Yan Wang

PI3K/mTOR Dual Inhibitor GSK458 and Arsenic Trioxide Exert Synergistic Antitumor Effects against Ovarian Clear-Cell Carcinoma

Abstract Ovarian clear-cell carcinoma (OCCC), particularly advanced or recurrent settings, is generally resistant to platinum-based chemotherapy, warranting novel therapeutic strategies. Mutations in the PI3K/AKT/mTOR pathway are frequently reported in OCCC. Therefore, we hypothesized that the PI3K/mTOR dual inhibitor, GSK458, and arsenic trioxide (As2O3) may exert synergistic antitumor effects on OCCC. We investigated the effects of GSK458, As2O3, and the combination of GSK458 and As2O3 on cell viability, colony formation, and apoptosis in seven OCCC cells. Mechanistically, transcriptomic differences were assessed among the groups. Additionally, their antitumor effects were evaluated on the three-dimensional cultures of OCCC patient-derived xenografts as well as in vivo. Low-dose combination of GSK458 and As2O3 exerted synergistic antitumor effects in vitro. Viability of the three-dimensional OCCC patient-derived xenograft cultures treated with the combination of GSK458 and As2O3 decreased to 23.8% of that of the control. RNA sequencing revealed that the mechanism was associated with cell cycle and DNA damage repair. The combination of GSK458 and As2O3 synergistically inhibited the PI3K/AKT/mTOR pathway and angiogenesis and increased apoptosis. Compared with any monotherapy, the combination treatment significantly suppressed tumor growth in vivo, thereby enhancing survival. Overall, our findings highlight the potential of the novel combination of GSK458 and As2O3 for OCCC treatment.

Bioinformatic analysis reveals MIR502 as a potential tumour suppressor in ovarian cancer

Abstract Background Ovarian cancer (OC) is a major cause of death among women due to the lack of early screening methods and its complex pathological progression. Increasing evidence has indicated that microRNAs regulate gene expression in tumours by interacting with mRNAs. Although the research regarding OC and microRNAs is extensive, the vital role of MIR502 in OC remains unclear. Methods We integrated two microRNA expression arrays from GEO to identify differentially expressed genes. The Kaplan–Meier method was used to screen for miRNAs that had an influence on survival outcome. Upstream regulators of MIR502 were predicted by JASPAR and verified by ChIP-seq data. The LinkedOmics database was used to study genes that were correlated with MIR502 . Gene Set Enrichment Analysis (GSEA) was conducted for functional annotation with GO and KEGG pathway enrichment analyses by using the open access WebGestalt tool. We constructed a PPI network by using STRING to further explore the core proteins. Results We found that the expression level of MIR502 was significantly downregulated in OC, which was related to poor overall survival. NRF1, as an upstream regulator of MIR502 , was predicted by JASPAR and verified by ChIP-seq data. In addition, anti-apoptosis and pro-proliferation genes in the Hippo signalling pathway, including CCND1, MYC, FGF1 and GLI2, were negatively regulated by MIR502 , as shown in the GO and KEGG pathway enrichment results. The PPI network further demonstrated that CCND1 and MYCN were at core positions in the development of ovarian cancer. Conclusions MIR502 , which is regulated by NRF1, acts as a tumour suppressor gene to accelerate apoptosis and suppress proliferation by targeting the Hippo signalling pathway in ovarian cancer.

Snail/PRMT5/NuRD complex contributes to DNA hypermethylation in cervical cancer by TET1 inhibition

AbstractThe biological function of PRMT5 remains poorly understood in cervical cancer metastasis. Here, we report that PRMT5 physically associates with the transcription factor Snail and the NuRD(MTA1) complex to form a transcriptional-repressive complex that catalyzes the symmetrical histone dimethylation and deacetylation. This study shows that the Snail/PRMT5/NuRD(MTA1) complex targets genes, such as TET1 and E-cadherin, which are critical for epithelial-mesenchymal transition (EMT). This complex also affects the conversion of 5mC to 5hmC. This study demonstrates that the Snail/PRMT5/NuRD(MTA1) complex promotes the invasion and metastasis of cervical cancer in vitro and in vivo. This study also shows that PRMT5 expression is upregulated in cervical cancer and various human cancers, and the PRMT5 inhibitor EPZ015666 suppresses EMT and the invasion potential of cervical cancer cells by disinhibiting the expression of TET1 and increasing 5hmC, suggesting that PRMT5 is a potential target for cancer therapy.

Unsupervised Domain Adaptive Dose Prediction via Cross-Attention Transformer and Target-Specific Knowledge Preservation

Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.e. the dose distribution maps, which cost dosimetrists considerable time and effort to acquire. For cancers of low-incidence, such as cervical cancer, it is often a luxury to collect an adequate amount of labeled data to train a well-performing deep learning (DL) model. To mitigate this problem, in this paper, we resort to the unsupervised domain adaptation (UDA) strategy to achieve accurate dose prediction for cervical cancer (target domain) by leveraging the well-labeled high-incidence rectal cancer (source domain). Specifically, we introduce the cross-attention mechanism to learn the domain-invariant features and develop a cross-attention transformer-based encoder to align the two different cancer domains. Meanwhile, to preserve the target-specific knowledge, we employ multiple domain classifiers to enforce the network to extract more discriminative target features. In addition, we employ two independent convolutional neural network (CNN) decoders to compensate for the lack of spatial inductive bias in the pure transformer and generate accurate dose maps for both domains. Furthermore, to enhance the performance, two additional losses, i.e. a knowledge distillation loss (KDL) and a domain classification loss (DCL), are incorporated to transfer the domain-invariant features while preserving domain-specific information. Experimental results on a rectal cancer dataset and a cervical cancer dataset have demonstrated that our method achieves the best quantitative results with [Formula: see text], [Formula: see text], and HI of 1.446, 1.231, and 0.082, respectively, and outperforms other methods in terms of qualitative assessment.