Dataset Information

A Comprehensive Analysis of Programmed Cell Death-Associated Genes for Tumor Microenvironment Evaluation Promotes Precise Immunotherapy in Patients with Lung Adenocarcinoma.

ABSTRACT: Immune checkpoint inhibitors (ICIs) represent a new hot spot in tumor therapy. Programmed cell death has an important role in the prognosis. We explore a programmed cell death gene prognostic model associated with survival and immunotherapy prediction via computational algorithms. Patient details were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. We used LASSO algorithm and multiple-cox regression to establish a programmed cell death-associated gene prognostic model. Further, we explored whether this model could evaluate the sensitivity of patients to anti-PD-1/PD-L1. In total, 1342 patients were included. We constructed a programmed cell death model in TCGA cohorts, and the overall survival (OS) was significantly different between the high- and low-risk score groups (HR 2.70; 95% CI 1.94-3.75; p < 0.0001; 3-year OS AUC 0.71). Specifically, this model was associated with immunotherapy progression-free survival benefit in the validation cohort (HR 2.42; 95% CI 1.59-3.68; p = 0.015; 12-month AUC 0.87). We suggest that the programmed cell death model could provide guidance for immunotherapy in LUAD patients.

SUBMITTER: Huang Y

PROVIDER: S-EPMC10058589 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Comprehensive Analysis of Programmed Cell Death-Associated Genes for Tumor Microenvironment Evaluation Promotes Precise Immunotherapy in Patients with Lung Adenocarcinoma.

Huang Yunxi Y Ouyang Wenhao W Wang Zehua Z Huang Hong H Ou Qiyun Q Lin Ruichong R Yu Yunfang Y Yao Herui H

Journal of personalized medicine 20230306 3

Immune checkpoint inhibitors (ICIs) represent a new hot spot in tumor therapy. Programmed cell death has an important role in the prognosis. We explore a programmed cell death gene prognostic model associated with survival and immunotherapy prediction via computational algorithms. Patient details were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. We used LASSO algorithm and multiple-cox regression to establish a programmed cell death-associated gene prognost ...[more]

PMID: 36983658

Similar Datasets

Project description:BackgroundLung adenocarcinoma (LUAD) is a highly heterogeneous disease, posing significant challenges to accurate prognosis prediction. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence programmed cell death (PCD) mechanisms, which are critical in tumorigenesis and cancer progression. However, the prognostic significance of the interplay between mitochondrial function and PCD in LUAD requires further investigation.MethodsWe analyzed data from 1231 LUAD patients across seven global cohorts to develop a mitochondrial-related PCD signature (MPCDS) using machine learning. Validation was done using six immunotherapy cohorts (LUAD, melanoma, clear cell renal cell carcinoma; n=935) and a pan-cancer cohort of 21 tumor types. An in-house LUAD tissue cohort (n=100) confirmed the prognostic significance of nucleoside diphosphate kinase 4 (NME4). In vivo and in vitro experiments explored NME4's role in immune exclusion.ResultsThe MPCDS demonstrated strong predictive performance for prognosis in LUAD patients, surpassing 114 previously published LUAD signatures. Additionally, MPCDS effectively predicted outcomes in immunotherapy patients (including those with LUAD, melanoma, and clear cell renal cell carcinoma). Biologically, MPCDS was significantly associated with immune features, with the high MPCDS group exhibiting reduced immune activity and a tendency towards cold tumors. NME4, a key gene within the MPCDS (correlation=0.55, p<0.05), was associated with poorer prognosis in LUAD patients with high expression, particularly in CD8 desert phenotypes, as validated by our in-house cohort. Multiplex immunofluorescence confirmed the spatial colocalization and exclusion relationship between NME4 and immune cells such as CD3+ T cells and CD20+ B cells. Further experiments revealed that NME4 regulated the proliferation and invasion of LUAD cells both in vitro and in vivo. Importantly, inhibiting NME4 increased the abundance and activity of CD8+ T cells and enhanced the antitumor immunity of anti-programmed cell death protein-1 therapy in vivo.ConclusionThe MPCDS provides personalized risk assessment and immunotherapy interventions for individual LUAD patients. NME4, a key gene within the MPCDS, has been identified as a novel oncogene associated with immune exclusion and may serve as a new target for LUAD intervention and immunotherapy.

Project description:BackgroundSuccinate dehydrogenase (SDH), one of the key enzymes in the tricarboxylic acid cycle, is mainly found in the mitochondria. SDH consists of four subunits encoding SDHA, SDHB, SDHC, and SDHD. The biological function of SDH is significantly related to cancer progression. Colorectal cancer (CRC) is one of the most common malignant tumors globally, whose most common histological subtype is colon adenocarcinoma (COAD). However, the correlation between SDH factors and COAD remains unclear.MethodsThe data on pan-cancer was obtained from The Cancer Genome Atlas (TCGA) database. Kaplan-Meier survival analysis showed the prognostic ability of SDHs. The cBioPortal database reflected genetic variations of SDHs. The correlation analysis was conducted between SDHs and mitochondrial energy metabolism genes (MMGs) and the protein-protein interaction (PPI) network was built. Consequently, Univariate and Multivariate Cox Regression Analysis on SDHs and other clinical characteristics were conducted. A nomogram was established. The ssGSEA analysis visualized the association between SDHs and immune infiltration. Immunophenoscore (IPS) explored the correlation between SDHs and immunotherapy, and the correlation between SDHs and targeted therapy was investigated through Genomics of Drug Sensitivity in Cancer. Finally, qPCR and immunohistochemistry detected SDHs' expression.ResultsAfter assessing SDHs differential expression in pan-cancer, we found that SDHB, SDHC, and SDHD benefit COAD patients. The cBioPortal database demonstrated that SDHA was the top gene in mutation frequency rank. Correlation analysis mirrored a strong link between SDHs and MMGs. We formulated a nomogram and found that SDHB, SDHC, SDHD, and clinical characteristics correlated with COAD patients' survival. For T helper cells, Th2 cells, and Tem, SDHA, SDHB, SDHC, and SDHD were significantly enriched in the high expression group. Moreover, COAD patients with high SDHA expression were more suitable for immunotherapy. And COAD patients with different SDHs' expression have different sensitivity to targeted drugs. Further verifying the gene and protein expression levels of SDHs, we found that the tissues were consistent with the bioinformatics analysis.ConclusionsOur study analyzed the expression and prognostic value of SDHs in COAD, explored the pathway mechanisms involved, and the immune cell correlations, indicating that SDHs might be biomarkers for COAD patients.

Dataset Information

A Comprehensive Analysis of Programmed Cell Death-Associated Genes for Tumor Microenvironment Evaluation Promotes Precise Immunotherapy in Patients with Lung Adenocarcinoma.

Publications

A Comprehensive Analysis of Programmed Cell Death-Associated Genes for Tumor Microenvironment Evaluation Promotes Precise Immunotherapy in Patients with Lung Adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets