Ppic modulates CCl4-induced liver fibrosis and TGF-ß-caused mouse hepatic stellate cell activation and regulated by miR-137-3p.

Hepatic stellate cell activation, characterized by hyperproliferation and increased release of collagens, is a critical event during the initiation and development of hepatic fibrosis. The deregulated genes among different expression profiles based on online datasets were analyzed, attempting to identify novel potential biomarkers and treatment targets for hepatic fibrosis. The abnormal upregulation of mouse peptidylprolyl isomerase C (Ppic) within the CCl4-caused hepatic fibrosis model in mice was identified according to bioinformatics and experimental analyses. The knockdown of Ppic in the CCl4-caused liver fibrosis murine model significantly improved CCl4-caused liver damage, decreased the fibrotic area, reduced ECM deposition, and reduced the hydroxyproline levels. The knockdown of Ppic in TGF-ß-stimulated mouse hepatic stellate cells inhibited cell proliferation and decreased ECM levels. Through direct targeting, miR-137-3p negatively regulated Ppic expression. Contrastingly to Ppic knockdown, miR-137-3p inhibition further promoted cell proliferation and boosted ECM levels; the effects of miR-137-3p inhibition could be partially reversed by Ppic knockdown. Altogether, mmu-miR-137-3p directly targets Ppic and forms a regulatory axis with Ppic, modulating CCl4-caused hepatic fibrosis in mice and TGF-ß-caused mouse hepatic stellate cell activation.

PPIC, EMP3 and CHI3L1 Are Novel Prognostic Markers for High Grade Glioma.

Current treatment methods for patients diagnosed with gliomas have shown limited success. This is partly due to the lack of prognostic genes available to accurately predict disease outcomes. The aim of this study was to investigate novel prognostic genes based on the molecular profile of tumor samples and their correlation with clinical parameters. In the current study, microarray data (GSE4412 and GSE7696) downloaded from Gene Expression Omnibus were used to identify differentially expressed prognostic genes (DEPGs) by significant analysis of microarray (SAM) between long-term survivors (>2 years) and short-term survivors (≤2 years). DEPGs generated from these two datasets were intersected to obtain a list of common DEPGs. The expression of a subset of common DEPGs was then independently validated by real-time reverse transcription quantitative PCR (qPCR). Survival value of the common DEPGs was validated using known survival data from the GSE4412 and TCGA dataset. After intersecting DEPGs generated from the above two datasets, three genes were identified which may potentially be used to determine glioma patient prognosis. Independent validation with glioma patients tissue (n = 70) and normal brain tissue (n = 19) found PPIC, EMP3 and CHI3L1 were up-regulated in glioma tissue. Survival value validation showed that the three genes correlated with patient survival by Kaplan-Meir analysis, including grades, age and therapy.