Anti-ZG16B (full length) polyclonal antibody

Liver metastasis in colorectal cancer is the major cause of cancer-related deaths. To identify and characterize proteins associated with colon cancer metastasis, we have compared the conditioned serum-free medium of highly metastatic KM12SM colorectal cancer cells with the parental, poorly metastatic KM12C cells using quantitative stable isotope labeling by amino acids in cell culture (SILAC) analyses on a linear ion trap-Orbitrap Velos mass spectrometer. In total, 1337 proteins were simultaneously identified in SILAC forward and reverse experiments. For quantification, 1098 proteins were selected in both experiments, with 155 proteins showing >1.5-fold change. About 52% of these proteins were secreted directly or using alternative secretion pathways. GDF15, S100A8/A9, and SERPINI1 showed capacity to discriminate cancer serum samples from healthy controls using ELISAs. In silico analyses of deregulated proteins in the secretome of metastatic cells showed a major abundance of proteins involved in cell adhesion, migration, and invasion. To characterize the tumorigenic and metastatic properties of some top up- and down-regulated proteins, we used siRNA silencing and antibody blocking. Knockdown expression of NEO1, SERPINI1, and PODXL showed a significant effect on cellular adhesion. Silencing or blocking experiments with SOSTDC1, CTSS, EFNA3, CD137L/TNFSF9, ZG16B, and Midkine caused a significant decrease in migration and invasion of highly metastatic cells. In addition, silencing of SOSTDC1, EFNA3, and CD137L/TNFSF9 reduced liver colonization capacity of KM12SM cells. Finally, the panel of six proteins involved in invasion showed association with poor prognosis and overall survival after dataset analysis of gene alterations. In summary, we have defined a collection of proteins that are relevant for understanding the mechanisms underlying adhesion, migration, invasion, and metastasis in colorectal cancer.

Purpose: In order to identify the molecular characteristics and improve the efficacy of early diagnosis of mucinous epithelial ovarian cancer (mEOC), here, the transcriptome profiling by weighted gene co-expression network analysis (WGCNA) has been proposed as an effective method. Methods: The gene expression dataset GSE26193 was reanalyzed with a systematical approach, WGCNA. mEOC-related gene co-expression modules were detected and the functional enrichments of these modules were performed at GO and KEGG terms. Ten hub genes in the mEOC-related modules were validated using two independent datasets GSE44104 and GSE30274. Results: 11 co-expressed gene modules were identified by WGCNA based on 4917 genes and 99 epithelial ovarian cancer samples. The turquoise module was found to be significantly associated with the subtype of mEOC. KEGG pathway enrichment analysis showed genes in the turquoise module significantly enriched in metabolism of xenobiotics by cytochrome P450 and steroid hormone biosynthesis. Ten hub genes (LIPH, BCAS1, FUT3, ZG16B, PTPRH, SLC4A4, MUC13, TFF1, HNF4G and TFF2) in the turquoise module were validated to be highly expressed in mEOC using two independent gene expression datasets GSE44104 and GSE30274. Conclusion: Our work proposed an applicable framework of molecular characteristics for patients with mEOC, which may help us to obtain a precise and comprehensive understanding on the molecular complexities of mEOC. The hub genes identified in our study, as potential specific biomarkers of mEOC, may be applied in the early diagnosis of mEOC in the future.