Identification of 4-Genes Model in Papillary Renal Cell Tumor Microenvironment Based on Comprehensive Analysis

Autor: Haiyi Zhou, Hao Su, Liang Luo
Rok vydání: 2021
Předmět:
Male
0301 basic medicine
Oncology
Cancer Research
Datasets as Topic
Kaplan-Meier Estimate
Kidney
0302 clinical medicine
Tumor Microenvironment
Protein Interaction Maps
RC254-282
Papillary renal cell carcinomas
Age Factors
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Middle Aged
Prognosis
Kidney Neoplasms
Up-Regulation
Gene Expression Regulation
Neoplastic
030220 oncology & carcinogenesis
Female
CD79 Antigens
Hub genes
medicine.medical_specialty
Stromal cell
Biology
Risk Assessment
Papillary renal cell carcinoma
03 medical and health sciences
Sex Factors
Internal medicine
Biomarkers
Tumor
Genetics
medicine
Humans
Carcinoma
Renal Cell
Gene
Survival analysis
Aged
Tumor microenvironment
Models
Genetic
Interleukin-6
Proportional hazards model
Research
Cancer
Nomogram
medicine.disease
Chemokine CXCL13
Nomograms
030104 developmental biology
ROC Curve
Chemokine CCL19
Feasibility Studies
Neoplasm Grading
Zdroj: BMC Cancer
BMC Cancer, Vol 21, Iss 1, Pp 1-9 (2021)
DOI: 10.21203/rs.3.rs-138186/v1
Popis: Background The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA). Results We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors. Conclusions Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.
Databáze: OpenAIRE
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