FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220885909> ?p ?o ?g. }

• W4220885909 abstract "With the improved knowledge of disease biology and the introduction of immune checkpoints, there has been significant progress in treating renal cell carcinoma (RCC) patients. Individual treatment will differ according to risk stratification. As the clinical course varies in RCC, it has developed different predictive models for assessing patient's individual risk. However, among other prognostic scores, no transparent preference model was given. MicroRNA as a putative marker shown to have prognostic relevance in RCC, molecular analysis may provide an innovative benefit in the prophetic prediction and individual risk assessment. Therefore, this study aimed to establish a prognostic-related microRNA risk score model of RCC and further explore the relationship between the model and the immune microenvironment, immune infiltration, and immune checkpoints. This practical model has the potential to guide individualized surveillance protocols, patient counseling, and individualized treatment decision for RCC patients and facilitate to find more immunotherapy targets.Downloaded data of RCC from the TCGA database for difference analysis and divided it into a training set and validation set. Then the prognostic genes were screened out by Cox and Lasso regression analysis. Multivariate Cox regression analysis was used to establish a predictive model that divided patients into high-risk and low-risk groups. The ENCORI online website and the results of the RCC difference analysis were used to search for hub genes of miRNA. Estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment (TME) and immune infiltration. Based on Kaplan-Meier survival analysis, search for immune checkpoints related to the prognosis of RCC.There were nine miRNAs in the established model, with a concordance index of 0.702 and an area under the ROC curve of 0.701. Nine miRNAs were strongly correlated with the prognosis (P < 0.01), and those with high expression levels had a poor prognosis. We found a common target gene PDGFRA of hsa-miR-6718, hsa-miR-1269b and hsa-miR-374c, and five genes related to ICGs (KIR2DL3, TNFRSF4, LAG3, CD70 and TNFRSF9). The immune/stromal score, immune infiltration, and immune checkpoint genes of RCC were closely related to its prognosis and were positively associated with a risk score.The established nine-miRNAs prognostic model has the potential to facilitate prognostic prediction. Moreover, this model was closely related to the immune microenvironment, immune infiltration, and immune checkpoint genes of RCC." @default.
• W4220885909 created "2022-04-03" @default.
• W4220885909 creator A5009012652 @default.
• W4220885909 creator A5041801347 @default.
• W4220885909 creator A5059555627 @default.
• W4220885909 creator A5062806638 @default.
• W4220885909 creator A5077233021 @default.
• W4220885909 creator A5077751349 @default.
• W4220885909 creator A5084741386 @default.
• W4220885909 creator A5087985458 @default.
• W4220885909 date "2022-03-12" @default.
• W4220885909 modified "2023-10-03" @default.
• W4220885909 title "A novel nine-microRNA-based model to improve prognosis prediction of renal cell carcinoma" @default.
• W4220885909 cites W1976043651 @default.
• W4220885909 cites W2462159864 @default.
• W4220885909 cites W2463178543 @default.
• W4220885909 cites W2510046850 @default.
• W4220885909 cites W2588025094 @default.
• W4220885909 cites W2619907255 @default.
• W4220885909 cites W2726542547 @default.
• W4220885909 cites W2744882786 @default.
• W4220885909 cites W2745234558 @default.
• W4220885909 cites W2757487320 @default.
• W4220885909 cites W2765224588 @default.
• W4220885909 cites W2766990963 @default.
• W4220885909 cites W2767051969 @default.
• W4220885909 cites W2792333466 @default.
• W4220885909 cites W2799468412 @default.
• W4220885909 cites W2799553179 @default.
• W4220885909 cites W2864822467 @default.
• W4220885909 cites W2892004398 @default.
• W4220885909 cites W2892325025 @default.
• W4220885909 cites W2897731471 @default.
• W4220885909 cites W2902244724 @default.
• W4220885909 cites W2905075726 @default.
• W4220885909 cites W2907368400 @default.
• W4220885909 cites W2921126124 @default.
• W4220885909 cites W2921527695 @default.
• W4220885909 cites W2952130397 @default.
• W4220885909 cites W2969760968 @default.
• W4220885909 cites W2974029864 @default.
• W4220885909 cites W2975608733 @default.
• W4220885909 cites W2979790494 @default.
• W4220885909 cites W2994968455 @default.
• W4220885909 cites W2998421705 @default.
• W4220885909 cites W3013629088 @default.
• W4220885909 cites W3016712382 @default.
• W4220885909 cites W3045720587 @default.
• W4220885909 cites W3078201066 @default.
• W4220885909 cites W3090125481 @default.
• W4220885909 cites W3094824630 @default.
• W4220885909 cites W3095456389 @default.
• W4220885909 cites W3097073976 @default.
• W4220885909 cites W3106806047 @default.
• W4220885909 cites W3107951541 @default.
• W4220885909 cites W3110261743 @default.
• W4220885909 cites W3137675424 @default.
• W4220885909 doi "https://doi.org/10.1186/s12885-022-09322-9" @default.
• W4220885909 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35279104" @default.
• W4220885909 hasPublicationYear "2022" @default.
• W4220885909 type Work @default.
• W4220885909 citedByCount "3" @default.
• W4220885909 countsByYear W42208859092022 @default.
• W4220885909 countsByYear W42208859092023 @default.
• W4220885909 crossrefType "journal-article" @default.
• W4220885909 hasAuthorship W4220885909A5009012652 @default.
• W4220885909 hasAuthorship W4220885909A5041801347 @default.
• W4220885909 hasAuthorship W4220885909A5059555627 @default.
• W4220885909 hasAuthorship W4220885909A5062806638 @default.
• W4220885909 hasAuthorship W4220885909A5077233021 @default.
• W4220885909 hasAuthorship W4220885909A5077751349 @default.
• W4220885909 hasAuthorship W4220885909A5084741386 @default.
• W4220885909 hasAuthorship W4220885909A5087985458 @default.
• W4220885909 hasBestOaLocation W42208859091 @default.
• W4220885909 hasConcept C121608353 @default.
• W4220885909 hasConcept C126322002 @default.
• W4220885909 hasConcept C136764020 @default.
• W4220885909 hasConcept C143998085 @default.
• W4220885909 hasConcept C203014093 @default.
• W4220885909 hasConcept C2777472916 @default.
• W4220885909 hasConcept C2777701055 @default.
• W4220885909 hasConcept C2780140570 @default.
• W4220885909 hasConcept C37616216 @default.
• W4220885909 hasConcept C41008148 @default.
• W4220885909 hasConcept C50382708 @default.
• W4220885909 hasConcept C71924100 @default.
• W4220885909 hasConcept C8891405 @default.
• W4220885909 hasConceptScore W4220885909C121608353 @default.
• W4220885909 hasConceptScore W4220885909C126322002 @default.
• W4220885909 hasConceptScore W4220885909C136764020 @default.
• W4220885909 hasConceptScore W4220885909C143998085 @default.
• W4220885909 hasConceptScore W4220885909C203014093 @default.
• W4220885909 hasConceptScore W4220885909C2777472916 @default.
• W4220885909 hasConceptScore W4220885909C2777701055 @default.
• W4220885909 hasConceptScore W4220885909C2780140570 @default.
• W4220885909 hasConceptScore W4220885909C37616216 @default.
• W4220885909 hasConceptScore W4220885909C41008148 @default.
• W4220885909 hasConceptScore W4220885909C50382708 @default.
• W4220885909 hasConceptScore W4220885909C71924100 @default.
• W4220885909 hasConceptScore W4220885909C8891405 @default.

• next