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OBJECTIVE: The main purpose of this study was to elucidate the anti-apoptotic effects of curculigoside (CUR) on ovarian granulosa cells (GCs) in a mouse model of cyclophosphamide (CTX)-induced premature ovarian failure (POF). METHOD: Intraperitoneal injection of CTX (100 mg/kg body weight) induced POF in mice. Thirty-six female mice were divided into six groups: blank group; POF model group; low-dose CUR group; medium-dose CUR group; high-dose CUR group; and estradiol benzoate group. Mice were orally administered for 28 consecutive days. Twenty-four hours after the completion of treatment, mice were weighed and euthanized, and blood was collected from the eyeball under anesthesia. The ovaries were surgically separated and weighed, and the ovarian index was calculated. Hematoxylin-eosin (HE) staining was used to observe follicular development and corpus luteum morphology in the ovaries. Serum levels of follicle stimulating hormone (FSH), anti-Müllerian hormone (AMH) and estradiol (E2) were measured. Superoxide dismutase (SOD) activity, glutathione peroxidase (GSH-Px) content and malondialdehyde (MDA) levels in ovarian tissue were determined. The GC apoptosis level was measured. Western blotting was used to detect protein expression levels of Beclin-1, LC3, P62, AKT, p-AKT, mTOR and p-mTOR in the ovaries. RESULTS: The results showed that CUR can improve body weight and ovarian index; promote follicular development and reduce follicular atresia; improve FSH, AMH and E2 levels; downregulate MDA levels and restore antioxidant enzyme activity; inhibit the autophagy level; activate the AKT/mTOR signaling pathway; and alleviate GC apoptosis. CONCLUSION: CUR improves POF by activating the AKT/mTOR signaling pathway, inhibiting autophagy and alleviating GC apoptosis.
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BACKGROUND: The treatment of clear-cell renal cell carcinoma (ccRCC) remains challenge. Chemokines laid impact on the proliferation and metastasis of cancer cells. The objective was to identify the chemokine-related genes and construct a prognostic model for ccRCC. METHODS: Bulk transcriptomic data (n = 531), single-cell RNA sequencing (scRNA-seq) dataset GSE159115, and other validation cohorts were acquired from the Cancer Genome Atlas Program (TCGA) and GEO databases. All clustering analysis was conducted by Seurat R package. Gene set enrichment analysis (GSEA), immune infiltration analysis, single nucleotide variations (SNV) analysis, and predictive response analysis of immunotherapy/chemotherapy were conducted. 786-O and A498 cell lines were cultured and applied into CCK-8, Western blot, and RT-qPCR kits. RESULTS: Univariate Cox analysis was used to screen out chemokine-related genes related to survival. ZIC2, SMIM24, COL7A1, IGF2BP3, ITPKA, ADAMTS14, CYP3A7, and AURKB were identified and applied for the construction of the prognostic model. High-risk group had a poorer prognosis than the low-risk group in each dataset. Memory CD8+ T cells, macrophages, and memory B cells were higher in the high-risk group, while the content of basophils was higher in the low-risk group. Bortezomib_1191, Dactinomycin_1911, Docetaxel_1007, and Daporinad_1248 were more sensitive to high-risk groups than low-risk groups. Moreover, we found that IGF2BP3 significantly elevated in both 786-O and A498 cell lines resistance to sunitinib. Knockdown of IGF2BP3 markedly reduced ccRCC cell migration and viability. CONCLUSION: Our study has yielded a novel prognostic model of chemokine-related genes based on comprehensive transcriptional atlas of ccRCC patients, shedding light on the significant impact of the tumor microenvironment on biology and immunotherapy response of ccRCC. We identified IGF2BP3 as a pivotal regulator in regulating ccRCC resistance to sunitinib.
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Since the onset of the COVID-19 pandemic, energy price predictability has worsened. We evaluate the effectiveness of the two machine learning methods of shrinkage and combination on the spot prices of crude oil before and during the COVID-19 epidemic. The results demonstrated that COVID-19 increased economic uncertainty and diminished the predictive capacity of numerous models. Shrinkage methods have always been regarded as having an excellent out-of-sample forecast performance. However, during the COVID period, the combination methods provide more accurate information than the shrinkage methods. The reason is that the outbreak of the epidemic has altered the correlation between specific predictors and crude oil prices, and shrinkage methods are incapable of identifying this change, resulting in the loss of information.
