There is growing interest in early detection of colorectal cancer as current screening modalities lack compliance and specificity. This study systematically reviewed the literature to identify biomarkers for early detection of colorectal cancer and polyps. Literature searches were conducted for relevant papers since 2007. Human studies reporting on early detection of colorectal cancer and polyps using biomarkers were included. Methodologic quality was evaluated, and sensitivity, specificity, and the positive predictive value (PPV) were reported. The search strategy identified 3,348 abstracts. A total of 44 papers, examining 67 different tumor markers, were included. Overall sensitivities for colorectal cancer detection by fecal DNA markers ranged from 53% to 87%. Combining fecal DNA markers increased the sensitivity of colorectal cancer and adenoma detection. Canine scent detection had a sensitivity of detecting colorectal cancer of 99% and specificity of 97%. The PPV of immunochemical fecal occult blood test (iFOBT) is 1.26%, compared with 0.31% for the current screening method of guaiac fecal occult blood test (gFOBT). A panel of serum protein biomarkers provides a sensitivity and specificity above 85% for all stages of colorectal cancer, and a PPV of 0.72%. Combinations of fecal and serum biomarkers produce higher sensitivities, specificities, and PPVs for early detection of colorectal cancer and adenomas. Further research is required to validate these biomarkers in a well-structured population-based study. Cancer Epidemiol Biomarkers Prev; 23(9); 1712–28. ©2014 AACR.

Colorectal cancer is the third most commonly diagnosed cancer in the world. It is estimated that worldwide 1.23 million new cases of colorectal cancer are diagnosed annually, and around 608,000 deaths are due to colorectal cancer a year (1). Colorectal cancer is known as a “silent” disease, as many people do not develop indicators, such as bleeding or abdominal pain until the cancer is difficult to cure (2). Most colon cancers start as noncancerous growths called polyps. If the polyps are removed, then the cancer may be prevented. Survival from colorectal cancer is significantly affected by the stage of the disease at presentation. Those presenting with early cancers Dukes A (T1/2N0M0) have a 93.2% 5-year survival rate, in contrast to those presenting with a Dukes C (T3/4N1/2M0) cancer in which the 5-year survival rate drops to 47.7% (3). Therefore, early detection of precancerous colorectal lesions plays a key role in improving the 5-year survival rate.

Therefore, screening for colorectal cancer has the potential to reduce both the incidence and mortality from this disease (4). The key strategy for these screening programs is detecting and eliminating colonic lesions before they become cancerous or symptomatic, to remove them at an earlier stage of disease (4).

There are several screening modalities in practice for colorectal cancer, including fecal occult blood testing (FOBT), flexible sigmoidoscopy, and colonoscopy. Each one has its own merits and disadvantages. Pooled meta-analyses of randomized trials show that FOBT screening reduces colorectal cancer mortality by 16% and flexible sigmoidoscopy screening reduces colorectal cancer mortality by 30% (5). Despite certain degrees of success with these modalities, there are still overwhelming limitations.

Patient adherence to FOBT program is low at 40% to 50% (6). Other limitations of FOBT screening include its low sensitivity for polyps and detecting cancers located in the distal colon. In addition, the test has a relatively low specificity, and thus there are many false-positive screens, which, as can be seen from our cost analysis, have a significant cost implication. For it to be most effective, repetitive screening is necessary (7).

Flexible sigmoidoscopy is a fairly quick and safe test, which does not usually require the need for full bowel preparation and can be performed without sedation. There is also a lower risk of serious complications compared with colonoscopy, such as perforation or bleeding. However, compliance issues are still likely to be a problem, with pilot studies showing a likely uptake of only 50% (8). In addition, the quality of the prep can be very variable, which can limit its usefulness.

Colonoscopy is the gold-standard screening test, and is used in Germany, with a sensitivity and specificity for identifying polyps and cancers in excess of 98% (9). However, it is an invasive test, needs repeating frequently (3–5 years), and is expensive to implement, has poor compliance rates, and there is risk of perforation of between 1 in 1,000 and 10,000 colonoscopies. Therefore, these limitations render this test unsuccessful as a screening tool in terms of cost to implement in many countries. Computed tomography (CT) colonography is another alternative to colonoscopy, but has the same limitations as the latter, and radiation concerns limit its use in the general population (10).

There is anecdotal evidence that individuals who do not comply with the current screening programs are usually those with the highest risk of having a cancer (6). Therefore, a drive to identify simpler, less invasive tests to improve compliance has stimulated considerable interest in researching potential biomarkers.

A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Hundt and colleagues (11) in 2007 reviewed a wide variety of potential blood markers in their systematic review of colorectal cancer. In the last decade, there has been substantial experimental work in cancer research with significant improvements in our understanding of cancer biology and thereby identifying new potential targets. Several areas of interest in recent reviews have been the search for new epigenetic biomarkers (12), proteomic markers (13), and fecal DNA markers (14), in an attempt to develop a novel screening modality that can overcome the inherited limitations of the current screening modalities.

The aim of this study was to systematically review the recent literature to identify all published biomarkers for early detection of colorectal cancer and polyps, to summarize performance characteristics of each biomarker, to assess these characteristics within the context of disease prevalence, and evaluate their suitability to be used for designing new screening tests for colorectal cancer.

Search strategy

A comprehensive systematic review of published work was conducted according to the preferred reporting items of systematic review and meta-analysis (PRISMA) guidelines. Literature searches were performed in the Ovid SP versions of Medline, EMBASE, and PubMed using MeSH terms, search terms, and Boolean operators with synonyms and plurals in addition to keywords. The search strategy was designed by 3 reviewers (J.A. Conti, E. Jones, and N.K. Francis) and conducted by E. Jones and R. Shah. The search terms presented in Table 1 were used as keywords in several combinations to conduct the search strategy.

Table 1.

Search strategy

(CSC or “Cancer Stem Cell” or “molecular marker*” or biomarker* or “free cancer cells” or “stem cell*” or fecal or fecal).ti,ab. 
Limit 1 to y = “2006 -Current” 
(“early diagnosis” or “early detection” or diagnos* or detect*).ti,ab. 
Limit 3 to y = “2006 -Current” 
(Blood or serum or plasma or protein or DNA or RNA or tissue or assay).ti,ab. 
Limit 5 to y = “2006 -Current” 
(“colorectal cancer” or colorectal or colon* or colorectal or polyp or adenoma).ti,ab. 
Limit 7 to y = “2006 -Current” 
prognos*.ti,ab. 
10 Limit 9 to y = “2006–Current” 
11 2 and 4 and 6 and 8 
12 11 not 9 
(CSC or “Cancer Stem Cell” or “molecular marker*” or biomarker* or “free cancer cells” or “stem cell*” or fecal or fecal).ti,ab. 
Limit 1 to y = “2006 -Current” 
(“early diagnosis” or “early detection” or diagnos* or detect*).ti,ab. 
Limit 3 to y = “2006 -Current” 
(Blood or serum or plasma or protein or DNA or RNA or tissue or assay).ti,ab. 
Limit 5 to y = “2006 -Current” 
(“colorectal cancer” or colorectal or colon* or colorectal or polyp or adenoma).ti,ab. 
Limit 7 to y = “2006 -Current” 
prognos*.ti,ab. 
10 Limit 9 to y = “2006–Current” 
11 2 and 4 and 6 and 8 
12 11 not 9 

Two reviewers (J.A. Conti and N.K. Francis) independently assessed titles and abstracts of all abstracts as part of the primary screen. A secondary screen of titles and abstracts was then conducted by a further 3 reviewers (P.J.K. Kuppen, V. Vidart, and E. Jones). Following the second screen full text articles were obtained and reviewed by J.A. Conti, N.K. Francis, and R. Shah. The search results were supplemented with hand searching of the reference lists. The results were analyzed by R. Shah, J.A. Conti, and N.K. Francis. All authors contributed to drafting the manuscript.

Eligibility criteria

Studies published between January 1, 2007, and June 30, 2013, were included to ensure that all new published evidence on potential markers for colorectal cancer screening since the last large systematic review were encompassed. All study designs were included as well as validated and unvalidated measures. The review was limited to studies on humans published in English that addressed early detection of colorectal cancer and/or colorectal polyps using biomarkers.

Exclusion criteria

Reasons for exclusion included studies with less than 10 participants, those conducted on cell lines and not in part on human subjects. In addition, studies that were designed for prognostic purposes and/or to assess advanced cancer (defined as stage III or IV) or its response to chemotherapy were excluded. In addition, the study was limited to biomarkers; hence, all other conventional tumor blood markers such as carcinoembryonic antigen were excluded. Finally, abstracts and conference proceedings were excluded because of the probability of incomplete data for a thorough review.

Data extraction

The studies, which satisfied the inclusion criteria, were categorized into fecal assessment, blood or serum assessment, tissue assessment, and a combination of tissue and blood assessment. These were then further subdivided depending on the category of marker being examined: (i) DNA biomarkers, (ii) RNA biomarkers, (iii) protein biomarkers, or (iv) other. Information about the number of cases and controls was obtained from each article. The cases were separated into those with colorectal cancer or those with adenomas, and where data were available, these were further partitioned by tumor stage or by adenoma size.

Outcome measures

The sensitivity and specificity, alongside their 95% confidence interval (CI) ranges, of each tumor marker was sought in order to describe the tumor markers performance characteristics and usefulness of each diagnostic test in their ability to detect a person with colorectal cancer or exclude a patient without colorectal cancer. The sensitivity of a test was defined as the probability that an individual with the disease would screen positive, and the specificity of the test was defined as the probability that an individual without the disease would screen negative. Combining the sensitivity and specificity alone could not be performed to estimate the probability of disease in a patient, or the usefulness of the test as a screening tool. However, when used in conjunction with disease prevalence, a positive predictive value (PPV) and negative predictive value were obtained.

Positive and negative predictive values

Disease prevalence for colorectal cancer or adenoma was sought from the literature and applied to one nation for demonstration (the UK population size is 63,230,000; ref. 15). Out of this population, approximately 8,852,200 would fall between the ages 60 to 74 years (16), for inclusion in the colorectal cancer screening program. The number of new cases of colorectal cancer diagnosed per year in the UK is approximately 40,695 (17), which leads to a disease prevalence of 1 in 1,500 people in the United Kingdom with colorectal cancer. Approximately 20% of the screening population have adenomas (18) but relatively few of these long-term will become cancers. Combining these values with the biomarker sensitivity and specificity in this review is aimed to enable calculation of the predictive values. A PPV illustrated the probability that an individual with a positive screening result has the disease; where as the negative predictive value illustrated the probability of a disease-free individual being given a negative result. This level of analysis enables an accurate evaluation of the diagnostic utility of biomarkers for detecting colorectal cancer or adenomas.

Methodologic quality

Methodologic quality of the 44 included studies was assessed. Points relevant to laboratory studies taken from the Cochrane collaboration's tool for assessing risk of bias (19) included assessment of detection bias (blinding of outcome assessment) and selection bias (randomization can prevent selecting interventions to participants). Data were also extracted on age and gender matching and whether or not the test was repeated, as these are also known indicators of quality in laboratory studies. Studies were graded under 1 of 3 categories [A = adequate (yes); B = unclear (not specified); C = not used (no); Fig. 1].

Figure 1.

Risk of bias assessment.

Figure 1.

Risk of bias assessment.

Close modal

A PRISMA diagram of studies selected for this systematic review is summarized in Fig. 2. The search strategy identified 3,348 suitable abstracts, from which 3,125 were excluded by review of the title and abstract during the primary and secondary screens, as they did not meet the eligibility criteria. Full text articles were obtained for 223 studies. A total of 179 of these articles were excluded for differing reasons, including not being original research articles (32 articles); written in a foreign language without an English translation (18 articles); research conducted on animals or cell lines, not humans (14 articles); reported inappropriate outcomes (19 articles); were not specific to colorectal cancer detection (19 articles); or did not have enough participants (15 articles).

Figure 2.

PRISMA diagram of studies searched and selected.

Figure 2.

PRISMA diagram of studies searched and selected.

Close modal

A total of 44 papers, examining 67 different tumor markers were included in this review for data extraction and analysis. Included studies were conducted inGermany, UK, USA, Australia, China, Japan, Spain, India, Italy, Poland, Sweden, Netherlands, Denmark, Canada, and Greece. They described a total of 9,908 participants: 3,393 in fecal testing, 4,628 in blood testing, 1,665 in tissue testing, and 222 in combined blood and tissue testing.

Fecal biomarkers

A total of 16 papers (20–35) evaluated 17 different fecal tumor markers. The results of all papers on fecal biomarkers are summarized in Table 2. These were further subdivided into fecal DNA biomarkers, RNA markers, combined DNA and RNA markers, protein assay markers, and other markers.

Table 2.

Summary of fecal biomarkers

Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
1. Fecal DNA Biomarkers 
 Zhang 2013 (20) Intern J Med Sci MS-PCR SP20 DNA hypermethylation 30 96 CRC 80.2 NS 100 NS 
 Zhang 2012 (21) Intern J Cancer Epidemiol Fluorescent quantitative Alu PCR TFPI2 DNA promoter methylation 30 20 60 CRC 68.3  100  
   Long DNA     CRC 53.3 NS 83.3 NS 
   TFP12 + Long DNA     CRC 86.7  83.3  
   Long DNA     CRC 79 59–92 92 84–96 
        Ad 17 9–28   
 Kalimutho 2010 (22) Int J Colorectal Dis QdHPLC iFOBT DNA integrity 95 69 34 Ad >1 cm 28 CRC 52 32–71 98 93–100 
      18 Ad <1 cm  Ad 21 12–33   
   Calprotectin   17 HP  CRC 72 51–88 75.5 66–84 
        Ad 28 18–41   
   Long DNA + iFOBT     CRC 89.3 72–98 94.7 88–98 
        Ad 33 22–46   
 Azuara 2010 (23) Clin Colorectal Cancer MS-PCR 4 gene panel – RARB2, P16INK4A, MGMT, APC DNA hypermethylation 20 20 26 CRC 62 41–83 NS NS 
        Ad 40 19–62   
 Melotte 2009 (24) J Natl Cancer Inst MS-PCR NDRG4 DNA promoter methylation 75 75 CRC 61 43–79 93 90–97 
 Glockner 2009 (25) Cancer Res MS-PCR TFPI2 DNA promoter methylation 30 19 47 CRC 76 60–88 93 77–99 
        Ad 21 6–46 93 78–99 
 Wang 2008 (26) World J Gastroenterol Methylight SFRP2 DNA hypermethylation 30 60: 69 CRC 87    
      34 Ad > 1 cm  Ad 61.8 NS 76.8 NS 
      26 HP  HP 42.3    
2. Combined fecal DNA + RNA biomarkers 
 Leung 2007 (27) Am J Gastroenterol MS-PCR COX2 MRNA     CRC 50 27–73 93 77–92 
    Overexpression of mRNA + DNA hypermethylation 30 30 20 Ad 4 0–20   
   6 Gene panel—APC, ATM, hMLH1, sFRP2, HLTF, MGMT   8 Ad > 1 cm      
      17 Ad <1 cm  CRC 75 51–91 90 74–98 
      5 HP  Ad 68 47–85   
3. Fecal RNA biomarkers 
 Takai 2009 (28) Cancer Epidemiol Biomarker RT-PCR COX2 mRNA Over expression of mRNA    CRC 87 76–94 100 90–100 
        A/B/C/D    
        77/96/82/82    
     29 62     
   MMP7 mRNA    Dukes CRC 65 51–76 100 90–100 
       A/B/C/D A/B/C/D    
       13/27/11/11 38/78/73/55    
   iFOBT     CRC 73 60–83 90 73–98 
        A/B/C/D    
        38/81/91/73    
   COX2 mRNA + MMP7 mRNA     CRC 90 80–96   
4. Fecal protein assay biomarkers 
 Shastri 2008 (29) Am J Gastroenterol ELISA TuM2-PK  516 69 55 CRC 78.2 65–88 73.8 67–77 
    Isoenzyme expressionin proliferating cells  21 Ad > 1 cm  Ad 37.7 26–50   
      48 Ad < 1 cm      
   iFOBT     CRC 70.9 57–84 96.3 94–98 
        Ad 30.4 20–43   
 Mulder 2007 (30) Eur J Gastro & Hepatology ELISA TuM2-PK  63 47 52 CRC 85    
        A/B 67    
    Isoenzyme expression in proliferating cells    C/D 89 NS 90 NS 
        Ad 28    
   iFOBT     CRC 92    
        A/B 100 NS 97 NS 
        C/D 89    
        Ad 40    
 Koss 2007 (31) Int J Colorectal Dis ELISA TuM2-PK Isoenzyme expression in proliferating cells 13 10 32 CRC 91 76–97   
      5 Ad >1 cm Dukes Ad >1 cm 60 23–88 92 67–99 
      5 Ad <1 cm A/B/C Ad <1 cm 20 4–62   
       3/17/12     
   gFOBT     CRC 21 9–41   
        Ad >1 cm 20 4–62 100 76–100 
        Ad <1 cm 0 0–43   
 Haug 2007 (32) Br J Cancer ELISA TuM2-PK Isoenzyme expression in proliferating cells 917 65 CRC 68 55–79 79 NS 
       Dukes A/B/C/D    
       A/B/C/D 67/61/67/100    
       12/18/12/6     
 Shastri 2006 (33) Int J Cancer ELISA TuM2-PK Isoenzyme expression in proliferating cells 128 31 74 CRC 81 70–89 71.1 62–79 
      10 Ad > 1 cm  Ad 25.8 12–45   
      21 Ad < 1 cm      
   gFOBT     CRC 36.5 26–49 92.2 86–96 
        Ad 16.1 5–34   
 Tonus 2006 (34) World J Gastroenterol ELISA TuM2-PK Isoenzyme expression in proliferating cells 42 54 CRC 78 NS 93 NS 
        A/B/C/D    
        60/76/89/90    
5. Other fecal biomarkers 
 Sonoda 2011 (35) Gut Canine scent detection Volatile organic compounds Scent detection 148 37 CRC 97 NS 99 NS 
Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
1. Fecal DNA Biomarkers 
 Zhang 2013 (20) Intern J Med Sci MS-PCR SP20 DNA hypermethylation 30 96 CRC 80.2 NS 100 NS 
 Zhang 2012 (21) Intern J Cancer Epidemiol Fluorescent quantitative Alu PCR TFPI2 DNA promoter methylation 30 20 60 CRC 68.3  100  
   Long DNA     CRC 53.3 NS 83.3 NS 
   TFP12 + Long DNA     CRC 86.7  83.3  
   Long DNA     CRC 79 59–92 92 84–96 
        Ad 17 9–28   
 Kalimutho 2010 (22) Int J Colorectal Dis QdHPLC iFOBT DNA integrity 95 69 34 Ad >1 cm 28 CRC 52 32–71 98 93–100 
      18 Ad <1 cm  Ad 21 12–33   
   Calprotectin   17 HP  CRC 72 51–88 75.5 66–84 
        Ad 28 18–41   
   Long DNA + iFOBT     CRC 89.3 72–98 94.7 88–98 
        Ad 33 22–46   
 Azuara 2010 (23) Clin Colorectal Cancer MS-PCR 4 gene panel – RARB2, P16INK4A, MGMT, APC DNA hypermethylation 20 20 26 CRC 62 41–83 NS NS 
        Ad 40 19–62   
 Melotte 2009 (24) J Natl Cancer Inst MS-PCR NDRG4 DNA promoter methylation 75 75 CRC 61 43–79 93 90–97 
 Glockner 2009 (25) Cancer Res MS-PCR TFPI2 DNA promoter methylation 30 19 47 CRC 76 60–88 93 77–99 
        Ad 21 6–46 93 78–99 
 Wang 2008 (26) World J Gastroenterol Methylight SFRP2 DNA hypermethylation 30 60: 69 CRC 87    
      34 Ad > 1 cm  Ad 61.8 NS 76.8 NS 
      26 HP  HP 42.3    
2. Combined fecal DNA + RNA biomarkers 
 Leung 2007 (27) Am J Gastroenterol MS-PCR COX2 MRNA     CRC 50 27–73 93 77–92 
    Overexpression of mRNA + DNA hypermethylation 30 30 20 Ad 4 0–20   
   6 Gene panel—APC, ATM, hMLH1, sFRP2, HLTF, MGMT   8 Ad > 1 cm      
      17 Ad <1 cm  CRC 75 51–91 90 74–98 
      5 HP  Ad 68 47–85   
3. Fecal RNA biomarkers 
 Takai 2009 (28) Cancer Epidemiol Biomarker RT-PCR COX2 mRNA Over expression of mRNA    CRC 87 76–94 100 90–100 
        A/B/C/D    
        77/96/82/82    
     29 62     
   MMP7 mRNA    Dukes CRC 65 51–76 100 90–100 
       A/B/C/D A/B/C/D    
       13/27/11/11 38/78/73/55    
   iFOBT     CRC 73 60–83 90 73–98 
        A/B/C/D    
        38/81/91/73    
   COX2 mRNA + MMP7 mRNA     CRC 90 80–96   
4. Fecal protein assay biomarkers 
 Shastri 2008 (29) Am J Gastroenterol ELISA TuM2-PK  516 69 55 CRC 78.2 65–88 73.8 67–77 
    Isoenzyme expressionin proliferating cells  21 Ad > 1 cm  Ad 37.7 26–50   
      48 Ad < 1 cm      
   iFOBT     CRC 70.9 57–84 96.3 94–98 
        Ad 30.4 20–43   
 Mulder 2007 (30) Eur J Gastro & Hepatology ELISA TuM2-PK  63 47 52 CRC 85    
        A/B 67    
    Isoenzyme expression in proliferating cells    C/D 89 NS 90 NS 
        Ad 28    
   iFOBT     CRC 92    
        A/B 100 NS 97 NS 
        C/D 89    
        Ad 40    
 Koss 2007 (31) Int J Colorectal Dis ELISA TuM2-PK Isoenzyme expression in proliferating cells 13 10 32 CRC 91 76–97   
      5 Ad >1 cm Dukes Ad >1 cm 60 23–88 92 67–99 
      5 Ad <1 cm A/B/C Ad <1 cm 20 4–62   
       3/17/12     
   gFOBT     CRC 21 9–41   
        Ad >1 cm 20 4–62 100 76–100 
        Ad <1 cm 0 0–43   
 Haug 2007 (32) Br J Cancer ELISA TuM2-PK Isoenzyme expression in proliferating cells 917 65 CRC 68 55–79 79 NS 
       Dukes A/B/C/D    
       A/B/C/D 67/61/67/100    
       12/18/12/6     
 Shastri 2006 (33) Int J Cancer ELISA TuM2-PK Isoenzyme expression in proliferating cells 128 31 74 CRC 81 70–89 71.1 62–79 
      10 Ad > 1 cm  Ad 25.8 12–45   
      21 Ad < 1 cm      
   gFOBT     CRC 36.5 26–49 92.2 86–96 
        Ad 16.1 5–34   
 Tonus 2006 (34) World J Gastroenterol ELISA TuM2-PK Isoenzyme expression in proliferating cells 42 54 CRC 78 NS 93 NS 
        A/B/C/D    
        60/76/89/90    
5. Other fecal biomarkers 
 Sonoda 2011 (35) Gut Canine scent detection Volatile organic compounds Scent detection 148 37 CRC 97 NS 99 NS 

Abbreviations: Ad, adenoma; CRC, colorectal cancer.

Seven studies investigated fecal DNA markers, looking at DNA hypermethylation of a single gene, or of a panel of genes. Sample sizes for all 7 studies were relatively small with Kalimutho and colleagues (22) having the biggest sample size of 192 participants. Four of these studies (28, 30, 32, 34) assessed the tumor marker sensitivity according to colorectal cancer staging and 9 studies looked at the sensitivities for adenoma detection. Overall sensitivities for colorectal cancer detection by fecal DNA markers ranged from 53% to 87% with varying specificities, however, all above 76%. Adenoma detection sensitivity ranged from 17% to 61%.

Two studies (21, 25) examined the same tumor marker TFP12, obtaining similar results. Zhang and colleagues (21) however combined TFP12 with another marker, long DNA, to increase the sensitivity of colorectal cancer detection to 86%. Wang and colleagues (26), who evaluated SFRP2 expression, seemed to have very promising results with high sensitivities for both colorectal cancer and adenoma detection, however they have obtained these results with a significantly lower specificity (76%) than the other included studies. The fecal DNA markers, which obtained the highest sensitivities alongside high specificities, are SP20 (20) and long DNA, especially when long DNA is used in conjunction with another marker (TFP12 or iFOBT).

Two of the 16 papers evaluating fecal biomarkers examined mRNA markers. Takai and colleagues (28) looked at COX2 mRNA and MMP7 mRNA, whereas Leung and colleagues (27) solely looked at COX2 mRNA alongside a panel of DNA markers. Takai and colleagues (28) assessed different stages of colorectal cancer and Leung and colleagues (27) examined adenoma detection as well as colorectal cancer detection. Overall sensitivities for colorectal cancer detection with mRNA ranged from 38% to 96%, with Dukes B cancers having the higher sensitivity values.

Adenoma detection with COX2 mRNA only had a sensitivity of 4%. However, when COX2 mRNA and MMP7 mRNA where used as a combined marker, their sensitivity increased to 90% with a small 95% CI range. Leung and colleagues (27) assessed a 6-gene panel of DNA markers, which obtained a high sensitivity for adenoma detection (68%). This panel included SFRP2, which was also evaluated by Wang and colleagues (26) for adenoma detection, and showed a great improvement in specificity (90%) alongside a minor improvement in sensitivity when in combined use with other genes.

Six papers looking at fecal biomarkers assessed the same enzyme TuM2-PK as a potential biomarker in colorectal cancer detection, as this is derived from neoplastic colonocytes. These studies used a sandwich ELISA to measure TuM2-PK activity, obtaining overall sensitivities ranging from 68% to 91%. In the 2 studies by Shastri and colleagues (29, 33), they compared tumor M2-PK activity and guaiac based FOBT in the first study and subsequently immunologic FOBT in the second study. They found that although measuring tumor M2-PK activity was more sensitive than FOBT screening, when compared with iFOBT, the latter was more sensitive, cheaper, and faster than tumor M2-PK activity assays. Koss and colleagues (31) showed the tumor M2-PK assay could also be utilized to detect adenomas with a sensitivity of 60%. However, results were obtained on a sample size of 5 patients. When Shastri and colleagues (29) conducted sensitivities of M2-PK for adenoma detection for a larger sample size, sensitivities obtained were much lower at 37%.

The study by Sonoda and colleagues (35) looked at canine scent detection to determine whether odor material can become an effective tool in colorectal cancer screening. This test utilizes the olfactory ability of dogs to detect very low concentrations of the alkanes and aromatic compounds generated by tumors (volatile organic compounds, VOC). Canine scent detection had a sensitivity of detecting colorectal cancer of 99% and a specificity of 97% on a study of nearly 300 patients.

In summary, overall sensitivities for colorectal cancer detection by fecal DNA markers ranged from 53% to 87%, with varying specificities. Combining DNA markers increased the sensitivity of colorectal cancer detection and the use of a panel of fecal DNA biomarkers, as well as VOCs detection, seem promising options for future screening tools.

Blood/serum biomarkers

Table 3 lists the 24 (36–59) studies evaluating potential blood/plasma biomarkers in colorectal cancer detection. Overall sensitivity ranges from 30% to 94% with specificity greater than 46%. Eight papers assessed plasma DNA markers. Blood samples were analyzed for epigenetic changes of genes involved in the tumor progression sequence. Four of these papers (38, 40, 41, 43) evaluated a panel of 2 or more DNA markers. Only 3 of the 8 studies (40–42) reported results with a specificity >90%. Sample sizes ranged from 76 to 583 participants. All papers looked at differing tumor markers.

Table 3.

Summary of blood/serum biomarkers

Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
1. Serum/plasma DNA biomarkers 
 Pack 2013 (36) Int J Colorectal Dis MSP-SSCP E cadherin DNA hypermethylation 60 40 60 CRC 60 NS 84 NS 
       Stage Stage I 48  87  
       I/II/III/IV     
   APC    14/14/28/4 CRC 57 NS 86 NS 
        Stage I 57  89  
   SMAD4     CRC 52 NS 64 NS 
        Stage I 47  87  
   DAPK1     CRC 50 NS 74 NS 
        Stage I 43  70  
   FHIT     CRC 50 NS 84 NS 
        Stage I 29  67  
 Lange 2012 (37) PLos ONE Methylight THBD-M DNA hypermethylation 98 106 CRC 71 NS 80 NS 
       Stage Stage I/II 74    
       I/II/III/IV     
       28/30/45/3 NS    
   C9orf50-M       NS  
 Cassinotti 2012 (38) Int J Cancer MS-PCR 6 Gene panel - CYCD2, HIC1, PAX 5, RASSF1A, RB1, SRBC DNA hypermethylation 30 30 30 CRC 83.7 71–97 67.9 51–85 
      18 Ad >1 cm Stage Ad 54.6 37–72 64.5 47–82 
       I/II     
       11/19     
 Warren 2011 (39) BMC Med QRT-PCR SEPT9 DNA hypermethylation 94 50 CRC 90 77–96 88 80–94 
        Stage    
        I/II/III/IV    
        71/90/100/100    
        Stage I + II 86.8 71–95   
 Tanzer 2010 (40) PLoS ONE QRT-PCR ALX4 +SEPT9 DNA hypermethylation 22 49 Precancerous Colorectal lesion 71 30–95 95 75–99 
      36 ad Stage     
      13 HP I/III     
       4/1     
 Lee 2009 (41) Clin Cancer Res MS-PCR 4 gene panel - APC, MGMT, RASSF2A, Wif-1 DNA hypermethylation 276 64 243 CRC 86.5 82–91 92.1 88–95 
       Stage     
       I/II Ad 74.7 63–85 91.3 86–95 
       44/199     
 Lofton-Day 2008 (42) Clin Chem QRT-PCR TMEFF2 DNA hypermethylation 185 135 CRC 30  95  
   NGFR     CRC 33 NS 95 NS 
   SEPT9     CRC 52  95  
 Han 2008 (43) Clin Cancer Res QRT-PCR 5 gene panel - CDA, MGC20553, BANK1, BCNP1, MS4A1 DNA hypermethylation 57 58 94 NS 77 NS 
2. Blood/serum RNA biomarkers 
 Wang 2012 (44) PLoS ONE QRT-PCR miR601 RNA expression 58 43 90 CRC 69.2 NS 72.4 67–83 
       Stage Ad 72.1  51.7  
       I/II/III/IV     
   miR760    26/25/29/10 CRC 80 NS 72.4 71–86 
        Ad 69.8  62.1  
   miR760 + miR 29a + miR92a     CRC 83.3 NS 93.1 91–98 
 Kanaan 2012 (45) Ann Surg QRT-PCR miR21 Tumor-associated RNA expression 20 20 CRC 90 NS 90 NS 
 Huang 2010 (46) Int J Cancer QRT-PCR miR29a Tumor-associated RNA expression 59 37 100 CRC 69  89.1  
       Stage Ad 62.2  84.7  
       I/II/III/IV     
   miR92a    27/25/38/10 CRC 84 NS 71.2 NS 
        Ad 64.9  81.4  
   miR29a + miR92a     CRC 83  84.7  
        Ad 73  79.7  
 Ng 2009 (47) Gut QRT-PCR miR92 Tumor-associated RNA expression 69 90 CRC 89  70  
       Stage  NS  NS 
   miR17-3p    I/II/III/IV CRC 64  70  
       6/34/23/27     
3. Blood/serum protein assay biomarkers 
 Wilson 2012 (48) British J Cancer ELISA MMP9 Over expression of proteolytic enzymes 525 125 46 CRC 79 NS 70 NS 
 Liu 2011 (49) Int J Med Sci MALDI-TOFMS 4 molecular weights 2870.7, 3084, 9180.5, 13748.8 Protein finger-printing 120 144 CRC 92.85 NS 91.25 NS 
       Dukes     
       A/B/C/D     
       28/6/23/27     
 Chen 2011 (50) Clinic Chim Acta Western Blot RPH3AL auto-antibodies Auto-antibodies targeting tumor-associated antigens 63 84 CRC 72.6 NS 84.1 NS 
       Dukes     
       A+B/C+D Duke A+B 64.7    
       34/50     
        Dukes C+D 78    
 Mead 2011 (51) Br J Cancer ELISA Linel 79bp, Alu 247bp, Alu 115bp, Mitochondrail DNA Mitochondrial and small DNA fragments 35 26 24 CRC 83 NS 72 NS 
 Babel 2011 (52) Mol Cell Proteomics ELISA SULF1 Auto-antibodies targeting tumor-associated antigens 103 50 CRC 73.9  50  
   NHSL1     CRC 52.2  52  
   MST1     CRC 71.7  46  
   GTF2i     CRC 52.2 NS 58 NS 
   SREBF2     CRC 60.9  48  
   GRN     CRC 58.7  58  
   6 Combined     CRC 73.9  72  
 Pederson 2011 (53) Int J Cancer ELISA MUC1 + MUC4 Auto-antibodies with altered glycosylation and expression 53 58 CRC 79 NS 92 NS 
 Tagi 2010 (54) J Gastroeneterol ELISA 4 protein panel - DK-BLY, CEA, Ca 19-9, S-p53 Aberrantly expressed protein isoforms 25 130 CRC 60.6 NS 80.0 NS 
 Mroczko 2010 (55) Int J Colorectal Dis ELISA MMP9 Proteolytic enzyme degradation 70 35 75 CRC 55    
       Duke     
   TIMP-1    A/B/C/D CRC 61    
       0/28/27/20  NS 100  
   MMP9+TIMP-1     CRC 75    
   MMP9 + CEA     CRC 75    
 De Chiara 2010 (56) BMC Cancer ELISA sCD26 Diminished protein expression 68 108 33 CRC 81.8 65–93 79.4 68–88 
      48 Ad >1 cm Duke     
      40 Ad <1 cm A/B/C/D CRC+ Ad 58 47–69 75.5 69–81 
      18 HP 1/12/15/4     
 Kim 2009 (57) J Proteome Res Western Blot S100A8 Over expression of proteins in cancer tissue 21 11 77 CRC 41  95  
       Stage Ad+ Stage I CRC 32  95  
   S100A9    I/II/III/IV CRC 44  95  
       14/23/21/19 Ad + Stage I CRC 40 NS 95 NS 
   CEA     CRC 22  100  
        Ad + Stage I CRC 21  100  
 Fentz 2007 (58) Proteomics Clin Appl SELDI-TOF-MS Transthyretin Auto-antibodies targeting tumor-associated antigens 58 58 54 CRC 60.7  100  
       All stage III Ad 85.7  67.8  
   C3a-desArg     CRC 60.7 NS 92.5 NS 
        Ad 78.5  77.5  
   Transthyretin + C3a-desArg     CRC 60.7  100  
        Ad 96.4  70.3  
4. Other blood/serum biomarkers 
 Bellows 2011 (59) Canc Epidemiol Biomarkers Prev Flow cytometry MSCs Circulating progenitor cells 26 45 64  73  
   CPCs    Stage 51  58  
   LCs    I/II/III/IV 71 NS 81 NS 
   ECs    2/8/11/24 38  70  
   CD34 bright cells     77  66  
Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
1. Serum/plasma DNA biomarkers 
 Pack 2013 (36) Int J Colorectal Dis MSP-SSCP E cadherin DNA hypermethylation 60 40 60 CRC 60 NS 84 NS 
       Stage Stage I 48  87  
       I/II/III/IV     
   APC    14/14/28/4 CRC 57 NS 86 NS 
        Stage I 57  89  
   SMAD4     CRC 52 NS 64 NS 
        Stage I 47  87  
   DAPK1     CRC 50 NS 74 NS 
        Stage I 43  70  
   FHIT     CRC 50 NS 84 NS 
        Stage I 29  67  
 Lange 2012 (37) PLos ONE Methylight THBD-M DNA hypermethylation 98 106 CRC 71 NS 80 NS 
       Stage Stage I/II 74    
       I/II/III/IV     
       28/30/45/3 NS    
   C9orf50-M       NS  
 Cassinotti 2012 (38) Int J Cancer MS-PCR 6 Gene panel - CYCD2, HIC1, PAX 5, RASSF1A, RB1, SRBC DNA hypermethylation 30 30 30 CRC 83.7 71–97 67.9 51–85 
      18 Ad >1 cm Stage Ad 54.6 37–72 64.5 47–82 
       I/II     
       11/19     
 Warren 2011 (39) BMC Med QRT-PCR SEPT9 DNA hypermethylation 94 50 CRC 90 77–96 88 80–94 
        Stage    
        I/II/III/IV    
        71/90/100/100    
        Stage I + II 86.8 71–95   
 Tanzer 2010 (40) PLoS ONE QRT-PCR ALX4 +SEPT9 DNA hypermethylation 22 49 Precancerous Colorectal lesion 71 30–95 95 75–99 
      36 ad Stage     
      13 HP I/III     
       4/1     
 Lee 2009 (41) Clin Cancer Res MS-PCR 4 gene panel - APC, MGMT, RASSF2A, Wif-1 DNA hypermethylation 276 64 243 CRC 86.5 82–91 92.1 88–95 
       Stage     
       I/II Ad 74.7 63–85 91.3 86–95 
       44/199     
 Lofton-Day 2008 (42) Clin Chem QRT-PCR TMEFF2 DNA hypermethylation 185 135 CRC 30  95  
   NGFR     CRC 33 NS 95 NS 
   SEPT9     CRC 52  95  
 Han 2008 (43) Clin Cancer Res QRT-PCR 5 gene panel - CDA, MGC20553, BANK1, BCNP1, MS4A1 DNA hypermethylation 57 58 94 NS 77 NS 
2. Blood/serum RNA biomarkers 
 Wang 2012 (44) PLoS ONE QRT-PCR miR601 RNA expression 58 43 90 CRC 69.2 NS 72.4 67–83 
       Stage Ad 72.1  51.7  
       I/II/III/IV     
   miR760    26/25/29/10 CRC 80 NS 72.4 71–86 
        Ad 69.8  62.1  
   miR760 + miR 29a + miR92a     CRC 83.3 NS 93.1 91–98 
 Kanaan 2012 (45) Ann Surg QRT-PCR miR21 Tumor-associated RNA expression 20 20 CRC 90 NS 90 NS 
 Huang 2010 (46) Int J Cancer QRT-PCR miR29a Tumor-associated RNA expression 59 37 100 CRC 69  89.1  
       Stage Ad 62.2  84.7  
       I/II/III/IV     
   miR92a    27/25/38/10 CRC 84 NS 71.2 NS 
        Ad 64.9  81.4  
   miR29a + miR92a     CRC 83  84.7  
        Ad 73  79.7  
 Ng 2009 (47) Gut QRT-PCR miR92 Tumor-associated RNA expression 69 90 CRC 89  70  
       Stage  NS  NS 
   miR17-3p    I/II/III/IV CRC 64  70  
       6/34/23/27     
3. Blood/serum protein assay biomarkers 
 Wilson 2012 (48) British J Cancer ELISA MMP9 Over expression of proteolytic enzymes 525 125 46 CRC 79 NS 70 NS 
 Liu 2011 (49) Int J Med Sci MALDI-TOFMS 4 molecular weights 2870.7, 3084, 9180.5, 13748.8 Protein finger-printing 120 144 CRC 92.85 NS 91.25 NS 
       Dukes     
       A/B/C/D     
       28/6/23/27     
 Chen 2011 (50) Clinic Chim Acta Western Blot RPH3AL auto-antibodies Auto-antibodies targeting tumor-associated antigens 63 84 CRC 72.6 NS 84.1 NS 
       Dukes     
       A+B/C+D Duke A+B 64.7    
       34/50     
        Dukes C+D 78    
 Mead 2011 (51) Br J Cancer ELISA Linel 79bp, Alu 247bp, Alu 115bp, Mitochondrail DNA Mitochondrial and small DNA fragments 35 26 24 CRC 83 NS 72 NS 
 Babel 2011 (52) Mol Cell Proteomics ELISA SULF1 Auto-antibodies targeting tumor-associated antigens 103 50 CRC 73.9  50  
   NHSL1     CRC 52.2  52  
   MST1     CRC 71.7  46  
   GTF2i     CRC 52.2 NS 58 NS 
   SREBF2     CRC 60.9  48  
   GRN     CRC 58.7  58  
   6 Combined     CRC 73.9  72  
 Pederson 2011 (53) Int J Cancer ELISA MUC1 + MUC4 Auto-antibodies with altered glycosylation and expression 53 58 CRC 79 NS 92 NS 
 Tagi 2010 (54) J Gastroeneterol ELISA 4 protein panel - DK-BLY, CEA, Ca 19-9, S-p53 Aberrantly expressed protein isoforms 25 130 CRC 60.6 NS 80.0 NS 
 Mroczko 2010 (55) Int J Colorectal Dis ELISA MMP9 Proteolytic enzyme degradation 70 35 75 CRC 55    
       Duke     
   TIMP-1    A/B/C/D CRC 61    
       0/28/27/20  NS 100  
   MMP9+TIMP-1     CRC 75    
   MMP9 + CEA     CRC 75    
 De Chiara 2010 (56) BMC Cancer ELISA sCD26 Diminished protein expression 68 108 33 CRC 81.8 65–93 79.4 68–88 
      48 Ad >1 cm Duke     
      40 Ad <1 cm A/B/C/D CRC+ Ad 58 47–69 75.5 69–81 
      18 HP 1/12/15/4     
 Kim 2009 (57) J Proteome Res Western Blot S100A8 Over expression of proteins in cancer tissue 21 11 77 CRC 41  95  
       Stage Ad+ Stage I CRC 32  95  
   S100A9    I/II/III/IV CRC 44  95  
       14/23/21/19 Ad + Stage I CRC 40 NS 95 NS 
   CEA     CRC 22  100  
        Ad + Stage I CRC 21  100  
 Fentz 2007 (58) Proteomics Clin Appl SELDI-TOF-MS Transthyretin Auto-antibodies targeting tumor-associated antigens 58 58 54 CRC 60.7  100  
       All stage III Ad 85.7  67.8  
   C3a-desArg     CRC 60.7 NS 92.5 NS 
        Ad 78.5  77.5  
   Transthyretin + C3a-desArg     CRC 60.7  100  
        Ad 96.4  70.3  
4. Other blood/serum biomarkers 
 Bellows 2011 (59) Canc Epidemiol Biomarkers Prev Flow cytometry MSCs Circulating progenitor cells 26 45 64  73  
   CPCs    Stage 51  58  
   LCs    I/II/III/IV 71 NS 81 NS 
   ECs    2/8/11/24 38  70  
   CD34 bright cells     77  66  

Abbreviations: Ad, adenoma; CRC, colorectal cancer.

From the studies assessing DNA hypermethylation of a single gene, Warren and colleagues (39) had the most promising results. Evaluating SEPT9 expression in 144 participants achieved a sensitivity of 90% for all tumor–node–metastasis (TNM) stages of colorectal cancer, at 88% specificity, with 86% sensitivity for stage I + II detection. Lee and colleagues (41) reported performance characteristics for a panel of 4-gene expression. This study had the largest cohort of patients of 583 patients, and reported a sensitivity of 86% for colorectal cancer detection alongside sensitivity of 74% for adenoma detection with both specificities being above 91%.

Four studies (44–47) applied quantitative real-time polymerase chain reaction to detect microRNA (miRNA) expressed in circulating tumor cells. The miRNAs with most interest were miR601, miR760, miR21, miR29a, and miR92a. Huang and colleagues (46) reported performance characteristics for miR29a and miR92a. Combined use of these assays produced a sensitivity of 83% and specificity of 84% for colorectal cancer detection. Wang and colleagues (44) combined this panel of assays with a further miRNA, miR760, to maintain the sensitivity but improve the specificity to 93%. Kanaan and colleagues (45) investigated miR21 as a potential screening assay and obtained results with high sensitivities and specificities (90%); however, these were conducted on a small sample size of just 40.

Immune responses in patients with cancer may be initiated by alterations in the tumor itself that result in increased immunogenicity of self-antigens. Humoral immunity, or the development of autoantibodies against tumor-associated proteins, may be used as a marker for cancer exposure. A total of 11 papers reviewed protein assays, including autoantibodies. The overall sensitivities and specificities were lower in this group than those in the serum DNA and RNA assays. Liu and colleagues (49) demonstrated that protein finger printing could be used to screen critical proteins with differential expression in the serum of patients with colorectal cancer. They determined a panel of 4 proteins of different molecular weights, which were able to differentiate colorectal cancer from healthy controls with a sensitivity of 92% and specificity of 91%.

In summary, using panels of DNA or miRNAs seems to offer the most likely candidate serum markers, as a panel of protein markers maintains sensitivity, however increases specificity of all tumor stages.

Tissue and combined assessment biomarkers

The results of tissue, taken from biopsy samples, and combined tissue and serum biomarkers are summarized in Tables 4 and 5. Three articles (60–62) evaluated tissue biomarkers and only 1 paper (63) examined combined use of tissue and serum biomarkers. These papers looked at 10 potential biomarkers. Methylation loci, looking at a panel of 10 (60) in a study of approximately 100 patients, found that the VSX2 gene was most specific at identifying those at risk of colorectal cancer with a sensitivity of 83% and a specificity of 92%. The other papers did not mention their specificity values, however Magnusson and colleagues (62) combined 2 protein markers SATB2 and CK20 to achieve a sensitivity of 97% when tested on a large cohort of 1,074 carcinomas. Kanojia and colleagues (63) systematically investigated the sperm-associated antigen 9 gene (SPAG9) mRNA and protein expression in patients with colorectal cancer and their role in the tumorigenicity of colon cancer. SPAG9 was expressed in 74% of patients with colorectal cancer and demonstrated a sensitivity of 100% in blood and 88% in tissue samples in stages I + II of colorectal cancer development.

Table 4.

Summary of tissue biomarkers

Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
1. Tissue DNA biomarker 
 Mori 2011 (60) Endocr Relat Cancer QRT-PCR VSX2, BEND4, NPTX1, miR34b, GLP1R, HOMER2 DNA hypermethylation 34 51 VSX2 83.3 NS VSX2 92.3 NS 
 Lind 2011 (61) Oncogene MS-PCR SPG20 DNA hypermethylation 59 51 105 CRC 88 NS NS NS 
        Ad 82    
2. Tissue protein biomarkers 
 Magnusson 2011 (62) Am J Surg Pathol Immunohistochemistry Western Blot SATB2 Antibody expression 194 88 1074 SATB2 85 NS NS NS 
       Stage     
   CK20    I/II/III SATB2 + CK20 97    
       119/440/515     
Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
1. Tissue DNA biomarker 
 Mori 2011 (60) Endocr Relat Cancer QRT-PCR VSX2, BEND4, NPTX1, miR34b, GLP1R, HOMER2 DNA hypermethylation 34 51 VSX2 83.3 NS VSX2 92.3 NS 
 Lind 2011 (61) Oncogene MS-PCR SPG20 DNA hypermethylation 59 51 105 CRC 88 NS NS NS 
        Ad 82    
2. Tissue protein biomarkers 
 Magnusson 2011 (62) Am J Surg Pathol Immunohistochemistry Western Blot SATB2 Antibody expression 194 88 1074 SATB2 85 NS NS NS 
       Stage     
   CK20    I/II/III SATB2 + CK20 97    
       119/440/515     

Abbreviations: Ad, adenoma; CRC, colorectal cancer.

Table 5.

Summary of combined serum ± tissue biomarkers

Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
Kanoja 2011 (63) Am J Pathol RT-PCR SPAG9 Auto-antibody expression Tissue 40  78 Tissue stage NS NS NS 
  HIS     Stage I + II 88    
  ELISA     I + II/III + IV III + IV    
       26/52 67    
     Blood 50  54 Blood stage NS NS NS 
       Stage I + II 100    
       I + II/III + IV III + IV 62    
       12/42     
Number of participants included
Author + dateJournalMethodMarkerMechanismNCAdenomaColorectal cancerSensitivity (%)95% CISpecificity (%)95% CI
Kanoja 2011 (63) Am J Pathol RT-PCR SPAG9 Auto-antibody expression Tissue 40  78 Tissue stage NS NS NS 
  HIS     Stage I + II 88    
  ELISA     I + II/III + IV III + IV    
       26/52 67    
     Blood 50  54 Blood stage NS NS NS 
       Stage I + II 100    
       I + II/III + IV III + IV 62    
       12/42     

In summary, it is difficult to evaluate the true accuracy of the results obtained from tissue assessment of biomarkers, as only 1 study commented of specificity. However, VSX2 seems to be the most promising potential biomarker from this group.

Assessment of methodologic quality

Analysis revealed that overall methodologic quality, when judged against the criterions from the Cochrane collaboration's tool for assessing risk of bias, was poor. Blinding was the most well-reported methodologic standard with 41% of papers giving a clear description of samples being collected and prepared by independent blinded individuals (commonly both endoscopists and stool testing technicians) rendering risk of detection bias low. However, 54% of papers did not refer to blinding at all with 2 papers stipulating samples were conducted unblinded.

Twenty-seven percent of papers reported on the use of a random number table or random coding of samples before processing and testing. Repeated testing and age/gender matching was poorly reported with in-adequate description of the type of repeat testing and matching between normative and diseased groups. Data on attrition bias were not formally extracted. However, data on withdrawals were not identified in the initial screen and all participant data were included within analysis.

In laboratory studies it is important that assay techniques are quality assured and standardized. However, across the 44 full text papers identified there was a huge variation in the techniques used, with varying use of control populations. It is widely recognized that assay complexity, cost, and time factors play an important part in the choice of assay. Therefore, quality assurance and validation of techniques were not considered within this review.

Positive and negative predictive values of biomarkers within the context of disease prevalence

The current UK colorectal colorectal cancer program of gFOBT has a sensitivity of 36.5% and specificity of 92.2% (33). Using the screening population and disease prevalence figures calculated in the methodology section, we can estimate that from the bowel cancer screening population, 692,165 patients would have a positive screening test and be referred for further investigation with colonoscopy, from which only 2,154 patients would be truly positive for the disease. However, 3,746 patients would achieve a false-negative test, leading to a PPV for gFOBT of 0.31% (Supplementary Table S1).

If a different screening tool was implemented with a higher sensitivity and specificity for colorectal cancer detection, for example iFOBT, with a sensitivity of 70.9% and specificity of 96.3% (29), the number of patients undergoing further investigation for false-positive results would reduce to 327,313 and patients with colorectal cancer being missed through the screening program with a false-negative result would fall to 1,717. This increases the PPV to 1.26%, while maintaining a high-negative predictive value of 99.98% (Supplementary Table S2).

Applying prevalence to the detection of adenomas. Lee and colleagues (41) reported an adenoma detection sensitivity of 74.7% and specificity of 91.3% for a 4 gene panel—APC, MGMT, RASSF2A, and Wifi1. Looking at disease prevalence this would lend to a PPV of 68.22% and negative predictive value of 93.52%, with 1,938,632 patients in the screening population undergoing further investigation with colonoscopy, from which only 616 113 patients would be negative. However, approximately half a million patients from the screening population would have an adenoma missed by this screening tool. (Supplementary Table S3).

These calculations demonstrate that a small difference in the biomarkers performance characteristics has much larger consequences in terms of a potential screening tool as colorectal cancer has a relatively low prevalence.

The effectiveness of a screening program depends on the accuracy and the acceptance of the screening test used to detect the condition. An ideal screening test should have high compliance, sensitivity, and specificity, be minimally invasive and remain cost effective. Because of the limitations of the current screening modalities in colorectal cancer, there has been an increasing body of evidence researching on the role of biomarkers, as an alternative screening tool.

This systematic review, to our knowledge, is the first to report on all biomarkers across different mediums, including feces, blood, and tissue, that can detect colorectal cancer and adenomas. This appraisal also provides updated evidence on early detection of colorectal cancer using biomarkers since the last review on blood biomarkers by Hundt and colleagues (11) in 2007. In addition, this article also explores the performance characteristic of biomarkers within the context of disease prevalence of colorectal cancer and polyps.

The main finding of this review is supporting the use of combined tests to maximize the benefits of various systems of biomarkers for detection of colorectal cancer and polyps. This is likely to maximize the benefits of various biomarker systems, minimize the number of false positive tests, and the number of patients undergoing invasive investigations with the potential of complications. However, the difficulty at present is using these tests in a mass-screening program to produce reliable and reproducible results while remaining cost-effective. Further research is required.

This evaluation has identified that DNA markers are most likely to be of promise in the future as will detection of volatile organic compounds. Using panels of DNA (41) or miRNAs (46) seems to offer the most likely candidate serum or fecal markers, but further validation studies are required before considering them as a screening tool. Tissue markers are potentially useful when combined with endoscopy to help stratifying patients into high-risk groups, however the current available biomarkers are not suitable for this at present, because of high false-negative results.

This study however has its limitations. First, it can only report on the published data of the various tests and this can be limited by incomplete reporting of data in the original articles. For example, in many studies, characterization of the study population was rather scarce and some studies did not report on specificity and/or sensitivity. Second, because of heterogeneity between studies, a meta-analysis with pooling of results of different studies could not be conducted. Furthermore, reported sensitivities and specificities may provide an overoptimistic perspective because of publication bias, which may have led to selected publication of more promising results. Hence, we analyzed some of the results within the context of prevalence to generate PPVs.

This review has shown that fecal screening has been the mainstay in many screening programs. This is consistent with a recent expert panel recommending the use of a multitarget stool DNA test as a screening tool (64). The disadvantage with all fecal screening modalities is compliance as many people find this method of screening unpalatable and thus those that may benefit the most from it do not perform the test. Indeed, patient adherence to the current FOBT program is low at around 40% and 50% (6). The most reliable screening method demonstrated in this systematic review is canine scent detection for volatile organic compounds in feces (35). However, this requires further research to identify the optimum mechanism(s) of identifying these particular compounds.

A simple blood test, which can be included in a patients annual health check-up, could be the most successful screening test. This test is minimally invasive and requires little special preparation. Looking for panels of DNA and RNA markers seems to be the most promising test for identifying cancers. However, these all have limitations when it comes to identifying adenomas, although several miRNA markers (ref. 44; e.g., miR601, miR760, and miR29a) offer high sensitivities for identifying polyps and using panels of markers have increased their specificity.

Tissue biomarkers can be combined with both flexible sigmoidoscopy and colonoscopy screening to potentially identify patients with normal colons who are at increased risk of cancer and thereby potentially reduce the need for repeated screening. Looking at DNA hypermethylation seems to be a useful test with VSX2 expression (60) the most likely to be of use, and SATB2 antibody expression with CK20 (62) another candidate. However, if used as a screening method, it relies on patient compliance to have an endoscopy, which we expect will be about 50%, based on pilots (8).

One of the major problems with any potential biomarker as a screening tool candidate is that, although colorectal cancer and polyps are common conditions, the current screening options in terms of biomarkers do not have the necessary sensitivity and specificity to serve as general screening without a massive increase in costs. The prevalence of colorectal cancer and adenomas in the general population means that, with the current screening biomarker options, there would be low PPVs with many patients undergoing further investigation of a positive screening test with a colonoscopy. This would have a considerable cost impact, with 690,011 patients undergoing colonoscopies for false-positive screening tests with the current screening method of gFOBT, at an estimated annual cost of £800,000,000. There would also be a fall in the negative predictive value, meaning that more patients with the disease/adenoma would be missed through the screening program.

The current stage of evidence supports a call for prospectively planned, systematic evaluations of both the most promising fecal, blood, and tissue tests in a well-defined, large-scale screening population, with standardized sample collection, processing, and storage. This can be linked to national screening programs for either sigmoidoscopy or colonoscopy to ensure the representation of both participants from a screening population and adenoma carriers. It would also allow direct comparison of performance characteristics and practicality of single and multiple tests. Longitudinal studies are also required to assess the potential of quantifying biomarkers over time to provide increased sensitivity for an emergent malignancy.

There are other emerging biomarkers that are not included in this review, including urinary biomarkers and gut microbiomes, with recent studies evaluating their efficacy. Urinary PGE-M seems to be a promising biomarker for adenoma detection with high PGE-M urinary levels being associated with an increased risk of advanced or multiple adenomas (65). Several studies have recently looked at microbial dysbiosis, a pathologic imbalance in the microbial community, in the etiology of colorectal adenomas and colorectal cancer. These, however, are in the early stages with additional studies required to define further the best sampling location, mucosal or luminal, and to elucidate the exact connections between the host gut microbiome and the onset of colorectal cancer (66).

This systematic review has demonstrated that volatile organic metabolites have a great potential in the early detection of colorectal cancer and polyps. A recent study highlighted the potential of VOC profiling as a noninvasive test to identify those with esophagogastric cancer (67). Selected ion flow mass spectrometry was applied for the quantification of VOCs in exhaled breath, identifying 4 VOCs that were statistically different between the esophagogastric cancer group and the control group. Chemical analytical research could lead to the development of a noninvasive VOC-based test that could significantly contribute in the early diagnosis of colorectal cancer.

Further work is required to investigate further the potential role of volatile biomarker metabolites and the optimum techniques for their detection in order to predict early detection of colorectal cancer and polyps.

This review has demonstrated that there are several fecal and serum biomarkers that can predict colorectal cancer and polyps. However, when combined into biomarker panels, higher sensitivity, specificities, and PPV for early detection of colorectal cancer and adenomas are achieved. Further research is required to validate these biomarkers in a well-structured population-based study.

No potential conflicts of interest were disclosed.

The authors thank Dr. Ian Mitchell for his valuable contribution to this article.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Ferlay
J
,
Shin
HR
,
Bray
F
,
Forman
D
,
Mathers
C
,
Parkin
DM
. 
Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008
.
Int J Cancer
2010
;
127
:
2893
917
.
2.
Assess Your Risk for Colorectal Cancer. [cited 2014 Feb.] Available from
: http://www.fascrs.org/patients/treatments_and_screenings/assess_your_risk_for_colorectal_cancer/screening/
American Society of Colon and Rectal Surgeons 2014
.
3.
Colorectal Cancer Survival by Stage
.
National Cancer Intelligent Network Data
. 
June 2009
.
4.
Walsh
J
,
Terdiman
J
. 
Colorectal cancer screening – scientific review
.
JAMA
2003
;
289
:
1288
96
.
5.
Bretthauer
M
. 
Colorectal cancer screening
.
J Intern Med
2011
;
270
:
87
98
.
6.
Vernon
S
. 
Participation in colorectal cancer screening: a review
.
J Natl Cancer Inst
1997
;
89
:
1406
22
.
7.
Bond
JH
. 
Fecal occult blood test screening for colorectal cancer
.
Gastrointest Endosc Clin N Am
2002
;
12
:
11
21
.
8.
Robb
K
,
Power
E
,
Kralj-Hans
I
,
Edwards
R
,
Vance
M
,
Atkin
W
, et al
Flexible sigmoidoscopy screening for colorectal cancer: uptake in a population-based pilot program
.
J Med Screen
2010
;
17
:
75
8
.
9.
Pox
CP
,
Altenhofen
L
,
Brenner
H
,
Theilmeier
A
,
Von Stillfried
D
,
Schmiegel
W
. 
Efficacy of a nationwide screening colonoscopy program for colorectal cancer
.
Gastroenterology
2012
;
142
:
1460
7
.
10.
Johnson
CD
,
Chen
MH
,
Toledano
AY
,
Heiken
JP
,
Dachman
A
,
Kuo
MD
, et al
Accuracy of CT colonography for detection of large adenomas and cancers
.
N Engl J Med
2008
;
359
:
1207
17
.
11.
Hundt
S
,
Haug
U
,
Brenner
H
. 
Blood markers for early detection of colorectal cancer: a systematic review
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
1935
53
.
12.
Rawson
JB
,
Bapat
B
. 
Epigenetic biomarkers in colorectal cancer diagnostics
.
Expert Rev Mol Diagn
2012
;
12
:
499
509
.
13.
Ma
Y
,
Zhang
P
,
Wang
F
,
Qin
H
. 
Searching for consistently reported up- and down-regulated biomarkers in colorectal cancer: a systematic review of proteomic studies
.
Mol Biol Rep
2012
;
39
:
8483
90
.
14.
Berger
BM
,
Ahlquist
DA
. 
Stool DNA screening for colorectal neoplasia: biological and technical basis for high detection rates
.
Pathology
2012
;
44
:
80
8
.
15.
Population Estimates. [cited 2013 Dec.] Available from
: http://www.statistics.gov.uk.
The office of UK National Statistics
. 
2013
.
16.
Population Age Distribution. [cited 2013 Dec.] Available from: http://www.censusscope.org.
Age distribution in population
. 
2010
.
17.
Bowel Cancer Incidence. [cited 2013 Dec.] Available from: http://www.cancerresearchuk.org/cancerinfo/cancerstats/types/bowel/incidence/.
Cancer Research UK
. 
2010
.
18.
Colorectal Adenoma Incidence. [cited 2013 Dec.] Available from: http://www.fascrs.org/physicians/education/core_subjects/2002/polyps/.
American Society of Colon and Rectal Surgeons
. 
2014
.
19.
Higgins
J
,
Green
S
. 
Cochrane handbook for systematic reviews of interventions version 5.1.0. The Cochrane Collaboration, 2011
. www.cochrane-handbook.org.
20.
Zhang
H
,
Song
YC
,
Dang
CX
. 
Detection of hypermethylated spastic paraplegia-20 in stool samples of patients with colorectal cancer
.
Int J Med Sci
2013
10
:
230
4
.
21.
Zhang
J
,
Yand
S
,
Xie
Y
,
Chen
X
,
Zhao
Y
,
He
D
, et al
Detection of methylated tissue factor pathway inhibitor 2 and human long DNA in fecal samples of patients with colorectal cancer in China
.
Cancer Epidemiol
2012
;
36
:
73
7
.
22.
Kalimutho
M
,
Del Vecchio Blanco
G
,
Cretella
M
,
Mannisi
E
,
Sileri
P
,
Formosa
A
, et al
A simplified, non-invasive fecal-based DNA integrity assay and iFOBT for colorectal cancer detection
.
Int J Colorectal Dis
2011
;
26
:
583
92
.
23.
Azuara
D
,
Rodriguez-Moranta
F
,
De Oca
J
,
Soriano-Izquierdo
A
,
Mora
J
,
Guardiola
J
, et al
Novel methylation panel for the early detection of colorectal tumors in stool DNA
.
Clin Colorectal Cancer
2010
;
9
:
168
76
.
24.
Melotte
V
,
Lentjes
MH
,
Van Den Bosch
SM
,
Hellebreker
DM
,
De Hoon
JP
,
Wouters
KA
, et al
N-Myc downstream-regulated gene 4 (NDRG4): a candidate tumor suppressor gene and potential biomarker for colorectal cancer
.
J Natl Cancer Inst
2009
;
101
:
916
27
.
25.
Glockner
SC
,
Dhir
M
,
Joo
MY
,
McGarvey
KE
,
Van Neste
J
,
Louwagie
J
, et al
Methylation of TFPI2 in stool DNA: a potential novel biomarker for the detection of colorectal cancer
.
Cancer Res
2009
;
69
:
4691
9
.
26.
Wang
DR
,
Tang
D
. 
Hypermethylated SFRP2 gene in fecal DNA is a high potential biomarker for colorectal cancer noninvasive screening
.
World J Gastroenterol
2008
;
14
:
524
31
.
27.
Leung
WK
,
To
KF
,
Man
ES
,
Chan
MY
,
Hui
AJ
,
Ng
SM
, et al
Detection of hypermethylated DNA or cyclooxygenase-2 messenger RNA in fecal samples of patients with colorectal cancer or polyps
.
Am J Gastroenterol
2007
;
102
:
1070
6
.
28.
Takai
T
,
Kanaoka
S
,
Yoshida
KI
,
Hamaya
Y
,
Ikuma
M
,
Miura
N
, et al
Fecal cyclooxygenase 2 plus matrix metalloproteinase 7 mRNA assays as a marker for colorectal cancer screening
.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
1888
93
.
29.
Shastri
YM
,
Loitsch
S
,
Hoepffner
N
,
Povse
N
,
Hanisch
E
,
Rösch
W
, et al
Comparison of an established simple office-based immunological FOBT with fecal tumor pyruvate kinase type M2 (M2-PK) for colorectal cancer screening: prospective multicenter study
.
Am J Gastroenterol
2008
;
103
:
1496
504
.
30.
Mulder
SA
,
Van Leerdam
ME
,
Van Vuuren
AJ
,
Francke
J
,
Van Toorenenbergen
AW
,
Kuipers
EJ
, et al
Tumor pyruvate kinase isoenzyme type M2 and immunochemical fecal occult blood test: performance in screening for colorectal cancer
.
Eur J Gastroenterol Hepatol
2007
;
19
:
878
82
.
31.
Koss
K
,
Maxton
D
,
Jankowski
J
. 
Fecal dimeric M2 pyruvate kinase in colorectal cancer and polyps correlates with tumor staging and surgical intervention
.
Colorectal Dis
2008
;
10
:
244
8
.
32.
Haug
U
,
Rothenbacher
D
,
Wente
N
,
Seiler
C
,
Stegmaier
C
,
Brenner
H
. 
Tumor M2-PK as a stool marker for colorectal cancer: comparative analysis in a large sample of unselected older adults vs. colorectal cancer patients
.
Br J Cancer
2007
;
96
:
1329
34
.
33.
Shastri
YM
,
Naumann
M
,
Oremek
GM
,
Hanisch
E
,
Rösch
W
,
Mössner
J
, et al
Prospective multicenter evaluation of fecal tumor pyruvate kinase type M2 (M2-PK) as a screening biomarker for colorectal neoplasia
.
Int J Cancer
2006
;
119
:
2651
56
.
34.
Tonus
C
,
Neupert
G
,
Sellinger
M
. 
Colorectal cancer screening by non-invasive metabolic biomarker fecal tumor M2-PK
.
World J Gastroenterol
2006
;
12
:
7007
11
.
35.
Sonoda
H
,
Kohnoe
S
,
Yamazato
T
,
Satoh
Y
,
Morizono
G
,
Shikata
K
, et al
Colorectal cancer screening with odor material by canine scent detection
.
Gut
2011
;
60
:
814
9
.
36.
Pack
SC
,
Kim
HR
,
Lim
SW
,
Kim
HY
,
Ko
JY
,
Lee
KS
, et al
Usefulness of plasma epigenetic changes of five major genes involved in the pathogenesis of colorectal cancer
.
Int J Colorectal Dis
2013
;
28
:
139
47
.
37.
Lange
CE
,
Campan
M
,
Hinoue
T
,
Schmitz
RF
,
Van Der Meulen de Jong
AE
,
Slingerland
H
, et al
Genome-scale discovery of DNA-methylation biomarkers for blood-based detection of colorectal cancer
.
PLoS ONE
2012
;
7
:
e50266
.
38.
Cassinotti
E
,
Melson
J
,
Liggett
T
,
Melnikov
A
,
Yi
Q
,
Replogle
C
, et al
DNA methylation patterns in blood of patients with colorectal cancer and adenomatous colorectal polyps
.
Int J Cancer
2012
;
131
:
1153
7
.
39.
Warren
JD
,
Xiong
W
,
Bunker
AM
,
Vaughn
CP
,
Furtado
LV
,
Roberts
WL
, et al
Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer
.
BMC Med
2011
;
9
:
133
.
40.
Tanzer
M
,
Balluff
B
,
Distler
J
,
Hale
K
,
Leodolter
A
,
Rocken
C
, et al
Performance of epigenetic markers SEPT9 and ALX4 in plasma for detection of colorectal precancerous lesions
.
PLoS ONE
2010
;
5
:
e9061
.
41.
Lee
BB
,
Lee
EJ
,
Jung
EH
,
Chun
HK
,
Chang
DK
,
Song
SY
, et al
Aberrant methylation of APC, MGMT, RASSF2A, and Wif-1 genes in plasma as a biomarker for early detection of colorectal cancer
.
Clin Cancer Res
2009
;
15
:
6185
91
.
42.
Lofton-Day
C
,
Model
F
,
Devos
T
,
Tetzner
R
,
Distler
J
,
Schuster
M
, et al
DNA methylation biomarkers for blood-based colorectal cancer screening
.
Clin Chem
2008
;
54
:
414
23
.
43.
Han
M
,
Choong
TL
,
Hong
WZ
,
Chao
S
,
Zheng
R
,
Kok
TY
, et al
Novel blood-based, five-gene biomarker set for the detection of colorectal cancer
.
Clin Cancer Res
2008
;
14
:
455
60
.
44.
Wang
Q
,
Huang
Z
,
Ni
S
,
Xiao
X
,
Xu
Q
,
Huang
D
, et al
Plasma miR-601 and miR-760 are novel biomarkers for the early detection of colorectal cancer
.
PLoS ONE
2012
;
7
:
e44398
.
45.
Kanaan
Z
,
Rai
SN
,
Eichenberger
MR
,
Roberts
H
,
Keskey
B
,
Pan
J
, et al
Plasma MiR-21: a potential diagnostic marker of colorectal cancer
.
Ann Surg
2012
;
256
:
544
51
.
46.
Huang
Z
,
Huang
D
,
Ni
S
,
Peng
Z
,
Sheng
W
,
Du
X
. 
Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer
.
Int J Cancer
2010
;
127
:
118
26
.
47.
Ng
EO
,
Chong
WS
,
Jin
H
,
Lam
EY
,
Shin
VY
,
Yu
J
, et al
Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening
.
Gut
2009
;
58
:
1375
81
.
48.
Wilson
S
,
Damery
S
,
Stocken
D
,
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
:
1431
8
.
49.
Liu
C
,
Pan
C
,
Shen
J
,
Wang
H
,
Yong
L
. 
MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of colorectal cancer
.
Int J Med Sci
2011
;
8
:
39
47
.
50.
Chen
JS
,
Kuo
YB
,
Chou
YP
,
Chan
CC
,
Fan
CW
,
Chen
KT
, et al
Detection of autoantibodies against Rabphilin-3A-like protein as a potential biomarker in patient's sera of colorectal cancer
.
Clin Chim Acta
2011
;
412
:
1417
22
.
51.
Mead
R
,
Duku
M
,
Bhandari
P
,
Cree
IA
. 
Circulating tumor markers can define patients with normal colons, benign polyps, and cancers
.
Br J Cancer
2011
;
105
:
239
45
.
52.
Babel
I
,
Barderas
R
,
Diaz-Uriarte
R
,
Moreno
V
,
Suarez
A
,
Fernandez-Acenero
M
, et al
Identification of MST1/STK4 and SULF1 proteins as autoantibody targets for the diagnosis of colorectal cancer by using phage microarrays
.
Mol Cell Proteomics
2011
;
10
:
M110.001784
.
53.
Pederson
J
,
Blixt
O
,
Bennett
E
,
Tarp
M
,
Dar
I
,
Mandel
U
, et al
Seromic profiling of colorectal cancer patients with novel glycopeptide microarray
.
Int J Cancer
2011
;
128
:
1860
71
.
54.
Tagi
T
,
Matsui
T
,
Kikuchi
S
,
Hoshi
S
,
Ochiai
T
,
Kokuba
Y
, et al
Dermokine as a novel biomarker for early-stage colorectal cancer
.
J Gastroenterol
2010
;
45
:
1201
11
.
55.
Mroczko
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
:
1177
84
.
56.
De Chiara
L
,
Rodriguez-Pineiro
AM
,
Rodriguez-Berrocal
FJ
,
Cordero
OJ
,
Martinez-Ares
D
,
Paez 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
.
57.
Kim
HJ
,
Kang
HJ
,
Lee
H
,
Lee
ST
,
Yu
MH
,
Kim
H
, et al
Identification of S100A8 and S100A9 as serological markers for colorectal cancer
.
J Proteome Res
2009
;
8
:
1368
79
.
58.
Fentz
AK
,
Spori
M
,
Spangenberg
J
,
List
HJ
,
Zornig
C
,
Dorner
A
, et al
Detection of colorectal adenoma and cancer based on transthyretin and C3a-desArg serum levels
.
Proteomics Clin Appl
2007
;
1
:
536
44
.
59.
Bellows
CF
,
Zhang
Y
,
Chen
J
,
Frazier
ML
,
Kolonin
MG
. 
Circulation of progenitor cells in obese and lean colorectal cancer patients
.
Cancer Epidemiol Biomarkers Prev
2011
;
20
:
2461
8
.
60.
Mori
Y
,
Olaru
A
,
Cheng
Y
,
Agarwal
R
,
Yang
J
,
Luvsanajv
D
, et al
Novel candidate colorectal cancer biomarkers identified by methylation microarray-based scanning
.
Endocr Relat Cancer
2011
;
18
:
465
78
.
61.
Lind
GE
,
Raiborg
C
,
Danielsen
SA
,
Rognum
TO
,
Thiis-Evensen
E
,
Hoff
G
, et al
SPG20, a novel biomarker for early detection of colorectal cancer, encodes a regulator of cytokinesis
.
Oncogene
2011
;
30
:
3967
78
.
62.
Magnusson
K
,
de Wit
M
,
Brennan
D
,
Johnson
L
,
McGee
S
,
Lundberg
E
, et al
SATB2 in combination with cytokeratin 20 identifies over 95% of all colorectal carcinomas
.
Am J Surg Pathol
2011
;
35
:
937
48
.
63.
Kanojia
D
,
Gard
M
,
Gupta
S
,
Gupta
A
,
Suri
A
. 
Sperm-associated antigen 9 is a novel biomarker for colorectal cancer and is involved in tumor growth and tumorigenicity
.
Am J Pathol
2011
;
178
:
1009
20
.
64.
DNA stool test recommended as screening tool
.
Cancer Discov
2014
;
4
:
0F4
.
65.
Shrubsole
M
,
Cai
Q
,
Wen
W
,
Milne
G
,
Smalley
WE
,
Chen
Z
, et al
Urinary prostaglandin E2 metabolite and risk for colorectal adenoma
.
Cancer Prev Res
2012
;
5
:
336
42
.
66.
Dulal
S
,
Keku
T
. 
Gut microbiome and colorectal adenomas
.
Cancer J
2014
;
20
:
225
31
.
67.
Kumar
S
,
Huang
J
,
Abbassi-Ghadi
N
,
Španěl
P
,
Smith
D
,
Hanna
GB
. 
Selected ion flow tube mass spectrometry analysis of exhaled breath for volatile organic compound profiling of esophagogastric cancer
.
Anal Chem
2013
;
85
:
6121
8
.