scholarly journals Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingyuan Zhang ◽  
Xinkui Liu ◽  
Wei Zhou ◽  
Shan Lu ◽  
Chao Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Differential Expression ◽  
Chronic Inflammation ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Gene Markers ◽  
Key Genes

BackgroundHepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.MethodsTwo groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.ResultsWe identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.ConclusionThe findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

scholarly journals Construction of circRNA-Based ceRNA Network to Reveal the Role of circRNAs in the Progression and Prognosis of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Rong Deng ◽  
Xiaohan Cui ◽  
Yuxiang Dong ◽  
Yanqiu Tang ◽  
Xuewen Tao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Differential Expression Analysis ◽  
Reduction Process ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Mirna Sequence

BackgroundCircular RNAs (circRNAs) are now under hot discussion as novel promising biomarkers for patients with hepatocellular carcinoma (HCC). The purpose of our study is to identify several competing endogenous RNA (ceRNA) networks related to the prognosis and progression of HCC and to further investigate the mechanism of their influence on tumor progression.MethodsFirst, we obtained gene expression data related to liver cancer from The Cancer Genome Atlas (TCGA) database (http://www.portal.gdc.cancer.gov/), including microRNA (miRNA) sequence, RNA sequence, and clinical information. A co-expression network was constructed through the Weighted Correlation Network Analysis (WGCNA) software package in R software. The differentially expressed messenger RNAs (DEmRNAs) in the key module were analyzed with the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA were utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module.ResultsThe 201 differentially expressed miRNAs (DEmiRNAs) and 3,783 DEmRNAs were preliminarily identified through differential expression analysis. The co-expression networks of DEmiRNAs and DEmRNAs were constructed with WGCNA. Further analysis confirmed four miRNAs in the most significant module (blue module) were associated with the overall survival (OS) of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p, and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The GO analysis results showed that the top enriched GO terms were oxidation–reduction process, extracellular exosome, and iron ion binding. In KEGG pathway analysis, the top three enriched terms included metabolic pathways, fatty acid degradation, and valine, leucine, and isoleucine degradation. In addition, we intersected the miRNA–mRNA interaction prediction results with the differentially expressed and prognostic mRNAs. We found that hsa-miR-92b-3p can be related to CPEB3 and ACADL. By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/cytoplasmic polyadenylation element binding protein-3 (CPEB3) and acyl-Coenzyme A dehydrogenase, long chain (ACADL) were validated in HCC tissue.ConclusionOur research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve a momentous therapeutic role to restrain the occurrence and development of HCC.

scholarly journals Construction of circRNA-based ceRNA Network to Reveal the Role of circRNAs in the Progression and Prognosis of Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Rong Deng ◽  
XiaoHan Cui ◽  
Yuxiang Dong ◽  
Yanqiu Tang ◽  
Xuewen Tao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Ontology ◽  
Liver Cancer ◽  
Differential Expression Analysis ◽  
Reduction Process ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed

Abstract Background: Circular RNAs (circRNAs) are now under hot discussion as novel promising bio-markers for patients with hepatocellular carcinoma. The purpose of our study is to identify several competing endogenous RNAs (ceRNAs) networks related to the prognosis and progression of hepatocellular carcinoma, and to further investigate the mechanism of their influence on tumor progression.Methods: First, we obtained gene expression data related to liver cancer from the TCGA database (http://www.portal.gdc.cancer.gov/), including miRNA-seq, RNA-seq and clinical information. A co-expression network was constructed through the WGCNA software package in R software, with the purpose of identifying important microRNAs (miRNAs) and messenger RNAs (mRNAs) related to liver cancer. The DEmRNAs in the key module were analyzed with DAVID (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA was utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module. Results：201 DEmiRNAs and 3783 DEmRNAs were finally identified through differential expression analysis. The co-expression networks of DEmiRNA and DEmRNA were constructed by using WGCNA. Further analysis confirmed 4 miRNAs in the most significant module (blue module) were associated with the OS of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The Gene Ontology (GO) analysis results showed that the top enriched GO terms were oxidation-reduction process, extracellular exosome and iron ion binding. In Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the top 3 enriched terms included metabolic pathways, fatty acid degradation and valine, leucine and isoleucine degradation. In addition, we corssed the miRNA-mRNA interactions prediction results with the differentially expressed and prognostic mRNAs, and found that hsa-miR-92b-3p can be related to cytoplasmic polyadenylation element binding protein 3 (CPEB3) and Acyl-CoA Dehydrogenase Long Chain (ACADL). By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/CPEB3&ACADL were validated in hepatic cell carcinoma (HCC) tissues and human protein atlas (HPA) database.Conclusion: Our research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve as an important biomarker to promote the occurrence and development of HCC.

scholarly journals Bioinformatic Analysis of Key Genes Related to Tumor Microenvironment in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Jingxu Zhang ◽  
Hao Liu ◽  
Keyi Zhao ◽  
Zhiye Bao ◽  
Zhishuo Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Expression Data ◽  
Hub Genes ◽  
Oncomine Database ◽  
Key Genes ◽  
Low Expression

Abstract Background: Tumor microenvironment (TME) plays important roles in the development of different types of cancer. However, the critical regulatory members of TME related to hepatocellular carcinoma (HCC) remain unclear. In this study, a bioinformatic analysis based on Cancer Genome Atlas (TCGA) and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) datasets was conducted to predict the key genes affecting TME in HCC.Material and Methods: First, 340 patients and 20531 genes’ expression data with ESTIMATE scores were filtered and combined to identify differentially expressed genes. Next, protein-protein interaction (PPI) network and functional enrichment analysis were conducted to find hub genes. Then, log-rank test and functional enrichment analysis were conducted on the consensus genes and hub genes. Finally, Kaplan-Meier curves of the hub genes were drawn. As verification, those genes were searched on Oncomine database.Results: Among all differentially expressed genes, 916 genes were expressed in both the immune and stromal groups. The Gene Ontology (GO) terms they enriched were T cell activation, leukocyte migration, collagen-containing matrix, external side of plasma membrane, receptor ligand and activator activity. Cytokine-cytokine receptor interaction was the most significant Kyoto Encyclopedia of Genes and Genomes (KEGG) term. Furthermore, cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 hub genes were low expressed in 304 patients, participating in a variety of responses including immune response−activating cell surface receptor signaling, immune response−activating signal transduction, clathrin−coated vesicle membrane, immune receptor activity， peptide binding and amide binding pathways. Their low expression was also verified on Oncomine database.Conclusion: cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 participated in many aspects related to TME, and their low expression constructs a signature, may predict a poor 5 years’ survival in hepatocellular carcinoma.

scholarly journals Identification of Key Genes and Evaluation of Their Prognosis Potential in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Jankun Liu ◽  
zy liu ◽  
Wei Li ◽  
Xinghua Pan ◽  
Zongjiang Fan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Target Genes ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Survival Curve ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Key Genes

Abstract Background Hepatocellular carcinoma (HCC) is a malignancy causing highly death rate in the world. Despite the development of treatment strategies for HCC, prognosis of this malignancy remains unsatisfactory. In this study, we aimed to identify the target genes associated with the prognosis of HCC patients. Methods Three expression profiles of HCC tissues were extracted from the Gene Expression Omnibus database to explore the differentially expressed genes (DEGs) using GEO2R method. Functional enrichment analysis was performed to reveal the biological characteristics of DEGs. Protein-protein interaction (PPI) network was constructed using Cytoscape software. The survival curve of identified DEGs were tested by Kaplan-Meier analysis. Results We identified 15 DEGs (CYP39A1, CYR61, FOS, FOXO1, GADD45B, ID1, IL1RAP, MT1M, PHLDA1, RND3, SDS, SOCS2, TAT, S100P, and SPINK1) in HCC tissues. Prognosis analysis showed that 4 DEGs (FOXO1,SPINK1༌SOCS2, and TAT) correlated with overall survival time of HCC patients, which might serve as therapeutic targets for HCC patients. Conclusions By integrated bioinformatics analysis, we proposed a novel way to reveal key genes that closely relate to HCC development.

scholarly journals Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11321
Author(s):  
Di Zhang ◽  
Pengguang Yan ◽  
Taotao Han ◽  
Xiaoyun Cheng ◽  
Jingnan Li
Keyword(s):  
Ulcerative Colitis ◽  
Mitochondrial Dysfunction ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Junction ◽  
Biological Processes ◽  
Hub Genes ◽  
Key Genes

Background Ulcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC. Methods Microarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the “limma” R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the “clusterProfiler” R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene. Results Six functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3. Conclusions Using WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

scholarly journals Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data

Nanomaterials ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 903 ◽  
Author(s):  
Antonio Federico ◽  
Angela Serra ◽  
My Kieu Ha ◽  
Pekka Kohonen ◽  
Jang-Sik Choi ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Data Preprocessing ◽  
Functional Enrichment Analysis ◽  
Toxicity Assessment ◽  
Functional Enrichment ◽  
Quality Data ◽  
Chemical Toxicity ◽  
Transcriptomics Data

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

scholarly journals Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

2020 ◽  
Vol 9 (2) ◽  
pp. LMT30
Author(s):  
Chuanli Ren ◽  
Weixiu Sun ◽  
Xu Lian ◽  
Chongxu Han
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Kaplan Meier ◽  
Key Genes ◽  
Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

scholarly journals Weighted Gene Correlation Network Analysis Identifies Specific Functional Modules and Genes in Esophageal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Xu ◽  
Jian Xu ◽  
Zhiqiang Wang ◽  
Yuequan Jiang
Keyword(s):  
Esophageal Cancer ◽  
Network Analysis ◽  
Cell Cycle Progression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Future Research ◽  
Hub Genes ◽  
Pathological Staging

Objective. Esophageal cancer (ESCA) is one of the most aggressive malignancies globally with an undesirable five-year survival rate. Here, this study was conducted for determining specific functional genes linked with ESCA initiation and progression. Methods. Gene expression profiling of ESCA was curated from TCGA (containing 160 ESCA and 11 nontumor specimens) and GSE38129 (30 paired ESCA and nontumor tissues) datasets. Differential expression analysis was conducted between ESCA and nontumor tissues with adjusted p value <0.05 and |log2fold-change|>1. Weighted gene coexpression network analysis (WGCNA) was conducted for determining the ESCA-specific coexpression modules and genes. Thereafter, ESCA-specific differentially expressed genes (DEGs) were intersected. Functional enrichment analysis was then presented with clusterProfiler package. Protein-protein interaction was conducted, and hub genes were determined. Association of hub genes with pathological staging was evaluated, and survival analysis was presented among ESCA patients. Results. This study determined 91 ESCA-specific DEGs following intersection of DEGs and ESCA-specific genes in TCGA and GSE38129 datasets. They were remarkably linked to cell cycle progression and carcinogenic pathways like the p53 signaling pathway, cellular senescence, and apoptosis. Ten ESCA-specific hub genes were determined, containing ASPM, BUB1B, CCNA2, CDC20, CDK1, DLGAP5, KIF11, KIF20 A, TOP2A, and TPX2. They were prominently associated with pathological staging. Among them, KIF11 upregulation was in relation to undesirable prognosis of ESCA patients. Conclusion. Collectively, we determined ESCA-specific coexpression modules and hub genes, which offered the foundation for future research concerning the mechanistic basis of ESCA.

scholarly journals Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiarong Yi ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
Xiazi Zouxu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

scholarly journals MiR-103a promotes tumour growth and influences glucose metabolism in hepatocellular carcinoma

2021 ◽  
Vol 12 (6) ◽  
Author(s):  
Yuling Liu ◽  
Yuanzhou Zhang ◽  
Bowen Xiao ◽  
Ning Tang ◽  
Jingying Hu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Death ◽  
Cell Growth ◽  
Glucose Metabolism ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Deep Sequencing Technology ◽  
Subcutaneous Xenograft ◽  
Patient Prognosis

AbstractHepatocellular carcinoma (HCC) is a common and high-mortality cancer worldwide. Numerous microRNAs have crucial roles in the progression of different cancers. However, identifying the important microRNAs and the target biological function of the microRNA in HCC progression is difficult. In this study, we selected highly expressed microRNAs with different read counts as candidate microRNAs and then tested whether the microRNAs were differentially expressed in HCC tumour tissues, and we found that their expression was related to the HCC prognosis. Then, we investigated the effects of microRNAs on the cell growth and mobility of HCC using a real-time cell analyser (RTCA), colony formation assay and subcutaneous xenograft models. We further used deep-sequencing technology and bioinformatic analyses to evaluate the main functions of the microRNAs. We found that miR-103a was one of the most highly expressed microRNAs in HCC tissues and that it was upregulated in HCC tissue compared with the controls. In addition, high miR-103a expression was associated with poor patient prognosis, and its overexpression promoted HCC cell growth and mobility. A functional enrichment analysis showed that miR-103a mainly promoted glucose metabolism and inhibited cell death. We validated this analysis, and the data showed that miR-103a promoted glucose metabolism-likely function and directly inhibited cell death via ATP11A and EIF5. Therefore, our study revealed that miR-103a may act as a key mediator in HCC progression.

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