Dataset Information

A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis.

ABSTRACT: Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were further screened using LASSO Cox regression and integrated into a prognostic risk score model. Then, KIRC patients were divided into high- and low-risk groups. A univariate cox regression analysis revealed a significant association between the high-risk group and a poor prognosis. The time-dependent receiver operating characteristic (ROC) curve shows that the risk group performs well in predicting overall survival. Furthermore, the risk group is contained in the best multivariate model that was obtained by a multivariate stepwise analysis, which further confirms that the risk group can be used as a potential prognostic biomarker. In addition, a nomogram was established for the best multivariate model and shown to perform well in predicting the survival of KIRC patients. In summary, a seven-MDEG signature is a powerful prognosis factor for KIRC patients and may provide useful suggestions for their personalized therapy.

SUBMITTER: Hu F

PROVIDER: S-EPMC6888680 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis.

Hu Fuyan F Zeng Wenying W Liu Xiaoping X

International journal of molecular sciences 20191114 22

Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were ...[more]

PMID: 31739630

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Project description:BackgroundFerredoxin 1 (FDX1) is a newly discovered gene regulating cuprotosis. However, the effect of FDX1 expression on clear renal cell carcinoma (ccRCC) is unknown.MethodsGene expression profiles and clinical data of ccRCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. The differences in FDX1 expression between ccRCC and nonneoplastic tissues adjacent to cancer were analyzed by R software. The results were validated by GEO data, quantitative real-time polymerase chain reaction (qRT-PCR), western blotting (WB), and immunohistochemistry (IHC). Chi-square test was used to analyze the clinical pathological parameters. Kaplan-Meier survival analysis and Cox proportional hazard regression model selection were used to evaluate the effect of FDX1 expression on overall survival. Protein interaction networks were used to analyze other proteins that interact with FDX1. Signal pathway analysis was performed for possible FDX1 enrichment using GSEA and ssGSEA algorithms. Pan-cancer analysis of FDX1 was carried out through TCGA database.ResultsThe FDX1 expression in nontumor tissues was significantly higher than that in ccRCC, and the expression difference was verified by GEO data, qRT-PCR, WB, and IHC. The high expression of FDX1 was significantly related to the well overall survival rate (P < 0.05). The chi-square test showed that the high expression of FDX1 was related to gender, TNM stage, T stage, lymph node metastasis, and pathological grade. Additionally, the FDX1 expression level was different in groups classified based on pathological grade, gender, TNM stage, T stage, lymph node metastasis, and distant metastasis (P < 0.05). The multivariate analysis revealed the high expression of FDX1 as an important independent predictor for overall survival. STRING database results showed that LIAS and LIPT1 may interact with FDX1 in the PPI network, which are also involved in the regulation of cuprotosis. The GSEA and ssGSEA results showed that the FDX1 was enriched in the anticancer pathway. The FDX1 high expression is associated with better prognosis in many cancers, as revealed by pan-cancer analysis.ConclusionFDX1 may play a role in the progression of ccRCC as a tumor suppressor gene. It can be used as a potential prognostic indicator and therapeutic target of ccRCC. However, the cuprotosis regulatory role in the development of ccRCC needs to be further verified.

Project description:The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.

Project description:Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

Dataset Information

A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis.

Publications

A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis.

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