AlZaabi Adhari, Piccolo Stephen, Graves Steven, Hansen Marc
Department of Human and Clinical Anatomy, Sultan Qaboos University, 35, Muscat 123, Oman.
Department of Physiology and Developmental Biology, Brigham Young University, Provo, UT 84602, USA.
Cancers (Basel). 2024 Jun 27;16(13):2365. doi: 10.3390/cancers16132365.
Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers with / 761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting.
在此，我们评估低分子量血清肽的差异表达如何能够高置信度地预测乳腺癌进展。我们对乳腺癌患者的血清样本进行基于液相色谱/串联质谱的无偏“组学”分析，以鉴定在I期和III期乳腺癌中差异表达的分子。结果通过标准和基于机器学习的分析流程生成。采用标准流程，一项探索性研究产生了65个具有统计学显著差异表达的循环生物标志物候选物。另一项研究证实了这些标志物中一部分的差异表达。使用一种探索性算法生成基于多种生物标志物组合的模型，该算法旨在产生比任何单个标志物更高的诊断能力和准确性。然后在一项盲法验证研究中对单个生物标志物和更复杂的多标志物模型进行测试。多标志物模型在验证研究中保留了其预测能力，其中最佳模型的曲线下面积（AUC）为0.84，灵敏度为43%，特异性为88%。其中一个分子量为761.38且表达下调的标志物被鉴定为纤维蛋白原α链。基于机器学习的分析产生了一个分类器，该分类器能够正确地对研究中的每个受试者进行分类，并展示了对分类器输出具有高置信度所需的参数约束。这些结果表明，血清肽生物标志物模型可在临床环境中进行优化，以评估乳腺癌分期。
