Identification of differentially expressed MiRNA clusters in cervical cancer
Abstract Background Aberrant miRNA expression has been associated with cervical cancer (CC) progression. The present study aimed to identify the miRNA clusters (MCs) altered in CC, identify their clinical utility, and understand their biological functions via computational analysis. Methods We used...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
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| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-01946-0 |
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| Summary: | Abstract Background Aberrant miRNA expression has been associated with cervical cancer (CC) progression. The present study aimed to identify the miRNA clusters (MCs) altered in CC, identify their clinical utility, and understand their biological functions via computational analysis. Methods We used small RNA sequencing and qRT‒PCR to identify and validate abnormally expressed MCs in cervical squamous cell carcinoma (CSCC) samples. We compared our data with publicly available CC datasets to identify the differentially expressed MCs in CC. The potential targets, pathways, biological functions, and clinical utility of abnormally expressed MCs were predicted via several computational tools. Results Small RNA sequencing revealed that 229 miRNAs belonging to 48 MCs were significantly differentially expressed in CSCC (p-value ≤ 0.05). Validation by qRT‒PCR confirmed the downregulation of members of the miR-379/656, namely, hsa-miR-376c-3p (2.8-fold; p-value 0.03), hsa-miR-494-3p (3.4-fold; p-value 0.02), hsa-miR-495-3p (eightfold; p-value 0.01), and hsa-miR-409-3p (fivefold; p-value 0.03), in CSCC samples compared with normal samples. The prognostic model generated via miRNA expression and random forest analysis showed robust sensitivity and specificity (0.88 to 0.92) in predicting overall survival. In addition, we report 22 prognostically important miRNAs in CC. Pathway analysis revealed the enrichment of several cancer-related pathways, notably p53, the cell cycle, viral infection and MAPK signalling. CDC25A, CCNE1, E2F1, CCNE2, RBL1, E2F3, CDK2, RBL2, E2F2 and CCND2 were identified as the top ten gene targets of MC. Drug‒gene interaction analysis revealed enrichment of 548 approved drugs and 62 unique genes. Conclusion Our study identified MCs, their target genes, their prognostic utility, and their potential functions in CC and recommended their usefulness in CC management. |
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| ISSN: | 2730-6011 |