BUB1 Predicts Poor Prognosis and Immune Status in Liver Hepatocellular Carcinoma

2021 ◽  
Author(s):  
wenbo qi ◽  
Yuping Bai ◽  
Le Liu ◽  
Hao Chen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Family Member ◽  
Western Blotting ◽  
Immune Status ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Cell Counting ◽  
Immune Microenvironment ◽  
Hub Genes ◽  
Liver Hepatocellular Carcinoma

Abstract Background: Accurate assessment of the tumour immune microenvironment helps develop individualised immunotherapy regimens and screen dominant populations suitable for immunotherapy. Therefore, potential molecular markers were investigated to make an overall assessment of the immune microenvironment status of liver hepatocellular carcinoma (LIHC). Methods: Differentially expressed genes (DEGs) in LIHC were extracted from the International Cancer Genome Consortium and The Cancer Genome Atlas databases. Gene set enrichment analysis was employed to assess the function of DEGs. Hub genes were identified using the STRING tool. The prognostic value of the hub genes was evaluated through Kaplan–Meier analysis and Cox regression. The correlation between genes and immunity was analysed using the TIMER tool. Further, tissue samples from 42 LIHC patients were collected for immunohistochemistry. HuH7 and SKP1 cells were analysed via western blotting, Cell Counting kit-8 assay, and Transwell assay. Results: A total of 121 DEGs were identified, and DEGs were enriched in the epithelial-mesenchymal transition, hypoxia, myogenesis, and p53 pathways. A total of 20 hub genes were selected and a strong correlation was identified between these hub genes and prognosis. The expression of budding uninhibited by benzimidazoles 1 (BUB1), which is known to play a role in cancer progression, was found to be upregulated in LIHC (compared to normal tissues). Furthermore, BUB1 expression was strongly related to immune cells and immune checkpoint molecule expression. Immunohistochemistry indicated that BUB1 expression was higher in LIHC tissues than in normal liver tissues. Western blotting showed that BUB1 expression was the highest in HuH7 and SKP1 cells. BUB1 knockdown resulted in reduced proliferation and vertical migration ability of LIHC cells, and reduced the expression of phospho-SMAD family member 2 and phospho-SMAD family member 3 proteins. Immunohistochemistry showed that BUB1 expression was accompanied by immune cell infiltration into LIHC tissues.Conclusions: These results suggest that BUB1 may serve as a potential prognostic biomarker for LIHC and as an indicator of its immune status.

scholarly journals Identification and Validation of the N6-Methyladenosine RNA Methylation Regulator YTHDF1 as a Novel Prognostic Marker and Potential Target for Hepatocellular Carcinoma

2020 ◽  
Vol 7 ◽  
Author(s):  
Saiyan Bian ◽  
Wenkai Ni ◽  
Mengqi Zhu ◽  
Qianqian Song ◽  
Jianping Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Biological Activities ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Cell Counting ◽  
Migration And Invasion ◽  
Mesenchymal Transition ◽  
Rna Methylation ◽  
Hcc Cells

Purpose: N6-methyladenosine (m6A) RNA methylation has been implicated in various malignancies. This study aimed to identify the m6A methylation regulator-based prognostic signature for hepatocellular carcinoma (HCC) as well as provide candidate targets for HCC treatment.Methods: The least absolute shrinkage and selection operator (LASSO) analyses were performed to identify a risk signature in The Cancer Genome Atlas (TCGA) datasets. The risk signature was further validated in International Cancer Genome Consortium (ICGC) and Pan-Cancer Analysis of Whole Genomes (PCAWG) datasets. Following transfection of short hairpin RNA (shRNA) targeting YTHDF1, the biological activities of HCC cells were evaluated by Cell Counting Kit-8 (CCK-8), wound-healing, Transwell, flow cytometry, and xenograft tumor assays, respectively. The potential mechanisms mediated by YTHDF1 were predicted by overrepresentation enrichment analysis (ORA)/gene set enrichment analysis (GSEA) and validated by Western blotting.Results: Overexpression of m6A RNA methylation regulators was correlated with malignant clinicopathological characteristics of HCC patients. The Cox regression and LASSO analyses identified a risk signature with five m6A methylation regulators (KIAA1429, ZC3H13, YTHDF1, YTHDF2, and METTL3). In accordance with HCC cases in TCGA, the prognostic value of risk signature was also determined in ICGC and PCAWG datasets. Following analyzing the expression and clinical implications in TCGA and Gene Expression Omnibus (GEO), YTHDF1 was chosen for further experimental validation. Knockdown of YTHDF1 significantly inhibited the proliferation, migration, and invasion of HCC cells, as well as enhanced the apoptosis in vitro. Moreover, silencing YTHDF1 repressed the growth of xenograft tumors in vivo. Mechanism investigation indicated that YTHDF1 might promote the aggressive phenotypes by facilitating epithelial–mesenchymal transition (EMT) and activating AKT/glycogen synthase kinase (GSK)-3β/β-catenin signaling.Conclusion: The current study identified a robust risk signature consisting of m6A RNA methylation regulators for HCC prognosis. In addition, YTHDF1 was a potential molecular target for HCC treatment.

scholarly journals Diagnostic significance and carcinogenic mechanism of pan-cancer gene POU5F1 in liver hepatocellular carcinoma

2020 ◽  
Author(s):  
Dingdong He ◽  
Xiaokang Zhang ◽  
Jiancheng Tu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Meta Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
Clinicopathological Parameters ◽  
Cancer Gene ◽  
Hub Genes ◽  
Liver Hepatocellular Carcinoma ◽  
The Impact ◽  
Pan Cancer

Abstract Background The prognostic and clinicopathological significance of POU Class 5 Homeobox 1 (POU5F1) among various cancers is disputable heretofore. The diagnostic value and function mechanism of POU5F1 in liver hepatocellular carcinoma (LIHC) have not been studied thoroughly. Methods An integrative strategy of meta-analysis, bioinformatics and wet-lab approach was used to explore the diagnostic and prognostic significance of POU5F1 in various types of tumors, especially in LIHC. Meta-analysis was utilized to investigate the impact of POU5F1 on prognosis and clinicopathological parameters in various cancers. The expression level and diagnostic value of POU5F1 were assessed by qPCR in plasma collected from LIHC patients and controls. The correlation between POU5F1 and tumor infiltrating immune cells (TIICs) in LIHC was evaluated by CIBERSORT. Gene set enrichment analysis (GSEA) was performed based on TCGA. Hub genes and related pathways were identified on the basis of co-expression genes of POU5F1. Results Elevated POU5F1 was associated with poor OS, DFS, RFS and DSS in various cancers. POU5F1 was confirmed as an independent risk factor for LIHC and correlated with tumor occurrence, stage and invasion depth. The combination of POU5F1 and AFP in plasma was with high diagnostic validity (AUC = 0.902, P < 0.001). Specifically, the level of POU5F1 was correlated with infiltrating levels of B cells, T cells, dendritic cells and monocytes in LIHC. GSEA indicated POU5F1 participated in multiple cancer related pathways and cell proliferation pathways. Moreover, CBX3, CCHCR1 and NFYC were filtered as the central hub genes of POU5F1. Conclusions Our study identified POU5F1 as a pan-cancer gene could not only be a prognostic and diagnostic biomarker in various cancers, especially in LIHC, but functionally carcinogenic in LIHC.

scholarly journals Systematic Analyses of the Role of the Reader Protein of N6-Methyladenosine RNA Methylation, YTH Domain Family 2, in Liver Hepatocellular Carcinoma

2020 ◽  
Vol 7 ◽  
Author(s):  
Xiang-yang Shao ◽  
Jin Dong ◽  
Han Zhang ◽  
Ying-song Wu ◽  
Lei Zheng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Domain Family ◽  
Rna Methylation ◽  
Yth Domain ◽  
Liver Hepatocellular Carcinoma

BackgroundYTH domain family (YTHDF) 2 acts as a “reader” protein for RNA methylation, which is important in tumor regulation. However, the effect of YTHDF2 in liver hepatocellular carcinoma (LIHC) has yet to be elucidated.MethodsWe explored the role of YTHDF2 in LIHC based on publicly available datasets [The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO)]. A bioinformatics approach was employed to analyze YTHDF2. Logistic regression analyses were applied to analyze the correlation between YTHDF2 expression and clinical characteristics. To evaluate the effect of YTHDF2 on the prognosis of LIHC patients, we used Kaplan–Meier (K–M) curves. Gene set enrichment analysis (GSEA) was undertaken using TCGA dataset. Univariate and multivariate Cox analyses were used to ascertain the correlations between YTHDF2 expression and clinicopathologic characteristics with survival. Genes co-expressed with YTHDF2 were identified and detected using publicly available datasets [LinkedOmics, University of California, Santa Cruz (UCSC), Gene Expression Profiling Interactive Analysis (GEPIA), and GEO]. Correlations between YTHDF2 and infiltration of immune cells were investigated by Tumor Immune Estimation Resource (TIMER) and GEPIA.ResultsmRNA and protein expression of YTHDF2 was significantly higher in LIHC tissues than in non-cancerous tissues. High YTHDF2 expression in LIHC was associated with poor prognostic clinical factors (high stage, grade, and T classification). K–M analyses indicated that high YTHDF2 expression was correlated with an unfavorable prognosis. Univariate and multivariate Cox analyses revealed that YTHDF2 was an independent factor for a poor prognosis in LIHC patients. GSEA revealed that the high-expression phenotype of YTHDF2 was consistent with the molecular pathways implicated in LIHC carcinogenesis. Analyses of receiver operating characteristic curves showed that YTHDF2 might have a diagnostic value in LIHC patients. YTHDF2 expression was associated positively with SF3A3 expression, which implied that they may cooperate in LIHC progression. YTHDF2 expression was associated with infiltration of immune cells and their marker genes. YTHDF2 had the potential to regulate polarization of tumor-associated macrophages, induce T-cell exhaustion, and activate T-regulatory cells.ConclusionYTHDF2 may be a promising biomarker for the diagnosis and prognosis of LIHC and may provide new directions and strategies for LIHC treatment.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

scholarly journals Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhuomao Mo ◽  
Daiyuan Liu ◽  
Dade Rong ◽  
Shijun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Immunosuppressive Microenvironment

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

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 Prognostic value of an immune long non-coding RNA signature in liver hepatocellular carcinoma

2019 ◽  
Author(s):  
rui kong ◽  
Nan Wang ◽  
Wei Han ◽  
Yuejuan Zheng ◽  
Jie Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Liver Hepatocellular Carcinoma ◽  
Long Non Coding Rna

Abstract Background: In recent years, long non-coding RNAs (lncRNAs) are emerging as crucial regulators in the immunological process of liver hepatocellular carcinoma (LIHC). Increasing studies have found that some lncRNAs could be used as a diagnostic or therapeutic target for clinical management, but little research has investigated the role of immune-related lncRNA in tumor prognosis. In this study, we aimed to develop an immune lncRNA signature for the precise diagnosis and prognosis of liver hepatocellular carcinoma. Methods: Gene expression profiles of LIHC samples obtained from TCGA were screened for immune-related genes using two reference gene sets. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate cox analysis. Then the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were carried out to evaluate the capability of immune lncRNA signature as a prognostic indicator. Results: Six long non-coding RNA MSC−AS1, AC009005.1, AL117336.3, AL031985.3, AL365203.2, AC099850.3 were identified via correlation analysis and cox regression analysis considering their interactions with immune genes. Next, tumor samples were separated into two risk groups by the signature with different clinical outcomes. Stratification analysis showed the prognostic ability of this signature acted as an independent factor. The AUC value of ROC curve was 0.779. The Kaplan-Meier method was used in survival analysis and results showed a statistical difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Data from gene set enrichment analysis (GSEA) further unveiled several potential biological processes of these biomarkers may involve in. Conclusion: In summary, the study demonstrated the potential role of the six-lncRNA signature served as an independent prognostic factor for LIHC patients.

scholarly journals A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Dengliang Lei ◽  
Yue Chen ◽  
Yang Zhou ◽  
Gangli Hu ◽  
Fang Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Gene Set Enrichment

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

scholarly journals Relationship between IL10 and PD-L1 in Liver Hepatocellular Carcinoma Tissue and Cell Lines

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qian Qian ◽  
Changping Wu ◽  
Jianping Chen ◽  
Weibing Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Monoclonal Antibody ◽  
Cell Lines ◽  
Western Blotting ◽  
Large Scale ◽  
Expression Level ◽  
Survival Duration ◽  
Great Promise ◽  
Clinical Response Rate ◽  
Liver Hepatocellular Carcinoma

Background. Despite the large-scale clinical application of programmed death-ligand 1 (PD-L1) monoclonal antibody, reduction in its clinical response rate has become a gradual problem. As such, use of PD-L1 monoclonal antibody in combination with other anticarcinoma drugs has been the main strategy in improving its efficacy. Interleukin 10 (IL10) is a recognized inflammatory and immunosuppressive factor. Previous studies have suggested that there is a link between PD-L1 and IL10. Objective. This study was aimed at clarifying the relationship between PD-L1 and IL10 in liver hepatocellular carcinoma (LIHC) and whether IL10 enhances the efficacy of PD-L1 inhibitor. Methods. Expression levels of PD-L1 and IL10 in carcinoma and adjacent tissues were tested by immunochemistry, Western blotting, and RT-PCR. Survival duration and follow-up data of each patient were recorded. LIHC cell lines Bel7405 and MHCC 97-H were used for in vitro experiments. Exogenous IL10 and anti-IL10 were added to cell supernatant. Expression level of PD-L1 in the LIHC cell lines was determined using Western blotting and ELISA. CCK8 and transwell assays were adopted to examine the effect of PD-L1 combined with IL10 on proliferation, invasion, and metastasis of LIHC cells. Results. The survival period of patients with low expression of IL10 was longer than that of patients with high expression (P=0.01). Overexpression of PD-L1 increased the IL10 and Met levels in LIHC tissues and cell lines. IL10 downregulated the expression level of PD-L1 and enhanced the efficacy of crizotinib via the Met signaling pathway in the LIHC cells. Conclusions. A combination of IL10 and PD-L1 inhibitor holds great promise as an effective treatment for LIHC.

scholarly journals A novel prognostic index model constructed with five autophagy-related genes may be a potential prognostic biomarker and therapeutic target in liver hepatocellular carcinoma

2020 ◽  
Author(s):  
Qingqing Zhu ◽  
Jia Wang ◽  
Menghan Liu ◽  
Qiujing Zhang ◽  
Miaomiao Shen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Rate ◽  
Therapeutic Target ◽  
Prognostic Biomarker ◽  
Prognostic Index ◽  
Cancer Genome ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Liver Hepatocellular Carcinoma ◽  
Testing Group

Abstract Background Early diagnosis and effective treatment of liver hepatocellular carcinoma (LIHC) are keys to improving the prognosis of patients. Increasing evidences clarify that autophagy-related genes (ARGs) make great differences to the generation and progression of LIHC, and may serve as prognostic biomarkers for LIHC. Methods We randomly divided the LIHC patients in The Cancer Genome Atlas (TCGA) into the training and testing group. Next, use the training group to perform univariate Cox, LASSO and multivariate Cox analysis to construct our prognostic index (PI) model for LIHC; use the testing group and the whole TCGA set to make internal validations; use the International Cancer Genome Consortium to make external validations; and use the whole TCGA, GSE14520 and Oncomine to exam the expression patterns of the five ARGs. Then, we performed the ROC curve as well as univariate and multivariate analysis to evaluate the independent prognostic prediction power of the PI model, and made nomograms to estimate 1,3,5-year survival rate of LIHC patients. Besides, we conducted functional enrichment analyses of differentially expressed ARGs with GO, KEGG and GSEA, and made drug sensitivity analysis for the PI model via the GDSC database. Results A novel PI model which was composed of five key ARGs ( ATG9A , EIF2S1 , GRID1 , SAR1A and SQSTM1 ) succeeded to be constructed. All the internal and external validations testified that the PI model could well distinguish high-risk patients from low-risk ones, with AUC values > 0.60. Further comparison analysis showed that the PI model was no less than some common prognostic factors. People can estimate the 1,3,5-year survival rate of individual LIHC patient with the nomograms. Additionally, we obtained 62 differentially expressed ARGs and studied the potential mechanisms or pathways. Furthermore, we also found some potential targeted drugs associated to the five ARGs for LIHC patients. Conclusions The novel five-ARGs PI model has great potential to serve as a diagnostic or prognostic biomarker and therapeutic target in LIHC, which may guide future clinical applications to some extent and improve the outcome of LIHC patients.

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