Identification of an RFC4, CKS2, MCM5 Genes Based Prognostic Signature in Cervical Cancer Using Systems Biology and Machine Learning

Rizwana Rohoofur Rahman 1, Narmadha Ramasamy 1, Moksha Pradha Prabakar 1, Usharani Nagarajan 1, Kannan Muthu 1 *
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1 Department of Bioinformatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
* Corresponding Author
J CLIN MED KAZ, Volume 23, Issue 4, pp. 33-48. https://doi.org/10.23950/jcmk/18885
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Author Contributions: Conceptualization, M.K.; methodology, R.R. and P.M. and M.K.; validation, M.K. and R.R.; formal analysis, R.R.; investigation, R.R.; data curation, R.R., P.M., and R.N; writing – original draft preparation, P.P. and R.N.*; writing – review and editing, R.N. and P.P.; visualization, R.N. and K.M.; supervision, M.K.; project administration, M.K. All authors have read and agreed to the published version of the manuscript.
*Both authors R.R. and R.N. contributed equally.

Data availability statement: All raw data publicly available from TCGA-CESC (n=306 tumors, n=13 normal) via GDC portal and GEO (GSE39001). Custom analysis scripts and processed datasets available from corresponding author upon reasonable request.

Artificial Intelligence (AI) Disclosure Statement: The authors declare that no generative AI or AI-assisted tools were used in the writing or preparation of this manuscript.

Ethics statement: All data used in this study were obtained from publicly accessible datasets in the GEO database. No human participants or animal subjects were directly involved, and ethical approval was not required.

ABSTRACT

Introduction: Among women, cervical cancer continues to be a leading source of morbidity and death, yet there are few accurate prognostic markers. The study was performed to create a good prognostic molecular signature for cervical cancer by utilizing systems-level transcriptome analysis and machine learning methodologies together.
Methods: To find genes that were differentially expressed in TCGA-CESC (n=306 tumors,13 normals) and GEO GSE39001(39 tumors, 4 normals), GSEA  was used to analyse gene expression datasets, then WGCNA was done to find disease-related modules. The most crucial genes were then identified through analysis of protein-protein interactions. LASSO and Random Forest machine learning approaches were used to obtain a minimum prognostic gene set, externally validated via Kaplan-Meier survival analysis, time-dependent ROC, and Cox regression in TCGA-CESC. Immune infiltration correlations were determined, and possible therapeutic drugs were found by using 3D protein modelling with molecular docking.
Results: A variety of genes with differential expression were able to successfully differentiate cervical cancer samples from healthy cells (5,097 DEGs: 2,691 up, 2,406 down). The pathways linked to immunological responses, DNA replication, and cell cycle regulation were the most abundant in tumor-associated co-expression modules (r>0.7 correlation with cancer). RFC4, CKS2, and MCM5 were found to be important hub genes by combining DEG, WGCNA, and PPI studies (top-5 ranked across 4 centrality metrics).
Conclusion: In the TCGA-CESC cohort, the three-gene prognostic model served as an independent prognostic indicator and effectively classified patients into high- and low-risk categories. Docking experiments showed substantial binding affinities, especially for RFC4, suggesting its potential relevance as a therapeutic target in cervical cancer, and gene expression levels were closely associated with immune cell infiltration.
Keywords: Cervical cancer, Gene expression profiling, Prognostic signature, TCGA-CESC, Therapeutic compounds.

CITATION

Rohoofur Rahman R, Ramasamy N, Prabakar MP, Nagarajan U, Muthu K. Identification of an RFC4, CKS2, MCM5 Genes Based Prognostic Signature in Cervical Cancer Using Systems Biology and Machine Learning. J CLIN MED KAZ. 2026;23(4):33-48. https://doi.org/10.23950/jcmk/18885

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