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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

Haixia Wang1, Xuemei Zhu2, Fangchao Zhao3, Pengfei Guo3, Jing Li3, Jingfang Du4, Guoyong Shan1, Yishuai Li5, Juan Li6
  • 1Department of Radiation Oncology, The Fifth Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou People’s Hospital, Zhengzhou 450003, China
  • 2Department of Ultrasound, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang 212000, China
  • 3Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
  • 4Department of Clinical Medicine, Hebei University of Engineering, Handan 056002, China
  • 5Department of Thoracic Surgery, Hebei Chest Hospital, Shijiazhuang 050000, China
  • 6School of Nursing, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian 271000, China
* Equal contribution
Received: April 18, 2023Accepted: November 2, 2023Published: February 5, 2024
https://doi.org/10.18632/aging.205509

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.