Identification of four prognostic LncRNAs for survival prediction of patients with hepatocellular carcinoma

Background As the fifth most common cancer worldwide, Hepatocellular carcinoma (HCC) is also the third most common cause of cancer-related death in China. Several lncRNAs have been demonstrated to be associated with occurrence and prognosis of HCC. However, identification of prognostic lncRNA signature for HCC with expression profiling data has not been conducted yet. Methods With the reuse of public available TCGA data, expression profiles of lncRNA for 371 patients with HCC were obtained and analyzed to find the independent prognostic lncRNA. Based on the expression of lncRNA, we developed a risk score model, which was evaluated by survival analysis and ROC (receiver operating characteristic) curve. Enrichment analysis was performed to predict the possible role of the identified lncRNA in HCC prognosis. Results Four lncRNAs (RP11-322E11.5, RP11-150O12.3, AC093609.1, CTC-297N7.9) were found to be significantly and independently associated with survival of HCC patients. We used these four lncRNAs to construct a risk score model, which exhibited a strong ability to distinguish patients with significantly different prognosis (HR = 2.718, 95% CI [2.103–3.514], p = 2.32e−14). Similar results were observed in the subsequent stratification survival analysis for HBV infection status and pathological stage. The ROC curve also implied our risk score as a good indicator for 5-year survival prediction. Furthermore, enrichment analysis revealed that the four signature lncRNAs may be involved in multiple pathways related to tumorigenesis and prognosis. Discussion Our study recognized four lncRNAs to be significantly associated with prognosis of liver cancer, and could provide novel insights into the potential mechanisms of HCC progression. Additionally, CTC-297N7.9 may influence the downstream TMEM220 gene expression through cis-regualtion. Nevertheless, further well-designed experimental studies are needed to validate our findings.

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Integrated analysis of autophagy-related genes(ATGs) reveals the prognostic value of oral squamous cell carcinoma

10.21203/rs.3.rs-17099/v1 ◽

2020 ◽

Author(s):

Guangzhao Huang ◽

Zhi-yun Li ◽

Yu Rao ◽

Xiao-zhi Lv

Keyword(s):

Survival Analysis ◽

Signaling Pathway ◽

Risk Score ◽

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Differentially Expressed ◽

Integrated Analysis ◽

Score Model

Abstract Background: Increasing evidence demonstrated that autophagy paly a crucial role in initiation and progression of OSCC. The aim of this study was to explore the prognostic value of autophagy-related genes(ATGs) in patients with OSCC. RNA-seq and clinical data were downloaded from TCGA database following extrating ATGs expression profiles. Then, differentially expressed analysis was performed in R software EdgeR package, and the potential biological function of differentially expressed ATGs were explored by GO and KEGG enrichment analysis. Furthermore, a risk score model based on ATGs was constructed to predict the overall survival. Moreover, univariate, multivariate cox regression and survival analysis were used to select autophagy related biomarkers which were identified by RT-qPCR in OSCC cell lines, OSCC tissues and matched normal mucosal tissues. Results: Total of 232 ATGs were extrated and 37 genes were differentially expressed in OSCC. GO and KEGG analysis indicated that these differentially expressed genes were mainly located in autophagosome membrane, and associated with apoptosis, platinum drug resistance, ErbB signaling pathway and TNF signaling pathway. Furthermore, a risk score model including 9 variables was constructed and subsequently identified with univariate, multivariate cox regression, survival analysis and Receiver Operating Characteristic curve(ROC). Moreover, ATG12 and BID were identified as potential autophagy related biomakers. Conclusion: This study successfully constructed a risk model to predict the prognosis of patients with OSCC, and the risk score may be as a independent prognostic biomarker in OSCC. ATG12 and BID were identified as potential biomarkers in tumor diagnosis and treatment of OSCC.

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Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

BioMed Research International ◽

10.1155/2020/8579651 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Xiaoqing Yu ◽

Jingsong Zhang ◽

Rui Yang ◽

Chun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Single Sample ◽

Survival Prediction ◽

Prediction Ability ◽

Cox Regression Analysis ◽

Score Model ◽

Function Enrichment

Objective. Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need. Methods. The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape. Results. Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions. Conclusion. This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

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Development and validation of a nomogram with an autophagy-related gene risk model for predicting survival in patients with melanoma

10.21203/rs.2.24382/v1 ◽

2020 ◽

Author(s):

Hao Zuo ◽

Luojun Chen ◽

Na Li ◽

Qibin Song

Keyword(s):

Overall Survival ◽

Risk Score ◽

Risk Model ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Survival Prediction ◽

High Morbidity ◽

Clinical Variables ◽

Kaplan Meier ◽

Score Model

Abstract Background: Melanoma is the third most common skin malignant tumor in the clinic, with high morbidity and mortality. Autophagy plays an important role in the development and progression of melanoma. We aimed to establish an autophagy-related genes(ARGs) expression based risk model for individualized prognosis prediction in patients with melanoma.Methods: Differentially expressed autophagy-related genes (DEARGs) in melanoma and normal skin samples were screened using TCGA and GTEx database. These DEARGs were used to perform KEGG functional enrichment analysis and GO analysis. Univariate and multivariate Cox regression analyses were performed on DEARGs to identify the optimal prognosis-related genes. These prognosis-related DEARGs were used to construct a risk score model, and the predictive effect of this risk model on the prognosis of melanoma patients was tested by the Kaplan-Meier curve, log-rank test, and ROC curve. Method of univariate and multivariate analysis were used to confirmed that the risk model of independent predictive value relative to other clinical variables, and build a nomogram based on the independent prognostic factors in the univariate analysis to predict overall survival(OS) in patients with melanoma, we used internal validation and calculation of concordance index (C-index) to test prediction effect of the nomogram. We also used the t-test to analyze the relationship between risk factors (risk genes and risk score) and clinical variables in the risk model.Results: We screened and finally obtained 6 optimal DEARGs (risk gene) through univariate and multivariate Cox analysis to construct the risk model: EIF2AK2(HR=0.403, P=0.007), IFNG(HR=0.659, P=0.003), DAPK2(HR=0.441, P=0.022), PTK6(HR=1.609, P=6.04E-05), BIRC5(HR=2.479, P=0.001), and EGFR(HR=1.474, P=0.004) were selected to establish the prognostic risk score model and validated in the entire melanoma cohort. The results of GO enrichment analysis showed that the gene function of the DEARGs was concentrated in the functions of gland morphogenesis, protein insertion into membrane, and autophagy. The results of KEGG enrichment analysis showed that the function of the DEARGs was concentrated in the autophagy–animal, p53 signaling pathway, and platinum drug resistance. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P=6.402E−11). The model was identified as an independent prognostic factor. Finally, a prognostic nomogram including the risk model, T-stage, N-stage, and radiotherapy was constructed, and the calibration plots indicated its excellent predictive performance.Conclusion: The autophagy-related six-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for melanoma. The prognostic nomogram could help individualized survival prediction and improve treatment strategies.

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Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma

Cancer Cell International ◽

10.1186/s12935-021-02049-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qianhui Xu ◽

Hao Xu ◽

Rongshan Deng ◽

Zijie Wang ◽

Nanjun Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Enrichment Analysis ◽

R Package ◽

Immune Checkpoint Blockade ◽

Differential Analysis ◽

Bioinformatics Analyses ◽

Tumor Mutation Burden ◽

Mutation Burden

Abstract Background Hepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure. Method The mutation data was analyzed by employing “maftools” package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package “limma”. Gene ontology (GO) enrichment analysis was implemented with “clusterProfiler”, “enrichplot” and “ggplot2” packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3. Results TMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction. Conclusion Collectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC.

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An apoptosis-related gene signature for the prognosis of hepatocellular carcinoma

10.21203/rs.3.rs-19086/v1 ◽

2020 ◽

Author(s):

Pengfei Zhu ◽

Zhang Lei ◽

Du Zhicheng ◽

Liao Yuan ◽

Yan Lei ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Data Set ◽

Public Health Burden ◽

Apoptotic Genes

Abstract Hepatocellular carcinoma (HCC) is a major public health burden worldwide owning to high incidence and poor prognosis. Although a mushrooming number of apoptosis-related genes had been disclosed in HCC, the prognostic value and clinical utility of them remain to be illustrated. Here, we defined the data from Gene Expression Omnibus (GEO) as a training cohort and data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma data set (TCGA-LIHC) as a validation cohort. The apoptosis-related differentially expressed genes (AR-DEGs) were identified with the two cohorts and the Gene Set Enrichment Analysis. Then, we constructed a Lasso-penalized Cox regression model using AR-DEGs and conducted a signature including 14 apoptotic genes to calculate the risk score. Patients with a high risk score indicated worse overall survival than those with low risk. Besides, the 3-year and 5-year area under curve (AUC) values of the signature were above 0.7 in both training and validation cohorts (0.762, 0.818, 0.717, 0.745, respectively). Moreover, a nomogram containing the signature and clinical characteristics presented reliable net benefits for the survival prediction. And the nomogram was tested by probability calibration curves and Decision Curve Analysis (DCA). Furthermore, protein-protein interaction (PPI) and Gene Ontology (GO) enrichment analysis disclosed several noticeable pathways that might clarify the hidden mechanism. Collectively, the present study formed a novel signature based on the 14 apoptotic genes and this possibly predicted prognosis and strengthened the communication with HCC patients about the likely treatment.

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POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2323 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 684.1-684

Author(s):

J. Q. Zhang ◽

S. X. Zhang ◽

R. Zhao ◽

J. Qiao ◽

M. T. Qiu ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Expression Profiles ◽

Inflammatory Myopathy ◽

Shanxi Province ◽

Survival Prediction ◽

Training Set ◽

Redox Biology ◽

Score Model ◽

Validation Set

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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

Clinical & Investigative Medicine ◽

10.25011/cim.v44i3.37124 ◽

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.

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GCSF As a Potential Molecular Target for Overall Survival of Hepatocellular Carcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210322124003 ◽

2021 ◽

Vol 24 ◽

Author(s):

Heng Cao ◽

Peng Guo ◽

Xiaohui Wu ◽

Jiankun Li ◽

Chenlong Ge ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Survival Analysis ◽

Molecular Mechanisms ◽

Malignant Tumors ◽

Basic Research ◽

Clinical Samples ◽

Tumor Diameter ◽

Overall Survival Analysis

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of digestive tract in the world. Therefore, it is important to carry out studies on the molecular mechanisms of early diagnosis and treatment of HCC to reduce mortality. Methods: Bioinformatic analysis was performed to explore the significant role of GCSF on the occurrence and development of neoplasm. Differently expressed genes (DEGs) were screened, and the significant hub genes related with GCSF were identified by the multiple algorithms of Cytoscape. Functional annotation for DEGs, pathological stage and overall survival analysis were implemented. In addition, the verification for the role of GCSF on HCC was made via the clinical samples. A total of 70 participates diagnosed as HCC were recruited from November 2014 to November 2019. The immunohistochemistry assay, qRT-PCR, receiver operating characteristic (ROC) curves, and overall survival analysis were carried out. Results: GCSF was related with the tumor size, and the expression of GCSF was up-regulated in hepatocellular carcinoma tissues. The enrichment results of GO and KEGG analysis were mainly enriched in “Inflammatory response”, “Protein binding”, “Metabolic pathways”, and “Proteasome”. The tumor diameter (P < 0.001), and survival time (P < 0.001) were significantly associated with expression of GCSF via the verification of clinical data. The univariate and multivariate Cox proportional regression analysis manifested that high expression of GCSF in patients with HCC was related to poor OS. Conclusion: The expression level of GCSF is significantly associated with the prognostic survival of HCC, and it is expected to become a new prognostic marker of HCC, providing a novel idea for future basic research as well as targeted therapy.

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Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20203263 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Xiaofei Wang ◽

Jie Qiao ◽

Rongqi Wang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Score ◽

Expression Profiles ◽

Univariate Analysis ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Good Prediction ◽

Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

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Eight-Gene Metabolic Signature Related with Tumor-Associated Macrophages Predicting Overall Survival for Hepatocellular Carcinoma

10.21203/rs.3.rs-36554/v1 ◽

2020 ◽

Author(s):

Junyu Huo ◽

Yunjin Zang ◽

Hongjing Dong ◽

Xiaoqiang Liu ◽

Fu He ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Survival Analysis ◽

Risk Score ◽

Risk Group ◽

Cancer Genome ◽

Metabolic Reprogramming ◽

Tumor Associated Macrophages ◽

Kaplan Meier ◽

The Relationship

Abstract Background: In recent years, the relationship between tumor associated macrophages (TAMs) and solid tumors has become a research hotspot. The study aims at exploring the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma(HCC), in order to provide a new way of treatment for HCC.Materials and methods: The study selected 343 HCC patients with complete survival information(survival time >= 1month) in the Cancer Genome Atlas (TCGA) as the study objects. Kaplan-Meier survival analysis assisted in figuring out the relationship between macrophage infiltration level and overall survival (OS), and Pearson correlation test to identify metabolic reprogramming genes(MRGs) related to tumor macrophage abundance. Lasso regression algorithm were conducted on prognosis related MRGs screened by Univariate Cox regression analysis and Kaplan-Meier survival analysis to construct the riskscore, another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) were used for external validation regarding the prognostic signature.Results: A risk score composed of 8 metabolic genes can accurately predict the OS of training cohort(TCGA) and testing cohort(ICGC). It is important that the risk score could widely used for people with different clinical characteristics, and is an independent predictor independent of other clinical factors affecting prognosis. As expected, high-risk group exhibited an obviously higher macrophage abundance relative to low-risk group, and the risk score presented a positive relation to the expression level of three commonly used immune checkpoints(PD1,PDL1,CTLA4).Conclusion: Our study constructed and validated a novel eight‑gene signature for predicting HCC patients’ OS, which possibly contributed to making clinical treatment decisions.

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