ArticleDisease markers2022
Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma.
Article in Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Integrative analysis identifies a glycosylation-related lncRNA signature associated with prognosis in kidney renal clear cell carcinoma.Translational andrology and urology · 2026Article
- Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.Journal of translational medicine · 2024Article
- Single-cell disulfidptosis regulator patterns guide intercellular communication of tumor microenvironment that contribute to kidney renal clear cell carcinoma progression and immunotherapy.Frontiers in immunology · 2024Article
- The fate and function of non-coding RNAs during necroptosis.Epigenomics · 2024Review
- The expression and significance of long non-coding RNA ITGB2-AS1 in renal clear cell carcinoma.Saudi medical journal · 2023Article
- Identification and Validation of a Necroptosis-Related Prognostic Signature for Kidney Renal Clear Cell Carcinoma.Stem cells international · 2023Article
- Article
- Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma.Journal of oncology · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods: The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results: Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions: Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.
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