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Background <p>RNA-binding proteins (RBPs) are key regulators of cellular transcription and are associated with the occurrence and development of diseases.</p> Objective <p>This study aimed to validate the biological characteristics and clinical value of RBPs in testicular cancer, and then construct prediction models for testicular cancer metastasis and treatment outcome.</p> Methods <p>RNA sequencing data from 150 testicular tumors and 6 normal tissues were obtained from the cancer genome atlas (TCGA). Additionally, RNA sequencing data from 165 normal testicular tissues were downloaded from the genotype-tissue expression (GTEx) portal. The chemotherapy sensitivity of testicular tumor was evaluated based on the genomics of drug sensitivity in cancer (GDSC) and cancer therapeutics response portal (CTRP) databases. RNA sequencing data was analyzed and predicted for tumor metastasis and treatment outcomes through machine learning models such as artificial neural networks (ANN), random forests (RF), support vector machines (SVM), and logistic regression models (LR).</p> Results <p>A RBP risk-score model was developed with the genes: GAPDH, APOBEC3G, KRT18, NOSIP, KCTD12, ENO1, HMGA1, LDHB, ANXA2, ELOVL6, TCF7, BICD1. Those biomarkers were enriched in growth factor activity, hormone receptor binding, and cell killing signaling pathway. Risk-score model can predict the progress free interval (PFI), disease free interval (DFI), and metastasis status of patients with testicular cancer. Patients with high risk-score tumor had an increased tumor infiltrating M2 macrophage, and were more likely to progress after anti-PD-L1 immunotherapy. High risk patients seemed to benifit more from cisplatin-based chemotherapy, but less from bleomycin chemotherapy. Machine learning models basing on RBPs were able to predict tumor metastasis and the effects of chemotherapy and radiotherapy. ANN model achieved the highest accuracy in predicting tumor lymph node metastasis and radiotherapy sensitivity.</p> Conclusion <p>RBP signature genes can serve as biomarkers for testicular cancer and play a role in predicting tumor metastasis and therapeutic efficacy.</p>

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RNA-binding protein expression based machine learning model predicts metastasis and treatment outcome of testicular cancer

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • Lin-jian Mo,
  • Hai-qi Liang,
  • Zhen-yuan Yu,
  • Yao-wen Liang,
  • Chuan-xin Gu,
  • Qiu-ju Wei,
  • Qi-huan He,
  • Fa-ye Wei,
  • Ji-wen Cheng,
  • Zeng-nan Mo

摘要

Background

RNA-binding proteins (RBPs) are key regulators of cellular transcription and are associated with the occurrence and development of diseases.

Objective

This study aimed to validate the biological characteristics and clinical value of RBPs in testicular cancer, and then construct prediction models for testicular cancer metastasis and treatment outcome.

Methods

RNA sequencing data from 150 testicular tumors and 6 normal tissues were obtained from the cancer genome atlas (TCGA). Additionally, RNA sequencing data from 165 normal testicular tissues were downloaded from the genotype-tissue expression (GTEx) portal. The chemotherapy sensitivity of testicular tumor was evaluated based on the genomics of drug sensitivity in cancer (GDSC) and cancer therapeutics response portal (CTRP) databases. RNA sequencing data was analyzed and predicted for tumor metastasis and treatment outcomes through machine learning models such as artificial neural networks (ANN), random forests (RF), support vector machines (SVM), and logistic regression models (LR).

Results

A RBP risk-score model was developed with the genes: GAPDH, APOBEC3G, KRT18, NOSIP, KCTD12, ENO1, HMGA1, LDHB, ANXA2, ELOVL6, TCF7, BICD1. Those biomarkers were enriched in growth factor activity, hormone receptor binding, and cell killing signaling pathway. Risk-score model can predict the progress free interval (PFI), disease free interval (DFI), and metastasis status of patients with testicular cancer. Patients with high risk-score tumor had an increased tumor infiltrating M2 macrophage, and were more likely to progress after anti-PD-L1 immunotherapy. High risk patients seemed to benifit more from cisplatin-based chemotherapy, but less from bleomycin chemotherapy. Machine learning models basing on RBPs were able to predict tumor metastasis and the effects of chemotherapy and radiotherapy. ANN model achieved the highest accuracy in predicting tumor lymph node metastasis and radiotherapy sensitivity.

Conclusion

RBP signature genes can serve as biomarkers for testicular cancer and play a role in predicting tumor metastasis and therapeutic efficacy.