Depression is a debilitating psychiatric disorder with a huge socioeconomic burden, and its treatment relies on antidepressants including selective serotonin reuptake inhibitors (SSRIs). Recently, the melatonergic system that is closely associated with the serotonergic system has been implicated in the pathophysiology and treatment of depression. However, it remains unknown whether combined treatment with SSRI and melatonin has synergistic antidepressant effects. In this study, we applied a sub-chronic restraint stress paradigm, and evaluated the potential antidepressant effects of combined fluoxetine and melatonin in adult male mice. Sub-chronic restraint stress (6 h/day for 10 days) induced depression-like behavior as shown by deteriorated fur state, increased latency to groom in the splash test, and increased immobility time in the forced-swim test. Repeated administration of either fluoxetine or melatonin at 10 mg/kg during stress exposure failed to prevent depression-like phenotypes. However, combined treatment with fluoxetine and melatonin at the selected dose attenuated stress-induced behavioral abnormalities. Moreover, we found that the antidepressant effects of combined treatment were associated with the normalization of brain-derived neurotrophic factor (BDNF)-tropomyosin receptor kinase B (TrkB) signaling in the hippocampus, but not in the prefrontal cortex. Our findings suggest that combined fluoxetine and melatonin treatment exerts synergistic antidepressant effects possibly by restoring hippocampal BDNF-TrkB signaling.

BackgroundGroup cognitive–behavioral therapy (GCBT) might meet the considerable treatment demand of insomnia, but its effectiveness needs to be addressed.ParticipantsThis study recruited 27 insomnia patients treated with 16-weeks of zolpidem (zolpidem group), 26 patients treated with 4-weeks of zolpidem and also treated with 12-weeks of GCBT (GCBT group), and 31 healthy control volunteers.MethodsBefore treatment and 16 weeks after intervention, participants were evaluated using the Patient Health Questionnaires (Patient Health Questionnaire-9 [PHQ-9] and Patient Health Questionnaire-15 [PHQ-15]), the Dysfunctional Beliefs and Attitudes about Sleep-16 (DBAS-16), and the Pittsburgh Sleep Quality Index (PSQI).ResultsCompared to the zolpidem and healthy control groups, the scale scores of PHQ-9, PHQ-15, DBAS-16 and PSQI were significantly reduced after intervention in the GCBT group. Regarding the score changes, there were correlations between PSQI, DBAS-16, PHQ-9, and PHQ-15 scales in the zolpidem group, but there were limited correlations between PSQI and some DBAS-16 scales in the GCBT group.ConclusionOur results indicate that GCBT is effective to treat insomnia by improving sleep quality and reducing emotional and somatic disturbances; thus, the study supports the advocacy of applying group psychotherapy to the disorder.

N6-methyladenosine (m6A) lncRNA plays a pivotal role in cancer. However, little is known about its role in pancreatic ductal adenocarcinoma (PDAC) and its tumor immune microenvironment (TIME). Based on The Cancer Genome Atlas (TCGA) cohort, m6A-related lncRNAs (m6A-lncRNA) with prognostic value were filtered using Pearson analysis and univariate Cox regression analysis. Distinct m6A-lncRNA subtypes were divided using unsupervised consensus clustering. Least absolute shrinkage and selection operator (LASSO) Cox regression was applied to establish an m6A-lncRNA-based risk score signature. The CIBERSORT and ESTIMATE algorithms were employed to analyze the TIME. The expression pattern of TRAF3IP2-AS1 was examined using qRT-PCR. The influence of TRAF3IP2-AS1 knockdown on cell proliferation was estimated by performing CCK8, EdU and colony-formation assays. Flow cytometry was applied to measure the effect of TRAF3IP2-AS1 knockdown on cell cycle and apoptosis. The in vivo anti-tumor effect of TRAF3IP2-AS1 was validated in a tumor-bearing mouse model. Two m6A-lncRNA subtypes with different TIME features were clarified. A risk score signature was constructed as a prognostic predictor based on m6A-lncRNAs. The risk score also correlated with TIME characterization, which facilitated immunotherapy. Finally, the m6A-lncRNA TRAF3IP2-AS1 was proved to be a tumor suppressor in PDAC. We comprehensively demonstrated m6A-lncRNAs to be useful tools for prognosis prediction, TIME depiction and immunotherapeutic guidance in PDAC.

Background: Mounting research studies have suggested the indispensable roles of N6-methyladenosine (m6A) RNA modification in carcinogenesis. Nevertheless, it was little known about the potential function of m6A-related lncRNAs in sample clustering, underlying mechanism, and anticancer immunity of pancreatic ductal adenocarcinoma (PDAC).Methods: PDAC sample data were obtained from TCGA-PAAD project, and a total of 23 m6A regulators were employed based on published articles. Pearson correlation and univariate Cox regression were analyzed to determine m6A-related lncRNAs with prognostic significance to identify distinct m6A-related lncRNA subtypes by consensus clustering. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was applied for constructing an m6A-related lncRNA scoring system, further quantifying the m6A-related lncRNA patterns in individual samples. Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual samples. To decode the comprehensive landscape of TME, the CIBERSORT method and ESTIMATE algorithm were analyzed. The half-maximal inhibitory concentration (IC50) of chemotherapeutic agents was predicted with the R package pRRophetic. Finally, a quantitative real-time polymerase chain reaction was used to determine TRPC7-AS1 mRNA expression in PDAC.Results: Two distinct m6A-related lncRNA patterns with different clinical outcomes, TEM features, and biological enrichment were identified based on 45 prognostic m6A-related lncRNAs. The identification of m6A-related lncRNA patterns within individual samples based on risk scores contributed to revealing biological signatures, clinical outcomes, TEM characterization, and chemotherapeutic effects. A prognostic risk-clinical nomogram was constructed and confirmed to estimate m6A-related lncRNA patterns in individual samples. Finally, the biological roles of TRPC7-AS1 were revealed in PDAC.Conclusion: This work comprehensively elucidated that m6A-related lncRNA patterns served as an indispensable player in prognostic prediction and TEM features. Quantitative identification of m6A-related lncRNA patterns in individual tumors will contribute to sample stratification for further optimizing therapeutic strategies.