Research Paper Volume 16, Issue 2 pp 1463—1483
Identification and validation of anoikis-related lncRNAs for prognostic significance and immune microenvironment characterization in ovarian cancer
- 1 Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China
- 2 Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
- 3 Department of Breast Surgery, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
- 4 Department of Gynecology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- 5 Biomedicine Research Centre, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
- 6 Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Guangdong Engineering Research Center for Antimicrobial Agent and Immunotechnology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
Received: August 31, 2023 Accepted: December 4, 2023 Published: January 15, 2024
https://doi.org/10.18632/aging.205439How to Cite
Copyright: © 2024 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Anoikis, a form of apoptotic cell death resulting from inadequate cell-matrix interactions, has been implicated in tumor progression by regulating tumor angiogenesis and metastasis. However, the potential roles of anoikis-related long non-coding RNAs (arlncRNAs) in the tumor microenvironment are not well understood. In this study, five candidate lncRNAs were screened through least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis based on differentially expressed lncRNAs associated with anoikis-related genes (ARGs) from TCGA and GSE40595 datasets. The prognostic accuracy of the risk model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) analyses revealed significant differences in immune-related hallmarks and signal transduction pathways between the high-risk and low-risk groups. Additionally, immune infiltrate analysis showed significant differences in the distribution of macrophages M2, follicular T helper cells, plasma cells, and neutrophils between the two risk groups. Lastly, silencing the expression of PRR34_AS1 and SPAG5_AS1 significantly increased anoikis-induced cell death in ovarian cancer cells. In conclusion, our study constructed a risk model that can predict clinicopathological features, tumor microenvironment characteristics, and prognosis of ovarian cancer patients. The immune-related pathways identified in this study may offer new treatment strategies for ovarian cancer.
Abbreviations
ARGs: anoikis-related genes; arlncRNAs: anoikis-related long non-coding RNAs; AUC: the area under the curve; BCL: B-cell lymphoma; CTLs: cytotoxic T lymphocytes; DCs: dendritic cells; DEGs: differentially expressed lncRNAs; ECM: extracellular matrix; GAPDH: glyceraldehyde 3-phosphate dehydrogenase; GEO: Gene Expression Omnibus; GPX3: glutathione peroxidase 3; GSEA: gene set enrichment analysis; HR: hazard ratio; IFN-γ: interferon gamma response; KEGG: Kyoto Encyclopedia of Genes and Genomes; LASSO: least absolute shrinkage and selection operator; NETs: neutrophil extracellular traps; NKs: natural killer cells; OS: overall survival; OV: ovarian cancer; OXPHOS: oxidative phosphorylation; ROC: receiver operating characteristic; ROS: reactive oxygen species; siRNAs: small interfering RNAs; TME: tumor microenvironment; TCGA: The Cancer Genome Atlas.