In Silico Study of Clinical Prognosis Associated MicroRNAs for patients with Metastasis in Clear Cell Renal Carcinoma

IF 2.4 3区生物学 Q3 BIOCHEMICAL RESEARCH METHODS

Current Bioinformatics Pub Date : 2023-09-05 DOI:10.2174/1574893618666230905154441

Ezra B. Wijaya, Venugopala Reddy Mekala, Efendi Zaenudin, Ka-Lok Ng

{"title":"In Silico Study of Clinical Prognosis Associated MicroRNAs for patients with Metastasis in Clear Cell Renal Carcinoma","authors":"Ezra B. Wijaya, Venugopala Reddy Mekala, Efendi Zaenudin, Ka-Lok Ng","doi":"10.2174/1574893618666230905154441","DOIUrl":null,"url":null,"abstract":"Background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. Objective: This study aims to propose a computational research framework that hypothesizes that a set of miRNAs functions as key regulators in modulating gene expression networks of kidney cancer survival. Method: We retrieved the NGS data from the TCGA-KIRC extracted from UCSC Xena. A set of prognostic miRNAs was acquired through multiple Cox regression analyses. We adopted machine learning approaches to evaluate miRNA prognosis's classification performance between normal, primary (M0), and metastasis (M1) samples. The molecular mechanism between primary cancer and metastasis was investigated by identifying the regulatory networks of miRNA's target genes. Result: A total of 14 miRNAs were identified as potential prognostic indicators. A combination of high-expression miRNAs was associated with survival probability. Machine learning achieved an average accuracy of 95% in distinguishing primary cancer from normal tissue and 79% in predicting the metastasis from primary tissue. Correlation analysis of miRNA prognostics with target genes unveiled regulatory network disparities between metastatic and primary tissues. Conclusion: This study has identified 14 miRNAs that could potentially serve as vital biomarkers for diagnosing and prognosing ccRCC. Differential regulatory networks between metastatic and primary tissues in this study provide the molecular basis for assessment and therapeutic treatment for ccRCC patients","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1574893618666230905154441","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. Objective: This study aims to propose a computational research framework that hypothesizes that a set of miRNAs functions as key regulators in modulating gene expression networks of kidney cancer survival. Method: We retrieved the NGS data from the TCGA-KIRC extracted from UCSC Xena. A set of prognostic miRNAs was acquired through multiple Cox regression analyses. We adopted machine learning approaches to evaluate miRNA prognosis's classification performance between normal, primary (M0), and metastasis (M1) samples. The molecular mechanism between primary cancer and metastasis was investigated by identifying the regulatory networks of miRNA's target genes. Result: A total of 14 miRNAs were identified as potential prognostic indicators. A combination of high-expression miRNAs was associated with survival probability. Machine learning achieved an average accuracy of 95% in distinguishing primary cancer from normal tissue and 79% in predicting the metastasis from primary tissue. Correlation analysis of miRNA prognostics with target genes unveiled regulatory network disparities between metastatic and primary tissues. Conclusion: This study has identified 14 miRNAs that could potentially serve as vital biomarkers for diagnosing and prognosing ccRCC. Differential regulatory networks between metastatic and primary tissues in this study provide the molecular basis for assessment and therapeutic treatment for ccRCC patients

查看原文本刊更多论文

透明细胞肾癌转移患者临床预后相关microrna的计算机研究

背景:转移涉及多个阶段和各种遗传和表观遗传改变。MicroRNA已被研究作为生物标志物和预后工具在各种癌症类型和分期。然而，考虑到单个miRNA能够靶向生物网络和途径中的多个基因，探索miRNA在肾癌中的作用仍然是一个重大挑战。背景:转移涉及多个阶段和各种遗传和表观遗传改变。MicroRNA已被研究作为生物标志物和预后工具在各种癌症类型和分期。然而，考虑到单个miRNA能够靶向生物网络和途径中的多个基因，探索miRNA在肾癌中的作用仍然是一个重大挑战。目的:本研究旨在提出一个计算研究框架，该框架假设一组mirna在调节肾癌生存的基因表达网络中起关键调节作用。方法:我们从UCSC Xena提取的TCGA-KIRC中检索NGS数据。通过多重Cox回归分析获得一组预后mirna。我们采用机器学习方法来评估正常、原发(M0)和转移(M1)样本之间的miRNA预后分类性能。通过鉴定miRNA靶基因的调控网络，探讨原发肿瘤与转移之间的分子机制。结果:共有14个mirna被确定为潜在的预后指标。高表达mirna的组合与生存率相关。机器学习在区分原发癌和正常组织方面的平均准确率为95%，在预测原发癌转移方面的平均准确率为79%。miRNA预后与靶基因的相关性分析揭示了转移性组织和原发组织之间的调控网络差异。结论:本研究已鉴定出14种mirna，可能作为ccRCC诊断和预后的重要生物标志物。本研究中转移组织和原发组织之间的差异调控网络为ccRCC患者的评估和治疗提供了分子基础

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Current Bioinformatics 生物-生化研究方法

CiteScore

6.60

自引率

2.50%

发文量

审稿时长

>12 weeks

期刊介绍： Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.