Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

IF 2.5 3区医学 Q3 ONCOLOGY

Oncology Pub Date : 2024-09-04 DOI:10.1159/000540329

Jason Y Tang, Valentina L Kouznetsova, Santosh Kesari, Igor F Tsigelny

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引用次数: 0

Abstract

Introduction: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC. They are promising biomarkers because of stability in the blood, ease of non-invasive detection, and convenient screening methods. This study aimed to use blood-based miRNA biomarkers and various analysis methods in the development of a machine-learning (ML) model for PDAC.

Methods: Blood-based miRNAs associated with PDAC were collected from open sources. miRNA sequences, targeted genes, and involved pathways were used to construct a set of descriptors for an ML model.

Results: Bioinformatics analysis revealed that most genes in pancreatic cancer and insulin signaling pathways were targeted by the PDAC-related miRNAs. The best-performing ML model with the Random Forest classifier was able to achieve an accuracy of 88.4%. Model evaluations of an independent PDAC-associated miRNAs test set had 100% accuracy while non-cancer miRNAs had 52.4% accuracy, indicating specificity to PDAC.

Conclusions: Our results suggest an ML model developed using blood-based miRNA biomarkers' target gene, pathway, and sequence features could be potentially implicated in PDAC diagnostics.

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利用机器学习和基于血液的 miRNA 开发胰腺导管腺癌诊断模型。

导言：在所有主要癌症中，胰腺导管腺癌（PDAC）的存活率最低，原因是缺乏早期症状、早期检测工具以及针对晚期患者的最佳疗法。因此，PDAC 的早期诊断至关重要。最近，有报道称循环 miRNA 在 PDAC 中发生了改变。这些miRNA在血液中稳定，易于无创检测，筛查方法简便，是很有前景的生物标志物。本研究旨在利用血液中的miRNA生物标记物和各种分析方法来开发PDAC的机器学习（ML）模型：方法：从公开来源收集与 PDAC 相关的血液 miRNA，并利用 miRNA 序列、靶基因和相关通路构建一组用于 ML 模型的描述因子：生物信息学分析表明，胰腺癌和胰岛素信号通路中的大多数基因都是 PDAC 相关 miRNA 的靶基因。采用随机森林分类器的ML模型表现最好，准确率达到88.4%。对独立的PDAC相关miRNA测试集进行的模型评估准确率为100%，而非癌症miRNA的准确率为52.4%，这表明了对PDAC的特异性：我们的研究结果表明，利用基于血液的 miRNA 生物标记物的靶基因、通路和序列特征开发的 ML 模型可用于 PDAC 诊断。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Oncology 医学-肿瘤学

CiteScore

6.00

自引率

2.90%

发文量

审稿时长

6-12 weeks

期刊介绍： Although laboratory and clinical cancer research need to be closely linked, observations at the basic level often remain removed from medical applications. This journal works to accelerate the translation of experimental results into the clinic, and back again into the laboratory for further investigation. The fundamental purpose of this effort is to advance clinically-relevant knowledge of cancer, and improve the outcome of prevention, diagnosis and treatment of malignant disease. The journal publishes significant clinical studies from cancer programs around the world, along with important translational laboratory findings, mini-reviews (invited and submitted) and in-depth discussions of evolving and controversial topics in the oncology arena. A unique feature of the journal is a new section which focuses on rapid peer-review and subsequent publication of short reports of phase 1 and phase 2 clinical cancer trials, with a goal of insuring that high-quality clinical cancer research quickly enters the public domain, regardless of the trial’s ultimate conclusions regarding efficacy or toxicity.