KRAS mutation is the most frequent oncogenic aberration in colorectal cancer (CRC). The molecular mechanism and clinical implications of KRAS mutation in CRC remain unclear and show high heterogeneity within these tumors.

Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.

Objectives: Limited epidemiological study has examined the association between rotating shift work and risk of irritable bowel syndrome (IBS). This study aimed to investigate the association between shift work and risk of IBS and explore the potential mediating factors for the association.Methods: A total of 268,290 participants from the UK Biobank were included. Cox proportional hazards model was used to examine the associations between shift work and the incidence of IBS. The mediation analyses were performed to investigate the mediating effects.Results: Participants engaged in always/usually shift work showed a significantly increased risk of developing IBS (HR: 1.12, 95% CI: 1.03–1.23). Joint analysis indicated that, participants with both always/usually shift work and inadequate sleep duration had a 54% increased risk of IBS (HR: 1.54, 95% CI: 1.35–1.82) compared to those with adequate sleep duration and never/rarely shift work; while participants with both always/usually shift work and insomnia-always had a 65% increased risk of IBS (HR: 1.65, 95% CI: 1.43–1.90) compared to those with never/rarely shift work and never/sometimes insomnia. Mediation analysis revealed that sleep quality and anxiety/depression partially mediated the relationship between shift work and IBS incidence, contributing 16.1% (6.8–25.4%) and 3.6% (0.4–6.8%) of the mediation effect, respectively.Conclusion: This study found that participants with always/usually shiftwork status had significantly increased risk of IBS, and this association may partially be mediated by anxiety/depression and sleep quality. Moreover, inadequate sleep duration and usually insomnia may intensify the effect of rotating shift work on the risk of incident IBS.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive types of squamous cell carcinoma and represents a significant proportion of esophageal cancer. Metabolic reprogramming plays a key role in the occurrence and development of ESCC. Unsupervised clustering analysis was employed to stratify ESCC samples into three clusters: MPC1-lipid type, MPC2-amino acid type, and MPC3-energy type, based on the enrichment scores of metabolic pathways extracted from the Reactome database. The MPC3 cluster exhibited characteristics of energy metabolism, with heightened glycolysis, cofactors, and nucleotide metabolism, showing a trend toward increased aggressiveness and poorer survival rates. On the other hand, MPC1 and MPC2 primarily involved lipid and amino acid metabolism, respectively. In addition, liquid chromatography‒mass spectrometry-based metabolite profiles and potential therapeutic agents were explored and compared among ESCC cell lines with different MPCs. MPC3 amplified energy metabolism markers, especially carnitines. In contrast, MPC1 and MPC2 predominantly had elevated levels of lipids (primarily triacylglycerol) and amino acids, respectively. Furthermore, MPC3 demonstrated a suboptimal clinical response to PD-L1 immunotherapy but showed increased sensitivity to the doramapimod chemotherapy regimen, as evident from drug sensitivity evaluations. These insights pave the way for a more personalized therapeutic approach, potentially enhancing treatment precision for ESCC patients.

Currently, there is an absence of large-scale research focusing on the metabolome profiles of individuals prior to the development of sepsis. This study aimed to evaluate the associations of circulating Nuclear Magnetic Resonance (NMR) metabolic biomarkers with the risk of incident sepsis and the predictive ability of these metabolites for sepsis. The analysis utilized plasma metabolomic data measuring through NMR from the UK Biobank, which involved baseline plasma samples of 106,533 participants. The multivariable-adjusted Cox proportional hazard models were used to assess the associations of each circulating NMR metabolite biomarker with risk of incident sepsis. The full cohort was randomly assigned to a training set (n = 53,267) and a test set (n = 53,266) to develop and validate the sepsis risk prediction model. In training set, the least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses were used to develop the prediction model. In test set, the predictive ability of conventional risk factors-based and combined metabolic biomarkers prediction model was assessed by Harrell’s C-index. The incremental predictive power of the metabolic biomarkers was evaluated with continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). A total of 90 circulating metabolic biomarkers were significantly associated with risk of incident sepsis (all FDR adjusted P value < 0.05). Of these, triglycerides related lipid sub-classes, glycolysis, ketone bodies, and inflammation related metabolite biomarkers, creatinine, and phenylalanine were positively associated with risk of incident sepsis, while most of other lipid sub-classes, albumin, histidine, fatty acid and cholines related metabolic biomarkers were negatively associated with risk of sepsis. The Harrell’s C-index of the conventional prediction model was 0.733 (95% CI: 0.722, 0.745) for incident sepsis; after adding the circulating NMR metabolic biomarkers to the conventional prediction model, the Harrell’s C-index increased to 0.741 (95% CI: 0.730, 0.753) for incident sepsis. In addition, the continuous NRI and IDI were 0.022 (95% CI: 0.015, 0.043, P < 0.05) and 0.009 (95% CI: 0.006, 0.014, P < 0.05). This study identified multiple plasma metabolic biomarkers were associated with risk of incident sepsis. The addition of these metabolic biomarkers to the conventional risk factors-based model significantly improved the prediction precision.