AUTHOR=Li Guoqi , Huo Diwei , Guo Naifu , Li Yi , Ma Hongzhe , Liu Lei , Xie Hongbo , Zhang Denan , Qu Bo , Chen Xiujie 

TITLE=Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs

JOURNAL=Frontiers in Genetics

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1106724

DOI=10.3389/fgene.2023.1106724

ISSN=1664-8021

ABSTRACT=Background: Long noncoding RNAs(lncRNAs) play an important role in the immune regulation of gastric cancer(GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance for the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.
Methods: In this study, R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) and univariate Cox regression analysis was done to find prognostic immune-related lncRNAs. 117 combinations based on 10 algorithms are integrated to determine the optimization model- immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERPORT algorithm, R package maftools, pRRopheti, clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, and drug sensitivity analysis, pathway enrichment score.
Results: A total of 321 immune-related lncRNAs were found and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the Risk Score(RS) developed by the 18-lncRNAs signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high - and low-risk groups according to the median RS, the low-risk group had a better prognosis, higher metabolism, tumor immune microenvironment, and tumor signatures enrichment score, higher frequency of genomic mutations, a higher proportion of immune cell infiltration, and higher antitumor drug resistance. Furthermore, there were 86 differentially expressed genes(DEGs) between high - and low-risk groups, which were mainly enriched in immune-related pathways.
Conclusions: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high and low-risk groups according to the RS.