Anti-ASGR2 monoclonal antibody, clone 3B23

Previously, we identified the regions of chromosomes 10q12-q31 and 15p16-q21 harbor quantitative trait loci (QTLs) for lumbar volumetric bone mineral density (vBMD) in female F2 rats derived from Fischer 344 (F344) x Lewis (LEW) and Copenhagen 2331 (COP) x Dark Agouti (DA) crosses. The purpose of this study is to identify the candidate genes within these QTL regions contributing to the variation in lumbar vBMD. RNA was extracted from bone tissue of F344, LEW, COP, and DA rats. Microarray analysis was performed using Affymetrix Rat Genome 230 2.0 Arrays. Genes differentially expressed among the rat strains were then ranked based on the strength of the correlation with lumbar vBMD in F2 animals derived from these rats. Quantitative PCR (qPCR) analysis was performed to confirm the prioritized candidate genes. A total of 285 genes were differentially expressed among all strains of rats with a false discovery rate less than 10%. Among these genes, 18 candidate genes were prioritized based on their strong correlation (r (2) > 0.90) with lumbar vBMD. Of these, 14 genes (Akap1, Asgr2, Esd, Fam101b, Irf1, Lcp1, Ltc4s, Mdp-1, Pdhb, Plxdc1, Rabep1, Rhot1, Slc2a4, Xpo4) were confirmed by qPCR. We identified several novel candidate genes influencing spinal vBMD in rats.

BACKGROUND: Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world and 5-year overall survival (OS) rate is less than 10%. So, it is urgent to identified novel diagnostic and prognostic biomarkers. METHODS: Twelve GEO (gene expression omnibus) datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between GC and normal tissues were screened and integrated using limma and RobustRankAggreg (RRA) packages in R software. Protein-protein interaction (PPI) network, GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses for DEGs were conducted via STRING and DAVID, respectively. Moreover, Cox regression model was used to construct a gene prognosis signature. RESULTS: Ten genes (COL1A1, CXCL8, COL3A1, SPP1, COL1A2, TIMP1, CXCL1, BGN, MMP3 and SERPINE1) were identified and might be highly related to GC. Further analysis showed high expression of CXCL8, COL3A1, CXCL1, MMP3 and SERPINE1, were significantly associated with late stage of GC. Lastly, we build a seven-gene prognosis signature (CYP19A1, SERPINE1, CGB5, CALCR, ASGR2, CYTL1 and ABCB5), which can give a good prediction of OS. CONCLUSIONS: Our article screened out key genes highly associating with GC's developments and prognosis, and it is useful for researcher to further understand GC's molecular basis and direct the synthesis medicine of GC.