ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Genetics
Identification of unique biomarkers in colorectal cancer based on comprehensive analysis and machine learning
Provisionally accepted
- First Hospital of Shanxi Medical University, Taiyuan, China
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The aim of this study was to identify unique biomarkers related to colorectal cancer (CRC). Gene expression profile data were obtained from the TCGA database and the GSE39582 dataset. After strict pretreatment, the limma package was used to screen for differentially expressed genes (DEGs). Hub genes were screened by various methods such as WGCNA analysis, construction of miRNA-hub gene networks and TF-hub gene networks, LASSO and SVM-RFE algorithms. Fourteen genes related to ferroptosis, mitochondria and RBPs (IMRBPs) were identified, some of which were downregulated and the others were upregulated in colorectal cancer tissues; the expression patterns of seven hub RNA-binding proteins (RBP) genes (APEX1, BRCA1, DNMT1, EZH2, PTTG1, SND1, UHRF1) differed in different datasets. Subtype analysis of the hub RBP genes revealed that they were related to stemness index, immune infiltration, etc., and ten potential therapeutic drugs were screened out. The study indicates that these biomarkers are related to clinical features, which BRCA1, DNMT1, EZH2, PTTG1, SND1, UHRF1were upregulated in CRC, APEX1 were downregulated in CRC by PCR and WB, and these seven hub genes were associated with ferroptosis suppression by regulating GSH/GSSG and Fe2+ levels. However, the observed associations are correlative and require functional validation to establish causality. This study provides a new perspective and potential therapeutic targets for colorectal cancer research. However, our sample size is too small. More samples and multi-center samples are needed, and further in-depth research is needed to promote clinical application.
Keywords: colorectal cancer, Bioinformatics analysis, machine learning, biomarkers, ferroptosis
Received: 04 Aug 2025; Accepted: 20 Nov 2025.
Copyright: © 2025 Wang, Ren, Cui, Shen and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Liwei Wang, wlw13203510066@163.com
Qingxing Huang, hqx@sxmu.edu.cn
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