A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma
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RESEARCH
Open Access
A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma Ling Chen1, Zijin Xiang2, Xueru Chen2, Xiuting Zhu2 and Xiangdong Peng2*
Abstract Background: Kidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients. Results: We identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set. Conclusion: Our findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients. Keywords: Kidney renal clear cell carcinoma, Bioinformatics, Prognostic model, LASSO penalty
Background Kidney renal clear cell carcinoma (KIRC) is a type of renal cortical tumour characterized by a growth pattern of the cytoplasm that is associated with malignant epithelial cells and accounts for 80–90% of renal cell carcinomas. In addition, KIRC tends to be resistant to radiation and chemotherapy, which makes surgery the primary treatment [1]. However, 30% of patients who undergo surgery still experience metastasis [2]. Early * Correspondence: [email protected] 2 Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan, China Full list of author information is available at the end of the article
identification of risk in KIRC patients can help with more accurate clinical treatment. Therefore, there is a strong demand to discover new and reliable markers to predict patient prognosis. Many studies show that predictive models of gene expression have great significance in clinical prognosis applications. For example, Fatai et al. built a model to demonstrate that a 35-gene signature can discriminate between rapidly and slowly progressing glioblastoma multiforme and predict survival in known subtypes of
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