3 results on '"Sun, Yaoqi"'
Search Results
2. Identification of an Autophagy-Related Signature for Prognosis and Immunotherapy Response Prediction in Ovarian Cancer.
- Author
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Ding, Jinye, Wang, Chunyan, Sun, Yaoqi, Guo, Jing, Liu, Shupeng, and Cheng, Zhongping
- Subjects
OVARIAN cancer ,INDUCED ovulation ,IMMUNE checkpoint inhibitors ,DISEASE risk factors ,CELL communication ,PROGNOSIS ,PROPORTIONAL hazards models ,PROGRESSION-free survival - Abstract
Background: Ovarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it easier to refine risk stratification for OC patients. Autophagy is closely implicated in the occurrence and development of tumors, including OC. Whether autophagy -related genes can be used as prognostic markers for OC patients remains unclear. Methods: The gene transcriptome data of 374 OC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between the autophagy levels and outcomes of OC patients was identified through the single sample gene set enrichment analysis (ssGSEA). Recognized molecular markers of autophagy in different clinical specimens were detected by immunohistochemistry (IHC) assay. The gene set enrichment analysis (GSEA), ESTIMATE, and CIBERSORT analysis were applied to explore the correlation of autophagy with the tumor immune microenvironment (TIME). Single-cell RNA-sequencing (scRNA-seq) data from seven OC patients were included for characterizing cell-cell interaction patterns of autophagy-high or low tumor cells. Machine learning, Stepwise Cox regression and LASSO-Cox analysis were used to screen autophagy hub genes, which were used to establish an autophagy-related signature for prognosis evaluation. Four tumor immunotherapy cohorts were obtained from the GEO (Gene Expression Omnibus) database and the literature for autophagy risk score validation. Results: The autophagy levels were closely related to the prognosis of the OC patients. Additionally, the autophagy levels were correlated with TIME status including immune score, and immune-cell infiltration. The scRNA-seq analysis found that tumor cells with high or low autophagy levels had different interactions with immune cells, especially macrophages. Eight autophagy-hub genes (ZFYVE1, AMBRA1, LAMP2, TRAF6, PDPK1, ATG2B, DAPK1 and TP53INP2) were screened for an autophagy-related signature. According to this signature, higher risk score was correlated with poor prognosis and better immunotherapy response in the OC patients. Conclusions: The autophagy-related signature is applicable to predict the prognosis and immune checkpoint inhibitors (ICIs) therapy efficiency in OC patients. It is possible to identify OC patients who will respond to ICIs therapy and have a favorable prognosis, although more verification is needed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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3. Pixantrone as a novel MCM2 inhibitor for ovarian cancer treatment.
- Author
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Chen, Qingshan, Sun, Yaoqi, Li, Hao, Liu, Shupeng, Zhang, Hai, Cheng, Zhongping, and Wang, Yu
- Subjects
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SURFACE plasmon resonance , *CANCER cell growth , *CANCER cell proliferation , *OVARIAN cancer , *MOLECULAR dynamics - Abstract
Mini-chromosome maintenance protein 2 (MCM2) is a potential target for the development of cancer therapeutics. However, small molecule inhibitors targeting MCM2 need further investigation. Molecular dynamics simulation was performed to identify active pockets in the MCM2 protein structure (6EYC). The active pocket was used as a docking model to discover MCM2 inhibitors by using structure-based virtual screening and surface plasmon resonance (SPR) assay. Furthermore, the efficacy of pixantrone targeting MCM2 in ovarian cancer was evaluated in vitro and in vivo. Pixantrone was identified as a novel inhibitor of MCM2 by virtual screening. SPR binding affinity analysis confirmed the direct binding of pixantrone to MCM2 protein. Pixantrone significantly reduced the viability of ovarian cancer cells A2780 and SKOV3 in a dose- and time-dependent manner. In addition, pixantrone inhibited DNA replication, and induced cell cycle arrest and apoptosis in ovarian cancer cells via targeting MCM2. Knockdown of MCM2 could attenuate the inhibitory activity of pixantrone in ovarian cancer cells. Furthermore, pixantrone significantly suppressed ovarian cancer growth in the A2780 cell xenograft mouse model and showed favorable safety. These findings suggest that pixantrone may be a promising drug for ovarian cancer patients by targeting MCM2 in the clinic. [Display omitted] • Pixantrone identified as a novel MCM2 inhibitor through virtual screening. • Surface plasmon resonance confirms direct binding of pixantrone to MCM2 protein. • Pixantrone inhibits ovarian cancer cell proliferation and induces apoptosis. • Knockdown of MCM2 attenuates the inhibitory effect of pixantrone on cancer cells. • Pixantrone significantly suppresses ovarian cancer growth in a mouse model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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