scholarly journals Bioinformatics analysis to identify the key genes affecting the progression and prognosis of hepatocellular carcinoma

2019 ◽  
Vol 39 (2) ◽  
Author(s):  
Yingai Zhang ◽  
Shunlan Wang ◽  
Jingchuan Xiao ◽  
Hailong Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Primary Liver Cancer ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Sequencing Data ◽  
Protein Coding ◽  
Qrt Pcr ◽  
Protein Coding Genes ◽  
Key Genes

Abstract Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer, which has poor outcome. The present study aimed to investigate the key genes implicated in the progression and prognosis of HCC. The RNA-sequencing data of HCC was extracted from The Cancer Genome Atlas (TCGA) database. Using the R package (DESeq), the differentially expressed genes (DEGs) were analyzed. Based on the Cluepedia plug-in in Cytoscape software, enrichment analysis for the protein-coding genes amongst the DEGs was conducted. Subsequently, protein–protein interaction (PPI) network was built by Cytoscape software. Using survival package, the genes that could distinguish the survival differences of the HCC samples were explored. Moreover, quantitative real-time reverse transcription-PCR (qRT-PCR) experiments were used to detect the expression of key genes. There were 2193 DEGs in HCC samples. For the protein-coding genes amongst the DEGs, multiple functional terms and pathways were enriched. In the PPI network, cyclin-dependent kinase 1 (CDK1), polo-like kinase 1 (PLK1), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serum amyloid A1 (SAA1), and lysophosphatidic acid receptor 3 (LPAR3) were hub nodes. CDK1 interacting with PLK1 and FOS, and LPAR3 interacting with FOS and SAA1 were found in the PPI network. Amongst the 40 network modules, 4 modules were with scores not less than 10. Survival analysis showed that anterior gradient 2 (AGR2) and RLN3 could differentiate the high- and low-risk groups, which were confirmed by qRT-PCR. CDK1, PLK1, FOS, SAA1, and LPAR3 might be key genes affecting the progression of HCC. Besides, AGR2 and RLN3 might be implicated in the prognosis of HCC.

scholarly journals Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junwei Liu ◽  
Fang Han ◽  
Jianyi Ding ◽  
Xiaodong Liang ◽  
Jie Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Functional Study ◽  
Ppi Network ◽  
Hub Genes ◽  
Qrt Pcr

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

scholarly journals Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

2020 ◽  
Author(s):  
Yang Wang ◽  
Chengping Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Protein Coding ◽  
Cox Regression Analysis ◽  
Protein Coding Genes

Abstract Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

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 Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Wang ◽  
Rui Peng ◽  
Zheng Zhang ◽  
Yixi Zhang ◽  
Yuke Dai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Primary Liver Cancer ◽  
Interaction Network ◽  
Chemical Carcinogenesis ◽  
Parameter Analysis ◽  
Qrt Pcr ◽  
Key Genes

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Integrative Analysis Reveals the Prognostic Value and Functions of Splicing Factors Implicated in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Yue Wang ◽  
Fan Yang ◽  
Jiaqi Shang ◽  
Haitao He ◽  
Qing Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Splicing Factors ◽  
Biological Functions ◽  
Clinical Value ◽  
Multiple Cancer ◽  
Protein Coding ◽  
Protein Coding Genes

Abstract Splicing factors (SFs) play critical roles in the pathogenesis of various cancers through regulating tumor-associated alternative splicing (AS) events. However, the clinical value and biological functions of SFs in hepatocellular carcinoma (HCC) remain obscure. In this study, we identified 40 dysregulated SFs in HCC and established a prognostic model composed of four SFs (DNAJC6, ZC3H13, IGF2BP3, DDX19B). The predictive efficiency and independence of the prognostic model were confirmed to be satisfactory. Gene Set Enrichment Analysis (GSEA) illustrated the risk score calculated by our prognostic model was significantly associated with multiple cancer-related pathways and metabolic processes. Furthermore, we constructed the SFs-AS events regulatory network and extracted 108 protein-coding genes from the network for following functional explorations. Protein-protein interaction (PPI) network delineated the potential interactions among these 108 protein-coding genes. GO and KEGG pathway analyses investigated ontology gene sets and canonical pathways enriched by these 108 protein-coding genes. Overlapping the results of GSEA and KEGG, seven pathways were identified to be potential pathways regulated by our prognostic model through triggering aberrant AS events in HCC. In conclusion, the present study established an effective prognostic model based on SFs for HCC patients. Functional explorations of SFs and SFs-associated AS events provided directions to explore biological functions and mechanisms of SFs in HCC tumorigenesis.

scholarly journals Identification of immune-infiltrating cell-related biomarkers in hepatocellular carcinoma based on gene co-expression network analysis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yinghui Hou ◽  
Guizhi Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Early Diagnosis ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Sequencing Data ◽  
Hub Genes ◽  
Survival Prognosis ◽  
Expression Levels ◽  
Cancer Tissues

Abstract Background Hepatocellular carcinoma (HCC) is often caused by chronic liver infection or inflammation. Searching for potential immunotherapy targets will aid the early diagnosis and treatment of HCC. Methods Firstly, detailed HCC data were downloaded from The Cancer Genome Atlas database. GDCRNATools was used for the comprehensive analysis of RNA sequencing data. Subsequently, the CIBERSORT package was used to estimate infiltration scores of 22 types of immune cells in complex samples. Furthermore, hub genes were identified via weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis. In addition, multiple databases were used to validate the expression of hub gene in the tumor tissue. Finally, prognostic, diagnostic and immunohistochemical analysis of key hub genes was performed. Results In the present study, 9 hub genes were identified using WGCNA and PPI network analysis. Furthermore, the expression levels of 9 genes were positively correlated with the infiltration levels of CD8-positive T (CD8+ T) cells. In multiple dataset validations, the expression levels of CCL5, CXCR6, CD3E, and LCK were decreased in cancer tissues. In addition, survival analysis revealed that patients with LCK low expression had a poor survival prognosis (P < 0.05). Immunohistochemistry results demonstrated that CCL5, CD3E and LCK were expressed at low levels in HCC cancer tissues. Conclusion The identification of CCL5, CXCR6, CD3E and LCK may be helpful in the development of early diagnosis and therapy of HCC. LCK may be a potential prognostic biomarker for immunotherapy for HCC.

scholarly journals Microarray Data Mining and Preliminary Bioinformatics Analysis of Hepatitis D Virus-Associated Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zhe Yu ◽  
Xuemei Ma ◽  
Wei Zhang ◽  
Xiujuan Chang ◽  
Linjing An ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
The Cancer Genome Atlas ◽  
Venn Diagram ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hepatitis D ◽  
Increased Risk ◽  
Key Genes

Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV-associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV-associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV-associated HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV-associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein-to-protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV-associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.

scholarly journals Up-regulated Expression and Related Gene Regulatory Networks of ANLN Predict Poor Prognosis and Correlate with Immune Infiltration in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Bo Hu ◽  
Xiao-Bo Yang ◽  
Xinting Sang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Immune Infiltration ◽  
Dismal Prognosis ◽  
Mrna Binding

Abstract Background: The aberrant Anillin (ANLN) expression is reported to be associated with carcinogenesis. In this study, sequencing data collected from the Cancer Genome Atlas database were utilized to analyze ANLN expression in hepatocellular carcinoma (HCC).Methods: The relationships of clinicopathological features with ANLN were investigated, and gene set enrichment analysis (GSEA) was performed to reveal the ANLN-related functions. LinkedOmics was employed to identify the co-expressed genes of ANLN and to examine the target networks of kinases, microRNAs (miRNAs) and transcription factors (TFs). Besides, the correlation of ANLN expression with cancer immune infiltrates was analyzed by TIMER. Results: ANLN over-expression predicted dismal prognosis, and GESA results revealed several functions that were related to cell cycle and mRNA binding. Moreover, functional network analysis indicated that, ANLN might regulate DNA replication and cell cycle signaling through pathways that involved several cancer-related kinases, miRNAs and E2F1. Additionally, ANLN was suggested to be associated with the infiltration of several immune cells, which was proved to be upregulated in both HCC cells and tissues. Conclusion: Those efficiently mined data reveal information regarding ANLN expression, the potential regulatory networks and the relationship with immune infiltration in HCC, which lay a foundation for further study on the role of ANLN in carcinogenesis.

scholarly journals Identification of Novel Biomarkers in Hepatocellular Carcinoma by Integrated Bioinformatical Analysis and Experimental Validation

2021 ◽  
Author(s):  
Chen Liao ◽  
Lanlan Wang ◽  
Xiaoqiang Li ◽  
Jinyu Bai ◽  
Jieqiong Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Small Molecule ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Venn Diagram ◽  
Ppi Network ◽  
Qrt Pcr ◽  
Small Molecule Drugs ◽  
Novel Biomarkers

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common poorly prognosed virulent neoplasms of the digestive system. In this study, we identified novel biomarkers associated with the pathogenesis of HCC aiming to provide new diagnostic and therapeutic approaches for HCC. Methods: Gene expression profiles of GSE62232, GSE84402,GSE121248 and GSE45267 were obtained in GEO database. Differential expressed genes (DEGs) between HCC and normal samples were identified using the GEO2R tool and Venn diagram software.Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to carry out enrichment analysis on gene ontology (GO) and the Kyoto Encyclopaedia of Genes and Genomes pathway (KEGG). The protein-protein interaction (PPI) network of DEGs was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized by Cytoscape. Expressions and prognostic values of hub genes were validated through Kaplan-Meier plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas Database (HPA), western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, potential small molecule drugs were screened by Connectivity Map (CMAP). Results: A total of 100 overlapped DEGs were detected and results showed 23 of which were up-regulated with the rest being down-regulated. STRING screened the 70 edges and the 199 nodes in the PPI network. Survival analysis showed that aberrant mRNA expression of TOP2A, DTL, ANLN, CDKN3, BUB1B, CDK1, PBK, RRM2, RACGAP1, PRC1, NEK2, ECT2, CCNB1, HMMR, ASPM was significantly associated with a low survival rate. Results of WB and qRT-PCR showed that the expression levels of ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A were all increased in HCC tissues. Furthermore, CMAP predict suggest the 10 most vital small molecule drugs could reverse the progression of HCC. Conclusions: Core DEGs (ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A) with poor prognosis and candidate drugs for HCC treatment were identified through integrated bioinformatic analysis.This study will contribute to providing prognostic biomarker and therapeutic strategies in HCC. Background : Hepatocellular carcinoma (HCC) is one of the most common poorly prognosed virulent neoplasms of the digestive system. In this study, we identified novel biomarkers associated with the pathogenesis of HCC aiming to provide new diagnostic and therapeutic approaches for HCC. Methods : Gene expression profiles of GSE62232, GSE84402,GSE121248 and GSE45267 were obtained in GEO database. Differential expressed genes (DEGs) between HCC and normal samples were identified using the GEO2R tool and Venn diagram software.Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to carry out enrichment analysis on gene ontology (GO) and the Kyoto Encyclopaedia of Genes and Genomes pathway (KEGG). The protein-protein interaction (PPI) network of DEGs was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized by Cytoscape. Expressions and prognostic values of hub genes were validated through Kaplan-Meier plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas Database (HPA), western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, potential small molecule drugs were screened by Connectivity Map (CMAP). Results : A total of 100 overlapped DEGs were detected and results showed 23 of which were up-regulated with the rest being down-regulated. STRING screened the 70 edges and the 199 nodes in the PPI network. Survival analysis showed that aberrant mRNA expression of TOP2A, DTL, ANLN, CDKN3, BUB1B, CDK1, PBK, RRM2, RACGAP1, PRC1, NEK2, ECT2, CCNB1, HMMR, ASPM was significantly associated with a low survival rate. Results of WB and qRT-PCR showed that the expression levels of ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A were all increased in HCC tissues. Furthermore, CMAP predict suggest the 10 most vital small molecule drugs could reverse the progression of HCC. Conclusions : Core DEGs (ANLN, CCNB1, DTL, RACGAP1, RRM2 and TOP2A) with poor prognosis and candidate drugs for HCC treatment were identified through integrated bioinformatic analysis.This study will contribute to providing prognostic biomarker and therapeutic strategies in HCC.

scholarly journals Co-expression analysis and ceRNA network reveal eight novel potential lncRNA biomarkers in hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8101
Author(s):  
Ren-chao Zou ◽  
Zhi-tian Shi ◽  
Shu-feng Xiao ◽  
Yang Ke ◽  
Hao-ran Tang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Primary Liver Cancer ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Transcriptome Data ◽  
Cerna Network ◽  
Cancer Genome Atlas ◽  
Degree Of Malignancy

Background Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world, with a high degree of malignancy and recurrence. The influence of the ceRNA network in tumor on the biological function of liver cancer is very important, It has been reported that many lncRNA play a key role in liver cancer development. In our study, integrated data analysis revealed potential eight novel lncRNA biomarkers in hepatocellular carcinoma. Methods Transcriptome data and clinical data were downloaded from the The Cancer Genome Atlas (TCGA) data portal. Weighted gene co-expression network analysis was performed to identify the expression pattern of genes in liver cancer. Then, the ceRNA network was constructed using transcriptome data. Results The integrated analysis of miRNA and RNAseq in the database show eight novel lncRNAs that may be involved in important biological pathways, including TNM and disease development in liver cancer. We performed function enrichment analysis of mRNAs affected by these lncRNAs. Conclusions By identifying the ceRNA network and the lncRNAs that affect liver cancer, we showed that eight novel lncRNAs play an important role in the development and progress of liver cancer.

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