Ovarian Cancer: Advances in Pathophysiology and Therapies

Tossetta, Giovanni; Inversetti, Annalisa

doi:10.3390/ijms24108930

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…Among these, more than 90% of ovarian cancers are epithelial tumors. Currently, epithelial ovarian cancers are further classi ed into ve types based on histopathology: high-grade serous ovarian cancer (HGSOC); 70%), endometrioid carcinoma (EC; 10%), clear cell carcinoma (CCC; 10%), mucinous carcinoma (MC; 3%), and low-grade serous carcinoma (LGSC; < 5%) (25,26). As the most lethal type of gynecologic cancer, HGSOC exhibits strong heterogeneity, posing challenges to effective treatment.…”

Section: Discussionmentioning

confidence: 99%

“…As the most lethal type of gynecologic cancer, HGSOC exhibits strong heterogeneity, posing challenges to effective treatment. (24,25).In this study, we aimed to investigate the value of VORGs in HGSOC prognosis, tumor microenvironment (TME) in ltration, and therapeutic response through the integration of scRNA-seq and bulk RNA-seq data. Of particular note, this study highlights MARVELD1 as a potential oncogene and a promising therapeutic target for HGSOC.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning visceral obesity-related genes based on MARVELD1 model for predicting the prognosis and immune cell infiltration of ovarian cancer

Qiao,

Wu,

et al. 2024

Preprint

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Background: Visceral obesity elevates high-grade serous carcinoma (HGSOC) risk and prognosis but its effect on biology is underexplored. This study examines obesity-related genes (VORGs) in HGSOC. Methods Using consensus clustering, we analyzed TCGA and GSE53963 datasets with VORGs, uncovering unique immune cell patterns and gene functions. Our prognostic model integrates OS, tumor immunity, ICI response, and chemotherapy outcomes. Single-cell data enrich our understanding of gene expression, particularly highlighting MARVELD1 in tumor stem cells. Experimental validation comprised CCK-8, transwell, and colony formation assays. Results Based on the expression profiles of VORGs associated with OS, HGSOC patients were stratified into four distinct molecular subtypes. The investigation entailed an exploration of the interplay between each molecular subtype, patient prognosis, and immune infiltration dynamics. Subsequently, a robust prognostic prediction model, encompassing four genes (CXCL9, IGF2, FCGBP, and MARVELD1), was meticulously developed. The model utilized differential genes linked to prognosis through molecular subtyping. It categorized patients into high- and low-risk groups, where the high-risk group exhibited significantly worse outcomes (P < 0.001). Elevated scores were associated with increased stem cell indices, reduced mutation burdens, and heightened immunosuppression. Moreover, these scores showed significant correlations with immune checkpoint genes and chemotherapy sensitivity, emphasizing their crucial role in predicting treatment responses. Turning our attention to finer granularity, analyzing single-cell data revealed diverse gene expression among the four models, especially MARVELD1's prominence in tumor stem cells. MARVELD1 impacted early cellular functions through pathways like reactive oxygen species. Lastly, to validate our findings, in vitro experiments provided compelling evidence that interfering with MARVELD1 effectively curbed the proliferation, invasion, and clone formation potential of ovarian cancer cells. Conclusion This study examines VORGs' impact on HGSOC biology, using scRNA-seq and bulk RNA-seq data, presenting a novel risk model correlated with prognosis, immune microenvironment, and drug effectiveness.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine learning visceral obesity-related genes based on MARVELD1 model for predicting the prognosis and immune cell infiltration of ovarian cancer

Qiao,

Wu,

et al. 2024

Preprint

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“…Висока летальність за даної патології безпосередньо пов'язана з діагностуванням на запущених (III-IV) стадіях захворювання, тобто метастазування. Це ,у свою чергу, відображається на перебігу та прогнозі даного захворювання [3]. П'ятирічна виживаність при діагностуванні на пізніх стадіях захворювання становить лише 29 % [4].…”

Section: імуногістохімічні особливості раку яєчників ім нікітіна тв к...unclassified

Immunohistochemical features of ovarian cancer

Nikitina,

Kopytsia,

Sukhostavets

et al. 2023

Buk. Med. Herald

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Ovarian cancer is the leading cause of death among women with gynecological oncological diseases. Diagnosis at the early stages of the disease allows for better chances of treatment and prognosis of ovarian cancer. It is reflected in the reduction of morbidity and mortality rates. An immunohistochemical study is used for differential diagnosis and accurate establishment of the histotype of ovarian cancer. Objective: to systematize literature data about the immunohistochemical features of ovarian cancer. In 90% of cases, ovarian cancer is epithelial-stromal, among which the main histological types are distinguished: serous (low- and high-grade), endometrioid, mucinous, and clear cell. In serous and other ovarian tumors, cytokeratins (CK) are widely used. CK 7 is present in all tumors, including mucinous tumors, expressing CK 20. Mucinous tumors also express villin and are negative for CDX-2. Endometrioid tumors react positively to vimentin, which is not observed in serous and mucinous tumors. Clear cell carcinoma expresses CK 8, 18, and EMA (epithelial membrane antigen).Conclusion. Therefore, immunohistochemical features of ovarian cancer ensure accuracy in the differential diagnosis of one or another histological type.

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“…Ovarian cancer (OC) refers to malignant tumor growth in the ovaries. Typically, OC is often not diagnosed early and since it has highly aggressive characteristics, coupled with a 5-year survival rate of less than 40%, it has one of the highest mortality rates of gynecological cancers [1]. The current treatment strategies include cytoreductive surgery and platinumbased chemotherapy, but the treatment effect is still suboptimal [2].…”

Section: Introductionmentioning

confidence: 99%

FBXL18 is required for ovarian cancer cell proliferation and migration through activating AKT signaling

Zhuang

2024

Am J Transl Res

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Background: F-box and leucine-rich repeat protein 18 (FBXL18) is an F-box protein that functions as an E3-ubiquitin ligase, and it plays pivotal roles in multiple disease processes. However, its role and underlying mechanism in ovarian cancer (OC) are still unknown. We investigated the impact and mechanism of FBXL18 in OC cell growth and tumorigenesis. Methods: Silent interfering RNAs and overexpression plasmids were employed to knock down and overexpress FBXL18 in OC cells (A2780 and OVCAR3). CCK-8, colony formation, cell migration, and nude mouse xenograft assays were used to assess the effect of FBXL18 on OC cell proliferation and migration. Western blotting and co-immunoprecipitation followed by ubiquitination assays were performed to detect the mechanism of the FBXL18/AKT axis in OC. Results: FBXL18 knockdown inhibited OC cell proliferation and migration, whereas FBXL18 overexpression showed the opposite results. Phosphorylated-AKT (S473) protein expression was increased by FBXL18 overexpression and markedly decreased after phosphorylated-AKT inhibitor (MK-2206) treatment. Coimmunoprecipitation assays demonstrated that FBXL18 strongly interacted with AKT in OC cells. Ubiquitination assays revealed that FBXL18 promoted K63-linked AKT ubiquitination to activate AKT. MK-2206 treatment reversed the increase in proliferation and migration of OC cells induced by FBXL18 overexpression. Conclusions: FBXL18 promoted OC cell proliferation and migration and facilitated OC tumorigenesis. Mechanically, FBXL18 interacted with AKT and promoted K63-linked ubiquitination of AKT to activate AKT in OC cells. Our study revealed that the FBXL18/AKT axis plays a crucial role in the OC process, indicating that FBXL18 may be a valuable target for OC diagnosis and treatment.

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Ovarian Cancer: Advances in Pathophysiology and Therapies

Abstract: We are pleased to present this Special Issue of the International Journal of Molecular Sciences, entitled “Ovarian Cancer: Advances in Pathophysiology and Therapies” [...]

Cited by 13 publications

References 23 publications

Machine learning visceral obesity-related genes based on MARVELD1 model for predicting the prognosis and immune cell infiltration of ovarian cancer

Machine learning visceral obesity-related genes based on MARVELD1 model for predicting the prognosis and immune cell infiltration of ovarian cancer

Immunohistochemical features of ovarian cancer

FBXL18 is required for ovarian cancer cell proliferation and migration through activating AKT signaling

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