A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma

Qixuan Li; Tianyi Wang; Jiaqi Zhu; Anping Zhang; Anqi Wu; Youlang Zhou; Jiahai Shi

doi:10.21037/atm-22-3195

A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma

Ann Transl Med. 2023 Mar 15;11(5):198. doi: 10.21037/atm-22-3195. Epub 2023 Mar 7.

Authors

Qixuan Li^#^{1

2

3}, Tianyi Wang^#^{1

2

3}, Jiaqi Zhu^#^{1

2

3}, Anping Zhang^{1

2

3}, Anqi Wu^{1

2

3}, Youlang Zhou⁴, Jiahai Shi^{1

2

5}

Affiliations

¹ Nantong Key Laboratory of Translational Medicine in Cardiothoracic Diseases, and Research Institution of Translational Medicine in Cardiothoracic Diseases, Affiliated Hospital of Nantong University, Nantong, China.
² Department of Thoracic Surgery, Affiliated Hospital of Nantong University, Nantong, China.
³ Medical School of Nantong University, Nantong, China.
⁴ Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, China.
⁵ School of Public Health, Nantong University, Nantong, China.

^# Contributed equally.

Abstract

Background: Cuproptosis, one of the newest forms of cell death induction, is attracting mounting attention. However, the role of cuproptosis in lung cancer is currently unclear. In this study, we constructed a prognostic signature utilizing cuproptosis-related long noncoding RNAs (CRL) in lung adenocarcinoma (LUAD) and researched its clinical and molecular function.

Methods: RNA-related and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed CRLs were screened using the 'limma' package of R software. We used coexpression analysis and univariate Cox analysis to further identify prognostic CRLs. Applying least absolute shrinkage and selection operator (LASSO) regression and Cox regression models, a prognostic risk model based on 16 prognostic CRLs was constructed. To validate prognostic CRL function in LUAD, vitro experiments were conducted to explore the expression of GLIS2-AS1, LINC01230, and LINC00592 in LUAD. Subsequently, according to a formula, patients in the training, test, and overall groups were split into high- and low-risk groups. Kaplan-Meier and receiver operating characteristic (ROC) analyses were applied to assess the predictability of the risk model. Finally, the associations between risk signature and immunity-related analysis, somatic mutation, principal component analysis (PCA), enriched molecular pathways, and drug sensitivity was investigated.

Results: A cuproptosis-related long noncoding RNAs (lncRNAs) signature was constructed. Using quantitative polymerase chain reaction (qPCR) trial, we verified that the expressions of GLIS2-AS1, LINC01230, and LINC00592 in LUAD cell lines and tissues were consistent with the above screening results. Based on this signature, a total of 471 LUAD samples from TCGA data set were split into two risk groups based on the computed risk score. The risk model showed a better capacity in predicting prognosis than traditional clinicopathological features. Moreover, significant differences were found in immune cell infiltration, drug sensitivity, and immune checkpoint expression between the two risk groups.

Conclusions: The CRLs signature was shown to be a prospective biomarker to forecast prognosis in patients with LUAD and presents new insights for personalized treatment of LUAD.

Keywords: Long noncoding RNAs (lncRNAs); cuproptosis; immune microenvironment; lung adenocarcinoma (LUAD); prognostic signature.