The development and validation of a prediction model for imminent vertebral osteoporotic fracture in postmenopausal women.

PURPOSE: This study aimed to develop and validate a new model that focused on the risk of imminent vertebral fractures in women with osteoporosis. METHODS: Data from 2,048 patients were extracted from three hospitals, of which 1,720 patients passed the inclusion and exclusion screen. The patients from Nanfang Hospital (NFH) were randomized at a 2:1 ratio to create a training cohort (n = 709) and an internal validation cohort (n = 355), with the patients from the other two hospitals (n = 656) used for external validation. The risk factors included in the imminent osteoporotic vertebral compression fractures (OVCFs) prediction model (labelled TVF) were sorted by the least absolute shrinkage and selection operator and constructed by logistic regression. The area under the receiver operating characteristic curve (AUC), the decision curve, and the clinical impact curves of the optimal model were analyzed to verify the model. RESULTS: There were 138 and 161 fresh fractures in NFH and the other two hospitals, respectively. The lowest BMD T value and the history of vertebral fracture were integrated into the TVF model. The prediction power of TVF was demonstrated by the AUCs of 0.788 (95% confidence interval [CI], 0.728-0.849) in the training cohort and 0.774 (95% CI, 0.705-0.842) in the internal validation cohort, and 0.790 (95% CI, 0.742-0.839) and 0.741 (95% CI, 0.668-0.813) in the external validation cohorts. CONCLUSION: The TVF model demonstrated good discrimination to stratify the imminent risk of OVCFs. We therefore consider the model as a pertinent commencement in the search for more accurate imminent OVCFs prediction.

Tumor microenvironment-associated lactate metabolism regulates the prognosis and precise checkpoint immunotherapy outcomes of patients with lung adenocarcinoma.

BACKGROUND: Despite the wide clinical application of checkpoint inhibitor immunotherapy in lung adenocarcinoma, its limited benefit to patients remains puzzling to researchers. One of the mechanisms of immunotherapy resistance may be the dysregulation of lactate metabolism in the immunosuppressive tumor microenvironment (TME), which can inhibit dendritic cell maturation and prevent T-cell invasion into tumors. However, the key genes related to lactate metabolism and their influence on the immunotherapeutic effects in lung adenocarcinoma have not yet been investigated in depth. METHODS: In this study, we first surveyed the dysregulated expression of genes related to lactate metabolism in lung adenocarcinoma and then characterized their biological functions. Using machine learning methods, we constructed a lactate-associated gene signature in The Cancer Genome Atlas cohort and validated its effectiveness in predicting the prognosis and immunotherapy outcomes of patients in the Gene Expression Omnibus cohorts. RESULTS: A 7-gene signature based on the metabolomics related to lactate metabolism was found to be associated with multiple important clinical features of cancer and was an independent prognostic factor. CONCLUSIONS: These results suggest that rather than being simply a metabolic byproduct of glycolysis, lactate in the TME can affect immunotherapy outcomes. Therefore, the mechanism underlying this effect of lactate is worthy of further study.