Deqian Xie, Lu Dai, Xiaolei Yang, Tao Huang, Wei Zheng
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A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma
Kidney renal clear cell carcinoma (KIRC) is increasing in incidence worldwide, with poor and unpredictable patient prognosis limited by diagnostic and therapeutic approaches. New genes are urgently needed to improve this situation. The ankyrin repeat and suppressor of the cytokine signaling (SOCS) box (ASB) family are a promising class of tumorigenesis-related genes. We examined the expression and mutation of 18 ASB genes in various tumors for this study. The findings revealed that ASB genes exhibit significant copy number variation (CNV) and single nucleotide variation (SNV). There were substantial variations in ASB gene expression in different tumor tissues, and different levels of methylation of ASB genes affected the gene expression and tumor progression. By applying LASSO regression analysis, we established a KIRC survival model based on five ASB genes (ASB6, ASB7, ASB8, ASB13, and ASB17). Additionally, ROC curve analysis was used to assess the survival model’s accuracy. Through univariate and multivariate COX regression analysis, we demonstrated that the model’s risk score might be an independent risk factor for individuals with KIRC. In summary, our KIRC survival model could accurately predict patients’ future survival. Further, we also quantified the survival model through a nomogram. This series of findings confirmed that ASB genes are potential predictive markers and targeted therapies for KIRC. Our KIRC survival model based on five ASB genes can help more clinical practitioners make accurate judgments about the prognosis of KIRC patients.
期刊介绍:
Genetics Research is a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations and molecular interactions, extending out to developmental, evolutionary, and population genetics as well as ethical, legal and social aspects. Our aim is to lead to a better understanding of genetic processes in health and disease. The journal focuses on the use of new technologies, such as next generation sequencing together with bioinformatics analysis, to produce increasingly detailed views of how genes function in tissues and how these genes perform, individually or collectively, in normal development and disease aetiology. The journal publishes original work, review articles, short papers, computational studies, and novel methods and techniques in research covering humans and well-established genetic organisms. Key subject areas include medical genetics, genomics, human evolutionary and population genetics, bioinformatics, genetics of complex traits, molecular and developmental genetics, Evo-Devo, quantitative and statistical genetics, behavioural genetics and environmental genetics. The breadth and quality of research make the journal an invaluable resource for medical geneticists, molecular biologists, bioinformaticians and researchers involved in genetic basis of diseases, evolutionary and developmental studies.