Interaction between cadherins, vimentin, and V-set and immunoglobulin domain containing 1 in gastric-type hepatocellular carcinoma.
Simona GurzuHaruhiko SugimuraJanos SzederjesiRita SzodoraiCornelia BraicuLaszlo KoboriDecebal FodorIoan JungPublished in: Histochemistry and cell biology (2021)
In hepatocellular carcinomas (HCCs), the role of the cell surface protein V-set and immunoglobulin domain containing 1 (VSIG1), which is known as a specific marker of the gastric mucosa and testis, has not yet been determined. We examined VSIG1 immunohistochemical (IHC) expression in 105 consecutive samples provided by HCC patients, along with the IHC expression of three of the biomarkers known to be involved in the epithelial-mesenchymal transition (EMT): vimentin (VIM), and E- and N-cadherin (encoded by CDH1 and CDH2 genes). IHC subcellular localization of thyroid transcription factor 1 (TTF1), in which nuclear-to-cytoplasmic translocation is known to cause a lineage shift from lung to gastric-type adenocarcinoma, was also checked. The obtained data were validated using the miRNET program. In the examined HCC samples, VSIG1 expression was observed in the cytoplasm of normal hepatocytes and downregulated in 47 of the 105 HCCs (44.76%). In 29 cases (27.62%), VSIG1 was co-expressed with cytoplasmic TTF1. VSIG1 expression was positively correlated with both E-cadherin and N-cadherin and negatively correlated with VIM (p < 0.0001). The VSIG1+/E-cadherin+/N-cadherin-/VIM phenotype was seen in 13 cases (12.4%) and was characteristic of well-differentiated (G1/2) carcinomas diagnosed in pT1/2 stages. Like pulmonary carcinomas, simultaneous cytoplasmic positivity of HCC cells for VSIG1 and TTF1 may be a potential indicator of a lineage shift from conventional to gastric-type HCC. The E-cadherin/VSIG1 complex can help suppress tumor growth by limiting HCC dedifferentiation. The miRNET-based interaction between VSIG1/VIM/CDH1/CDH2 genes might be interconnected by miR-200b-3p, a central regulator of EMT which also targets VIM and VSIG1.
Keyphrases
- epithelial mesenchymal transition
- poor prognosis
- transcription factor
- binding protein
- end stage renal disease
- genome wide
- squamous cell carcinoma
- high grade
- cell surface
- newly diagnosed
- pulmonary hypertension
- signaling pathway
- machine learning
- transforming growth factor
- dna methylation
- induced apoptosis
- prognostic factors
- artificial intelligence
- cell proliferation
- cell cycle arrest
- electronic health record
- deep learning
- big data
- peritoneal dialysis
- human health
- cell death
- locally advanced
- patient reported
- genome wide analysis