解密皮肤病的区别:通过电子活组织检查和机器学习检测基底细胞癌和鳞状细胞癌之间作为判别生物标志物的玉米蛋白

IF 3.5 3区 医学 Q1 DERMATOLOGY
Edward Vitkin, Julia Wise, Ariel Berl, Ofir Shir-az, Vladimir Kravtsov, Zohar Yakhini, Avshalom Shalom, Alexander Golberg
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引用次数: 0

摘要

皮肤鳞状细胞癌(cSCC)和基底细胞癌(BCC)之间的临床误诊给治疗带来了挑战,并可能导致复发、转移以及发病率和死亡率的增加。我们的目标是利用一种称为电子活检的微创蛋白质组取样方法,采用电穿孔技术使细胞非热透化,并利用机器学习技术,找出区分 cSCC 和 BCC 的蛋白质标记物。电子活检有助于从 21 例 cSCC 和 21 例 BCC 病理验证人类癌症中提取体外蛋白质组。对126个蛋白质组进行LC/MS/MS分析,然后进行机器学习分析,以确定区分cSCC和BCC的蛋白质。为了对已确定的蛋白质组进行验证,我们使用了从无关联的 20 例 cSCC 和 46 例 BCC 人类癌症的电子活组织检查中提取的蛋白质组,并对已发表的转录组学进行了差异表达分析。我们还使用荧光免疫组化方法验证了机器学习模型最常选择的判别生物标记物玉米素。分析了来自 188 名患者的 192 个蛋白质组样本。基于机器学习的方法得出了一组 11 个潜在的生物标记蛋白,可用于构建患者分类模型,其平均交叉验证准确率为 95.2%,BCC 精确度为 93.6 ± 14.5%,cSCC 精确度为 98.4 ± 7.2%,特异性为 97.7 ± 11.8%,灵敏度为 92.7 ± 15.3%。蛋白-蛋白相互作用分析表明,11 个结果蛋白中有 10 个连接着一个新的相互作用网络。组织学和转录组学验证证实,玉米蛋白作为一种判别标志物,在 cSCC 中的含量明显低于 BCC。电子活组织检查与机器学习相结合,为皮肤分子取样提供了一种新方法,可用于生物标记物检测和 cSCC 与 BCC 之间的差异表达分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deciphering dermatological distinctions: Cornulin as a discriminant biomarker between basal cell carcinoma and squamous cell carcinoma detected through e-biopsy and machine learning

Deciphering dermatological distinctions: Cornulin as a discriminant biomarker between basal cell carcinoma and squamous cell carcinoma detected through e-biopsy and machine learning

Clinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) poses treatment challenges and carries risks of recurrence, metastases and increased morbidity and mortality. We aimed to identify discriminant proteins markers for cSCC and BCC using a minimally invasive proteome sampling method called e-biopsy, employing electroporation for non-thermal cell permeabilization and machine learning. E-biopsy facilitated ex vivo proteome extraction from 21 cSCC and 21 BCC pathologically validated human cancers. LC/MS/MS profiling of 126 proteomes was followed by machine learning analysis to identify proteins distinguishing cSCC from BCC. For identified panel validation, we used proteomes sampled by e-biopsy from unrelated 20 cSCC and 46 BCC human cancers, and differential expression analysis of published transcriptomics. Cornulin, the most commonly chosen discriminative biomarker by machine learning models, was also validated using fluorescent immunohistochemistry. One hundred and ninety-two proteomes sampled from one hundred eight patients were analysed. Machine learning-based approaches resulted in a set of 11 potential biomarker proteins that can be used to construct a patient classification model with 95.2% average cross-validation accuracy, BCC precision of 93.6 ± 14.5%, cSCC precision of 98.4 ± 7.2%, specificity of 97.7 ± 11.8% and sensitivity 92.7 ± 15.3%. Protein–protein interaction analysis revealed a novel interaction network connecting 10 of the 11 resulted proteins. Histological and transcriptomic validation confirmed cornulin as a discriminant marker significantly lower in cSCC than in BCC. E-biopsy combined with machine learning provides a novel approach to molecular sampling from skin for biomarker detection and differential expression analysis between cSCC and BCC.

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来源期刊
Experimental Dermatology
Experimental Dermatology 医学-皮肤病学
CiteScore
6.70
自引率
5.60%
发文量
201
审稿时长
2 months
期刊介绍: Experimental Dermatology provides a vehicle for the rapid publication of innovative and definitive reports, letters to the editor and review articles covering all aspects of experimental dermatology. Preference is given to papers of immediate importance to other investigators, either by virtue of their new methodology, experimental data or new ideas. The essential criteria for publication are clarity, experimental soundness and novelty. Letters to the editor related to published reports may also be accepted, provided that they are short and scientifically relevant to the reports mentioned, in order to provide a continuing forum for discussion. Review articles represent a state-of-the-art overview and are invited by the editors.
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