Significance of Spliceosome-Related Genes in the Prediction of Prognosis and Treatment Strategies for Lung Adenocarcinoma

Ying Yang; Tianyi Huang; Yihui Fan; Haimin Lu; Jingjing Shao; Yilang Wang; Aiguo Shen

doi:10.1155/2022/1753563

Significance of Spliceosome-Related Genes in the Prediction of Prognosis and Treatment Strategies for Lung Adenocarcinoma

Biomed Res Int. 2022 Nov 4:2022:1753563. doi: 10.1155/2022/1753563. eCollection 2022.

Authors

Ying Yang¹, Tianyi Huang², Yihui Fan³, Haimin Lu³, Jingjing Shao¹, Yilang Wang⁴, Aiguo Shen¹

Affiliations

¹ Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China.
² Department of Pharmacy, Nantong University Xinling College, Nantong, China.
³ Department of Thoracic Surgery, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China.
⁴ Department of Oncology, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China.

Abstract

Background: The leading cause of cancer-related fatalities globally is lung cancer; lung adenocarcinoma (LUAD) is the most common histological type in it. The spliceosome plays an important role in a majority of malignancies. However, it is yet unclear how spliceosome-related genes affect patients with LUAD in terms of treatment course and prognosis.

Methods: Spliceosome-related genes were assessed from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database to obtain clinical information and gene expression in patients with LUAD. A spliceosome-related gene signature and prognostic model were constructed by using the least absolute shrinkage and selection operator (LASSO), time-dependent receiver operating characteristic (ROC), and nomogram. Immune infiltrate levels, mutation analysis, and pathway enrichment were predicted potential mechanisms of the signature by using single-sample gene set enrichment analysis (ssGSEA), Gene Set Cancer Analysis (GSCA) database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) database. Then, a protein-protein interaction (PPI) network and transcription factor- (TF-) hub gene and drug mining network were also established by Cytoscape software.

Results: Firstly, we constructed a prognostic model for 11 spliceosome signature genes. Based on the prognostic risk score, we stratified patients with LUAD into high- and low-risk groups. The high- and low-risk groups were closely related to the OS, tumor immune infiltration level, immune checkpoint molecules, and tumor mutation burden (TMB) of LUAD patients. Based on PPI networks, we also predict relevant TF genes that may regulate signature prognostic genes. Finally, drugs including oxaliplatin, arsenic trioxide, cisplatin, and sunitinib were excavated for the treatment of the 11 spliceosome signature genes in LUAD patients.

Conclusion: In conclusion, this study is the first to explore the importance of spliceosome-related genes in the prognosis and treatment of LUAD. Through our study, we have innovatively provided potential prognosis genes and new therapeutic drug targets for the treatment of LUAD patients.

MeSH terms

Adenocarcinoma of Lung* / pathology
Gene Expression Regulation, Neoplastic
Humans
Lung Neoplasms* / genetics
Lung Neoplasms* / metabolism
Lung Neoplasms* / therapy
Prognosis
Spliceosomes / genetics