PO.BCS02.06 · 生物信息与计算
利用 PVT1 生物标志物和 PSA 预测前列腺癌的机器学习模型的比较评估
A comparative assessment of machine learning models for predicting prostate cancer using PVT1 biomarkers and PSA
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
前列腺癌（PCa）是男性癌症相关死亡的主要原因之一，尤其在非洲血统人群中。虽然前列腺特异性抗原（PSA）检测是最常用的诊断工具，但其低特异性导致过度诊断和过度治疗。近期研究表明，PVT1 基因的特定外显子，如外显子 4A、外显子 4B 和外显子 9，当与 PSA 水平结合时，可能作为改善 PCa 风险分层的有前景的生物标志物。在此项工作的基础上，我们的研究评估了机器学习技术在识别任何前列腺癌或高级别前列腺癌患者方面的预测准确度，数据来自 108 名 PSA 升高的多种族男性。我们使用 logistic 回归和支持向量机（SVM）探索了这些特征的不同组合。我们发现，在所有人群组和终点中不存在单一的最佳方法。最优分类器随具体预测任务而变化。对于预测一般人群中的任何癌症，当 PSA 与 PVT1 外显子 4a 结合时，表现最佳的模型是 logistic 回归（F1 评分为 0.72）。对于一般人群中的癌症分级预测，SVM 使用 PSA、PVT1 外显子 4a 和 PVT1 外显子 9 取得了最佳性能（F1 评分为 0.93）。对于预测非洲血统男性（MoAA）中任何癌症的风险，logistic 回归通过将 PSA 与 PVT1 外显子 4a 结合取得了最佳准确度（F1 评分为 0.89）。我们的结果表明，PVT1 生物标志物与 PSA 的结合可提高不同人群和终点中风险预测的准确度。此外，logistic 回归对任何癌症提供了更准确的预测，而 SVM 在预测高级别癌症方面表现更好。未来的工作将聚焦于在更大的临床数据上评估我们的模型。
查看英文原文 English abstract
Prostate cancer (PCa) is one of the leading causes of cancer-related deaths among men, especially in populations of African ancestry. While prostate-specific antigen (PSA) testing is the most common diagnostic tool, its low specificity results in overdiagnosis and overtreatment. Recent studies have shown that specific exons of the PVT1 gene, such as exon 4A, exon 4B and exon 9, may serve as promising biomarkers to improve PCa risk stratification when combined with PSA levels. Building on this work, our study evaluates the predictive accuracy of machine learning techniques in identifying patients with any prostate cancer or high-grade prostate cancer on data from 108 multiracial men with elevated PSA. We explore different combinations of these features using logistic regression and support vector machines (SVMs).We found no single best method across all population groups and endpoints. The optimal classifier varied based on the specific prediction task. For predicting any cancer in the general population, the best performing model was Logistic Regression (F1-score of 0.72) when PSA is combined with PVT1 exon 4a. For cancer grade prediction in the general population, SVM achieved the best performance (F1 score of 0.93) using PSA, PVT1 exon 4a and PVT1 exon 9. For predicting the risk of any cancer among Men of African Ancestry (MoAA) Logistic Regression achieved the best accuracy (F1 score of 0.89) by combining PSA with PVT1 exon 4a.Our results show that combination of PVT1 biomarkers with PSA improves the accuracy of risk prediction across different populations and endpoints. Additionally, Logistic Regression provides more accurate predictions of any cancer whereas SVMs perform better for predicting high grade cancer. Future work will focus on evaluating our models on larger clinical data
利益披露 Disclosure
P. Kadel, None..
R. E. Bonacci, None..
E. O. Asante-Asamani, None.