PXD050713-3

PXD050713 is an original dataset announced via ProteomeXchange.

Title	Deciphering dermatological distinctions: Cornulin as a discriminant biomarker between Basal Cell Carcinoma and Squamous Cell Carcinoma detected through E-biopsy and Machine Learning
Description	Background: 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. Objective: 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. Methods: 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. The most commonly chosen discriminant biomarker by machine learning models, cornulin, was also validated using fluorescent immunohistochemistry. Results: 192 proteomes sampled from 108 patients were analyzed. Machine Learning-based approaches resulted in a set of 11 potential biomarker proteins that can be used to construct a 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 per-patient 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. Conclusions: E-biopsy combined with machine learning provides a novel approach to molecular biomarkers sampling from skin for biomarker detection and differential expression analysis between cSCC and BCC
HostingRepository	PRIDE
AnnounceDate	2024-10-22
AnnouncementXML	Submission_2024-10-22_06:57:55.532.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Alexander Golberg
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	acetylated residue; monohydroxylated residue; iodoacetamide derivatized residue
Instrument	Q Exactive HF-X

Revision	Datetime	Status	ChangeLog Entry
0	2024-03-18 02:53:54	ID requested
1	2024-09-10 12:41:37	announced
2	2024-09-12 04:44:40	announced	2024-09-12: Updated project metadata.
⏵ 3	2024-10-22 06:57:55	announced	2024-10-22: Updated project metadata.

10.1016/J.XJIDI.2024.100304;

submitter keyword: Carcinoma, Machine learning, Proteomics

Alexander Golberg
contact affiliation	Tel-aviv University
contact email	agolberg@tauex.tau.ac.il
lab head
Alexander Golberg
contact affiliation	Tel Aviv University
contact email	agolberg@tauex.tau.ac.il
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD050713
     1. Label: PRIDE project
     2. Name: Deciphering dermatological distinctions: Cornulin as a discriminant biomarker between Basal Cell Carcinoma and Squamous Cell Carcinoma detected through E-biopsy and Machine Learning