Research Paper Volume 14, Issue 15 pp 6358—6376
Clinical outcomes and potential therapies prediction of subgroups based on a ferroptosis-related long non-coding RNA signature for gastric cancer
- 1 Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- 2 State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Received: November 10, 2021 Accepted: July 26, 2022 Published: August 14, 2022
https://doi.org/10.18632/aging.204227How to Cite
Copyright: © 2022 Geng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Gastric cancer (GC) is one of the most aggressive malignant tumors worldwide. Ferroptosis is a kind of iron-dependent cell death, which is proved to be closely related to tumor progression. In this study, we aim at constructing a ferroptosis-related lncRNAs signature to predict the prognosis of GC and explore potential therapies.
Methods: Ferroptosis-Related LncRNAs Signature for GC patients (FRLSG) was constructed through univariate Cox regression, the LASSO algorithm, and multivariate Cox regression. Kaplan–Meier analysis, receiver operating characteristic curves, and risk score plot were applied to verify the predictive power of FRLSG. Gene Set Enrichment Analysis (GSEA) and immune infiltration analyses were conducted to explore the potential clinical value of the FRLSG. In addition, drug sensitivity prediction was applied to identify chemotherapeutic drugs with potential therapeutic effect.
Results: Five ferroptosis-related lncRNAs (AC004816.1, AC005532.1, LINC01357, AL355574.1 and AL049840.4) were identified to construct FRLSG, whose expression level in GC were confirmed by experimental validation. Kaplan-Meier curve and ROC curve proved the reliability and effectiveness of the FRLSG in predicting the prognosis for GC patients. Several immune-related pathways were enriched in the high-FRLSG group, and further immune infiltration analyses demonstrated the high immune infiltration status of the high-FRLSG group. In addition, 19 and 24 candidate drugs with potential therapeutic effect were identified for the high- and low-FRLSG groups, respectively.
Conclusions: FRLSG was an effective tool in predicting the prognosis of GC, which might help to prioritize potential therapeutics for GC patients.
Abbreviations
GC: Gastric cancer; TCGA: The Cancer Genome Atlas; ROC: receiver operating characteristic; GSEA: Gene Set Enrichment Analysis; TNM: tumor-node-metastasis; lncRNAs: long non-coding RNAs; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSVA: Gene Set Variation Analysis; FPKM: Fragments Per Kilobase of transcript per Million fragments mapped; ceRNAs: competing endogenous RNAs; OS: Overall Survival; ssGESA: Single Sample Gene Set Enrichment Analysis; ESTIMATE: The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data; CTRP: the Cancer Therapeutics Response Portal; PRISM: PRISM Repurposing dataset; GDSC: the Genomics of Drug Sensitivity in Cancer; PCA: principal component analysis; t-SNE: t-distribution random neighbor embedding; AUC: the area under curve; PD-1: programmed cell death protein-1; CTLA4: cytotoxic T-lymphocyte-associated protein-4.