Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Biomarkers of Kidney Cancer.
Proteomics Clin Appl
; 11(11-12)2017 Dec.
Article
in En
| MEDLINE
| ID: mdl-28975715
ABSTRACT
PURPOSE:
Renal cell carcinoma (RCC) is a malignant and metastatic cancer with 95% mortality, and clear cell RCC (ccRCC) is the most observed among the five major subtypes of RCC. Specific biomarkers that can distinguish cancer tissues from adjacent normal tissues should be developed to diagnose this disease in early stages and conduct a reliable prognostic evaluation. EXPERIMENTALDESIGN:
Data-independent acquisition (DIA) strategy has been widely employed in proteomic analysis because of various advantages, including enhanced protein coverage and reliable data acquisition. In this study, a DIA workflow is constructed on a quadrupole-Orbitrap LC-MS platform to reveal dysregulated proteins between ccRCC and adjacent normal tissues.RESULTS:
More than 4000 proteins are identified, 436 of these proteins are dysregulated in ccRCC tissues. Bioinformatic analysis reveals that multiple pathways and Gene Ontology items are strongly associated with ccRCC. The expression levels of L-lactate dehydrogenase A chain, annexin A4, nicotinamide N-methyltransferase, and perilipin-2 examined through RT-qPCR, Western blot, and immunohistochemistry confirm the validity of the proteomic analysis results. CONCLUSIONS AND CLINICAL RELEVANCE The proposed DIA workflow yields optimum time efficiency and data reliability and provides a good choice for proteomic analysis in biological and clinical studies, and these dysregulated proteins might be potential biomarkers for ccRCC diagnosis.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Carcinoma, Renal Cell
/
Proteomics
/
Kidney Neoplasms
Type of study:
Prognostic_studies
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
Proteomics Clin Appl
Journal subject:
BIOQUIMICA
Year:
2017
Type:
Article
Affiliation country:
China