ArticleTranslational cancer research2024
A novel immune-related long noncoding RNA (lncRNA) pair model to predict the prognosis of triple-negative breast cancer.
Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- A micropeptide encoded by the lncRNA USP30-AS1 promotes tumor growth by attenuating cGAS-STING-type I IFN signaling in macrophages.Nature cancer · 2026Article
- State-Aware RNA Biomarkers in Triple-Negative Breast Cancer (TNBC): Integrating Tumor Plasticity, Spatial Architecture, and Temporal Monitoring.International journal of molecular sciences · 2026Review
- Dual Immunological Prognostic Models for Risk Stratification and Treatment Insights in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- Spotlight on USP30: structure, function, disease and target inhibition.Frontiers in pharmacology · 2025Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Breast cancer (BC) is the most prevalent cancer type and is the principal cause of cancer-related death in women. Anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) immunotherapy has shown promising effects in metastatic triple-negative breast cancer (TNBC), but the potential factors affecting its efficacy have not been elucidated. Immune-related long noncoding RNAs (irlncRNAs) have been reported to be involved in immune escape to influence the carcinogenic process through the PD-1/PD-L1 signaling pathway. Therefore, exploring the potential regulatory mechanism of irlncRNAs in PD-1/PD-L1 immunotherapy in TNBC is of great importance. Methods: We retrieved transcriptome profiling data from The Cancer Genome Atlas (TCGA) and identified differentially expressed irlncRNA (DEirlncRNA) pairs. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct a risk assessment model. Results: Receiver operating characteristic (ROC) curve analysis indicated that the risk model may serve as a potential prediction tool in TNBC patients. Clinical stage and risk score were proved to be independent prognostic predictors by univariate and multivariate Cox regression analyses. Subsequently, we investigated the correlation between the risk model and tumor-infiltrating immune cells and immune checkpoints. Finally, we identified USP30-AS1 through the StarBase and Multi Experiment Matrix (MEM) databases, predicted the potential target genes of USP30-AS1, and then discovered that these target genes were closely associated with immune responses. Conclusions: Our study constructed a risk assessment model by irlncRNA pairs regardless of expression levels, which contributed to predicting the efficacy of immunotherapy in TNBC. Furthermore, the lncRNA USP30-AS1 in the model was positively correlated with the expression of PD-L1 and provided a potential therapeutic target for TNBC.
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