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Genome-wide differential methylation analysis of ovarian cancer and normal tissue samples

ABSTRACT: Purpose: MeDIP based Next-generation sequencing (NGS) has revolutionized differential and funtional mapping of genome-wide methylation signature. The goals of this study are to compare MeDIP-seq derived methylome profiling of miRNA in EOC samples and normal ovary tissue samples, and their downstream expression analysis through quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods. Methods: Six EOC tissue and two normal ovary samples were processed and genomic DNA was isolated. MeDIP-seq based library was prepared and for all eight samples and cluster generation and pair end sequencing was performed on Illumina TrueSeq 500 platform. Adptor triming, low quality read and duplicate reads were removed. High qulity read were aligned using BWA-Mem tools. Methylated genome regions and differential methylation analysis was performed using diffReps (v1.55.6). In addition, hypomethylation/hypermethylation of miRNA genes and thier downstream expression analysis was evaluated using qRT–PCR SYBR Green based assays Results: Using an optimized data analysis workflow, we mapped about ~60-80 million sequence reads per sample to the human. arround 2,24,929 DMR (p<0.05) were identified as differentially methylated regions and out of them 45% were hypermethylated and 55% were hypomethylated compared to normal samples. genomic distribution of DMRs revealed higher enrichment in Gene body, and in other intergenic region. Arround 50 proximal promoter regions of miRNA genes were hypomethylated, while 80 were fall in hypermethylated region. Out of these three miRNA from hypomethylated were screened for further qRT-PCR based expression analysis. qRT-PCR expression analysis revealed upregulation of candidate miRNA in EOC compared to normal samples. Upregulated expression profile of three miRNA obtained from qRT-PCR confiremed there association with hypomethylation of miRNA genes and their downstream expression. Conclusions: Our study revealed genome-wide methylome analysis of ovarian cancer tissue samples, using MeDIP NGS based approach. The optimized data analysis workflows reported here should provide a association between gene methylation and their downream expression profiles. Our results show that MeDIP-seq offers a comprehensive and more accurate genome-wide differential methylation analysis of ovarian cancer tissue. We conclude that MeDIP-seq based analysis would help in understanding of differential methylation characteristics of cancerous and non-cancerous ovarian tissue samples and could allow us to understand complex biological functions

ORGANISM(S): Homo sapiens

PROVIDER: GSE180292 | GEO | 2021/10/05

REPOSITORIES: GEO

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Project description:Epithelial ovarian cancer (EOC) are a heterogeneous collection of malignancies, each with their own developmental origins, clinical behavior and molecular profile. With less than 5% of EOC cases, mucinous ovarian carcinoma is a rare form with a poor prognosis with a 5-year survival of 11% for advanced stages (III/IV). Clinically, due to the large size and heterogeneity of mu-cinous tumors, Malignant forms are difficult to distinguish from Borderline (15%) and benign (80%) forms with a better prognosis. Improving their diagnosis is therefore a challenge with regard to the risk of under-treating a malignant form or of unnecessarily undertaking radical surgical excision. The involvement of microRNAs (miRNAs) in cancer and their potential as biomarkers of diagnosis are becoming increasingly recognized. In this study, miRNA expres-sion profiling generates using FFPE patient samples followed by qRT-PCR validation allows to identify 10 down-regulated and 5 up-regulated miRNAs between Malignant and Borderline tumors. To overcome issues linked to data normalization and to improve the accuracy of the results, a ratio analysis combining up-regulated and down-regulated miRNAs was used. Alt-hough 21/50 miRNA expression ratio were significantly different between Malignant and Bor-derline tumor samples, any ratio could perfectly discriminate the two groups. However, a combination of 14 miRNAs expression ratio (double ratio) showed high discriminatory poten-tial, with 100% of accuracy in distinguishing Malignant and Borderline ovarian tumors, and suggests that miRNAs may hold significant clinical potential as a diagnostic tool. Epithelial ovarian cancer (EOC) are a heterogeneous collection of malignancies, each with their own developmental origins, clinical behavior and molecular profile. With less than 5% of EOC cases, mucinous ovarian carcinoma is a rare form with a poor prognosis with a 5-year survival of 11% for advanced stages (III/IV). Clinically, due to the large size and heterogeneity of mu-cinous tumors, Malignant forms are difficult to distinguish from Borderline (15%) and benign (80%) forms with a better prognosis. Improving their diagnosis is therefore a challenge with regard to the risk of under-treating a malignant form or of unnecessarily undertaking radical surgical excision. The involvement of microRNAs (miRNAs) in cancer and their potential as biomarkers of diagnosis are becoming increasingly recognized. In this study, miRNA expres-sion profiling generates using FFPE patient samples followed by qRT-PCR validation allows to identify 10 down-regulated and 5 up-regulated miRNAs between Malignant and Borderline tumors. To overcome issues linked to data normalization and to improve the accuracy of the results, a ratio analysis combining up-regulated and down-regulated miRNAs was used. Alt-hough 21/50 miRNA expression ratio were significantly different between Malignant and Bor-derline tumor samples, any ratio could perfectly discriminate the two groups. However, a combination of 14 miRNAs expression ratio (double ratio) showed high discriminatory poten-tial, with 100% of accuracy in distinguishing Malignant and Borderline ovarian tumors, and suggests that miRNAs may hold significant clinical potential as a diagnostic tool.

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Genome-wide differential methylation analysis of ovarian cancer and normal tissue samples

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