Identification and validation of the clinical prediction model and biomarkers based on chromatin regulators in colon cancer by integrated analysis of bulk- and single-cell RNA sequencing data.

Background: Chromatin regulators (CRs) are implicated in the development of cancer, but a comprehensive investigation of their role in colon adenocarcinoma (COAD) is inadequate. The purpose of this study is to find CRs that can provide recommendations for clinical diagnosis and treatment, and to explore the reasons why they serve as critical CRs. Methods: We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted Gene Co-Expression Network Analysis (WGCNA) screened tumor-associated CRs. LASSO-Cox regression was used to construct the model and to screen key CRs together with support vector machine (SVM), the univariate Cox regression. We used single-cell data to explore the expression of CRs in cells and their communication. Immune infiltration, immune checkpoints, mutation, methylation, and drug sensitivity analyses were performed. Gene expression was verified by quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Pan-cancer analysis was used to explore the importance of hub CRs. Results: We finally obtained 32 tumor-associated CRs. The prognostic model was constructed based on RCOR2, PPARGC1A, PKM, RAC3, PHF19, MYBBP1A, ORC1, and EYA2 by the LASSO-Cox regression. Single-cell data revealed that the model was immune-related. Combined with immune infiltration analysis, immune checkpoint analysis, and tumor immune dysfunction and exclusion (TIDE) analysis, the low-score risk group had more immune cell infiltration and better immune response. Mutation and methylation analysis showed that multiple CRs may be mutated and methylated in colon cancer. Drug sensitivity analysis revealed that the low-risk group may be more sensitive to several drugs and PKM was associated with multiple drugs. Combined with machine learning, PKM is perhaps the most critical gene in CRs. Pan-cancer analysis showed that PKM plays a role in the prognosis of cancers. Conclusions: We developed a prognostic model for COAD based on CRs. Increased expression of the core gene PKM is linked with a poor prognosis in several malignancies.

The Association Between Gut Microbiota, Toll-Like Receptors, and Colorectal Cancer.

The large number of microbes found in the gut are involved in various critical biological processes in the human body and have dynamic and complex interactions with the immune system. Disruptions in the host's gut microbiota and the metabolites produced during fermentation promote the development of intestinal inflammation and colorectal cancer (CRC). Toll-like receptors (TLRs) recognize specific microbial-associated molecular patterns specific to microorganisms whose signaling is involved in maintaining intestinal homeostasis or, under certain conditions, mediating dysbiosis-associated intestinal inflammation. The signaling pathways of TLRs are described first, followed by a discussion of the interrelationship between gut microbes and TLRs, including the activation of TLRs by gut microbes and the effect of TLRs on the distribution of gut microbiota, particularly the role of microbes in colorectal carcinogenesis via TLRs. Finally, we discuss the potential roles of various TLRs in colorectal cancer.

Mapping Lymph Node during Indocyanine Green Fluorescence-Imaging Guided Gastric Oncologic Surgery: Current Applications and Future Directions.

Huge strides have been made in the navigation of gastric cancer surgery thanks to the improvement of intraoperative techniques. For now, the use of indocyanine green (ICG) enhanced fluorescence imaging has received promising results in detecting sentinel lymph nodes (SLNs) and tracing lymphatic drainages, which make it applicable for limited and precise lymphadenectomy. Nevertheless, issues of the lack of specificity and unpredictable false-negative lymph nodes were encountered in gastric oncologic surgery practice using ICG-enhanced fluorescence imaging (ICG-FI), which restrict its application. Here, we reviewed the current application of ICG-FI and assessed potential approaches to improving ICG-FI.

Effects of linaclotide in the treatment of chronic constipation and irritable bowel syndrome with constipation: a meta-analysis.

OBJECTIVE: Linaclotide is a guanylate cyclase-C (GCC) agonist that is found in intestinal epithelial cells and is used when treating chronic constipation (CC) and irritable bowel syndrome with constipation (IBS-C). Several randomized controlled trials (RCTs) were conducted for evaluating its efficacy and safety. METHODS: The PubMed, EMBASE, and Cochrane databases and the Web of Science were searched to find multiple RCTs of patients with CC or IBS-C. The Jadad scoring system was used for evaluating each study's methodological quality, and RevMan5.3 was used for meta-analysis. The composite endpoint reaction approved by the FDA, abdominal pain and discomfort relief, symptom improvement, and diarrhea-related adverse reactions were chosen as observation indicators, and relative risk (RR) and 95% confidence interval (CI) were obtained for quantitative and comprehensive evaluation. RESULTS: Eleven randomized controlled studies were included, consisting of 5 cases of CC and 6 cases of IBS-C. Linaclotide reached the composite endpoint response approved by FDA in the treatment of CC (RR = 3.26, 95% CI: 2.45-4.33), and the composite endpoint response approved by FDA for the treatment of IBS-C (RR = 2.26, 95% CI: 1.86-2.74) was greater than the placebo (both p < 0.00001). The main adverse reactions of linaclotide were gastrointestinal, mostly diarrhea, which was higher than that of the placebo when treating CC (RR = 3.56, 95% CI: 2.76-4.60) and IBS-C (RR = 8.23, 95% CI: 5.69-11.90) (both p < 0.00001). CONCLUSION: Linaclotide proved to be effective and safe for the treatment of CC and IBS-C compared to the placebo. However, diarrhea is the primary adverse reaction.

The roles of microbial products in the development of colorectal cancer: a review.

A large number of microbes exist in the gut and they have the ability to process and utilize ingested food. It has been reported that their products are involved in colorectal cancer development. The molecular mechanisms which underlie the relationship between gut microbial products and CRC are still not fully understood. The role of some microbial products in CRC is particularly controversial. Elucidating the effects of gut microbiota products on CRC and their possible mechanisms is vital for CRC prevention and treatment. In this review, recent studies are examined in order to describe the contribution metabolites and toxicants which are produced by gut microbes make to CRC, primarily focusing on the involved molecular mechanisms.Abbreviations: CRC: colorectal cancer; SCFAs: short chain fatty acids; HDAC: histone deacetylase; TCA cycle: tricarboxylic acid cycle; CoA: cytosolic acyl coenzyme A; SCAD: short chain acyl CoA dehydrogenase; HDAC: histone deacetylase; MiR-92a: microRNA-92a; KLF4: kruppel-like factor; PTEN: phosphatase and tensin homolog; PI3K: phosphoinositide 3-kinase; PIP2: phosphatidylinositol 4, 5-biphosphate; PIP3: phosphatidylinositol-3,4,5-triphosphate; Akt1: protein kinase B subtype α; ERK1/2: extracellular signal-regulated kinases 1/2; EMT: epithelial-to-mesenchymal transition; NEDD9: neural precursor cell expressed developmentally down-regulated9; CAS: Crk-associated substrate; JNK: c-Jun N-terminal kinase; PRMT1: protein arginine methyltransferase 1; UDCA: ursodeoxycholic acid; BA: bile acids; CA: cholic acid; CDCA: chenodeoxycholic acid; DCA: deoxycholic acid; LCA: lithocholic acid; CSCs: cancer stem cells; MHC: major histocompatibility; NF-κB: NF-kappaB; GPR: G protein-coupled receptors; ROS: reactive oxygen species; RNS: reactive nitrogen substances; BER: base excision repair; DNA: deoxyribonucleic acid; EGFR: epidermal growth factor receptor; MAPK: mitogen activated protein kinase; ERKs: extracellular signal regulated kinases; AKT: protein kinase B; PA: phosphatidic acid; TMAO: trimethylamine n-oxide; TMA: trimethylamine; FMO3: flavin-containing monooxygenase 3; H2S: Hydrogen sulfide; SRB: sulfate-reducing bacteria; IBDs: inflammatory bowel diseases; NSAID: non-steroidal anti-inflammatory drugs; BFT: fragile bacteroides toxin; ETBF: enterotoxigenic fragile bacteroides; E-cadherin: extracellular domain of intercellular adhesive protein; CEC: colonic epithelial cells; SMOX: spermine oxidase; SMO: smoothened; Stat3: signal transducer and activator of transcription 3; Th17: T helper cell 17; IL17: interleukin 17; AA: amino acid; TCF: transcription factor; CDT: cytolethal distending toxin; PD-L1: programmed cell death 1 ligand 1.