Singh Prithvi, Solanki Rubi, Tasneem Alvea, Suri Simran, Kaur Harleen, Shah Sapna Ratan, Dohare Ravins
Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India.
School of Interdisciplinary Sciences and Technology, Jamia Hamdard, New Delhi 110062, India.
J Genet Eng Biotechnol. 2024 Mar;22(1):100337. doi: 10.1016/j.jgeb.2023.100337. Epub 2024 Jan 24.
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.
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.
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.
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.
尽管科学界和管理机构进行了所有研究并付出了努力，但肝细胞癌（HCC）的发病率仍逐年逐渐上升。所有肝癌病例中约90%属于HCC。通常，HCC患者在这种恶性肿瘤的晚期才接受治疗，这成为高死亡率的主要原因。关于HCC分子发病机制的知识有限，需要研究人员更多关注以识别驱动基因和miRNA，从而将这些信息转化为临床实践。因此，识别miRNA-mRNA调控网络的关键调节因子对于识别HCC相关基因至关重要。
我们从UCSC Xena中提取了正常和肿瘤HCC患者样本的微小RNA（miRNA）和信使核糖核酸（mRNA）表达数据集，随后识别差异表达基因（DEG）和差异表达miRNA（DEM）。使用单变量和多变量cox比例风险模型来识别与总生存期（OS）有显著关联的DEM。使用Kaplan-Meier（KM）绘图仪验证预后DEM的存在。使用风险评分模型评估经KM绘图仪验证的DEM组合对样本风险的有效性。通过miRTargetLink和miRWalk等来源识别预后miRNA的靶标DEG，随后在外部微阵列队列中对其进行验证并进行富集分析。
在单变量、多变量、风险评分模型评估和KM绘图仪分析后，识别出562个DEG和388个DEM，以及7个预后miRNA（即miR-19a、miR-19b、miR-30d-5p、miR-424-5p、miR-3677-5p、miR-3913-5p、miR-7705）。ANLN、MRO、CPEB3是它们的靶标，并且也在GSE84005数据集中得到验证。
本研究结果表明，最显著的miRNA及其识别出的靶标基因与细胞凋亡、炎症、细胞周期调控和癌症相关途径有关，这似乎有助于HCC发病机制，因此有助于发现新的靶标。
