scholarly journals Identification of Aging-Related Genes Associated With Clinical and Prognostic Features of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingte Chen ◽  
Lei Wang ◽  
Liang Hong ◽  
Zhixiong Su ◽  
Xiaohong Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Prognostic Features

Background: Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis.Methods: Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses.Results: Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p < 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p < 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p < 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity.Conclusion: The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.

Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

2020 ◽  
Author(s):  
Wanli Yang ◽  
Liaoran Niu ◽  
Xinhui Zhao ◽  
Lili Duan ◽  
Yiding Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Survival Rate ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Survival Model ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Prognostic Prediction

Abstract Background: Hepatocellular carcinoma (HCC) is one of the devastating tumors with increasing incidence. Autophagy-associated genes (ARGs) are widely participated in the cellular processes of HCC. This study proposed to identify the novel prognostic gene signature based on ARGs in HCC. Methods: We downloaded the RNA sequencing data and clinical information of HCC and normal tissues from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Functional enrichment analyses were conducted to explore the biological implications and mechanisms of ARGs in HCC. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs which related to overall survival (OS). The OS-related ARGs were then used to establish a prognostic prediction model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were both applied to evaluate the accuracy of the model. GSE14520 dataset was downloaded as the testing cohort to validate the prognostic risk model in TCGA. A nomogram based on the clinical features and risk signature was established to predict the 3-year and 5-year survival rate of HCC patients. Results: Totally 27 differentially expressed ARGs were screened in this study. Then, 3 OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these 3 ARGs, a prognostic prediction model was constructed. HCC patients in high-risk group presented poorer prognosis than those with low risk score in TCGA cohort (3-year OS, 53.7% vs 70.2%; 5-year OS, 42.0 % vs 55.2%; P=4.478e-04) and in the testing group (3-year OS, 57.7% vs 73.5%; 5-year OS, 43.2% vs 63.0%; P=1.274e-03). The risk score curve showed a well feasibility in predicting the patients’ survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict the 3-year and 5-year survival rate in HCC patients. Conclusions: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.

scholarly journals Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiwen Wu ◽  
Tian Lan ◽  
Muqi Li ◽  
Junfeng Liu ◽  
Xukun Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Validation Cohort ◽  
Risk Group ◽  
Cancer Genome ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Cohort

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive solid malignant tumors and current research regards HCC as a type of metabolic disease. This study aims to establish a metabolism-related mRNA signature model for risk assessment and prognosis prediction in HCC patients.Methods: HCC data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) was used to screen out the candidate mRNAs and calculate the risk coefficient to establish the prognosis model. A high-risk group and low-risk group were separated for further study depending on their median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort.Results: A total of 548 differential mRNAs were identified from HCC samples (n = 374) and normal controls (n = 50), 45 of which were correlated with prognosis. A total of 373 samples met the screening criteria and there were randomly divided into the training cohort (n = 186) and the validation cohort (n = 187). In the training cohort, six metabolism-related mRNAs were used to construct a prognostic model with a LASSO regression model. Based on the risk model, the overall survival rate of the high-risk cohort was significantly lower than that of the low-risk cohort. The results of a time-ROC curve proved that the risk score (AUC = 0.849) had a higher prognostic value than the pathological grade, clinical stage, age or gender.Conclusion: The model constructed by the six metabolism-related mRNAs has a significant value for survival prediction and can be applied to guide the evaluation of HCC and the designation of clinical therapy.

scholarly journals The Landscape of Iron metabolism-related Genes for Overall survival prediction in Patients with Hepatocellular carcinoma

2021 ◽  
Author(s):  
Zhipeng Zhu ◽  
Huang Cao ◽  
Anran Sun ◽  
Hongliang zhan ◽  
Zhengsheng Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
High Risk ◽  
Iron Metabolism ◽  
Gene Signature ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Survival Outcomes ◽  
Survival Prediction ◽  
Novel Model

Abstract Background Hepatocellular carcinoma (HCC) is the seventh most commonly occurring cancer and the second most common cause of cancer-related death worldwide. Despite improvements in early detection and treatment, the morbidity and mortality remain high because of complex molecular mechanisms and cellular heterogeneity in HCC. However, novel model is still needed to predict the survival and clinical immunotherapy response in HCC.Methods 13 iron metabolism-related gene sets were identified from the GSEA. DEGs associated with iron metabolism were calculated between patients who survived < 1 year and more than 3 years for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis, Principal Component Analysis (PCA) analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in external testing cohort, internal testing cohort. Moreover, Weighted gene co-expression network analysis(WGCNA), Gene set enrichment analysis(GSEA), Gene Ontology (GO) and KEGG analysis were performed to reveal signaling pathways, and two independent prognostic factors were combined to build Nomogram for predicting HCC prognosis. Finally, expression level of genes of gene signature in clinical samples was performed using quantitative real-time RT-PCR (qRT-PCR) analyses.Results Here we established a gene signature using iron metabolism-related genes form the Cancer Genome Atlas (TCGA), and the survival outcomes were validated from International Cancer Genome Consortium(ICGC). Distinct subtypes (high- and low-risk subtypes) were characterized by different clinical outcomes. The high-risk subtype was featured by better survival outcomes, upregulated cell cycle relevant pathways and better response for immunotherapy.Conclusions Our finding suggested that the novel model may be useful as a biomarker for prognostic predication, immunotherapy and cell cycle inhibitors may be efficacious for high-risk subtype of HCC patients.

scholarly journals A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

2021 ◽  
Author(s):  
Shaopei Ye ◽  
Wenbin Tang ◽  
Ke Huang
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

scholarly journals Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Mu-xing Li ◽  
Hang-yan Wang ◽  
Chun-hui Yuan ◽  
Zhao-lai Ma ◽  
Bin Jiang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Phenotypic Switch

IntroductionMacrophage phenotype switch plays a vital role in the progression of malignancies. We aimed to build a prognostic signature by exploring the expression pattern of macrophage phenotypic switch related genes (MRGs) in the Cancer Genome Atlas (TCGA)—pancreatic adenocarcinoma (PAAD), Genotype-Tissue Expression (GTEx)-Pancreas, and Gene Expression Omnibus (GEO) databases.MethodsWe identified the differentially expressed genes between the PAAD and normal tissues. We used single factor Cox proportional risk regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis to establish the prognosis risk score by the MRGs. The relationships between the risk score and immune landscape, “key driver” mutations and clinicopathological factors were also analyzed. Gene-set enrichment analysis (GSEA) analysis was also performed.ResultsWe detected 198 differentially expressed MRGs. The risk score was constructed based on 9 genes (KIF23, BIN1, LAPTM4A, ERAP2, ATP8B2, FAM118A, RGS16, ELMO1, RAPGEFL1). The median overall survival time of patients in the low-risk group was significantly longer than that of patients in the high-risk group (P &lt; 0.001). The prognostic value of the risk score was validated in GSE62452 dataset. The prognostic performance of nomogram based on risk score was superior to that of TNM stage. And GSEA analysis also showed that the risk score was closely related with P53 signaling pathway, pancreatic cancer and T cell receptor signaling pathway. qRT-PCR assay showed that the expressions of the 9 MRGs in PDAC cell lines were higher than those in human pancreatic ductal epithelium cell line.ConclusionsThe nine gene risk score could be used as an independent prognostic index for PAAD patients. Further studies validating the prognostic value of the risk score are warranted.

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 Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Peng Liu ◽  
Jinhong Wei ◽  
Feiyu Mao ◽  
Zechang Xin ◽  
Heng Duan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Endoplasmic Reticulum ◽  
Regression Analysis ◽  
Endoplasmic Reticulum Stress ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Cancer Genome ◽  
Survival Prediction ◽  
Training Cohort ◽  
Cox Regression Analysis

Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide and its incidence continues to increase year by year. Endoplasmic reticulum stress (ERS) caused by protein misfolding within the secretory pathway in cells and has an extensive and deep impact on cancer cell progression and survival. Growing evidence suggests that the genes related to ERS are closely associated with the occurrence and progression of HCC. This study aimed to identify an ERS-related signature for the prospective evaluation of prognosis in HCC patients. RNA sequencing data and clinical data of patients from HCC patients were obtained from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Using data from TCGA as a training cohort (n=424) and data from ICGC as an independent external testing cohort (n=243), ERS-related genes were extracted to identify three common pathways IRE1, PEKR, and ATF6 using the GSEA database. Through univariate and multivariate Cox regression analysis, 5 gene signals in the training cohort were found to be related to ERS and closely correlated with the prognosis in patients of HCC. A novel 5-gene signature (including HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker. The prognosis of patients with high-risk HCC was worse than that of patients with low-risk HCC. Multivariate Cox regression analysis confirmed that the signature was an independent prognostic biomarker for HCC. The results were further validated in an independent external testing cohort (ICGC). Also, GSEA indicated a series of significantly enriched oncological signatures and different metabolic processes that may enable a better understanding of the potential molecular mechanism mediating the progression of HCC. The 5-gene biomarker has a high potential for clinical applications in the risk stratification and overall survival prediction of HCC patients. In addition, the abnormal expression of these genes may be affected by copy number variation, methylation variation, and post-transcriptional regulation. Together, this study indicated that the genes may have potential as prognostic biomarkers in HCC and may provide new evidence supporting targeted therapies in HCC.

scholarly journals Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260876
Author(s):  
Jun Yang ◽  
Jiaying Zhou ◽  
Cuili Li ◽  
Shaohua Wang
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Risk Model ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Differentially Expressed ◽  
Lasso Regression

Background Neuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB. Methods We obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset. Results In total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368–1.722, P = 2.69E-13). Conclusions This study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

scholarly journals A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoping Li ◽  
Jishang Chen ◽  
Qihe Yu ◽  
Hui Huang ◽  
Zhuangsheng Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P &lt; 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

scholarly journals Identification of the Pyroptosis-Related Gene Signature for Overall Survival Prediction in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Liu ◽  
Ruonan Shao ◽  
Xiaoyun Bu ◽  
Yujie Xu ◽  
Ming Shi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Multivariable Analysis ◽  
Low Risk ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Incidence And Mortality ◽  
Risk Patients

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor worldwide, with an increasing incidence and mortality. Due to general resistance to antitumor drugs, only limited therapies are currently available for advanced HCC patients, leading to a poor prognosis with a 5-year survival rate less than 20%. Pyroptosis is a type of inflammation-related programmed cell death and may become a new potential target for cancer therapy. However, the function and prognostic value of pyroptosis-related genes (PRGs) in HCC remain unknown. Here, we identified a total of 58 PRGs reported before and conducted a six-PRG signature via the LASSO regression method in the GEO training cohort, and model efficacy was further validated in an external dataset. The HCC patients can be classified into two subgroups based on the median risk score. High-risk patients have significantly shorter overall survival (OS) than low-risk patients in both training and validation cohorts. Multivariable analysis indicated that the risk score was an independent prognostic factor for OS of HCC patients. Functional enrichment analysis and immune infiltration evaluation suggested that immune status was more activated in the low-risk group. In summary, PRGs can be a prediction factor for prognosis of HCC patients and targeting pyroptosis is a potential therapeutic alternative in HCC.

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