PXD050863

PXD050863 is an original dataset announced via ProteomeXchange.

Title	High-throughput proteomics and statistical approach identify a protein panel of potential biomarkers of colorectal cancer categorizing intra-tumoral heterogeneity
Description	Colorectal cancer (CRC) is one of the major causes of cancer-related death worldwide, for which diagnosis and prognosis are still inadequate mainly due to local recurrence and metastasis, although medical and surgical treatment in cancer therapy advanced. Diagnosis is frequently late, the high capacity of CRC to infiltrate and develop metastasis has the consequence that 40% of patients with CRC have metastasis in liver already at the time of diagnosis. The prognosis of CRC is closely related to the stage, but classification of patients is affected by a great variability in response to therapy and clinical outcome. The localization of the tumor determines a huge inter-tumoral heterogeneity and the intra-tumoral heterogeneity connected to the tumor microenvironment increases the complexity. The heterogeneity typical of CRC is also associated to the several oncogenic signaling pathways, among them the glucose-related pathways, indeed, the glucose metabolic reprogramming of cancer cells appears to be implicated in the malignant progression of CRC. This metabolic alteration seems to be associated to the epithelial-mesenchymal transition (EMT), which is considered the main event promoting the invasion and migration of CRC cells. However, the comprehensive understanding of the molecular mechanisms associated to EMT in CRC is still a challenge and despite decades of research, the process of tumor dissemination is insufficiently understood. Therefore, it is necessary to identify novel biomarkers associated with the prevention, diagnosis and treatment of CRC, as well as potential therapeutic targets. In this context, we performed a large-scale shotgun proteomic investigation on CRC with the aim of discover novel potential protein hallmarks able to distinguish with high specificity and sensitivity tumoral tissue from the health one, and to classify the superficial and the internal deep tumoral tissues. The EMT process is driven by the deep tumoral cells in CRC, therefore, to find significant differences at proteomic levels with respect to the peripheric tumoral tissue and to the healthy mucosa, and to identify specific classifying factors can be useful to individuate potential biomarkers of CRC invasiveness and potential therapy targets. Moreover, the functional enrichment analysis of proteomic data can provide information on biological processes implicated and associated to the variations of the protein profiles, and thus, help to understand the molecular mechanism associated to the CRC progression and malignance. Our approaches coupling high-throughput proteomic strategy with highly accurate statistical and enrichments analysis highlighted not only confirmation on potential biomarkers already proposed in other studies but also novel biomarkers able to distinguish with high sensitivity and specificity the deep tumor from the superficial one and to the healthy mucosa. Our investigation suggested a strong contribution of proteins implicated in metabolic pathways as catalytic and regulatory activities, some of them with high classifying power and potentially useful as biomarker and therapeutic target.
HostingRepository	PRIDE
AnnounceDate	2024-10-17
AnnouncementXML	Submission_2024-10-17_05:23:01.705.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Cristina Contini
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	2-pyrrolidone-5-carboxylic acid (Gln); phosphorylated residue; acetylated residue; monohydroxylated residue; iodoacetamide derivatized residue
Instrument	LTQ Orbitrap XL

Revision	Datetime	Status	ChangeLog Entry
0	2024-03-21 08:42:12	ID requested
⏵ 1	2024-10-17 05:23:02	announced

10.3390/cells13161311;

Contini C, Manconi B, Olianas A, Guadalupi G, Schirru A, Zorcolo L, Castagnola M, Messana I, Faa G, Diaz G, Cabras T, Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer. Cells, 13(16):(2024) [pubmed]

submitter keyword: proteomics, Colorectal cancer, biomarkers,LC-MSMS

Tiziana Cabras
contact affiliation	University of Cagliari, Italy, Department of Environmental and Life Sciences
contact email	tcabras@unica.it
lab head
Cristina Contini
contact affiliation	Università di Cagliari
contact email	cristina.contini93@unica.it
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2024/10/PXD050863

PRIDE project URI

• PRIDE
  1. PXD050863
     1. Label: PRIDE project
     2. Name: High-throughput proteomics and statistical approach identify a protein panel of potential biomarkers of colorectal cancer categorizing intra-tumoral heterogeneity