TREM-1 amplifies trophoblastic inflammation via activating NF-κB pathway during preeclampsia.

INTRODUCTION: Excessive activation of maternal systemic inflammation is one of the underlying causes of pathology during the disease course of preeclampsia (PE). The triggering receptor expressed on myeloid cells-1 (TREM-1) participates in the development and persistence of inflammation. We hypothesized that dysregulated TREM-1 may be involved in the pathogenesis of PE by promoting the secretion of trophoblastic pro-inflammatory cytokines that augment inflammation. METHODS: The localization of TREM-1 in placenta and the extravillous trophoblast cell line (TEV-1) was determined by immunohistochemical staining. The expression level of TREM-1 and pro-inflammatory cytokines in placentas were compared between normal pregnancies and PE. We used lipopolysaccharide (LPS) to simulate trophoblastic inflammation. TEV-1 cells were transfected with TREM-1 plasmid and si-TREM-1 respectively, and then were incubated with LPS. The expression levels of pro-inflammatory cytokines and key molecules featured in nuclear transcription factor-kappaB (NF-κB) pathway were detected. Transwell assays were used to detect the effects of TREM-1 on cell migration and invasion. RESULTS: TREM-1 was localized on both villous trophoblasts (VTs) and extravillous trophoblasts (EVTs). TREM-1 and pro-inflammatory cytokines were up-regulated in preeclamptic placenta. Overexpression of TREM-1 promoted the activation of NF-κB pathway and the release of pro-inflammatory factors induced by LPS, and enhanced migration and invasion of TEV-1 cells. Inhibition of TREM-1 significantly attenuated LPS-induced effects and suppressed migration and invasion. DISCUSSION: This study suggested that TREM-1 was up-regulated in PE, and may promote the production of downstream inflammatory factors by activating NF-κB pathway in trophoblastic cells, thus exerting pro-inflammatory effects in the pathogenesis of PE.

Integrated analysis of multiple microarray studies to identify potential pathogenic gene modules in preeclampsia.

BACKGROUND: Preeclampsia is a life-threatening hypertensive disorder during pregnancy, while underlying pathogenesis and its diagnosis are incomplete. METHODS: In this study, we utilized the Robust Rank Aggregation method to integrate 6 eligible preeclampsia microarray datasets from Gene Expression Omnibus database. We used linear regression to assess the associations between significant differentially expressed genes (DEGs) and blood pressure. Functional annotation, protein-protein interaction, Gene Set Enrichment Analysis (GSEA) and single sample GSEA were employed for investigating underlying pathogenesis in preeclampsia. RESULTS: We filtered 52 DEGs and further screened for 5 hub genes (leptin, pappalysin 2, endoglin, fms related receptor tyrosine kinase 1, tripartite motif containing 24) that were positively correlated with both systolic blood pressure and diastolic blood pressure. Receiver operating characteristic indicated that hub genes were potential biomarkers for diagnosis and prognosis in preeclampsia. GSEA for single hub gene revealed that they were all closely related to angiogenesis and estrogen response in preeclampsia. Moreover, single sample GSEA showed that the expression levels of 5 hub genes were correlated with those of immune cells in immunologic microenvironment at maternal-fetal interface. CONCLUSIONS: These findings provide new insights into underlying pathogenesis in preeclampsia; 5 hub genes were identified as biomarkers for diagnosis and prognosis in preeclampsia.

Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma.

BACKGROUND: Immune cell infiltration in the tumor microenvironment (TME) affects tumor initiation, patients' prognosis and immunotherapy strategies. However, their roles and interactions with genomics and molecular processes in hepatocellular carcinoma (HCC) still have not been systematically evaluated. METHODS: We performed unsupervised clustering of total 1000 HCC samples including discovery and validation group from available public datasets. Immune heterogeneity of each subtype was explored by multi-dimension analysis. And a support vector machine (SVM) model based on multi-omics signatures was trained and tested. Finally, we performed immunohistochemistry to verify the immune role of signatures. RESULTS: We defined three immune subtypes in HCC, with diverse clinical, molecular, and genomic characteristics. Cluster1 had worse prognosis, better anti-tumor characteristics and highest immune scores, but also accompanied by immunosuppression and T cell dysfunction. Meanwhile, a better anti-PD1/CTLA4 immunotherapeutic response was predicted in cluster1. Cluster2 was enriched in TAM-M2 and stromal cells, indicating immunosuppression. Cluster3, with better prognosis, had lowest CD8 T cell but highest immune resting cells. Further, based on genomic signatures, we developed an SVM classifier to identify the patient's immunological status, which was divided into Type A and Type B, in which Type A had poorer prognosis, higher T cell dysfunction despite higher T cell infiltration, and had better immunotherapeutic response. At the same time, MMP9 may be a potential predictor of the immune characteristics and immunotherapeutic response in HCC. CONCLUSIONS: Our work demonstrated 3 immune clusters with different features. More importantly, multi-omics signatures, such as MMP9 was identified based on three clusters to help us recognize patients with different prognosis and responses to immunotherapy in HCC. This study could further reveal the immune status of HCC and provide potential predictors for immune checkpoint treatment response.