Targeting GSDME-mediated macrophage polarization for enhanced antitumor immunity in hepatocellular carcinoma.

Despite the notable efficacy of anti-PD1 therapy in the management of hepatocellular carcinoma (HCC) patients, resistance in most individuals necessitates additional investigation. For this study, we collected tumor tissues from nine HCC patients receiving anti-PD1 monotherapy and conducted RNA sequencing. These findings revealed significant upregulation of GSDME, which is predominantly expressed by tumor-associated macrophages (TAMs), in anti-PD1-resistant patients.

Association between polygenic risk scores combined with clinical characteristics and antidepressant efficacy.

Background: While millions of people suffer from major depressive disorder (MDD), research has shown that individual differences in antidepressant efficacy exist, potentially attributable to various factors. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response are seldom reported. Here, we examined whether PRSs for MDD and schizophrenia (SCZ) are associated with antidepressant effectiveness and the influence of other factors.

Targeting SRSF10 might inhibit M2 macrophage polarization and potentiate anti-PD-1 therapy in hepatocellular carcinoma.

Background: The efficacy of immune checkpoint blockade therapy in patients with hepatocellular carcinoma (HCC) remains poor. Although serine- and arginine-rich splicing factor (SRSF) family members play crucial roles in tumors, their impact on tumor immunology remains unclear. This study aimed to elucidate the role of SRSF10 in HCC immunotherapy.

Association between sleep duration, depression and breast cancer in the United States: a national health and nutrition examination survey analysis 2009-2018.

Objective: Breast cancer is the most common cancer in women, threatening both physical and mental health. The epidemiological evidence for association between sleep duration, depression and breast cancer is inconsistent. The aim of this study was to determine the association between them and build machine-learning algorithms to predict breast cancer.