Establishment and validation of a prognostic model based on grouping of vasculogenic mimicry-related genes in ovarian cancer

Establishment and validation of a prognostic model based on grouping of vasculogenic mimicry-related genes in ovarian cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Establishment and validation of a prognostic model based on grouping of vasculogenic mimicry-related genes in ovarian cancer Xueyuan Zhao, Yan Jia, Weijia Wen, Caixia Shao, Qiaojian Zou, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4336317/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Vasculogenic mimicry (VM) is a vascular-like microcirculatory delivery system directly formed by tumor cells. It plays significant roles in tumor invasion and resistance to antiangiogenic drugs. However, there is no model based on VM for ovarian cancer (OC) prognosis. Results The occurrence and progression of OC is associated with vasculogenic mimicry-related genes (VMGs) interactions in a cellular network. The prognostic VMGs can distinguish between two subgroups with significantly different prognoses. Differentially expressed genes (DEGs) between the subgroups suggest that certain signaling pathways and cellular functions are involved in the formation or promotion of VM. Based on filtered DEGs, a prognostic model was constructed and demonstrated outstanding performance in internal and external validations. The model results do not affect the distribution of clinical features and can be used to construct a nomogram. There are differences in immune cell composition and immune infiltration between the high-risk group and the low-risk group. Differences also exist in their resistance and sensitivity to certain drugs. Finally, subgroups analysis demonstrated differences in ovarian cancer VM structures which may be associated with different expressions of VMGs and VM-related prognostic indices (VMRPI). Conclusions VM may play a crucial role in the development and tumor immune microenvironment of serous ovarian cancer. VMRPI holds promise as a valuable prognostic biomarker. This is the first time that it is used to identify high risk OC patients with ovarian cancer and may indicate responsiveness to specific drug treatments. ovarian cancer (OC) vasculogenic mimicry prognostic model immune microenvironment therapeutic response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Ovarian cancers (OCs) are prevalent forms of tumors affecting women. According to recent reports, most OCs are epithelial serous carcinomas that can pose danger to health and significantly reduce their longevity 123 . Ovarian cancers are frequently identified in advanced clinical stages due to their subtle onset and tendency for metastasis, leading to an unfavorable prognosis 45 . Currently, the primary treatment for ovarian cancers involves surgery combined with platinum-based chemotherapy 67 . The rapid development of chemoresistance, especially for antiangiogenic agents, poses a significant challenge in managing ovarian cancers 89 . The prognosis of ovarian cancer can assist gynecologists in determining the timing and plan of treatment and providing patients with information about the tumors. Therefore, the identification and validation of relevant prognostic markers are urgently needed to improve the prognosis assessment and treatment guidance for ovarian cancer patients. A condition known as "vasculogenic mimicry" (VM) occurs when tumor cells directly create luminal structures without the assistance of endothelial cells, thereby facilitating the supply of nutrients and oxygen to tumor cells 10 . This phenomenon was first discovered by Maniotis in melanoma studies in 1999, and subsequent research has confirmed its widespread presence in various cancers, including breast and lung cancers, glioblastoma, and others 1013141516 . Current studies indicate that tumor cells are most likely to produce VM structures under hypoxic conditions, due to the presence of cells that can transdifferentiate into an endothelial phenotype, establish intercellular connections, and create a nutrient channel like blood vessel 112123 . According to Ayala-Domínguez L et al., three major pathways and their genes are involved in this phenomenon. These include the VE-cadherin/EphA2/MMPs pathway, the hypoxia-induced HIF-1A pathway, and the EMT pathway 15 . Other genes such as FOXC2, TGFB1, SNAI families genes also play important role in VM 123133 . Currently, research on inhibiting VM in tumor cells is limited, but researchers have successfully demonstrated the inhibition of VM in vitro using VM inhibitors 1920 . Jui-Ling Hsu et al. revealed that strategies to inhibit vasculogenic mimicry hold promise for cancer treatment 19 . Additionally, Gao Y et al. confirmed the presence of VM in ovarian cancer and its association with recurrence, metastasis, and prognosis 1718 . Increasing evidence suggests that certain genes play a significant role in ovarian cancer VM 21 , however, no reported research established a prognostic model on the use of biomarkers for ovarian cancer VM. The identification of the molecular mechanisms underlying vasculogenic mimicry in ovarian cancer, may unveil potential therapeutic targets. In this article, two subgroups were identified based on VM genes (VMGs), and a comprehensive bioinformatic analysis of datasets was performed. We successfully established and verified a model by VM-related prognostic index (VMRPI) that provides ovarian cancer prognosis and reveals the landscape of immune and drugs’ responses. Materials and methods Data collection and processing RNA sequencing data and clinical details were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) database for serous ovarian cancer (SOC) patients (n = 429) in our study, as well as from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/ ) for GSE51088 (n = 100), comprising the main set for both training and test data. Additionally, an external validation set was obtained from GSE:17260 (n = 110) in GEO. After exclusion, 522 samples remained in the main set, and 110 samples were included in the external validation set. The fallopian tube RNA sequencing of normal people (n = 180) was acquired from a professional website-The Genotype-Tissue Expression (GTEx, https://www.gtexportal.org/ ) portal. Every dataset from the TCGA and GEO databases was available to the general public. And unnecessarily for ethics committee approval for those data. The data extraction policies of these databases also were adhered strictly. To subject the TPM data to log2(x + 1) and realize standardization, the “limma” package was used for transformation the RNA-seq data. The 'Combat' function in 'sva' package was the protocol to remove the batch effects. To analyze and validate the distribution and batching differences in the merged expression data from TCGA and GEO, this research conducted principal component analysis (PCA), and the 'prcomp' mathematical function from the'stats' package was utilized. The protein-protein interaction (PPI) network was established using version 12.0 of the Search Tool for the Retrieval of Interacting Genes (STRING) database ( https://cn.string-db.org/ ). This database serves as an extensive repository for both confirmed and anticipated protein-protein interactions. Identification of VM-related genes in patients with SOC The VM-related genes (VMGs) in our research were collected from the previously published reviews and research articles 123132333435 . The univariate Cox analysis was utilized to identify VMGs, including SNAI1, MMP2, MMP14, SNAI2, ZEB1, and TWIST1, as the core genetic determinants. The association between the expression of prognostically relevant genes and survival duration were illustrated by Kaplan-Meier curves. To confirm the differentially expressed genes (DEGs) identified in the "limma" software package, we set the significance level at a p-value of less than 0.05. Consensus clustering To identify vasculogenic mimicry subtypes, a rigorous unsupervised classification algorithm, named consensus clustering analysis in R package “ConsensuClusterPlus”, was performed using the expression of the determinant core genes and euclidean distance was set as 1000 times repetition 26 . In the experiment, we aim to determine two values: the optimum cluster number (k) and the degree of consensus stability. Finding the output from the cumulative distribution function (CDF) plots and determining whether the CDF curve is flat is necessary for this. We also need to confirm the differences in overall survival between clusters. To complete this stage, use R's "survival" package, employing the Cox Proportional-Hazards model, and determining the statistical differences based on the results of the log-rank test. Enrichment analysis for GO and KEGG DEGs derived from contrasting the risk-high and risk-low subgroups in the main set were investigated to assess both molecular function (MF), biological process (BP), and cellular component (CC) from Gene Ontology (GO) 28 , and intracellular metabolic pathways from and gene functions Genes and Genomes (KEGG) enrichment analysis 38 . The parameter of filter was set according to logFC = 1 and FDR < 0.05. The R package used here is called the “clusterProfiler” 27 . Construction and validation of the Risk Score prognostic model There were 260 DEGs showed significant prognostic association with their expression from DEGs (n = 758) identified between the clusters by the “survival” R package using the univariate Cox regression analysis when the level was set at p < 0.05. In the main set, 4/5 of the patients were divided into training set while 1/5 samples was divided into internal validation by the ‘createDataPartition’ function in the ‘caret’ R package. The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was employed to reduce the candidate gene pool for developing the prognostic model by using the R package "glmnet". The penalty parameter (λ) was determined using a ten-fold cross-validation approach, selecting the value that met the minimum criteria. After calculations, a risk score model, named Risk Score, was established using β (coefficient) value multiplied by the expression of risk genes. The risk score formula was as follows: Risk Score = (β1*Exp1 + β2*Exp2+ … +βn*Expn). The determination of the correlation coefficients was computed through a multifactorial Cox analysis by ‘coxph’ function. Finally, 9 genes were identified to construct the prognostic signature. The median risk score is employed to differentiate subgroups into low-risk and high-risk cohorts. The receiver operating characteristic (ROC) curve was generated through ROC curve analysis employing the R packages "survival", "survminer", and "timeROC" to assess the diagnostic effectiveness of the risk model. Additionally, external validation steps were executed in the external validation set. Mutation profile analysis Waterfall plots were utilized to illustrate the fluctuation in mutations between groups with high and low risk depending on the evaluation of risk scores for patient individually. This process was analyzed using the "maftools" package in R 29 . Additionally, every sample somatic mutation data were collected from the TCGA database online. Independent prognostic analysis Additional independent prognostic examination of the samples' clinical features was done in the primary set, such as age, tumor grade, and tumor stage (FIGO), as well as risk scores, to assess independence and construct a nomogram model. The assessment of the connection between factors and prognosis utilized Univariate Cox regression analysis, while the coefficients in the nomogram were computed using multivariate Cox regression analyses. Evaluation of microenvironment and cell infiltration With the aim of revealling the microenvironment for immune of the SOC, the chosen method is the "estimate" R package calculating the grades of stromal, immune and ESTIMATE. The CIBERSORT algorithm was utilized for quantitative analysis of the relative abundance of 20 immune cell types within the data from TCGA, which can illustrate the subtypes of diverse immune cells. Spearman correlation tests were conducted to obtain the test values, and a heatmap was generated using the "ggplot" package. Development and validation of the nomogram model Through univariate and multivariate Cox analyses, we identified clinically significant pathological parameters with statistical significance. Subsequently, leveraging the "rms" and "survival" R packages, a nomogram model was established including age, tumor grade, tumor stage, and Risk Score index. This model aimed to predict the overall survival probabilities at 1 year, 3 years, and 5 years have been computed for patients. Additionally, calibration curves in the form of column charts have been generated to visually assess the predictive accuracy of prognosis outcomes for SOC patients. Clinical specimens We performed relevant experimental verification on tumor tissue of 36 new patients who met the inclusion criteria and failed to meet the exclusion criteria in 2023 in the First Affiliated Hospital of Sun Yat-sen University. The relevant cases were selected according to the following inclusion and exclusion criteria. Inclusion criteria: 1. Patients who was diagnosed ovarian cancer according to the 2022 edition of the Chinese Guidelines for the Diagnosis and Treatment of Ovarian Cancer; 2. Aged between 18 and 75 years old; 3. Enrolled cases must present with a histologically confirmed primary tumor that fulfills the specified criteria; 4. Adequate other organ and bone marrow function; 5. No history of other malignant tumors. Exclusion criteria: 1. Individuals with severe underlying diseases that are poorly controlled; 2. Lactating or pregnant women; 3. Those with a history of other illness, including serious infectious disease; 4. Persons with incapacitated or limited ability to act; 5. Patients received radiotherapy or chemotherapy before surgery. Immunohistochemical staining A total of 35 paraffin-embedded tissue from patients with SOC was stained by immunohistochemical (IHC) who received treatment at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). The formalin-fixed paraffin-embedded (FFPE) slides underwent deparaffinization in a graded series of xylene and ethanol. Epitopes were unmasked by immersing the slides in a boiling antigen retrieval solution for 5 minutes. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide for 10 minutes and then incubated at room temperature for 25 minutes using goat serum blocking solution to eliminate non-specific staining. After incubation with mouse-derived anti-CD31 antibody (1:1000, CST, 3528S), the slides were placed at 4 degrees Celsius for 18 hours. Following PBS washing, an anti-mouse horseradish peroxidase-labeled secondary antibody (Vector Laboratories) was applied and incubated at 25℃ for 45 minutes. After 2 min of with 0.05% 3′,3-diaminobenzidine tetrahydrochloride (DAB, ZSGB-BIO, ZLI-9017) staining, the PAS staining procedure was carried out in accordance with the ZSGB-BIO, BSBA-4080A manufacturer's instructions. The slides were then dehydrated, mounted, and counterstained with hematoxylin. VM detection and distinguish After the staining and neutral resin sealing process, the pathology slides were captured as electronic images at 40x magnification using a bright-field scanner. Subsequently, all channel-like structures were systematically examined based on the established criteria for typical vascular malformations. These criteria include the following: ( 1 ) VM vascular-like channels surrounded by tumor cells; ( 2 ) VM vessel channels staining PAS positive and CD31/CD34 negative (PAS+/CD34−), in contrast to endothelial vessel channels staining PAS positive and CD31/CD34 positive (PAS+/CD34+); ( 3 ) VM vascular-like channels containing erythrocytes 1223 . According to the results of IHC staining for VM, all samples of clinical specimens were categorized into two subgroups— VM(+) group meanings those with typical VM structures and VM(-) group meanings those without. Quantitative real-time PCR In accordance with the manufacturer's instructions, the total bulk RNA was extracted and purified through Trizol reagent (Invitrogen) and the SteadyPure Universal RNA Extraction Kit (ACCURATE BIOTECHNOLOGY (Human) CO., LTD, Changsha, China), and then it was reverse transcribed to cDNA through the Reverse Transcription Supermix (ACCURATE BIOTECHNOLOGY (Human) CO., LTD, Changsha, China, AG11706). The SYBR Green PCR Kit was utilized for conducting the qRT-PCR (ACCURATE BIOTECHNOLOGY (Human) CO., LTD, Changsha, China, AG11701) and in a Real-time fluorescence PCR instrument (Bio-Rad Laboratories, Inc, United States, Bio-Rad CFX Connect Real-Time PCR System 1855201). The expression of Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was set as an internal control to realize normalization. The Primers sequences in this study are presented in Supplementary Data. The comparative expression level was evaluated by 2-ΔΔCt method. Drug sensitivity To investigate, by comparison, how risk scores affect medication therapy, follow the advanced instructions of the "oncoPredict" package 25 , utilizing the CTRP v2.0 dataset sourced from the CTRP website( https://portals.broadinstitute.org/ctrp/ ) and associated drug experimental results (IC50) to construct a drug sensitivity model in R. The training set comprises TPM gene expression data of SOC-related cell lines (n = 28) and their drug (497 drugs) experimental results. Consequently, the drug sensitivity scores for samples within the main set will be calculated. Statistical analysis Using R software, statistical analyses were carried out. (version 4.3.2; http://www.Rproject.org ). The categorical variables were compared using the Chi-square test. The Wilcoxon test was employed to compare the drug sensitivity and gene expression levels of groups. A p-value of less than 0.05 was taken into consideration as the criterion for statistical significance in this investigation. Results Comparison vasculogenic mimicry gene expression between normal tissues and Serous Ovarian Cancers (SOCs) We created a heatmap of the data from TCGA and GTEx databases to compare the expression levels of VM-related genes in SOC and normal tissues of the fallopian tube (Fig. 1 A). We discovered significant differences in the expression of 11 VM-related genes between TCGA OV tumors and GTEx normal samples (Fig. 1 B). Moreover, a network of protein-protein interactions was created and we uncovered robust interactions among these genes (Fig. 1 C). Based on mRNA expression levels in our dataset, a correlation network of the VMGs was created to show the negative (red hemisphere) and positive (favorable factors) risks associated with the hazard level among these VMGs (Fig. 1 D). Furthermore, a notable association among these VMGs is evident. These findings suggest that VMGs may be very important for the onset and progression of SOC. Identification of subgroups associated with gene expression and prognosis After elimination of batch effects, TCGA and GEO combined dataset was referred to as the main set. Principal component analysis (PCA) confirmed a relatively consistent distribution (Fig. 2 A). Considering that the occurrence of vasculogenic mimicry is the result of multiple cellular signaling pathways, and that there is mutual exclusion in gene expression levels among those pathways, VM important prognostic genes were kept as categorization criteria. To identify predictive vasculogenic mimicry genes (VMGs), univariate Cox regression analysis was performed using the R "survival" package. According to the analysis, there is a statistically significant negative association between patient prognosis and SNAI1, MMP2, MMP14, SNAI2, ZEB1, and TWIST1. Subsequently, Kaplan-Meier (K-M) survival curves stratified by cutoff values, were subsequently generated to investigate the associations between the expression of these genes and overall survival in SOC patients (Fig. 2 B, C). Consensus clustering was used to identify subtype groups based on the expression of prognostic VM-related genes. In the main dataset, the 520 SOC patients were partitioned into two clusters: The VM-high subgroup comprising 243 patients and the VM-low subgroup comprising 277 patients. The choice of a clustering variable (k) of 2 was made due to maximal intragroup correlations and minimal intergroup correlations (Fig. 2 D). Remarkably, the overall survival of the identified clusters varied significantly, with VM-high patients exhibiting a better prognosis compared to VM-low patients (p = 0.01) (Fig. 2 B). A distributional map was generated, illustrating the origins of the sequenced data and key clinical features such as tumor stage, tumor grade, and age (Fig. 2 F). Enrichment analysis of DEGs and construction of the VM-related prognostic index (VMRPI) The main dataset, which included TCGA and GEO cohorts, was screened for the DEGs between the clusters, when parameters were set to log fold change (log FC) = 0.585, and the false discovery rate (FDR) < 0.05. As a result, 758 DEGs were found from all genes (Fig. 3 A). To identify the involved pathways and functions that are significantly associated with the DEGs, enrichment analyses, including GO and KEGG analyses, were conducted. GO analysis revealed that the top five enriched biological processes (BP) terms were “extracellular matrix organization”, “extracellular structure organization”, “external encapsulating structure organization”, “ossification” and “connective tissue development”. The top five enriched cellular component (CC) terms were “collagen − containing extracellular matrix”, “endoplasmic reticulum lumen”, “cell − substrate junction”, “focal adhesion” and “external side of plasma membrane”. The top five enriched molecular function (MF) terms were “extracellular matrix structural constituent”, “receptor ligand activity”, “glycosaminoglycan binding”, “integrin binding” and “sulfur compound binding”. The KEGG analysis showed that DEGs were mainly involved in proteoglycans in the cancer signaling pathway, the ECM − receptor interaction signaling pathway, the Malaria signaling pathway, the focal adhesion signaling pathway, the PI3K − Akt signaling pathway signaling pathway, the AGE − RAGE signaling pathway in the diabetic complications signaling pathway, The protein digestion and absorption signaling pathway, the complement and coagulation cascades signaling pathway, the cytokine − cytokine receptor interaction signaling pathway, and the staphylococcus aureus infection signaling pathway. These results suggest that these signaling pathways and cellular functions may be involved in the formation or promotion of VM. Furthermore, a univariate Cox prognostic analysis was conducted on the DEGs, revealing 260 DEGs with prognostic significance. The main set was randomly partitioned, with 80% of the samples designated as the training set and the remaining samples as the test set for internal validation. Using LASSO regression on the 260 prognostic DEGs, a subset was selected, and subsequently, a multivariable Cox analysis was performed to calculate the selected genes’ prognostic coefficients (β). The process of parameter selection is illustrated in Fig. 3 C. The results of the established Risk Score were presented in a forest plot format (Figure D). These findings enabled the creation of a final model that comprised 9 genes. Among these, FPR1, ADH1B, PAPPES1, and WNT11 gene expression levels exhibited strong correlations with a higher prognosis score. TSPAN8, FOXJ1, CXCL13, CXCL9, and SST were associated with a lower prognosis score. Validation of the prognostic model and gene level differences based on VM-related prognostic indices (VMRPI) The effectiveness of the prognostic model was assessed using ROC analysis, which demonstrated promising performance. The AUC values of the training set for 1, 3, and 5 years were 0.694, 0.746, and 0.727, respectively. Simultaneously, the internal validation showed AUC values of 0.752, 0.667, and 0.663, respectively (Fig. 4 A). The AUC values of the main set ROC at 1, 3 and 5 years were 0.708, 0.732, and 0.720. To further validate its applicability, GSE17260 was chosen as an external validation set for our Risk Score model. This set showed AUC values of 0.731, 0.633, and 0.723 at 1, 3, and 5 years, respectively, demonstrating robust performance (Fig. 4 B). Additionally, and according to the median risk score, the partitioning all samples into two groups, risk-high and risk-low, resulted in significant prognostic differences with p-values lower than 0.01 for the main set and p-values of 0.03 for the external validation set (Fig. 4 C, 4 D). Furthermore, the risk score was validated for assessing the VM gene expression levels. Differential expressions of the VM-associated genes, BCAR3, CD44, CDH5, EPHA2, FOXC2, IL6, LAMC2, MMP14, MMP2, PLAU, TGFB1, TWIST1, ZEB1, SNAI1, and SNAI2, were observed between the groups (Fig. 4 E). These results comprehensively demonstrate the meaningful implications of the risk score. Moreover, the exploration of intergroup mutation patterns using TCGA mutation data revealed that TP53 and TTN were the most frequently mutated genes. The other top five mutations in the high-risk group included CSMD3, MUC16, USH2A, TG, and FLG2, whereas the prominent mutations in the low-risk group were RYR2, CSMD3, USH2A, NF1, FAT3, and DST (Fig. 4 F). Evaluation of the clinical features of risk subgroups and calculation of the nomogram To investigate the relationships between clinical features and scores, we conducted a differential analysis of scores among different clinical characteristics. The analysis found no significant differences in risk scores among different age groups, tumor grades or tumor stages (Fig. 5 A). This implied that risk scoring is an independent prognostic factor irrespective of clinical features. Furthermore, considering that the immune status is a critical clinical feature and prognostic factor, exploring the relationships between the risk model and immune infiltration, including immune cells category, is imperative. Our initial analysis of immune cell correlations suggested that high- or low-risk scores are positively correlated with neutrophils, monocytes, activated mast cells, M2 macrophages, and resting memory CD4 T cells. However, they negatively correlated with follicular helper T cells, plasma cells, regulatory T cells (Tregs), M1 macrophages, CD8 T cells, and activated memory CD4 T cells (Fig. 5 B). Furthermore, an evaluation of the immunological microenvironment in the high- and low-risk groups showed that the high-risk group had significantly higher immune, stromal, and ESTIMAT scores than those of the low-risk group (Fig. 5 C). We carried out a multivariate analysis combining clinical characteristics to effectively incorporate a multifactorial assessment (age, grading, and staging) with risk scores (Fig. 5 D) and constructed a prognostic nomogram (Fig. 5 E) for a comprehensive prognosis evaluation. Moreover, calibration curves indicated a favorable predictive performance at 3 and 5 years (Fig. 5 F). Experimental expression validation of cluster genes and VMRPI genes To further investigate whether differences in related outcomes within ovarian cancer biospecimens exist, we used our center paraffin-embedded specimens of primary SOCs from the past six months for PAS-CD31 immunohistochemistry (IHC) staining (n = 35). Among these specimens, only six exhibited typical vasculogenic mimicry (VM) features. Subsequently, we matched fresh frozen tissues of identical age, grade, and stage based on IHC results, and classified them into VM + and VM- groups. Three cases were chosen from each group for presentation (Fig. 6 A). A verification of the differences in VM-associated gene expression profiles (VMGs) and VM-related prognostic indices (VMRPIs) between the groups was performed. The results of qRT-PCR revealed statistically significant differences in the expression of the representative VMGs, MMP2, MMP14, SNAI2, ZEB1, and TWIST1, between the VM + and VM- groups. Moreover, FPR1, ADH1B, and WNT11, identified as VMRPIs, also exhibited statistically significant gene expression differences and demonstrated a relationship with VM that aligned with the prognostic risk analysis (Fig. 6 B). Hence, these results indicate the potential for further exploration into the upregulation of FPR1, ADH1B, and WNT11 expression in relation to the mechanisms associated with vasculogenic mimicry. Prediction of therapeutic response to drugs based on the risk score In addition to conventional chemotherapy drugs, numerous studies investigated new therapeutic targets for ovarian cancer to enhance the effectiveness of chemotherapy, reduce drug resistance, and minimize tumor recurrence. Therefore, we conducted a drug sensitivity analysis using the CTRP (Cancer Therapeutics Response Portal) and CCLE (Cancer Cell Line Encyclopedia) databases. Based on risk score-based stratification, the sensitivity scores of various drugs indicated that drugs, such as BRD-K61166597, apicidin, AZD8055, bardoxolone methyl, curcumin, doxorubicin, KU-0063794, lovastatin, NSC48300, leptomycin B, sirolimus, and temsirolimus, exhibit higher IC50 values within the high-risk group. Conversely, compound 1B displayed lower IC50 values (Fig. 7 ). These findings revealed differences in partial drug sensitivity among subgroups stratified based on risk scores. This information can aid in guiding clinical drug administration strategies, exploring drug targets, and identifying potential drug resistance markers. Discussion Ovarian cancer exhibits a poor prognosis due to its insidious onset and high propensity for metastasis and recurrence 17 . Among ovarian cancer subtypes, mucinous ovarian cancer accounts for the majority of occurrences and fatalities 23 . Additionally, inadequate responsiveness to antiangiogenic drugs in clinical settings warrants thoughtful consideration 959697 . It has been established that ovarian cancer has a perfusion-enhancing mechanism, known as vasculogenic mimicry, which significantly correlates with prognosis. Previous studies on ovarian cancer vasculogenic mimicry (VM) have underscored the crucial roles of genes such as MMP2, MMP14, TWIST1 in VM and have validated the pivotal involvement of pathways such as the VE-cadherin/EphA2/MMPs pathway, the hypoxia-induced HIF-1A pathway and the EMT pathway in generating and regulating ovarian cancer VM. Despite the significance of VM in cancer, the use of inhibitors has been preliminarily explored in lung cancer, and further assessment of their effectiveness and specificity remains pending 19 . Moreover, the current state lacks prognostic evaluation mechanisms centered around VM. Therefore, the goal of this work was to create a prognostic model within the standard of care (SOC), based on the transcriptional status of VM-related genes (Fig. 8 ). Based on previously published research, we constructed a gene set consisting of 28 encoding genes related to VM. This was performed after excluding genes whose expression data could not be utilized in the dataset. Initially, a comparison between tumor tissues and normal tissues revealed significant differential expression of some genes, indicating the potential involvement of VM genes in the onset and progression of cancer. Furthermore, univariate Cox analysis identified significant associations between SNAI1, MMP2, MMP14, SNAI2, ZEB1, and TWIST1 expression levels and SOC prognosis. The HR values of these genes were all greater than 1, strongly indicating a poorer prognosis associated with VM. Utilizing these prognostically relevant VMGs, we employed the widely used classification algorithm "ConsensusClusterPlus". To avoid single-platform overfitting and enhance inclusivity, sequencing data from multi-databases were merged to construct the main set for subtype grouping: VM-high correlation and VM-low correlation subgroups. The validation of VM-related genes between the two groups confirmed the presence of significant differences in gene expressions of BCAR3, CD44, CDH5, EPHA2, FOXC2, IL6, LAMC2, MMP14, MMP2, PLAU, TGFB1, TWIST1, ZEB1, SNAI1, and SNAI2, indicating a robust grouping based on VM gene expression. Subsequently, 758 DEGs were found between the two subgroups; with 260 genes linked to prognosis. Using these prognostically relevant DEGs, we employed the LASSO model for dimension reduction and variable selection, followed by multivariate Cox regression, to comprehensively calculate the prognostic risk. Ultimately, a risk-scoring model was constructed based on nine core genes: FPR1, ADH1B, RARRES1, TSPAN8, FOXJ1, CXCL13, WNT11, CXCL9, and SST. Internal and external validations of the model demonstrated good performance and multiplatform applicability. Moreover, based on the risk score-based grouping, significant differences in immune cells and the immune microenvironment were observed between groups. A higher risk score indicated a poorer cellular immune status, potentially attributed to a VM-associated increase in perfusion. To enhance clinical prognosis evaluation, a line graph was constructed based on the risk model and combined with clinical features for better quantitative survival rate calculations. For experimental validation, histopathological staining of VM structures were conducted on our center specimens and paired with the grouping. Using qRT‒PCR on fresh frozen tissues, the expression status of VMGs and VMRPIs were explored, revealing significant differences in the expression of MMP2, MMP14, SNAI2, ZEB1, and TWIST1 as subtype genes in our center's samples. These results highlighted the close association between VM-related genes and VM structures. Similarly, FPR1, ADH1B, and WNT11, as VMRPIs, were highly expressed in VM + patients, indicating consistency between the data analysis and pathological specimens. Finally, utilizing public databases, drug sensitivity prediction was performed for the risk score-based groups. The results suggested that the presence of VM may lead to chemoresistance for drugs such as BRD-K61166597, apicidin, AZD8055, bardoxolone methyl, curcumin, doxorubicin, KU-0063794, lovastatin, NSC48300, leptomycin B, sirolimus, and temsirolimus. However, compound 1B, an NF-KB inhibitor, demonstrated better efficacy in the high-score VM group, indicating its potential as a combined therapeutic agent for antiangiogenic drugs. The existing literature has provided insights into the genes included in the risk score model. C-X-C motif chemokine ligand 13 (CXCL13), known as a fundamental regulator of B-cell recruitment and organization, can coordinate the development of tertiary lymphoid structures 37 . Its expression level is associated with long-term survival can enhance the effectiveness of PD-1 checkpoint blockade in high-grade serous ovarian cancer (HGSOC) 3839 . Formyl peptide receptor 1 (FPR1) is not only in proinflammatory and antibacterial host responses but also involved in cell chemotaxis, proliferation, and tumor progression 404142 . Activated FPR may contribute to these processes and has been identified as a potential biomarker and treatment target for aggressive epithelial ovarian cancer (EOC) 4344 . Tetraspanin 8 (TSPAN8) is a member of the tetraspanin family, implicated in various human cancers through its role in regulating intercellular interactions and cell motility. It is a potential therapeutic target for the inhibition of invasion and metastasis in OCs 454647484950 . Forkhead box J1 (FOXJ1) is a 3-exon transcription factor and a master regulator of motile ciliogenesis. It is expressed in various tissues such as the respiratory tract, brain, and reproductive tract, where it plays a pivotal role in regulating transcriptional programs governing motile cilia assembly 51525354 . This activity showcases its diverse implications for cancer biology and prognosis. High FOXJ1 expression is correlated with better tumor differentiation and favorable prognosis in various cancers such as gastric cancer, ependymomas, choroid plexus tumors, and ovarian cancer 555657 . Alcohol dehydrogenase class I beta polypeptide (ADH1B) is pivotal for alcohol metabolism and is implicated in tumorigenesis, particularly, in esophageal squamous cell and colorectal cancers 586061 . High FABP4 and ADH1B expressions in high-grade serous ovarian cancer suggest an increased risk of residual disease, possibly guiding neoadjuvant chemotherapy candidacy 62 . Wnt family member 11 (WNT11) is a noncanonical Wnt protein that regulates cell movement and organ formation through specific receptors and signaling pathways 636465666768 . It plays a dual role by promoting migration in certain cancers including breast cancer, colon cancer, and leukemia, while suppressing cell migration in hepatocellular carcinoma 6970 . Additionally, WNT11 influences cell adhesion and migration by modulating the expression of E-cadherin and integrin subunits 7172 . Retinoic acid receptor responder 1 (RARRES1, also known as tazarotene-induced gene 1, TIG1) is upregulated by tazarotene in skin culture and resembles CD38 and is frequently silenced in cancers due to promoter hypermethylation 737475 . It shows potential as a tumor suppressor in prostate and endometrial cancers 7677 . Notably, RARRES1 plays a crucial role in promoting tumor growth and invasion in IBC through Axl, indicating its promise as a therapeutic target for IBC patients 78 . C-X-C motif chemokine ligand 9 (CXCL9), in conjunction with CXCL10 and CXCL11, boosts T-cell infiltration in ovarian cancer, and is associated with improved survival rates 79 . Research indicates that CXCL9 and its chemokine counterparts are indicators of an inflammation-rich subtype of ovarian cancer 8081828384 . Recent evidence highlighted the predictive value of CXCL9 for positive outcomes and favorable responses to anti-PD-1 therapy in cancer patients 8586 . Somatostatin (SST) is cyclic peptide that inhibits hormone secretion and suppresses immune functions 87888990 . Its signaling pathway also regulates tumor characteristics such as angiogenesis, cell migration, and growth factors, promoting tumor neovascularization and cell growth 91929394 . Despite numerous reviews and studies on ovarian cancer VM, our investigation represents the inaugural exploration of the VM-associated prognostic risk model in ovarian cancer. Utilizing multiplatform datasets for model construction and external validation has enhanced the generalizability of the risk model and Kaplan‒Meier plots. However, there are several limitations: Primarily, our findings indicate an increased immune score due to VM; however, a deeper exploration of the underlying mechanisms, contributing to poorer prognosis, is warranted; Secondly, within the sample set, used for Kaplan‒Meier plot construction, there might be an insufficient number of early-stage and low-grade samples, necessitating further analysis to enhance the accuracy of staging and grading within the Kaplan‒Meier plots; and finally, there is an urgent need for more mechanistic studies to elucidate the roles of the identified risk genes in mediating VM in ovarian cancer. Conclusion In summary, our research has established a prognostic risk model centered around the genes FPR1, ADH1B, RARRES1, TSPAN8, FOXJ1, CXCL13, WNT11, CXCL9, and SST within the SOC based on VM-related gene subgroups. This risk model has a strong predictive value in both internal and external validations. We also explored mutations and immune-related situations based on risk score status. Furthermore, in conjunction with age, grading, and staging, we constructed a robust performance nomogram for prognostic assessment. Additionally, we have experimentally validated the differential expression of relevant risk genes among IHC-VM subgroups and explored potential drug therapeutic targets based on risk scores. Declarations Ethics statement and consent to participate The studies involving humans were approved by Committee on the Use of Clinical Research of the First Affiliated Hospital of Sun Yat-sen University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The design of this study follows the tenets of the Declaration of Helsinki. Availability of data and materials The data used and/or analysed during the current study are available upon reasonable requests. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the National Key R&D Program of China (2022YFC2704201 to Shuzhong Yao), National Natural Science Foundation of China (82273365, 82072874 to Shuzhong Yao), China Postdoctoral Science Foundation (2023M744070 to Chunyu Zhang), Postdoctoral Fellowship Program of CPSF (GZC20233282 to Chunyu Zhang), Guangdong Medical Science and Technology Research Foundation (A2024280 to Chunyu Zhang), Science and Technology Plan of Guangdong Province (2023A0505050102 to Shuzhong Yao), Guangzhou Science and Technology Program (2024B03J1336 to Shuzhong Yao), Sun Yat-sen University Clinical Research Foundation of 5010 Project (2017006 to Shuzhong Yao), and Guangdong Basic and Applied Basic Research Foundation (2023A1515012214 to Wei Wang，2023A1515110333 to Chunyu Zhang). Author contributions Xueyuan Zhao and Yan Jia performed the experiment and analyzed the data. Xueyuan Zhao and Weijia Wen wrote the manuscript. Caixia Shao and Qianjian Zou participated in the study design and helped in experiments. Linna Chen and Hongye Jiang collected and downloaded the data. Guofen Yang helped obtain funding. Chunyu Zhang conceived this project and obtained funding. Wei Wang and Shuzhong Yao provided the resources, obtained funding and supervised the project. All authors contributed to the article and approved the submitted version. Acknowledgements Not applicable Declaration of competing interest The authors declare that they have no conflicts of interest. References Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021 Jan;71(1):7-33. doi: 10.3322/caac.21654. Epub 2021 Jan 12. Erratum in: CA Cancer J Clin. 2021 Jul;71(4):359. PMID: 33433946. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. 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Cytokine Netw. 2000;11:161–176. Chowers Y., Cahalon L., Lahav M., Schor H., Tal R., Bar-Meir S., Levite M. Somatostatin Through Its Specific Receptor Inhibits Spontaneous and TNF-α- and Bacteria-Induced IL-8 and IL-1β Secretion from Intestinal Epithelial Cells. J. Immunol. 2000;165:2955–2961. doi: 10.4049/jimmunol.165.6.2955. García De La Torre N., Wass J.A.H., Turner H.E. Antiangiogenic Effects of Somatostatin Analogues. Clin. Oncol. 2002;57:425–441. doi: 10.1046/j.1365-2265.2002.01619.x. Rai U., Thrimawithana T.R., Valery C., Young S.A. Therapeutic Uses of Somatostatin and Its Analogues: Current View and Potential Applications. Pharmacol. Ther. 2015;152:98–110. doi: 10.1016/j.pharmthera.2015.05.007. Annunziata M., Luque R.M., Durn-Prado M., Baragli A., Grande C., Volante M., Gahete M.D., Deltetto F., Camanni M., Ghigo E., et al. Somatostatin and Somatostatin Analogues Reduce PDGF-Induced Endometrial Cell Proliferation and Motility. Hum. Reprod. 2012;27:2117–2129. doi: 10.1093/humrep/des144. Pola S., Cattaneo M.G., Vicentini L.M. Anti-Migratory and Anti-Invasive Effect of Somatostatin in Human Neuroblastoma Cells: Involvement of Rac and Map Kinase Activity. J. Biol. Chem. 2003;278:40601–40606. doi: 10.1074/jbc.M306510200. Monk BJ, Minion LE, Coleman RL. Anti-angiogenic agents in ovarian cancer: past, present, and future. Ann Oncol. 2016 Apr;27 Suppl 1(Suppl 1):i33-i39. doi: 10.1093/annonc/mdw093. PMID: 27141068; PMCID: PMC6283356. Matulonis UA, Sood AK, Fallowfield L, Howitt BE, Sehouli J, Karlan BY. Ovarian cancer. Nat Rev Dis Primers. 2016 Aug 25;2:16061. doi: 10.1038/nrdp.2016.61. PMID: 27558151; PMCID: PMC7290868. Li J, Zheng C, Mai Q, Huang X, Pan W, Lu J, Chen Z, Zhang S, Zhang C, Huang H, Chen Y, Guo H, Wu Z, Deng C, Jiang Y, Li B, Liu J, Yao S, Pan C. Tyrosine catabolism enhances genotoxic chemotherapy by suppressing translesion DNA synthesis in epithelial ovarian cancer. Cell Metab. 2023 Nov 7;35(11):2044-2059.e8. doi: 10.1016/j.cmet.2023.10.002. Epub 2023 Oct 26. PMID: 37890478. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4336317","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298917096,"identity":"5ef085a7-46d1-47a3-a30e-f26aa29d5658","order_by":0,"name":"Xueyuan Zhao","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, the First Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xueyuan","middleName":"","lastName":"Zhao","suffix":""},{"id":298917099,"identity":"516a6299-3a7a-45db-bd8d-de04bbe00404","order_by":1,"name":"Yan Jia","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, the First Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Jia","suffix":""},{"id":298917102,"identity":"cee22e0c-b53b-47b1-ace4-be944123984b","order_by":2,"name":"Weijia Wen","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, the First Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Weijia","middleName":"","lastName":"Wen","suffix":""},{"id":298917106,"identity":"1e7bc9a6-ed2a-4a75-a254-c0739890622f","order_by":3,"name":"Caixia Shao","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, the First Affiliated Hospital, Sun Yat-sen 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Gynecology, the First Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Zhang","suffix":""},{"id":298917133,"identity":"fd1ce909-aa97-4a42-afea-74fe7eb37d69","order_by":10,"name":"Shuzhong Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYJACZiBO4AcSBx7AhHiI0SLZANSSQJIWgwMgkhgtBscPH/5c2FaXZ3zt8EOgLXWJ82ckMD5428Ygb45Ly5m0NOmZbWzFZrfTDIBaDiduuJHAbDi3jcFwZwMOLQdyzJh523gSt91OAGk5kLhBIoFNmrcN6lRsWs6/Mf7M2yaRuHl2+geYw9h/49VyI8cAaKZB4gbpHJAtzIkNNxLYmPFpkbzxLE2a51xC4ozbOQUHEgwOG28487BZcs45CcMNOLTwnU8+/JmnrC6xf3b65g8fKupk57cnH/zwpsxGHpctCqjiBgyODQyMDUCWBHb1QCDfgCZgj1PpKBgFo2AUjFgAAHA6YoikQa4VAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Obstetrics and Gynecology, the First Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Shuzhong","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2024-04-28 05:39:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4336317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4336317/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56037367,"identity":"1b32d09b-5daf-497e-9930-f6a201d0e55a","added_by":"auto","created_at":"2024-05-07 18:50:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2953939,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and interactions of 30 VM-related genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Heatmap showing the differences in VMGs between GTEx normal samples (n=100) and TCGA OV tumor (n=429). Upregulation is represented by green, and downregulation by red. \u003cstrong\u003eB:\u003c/strong\u003e A heatmap showing the differences in VMGs between GTEx normal samples (n=180) and TCGA OV tumors (n=429). Upregulation is represented by green, and downregulation by red. A threshold of p \u0026lt; 0.05 was chosen for the filtering. \u003cstrong\u003eC:\u003c/strong\u003e VMGs’ network of protein-protein interactions (PPIs) (interaction score = 0.4). Both direct (physical) and indirect (functional) linkages are present in the exchanges. \u003cstrong\u003eD: \u003c/strong\u003eVMGs' mRNA expression-based correlation network. Positive and negative associations are respectively by the sky blue and dark blue lines. The red hemisphere represents risk factors, while the green hemisphere represents favorable factors. The size of the sphere depicts the strength of the hazard level.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/2d516621b9c11404c8e9682b.jpg"},{"id":56037369,"identity":"6f2be421-434f-49d8-8fca-25a043bcec88","added_by":"auto","created_at":"2024-05-07 18:50:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1628204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubtypes based on VM-related gene expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e­\u003cstrong\u003eA:\u003c/strong\u003e PCA plot illustrating the combined TCGA (n=429) and GEO (n=100) data after batch effect removal. \u003cstrong\u003eB:\u003c/strong\u003e A forest map of the most important VM-related genes for prognosis identified by univariate Cox regression analysis in OV patients. \u003cstrong\u003eC: \u003c/strong\u003eKaplan–Meier curves of important VM-related genes for prognosis. \u003cstrong\u003eD: \u003c/strong\u003eConsensus clustering matrix (k=2) showing two clusters (VM-high = 243; VM-low = 277) based on the expression of important genes in 30 VMGs. \u003cstrong\u003eE:\u003c/strong\u003e Overall survival showing a significant difference (p\u0026lt;0.01) in the survival plot. \u003cstrong\u003eF: \u003c/strong\u003eA heatmap showing the relationships among the patients' clusters, clinicopathological characteristics, and data sources for ovarian cancer.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/044a61300d4f1a46c4dde746.jpg"},{"id":56037366,"identity":"d0282aa9-a814-4981-8a94-2ef7bc900255","added_by":"auto","created_at":"2024-05-07 18:50:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1747901,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of a risk signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Volcano map showing DEGs between the VMG clusters (Figure A). DEGs were sifted using the subsequent standards: log fold alteration (log FC) =0.585, and a false discovery rate (FDR) \u0026lt; 0.05. \u003cstrong\u003eB:\u003c/strong\u003e The bar plots display the results of GO and KEGG enrichment analysis for the DEGs. \u003cstrong\u003eC:\u003c/strong\u003e A total of 260 OS-related genes were identified by univariate Cox regression analysis and subjected to LASSO regression, using cross-validation to fine-tune the LASSO regression's parameter selection. \u003cstrong\u003eD:\u003c/strong\u003e Forest plot of the important genes and coefficients in the gene model by multivariate Cox regression analysis.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/10b89ccd22af39d7612d4daf.jpg"},{"id":56037374,"identity":"09ab61d4-cfc6-4f23-920b-b993cddb4fad","added_by":"auto","created_at":"2024-05-07 18:50:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1877798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation and assessment of the risk model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e The predictive effectiveness of the risk score in the training and test sets is shown by the time-dependent receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses. \u003cstrong\u003eB:\u003c/strong\u003e Shows the predictive effectiveness of the risk score in all sets and in the external validation set using time-dependent ROC curves and AUC analyses. \u003cstrong\u003eC:\u003c/strong\u003e Kaplan-Meier curves for the OS of VMRPI-High and VMRPI-Low patients in the total cohort. \u003cstrong\u003eD:\u003c/strong\u003e External validation cohort. \u003cstrong\u003eE: \u003c/strong\u003eIn TCGA cohort, the expression levels of VM-related genes were compared between the VMRPI-High and VMRPI-Low subgroups. \u003cstrong\u003eF:\u003c/strong\u003e An oncoplot showing the frequency of mutations in the top 20 mutated genes in the high- and low-risk groups.\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0. 001, Non statistically significant results are indicated by ns.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/cc6d1890ee2929ddad4552ec.jpg"},{"id":56037373,"identity":"e925ac7b-88ec-4be1-ad1e-95abde2f30fa","added_by":"auto","created_at":"2024-05-07 18:50:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1234971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical analysis and nomogram construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Boxplot showing the age, grade, and FIGO stage for the groups at high and low risks. \u003cstrong\u003eB:\u003c/strong\u003e Spearman's correlation between VMRPI genes and immune cells in the TCGA OV dataset was determined using the TIMER database. \u003cstrong\u003eC:\u003c/strong\u003e Distribution of immunological, stromal, and ESTIMATE scores based on VMRPI. \u003cstrong\u003eD:\u003c/strong\u003e A forest map of prognostic clinical indices using a nomograph. \u003cstrong\u003eE:\u003c/strong\u003e Nomogram using tumor grade, Figo staging system, age, and VMRPI. \u003cstrong\u003eF:\u003c/strong\u003e Nomogram's calibration curves. The nomogram-predicted likelihood of invasive adenocarcinoma is represented by the x-axis, whereas the actual probability is represented by the y-axis.\u003c/p\u003e\n\u003cp\u003eIn results’ figures, ns means not significant, *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/33c998db0340497864fd5191.jpg"},{"id":56037372,"identity":"d7db7cab-74f0-42f5-b801-ee05354d18cc","added_by":"auto","created_at":"2024-05-07 18:50:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1810993,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental validation of VM genes and VMRPIs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Vascular structures in HGSOC. The black arrows denote CD31+/PAS+ endothelial angiogenic structures, whereas the red arrows denote CD31−/PAS+ vasculogenic mimicry structures. \u003cstrong\u003eB:\u003c/strong\u003e qRT‒PCR was performed to validate the differences in VM cluster genes and VMRPIs between the two groups. The data are presented as the mean ± SD (n = 6 in each group).\u003c/p\u003e\n\u003cp\u003eThe statistical study was conducted using the Wilcoxon test.\u003c/p\u003e\n\u003cp\u003eIn the figures of results, ns means not significant, * p \u0026lt; 0.05, ** P \u0026lt; 0.01, and *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/9996c0d636dde891a6a29c99.jpg"},{"id":56037370,"identity":"cd21bea7-ea7f-4a40-8efb-b5797fea063e","added_by":"auto","created_at":"2024-05-07 18:50:44","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":847607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of therapy response to drugs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoxplot of therapeutic response to drugs in the high- and low-risk groups. In the figures of results, ns means not significant, *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/e024759e29edebfc2cbc4974.jpg"},{"id":56037371,"identity":"f3cee566-3851-465a-a601-b79359e80f0e","added_by":"auto","created_at":"2024-05-07 18:50:44","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1577228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe study research process.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/a043d6898b2ba7548194afae.jpg"},{"id":56038901,"identity":"494915d4-06d5-4310-b31b-96e0953bc357","added_by":"auto","created_at":"2024-05-07 19:06:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2035004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4336317/v1/4ac0fa9b-3d42-4ead-a668-aeac6435effb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishment and validation of a prognostic model based on grouping of vasculogenic mimicry-related genes in ovarian cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancers (OCs) are prevalent forms of tumors affecting women. According to recent reports, most OCs are epithelial serous carcinomas that can pose danger to health and significantly reduce their longevity \u003csup\u003e123\u003c/sup\u003e. Ovarian cancers are frequently identified in advanced clinical stages due to their subtle onset and tendency for metastasis, leading to an unfavorable prognosis\u003csup\u003e45\u003c/sup\u003e. Currently, the primary treatment for ovarian cancers involves surgery combined with platinum-based chemotherapy \u003csup\u003e67\u003c/sup\u003e. The rapid development of chemoresistance, especially for antiangiogenic agents, poses a significant challenge in managing ovarian cancers \u003csup\u003e89\u003c/sup\u003e. The prognosis of ovarian cancer can assist gynecologists in determining the timing and plan of treatment and providing patients with information about the tumors. Therefore, the identification and validation of relevant prognostic markers are urgently needed to improve the prognosis assessment and treatment guidance for ovarian cancer patients.\u003c/p\u003e \u003cp\u003eA condition known as \"vasculogenic mimicry\" (VM) occurs when tumor cells directly create luminal structures without the assistance of endothelial cells, thereby facilitating the supply of nutrients and oxygen to tumor cells\u003csup\u003e10\u003c/sup\u003e. This phenomenon was first discovered by Maniotis in melanoma studies in 1999, and subsequent research has confirmed its widespread presence in various cancers, including breast and lung cancers, glioblastoma, and others \u003csup\u003e1013141516\u003c/sup\u003e. Current studies indicate that tumor cells are most likely to produce VM structures under hypoxic conditions, due to the presence of cells that can transdifferentiate into an endothelial phenotype, establish intercellular connections, and create a nutrient channel like blood vessel\u003csup\u003e112123\u003c/sup\u003e. According to Ayala-Dom\u0026iacute;nguez L et al., three major pathways and their genes are involved in this phenomenon. These include the VE-cadherin/EphA2/MMPs pathway, the hypoxia-induced HIF-1A pathway, and the EMT pathway\u003csup\u003e15\u003c/sup\u003e. Other genes such as FOXC2, TGFB1, SNAI families genes also play important role in VM \u003csup\u003e123133\u003c/sup\u003e. Currently, research on inhibiting VM in tumor cells is limited, but researchers have successfully demonstrated the inhibition of VM in vitro using VM inhibitors\u003csup\u003e1920\u003c/sup\u003e. Jui-Ling Hsu et al. revealed that strategies to inhibit vasculogenic mimicry hold promise for cancer treatment\u003csup\u003e19\u003c/sup\u003e. Additionally, Gao Y et al. confirmed the presence of VM in ovarian cancer and its association with recurrence, metastasis, and prognosis\u003csup\u003e1718\u003c/sup\u003e. Increasing evidence suggests that certain genes play a significant role in ovarian cancer VM \u003csup\u003e21\u003c/sup\u003e, however, no reported research established a prognostic model on the use of biomarkers for ovarian cancer VM. The identification of the molecular mechanisms underlying vasculogenic mimicry in ovarian cancer, may unveil potential therapeutic targets.\u003c/p\u003e \u003cp\u003eIn this article, two subgroups were identified based on VM genes (VMGs), and a comprehensive bioinformatic analysis of datasets was performed. We successfully established and verified a model by VM-related prognostic index (VMRPI) that provides ovarian cancer prognosis and reveals the landscape of immune and drugs\u0026rsquo; responses.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and processing\u003c/h2\u003e \u003cp\u003eRNA sequencing data and clinical details were obtained from The Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database for serous ovarian cancer (SOC) patients (n\u0026thinsp;=\u0026thinsp;429) in our study, as well as from the Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for GSE51088 (n\u0026thinsp;=\u0026thinsp;100), comprising the main set for both training and test data. Additionally, an external validation set was obtained from GSE:17260 (n\u0026thinsp;=\u0026thinsp;110) in GEO. After exclusion, 522 samples remained in the main set, and 110 samples were included in the external validation set. The fallopian tube RNA sequencing of normal people (n\u0026thinsp;=\u0026thinsp;180) was acquired from a professional website-The Genotype-Tissue Expression (GTEx, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) portal. Every dataset from the TCGA and GEO databases was available to the general public. And unnecessarily for ethics committee approval for those data. The data extraction policies of these databases also were adhered strictly. To subject the TPM data to log2(x\u0026thinsp;+\u0026thinsp;1) and realize standardization, the \u0026ldquo;limma\u0026rdquo; package was used for transformation the RNA-seq data. The 'Combat' function in 'sva' package was the protocol to remove the batch effects. To analyze and validate the distribution and batching differences in the merged expression data from TCGA and GEO, this research conducted principal component analysis (PCA), and the 'prcomp' mathematical function from the'stats' package was utilized. The protein-protein interaction (PPI) network was established using version 12.0 of the Search Tool for the Retrieval of Interacting Genes (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This database serves as an extensive repository for both confirmed and anticipated protein-protein interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of VM-related genes in patients with SOC\u003c/h2\u003e \u003cp\u003eThe VM-related genes (VMGs) in our research were collected from the previously published reviews and research articles\u003csup\u003e123132333435\u003c/sup\u003e. The univariate Cox analysis was utilized to identify VMGs, including SNAI1, MMP2, MMP14, SNAI2, ZEB1, and TWIST1, as the core genetic determinants. The association between the expression of prognostically relevant genes and survival duration were illustrated by Kaplan-Meier curves. To confirm the differentially expressed genes (DEGs) identified in the \"limma\" software package, we set the significance level at a p-value of less than 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConsensus clustering\u003c/h2\u003e \u003cp\u003eTo identify vasculogenic mimicry subtypes, a rigorous unsupervised classification algorithm, named consensus clustering analysis in R package \u0026ldquo;ConsensuClusterPlus\u0026rdquo;, was performed using the expression of the determinant core genes and euclidean distance was set as 1000 times repetition\u003csup\u003e26\u003c/sup\u003e. In the experiment, we aim to determine two values: the optimum cluster number (k) and the degree of consensus stability. Finding the output from the cumulative distribution function (CDF) plots and determining whether the CDF curve is flat is necessary for this. We also need to confirm the differences in overall survival between clusters. To complete this stage, use R's \"survival\" package, employing the Cox Proportional-Hazards model, and determining the statistical differences based on the results of the log-rank test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis for GO and KEGG\u003c/h2\u003e \u003cp\u003eDEGs derived from contrasting the risk-high and risk-low subgroups in the main set were investigated to assess both molecular function (MF), biological process (BP), and cellular component (CC) from Gene Ontology (GO) \u003csup\u003e28\u003c/sup\u003e, and intracellular metabolic pathways from and gene functions Genes and Genomes (KEGG) enrichment analysis\u003csup\u003e38\u003c/sup\u003e. The parameter of filter was set according to logFC\u0026thinsp;=\u0026thinsp;1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The R package used here is called the \u0026ldquo;clusterProfiler\u0026rdquo;\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the Risk Score prognostic model\u003c/h2\u003e \u003cp\u003eThere were 260 DEGs showed significant prognostic association with their expression from DEGs (n\u0026thinsp;=\u0026thinsp;758) identified between the clusters by the \u0026ldquo;survival\u0026rdquo; R package using the univariate Cox regression analysis when the level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In the main set, 4/5 of the patients were divided into training set while 1/5 samples was divided into internal validation by the \u0026lsquo;createDataPartition\u0026rsquo; function in the \u0026lsquo;caret\u0026rsquo; R package. The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was employed to reduce the candidate gene pool for developing the prognostic model by using the R package \"glmnet\". The penalty parameter (λ) was determined using a ten-fold cross-validation approach, selecting the value that met the minimum criteria. After calculations, a risk score model, named Risk Score, was established using β (coefficient) value multiplied by the expression of risk genes. The risk score formula was as follows: Risk Score = (β1*Exp1\u0026thinsp;+\u0026thinsp;β2*Exp2+ \u0026hellip; +βn*Expn). The determination of the correlation coefficients was computed through a multifactorial Cox analysis by \u0026lsquo;coxph\u0026rsquo; function. Finally, 9 genes were identified to construct the prognostic signature. The median risk score is employed to differentiate subgroups into low-risk and high-risk cohorts. The receiver operating characteristic (ROC) curve was generated through ROC curve analysis employing the R packages \"survival\", \"survminer\", and \"timeROC\" to assess the diagnostic effectiveness of the risk model. Additionally, external validation steps were executed in the external validation set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMutation profile analysis\u003c/h2\u003e \u003cp\u003eWaterfall plots were utilized to illustrate the fluctuation in mutations between groups with high and low risk depending on the evaluation of risk scores for patient individually. This process was analyzed using the \"maftools\" package in R\u003csup\u003e29\u003c/sup\u003e. Additionally, every sample somatic mutation data were collected from the TCGA database online.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIndependent prognostic analysis\u003c/h2\u003e \u003cp\u003eAdditional independent prognostic examination of the samples' clinical features was done in the primary set, such as age, tumor grade, and tumor stage (FIGO), as well as risk scores, to assess independence and construct a nomogram model. The assessment of the connection between factors and prognosis utilized Univariate Cox regression analysis, while the coefficients in the nomogram were computed using multivariate Cox regression analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of microenvironment and cell infiltration\u003c/h2\u003e \u003cp\u003eWith the aim of revealling the microenvironment for immune of the SOC, the chosen method is the \"estimate\" R package calculating the grades of stromal, immune and ESTIMATE. The CIBERSORT algorithm was utilized for quantitative analysis of the relative abundance of 20 immune cell types within the data from TCGA, which can illustrate the subtypes of diverse immune cells. Spearman correlation tests were conducted to obtain the test values, and a heatmap was generated using the \"ggplot\" package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of the nomogram model\u003c/h2\u003e \u003cp\u003eThrough univariate and multivariate Cox analyses, we identified clinically significant pathological parameters with statistical significance. Subsequently, leveraging the \"rms\" and \"survival\" R packages, a nomogram model was established including age, tumor grade, tumor stage, and Risk Score index. This model aimed to predict the overall survival probabilities at 1 year, 3 years, and 5 years have been computed for patients. Additionally, calibration curves in the form of column charts have been generated to visually assess the predictive accuracy of prognosis outcomes for SOC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical specimens\u003c/h2\u003e \u003cp\u003eWe performed relevant experimental verification on tumor tissue of 36 new patients who met the inclusion criteria and failed to meet the exclusion criteria in 2023 in the First Affiliated Hospital of Sun Yat-sen University. The relevant cases were selected according to the following inclusion and exclusion criteria. Inclusion criteria: 1. Patients who was diagnosed ovarian cancer according to the 2022 edition of the Chinese Guidelines for the Diagnosis and Treatment of Ovarian Cancer; 2. Aged between 18 and 75 years old; 3. Enrolled cases must present with a histologically confirmed primary tumor that fulfills the specified criteria; 4. Adequate other organ and bone marrow function; 5. No history of other malignant tumors. Exclusion criteria: 1. Individuals with severe underlying diseases that are poorly controlled; 2. Lactating or pregnant women; 3. Those with a history of other illness, including serious infectious disease; 4. Persons with incapacitated or limited ability to act; 5. Patients received radiotherapy or chemotherapy before surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical staining\u003c/h2\u003e \u003cp\u003eA total of 35 paraffin-embedded tissue from patients with SOC was stained by immunohistochemical (IHC) who received treatment at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). The formalin-fixed paraffin-embedded (FFPE) slides underwent deparaffinization in a graded series of xylene and ethanol. Epitopes were unmasked by immersing the slides in a boiling antigen retrieval solution for 5 minutes. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide for 10 minutes and then incubated at room temperature for 25 minutes using goat serum blocking solution to eliminate non-specific staining. After incubation with mouse-derived anti-CD31 antibody (1:1000, CST, 3528S), the slides were placed at 4 degrees Celsius for 18 hours. Following PBS washing, an anti-mouse horseradish peroxidase-labeled secondary antibody (Vector Laboratories) was applied and incubated at 25℃ for 45 minutes. After 2 min of with 0.05% 3\u0026prime;,3-diaminobenzidine tetrahydrochloride (DAB, ZSGB-BIO, ZLI-9017) staining, the PAS staining procedure was carried out in accordance with the ZSGB-BIO, BSBA-4080A manufacturer's instructions. The slides were then dehydrated, mounted, and counterstained with hematoxylin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eVM detection and distinguish\u003c/h2\u003e \u003cp\u003eAfter the staining and neutral resin sealing process, the pathology slides were captured as electronic images at 40x magnification using a bright-field scanner. Subsequently, all channel-like structures were systematically examined based on the established criteria for typical vascular malformations. These criteria include the following: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) VM vascular-like channels surrounded by tumor cells; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) VM vessel channels staining PAS positive and CD31/CD34 negative (PAS+/CD34\u0026minus;), in contrast to endothelial vessel channels staining PAS positive and CD31/CD34 positive (PAS+/CD34+); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) VM vascular-like channels containing erythrocytes\u003csup\u003e1223\u003c/sup\u003e. According to the results of IHC staining for VM, all samples of clinical specimens were categorized into two subgroups\u0026mdash; VM(+) group meanings those with typical VM structures and VM(-) group meanings those without.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR\u003c/h2\u003e \u003cp\u003eIn accordance with the manufacturer's instructions, the total bulk RNA was extracted and purified through Trizol reagent (Invitrogen) and the SteadyPure Universal RNA Extraction Kit (ACCURATE BIOTECHNOLOGY (Human) CO., LTD, Changsha, China), and then it was reverse transcribed to cDNA through the Reverse Transcription Supermix (ACCURATE BIOTECHNOLOGY (Human) CO., LTD, Changsha, China, AG11706). The SYBR Green PCR Kit was utilized for conducting the qRT-PCR (ACCURATE BIOTECHNOLOGY (Human) CO., LTD, Changsha, China, AG11701) and in a Real-time fluorescence PCR instrument (Bio-Rad Laboratories, Inc, United States, Bio-Rad CFX Connect Real-Time PCR System 1855201). The expression of Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was set as an internal control to realize normalization. The Primers sequences in this study are presented in Supplementary Data. The comparative expression level was evaluated by 2-ΔΔCt method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDrug sensitivity\u003c/h2\u003e \u003cp\u003eTo investigate, by comparison, how risk scores affect medication therapy, follow the advanced instructions of the \"oncoPredict\" package\u003csup\u003e25\u003c/sup\u003e, utilizing the CTRP v2.0 dataset sourced from the CTRP website(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portals.broadinstitute.org/ctrp/\u003c/span\u003e\u003cspan address=\"https://portals.broadinstitute.org/ctrp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and associated drug experimental results (IC50) to construct a drug sensitivity model in R. The training set comprises TPM gene expression data of SOC-related cell lines (n\u0026thinsp;=\u0026thinsp;28) and their drug (497 drugs) experimental results. Consequently, the drug sensitivity scores for samples within the main set will be calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUsing R software, statistical analyses were carried out. (version 4.3.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The categorical variables were compared using the Chi-square test. The Wilcoxon test was employed to compare the drug sensitivity and gene expression levels of groups. A p-value of less than 0.05 was taken into consideration as the criterion for statistical significance in this investigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eComparison vasculogenic mimicry gene expression between normal tissues and Serous Ovarian Cancers (SOCs)\u003c/h2\u003e\n\u003cp\u003eWe created a heatmap of the data from TCGA and GTEx databases to compare the expression levels of VM-related genes in SOC and normal tissues of the fallopian tube (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). We discovered significant differences in the expression of 11 VM-related genes between TCGA OV tumors and GTEx normal samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Moreover, a network of protein-protein interactions was created and we uncovered robust interactions among these genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Based on mRNA expression levels in our dataset, a correlation network of the VMGs was created to show the negative (red hemisphere) and positive (favorable factors) risks associated with the hazard level among these VMGs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Furthermore, a notable association among these VMGs is evident. These findings suggest that VMGs may be very important for the onset and progression of SOC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eIdentification of subgroups associated with gene expression and prognosis\u003c/h2\u003e\n\u003cp\u003eAfter elimination of batch effects, TCGA and GEO combined dataset was referred to as the main set. Principal component analysis (PCA) confirmed a relatively consistent distribution (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Considering that the occurrence of vasculogenic mimicry is the result of multiple cellular signaling pathways, and that there is mutual exclusion in gene expression levels among those pathways, VM important prognostic genes were kept as categorization criteria. To identify predictive vasculogenic mimicry genes (VMGs), univariate Cox regression analysis was performed using the R \"survival\" package. According to the analysis, there is a statistically significant negative association between patient prognosis and SNAI1, MMP2, MMP14, SNAI2, ZEB1, and TWIST1. Subsequently, Kaplan-Meier (K-M) survival curves stratified by cutoff values, were subsequently generated to investigate the associations between the expression of these genes and overall survival in SOC patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). Consensus clustering was used to identify subtype groups based on the expression of prognostic VM-related genes. In the main dataset, the 520 SOC patients were partitioned into two clusters: The VM-high subgroup comprising 243 patients and the VM-low subgroup comprising 277 patients. The choice of a clustering variable (k) of 2 was made due to maximal intragroup correlations and minimal intergroup correlations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Remarkably, the overall survival of the identified clusters varied significantly, with VM-high patients exhibiting a better prognosis compared to VM-low patients (p\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). A distributional map was generated, illustrating the origins of the sequenced data and key clinical features such as tumor stage, tumor grade, and age (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eEnrichment analysis of DEGs and construction of the VM-related prognostic index (VMRPI)\u003c/h2\u003e\n\u003cp\u003eThe main dataset, which included TCGA and GEO cohorts, was screened for the DEGs between the clusters, when parameters were set to log fold change (log FC)\u0026thinsp;=\u0026thinsp;0.585, and the false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. As a result, 758 DEGs were found from all genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). To identify the involved pathways and functions that are significantly associated with the DEGs, enrichment analyses, including GO and KEGG analyses, were conducted. GO analysis revealed that the top five enriched biological processes (BP) terms were \u0026ldquo;extracellular matrix organization\u0026rdquo;, \u0026ldquo;extracellular structure organization\u0026rdquo;, \u0026ldquo;external encapsulating structure organization\u0026rdquo;, \u0026ldquo;ossification\u0026rdquo; and \u0026ldquo;connective tissue development\u0026rdquo;. The top five enriched cellular component (CC) terms were \u0026ldquo;collagen\u0026thinsp;\u0026minus;\u0026thinsp;containing extracellular matrix\u0026rdquo;, \u0026ldquo;endoplasmic reticulum lumen\u0026rdquo;, \u0026ldquo;cell\u0026thinsp;\u0026minus;\u0026thinsp;substrate junction\u0026rdquo;, \u0026ldquo;focal adhesion\u0026rdquo; and \u0026ldquo;external side of plasma membrane\u0026rdquo;. The top five enriched molecular function (MF) terms were \u0026ldquo;extracellular matrix structural constituent\u0026rdquo;, \u0026ldquo;receptor ligand activity\u0026rdquo;, \u0026ldquo;glycosaminoglycan binding\u0026rdquo;, \u0026ldquo;integrin binding\u0026rdquo; and \u0026ldquo;sulfur compound binding\u0026rdquo;. The KEGG analysis showed that DEGs were mainly involved in proteoglycans in the cancer signaling pathway, the ECM\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction signaling pathway, the Malaria signaling pathway, the focal adhesion signaling pathway, the PI3K\u0026thinsp;\u0026minus;\u0026thinsp;Akt signaling pathway signaling pathway, the AGE\u0026thinsp;\u0026minus;\u0026thinsp;RAGE signaling pathway in the diabetic complications signaling pathway, The protein digestion and absorption signaling pathway, the complement and coagulation cascades signaling pathway, the cytokine\u0026thinsp;\u0026minus;\u0026thinsp;cytokine receptor interaction signaling pathway, and the staphylococcus aureus infection signaling pathway. These results suggest that these signaling pathways and cellular functions may be involved in the formation or promotion of VM. Furthermore, a univariate Cox prognostic analysis was conducted on the DEGs, revealing 260 DEGs with prognostic significance. The main set was randomly partitioned, with 80% of the samples designated as the training set and the remaining samples as the test set for internal validation. Using LASSO regression on the 260 prognostic DEGs, a subset was selected, and subsequently, a multivariable Cox analysis was performed to calculate the selected genes\u0026rsquo; prognostic coefficients (\u0026beta;). The process of parameter selection is illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC. The results of the established Risk Score were presented in a forest plot format (Figure D). These findings enabled the creation of a final model that comprised 9 genes. Among these, FPR1, ADH1B, PAPPES1, and WNT11 gene expression levels exhibited strong correlations with a higher prognosis score. TSPAN8, FOXJ1, CXCL13, CXCL9, and SST were associated with a lower prognosis score.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation of the prognostic model and gene level differences based on VM-related prognostic indices (VMRPI)\u003c/h2\u003e\n\u003cp\u003eThe effectiveness of the prognostic model was assessed using ROC analysis, which demonstrated promising performance. The AUC values of the training set for 1, 3, and 5 years were 0.694, 0.746, and 0.727, respectively. Simultaneously, the internal validation showed AUC values of 0.752, 0.667, and 0.663, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The AUC values of the main set ROC at 1, 3 and 5 years were 0.708, 0.732, and 0.720. To further validate its applicability, GSE17260 was chosen as an external validation set for our Risk Score model. This set showed AUC values of 0.731, 0.633, and 0.723 at 1, 3, and 5 years, respectively, demonstrating robust performance (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, and according to the median risk score, the partitioning all samples into two groups, risk-high and risk-low, resulted in significant prognostic differences with p-values lower than 0.01 for the main set and p-values of 0.03 for the external validation set (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Furthermore, the risk score was validated for assessing the VM gene expression levels. Differential expressions of the VM-associated genes, BCAR3, CD44, CDH5, EPHA2, FOXC2, IL6, LAMC2, MMP14, MMP2, PLAU, TGFB1, TWIST1, ZEB1, SNAI1, and SNAI2, were observed between the groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). These results comprehensively demonstrate the meaningful implications of the risk score. Moreover, the exploration of intergroup mutation patterns using TCGA mutation data revealed that TP53 and TTN were the most frequently mutated genes. The other top five mutations in the high-risk group included CSMD3, MUC16, USH2A, TG, and FLG2, whereas the prominent mutations in the low-risk group were RYR2, CSMD3, USH2A, NF1, FAT3, and DST (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\n\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n\u003ch2\u003eEvaluation of the clinical features of risk subgroups and calculation of the nomogram\u003c/h2\u003e\n\u003cp\u003eTo investigate the relationships between clinical features and scores, we conducted a differential analysis of scores among different clinical characteristics. The analysis found no significant differences in risk scores among different age groups, tumor grades or tumor stages (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). This implied that risk scoring is an independent prognostic factor irrespective of clinical features. Furthermore, considering that the immune status is a critical clinical feature and prognostic factor, exploring the relationships between the risk model and immune infiltration, including immune cells category, is imperative. Our initial analysis of immune cell correlations suggested that high- or low-risk scores are positively correlated with neutrophils, monocytes, activated mast cells, M2 macrophages, and resting memory CD4 T cells. However, they negatively correlated with follicular helper T cells, plasma cells, regulatory T cells (Tregs), M1 macrophages, CD8 T cells, and activated memory CD4 T cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). Furthermore, an evaluation of the immunological microenvironment in the high- and low-risk groups showed that the high-risk group had significantly higher immune, stromal, and ESTIMAT scores than those of the low-risk group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). We carried out a multivariate analysis combining clinical characteristics to effectively incorporate a multifactorial assessment (age, grading, and staging) with risk scores (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD) and constructed a prognostic nomogram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE) for a comprehensive prognosis evaluation. Moreover, calibration curves indicated a favorable predictive performance at 3 and 5 years (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n\u003ch2\u003eExperimental expression validation of cluster genes and VMRPI genes\u003c/h2\u003e\n\u003cp\u003eTo further investigate whether differences in related outcomes within ovarian cancer biospecimens exist, we used our center paraffin-embedded specimens of primary SOCs from the past six months for PAS-CD31 immunohistochemistry (IHC) staining (n\u0026thinsp;=\u0026thinsp;35). Among these specimens, only six exhibited typical vasculogenic mimicry (VM) features. Subsequently, we matched fresh frozen tissues of identical age, grade, and stage based on IHC results, and classified them into VM\u0026thinsp;+\u0026thinsp;and VM- groups. Three cases were chosen from each group for presentation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). A verification of the differences in VM-associated gene expression profiles (VMGs) and VM-related prognostic indices (VMRPIs) between the groups was performed. The results of qRT-PCR revealed statistically significant differences in the expression of the representative VMGs, MMP2, MMP14, SNAI2, ZEB1, and TWIST1, between the VM\u0026thinsp;+\u0026thinsp;and VM- groups. Moreover, FPR1, ADH1B, and WNT11, identified as VMRPIs, also exhibited statistically significant gene expression differences and demonstrated a relationship with VM that aligned with the prognostic risk analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Hence, these results indicate the potential for further exploration into the upregulation of FPR1, ADH1B, and WNT11 expression in relation to the mechanisms associated with vasculogenic mimicry.\u003c/p\u003e\n\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n\u003ch2\u003ePrediction of therapeutic response to drugs based on the risk score\u003c/h2\u003e\n\u003cp\u003eIn addition to conventional chemotherapy drugs, numerous studies investigated new therapeutic targets for ovarian cancer to enhance the effectiveness of chemotherapy, reduce drug resistance, and minimize tumor recurrence. Therefore, we conducted a drug sensitivity analysis using the CTRP (Cancer Therapeutics Response Portal) and CCLE (Cancer Cell Line Encyclopedia) databases. Based on risk score-based stratification, the sensitivity scores of various drugs indicated that drugs, such as BRD-K61166597, apicidin, AZD8055, bardoxolone methyl, curcumin, doxorubicin, KU-0063794, lovastatin, NSC48300, leptomycin B, sirolimus, and temsirolimus, exhibit higher IC50 values within the high-risk group. Conversely, compound 1B displayed lower IC50 values (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings revealed differences in partial drug sensitivity among subgroups stratified based on risk scores. This information can aid in guiding clinical drug administration strategies, exploring drug targets, and identifying potential drug resistance markers.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOvarian cancer exhibits a poor prognosis due to its insidious onset and high propensity for metastasis and recurrence\u003csup\u003e17\u003c/sup\u003e. Among ovarian cancer subtypes, mucinous ovarian cancer accounts for the majority of occurrences and fatalities\u003csup\u003e23\u003c/sup\u003e. Additionally, inadequate responsiveness to antiangiogenic drugs in clinical settings warrants thoughtful consideration\u003csup\u003e959697\u003c/sup\u003e. It has been established that ovarian cancer has a perfusion-enhancing mechanism, known as vasculogenic mimicry, which significantly correlates with prognosis. Previous studies on ovarian cancer vasculogenic mimicry (VM) have underscored the crucial roles of genes such as MMP2, MMP14, TWIST1 in VM and have validated the pivotal involvement of pathways such as the VE-cadherin/EphA2/MMPs pathway, the hypoxia-induced HIF-1A pathway and the EMT pathway in generating and regulating ovarian cancer VM. Despite the significance of VM in cancer, the use of inhibitors has been preliminarily explored in lung cancer, and further assessment of their effectiveness and specificity remains pending\u003csup\u003e19\u003c/sup\u003e. Moreover, the current state lacks prognostic evaluation mechanisms centered around VM. Therefore, the goal of this work was to create a prognostic model within the standard of care (SOC), based on the transcriptional status of VM-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on previously published research, we constructed a gene set consisting of 28 encoding genes related to VM. This was performed after excluding genes whose expression data could not be utilized in the dataset. Initially, a comparison between tumor tissues and normal tissues revealed significant differential expression of some genes, indicating the potential involvement of VM genes in the onset and progression of cancer. Furthermore, univariate Cox analysis identified significant associations between SNAI1, MMP2, MMP14, SNAI2, ZEB1, and TWIST1 expression levels and SOC prognosis. The HR values of these genes were all greater than 1, strongly indicating a poorer prognosis associated with VM. Utilizing these prognostically relevant VMGs, we employed the widely used classification algorithm \"ConsensusClusterPlus\". To avoid single-platform overfitting and enhance inclusivity, sequencing data from multi-databases were merged to construct the main set for subtype grouping: VM-high correlation and VM-low correlation subgroups. The validation of VM-related genes between the two groups confirmed the presence of significant differences in gene expressions of BCAR3, CD44, CDH5, EPHA2, FOXC2, IL6, LAMC2, MMP14, MMP2, PLAU, TGFB1, TWIST1, ZEB1, SNAI1, and SNAI2, indicating a robust grouping based on VM gene expression. Subsequently, 758 DEGs were found between the two subgroups; with 260 genes linked to prognosis. Using these prognostically relevant DEGs, we employed the LASSO model for dimension reduction and variable selection, followed by multivariate Cox regression, to comprehensively calculate the prognostic risk. Ultimately, a risk-scoring model was constructed based on nine core genes: FPR1, ADH1B, RARRES1, TSPAN8, FOXJ1, CXCL13, WNT11, CXCL9, and SST. Internal and external validations of the model demonstrated good performance and multiplatform applicability. Moreover, based on the risk score-based grouping, significant differences in immune cells and the immune microenvironment were observed between groups. A higher risk score indicated a poorer cellular immune status, potentially attributed to a VM-associated increase in perfusion. To enhance clinical prognosis evaluation, a line graph was constructed based on the risk model and combined with clinical features for better quantitative survival rate calculations. For experimental validation, histopathological staining of VM structures were conducted on our center specimens and paired with the grouping. Using qRT‒PCR on fresh frozen tissues, the expression status of VMGs and VMRPIs were explored, revealing significant differences in the expression of MMP2, MMP14, SNAI2, ZEB1, and TWIST1 as subtype genes in our center's samples. These results highlighted the close association between VM-related genes and VM structures. Similarly, FPR1, ADH1B, and WNT11, as VMRPIs, were highly expressed in VM\u0026thinsp;+\u0026thinsp;patients, indicating consistency between the data analysis and pathological specimens. Finally, utilizing public databases, drug sensitivity prediction was performed for the risk score-based groups. The results suggested that the presence of VM may lead to chemoresistance for drugs such as BRD-K61166597, apicidin, AZD8055, bardoxolone methyl, curcumin, doxorubicin, KU-0063794, lovastatin, NSC48300, leptomycin B, sirolimus, and temsirolimus. However, compound 1B, an NF-KB inhibitor, demonstrated better efficacy in the high-score VM group, indicating its potential as a combined therapeutic agent for antiangiogenic drugs.\u003c/p\u003e \u003cp\u003eThe existing literature has provided insights into the genes included in the risk score model. C-X-C motif chemokine ligand 13 (CXCL13), known as a fundamental regulator of B-cell recruitment and organization, can coordinate the development of tertiary lymphoid structures\u003csup\u003e37\u003c/sup\u003e. Its expression level is associated with long-term survival can enhance the effectiveness of PD-1 checkpoint blockade in high-grade serous ovarian cancer (HGSOC)\u003csup\u003e3839\u003c/sup\u003e. Formyl peptide receptor 1 (FPR1) is not only in proinflammatory and antibacterial host responses but also involved in cell chemotaxis, proliferation, and tumor progression\u003csup\u003e404142\u003c/sup\u003e. Activated FPR may contribute to these processes and has been identified as a potential biomarker and treatment target for aggressive epithelial ovarian cancer (EOC)\u003csup\u003e4344\u003c/sup\u003e. Tetraspanin 8 (TSPAN8) is a member of the tetraspanin family, implicated in various human cancers through its role in regulating intercellular interactions and cell motility. It is a potential therapeutic target for the inhibition of invasion and metastasis in OCs \u003csup\u003e454647484950\u003c/sup\u003e. Forkhead box J1 (FOXJ1) is a 3-exon transcription factor and a master regulator of motile ciliogenesis. It is expressed in various tissues such as the respiratory tract, brain, and reproductive tract, where it plays a pivotal role in regulating transcriptional programs governing motile cilia assembly \u003csup\u003e51525354\u003c/sup\u003e. This activity showcases its diverse implications for cancer biology and prognosis. High FOXJ1 expression is correlated with better tumor differentiation and favorable prognosis in various cancers such as gastric cancer, ependymomas, choroid plexus tumors, and ovarian cancer\u003csup\u003e555657\u003c/sup\u003e. Alcohol dehydrogenase class I beta polypeptide (ADH1B) is pivotal for alcohol metabolism and is implicated in tumorigenesis, particularly, in esophageal squamous cell and colorectal cancers\u003csup\u003e586061\u003c/sup\u003e. High FABP4 and ADH1B expressions in high-grade serous ovarian cancer suggest an increased risk of residual disease, possibly guiding neoadjuvant chemotherapy candidacy \u003csup\u003e62\u003c/sup\u003e. Wnt family member 11 (WNT11) is a noncanonical Wnt protein that regulates cell movement and organ formation through specific receptors and signaling pathways\u003csup\u003e636465666768\u003c/sup\u003e. It plays a dual role by promoting migration in certain cancers including breast cancer, colon cancer, and leukemia, while suppressing cell migration in hepatocellular carcinoma\u003csup\u003e6970\u003c/sup\u003e. Additionally, WNT11 influences cell adhesion and migration by modulating the expression of E-cadherin and integrin subunits\u003csup\u003e7172\u003c/sup\u003e. Retinoic acid receptor responder 1 (RARRES1, also known as tazarotene-induced gene 1, TIG1) is upregulated by tazarotene in skin culture and resembles CD38 and is frequently silenced in cancers due to promoter hypermethylation\u003csup\u003e737475\u003c/sup\u003e. It shows potential as a tumor suppressor in prostate and endometrial cancers\u003csup\u003e7677\u003c/sup\u003e. Notably, RARRES1 plays a crucial role in promoting tumor growth and invasion in IBC through Axl, indicating its promise as a therapeutic target for IBC patients\u003csup\u003e78\u003c/sup\u003e. C-X-C motif chemokine ligand 9 (CXCL9), in conjunction with CXCL10 and CXCL11, boosts T-cell infiltration in ovarian cancer, and is associated with improved survival rates\u003csup\u003e79\u003c/sup\u003e. Research indicates that CXCL9 and its chemokine counterparts are indicators of an inflammation-rich subtype of ovarian cancer\u003csup\u003e8081828384\u003c/sup\u003e. Recent evidence highlighted the predictive value of CXCL9 for positive outcomes and favorable responses to anti-PD-1 therapy in cancer patients \u003csup\u003e8586\u003c/sup\u003e. Somatostatin (SST) is cyclic peptide that inhibits hormone secretion and suppresses immune functions\u003csup\u003e87888990\u003c/sup\u003e. Its signaling pathway also regulates tumor characteristics such as angiogenesis, cell migration, and growth factors, promoting tumor neovascularization and cell growth\u003csup\u003e91929394\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e Despite numerous reviews and studies on ovarian cancer VM, our investigation represents the inaugural exploration of the VM-associated prognostic risk model in ovarian cancer. Utilizing multiplatform datasets for model construction and external validation has enhanced the generalizability of the risk model and Kaplan‒Meier plots. However, there are several limitations: Primarily, our findings indicate an increased immune score due to VM; however, a deeper exploration of the underlying mechanisms, contributing to poorer prognosis, is warranted; Secondly, within the sample set, used for Kaplan‒Meier plot construction, there might be an insufficient number of early-stage and low-grade samples, necessitating further analysis to enhance the accuracy of staging and grading within the Kaplan‒Meier plots; and finally, there is an urgent need for more mechanistic studies to elucidate the roles of the identified risk genes in mediating VM in ovarian cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our research has established a prognostic risk model centered around the genes FPR1, ADH1B, RARRES1, TSPAN8, FOXJ1, CXCL13, WNT11, CXCL9, and SST within the SOC based on VM-related gene subgroups. This risk model has a strong predictive value in both internal and external validations. We also explored mutations and immune-related situations based on risk score status. Furthermore, in conjunction with age, grading, and staging, we constructed a robust performance nomogram for prognostic assessment. Additionally, we have experimentally validated the differential expression of relevant risk genes among IHC-VM subgroups and explored potential drug therapeutic targets based on risk scores.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by Committee on the Use of Clinical Research of the First Affiliated Hospital of Sun Yat-sen University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The design of this study follows the tenets of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used and/or analysed during the current study are available upon reasonable requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2022YFC2704201 to Shuzhong Yao), National Natural Science Foundation of China (82273365, 82072874 to Shuzhong Yao), China Postdoctoral Science Foundation (2023M744070 to Chunyu Zhang), Postdoctoral Fellowship Program of CPSF (GZC20233282 to Chunyu Zhang), Guangdong Medical Science and Technology Research Foundation (A2024280 to Chunyu Zhang), Science and Technology Plan of Guangdong Province (2023A0505050102 to Shuzhong Yao), Guangzhou Science and Technology Program (2024B03J1336 to Shuzhong Yao), Sun Yat-sen University Clinical Research Foundation of 5010 Project (2017006 to Shuzhong Yao), and Guangdong Basic and Applied Basic Research Foundation (2023A1515012214 to Wei Wang，2023A1515110333 to Chunyu Zhang).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXueyuan Zhao and Yan Jia performed the experiment and analyzed the data. Xueyuan Zhao and Weijia Wen wrote the manuscript. Caixia Shao and Qianjian Zou participated in the study design and helped in experiments. Linna Chen and Hongye Jiang collected and downloaded the data. Guofen Yang helped obtain funding. Chunyu Zhang conceived this project and obtained funding. Wei Wang and Shuzhong Yao provided the resources, obtained funding and supervised the project. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021 Jan;71(1):7-33. doi: 10.3322/caac.21654. 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PMID: 37890478.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ovarian cancer (OC), vasculogenic mimicry, prognostic model, immune microenvironment, therapeutic response","lastPublishedDoi":"10.21203/rs.3.rs-4336317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4336317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eVasculogenic mimicry (VM) is a vascular-like microcirculatory delivery system directly formed by tumor cells. It plays significant roles in tumor invasion and resistance to antiangiogenic drugs. However, there is no model based on VM for ovarian cancer (OC) prognosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe occurrence and progression of OC is associated with vasculogenic mimicry-related genes (VMGs) interactions in a cellular network. The prognostic VMGs can distinguish between two subgroups with significantly different prognoses. Differentially expressed genes (DEGs) between the subgroups suggest that certain signaling pathways and cellular functions are involved in the formation or promotion of VM. Based on filtered DEGs, a prognostic model was constructed and demonstrated outstanding performance in internal and external validations. The model results do not affect the distribution of clinical features and can be used to construct a nomogram. There are differences in immune cell composition and immune infiltration between the high-risk group and the low-risk group. Differences also exist in their resistance and sensitivity to certain drugs. Finally, subgroups analysis demonstrated differences in ovarian cancer VM structures which may be associated with different expressions of VMGs and VM-related prognostic indices (VMRPI).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eVM may play a crucial role in the development and tumor immune microenvironment of serous ovarian cancer. VMRPI holds promise as a valuable prognostic biomarker. This is the first time that it is used to identify high risk OC patients with ovarian cancer and may indicate responsiveness to specific drug treatments.\u003c/p\u003e","manuscriptTitle":"Establishment and validation of a prognostic model based on grouping of vasculogenic mimicry-related genes in ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 18:50:39","doi":"10.21203/rs.3.rs-4336317/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd6bff2e-dccd-4910-a664-731f320b5abe","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-15T04:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 18:50:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4336317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4336317","identity":"rs-4336317","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}