Background Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches. Results In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer. Conclusions This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.

Background In order to test the predictive effect of the XGBoost model in the machine learning algorithm for early lung cancer diagnosis and the importance of amino acid and carnitine indicators in affecting the occurrence of lung cancer, this study used data from 848 patients with lung cancer and benign lung nodules. Results The study selected 49 serum amino acid and carnitine indexes, as well as age and gender demographic indexes of subjects from a hospital in Dalian City, Liaoning Province. After screening with stepwise regression algorithm, 16 indicators were included.Draw the ROC curve of 5 models, comparing the accuracy, precision, recall, and F1 score of the lung cancer prediction model with the nomogram and the machine learning algorithm, the optimal model (XGBoost) was obtained, and the feature importance ranking was obtained using the XGBoost model.In the method of screening indicators, the index modeling accuracy after screening using the stepwise regression algorithm was the highest, which was 75.29%, and the AUC value was 0.81. In the model performance comparison, the accuracy of the nomogram is 68.24%, and the AUC value is 0.74. Among the machine learning models, the XGBoost algorithm has the best performance, with an accuracy rate of 75.29% and an ROC value of 0.81. In the model feature importance score results, the top three important features were ornithine, valine and palmitoylcarnitine. Conclusions The combined use of nomogram and machine learning model(XGBoost) not only improves the accuracy of the lung cancer prediction model, but also makes the understanding of the results more intuitive and convenient for doctors and patients.

Background Continuous hyperglycaemia has been related with dementia. However, it remains unclear whether prediabetes poses a higher risk of dementia. A meta‐analysis was therefore conducted to comprehensively investigate the possible role of prediabetes as a risk factor of dementia. Methods Prospective cohort studies reporting the association of prediabetes and dementia were identified from PubMed, Web of Science, and Embase databases. A random‐effects model was applied to combine the results by incorporating the influence of heterogeneity. Subgroup analyses were also conducted to explore the influences of study features on the relationship. Sensitivity analysis re‐estimated the combined effect size after excluding single studies separately to explore the robustness of the results. Results Nine studies involving 29 986 adults from the general population, 6265 (20.9%) of whom had prediabetes, were included. It was shown that prediabetes was not independently associated with a higher incidence of dementia compared with normoglycaemia (adjusted risk ratio (RR): 1.01, 95% confidence interval (CI): 0.85–1.21, P = 0.89, I2 = 39%). Subgroup analyses according to the definitions of prediabetes, follow‐up duration, method for diagnosis of dementia, and quality score produced similar findings (P for all subgroup differences >0.05). In addition, prediabetes was not independently associated with the incidence of Alzheimer's disease (RR: 1.24, 95% CI: 0.98–1.56, P = 0.07, I2 = 0%) or vascular dementia (RR: 1.16, 95% CI: 0.70–1.92, P = 0.56, I2 = 0%). Different definitions of prediabetes have the potential to influence the results, as reflected in the subgroup analysis for Alzheimer's disease (RR: 1.30, 95% CI: 1.06–1.60, P = 0.01, I2 = 0%). Conclusions Prediabetes may not be an independent risk factor of all‐cause dementia or vascular dementia in the general adult population. However, changing the definition of prediabetes may have an impact on the outcome for Alzheimer's disease.

Background The study of CCR7/CCL19 chemokine axis and BC (BC) prognosis and metastasis is a current hot topic. We constructed a ceRNA network and risk-prognosis model based on CCR7/CCL19. Methods Based on the lncRNA, miRNA and mRNA expression data downloaded from the TCGA database, we used the starbase website to find the lncRNA and miRNA of CCR7/CCL19 and established the ceRNA network. The 1008 BC samples containing survival data were divided into Train group (504 cases) and Test group (504 cases) using R "caret" package. Then we constructed a prognostic risk model using RNA screened by univariate Cox analysis in the Train group and validated it in the Test and All groups. In addition, we explored the correlation between riskScores and clinical trials and immune-related factors (22 immune-infiltrating cells, tumor microenvironment, 13 immune-related pathways and 24 HLA genes). After transfection with knockdown CCR7, we observed the activity and migration ability of MDA-MB-231 and MCF-7 cells using CCK8, scratch assays and angiogenesis assays. Finally, qPCR was used to detect the expression levels of five RNAs in the prognostic risk model in MDA-MB-231 and MCF-7 cell. Results Patients with high expression of CCR7 and CCL19 had significantly higher overall survival times than those with low expression. The ceRNA network is constructed by 3 pairs of mRNA-miRNA pairs and 8 pairs of miRNA-lncRNA. After multivariate Cox analysis, we obtained a risk prognostic model: riskScore= -1.544 *`TRG-AS1`+ 0.936 * AC010327.5 + 0.553 *CCR7 -0.208 *CCL19 -0.315 *`hsa-let-7b-5p. Age, stage and riskScore can all be used as independent risk factors for BC prognosis. By drug sensitivity analysis, we found 5 drugs targeting CCR7 (convolamine, amikacin, AH-23848, ondansetron, flucloxacillin). After transfection with knockdown CCR7, we found a significant reduction in cell activity and migration capacity in MDA-MB-231 cells. Conclusion we constructed the first prognostic model based on the CCR7/CCL19 chemokine axis in BC and explored its role in immune infiltration, tumor microenvironment, and HLA genes.

Background Curcumin, as a lipid-lowering drug, has been reported to be effective in the treatment of breast cancer. However, the underlying molecular mechanisms have not been completely investigated. Methods MTT assay was used to determine the effect of curcumin on survival rate of MCF-7 cells. The effects of curcumin on tumor growth were observed in animal models of breast cancer. The positive reactions of Caspase-1, IL-1β and IL-18 were detected by immunohistochemistry. LC3, p62, CTSB, ASC, Pro-Caspase-1, GSDMD, NLRP3, Caspase-1, GSDMD-N, IL-1β and IL-18 were determined by Western blot in vitro and vivo. The release of extracellular IL-1β and IL-18 was determined by ELISA. LDH release was measured. The expression level of CTSB in cytoplasm were determined by immunofluorescence assay. Cell proliferation, cell migration and tube formation assays were used to determine the abilities of cells. In this study, NLRP3 inflammasome inhibitor MCC950, cathepsin B inhibitor CA-074 ME and autophagy inhibitor 3-MA were used to act on cells to investigate the role of NLRP3 inflammasome, cathepsin B and autophagy in curcumin-induced pyroptosis of MCF-7 breast cancer cells. Results In mouse model of breast cancer, we observed that curcumin treatment significantly induced cell autophagy and pyroptosis. In human breast cancer MCF-7 cells, we found that curcumin induced pyroptotic cell death was dependent on the activation of NLRP3/Caspase-1/GSDMD signaling pathway, which was CTSB-dependent. In addition, curcumin-induced cell autophagy caused lysosomal rupture and CTSB release. Furthermore, NLRP3 inhibitor (MCC950) significantly suppressed curcumin-induced pyroptosis, as well as CTSB inhibitor (CA074 Me) and autophagy inhibitor (3-MA). Besides, we also found that curcumin suppressed cell proliferation, cell migration and tube formation, which could be reversed by inhibitors. Conclusions In summary, our results demonstrated that curcumin induced MCF-7 cell pyroptosis by the activation of autophagy/CTSB/NLRP3/Caspase-1/GSDMD signaling pathway. These findings offer novel insights into the potential molecular mechanisms of curcumin in treatment of breast cancer.