FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386119683> ?p ?o ?g. }

• W4386119683 endingPage "8407" @default.
• W4386119683 startingPage "8384" @default.
• W4386119683 abstract "Background: Numerous types of research revealed that long noncoding RNAs (lncRNAs) played a significant role in immune response and the tumor microenvironment of bladder cancer (BLCA). Dysregulated lipid metabolism is considered to be one of the major risk factors for BLCA, the study aimed to detect the lipid metabolism-related lncRNAs (LMRLs) along with their potential prognostic values and immune correlations in BLCA. Methods: We collected lipid metabolism-related genes, expression profiles, and clinical information on BLCA from the Molecular Signature Database (MSigDB) and the TCGA database, respectively. Differentially expressed lipid metabolism genes (DE-LMRGs) and differentially expressed long non-coding RNAs (DE-lncRNAs) were selected using the limma package. Spearman correlation analysis was employed to explore the correlations between DE-lncRNAs and DE-LMRGs and to further develop protein-protein interaction (PPI) networks and perform mutational analysis. The least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis were then employed to construct a prognostic risk model. The performance of the model was evaluated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and consistency indices. In addition, we downloaded the GSE31684 dataset for external validation of the prognostic signature. Moreover, we explored the association of the risk model with immune cell infiltration and chemotherapy response analysis to reveal the tumor immune microenvironment of BLCA. Finally, RT-qPCR was utilized to validate the expression of prognostic genes. Results: A total of 48 DE-LncRNAs and 33 DE-LMRGs were found to be robustly correlated, and were used to construct a lncRNA-mRNA co-expression network, in which ACACB, ACOX2, and BCHE showed high mutation rates. Then, a risk model based on three LMRLs (RP11-465B22.8, MIR100HG, and LINC00865) was constructed. The risk model effectively distinguished between the clinical outcomes of BLCA patients, with high-risk scores indicating a worse prognosis and with substantial prognostic prediction accuracy. The model's results were consistent in the GSE31684 dataset. In addition, a nomogram was constructed based on the risk score, age, pathological T-stage, and pathological N-stage, which showed robust predictive power. Immune landscape analysis indicated that the risk model was significantly associated with T-cell CD4 memory activation, M1 macrophage, M2 macrophage, dendritic cell activation, and T-cell regulatory. We predicted that 49 drugs would perform satisfactorily in the high-risk group. Additionally, we found five m6A regulators associated with the high- and low-risk groups, suggesting that upstream regulation of LncRNA could be a novel target for BLCA treatment. Finally, RT-qPCR showed that RP11-465B22.8 was highly expressed in BLCA, while MIR100HG and LINC00865 were downregulated in BLCA. Conclusion: Our findings suggest that the three LMRLs may serve as potential prognostic and immunotherapeutic biomarkers in BLCA. In addition, our study provides new ideas for understanding the pathogenic mechanisms and developing therapeutic strategies for BLCA patients." @default.
• W4386119683 created "2023-08-25" @default.
• W4386119683 creator A5000815347 @default.
• W4386119683 creator A5001129223 @default.
• W4386119683 creator A5011976467 @default.
• W4386119683 creator A5035462540 @default.
• W4386119683 creator A5043069455 @default.
• W4386119683 date "2023-08-24" @default.
• W4386119683 modified "2023-10-14" @default.
• W4386119683 title "Prognosis analysis and validation of lipid metabolism-associated lncRNAs and tumor immune microenvironment in bladder cancer" @default.
• W4386119683 cites W1601070312 @default.
• W4386119683 cites W2035618305 @default.
• W4386119683 cites W2049372134 @default.
• W4386119683 cites W2093260830 @default.
• W4386119683 cites W2146512944 @default.
• W4386119683 cites W2314946743 @default.
• W4386119683 cites W2417662509 @default.
• W4386119683 cites W2529608586 @default.
• W4386119683 cites W2766946295 @default.
• W4386119683 cites W2771657918 @default.
• W4386119683 cites W2793688609 @default.
• W4386119683 cites W2898799689 @default.
• W4386119683 cites W2950126013 @default.
• W4386119683 cites W2966710588 @default.
• W4386119683 cites W2981145485 @default.
• W4386119683 cites W2985479685 @default.
• W4386119683 cites W3003977949 @default.
• W4386119683 cites W3023288864 @default.
• W4386119683 cites W3034809608 @default.
• W4386119683 cites W3036761984 @default.
• W4386119683 cites W3097446375 @default.
• W4386119683 cites W3119005666 @default.
• W4386119683 cites W3119996920 @default.
• W4386119683 cites W3120849050 @default.
• W4386119683 cites W3146647783 @default.
• W4386119683 cites W3165482261 @default.
• W4386119683 cites W3198719997 @default.
• W4386119683 cites W3200040834 @default.
• W4386119683 cites W3200190424 @default.
• W4386119683 cites W3209105008 @default.
• W4386119683 cites W3210317168 @default.
• W4386119683 cites W3215258178 @default.
• W4386119683 cites W3217221814 @default.
• W4386119683 cites W4205791393 @default.
• W4386119683 cites W4206494082 @default.
• W4386119683 cites W4220751013 @default.
• W4386119683 cites W4220781843 @default.
• W4386119683 cites W4220925293 @default.
• W4386119683 cites W4223542278 @default.
• W4386119683 cites W4224282089 @default.
• W4386119683 cites W4322772843 @default.
• W4386119683 doi "https://doi.org/10.18632/aging.204975" @default.
• W4386119683 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37632832" @default.
• W4386119683 hasPublicationYear "2023" @default.
• W4386119683 type Work @default.
• W4386119683 citedByCount "0" @default.
• W4386119683 crossrefType "journal-article" @default.
• W4386119683 hasAuthorship W4386119683A5000815347 @default.
• W4386119683 hasAuthorship W4386119683A5001129223 @default.
• W4386119683 hasAuthorship W4386119683A5011976467 @default.
• W4386119683 hasAuthorship W4386119683A5035462540 @default.
• W4386119683 hasAuthorship W4386119683A5043069455 @default.
• W4386119683 hasBestOaLocation W43861196831 @default.
• W4386119683 hasConcept C104317684 @default.
• W4386119683 hasConcept C10515644 @default.
• W4386119683 hasConcept C119857082 @default.
• W4386119683 hasConcept C121608353 @default.
• W4386119683 hasConcept C126322002 @default.
• W4386119683 hasConcept C134018914 @default.
• W4386119683 hasConcept C136764020 @default.
• W4386119683 hasConcept C143998085 @default.
• W4386119683 hasConcept C161584116 @default.
• W4386119683 hasConcept C199163554 @default.
• W4386119683 hasConcept C203014093 @default.
• W4386119683 hasConcept C2776107976 @default.
• W4386119683 hasConcept C2780352672 @default.
• W4386119683 hasConcept C37616216 @default.
• W4386119683 hasConcept C41008148 @default.
• W4386119683 hasConcept C4733338 @default.
• W4386119683 hasConcept C502942594 @default.
• W4386119683 hasConcept C50382708 @default.
• W4386119683 hasConcept C54355233 @default.
• W4386119683 hasConcept C58471807 @default.
• W4386119683 hasConcept C60644358 @default.
• W4386119683 hasConcept C70721500 @default.
• W4386119683 hasConcept C71924100 @default.
• W4386119683 hasConcept C86803240 @default.
• W4386119683 hasConcept C8891405 @default.
• W4386119683 hasConceptScore W4386119683C104317684 @default.
• W4386119683 hasConceptScore W4386119683C10515644 @default.
• W4386119683 hasConceptScore W4386119683C119857082 @default.
• W4386119683 hasConceptScore W4386119683C121608353 @default.
• W4386119683 hasConceptScore W4386119683C126322002 @default.
• W4386119683 hasConceptScore W4386119683C134018914 @default.
• W4386119683 hasConceptScore W4386119683C136764020 @default.
• W4386119683 hasConceptScore W4386119683C143998085 @default.
• W4386119683 hasConceptScore W4386119683C161584116 @default.
• W4386119683 hasConceptScore W4386119683C199163554 @default.

• next