A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer.

Journal of Oncology
Zongtai ZhengXudong Yao

Abstract

Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa. Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Univariate Cox regression analysis and minimum redundancy maximum relevance were performed for gene selection. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group (LNI group). Machine learning models were constructed in the training set and assessed in the validation set. Univariable logistic regression and multivariable logistic regression were applied to build a nomogram. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of hub genes between five matched primary PCa and the corresponding LNI samples. The 37-gene-based support vector machine (SVM) model had the optimal synthesized performance in the SMOTE-balanced training (area under the curve (AUC): 0.947) and validation (AUC: 0.901) sets. Incorporating the SVM-based risk score and the Gleason grade, the genomic-clinicopathologic nomogram demonstrated good prediction and ...Continue Reading

References

Aug 27, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Hanchuan PengChris Ding
Jan 25, 2011·Urology·Daniar Kurmanbekovich OsmonovKlaus Peter Juenemann
May 23, 2013·Annals of Oncology : Official Journal of the European Society for Medical Oncology·S Riggs, R T Burks
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Mar 18, 2020·International Journal of Environmental Research and Public Health·Yafei Wu, Ya Fang

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R
SMOTE
GSEA

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