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Bing-Zheng Zhong, Qiang Wang, Feng Liu, Jia-Li He, Yi Xiong, Jie Cao Department of Gastroenterology, Guangzhou First People’s Hospital, South China University of Technology School of Medicine, Guangzhou, Guangdong Province 510000, People’s Republic of ChinaCorrespondence: Jie CaoDepartment of Gastroenterology, Guangzhou First People’s Hospital, South China University of Technology School of Medicine, No. 1 Panfu Road, Yuexiu District, Guangzhou, Guangdong Province 510000, People’s Republic of ChinaEmail JieCao159@outlook.comObjective: To explore the related influencing mechanism of miR-384 and miR-134-5p acting on Yin Yang 1 (YY1) signaling transduction on the biological function of gastric cancer (GC) cells.Methods: miR-384, miR-134-5p and YY1 levels in human GC cell lines KATO III, MKN-45, SNU-1 and normal gastric cell line GES-1 were measured by polymerase chain reaction (PCR). Dual luciferase reporter (DLR) gene assay and Western blot (WB) were employed for correlation analysis between miR-384, miR-134-5p and YY1. miR-384-inhibitor, miR-384-mimics, empty plasmid (miRNA-NC) and sh-YY1 were transfected into KATO III cells. Cell proliferation was determined by 3-(4,5-Dimethylthiazolyl-2)-2,5-Diphenyl Tetrazolium Bromide (MTT), cell invasion was measured by Transwell, and apoptosis was analyzed by flow cytometry (FC).Results: In KATO III, MKN-45 and SNU-1 cell lines, YY1 was upregulated while miR-384 and miR-134-5p were downregulated (P< 0.001). The expression of miR-134-5p in the miR-134-5p-inhibitor group was significantly lower (P< 0.001), while that in the miR-134-5p-mimics group was significantly higher (P< 0.001). The expression of miR-384 in the miR-384-inhibitor group was significantly lower (P< 0.001), and that in the miR-384-mimics group was significantly higher as compared to the NC group (P< 0.001). Both miR-384 and miR-134-5p overexpression could inhibit cell proliferation and invasion, and promote apoptosis. As detected by WB, overexpressed miR-384 and miR-134-5p inhibited the expression of EMT-related molecular markers. Compared with sh-YY1, the number of cells in S phase decreased, the pro-apoptotic proteins boosted statistically, and the anti-apoptotic proteins declined notably after transfecting miR-134-5p-mimics/sh-YY1 or miR-384-mimics/sh-YY1 (P< 0.05). The tumor growth rate of nude mice in miR-134-5p/sh-YY1 and miR-384/sh-YY1 groups were significantly lower than those in sh-YY1 group (all P< 0.001).Conclusion: By targeting YY1 signaling transduction, miR-134-5p and miR-384 can alter the growth and apoptosis of GC cells, which are promising targets for new therapeutics of GC.Keywords: miR-384, miR-134-5p, YY1, gastric cancer cells, proliferation

Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior probability of protein identification and integrated to re-score in PSM using the binomial probability distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.