Hui Luo

PLIN2 Promotes Lipid Accumulation in Ascites‐Associated Macrophages and Ovarian Cancer Progression by HIF1α/SPP1 Signaling

AbstractA major characteristic of ovarian cancer (OC) is its unique route of metastasis via ascites. The immune microenvironment in ascites remains understudied, leaving the mechanism of ascites‐mediated abdominal metastasis obscure. Here, a single‐cell transcriptomic landscape of CD45+ immune cells across multiple anatomical sites is depicted, including primary tumors, metastatic lesions, and ascites, from patients diagnosed with high‐grade serous ovarian carcinoma (HGSOC). A novel subset of perilipin 2 high (PLIN2hi) macrophages are identified that are enriched in ascites and positively correlated with OC progression, hence being designated as “ascites‐associated macrophages (AAMs)”. AAMs are lipid‐loaded with overexpression of the lipid droplet protein PLIN2. Overexpression or suppression of PLIN2 can enhance or inhibit tumor cell migration, invasion, and vascular permeability in vitro, which is also confirmed in vivo. Mechanistically, it is demonstrated that PLIN2 boosts HIF1α/SPP1 signaling in macrophages, thereby exerting pro‐tumor functions. Finally, a PLIN2‐targeting liposome is designed to efficiently suppress ascites production and tumor metastasis. Taken together, this work provides a comprehensive characterization of the cancer‐promoting function and lipid‐rich property of ascites‐enriched PLIN2hi macrophages, establishes a link between lipid metabolism and hypoxia within the context of the ascites microenvironment, and elucidates the pivotal role of ascites in trans‐coelomic metastasis of OC.

Application of O‐RADS Ultrasound Lexicon‐Based Logistic Regression Analysis Model in the Diagnosis of Solid Component‐Containing Ovarian Malignancies

Objective. To use the logistic regression model to evaluate the value of ultrasound characteristics in the Ovarian‐Adnexal Reporting and Data System ultrasound lexicon in determining ovarian solid component‐containing mass benignancy/malignancy. Methods. We retrospectively analyzed the data of 172 patients with adnexal masses discovered by ultrasound, and diagnosis was confirmed by postoperative pathological tests from January 2019 to December 2021. Thirteen ovarian tumor‐related parameters in the benign and malignant ovarian tumor groups were selected for univariate analyses. Statistically significant parameters were included in multivariate logistic regression analyses to construct a logistic regression diagnosis model, and the diagnostic performance of the model in predicting ovarian malignancies was calculated. Results. Of the 172 adnexal tumors, 104 were benign, and 68 were malignant. There were differences in cancer antigen 125, maximum mass diameter, maximum solid component diameter, multilocular cyst with solid component, external contour, whether acoustic shadows were present in the solid component, number of papillae, vascularity, presence/absence of ascites, and presence/absence of peritoneal thickening or nodules between the benign ovarian tumor and malignancy groups (p < 0.05). Logistic regression analyses showed that maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites were included in the logistic regression model, and the area under the receiver operating characteristic curve for this regression model in predicting ovarian malignancy was 0.962 (95% confidence interval: 0.933~0.990; p < 0.001). Logit (p) ≥ −0.02 was used as the cutoff value, and the prediction accuracy, sensitivity, specificity, positive predictive value, and negative predictive values were 93.6%, 86.8%, 98.1%, 96.7%, and 91.9%, respectively. Conclusion. The logistic regression model containing the maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites can help in determining the benignancy/malignancy of solid component‐containing masses.