bioRxivCancer Biology
A novel 3-miRNA network regulates tumour progression in oral squamous cell carcinoma
A. Patel, P. Patel, D. Mandlik, K. Patel, P. Malaviya, K. Johar, K. B. Swamy, S. Patel, V. Tanavde
Abstract
Oral squamous cell carcinoma (OSCC) is often diagnosed late, leading to poor patient outcomes. This study aims to identify potential miRNA-based biomarkers for predicting disease progression using salivary exosomes derived from OSCC patients. Further, we identify crucial miRNA-mRNA networks involved in tumorigenesis and uncover the underlying mechanism responsible for OSCC progression. Small RNA (n=23) sequencing analysis along with data available from The Cancer Genome Atlas (TCGA) (n=114) identified 12 differentially expressed miRNAs in OSCC patients as compared to controls. Validating these findings, miR-140-5p, miR-143-5p, and miR-145-5p were significantly downregulated in a larger cohort of OSCC patients (n=70). This 3-miRNA signature demonstrated higher efficacy of salivary exosomes (p<0.0001) in early detection and clinically correlated with disease progression and overall survival of OSCC patients (p<0.05). Further, analysis of the transcriptome, TCGA datasets and miRNA-mRNA networks, identified top hub genes (HIF1a, CDH1, CD44, EGFR, and CCND1) which were regulated by a 3-miRNA signature. Based on pathway analysis, these miRNA-mRNA interactions were found to be involved in regulating epithelial-mesenchymal transition (EMT). Further, transfection-mediated upregulation of the 3-miRNA signature significantly decreased cell proliferation, induced apoptosis, resulted in G2/M phase cell cycle arrest and reduced the invasive and migratory potential by reversing the EMT process in OECM-1 cell line. Thus, this study identifies a 3-miRNA signature that can be utilized as a potential biomarker for early detection of OSCC and uncovers the underlying mechanisms responsible for converting a normal epithelial cell into a malignant phenotype.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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