A cuproptosis-related lncRNA signature-based prognostic model featuring on metastasis and drug selection strategy for patients with lung adenocarcinoma

Mengzhe Zhang; Zengtuan Xiao; Yongjie Xie; Zekun Li; Lianmin Zhang; Zhenfa Zhang

doi:10.3389/fphar.2023.1236655

A cuproptosis-related lncRNA signature-based prognostic model featuring on metastasis and drug selection strategy for patients with lung adenocarcinoma

Front Pharmacol. 2023 Sep 7:14:1236655. doi: 10.3389/fphar.2023.1236655. eCollection 2023.

Authors

Mengzhe Zhang^#¹, Zengtuan Xiao^#^{1

2}, Yongjie Xie³, Zekun Li³, Lianmin Zhang¹, Zhenfa Zhang¹

Affiliations

¹ Department of Lung Cancer Surgery, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
² Department of Immunology, Biochemistry and Molecular Biology, Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Medical University, Tianjin, China.
³ Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.

^# Contributed equally.

Abstract

Introduction: Lung adenocarcinoma is a common cause of mortality in patients with cancer. Recent studies have indicated that copper-related cell death may not occur in the same way as previously described. Long non-coding RNAs (lncRNAs) play a key role in the occurrence and development of tumors; however, the relationship between cuproptosis and lncRNAs in tumorigenesis and lung adenocarcinoma (LUAD) treatment has not been well established. Our study aimed to construct a model to analyze the prognosis of lung adenocarcinoma in patients using a carcinogenesis-related lncRNA (CR) signature. Methods: The transcriptional profiles of 507 samples from The Cancer Genome Atlas were assessed. Cox regression and co-expression analyses, and the least absolute shrinkage and selection operator (LASSO) were used to filter the CR and develop the model. The expression status of the six prognostic CRs was used to classify all samples into high- and low-risk groups. The overall disease-free survival rate was compared between the two groups. The Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used to identify the pathways and mechanisms involved in this model. Subsequently, immunotherapy response, sensitivity, and correlation analyses for several anti-tumor medications were performed. In vitro experiments, including qPCR, were conducted in nine lung adenocarcinoma cell lines and 16 pairs of lung adenocarcinoma and para-carcinoma tissues. Results: After confirmation using the ROC curve, patients in the low-risk category benefited from both overall and disease-free survival. Gene Ontology analysis highlighted cell movement in the model. In the in vitro experiments, qPCR results showed the expression levels of six CRs in 16 pairs of carcinoma and para-carcinoma tissues, which were in accordance with the results of the model. AL138778.1 is a protective factor that can weaken the invasion and migration of A549 cells, and AL360270.1 is a hazardous factor that promotes the invasion and migration of A549 cells. According to this model, targeted treatments such as axitinib, gefitinib, linsitinib, pazopanib, and sorafenib may be more appropriate for low-risk patients. Conclusion: Six CR profiles (AL360270.1, AL138778.1, CDKN2A-DT, AP003778.1, LINC02718, and AC034102.8) with predictive values may be used to evaluate the prognosis of patients with lung adenocarcinoma undergoing therapy.

Keywords: anti-cancer drug; biofunction; cuproptosis; lncRNA; lung adenocarcinoma; lung cancer; metastasis; prognostic model.