A novel prognostic model based on immunogenomics for clear cell renal cell carcinoma

Autor: Zhipeng Wu, Kai Wang, Jinhui Liu, Dongming Chen, Xianlin Xu, DeSen Fan, Yanhao Shen
Rok vydání: 2021
Předmět:
CD4-Positive T-Lymphocytes
Male
0301 basic medicine
Oncology
medicine.medical_specialty
Multivariate statistics
medicine.medical_treatment
Clinical Decision-Making
Immunology
Risk Assessment
Decision Support Techniques
Immunophenotyping
Transcriptome
03 medical and health sciences
Lymphocytes
Tumor-Infiltrating
0302 clinical medicine
Predictive Value of Tests
Risk Factors
Internal medicine
Databases
Genetic
Biomarkers
Tumor
Tumor Microenvironment
medicine
Humans
Immunology and Allergy
Gene Regulatory Networks
Carcinoma
Renal Cell
Pharmacology
Framingham Risk Score
business.industry
Proportional hazards model
Gene Expression Profiling
Univariate
Dendritic Cells
Middle Aged
Nomogram
Prognosis
medicine.disease
Kidney Neoplasms
Gene Expression Regulation
Neoplastic
Nomograms
Clear cell renal cell carcinoma
Phenotype
030104 developmental biology
Cytokine
030220 oncology & carcinogenesis
Female
business
Zdroj: International Immunopharmacology. 90:107119
ISSN: 1567-5769
Popis: Background Immune cell infiltration into tumor tissue is closely related to the clinical outcomes of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to screen out potential immune genes associated with ccRCC, analyze their relationships with clinical outcomes, and construct a signature to predict ccRCC. Methods The transcriptome RNA-sequencing data in 539 ccRCC and 72 adjacent normal tissues were obtained from TCGA database. Biomedical computational algorithms were conducted to identify immune-related differential expressed genes (IRDGs) and enriched pathways. Then, LASSO Cox and multivariate Cox analyses were performed to screen out genes that were then used to construct the prognostic model. Results A total of 116 down-regulated and 565 up-regulated IRDGs were identified. Pathway enrichment analysis suggested that IRDGs was mainly enriched in the pathway of “cytokines and cytokine receptors”. The entire data of ccRCC were randomly divided into the training set and the test set with a ratio of 1:1. A 4-gene signature was then constructed using LASSO Cox analysis and multivariate Cox analysis in the training set. This prognostic signature could stratify patients into high- and low-risk groups successfully, and serve as an independent predictor when adjusted with clinical factors by univariate and multivariate Cox regression analysis. These results were verified in the test set and the entire set. Besides, the abundance of CD4 + T cells and dendritic cells increased in the high-risk group. Finally, we built a nomogram incorporating risk score and clinical factors to predict the overall survival of ccRCC patients. Conclusions These findings may contribute to the research of ccRCC in immunization part.
Databáze: OpenAIRE
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