Volume 18, Issue 11 pp. 2629-2648

Systematic Review

Open Access

Where do we stand with screening for colorectal cancer and advanced adenoma based on serum protein biomarkers? A systematic review

Adrien Grancher,

Corresponding Author

Adrien Grancher

[email protected]

orcid.org/0000-0003-2338-891X

U1086 “ANTICIPE” INSERM-University of Caen Normandy, Centre François Baclesse, Caen, France

Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, France

Correspondence

A. Grancher, Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, 1 rue de Germont, 76000 Rouen, France

Fax: +33232888669

Tel: +33232886293

E-mail: [email protected]

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Steven Cuissy,

Steven Cuissy

Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, France

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Hélène Girot,

Hélène Girot

Department of Medical Biochemistry, Rouen University Hospital, France

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André Gillibert,

André Gillibert

Department of Biostatistics, Rouen University Hospital, France

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Frédéric Di Fiore,

Frédéric Di Fiore

Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, France

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Lydia Guittet,

Lydia Guittet

U1086 “ANTICIPE” INSERM-University of Caen Normandy, Centre François Baclesse, Caen, France

Public Health Department, Caen University Hospital, France

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Adrien Grancher,

Corresponding Author

Adrien Grancher

[email protected]

orcid.org/0000-0003-2338-891X

U1086 “ANTICIPE” INSERM-University of Caen Normandy, Centre François Baclesse, Caen, France

Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, France

Correspondence

A. Grancher, Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, 1 rue de Germont, 76000 Rouen, France

Fax: +33232888669

Tel: +33232886293

E-mail: [email protected]

Search for more papers by this author

Steven Cuissy,

Steven Cuissy

Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, France

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Hélène Girot,

Hélène Girot

Department of Medical Biochemistry, Rouen University Hospital, France

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André Gillibert,

André Gillibert

Department of Biostatistics, Rouen University Hospital, France

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Frédéric Di Fiore,

Frédéric Di Fiore

Department of Hepato-Gastroenterology and Digestive Oncology, Rouen University Hospital, France

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Lydia Guittet,

Lydia Guittet

U1086 “ANTICIPE” INSERM-University of Caen Normandy, Centre François Baclesse, Caen, France

Public Health Department, Caen University Hospital, France

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First published: 30 September 2024

https://doi.org/10.1002/1878-0261.13734

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Abstract

Colorectal cancer (CRC) screening has been proven to reduce both mortality and the incidence of this disease. Most CRC screening programs are based on fecal immunochemical tests (FITs), which have a low participation rate. Searching for blood protein biomarkers can lead to the development of a more accepted screening test. The aim of this systematic review was to compare the diagnostic potential of the most promising serum protein biomarkers. A systematic review based on PRISMA guidelines was conducted in the PubMed and Web of Science databases between January 2010 and December 2023. Studies assessing blood protein biomarkers for CRC screening were included. The sensitivity, specificity, and area under the ROC curve of each biomarker were collected. Among 4685 screened studies, 94 were considered for analysis. Most of them were case–control studies, leading to an overestimation of the performance of candidate biomarkers. The performance of no protein biomarker or combination of biomarkers appears to match that of the FIT. Studies with a suitable design and population, testing new assay techniques, or based on algorithms combining FIT with serum tests are needed.

Abbreviations

AA: advanced adenoma
AN: advanced neoplasia
AUC: area under the ROC curve
CRC: colorectal cancer
ELISA: enzyme-linked immunosorbent assay
FIT: fecal immunochemical test
HC: healthy control
NAA: non advanced adenoma
OS: overall survival

1 Introduction

Colorectal cancer (CRC) is a worldwide public health problem that affects 1.9 million people each year and causes 900 000 deaths [[1]]. Overall survival (OS) highly depends on the stage at diagnosis. The 5-year survival rate is 92% for stage I patients, compared with 12% for stage IV patients [[2]]. CRC screening allows early detection at a preneoplastic stage or at a localized stage. Screen-detected CRCs are more likely to be localized than nonscreen-detected CRCs [[3]]. CRC screening programs have been proven to reduce both CRC-specific mortality and CRC incidence [[4]]. Screening programs in several countries are based on noninvasive fecal occult blood tests. The fecal immunochemical test (FIT) offers good performance for CRC screening, with a sensitivity of 65–91% and specificity of 91–96% for CRC detection, varying according to the positivity threshold selected by the screening program [[5-8]]. The global area under the ROC curve (AUC) of the FIT for CRC detection was estimated to be 0.95 (95% confidence interval [CI]: 0.93–0.97) [[5]]. However, participation rates remain low, under the European recommended threshold of 45% [[9]]. Among the explanatory factors, fecal aversion and the complexity of stool collection are important points [[10]]. Some data have suggested that a screening test based on blood sample collection would be more accepted by the general population than a stool-based test [[11, 12]], or could be proposed in patients who declined colonoscopy and FIT [[13]]. Thus, developing a blood-based screening test would increase the participation rate and therefore the effectiveness of screening.

Many blood biomarkers for CRC screening have been identified in the literature [[14]]. One blood-based test detecting SEPT9 methylation (Epi proColon®, Epigenomics AG, Berlin, Germany) is accredited by the FDA [[15]]. Recent results from the ECLIPSE clinical trial, evaluating a cell-free DNA blood-based test, have shown promising results [[16]]. However, these tests are still expensive and are not widely adopted as part of an organized screening program. Serum protein biomarkers could represent another promising option [[14, 17]]. Numerous studies have reported the use of several protein biomarkers for CRC and/or advanced adenoma (AA) detection. Nonetheless, most of these studies were retrospective studies with limited sample sizes that compared patients with invasive CRC to healthy controls (HCs). Few studies have included a study population that is representative of the population concerned by screening, on which the performance of biomarkers is to be evaluated [[18]]. Thus, studies including incident CRC cases diagnosed by screening colonoscopy in asymptomatic patients are to be preferred. Similarly, distinguishing a polyp or an adenoma group from a healthy control group is also an important criterion for assessing the specificity of biomarkers for CRC screening, but also for analyzing the performance of these biomarkers in detecting preneoplastic lesions [[14, 17]].

It is therefore necessary to sort through all this information to identify the most relevant serum protein biomarkers. The aim of this systematic review was to collect and compare the diagnostic ability of the most promising serum protein biomarkers for CRC screening.

2 Materials and Methods

This systematic review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines for the literature search [[19]].

2.1 Search strategy

A full literature search of the PubMed and Web of Science databases was conducted on May 6, 2024. The following equation was used: (marker OR biomarker) AND (serum OR blood) AND (diagnosis OR screening) AND (colorectal OR colon OR rectal) AND (cancer OR carcinoma OR neoplasia). Additional terms were added to delete biomarkers of other cancers and genetic biomarkers: NOT (prostate OR lung OR breast OR gastric OR pancreatic OR ovarian OR thyroid) and NOT (DNA OR RNA OR microRNA).

One researcher (A.Gr) performed the data extraction. One reviewer (A.Gr) used the rayyan online software to delete duplicates and screen title and abstract of the extracted articles. The remaining articles were then assessed for eligibility in the full-text review stage, blindly conducted by two reviewers (A.Gr and S.C.). In the event of disagreement between the two reviewers on the eligibility of an article, a collegial decision was made after rereading.

2.2 Study selection

Only articles in the English language published between January 1, 2010, and December 31, 2023, were considered. Animal or in vitro studies were excluded. Meta-analyses and reviews without original data were excluded, as well as case report studies. Studies involving nonserum biomarkers (plasma, fecal, urine, saliva) were excluded. Studies reporting the ratio or index of interest between cells, or cells and proteins, were excluded. Studies involving genetic biomarkers (DNA, RNA, miRNA) were excluded. Studies focusing only on the prognostic value of biomarkers for CRC recurrence or metastasis were excluded, as well as the predictive value of treatment efficacy. Studies assessing serum protein biomarkers for CRC and/or colorectal adenoma diagnosis or screening were included. Studies that did not include the following three groups were excluded: a control group (with HC and/or other nonmalignant diseases), a polyp group (including adenomas), and a CRC group. Studies that did not specify the sensitivity, specificity, or AUC of the biomarkers were excluded.

2.3 Data collection process

Two researchers (A.Gr and S.C.) independently reviewed and collected the data from the included articles. Previous data on CRC carcinogenesis involving candidate biomarker(s), including population, study design, test(s) used to measure biomarker(s), diagnostic performance for CRC and/or adenoma detection, and comparisons with other well-known biomarkers, have been systematically reported.

No sensitivity, specificity, or AUC calculations were performed, and only raw data from included studies were reported. Reported AUC, Sensitivity and Specificity of included studies for diagnosing CRC or advanced neoplasia (AN) were collected. When specified, the AUC, sensitivity, and specificity for adenoma or AA diagnosis were also collected. The AUC, sensitivity, and specificity for localized versus advanced CRC were also collected when available. AUC, sensitivity, or specificity of other standard biomarkers, such as CEA or CA19-9 levels, were also collected if available. AUC, sensitivity, and specificity of other screening tests performed—such as fecal immunochemical test—were also collected if available.

2.4 Reporting quality and level of evidence

Two reviewers (A.Gr and S.C.) assessed the methodological quality of each included study using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool from the Cochrane Library [[20]]. The QUADAS-2 tool was used to assess the risk of bias and the applicability of the study to answer the research question in four domains: patient selection, index test, reference standard, flow and timing (see Fig. S1). Each domain was evaluated as low risk, unclear risk, or high risk of bias.

The global ability to evaluate performance of biomarker(s) for CRC screening was determined through a four-step algorithm. First, design of the study was analyzed. Patient-control designs were considered as poor-ability studies. The other studies moved to the second step. Second, the type of included CRC was checked. Studies that included prevalent CRC were considered as poor-ability studies. The other studies moved to a third step. Third, the type of included controls was checked. Studies that included control patients who did not undergo colonoscopy were considered as poor-ability studies. The other studies moved to the fourth step. Finally, risk of bias and applicability concerns following the QUADAS score and statistical validation were analyzed. Studies with a majority of light gray rectangles in the QUADAS score, and with a statistical validation method, were considered as good-ability studies. Studies with a majority of black/dark rectangles in the QUADAS score, or without a statistical validation method, were considered as medium-ability studies.

2.5 Statistical analysis

The estimation of the AUC for CRC or AN screening reported in the included studies for each biomarker or combination of biomarkers was represented through forest plots, with a color code indicating the global ability to evaluate biomarkers for CRC screening (black square for poor ability, gray square for medium ability and white square for good ability). r software (Vienna, Austria) was used to perform the analysis and construct the forest plots.

3 Results

3.1 Study selection

A literature search was conducted on May 6, 2024, and 4685 articles published between January 2010 and December 2023 were identified; 431 of these articles were eligible for inclusion in the review. After a full-text assessment for inclusion, only 94 studies were included [[21-114]] (Fig. 1). A total of 336 studies were excluded: 269 because of the lack of an adenoma or a polyp group and 55 because of the lack of communication of AUC, sensitivity, or specificity for the studied biomarker(s).

Details are in the caption following the image — **Fig. 1**
Open in figure viewer PowerPoint

Flow chart of studies included in this systematic review. *Other cancers than CRC, other colorectal diseases (IBD, diverticulitis…), obesity, diabetes. **Stool biomarkers only, urinary biomarkers, circulating DNA/RNA/microRNA biomarkers, circulating cells biomarkers. AN, advanced neoplasia; AUC, area under the curve; CRC, colorectal cancer; Se, sensitivity; Sp, specificity; WOS, Web of Science.

3.2 Study characteristics

The most common designs were case–control studies (49/94, 52.1%), retrospective analysis of biobanks (21/94, 22.3%), and prospective recruitment for colonoscopy (24/94, 25.5%), including only two studies restricted to FIT-positive patients [[53, 74]] (Table 1).

Table 1. Characteristics of the included studies.

Study (authors/year)	Design^a	Statistical validation^b	Population of the study					Number of biomarkers tested	CEA assessment^g	QUADAS-score^h							Global ability to assess biomarkers for CRC screeningⁱ
			Sample size (CRC + AA)	Mean age (years)^c (CRC/adenoma/HC)	CRC or adenoma history^d	Type of included CRC^e	Colonoscopy in controls^f			Risk of bias				Applicability concerns
			Sample size (CRC + AA)	Mean age (years)^c (CRC/adenoma/HC)	CRC or adenoma history^d	Type of included CRC^e	Colonoscopy in controls^f			Patient selection	Index test	Reference standard	Flow and timing	Patient selection	Index test	Reference standard
Attallah 2018	P	None	235 (150 + NG)	NP	NP	2	0	3	1								Poor
Atwa 2020	P	None	251 (120 + NG)	48 (51/44/49)	0	2	0	1	0								Poor
Bedin 2015	C	None	151 (90 + 27)	65 (68/61/62)	NP	3	1	88	0								Poor
Bhardwaj 2020a	B	T + V (bs)	457 (154 + 101)	65 (66/65/65)	NP	1	1	275	0								Good
Bhardwaj 2020b	B	T + V (bs)	450 (156 + 99)	65 (66/65/65)	NP	1	1	275	0								Good
Bünger 2012	B	T + V (bs)	317 (164 + NG)	66 (70/64/62)	NP	1	1	9	1								Good
Cai 2022	C	T + V	288 (96 + NG)	57 (56/57/57)	NP	3	1	4	1								Poor
Chen H 2016	B	T + V	753 (401 + 99)	65 (66/64/62)	NP	1	1	64	0								Good
Chen H 2017	B	T + V	598 (267 + NG)	65 (68/64/62)	NP	1	1	5	0								Good
Chen M 2020	C	None	601 (301 + NG)	62 (62/62/61)	NP	3	1	4	0								Poor
Christensen 2015	B	None	1965 (32 + NG)	59 (67/65/56)	NP	2	1	2	1								Med
Dai 2021	P	None	90 (19 + 47)	58 (56/55/50)	1	1	1	4	1								Med
De Chiara 2010	P	None	299 (33 + 50)	NP	NP	2	1	1	0								Med
De Chiara 2022	P	None	1703 (249 + 372)	62 (NP)	NP	2	1	2	0								Med
Deng 2013	C	T + V	187 (67 + NG)	62 (60/58/55)	NP	3	1	4	1								Poor
Dowling 2014	C	T + V	106 (56 + NG)	62 (62/60/65)	NP	2	1	4	0								Poor
Dressen 2017	C	None	106 (35 + NG)	52 (69/55/39)	NP	3	NP	24	1								Poor
Farshidfar 2016	B	T + V	605 (320 + NG)	64 (65/60/62)	NP	3	1	48	0								Poor
Fitzgerald 2018	P	None	114 (24 + NG)	67 (67/67/67)	NP	2	1	7	0								Med
Garranzo-A. 2019	B	None	96 (40 + NG)	59 (66/59/61)	NP	3	1	9	0								Poor
Gawel 2019	B	T + V	4493 (512 + 399)	NP	NP	2	1	8	1								Good
Gimeno-G. 2015	P	None	150 (25 + 25)	63 (68/63/62)	0	2	1	2	0								Med
Groblewska 2010	C	None	210 (91 + NG)	NP	NP	3	NP	4	1								Poor
Gu J 2019	C	None	110 (40 + NG)	NP	NP	3	0	23	0								Poor
Gu Y 2022	C	None	382 (120 + 130)	60 (62/59/60)	NP	3	0	5	1								Poor
Han 2014	C	None	174 (113 + NG)	61 (62/58/60)	NP	3	NP	2	1								Poor
Huang Z 2022	C	None	460 (227 + NG)	62 (NP)	NP	3	NP	3	1								Poor
Huang M 2023	P	None	291 (101 + NG)	58 (67/55/41)	NP	2	1	3	1								Med
Ivancic 2020	C	T + V (cv)	259 (47 + 72)	60 (NP)	NP	3	1	17	0								Poor
Jiang 2021	C	T + V	1729 (148 + NG)	NP	NP	3	1	2	1								Poor
Johansen 2014	P	None	4496 (293 + NG)	61 (NP)	1	2	1	2	1								Med
Ke 2023	C	T + V	209 (74 + NG)	62 (65/62/58)	NP	3	0	6	1								Poor
Kleif 2022	P	None	4048 (242 + 548)	NP	NP	1	1	9	1								Med
Kraus 2015	C	T + V	297 (98 + NG)	59 (63/62/55)	0	3	1	1	0								Poor
Li Q 2016	C	None	204 (127 + NG)	64 (66/62/60)	0	3	1	2	1								Poor
Li S 2019	C	None	327 (160 + NG)	NP	NP	3	NP	2	1								Poor
Li B 2020	C	T + V	250 (165 + NG)	54 (56/52/54)	0	1	1	1	0								Poor
Liu L 2020	C	None	313 (151 + NG)	60 (61/57/60)	NP	3	1	3	1								Poor
Liu Z 2021	C	T + V	243 (120 + NG)	NP	NP	3	0	4	0								Poor
Meng 2012	P	None	483 (93 + 41)	59 (59/60/57)	NP	3	1	2	1								Poor
Montero-C. 2023	C	None	110 (31 + NG)	67 (71/64/67)	NP	3	0	2	0								Poor
Moravkova 2019	C	None	84 (22 + 22)	63 (69/64/55)	0	3	1	4	0								Poor
Mroczko 2010	C	None	180 (75 + NG)	NP	NP	3	NP	3	1								Poor
Nielsen 2011	P	None	4486 (294 + NG)	61 (NP)	NP	2	1	1	0								Med
Otero-E. 2014	P	None	516 (4 + 53)	54 (NP)	1	1	1	1	0								Med
Otero-E. 2015	P	None	516 (4 + 53)	54 (NP)	1	1	1	1	0								Med
Otero-E. 2016	P	None	511 (4 + 53)	NP	1	1	1	1	0								Med
Overholt 2015	P	None	448 (134 + NG)	57 (NP)	NP	3	1	1	0								Poor
Ozemir 2016	C	None	80 (27 + 8)	56 (NP)	NP	3	1	3	1								Poor
Ozgur 2019	P	None	185 (40 + NG)	57 (61/59/55)	NP	2	1	2	0								Med
Pan Y 2021	C	T + V	362 (163 + 98)	60 (61/60/58)	NP	3	0	52	1								Poor
Pan Z 2022	C	T + V	127 (65 + NG)	56 (57/55/55)	NP	3	NP	3	1								Poor
Peltier 2016	B	T + V	85 (24 + NG)	61 (67/66/53)	NP	3	1	3	0								Poor
Petersen 2023	P	None	4048 (242 + 548)	65 (68/66/63)	0	1	1	9	1								Med
Qian 2018	B	T + V	651 (45 + 120)	NP	NP	1	1	1	0								Good
Qiu 2019	P	None	164 (20 + NG)	60 (64/67/57)	0	2	1	1	0								Med
Rasmussen 2021	B	None	784 (196 + 98)	66 (70/66/64)	0	1	1	1	0								Med
Rho 2016	B	T + V	1179 (653 + NG)	NP	NP	2	1	5	0								Good
Rigi 2020	C	None	178 (56 + NG)	57 (NP)	1	3	1	1	0								Poor
Solé 2015	C	None	180 (80 + NG)	66 (66/60/67)	NP	3	1	1	0								Poor
Song Y 2016	C	None	382 (137 + NG)	58 (59/57/57)	NP	3	0	3	1								Poor
Song W 2020	C	None	1010 (350 + NG)	NP	NP	3	0	37	1								Poor
Storm 2015	C	T + V	991 (99 + NG)	63 (69/65/62)	0	3	1	3	0								Poor
Sun 2018	C	None	1365 (455 + NG)	57 (58/57/55)	NP	3	0	4	1								Poor
Taguchi 2015	B	None	180 (60 + NG)	60 (63/62/56)	NP	3	1	3	1								Poor
Tao 2012	B	None	597 (179 + 193)	65 (68/65/62)	NP	2	1	3	0								Med
Thomas 2015	B	None	100 (40 + NG)	62 (62/62/62)	0	2	NP	2	1								Poor
Thorsen 2013	B	T + V (cv)	280 (70 + NG)	72 (72/72/72)	1	2	1	74	1								Med
Uchiyama 2017	C	None	175 (56 + NG)	69 (70/70/68)	NP	3	1	334	0								Poor
Uchiyama 2018	C	T + V	304 (140 + NG)	69 (70/70/68)	NP	3	1	5	0								Poor
Van Den B. 2010	P	None	616 (69 + NG)	60 (69/61/58)	NP	1	1	3	0								Med
Wang J 2013	C	None	366 (201 + 80)	54 (55/52/53)	NP	3	0	2	1								Poor
Wang X 2017	C	None	987 (473 + NG)	NP	NP	3	0	2	1								Poor
Wang D 2019	C	T + V	428 (187 + NG)	57 (58/56/56)	NP	3	NP	3	1								Poor
Wang Z 2021	C	None	173 (81 + NG)	53 (54/47/56)	NP	3	0	2	1								Poor
Wang H 2023	C	None	817 (204 + 186)	57 (58/56/57)	NP	3	0	26	1								Poor
Wang Q 2023	C	None	313 (202 + NG)	NP	0	3	1	3	1								Poor
Watany 2018	C	None	120 (49 + NG)	NP	NP	3	NP	3	1								Poor
Weiss 2011	C	None	178 (59 + NG)	56 (68/45/55)	NP	3	0	2	1								Poor
Werner 2016	B	None	1660 (40 + NG)	62 (66/64/62)	NP	1	1	5	1								Med
Wild 2010	B	T + V	710 (301 + 143)	65 (67/67/62)	NP	3	1	6	1								Poor
Wilhelmsen 2017	P	None	4698 (512 + 399)	NP	0	2	1	8	1								Med
Wilson 2012	P	None	748 (3 + 43)	59 (NP)	NP	2	1	1	0								Med
Wu 2015	B	None	93 (49 + NG)	60 (62/59/56)	NP	3	0	2	1								Poor
Xie 2017	C	T + V	870 (346 + NG)	57 (59/57/54)	NP	3	0	2	1								Poor
Xu 2015	C	None	152 (125 + NG)	NP	NP	3	NP	7	0								Poor
Yao 2012	B	None	215 (122 + NG)	NP	NP	3	NP	2	1								Poor
Ye 2014	C	None	199 (120 + NG)	64 (NP)	NP	3	NP	3	1								Poor
Yildrim 2018	P	None	68 (23 + NG)	59 (63/63/49)	NP	2	1	2	1								Med
Zekri 2015	P	None	114 (34 + NG)	42 (41/40/42)	NP	2	1	3	0								Med
Zhang 2015	C	None	249 (138 + NG)	58 (NP)	NP	3	1	2	1								Poor
Zhou 2019	C	None	460 (258 + NG)	NP	1	3	NP	4	1								Poor
Zhu C 2010	C	None	398 (144 + NG)	66 (68/64/66)	NP	3	1	2	1								Poor
Zhu J 2014	C	T + V (cv)	234 (66 + NG)	57 (58/56/57)	NP	3	1	13	0								Poor

Abbreviations: AA, advanced adenoma; CRC, colorectal cancer; HC, Healthy controls; NG, details of the proportion of advanced or high-risk adenomas were not given for this adenoma group; NP, not precised.
^a P = Prospective, C = Case–control, B = Biobank.
^b T + V = training + validation set, bs = bootstrap, cv = cross-validation.
^c Mean age in years in the overall population and in the CRC, the adenoma and the HC subgroups.
^d 0 = no personal of family history of adenoma or CRC, 1 = possible personal or family history of CRC.
^e 1 = Incident CRC diagnosed screening colonoscopy in asymptomatic patients, 2 = Incident CRC diagnosed after colonoscopy in symptomatic patients, 3 = Prevalent case of CRC.
^f 1 = total colonoscopy performed in all healthy controls, 0 = total colonoscopy not performed in all healthy controls.
^g CEA assessment was rated as 1 if performed and 0 if not.
^h QUADAS score items were rated as high risk (black rectangle), unclear risk (dark gray rectangle) or low risk of bias or applicability concerns (light gray rectangle), following the recommendations of the Cochrane Library [[20]].
ⁱ Global ability to evaluate biomarkers for CRC screening was determined through a four-steps algorithm, as follows: 1st step: design of the study. Patient-control designs were considered as poor-ability studies. The other studies moved to step 2. 2nd step: type of included CRC. Studies that included prevalent CRC were considered as poor-ability studies. The other studies moved to step 3. 3rd step: type of included controls. Studies that included control patients who did not undergo colonoscopy were considered as poor-ability studies. The other studies moved to step 4. 4th step: risk of bias and applicability concerns following QUADAS score, and statistical validation. Studies with a majority of light gray rectangles in QUADAS score, and with a statistical validation method, were considered as good-ability studies. Studies with majority of black/dark rectangles in QUADAS score, or without a statistical validation method, were considered as medium-ability studies.

Quantitative assay results raise the question of cutoff value selection. There was a high heterogeneity between included studies to determine the cutoff value of candidate biomarkers for CRC screening. The best procedure was based on a 2-step strategy. First, a training set was used to identify the cutoff value associated with the best performance (sensitivity and specificity), or with the best sensitivity for a prespecified specificity (often set at 90%). Then, performance of the biomarker at this cutoff value was tested through a second cohort, in the validation set. A statistical validation with a training set and a validation set was performed in 28 studies only (29.8%), with six using cross-validation or bootstrap resampling. Six studies (6.4%) determined the cutoff value based on previous data or results described in the literature for the considered biomarker(s). In this case, the rationale behind the choice of the cutoff value was then clearly explained in Section 2. However, the remaining 60 studies have calculated biomarker performance based on the most interesting cutoff value, without any preexisting rationale, and without statistical validation in a second cohort (63.8%).

The median number of patients included in the studies was 302, ranging from 68 to 4698. Despite the inclusion of studies involving groups of patients with adenoma or polyps, only 28 of them (29.8%) specified the AA (or high-risk polyp) population. This detail is important for assessing the diagnostic capacity of the test for precancerous lesions, since one in four AAs will develop into cancer. The risk of progression from adenoma to cancer is much lower for simple adenomas than for AA [[115]].

The mean age of the overall population was available in 73 studies (77.6%) and detailed in the three subgroups in 60 studies (63.8%). Among these studies, the mean age was 61 years for the overall population, 63 years for CRCs, 62 years for adenomas, and 60 years for HC.

Personal or family history of polyp or CRC in the study population was not recorded or detailed in most studies (73/94, 77.6%).

Selection of a study population representative of the asymptomatic patients targeted by CRC-screening is a crucial point. Among the 94 studies, only 16 included screen-detected incident CRC (17.0%), which was the targeted CRC population. Other studies included incident CRC diagnosed in symptomatic patients (21/94, 22.3%) or prevalent CRC (57/94, 60.1%). Furthermore, only 62 (66.0%) included a colonoscopy-validated control group, including two with part of flexible sigmoidoscopy [[51, 64]]. Nineteen studies had a noncolonoscopy-validated control group (20.1%), and the results were unclear for 13 other studies (13.9%).

Several exploratory studies have tested numerous biomarkers, with 29 studies evaluating five biomarkers or more (30.1%), which raises the problem of risk alpha inflation.

Numerous studies measured CEA (51/94, 54.3%) and/or CA19-9 (18/94, 19.1%). These two biomarkers have been extensively studied in the context of CRC screening, but their accuracy in CRC screening is limited. A systematic review reported a sensitivity between 40% and 60% for CRC detection and between 5% and 30% for AA detection and a specificity between 85% and 97% for CEA. The sensitivity of CA19-9 for detecting CRC was between 16% and 52%, and the specificity was between 80% and 97% [[116]]. The accuracy of these two biomarkers therefore sheds light on the study carried out and may argue for an overestimation of the accuracy of the candidate biomarker in the case of high accuracy for these two biomarkers.

3.3 Risk of bias and applicability of studies

Table 1 also presents the quality assessment and level of evidence of the included studies.

Most of the included studies presented a high or an unclear risk of selection bias (66.0%). This bias was explained by the case–control design, or the absence of description of consecutive or random sample for prospective enrolment. Thus, the cases are not representative of the screening population because of the importance of advanced and/or symptomatic CRC. The proteomic profile of these patients could be different from that of asymptomatic patients with early-onset CRC (or AA) targeted by CRC screening.

Furthermore, most of the studies also presented measurement bias because the cutoff was established without prior data, and no confirmation was made through a two-step design with training or validation sets.

The timing of blood collection was unclear or not specified in 39 studies (41.5%), which can lead to confusion concerning the quality of the presented results.

Finally, some techniques for biomarker measurement are difficult to reproduce, raising the question of the external validity of the results, especially for spectrometry techniques.

When analyzing the included studies following our four-steps algorithm (as described above), only eight studies were classified as good ability to evaluate the performance of the biomarker for CRC screening (8.5%). Twenty-four were classified as medium-ability (25.5%), and the remaining 62 studies as poor-ability (66.0%).

3.4 Global assessment of serum protein biomarkers for CRC screening

Several serum protein biomarkers have been identified as potentially interesting for CRC screening (see Table S1). However, these results must be interpreted based on the ability of the studies to answer the question.

Figure 2 summarizes the AUCs for CRC or AN detection for the main biomarkers identified, with a color code weighing the ability to evaluate biomarkers for CRC screening. This figure highlights the global tendency to overestimate the intrinsic performance of biomarkers, since the majority of results come from studies not properly designed to evaluate biomarkers for CRC screening. All the biomarkers with an AUC greater than 0.90 were identified in case–control or retrospective analyses of biobank studies, with poor ability to evaluate biomarkers for CRC screening (15/15, 100%).

When looking at reported AUCs without weighing for study heterogeneity, the median AUC for CRC or AN detection for tested biomarkers was 0.75, ranging from 0.52 to 0.98. The median AUC was 0.60 for good-ability studies (ranging from 0.52 to 0.71), 0.69 for medium-ability studies (ranging from 0.55 to 0.85), and 0.82 for poor-ability studies (0.55–0.98). The influence of the methodological ability to evaluate biomarkers for CRC screening on the AUC was striking when looking at interleukin (IL)-8, with an AUC for CRC detection between 0.92 and 0.98 in poor-ability studies, 0.76 in a medium-ability study, and only 0.68 in a good-ability study (Fig. 2). Median AUC was 0.77 in studies without methodological validation (ranging from 0.55 to 0.98) and 0.68 in studies with methodological validation (ranging from 0.52 to 0.97).

When considering results of studies with a good- or medium-ability to evaluate biomarkers for CRC screening, the most promising biomarkers are IL-8 (AUC = 0.76) [[77]], MIC-1 (AUC = 0.82) [[32]], YKL-40 (AUC = 0.77) [[51]], FGF-21 (AUC = 0.71) [[75]], TIMP-1 (AUC = 0.70) [[31, 64]], MMP-9 (AUC = 0.77) [[103]], adiponectin (AUC = 0.85) [[110]], sCD26 (AUC = 0.81) [[33, 65]], cystatin 4 (AUC = 0.77) [[48]] and histone 4 (AUC = 0.79) [[70]]. However, when comparing these results to the FIT (AUC = 0.95), no single biomarker could currently challenge the fecal test.

3.5 Global assessment of combinations of biomarkers for CRC screening

One way of increasing the diagnostic performance of a blood test is to combine several protein biomarkers. Table S2 presents the different combinations of protein biomarkers. Most of these studies were based on the enzyme-linked immunosorbent assay (ELISA). Several combinations of CEA with one or more other biomarkers of interest were evaluated, with AUCs ranging from 0.75 to 0.99 for CRC detection and from 0.56 to 0.93 for AA detection [[31, 32, 50-52, 57, 58, 64, 72, 81, 84, 85, 95, 96, 100, 101, 105, 108, 111-113]]. Other combinations of 4–11 protein biomarkers measured by different multiplex immunoassays (Proximity Extension Assays [Olink®, Biogenity ApS, Aalbord, Denmark] [[24, 78, 88]] or the Abbott Architect® i2000R automated platform [[41, 53, 102]]) have also shown promising results. In addition, serum peptide profiles were studied using various spectrometry techniques, with AUCs ranging from 0.67 to 0.93 for CRC detection and from 0.65 to 0.83 for adenoma detection [[25, 35, 38, 44, 45, 49, 72, 90, 106, 114]].

Figure 3 summarizes the AUCs for CRC or AN detection for the main combinations identified, with the color code weighting the ability to evaluate biomarkers for CRC screening. Among the 13 combinations of biomarkers with an excellent diagnostic performance, with an AUC greater than 0.90, only one was identified in a medium-ability study [[32]].

When considering the results of good- or medium-ability studies, four combinations of biomarkers presented an AUC between 0.80 and 0.90: CEA + anti-p53 (AUC = 0.85) [[100]], CEA + CA19-9 + AFP + TIMP-1 + CyFra21-1 + hsCRP + Ferritin + Galactin-3 (AUC = 0.84) [[41, 102]], and BAG-4 + IL6ST + vWF + CD44 + EGFR (AUC = 0.84) [[78]]. Only one combination of biomarker presented an AUC higher than 0.90: the association of CEA + CA19-9 + CA24-2 + MIC-1 (AUC = 0.94). This promising result comes from a Chinese study, with an adapted study population [[32]]. However, no statistical validation method was performed, and confirmation of the performance of this combination in another prospective cohort is pending. Thus, no combination of biomarkers could currently challenge the FIT in the diagnostic ability for CRC screening.

3.6 Combination of serum protein biomarkers with the FIT for CRC screening

Another way of increasing the performance of a screening program would be to combine a blood screening test with a fecal test (immunological Fecal Occult Blood Test or FIT) (Table S3). However, this solution has been understudied. One study assessing the sCD26 + FIT combination found an interesting sensitivity of 64.9% for AN detection and 62.3% for AA detection at 90% specificity, with a FIT cutoff of 20 μg Hb·g⁻¹ feces [[65]]. When combining sCD26, DDP4, and FIT results through a decision algorithm, the sensitivity was 95.2% for CRC detection and 80.3% for AA detection. Thus, a sCD26/DDP4 blood test could be proposed after a FIT-positive test to avoid unnecessary colonoscopy. However, this algorithm must be validated in a prospective study [[34]]. A recent Danish study found a better AUC for a two-step algorithm approach combining FIT, patients characteristics (age and sex), and serum biomarkers (CEA, hsCRP, Ferritin, TIMP-1, Pepsinogen-2, HE4, Cyfra21-1, Galectin-3, and β2-microglobulin) as compared with FIT alone, with a cutoff value set at 100 ng·mL⁻¹ of hemoglobin (AUC = 0.75 [0.72–0.78] vs AUC = 0.69 [0.66–0.72], respectively) [[74]]. These two recent works open up the prospect of a decision-making algorithm based on the combination of the FIT result and one or a combination of more routinely assayable protein biomarkers of interest, making it possible to increase the performance of the screening program.

4 Discussion

This systematic review reports the most interesting studies assessing serum protein biomarkers for CRC, AN, or AA detection in the past 13 years. A recent consensus of international experts reported that an accuracy as good as that of the FIT is needed to determine whether blood protein biomarkers are an option [[117]]. Thus, these biomarkers should have at least a 0.95 AUC for CRC detection, which corresponds to 79% sensitivity and 94% specificity [[5]]. Despite the extensive literature in this field, there are currently no serum protein biomarkers nor combinations of biomarkers that present sufficient accuracy for replacing the FIT.

4.1 How to conduct new studies assessing biomarkers for CRC screening?

This review highlights the significant heterogeneity of the study population, despite stringent inclusion criteria. Most of the included studies were case–control studies, with prevalent CRC, which overestimate the performance of candidate biomarkers. The question of selecting a study population representative of the population targeted by screening is crucial. The performance of a protein biomarker for CRC screening can only be properly assessed in a population of asymptomatic patients targeted by CRC screening [[18]]. This review highlights the importance of conducting better-designed studies. Some European biological collections, such as the German BLITZ cohort [[24]] or the Danish cohort [[53]], are currently the best studies to rely on in terms of methodological examples to assess the usefulness of biomarkers for CRC or AN detection. These cohorts included many asymptomatic patients referred for a screening colonoscopy.

In our opinion, future studies conducted on the subject should follow the recent guidelines for the assessment of new noninvasive screening tests for CRC [[117]]. Prospective studies are to be preferred to case–control studies, with a low level of evidence and a high risk of bias. When performing a prospective study, only asymptomatic patients referred for a screening colonoscopy must be included. If a case–control study, only incident CRC diagnosed in asymptomatic patients after a screening colonoscopy, must be included. Patients in the control group must have undergone a total colonoscopy and be eligible for CRC screening. Then, timing of the blood sampling and the preanalysis process must be clearly defined to be able to reproduce the study.

Furthermore, our review underlines the heterogeneity of included studies to determine the cutoff value. Only 29.8% of the included studies have the recommended two-step strategy, with the establishment of the cutoff value in a first training cohort, and the statistical validation of this value in a second cohort. This two-step design must be encouraged in future studies, to be able to compare the results. Data sharing, with detailed biomarker results and study population characteristics, should be encouraged. Indeed, 50% of the studies included had fewer than 302 patients, which raises the problem of a lack of power, potentially explaining the disappointing results. The sharing of detailed, open-access data would enable an individual participant data network meta-analyses to be carried out, taking into account the heterogeneity of the study population due to the characteristics transmitted, and thus coupling the efforts of different teams to make progress in this field of research.

Another issue is the presence of publication bias in the field of serum protein biomarkers for CRC. The tendency to publish significant or favorable results while neglecting null findings can distort the overall understanding of biomarker performance and hinder reproducibility. Efforts to mitigate publication bias, such as establishment of registries and journal that accept null findings, has to be encouraged [[118]].

4.2 Heterogeneity of the methodological process used to combine biomarkers: which statistical method is the best?

Calculating the diagnostic performance of biomarker combinations is also a fundamental issue. Several statistical methods could be used. The most frequently used tool was the construction of an ROC curve using a multivariate logistic regression model that included biomarkers. A least absolute shrinkage and selection operator (LASSO) logistic regression model was used in some studies. Another intuitive method, “and-or combination,” following logic combination rules could be used [[119]]. A combination was also considered positive if one or more of the biomarkers were positive or if each biomarker was positive. However, these techniques are not suitable for combinations of numerous biomarkers. More complex analysis models, such as Monte Carlo cross-validation, partial least-squares discriminant analysis, or support vector machine analysis, are then needed.

4.3 Future serum protein biomarkers: which way to look?

Among the serum protein biomarkers, different families of serum proteins are interesting because of their proven or suspected involvement in colorectal tumorigenesis (Table S1). This systematic review updates known data on serum protein biomarkers for CRC screening, one of the currently explored pathways for the development of a blood-based CRC screening test. The conclusions are similar to those of the review conducted by Liu et al. [[17]] a decade ago; namely, that there is currently no credible serum protein biomarker or combination of serum protein biomarkers to replace FIT. Considering the results of the best-designed studies, only a combination of biomarkers (associating CEA, CA19-9 CA24-2, and MIC-1) approaches the AUC of FIT with an AUC of 0.94. However, this result comes from a study with a small sample size (90 patients, including 19 CRCs), and needs to be confirmed on a larger cohort of asymptomatic subjects eligible for screening [[32]].

Despite the so-far disappointing results, there are still many avenues to explore. First, thanks to new bioassay methods, further studies will be carried out to precisely analyze the proteome and the metabolome of asymptomatic patients to identify a profile associated with the presence of CRC or AA. Then the advent of artificial intelligence is enabling the development of increasingly complex algorithms, which can take into account the results of several serum protein assays, as well as individual patient characteristics.

One of the most promising short-term avenues seems to be the development of multistage algorithms combining the FIT result with certain protein assays to increase the performance of the screening program. A recent Danish study found that a multistage algorithm combining FIT, clinical features, and routine serum protein assays outperformed FIT alone [[74]]. However, replacing or adding a blood-based test in the CRC screening program in countries where organized screening based on a fecal test has already been in place for many years raises serious questions. Blood screening is likely to come up against new barriers to participation, such as the need to go to a healthcare professional to have a sample taken, and some patients' fear of taking a blood sample. Medico-economic studies will also have to determine the cost-effectiveness of a blood screening test, which will probably have a difficulty in challenging the low cost of fecal testing. For example, the Epi proColon® test, based on the detection of SEPT9 gene methylation, the only blood test currently accredited by the US Food and Drug Administration (FDA), is currently far more expensive than the FIT, limiting possible extension to the entire population targeted by screening [[15]].

4.4 Strengths of our review

To our knowledge, this systematic review is the first to focus on the use of serum protein biomarkers for CRC screening by involving studies including CRC patients, adenoma patients, and control subjects. In fact, a biomarker for CRC screening is relevant only if it reveals a preneoplastic lesion or early-stage CRC and not an advanced or metastatic CRC in a symptomatic patient. This systematic review was conducted following the PRISMA-DTA guidelines, and the Cochrane QUADAS-2 tool was used to analyze bias in the included studies.

4.5 Limitations of our review

Only studies indexed in the NCBI and Web of Science databases were considered. Studies conducted before January 1, 2010, were not included, as the results from previous studies on serum protein biomarkers for CRC diagnosis conducted before were already reported by another systematic review [[17]]. However, most of the innovative proteomics and metabolomics techniques were not available at that time. Despite these two limitations, more than 4600 studies were initially screened for inclusion.

This review excluded several articles without a polyp or adenoma group, which may exclude biomarkers of interest for CRC screening. This clearcut choice is based on remarks provided by previous systematic reviews on the subject, which highlight the absence of consideration of a polyp or adenoma group in addition to a healthy control group as a potential bias when analyzing the specificity of a biomarker for the diagnosis of CRC [[14, 17]]. Indeed, the presence of a control group that does not distinguish healthy controls from polyps or adenomas may induce a bias, limiting polyp specificity. As the prevalence of polyps and adenomas in the general population is between 20% and 30%, this subgroup cannot be neglected [[120]]. In addition, the presence of an adenoma or advanced adenoma group makes it possible to calculate the sensitivity of the biomarker for the detection of preneoplastic lesions, which is a necessity, given FIT's limited capacity. Finally, the a priori distinction of an adenoma group when drafting the study protocol often reflects a study population that is more representative of the population targeted by CRC screening. By excluding studies that do not include these three groups, we focused on studies that are the most likely to answer the question.

This systematic review excluded studies assessing blood protein biomarkers for other cancers, as studies including a group of patients with noncolorectal cancer, patients with colonic polyps, and patients with CRC were considered improbable. However, while a blood protein biomarker has good sensitivity for diagnosing CRC or AA, it is also important to measure its specificity in a wider population, which may have other early-stage cancers. Indeed, the identification of a promising biomarker or combination of biomarkers should be evaluated in a group of patients with noncolorectal cancer to assess the specificity of these biomarkers. Similarly, a group of patients with other noncancerous colonic diseases (e.g. inflammatory bowel disease [IBD], diverticulitis) should also be included to assess biomarker specificity. The inclusion of patients from these two groups in addition to HCs is therefore a quality criterion and should be encouraged in future studies.

5 Conclusion

No serum protein biomarkers—or their combination—is currently able to replace the 0.95 AUC, 65% sensitivity, and 95% specificity of the FIT in CRC screening programs. However, the development of new proteomic or metabolomic techniques would allow us to identify profiles associated with CRC or AN. Most of the studies conducted in these fields had poor designs and several biases. Further studies should focus on asymptomatic individuals targeted by screening to avoid overestimation of sensitivity. Research must continue with better-designed studies to develop new screening tests based on one or more serum protein biomarkers. Development of new algorithms, based on the FIT results, patients' characteristics, and results of serum protein biomarkers, represent a promising option.

Acknowledgments

None.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

AGr, HG, LG, and FDF designed the study research; AGr performed data extraction and screened all titles and abstracts; AGr and SC reviewed independently the eligibility of articles, collected data from included articles, and assessed the methodological quality of each included article; AGr and AGi performed statistical analyses and figures conception; and AGr, LG, FDF, and HG wrote the article. All authors reviewed the article and approved the final version.

Open Research

Data accessibility

The data presented in the current study are available upon reasonable request from the corresponding author.

Supporting Information

References

1Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71(3): 209–249.
10.3322/caac.21660
PubMedWeb of Science®Google Scholar
2Rawla P, Sunkara T, Barsouk A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Prz Gastroenterol. 2019; 14(2): 89–103.
CASPubMedGoogle Scholar
3Cardoso R, Guo F, Heisser T, De Schutter H, Van Damme N, Nilbert MC, et al. Proportion and stage distribution of screen-detected and non-screen-detected colorectal cancer in nine European countries: an international, population-based study. Lancet Gastroenterol Hepatol. 2022; 7(8): 711–723.
10.1016/S2468-1253(22)00084-X
CASPubMedGoogle Scholar
4Cardoso R, Guo F, Heisser T, Hackl M, Ihle P, De Schutter H, et al. Colorectal cancer incidence, mortality, and stage distribution in European countries in the colorectal cancer screening era: an international population-based study. Lancet Oncol. 2021; 22(7): 1002–1013.
10.1016/S1470-2045(21)00199-6
PubMedWeb of Science®Google Scholar
5Lee JK, Liles EG, Bent S, Levin TR, Corley DA. Accuracy of fecal immunochemical tests for colorectal cancer: systematic review and meta-analysis. Ann Intern Med. 2014; 160(3): 171.
10.7326/M13-1484
PubMedWeb of Science®Google Scholar
6Selby K, Levine EH, Doan C, Gies A, Brenner H, Quesenberry C, et al. Effect of sex, age, and positivity threshold on fecal immunochemical test accuracy: a systematic review and meta-analysis. Gastroenterology. 2019; 157(6): 1494–1505.
10.1053/j.gastro.2019.08.023
CASPubMedGoogle Scholar
7Grobbee EJ, Wisse PH, Schreuders EH, van Roon A, van Dam L, Zauber AG, et al. Guaiac-based faecal occult blood tests versus faecal immunochemical tests for colorectal cancer screening in average-risk individuals. Cochrane Database Syst Rev. 2022; 6(6):CD009276.
PubMedGoogle Scholar
8Imperiale TF, Gruber RN, Stump TE, Emmett TW, Monahan PO. Performance characteristics of fecal immunochemical tests for colorectal cancer and advanced adenomatous polyps: a systematic review and meta-analysis. Ann Intern Med. 2019; 170(5): 319–329.
10.7326/M18-2390
PubMedWeb of Science®Google Scholar
9Moss S, Ancelle-Park R, Brenner H, International Agency for Research on Cancer. European guidelines for quality assurance in colorectal cancer screening and diagnosis. First edition—evaluation and interpretation of screening outcomes. Endoscopy. 2012; 44(Suppl 3): SE49–SE64.
PubMedWeb of Science®Google Scholar
10Le Bonniec A, Meade O, Fredrix M, Morrissey E, O'Carroll RE, Murphy PJ, et al. Exploring non-participation in colorectal cancer screening: a systematic review of qualitative studies. Soc Sci Med. 2023; 329:116022.
10.1016/j.socscimed.2023.116022
PubMedGoogle Scholar
11Osborne J, Wilson C, Moore V, Gregory T, Flight I, Young G. Sample preference for colorectal cancer screening tests: blood or stool? Open J Prev Med. 2012; 2: 326–331. https://doi.org/10.4236/ojpm.2012.23047
10.4236/ojpm.2012.23047
Google Scholar
12Osborne JM, Flight I, Wilson CJ, Chen G, Ratcliffe J, Young GP. The impact of sample type and procedural attributes on relative acceptability of different colorectal cancer screening regimens. Patient Prefer Adherence. 2018; 12: 1825–1836.
10.2147/PPA.S172143
PubMedWeb of Science®Google Scholar
13Liang PS, Zaman A, Kaminsky A, Cui Y, Castillo G, Tenner CT, et al. Blood test increases colorectal cancer screening in persons who declined colonoscopy and fecal immunochemical test: a randomized controlled trial. Clin Gastroenterol Hepatol. 2023; 21(11): 2951–2957.e2.
10.1016/j.cgh.2023.03.036
CASPubMedGoogle Scholar
14Nikolaou S, Qiu S, Fiorentino F, Rasheed S, Tekkis P, Kontovounisios C. Systematic review of blood diagnostic markers in colorectal cancer. Tech Coloproctol. 2018; 22(7): 481–498.
10.1007/s10151-018-1820-3
PubMedWeb of Science®Google Scholar
15Church TR, Wandell M, Lofton-Day C, Mongin SJ, Burger M, Payne SR, et al. Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut. 2014; 63(2): 317–325.
10.1136/gutjnl-2012-304149
CASPubMedWeb of Science®Google Scholar
16Chung DC, Gray DM, Singh H, Issaka RB, Raymond VM, Eagle C, et al. A cell-free DNA blood-based test for colorectal cancer screening. N Engl J Med. 2024; 390(11): 973–983.
10.1056/NEJMoa2304714
CASPubMedGoogle Scholar
17Liu Z, Zhang Y, Niu Y, Li K, Liu X, Chen H, et al. A systematic review and meta-analysis of diagnostic and prognostic serum biomarkers of colorectal cancer. PLoS One. 2014; 9(8):e103910.
10.1371/journal.pone.0103910
PubMedWeb of Science®Google Scholar
18Qian J, Tikk K, Werner S, Balavarca Y, Saadati M, Hechtner M, et al. Biomarker discovery study of inflammatory proteins for colorectal cancer early detection demonstrated importance of screening setting validation. J Clin Epidemiol. 2018; 104: 24–34.
10.1016/j.jclinepi.2018.07.016
PubMedGoogle Scholar
19McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM, the PRISMA-DTA Group, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA. 2018; 319(4): 388–396.
10.1001/jama.2017.19163
PubMedWeb of Science®Google Scholar
20Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011; 155(8): 529–536.
10.7326/0003-4819-155-8-201110180-00009
PubMedWeb of Science®Google Scholar
21Attallah AM, El-Far M, Ibrahim AR, El-Desouky MA, Omran MM, Elbendary MS, et al. Clinical value of a diagnostic score for colon cancer based on serum CEA, CA19-9, cytokeratin-1 and mucin-1. Br J Biomed Sci. 2018; 75(3): 122–127.
10.1080/09674845.2018.1456309
CASPubMedGoogle Scholar
22Atwa RA, Abdelrazek MA, Attallah AM. Evaluation of metalloprotenase-1 in early diagnosis of colon cancer. Biosci Res. 2020; 17: 2816–2824.
Google Scholar
23Bedin C, Crotti S, Ragazzi E, Pucciarelli S, Agatea L, Tasciotti E, et al. Alterations of the plasma peptidome profiling in colorectal cancer progression. J Cell Physiol. 2016; 231(4): 915–925.
10.1002/jcp.25196
CASPubMedWeb of Science®Google Scholar
24Bhardwaj M, Weigl K, Tikk K, Benner A, Schrotz-King P, Brenner H. Multiplex screening of 275 plasma protein biomarkers to identify a signature for early detection of colorectal cancer. Mol Oncol. 2020; 14(1): 8–21.
10.1002/1878-0261.12591
CASPubMedWeb of Science®Google Scholar
25Bhardwaj M, Weigl K, Tikk K, Holland-Letz T, Schrotz-King P, Borchers CH, et al. Multiplex quantitation of 270 plasma protein markers to identify a signature for early detection of colorectal cancer. Eur J Cancer. 2020; 127: 30–40.
10.1016/j.ejca.2019.11.021
CASPubMedGoogle Scholar
26Bünger S, Haug U, Kelly M, Posorski N, Klempt-Giessing K, Cartwright A, et al. A novel multiplex-protein array for serum diagnostics of colon cancer: a case-control study. BMC Cancer. 2012; 12: 393.
10.1186/1471-2407-12-393
CASPubMedGoogle Scholar
27Cai L, Tu M, Yin X, Zhang S, Zhuang W, Xia Y, et al. Combination of serum CST4 and DR-70 contributes to early diagnosis of colorectal cancer. Clin Chim Acta. 2022; 531: 318–324.
10.1016/j.cca.2022.04.1000
CASPubMedGoogle Scholar
28Chen H, Werner S, Butt J, Zörnig I, Knebel P, Michel A, et al. Prospective evaluation of 64 serum autoantibodies as biomarkers for early detection of colorectal cancer in a true screening setting. Oncotarget. 2016; 7(13): 16420–16432.
10.18632/oncotarget.7500
PubMedGoogle Scholar
29Chen H, Qian J, Werner S, Cuk K, Knebel P, Brenner H. Development and validation of a panel of five proteins as blood biomarkers for early detection of colorectal cancer. Clin Epidemiol. 2017; 9: 517–526.
10.2147/CLEP.S144171
PubMedWeb of Science®Google Scholar
30Chen M, Lin X, Zhang L, Yu L, Wu Q, Zhang S, et al. Development of a panel of serum IgG and IgA autoantibodies for early diagnosis of colon cancer. Int J Med Sci. 2020; 17(17): 2744–2750.
10.7150/ijms.50169
CASPubMedGoogle Scholar
31Christensen IJ, Brünner N, Dowell B, Davis G, Nielsen HJ, Newstead G, et al. Plasma TIMP-1 and CEA as markers for detection of primary colorectal cancer: a prospective validation study including symptomatic and non-symptomatic individuals. Anticancer Res. 2015; 35(9): 4935–4941.
CASPubMedGoogle Scholar
32Dai C, Zhang X, Ma Y, Chen Z, Chen S, Zhang Y, et al. Serum macrophage inhibitory cytokine-1 serves as a novel diagnostic biomarker of early-stage colorectal cancer. Biomarkers. 2021; 26(7): 598–605.
10.1080/1354750X.2021.1950209
CASPubMedGoogle Scholar
33De Chiara L, Rodríguez-Piñeiro AM, Rodríguez-Berrocal FJ, Cordero OJ, Martínez-Ares D, Páez de la Cadena M. Serum CD26 is related to histopathological polyp traits and behaves as a marker for colorectal cancer and advanced adenomas. BMC Cancer. 2010; 10: 333.
10.1186/1471-2407-10-333
PubMedGoogle Scholar
34De Chiara L, Barcia-Castro L, Gallardo-Gomez M, Paez de la Cadena M, Martinez-Zorzano VS, Rodriguez-Berrocal FJ, et al. Evaluation of blood soluble CD26 as a complementary biomarker for colorectal cancer screening programs. Cancers (Basel). 2022; 14(19):4563.
10.3390/cancers14194563
CASPubMedGoogle Scholar
35Deng B-G, Yao J-H, Liu Q-Y, Feng X-J, Liu D, Zhao L, et al. Comparative serum proteomic analysis of serum diagnosis proteins of colorectal cancer based on magnetic bead separation and maldi-tof mass spectrometry. Asian Pac J Cancer Prev. 2013; 14(10): 6069–6075.
10.7314/APJCP.2013.14.10.6069
PubMedGoogle Scholar
36Dowling P, Hughes DJ, Larkin AM, Meiller J, Henry M, Meleady P, et al. Elevated levels of 14-3-3 proteins, serotonin, gamma enolase and pyruvate kinase identified in clinical samples from patients diagnosed with colorectal cancer. Clin Chim Acta. 2015; 441: 133–141.
10.1016/j.cca.2014.12.005
CASPubMedWeb of Science®Google Scholar
37Dressen K, Hermann N, Manekeller S, Walgenbach-Bruenagel G, Schildberg FA, Hettwer K, et al. Diagnostic performance of a novel multiplex immunoassay in colorectal cancer. Anticancer Res. 2017; 37(5): 2477–2486.
10.21873/anticanres.11588
CASPubMedGoogle Scholar
38Farshidfar F, Weljie AM, Kopciuk KA, Hilsden R, McGregor SE, Buie WD, et al. A validated metabolomic signature for colorectal cancer: exploration of the clinical value of metabolomics. Br J Cancer. 2016; 115(7): 848–857.
10.1038/bjc.2016.243
CASPubMedWeb of Science®Google Scholar
39Fitzgerald S, O'Reilly J-A, Wilson E, Joyce A, Farrell R, Kenny D, et al. Measurement of the IgM and IgG autoantibody immune responses in human serum has high predictive value for the presence of colorectal cancer. Clin Colorectal Cancer. 2019; 18(1): e53–e60.
10.1016/j.clcc.2018.09.009
PubMedWeb of Science®Google Scholar
40Garranzo-Asensio M, San Segundo-Acosta P, Povés C, Fernández-Aceñero MJ, Martínez-Useros J, Montero-Calle A, et al. Identification of tumor-associated antigens with diagnostic ability of colorectal cancer by in-depth immunomic and seroproteomic analysis. J Proteomics. 2020; 214:103635.
10.1016/j.jprot.2020.103635
CASPubMedWeb of Science®Google Scholar
41Gawel SH, Lucht M, Gomer H, Treado P, Christensen IJ, Nielsen HJ, et al. Evaluation of algorithm development approaches: development of biomarker panels for early detection of colorectal lesions. Clin Chim Acta. 2019; 498: 108–115.
10.1016/j.cca.2019.08.007
CASPubMedGoogle Scholar
42Gimeno-García AZ, Triñanes J, Quintero E, Salido E, Nicolás-Pérez D, Adrián-de-Ganzo Z, et al. Plasma matrix metalloproteinase 9 as an early surrogate biomarker of advanced colorectal neoplasia. Gastroenterol Hepatol. 2016; 39(7): 433–441.
10.1016/j.gastrohep.2015.10.002
PubMedGoogle Scholar
43Groblewska M, Mroczko B, Gryko M, Kędra B, Szmitkowski M. Matrix metalloproteinase 2 and tissue inhibitor of matrix metalloproteinases 2 in the diagnosis of colorectal adenoma and cancer patients. Folia Histochem Cytobiol. 2010; 48(4): 564–571.
PubMedGoogle Scholar
44Gu J, Xiao Y, Shu D, Liang X, Hu X, Xie Y, et al. Metabolomics analysis in serum from patients with colorectal polyp and colorectal cancer by 1H-NMR spectrometry. Dis Markers. 2019; 2019:3491852.
10.1155/2019/3491852
PubMedGoogle Scholar
45Gu Y, Duan B, Sha J, Zhang R, Fan J, Xu X, et al. Serum IgG N-glycans enable early detection and early relapse prediction of colorectal cancer. Int J Cancer. 2023; 152(3): 536–547.
10.1002/ijc.34298
CASPubMedWeb of Science®Google Scholar
46Han S, Wang J, Wang L, Jin G, Ying X, He C, et al. The role of RCAS1 as a biomarker in diagnosing CRC and monitoring tumor recurrence and metastasis. Tumour Biol. 2014; 35(6): 6149–6157.
10.1007/s13277-014-1814-3
CASPubMedGoogle Scholar
47Huang Z, Li Z, Chen X, Zhu X, Zhang J, Song Y, et al. Comparison between clinical utility of CXCL-8 and clinical practice tumor markers for colorectal cancer diagnosis. Biomed Res Int. 2022; 2022:1213968.
10.1155/2022/1213968
PubMedGoogle Scholar
48Huang M, Yang Z, Ren J, Wang T, Chen D, Zhan Y, et al. The diagnosis significance of serum cysteine protease inhibitors (CST4) in colorectal cancer. Technol Cancer Res Treat. 2023; 22:15330338231164232.
10.1177/15330338231164232
Google Scholar
49Ivancic MM, Megna BW, Sverchkov Y, Craven M, Reichelderfer M, Pickhardt PJ, et al. Noninvasive detection of colorectal carcinomas using serum protein biomarkers. J Surg Res. 2020; 246: 160–169.
10.1016/j.jss.2019.08.004
CASPubMedWeb of Science®Google Scholar
50Jiang X, Wang J, Wang M, Xuan M, Han S, Li C, et al. ITGB4 as a novel serum diagnosis biomarker and potential therapeutic target for colorectal cancer. Cancer Med. 2021; 10(19): 6823–6834.
10.1002/cam4.4216
CASPubMedWeb of Science®Google Scholar
51Johansen JS, Christensen IJ, Jørgensen LN, Olsen J, Rahr HB, Nielsen KT, et al. Serum YKL-40 in risk assessment for colorectal cancer: a prospective study of 4,496 subjects at risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2015; 24(3): 621–626.
10.1158/1055-9965.EPI-13-1281
CASPubMedWeb of Science®Google Scholar
52Ke X, Liu W, Shen L, Zhang Y, Liu W, Wang C, et al. Early screening of colorectal precancerous lesions based on combined measurement of multiple serum tumor markers using artificial neural network analysis. Biosensors (Basel). 2023; 13(7): 685.
10.3390/bios13070685
CASPubMedGoogle Scholar
53Kleif J, Jørgensen LN, Hendel JW, Madsen MR, Vilandt J, Brandsborg S, et al. Early detection of colorectal neoplasia: application of a blood-based serological protein test on subjects undergoing population-based screening. Br J Cancer. 2022; 126(10): 1387–1393.
10.1038/s41416-022-01712-x
CASPubMedGoogle Scholar
54Kraus S, Shapira S, Kazanov D, Naumov I, Moshkowitz M, Santo E, et al. Predictive levels of CD24 in peripheral blood leukocytes for the early detection of colorectal adenomas and adenocarcinomas. Dis Markers. 2015; 2015:916098.
10.1155/2015/916098
PubMedGoogle Scholar
55Li B, Shen K, Zhang J, Jiang Y, Yang T, Sun X, et al. Serum netrin-1 as a biomarker for colorectal cancer detection. Cancer Biomark. 2020; 28(3): 391–396.
10.3233/CBM-190340
CASPubMedGoogle Scholar
56Li Q, Wang K, Su C, Fang J. Serum trefoil factor 3 as a protein biomarker for the diagnosis of colorectal cancer. Technol Cancer Res Treat. 2017; 16(4): 440–445.
10.1177/1533034616674323
CASPubMedGoogle Scholar
57Li S, Huang M, Liu Q, Wang D, Wu R, Zhang X, et al. Serum expression of β-catenin is a potential detection marker in patients with colorectal cancer. Dis Markers. 2019; 2019:5070524.
10.1155/2019/5070524
PubMedGoogle Scholar
58Liu L, He Q, Li Y, Zhang B, Sun X, Shan J, et al. Serum SYPL1 is a promising diagnostic biomarker for colorectal cancer. Clin Chim Acta. 2020; 509: 36–42.
10.1016/j.cca.2020.05.048
CASPubMedGoogle Scholar
59Liu Z, Tang H, Zhang W, Wang J, Wan L, Li X, et al. Coupling of serum CK20 and hyper-methylated CLIP4 as promising biomarker for colorectal cancer diagnosis: from bioinformatics screening to clinical validation. Aging (Albany NY). 2021; 13(24): 26161–26179.
10.18632/aging.203804
CASPubMedGoogle Scholar
60Meng W, Zhu H-H, Xu Z-F, Cai S-R, Dong Q, Pan Q-R, et al. Serum M2-pyruvate kinase: a promising non-invasive biomarker for colorectal cancer mass screening. World J Gastrointest Oncol. 2012; 4(6): 145–151.
10.4251/wjgo.v4.i6.145
PubMedGoogle Scholar
61Montero-Calle A, Garranzo-Asensio M, Torrente-Rodríguez RM, Ruiz-Valdepeñas Montiel V, Poves C, Dziaková J, et al. p53 and p63 proteoforms derived from alternative splicing possess differential seroreactivity in colorectal cancer with distinct diagnostic ability from the canonical proteins. Cancers (Basel). 2023; 15(7): 2102.
10.3390/cancers15072102
CASPubMedGoogle Scholar
62Moravkova P, Kohoutova D, Vavrova J, Bures J. Serum S100A6, S100A8, S100A9 and S100A11 proteins in colorectal neoplasia: results of a single centre prospective study. Scand J Clin Lab Invest. 2020; 80(3): 173–178.
10.1080/00365513.2019.1704050
CASPubMedGoogle Scholar
63Mroczko B, Groblewska M, Okulczyk B, Kedra B, Szmitkowski M. The diagnostic value of matrix metalloproteinase 9 (MMP-9) and tissue inhibitor of matrix metalloproteinases 1 (TIMP-1) determination in the sera of colorectal adenoma and cancer patients. Int J Colorectal Dis. 2010; 25(10): 1177–1184.
10.1007/s00384-010-0991-9
PubMedGoogle Scholar
64Nielsen HJ, Brünner N, Jorgensen LN, Olsen J, Rahr HB, Thygesen K, et al. Plasma TIMP-1 and CEA in detection of primary colorectal cancer: a prospective, population based study of 4509 high-risk individuals. Scand J Gastroenterol. 2011; 46(1): 60–69.
10.3109/00365521.2010.513060
CASPubMedGoogle Scholar
65Otero-Estévez O, De Chiara L, Rodríguez-Berrocal FJ, Páez de la Cadena M, Cubiella J, Castro I, et al. Serum sCD26 for colorectal cancer screening in family-risk individuals: comparison with faecal immunochemical test. Br J Cancer. 2015; 112(2): 375–381.
10.1038/bjc.2014.605
CASPubMedGoogle Scholar
66Otero-Estévez O, De Chiara L, Rodríguez-Girondo M, Rodríguez-Berrocal FJ, Cubiella J, Castro I, et al. Serum matrix metalloproteinase-9 in colorectal cancer family-risk population screening. Sci Rep. 2015; 5: 13030.
10.1038/srep13030
CASPubMedWeb of Science®Google Scholar
67Otero-Estévez O, De Chiara L, Barcia-Castro L, Páez de la Cadena M, Rodríguez-Berrocal FJ, Cubiella J, et al. Evaluation of serum nucleoside diphosphate kinase A for the detection of colorectal cancer. Sci Rep. 2016; 6:26703.
10.1038/srep26703
CASPubMedWeb of Science®Google Scholar
68Overholt BF, Wheeler DJ, Jordan T, Fritsche HA. CA11-19: a tumor marker for the detection of colorectal cancer. Gastrointest Endosc. 2016; 83(3): 545–551.
10.1016/j.gie.2015.06.041
PubMedGoogle Scholar
69Ozemir IA, Aslan S, Eren T, Bayraktar B, Bilgic C, Isbilen B, et al. The diagnostic and prognostic significance of serum neutrophil gelatinase-associated lipocalin levels in patients with colorectal cancer. Chirurgia (Bucur). 2016; 111(5): 414–421.
10.21614/chirurgia.111.5.414
PubMedGoogle Scholar
70Özgür E, Keskin M, Yörüker EE, Holdenrieder S, Gezer U. Plasma histone H4 and H4K20 trimethylation levels differ between colon cancer and precancerous polyps. In Vivo. 2019; 33(5): 1653–1658.
10.21873/invivo.11651
PubMedGoogle Scholar
71Pan Y, Zhang L, Zhang R, Han J, Qin W, Gu Y, et al. Screening and diagnosis of colorectal cancer and advanced adenoma by bionic glycome method and machine learning. Am J Cancer Res. 2021; 11(6): 3002–3020.
CASPubMedGoogle Scholar
72Pan Z, Hu Z, Guan L, Zhang L, Gao X, Yang L, et al. Diagnostic value of serum sphingolipids in patients with colorectal cancer. Analyst. 2022; 147(10): 2189–2197.
10.1039/D1AN02239C
CASPubMedWeb of Science®Google Scholar
73Peltier J, Roperch J-P, Audebert S, Borg J-P, Camoin L. Quantitative proteomic analysis exploring progression of colorectal cancer: modulation of the serpin family. J Proteomics. 2016; 148: 139–148.
10.1016/j.jprot.2016.07.031
CASPubMedWeb of Science®Google Scholar
74Petersen MM, Kleif J, Jørgensen LN, Hendel JW, Seidelin JB, Madsen MR, et al. Optimizing screening for colorectal cancer: an algorithm combining fecal immunochemical test, blood-based cancer-associated proteins and demographics to reduce colonoscopy burden. Clin Colorectal Cancer. 2023; 22(2): 199–210.
10.1016/j.clcc.2023.02.001
PubMedGoogle Scholar
75Qian J, Tikk K, Weigl K, Balavarca Y, Brenner H. Fibroblast growth factor 21 as a circulating biomarker at various stages of colorectal carcinogenesis. Br J Cancer. 2018; 119(11): 1374–1382.
10.1038/s41416-018-0280-x
CASPubMedWeb of Science®Google Scholar
76Qiu S, Nikolaou S, Fiorentino F, Rasheed S, Darzi A, Cunningham D, et al. Exploratory analysis of plasma neurotensin as a novel biomarker for early detection of colorectal polyp and cancer. Horm Cancer. 2019; 10(2–3): 128–135.
10.1007/s12672-019-00364-3
CASPubMedGoogle Scholar
77Rasmussen L, Nielsen HJ, Christensen IJ. Evaluation of a 92 multiplex protein panel in detection of colorectal cancer and high-risk adenoma in 784 symptomatic individuals. Cancer Biomark. 2021; 32(1): 73–84.
10.3233/CBM-203211
CASPubMedGoogle Scholar
78Rho J-H, Ladd JJ, Li CI, Potter JD, Zhang Y, Shelley D, et al. Protein and glycomic plasma markers for early detection of adenoma and colon cancer. Gut. 2018; 67(3): 473–484.
10.1136/gutjnl-2016-312794
CASPubMedWeb of Science®Google Scholar
79Rigi F, Jannatabad A, Izanloo A, Roshanravan R, Hashemian HR, Kerachian MA. Expression of tumor pyruvate kinase M2 isoform in plasma and stool of patients with colorectal cancer or adenomatous polyps. BMC Gastroenterol. 2020; 20(1): 241.
10.1186/s12876-020-01377-x
CASPubMedGoogle Scholar
80Solé X, Crous-Bou M, Cordero D, Olivares D, Guinó E, Sanz-Pamplona R, et al. Discovery and validation of new potential biomarkers for early detection of colon cancer. PLoS One. 2014; 9(9):e106748.
10.1371/journal.pone.0106748
PubMedWeb of Science®Google Scholar
81Song YF, Xu ZB, Zhu XJ, Tao X, Liu JL, Gao FL, et al. Serum Cyr61 as a potential biomarker for diagnosis of colorectal cancer. Clin Transl Oncol. 2017; 19(4): 519–524.
10.1007/s12094-016-1560-7
CASPubMedGoogle Scholar
82Song W-Y, Zhang X, Zhang Q, Zhang P-J, Zhang R. Clinical value evaluation of serum markers for early diagnosis of colorectal cancer. World J Gastrointest Oncol. 2020; 12(2): 219–227.
10.4251/wjgo.v12.i2.219
PubMedGoogle Scholar
83Storm L, Christensen IJ, Jensenius JC, Nielsen HJ, Thiel S, Danish Study Group on Early Detection of Colorectal Cancer. Evaluation of complement proteins as screening markers for colorectal cancer. Cancer Immunol Immunother. 2015; 64(1): 41–50.
10.1007/s00262-014-1615-y
CASPubMedGoogle Scholar
84Sun F, Tan Y-A, Gao Q-F, Li S-Q, Zhang J, Chen Q-G, et al. Circulating fibrinogen to pre-albumin ratio is a promising biomarker for diagnosis of colorectal cancer. J Clin Lab Anal. 2019; 33(1):e22635.
10.1002/jcla.22635
PubMedWeb of Science®Google Scholar
85Taguchi A, Rho J-H, Yan Q, Zhang Y, Zhao Y, Xu H, et al. MAPRE1 as a plasma biomarker for early-stage colorectal cancer and adenomas. Cancer Prev Res (Phila). 2015; 8(11): 1112–1119.
10.1158/1940-6207.CAPR-15-0077
CASPubMedWeb of Science®Google Scholar
86Tao S, Haug U, Kuhn K, Brenner H. Comparison and combination of blood-based inflammatory markers with faecal occult blood tests for non-invasive colorectal cancer screening. Br J Cancer. 2012; 106(8): 1424–1430.
10.1038/bjc.2012.104
CASPubMedWeb of Science®Google Scholar
87Thomas DS, Fourkala E-O, Apostolidou S, Gunu R, Ryan A, Jacobs I, et al. Evaluation of serum CEA, CYFRA21-1 and CA125 for the early detection of colorectal cancer using longitudinal preclinical samples. Br J Cancer. 2015; 113(2): 268–274.
10.1038/bjc.2015.202
CASPubMedWeb of Science®Google Scholar
88Thorsen SB, Lundberg M, Villablanca A, Christensen SLT, Belling KC, Nielsen BS, et al. Detection of serological biomarkers by proximity extension assay for detection of colorectal neoplasias in symptomatic individuals. J Transl Med. 2013; 11: 253.
10.1186/1479-5876-11-253
PubMedWeb of Science®Google Scholar
89Uchiyama K, Yagi N, Mizushima K, Higashimura Y, Hirai Y, Okayama T, et al. Serum metabolomics analysis for early detection of colorectal cancer. J Gastroenterol. 2017; 52(6): 677–694.
10.1007/s00535-016-1261-6
CASPubMedGoogle Scholar
90Uchiyama K, Naito Y, Yagi N, Mizushima K, Higashimura Y, Hirai Y, et al. Selected reaction monitoring for colorectal cancer diagnosis using a set of five serum peptides identified by BLOTCHIP®-MS analysis. J Gastroenterol. 2018; 53(11): 1179–1185.
10.1007/s00535-018-1448-0
CASPubMedGoogle Scholar
91van den Broek I, Sparidans RW, Engwegen JYMN, Cats A, Depla ACTM, Schellens JHM, et al. Evaluation of human neutrophil peptide-1, -2 and -3 as serum markers for colorectal cancer. Cancer Biomark. 2010; 7(2): 109–115.
10.3233/CBM-2010-0153
PubMedGoogle Scholar
92Wang J, Wang X, Lin S, Chen C, Wang C, Ma Q, et al. Identification of kininogen-1 as a serum biomarker for the early detection of advanced colorectal adenoma and colorectal cancer. PLoS One. 2013; 8(7):e70519.
10.1371/journal.pone.0070519
CASPubMedGoogle Scholar
93Wang X, Yang Z, Tian H, Li Y, Li M, Zhao W, et al. Circulating MIC-1/GDF15 is a complementary screening biomarker with CEA and correlates with liver metastasis and poor survival in colorectal cancer. Oncotarget. 2017; 8(15): 24892–24901.
10.18632/oncotarget.15279
PubMedWeb of Science®Google Scholar
94Wang D, Yuan W, Wang Y, Wu Q, Yang L, Li F, et al. Serum CCL20 combined with IL-17A as early diagnostic and prognostic biomarkers for human colorectal cancer. J Transl Med. 2019; 17(1): 253.
10.1186/s12967-019-2008-y
PubMedWeb of Science®Google Scholar
95Wang Z, Wang S, Liu Y, Gao S, Yu Y, Hu Z. Serum levels of BDNF in patients with adenoma and colorectal cancer. Dis Markers. 2021; 2021:8867368.
10.1155/2021/8867368
PubMedGoogle Scholar
96Wang H, Zhou Z, Li H, Xiang W, Lan Y, Dou X, et al. Blood biomarkers panels for screening of colorectal cancer and adenoma on a machine learning-assisted detection platform. Cancer Control. 2023; 30:10732748231222109.
10.1177/10732748231222109
Google Scholar
97Wang Q-Q, Zhou L, Qin G, Tan C, Zhou Y-C, Yao S-K. Leukocyte immunoglobulin-like receptor B2 overexpression as a promising therapeutic target and noninvasive screening biomarker for colorectal cancer. World J Gastroenterol. 2023; 29(37): 5313–5326.
10.3748/wjg.v29.i37.5313
CASPubMedGoogle Scholar
98Watany MM, Elmashad NM, Badawi R, Hawash N. Serum FBLN1 and STK31 as biomarkers of colorectal cancer and their ability to noninvasively differentiate colorectal cancer from benign polyps. Clin Chim Acta. 2018; 483: 151–155.
10.1016/j.cca.2018.04.038
CASPubMedWeb of Science®Google Scholar
99Weiss JV, Klein-Scory S, Kübler S, Reinacher-Schick A, Stricker I, Schmiegel W, et al. Soluble E-cadherin as a serum biomarker candidate: elevated levels in patients with late-stage colorectal carcinoma and FAP. Int J Cancer. 2011; 128(6): 1384–1392.
10.1002/ijc.25438
CASPubMedWeb of Science®Google Scholar
100Werner S, Krause F, Rolny V, Strobl M, Morgenstern D, Datz C, et al. Evaluation of a 5-marker blood test for colorectal cancer early detection in a colorectal cancer screening setting. Clin Cancer Res. 2016; 22(7): 1725–1733.
10.1158/1078-0432.CCR-15-1268
CASPubMedWeb of Science®Google Scholar
101Wild N, Andres H, Rollinger W, Krause F, Dilba P, Tacke M, et al. A combination of serum markers for the early detection of colorectal cancer. Clin Cancer Res. 2010; 16(24): 6111–6121.
10.1158/1078-0432.CCR-10-0119
CASPubMedWeb of Science®Google Scholar
102Wilhelmsen M, Christensen IJ, Rasmussen L, Jørgensen LN, Madsen MR, Vilandt J, et al. Detection of colorectal neoplasia: combination of eight blood-based, cancer-associated protein biomarkers. Int J Cancer. 2017; 140(6): 1436–1446.
10.1002/ijc.30558
CASPubMedWeb of Science®Google Scholar
103Wilson S, Damery S, Stocken DD, Dowswell G, Holder R, Ward ST, et al. Serum matrix metalloproteinase 9 and colorectal neoplasia: a community-based evaluation of a potential diagnostic test. Br J Cancer. 2012; 106(8): 1431–1438.
10.1038/bjc.2012.93
CASPubMedGoogle Scholar
104Wu S, Qi Y, Niu LM, Xie DX, Cui XL, Liu ZH. Activin A as a novel biomarker for colorectal adenocarcinoma in humans. Eur Rev Med Pharmacol Sci. 2015; 19(22): 4371–4378.
CASPubMedGoogle Scholar
105Xie H, Guo J-H, An W-M, Tian S-T, Yu H-P, Yang X-L, et al. Diagnostic value evaluation of trefoil factors family 3 for the early detection of colorectal cancer. World J Gastroenterol. 2017; 23(12): 2159–2167.
10.3748/wjg.v23.i12.2159
CASPubMedGoogle Scholar
106Xu W, Hu Y, Li J, He X, Fu Z, Pan T, et al. Study of distinct serum proteomics for the biomarkers discovery in colorectal cancer. Discov Med. 2015; 20(110): 239–253.
PubMedGoogle Scholar
107Yao L, Lao W, Zhang Y, Tang X, Hu X, He C, et al. Identification of EFEMP2 as a serum biomarker for the early detection of colorectal cancer with lectin affinity capture assisted secretome analysis of cultured fresh tissues. J Proteome Res. 2012; 11(6): 3281–3294.
10.1021/pr300020p
CASPubMedWeb of Science®Google Scholar
108Ye H-M, Lu Y-Z, Liang X-M, Lin Y-Z, Li Y, Zhang Z-Y, et al. Clinical significance of combined testing of YKL-40 with CEA in Chinese colorectal cancer patients. Clin Lab. 2014; 60(3): 397–405.
CASPubMedGoogle Scholar
109Yildirim K, Colak E, Aktimur R, Gun S, Taskin MH, Nigdelioglu A, et al. Clinical value of CXCL5 for determining of colorectal cancer. Asian Pac J Cancer Prev. 2018; 19(9): 2481–2484.
CASPubMedGoogle Scholar
110Zekri A-RN, Bakr YM, Ezzat MM, Zakaria MSE, Elbaz TM. Circulating levels of adipocytokines as potential biomarkers for early detection of colorectal carcinoma in Egyptian patients. Asian Pac J Cancer Prev. 2015; 16(16): 6923–6928.
10.7314/APJCP.2015.16.16.6923
PubMedGoogle Scholar
111Zhang S-Y, Lin M, Zhang H-B. Diagnostic value of carcinoembryonic antigen and carcinoma antigen 19-9 for colorectal carcinoma. Int J Clin Exp Pathol. 2015; 8(8): 9404–9409.
PubMedGoogle Scholar
112Zhou M, Li M, Liang X, Zhang Y, Huang H, Feng Y, et al. The significance of serum S100A9 and TNC levels as biomarkers in colorectal cancer. J Cancer. 2019; 10(22): 5315–5323.
10.7150/jca.31267
CASPubMedGoogle Scholar
113Zhu C-B, Wang C-X, Zhang X, Zhang J, Li W. Serum sHLA-G levels: a useful indicator in distinguishing colorectal cancer from benign colorectal diseases. Int J Cancer. 2011; 128(3): 617–622.
10.1002/ijc.25372
CASPubMedWeb of Science®Google Scholar
114Zhu J, Djukovic D, Deng L, Gu H, Himmati F, Chiorean EG, et al. Colorectal cancer detection using targeted serum metabolic profiling. J Proteome Res. 2014; 13(9): 4120–4130.
10.1021/pr500494u
CASPubMedWeb of Science®Google Scholar
115Click B, Pinsky PF, Hickey T, Doroudi M, Schoen RE. Association of colonoscopy adenoma findings with long-term colorectal cancer incidence. JAMA. 2018; 319(19): 2021–2031.
10.1001/jama.2018.5809
PubMedWeb of Science®Google Scholar
116Hundt S, Haug U, Brenner H. Blood markers for early detection of colorectal cancer: a systematic review. Cancer Epidemiol Biomarkers Prev. 2007; 16(10): 1935–1953.
10.1158/1055-9965.EPI-06-0994
CASPubMedWeb of Science®Google Scholar
117Bresalier RS, Senore C, Young GP, Allison J, Benamouzig R, Benton S, et al. An efficient strategy for evaluating new non-invasive screening tests for colorectal cancer: the guiding principles. Gut. 2023; 72(10): 1904–1918.
10.1136/gutjnl-2023-329701
PubMedWeb of Science®Google Scholar
118Dwan K, Gamble C, Williamson PR, Kirkham JJ, Reporting Bias Group. Systematic review of the empirical evidence of study publication bias and outcome reporting bias—an updated review. PLoS One. 2013; 8(7):e66844.
10.1371/journal.pone.0066844
CASPubMedWeb of Science®Google Scholar
119Etzioni R, Kooperberg C, Pepe M, Smith R, Gann PH. Combining biomarkers to detect disease with application to prostate cancer. Biostatistics. 2003; 4(4): 523–538.
10.1093/biostatistics/4.4.523
PubMedWeb of Science®Google Scholar
120Rex DK, Lehman GA, Ulbright TM, Smith JJ, Pound DC, Hawes RH, et al. Colonic neoplasia in asymptomatic persons with negative fecal occult blood tests: influence of age, gender, and family history. Am J Gastroenterol. 1993; 88(6): 825–831.
CASPubMedGoogle Scholar

Volume18, Issue11

November 2024

Pages 2629-2648

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Where do we stand with screening for colorectal cancer and advanced adenoma based on serum protein biomarkers? A systematic review

Abstract

Abbreviations

1 Introduction

2 Materials and Methods

2.1 Search strategy

2.2 Study selection

2.3 Data collection process

2.4 Reporting quality and level of evidence

2.5 Statistical analysis

3 Results

3.1 Study selection

3.2 Study characteristics

3.3 Risk of bias and applicability of studies

3.4 Global assessment of serum protein biomarkers for CRC screening

3.5 Global assessment of combinations of biomarkers for CRC screening

3.6 Combination of serum protein biomarkers with the FIT for CRC screening

4 Discussion

4.1 How to conduct new studies assessing biomarkers for CRC screening?

4.2 Heterogeneity of the methodological process used to combine biomarkers: which statistical method is the best?

4.3 Future serum protein biomarkers: which way to look?

4.4 Strengths of our review

4.5 Limitations of our review

5 Conclusion

Acknowledgments

Conflict of interest

Author contributions

Open Research

Data accessibility

Supporting Information

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