利用基于生存期的生物信息学方法筛选作为肝细胞癌预后生物标志物的 miRNA 及其相关枢纽靶点

IF 3.5 Q3 Biochemistry, Genetics and Molecular Biology
Prithvi Singh , Rubi Solanki , Alvea Tasneem , Simran Suri , Harleen Kaur , Sapna Ratan Shah , Ravins Dohare
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引用次数: 0

摘要

背景尽管科学界和管理机构进行了各种研究和努力,但肝细胞癌(HCC)的发病率仍在逐年上升。约 90% 的肝癌病例属于 HCC。通常,HCC 患者在恶性肿瘤晚期才开始接受治疗,这也是导致高死亡率的主要原因。目前,人们对 HCC 分子发病机制的了解还很有限,需要研究人员更多地关注如何识别驱动基因和 miRNA,从而将这些信息转化为临床实践。方法我们从 UCSC Xena 提取了正常和肿瘤 HCC 患者样本的 microRNA(miRNA)和信使 RNA(mRNA)表达数据集,然后鉴定了差异表达基因(DEGs)和差异表达 miRNA(DEMs)。利用单变量和多变量cox-比例危险模型来确定与总生存期(OS)有显著关联的DEMs。Kaplan-Meier (KM) plotter用于验证预后DEMs的存在。风险评分模型用于评估KM-plotter验证的DEMs组合对样本风险的影响。通过 miRTargetLink 和 miRWalk 等来源确定了预后 miRNA 的靶 DEGs,然后在外部微阵列队列中进行了验证和富集分析、miR-19a、miR-19b、miR-30d-5p、miR-424-5p、miR-3677-5p、miR-3913-5p、miR-7705)进行单变量、多变量、风险评分模型评估和 KM 绘图仪分析。结论这项研究的结果揭示了大多数重要的 miRNAs 及其确定的靶基因与细胞凋亡、炎症、细胞周期调控和癌症相关通路有关,这些通路似乎有助于 HCC 的发病机制,因此需要发现新的靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Screening of miRNAs as prognostic biomarkers and their associated hub targets across Hepatocellular carcinoma using survival-based bioinformatics approach

Background

The hepatocellular carcinoma (HCC) incident rate is gradually increasing yearly despite all the research and efforts taken by scientific communities and governing bodies. Approximately 90% of all liver cancer cases belong to HCC. Usually, HCC patients approach the treatment in the late stages of this malignancy which becomes the primary cause of high mortality rate. The knowledge about molecular pathogenesis of HCC is limited and needs more attention from researchers to identify the driver genes and miRNAs, which causes to translate this information into clinical practice. Therefore, the key regulators identification of miRNA-mRNA regulatory network is essential to identify HCC-associated genes.

Methodology

We extracted microRNA (miRNA) and messenger RNA (mRNA) expression datasets of normal and tumor HCC patient samples from UCSC Xena followed by identifying differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Univariate and multivariate cox-proportional hazard models were utilized to identify DEMs having significant association with overall survival (OS). Kaplan-Meier (KM) plotter was used to validate the presence of prognostic DEMs. A risk-score model was used to evaluate the effectiveness of KM-plotter validated DEMs combination on risk of samples. Target DEGs of prognostic miRNAs were identified via sources such as miRTargetLink and miRWalk followed by their validation in an external microarray cohort and enrichment analysis.

Results

562 DEGs and 388 DEMs were identified followed by seven prognostic miRNAs (i.e., miR-19a, miR-19b, miR-30d-5p, miR-424-5p, miR-3677-5p, miR-3913-5p, miR-7705) post univariate, multivariate, risk-score model evaluation and KM-plotter analyses. ANLN, MRO, CPEB3 were their targets and were also validated in GSE84005 dataset.

Conclusions

The findings of this study decipher that most significant miRNAs and their identified target genes have association with apoptosis, inflammation, cell cycle regulation and cancer-related pathways, which appear to contribute to HCC pathogenesis and therefore, the discovery of new targets.

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来源期刊
Journal of Genetic Engineering and Biotechnology
Journal of Genetic Engineering and Biotechnology Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
5.70
自引率
5.70%
发文量
159
审稿时长
16 weeks
期刊介绍: Journal of genetic engineering and biotechnology is devoted to rapid publication of full-length research papers that leads to significant contribution in advancing knowledge in genetic engineering and biotechnology and provide novel perspectives in this research area. JGEB includes all major themes related to genetic engineering and recombinant DNA. The area of interest of JGEB includes but not restricted to: •Plant genetics •Animal genetics •Bacterial enzymes •Agricultural Biotechnology, •Biochemistry, •Biophysics, •Bioinformatics, •Environmental Biotechnology, •Industrial Biotechnology, •Microbial biotechnology, •Medical Biotechnology, •Bioenergy, Biosafety, •Biosecurity, •Bioethics, •GMOS, •Genomic, •Proteomic JGEB accepts
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