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 Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

2021 ◽  
Vol 27 ◽  
Author(s):  
Wanbang Zhou ◽  
Yiyang Chen ◽  
Ruixing Luo ◽  
Zifan Li ◽  
Guanwei Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Tumor Progression ◽  
Protein Interactions ◽  
Immune Cells ◽  
Immune Cell ◽  
Immune Microenvironment ◽  
Hub Genes ◽  
Expression Levels ◽  
Immune Infiltration

Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.

scholarly journals Multi-Omic Analyses of the m5C Regulator ALYREF Reveal Its Essential Roles in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chen Xue ◽  
Yalei Zhao ◽  
Ganglei Li ◽  
Lanjuan Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Hub Genes ◽  
Specific Expression ◽  
Tissue Samples ◽  
Expression Levels ◽  
Advanced Tumor

The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

scholarly journals Exploration of the regulatory mechanism for hepatocellular carcinoma based on ceRNA networks analysis and immune infiltration

2021 ◽  
Author(s):  
Ning Huang ◽  
Qiang Chen ◽  
Xiaoyi Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Network Analysis ◽  
Correlation Analysis ◽  
Immune Cells ◽  
Regulatory Mechanism ◽  
Immune Cell ◽  
Risk Model ◽  
The Cancer Genome Atlas ◽  
Ppi Network

Abstract Background Hepatocellular carcinoma (HCC) as malignant cancer has been deeply investigated for its widespread distribution and extremely high mortality rate worldwide. Despite efforts to understand the regulatory mechanism in HCC, it remains largely unknown. Methods The RNA (mRNAs, lncRNAs, and miRNAs) profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the Weighted Gene Co-expression Network Analysis (WGCNA), the hub differentially expressed RNAs (DERNAs) were screened out. The competing endogenous RNA (ceRNA) and Protein and Protein Interaction (PPI) network were constructed based on the hub DERNAs. The Cox and LASSO regression analysis were used to find the independent prognostic ceRNAs. We performed the “CIBERSORT” algorithm estimate the abundance of immune cells. The correlation analysis was applied to determine the relationship between HCC-related immune cells and prognostic ceRNAs. GEPIA and TIMER database were used to explore the association of critical genes with survival and immune cell infiltration, respectively. Results A total of 524 hub RNAs (507 DEmRNAs, 13 DElncRNAs and 4 DEmiRNAs) were identified in the turquoise module (cor = 0.78, P = 4.7e − 198) using WGCNA algorithm. PPI network analysis showed that NDC80, BUB1B and CCNB2 as the critical genes in HCC. Subsequently, survival analysis revealed that the low expression of NDC80 and BUB1B resulted in a longer overall survival (OS) time for HCC patients in GEPIA database. These critical genes and several immune cells were all significantly positive correlated in TIMER database. The ceRNA network were establish, and were incorporated to risk model. Subsequently, ROC curve showed that the area under the curve (AUC) of the 1-, 3-, and 5-year were 0.762, 0.705, and 0.688, respectively. Out of the 22 cell types, T cells CD4 memory resting were identified as the HCC-related immune cells by systematic analysis. The correlation analysis shown that T cells CD4 memory resting is negatively associated with both AL021453.1 (R = − 0.44, P = 0.00049) and CCDC137 (R = − 0.47, P = 2e-04). Conclusion The current study provide potential prognostic signatures and therapeutic targets for HCC.

scholarly journals RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Benchen Rao ◽  
Jianhao Li ◽  
Tong Ren ◽  
Jing Yang ◽  
Guizhen Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Tumor Progression ◽  
Poor Prognosis ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Bile Acid Metabolism ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Tumor Tissues

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.MethodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.ResultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p &lt; 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.ConclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

scholarly journals Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

2020 ◽  
Author(s):  
Xueping Jiang ◽  
Yanping Gao ◽  
Nannan Zhang ◽  
Cheng Yuan ◽  
Yuan Luo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Venn Diagram ◽  
Ppi Network ◽  
Survival Curves ◽  
Hub Genes ◽  
Expression Levels ◽  
Kegg Analysis

Abstract Background As the most diagnosed malignancy, lung cancer is also the primary cause of cancer death in the entire world. The therapy of lung adenocarcinoma (LUAD), which is the most prevalent subtype of lung cancer, draw researchers’ increasing attentions. This research aimed to investigate the tumor microenvironment (TME)-related hub genes which might be novel targets for treatment. Materials and methods LUAD-associated data packages, including RNA-Seq information and clinical data of 522 patients, were obtained from The Cancer Genome Atlas (TCGA). For better evaluation of stromal and immune cell components, immune scores, stromal scores and estimate scores were obtained with ESTIMATE algorithm based on gene expression levels in tumors. The R package heatmap and clustering analysis were used to explore interested genes. Differentially expressed genes (DEGs) were identified by Venn diagram. Protein-protein interaction (PPI) network was applied to explore intrinsic connections of DEGs. Kaplan-Meier (K-M) survival curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to investigate the prognostic values and intricate biological functions of DEGs. The relationships between 4 survival-related hub genes and 6 types of immune cells were examined using TIMER database. The LinkedOmics database was applied to look for kinase targets of hub genes. Results The immune/stromal/estimate scores were significantly correlated with clinical features, including the grades and sizes of LUAD, distant metastasis and outcomes. A total of 702 DEGs, 589 up-regulated and 113 down-regulated, were identified. GO and KEGG analysis showed that the DEGs had significant correlations with tumor immunology. PPI network suggested that the top 8 nodes were FPR2, C3AR1, MCHR1, CCR5, FPR1, CCL19, CCR2 and CXCL10. K-M survival curves indicated that FPR2, C3AR1, MCHR1 and CCR5, as hub genes, were significantly correlated with the overall survival (OS) of LUAD patients. The expression levels of C3AR1 and CCR5 were positively correlated with immune cell infiltration. LYN, LCK and SYK were the targeted kinases of these hub genes. Conclusion FPR2, C3AR1, MCHR1 and CCR5 were TME-related genes and potential biomarkers for the therapy and prognosis of LUAD.

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 Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Dechao Wei ◽  
Qiankun Li ◽  
Xiaobing Yang ◽  
Yili Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Single Gene ◽  
Characteristic Curve ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Hub Genes ◽  
Gene Set

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e–26) and tumor stage (r = 0.38, p = 2e–17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

scholarly journals Identification of miR-589-5p as a Potential Prognostic Biomarker of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Zhijia Yao ◽  
Chunlin Jiang ◽  
Huiwen Weng ◽  
Shi Fang ◽  
Heping Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
In Situ Hybridization ◽  
Early Diagnosis ◽  
Real Time ◽  
Real Time Pcr ◽  
Human Cancer ◽  
Clinical Stage ◽  
Survival Prognosis ◽  
Expression Levels

Abstract Background: MicroRNAs (miRNAs) are small endogenous non-coding RNAs with 22 nucleotides approximately. Numerous studies have reported that microRNAs may hold the potential to serve as biomarkers in diagnosing cancer and predicting prognosis. miR-589-5p is a well-documented oncogenic miRNA implicated in several human cancer types. However, the potential value of miR-589-5p as a biomarker to facilitate early diagnosis and predict prognosis in hepatocellular carcinoma (HCC) patients remains obscure.Methods: miR-589-5p expression was examined by in situ hybridization (ISH) in 10 adjacent normal tissues (ANT), 21 liver fibrosis tissues, 113 HCC tissues, including 22 HCC with grade I, 37 HCC with grade II, 49 HCC with grade III and 5 hepatocholangiocellular carcinoma tissues. Statistical analysis and Kaplan–Meier survival analysis was performed to evaluate the clinical correlation between miR-589-5p expression and clinicopathological features and survival prognosis in HCC patients.Results: miR-589-5p expression was dramatically upregulated in HCC tissues by in situ hybridization (ISH). Overexpression of miR-589-5p was not only positively correlated with poor tumor differentiation degree, increased AFP levels, advanced clinical stage, distant metastatic status and venous invasion, but also predicted poor overall and relapse-free survival in HCC patients. Importantly, the expression levels of miR-589-5p measured by real-time PCR in high-ISH scores tissues were dramatically upregulated compared with those in low-ISH scores tissues, and a strong and positive correlation of miR-589-5p expression levels in real-time PCR with ISH staining index was demonstrated in HCC tissues.Conclusion: our results support the notion that miR-589-5p might hold a promising value as a novel biomarker to facilitate early diagnosis and predict prognosis in HCC patients.

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 Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhuolin Li ◽  
Yao Lin ◽  
Bizhen Cheng ◽  
Qiaoxin Zhang ◽  
Yingmu Cai
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Reduction Process ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Poor Overall Survival ◽  
Primary Liver Tumor ◽  
Key Genes

BackgroundHepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.MethodsThe bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.ResultsWe ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.ConclusionThe present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

Sign in / Sign up

Export Citation Format

Share Document