A novel prognostic model based on vasculogenic mimicry in ovarian cancer

Introduction

Ovarian cancer (OC) is a broad category of diseases with a wide range of clinical manifestations, pathological characteristics, and molecular profiles. It ranks as the second most fatal gynecological malignancy worldwide, with consistently elevated rates of incidence and mortality (1). High-grade serious OC (HGSOC), the most frequent and aggressive histological subtype, is characterized by insidious onset, a propensity for peritoneal dissemination, and the frequent development of significant ascites (2). The prognosis for patients with HGSOC remains unfavorable despite major improvements in surgical methods, chemotherapeutic treatments, and targeted therapies, especially for those who are diagnosed at an advanced stage. To improve the therapeutic strategy and enhance disease management for patients with HGSOC, it is vitally crucial to identify novel diagnostic and prognostic biomarkers.

Vasculogenic mimicry (VM) is a distinctive biological phenomenon in which tumor cells generate a vessel-like structure to facilitate nutrient transport and metastasis, circumventing the requirement for endothelial cells (3). This mechanism enhances blood circulation, allowing tumor cells to obtain oxygen and nutrients, thus significantly contributing to tumor growth and progression. VM has been linked to advanced tumor grade, enhanced invasiveness, metastatic potential, and poor patient prognosis in various malignancies (4). The formation of VM is regulated by intricate signaling pathways, such as those involving vascular endothelial growth factor (VEGF), matrix metalloproteinases, and molecules associated with epithelial-mesenchymal transition (5,6). Hypoxia is a key driver in VM development, with hypoxia-inducible factors and hypoxia response elements playing crucial roles (7). In preclinical OC models, anti-angiogenic therapy employing bevacizumab, a monoclonal antibody targeting VEGF-A, has demonstrated limited therapeutic efficacy, accompanied by increased metastasis, hypoxia, and VM (8). This implies that compensatory alternative vascularization processes like VM, which are initiated by aggressive tumor cells, may make anti-angiogenic medicines less effective in slowing the progression of cancer (9). Therefore, elucidating the mechanisms of VM and constructing a prognostic risk model based on VM-related biomarkers are crucial to assess their prognostic significance and explore their potential as therapeutic targets for more effective cancer treatment strategies.

In this study, we aim to construct a prognostic risk model based on VM-related biomarkers. By analyzing transcriptomic data from HGSOC patients, we identified a set of genes associated with VM. Using the least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, we developed a predictive risk model for HGSOC based on the expression profiles of these VM-related genes. Survival analysis in an independent patient cohort validated the model’s predictive performance. Subsequently, the survival analysis showed that LRIG1 could be used as a potentially good prognostic biomarker for HGSOC, and an experiment in vitro illustrated that LRIG1 inhibited tube formation of OVCAR3. Our findings not only provide insights into the molecular mechanisms of VM and angiogenesis in HGSOC progression but also offer novel biomarkers for prognostic assessment. This research may open new avenues for targeted therapies and improve the prognosis of HGSOC patients. We present this article in accordance with the TRIPOD and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1849/rc).

Methods

Data collection

We conducted a comprehensive literature search to identify 42 VM-related genes (10). To systematically investigate the role of VM in HGSOC, we utilized two independent public databases: The Cancer Genome Atlas (TCGA)-OV and GSE9891 from the Gene Expression Omnibus (GEO) to develop a prognostic scoring model. The TCGA repository (https://cancergenome.nih.gov/) provided the messenger RNA (mRNA) expression profiles and related clinical information. The mRNA expression profiles of GSE9891 are available at the GEO (https://www.ncbi.nlm.nih.gov/geo/) webserver under accession ID GSE9891, and related clinical information was accessed from the R package curatedOvarianData. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

RNA sequencing (RNA-seq) data processing

We employed the R package DESeq2 v1.16.1 to conduct the differential expression gene analysis. Differential expression genes were defined as follows: |log₂fold change| >1 and false discovery rate (FDR) <0.05, unless otherwise indicated.

Development of a risk assessment model for determining the risk score

We performed LASSO regression using a 10-fold cross-validation at a significance threshold of 0.05 after starting with a univariate analysis. A total of 1,000 random simulations were used in each iteration of this regression. The frequency of each variable combination in these iterations was subsequently calculated. The variable combinations that occurred more than 100 times were retained for the subsequent univariate and multivariate Cox proportional hazards regression analysis and model construction. Additionally, the area under the curve (AUC) values for each model were computed and graphically illustrated. The computational process was terminated when the graphical representation reached its peak, which indicated the utmost achievable AUC value, indicating that the model was the optimal choice. Subsequently, we conducted the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves for the model. The risk score for each patient was calculated using the following formula: LATS2 × 0.860050132558781 + LRIG1 × (−0.564458518190914) + MMP13 × (0.0384058072846764) + MMP9 × (−0.0116548695663016) + PTGS2 × (−0.07567136399959) + RGS3 × (−0.0574830827223467) + TF × (0.0575021158492538).

Survival analysis stratified by low- and high-risk scores

We utilized the Kaplan-Meier method to perform the survival analysis and employed the log-rank test to evaluate the difference in overall survival between the high- and low-risk groups. The R packages “survival” and “survminer” were used to conduct the survival analysis. We created a risk score curve based on each patient’s risk scores.

Verification of risk score through univariate and multivariate Cox regression analyses

We tallied the pathological characteristics and risk scores using both univariate and multivariate Cox proportional hazards regression to ascertain the risk score’s role as an independent predictor of overall survival. The factor was deemed an independent risk factor with a P value below 0.05.

Establishment of risk prediction model

To forecast the overall survival rates at 1-, 2-, and 3-year for patients with HGSOC, we created a nomogram that combines risk scores and clinical information. The R package “rms” was used to generate this nomogram. We used bootstrapping with 1,000 resamples to construct the Concordance index, which was used to assess the nomogram’s discriminative power.

Gene ontology (GO) analysis

We calculated the differential gene expression profiles between the high- and low-risk groups and conducted GO enrichment analysis using the R package ClusterProfiler (version 3.6.0). The enrichment results were plotted using the R package ggplot2 (version 3.5.1).

Cell culture

We obtained the human HGSOC cell line OVCAR3 from the American Type Culture Collection. OVCAR3 were maintained in Dulbecco’s modified Eagle’s medium (DMEM)/F12 medium (Gibco, C11330500BT) containing 10% fetal bovine serum and 1% penicillin-streptomycin. All cells were cultured at 37 ℃ in an atmosphere with 5% (v/v) carbon dioxide (CO₂). We regularly checked cell cultures for mycoplasma contamination through polymerase chain reaction (PCR) testing. LRIG1 overexpression lentivirus and corresponding vector lentivirus were purchased from TsingkeBiotechnology (Beijing, China). OVCAR3 cells were infected following the instructions of the manufacturer.

RNA extraction and quantitative real-time PCR (qRT‑PCR)

The TRIzol reagent (Ambion, Austin, TX, USA; #15596026) was used to isolate total RNA, and the PrimeScript RT reagent Kit with gDNA Eraser (Vazyme, Nanjing, China; #R323-01) was used to convert RNA to complementary DNA (cDNA). We performed qRT-PCR using SYBR Green PCR Master Mix (Vazyme, #Q331-02) and corresponding primers (Table 1). Relative expression levels were calculated by 2^−ΔΔCT method.

In vitro tube formation assay

To allow the Matrigel to solidify, we coated twenty-four-well plates with ice-cold Matrigel solution (Corning Inc., Corning, NY, USA) and incubated them at 37 ℃ for at least 30 min. After harvesting OVCAR3, we suspended them in 2% FBS-reduced medium. Then, we seeded them in the Matrigel-coated wells at a density of 5×10⁴ cells/500 µL/well, and pre-incubated the cells for 30 min at 37 ℃ for cell attachment. Images of the tubular structures were captured after 24 h.

Statistical analysis

An independent t-test was applied for the two-group comparison. All statistical analyses were performed using GraphPad Prism version 9.5.0. Statistical significance was defined as a P value of less than 0.05.

Results

Construction of a prognostic risk model of VM in HGSOC

We conducted LASSO-Cox regression analysis in the testing cohort GSE9891, and established a prognostic risk model composed of seven VM-related genes (Figure 1A,1B). To further investigate the impact of these biomarkers on prognosis, we obtained their coefficient values and sorted them in absolute order (Figure 1C). LATS2 exhibited a prominent positive coefficient value, indicating that higher expression of LATS2 was significantly associated with a worse prognosis. Conversely, LRIG1 showed a negative coefficient value, suggesting that increased expression of LRIG1 might be related to a better prognosis. After that, we used univariate and multivariate logistic regression analysis to assess the relationship between these seven VM-related genes and the prognosis of HGSOC (Figure 1D). In the univariate analysis, the expression levels of LATS2 and MMP13 were negatively correlated with HGSOC, while only LATS2 was found to be negatively connected with prognosis in the multivariate analysis (Figure 1D).

Figure 1 Construction of a prognostic risk model for HGSOC by VM. (A) Seven biomarkers were analyzed by LASSO regression, in which biomarkers were eventually removed from the model as the penalty (lambda) increased. (B) The graph of partial likelihood deviance for tuning parameter selection in the LASSO model. (C) The impact direction of each biomarker on the prognosis in the model. (D) The univariate and multivariable logistic regression analysis of the seven biomarkers used to construct risk assessment model. (E,F) The 1-, 3-, and 5-year ROC of the GSE9891-OC (E) and the TCGA-OV (F). (G,H) Patients in the low-risk group experienced a longer survival time in the GSE9891-OC (G) and the TCGA-OV (H). AUC, area under the curve; CI, confidence interval; HGSOC, high-grade serious ovarian cancer; LASSO, least absolute shrinkage and selection operator; OC, ovarian cancer; OR, odds ratio; OV, ovarian cancer; ROC, receiver operating characteristic; SD, standard deviation; TCGA, The Cancer Genome Atlas; VM, vasculogenic mimicry.

The VM-related prognostic risk model’s clinical significance was confirmed by creating ROC curves for 1-, 3-, and 5-year survival. The predictive performance of the risk model was evaluated using time-dependent ROC curve analysis and showing AUCs of 0.720, 0.766, and 0.702 for 1-, 3-, and 5-year overall survival in training cohort GSE9891 (Figure 1E) and AUCs of 0.638, 0.653, and 0.572 for 1-, 3-, and 5-year overall survival in testing cohort TCGA respectively (Figure 1F) which demonstrated that the model performed well in terms of prediction. The survival curve analysis indicated that patients in the high-risk group had poorer prognosis, in both the training cohort (Figure 1G) and the testing cohort (Figure 1H).

Developing a nomogram

The univariate and multivariate Cox regression analysis was employed to identify independent predictive markers in HGSOC, which guarantees the model’s independence from other clinical variables (Figure 2A,2B). The results confirmed that the risk score, age, and International Federation of Obstetrics and Gynecology (FIGO) stage were significant independent predictors of prognosis (Figure 2A,2B). Subsequently, we integrated age and FIGO stage with the risk score to construct a nomogram to improve prognostic precision in HGSOC (Figure 2C). The link between age, FIGO stage, risk score, and survival probability at specific time points was graphically shown by this nomogram (Figure 2C). The calibration curves demonstrated good agreement between nomogram-predicted and observed survival probabilities in GSE9891 (Figure 2D). Taken together, the univariate and multivariate Cox regression identified independent prognostic factors, and the subsequent nomogram effectively predicted overall survival in HGSOC patients. This integrated tool served as a valuable asset for clinical prognosis assessment and personalized treatment planning.

Figure 2 The establishment of a nomogram for estimating the prognosis of OC. (A) Univariate Cox analysis of risk score and clinicopathological characteristics in GSE9891. (B) Multivariate Cox analysis of risk score and clinicopathological characteristics in GSE9891. (C) Nomogram model integrating the risk score, age, treatment and stage was constructed. (D) Calibration curves for OS prediction of nomogram. *, P<0.05; ***, P<0.001. CI, confidence interval; HR, hazard ratio; OC, ovarian cancer; OS, overall survival.

Enrichment analyses based on the risk score of VM

To elucidate the molecular mechanisms underlying HGSOC progression, particularly those related to VM, differentially expressed genes between the high- and low-risk groups were calculated and used for GO enrichment analysis. Genes in the high-risk group were significantly enriched in extracellular matrix (ECM)-related processes, prominently featuring terms like collagen-containing ECM, ECM structural constituent, extracellular structure organization, and ECM organization (Figure 3A). Additional up-regulated pathways included integrin binding and regulation of cellular response to growth factor stimulus (Figure 3A). In contrast, down-regulated genes were primarily associated with serine protease activity, specifically serine hydrolase activity, serine-type peptidase activity, and serine-type endopeptidase activity (Figure 3B). The dysregulation of these ECM-associated and proteolytic pathways likely contributed to the aggressive biology of high-risk HGSOC and represents potential targets for therapeutic intervention.

Figure 3 GO enrichment analyses of the training set from GSE9891. (A) GO terms of upregulated genes. (B) GO terms of downregulated genes. GO, gene ontology.

LRIG1 as a favorable prognostic biomarker and inhibited tube formation in OVCAR3

To explore the prognostic role of LRIG1 in HGSOC, we divided patients into high and low LRIG1 expression groups according to the median expression level. The Kaplan-Meier survival curves revealed that high LRIG1 expression was significantly associated with better overall survival in both GSE9891 datasets (Figure 4A) and the TCGA datasets (Figure 4B). This indicates that LRIG1 may act as a potential favorable prognostic biomarker for HGSOC.

Figure 4 LRIG1 may serve as a valuable prognostic biomarker for HGSOC. (A,B) Survival analysis plots of HGSOC patients with high- and low-LRIG1 in GSE9891 (A) and TCGA-OV (B). (C) The relative expression of LRIG1 in control and OVCAR3 was assessed by qRT-PCR. (D) Representative images of tube formation assay in control and LRIG1-overexpressing OVCAR3 (using bright-field microscopy without staining). (E,F) The number of junction points (E) and total segment lengths (F) in control group and LRIG1-overexpressing group. ***, P<0.001. HGSOC, high-grade serious ovarian cancer; LV, lentivirus; NC, negative control; OV, ovarian cancer; qRT-PCR, quantitative real-time polymerase chain reaction; TCGA, The Cancer Genome Atlas.

To further investigate the biological function of LRIG1 in OC progression, we established LRIG1-overexpressing HGSOC cell line OVCAR3 using lentivirus, and validated by qRT-PCR (Figure 4C). Tube formation experiment showed that LRIG1 overexpression significantly reduced the tube formation capacity of OVCAR3 compared to the control group (Figure 4D). Specifically, the number of junction points (Figure 4E) and the total segment length (Figure 4F) of the LRIG1 overexpression group were significantly reduced. These findings suggested that LRIG1 might impede VM by suppressing the tubulin capability of HGSOC cells and hinder the progression of HGSOC, which aligned with its prognostic significance demonstrated in survival analysis.

Discussion

Presently, prognostic predictions for HGSOC primarily rely on histological type and the FIGO staging system (11,12). However, these traditional methods are insufficient for accurately predicting clinical outcomes in HGSOC. There is a pressing need to enhance early detection and prognostic assessment of HGSOC to aid clinicians in optimizing treatment strategies. In recent years, advancements in genome sequencing technology and bioinformatics analysis have been widely applied in tumor research, playing an increasingly vital role (13). Numerous studies have identified genes associated with HGSOC prognosis by analyzing differentially expressed genes in HGSOC tissues and adjacent tissues, leading to the development of risk prediction models (14,15). Extensive research over the past decades has revealed that VM plays a crucial regulatory role in the initiation and progression of tumors. Meanwhile, anti-angiogenic therapy has demonstrated limited therapeutic efficacy in HGSOC, potentially due to VM formation by tumor cells. Therefore, in this study, to assess the prognostic significance of VM and explore their potential as therapeutic targets for more effective cancer treatment strategy, we elucidated the mechanisms of VM and developed a VM-related prognostic model for predicting survival outcomes in HGSOC.

In this study, we systematically identified VM-related genes and constructed a prognostic risk model for HGSOC based on seven VM-related genes. As a practical tool, mathematical nomograms have been used to calculate expected prognostic risks by integrating multiple clinicopathological factors (16). An established and validated nomogram based on Cox regression can predict the probability of overall survival time. Therefore, building on the VM-related risk model, we further integrated classical clinicopathological factors such as age and FIGO stage, developing a new nomogram that combines risk characteristics with clinicopathological features. This nomogram demonstrated strong predictive performance for the clinical prognosis of HGSOC patients.

GO enrichment analysis revealed enrichment of extracellular matrix-related pathways, integrin signaling, and growth factor response in high-risk HGSOC patients. Conversely, serine hydrolase, peptidase, and endopeptidase activities were suppressed. These functional pathways align with known VM mechanisms in HGSOC progression. Based on the preceding LASSO Cox regression analysis, we identified seven VM-associated genes linked to these functional pathways: LATS2, LRIG1, MMP13, MMP9, PTGS2, RGS3, and TF.

LATS2 has been identified as a potential tumor suppressor and a prognostic biomarker in various cancers (17). A study indicates that LATS2 expression is downregulated in breast, non-small cell lung cancer, and gastric cancer, yet upregulated in nasopharyngeal cancer, highlighting that its function depends on the tissue type of cancer (18). In HGSOC, the role of LATS2 is less defined. Existing evidence suggests that LATS2 possesses tumor suppressive potential (19); however, other studies have not verified its role in serous HGSOC. In our study, LATS2 was associated with poor prognosis in HGSOC. The conflicting relationship between LATS2 levels and patient outcomes in HGSOC underscores the complexity of tumor biology and highlights the need for further research to clarify the precise role of LATS2 in the progression of this aggressive cancer subtype.

LRIG1 is associated with the aggressive progression of various tumors, with its downregulation noted in multiple cancer types (20). LRIG1 generally acts as an inhibitor of tumorigenesis by negatively regulating epidermal growth factor receptor signaling (21). Current studies suggest that hypoxia exposure enhances VM formation, while LRIG1 overexpression counteracts VM formation (22). The tube formation assay is a well-recognized in vitro experiment that recapitulates VM formation by aggressive tumor cells. In this study, tube formation experiment proved that LRIG1 overexpression significantly inhibited the formation of vascular-like structures of HGSOC cells, which was consistent with the logic of LRIG1’s negative regulation of VM formation. In this study, LRIG1 not only demonstrated prognostic value in survival analysis but also confirmed its role by inhibiting VM-related biological behavior, indicating that LRIG1 was a key molecule connecting VM phenotype and HGSOC prognosis.

Despite these promising findings, several limitations must be acknowledged. One key limitation to acknowledge is that our nomogram was developed using a limited set of prognostic variables, namely age and tumor stage, and did not integrate other critical clinical factors, as it relied on public databases. Due to the lack of detailed clinical features in the TCGA and GSE9891 datasets, we aim to establish our own clinical cohort in subsequent studies to further refine the predictive model, thereby improving its comprehensiveness and reliability. HGSOC cell lines cannot fully represent the biological characteristics in HGOSC owing to tumor heterogeneity. Consequently, alternative models such as patient-derived organoids are required. In the future, we will conduct further experiments to explore the role of LRIG1 in the pathogenesis, prognosis, and treatment of HGSOC.

Conclusions

Based on public datasets and bioinformatics, we systematically identified VM-related genes and constructed a prognostic risk model of HGSOC based on seven prognostic genes. To enhance practical utility, we further integrated this VM-related risk model with classical clinicopathological factors (age and FIGO stage) to develop a novel nomogram, which demonstrated good predictive performance for HGSOC patient prognosis, offering clinicians a valuable tool to optimize treatment strategies. Finally, we demonstrated that LRIG1 might inhibit HGSOC progression by reducing angiogenesis simulation, which can be used as a potentially good prognostic biomarker and therapeutic target for HGSOC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1849/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1849/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1849/prf

Funding: This work was supported by the Science and Technology Planning Project of Ganzhou (No. 2023NS127387) and the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515010779).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1849/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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