Prognosis Of Ovarian Cancer Research Articles

Integrated multiomics characterization reveals cuproptosis-related hub genes for predicting the prognosis and clinical efficacy of ovarian cancer

BackgroundAs a prevalent malignancy in women, ovarian cancer (OC) presents a challenge in clinical practice because of its poor prognosis and poor therapeutic efficacy. The mechanism by which cuproptosis activity is accompanied by immune infiltration in OC remains unknown. Here, we investigated cuproptosis-related OC subtypes and relevant immune landscapes to develop a risk score (RS) model for survival prediction.MethodsCuproptosis-related genes (CRGs) were identified to construct molecular subtypes via an unsupervised clustering algorithm based on the expression profiles of survival-related CRGs in the GEO database. Single-cell datasets were used to estimate immune infiltration among subtypes. The RS oriented from molecular subtypes was developed via LASSO Cox regression in the TCGA OC dataset and independently validated in the GEO and TCGA datasets. Hub markers from RS were identified in tissues and cell lines. The function of the key gene from RS was identified in vitro.ResultsWe investigated cuproptosis activity and immune infiltration to establish three clinical subtypes of OC based the differentially expressed genes (DEGs) from CRGs to create an RS model validated for clinical efficacy and prognosis. Six hub genes from the RS served as ongenic markers in OC tissues and cell lines. The function of GAS1 in the RS model revealed that it exerts oncogenic effects.ConclusionsOur study provides a novel RS model including 6 hub genes associated with cuproptosis and immune infiltration to predict OC prognosis as well as clinical efficacy.

CD1a affects the recurrence and prognosis of ovarian cancer.

Explored the correlation between CD1a expression in recurrence and prognosis of ovarian cancer (OV). The CD1a expression profile in OV, recurrent OV, and normal tissues, as well as corresponding clinical data, were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), and Genotype Tissue Expression (GTEx) databases. Meanwhile, immunohistochemical detection of CD1a expression in normal and OV tissues. Kaplan-Meier curves were plotted to estimate the hazard ratio (HR) of survival in OV. In addition, the correlation between CD1a and immune cells in OV, as well as the CD1a expression profile and corresponding survival time in pan-cancer were obtained from TCGA database. CD1a was overexpressed in OV and was significantly under-expressed in recurrent OV (TCGA-OV, p < 0.0001 and ICGC-OV, p < 0.0001). CD1a immunohistochemistry is significantly overexpressed in OV compared to normal tissue (p < 0.05). Recurrent OV (ICGC, p < 0.001; GSE17260, p < 0.001; GSE32062, p < 0.05). The prognosis in OV was significantly better when CD1a is overexpressed compared to under-expressed (HR [low], 1.426: 95% confidence interval [CI], 0.912-2.128; p = 0.050). Meanwhile, the overexpression of CD1a has a better prognosis than low expression in OV and recurrent OV (p = 0.004, HR [low] = 2.462, 95%CI [1.346-4.504] and p = 0.011, HR [low] = 2.199, 95%CI [1.202-4.024]). In addition, CD1a expression was closely correlated with immune cells, the CD8+ T cells, macrophages, and NK cells, while uncharacterized cells were significantly different (p = 2.65e-6, p = 7.52e-13, p = 8.28e-12, and p = 5.89e-8, respectively). Moreover, CD1a expression affected the prognosis in various other cancers. CD1a expression affected the recurrence and prognosis of OV and is closely related to various immune cell levels.

Attenuated titin protein expression correlates with poor prognosis in ovarian cancer

Attenuated titin protein expression correlates with poor prognosis in ovarian cancer

The role of molecular subtypes and immune infiltration characteristics based on disulfidptosis-related genes in ovarian cancer

Ovarian cancer (OC) is the most fatal, gynecological malignancy. Compared with advanced ovarian cancer, the 5 year survival rate of early ovarian cancer is significantly improved, and predicting early detection and diagnosis is very important to improve the prognosis of OC. Recent research has found a new way of cell death: disulfidptosis. Under glucose starvation, abnormal accumulation of disulfide molecules such as Cystine in SLC7A11 overexpression cells induced disulfide stress to trigger cell death. Studies of disulfidptosis are still in their infancy and its role in ovarian cancer progression is unclear. In this study, we used a public database to detect the expression and mutations of disulfidptosis-related genes in OC. Cluster analysis was performed based on disulfidptosis-related genes, and disulfidptosis differential expression genes were analyzed. A prognostic risk model was constructed using three disulfidptosis-related genes, and the reasons for differences in prognosis were explored through immune infiltration analysis and drug sensitivity analysis. The prognostic characteristics of transcriptome based on disulfidptosis-related genes are closely related to the prognosis of OC patients. Finally, quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of three prognostic genes in clinical OC samples.Our study establishes a link between disulfidptosis and OC, providing new ideas for personalized and precise treatment of OC.

Identification of three subtypes of ovarian cancer and construction of prognostic models based on immune-related genes

BackgroundImmunotherapy has revolutionized the treatment of ovarian cancer (OC), but different immune microenvironments often constrain the efficacy of immunotherapeutic interventions. Therefore, there is an imperative to delineate novel immune subtypes for development of efficacious immunotherapeutic strategies.MethodsThe immune subtypes of OC were identified by consensus cluster analysis. The differences in clinical features, genetic mutations, mRNA stemness (mRNAsi) and immune microenvironments were analyzed among subtypes. Subsequently, prognostic risk models were constructed based on differentially expressed genes (DEGs) of the immune subtypes using weighted correlation network analysis.ResultsOC patients were classified into three immune subtypes with distinct survival rates and clinical features. Different subtypes exhibited varying tumor mutation burdens, homologous recombination deficiencies, and mRNAsi levels. Significant differences were observed among immune subtypes in terms of immune checkpoint expression and immunogenic cell death. Prognostic risk models were validated as independent prognostic factors demonstrated great predictive performance for survival of OC patients.ConclusionIn this study, three distinct immune subtypes were identified based on gene sets related to vaccine response, with the C2 subtype exhibiting significantly worse prognosis. While no statistically significant differences in tumor mutation burden (TMB) were observed across the three subtypes, the homologous recombination deficiency (HRD) score and mRNA stemness index (mRNAsi) were notably elevated in the C2 group compared to the others. Immune infiltration analysis indicated that the C2 subtype may have an increased presence of regulatory T (Treg) cells, potentially contributing to a more favorable response to combination therapies involving PARP inhibitors and immunotherapy. These findings offer a precision medicine approach for tailoring immunotherapy in ovarian cancer patients. Moreover, the C3 subtype demonstrated significantly lower expression levels of immune checkpoint genes, a pattern validated by independent datasets, and associated with a better prognosis. Further investigation revealed that the immune-related gene FCRL5 correlates with ovarian cancer prognosis, with in vitro experiments showing that it influences the proliferation and migration of the ovarian cancer cell line SKOV3.

Tumor-infiltrating B cell-related lncRNA crosstalk reveals clinical outcomes and tumor immune microenvironment in ovarian cancer based on single-cell and bulk RNA-sequencing

BackgroundThe tumor immune microenvironment (TIME) plays a pivotal role in determining ovarian cancer (OC) prognosis. Long non-coding RNAs (lncRNAs) are key regulators of immune response and tumor progression in OC. Among these, tumor-infiltrating B cells represent an emerging target in immune response pathways. However, the specific involvement of B cell-related lncRNAs (BCRLs) in OC remains unclarified. MethodsLeveraging single-cell and bulk RNA-sequencing data, correlation analysis identified BCRLs in ovarian serous cystadenocarcinoma (OV) from the TCGA database. Subsequently, BCRLIs were filtered through COX survival analysis and the LASSO algorithm, leading to the development of a B cell-related lncRNA scoring system (BCRLss). The predictive accuracy of BCRLss for prognosis in TCGA-OV was assessed and externally validated in an independent cohort. Functional enrichment analyses were conducted to elucidate biological pathways associated with risk subgroups. Additionally, the relationship between BCRLss and TIME was investigated through multiple algorithms and consensus clustering, uncovering potential immune response targets. Drug sensitivity analyses further identified potential therapeutic options tailored to risk subgroups. The highest risk score lncRNA was selected for in vitro validation. ResultsThe BCRLss was constructed using six BCRLIs. Survival analysis revealed an improved prognosis in the low-risk group, with results corroborated by external validation in the ICGC-OV cohort. ROC analysis and nomogram construction confirmed the strong prognostic accuracy of BCRLss. Enrichment analysis highlighted associations between risk subgroups and tumor immune pathways, with the low-risk group demonstrating a more robust immune response and elevated expression of immune checkpoint-related genes. Drug sensitivity tests revealed notable differences across risk subgroups. In vitro experiments confirmed elevated LINC01150 expression in OC cells, and LINC01150 knockdown significantly inhibited the proliferation, invasion, and migration of SKOV3 cells. ConclusionsIn conclusion, BCRLss provides a reliable prognostic tool for predicting clinical outcomes and the immune landscape of patients with OC, offering valuable guidance for immunotherapy target selection and personalized treatment strategies.

Explore the expression of mitochondria-related genes to construct prognostic risk model for ovarian cancer and validate it, so as to provide optimized treatment for ovarian cancer.

The use of gene development data from public database has become a new starting point to explore mitochondrial related gene expression and construct a prognostic prediction model of ovarian cancer. Data were obtained from the TCGA and ICGC databases, and the intersection with mitochondrial genes was used to obtain the differentially expressed genes. q-PCR, Cox proportional risk regression, minimal absolute contraction and selection operator regression analysis were performed to construct the prognostic risk model, and ROC curve was used to evaluate the model for centralized verification. The association between risk scores and clinical features, tumor mutation load, immune cell infiltration, macrophage activation analysis, immunotherapy, and chemosensitivity was further evaluated. A prognostic risk score model for ovarian cancer patients was constructed based on 12 differentially expressed genes. The score was highly correlated with ovarian cancer macrophage infiltration and was a good predictor of the response to immunotherapy. M1 and M2 macrophages in the ovarian tissue in the OV group were significantly activated, providing a reference for the study of the polarity change of tumor-related macrophages for the prognosis and treatment of ovarian cancer. In terms of drug sensitivity, the high-risk group was more sensitive to vinblastine, Acetalax, VX-11e, and PD-0325901, while the low-risk group was more sensitive to Sabutoclax, SB-505124, cisplatin, and erlotinib. The prognostic risk model of ovarian cancer associated to mitochondrial genes built on the basis of public database better evaluated the prognosis of ovarian cancer patients and guided individual treatment.

Real-life treatment patterns and time to next treatment among patients with ovarian cancer in the pre-PARP inhibitor era: the OCRWE-Finland Study.

As the treatment landscape for advanced ovarian cancer (OC) evolves, it is important to understand patient outcomes in real-world clinical practice. OCRWE-Finland was an observational cohort study investigating OC outcomes, including treatment patterns, time to next treatment 1 (TTNT1), overall survival and healthcare resource utilisation, in Finland during the pre-PARPi era. Patients included in OCRWE-Finland were diagnosed with OC between 2014 and 2019. Here, we report treatment patterns and TTNT1 outcomes (as a surrogate for progression-free survival) for patients in the high-grade serous ovarian carcinoma (HGSOC) cohort. In OCRWE-Finland, there were 867 patients with HGSOC. Of the 811 patients who received first-line treatment, the most common regimen was surgery and adjuvant chemotherapy (53%), and 227 patients also received first-line bevacizumab. Median TTNT1 among 623 patients with stage III/IV disease was 19 months (95% confidence interval, 18-21 months), with no difference between patients with stage III or IV disease (p = 0.24). The presence versus absence of visible residual disease post-debulking surgery was associated with shorter TTNT1 among patients with stage III tumours (p = 0.031) but showed no impact for stage IV tumours (p = 0.55). First-line versus no first-line bevacizumab was associated with shorter TTNT1 among stages I-IV (p < 0.0001) but did not affect patients with stage III/IV tumours (p = 0.45). In the pre-PARPi era, prognosis for advanced OC was poor, particularly for patients with stage III tumours and visible residual disease or stage IV tumours regardless of the presence of residual disease. The increasing use of PARPis will hopefully help address the need for effective treatments in advanced OC.

Effects of Thyroid Hormones on Cellular Development in Human Ovarian Granulosa Tumor Cells (KGN).

Ovarian cancer is a common malignant tumor in the female reproductive system, and Granulosa cell tumor (GCT) of the ovary is a rare type of ovarian cancer, which significantly threatens women's reproductive health. It has been reported that dysregulation of thyroid hormones (THs) may be closely related to the progression and prognosis of ovarian cancer. Moreover, THs regulate phosphorylation of signal transducer and activator of transcription (STAT3) and Octamer-binding transcription factor 4 (OCT4) expression. It has been reported that STAT3 and OCT4 play important roles in cellular development and tumorigenesis. However, the mechanisms by which THs affect the development of GCT are still remained unclear. To evaluate the effect of THs on human ovarian granulosa tumor cells (KGN), cells were treated with 3,5,3' -triiodothyronine (T3). Oct4 small interfering (Oct4 siRNA) or STAT3 inhibitor C188-9 was also co-cultured with cells in some experiments, respectively. The cell viability, proliferation, and proteins content were detected by CCK-8, EdU, and Western Blotting, respectively. The results showed that T3 enhanced cell viability and proliferation. Moreover, T3 also increased the expression of thyroid hormone receptor (TR), p-STAT3, and OCT4 proteins. The effects of T3 on both p-STAT3 and OCT4 expression were blocked by TR antagonist 1-850. Meanwhile, C188-9, an inhibitor of STAT3, decreased T3-induced cellular viability, proliferation, and OCT4 expression, highlighting that p-STAT3 can regulate the expression of OCT4 and affect cellular viability, and proliferation. Furthermore, T3-induced cellular growth was reduced by Oct4 siRNA, which indicates that T3 regulates cellular development through OCT4. These findings suggest that T3 increases cellular development via OCT4, which is mediated by phosphorylation of STAT3, and TR is also involved in these processes.

Investigating PPT2’s role in ovarian cancer prognosis and immunotherapy outcomes

Ovarian cancer (OC) remains the primary cause of mortality among gynecological malignancies, and the identification of reliable molecular biomarkers to prognosticate OC outcomes is yet to be achieved. The gene palmitoyl protein thioesterase 2 (PPT2), which has been sparsely studied in OC, was closely associated with metabolism. This study aimed to determine the association between PPT2 expression, prognosis, immune infiltration, and potential molecular mechanisms in OC. We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) databases, then Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to assess and verify the role of PPT2. Gene set enrichment analysis (GSEA) was used to figure out the closely correlated pathways with PPT2. Overexpression experiment was performed to explore the function of PPT2. Our findings showed that PPT2 mRNA expression was apparent down-regulation in OC tissue compared to normal ovarian tissues in TCGA, GTEx datasets, and GEO datasets. This differential expression was also confirmed in our in-house datasets at both the mRNA and protein levels. Decreased PPT2 expression correlated with lower survival rates in TCGA, several GEO datasets, and our in-house datasets. Multivariate analysis revealed that PPT2 was an independent factor in predicting better outcomes for OC patients in TCGA and GEO. A negative correlation was revealed between immune infiltration and PPT2 expression through Single-sample GSEA (ssGSEA). Additionally, PPT2 was negatively correlated with an up-regulated immune score, stromal score, and estimate score, suggesting that patients with low PPT2 expression might benefit more from immunotherapy. Numerous chemical agents showed lower IC50 in patients with high PPT2 expression. In single-cell RNA sequencing (scRNA-seq) analysis of several OC datasets, we found PPT2 was mainly expressed in endothelial cells. Furthermore, we found that PPT2 inhibited OC cell proliferation in vitro. Our results demonstrated that PPT2 was considered a favorable prognostic biomarker for OC and may be vital in predicting response to immunotherapy and chemotherapy. Further research was needed to fully understand the relationship between PPT2 and immunotherapy efficacy in OC patients.

LncRNA LINC00261 associates with chemoresistance and clinical prognosis in patients with epithelial ovarian cancer.

The purpose of this experiment is to explore the role of long intergenic noncoding RNA 261 (LINC00261) gene in the chemoresistance and clinical prognosis of epithelial ovarian cancer (EOC). We used matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry to detect the methylation levels of the LINC00261 promoter region in EOC patient specimens. The expression levels of LINC00261, miR-545-3p, and MT1M in EOC patients were evaluated by quantitative real-time reverse transcriptase PCR (RT-qPCR). Spearman's correlation analysis was used for relevance analyses and clinical prognosis was counted by Kaplan-Meier analysis. Stable overexpressed LINC00261 SKOV3 cells were established to test the influence of LINC00261 on proliferation, platinum sensitivity, migration, and invasion. The promoter region methylation level of the LINC00261 was hypermethylated and LINC00261 was significantly downregulated in platinum-resistant EOC tissues. The methylation level of LINC00261was significantly negative correlated with its RNA expression in EOC. Moreover, hypermethylation and lower expression of LINC00261 in EOC patients were related to shorter progression-free survival (PFS) and overall survival (OS). Furthermore, Spearman's correlation analysis showed that the expression of miR-545-3p had a negative relevance with LINC00261. According to the website prediction, MT1M might be the downstream target gene of LINC00261. Expression of MT1M was negatively correlated with miR-545-3p and positively with LINC00261 in EOC tissues. And lower MT1M mRNA expression was correlated with chemotherapy resistance and worse prognosis. In vitro, overexpression of LINC00261 could inhibit cisplatin resistance, proliferation, and suppression of migration and invasion in SKOV3 cells. This research indicates that the aberrant hypermethylation and low expression of LINC00261 were associated with platinum resistance and adverse outcomes in EOC patients.

Identification and validation of an m7G-related lncRNAs signature for predicting prognosis, immune response and therapy landscapes in ovarian cancer.

Ovarian cancer is the most mortality malignancy in gynecology. N7-methylguanosine (m7G) is one of the most prevalent RNA modifications in the development and progression of cancer. The aim of this study is to investigate the effect of m7G-related lncRNA on ovarian cancer in terms of instruction prognosis and immunotherapy. After integrating and processing the RNA expression profiles with the clinical sample information in the TCGA database, we initially screened to the m7G-related lncRNAs by Spearman correlation analysis, and subsequently obtained a prognostic model constructed by five m7G-related lncRNAs with Univariate Cox analysis, LASSO regression analysis, and Multivariate Cox regression analysis, after which we further evaluated and validated the prognostic value of the model using Kaplan-Meier survival analysis, Principal component analysis, Nomogram, and ROC curve. In addition, based on this risk model, we explored the differentially enriched pathways and functions of the high and low risk groups, and characterized the immune cells, immune functions, gene mutations, and drug sensitivity between the two groups. After a series of rigorous filtering, we finally attained a prognostic risk model consisting of KRT7-AS, USP30-AS1, ZFHX4-AS1, ACAP2-IT1, and TWSG1-DT which is excellent in predicting the prognostic survival of ovarian cancer patients as well as existing as an independent prognostic factor. Moreover, the model has certain relevance in the immune cells and functions between high and low risk groups, and simultaneously, the signature has the role of guiding the option of immunotherapy and chemotherapeutic drugs. Altogether, our study established a tight connection between m7G-associated lncRNAs and ovarian cancer, with potential that the prognostic patterns contribute to steering the prognosis of ovarian cancer patients, measuring the efficacy of immunotherapeutic approaches, and detecting effective chemotherapeutic agents.

MCL1 inhibitor S63845 delivered by follicle-stimulating hormone modified liposome potentiates carboplatin efficacy in ovarian cancer

Platinum resistance cause therapeutic failure and poor prognosis in ovarian cancer, and evasion of apoptosis is a critical factor in chemoresistance. A limited number of FDA-approved anticancer drugs directly target apoptotic pathways. Here, we discovered that MCL1, a critical anti-apoptotic protein, is amplified and associated with platinum resistance and survival in ovarian cancer, assisting in personalized treatment. We further identified S63845 through drug-based screening, the most potent MCL1 inhibitor, which efficiently enhanced carboplatin (CBP) sensitivity in various ovarian cancer models, including primary ovarian cancer cells, orthotopic ovarian cancer, peritoneal metastasis, and human patient-derived xenograft (PDX) models. Mechanistically, S63845 competitively binds to MCL1, disrupts the binding of apoptosis effector (BAK and BAX) or pro-apoptotic BH3 protein (BIM) to MCL1 respectively, and eventually enhances CBP-induced apoptosis.To promote the clinical transformation of S63845, we developed follicle-stimulating hormone-modified liposome nanoparticles (S63845@Lipo-FSH) to enhance stability, membrane penetration, and tumor-targeting capabilities. S63845@Lipo-FSH exhibits a superior therapeutic efficacy and tumor targeting compared to free S63845, even when the dose of S63845 is reduced to one-fifth. Overall, targeting MCL1 by S63845@Lipo-FSH enhances CBP efficiency in ovarian cancer, with safety and efficacy, suggesting that this strategy is effective and promising for clinical application.

Berberine modulates ovarian cancer autophagy and glycolysis through the LINC01123/P65/MAPK10 signaling axis

BackgroundBerberine, a readily accessible natural compound known for its ease of synthesis and low toxicity, exhibits anti-tumor properties by modulating inflammatory responses. Recent studies have revealed that berberine can also treat malignant tumors by influencing tumor metabolic reprogramming, making it a potential candidate for metabolic therapy in ovarian cancer. MethodsThe anti-proliferative and anti-metastatic effects of berberine on ovarian cancer cells were investigated using CCK-8 assays, scratch assays, EDU proliferation assays, and assays related to glycolysis and autophagy. Differentially expressed lncRNAs in ovarian cancer were identified using data from the TCGA database. A specific lncRNA's role was delineated through RNA pulldown assays, silver staining, mass spectrometry analysis, CHIP assays, and immunoprecipitation experiments, focusing on its involvement in glycolysis and autophagy regulation in ovarian cancer. Additionally, the inhibitory mechanism of berberine on ovarian cancer cells was validated through cell thermal shift assays and cycloheximide protein degradation experiments to confirm its interaction with key targets. ResultsIn vitro experiments revealed that berberine reduces glycolysis and autophagy levels, leading to the inhibition of ovarian cancer cell proliferation and metastasis. Bioinformatics analysis of TCGA data identified LINC00123 as associated with poor prognosis in ovarian cancer. Experimental validation, including RNA pulldown assays, confirmed that the LINC00123/P65/MAPK10 signaling axis regulates glycolysis and autophagy in ovarian cancer. Furthermore, at the molecular level, berberine inhibits the interaction between LINC00123 and P65, thereby reducing P65 protein stability and impeding its transcriptional regulation of downstream MAPK10. These findings were further validated in animal models. ConclusionOur study highlights berberine's dual benefits of anti-inflammatory effects and inhibition of ovarian cancer proliferation and metastasis by modulating autophagy and glycolysis levels. Mechanistically, berberine targets the LINC00123/P65/MAPK10 signaling pathway to regulate glycolysis and autophagy in ovarian cancer. These insights not only expand the potential of berberine in ovarian cancer therapy but also provide new targets and therapeutic strategies for metabolic therapy in this cancer type.

Deep Learning Models for Early Detection of Ovarian Cancer: A Systematic Review of Ultrasound-Based Diagnostic Tools

Ovarian Cancer (OC) remains one of the most lethal gynecological malignancies, with a global 5-year survival rate of only 45%. This project aimed to assess the potential of deep learning-based diagnostic tools in improving early detection of ovarian cancer, particularly through ultrasound imaging. The primary research problem lies in the challenge of accurately diagnosing OC at an early stage, as traditional imaging techniques often rely on subjective interpretations, leading to inconsistent results. To address this issue, a systematic review was conducted following PRISMA guidelines, evaluating studies published between January 2018 and May 2023 in the Scopus and PubMed databases. These studies employed deep learning or artificial intelligence models for the diagnosis, prognosis, or identification of OC from ultrasound images. Of the 101 studies screened, 9 met the inclusion criteria. The included studies reported diagnostic accuracies of deep learning models ranging from 75% to 100%, with sensitivities between 85% and 99%. The conclusions indicate that deep learning models significantly enhance the diagnostic accuracy of ovarian cancer, offering a promising non-invasive tool for early detection. This research underscores the importance of integrating AI technologies into clinical practice to improve survival outcomes for OC patients.

Predicting the Recurrence of Ovarian Cancer Based on Machine Learning.

Recurrence is the main factor for poor prognosis in ovarian cancer, but few prognostic biomarkers were reported. In this study, we used machine learning methods based on multiple biomarkers to develop a specific prediction model for the recurrence of ovarian cancer. A total of 277 ovarian cancer patients were enrolled in this study and randomly classified into training and testing cohorts. The prediction information was obtained through 47 clinical parameters using six supervised clustering machine learning algorithms, including K-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). In predicting the recurrence of ovarian cancer, machine learning algorithm was superior to conventional logistic regression analysis. In this study, XGBoost showed the best performance in predicting the recurrence of ovarian cancer, with an accuracy of 0.95. In addition, neoadjuvant chemotherapy, Monocyte ratio (MONO%), Hematocrit (HCT), Prealbumin (PAB), Aspartate aminotransferase (AST), and carbohydrate antigen 125 (CA125) are the most important biomarkers to predict the recurrence of ovarian cancer. The machine learning techniques can achieve a more accurate assessment of the recurrence of ovarian cancer, which can help clinicians make decisions, and develop personalized treatment strategies.

Radiomics Signatures Based on Computed Tomography for Noninvasive Prediction of CXCL10 Expression and Prognosis in Ovarian Cancer.

Ovarian cancer (OC) is an aggressive gynecological tumor usually diagnosed with malignant ascites and even observed widespread metastasis or distant spread. We aimed to develop and identify radiomics models according to computed tomography (CT) for preoperative prediction of CXCL10 expression and prognosis in patients with OC. Genomic data with CT images and corresponding clinicopathological parameters were extracted from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To analyze the prognosis, we carried out the univariate Cox regression analysis (UCRA), multivariate Cox regression analysis (MCRA), and Kaplan-Meier (KM) analysis. For the data reduction, logistic regression, operator regression, least absolute shrinkage selection, radiomic feature construction, and feature selection were utilized. The predictive performance of the radiomic signatures was assessed using the analyses of the receiver operating characteristic (ROC) curve, decision curve (DCA), and precision-recall (PR) curve. To evaluate the correlation between the radiomic score (Rad-score) and CXCL10 expression, the Wilcoxon rank-sum test was applied. Three radiomics models effectively predicted CXCL10 expression levels (AUC = 0.791, 0.748, and 0.718 for the set of training; AUC = 0.761, 0.746, and 0.701 for the set of validation). A higher Rad-score significantly correlated with upregulated CXCL10 expression. CXCL10 expression can be predicted noninvasively and preoperatively via radiomic signatures based on contrast-enhanced CT images.

Association between pre-diagnosis and post-diagnosis Alternate Mediterranean Diet and ovarian cancer survival: evidence from a prospective cohort study

BackgroundThere is currently a lack of comprehensive evidence regarding the correlation between Alternate Mediterranean Diet (AMED) and the survival of patients with ovarian cancer (OC). This prospective cohort study first assessed the association of AMED, not only pre-diagnosis and post-diagnosis but also the change from pre-diagnosis to post-diagnosis with OC survival.MethodsA total of 560 OC patients were included in the study, and their dietary intake was assessed using a reliable 111-item food frequency questionnaire. The overall survival (OS) of the patients was monitored through active follow-up and review of medical records until February 16th, 2023. Cox proportional hazard regression models were utilized to compute the hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs).ResultsOut of the total 560 patients with OC, 211 (37.68%) succumbed during a median follow-up period of 44.40 months (interquartile range: 26.97–61.37). Comparative analysis indicated a significant association between the highest tertiles of pre-diagnosis (HR = 0.59; 95% CI 0.38–0.90; Ptrend < 0.05) and post-diagnosis (HR = 0.61; 95% CI 0.41–0.91; Ptrend < 0.05) AMED intake and improved OS as opposed to the lowest tertile. Additionally, a significant linear trend was observed for AMED and OC survival. Notably, decreased intake (more than 5% change) and significantly increased intake (more than 15% change) of AMED from pre-diagnosis to post-diagnosis were linked to worse and better OS, respectively, when compared to the stable intake group (change within 5%). Furthermore, patients displaying consistently higher AMED intake both before and after diagnosis experienced enhanced OS in comparison to those with consistently low AMED intake (HRHigh-High vs. Low-Low = 0.47; 95% CI 0.31–0.70).ConclusionHigh pre-diagnosis and post-diagnosis AMED was associated with an improved OS in patients with OC, suggesting that maintaining a consistently high intake of AMED could potentially benefit the prognosis of OC.

Use of Dietary Supplements Before, During and After Treatment for Ovarian Cancer: Results from the Ovarian Cancer Prognosis and Lifestyle (OPAL) Study

The use of dietary supplements by cancer patients is common but contentious, particularly during chemotherapy. Few studies have investigated this for ovarian cancer. In a prospective study of women with ovarian cancer, dietary supplement use was collected through questionnaires. Data on the use of supplements were available for 421 women before diagnosis, during chemotherapy, and after chemotherapy completion. Predictors of changes in supplement use were investigated using logistic regression. The use of ≥1 supplement pre-diagnosis, during, and after chemotherapy completion was reported by 72%, 57%, and 68% of women, respectively. Multivitamins, vitamin D, and fish oils were the most commonly used supplements at all time points. The supplements most commonly discontinued during treatment were fish oils (69% of pre-diagnosis users) and multivitamins (53% of users); while 9%–10% of pre-diagnosis non-users initiated vitamin D and multivitamins. Predictors of supplement initiation during chemotherapy included pre-diagnosis use of medications, such as statins (Odds Ratio, OR = 4.12, 95% confidence interval, CI = 1.28–13.3), antidepressants (5.39, 1.18–24.7), acetaminophen (3.13, 1.05–9.33), and NSAIDs (2.15, 0.81–5.72). Other factors included younger age, university education, neoadjuvant chemotherapy, and/or experiencing fatigue during treatment, although not statistically significant. In conclusion, a high proportion of women with ovarian cancer reported using supplements at all time points.

Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer.

Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined. Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells. A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells. Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.