An immune cell infiltration-related gene signature predicts prognosis for bladder cancer.
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ABSTRACT:
SUBMITTER: Chen H
PROVIDER: S-EPMC8370985 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe close relationship between histone deacetylase 9 (HDAC9) and immunity has attracted attention. We constructed an immune signature for HDAC9, a vital epigenetic modification, to predict the survival status and treatment benefits in bladder cancer (BC).MethodsAn exhaustive analysis of HDAC9 and immunology via the tumor and immune system interaction database (TISIDB) was performed, and an immune prognostic risk signature was developed based on genes enriched in the top five immune-related pathways under high HDAC9 status. Comprehensive analysis of survival curves and Cox regression were used to estimate the effectiveness of the risk signature. The relationship between immunological characteristics and the risk score was evaluated, and the mechanisms were also explored.ResultsIn the TISIDB, HDAC9 was closely related to various immunological characteristics. The risk signature was obtained based on genes related to prognosis enriched in the top five immune-related pathways under high HDAC9 status. The survival rate of the high-risk BC patients was poor. The risk score was closely related to multiple immunological characteristics, drug sensitivity, immunotherapy benefits and biofunctions.ConclusionAn immune-related prognostic signature established for HDAC9 expression status could independently predict the prognosis of BC patients. The use of this signature could help clinicians make personalized treatment decisions.
| S-EPMC8893690 | biostudies-literature
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.
| S-EPMC10369052 | biostudies-literature
Project description:BackgroundMitophagy plays essential role in the development and progression of colorectal cancer (CRC). However, the effect of mitophagy-related genes in CRC remains largely unknown.AimTo develop a mitophagy-related gene signature to predict the survival, immune infiltration and chemotherapy response of CRC patients.MethodsNon-negative matrix factorization was used to cluster CRC patients from Gene Expression Omnibus database (GSE39582, GSE17536, and GSE37892) based on mitophagy-related gene expression. The CIBERSORT method was applied for the evaluation of the relative infiltration levels of immune cell types. The performance signature in predicting chemotherapeutic sensitivity was generated using data from the Genomics of Drug Sensitivity in Cancer database.ResultsThree clusters with different clinicopathological features and prognosis were identified. Higher enrichment of activated B cells and CD4+ T cells were observed in cluster III patients with the most favorable prognosis. Next, a risk model based on mitophagy-related genes was developed. Patients in training and validation sets were categorized into low-risk and high-risk subgroups. Low risk patients showed significantly better prognosis, higher enrichment of immune activating cells and greater response to chemotherapy (oxaliplatin, irinotecan, and 5-fluorouracil) compared to high-risk patients. Further experiments identified CXCL3 as novel regulator of cell proliferation and mitophagy.ConclusionWe revealed the biological roles of mitophagy-related genes in the immune infiltration, and its ability to predict patients' prognosis and response to chemotherapy in CRC. These interesting findings would provide new insight into the therapeutic management of CRC patients.
| S-EPMC10052665 | biostudies-literature
Project description:Neuroblastoma is the most common pediatric extracranial solid tumor. The 5-year survival rate for high-risk neuroblastoma is less than 50%, despite multimodal treatment. Pyroptosis, an inflammatory type of programmed cell death, manifested pro-tumor and anti-tumor roles in the adult tumor. Thus, we aimed to elucidate the function of pyroptosis in neuroblastoma. We classified neuroblastoma patients into two clusters based on the pyroptosis gene expression. We found high pyroptosis neuroblastoma manifested favorable overall survival and more anti-tumor immune cell infiltration. Based on the results of a stepwise Cox regression analysis, we built a four-gene predictive model including NLRP3, CASP3, IL18, and GSDMB. The model showed excellent predictive performance in internal and external validation. Our findings highlight that high pyroptosis positively correlated with neuroblastoma outcomes and immune landscape, which may pave the way for further studies on inducing pyroptosis therapy in high-risk neuroblastoma treatment.
| S-EPMC9162481 | biostudies-literature
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.
| S-EPMC9081442 | biostudies-literature
Project description:BACKGROUND:There is no good prognostic model that could predict the prognosis of bladder cancer (BCa) and the benefit of immunotherapy. METHODS:Through the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a 13-mRNA immune signature from the TCGA cohort (n = 406). We validated its prognostic value and predictive value for the benefit of immunotherapy with four independent validation cohort (GSE13507 [n = 256], GSE31684 [n = 93], GSE32894 [n = 308], and IMvigor210 cohort [n = 298]). RESULTS:Our results indicating that high-risk group with higher inhibitory immune cell infiltration (regulatory T cells [Tregs] and macrophage, etc), higher expression of immune checkpoints, and more T cell suppressive pathways (transforming growth factor ? [TGF-?], epithelial-mesenchymal transition [EMT], etc) were activated. Besides, the immune signature showed a good predictive value for the benefit of immunotherapy in a cohort of urothelial carcinoma patients treated with PD-L1. CONCLUSIONS:The immune signature constructed is convenient to classify the immunotherapeutic susceptibility of patients with BCa, so as to achieve precision immunotherapy for BCa.
| S-EPMC7571842 | biostudies-literature
Project description:Background: Endometrial cancer (EC) is one of the most common gynecological malignancies in women. Cholesterol metabolism has been confirmed to be closely related to tumor proliferation, invasion and metastasis. However, the correlation between cholesterol homeostasis-related genes and prognosis of EC remains unclear. Methods: EC patients from the Cancer Genome Atlas (TCGA) database were randomly divided into training cohort and test cohort. Transcriptome analysis, univariate survival analysis and LASSO Cox regression analysis were adopted to construct a cholesterol homeostasis-related gene signature from the training cohort. Subsequently, Kaplan-Meier (KM) plot, receiver operating characteristic (ROC) curve and principal component analysis (PCA) were utilized to verify the predictive performance of the gene signature in two cohorts. Additionally, enrichment analysis and immune infiltration analysis were performed on differentially expressed genes (DEGs) between two risk groups. Results: Seven cholesterol homeostasis-related genes were selected to establish a gene signature. KM plot, ROC curve and PCA in two cohorts demonstrated that the gene signature was an efficient independent prognostic indicator. The enrichment analysis and immune infiltration analysis indicated that the high-risk group generally had lower immune infiltrating cells and immune function. Conclusion: We constructed and validated a cholesterol homeostasis-related gene signature to predict the prognosis of EC, which correlated to immune infiltration and expected to help the diagnosis and precision treatment of EC.
| S-EPMC8591263 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.Materials and methodsWe downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.ResultsA total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.ConclusionThe novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.
| S-EPMC10172511 | biostudies-literature
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.
| S-EPMC8653573 | biostudies-literature
Project description:Breast cancer (BRCA) is a common malignancy worldwide that is associated with a high mortality rate. Despite recent improvements in diagnosis and treatment, there is an urgent need to investigate the processes underlying cancer progression and identify novel prognostic indicators. Anoikis, which plays a role in the development of human malignant tumors, has been gaining increasing interest from researchers. However, the potential role of anoikis-related genes (ANRGs) in the advancement of BRCA remains unknown. In this study, we aimed to assess the predictive value of ANRGs in BRCA, construct a prognostic model based on ANRGs, and explore the tumor microenvironment in different prognostic score groups. This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect clinical information and RNA sequencing data from patients with BRCA. Information on ANRGs was gathered from GeneCards and Harmonizome portals. A risk score model based on ANRGs was created using least absolute shrinkage and selection operator Cox (LASSO) regression analysis. Additionally, the study explored the tumor microenvironment and enriched pathways in different risk groups. Finally, a novel ANRG-based nomogram is developed. A total of 142 differentially expressed genes associated with survival were identified, of which 5 genes were selected to create the ANRG signature. The risk score based on this signature proved to be an independent prognostic factor. Further analysis revealed that different risk subgroups exhibited variations in the tumor microenvironment and drug sensitivities. Subsequently, a nomogram was developed using risk scores and clinicopathological factors. The decision curve analysis results suggest that patients with BRCA might derive clinical treatment benefits from utilizing this prognostic model. Based on the results of this study, the ANRG signature and nomograph established can be used for clinical decision-making in patients with BRCA.
| S-EPMC10615559 | biostudies-literature