Background: Cancer-associated fibroblasts (CAFs) are essential components of the tumor microenvironment (TME). These cells play a supportive role throughout cancer progression. Their ability to modulate the immune system has also been noted. However, there has been limited investigation of CAFs in the TME of epithelial ovarian cancer (EOC).Methods: We comprehensively evaluated the CAF landscape and its association with gene alterations, clinical features, prognostic value, and immune cell infiltration at the pan-cancer level using multi-omic data from The Cancer Genome Atlas (TCGA). The CAF contents were characterized by CAF scores based on the expression levels of seven CAF markers using the R package “GSVA.” Next, we identified the molecular subtypes defined by CAF markers and constructed a CAF riskscore system using principal component analysis in the EOC cohort. The correlation between CAF riskscore and TME cell infiltration was investigated. The ability of the CAF riskscore to predict prognosis and immunotherapy response was also examined.Results: CAF components were involved in multiple immune-related processes, including transforming growth factor (TGF)-β signaling, IL2-STAT signaling, inflammatory responses, and Interleukin (IL) 2-signal transducer and activator of transcription (STAT) signaling. Considering the positive correlation between CAF scores and macrophages, neutrophils, and mast cells, CAFs may exert immunosuppressive effects in both pan-cancer and ovarian cancer cohorts, which may explain accelerated tumor progression and poor outcomes. Notably, two distinct CAF molecular subtypes were defined in the EOC cohort. Low CAF riskscores were characterized by favorable overall survival (OS) and higher efficacy of immunotherapy. Furthermore, 24 key genes were identified in CAF subtypes. These genes were significantly upregulated in EOC and showed a strong correlation with CAF markers.Conclusions: Identifying CAF subtypes provides insights into EOC heterogeneity. The CAF riskscore system can predict prognosis and select patients who may benefit from immunotherapy. The mechanism of interactions between key genes, CAF markers, and associated cancer-promoting effects needs to be further elucidated.

Background: Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis.Purpose: We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers.Methods: A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis.Results: ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001).Conclusions: Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

Ovarian cancer (OC) is the most lethal reproductive system cancer and a leading cause of cancer-related death. The high mortality rate and poor prognosis of OC are primarily due to its tendency for extensive abdominal metastasis, late diagnosis in advanced stages, an immunosuppressive tumor microenvironment, significant adverse reactions to first-line chemotherapy, and the development of chemoresistance. Current adjuvant chemotherapies face challenges such as poor targeting, low efficacy, and significant side effects. Targeted drug delivery systems (TDDSs) are designed to deliver drugs precisely to the tumor site to enhance efficacy and minimize side effects. This review highlights recent advancements in the use of TDDSs for OC therapies, including drug conjugate delivery systems, nanoparticle drug delivery systems, and hydrogel drug delivery systems. The focus is on employing TDDS to conduct direct, effective, and safer interventions in OC through methods such as targeted tumor recognition and controlled drug release, either independently or in combination. This review also discusses the prospects and challenges for further development of TDDSs. Undoubtedly, the use of TDDSs shows promise in the battle against OCs.

The initiation and progression of cancer depend on the genetic alterations inherent in cancer cells, coupled with the mutual interplay of cancer cells with the surrounding tumor stroma. The platelet-derived growth factor (PDGF) family, as a mesenchymal growth factor, was involved in tumor progression by affecting the surrounding tumor stroma in some cancer types. However, the association of the PDGF family with the ovarian cancer stroma remains elusive. In our study, we first explored the expression pattern of the PDGF family using RNA expression profiles from public databases. We found that the PDGF family was highly expressed in tumor stroma compared with the corresponding epithelial components of ovarian cancer. In particular, PDGF receptors were weakly expressed in ovarian cancer tissues compared with the respective normal tissues; even in tumor mass, PDGF receptors were predominantly expressed by tumor stroma rather than ovarian cancer cells. Importantly, functional enrichment analyses and correlation analyses revealed that the PDGF family was strongly associated with activated stromal scores in ovarian cancer, including higher stromal scores, enriched pathways related to the extracellular matrix (ECM) organization and remodeling, elevated cancer-associated fibroblasts (CAFs) infiltration, and increased tumor-associated macrophages (TAMs) infiltration, especially macrophage M2. Besides, the positive correlations of the PDGF family with CAFs infiltration and macrophage M2 infiltration were observed in other various cancer types. Of note, the PDGF family was also involved in tumor progression-related pathways, such as transforming growth factor β (TGF-β) signaling, epithelial-mesenchymal transition (EMT), angiogenesis, and phosphatidylinositol 3-kinase-Akt (PI3K-Akt) signaling. Higher expressions of PDGF receptors were also observed in ovarian cancer patients with venous or lymphatic invasion. Furthermore, we uncovered the prognostic prediction of the PDGF family in ovarian cancer and constructed a PDGF family-based risk prognosis model with a hazard ratio of 1.932 ( ) and value < 0.01 ( , 0.752 for 1 year and 2 years, respectively). Taken together, we demonstrated that ovarian cancers with high PDGF family expression biologically exhibit malignant progression behaviors as well as poor clinical survival, which is attributed to the activated tumor stroma in ovarian cancer.