Chemotherapy induced diarrhea (CID) is a common side effect in patients receiving chemotherapy for cancer. The aim of our study was to explore the association between gut microorganisms and CID from the CapeOX regimen in resected stage III colorectal cancer (CRC) patients. After screening and identification, 17 stool samples were collected from resected stage III CRC patients undergoing the CapeOX regimen. Bacterial 16S ribosomal RNA genes was sequenced, and a bioinformatics analysis was executed to screen for the distinctive gut microbiome and the functional metabolism associated with CID due to the CapeOX regimen. The gut microbial community richness and community diversity were lower in CID (p < 0.05 vs control group). Klebsiella pneumoniae was the most predominant species (31.22%) among the gut microbiome in CRC patients with CID. There were 75 microorganisms with statistically significant differences at the species level between the CRC patients with and without CID (LDA, linear discriminant analysis score > 2), and there were 23 pathways that the differential microorganisms might be involved in. The gut microbial community structure and diversity have changed in CRC patients with CID. It may provide novel insights into the prevention and treatment of CID.

Metabolic (dysfunction)-associated steatohepatitis (MASH) is the advanced stage of metabolic (dysfunction)-associated fatty liver disease (MAFLD) lacking approved clinical drugs. Adenosine A1 receptor (A₁R), belonging to the G-protein-coupled receptors (GPCRs) superfamily, is mainly distributed in the central nervous system and major peripheral organs with wide-ranging physiological functions; however, the exact role of hepatic A₁R in MAFLD remains unclear. Here, we report that liver-specific depletion of A₁R aggravates while overexpression attenuates diet-induced metabolic-associated fatty liver (MAFL)/MASH in mice. Mechanistically, activation of hepatic A₁R promotes the competitive binding of sterol-regulatory element binding protein (SREBP) cleavage-activating protein (SCAP) to sequestosome 1 (SQSTM1), rather than protein kinase A (PKA) leading to SCAP degradation in lysosomes. Reduced SCAP hinders SREBP1c/2 maturation and thus suppresses de novo lipogenesis and inflammation. Higher hepatic A₁R expression is observed in patients with MAFL/MASH and high-fat diet (HFD)-fed mice, which is supposed to be a physiologically adaptive response because A₁R agonists attenuate MAFL/MASH in an A₁R-dependent manner. These results highlight that hepatic A₁R is a potential target for MAFL/MASH therapy.

With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi) of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA). Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation.