Journal of Advanced Biomedical Sciences
JABS
Sun, Oct 19, 2025
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Autumn 2025 (In Press)
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1- Department of Animal Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
2- Department of Animal Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran ,safaralizadeh@tabrizu.ac.ir
3- Department of Biology, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran
2- Department of Animal Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran ,
3- Department of Biology, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran
Abstract: (9 Views)
Background & Objective: Gastric cancer (GC) remains one of the most prevalent and lethal malignancies worldwide, with cisplatin serving as a cornerstone in its chemotherapeutic regimen. However, the frequent and often rapid development of cisplatin resistance significantly compromises therapeutic efficacy. MicroRNAs (miRNAs) are key post-transcriptional regulators that modulate critical cellular mechanisms underlying chemoresistance, including apoptosis, DNA repair, drug efflux, and proteostasis. This study aimed to identify candidate miRNAs and molecular pathways associated with cisplatin resistance through an integrated bioinformatics approach.
Materials & Methods: miRNA expression profiles from the GEO dataset GSE86195, comprising cisplatin-sensitive and cisplatin-resistant GC cell lines, were analyzed. Differential expression analysis was conducted using limma, followed by functional enrichment analysis of validated miRNA targets via clusterProfiler. Family-level aggregation, Weighted Gene Co-expression Network Analysis (WGCNA), and Random Forest feature ranking were subsequently applied to identify potential hub and predictive miRNAs.
Results: Although no miRNAs survived false discovery rate (FDR) correction, an exploratory nominal p-value threshold of < 0.05 revealed 957 candidate differentially expressed miRNAs (538 upregulated and 416 downregulated). Enrichment analysis indicated the involvement of pathways related to nucleocytoplasmic transport, RNA splicing, ubiquitin-mediated proteolysis, and platinum drug resistance. A coordinated dysregulation of the miR-346, miR-421, and miR-139-5p families was identified. Machine learning further highlighted hsa-let-7e and hsa-miR-20a-star as top-ranked predictive candidates, although both WGCNA and Random Forest findings should be interpreted cautiously due to the limited sample size (n = 4).
Conclusion: This exploratory bioinformatics analysis identifies candidate miRNAs and signaling pathways that may underlie cisplatin resistance in GC. The findings should be considered hypothesis-generating and warrant validation in larger cohorts (e.g., TCGA) as well as experimental confirmation through functional assays prior to clinical translation.
Materials & Methods: miRNA expression profiles from the GEO dataset GSE86195, comprising cisplatin-sensitive and cisplatin-resistant GC cell lines, were analyzed. Differential expression analysis was conducted using limma, followed by functional enrichment analysis of validated miRNA targets via clusterProfiler. Family-level aggregation, Weighted Gene Co-expression Network Analysis (WGCNA), and Random Forest feature ranking were subsequently applied to identify potential hub and predictive miRNAs.
Results: Although no miRNAs survived false discovery rate (FDR) correction, an exploratory nominal p-value threshold of < 0.05 revealed 957 candidate differentially expressed miRNAs (538 upregulated and 416 downregulated). Enrichment analysis indicated the involvement of pathways related to nucleocytoplasmic transport, RNA splicing, ubiquitin-mediated proteolysis, and platinum drug resistance. A coordinated dysregulation of the miR-346, miR-421, and miR-139-5p families was identified. Machine learning further highlighted hsa-let-7e and hsa-miR-20a-star as top-ranked predictive candidates, although both WGCNA and Random Forest findings should be interpreted cautiously due to the limited sample size (n = 4).
Conclusion: This exploratory bioinformatics analysis identifies candidate miRNAs and signaling pathways that may underlie cisplatin resistance in GC. The findings should be considered hypothesis-generating and warrant validation in larger cohorts (e.g., TCGA) as well as experimental confirmation through functional assays prior to clinical translation.
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This work is licensed under a Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)