Dataset Information

A novel pyroptosis-related indicator of immune infiltration features and prognosis in breast cancer.

ABSTRACT: Breast cancer is the most common malignancy in women, and there is evidence for the dual role of cell pyroptosis in tumor development. However, little is known about the relationship between cell pyroptosis and breast cancer and its prognostic value. We aimed to construct a prognostic model using cell-pyroptosis-related genes to provide innovative insights into the prognosis and treatment of breast cancer. We screened candidate genes for pyroptosis using public databases and identified 10 cell pyroptosis signature genes with the random forest method. Finally, a nomogram for predicting 1-, 3-, and 5-year survival probabilities was constructed. The differences in immune cell distributions between survival periods were similar across the breast cancer datasets. The 10 identified key pyroptosis factors showed a significant correlation with Her2, tumor-node-metastasis (TNM) stage, and survival of breast cancer. The risk scores correlated positively with the infiltration features of naive B cells, CD8+ T cells, atpdelnd mast cells, while they correlated negatively with those of M0 macrophages and dendritic cells. In conclusion, our findings confirm that cell pyroptosis is closely associated with breast cancer. Importantly, the prognostic complex values generated from the 10 cell-pyroptosis-related genes based on various clinical features may provide an important basis for future studies on the prognosis of breast cancer.

SUBMITTER: Wang C

PROVIDER: S-EPMC9491236 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A novel pyroptosis-related indicator of immune infiltration features and prognosis in breast cancer.

Wang Cheng C Zhang Liyong L Ren Lin L Zhang Guozhi G Wan Andi A Xiong Siyi S Tian Hao H Peng Zaihui Z Zhao Tingting T Gao Pingping P Sun Na N Zhang Yi Y Qi Xiaowei X

Frontiers in oncology 20220907

Breast cancer is the most common malignancy in women, and there is evidence for the dual role of cell pyroptosis in tumor development. However, little is known about the relationship between cell pyroptosis and breast cancer and its prognostic value. We aimed to construct a prognostic model using cell-pyroptosis-related genes to provide innovative insights into the prognosis and treatment of breast cancer. We screened candidate genes for pyroptosis using public databases and identified 10 cell p ...[more]

PMID: 36158689

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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:BackgroundGlioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear.MethodsIn this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort.ResultsIn this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated.ConclusionWe established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma.

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.

Dataset Information

A novel pyroptosis-related indicator of immune infiltration features and prognosis in breast cancer.

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A novel pyroptosis-related indicator of immune infiltration features and prognosis in breast cancer.

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