ArticleAging2024
Identification and validation of anoikis-related lncRNAs for prognostic significance and immune microenvironment characterization in ovarian cancer.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 4 citations in OpenAlex.
- [Role of programmed cell death in platinum resistance in ovarian cancer].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Single-cell transcriptome analysis indicated that immune-related programmed cell death modification features could predict the clinical outcomes of patients with ovarian cancer.Translational oncology · 2026Article
- Single-cell sequencing-guided design of synergistic chemo-immunotherapy nanodrugs for cGAS-STING activation in prostate cancer therapy.Journal of nanobiotechnology · 2026Article
- Discovery of Anoikis-correlated Biomarkers for Ovarian Cancer Through Integrated Transcriptome and Single-cell RNA Sequencing Analyses.Current topics in medicinal chemistry · 2026Article
- Identification of key biomarkers in breast cancer based on bioinformatics analysis and experimental verification.Journal of the Egyptian National Cancer Institute · 2025Article
- Article
- Develop a Novel Signature to Predict the Survival and Affect the Immune Microenvironment of Osteosarcoma Patients: Anoikis-Related Genes.Journal of immunology research · 2024Article
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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