Integrated Transcriptomics-Proteomics Analysis Identifies Molecular Phenotypic Alterations Associated with Colorectal Cancer.

Understanding the pathogenesis and finding diagnostic markers for colorectal cancer (CRC) are the key to its diagnosis and treatment. Integrated transcriptomics and proteomics analysis can be used to characterize alterations of molecular phenotypes and reveal the hidden pathogenesis of CRC. This study employed a novel strategy integrating transcriptomics and proteomics to identify pathological molecular pathways and diagnostic biomarkers of CRC. First, differentially expressed proteins and coexpressed genes generated from weighted gene coexpression network analysis (WGCNA) were intersected to obtain key genes of the CRC phenotype. In total, 63 key genes were identified, and pathway enrichment analysis showed that the process of coagulation and peptidase regulator activity could both play important roles in the development of CRC. Second, protein-protein interaction analysis was then conducted on these key genes to find the central genes involved in the metabolic pathways underpinning CRC. Finally, Itih3 and Lrg1 were further screened out as diagnostic biomarkers of CRC by applying statistical analysis on central genes combining transcriptomics and proteomics data. The deep involvement of central genes in tumorigenesis demonstrates the accuracy and reliability of this novel transcriptomics-proteomics integration strategy in biomarker discovery. The identified candidate biomarkers and enriched metabolic pathways provide insights for CRC diagnosis and treatment.

Integrative analysis of the mouse fecal microbiome and metabolome reveal dynamic phenotypes in the development of colorectal cancer.

The gut microbiome and its interaction with host have been implicated as the causes and regulators of colorectal cancer (CRC) pathogenesis. However, few studies comprehensively investigate the compositions of gut bacteria and their interactions with host at the early inflammatory and cancerous stages of CRC. In this study, mouse fecal samples collected at inflammation and CRC were subjected to microbiome and metabolome analyses. The datasets were analyzed individually and integratedly using various bioinformatics approaches. Great variations in gut microbiota abundance and composition were observed in inflammation and CRC. The abundances of Bacteroides, S24-7_group_unidifineted, and Allobaculum were significantly changed in inflammation and CRC. The abundances of Bacteroides and Allobaculum were significantly different between inflammation and CRC. Furthermore, strong excluding and appealing microbial interactions were found in the gut microbiota. CRC and inflammation presented specific fecal metabolome profiling. Fecal metabolomic analysis led to the identification and quantification of 1,138 metabolites with 32 metabolites significantly changed in CRC and inflammation. 1,17-Heptadecanediol and 24,25,26,27-Tetranor-23-oxo-hydroxyvitamin D3 were potential biomarkers for CRC. 3α,7ß,12α-Trihydroxy-6-oxo-5α-cholan-24-oic Acid and NNAL-N-glucuronide were potential biomarkers for inflammation. The significantly changed bacterial species and metabolites contribute to inflammation and CRC diagnosis. Integrated microbiome and metabolomic analysis correlated microbes with host metabolites, and the variated microbe-metabolite association in inflammation and CRC suggest that microbes facilitate tumorigenesis of CRC through interfering host metabolism.

Proteome-wide analysis of protein alterations in response to aristolochic acids in rat kidney and liver tissues.

Aristolochic acids (AAs), nephrotoxic components of herbs, have been previously demonstrated to cause DNA damage by forming DNA-AA adducts. However, the changes of tissue proteome profiles revealing AA toxicity need to be further studied. We conducted a proteomic study on the kidney and liver tissues of AA treated rats by a shotgun proteomics approach coupled with LC-MS/MS technology. A total of 1543 and 1641 proteins were identified and quantified in the kidneys and liver. Due to AA dosage, 10 and 4 proteins significantly changed in kidneys and the liver after multiple testing correction. Pathway enrichment analysis results were variant in kidneys and the liver. The enrichment analysis of metabolic pathways showed that gene expression and protein biosynthesis disorders were the common causes of AA toxicity to organs. Biological processes that positively responded to AAs in the liver probably have a detoxification function. SEC14-like protein 2 and synaptic vesicle membrane protein VAT-1 homolog were the mostly downregulated proteins in the liver and kidneys respectively.