ArticleBiomedicines2023
Basement Membrane-Associated lncRNA Risk Model Predicts Prognosis and Guides Clinical Treatment in Clear Cell Renal Cell Carcinoma.
Article in Biomedicines, 2023. 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, 6 citations in OpenAlex.
- Advances in the Treatment of Kidney and Upper Urinary Tract Cancers.Biomedicines · 2024Article
- A Model to Predict Prognosis of Renal Cell Clear Cell Carcinoma Based on 3 Angiogenesis-related Long Non-coding RNAs.Journal of Cancer · 2024Article
- Prognosis prediction of head and neck squamous cell carcinoma through the basement membrane-related lncRNA risk model.Frontiers in molecular biosciences · 2024Article
- Up-regulation of Cuproptosis-related lncRNAS in Patients Receiving Immunotherapy for Metastatic Clear Cell Renal Cell Carcinoma Indicates Progressive Disease.In vivo (Athens, Greece)Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The basement membrane (BM) affects the invasion and growth of malignant tumors. The role and mechanism of BM-associated lncRNAs in clear cell renal cell carcinoma (ccRCC) are unknown. In this study, we identified biomarkers of ccRCC and developed a risk model to assess patient prognosis. We downloaded transcripts and clinical data from the Cancer Genome Atlas (TCGA). Differential analysis, co-expression analysis, Cox regression analysis, and lasso regression were used to identify BM-associated prognostic lncRNAs and create a risk prediction model. We evaluated and validated the accuracy of the model using multiple methods and constructed a nomogram to predict the prognosis of ccRCC. GO, KEGG, and immunity analyses were used to explore differences in biological function. We constructed a risk model containing six BM-associated lncRNAs (LINC02154, IGFL2-AS1, NFE4, AC112715.1, AC092535.5, and AC105105.3). The risk model has higher diagnostic efficiency compared to clinical characteristics and can be used to forecast patient prognoses. We used renal cancer cells and tissue microarrays to verify the expression of lncRNAs in the risk model. We found that knocking down LINC02154 and AC112715.1 could inhibit the invasion ability of renal cancer cells. The risk model based on BM-associated lncRNAs can well predict ccRCC and guide clinical treatment.
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Registered trials
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