Anti-MRPL35 polyclonal antibody

Mitochondrial ribosome proteins (MRPs), which are encoded by the nuclear genomic DNA, are important for mitochondrial-encoded protein synthesis and mitochondrial function. Emerging evidence suggests that several MRPs also exhibit important extra-mitochondrial. functions, such as involvement in apoptosis, protein biosynthesis, and signal transduction. In this study, we demonstrate a significant role of MRP L35 (MRPL35) in colorectal cancer (CRC). The expression of MRPL35 was higher in CRC tissues than in matched cancer-adjacent tissues and higher in CRC cells than in normal mucosal epithelial cells. Higher MRPL35 expression in CRC tissue correlated with shorter overall survival for CRC patients. In vitro, down-regulation of MRPL35 led to increased production of reactive oxygen species (ROS) together with DNA damage, loss of cell proliferation, G(2)/M arrest, a decrease in mitochondrial membrane potential, apoptosis, and autophagy induction. MRPL35 knockdown inhibited tumor proliferation in a CRC xenograft nude mouse model. Furthermore, overexpression of MRPL35 or treatment of cells with the ROS scavenger, N-acetyl cysteine, abrogated ROS production, cell cycle arrest, and apoptosis in vitro. These findings suggest that MRPL35 plays an essential role in the development of CRC and may be a potential therapeutic target for CRC.

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test usingLimma. Then we applied a deep learning method, "nnet" to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) <0.001. After performing deep learning analysis, we obtained average classification accuracy 90.69% (+/- 1.97%) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis usingCytoscape. We reported five top in-degree genes (PAIP2, GRWD1, VPS4B, CRADD and LLPH) and five top out-degree genes (MRPL35, FAM177A1, STAT4, ASPSCR1 and FABP7). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using toolWebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.