Dataset Information

Novel Risk Classification Based on Pyroptosis-Related Genes Defines Immune Microenvironment and Pharmaceutical Landscape for Hepatocellular Carcinoma.

ABSTRACT: Growing evidence has indicated that pyroptosis functions in the development of cancer. Nonetheless, specific roles of pyroptosis-related genes in tumor progression, immune response, prognosis, and immunotherapy have not been thoroughly elucidated. After a comprehensive evaluation of pyroptosis genes, unsupervised clustering was performed to generate three distinct clusters from hepatocellular carcinoma (HCC) samples. Three distinct pyroptosis-related molecular subtypes comprising three gene clusters that had differential prognostic effects on patient survival were then identified. Immune characteristics analyses revealed diversified immune cell infiltration among the subtypes. Two clusters served as immune-hot phenotypes associated with significantly poorer survival compared to a remaining third immune-cold cluster. Among these, the immune-hot clusters were characterized by abundant adaptive immune cell infiltration, active CD4+ and CD8+ T cells, high total leukocyte counts and tumor growth status, and lower Th17 cell and M2 macrophage densities. Then, risk scores indicated that low-risk patients were more sensitive to anti-tumor therapy. Subsequently, we found a significant correlation between pyroptosis and prognosis in HCC and that pyroptosis genes drive the heterogeneity of the tumor microenvironment. The risk scoring system, based on pyroptosis-related differentially expressed genes, was established to evaluate the individual outcomes and contribute to new insights into the molecular characterization of pyroptosis-related subtypes.

SUBMITTER: Wang J

PROVIDER: S-EPMC8773536 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Novel Risk Classification Based on Pyroptosis-Related Genes Defines Immune Microenvironment and Pharmaceutical Landscape for Hepatocellular Carcinoma.

Wang Jianye J Wang Ying Y Steffani Marcella M Stöß Christian C Ankerst Donna D Friess Helmut H Hüser Norbert N Hartmann Daniel D

Cancers 20220117 2

Growing evidence has indicated that pyroptosis functions in the development of cancer. Nonetheless, specific roles of pyroptosis-related genes in tumor progression, immune response, prognosis, and immunotherapy have not been thoroughly elucidated. After a comprehensive evaluation of pyroptosis genes, unsupervised clustering was performed to generate three distinct clusters from hepatocellular carcinoma (HCC) samples. Three distinct pyroptosis-related molecular subtypes comprising three gene clus ...[more]

PMID: 35053610

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Project description:Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan-Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.

Project description:BackgroundVarious studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression.MethodsWe integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients.ResultsTo describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration.ConclusionsTaken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

Project description:Lung squamous cell carcinoma (LUSC) is a common subtype of lung cancer that exhibits diverse pyroptosis regulatory patterns. Studies have highlighted the significance of pyroptosis in cancer invasion and immune responses. We aimed to explore the signatures of pyroptosis-related genes and their immune relevance in LUSC. Using The Cancer Genome Atlas (TCGA)-LUSC cohort and 5 gene expression omnibus (GEO) datasets, we performed consensus clustering based on 41 pyroptosis-related genes, and single sample gene set enrichment analysis (ssGSEA) was employed to calculate the infiltration levels of distinct clusters. A pyroptosis scoring scheme using the principal component analysis (PCA) method was used to quantify pyroptosis regulation in patients with LUSC and predict their prognosis. Four pyroptosis clusters were identified among 833 LUSC samples, which were associated with different Kyoto encyclopedia of genes and genome (KEGG) signaling pathways and tumor microenvironment infiltration features, and were highly consistent with 4 reported immune phenotypes: immune-responsive, immune-non-functional, immune-exclusion, and immune-ignorance. We then divided the patients into high- and low-pyroptosis score subgroups, and patients with higher scores were characterized by prolonged survival and attenuated immune infiltration. Moreover, higher scores were correlated with male patients, higher microsatellite instability, lower immune checkpoint inhibitor expression (such as CTLA-4 and GAL-9), and high mutation rates of typical mutated genes (e.g., TP53 and TTN). In particular, patients with lower pyroptosis scores showed better immune response to immune checkpoint inhibitor treatment. Pyroptosis regulatory patterns in the immune microenvironment can predict the clinical outcomes of patients with LUSC. Accurately quantifying the pyroptosis of individual patients will strengthen the understanding of heterogeneity within the LUSC tumor microenvironment infiltration areas.

Project description:Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis. Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups. Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score. Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

Dataset Information

Novel Risk Classification Based on Pyroptosis-Related Genes Defines Immune Microenvironment and Pharmaceutical Landscape for Hepatocellular Carcinoma.

Publications

Novel Risk Classification Based on Pyroptosis-Related Genes Defines Immune Microenvironment and Pharmaceutical Landscape for Hepatocellular Carcinoma.

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