Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma

Wentao Zhang; Haizeng Qu; Xiaoqing Ma; Liang Li; Yanjun Wei; Ye Wang; Renya Zeng; Yuanliu Nie; Chenggui Zhang; Ke Yin; Fengge Zhou; Zhe Yang

doi:10.3389/fimmu.2023.1179742

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma

Front Immunol. 2023 Aug 9:14:1179742. doi: 10.3389/fimmu.2023.1179742. eCollection 2023.

Authors

Wentao Zhang¹, Haizeng Qu², Xiaoqing Ma³, Liang Li⁴, Yanjun Wei⁵, Ye Wang⁶, Renya Zeng¹, Yuanliu Nie⁶, Chenggui Zhang⁷, Ke Yin⁸, Fengge Zhou¹, Zhe Yang¹

Affiliations

¹ Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
² Radiotherapy Department, Dongming People's Hospital, Heze, Shandong, China.
³ Radiotherapy and Minimally Invasive Group I, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
⁴ Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China.
⁵ Department of Radiation Oncology, Weifang People's Hospital, Weifang, China.
⁶ Tumor Research and Therapy Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
⁷ Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
⁸ Department of Pathology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.

Abstract

Background: Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients.

Methods: RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB).

Results: We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Keywords: LUAD; cuproptosis; immune; prognosis; signature.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adenocarcinoma of Lung* / genetics
Algorithms
Apoptosis
Humans
Lung Neoplasms* / genetics
Prognosis
Tumor Suppressor Proteins

Substances

ZNF671 protein, human
Tumor Suppressor Proteins

Grants and funding

This work was supported by the Beijing Xisike Clinical Oncology Research Foundation (grant number Y-2019AZMS-0522); the Natural Science Foundation of Shandong Province (grant number ZR2020MH229) ; the Xisike - 2019 Qilu Oncology Research Fund Project (grant number Y-QL2019-0149) and Natural Science Foundation of Shandong Province(ZR2022QH128).