Project description:We aimed to identify the differences between these two subtypes of lung cancers and their differentially clinical treatment. Through functional analysis, we obtained differentially expressed proteins which might be assossciated with lung cancer treatment.
Project description:Objectivethis study aimed to identify the relationships between gut microbiota, metabolism, and non-small cell lung cancer (NSCLC) treatment outcomes, which are presently unclear.Methodsin this single-center prospective cohort study, we investigated changes in the gut microbiota and serum metabolite profile in 60 patients with NSCLC after four cycles of anticancer therapy.ResultsThe microbial landscape of the gut exhibited a surge in Proteobacteria and Verrucomicrobiota populations, alongside a decline in Firmicutes, Actinobacteriota, and Bacteroidota. Furthermore, a significant shift in the prevalence of certain bacterial genera was noted, with an increase in Escherichia/Shigella and Klebsiella, contrasted by a reduction in Bifidobacterium. Metabolomic analysis uncovered significant changes in lipid abundances, with certain metabolic pathways markedly altered post-treatment. Correlation assessments identified strong links between certain gut microbial genera and serum metabolite concentrations. Despite these findings, a subgroup analysis delineating patient responses to therapy revealed no significant shifts in the gut microbiome's composition after four cycles of treatment.ConclusionsThis study emphasizes the critical role of gut microbiota changes in NSCLC patients during anticancer treatment. These insights pave the way for managing treatment complications and inform future research to improve patient care by understanding and addressing these microbiota changes.
Project description:Reliable identification of cancer markers can have substantial implications to early detection of cancer. We report here an integrated computational and experimental study on identification of gastric cancer markers in patients’ tissue and sera based on (i) genome-scale transcriptomic analyses on 80 paired gastric cancer/reference tissues, with the aim of identifying abnormally expressed genes at various subtypes/stages of gastric carcinoma (ii) a computational identification of differentially expressed genes that may have their proteins secreted into blood circulation, followed by experimental validations.
Project description:In depth label free quantitative mass spectrometry based proteomics for identification of potential biomarkers of drug resistance in lung cancer.