Gel-Based Proteomic Identification of Suprabasin as a Potential New Candidate Biomarker in Endometrial Cancer.
Fulvio CelsiLorenzo MonastaGiorgio ArrigoniIlaria BattistiDanilo LicastroMichelangelo AloisioGiovanni Di LorenzoFederica RomanoGiuseppe RicciBlendi UraPublished in: International journal of molecular sciences (2022)
Endometrial cancer (EC) is the most frequent gynaecologic cancer in postmenopausal women. We used 2D-DIGE and mass spectrometry to identify candidate biomarkers in endometrial cancer, analysing the serum protein contents of 10 patients versus 10 control subjects. Using gel-based proteomics, we identified 24 candidate biomarkers, considering only spots with a fold change in volume percentage ≥ 1.5 or intensity change ≤ 0.6, which were significantly different between cases and controls ( p < 0.05). We used Western blotting analysis both in the serum and tissue of 43 patients for data validation. Among the identified proteins, we selected Suprabasin (SBSN), an oncogene previously associated with poor prognosis in different cancers. SBSN principal isoforms were subjected to Western blotting analysis in serum and surgery-excised tissue: both isoforms were downregulated in the tissue. However, in serum, isoform 1 was upregulated, while isoform 2 was downregulated. Data-mining on the TCGA and GTEx projects, using the GEPIA2.0 interface, indicated a diminished SBSN expression in the Uterine Corpus Endometrial Cancer (UCEC) database compared to normal tissue, confirming proteomic results. These results suggest that SBSN, specifically isoform 2, in tissue or serum, could be a potential novel biomarker in endometrial cancer.
Keyphrases
- endometrial cancer
- poor prognosis
- end stage renal disease
- postmenopausal women
- mass spectrometry
- chronic kidney disease
- ejection fraction
- long non coding rna
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- minimally invasive
- high resolution
- machine learning
- small molecule
- big data
- patient reported outcomes
- high intensity
- coronary artery disease
- ms ms
- label free
- squamous cell
- acute coronary syndrome
- artificial intelligence
- protein protein
- tandem mass spectrometry
- amino acid