BackgroundPreeclampsia (PE) is an obstetric disorder with high morbidity and mortality rates but without clear pathogeny. The dysfunction of the blood coagulation-fibrinolysis system is a salient characteristic of PE that varies in severity, and necessitates different treatments. Therefore, it is necessary to find suitable predictors for the onset and severity of PE.ObjectivesWe aimed to evaluate blood coagulation parameters and platelet indices as potential predictors for the onset and severity of PE.MethodsBlood samples from 3 groups of subjects, normal pregnant women (n = 79), mild preeclampsia (mPE) (n = 53) and severe preeclampsia (sPE) (n = 42), were collected during early and late pregnancy. The levels of coagulative parameters and platelet indices were measured and compared among the groups. The receiver-operating characteristic (ROC) curves of these indices were generated, and the area under the curve (AUC) was calculated. The predictive values of the selected potential parameters were examined in binary regression analysis.ResultsDuring late pregnancy in the normal pregnancy group, the activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT) and platelet count decreased, while the fibrinogen level and mean platelet volume (MPV) increased compared to early pregnancy (p<0.05). However, the PE patients presented with increased APTT, TT, MPV and D-dimer (DD) during the third trimester. In the analysis of subjects with and without PE, TT showed the largest AUC (0.743) and high predictive value. In PE patients with different severities, MPV showed the largest AUC (0.671) and ideal predictive efficiency.ConclusionNormal pregnancy causes a maternal physiological hypercoagulable state in late pregnancy. PE may trigger complex disorders in the endogenous coagulative pathways and consume platelets and FIB, subsequently activating thrombopoiesis and fibrinolysis. Thrombin time and MPV may serve as early monitoring markers for the onset and severity of PE, respectively.

Gastric cancer, a highly invasive and aggressive malignancy, is the third leading cause of death from cancer worldwide. Genetic association studies have successfully revealed several important genes consistently associated with gastric cancer to date. However, these robust gastric cancer-associated genes do not fully elucidate the mechanisms underlying the development and progression of the disease. In the present study, we performed an alternative approach, a gene expression-based genome-wide association study (eGWAS) across 13 independent microarray experiments (including 251 gastric cancer cases and 428 controls), to identify top candidates (p<0.00001). Additionally, we conducted gene ontology analysis, pathway analysis and network analysis and identified aurora kinase A (AURKA) as our candidate. We observed that MLN8237, which is a specific inhibitor of AURKA, decreased the β-catenin and the phosphorylation of Akt1 and GSK-3β, as well as blocked the Akt and Wnt signaling pathways. Furthermore, MLN8237 arrested the cells in the G2/M phase. The activity of Wnt and Akt signaling pathways affected the level of histone methylation significantly, and we supposed that MLN8237 affected the level of histone methylation through these two signaling pathways. Additionally, the treatment of MLN8237 influenced the level of H3K4 me1/2/3 and H3K27 me1/2/3. Chip data on cell lines suggested that MLN8237 increases the level of H3K27 me3 on the promoter of Twist and inhibits EMT (epithelial-mesenchymal transition). In summary, AURKA is a potential therapeutic target in gastric cancer and induces EMT through histone methylation.

Background: Gastric cancer peritoneal metastasis has high mortality. At present, there is no effective way to cure the patients diagnosed with gastric cancer peritoneal metastasis due to its indistinct molecular mechanism. Therefore, to understand the pathogenesis and help for further target therapy, we conduct comprehensive analysis of peritoneal metastasis by bioinformatics in gastric cancer. Methods: Microarray sequencing was used to find differential mRNAs and long non-coding RNAs (lncRNAs) expression between primary foci and peritoneal metastases lesion in gastric cancer. RT-qPCR was used to verify the expression levels of lncRNAs in gastric cancer cells after co-culture with adipocytes. TCGA, Cytoscape, lnCAR, cBioPoratal and R packages (ggrisk, survival, survminer, timeROC, forestplot, immunedeconv, ggplot2, pheatmap and ggpubr) were applied in this work. Results: Adipocytes co-culture model was used to mimic the peritoneal microenvironment and found that LINC01151 (NR_126348), FAM27B (NR_027422) and LINC00924 (NR_027133) were up-regulated in co-culture group. Increased LINC00924 expression was significantly associated with reduced overall survival and up-regulated percentage abundance of tumorinfiltrating CD8 + T, B, macrophage and NK immune cells; moreover, immune checkpoint blockers (ICBs) had a worse effect on the LINC00924 high expression group. Furthermore, through univariate and multivariate Cox regression analysis, we found that LINC00924-related PEX5L in CNC network was an independent prognostic factor in gastric cancer progression. Conclusion: LINC00924 expression was associated with poor survival, immune infiltrations and worse response to ICBs. LINC00924 might be immunotherapeutic targets of advanced gastric cancer.

Tumor recurrence hinders treatment of ovarian cancer. The present study aimed to identify potential biomarkers for ovarian cancer recurrence prognosis and explore relevant mechanisms. RNA-sequencing of data from the TCGA database and GSE17260 dataset was carried out. Samples of the data were grouped according to tumor recurrence information. Following data normalization, differentially expressed genes/micro RNAs (miRNAs)/long non-coding (lncRNAs) (DEGs/DEMs/DELs) were selected between recurrent and non-recurrent samples. Their correlations with clinical information were analyzed to identify prognostic RNAs. A support vector machine classifier was used to find the optimal gene set with feature genes that could conclusively distinguish different samples. A protein-protein interaction (PPI) network was established for DEGs using relevant protein databases. An integrated ‘lncRNA/miRNA/mRNA’ competing endogenous RNA (ceRNA) network was constructed to reveal potential regulatory relationships among different RNAs. We identified 36 feature genes (e.g. TP53 and RBPMS) for the classification of recurrent and non-recurrent ovarian cancer samples. Prediction with this gene set had a high accuracy (91.8%). Three DELs (WT1-AS, NBR2 and ZNF883) were highly associated with the prognosis of recurrent ovarian cancer. Predominant DEMs with their targets were hsa-miR-375 (target: RBPMS), hsa-miR-141 (target: RBPMS), and hsa-miR-27b (target: TP53). Highlighted interactions in the ceRNA network were ‘WT1-AS-hsa-miR-375-RBPMS’ and ‘WT1-AS-hsa-miR-27b-TP53’. TP53, RBPMS, hsa-miR-375, hsa-miR-141, hsa-miR-27b, and WT1-AS may be biomarkers for recurrent ovarian cancer. The interactions of ‘WT1-AS-hsa-miR-375-RBPMS’ and ‘WT1-AS-hsa-miR-27b-TP53’ may be potential regulatory mechanisms during cancer recurrence.