Background
Epithelial ovarian carcinoma (EOC) is a malignant tumor with high motility in women. Our previous study found that dysregulated nucleoside-triphosphatase cancer-related (NTPCR) was associated with the prognosis of EOC patients, and thus, this present study attempted to explore the potential roles of NTPCR in disease progression.
Conclusion
NTPCR might serve as a tumor suppressor in EOC progression. Our results demonstrated that DEGs and differential metabolites were mainly related to several signaling pathways, which might be a crucial role in the progression of NTPCR regulation of EOC.
Methods
Expressed level of NTPCR was investigated in EOC tissues by RT-qPCR and Western blot analysis. NTPCR shRNA and overexpression vector were generated and transfected into OVCAR-3 or SKOV3 cells to detect the effect of NTPCR on cell proliferation, cell cycle, cell migration, and invasion. Transcriptomic sequencing and metabolite profiling analysis were performed in shNTPCR groups to identify transcriptome or metabolite alteration that might contribute to EOC. Finally, we searched the overlapped signaling pathways correlated with differential metabolites and differentially expressed genes (DEGs) by integrating analysis.
Results
Comparing para-cancerous tissues, we found that NTPCR is highly expressed in cancer tissues (p < 0.05). Overexpression of NTPCR inhibited cell proliferation, migration, and invasion and reduced the proportion of S- and G2/M-phase cells, while downregulation of NTPCR showed the opposite results. RNA sequencing analysis demonstrated cohorts of DEGs were identified in shNTPCR samples. Protein-protein interaction networks were constructed for DEGs. STAT1 (degree = 43) and OAS2 (degree = 36) were identified as hub genes in the network. Several miRNAs together with target genes were predicted to be crucial genes related to disease progression, including hsa-miR-124-3p, hsa-miR-30a-5p, hsa-miR-146a-5, EP300, GATA2, and STAT3. We also screened the differential metabolites from shNTPCR samples, including 22 upregulated and 22 downregulated metabolites. By integrating transcriptomics and metabolomics analysis, eight overlapped pathways were correlated with these DEGs and differential metabolites, such as primary bile acid biosynthesis, protein digestion, and absorption, pentose, and glucuronate interconversions.