Molecular map of disulfidptosis-related genes in lung adenocarcinoma: the perspective toward immune microenvironment and prognosis

• Published in: Clinical epigenetics 2024-02, Vol.16 (1), p.26-26, Article 26
• Main Authors: Zhao, Fangchao, Su, Lei, Wang, Xuefeng, Luan, Jiusong, Zhang, Xin, Li, Yishuai, Li, Shujun, Hu, Ling
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Algorithms; Apoptosis; Cancer; Cancer therapies; Cell death; Cell interactions; Chemotherapy; Clinical outcomes; Datasets; Development and progression; Disulfidptosis; Gene expression; Gene set enrichment analysis; Genes; Genetic aspects; Genetic transcription; Genomes; Genomics; Immune checkpoint inhibitors; Immunohistochemistry; Immunotherapy; Infiltration; Lung adenocarcinoma; Lung cancer; Medical diagnosis; Medical prognosis; Medical research; Medicine, Experimental; Metastases; Molecular subtypes; Mutation; Patients; Prognosis; Regression analysis; Risk assessment; Tumor microenvironment; Tumors
• ISSN: 1868-7083; 1868-7075; 1868-7083; 1868-7075
• DOI: 10.1186/s13148-024-01632-y

Disulfidptosis is a recently discovered form of programmed cell death that could impact cancer development. Nevertheless, the prognostic significance of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD) requires further clarification. This study systematically explores the genetic and transcriptional variability, prognostic relevance, and expression profiles of DRGs. Clusters related to disulfidptosis were identified through consensus clustering. We used single-sample gene set enrichment analysis and ESTIMATE to assess the tumor microenvironment (TME) in different subgroups. We conducted a functional analysis of differentially expressed genes between subgroups, which involved gene ontology, the Kyoto encyclopedia of genes and genomes, and gene set variation analysis, in order to elucidate their functional status. Prognostic risk models were developed using univariate Cox regression and the least absolute shrinkage and selection operator regression. Additionally, single-cell clustering and cell communication analysis were conducted to enhance the understanding of the importance of signature genes. Lastly, qRT-PCR was employed to validate the prognostic model. Two clearly defined DRG clusters were identified through a consensus-based, unsupervised clustering analysis. Observations were made concerning the correlation between changes in multilayer DRG and various clinical characteristics, prognosis, and the infiltration of TME cells. A well-executed risk assessment model, known as the DRG score, was developed to predict the prognosis of LUAD patients. A high DRG score indicates increased TME cell infiltration, a higher mutation burden, elevated TME scores, and a poorer prognosis. Additionally, the DRG score showed a significant correlation with the tumor mutation burden score and the tumor immune dysfunction and exclusion score. Subsequently, a nomogram was established for facilitating the clinical application of the DRG score, showing good predictive ability and calibration. Additionally, crucial DRGs were further validated by single-cell sequencing data. Finally, crucial DRGs were further validated by qRT-PCR and immunohistochemistry. Our new DRG signature risk score can predict the immune landscape and prognosis of LUAD. It also serves as a reference for LUAD's immunotherapy and chemotherapy.