Targeted drug conjugate systems for ovarian cancer chemotherapy

Identification and validation of genes associated with prognosis of cisplatin-resistant ovarian cancer

PURPOSE

To investigate the role of prognostic genes related to cisplatin resistance in ovarian cancer during disease progression.

METHOD

The gene expression profile of the NCI-60 cell line was acquired through comprehensive analysis of the GEO database accession GSE116439. We performed a thorough analysis of gene expression differences in samples from seven individuals exposed to cisplatin concentrations of 0 nM compared to seven samples exposed to 15000 nM over a 24-h period. Key genes were initially identified through LASSO regression, followed by their enrichment through differential gene function analysis (GO) and pathway enrichment analysis (KEGG). Subsequently, a prognostic risk model was established for these key genes. The prognostic model's performance was assessed through K-M survival curves and ROC curves. To examine the variance in immune cell infiltration between the high and low-risk groups, CIBERSORTx analysis was employed. Finally, validation of prognostic gene expression in cisplatin-resistant ovarian cancer was carried out using clinical samples, employing RT-qPCR and Western Blot techniques.

RESULTS

A total of 132 differential genes were found between cisplatin resistance and control group, and 8 key prognostic genes were selected by analysis, namely VPS13B, PLGRKT, CDKAL1, TBC1D22A, TAP1, PPP3CA, CUX1 and PPP1R15A. The efficacy of the risk assessment model derived from prognostic biomarkers, as indicated by favorable performance on both Kaplan-Meier survival curves and ROC curves. Significant variations in the abundance of Macrophages M1, T cells CD4 memory resting, T cells follicular helper, and T cells gamma delta were observed between the high and low-risk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming differential expression of the identified eight key genes between the two groups.

CONCLUSION

VPS13B, TBC1D22A, PPP3CA, CUX1 and PPP1R15A were identified as poor prognostic genes of cisplatin resistance in ovarian cancer, while PLGRKT, CDKAL1 and TAP1 were identified as good prognostic genes. This offers a novel perspective for future advancements in ovarian cancer treatment, suggesting potential avenues for the development of new therapeutic targets.

Myelopreservation with Trilaciclib in recurrent advanced ovarian cancer: a case report

Ovarian cancer is a prevalent malignant tumor of the female reproductive system, often remaining concealed until it reaches an advanced stage. The standard treatment protocol includes cytoreductive surgery for ovarian cancer plus postoperative consolidation chemotherapy and maintenance therapy, although it carries a high recurrence rate. During the treatment period, chemotherapy can lead to bone marrow suppression, a condition known as Chemotherapy-Induced Myelosuppression (CIM). This suppression may necessitate dose reduction or chemotherapy treatment cycle delay. In severe cases, CIM can result in infection, fever, and potential harm to the patient's life. Here, we report a case of a female patient with ovarian malignant tumor of biochemical recurrence who treated with chemotherapy combined with Trilaciclib, following previous perioperative chemotherapy with occurrence of severe CIM. It involves an intravenous injection of Trilaciclib before chemotherapy, which significantly abates the side effects of chemotherapy, reduces the occurrence of severe CIM, improves the patients' quality of life, and decreases the economic burden of hospitalization. We hope that this retrospective analysis of the case may serve as a reference in preventing and treating severe CIM during chemotherapy in some patients with malignant tumors, ultimately benefiting more patients with tumors.

Current Analytical Methods for the Sensitive Assay of New-Generation Ovarian Cancer Drugs in Pharmaceutical and Biological Samples

Ovarian cancer, which affects the female reproductive organs, is one of the most common types of cancer. Since this type of cancer has a high mortality rate from gynaecological cancers, the scientific community shows great interest in studies on its treatment. Chemotherapy, radiotherapy, and surgical treatment methods are used in its treatment. In the absence of targeted treatments in these treatment methods, side effects occur in patients, and patients show resistance to the drug. In addition, the underlying causes of ovarian cancer are still not fully known. The scientific world thinks that genetic factors, environmental conditions, and consumed foods may cause this cancer. The most important factor in the treatment of ovarian cancer is early diagnosis. Therefore, the drugs used in the treatment of ovarian cancer are platinum-based anticancer drugs. In addition to these drugs, the most preferred treatment method recently is targeted treatment approaches using poly(adenosine diphosphate ribose) polymerase (PARP) inhibitors. In this review, studies on the sensitive analysis of the treatment methods of these new-generation drugs used in the treatment of ovarian cancer have been comprehensively examined. In addition, the basic features, structural aspects, and biological data of analytical methods used in treatments with new-generation drugs are explained. Analytical studies carried out in the literature in recent years aim to show future developments in how these new-generation drugs are used today and to guide future studies by comprehensively examining and explaining the structure-activity relationship, mechanism of action, toxicity, and pharmacokinetic studies. Finally, in this study, the methods used in the analysis of drugs used in the treatment of ovarian cancer and the studies conducted between 2015 and 2023 were discussed in detail.

EM-transcriptomic signature predicts drug response in advanced stages of high-grade serous ovarian carcinoma based on ascites-derived primary cultures

Introduction: High-grade serous ovarian carcinoma (HGSOC) remains a medical challenge despite considerable improvements in the treatment. Unfortunately, over 75% of patients have already metastasized at the time of diagnosis. Advances in understanding the mechanisms underlying how ascites cause chemoresistance are urgently needed to derive novel therapeutic strategies. This study aimed to identify the molecular markers involved in drug sensitivity and highlight the use of ascites as a potential model to investigate HGSOC treatment options. Methods: After conducting an in silico analysis, eight epithelial-mesenchymal (EM)-associated genes related to chemoresistance were identified. To evaluate differences in EM-associated genes in HGSOC samples, we analyzed ascites-derived HGSOC primary cell culture (AS), tumor (T), and peritoneal nodule (NP) samples. Moreover, in vitro experiments were employed to measure tumor cell proliferation and cell migration in AS, following treatment with doxorubicin (DOX) and cisplatin (CIS) and expression of these markers. Results: Our results showed that AS exhibits a mesenchymal phenotype compared to tumor and peritoneal nodule samples. Moreover, DOX and CIS treatment leads to an invasive-intermediate epithelial-to-mesenchymal transition (EMT) state of the AS by different EM-associated marker expression. For instance, the treatment of AS showed that CDH1 and GATA6 decreased after CIS exposure and increased after DOX treatment. On the contrary, the expression of KRT18 has an opposite pattern. Conclusion: Taken together, our study reports a comprehensive investigation of the EM-associated genes after drug exposure of AS. Exploring ascites and their associated cellular and soluble components is promising for understanding the HGSOC progression and treatment response at a personalized level.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.