IL-37bΔ1-45 suppresses the migration and invasion of endometrial cancer cells by targeting the Rac1/NF-κB/MMP2 signal pathway.

Endometrial carcinoma is one of the most common malignancies in the female reproductive system. Interleukin-37 (IL-37) is a newly discovered anti-inflammatory factor belonging to the IL-1 family. IL-37 has five different isoforms, and IL-37b is the most biologically functional subtype. In recent years, the protective roles of IL-37 in different cancers, including lung and liver cancers, have been successively reported. IL-37 also plays an important role in some gynecological diseases such as endometriosis, adenomyosis, and cervical cancer. However, the role and mechanism of IL-37b, especially the mature form of IL-37b, in endometrial carcinoma have not been elucidated. The present study demonstrated that IL-37 protein was downregulated in endometrial carcinoma cells compared with the control endometrium. IL-37b did not affect the proliferation and colony-forming ability of endometrial cancer cells. A mature form of IL-37b (IL-37bΔ1-45) effectively suppressed the migration and invasion of endometrial cancer cells by decreasing the expression of matrix metalloproteinase 2 (MMP2) via Rac1/NF-κB signal pathway. However, it did not affect epithelial-mesenchymal transition (EMT) or filamentous actin (F-actin) depolymerization of endometrial cancer cells. IL-37bΔ1-45 attenuated tumor metastasis in a peritoneal metastatic xenograft model of endometrial cancer. To sum up, these results suggested IL-37b could be involved in the pathogenesis of endometrial carcinoma and provide a novel target for the diagnosis and treatment of endometrial carcinoma.

TRIM27 regulates the expression of PDCD4 by the ubiquitin­proteasome pathway in ovarian and endometrial cancer cells.

Programmed cell death 4 (PDCD4) is regarded as an important tumor suppressor that is lowly expressed or deleted in numerous human types of cancer, including ovarian and endometrial cancer. Tripartite motif­containing 27 (TRIM27) is closely related to the occurrence and development of tumors and is highly expressed in numerous types of cancer such as ovarian and endometrial cancer. PDCD4 can be degraded through ubiquitination, while TRIM27 has the E3 ubiquitin ligase activity. However, whether TRIM27 may regulate the expression of PDCD4 by ubiquitination effect remains unclear. In the present study, the expression of PDCD4 and TRIM27 in different ovarian and endometrial cancer cell lines was detected by reverse transcription­quantitative PCR (RT­qPCR), western blotting and immunocytochemistry. The impact of TRIM27 overexpression and knockdown on PDCD4 expression and the effective mechanism of TRIM27 regulating PDCD4 expression were also investigated in vitro by RT­qPCR, western blotting, co­immunoprecipitation assay, Transwell migration and Matrigel invasion assays. The results showed that the expression of TRIM27 and PDCD4 had a negative association at the protein level, and the distribution of TRIM27 and PDCD4 proteins had a phenomenon of co­localization in different ovarian and endometrial cancer cell lines. TRIM27 promoted the degradation of PDCD4 through the ubiquitin­proteasome pathway. To sum up, TRIM27 could increase the migration and invasion of ovarian and endometrial cancer cells by promoting the ubiquitination and degradation of PDCD4. The present findings may provide a new target for the treatment of ovarian and endometrial cancer.

Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic.

Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote health monitoring through telemedicine can help people discover potential health threats in time. In the COVID-19 pandemic, remote health monitoring can help obtain and analyze biomedical signals including human body temperature without direct body contact. This technique is of great significance to achieve safe and efficient health monitoring in the COVID-19 pandemic. Existing remote biomedical signal monitoring methods cannot effectively analyze the time series data. This paper designs a remote biomedical signal monitoring framework combining the Internet of Things (IoT), 5G communication and artificial intelligence techniques. In the constructed framework, IoT devices are used to collect biomedical signals at the perception layer. Subsequently, the biomedical signals are transmitted through the 5G network to the cloud server where the GRU-AE deep learning model is deployed. It is noteworthy that the proposed GRU-AE model can analyze multi-dimensional biomedical signals in time series. Finally, this paper conducts a 24-week monitoring experiment for 2000 subjects of different ages to obtain real data. Compared with the traditional biomedical signal monitoring method based on the AutoEncoder model, the GRU-AE model has better performance. The research has an important role in promoting the development of biomedical signal monitoring techniques, which can be effectively applied to some kinds of remote health monitoring scenario.