Polymorphic Single-Nucleotide Variants in miRNA Genes and the Susceptibility to Colorectal Cancer: Combined Evaluation by Pairwise and Network Meta-Analysis, Thakkinstian's Algorithm and FPRP Criterium

Life Sciences Biochemistry Oncology Medicine Genetics
colorectal cancer miRNA single-nucleotide polymorphism network meta-analysis Thakkinstian's algorithm false positive report probability susceptibility

Association Study of Polymorphic Single-Nucleotide Variants in miRNA Genes and Colorectal Cancer Susceptibility

Academic Background

Colorectal cancer (CRC) is one of the most common malignancies with high incidence and mortality rates worldwide. According to global cancer statistics in 2022, CRC ranks as the third most prevalent cancer, with over 1.9 million new cases and more than 900,000 deaths annually. Although the pathogenesis of CRC is complex, genetic factors play a significant role in its development. In particular, single nucleotide polymorphisms (SNPs) in miRNA genes are believed to be closely associated with CRC susceptibility.

miRNAs (microRNAs) are a class of short non-coding RNAs that regulate gene expression by targeting complementary sequences of mRNAs, thereby influencing biological processes such as cell proliferation, apoptosis, angiogenesis, and metastasis. Studies have shown that SNPs in miRNA genes may affect miRNA transcription, maturation, and interactions with mRNAs, ultimately impacting cancer development. However, existing research on the association between miRNA-SNPs and CRC susceptibility has yielded inconsistent results, partly due to limited sample sizes, variations in study designs, and ethnic heterogeneity.

To address this issue, this study systematically evaluated the association between miRNA-SNPs and CRC susceptibility by combining pairwise meta-analysis, network meta-analysis, Thakkinstian’s algorithm, and the false positive report probability (FPRP) criterion. The aim was to identify the most relevant miRNA-SNPs and their optimal genetic models.

Source of the Paper

This paper was co-authored by scholars from multiple research institutions, including Qing Liu, Ivan Archilla, Sandra Lopez-Prades, Ferran Torres, Jordi Camps, and Miriam Cuatrecasas. These researchers are affiliated with the Faculty of Medicine and Health Sciences at the University of Barcelona, the August Pi i Sunyer Biomedical Research Institute, the Pathology Department of the Hospital Clinic at the University of Barcelona, and the Department of Biostatistics at the Autonomous University of Barcelona, among others. The paper was published in 2025 in the journal Cancer Medicine, titled Polymorphic Single-Nucleotide Variants in miRNA Genes and the Susceptibility to Colorectal Cancer: Combined Evaluation by Pairwise and Network Meta-Analysis, Thakkinstian’s Algorithm and FPRP Criterium.

Research Process and Results

1. Literature Search and Screening

The research team conducted a comprehensive search of databases including Medline, Embase, Web of Science, and Cochrane Library, identifying relevant literature published up to May 2024. A total of 39 case-control studies were included, involving 18,028 CRC patients and 21,816 healthy controls. These studies covered 11 SNPs in miRNA genes, including miR-196a2 (rs11614913), miR-146a (rs2910164), and miR-27a (rs895819), among others.

2. Data Extraction and Quality Assessment

Two independent researchers extracted relevant data from each study, including author names, publication year, country, sample size, control sources, genotyping methods, and genotype frequencies. Study quality was assessed using a pre-established scoring scale, with a total score of 15. Studies scoring ≥11 were classified as high-quality, those scoring 7-10 as medium-quality, and studies scoring were excluded.

3. Pairwise Meta-Analysis

The research team performed pairwise meta-analysis for each SNP, calculating pooled odds ratios (ORs) and 95% confidence intervals (CIs) under six genetic models (allelic, homozygous, heterozygous, dominant, recessive, and over-dominant models). The results showed that miR-27a (rs895819) was significantly associated with CRC risk in both the overall population and the Asian population, with ORs of 1.58 (95% CI: 1.32-1.89) and 1.62 (95% CI: 1.31-2.01), respectively. The recessive model was identified as the optimal model. Additionally, miR-196a2 (rs11614913), miR-143⁄145 (rs41291957), and miR-34b/c (rs4938723) exhibited protective effects in the Asian population, with ORs of 0.75 (95% CI: 0.65-0.86), 0.72 (95% CI: 0.60-0.85), and 0.69 (95% CI: 0.56-0.85), respectively.

4. Network Meta-Analysis and Thakkinstian’s Algorithm

To determine the optimal genetic models, the research team conducted network meta-analysis and further validated the results using Thakkinstian’s algorithm. The results indicated that the recessive model of miR-27a (rs895819) performed best in predicting CRC risk. Additionally, the dominant model of miR-196a2 (rs11614913) and the recessive models of miR-143⁄145 (rs41291957) and miR-34b/c (rs4938723) were also identified as optimal.

5. False Positive Report Probability (FPRP) Analysis

To assess the significance of the findings, the research team calculated FPRP values. The results showed that the recessive model of miR-27a (rs895819) had FPRP values below 0.2 in both the overall and Asian populations, indicating statistical significance. Furthermore, the protective effects of miR-196a2 (rs11614913), miR-143⁄145 (rs41291957), and miR-34b/c (rs4938723) were validated in the Asian population.

6. Diagnostic Performance Evaluation

The research team also evaluated the diagnostic performance of miR-27a (rs895819) in CRC. The results showed that the area under the curve (AUC) for this SNP was 0.656 in the overall population and 0.639 in the Asian population, suggesting its potential diagnostic value.

Conclusions and Significance

This study comprehensively evaluated the association between miRNA-SNPs and CRC susceptibility, identifying a significant correlation between miR-27a (rs895819) and CRC risk, with the recessive model being the optimal predictive model. Additionally, miR-196a2 (rs11614913), miR-143⁄145 (rs41291957), and miR-34b/c (rs4938723) exhibited protective effects in the Asian population. These findings provide important genetic insights for early screening and personalized prevention of CRC.

Research Highlights

  1. Comprehensive Analytical Approach: This study is the first to combine pairwise meta-analysis, network meta-analysis, Thakkinstian’s algorithm, and the FPRP criterion to systematically evaluate the association between miRNA-SNPs and CRC susceptibility, enhancing the reliability and accuracy of the results.
  2. Ethnic Specificity: The study found that the protective effects of miRNA-SNPs were more pronounced in the Asian population, highlighting the significant role of genetic background and environmental factors in CRC development.
  3. Diagnostic Value: The recessive model of miR-27a (rs895819) demonstrated potential in CRC diagnosis, offering new directions for future clinical applications.

Additional Valuable Information

This study also identified that SNPs such as miR-149 (rs2292832) and miR-608 (rs4919510) may have protective effects in specific populations. However, due to limited sample sizes, these findings require further validation. Additionally, the research team called for more high-quality, large-scale studies to further explore the association between miRNA-SNPs and CRC risk, as well as potential gene-environment interactions.

Through this study, we have deepened our understanding of the role of miRNA-SNPs in CRC development and provided new research directions for future cancer prevention and personalized treatment.