Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human lung cancer patients with sqamous cell or adenocarcinoma at different stages of disease to identify gene signatures to predict prognosis

ABSTRACT: Gene signatures were derived to separate high risk patients from low risk ones. Experiment Overall Design: 130 samples from 129 patients

ORGANISM(S): Homo sapiens

SUBMITTER: Jack Yu

PROVIDER: E-GEOD-4573 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.

Raponi Mitch M Zhang Yi Y Yu Jack J Chen Guoan G Lee Grace G Taylor Jeremy M G JM Macdonald James J Thomas Dafydd D Moskaluk Christopher C Wang Yixin Y Beer David G DG

Cancer research 20060801 15

Non-small-cell lung cancers (NSCLC) compose 80% of all lung carcinomas with squamous cell carcinomas (SCC) and adenocarcinoma representing the majority of these tumors. Although patients with early-stage NSCLC typically have a better outcome, 35% to 50% will relapse within 5 years after surgical treatment. We have profiled primary squamous cell lung carcinomas from 129 patients using Affymetrix U133A gene chips. Unsupervised analysis revealed two clusters of SCC that had no correlation with tumo ...[more]

PMID: 16885343

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Project description:Abstract: Immunotherapy has emerged as a crucial treatment approach for lung adenocarcinoma (LUAD), yet a notable percentage of patients do not exhibit the anticipated prognosis and therapeutic response. In this study, we embarked on a comprehensive analysis of immune infiltration-related genes in LUAD by integrating transcriptome sequencing data and TCGA dataset. And an immune risk prognosis model was developed, which accurately forecasts the prognosis and predicts the immunotherapy efficacy in LUAD. Patients with LUAD were categorized into high-risk and low-risk groups based on the model's risk value. The high-risk group exhibited a poorer prognosis, higher tumor mutation burden (TMB), greater genomic heterogeneity and lower immune scores. In contrast, the low-risk group manifested characteristics conducive to immunotherapy, including a more favorable prognosis, heightened sensitivity to immunotherapy and reduced likelihood of immune escape. The robustness of the model's predictive capability was corroborated using the GEO dataset, Nomogram and an independent immunotherapy cohort. Furthermore, the model risk value was found to be significantly associated with the prognosis of LUAD patients, genomic heterogeneity, immune cell infiltration, immune regulation and immunotherapy outcomes. Additionally, we characterized the significance of the model's feature factors using single-cell clustering and pseudo-temporal analysis, revealing that the model's feature genes are predominantly located in tumor-infiltrating T cells, particularly in the exhausted and effector subtypes. TNFRSF11A emerged as a key gene in the model, promoting LUAD cell proliferation, migration, and apoptosis, and involvement in LUAD M6A methylation and PD-L1-mediated immune escape. TNFRSF11A was mainly expressed in T cell clusters and represented a potential immune system regulatory factor associated with T cell exhaustion, fostering malignant progression in LUAD cells. Our study offers new mechanistic insights and therapeutic targets for the biological behaviors and immune suppression in LUAD.

Dataset Information

Transcription profiling of human lung cancer patients with sqamous cell or adenocarcinoma at different stages of disease to identify gene signatures to predict prognosis

Publications

Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.

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