Epidemiological studies have reported an inconsistent relationship between maternal lipid levels and preterm birth (PTB). We performed this meta-analysis to evaluate the association between maternal dyslipidemia and PTB. Overall, three nested case-control studies and eight cohort studies were eligible. Effect estimates [odds ratio(OR)/relative risk] were pooled using a fixed-effects or a random-effects model. Subgroup and metaregression analyses were conducted to evaluate the sources of heterogeneity. Eleven studies involving 13,025 pregnant women were included. Compared with pregnant women with normal lipid levels, the women with elevated levels of lipids had an increased risk of PTB, and the pooled OR was 1.68 [95% confidence interval (CI): 1.25-2.26)]; meanwhile, women with lower levels of lipids also had a trend of an increased risk of PTB (OR=1.52, 95% CI=0.60-3.82). The pooled ORs for elevated levels of total cholesterol, triglycerides, low density lipoprotein-cholesterol, and lower levels of high density lipoprotein-cholesterol were 1.71 (95% CI: 1.05-2.79), 1.55 (95% CI: 1.13-2.12), 1.19 (95% CI: 0.95-1.48), and 1.33 (95% CI: 1.14-1.56), respectively. The present meta-analysis found that maternal dyslipidemia during pregnancy, either the elevated total cholesterol or triglycerides, was associated with an increased risk of PTB. These findings indicate that a normal level of maternal lipid during pregnancy may reduce the risk of PTB.

Listeria monocytogenes (L. monocytogenes), which is a facultative intracellular bacterial pathogen that causes listeriosis, is widely used to study the mammalian immune response to infection. After phagocytosis by professional phagocytes, L. monocytogenes is initially contained within phagosomes, which mature into phagolysosomes, where the bacteria are degraded. Although phagocytosis and subsequent phagosome maturation is essential for the clearance of infectious microbial pathogens, the underlying regulatory mechanisms are still unclear. SNX10 (Sorting nexin 10) has the simplest structure of the SNX family and has been reported to regulate endosomal morphology, which might be crucial for macrophage function, including phagocytosis and digestion of pathogens, inflammatory response, and antigen presentation. Our results showed that SNX10 expression was upregulated following L. monocytogenes infection in macrophages. It was also revealed that SNX10 promoted phagosome maturation by recruiting the Mon1-Ccz1 complex to endosomes and phagosomes. As a result, SNX10 deficiency decreased the bacterial killing ability of macrophages, and SNX10-deficient mice showed increased susceptibility to L. monocytogenes infection in vivo. Thus, this study revealed an essential role of SNX10 in controlling bacterial infection.

Gastric cancer (GC) is one of the leading causes of cancer-associated deaths worldwide. Due to the lack of typical symptoms and effective biomarkers for non-invasive screening, most patients develop advanced-stage GC by the time of diagnosis. Circulating microRNA (miRNA)-based panels have been reported as a promising tool for the screening of certain types of cancers. In this study, we performed differential expression analysis of miRNA profiles of plasma samples obtained from gastric cancer and non-cancer patients using two independent Gene Expression Omnibus (GEO) datasets: GSE113486 and GSE124158. We identified three miRNAs, hsa-miR-320a, hsa-miR-1260b, and hsa-miR-6515-5p, to distinguish gastric cancer cases from non-cancer controls. The three miRNAs showed an area under the curve (AUC) over 0.95 with high specificity (>93.0%) and sensitivity (>85.0%) in both the discovery datasets. In addition, we further validated these three miRNAs in two external datasets: GSE106817 [sensitivity: hsa-miR-320a (99.1%), hsa-miR-1260b (97.4%), and hsa-miR-6515-5p (92.2%); specificity: hsa-miR-320a (88.8%), hsa-miR-1260b (89.6%), and hsa-miR-6515-5p (88.7%); and AUC: hsa-miR-320a (96.3%), hsa-miR-1260b (97.4%), and hsa-miR-6515-5p (94.6%)] and GSE112264 [sensitivity: hsa-miR-320a (100.0%), hsa-miR-1260b (98.0%), and hsa-miR-6515.5p (98.0%); specificity: hsa-miR-320a (100.0%), hsa-miR-1260b (100.0%), and hsa-miR-6515.5p (92.7%); and AUC: hsa-miR-320a (1.000), hsa-miR-1260b (1.000), and hsa-miR-6515-5p (0.988)]. On the basis of these findings, the three miRNAs can be used as potential biomarkers for gastric cancer screening, which can provide patients with a higher chance of curative resection and longer survival.

Background Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters. Methods Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches. Results Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk. Conclusions By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.

Breast cancer is a common malignancy, but the understanding of its cellular and molecular mechanisms is limited. ZFHX3, a transcription factor with many homeodomains and zinc fingers, suppresses prostatic carcinogenesis but promotes tumor growth of liver cancer cells. ZFHX3 regulates mammary epithelial cells’ proliferation and differentiation by interacting with estrogen and progesterone receptors, potent breast cancer regulators. However, whether ZFHX3 plays a role in breast carcinogenesis is unknown. Here, we found that ZFHX3 promoted the proliferation and tumor growth of breast cancer cells in culture and nude mice; and higher expression of ZFHX3 in human breast cancer specimens was associated with poorer prognosis. The knockdown of ZFHX3 in ZFHX3-high MCF-7 cells decreased, and ZFHX3 overexpression in ZFHX3-low T-47D cells increased the proportion of breast cancer stem cells (BCSCs) defined by mammosphere formation and the expression of CD44, CD24, and/or aldehyde dehydrogenase 1. Among several transcription factors that have been implicated in BCSCs, MYC and TBX3 were transcriptionally activated by ZFHX3 via promoter binding, as demonstrated by luciferase-reporter and ChIP assays. These findings suggest that ZFHX3 promotes breast cancer cells’ proliferation and tumor growth likely by enhancing BCSC features and upregulating MYC, TBX3, and others.