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Multi-stage transcriptome analysis identifies hub genes and regulatory mechanisms driving cervical cancer progression

bioinformatics
molecular-biology
oncology
womens-health

Abstract

Background: Cervical cancer (CC) ranks as the fourth most prevalent cancer in women worldwide. Previous studies have reported some targeted molecules for CC, but few reports on biomarkers in different stages of CC were reported. This study aims to identify hub genes that persistently function throughout the CC development spectrum, thereby exploring the molecular mechanisms of CC malignant progression.

Methods: This study collected cervical epithelial tissue samples from 10 patients each with low-grade squamous intraepithelial lesions (LSIL), high-grade squamous intraepithelial lesions (HSIL), and squamous cell carcinoma (SCC) at Gansu Provincial Maternity and Child-care Hospital for transcriptome sequencing. The differentially expressed genes (DEGs) between different samples were obtained by differential expression analysis. MFuzz expression pattern clustering analysis, protein-protein interaction (PPI) network, and expression level verification were performed to obtain biomarkers. After that, Gene set enrichment analysis (GSEA) and immune infiltration analysis were performed. Then, the upstream molecules related to biomarkers were obtained in the databases. The expressions of biomarkers in clinical samples were validated by reverse transcription quantitative PCR.

Results: A total of 6,635 and 1,654 DEGs were identified in different groups. Then, 9 genes were screened by Mfuzz expression pattern clustering analysis and PPI network analysis. Finally, BUB1B, KIF14, and MELK were regarded as the biomarkers . GSEA results indicate that the three hub genes are significantly enriched in multiple shared pathways and exhibit stage-specific differences. The immune infiltration results showed that the 5 cells, such as naive B cells, were regarded as the key cells. The miRNAs such as hsa-miR-192-5p, hsa-miR-215-5p, and hsa-miR-193b-3p, as well as TFs such as AF4, E2F1, FOXA1, and KDM5B, were found to be associated with biomarkers. The lncRNAs such as KCNQ1OT1, LINC01089, and XIST3 are associated with miRNAs. Finally, the expressions of biomarkers in clinical samples were consistent with previous bioinformatics analysis.


Conclusion: This study identified 3 biomarkers (BUB1B, KIF14, and MELK). The results could offer novel insights into potential therapeutic targets for CC.
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