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Identification of Nephrogenic Therapeutic Biomarkers of Wilms Tumor Using Machine Learning
Hanxiang Liu1, Chaozhi Tang2, Yi Yang1
1Pediatric Urology, Shengjing Hospital of China Medical University, Shenyang 110001, China.
Abstract:
Wilms tumor is the most common renal malignancy in children, with a survival rate of more than 90%; however, treatment outcomes for certain patient subgroups, such as those with bilateral and recurrent diseases, remain significantly below this survival rate. Therefore, it remains essential to identify new biomarkers and develop effective therapeutic strategies. Based on the Therapeutically Applicable Research to Generate Effective Treatments and Gene Expression Omnibus RNA microarray datasets, we have identified eight differentially expressed genes in Wilms tumors as renal-specific in 33 randomly selected adult tumors. The risk model, constructed using survival forest and multivariate Cox regression, can effectively predict the prognosis; the risk score is an independent prognostic factor in Wilms tumor. Gene set enrichment analysis showed that most of the signature genes were involved in regulating human development-related pathways. At the same time, patients in the high-risk group exhibited more sensitive immunological and chemotherapeutic properties than those in the low-risk group. These results provide new insights into personalized and precise Wilms tumor treatment strategies.
Insights
Researchers identified eight key genes in Wilms tumor, developing a risk model to predict prognosis. This discovery aids in personalized treatment strategies for pediatric kidney cancer, improving outcomes for high-risk patients.
Area of Science:
- Pediatric Oncology
- Molecular Biology
- Genomics
Background:
- Wilms tumor is the leading childhood kidney cancer, with over 90% survival.
- Certain subgroups, like those with bilateral or recurrent disease, have poorer outcomes.
- Identifying novel biomarkers and therapies is crucial for improving treatment efficacy.
Purpose of the Study:
- To identify novel, renal-specific, differentially expressed genes in Wilms tumors.
- To develop a prognostic risk model for predicting patient outcomes.
- To explore potential therapeutic strategies based on identified gene signatures.
Main Methods:
- Utilized Therapeutically Applicable Research to Generate Effective Treatments (TREATS) and Gene Expression Omnibus (GEO) RNA microarray datasets.
- Identified eight differentially expressed genes in Wilms tumors from 33 adult tumor samples.
- Constructed a risk model using survival forest and multivariate Cox regression analysis.
Main Results:
- Eight differentially expressed genes were identified as renal-specific in Wilms tumors.
- The developed risk model effectively predicts prognosis, with the risk score being an independent prognostic factor.
- High-risk patients showed increased sensitivity to immunotherapy and chemotherapy.
Conclusions:
- The identified gene signature and risk model offer valuable tools for prognostic prediction in Wilms tumor.
- Findings suggest potential for developing personalized and precise treatment strategies.
- Further research into these genes may uncover new therapeutic targets for challenging Wilms tumor cases.
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