Unraveling the gut microbiota and short-chain fatty acids characteristics and associations in a cancer cachexia mouse model.

OBJECTIVE: Cachexia is a common pathological condition in cancer patients, affecting prognosis and treatment outcomes. The relationship between cachexia and gut microbiota and short-chain fatty acids (SCFAs) remains understudied. This research aimed to establish a cachexia mouse model and explore the gut microbiota-SCFAs connection. The study provides fundamental insights into the regulatory mechanisms of cancer cachexia and potential therapeutic strategies. METHODS: A cachexia mouse model was created using C26 cells, with relevant indicators measured. Histological and immunohistochemical analyses assessed muscle structure and protein expression. ELISA was performed to detect the levels of IL-1ß, IL-6, TNF-α, and LPS in serum to evaluate inflammation.16S rDNA sequencing and GC-MS quantified gut microbiota and SCFAs. Bioinformatics analysis identified indicator species and explored microbiota-SCFAs correlations.ROC analysis was performed to assess the potential of gut microbiota and SCFAs in identifying cachexia. RESULTS: The cachexia mouse model exhibited weight loss, muscle atrophy, and elevated inflammatory factors. Gut microbiota in cachexia mice showed decreased diversity and imbalance. Fourteen bacterial genera were identified as potential cachexia indicators. Functional prediction indicated alterations in the functional composition of gut microbial communities in cachexia mice, particularly in carbohydrate and lipid metabolism pathways. Four SCFAs showed significant changes, potentially serving as diagnostic factors. Specific microbial taxa were positively or negatively correlated with changes in SCFAs, and these microbial taxa and differential SCFAs were also correlated with inflammatory cytokines. CONCLUSION: Our study uncovers the gut microbiota and SCFAs features in a cachexia mouse model, revealing novel correlations between them. These newfound insights into the interplay between cachexia, gut microbiota, and SCFAs provide a crucial foundation for understanding the mechanisms behind cancer cachexia development and potential therapeutic approaches.

Construction of a predictive model for clinical survival in male patients with non-metastatic rectal adenocarcinoma.

BACKGROUND: No clinical prediction model is available for non-metastatic rectal adenocarcinoma in males. Based on demographic and clinicopathological characteristics, we constructed a survival prediction model for the study population. METHODS: At a ratio of 7:3, 3450 eligible patients were divided into training and validation sets. Optimal cutoff values were calculated using X-tile software. Cox proportional hazards regression was used to find prognostic factors for cancer-specific survival (CSS) and overall survival (OS). Corresponding nomogram prognostic models were also constructed based on predictors.The validity, discriminative ability, predictability, and clinical usefulness of the model were analyzed and assessed. RESULTS: We identified predictors of survival in the target population and successfully constructed nomograms. In the nomogram prediction model for OS and CSS, the C-index was 0.724 and 0.735, respectively, for the training group and 0.754 and 0.760, respectively, for the validation group. In the validation group, the area under the curve (AUC) of the receiver operating characteristic curve for OS and CSS nomograms was 0.768 and 0.769, respectively, for the 3-year survival rate and 0.755 and 0.747, respectively, for the 5-year survival rate. Kaplan-Meier Survival Curves showed excellent risk discrimination performance of the nomogram (P < 0.05) Calibration curves, time-dependent AUC and decision curve analysis showed that the prediction model constructed in this study had excellent clinical prediction and decision ability and performed better than the TNM staging system. CONCLUSION: Our nomogram is helpful to evaluate the prognosis of non-metastatic male patients with rectal adenocarcinoma and has guiding significance for clinical treatment.

The metabolite butyrate produced by gut microbiota inhibits cachexia-associated skeletal muscle atrophy by regulating intestinal barrier function and macrophage polarization.

OBJECTIVE: Cachexia, marked by muscle atrophy, poses substantial challenges for prevention and treatment. This study delves into the unclear role of butyrate, a gut microbiota metabolite, in cachexia by examining gut microbiota and short-chain fatty acid (SCFA) profiles in human and mouse fecal samples. METHODS: We analyzed cachexia-associated gut microbiota and SCFA profiles using 16S rRNA sequencing and metabolomic techniques. Mouse cachexia models were developed with C26 cells, and LPS was used to induce muscle cell atrophy in C2C12 cells. We evaluated butyrate's in vivo effects on intestinal health, muscle preservation, inflammation, and macrophage activity. In vitro studies focused on butyrate's influence on macrophage polarization and the subsequent effects on muscle cells. RESULTS: Both cachexia patients and mice exhibited gut microbiota imbalances, irregular butyrate concentrations, and a decline in butyrate-producing bacteria. In vivo tests showed that butyrate counteract cachexia-induced muscle atrophy by adjusting the Akt/mTOR/Foxo3a and Fbox32/Trim63 pathways. These butyrate also bolstered intestinal barrier integrity, minimized endotoxin migration, and mitigated oxidative stress. Furthermore, butyrate curtailed inflammation and macrophage penetration in muscles. In vitro experimental results demonstrate that butyrate inhibit macrophage polarization towards the M1 phenotype and promote polarization towards the M2 phenotype. Both M1 and M2 macrophages influence the aforementioned pathways and oxidative stress, participating in the regulation of muscle cell atrophy. CONCLUSION: Our study delineates the intricate interplay between gut microbiota dysbiosis, butyrate fluctuations, and cachexia progression. Butyrate not only reinforces the intestinal barrier but also orchestrates macrophage polarization, mitigating muscle atrophy and averting cachexia-induced muscle deterioration. Concurrently, the M1 and M2 macrophages play pivotal roles in modulating skeletal muscle cell atrophy. This highlights the potential of utilizing the gut-derived metabolite butyrate as a promising therapeutic approach for addressing cachexia-related issues.

Bioinformatic analysis of potential hub genes in gastric adenocarcinoma.

Gastric adenocarcinoma is the most common histologic type of gastric cancer; however, the pathogenic mechanisms remain unclear. To improve mechanistic understanding and identify new treatment targets or diagnostic biomarkers, we used bioinformatic tools to predict the hub genes related to the process of gastric adenocarcinoma development from public datasets, and explored their prognostic significance. We screened differentially expressed genes between gastric adenocarcinoma and normal gastric tissues in Gene Expression Omnibus datasets (GSE79973, GSE118916, and GSE29998) using the GEO2R tool, and their functions were annotated with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses in the DAVID database. Hub genes were identified based on the protein-protein network constructed in the STRING database with Cytoscape software. A total of 10 hub genes were selected for further analysis, and their expression patterns in gastric adenocarcinoma patients were investigated using the Oncomine GEPIA database. The expression levels of ATP4A, CA9, FGA, ALDH1A1, and GHRL were reduced, whereas those of TIMP1, SPP1, CXCL8, THY1, and COL1A1 were increased in gastric adenocarcinoma. The Kaplan-Meier online plotter tool showed associations of all hub genes except for CA9 with prognosis in gastric adenocarcinoma patients; CXCL8 and ALDH1A1 were positively correlated with survival, and the other genes were negatively correlated with survival. These 10 hub genes may be involved in important processes in gastric adenocarcinoma development, providing new directions for research to clarify the role of these genes and offer insight for improved treatment.

Construction of a clinical survival prognostic model for middle-aged and elderly patients with stage III rectal adenocarcinoma.

BACKGROUND: Nomograms for prognosis prediction in colorectal cancer patients are few, and prognostic indicators differ with age. AIM: To construct a new nomogram survival prediction tool for middle-aged and elderly patients with stage III rectal adenocarcinoma. METHODS: A total of 2773 eligible patients were divided into the training cohort (70%) and the validation cohort (30%). Optimal cutoff values were calculated using the X-tile software for continuous variables. Univariate and multivariate Cox proportional hazards regression analyses were used to determine overall survival (OS) and cancer-specific survival (CSS)-related prognostic factors. Two nomograms were successfully constructed. The discriminant and predictive ability and clinical usefulness of the model were also assessed by multiple methods of analysis. RESULTS: The 95%CI in the training group was 0.719 (0.690-0.749) and 0.733 (0.702-0.74), while that in the validation group was 0.739 (0.696-0.782) and 0.750 (0.701-0.800) for the OS and CSS nomogram prediction models, respectively. In the validation group, the AUC of the three-year survival rate was 0.762 and 0.770, while the AUC of the five-year survival rate was 0.722 and 0.744 for the OS and CSS nomograms, respectively. The nomogram distinguishes all-cause mortality from cancer-specific mortality in patients with different risk grades. The time-dependent AUC and decision curve analysis showed that the nomogram had good clinical predictive ability and decision efficacy and was significantly better than the tumor-node-metastases staging system. CONCLUSION: The survival prediction model constructed in this study is helpful in evaluating the prognosis of patients and can aid physicians in clinical diagnosis and treatment.

Prediction of cancer-specific survival and overall survival in middle-aged and older patients with rectal adenocarcinoma using a nomogram model.

OBJECTIVE: To develop a new nomogram tool for predicting survival in middle-aged and elderly patients with rectal adenocarcinoma. METHODS: A total of 6,116 patients were randomly assigned in a 7:3 ratio to training and validation cohorts. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS) in the training set, and two nomogram prognostic models were constructed. The validity, accuracy, discrimination, predictive ability, and clinical utility of the models were assessed based on the concordance index (C-index), area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), Kaplan-Meier survival curve, and decision curve analyses. RESULTS: Predictors of OS and CSS were identified, and nomograms were successfully constructed. The calibration discrimination for both the OS and CSS nomogram prediction models was good (C-index: 0.763 and 0.787, respectively). The AUC showed excellent predictive performance, and the calibration curve exhibited significant predictive power for both nomograms. The time-dependent AUC showed that the predictive ability of the predictor-based nomogram was better than that of the TNM stage. The nomograms successfully discriminated high-, medium-, and low-risk patients for all-cause and cancer-specific mortality. The decision curve demonstrated that the nomograms are useful with respect to good decision power. CONCLUSION: Our nomogram survival prediction models may aid in evaluating the prognosis of middle-aged and older patients with rectal adenocarcinoma and guiding the selection of the clinical treatment measures.