Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs

Miaosen Zheng; Hao Zhou; Jing Xie; Haifeng Zhang; Xiaojian Shen; Dongbing Zhu

doi:10.21037/jtd-22-1534

Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs

J Thorac Dis. 2022 Dec;14(12):4828-4845. doi: 10.21037/jtd-22-1534.

Authors

Miaosen Zheng^#¹, Hao Zhou^#², Jing Xie¹, Haifeng Zhang³, Xiaojian Shen¹, Dongbing Zhu¹

Affiliations

¹ Department of Pathology, The People's Hospital of Rugao, Rugao Hospital Affiliated to Nantong University, Rugao, China.
² Department of Thoracic Surgery, Affiliated Hospital of Nantong University, Nantong, China.
³ Department of Thoracic Surgery, The People's Hospital of Rugao, Rugao Hospital Affiliated to Nantong University, Rugao, China.

^# Contributed equally.

Abstract

Background: Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD.

Methods: The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the "ConsensusClusterPlus" package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy.

Results: Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group.

Conclusions: Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis.

Keywords: Lung adenocarcinoma (LUAD); cuprotosis; immune cells infiltration; long non-coding RNA (lncRNA); prognosis.