Human SMARCC2 peptide

Background: Gastric cancer is still a common cancer worldwide. Investigation of potential plasma biomarkers for gastric cancer diagnosis is essential for prevention strategies and early intervention for gastric cancer-control planning. Objectives: This study was aimed to explore the lncRNAs' promoter of CDKN1A antisense DNA-damage-activated RNA (PANDAR), FOXD2-AS1, and SMARCC2 as potential novel diagnostic biomarkers for gastric cancer. Method: 109 gastric cancer patients and 106 healthy controls were involved in this study. Plasma lncRNAs PANDAR, FOXD2-AS1, and SMARCC2 were detected by real-time PCR. Student's t-test, Mann-Whitney U test, and Chi-square test were used to verify the differences of clinical variables between two groups. Receiver operating characteristic curve (ROC) was used to evaluate the diagnostic value of every biomarker. Multivariable analysis of risk factors for gastric cancer was performed using logistic regression analysis. Results: There were significant differences in age, gender, carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 153 between gastric cancer and healthy controls (P<0.05). Compared with healthy subjects, the levels of plasma lncRNAs PANDAR, FOXD2-AS1, and SMARCC2 were all significantly higher in gastric cancer patients (P<0.05). These lncRNAs were significantly associated with clinicopathological parameters of gastric cancer, like pathological differentiation, TNM stage, and/or lymph nodes metastasis, and/or invasion depth (P<0.05). The AUC for lncRNA PANDAR was 0.767, for FOXD2-AS1 was 0.700, for SMARCC2 was 0.748, and the AUC of the combinative diagnostic value of these three lncRNAs was 0.839. Adjusted by other variables, these lncRNAs' expressions were significantly associated with gastric cancer. Conclusions: Plasma lncRNAs PANDAR, FOXD2-AS1, and SMARCC2 might be appropriate diagnostic biomarkers for gastric cancer.

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.