Identifying Dendritic Cell–Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

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Identification of Immune-Related Prognostic mRNA and lncRNA in Patients with Hepatocellular Carcinoma

Journal of Oncology ◽

10.1155/2022/5313149 ◽

2022 ◽

Vol 2022 ◽

pp. 1-16

Author(s):

Dan Chen ◽

Xiaoting Li ◽

Hui Li ◽

Kai Wang ◽

Xianghua Tian

Keyword(s):

Hepatocellular Carcinoma ◽

Flow Cytometry ◽

T Cells ◽

Regression Analysis ◽

Survival Analysis ◽

Immune Cells ◽

Cox Regression ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Immune Related Genes

Background. As the most common hepatic malignancy, hepatocellular carcinoma (HCC) has a high incidence; therefore, in this paper, the immune-related genes were sought as biomarkers in liver cancer. Methods. In this study, a differential expression analysis of lncRNA and mRNA in The Cancer Genome Atlas (TCGA) dataset between the HCC group and the normal control group was performed. Enrichment analysis was used to screen immune-related differentially expressed genes. Cox regression analysis and survival analysis were used to determine prognostic genes of HCC, whose expression was detected by molecular experiments. Finally, important immune cells were identified by immune cell infiltration and detected by flow cytometry. Results. Compared with the normal group, 1613 differentially expressed mRNAs (DEmRs) and 1237 differentially expressed lncRNAs (DElncRs) were found in HCC. Among them, 143 immune-related DEmRs and 39 immune-related DElncRs were screened out. These genes were mainly related to MAPK cascade, PI3K-AKT signaling pathway, and TGF-beta. Through Cox regression analysis and survival analysis, MMP9, SPP1, HAGLR, LINC02202, and RP11-598F7.3 were finally determined as the potential diagnostic biomarkers for HCC. The gene expression was verified by RT-qPCR and western blot. In addition, CD4 + memory resting T cells and CD8 + T cells were identified as protective factors for overall survival of HCC, and they were found highly expressed in HCC through flow cytometry. Conclusion. The study explored the dysregulation mechanism and potential biomarkers of immune-related genes and further identified the influence of immune cells on the prognosis of HCC, providing a theoretical basis for the prognosis prediction and immunotherapy in HCC patients.

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Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

10.21203/rs.3.rs-123214/v1 ◽

2020 ◽

Author(s):

Xiang Zhou ◽

Keying Zhang ◽

Fa Yang ◽

Chao Xu ◽

Jianhua Jiao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Risk Model ◽

Cox Regression ◽

Wilcoxon Test ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

10.21203/rs.3.rs-16346/v1 ◽

2020 ◽

Author(s):

Zaoqu Liu ◽

Dechao Jiao ◽

Xueliang Zhou ◽

Yuan Yao ◽

Zhaonan Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Functional Enrichment ◽

Differentially Expressed ◽

Risk Genes ◽

Cox Regression Analysis ◽

Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

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Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.781307 ◽

2021 ◽

Vol 8 ◽

Author(s):

Shenglan Huang ◽

Jian Zhang ◽

Xiaolan Lai ◽

Lingling Zhuang ◽

Jianbing Wu

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Immune Cells ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Kaplan Meier ◽

Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

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Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

10.21203/rs.3.rs-107220/v1 ◽

2020 ◽

Author(s):

Cuiyun Wu ◽

Yaosheng Luo ◽

Yinghui Chen ◽

Hongling Qu ◽

Lin Zheng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Differentially Expressed Genes ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Differentially Expressed ◽

Risk Scores ◽

Prognostic Gene ◽

Prognostic Gene Signature

Abstract Background: Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns.Methods: Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package “limma”. Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan–Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the “rms” package.Results: Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes,IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. Patients with lower risk scores generally had a better survival rate than those with higher risk scores. The mortality rate in the high-risk group was 42.02%, while that in the low-risk group was 12.50%. Results of the log-rank test showed significant differences in mortality between the two groups (HR: 4.86; 95% CI: 2.72–8.69; P = 1.01E-08). Subsequently, we assessed the prognostic accuracy of the gene signature using an ROC curve and the results show good sensitivity and specificity, with an average area under the curve (AUC) of 0.81 at 5 years (P < 0.01). Following multivariate adjustment for conventional clinical patterns, the prognostic gene signature remained a powerful and independent factor (HR: 4.70; 95% CI: 2.61–8.38; P = 2.06E-07), confirming its robust predictive ability of overall survival in HCC patients. Finally, a nomogram was established based on the gene signature and four clinicopathological features, which demonstrated an advantageous discriminating ability with the potential to facilitate clinical decision-making in HCC.Conclusion: Our prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

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Identification of a prognostic and therapeutic immune signature associated with hepatocellular carcinoma

Cancer Cell International ◽

10.1186/s12935-021-01792-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yanan Peng ◽

Chang Liu ◽

Mengting Li ◽

Wenjie Li ◽

Mengna Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Tumor Microenvironment ◽

Poor Prognosis ◽

Cox Regression ◽

Differentially Expressed ◽

Cell Infiltration ◽

Cox Regression Analysis ◽

Immune Signature ◽

Significant Difference ◽

Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is one of the most prevalent and inflammation-associated cancers. The tumor microenvironment (TME) plays an essential role in HCC development and metastasis, leading to poor prognosis. The overall TME immune cells infiltration characterizations mediated by immune-related genes (IRGs) remain unclear. In this study, we aimed to investigate whether immune-related genes could be indicators for the prognosis of HCC patients and TME cell infiltration characterization as well as responses to immunotherapy. Methods We obtained differentially expressed immune-related genes (DE IRGs) between normal liver tissues and liver cancer tissues from The Cancer Genome Atlas (TCGA) database. To identify the prognostic genes and establish an immune risk signature, we performed univariable Cox regression survival analysis and the Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the DE IRGs by robust rank aggregation method. Cox regression analysis was used to identify independent prognostic factors in HCC. We estimated the immune cell infiltration in TME via CIBERSORT and immunotherapy response through TIDE algorithm. Results We constructed an immune signature and validated its predictive capability. The immune signature included 7 differentially expressed IRGs: BIRC5, CACYBP, NR0B1, RAET1E, S100A8, SPINK5, and SPP1. The univariate and multivariate cox analysis showed that the 7-IRGs signature was a robust independent prognostic factor in the overall survival of HCC patients. The 7-IRG signature was associated with some clinical features, including gender, vascular invasion, histological grade, clinical stage, T stage. We also found that the 7-IRG signature could reflect the infiltration characterization of different immunocytes in the tumor microenvironment (TME) and had a good correlation with immune checkpoint molecules, revealing that the poor prognosis might be partly due to immunosuppressive TME. The Tumour Immune Dysfunction and Exclusion (TIDE) analysis data showed that the 7-IRG signature had great potential for indicating the immunotherapy response in HCC patients. The mutation analysis demonstrated a significant difference in the tumor mutation burden (TMB) between the high- and low-risk groups, partially explaining this signature's predictive value. Conclusion In a word, we constructed and validated a novel, immune-related prognostic signature for HCC patients. This signature could effectively indicate HCC patients' survival and immunotherapy response. And it might act as potential immunotherapeutic targets for HCC patients.

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Development of a prognostic model based on immunogenomic landscape analysis of hepatocellular carcinoma

10.21203/rs.3.rs-238684/v1 ◽

2021 ◽

Author(s):

ligong lu ◽

Shaoqing Liu ◽

Shengni Hua ◽

Zhenlin Zhang ◽

Meixiao Zhan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Risk Score ◽

Immune Cells ◽

Bioinformatics Analysis ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Prognostic Indicators

Abstract Background Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and the systematic exploration of its prognostic indicators is urgently needed. In this study, we obtained 12 IRGs for the construction of a risk score prediction model in HCC by bioinformatics analysis. Methods Differentially expressed genes were screened using the R software edgeR package. Functional enrichment analysis was performed through gene ontology analyses as well as the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Single factor and multi-factor Cox analysis were employed for survival analysis. We used the Timer software to examine the correlation between risk score and tumor-infiltrating immune cells. Results We identified 3,215 up-regulated and 1,044 down-regulated genes in HCC tissues based on a cohort from The Cancer Genome Atlas (TCGA). Differentially expressed immune-related genes (IRGs) and survival-associated IRGs were further identified. We also integrated multivariate Cox regression analyses to obtain 12 IRGs for the construction of a risk score prediction model, whose performance was verified using the Kaplan-Meier survival and receiver operating characteristic curve analyses. Our findings suggest that the risk score was associated with clinical characteristics and the infiltration of immune cells in HCC patients. Conclusions We obtained a risk score prediction model of 12 IRGs in HCC by bioinformatics analysis and confirmed its performance.

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Identification of Immune-Related Genes for Establishment of Prognostic Index in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.760079 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yinfang Li ◽

Ling Zou ◽

Xuejun Liu ◽

Judong Luo ◽

Hui Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Prognostic Index ◽

Hub Genes ◽

Cox Regression Analysis ◽

Risk Patients ◽

Immune Related Genes

Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level.Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy.

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A Novel Pyroptosis-related Genes Prognostic Signature Affects the Immune Infiltration Patterns in Hepatocellular Carcinoma

10.21203/rs.3.rs-1020197/v1 ◽

2021 ◽

Author(s):

Yiqun Jin ◽

Bai. Xue-song

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Checkpoint ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract PurposePyroptosis is an inflammatory form of cell death associated with tumorigenesis and progression. However, the prognostic value of pyroptosis-related genes (PRGs) in hepatocellular carcinoma (HCC) have not been elucidated.MethodsWe downloaded mRNA expression profiles and clinical information from TCGA and ICGC database. Then, differently expressed PRGs were screened to construct a multigene prognostic signature by least absolute contraction and selection operator (LASSO) Cox regression method in TCGA cohort. Date from ICGC was used to validate the robustness of this signature. Kaplan-Meier analysis was used to compare overall survival (OS) between high- and low-risk group. Univariate and multivariate Cox analysis were performed to identify the independent prognostic value of the signature. Gene set enrichment analysis (GSEA) was utilized to conduct GO and KEGG analysis. Single-sample gene set enrichment analysis was implemented to assess the immune cell infiltration and immune-related function. TIDE algorithm evaluated the significance of this signature in predicting immunotherapeutic sensitivity. ResultsAn 8-PRGs prognostic model was established. The OS of low-risk group was significantly increased compared to high-risk group. Receiver operating characteristic curve showed the model had a good prognostic predictive accuracy. Cox regression analysis proved the model an independent predictor for OS in HCC. GSEA indicated that the risk score was associated with immune response. Furthermore, different subgroups exhibited different immunoinfiltration patterns, different immune-checkpoint levels and different potential responses for immune-checkpoint blockade therapy.ConclusionAn 8-PRGs signature can predict the prognosis of HCC patients and may act as an immunotherapeutic potential target for HCC.

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