Identification of potential prognostic microRNA biomarkers for predicting survival in patients with hepatocellular carcinoma

Abstract

BackgroundThe aim of the present study was to identify potential prognostic microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) prognosis prediction based on a dataset from The Cancer Genome Atlas (TCGA).Materials and methodsA miRNA sequencing dataset and corresponding clinical parameters of HCC were obtained from TCGA. Genome-wide univariate Cox regression analysis was used to screen prognostic differentially expressed miRNAs (DEMs), and multivariable Cox regression analysis was used for prognostic signature construction. Comprehensive survival analysis was performed to evaluate the prognostic value of the prognostic signature.ResultsFive miRNAs were regarded as prognostic DEMs and used for prognostic signature construction. The five-DEM prognostic signature performed well in prognosis prediction (adjusted P < 0.0001, adjusted hazard ratio = 2.249, 95% confidence interval =1.491–3.394), and time-dependent receiver–operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.765, 0.745, 0.725, and 0.687 for 1-, 2-, 3-, and 5-year HCC overall survival (OS) prediction, respectively. Comprehensive survival analysis of the prognostic signature suggests that the risk score model could serve as an independent factor of HCC and perform better in prognosis prediction than other traditional clinical indicators. Functional assessment of the target genes of hsa-mir-139 and hsa-mir-5003 indicates that they were significantly enriched in multiple biological processes and pathways, including cell proliferation and cell migration regulation, pathways in cancer, and the cyclic adenosine monophosphate (cAMP) signaling pathway.ConclusionOur study indicates that the novel miRNA expression signature may be a potential prognostic biomarker for HCC patients.

Identify potential clinical significance of long noncoding RNA forkhead box P4 antisense RNA 1 in patients with early stage pancreatic ductal adenocarcinoma

“…Prognostic differences between different FOXP4‐AS1 expression groups were compared using Kaplan‐Meier with log‐rank test, univariate as well as multivariate Cox proportional hazard regression models. Time‐dependent receiver operating characteristic (ROC) curve was used for evaluating the accuracy of FOXP4‐AS1 expression in predicting the prognosis of PDAC, which was performed by survival package in the R platform . The nomogram was used to assess the contribution of FOXP4‐AS1 expression to PDAC prognosis after receiving pancreaticoduodenectomy treatment, which was performed by rms package in the R platform.…”

“…Time-dependent receiver operating characteristic (ROC) curve was used for evaluating the accuracy of FOXP4-AS1 expression in predicting the prognosis of PDAC, which was performed by survival package in the R platform. 12,16 The nomogram was used to assess the contribution of FOXP4-AS1 expression to PDAC prognosis after receiving pancreaticoduodenectomy treatment, which was performed by rms package in the R platform. Joint effects survival analysis was used to assess the predictive value of FOXP4-AS1 expression and clinical parameters combination for PDAC prognosis.…”

A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

“…Meanwhile, gene signatures can provide clues of potential therapeutic targets to improve clinical outcomes of HCC. Many prognostic gene signatures including mRNA, miRNA, or lncRNA were identified in previous studies, and the gene number of these gene signatures may be different [15][16][17][18][19][20][21][22][23]. In the present study, AATF, a key transcription factor contributing to the occurrence and development of HCC, was used to identify coexpressed genes which may play an important role in the molecular mechanisms in the AATF regulation process of gene transcription [24].…”

Identification of miRNA-mRNA associations in hepatocellular carcinoma using hierarchical integrative model

“…Dysregulated expression of hsa-miR-7 and hsa-miR-532-3p in HCC leads to increased proliferation, invasion, and metastasis through the PI3K/AKT signaling pathway [28]. Further, hsa-miR-532-3p along with hsa-miR-139 are associated with CHRD, FLAD1, MT1JP, and EBF2 in our analysis and have been identified as tumor suppressors in HCC that inhibit cell proliferation, migration, and invasion [29]. MT1JP is also thought to act as a tumor suppressor through regulating a series of pathways involving p53, such as the cell cycle, apoptosis and proliferation [30].…”