Research Paper Volume 16, Issue 3 pp 2753—2773
Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma
- 1 Department of Radiation Oncology, The Fifth Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou People’s Hospital, Zhengzhou 450003, China
- 2 Department of Ultrasound, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang 212000, China
- 3 Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
- 4 Department of Clinical Medicine, Hebei University of Engineering, Handan 056002, China
- 5 Department of Thoracic Surgery, Hebei Chest Hospital, Shijiazhuang 050000, China
- 6 School of Nursing, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian 271000, China
Received: April 18, 2023 Accepted: November 2, 2023 Published: February 5, 2024
https://doi.org/10.18632/aging.205509How to Cite
Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis.
Methods: Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression.
Results: Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR.
Conclusions: Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.
Abbreviations
GEO: Gene Expression Omnibus; TCGA: The Cancer Genome Atlas; LUAD: lung adenocarcinoma; TME: tumor microenvironment; DRGs: disulfidoptosis-related genes; ICGs: immune checkpoint genes; CRSGs: chemoradiotherapy sensitivity-related genes; TIDE: tumor immune dysfunction and exclusion; PCA: principal component analysis; OS: overall survival; GSVA: Gene Set Variation Analysis; GSEA: Gene Set Enrichment Analysis; DEGs: differentially expressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; LASSO: least absolute shrinkage and selection operator; ROC: receiver operating characteristic; AUC: area under the curve; DCA: decision curve analysis; GTEx: Genotype-Tissue Expression; HPA: Human Protein Atlas; IHC: immunohistochemistry.