scholarly journals Genome‐wide identification of CpG island methylator phenotype related gene signature as a novel prognostic biomarker of gastric cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9624 ◽  
Author(s):  
Zhuo Zeng ◽  
Daxing Xie ◽  
Jianping Gong
Keyword(s):  
T Cells ◽  
Cox Regression ◽  
Cpg Island ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cpg Island Methylator Phenotype ◽  
Cox Regression Analysis ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Background Gastric cancer (GC) is one of the most fatal cancers in the world. Results of previous studies on the association of the CpG island methylator phenotype (CIMP) with GC prognosis are conflicting and mainly based on selected CIMP markers. The current study attempted to comprehensively assess the association between CIMP status and GC survival and to develop a CIMP-related prognostic gene signature of GC. Methods We used a hierarchical clustering method based on 2,082 GC-related methylation sites to stratify GC patients from the cancer genome atlas into three different CIMP subgroups according to the CIMP status. Gene set enrichment analysis, tumor-infiltrating immune cells, and DNA somatic mutations analysis were conducted to reveal the genomic characteristics in different CIMP-related patients. Cox regression analysis and the least absolute shrinkage and selection operator were performed to develop a CIMP-related prognostic signature. Analyses involving a time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to assess the performance of the prognostic signature. Results We found a positive relationship between CIMP and prognosis in GC. Gene set enrichment analysis indicated that cancer-progression-related pathways were enriched in the CIMP-L group. High abundances of CD8+ T cells and M1 macrophages were found in the CIMP-H group, meanwhile more plasma cells, regulatory T cells and CD4+ memory resting T cells were detected in the CIMP-L group. The CIMP-H group showed higher tumor mutation burden, more microsatellite instability-H, less lymph node metastasis, and more somatic mutations favoring survival. We then established a CIMP-related prognostic gene signature comprising six genes (CST6, SLC7A2, RAB3B, IGFBP1, VSTM2L and EVX2). The signature was capable of classifying patients into high‐and low‐risk groups with significant difference in overall survival (OS; p < 0.0001). To assess performance of the prognostic signature, the area under the ROC curve (AUC) for OS was calculated as 0.664 at 1 year, 0.704 at 3 years and 0.667 at 5 years. When compared with previously published gene-based signatures, our CIMP-related signature was comparable or better at predicting prognosis. A multivariate Cox regression analysis indicated the CIMP-related prognostic gene signature was an independent prognostic indicator of GC. In addition, Gene ontology analysis indicated that keratinocyte differentiation and epidermis development were enriched in the high-risk group. Conclusion Collectively, we described a positive association between CIMP status and prognosis in GC and proposed a CIMP-related gene signature as a promising prognostic biomarker for GC.

scholarly journals Comprehensive Analysis of Ferroptosis-Related LncRNAs in Breast Cancer Patients Reveals Prognostic Value and Relationship With Tumor Immune Microenvironment

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhengjie Xu ◽  
Suxiao Jiang ◽  
Juan Ma ◽  
Desheng Tang ◽  
Changsheng Yan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Cox Regression ◽  
Comprehensive Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Breast cancer (BC) is a heterogeneous malignant tumor, leading to the second major cause of female mortality. This study aimed to establish an in-depth relationship between ferroptosis-related LncRNA (FRlncRNA) and the prognosis as well as immune microenvironment of the patients with BC.Methods: We downloaded and integrated the gene expression data and the clinical information of the patients with BC from The Cancer Genome Atlas (TCGA) database. The co-expression network analysis and univariate Cox regression analysis were performed to screen out the FRlncRNAs related to prognosis. A cluster analysis was adopted to explore the difference of immune microenvironment between the clusters. Furthermore, we determined the optimal survival-related FRLncRNAs for final signature by LASSO Cox regression analysis. Afterward, we constructed and validated the prediction models, which were further tested in different subgroups.Results: A total of 31 FRLncRNAs were filtrated as prognostic biomarkers. Two clusters were determined, and C1 showed better prognosis and higher infiltration level of immune cells, such as B cells naive, plasma cells, T cells CD8, and T cells CD4 memory activated. However, there were no significantly different clinical characters between the clusters. Gene Set Enrichment Analysis (GSEA) revealed that some metabolism-related pathways and immune-associated pathways were exposed. In addition, 12 FRLncRNAs were determined by LASSO analysis and used to construct a prognostic signature. In both the training and testing sets, patients in the high-risk group had a worse survival than the low-risk patients. The area under the curves (AUCs) of receiver operator characteristic (ROC) curves were about 0.700, showing positive prognostic capacity. More notably, through the comprehensive analysis of heatmap, we regarded LINC01871, LINC02384, LIPE-AS1, and HSD11B1-AS1 as protective LncRNAs, while LINC00393, AC121247.2, AC010655.2, LINC01419, PTPRD-AS1, AC099329.2, OTUD6B-AS1, and LINC02266 were classified as risk LncRNAs. At the same time, the patients in the low-risk groups were more likely to be assigned to C1 and had a higher immune score, which were consistent with a better prognosis.Conclusion: Our research indicated that the ferroptosis-related prognostic signature could be used as novel biomarkers for predicting the prognosis of BC. The differences in the immune microenvironment exhibited by BC patients with different risks and clusters suggested that there may be a complementary synergistic effect between ferroptosis and immunotherapy.

scholarly journals A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guichuan Huang ◽  
Jing Zhang ◽  
Ling Gong ◽  
Yi Huang ◽  
Daishun Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

scholarly journals Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Jiang ◽  
Jiameng Xu ◽  
Zirui Liao ◽  
Guangbin Li ◽  
Chengpeng Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Training Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
External Test

ObjectiveTo screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.MethodsThe GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.ResultsWe identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.ConclusionThis prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

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 A novel pyroptosis-associated gene signature for immune status and prognosis of cutaneous melanoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12304
Author(s):  
Zhengyuan Wu ◽  
Leilei Chen ◽  
Chaojie Jin ◽  
Jing Xu ◽  
Xingqun Zhang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Immune Status ◽  
Immune Cell ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Tumor Cell Death ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Background Cutaneous melanoma (CM) is a life-threatening destructive malignancy. Pyroptosis significantly correlates with programmed tumor cell death and its microenvironment through active host-tumor crosstalk. However, the prognostic value of pyroptosis-associated gene signatures in CM remains unclear. Methods Gene profiles and clinical data of patients with CM were downloaded from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes associated with pyroptosis and overall survival (OS). We constructed a prognostic gene signature using LASSO analysis, then applied immune cell infiltration scores and Kaplan-Meier, Cox, and pathway enrichment analyses to determine the roles of the gene signature in CM. A validation cohort was collected from the Gene Expression Omnibus (GEO) database. Results Four pyroptosis-associated genes were identified and incorporated into a prognostic gene signature. Integrated bioinformatics findings showed that the signature correlated with patient survival and was associated with tumor growth and metastasis. The results of Gene Set Enrichment Analysis of a risk signature indicated that several enriched pathways are associated with cancer and immunity. The risk signature for immune status significantly correlated with tumor stem cells, the immune microenvironment, immune cell infiltration and immune subtypes. The expression of four pyroptosis genes significantly correlated with the OS of patients with CM and was related to the sensitivity of cancer cells to several antitumor drugs. A signature comprising four genes associated with pyroptosis offers a novel approach to the prognosis and survival of patients with CM and will facilitate the development of individualized therapy.

scholarly journals Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guomin Wu ◽  
Qihao Wang ◽  
Ting Zhu ◽  
Linhai Fu ◽  
Zhupeng Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Clinical Features ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

scholarly journals An Immune-Related Prognostic Signature for Predicting Clinical Outcomes and Immune Landscape in IDH-Mutant Lower-Grade Gliomas

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Gang Xiao ◽  
Xuan Gao ◽  
Lifeng Li ◽  
Chao Liu ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Outcomes ◽  
Immune Cell ◽  
Cox Regression ◽  
Immune Escape ◽  
Gene Signature ◽  
Individual Therapy ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Background. IDH mutation is the most common in diffuse LGGs, correlated with a favorable prognosis. However, the IDH-mutant LGGs patients with poor prognoses need to be identified, and the potential mechanism leading to a worse outcome and treatment options needs to be investigated. Methods. A six-gene immune-related prognostic signature in IDH-mutant LGGs was constructed based on two public datasets and univariate, multivariate, and LASSO Cox regression analysis. Patients were divided into low- and high-risk groups based on the median risk score in the training and validation sets. We analyzed enriched pathways and immune cell infiltration, applying the GSEA and the immune evaluation algorithms. Results. Stratification and multivariate Cox analysis unveiled that the six-gene signature was an independent prognostic factor. The signature (0.806/0.795/0.822) showed a remarkable prognostic performance, with 1-, 3-, and 5-year time-dependent AUC, higher than for grade (0.612/0.638/0.649) and 1p19q codeletion status (0.606/0.658/0.676). High-risk patients had higher infiltrating immune cells. However, the specific immune escape was observed in the high-risk group after immune activation, owing to increasing immunosuppressive cells, inhibitory cytokines, and immune checkpoint molecules. Moreover, a novel nomogram model was developed to evaluate the survival in IDH-mutant LGGs patients. Conclusion. The six-gene signature could be a promising prognostic biomarker, which is promising to promote individual therapy and improve the clinical outcomes of IDH-mutant gliomas. The study also refined the current classification system of IDH-mutant gliomas, classifying patients into two subtypes with distinct immunophenotypes and overall survival.

scholarly journals Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

2021 ◽  
Author(s):  
Jixiang Cao ◽  
Xi Chen ◽  
Guang Lu ◽  
Haowei Wang ◽  
Xinyu Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Risk Scores ◽  
Tumor Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

scholarly journals Development and validation of an immune-related prognostic signature in cervical cancer

2021 ◽  
Author(s):  
Rongjia Su ◽  
Chengwen Jin ◽  
Hualei Bu ◽  
Xiaoyun Wang ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Gynecological Malignancy ◽  
Immune Infiltration

Abstract Background Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

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