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 Integrative analysis reveals the prognostic value and functions of splicing factors implicated in hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
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

AbstractSplicing 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.

A Six-Gene Prognostic Risk Prediction Model In Hepatitis B Virus-Associated Hepatocellular Carcinoma

2021 ◽  
Vol 44 (3) ◽  
pp. E32-44
Author(s):  
Jia Shen ◽  
Ming Shu ◽  
Shujie Xie ◽  
Jia Yan ◽  
Kaile Pan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Prognostic Score ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Gene Set Enrichment ◽  
B Virus ◽  
Score Model ◽  
Normal Controls

Purpose: This study aimed to screen hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC)-related feature ribonucleic acids (RNAs) and to establish a prognostic model. Methods: The transcriptome expression data of HBV-associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus database. Differential RNAs between HBV-associated HCC and normal controls were identified by a meta-analysis of TCGA, GSE55092 and GSE121248. Weighted gene co-expression network analysis was performed to identify key RNAs and modules. A prognostic score model was established using TCGA as a training set by Cox regression analysis and was validated in E-TABM-36 dataset. Additionally, independent prognostic clinical factors were screened, and the function of lncRNAs waspredicted through Gene Set Enrichment Analysis. Results: A total of 710 consistent differential RNAs between HBV-associated HCC and normal controls were obtained, including five lncRNAs and 705 mRNAs. An optimized combination of six differential RNAs (DSCR4, DBH, ECM1, GDAP1, MATR3 and RFC4) was selected and a prognostic score model was constructed. Kaplan-Meier analysis demonstrated that the prognosis of the high-risk and low-risk groups separated by this model was significantly different in the training set and the validation set. Gene Set Enrichment Analysis showed that the co-expression genes of DSCR4 were significantly correlated with neuroactive ligand receptor interactionpathway. Conclusion: A prognostic model based on DSCR4, DBH, ECM1, GDAP1, MATR3 and RFC4 was developed that can accurately predict the prognosis of patients with HBV-associated HCC. These genes, as well as histologic grade, may serve as independent prognostic factors in HBV-associated HCC.

scholarly journals Prognostic significance and oncogene function of cathepsin A in hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Huaxiang Wang ◽  
Fengfeng Xu ◽  
Fang Yang ◽  
Lizhi Lv ◽  
Yi Jiang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Oxidative Phosphorylation ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Normal Liver ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Lysosomal Protease

AbstractCathepsin A (CTSA) is a lysosomal protease that regulates galactoside metabolism. The previous study has shown CTSA is abnormally expressed in various types of cancer. However, rarely the previous study has addressed the role of CTSA in hepatocellular carcinoma (HCC) and its prognostic value. To study the clinical value and potential function of CTSA in HCC, datasets from the Cancer Genome Atlas (TCGA) database and a 136 HCC patient cohort were analyzed. CTSA expression was found to be significantly higher in HCC patients compared with normal liver tissues, which was supported by immunohistochemistry (IHC) validation. Both gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that CTSA co-expressed genes were involved in ATP hydrolysis coupled proton transport, carbohydrate metabolic process, lysosome organization, oxidative phosphorylation, other glycan degradation, etc. Survival analysis showed a significant reduction both in overall survival (OS) and recurrence-free survival (RFS) of patients with high CTSA expression from both the TCGA HCC cohort and 136 patients with the HCC cohort. Furthermore, CTSA overexpression has diagnostic value in distinguishing between HCC and normal liver tissue [Area under curve (AUC) = 0.864]. Moreover, Gene set enrichment analysis (GSEA) showed that CTSA expression correlated with the oxidative phosphorylation, proteasome, and lysosome, etc. in HCC tissues. These findings demonstrate that CTSA may as a potential diagnostic and prognostic biomarker in HCC.

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 m6A methyltransferase-related lncRNA signature for predicting immunotherapy and prognosis in patients with hepatocellular carcinoma

2021 ◽  
Author(s):  
Lili Li ◽  
Rongrong Xie ◽  
Guangrong Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Regression Analyses ◽  
Protein Coding ◽  
Normal Tissues ◽  
Kaplan Meier

N6-methyladenosine (m6A) methyltransferase has been shown to be an oncogene in a variety of cancers. Nevertheless, the relationship between the long non-coding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains elusive. We integrated the gene expression data of 371 HCC and 50 normal tissues from The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression & Kaplan-Meier (K-M) analysis were performed to identify m6A methyltransferase‑related lncRNAs. Three prognostic lncRNAs were selected by univariate and LASSO Cox regression analyses to construct the m6A methyltransferase-related lncRNA signature. Multivariate Cox regression analyses illustrated that this signature was an independent prognostic factor for overall survival (OS) prediction. The Gene Set Enrichment Analysis (GSEA) suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes and pathways. Besides, we discovered that the lncRNAs signature was correlated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that the lncRNAs could predict the clinical response to immunotherapy. Our study had originated a prognostic signature for HCC based on the potential prognostic m6A methyltransferase-related lncRNAs. This study had deepened the understanding of the TME status of HCC patients and laid a theoretical foundation for the choice of immunotherapy.

scholarly journals Arp2/3 Complex Subunits as Prognostic Biomarkers and Their Correlations With Immune Infiltration in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Dan Li ◽  
LingLing Zhuang ◽  
Liying Sun ◽  
Jianbing WU
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Patterns ◽  
Family Members ◽  
Enrichment Analysis ◽  
Normal Liver ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Biomarkers ◽  
Migration And Invasion ◽  
Multiple Cancer ◽  
Immune Infiltration

Abstract Introduction: Actin‐related protein 2/3 complex (Arp2/3) is a major actin nucleator containing seven subunits in humans, which has been widely reported that Arp2/3 plays an important role in promoting migration and invasion of various kinds of cancers. Nevertheless, the expression patterns and prognostic values of Arp2/3 subunits and the correlation between ARP2/3 subunits with immune infiltration in hepatocellular carcinoma remains unclear.Methods: TCGA-FPKM dataset, Oncomine, CCLE and UCSC Xena database was used to obtained mRNA expression and clinical information of Arp2/3 subunits in HCC. The Arp2/3 subunits differential expression in HCC tissues and cell lines was analyzed by utilizing the Perl 5.30.0 and R 4.0.4 software. The protein expression data of Arp2/3 subunits was obtained from The Human Protein Atlas (HPA). The prognostic value of each individual Arp2/3 subunits was identified by R 4.0.4 software. The association between the mRNA expressions of Arp2/3 members in HCC tissues with their clinicopathologic parameters was analyzed by UALCAN. The relevance between tumor immunocyte infiltration and the Arp2/3 complex members were determined by the TIMER 2.0 platform and GEPIA database. A gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of Arp2/3 complex members in the carcinogenesis of HCC.Results：The results revealed that expression of Arp2/3 family members(ACTR2,ACTR3,ARPC1A, APRC1B,ARPC2,ARPC3,ARPC4, ARPC5, ARPC5L) were up-regulated in hepatocellular carcinoma (HCC). Moreover, higher protein expressions of of ACTR3, ARPC1A, ARPC1B, ARPC2 were found in HCC tissues compared with normal liver tissues. The higher expression of of Arp2/3 family members were significantly correlated with poor survival and cancer stages in HCC patients. Multivariate analysis also demonstrated that ACTR3, ARPC2 and ARPC5 were independent prognostic biomarkers of survival in HCC patients. Meanwhile, ACTR3, ARPC2 and ARPC5 in HCC has positive significant correlations with the infiltration of immune cells. The GSEA results indicated that Arp2/3 members significantly involve in multiple cancer-related pathways by which promoted development of HCC. Conclusions：Various analysis indicated that certain Arp2/3 complex subunits were noticeably upregulated and predicted worse survival in HCC, which may be applied as promising molecular targets for diagnosis and therapy of HCC in the future.

scholarly journals Prognostic Significance and Oncogene Function of Cathepsin A in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Huaxiang Wang ◽  
Fengfeng Xu ◽  
Fang Yang ◽  
Lizhi Lv ◽  
Yi Jiang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Oxidative Phosphorylation ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Normal Liver ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Lysosomal Protease

Abstract Cathepsin A (CTSA) is a lysosomal protease that regulates galactoside metabolism. The previous study has shown CTSA is abnormally expressed in various types of cancer. However, rarely the previous study has addressed the role of CTSA in hepatocellular carcinoma (HCC) and its prognostic value. To study the clinical value and potential function of CTSA in HCC, datasets from the Cancer Genome Atlas (TCGA) database and a 136 HCC patient cohort were analyzed. CTSA expression was found to be significantly higher in HCC patients compared with normal liver tissues, which was supported by immunohistochemistry (IHC) validation. Both gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that CTSA co-expressed genes were involved in ATP hydrolysis coupled proton transport, carbohydrate metabolic process, lysosome organization, oxidative phosphorylation, other glycan degradation, etc. Survival analysis showed a significant reduction both in overall survival (OS) and recurrence-free survival (RFS) of patients with high CTSA expression from both the TCGA HCC cohort and 136 patients with the HCC cohort. Furthermore, CTSA overexpression has diagnostic value in distinguishing between HCC and normal liver tissue (Area under curve (AUC) = 0.864). Moreover, Gene set enrichment analysis (GSEA) showed that CTSA expression correlated with the oxidative phosphorylation, proteasome, and lysosome, etc. in HCC tissues. These findings demonstrate that CTSA may as a potential diagnostic and prognostic biomarker in HCC.

scholarly journals Cyclin B1 acts as a tumor microenvironment-related cancer promoter and prognostic biomarker in hepatocellular carcinoma

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110162
Author(s):  
Yangming Hou ◽  
Xin Wang ◽  
Junwei Wang ◽  
Xuemei Sun ◽  
Xinbo Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Proportional Hazards ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Cyclin B1 ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
Tumor Promoter ◽  
Protein Protein Interaction

Objectives The present study aimed to develop a gene signature based on the ESTIMATE algorithm in hepatocellular carcinoma (HCC) and explore possible cancer promoters. Methods The ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cells (TICs) in a cohort of HCC patients. The differentially expressed genes (DEGs) were screened by Cox proportional hazards regression analysis and protein–protein interaction (PPI) network construction. Cyclin B1 (CCNB1) function was verified using experiments. Results The stromal and immune scores were associated with clinicopathological factors and recurrence-free survival (RFS) in HCC patients. In total, 546 DEGs were up-regulated in low score groups, 127 of which were associated with RFS. CCNB1 was regarded as the most predictive factor closely related to prognosis of HCC and could be a cancer promoter. Gene Set Enrichment Analysis (GSEA) and CIBERSORT analyses indicated that CCNB1 levels influenced HCC tumor microenvironment (TME) immune activity. Conclusions The ESTIMATE signature can be used as a prognosis tool in HCC. CCNB1 is a tumor promoter and contributes to TME status conversion.

scholarly journals In Silico Identification of Contradictory Role of ADAMTS5 in Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382098682
Author(s):  
Zhipeng Zhu ◽  
Jiuhua Xu ◽  
Xiaofang Wu ◽  
Sihao Lin ◽  
Lulu Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Subgroup Analysis ◽  
Prognostic Value ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Clinicopathological Parameters ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
The Impact

Background: ADAMTS5 has different roles in multiple types of cancers and participates in various molecular mechanisms. However, the prognostic value of ADAMTS5 in patients with hepatocellular carcinoma (HCC) still remains unclear. We carried the study to evaluate the prognostic value and identified underlying molecular mechanisms in HCC. Methods: Firstly, the association of ADAMTS5 expression and clinicopathological parameters was evaluated by in GSE14520. Next, ADAMTS5 expression in HCC was performed using GSE14520, GSE36376, GSE76427 and The Cancer Genome Atlas (TCGA) profile. Furthermore, Kaplan-Meier analysis, Univariate and Multivariate Cox regression analysis, subgroup analysis was performed to evaluate the prognostic value of ADAMTS5 in HCC. Finally, GO enrichment analysis, gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA) were performed to revealed underlying molecular mechanisms. Result: The expression of ADAMTS5 was positively correlated with the development of HCC. Next, high ADAMTS5 expression was significantly associated with poorer survival (all P < 0.05) and the impact of ADAMTS5 on all overall survival (OS), disease-free survival (DFS), relapse-free survival (RFS), disease specific survival (DSS) and progression free interval (PFI) was specific for HCC among other 29 cancer types. Subgroup analysis showed that ADAMTS5 overexpression was significantly associated with poorer OS in patients with HCC. Finally, ADAMTS5 might participate in the status conversion from metabolic-dominant to extracellular matrix-dominant, and the activation of ECM-related biological process might contribute to high higher mortality risk for patients with HCC. Conclusion: ADAMTS5 may play an important role in the progression of HCC, and may be considered as a novel and effective biomarker for predicting prognosis for patients with HCC.

scholarly journals A Novel Prognostic Model Based On Cytosolic DNA Sensing-Related Gene Signature for Pancreatic Cancer

2021 ◽  
Author(s):  
Chuan-Qi Xu ◽  
Kui-Sheng Yang ◽  
Shu-Xian Zhao ◽  
Jian Lv
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factor ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Independent Prognostic Factor ◽  
Gene Set Enrichment Analysis ◽  
Dna Sensing ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Objective: Pancreatic cancer (PC) is one of the most malignant tumors. Cytosolic DNA sensing have been found to play an essential role in tumor. In this study, a cytosolic DNA sensing-related genes (CDSRGs) signature was constructed and the potential mechanisms also been discussed.Methods: The RNA expression and clinical data of PC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Subsequently, univariate (UCR) and multivariate Cox regression (MCR) analyses were conducted to establish a prognostic model in the TCGA patients, which was verified by GEO patients. Cancer immune infiltrates were investigated via single sample gene set enrichment analysis (ssGSEA) and Tumor Immune Estimation Resource (TIMER). Finally, Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways.Results: A prognostic model comprising four genes (POLR2E,IL18, MAVS, and FADD) was established. The survival rate of patients in the low-risk group was significantly higher than that of patients in the high-risk group. In addition, CDSRGs-risk score was proved as an independent prognostic factor in PC. Immune infiltrates and drug sensitivity are associated with POLR2E,IL18, MAVS, and FADD expression.Conclusions: In summary, we present and validated a CDSRGs risk model that is an independent prognostic factor and indicates the immune characteristics of PC. This prognostic model may facilitate the personalized treatment and monitoring.

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