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Transcriptome sequencing of in vivo cervical tissue revealed lncRNA-miRNA-mRNA co-regulatory functions in cervical cancer of Uygur women

ABSTRACT: Background: Non-coding RNA has been shown to participate in numerous biological and pathological processes and has attracted increasing attention in recent years. Recent studies have demonstrated that long non-coding RNA and microRNA can interact through various mechanisms to regulate mRNA. Yet the gene-gene interaction has not been investigated in cervical cancer of Uygur woman. High throughput sequencing were used to identify differentially expressed lncRNA, miRNA, and mRNA profiles between normal, cervical intraepithelial neoplasia and cervix carcinoma tissues. Gene Oncology (GO), KEGG enrichment analysis were performed. Gene co-expression network were constructed and pivotal genes were screened out. A total of 1464 lncRNAs were differentially expressed between normal, cervical intraepithelial neoplasia and cervical cancer tissues. GO and KEGG analysis show that related molecular mechanisms and biological processes play a role in cervical cancer. The gene-gene interaction network was constructed and visualized based on Pearson correlation coefficients and TargetScan prediction. These results have shown the potential of lncRNA, miRNA, and mRNA as clinical biomarkers and in elucidating pathological mechanisms of cervical cancer from a transcriptomic perspective.

ORGANISM(S): Homo sapiens

PROVIDER: GSE149763 | GEO | 2021/03/10

REPOSITORIES: GEO

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Project description:Analysis of various of up-regulated and down-regulated genes in Normal Cervical mucosa, Cervical intraepithelial neoplasia and Cervical squamous cell carcinoma. The report provides a data analysis methodology for identification of co-expressed gene patterns, as emerging clusters, in global transcriptome of cervical mucosal pre-malignant and malignant conditions in comparison to their normal counterparts. Microarray based study of global gene expression is often used to extract molecular signatures underlying cancer progression. Such endeavors endorse self organizing map, a type of artificial neural network to analyze high dimensional pre-processed transcriptome data to segregate hotspot genes in component plane for disease subtypes. This report provides a data analysis methodology for identification of coexpressed gene patterns, as emerging clusters, in global transcriptome of oral and cervical mucosal premalignant and malignant conditions in comparison to their normal counterparts. Four exclusive cluster patterns, each involving 100 − 300 genes, were identified from component planes for oral study groups. Gene expression associated with each pattern belonged to 32 biological processes. Analysis on cervical biopsies, where cancer was compared to cervical interepithelial neoplasia and normal counterpart, it revealed three non-overlapping patterns for each condition. In cervical interepithelial neoplasia an intermediate pattern with nine different dominant functional processes was identified, whereas, in cervical squamous cell carcinoma pattern showed dominance for seven different functions. This analysis demonstrated utility of self organizing map to capture dominant enriched patterns as visual plots and revealed six common biological processes like transcription and RNA processing, cytoskeleton reorganization, angiogenesis, immunity, neuron signalling, and connective tissue remodelling in the pathogenesis of oral and cervical cancers. In fact it could provide an intuitive understanding of molecular course in carcinogenesis and may contribute for combinatorial biomarker discovery.

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Transcriptome sequencing of in vivo cervical tissue revealed lncRNA-miRNA-mRNA co-regulatory functions in cervical cancer of Uygur women

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