skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

A novel nine-microRNA-based model to improve prognosis prediction of renal cell carcinoma

BMC cancer, 2022-03, Vol.22 (1), p.264-264, Article 264 [Peer Reviewed Journal]

2022. The Author(s). ;COPYRIGHT 2022 BioMed Central Ltd. ;The Author(s) 2022 ;ISSN: 1471-2407 ;EISSN: 1471-2407 ;DOI: 10.1186/s12885-022-09322-9 ;PMID: 35279104

Full text available

Citations Cited by
  • Title:
    A novel nine-microRNA-based model to improve prognosis prediction of renal cell carcinoma
  • Author: Xu, Chen ; Zeng, Hui ; Fan, Junli ; Huang, Wenjie ; Yu, Xiaosi ; Li, Shiqi ; Wang, Fubing ; Long, Xinghua
  • Subjects: Analysis ; Carcinoma, Renal cell ; Care and treatment ; Diagnosis ; Health risk assessment ; Immune microenvironment ; Immunotherapy ; Medical research ; Medicine, Experimental ; Methods ; MicroRNA ; miRNA ; Patient outcomes ; Prognosis ; Renal cell carcinoma ; Risk assessment ; Risk factors
  • Is Part Of: BMC cancer, 2022-03, Vol.22 (1), p.264-264, Article 264
  • Description: 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.
  • Publisher: England: BioMed Central Ltd
  • Language: English
  • Identifier: ISSN: 1471-2407
    EISSN: 1471-2407
    DOI: 10.1186/s12885-022-09322-9
    PMID: 35279104
  • Source: GFMER Free Medical Journals
    PubMed Central
    Springer Nature OA/Free Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait