ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1581915
This article is part of the Research TopicImmune Predictive and Prognostic Biomarkers in Immuno-Oncology: Refining the Immunological Landscape of CancerView all 15 articles
Toward Precision Oncology in LUAD: A Prognostic Model Using Single-Cell Sequencing and WGCNA Based on a Disulfidptosis Relative Gene Signature
Provisionally accepted- 1Shandong University, Jinan, China
- 2Tianjin Chest Hospital, Tianjin, Tianjin Municipality, China
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AbstractBackgroundDisulfidptosis, a recently identified mechanism of cell death characterized by intracellular sulfide accumulation, leading to cellular exhaustion. Our objective is to create a prognostic model using a cohort of disulfidptosis-related genes (DRGs) to assess their prognostic value in lung adenocarcinoma (LUAD). This research not only deepens our understanding of the molecular mechanisms underpinning LUAD but also offers promising avenues for new clinical treatment biomarkers and therapeutic targets.MethodsWe employed various methodologies to assess DRGs in LUAD. Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. Differential gene analysis was conducted using the limma package and intersected with core gene sets. Univariate Cox regression analysis revealed genes with significant effects on LUAD prognosis. A prognostic model was developed using LASSO and Cox regression, utilizing median model scores for stratifying patient risk. Kaplan-Meier curves assessed prognostic differences between risk groups. Comprehensive analyses were performed on the tumor microenvironment (TME) and mutational landscape across different risk groups. Immune response characteristics and functional enrichment patterns were further evaluated in these cohorts.ResultsOur study delved into disulfidptosis in LUAD through a series of analyses: scRNA-seq data processing, WGCNA analysis, construction of a prognostic model, evaluation of clinical features and risk, enrichment analysis, mutation landscape assessment, and examination of the tumor microenvironment. We identified core genes related to disulfidptosis and established a prognostic model to classify patients based on risk scores. Notable differences in TME characteristics, immune cell infiltration, mutation landscape, and biological pathway activities were observed between risk groups, shedding new light on LUAD clinical treatment and biomarker discovery. Cell experiments highlighted the significance of KCNK1 in LUAD cells, suggesting its potential as a therapeutic target.ConclusionA prognostic model centered on DRGs was effectively developed to predict prognosis of LUAD and immunotherapy response. Our initial investigations unveiled KCNK1's oncogenic role in LUAD, identifying it as a potential therapeutic target.
Keywords: disulfidptosis1, LUAD2, TME3, immunotherapy4, ScRNA-seq5
Received: 23 Feb 2025; Accepted: 29 Apr 2025.
Copyright: © 2025 Li, Zhang, Sun and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Panpan Li, Shandong University, Jinan, China
Limin Sun, Shandong University, Jinan, China
Xiaojuan Wu, Shandong University, Jinan, China
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