Construction of an STK11 Mutation and Immune-Related Prognostic Prediction Model in Lung Adenocarcinoma
Author(s):
Bo Tang , Xia Zhao , Hongbing Liu , Qingfeng Zhang , Kui Liu , Xiaoyan Yang , Yun Huang *
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Background
STK11 mutation in LUAD affects immune cell infiltration in tumor tissue, and is associated with tumor prognosis.Objective
This study aimed to construct a STK11 mutation and immune-related LUAD prognostic model.Materials and Methods
The mutation frequency of STK11 in LUAD was queried via cBioPortal in TCGA and PanCancer Atlas databases. The degree of immune infiltration was analyzed by CIBERSORT analysis. DEGs in STK11mut and STK11wt samples were analyzed. Metascape, GO and KEGG methods were adopted for functional and signaling pathway enrichment analysis of DEGs. Genes related to immune were overlapped with DEGs to acquire immune-related DEGs, whose Cox regression and LASSO analyses were employed to construct prognostic model. Univariate and multivariate Cox regression analyses verified the independence of riskscore and clinical features. A nomogram was established to predict the OS of patients. Additionally, TIMER was introduced to analyze relationship between infiltration abundance of 6 immune cells and expression of feature genes in LUAD.Results
The mutation frequency of STK11 in LUAD was 16%, and the degrees of immune cell infiltration were different between the wild-type and mutant STK11. DEGs of STK11 mutated and unmutated LUAD samples were mainly enriched in immune-related biological functions and signaling pathways. Finally, 6 feature genes were obtained, and a prognostic model was established. Riskscore was an independent immuno-related prognostic factor for LUAD. The nomogram diagram was reliable.Conclusion
Collectively, genes related to STK11 mutation and immunity were mined from the public database, and a 6-gene prognostic prediction signature was generated.Keywords:
Language:
English
Published:
Iranian Journal of Biotechnology, Volume:21 Issue: 2, Spring 2023
Pages:
1 to 14
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