PXD072964-1

PXD072964 is an original dataset announced via ProteomeXchange.

Title	Combining proteomics and machine learning to identify triple negative breast cancer biomarkers binding telomeric G-Quadruplex
Description	Triple-negative breast cancer (TNBC) lacks ER, PR and HER2 expression, represents ~10–20% of invasive breast cancers, and is clinically aggressive with limited targeted treatment options. Its pronounced molecular heterogeneity challenges single-marker diagnostics, motivating the development of robust biomarker panels for early detection and disease monitoring. Aptamers, and particularly guanine-rich DNA sequences forming G-quadruplexes (G4s), provide stable and versatile molecular recognition tools. Telomeric G4 structures are biologically relevant in genome maintenance and can enrich disease-related protein interactors, making them attractive baits for translational biomarker discovery. Here, we employed an overhang human telomere model capable of forming two consecutive G4s (tel46) immobilized on Controlled Pore Glass (CPG) to profile the nuclear G4 interactome in two TNBC cell lines, MDA-MB-231 and BT-549. Using affinity purification–mass spectrometry (AP-MS) with CPG-tel46 combined with quantitative proteomics and stringent background subtraction, we identified tel46-associated proteins consistently upregulated in both tumour models and prioritized 11 candidates supported by downstream bioinformatic validation. To move beyond single-marker evaluation, we implemented a machine-learning framework to assess candidate proteins as a coordinated molecular signature. Regularized models with embedded feature selection and cross-validation were used to identify stable, discriminative combinations while controlling overfitting. This integrative strategy supports G4-based capture as a practical approach to enrich clinically relevant interactors and prioritize diagnostic panels. We propose a five-protein signature (KIF4A, ACIN1, RBM12, FOXK1 and NCAPD2) as a candidate classifier for TNBC early diagnosis, providing a foundation for independent validation in clinical cohorts.
HostingRepository	PRIDE
AnnounceDate	2026-05-29
AnnouncementXML	Submission_2026-05-29_06:45:05.920.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	ilaria iacobucci
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: NEWT:9606;
ModificationList	monohydroxylated residue; iodoacetamide derivatized residue
Instrument	Orbitrap Exploris 240

Revision	Datetime	Status	ChangeLog Entry
0	2026-01-12 11:04:26	ID requested
⏵ 1	2026-05-29 06:45:06	announced

10.1186/s12935-025-03955-z;

Iacobucci I, Cipollone I, Cozzolino F, Gaglione R, Mentino MR, Platella C, Musumeci D, Arciello A, Montesarchio D, Monti M, Integrating proteomics and bioinformatics for the identification of breast cancer biomarkers interacting with telomeric G-quadruplex. Cancer Cell Int, 25(1):319(2025) [pubmed]

submitter keyword: tel46, TNBC, Gquadruplex, CANCER, BREAST, DNA,quantitative proteomics, biomarkes

Maria Monti
contact affiliation	Università degli studi di Napoli Federico II - Dipartimento di Chimica
contact email	montimar@unina.it
lab head
ilaria iacobucci
contact affiliation	Department of Chemical Sciences, University of Naples Federico II, Naples 80126, Italy CEINGE Advanced Biotechnologies, University of Naples Federico II, Naples 80145, Italy
contact email	iacobucci@ceinge.unina.it
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD072964
     1. Label: PRIDE project
     2. Name: Combining proteomics and machine learning to identify triple negative breast cancer biomarkers binding telomeric G-Quadruplex