Identification of RNA binding protein genes associated with colorectal cancer by bioinformatics analysis

Colorectal cancer (CRC) is a prevalent malignant neoplasm on a global scale, with tumor heterogeneity driving therapeutic resistance and poor prognosis. RNA-binding proteins (RBPs), which are involved in regulating post-transcriptional processes, are becoming more recognized for their involvement in the advancement of cancer. This research conducted a thorough examination of the prognostic and functional significance of RBP-related gene sets (RBPGs) in CRC by utilizing transcriptomic data from the cancer genome atlas (TCGA). Differential expression analysis identified 406 RBPGs (268 upregulated, 138 downregulated) in CRC. Protein-protein interaction (PPI) network analysis revealed key modules involving 394 nodes, highlighting their functional interconnectivity. A prognostic model was constructed using 8 RBPGs (GTPBP4, KPNA2, CTNNA1, RRS1, SLFN11, CDKN2A, CHD3, TRAP1). This model effectively stratifies patients into 2 risk categories, each associated with differing survival outcomes. The model demonstrated robust predictive accuracy and was validated through subgroup analyses, receiver operating characteristic (ROC) curves, principal component analysis (PCA), and consistency index (C-index) curves. Integrating risk score, stage, and age, a nomogram was established to further enhance clinical applicability. Enrichment analysis linked high-risk groups to extracellular matrix remodeling, PI3K-Akt signaling, and immune evasion, while low-risk groups exhibited metabolic and immune activation pathways. Analysis of the tumor microenvironment (TME) indicated that high-risk patients exhibited elevated stromal and immune scores, alongside reduced tumor purity, which is associated with more aggressive phenotypic characteristics. Although tumor mutation burden (TMB) did not differ between risk groups, high TMB correlated with poorer survival. High-risk patients showed elevated tumor immune dysfunction and exclusion (TIDE) score and increased sensitivity to oxaliplatin, gemcitabine, and targeted therapies. In addition, we further experimentally verified the expression of the hub gene in the tissues of colorectal cancer patients. This study establishes RBP-based prognostic signatures, elucidates their mechanistic roles in CRC progression, and identifies potential therapeutic targets, providing a framework for personalized CRC management. Further validation in multicenter cohorts is warranted to translate findings into clinical practice.

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Publication Details

Journal
Discover Oncology
Published
2025-08-21
DOI
https://doi.org/10.1007/s12672-025-03439-6
Primary Topic
RNA modifications and cancer
Type
article
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article

Identification of RNA binding protein genes associated with colorectal cancer by bioinformatics analysis

Han Wang, Haibo Wang, Jun He, Zhong Shen et al.
Discover Oncology
RNA modifications and cancer
article

Identification of RNA binding protein genes associated with colorectal cancer by bioinformatics analysis

Han Wang, Haibo Wang, Jun He, Zhong Shen, Ran He
article en

Abstract

Colorectal cancer (CRC) is a prevalent malignant neoplasm on a global scale, with tumor heterogeneity driving therapeutic resistance and poor prognosis. RNA-binding proteins (RBPs), which are involved in regulating post-transcriptional processes, are becoming more recognized for their involvement in the advancement of cancer. This research conducted a thorough examination of the prognostic and functional significance of RBP-related gene sets (RBPGs) in CRC by utilizing transcriptomic data from the cancer genome atlas (TCGA). Differential expression analysis identified 406 RBPGs (268 upregulated, 138 downregulated) in CRC. Protein-protein interaction (PPI) network analysis revealed key modules involving 394 nodes, highlighting their functional interconnectivity. A prognostic model was constructed using 8 RBPGs (GTPBP4, KPNA2, CTNNA1, RRS1, SLFN11, CDKN2A, CHD3, TRAP1). This model effectively stratifies patients into 2 risk categories, each associated with differing survival outcomes. The model demonstrated robust predictive accuracy and was validated through subgroup analyses, receiver operating characteristic (ROC) curves, principal component analysis (PCA), and consistency index (C-index) curves. Integrating risk score, stage, and age, a nomogram was established to further enhance clinical applicability. Enrichment analysis linked high-risk groups to extracellular matrix remodeling, PI3K-Akt signaling, and immune evasion, while low-risk groups exhibited metabolic and immune activation pathways. Analysis of the tumor microenvironment (TME) indicated that high-risk patients exhibited elevated stromal and immune scores, alongside reduced tumor purity, which is associated with more aggressive phenotypic characteristics. Although tumor mutation burden (TMB) did not differ between risk groups, high TMB correlated with poorer survival. High-risk patients showed elevated tumor immune dysfunction and exclusion (TIDE) score and increased sensitivity to oxaliplatin, gemcitabine, and targeted therapies. In addition, we further experimentally verified the expression of the hub gene in the tissues of colorectal cancer patients. This study establishes RBP-based prognostic signatures, elucidates their mechanistic roles in CRC progression, and identifies potential therapeutic targets, providing a framework for personalized CRC management. Further validation in multicenter cohorts is warranted to translate findings into clinical practice.

Discover OncologyVol. 16(1)
Third People's Hospital of Hangzhou (CN), Zhejiang University (CN)
Good health and well-being
Openalex Percentile: Top 81%
RNA modifications and cancer
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