Dataset Information

Establishment and validation of a polygene prognostic model for clear cell renal cell carcinoma.

ABSTRACT: Purpose: To establish an effective prognostic model for patients with clear cell renal cell carcinoma (ccRCC). Methods: We identified four hub differentially expressed genes (DEGs) in Gene Expression Omnibus (GEO) database and verified them in the Cancer Gene Atlas (TCGA), STRING, UALCAN, TIMER, and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We then used TCGA and International Cancer Genome Consortium (ICGC) to identify tumor pathway molecules highly correlated with hub DEGs. And by further LASSO and Cox regression analysis, we successfully identified five genes as prognostic factors. Results: We successfully identified a risk prediction model consisting of five genes: IGF2BP3, CDKN1A, GSDMB, FABP5, RBMX. We next distributed patients into low-risk and high-risk groups using the median as a cutoff. The low-risk group obviously had better survival than those in the predicted high-risk group. The results showed discrepancies in tumor-associated immune cell infiltration between risk groups. We also combined the risk model with clinical variables to create a nomogram. Conclusion: Our model has a satisfactory predictive effect on the prognosis of ccRCC patients and may provide new ideas for future immune therapy.

SUBMITTER: Gan K

PROVIDER: S-EPMC9630580 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Establishment and validation of a polygene prognostic model for clear cell renal cell carcinoma.

Gan Kai K Zhang Keying K Li Yu Y Zhao Xiaolong X Li Hongji H Xu Chao C Liu Shaojie S Zhang Chao C Han Donghui D Wen Weihong W Qin Weijun W

Frontiers in genetics 20221020

<b>Purpose:</b> To establish an effective prognostic model for patients with clear cell renal cell carcinoma (ccRCC). <b>Methods:</b> We identified four hub differentially expressed genes (DEGs) in Gene Expression Omnibus (GEO) database and verified them in the Cancer Gene Atlas (TCGA), STRING, UALCAN, TIMER, and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We then used TCGA and International Cancer Genome Consortium (ICGC) to identify tumor pathway molecules highly correlat ...[more]

PMID: 36338999

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Project description:BackgroundKidney clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma. Peroxisomes play a role in the regulation of tumorigenesis and cancer progression, yet the prognostic significance of peroxisome-related genes (PRGs) remains rarely studied. The study aimed to establish a novel prognostic risk model and identify potential biomarkers in KIRC.MethodsThe significant prognostic PRGs were screened through differential and Cox regression analyses, and LASSO Cox regression analysis was performed to establish a prognostic risk model in the training cohort, which was validated internally in the testing and entire cohorts, and further assessed in the GSE22541 cohort. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function and pathway differences between the high-risk and low-risk groups. The relationship between risk score and immune cell infiltration levels was evaluated in the CIBERSORT, ESTIMATE and TIMER databases. Finally, potential biomarkers were identified and validated from model genes, using immunohistochemistry.ResultsFourteen significant prognostic PRGs were identified using multiple analyses, and 9 genes (ABCD1, ACAD11, ACAT1, AGXT, DAO, EPHX2, FNDC5, HAO1, and HNGCLL1) were obtained to establish a prognostic model via LASSO Cox regression analysis. Combining the risk score with clinical factors to construct a nomogram, which provided support for personalized treatment protocols for KIRC patients. GO and KEGG analyses highlighted associations with substance metabolism, transport, and the PPAR signaling pathways. Tumor immune infiltration indicated immune suppression in the high-risk group, accompanied by higher tumor purity and the expression of 9 model genes was positively correlated with the level of immune cell infiltration. ACAT1 has superior prognostic capabilities in predicting the outcomes of KIRC patients.ConclusionsThe peroxisome-related prognostic risk model could better predict prognosis in KIRC patients.

Project description:Background: Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the human urinary system. Macrophage differentiation is associated with tumorigenesis. Therefore, exploring the prognostic value of macrophage differentiation-associated genes (MDGs) may contribute to better clinical management of ccRCC patients. Methods: The RNA sequence data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed MDGs were unveiled in ccRCC and normal samples. The prognostic model was established according to the univariate and multivariate Cox regression analyses. By combining clinico-pathological features and prognostic genes, a nomogram was established to predict individual survival probability. The Tumor Immune Estimation Resource (TIMER) database was utilized to analyze the correlation between prognostic genes and immune infiltrating cells. Eventually, the mRNA and protein expression levels of prognostic genes were verified. Results: A total of 52 differentially expressed prognosis-related MDGs were identified in ccRCC. Afterward, a six-gene prognostic model (ABCG1, KDF1, KITLG, TGFA, HAVCR2, and CD14) was constructed through the Cox analysis. The overall survival in the high-risk group was relatively poor. Moreover, the risk score was identified as an independent prognostic factor. We constructed a prognostic nomogram with a well-fitted calibration curve based on risk score and clinical data. Furthermore, the prognostic genes were significantly related to the level of immune cell infiltration including B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells. Finally, the mRNA expression of prognostic genes in clinical ccRCC tissues showed that the ABCG1, HAVCR2, CD14, and TGFA mRNA in tumor samples were increased compared with the adjacent control tissue samples, while KDF1 and KITLG were decreased, which was consistent with the verification results in the GSE53757. Conclusion: In conclusion, this study identified and validated a macrophage differentiation-associated prognostic model for ccRCC that could be used to predict the outcomes of the ccRCC patients.

Dataset Information

Establishment and validation of a polygene prognostic model for clear cell renal cell carcinoma.

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Establishment and validation of a polygene prognostic model for clear cell renal cell carcinoma.

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