ArticleComputational and structural biotechnology journal2023
Development and validation of a novel DNA damage repair-related long non-coding RNA signature in predicting prognosis, immunity, and drug sensitivity in uterine corpus endometrial carcinoma.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- AI-driven peptide discovery for endometrial cancer: deep generative modeling and molecular simulation in the big data era.Journal of computer-aided molecular design · 2026Article
- Recent advances in characterizing the immune microenvironment and biomarkers of endometrial carcinoma.Frontiers in immunology · 2026Review
- Explainable machine learning for preoperative relapse prediction in molecularly stratified endometrial cancer: A single-center finnish cohort study.Computational and structural biotechnology journal · 2026Article
- Sialylation-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in uterine corpus endometrial carcinoma.Cancer cell international · 2024Article
- lncRNA-microRNA axis in cancer drug resistance: particular focus on signaling pathways.Medical oncology (Northwood, London, England) · 2024Review
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Background: DNA damage response (DDR) confer resistance to chemoradiotherapy in cancer cells. However, the role of DDR-related lncRNAs (DRLs) in uterine corpus endometrial carcinoma (UCEC) is poorly understood. In this study, we aimed to identify a DRL-related prognostic signature that could guide the clinical treatment of UCEC. Methods: We extracted transcriptome and clinical data of patients with UCEC from The Cancer Genome Atlas (TCGA) database and identified DRLs using Spearman correlation analysis. Univariate and multivariate Cox analyses were used to determine candidate prognostic DRLs. The samples were randomly divided into training and test cohorts in a 1:1 ratio. A DRL-related risk signature was constructed from the training cohort data using the least absolute shrinkage and selection operator (LASSO) algorithm, and validated using the test and entire cohorts. Subsequently, a prognostic nomogram was developed using a multivariate Cox regression analysis. The functional annotation, immune microenvironment, tumor mutation burden (TMB), immune checkpoint blockade (ICB) efficacy, and drug sensitivity were also comprehensively analyzed between different risk groups. Finally, the function of AC019069.1 was validated in vitro. Results: A novel risk signature was developed based on nine DRLs. The risk score efficiently predicted the prognosis of patients with UCEC. Based on the median risk score, two subgroups were identified. The DDR-related pathways were upregulated in the high-risk group. Additionally, high-risk patients have low immune activity, poor response to ICB, and weak sensitivity to chemotherapeutic agents, possibly because of the proficient DDR system. Finally, we demonstrated AC019069.1 could promote cell proliferation, decrease apoptosis and maintain genome stability of UCEC cells. Conclusions: The developed DRL-related signature can predict the prognosis, immune microenvironment, immunotherapy, and chemoradiotherapy responsiveness of UCEC. Our study also revealed the potential value of DDR-targeted therapy in treating high-risk patients with UCEC.
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