bioRxivMolecular BiologyPreprint
Subtype-Specific miRNA Expression Profiling in HPV-Associated Cervical Cancer: Insights into Oncogenic Pathways and Biomarker Potential
Nur Sabrina Abd RashidiD, Ahmad Aizat Abdul AziziD, Sarina SulongiD, Mohd Pazudin IsmailiD, Nur Asyilla Che JaliliD, Marjanu Hikmah, Marahaini MusaiD, Daniel Roza Duski, Nazihah Mohd YunusiD
Abstract
Cervical cancer driven by high-risk human papillomavirus (HPV) infections, remains a global health concern. Despite advances in understanding HPV-mediated carcinogenesis, the role of microRNAs (miRNAs) in subtype-specific oncogenesis remains underexplored. This study aims to profile miRNA expression patterns in cervical cancer cell lines emphasizing subtype-specific dysregulation and its implications for diagnostic and therapeutic strategies. Four cervical cancer cell lines representing HPV16, HPV18, HPV68, and HPV-negative were profiled for miRNAs expression using NanoString nCounter Human V3 miRNA Panel. Differentially expressed miRNAs were identified based on fold-change [≥]2.0, p-value <0.05, and false discovery rate (FDR) < 0.3. Predicted target gene of miRNAs were identified from TarBase, TargetScan, and microT-CDS. Further enrichment analysis and protein-protein interaction (PPI) network were performed using DIANA-miRPath and STRING software respectively. Subtype-specific clustering revealed 18 distinct miRNA signatures unique to different HPV subtypes with miR-205-5p and miR-125b-5p were the most up-regulated and down-regulated, respectively. Bioinformatic analyses, identified PI3K-Akt signaling as a key cancer-associated pathway implicated in tumor proliferation and survival. These findings advance understanding of miRNA-driven molecular mechanisms in HPV subtype-specific cervical carcinogenesis. miR-205-5p and miR-125b-5p are potential biomarkers and therapeutic targets toward precision medicine approaches in cervical cancer management. Further validation is warranted to integrate these findings into clinical practice.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 2
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