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) b...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Genetics Research |
| Online Access: | http://dx.doi.org/10.1155/2023/6254023 |
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| _version_ | 1849397914965639168 |
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| author | Deqian Xie Lu Dai Xiaolei Yang Tao Huang Wei Zheng |
| author_facet | Deqian Xie Lu Dai Xiaolei Yang Tao Huang Wei Zheng |
| author_sort | Deqian Xie |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a65f1e01aa0a445f866e08dc88756943 |
| institution | Kabale University |
| issn | 1469-5073 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Genetics Research |
| spelling | doaj-art-a65f1e01aa0a445f866e08dc887569432025-08-20T03:38:48ZengWileyGenetics Research1469-50732023-01-01202310.1155/2023/6254023A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell CarcinomaDeqian Xie0Lu Dai1Xiaolei Yang2Tao Huang3Wei Zheng4Department of UrologyDepartment of Plastic SurgeryJinan Fourth People’s HospitalDepartment of UrologyDepartment of UrologyKidney 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.http://dx.doi.org/10.1155/2023/6254023 |
| spellingShingle | Deqian Xie Lu Dai Xiaolei Yang Tao Huang Wei Zheng A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma Genetics Research |
| title | A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma |
| title_full | A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma |
| title_fullStr | A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma |
| title_full_unstemmed | A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma |
| title_short | A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma |
| title_sort | survival model based on the asb genes and used to predict the prognosis of kidney renal clear cell carcinoma |
| url | http://dx.doi.org/10.1155/2023/6254023 |
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