Identification of a Gene Set Correlated With Immune Status in Ovarian Cancer by Transcriptome-Wide Data Mining

Metabolism-Related Programmed Cell Death: Unveiling Prognostic Biomarkers, Immune Checkpoints, and Therapeutic Strategies in Ovarian Cancer

BACKGROUND

Ovarian cancer (OC), the gynecologic malignancy with the poorest prognosis, is driven by metabolic reprogramming and dysregulated programmed cell death (PCD). However, their interplay and prognostic significance remain inadequately understood.

METHODS

Transcriptomic data from OC patients and healthy controls (TCGA and GTEx) were analyzed to identify differentially expressed genes (DEGs) intersecting with metabolism-related (MRGs) and PCD-related genes (PCDRGs). Prognostic genes were determined using univariate Cox regression, LASSO, multivariate Cox regression, and stepwise analyses. Consensus clustering revealed enrichment differences, while a risk model and nomogram were developed for outcome prediction. Associations between prognostic genes, immune microenvironment, and drug sensitivity were also assessed.

RESULTS

A total of 166 candidate genes were identified, with PLA2G2D, LPCAT3, ARG1, PLA2G4A, and EXOSC3 emerging as significant prognostic markers. The risk model demonstrated marked survival differences, while the nomogram showed robust calibration for survival prediction. Differential immune cell infiltration was observed between risk groups. Additionally, Sinularin and Fulvestrant exhibited variable sensitivity, validated through molecular docking models.

CONCLUSION

Metabolism-related PCD genes were identified as pivotal prognostic markers in OC, providing critical insights for prognostic evaluation and targeted therapy development.

Revisiting the CXCL13/CXCR5 axis in the tumor microenvironment in the era of single-cell omics: Implications for immunotherapy

As one of the important members of the family of chemokines and their receptors, the CXCL13/CXCR5 axis is involved in follicle formation in normal lymphoid tissues and the establishment of somatic cavity immunity under physiological conditions, as well as being associated with a wide range of infectious, autoimmune, and tumoral diseases. Here in this review, we focus on its role in tumors. Traditional studies have found the axis to be both pro- and anti-tumorigenic, involving a variety of immune cells, including the tumor cells themselves and those in the tumor microenvironment (TME), and the prognostic significance of this axis is clinical context-dependent. With the development of techniques at the single-cell level, we were able to explain in detail the status of the CXCL13/CXCR5 axis in the TME based on real clinical samples and found that it involves a range of crucial intrinsic anti-tumor immune processes in the TME and is therefore important in tumor immunotherapy. We summarize the cellular subsets, physiological functions, and prognostic significance associated with this axis in the most promising immune checkpoint inhibitor (ICI) therapies of the day and summarize possible therapeutic ideas based on this axis. As with any TME study, the most important takeaway is that the complexity of the CXCL13/CXCR5 axis in TME suggests the importance of personalized therapy in tumor therapy.

Extracellular vesicles as a hydrolytic platform of secreted phospholipase A2

Extracellular vesicles (EVs) represent small vesicles secreted from cells, including exosomes (40-150 nm in diameter), which are released via the multivesicular endosomal pathway, and microvesicles and ectosomes (100-1000 nm), which are produced by plasma membrane budding. Broadly, EVs also include vesicles generated from dying cells, such as apoptotic bodies (5-10 μm), as well as exomeres (< 50 nm), which are very small, non-membranous nanoparticles. EVs play important roles in cell-to-cell signaling in various aspects of cancer, immunity, metabolism, and so on by transferring proteins, microRNAs (miRNAs), and metabolites as cargos from donor cells to recipient cells. Although lipids are one of the major components of EVs, they have long been recognized as merely the "wall" that partitions the lumen of the vesicle from the outside. However, it has recently become obvious that lipid composition of EVs influences their properties and functions, that EVs act as a carrier of a variety of lipid mediators, and that lipid mediators are produced in EV membranes by the hydrolytic action of secreted phospholipase A2s (sPLA2s). In this article, we will make an overview of the roles of lipids in EVs, with a particular focus on sPLA2-driven mobilization of lipid mediators from EVs and its biological significance.

Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer

BACKGROUND

Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy.

METHODS

The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics.

RESULTS

We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group.

CONCLUSIONS

The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies.

PLA2G2D promotes immune escape in non-small cell lung cancer by regulating T cell immune function through PD-L1-expressing extracellular vesicles

It is urgent to explore factors affecting immunotherapy efficacy to benefit non-small cell lung cancer (NSCLC) patient survival. Bioinformatics predicted genes associated with programmed cell death ligand 1 (PD-L1) expression and analysed phospholipase A2 group IID (PLA2G2D) expression in NSCLC. BODIPY 493/503 dye staining and kits detected lipids, triglycerides, and phospholipids in H1299 cells, respectively. Extracellular vesicles (EVs) were extracted for morphology and size assessment using electron microscopy. Western blot assayed CD9, CD63, HSP90, EVs-PD-L1, PD-L1, and PLA2G2D expression. CCK-8, LDH, and ELISA tested proliferation and toxicity of CD8+ T cells, interleukin-2, and interferon-gamma secretion, respectively. PLA2G2D, PD-L1, and Ki67 expression was detected by immunohistochemistry. Immunofluorescence assayed PLA2G2D localisation and CD8+ T cell content. Flow cytometry assessed PD-L1 and CD8 expression. In NSCLC, upregulated EVs-PD-L1 and clinical characteristics showed a strong correlation. H1299 cells with overexpression PD-L1 significantly reduced proliferation, toxicity of CD8+ T cells, and interleukin-2 and interferon-gamma levels. Bioinformatics revealed positive correlations between PLA2G2D and overexpressed PD-L1. PLA2G2D was expressed in macrophages and dendritic cells in NSCLC tissue. Overexpression PLA2G2D (oe-PLA2G2D) increased lipids, triglycerides, and phospholipids contents in H1299 cells. oe-PLA2G2D significantly reduced proliferation, toxicity of CD8+ T cells, and interleukin-2 and interferon-gamma levels. si-PD-L1 restored inhibition of oe-PLA2G2D on CD8+ T cells. oe-PLA2G2D significantly increased mice tumour volume and weight, upregulated expression of blood EVs-PD-L1 and tissue PD-L1, PLA2G2D, Ki67, and decreased CD8+ T cell content. PLA2G2D facilitated immune escape in NSCLC by regulating CD8+ T cell immune function by upregulating EVs-PD-L1.

Two Different Immune Profiles Are Identified in Sentinel Lymph Nodes of Early-Stage Breast Cancer

In the management of early-stage breast cancer (BC), lymph nodes (LNs) are typically characterised using the One-Step Nucleic Acid Amplification (OSNA) assay, a standard procedure for assessing subclinical metastasis in sentinel LNs (SLNs). The pivotal role of LNs in coordinating the immune response against BC is often overlooked. Our aim was to improve prognostic information provided by the OSNA assay and explore immune-related gene signatures in SLNs. The expression of an immune gene panel was analysed in SLNs from 32 patients with Luminal A early-stage BC (cT1-T2 N0). Using an unsupervised approach based on these expression values, this study identified two clusters, regardless of the SLN invasion: one evidencing an adaptive anti-tumoral immune response, characterised by an increase in naive B cells, follicular T helper cells, and activated NK cells; and another with a more undifferentiated response, with an increase in the activated-to-resting dendritic cells (DCs) ratio. Through a protein-protein interaction (PPI) network, we identified seven immunoregulatory hub genes: CD80, CD40, TNF, FCGR3A, CD163, FCGR3B, and CCR2. This study shows that, in Luminal A early-stage BC, SLNs gene expression studies enable the identification of distinct immune profiles that may influence prognosis stratification and highlight key genes that could serve as potential targets for immunotherapy.

Fc receptor-like A promotes malignant behavior in renal cell carcinoma and correlates with tumor immune infiltration

BACKGROUND

Our study aims to investigate the mechanisms through which Fc receptor-like A (FCRLA) promotes renal cell carcinoma (RCC) and to examine its significance in relation to tumor immune infiltration.

MATERIALS AND METHODS

The correlation between FCRLA and data clinically related to RCC was explored using The Cancer Genome Atlas (TCGA), then validated using Gene Expression Omnibus (GEO) gene chip data. Enrichment and protein-protein interaction (PPI) network analyses were performed for FCRLA and its co-expressed genes. FCRLA was knocked down in RCC cell lines to evaluate its impact on biological behavior. Then the potential downstream regulators of FCRLA were determined by western blotting, and rescue experiments were performed for verification. The relevance between FCRLA and various immune cells was analyzed through GSEA, TIMER, and GEPIA tools. TIDE and ESTIMATE algorithms were used to predict the effect of FCRLA in immunotherapy.

RESULTS

Fc receptor-like A was associated with clinical and T stages and could predict the M stage (AUC = 0.692) and 1-3- and 5-year survival rates (AUC = 0.823, 0.834, and 0.862) of RCC patients. Higher expression of FCLRA predicted an unfavorable overall survival (OS) in TCGA-RCC and GSE167573 datasets (p = 0.03, p = 0.04). FCRLA promoted the malignant biological behavior of RCC cells through the pERK1/2/-MMP2 pathway and was associated with tumor immune microenvironment in RCC.

CONCLUSION

Fc receptor-like A is positively correlated with poor outcomes in RCC patients and plays an oncogenic role in RCC through the pERK1/2-MMP2 pathway. Patients with RCC might benefit from immunotherapy targeting FCRLA.

SYNE1 Mutation Is Associated with Increased Tumor Mutation Burden and Immune Cell Infiltration in Ovarian Cancer

SYNE1, a nuclear envelope protein critical for cellular structure and signaling, is downregulated in numerous malignancies. SYNE1 alterations are found in 10% of gynecologic malignancies and 5% of epithelial ovarian cancers. Previous studies demonstrated an association between SYNE1 mutation, increased tumor mutation burden (TMB), and immunotherapy response. This study evaluates the SYNE1 mutation frequency, association with TMB, and downstream effects of SYNE1 mutation in ovarian cancer. Genetic information, including whole-exome sequencing, RNA analysis, and somatic tumor testing, was obtained for consenting ovarian cancer patients at an academic medical center. Mutation frequencies were compared between the institutional cohort and The Cancer Genome Atlas (TCGA). Bioinformatics analyses were performed. In our cohort of 50 patients, 16 had a SYNE1 mutation, and 15 had recurrent disease. Median TMB for SYNE1 mutated patients was 25 compared to 7 for SYNE1 wild-type patients (p < 0.0001). Compared to the TCGA cohort, our cohort had higher SYNE1 mutation rates (32% vs. 6%, p < 0.001). Gene expression related to immune cell trafficking, inflammatory response, and immune response (z > 2.0) was significantly increased in SYNE1 mutated patients. SYNE1 mutation is associated with increased TMB and immune cell infiltration in ovarian cancer and may serve as an additional biomarker for immunotherapy response.

LncRNA HOTAIR as a ceRNA is related to breast cancer risk and prognosis

PURPOSE

Breast cancer (BC) is one of the biggest threats to women's health. LncRNA HOTAIR is related to the recurrence and metastasis of BC. Whether HOTAIR can serve as an effective biomarker to distinguish BC patients with different prognosis need to be further studied.

METHODS

The miRNA and mRNA expression profile data of BC patients were downloaded from TCGA database. Univariate Cox regression was used to screen differential expression genes (DEGs). The miRcode database and miRWalk database were used to predict miRNA binding to HOTAIR and binding sites of miRNAs, respectively. Kaplan-Meier (KM) analysis was used to estimate the overall survival rate of BC patients. Finally, qRT-PCR and western blot were applied to evaluate the expression level of HOTAIR and mRNAs between BC cells and normal mammary cells.

RESULTS

The patients with high HOTAIR expression had poor prognosis in BC. Totally 10 genes correlated with BC prognosis were identified from 170 DEGs, among which PAX7, IYD, ZIC2, MS4A1, TPRXL, CD24, LHX1 were positively correlated with HOTAIR, while CHAD, NPY1R, TPRG1 were opposite. The levels of IYD, ZIC2, CD24 mRNA and protein were increased in BC tissues and BC cells. In BC cells, the levels of IYD, ZIC2 and CD24 mRNA and protein were significantly increased in HOTAIR overexpressed group. HOTAIR had the strongest interaction with hsa-miR-129-5p, followed by hsa-miR-107.

CONCLUSION

HOTAIR regulated the expression of downstream genes by interacting with 8 miRNAs and ultimately affected the prognosis of BC patients.

Peripheral and tumor-infiltrating immune cells are correlated with patient outcomes in ovarian cancer

OBJECTIVE

At present, there is still a lack of reliable biomarkers for ovarian cancer (OC) to guide prognosis prediction and accurately evaluate the dominant population of immunotherapy. In recent years, the relationship between peripheral blood markers and tumor-infiltrating immune cells (TICs) with cancer has attracted much attention. However, the relationship between the survival of OC patients and intratumoral- or extratumoral-associated immune cells remains controversial.

METHODS

In this study, four machine-learning algorithms were used to predict overall survival in OC patients based on peripheral blood indicators. To further screen out immune-related gene and molecular targets, we systematically explored the correlation between TICs and OC patient survival based on The Cancer Genome Atlas database. Using the TICs score method, patients were divided into a low immune infiltrating cell group and a high immune infiltrating cell group.

RESULTS

The results showed that there was a significant statistical significance between the peripheral blood indicators and the survival prognosis of OC patients. Survival analysis showed that TICs play a crucial role in the survival of OC patients. Four core genes, CXCL9, CD79A, MS4A1, and MZB1, were identified by cross-PPI and COX regression analysis. Further analysis found that these genes were significantly associated with both TICs and survival in OC patients.

CONCLUSIONS

These results suggest that both peripheral blood markers and TICs can be used as prognostic predictors in patients with OC, and CXCL9, CD79A, MS4A1, and MZB1 may be potential therapeutic targets for OC immunotherapy.

The current landscape of predictive and prognostic biomarkers for immune checkpoint blockade in ovarian cancer

Immune checkpoint blockade (ICB) therapy has evoked a prominent shift in anticancer therapy. Durable clinical antitumor activity to ICB has been observed in patients with ovarian cancer (OC). However, only a subset of patients derive clinical benefit, and immune-related adverse events (irAEs) caused by ICB therapy can lead to permanent tissue damage and even fatal consequences. It is thus urgent to develop predictive biomarkers to optimize patient outcomes and minimize toxicity risk. Herein, we review current predictive and prognostic biomarkers for checkpoint immunotherapy in OC and highlight emerging biomarkers to guide treatment with ICB. The prevalent biomarkers, such as PD-L1 expression status, tumor-infiltrating lymphocytes, mutational burden, and immune gene signatures, are further discussed. We provide a state-of-the-art survey on prognostic and predictive biomarkers for checkpoint immunotherapy and offer valuable information for guiding precision immunotherapy

Single-Cell Transcriptome Analysis Reveals Different Immune Signatures in HPV- and HPV + Driven Human Head and Neck Squamous Cell Carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is a significant health problem and related to poor long-term outcomes, indicating more research to be done to deeply understand the underlying pathways.

Objective

This current study aimed in the assessment of the viral- (especially human papilloma virus [HPV]) and carcinogen-driven head and neck squamous cell carcinoma (HNSCC) microenvironment based on single-cell sequencing analysis.

Methods

Data were downloaded from GEO database (GSE139324), including 131224 cells from 18 HP- HNSCC patients and 8 HPV+ HNSCC patients. Following data normalization, all highly variable genes in single cells were identified, and batch correction was applied. Differentially expressed genes were identified using Wilcoxon rank sum test. A gene enrichment analysis was performed in each cell cluster using KEGG analysis. Single-cell pseudotime trajectories were constructed with MONOCLE (version 2.6.4). Cell-cell interactions were analyzed with CellChat R package. Additionally, cell-cell communication patterns in key signal pathways were compared in different tissue groups. A hidden Markov model (HMM) was used to predict gene expression states (on or off) throughout pseudotime. Five-year overall survival outcomes were compared in both HPV+ and HPV- subsets.

Results

20,978 high-quality individual cells passed quality control. RNA-seq data were used from 522 HNSCC primary tumor samples. 1,137 differentially expressed genes between HPV+ and HPV- HNSCC patients were investigated. 96 differentially expressed genes were associated with overall survival and highly enriched in B cell associated biological process. Cell composition differed between types of samples. MHC-I, MHC-II, and MIF signaling pathways were found to be most relevant. Within these pathways, some cells were either signal receiver or signal sender, depending on sample type, respectively. Six genes were obtained, AREG and TGFBI (upregulation), CD27, CXCR3, MS4A1, and CD19 (downregulation), whose expression and HPV types were highly associated with worse overall survival. AREG and TGFBI were pDC marker genes, CXCR3 and CD27 were significantly expressed in T cell-related cells, while MS4A1 and CD19 were mainly expressed in B naïve cells.

Conclusions

This study revealed dynamic changes in cell percentage and heterogeneity of cell subtypes of HNSCC. AREG, TGFBI, CD27, CXCR3, MS4A1, and CD19 were associated with worse overall survival in HPV-related HNSCC. Especially B-cell related pathways were revealed as particularly relevant in HPV-related HNSCC. These findings are a basis for the development of biomarkers and therapeutic targets in respective patients.

Identification of a Prognostic Model Based on Fatty Acid Metabolism-Related Genes of Head and Neck Squamous Cell Carcinoma

The fatty acid metabolism (FAM) is known to impact tumorigenesis, tumor progression and treatment resistance via enhancing lipid synthesis, storage and catabolism. However, the role of FAM in head and neck squamous cell carcinoma (HNSCC) has remained elusive. In the present study, we obtained a total of 69 differentially expressed FAM-related genes between 502 HNSCC samples and 44 normal samples from The Cancer Genome Atlas (TCGA) database. The HNSCC samples were divided into 2 clusters according to 69 differentially expressed genes (DEGs) via cluster analysis. Then DEGs in the two clusters were found, and 137 prognostic DEGs were identified by univariate analysis. Subsequently, combined with the clinical information of 546 HNSCC patients from TCGA database, a 12-gene prognostic risk model was established (FEPHX3, SPINK7, FCRLA, MASP1, ZNF541, CD5, BEST2 and ZAP70 were down-regulation, ADPRHL1, DYNC1I1, KCNG1 and LINC00460 were up-regulation) using multivariate Cox regression and LASSO regression analysis. The risk scores of 546 HNSCC samples were calculated. According to the median risk score, 546 HNSCC patients were divided into the high- and low-risk (high- and low score) groups. The Kaplan-Meier survival analysis showed that the survival time of HNSCC patients was significantly shorter in the high-risk group than that in the low-risk group (p < 0.001). The same conclusion was obtained in the Gene Expression Omnibus (GEO) dataset. After that, the multivariate Cox regression analysis indicated that the risk score was an independent factor for patients with HNSCC in the TCGA cohort. In addition, single-sample gene set enrichment analysis (ssGSEA) indicated that the level of infiltrating immune cells was relatively low in the high-risk group compared with the low-risk group. In summary, FAM-related gene expression-based risk signature could predict the prognosis of HNSCC independently.

Identification of NAD+ Metabolism-Derived Gene Signatures in Ovarian Cancer Prognosis and Immunotherapy

Background: Nicotinamide adenine dinucleotide (NAD⁺) has emerged as a critical regulator of cell signaling and survival pathways, affecting tumor initiation and progression. In this study it was investigated whether circulating NAD⁺ metabolism-related genes (NMRGs) could be used to predict immunotherapy response in ovarian cancer (OC) patients.

Method: In this study, NMRGs were comprehensively examined in OC patients, three distinct NMRGs subtypes were identified through unsupervised clustering, and an NAD⁺-related prognostic model was generated based on LASSO Cox regression analysis and generated a risk score (RS). ROC curves and an independent validation cohort were used to assess the model’s accuracy. A GSEA enrichment analysis was performed to investigate possible functional pathways. Furthermore, the role of RS in the tumor microenvironment, immunotherapy, and chemotherapy was also investigated.

Result: We found three different subgroups based on NMRGs expression patterns. Twelve genes were selected by LASSO regression to create a prognostic risk signature. High-RS was founded to be linked to a worse prognosis. In Ovarian Cancer Patients, RS is an independent prognostic marker. Immune infiltrating cells were considerably overexpressed in the low-RS group, as immune-related functional pathways were significantly enriched. Furthermore, immunotherapy prediction reveal that patients with low-RS are more sensitive to immunotherapy.

Conclusion: For a patient with OC, NMRGs are promising biomarkers. Our prognostic signature has potential predictive value for OC prognosis and immunotherapy response. The results of this study may help improve our understanding of NMRG in OCs.

Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

BACKGROUND

The tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.

METHODS AND RESULTS

The stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.

CONCLUSION

These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.