Gene Expression Signatures and Hub Genes in Cervical Cancer: A Bioinformatics-Based Roadmap to Early Diagnosis and Targeted Therapy

Introduction Globally, cervical cancer is the fourth leading cause of cancer among women and associated with high mortality. (Cancer Genome Atlas, 2017; Xie et al., 2019). Is the most common malignant tumour among women and is the fourth most frequent gynaecological cancer, with 604,000 cases and 342, 000. According to histology, cervical cancer is classified as squamous cell carcinoma, adenocarcinoma, or adeno-sqamous carcinoma (Gurram et al., 2020). Over time, cervical cancer develops gradually. The cervix cells undergo changes called dysplasia until cancer occurs inside the cervix, in which irregular cells will become cancer cells and begin to expand and spread more widely into the cervix and the surrounding areas. Aim This study aimed to analyse cervical cancer gene expression profiles and identify new biomarkers for diagnostic and therapeutic targets using Bioinformatics analysis. Methods We analysed gene expression profiles from GSE63514, containing 11 normal and 11 cancer samples. Results A total of 155 Differentially Expressed Genes (DEGs) met the strict statistical cut-off parameters (log2 FC > 1.0 and adjusted p-value < 0.05). Among these significant DEGs, 90 genes were up-regulated and 65 genes were down-regulated in cervical cancer tissues compared to non-cancerous controls. An additional 71 genes analysed showed no statistically significant expression changes and were excluded from the DEG target pool. Gene enrichment analysis using GeneCodis4 revealed significant enrichment in biological processes like regulation of cell adhesion and cellular response to cytokine stimulus. In cellular components, enrichment was found in the hinge region between urothelial plaques and the apical plasma membrane. Regarding molecular function, the DEGs were enriched in C-X-C motif chemokine 12 receptor activity and arachidonate 12(S)-lipoxygenase activity. KEGG pathway analysis identified enrichment in pathways like Pathways in cancer, p53 signaling pathway, and viral protein interaction with cytokine and cytokine receptor. Protein-protein interaction (PPI) network analysis identified six hub genes ( CRNN, SPINK5, GBP6, IFI44, CDKN2A, and CXCR4 ) encoding key proteins. Conclusion CRNN, SPINK5, GBP6 and IFI44 , could be used as biomarkers for diagnostic and therapeutic targets in cervical cancer potentially associated with cervical cancer development. Notably, CDKN2A and CXCR4 have been identified as biomarkers for cervical cancer diagnosis and treatments based on existing literature. This study provide an excellent idea to understand the molecular mechanisms of cervical cancer development and progression, to ultimately improve diagnosis and reveal advanced therapeutic targets to detect cervical cancer early to minimize mortality rates among women.