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Decoding Colon Cancer Heterogeneity Through Integrated miRNA–Gene Network Analysis

Author

Listed:
  • Qingcai He

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    LMIB and SKLCCSE, Beihang University, Beijing 100191, China
    Shen Yuan Honors College, Beihang University, Beijing 100191, China)

  • Zhilong Mi

    (LMIB and SKLCCSE, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China)

  • Tianyue Liu

    (LMIB and SKLCCSE, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China)

  • Taihang Huang

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    LMIB and SKLCCSE, Beihang University, Beijing 100191, China)

  • Mao Li

    (LMIB and SKLCCSE, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China)

  • Binghui Guo

    (LMIB and SKLCCSE, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China)

  • Zhiming Zheng

    (LMIB and SKLCCSE, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China)

Abstract

Colon adenocarcinoma (COAD) demonstrates significant clinical heterogeneity across disease stages, gender, and age groups, posing challenges for unified therapeutic strategies. This study establishes a multi-dimensional stratification framework through integrative analysis of miRNA–gene co-expression networks, employing the MRNETB algorithm coupled with Markov flow entropy (MFE) centrality quantification. Analysis of TCGA-COAD cohorts revealed stage-dependent regulatory patterns centered on CDX2-hsa-miR-22-3p-MUC13 interactions, with progressive dysregulation mirroring tumor progression. Gender-specific molecular landscapes have emerged, characterized by predominant SLC26A3 expression in males and GPA33 enrichment in females, suggesting divergent pathogenic mechanisms between genders. Striking age-related disparities were observed, where early-onset cases exhibited molecular signatures distinct from conventional COAD, highlighted by marked XIST expression variations. Drug-target network analysis identified actionable candidates including CEACAM5-directed therapies and differentiation-modulating agents. Our findings underscore the critical need for heterogeneity-aware clinical decision-making, providing a roadmap for stratified intervention paradigms in precision oncology.

Suggested Citation

  • Qingcai He & Zhilong Mi & Tianyue Liu & Taihang Huang & Mao Li & Binghui Guo & Zhiming Zheng, 2025. "Decoding Colon Cancer Heterogeneity Through Integrated miRNA–Gene Network Analysis," Mathematics, MDPI, vol. 13(6), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:1020-:d:1616938
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