Dataset Information

Deciphering the Pyroptosis-Related Prognostic Signature and Immune Cell Infiltration Characteristics of Colon Cancer.

ABSTRACT: Background: Colon cancer (CC) remains one of the most common malignancies with a poor prognosis. Pyroptosis, referred to as cellular inflammatory necrosis, is thought to influence tumor development. However, the potential effects of pyroptosis-related regulators (PRRs) on the CC immune microenvironment remain unknown. Methods: In this study, 27 PRRs reported in the previous study were used to cluster the 1,334 CC samples into three pyroptosis-related molecular patterns. Through subtype pattern differential analysis and structure network mining using Weighted Gene Co-expression Network Analysis (WGCNA), 854 signature genes associated with the PRRs were discovered. Further LASSO-penalized Cox regression of these genes established an eight-gene assessment model for predicting prognosis. Results: The CC patients were subtyped based on three distinct pyroptosis-related molecular patterns. These pyroptosis-related patterns were correlated with different clinical outcomes and immune cell infiltration characteristics in the tumor microenvironment. The pyroptosis-related eight-signature model was established and used to assess the prognosis of CC patients with medium-to-high accuracy by employing the risk scores, which was named "PRM-scores." Greater inflammatory cell infiltration was observed in tumors with low PRM-scores, indicating a potential benefit of immunotherapy in these patients. Conclusions: This study suggests that PRRs have a significant effect on the tumor immune microenvironment and tumor development. Evaluating the pyroptosis-related patterns and related models will promote our understanding of immune cell infiltration characteristics in the tumor microenvironment and provide a theoretical basis for future research targeting pyroptosis in cancer.

SUBMITTER: Wei R

PROVIDER: S-EPMC8546261 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deciphering the Pyroptosis-Related Prognostic Signature and Immune Cell Infiltration Characteristics of Colon Cancer.

Wei Ran R Li Shuofeng S Yu Guanhua G Guan Xu X Liu Hengchang H Quan Jichuan J Jiang Zheng Z Wang Xishan X

Frontiers in genetics 20211012

<b>Background:</b> Colon cancer (CC) remains one of the most common malignancies with a poor prognosis. Pyroptosis, referred to as cellular inflammatory necrosis, is thought to influence tumor development. However, the potential effects of pyroptosis-related regulators (PRRs) on the CC immune microenvironment remain unknown. <b>Methods:</b> In this study, 27 PRRs reported in the previous study were used to cluster the 1,334 CC samples into three pyroptosis-related molecular patterns. Through sub ...[more]

PMID: 34712271

Similar Datasets

Project description:BackgroundColon cancer (CC) is a highly heterogeneous malignancy associated with high morbidity and mortality. Pyroptosis is a type of programmed cell death characterized by an inflammatory response that can affect the tumor immune microenvironment and has potential prognostic and therapeutic value. The aim of this study was to evaluate the association between pyroptosis-related gene (PRG) expression and CC.MethodsBased on the expression profiles of PRGs, we classified CC samples from The Cancer Gene Atlas and Gene Expression Omnibus databases into different clusters by unsupervised clustering analysis. The best prognostic signature was screened and established using least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. Subsequently, a nomogram was established based on multivariate COX regression analysis. Next, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential molecular mechanisms between the high- and low-risk groups and to explore the differences in clinicopathological characteristics, gene mutation characteristics, abundance of infiltrating immune cells, and immune microenvironment between the two groups. We also evaluated the association between common immune checkpoints and drug sensitivity using risk scores. The immunohistochemistry staining was utilized to confirm the expression of the selected genes in the prognostic model in CC.ResultsThe 1163 CC samples were divided into two clusters (clusters A and B) based on the expression profiles of the 33 PRGs. Genes with prognostic value were screened from the DEGs between the two clusters, and an eight PRGs prognostic model was constructed. GSEA and GSVA of the high- and low-risk groups revealed that they were mainly enriched in inflammatory response-related pathways. Compared to those in the low-risk group, patients in the high-risk group had worse overall survival, an immunosuppressive microenvironment, and worse sensitivity to immunotherapy and drug treatment.ConclusionOur findings provide a foundation for future research targeting pyroptosis and new insights into prognosis and immunotherapy from the perspective of pyroptosis in CC.

Project description:IntroductionThis study investigated pyroptosis- and immunity-related long non-coding RNAs (lncRNAs) to identify promising therapeutic targets for breast cancer (BC), and constructed lncRNA signatures to determine the prognosis and immunotherapy responses of BC patients.MethodsPearson's correlation coefficient was used to identify pyroptosis- and immune-related differentially expressed lncRNAs (DE-pyrolncRNAs and DE-ImmlncRNAs, respectively). The Cancer Genome Atlas dataset was allocated to training and testing subsets. Prognostic lncRNA signatures were derived based on the training subset using univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator methods. Stepwise Cox regression was used to refine these signatures and to select the optimal lncRNA signature. The median risk score of the training subset was applied as a threshold to divide patients into high-risk (HR) and low-risk (LR) groups. The Wilcoxon test was used to reveal differences in immune scores, cell types, functions, and checkpoint genes between these groups. Single-cell sequencing data from GSE176078 were used to validate the immune cell infiltration landscape of the identified lncRNA signatures.ResultsWe identified a six-lncRNA pyroptosis-immune signature comprising MAPT.AS1, CTA.384, D8.34, RP11.561, I11.3, HID1.AS1, AC097713.3, and USP2.AS1. Patients in the HR group demonstrated inferior prognoses in the training, testing, and full datasets (P=3.622e-07, P=3.736e-03, and P=1.151e-08, respectively). Immune scores were significantly enhanced in the LR group, whereas tumor purity was elevated in the HR group. Fifty-eight immune scores showed significant differences between the groups (P<0.05). Immune function (APC coinhibition, CCR, and checkpoints) more significantly impaired in the HR group. Expression levels of 38 immune checkpoint genes, including KIR2DS4, KIR3DL2, CD40LG, KIR3DL1, and PDCD1, were significantly higher in the LR group. Conversely, the TDO2, PVR, and CD276 levels were elevated in the HR group. Single-cell sequencing data from GSE176078 showed sparse T cell, B cell, myeloid, and plasmablast clusters in the HR group, whereas the LR group displayed significant clustering of B cells, myeloids, and plasmablasts.ConclusionThe six-lncRNA pyroptosis-immune signature effectively predicted BC prognosis and highlighted distinct immune cell infiltration patterns. This holds promise for evaluating immunotherapy responses and guiding therapeutic target identification in BC.

Project description:BackgroundPyroptosis has been demonstrated to be an inflammatory form of programmed cell death recently. However, the expression of pyroptosis-related genes (PRGs) in colon adenocarcinoma (COAD) and their correlations with prognosis remain unclear.MethodsData of COAD patients were obtained from The Cancer Genome Atlas (TCGA) database to screen differentially expressed genes (DEGs). Univariate Cox regression analysis and the LASSO Cox regression analysis were applied to construct a gene signature. All COAD patients in TCGA cohort were separated into low-risk subgroup or high-risk subgroup via the risk score. Kaplan-Meier survival analysis and receiver operator characteristic (ROC) curves were adopted to assess its prognostic efficiency. COAD data from the GSE17537 datasets was used for validation. A prognostic nomogram was established to predict individual survival. The correlation between PRGs and immune cell infiltration in COAD was verified based on TIMER database. CIBERSORT analysis was utilized on risk subgroup as defined by model. The protein and mRNA expression level of PRGs were verified by HPA database and qPCR.ResultsA total of 51 differentially expressed PRGs were identified in TCGA cohort. Through univariate Cox regression analysis and LASSO Cox regression analysis, a prognostic model containing 7 PRGs was constructed. Kaplan-Meier survival analysis indicated that patients in the low-risk subgroup exhibited better prognosis compared to those in the high-risk subgroup. Additionally, the area under the curve (AUC) of ROC is 0.60, 0.63, and 0.73 for 1-, 3-, and 5-year survival in TCGA cohort and 0.63, 0.65, and 0.64 in validation set. TIMER database showed a strong correlation between 7 PRGs and tumor microenvironment in COAD. Moreover, CIBERSORT showed significant differences in the infiltration of plasma cells, M0 macrophages, resting dendritic cells, and eosinophils between low-risk subgroup and high-risk subgroup. HPA database showed that protein expression level of SDHB, GZMA, BTK, EEF2K, and NR1H2 was higher in normal tissues. And the transcriptional level of CASP5, BTK, SDHB, GZMA, and RIPK3 was high in normal tissues.ConclusionsOur study identified a novel PRGs signature that could be used to predict the prognosis of COAD patients, which might provide a new therapeutic target for the treatment of COAD patients.

Project description:Increasing evidence has demonstrated that pyroptosis, a type of inflammatory programmed cell death, plays an important role in the pathogenesis and progression of gastric cancer. However, it remains unclear whether pyroptosis-related long non-coding RNAs (lncRNAs) can be used to predict the diagnosis and prognosis of gastric adenocarcinoma. This study aimed to evaluate and test the role of the lncRNA signature associated with pyroptosis as a prognostic tool for stomach adenocarcinoma (STAD) and to ascertain their immune value. Relative RNA-sequencing data were extracted from The Cancer Genome Atlas database (TCGA), and data preprocessing was performed for STAD. Pearson correlation analysis was used to determine whether lncRNAs were significantly correlated with pyroptosis based on 23 genes related to pyroptosis. Univariate Cox regression and least absolute shrinkage and selection operator(LASSO) analyses were both adopted to select features and establish the pyroptosis-related lncRNA (PRL) prognostic signature. Kaplan-Meier(KM) survival analysis of the different risk groups was conducted according to the risk scores. We further examined the functional enrichment, tumor microenvironment, and landscape of mutation status among the different risk groups, and these analyses further explained the reasons for the differences in the prediction as well as survival value of the different risk groups. Four lncRNAs, including HAND2-AS1, LINC01354, RP11-276H19.1, and PGM5-AS1, were involved in the PRL signature and used to split STAD patients into two risk groups. Overall survival time(OS) was significantly higher in the low-risk group than in the high-risk group in both the training and validation groups. Functional enrichment analysis was further employed to analyze differentially expressed genes in high- and low-risk groups to identify potential molecular functions and pathways associated with pyroptosis in the gastric cancer microenvironment. Protein-protein interaction (PPI) and Friends analysis identified hub genes that may play a key role in differentially expressed genes in high- and low-risk groups. In addition, there were remarkable discrepancies between the different risk groups in the tumor stage (P < 0.01) and histologic grade (P < 0.05). Furthermore, drug-susceptibility testing indicated potential sensitive chemotherapeutic drugs for each risk group. This study is the first to establish and validate STAD-associated PRLs that can effectively guide the prognosis and the immune microenvironment in STAD patients and provide evidence for the development of molecularly targeted therapies related to pyroptosis.

Project description:Clear cell renal cell carcinoma (ccRCC) is a common urinary system malignant tumor with a high incidence and recurrence rate. Pyroptosis is a kind of programmed cell death caused by inflammasomes. More and more evidence had confirmed that pyroptosis plays a very significant part in cancer, and it is controversial whether pyroptosis promotes or inhibits tumors. Consistently, its potential role in ccRCC treatment efficacy and prognosis remains unclear. In this study, we systematically investigated the role of pyroptosis in the ccRCC samples from The Cancer Genome Atlas (TCGA) database. Based on the differentially expressed pyroptosis-related genes (DEPRGs), we identified three pyroptosis subtypes with different clinical outcomes, immune signatures, and responses to immunotherapy. Gene set variation analysis (GSVA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that pyroptosis activation meant infiltration of more immune cells that is conducive to tumor progression. To further investigate the immunomodulatory effect of pyroptosis in ccRCC, we constructed a pyroptosis-score based on the common differential prognostic genes of the three pyroptosis subtypes. It was found that patients with high pyroptosis-score were in an unfavorable immune environment and the prognosis was worse. Gene set enrichment analysis suggested that immune-related biological processes were activated in the high pyroptosis-score group. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression was implemented for constructing a prognostic model of eight pyroptosis-related long noncoding RNAs (PRlncRNAs) in the TCGA dataset, and the outcomes revealed that, compared with the low-risk group, the model-based high-risk group was intently associated with poor overall survival (OS). We further explored the relationship between high- and low-risk groups with tumor microenvironment (TME), immune infiltration, and drug therapy. Finally, we constructed and confirmed a robust and reliable PRlncRNA pairs prediction model of ccRCC, identified PRlncRNA, and verified it by experiments. Our findings suggested the potential role of pyroptosis in ccRCC, offering new insights into the prognosis of ccRCC and guiding effectual targeted therapy and immunotherapy.

Dataset Information

Deciphering the Pyroptosis-Related Prognostic Signature and Immune Cell Infiltration Characteristics of Colon Cancer.

Publications

Deciphering the Pyroptosis-Related Prognostic Signature and Immune Cell Infiltration Characteristics of Colon Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets