Purpose:The efficacy of post-surgery platinum-based chemotherapy, the primary choice for the treatment of ovarian cancer (OC), is greatly reduced by the development of drug-resistance. In this study, we investigated the association of expression low-density lipoprotein receptor (LDLR) and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), two cholesterol metabolism-related proteins, in OC tissues and chemoresistance and patient prognosis. Methods: Survival analysis using LDLR and HMGCR expression in the ovarian cancer patients using the dataset of Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) was carried out online. A retrospective study was performed on 65 patients who had undergone surgery for ovarian cancer. In addition, patients were divided into 2 groups: platinum resistance group and platinum sensitivity group. Serum lipid metabolism data were collected and analyzed. Protein expressions of LDLR and HMGCR in ovarian cancer tissue were detected by immunohistochemistry. Results: Online survival analysis showed that patients with higher LDLR expression had poorer prognosis than those with lower LDLR expression in ovarian cancer cells, while a higher HMGCR expression was associated with better OC prognosis. Overall survival (OS) and disease-free survival (DFS) were lower in patients with higher LDLR levels (OS: P=0.046, DFS: P=0.009). Platinum-resistant patients had higher levels of low-density lipoprotein (LDL) and cholesterol in serum as compared with platinum-sensitive patients (P<0.001). Immunohistochemistry showed that LDLR expression was high and HMGCR was low in platinum-resistant patients. Conclusion:The expression of LDLR and HMGCR proteins, involved in the regulation of cholesterol metabolism and the plasma LDL and cholesterol levels were significantly different in platinum-resistant and platinum-sensitive ovarian cancer patients. We postulate that cholesterol metabolic reprogramming might play a role in platinum resistance in ovarian cancer.

Ovarian cancer (OC) has the lowest survival rate among gynecologic malignancies. Ectopic lymphocyte aggregates, namely tertiary lymphoid structures (TLSs), have been reported as positive biomarkers for tumor prognosis. However, the related gene signature of tertiary lymphoid structure in ovarian cancer was less understood. Therefore, this study first exhibited the organizational patterns of tertiary lymphoid structure by H&amp;E staining and immunohistochemistry (IHC), and confirmed the improved survival values of tertiary lymphoid structure and quantified tumor-infiltrating lymphocytes (CD20+ B cells and CD8+ T cells) in ovarian cancer patients. Secondly, we collected the genes involved in tertiary lymphoid structure from databases. By the univariate regression analysis, the tertiary lymphoid structure gene signature (CETP, CCR7, SELL, LAMP3, CCL19, CXCL9, CXCL10, CXCL11, and CXCL13) with prognostic value, characteristically of ovarian cancer, was constructed in the TCGA dataset and validated in the GSE140082 dataset. Thirdly, by performing CIBERSORT and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, we found that the high expression of this gene signature was positively correlated with developed immune infiltration and reduced immune escape. The improved IPS score and application in the IMvigor210 dataset received PD-L1 proved the predictive value of immunotherapy for this gene signature. Furthermore, this signature showed a better correlation between tumor mutation burden and classical checkpoint genes. In conclusion, Tertiary lymphoid structure plays important role in tumor immunity and the gene signature can be evaluated as a biomarker for predicting prognosis and guiding immunotherapy in ovarian cancer.

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Nutritional and inflammatory states are crucial in cancer development. The purpose of this study is to construct a scoring system grounded on peripheral blood parameters associated with nutrition and inflammation and explore its value in stage, overall survival (OS), and progression-free survival (PFS) prediction for epithelial ovarian cancer (EOC) patients. Patients and Methods: Four hundred and fifty-three EOC patients were retrospectively identified and their clinical data and relevant peripheral blood parameters were collected. The ratio of neutrophil to lymphocyte, lymphocyte to monocyte, fibrinogen to lymphocyte, total cholesterol to lymphocyte and albumin level were calculated and dichotomized. A scoring system named peripheral blood score (PBS) was constructed. Univariate and multivariate Logistic or Cox regression analyses were used to select independent factors; these factors were then used to develop nomogram models of advanced stage and OS, PFS, respectively. The internal validation and DCA analysis were performed to evaluate models. Results: Lower PBS indicated a better prognosis and higher PBS indicated inferior. High PBS is associated with advanced stage, high CA125, serous histological type, poor differentiation, and accompanied ascites. The logistic regression showed age, CA125, and PBS were independent factors for the FIGO III-IV stage. The nomogram models for advanced FIGO stage based on these factors showed good efficiency. FIGO stage, residual disease, and PBS were independent factors affecting OS and PFS, the nomogram models composed of these factors had good performance. DCA curves revealed the models augmented net benefits. Conclusion: PBS can be a noninvasive biomarker for EOC patients' prognosis. The related nomogram models could be powerful, cost-effective tools to provide information of advanced stage, OS, and PFS for EOC patients.

In recent years, the emergence of soybean stay-green syndrome (SGS), also referred to as ‘zhengqing’, in the Huang-Huai-Hai region of China has resulted in significant yield losses. SGS is a phenomenon characterized by the delayed senescence of soybean, resulting in stay-green leaves, flat pods, and stunted seed development at harvest. We previously identified a distinct geminivirus, named soybean stay-green associated geminivirus (SoSGV), as the causative agent of SGS by fulfilling Koch’s postulates. To further understand the epidemiology of SoSGV, in this study, we collected 368 stay-green samples from 17 regions in 8 provinces including the Huang-Huai-Hai region and surrounding areas. The results showed that 228 samples tested positive for SoSGV (61.96%), and 96.93% of these positive samples showed severe pod deflation. Our epidemiological assessment reveals that SGS caused by the SoSGV is prevalent in the fields, and it is undergoing geographical expansion and genetic differentiation. Additionally, we determined other natural hosts grown in the Huang-Huai-Hai region. By capturing insects in the field and conducting laboratory vector transmission tests, we confirmed that the common brown leafhopper (Orosius orientalis) is the transmission vector of SoSGV. With a better understanding of the transmission and epidemiology of SoSGV, we can develop more effective strategies for managing and mitigating its impact on soybean yields.