Dapagliflozin inhibits the activity of lateral habenula to alleviate diabetes mellitus-induced depressive-like behavior.

The prevalence of depression in diabetes mellitus (DM) patients is very high, and it severely impacts the prognosis and quality of life of these patients. Sodium-glucose co-transporter 2 (SGLT2) inhibitors, a new type of oral hypoglycemic drugs, have been shown to alleviate depressive symptoms in DM patients; however, the mechanism underlying this effect is not well understood. The lateral habenula (LHb) plays an important role in the pathogenesis of depression expresses SGLT2, suggesting that the LHb may mediate antidepressant effects of SGLT2 inhibitors. The current study aimed to investigate the involvement of the LHb in the antidepressant effects of the SGLT2 inhibitor dapagliflozin. Chemogenetic methods were used to manipulate the activity of LHb neurons. Behavioral tests, Western blotting, immunohistochemistry, and neurotransmitter assays were used to determine the effects of dapagliflozin on the behavior of DM rats, AMP-activated protein kinase (AMPK) pathway and c-Fos expression in the LHb and 5-hydroxyindoleacetic acid (5-HIAA)/5-hydroxytryptamine (5-HT) ratio in the dorsal raphe nucleus (DRN). We found that DM rats demonstrated depressive-like behavior, increased c-Fos expression, and decreased AMPK pathway activity in the LHb. Inhibition of LHb neurons alleviated the depressive-like behavior of DM rats. Both systemic and local LHb administration of dapagliflozin alleviated the depressive-like behavior and reversed the changes of the AMPK pathway and c-Fos expression in the LHb of DM rats. Dapagliflozin, when microinjected into the LHb, also increased 5-HIAA /5-HT in the DRN. These results suggest that dapagliflozin directly acts on the LHb to alleviate DM-induced depressive-like behavior and that the underlying mechanism involves activating the AMPK signaling pathway, leading to the inhibition of LHb neuronal activity, which in turn increases serotonergic activity in the DRN. These results will help develop new strategies for the treatment of DM-induced depression.

Development and validation of a tumor immune cell infiltration-related gene signature for recurrence prediction by weighted gene co-expression network analysis in prostate cancer.

Introduction: Prostate cancer (PCa) is the second most common malignancy in men. Despite multidisciplinary treatments, patients with PCa continue to experience poor prognoses and high rates of tumor recurrence. Recent studies have shown that tumor-infiltrating immune cells (TIICs) are associated with PCa tumorigenesis. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to derive multi-omics data for prostate adenocarcinoma (PRAD) samples. The CIBERSORT algorithm was used to calculate the landscape of TIICs. Weighted gene co-expression network analysis (WGCNA) was performed to determine the candidate module most significantly associated with TIICs. LASSO Cox regression was applied to screen a minimal set of genes and construct a TIIC-related prognostic gene signature for PCa. Then, 78 PCa samples with CIBERSORT output p-values of less than 0.05 were selected for analysis. WGCNA identified 13 modules, and the MEblue module with the most significant enrichment result was selected. A total of 1143 candidate genes were cross-examined between the MEblue module and active dendritic cell-related genes. Results: According to LASSO Cox regression analysis, a risk model was constructed with six genes (STX4, UBE2S, EMC6, EMD, NUCB1 and GCAT), which exhibited strong correlations with clinicopathological variables, tumor microenvironment context, antitumor therapies, and tumor mutation burden (TMB) in TCGA-PRAD. Further validation showed that the UBE2S had the highest expression level among the six genes in five different PCa cell lines. Discussion: In conclusion, our risk-score model contributes to better predicting PCa patient prognosis and understanding the underlying mechanisms of immune responses and antitumor therapies in PCa.

A Prognostic Survival Model of Pancreatic Adenocarcinoma Based on Metabolism-Related Gene Expression.

Accurately predicting the survival prospects of patients suffering from pancreatic adenocarcinoma (PAAD) is challenging. In this study, we analyzed RNA matrices of 182 subjects with PAAD based on public datasets obtained from The Cancer Genome Atlas (TCGA) as training datasets and those of 63 subjects obtained from the Gene Expression Omnibus (GEO) database as the validation dataset. Genes regulating the metabolism of PAAD cells correlated with survival were identified. Furthermore, LASSO Cox regression analyses were conducted to identify six genes (XDH, MBOAT2, PTGES, AK4, PAICS, and CKB) to create a metabolic risk score. The proposed scoring framework attained the robust predictive performance, with 2-year survival areas under the curve (AUCs) of 0.61 in the training cohort and 0.66 in the validation cohort. Compared with the subjects in the low-risk cohort, subjects in the high-risk training cohort presented a worse survival outcome. The metabolic risk score increased the accuracy of survival prediction in patients suffering from PAAD.