No abstract

The transformation of tumor cells from an epithelial to a mesenchymal-like phenotype, designated as epithelial-to-mesenchymal transition (EMT), represents a key hallmark of human cancer metastasis, including gastric cancer (GC). However, a large set of non-coding RNAs have been studied for their functions that initiate or inhibit this phenotypic switch in GC cells by regulating oncogenes or tumor suppressors. In this paper, we aimed to identify lncRNA SND1-IT1, miR-124, and COL4A1 gene in the context of GC with a specific focus on their effects on transforming growth factor β1 (TGF-β1)-induced EMT. The study included 52 paired samples of lesion tissues and adjacent lesion-free tissues surgically resected from patients diagnosed with GC. HGC-27 cells were stimulated with exogenous TGF-β1 (2 ng/mL). Expression of lncRNA SND1-IT1, miR-124, and COL4A1 was determined by RT-qPCR. CCK-8 assays, Transwell assays, immunoblotting analysis of EMT-specific markers, and tumor invasion markers were performed to evaluate cell viability, migration, and invasion of cultured HGC-27 cells. Luciferase activity assay was employed to examine miR-124 binding with lncRNA SND1-IT1 and COL4A1, respectively. LncRNA SND1-IT1 was upregulated in GC tissues and cells. TGF-β1-stimulated EMT and regulated lncRNA SND1-IT1, miR-124, and COL4A1 expressions in HGC-27 cells. LncRNA SND1-IT1 knockdown tempered HGC-27 cell viability, migration and invasion. LncRNA SND1-IT1 participated in TGF-β1-stimulated EMT in GC by sponging miR-124. MiR-124 attenuated TGF-β1-stimulated EMT in GC by targeting COL4A1. These results primarily demonstrated TGF-β1 can regulate cancer cell migration, invasion and stimulate EMT through the SND1-IT1/miR-124/COL4A1 axis in GC.

Objectives. Prolyl 4-hydroxylase subunit alpha 1 (P4HA1) plays a crucial role in modulating extracellular matrix component and promoting tumor progression by changing tumor adhesion, migration, and other biological behaviors in some cancers. However, its expression pattern, biological function, and underlying mechanism in pancreatic cancer remain largely unclear. Materials and Methods. In this study, a set of bioinformatics tools were used to analyze the expression of P4HA1 and its prognostic value in pancreatic cancer. In addition, the mechanism through which P4HA1 promotes the progression of pancreatic cancer was explored by constructing a competing endogenous RNA (ceRNA) regulatory axis. Results. It was found that the mRNA and protein expression of P4HA1 was significantly higher in pancreatic cancer tissues than in normal tissues. Its high P4HA1 expression correlated with poor clinicopathological features (T stage: P = 0.0078 ; N stage: P = 0.0124 ; TNM stage: P = 0.0013 ; pathological grade: P = 0.0108 ) and poor prognosis [OS: HR = 1 , 95% CI (1-1.01), P = 0.00028 ; DSS: HR = 1 , 95% CI (1-1.01), P = 0.00049 ; PFI: HR = 1.01 , 95% CI (1.01-1.02), P = 0.0057 ; and DFI: HR = 1 , 95% CI (1-1.01), P = 0.0034 ]. The LINC01503/miR-335-5p/P4HA1 axis might mediate the effects of P4HA1 in promoting the progression on pancreatic cancer. Conclusions. Collectively, our findings suggest that high expression of P4HA1 may be used as a promising prognostic biomarker and could be considered for the development of a novel therapeutic strategy for pancreatic cancer in the future.

Background Zeste White 10 interactor (ZW10 interactor, ZWINT) is a centromeric complex required for a mitotic spindle checkpoint. According to previous studies, it was overexpressed in people with recurrent tumors. However, the expression of ZWINT in breast cancer has not been thoroughly studied. In addition, the correlations of ZWINT to prognosis in breast cancer remain unclear. Methods In this study, the expression of ZWINT in different types of tumors was analyzed based on the Oncomine database, and the effect of ZWINT expression on clinical prognosis was evaluated by Kaplan-Meier plotter. Results In breast cancer, lung cancer, sarcoma, ovarian cancer, bladder cancer, liver cancer and cervical cancer, the expression of ZWINT was higher than that in normal tissues, but in gastric cancer, prostate cancer, myeloma, renal cancer and pancreatic cancer, the expression of ZWINT was lower. In addition, a meta-analysis of 22 cancer database studies found that the ZWINT gene was over-expressed in breast cancer tissues compared with normal tissues (P=4.05×10 −6 ). Through the survival analysis of Kaplan-Meier plotter, it is found that the high expression of ZWINT is related to the worse overall survival (OS) [hazard ratio (HR) =1.73, 95% confidence interval (CI): 1.39–2.51, P=5.4×10 −7 ], RFS (HR =1.68, 95% CI: 1.51–1.88, P<1×10 −16 ) and distant metastasis-free survival (DMFS) (HR =1.55, 95% CI: 1.28–1.89, P=7.9×10 −6 ) in all BC patients. Conclusions Our results strongly suggest that over expression of ZWINT is closely related to poor prognosis of breast cancer. ZWINT may be a prognostic biomarker for the treatment of BC.

Background: We aimed to identify the key differentially expressed genes (DEGs) associated with poor prognosis in gastric cancer (GC) and to elucidate the underlying molecular mechanisms in order to provide a therapeutic target for this disease.Methods: The DEGs common in two datasets, GSE54129 and GSE79973, were screened. GO and KEGG enrichment analyses were then performed for these DEGs using DAVID's tool. STRING and the Cytoscope software were also used to analyze the protein-protein interaction (PPI) networks of the DEGs common between the two datasets.Results: A total of 164 common DEGs were identified from GSE79973 and GSE54129 datasets, 42 were up-regulated and 122 were down-regulated in GC. KEGG analysis demonstrated that up-regulated DEGs were mainly enriched for focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathway, protein digestion and absorption, and vascular smooth muscle contraction, while down-regulated DEGs were enriched for chemical carcinogenesis, metabolism of xenobiotics by cytochrome P450, drug metabolismcytochrome P450, and retinol metabolism (P<0.05). Obtained PPI network for the 164 DEGs via Cytotype software, using MCODE app of Cytotype software we identified 13 hub genes. Twelve of these genes were found to be associated with poor prognosis in GC by survival analysis. Post validation by the GEPIA, Oncomine, and Human Protein Atlas databases, eight genes (COL4A1,