Dataset Information

Pyroptosis-Related lncRNAs for Predicting the Prognosis and Identifying Immune Microenvironment Infiltration in Breast Cancer Lung Metastasis.

ABSTRACT: Breast cancer (BC) is the second leading cause of death among women and is highly heterogeneous. Three pyroptosis (PR) subtypes were identified in patients with BC from the Cancer Genome Atlas Database (TCGA) cohorts using 20 PR-related regulators, which illustrate a strong association between BC prognosis and PR. Lung metastasis commonly occurs in the advanced stages of BC, resulting in a poor quality of life. Eight differentially expressed (DE) lncRNAs were identified using LASSO-Cox analysis between PR-related and BC lung metastasis. Moreover, a BRCA risk-score (RS) model was established using multivariate Cox regression, which correlated with prognosis in TCGA-BRCA. Clinical characteristics, tumor mutation burden, and tumor immune cell infiltration were extensively investigated between high- and low-RS groups. Similarly, a lower RS implied longer overall survival, greater inflammatory cell infiltration, and better immunotherapeutic response to PD-1 blockers. Our findings provide a foundation for future studies targeting PR and confirme that RS could predict the prognosis of patients with BC.

SUBMITTER: Liu L

PROVIDER: S-EPMC8931340 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Pyroptosis-Related lncRNAs for Predicting the Prognosis and Identifying Immune Microenvironment Infiltration in Breast Cancer Lung Metastasis.

Liu Li L Chen Chenxi C Tu Gang G Peng Yang Y Shen Meiying M Xu Yingkun Y Liu Shengchun S

Frontiers in cell and developmental biology 20220304

Breast cancer (BC) is the second leading cause of death among women and is highly heterogeneous. Three pyroptosis (PR) subtypes were identified in patients with BC from the Cancer Genome Atlas Database (TCGA) cohorts using 20 PR-related regulators, which illustrate a strong association between BC prognosis and PR. Lung metastasis commonly occurs in the advanced stages of BC, resulting in a poor quality of life. Eight differentially expressed (DE) lncRNAs were identified using LASSO-Cox analysis ...[more]

PMID: 35309942

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Project description:Background: Accumulating evidence shows that pyroptosis plays a crucial role in hepatocellular carcinoma (HCC). However, the relationship between pyroptosis-related long non-coding RNAs (lncRNAs) and HCC tumor characteristics remains enigmatic. We aimed to explore the predictive effect of pyroptosis-related lncRNAs (PRLs) in the prognosis of HCC. Methods: We comprehensively analyzed the role of the PRLs in the tumor microenvironment and HCC prognosis by integrating genomic data from patients of HCC. Consensus clustering analysis of PRLs was applied to identify HCC subtypes. A prognostic model was then established with a training cohort from The Cancer Genome Atlas (TCGA) using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Further, we evaluated the accuracy of this predictive model using a validation set. We predicted IC50s of commonly used chemotherapeutic and targeted drugs through the R package pRRophetic. Results: Based on pyroptosis-related lncRNAs, a prognostic risk signature composed of seven PRLs (MKLN1AS, AL031985.3, SNHG4, GHRLOS, AC005479.2, AC099850.4, and AC026412.3) was established. For long-term prognosis of HCC patients, our model shows excellent accuracy to forecast overall survival of HCC individuals both in training set and testing set. We found a significant correlation between clinical features and the risk score. Patients in the high-risk group had tumor characteristics associated with progression such as aggressive pathological grade and stage. Besides that, gene set enrichment analysis (GSEA) showed that cell cycle and focal adhesion were significantly enriched in the high-risk group. Conclusion: The association of the risk model constituted by these seven pyroptosis-related lncRNAs with clinical prognosis, tumor microenvironment, chemotherapy and small molecule drugs was evaluated. Our study provides strong evidence for individualized prediction of prognosis, shedding light on immunotherapy in HCC patients.

Project description:Objective: Gliomas are the most common primary tumors in the central nervous system with a bad prognosis. Pyroptosis, an inflammatory form of regulated cell death, plays a vital role in the progression and occurrence of tumors. However, the value of pyroptosis related genes (PRGs) in glioma remains poorly understood. This study aims to construct a PRGs signature risk model and explore the correlation with clinical characteristics, prognosis, tumor microenviroment (TME), and immune checkpoints. Methods: RNA sequencing profiles and the relevant clinical data were obtained from the Chinese Glioma Genome Atlas (CGGA), the Cancer Genome Atlas (TCGA), the Repository of Molecular Brain Neoplasia Data (REMBRANDT), and the Genotype-Tissue Expression Project (GTEx-Brain). Then, the differentially expressed pyroptosis related genes (PRGs) were identified, and the least absolute shrinkage and selection operator (LASSO) and mutiCox regression model was generated using the TCGA-train dataset. Then the expression of mRNA and protein levels of PRGs signature was detected through qPCR and human protein atlas (HPA). Further, the predictive ability of the PRGs-signature, prognostic analysis, and stratification analysis were utilized and validated using TCGA-test, CGGA, and REMBRANDT datasets. Subsequently, we constructed the nomogram by combining the PRGs signature and other key clinical features. Moreover, we used gene set enrichment analysis (GSEA), GO, KEGG, the tumor immune dysfunction and exclusion (TIDE) single-sample GSEA (ssGSEA), and Immunophenoscore (IPS) to determine the relationship between PRGs and TME, immune infiltration, and predict the response of immune therapy in glioma. Results: A four-gene PRGs signature (CASP4, CASP9, GSDMC, IL1A) was identified and stratified patients into low- or high-risk group. Survival analysis, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group than in the low-risk group. Correlation analysis showed that the risk score was correlated with poor disease features. Furthermore, GSEA and immune infiltrating and IPS analysis showed that the PRGs signature could potentially predict the TME, immune infiltration, and immune response in glioma. Conclusion: The newly identified four-gene PRGs signature is effective in diagnosis and could robustly predict the prognosis of glioma, and its impact on the TME and immune cell infiltrations may provide further guidance for immunotherapy.

Project description:BackgroundPyroptosis is a newly found form of programmed cell death, accompanied by inflammatory response as well as immune response. Here, the specific function and prognosis predictive of pyroptosis-related genes (PRGs) were systematically explored in lung adenocarcinoma (LUAD).MethodsThe gene expression data and corresponding clinical information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA), and the expression level of PRGs was identified between normal and tumor tissues. Furthermore, univariate Cox proportional hazards regression was conducted to filter the PRGs related to overall survival, and least absolute shrinkage and selection operator (LASSO) regression was subsequent employed to establish the PRGs risk model. Besides, the correlation of risk score with patients' clinical features, tumor mutational burden (TMB) as well as tumor microenvironment (TME) was also investigated.ResultsA total of 5 PRGs (NLRC4, NLRP1, NLRP3, NOD1, PLCG1, and BAK1) was used to establish the risk prognostic model. According the median value of risk score, all the patients were classified into low- and high-risk score group. Kaplan-Meier analysis indicted that the LUAD patients in low-risk group exhibited a better survival outcome compared the patients in high-risk group (P<0.001). After adjusting for age, gender, and clinical stage, the risk score was also considered as and independent risk factor affecting the overall survival of LUAD patients (HR =2.949, 95% CI: 1.762-4.937). Moreover, low-risk score group exhibited a higher Immune score and lower Tumor purity compared with high-risk score group. ssGSEA results proved that the enrichment scores of most immune cells and immune related signal pathway in low-risk score group was significant higher than that in high-risk score group. In addition, the PRGs risk score was also positive correlated with TMB in LUAD tissues.ConclusionsIn this study, a novel prognostic model based on PRGs was constructed and used to predict the survival outcome of LUAD patients. In addition, the PRGs risk signature was also associated with TMB and anti-tumor immune environment. The induction of pyroptosis inside tumors might be considered a potential strategy in cancer treatments.

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.

Dataset Information

Pyroptosis-Related lncRNAs for Predicting the Prognosis and Identifying Immune Microenvironment Infiltration in Breast Cancer Lung Metastasis.

Publications

Pyroptosis-Related lncRNAs for Predicting the Prognosis and Identifying Immune Microenvironment Infiltration in Breast Cancer Lung Metastasis.

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