TianQin is a planned space-based gravitational wave (GW) observatory consisting of three Earth-orbiting satellites with an orbital radius of about $10^5 \, {\rm km}$. The satellites will form an equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4} \, {\rm Hz}$ and $1 \, {\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe and exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new-generation $17 \, {\rm cm}$ single-body hollow corner-cube retro-reflector which was launched with the QueQiao satellite on 21 May 2018; a new laser-ranging station equipped with a $1.2 \, {\rm m}$ telescope has been constructed and the station has successfully ranged to all five retro-reflectors on the Moon; and the TianQin-1 experimental satellite was launched on 20 December 2019—the first-round result shows that the satellite has exceeded all of its mission requirements.
Background Ovarian cancer is one of the three major malignant tumors of the female reproductive system, and the mortality associated with ovarian cancer ranks first among gynecologic malignant tumors. The pathogenesis of ovarian cancer is not yet clearly defined but elucidating this process would be of great significance for clinical diagnosis, prevention, and treatment. For this study, we used bioinformatics to identify the key pathogenic genes and reveal the potential molecular mechanisms of ovarian cancer; we used immunohistochemistry to validate them. Methods We analyzed and integrated four gene expression profiles (GSE14407, GSE18520, GSE26712, and GSE54388), which were downloaded from the Gene Expression Omnibus (GEO) database, with the aim of obtaining a common differentially expressed gene (DEG). Then, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We then established a protein–protein interaction (PPI) network of the DEGs through the Search Tool for the Retrieval of Interacting Genes (STRING) database and selected hub genes. Finally, survival analysis of the hub genes was performed using a Kmplotter online tool. Results A total of 226 DEGs were detected after the analysis of the four gene expression profiles; of these, 87 were upregulated genes and 139 were downregulated. GO analysis results showed that DEGs were significantly enriched in biological processes including the G2/M transition of the mitotic cell cycle, the apoptotic process, cell proliferation, blood coagulation, and positive regulation of the canonical Wnt signaling pathway. KEGG analysis results showed that DEGs were particularly enriched in the cell cycle, the p53 signaling pathway, the Wnt signaling pathway, the Ras signaling pathway, the Rap1 signaling pathway, and tyrosine metabolism. We selected 50 hub genes from the PPI network, which had 147 nodes and 655 edges, and 30 of them were associated with the prognosis of ovarian cancer. We performed immunohistochemistry on phosphoserine aminotransferase 1 (PSAT1). PSAT1 was highly expressed in cancer tissues, and its expression level was related to clinical stage and tissue differentiation in ovarian cancer. A Cox proportional risk model suggested that high expression of PSAT1 and late clinical stage were independent risk factors for survival and prognosis of ovarian cancer patients. Conclusion The detection of DEGs using bioinformatics analysis might be crucial to understanding the pathogenesis of ovarian cancer, especially the molecular mechanisms of its development. The association between PSAT1 expression and the occurrence, development, and prognosis of ovarian cancer was further verified by immunohistochemistry. The PSAT1 expression can be used as a prognostic marker to provide a potential target for the diagnosis and treatment of ovarian cancer.
The TianQin-1 satellite (TQ-1), which is the first technology demonstration satellite for the TianQin project, was launched on
Ovarian carcinoma has the highest mortality among the malignant tumours in gynaecology, and new treatment strategies are urgently needed to improve the clinical status of ovarian carcinoma patients. The Cancer Genome Atlas (TCGA) cohort were performed to explore the immune function of the internal environment of tumours and its clinical correlation with ovarian carcinoma. Finally, four molecular subtypes were obtained based on the global immune‐related genes. The correlation analysis and clinical characteristics showed that four subtypes were all significantly related to clinical stage; the immune scoring results indicated that most immune signatures were upregulated in C3 subtype, and the majority of tumour‐infiltrating immune cells were upregulated in both C3 and C4 subtypes. Compared with other subtypes, C3 subtype had a higher BRCA1 mutation, higher expression of immune checkpoints, and optimal survival prognosis. These findings of the immunological microenvironment in tumours may provide new ideas for developing immunotherapeutic strategies for ovarian carcinoma.
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