The diagnostic efficiency of tissue factor pathway inhibitor 2 methylation in the detection of colorectal cancer: a systematic review and meta-analysis

Introduction

Colorectal cancer (CRC), which is also referred to as bowel and colon cancer, is one of the most common malignant tumors of the digestive tract (1,2). CRC is a disease with high morbidity and mortality; it is the third most common cancer worldwide and the second leading cause of cancer-related death worldwide (3). In addition, since early CRC generally lacks specific symptoms and there is currently no efficient early diagnostic method, more than 50% of CRC patients are diagnosed at a stage III–IV at their initial diagnosis, and even after undergoing curative surgical resection, about 30% of patients suffer from recurrence and metastasis (4). Globally, the age-standardized 5-year survival rate for colorectal cancer ranges from 12.3% to 76.1%, with a median of 60.1% (5). However, if CRC patients are diagnosed in the early stages of CRC development, the 5-year survival rate can be as high as 70% to 90% (6). Therefore, early diagnosis is essential in improving the survival rates of CRC patients.

Common CRC screening tests include sigmoidoscopy, colonoscopy, computed tomography colonography, fecal occult blood test (FOBT), fecal immunochemical test (FIT), stool DNA test, and double-contrast barium enemas (7,8). For individuals aged 50–74 years, a combination of conventional endoscopy and/or FOBT is most commonly used (7,8). At present, colonoscopy is the gold standard for the early screening and diagnosis of CRC. However, it is prone to cause intestinal perforation, bleeding, and infection (9). Additionally, this method is not suitable for early and extensive screening. Therefore, a non-invasive, easily accepted, convenient method for the early diagnosis of CRC urgently needs to be established.

With advances in the understanding of the genetic and epigenetic changes in the development of CRC, the DNA in biological vectors (e.g., blood, feces and urine), as a potential source of biomarker for the non-invasive screening and early diagnosis of CRC, has attracted much attention. Abnormal DNA methylation such as hypermethylation has already been shown to be an early event in tumorigenesis and development (10,11). Unlike markers of genetic mutation, changes in DNA methylation can be measured quantitatively, and the frequency of methylation change is higher than that of gene mutation (12,13). Given these advantages, DNA methylation has great application potential in the early screening and diagnosis of CRC. A number of methylated DNA associated with CRC have already been reported; however, only a few have been successfully used in clinical trials. Many studies have examined the sensitivity and specificity of a panel of methylation biomarkers in the detection of CRC, most of which focused on methylated genes such as tissue factor pathway inhibitor 2 (TFPI2), N-Myc downstream-regulated gene 4 (NDRG4), bone morphogenetic protein 3 (BMP3), syndecan-2 (SDC2), and secreted frizzled-related protein 2 (SFRP2) (11,14-16).

TFPI2 is a member of the Kunitz-type serine proteinase inhibitor family, which protects the extracellular matrix of cancer cells from degradation, and inhibits in vitro colony formation and proliferation (17). Many studies have reported that TFPI2 methylation is frequent in human CRC and thus could be used to detect CRC (18-21). A higher percentage of TFPI2 methylation was correlated with the higher stage of CRC (22). TFPI2 methylation exhibited high sensitivity and specificity in the peripheral blood mononuclear cells of CRC patients, making it a novel non-invasive screening method (22). TFPI2 methylation [area under the curve (AUC) =0.85] demonstrated significantly higher diagnostic performance for CRC adenocarcinoma compared to SDC2 (AUC =0.64) (16), reflecting that TFPI2 methylation may be a favourable biomarker for distinguishing CRC patients from healthy subjects. The current challenge surrounding TFPI2 methylation lies in its marked variability in sensitivity (approximately ranging from 0.7 to 0.9) in the most studies (14,16,19,21-31). Notably, critical factors such as ethnic disparities, detection methodologies, and sample size effects on diagnostic accuracy remain poorly characterized. To address these uncertainties, a meta-analysis offers a systematic solution by employing random-effects models to pool sensitivity and specificity estimates, this approach enhances statistical power beyond individual studies. Subgroup analyses can further dissect heterogeneity origins, such as ethnicity-specific methylation patterns or methodology-driven variability. Rigorous bias assessment, via Egger’s test for publication bias and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) for study quality, ensures methodological robustness. Additionally, cumulative meta-analysis evaluates the stability of effect sizes with accumulating data, while synthesized metrics like the diagnostic odds ratio (DOR) and AUC provide clinically interpretable benchmarks. Collectively, meta-analysis serves as a comprehensive tool to resolve conflicting evidence, clarify contextual limitations, and establish standardized criteria for TFPI2 methylation’s diagnostic utility in CRC. However, to date, no meta-analysis has been conducted to evaluate the diagnostic value of TFPI2 methylation in CRC screening. Thus, we conducted a meta-analysis to investigate the diagnostic efficiency of TFPI2 methylation for CRC, and evaluated its clinical performance in the early detection of CRC. We present this article in accordance with the PRISMA-DTA reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-319/rc).

Methods

Literature search strategy

The PubMed, Web of Science, and EMBASE databases were searched using the following key words: “(colorectal* or large intestine* or colon* or rectum*) AND (cancer or carcinoma or tumor* or neoplasm* or adenocarcinoma* or polyp*) AND (tissue-factor-pathway inhibitor 2 or tissue factor pathway inhibitor 2 or tissue factor pathway inhibitor 2 protein, human or TFPI2 protein, human or TFPI2)”.

Inclusion and exclusion criteria

To be eligible for inclusion in the meta-analysis, the studies had to meet the following inclusion criteria: (I) include patients diagnosed with CRC via pathological examination; (II) examine the DNA methylation of the TFPI2 gene in either blood or stool samples; (III) have available data on the number of cases and controls, sensitivity, specificity, and AUC; (IV) describe the methylation detection method used; and (V) be published in the English language. Studies were excluded from the meta-analysis if they met any of the following exclusion criteria: (I) the type of disease studied was not CRC; (II) the study had no set control group; and/or (III) data were not available.

Data extraction

Two evaluators independently extracted the data from the included studies to avoid errors. If any disagreement arose, the decision as to which data to extract was resolved by discussion. The main data extracted included the name of the first author of the study; the year of publication; the country; the ethnicity of participants; the sample source; the total number of CRC patients; and the total number of controls. Literature was screened by the Population, Intervention, Comparison, Outcomes and Study (PICOS) principle. The quality of the included studies was evaluated using the QUADAS-2.

Statistical analysis

Reviewer manager 5.3 was used to assess the methodological quality of the included studies. Stata 13.0 and MetaDisc 1.4 were used for the meta-analysis. The diagnostic value of TFPI2 methylation for CRC was evaluated based on the combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), DOR, and AUC. First, we measured the threshold effect. If the Spearman correlation coefficient’s P value was >0.05, there was no threshold effect, and the heterogeneity produced via the non-threshold effect underwent further analysis. The heterogeneity of the studies was assessed using Cochran’s Q test and the I² test; if the P value was >0.10 and the I² value was <50%, a fixed-effects model was used; otherwise, the random-effects model was used. A meta-regression analysis and subgroup analysis were also performed to further analyze heterogeneity. The Fagan nomogram was used to calculate the pre-test probability and post-test probability of the PLR and NLR. Further, Deeks’ funnel plot was used for the assessment of the publication bias. A two-sided P value <0.05 was considered statistically significant.

Results

Characteristics of the selected studies

In total, 14 studies were included in the meta-analysis (14,16,19,21-31). Based on the search strategy, a total of 681 studies were initially retrieved. Next, 83 duplicate articles were removed. Subsequently, based on a title and abstract review, 561 additional articles were excluded (382 were excluded because they were not related to the search topic, 118 because they were not about CRC, 38 because they were animal studies, and 23 because they were reviews or meta-analyses). After a full reading of the remaining articles, 19 articles were excluded. The remaining 18 articles met the full text requirements, but there were 4 articles whose data did not meet the requirements. Ultimately, 14 articles were selected for the meta-analysis. The screening process is shown in Figure 1. Table 1 summarizes the characteristics of the 14 included studies. The 14 articles were published between 2009 and 2022, and included 3,330 subjects (1,983 CRC patients and 1,347 non-CRC controls). In addition, QUADAS-2 was used to assess the quality of the included articles, and the results are shown in Figure 2. As Figure 2 shows, the included articles had a high level of bias in terms of patient selection and the index test, but a low level of bias in terms of reference standards, and flow and timing.

Figure 1 Flowchart showing the process used to identify the included articles.

Figure 2 Methodological quality of the studies on the use of TFPI2 methylation in the diagnosis of CRC. TFPI2, tissue factor pathway inhibitor; CRC, colorectal cancer.

Meta-analysis

According to the meta-analysis, the combined sensitivity and specificity of TFPI2 methylation for the diagnosis of CRC were 0.83 [95% confidence interval (CI): 0.72–0.91] and 0.96 (95% CI: 0.93–0.97), respectively (Figure 3). The combined PLR and NLR were 19.2 (95% CI: 11.1–33.5) and 0.18 (95% CI: 0.10–0.31) (Figure 4), respectively. The combined DOR was 109 (95% CI: 45–261) (Figure 4). The summary receiver operating characteristic (SROC) curve for the included studies showed that the AUC was 0.97 (95% CI: 0.95–0.98) (Figure 5).

Figure 3 Forest plots of pooled sensitivity and specificity. CI, confidence interval.

Figure 4 Forest plots of positive likelihood ratios, negative likelihood ratios (A), and diagnostic score, odds ratios (B). CI, confidence interval; DLR, diagnostic likelihood ratio.

Figure 5 SROC curve for the diagnosis of TFPI2 methylation in CRC. AUC, area under the curve; CRC, colorectal cancer; SENS, sensitivity; SPEC, specificity; SROC, summary receiver operating characteristic; TFPI2, tissue factor pathway inhibitor.

Heterogeneity and subgroup analyses

The threshold effect measured was 0.206 (P>0.05), which indicated that there was no threshold effect. For the meta-analysis, the I² of sensitivity was 97.85%, and that of specificity was 72.89%. As the I² value >50%, a random-effects model was used. To determine the sources of the between-study heterogeneity, meta-regression and subgroup analyses were conducted. The results of the meta-regression analysis indicated that country, method, and specimen contributed to the main heterogeneity in specificity (Figure 6). In the subgroup analysis, the diagnostic parameters were calculated according to country, the number of cases, the sample size, specimen, and the methylation detection method. The subgroup analysis results for the diagnostic sensitivity, specificity, PLR, NLR, DOR, and AUC are set out in Table 2. We found that when the sample size was ≥100, the pooled sensitivity (0.86 vs. 0.72), DOR (136 vs. 49), and AUC (0.97 vs. 0.96) of TFPI2 methylation increased, which suggested that expanding the sample size improved the diagnostic value of TFPI2 methylation for CRC. Moreover, TFPI2 methylation detected by methylation-specific polymerase chain reaction (MSP) had a pooled DOR and AUC of 116 of 0.97, respectively, which were higher than those of other methods. In addition, other types, including tissue and blood, had a higher diagnostic value than stool types: sensitivity (0.87 vs. 0.79), specificity (0.95 vs. 0.96), DOR (115 vs. 101), and AUC (0.96 vs. 0.95) (Table 2).

Figure 6 Forest plots of multivariable meta-regression for sensitivity and specificity. *, P<0.05; **, P<0.01. CI, confidence interval.

Clinical diagnostic efficiency

A Fagan nomogram was used to calculate the pre-test probability and post-test probability of the PLR and NLR. As Figure 7 shows, the pre-test probability of the PLR was 20%, and the post-test probability was 83%. While the pre-test probability of the NLR was 20%, and the post-test probability was only 4%.

Figure 7 Fagan nomogram. LR, likelihood ratio; neg, negative; pos, positive; prob, probability.

Publication bias

The Deeks’ funnel chart asymmetry test was used to assess the publication bias of all the included studies. The results revealed a P value of 0.29, indicating that there was no publication bias in this meta-analysis (Figure 8).

Figure 8 Deeks’ funnel plot for the assessment of the publication bias. ESS, explained sum of squares.

Discussion

The CRC mortality rate remains high, and CRC is showing a trend of younger onset (2). It is obvious that CRC patients could benefit from early diagnosis. However, current methods for the early, non-invasive screening of CRC, such as the FOBT (32,33) and the detection of carcinoembryonic antigen, carbohydrate antigen 724, carbohydrate antigen 19-9, and squamous cell carcinoma antigen (SCC-Ag) in serum, have poor sensitivity and specificity (34,35). Thus, new markers for the early diagnosis of CRC need to be identified.

Abnormal DNA methylation, an early event in tumorigenesis and development, has been found to be related to the occurrence of cancer, and could become a tumor diagnostic biomarker (36,37). Many studies have reported that TFPI2 could serve as a new diagnostic biomarker for CRC (18-21). The TFPI2 gene is minimally methylated in normal colonic epithelial cells, but is hypermethylated in CRC cells (17,38). The TFPI2 methylation can be tested in stool and serum samples as a non-invasive assay.

We searched various databases and included 14 articles to examine the value of TFPI2 methylation in the diagnosis of CRC. The pooled sensitivity of TFPI2 methylation in the diagnosis of CRC was 0.83 (95% CI: 0.72–0.91). The pooled specificity of TFPI2 methylation in the diagnosis of CRC was 0.96 (95% CI: 0.93–0.97). The DOR and SROC curve were used to evaluate the diagnostic performance TFPI2 methylation. The greater the DOR value, the better the diagnostic performance, and the higher the diagnostic accuracy (39). The closer the SROC curve is to the top left corner, and the closer the AUC is to 1, the higher the diagnostic accuracy (40). In our study, the meta-analysis results showed that the AUC was 0.97 (95% CI: 0.95–0.98) and the DOR was 109 (95% CI: 45–261), indicating that the diagnostic performance of TFPI2 methylation for CRC was high. We also calculated the PLR and NLR, which were 19.2 and 0.18, respectively. The diagnostic criteria showed that a PLR >10 and a NLR <0.1 represented rule out CRC and confirmation CRC, respectively.

Due to the heterogeneity, we conducted meta-regression and subgroup analyses of the extracted data. The subgroup analysis was performed according to the country, sample size, detection method, and specimen. The results showed that when the sample size was ≥100, the pooled sensitivity (0.86 vs. 0.72), DOR (136 vs. 49), and AUC (0.97 vs. 0.96) increased, suggesting that the diagnostic value of TFPI2 methylation increased by expanding the sample size. Moreover, TFPI2 methylation detected by MSP had a pooled DOR and an AUC of 116 and 0.97, respectively, which were higher than those of other methods. The results showed the high effectiveness of MSP in the detection of TFPI2 methylation. It may be that MSP and other methods require particular gene sequence information for the design of polymerase chain reaction primers, which might lead to different results (41). Other types, including tissue and blood, had a higher diagnostic value than stool types: sensitivity (0.87 vs. 0.79), specificity (0.95 vs. 0.96), DOR (115 vs. 101), and AUC (0.98 vs. 0.95). The outcomes indicated that the detection of tissue or plasma TFPI2 methylation was more suitable for diagnosing CRC than the detection of stool TFPI2 methylation. TFPI2 methylation, as a non-invasive method for the early diagnosis of CRC, should be combined with other biomarkers to further improve diagnostic sensitivity. Previous studies have evaluated the diagnostic value of the TFPI2 gene combined with SDC2 (16,21), which is an approved commercial test by Wuhan Ammunition Life-tech Co. (Wuhan, China), for the early screening of CRC, NDRG4 (22), and other gene (24) methylation for the early detection of CRC, and reported that the integrated detection of methylated TFPI2 in combination with SDC2 or NDRG4 could be an effective and non-invasive tool for the early screening of CRC.

Finally, the Deeks’ funnel chart asymmetry test did not reveal any publication bias (P=0.29). The results of the Fagan nomogram showed that TFPI2 methylation had significant value in the diagnosis of CRC. The expression of TFPI2 in CRC was significantly upregulated. The pre-test probability of the PLR was 20%, and the post-test probability was 83%. While the pre-test probability of the NLR was 20%, and the post-test probability was only 4%.

This comprehensive meta-analysis evaluated the diagnostic value of TFPI2 methylation for CRC. However, this study also had several limitations—(I) sample diversity: most studies included Asian populations, limiting generalizability to other ethnic groups. (II) Advanced lesions: the diagnostic value for advanced adenomas (precursors to CRC) was not assessed, representing a critical gap. (III) Heterogeneous controls: some studies included adenoma patients in control groups, potentially skewing specificity estimates. (IV) Methodological variability: detection assays [e.g., MSP vs. quantitative MSP (qMSP)] and sample types (stool vs. blood) varied, introducing confounding. (V) Publication bias: though Deeks’ test indicated no bias (P=0.29), reliance on English-language publications may exclude relevant non-English studies.

Conclusions

The results of the current meta-analysis indicate the TFPI2 methylation is a promising non-invasive diagnostic biomarker for CRC. However, due to its low sensitivity and high specificity, TFPI2 should be combined with other biomarkers to further improve its sensitivity. In order to develop new ways to diagnose CRC patients early, we recommend the implementation of large-scale multi-center clinical studies in the future.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA-DTA reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-319/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-319/prf

Funding: This work was supported by the Health Commission Fund of Shanxi Province (No. 2021081); the Shanxi Administration of Traditional Chinese Medicine (No. 2023ZYYDA2001); the Young Scientists Fund of the Basic Research Project of Shanxi Province (No. 202303021222369); and the Shanxi Academy of Traditional Chinese Medicine (Nos. 202301 and 202404).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-319/coif). M.E. is from Mainz Biomed Germany GmbH, Mainz, Germany. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: L. Huleatt)