This study aimed to develop an integrative prognostic model combining genetic, clinical, and immunological data to improve outcome prediction for ovarian cancer patients. By analyzing data from The Cancer Genome Atlas (TCGA), we identified key prognostic genes and developed a risk score that effectively stratifies patients into high- and low-risk groups with significant survival differences. Unlike previous models, which primarily focused on genetic or clinical parameters, our model incorporates immune landscape data, including immune cell infiltration and checkpoint expression, to provide a more comprehensive understanding of ovarian cancer progression and treatment response. These findings not only validate the prognostic significance of the tumor microenvironment but also highlight potential therapeutic targets, offering a robust tool for personalized treatment strategies.

Several studies have previously attempted to construct prognostic models for ovarian cancer; however, most focused primarily on genetic or clinical parameters independently. For instance, Konecny et al. developed a prognostic model based on genetic alterations in ovarian cancer, which significantly enhanced the prediction of patient outcomes compared to traditional clinical models alone[28]. While this model was groundbreaking, it did not incorporate the immunological context of the tumor microenvironment, which has been shown to play a critical role in cancer progression and response to treatment.

In contrast, our study's inclusion of immune cell infiltration and immune function data represents a significant advancement. The analysis of immune cell presence and activity, as seen through ssGSEA and immune checkpoint expression, provides a deeper insight into the tumor’s interaction with the host immune system. This approach is supported by recent studies suggesting that the immune microenvironment can influence the efficacy of standard treatments and the success of emerging therapies like immunotherapy. By integrating these immunological insights with genetic and clinical data, our model aligns with the shift towards a more holistic view of cancer that encompasses the dynamic interactions within the tumor microenvironment.

The risk score developed in this study demonstrated strong predictive power for patient outcomes, as evidenced by survival analyses and validation across multiple cohorts. High-risk patients identified by our model were associated with poor prognosis, a finding consistent with studies that have linked aggressive molecular profiles and immune evasion mechanisms with advanced disease stages and resistance to treatment. To further refine the immune analysis, we evaluated specific immune cell subtypes, such as CD8 + T cells, regulatory T cells, and macrophage polarization (M1/M2), revealing distinct differences in immune cell infiltration between high- and low-risk groups. Additionally, integrating immune checkpoint expression (e.g., PD-1, CTLA-4) and neoantigen load into the analysis highlighted a more suppressive immune environment in high-risk patients, suggesting potential responsiveness to immune checkpoint blockade therapies. These refinements provide deeper insights into the role of the tumor microenvironment and its contribution to prognosis, further enhancing the model’s clinical relevance and potential for guiding personalized treatment strategies.

Furthermore, our study expanded on these findings by correlating high-risk scores with increased expression of immune checkpoint inhibitors like PD-L1, suggesting potential responsiveness to checkpoint blockade therapies. This correlation aligns with recent clinical trials that have explored the efficacy of PD-1 and PD-L1 inhibitors in ovarian cancer, emphasizing the importance of predictive biomarkers for selecting suitable candidates for immunotherapy [29, 30].

Our study’s emphasis on the differential immune profiles between high and low-risk groups provides a valuable framework for understanding the variability in treatment responses among ovarian cancer patients. For example, the high-risk group exhibited a more suppressive immune environment, which could explain the lower effectiveness of certain therapies in these patients. These findings are in line with research by Zhang et al., which demonstrated that a suppressive immune microenvironment could hinder the efficacy of both chemotherapy and immunotherapy in ovarian cancer [31]. By analyzing the immune profiles in conjunction with genetic data, our model provides a comprehensive overview that can inform more targeted and effective therapeutic strategies. For instance, patients with high immune cell infiltration but low mutational burden might benefit from therapies that aim to activate the immune response, whereas those with high mutational burden could be better candidates for immunotherapy.

The analysis of tumor mutational burden (TMB) in our study highlighted its potential as a prognostic and predictive marker in ovarian cancer. High TMB was associated with better outcomes in low-risk patients, which supports the hypothesis that a higher neoantigen load may enhance the immunogenicity of tumors, making them more susceptible to immune checkpoint inhibitors. This finding is corroborated by recent studies indicating that TMB can serve as a biomarker for immunotherapy response across various cancers, including melanoma and non-small cell lung cancer.

However, our study also revealed that high TMB in the context of a high-risk score was associated with poorer prognosis, suggesting that the benefits of high TMB might be counteracted by other aggressive tumor features or a more suppressive immune environment. This complexity highlights the need for multifaceted models like ours that can dissect these interactions and provide more nuanced guidance for treatment planning.

While our study represents a significant step forward in the prognostic modeling of ovarian cancer, it has several limitations. The retrospective nature of the data and reliance on public datasets, such as TCGA and GEO, may introduce selection biases and limit the generalizability of the findings to diverse patient populations, particularly due to incomplete clinical annotations, such as detailed treatment histories and co-morbidities. Furthermore, immune-related analyses based on in silico estimations may not fully capture the spatial and temporal heterogeneity of the tumor microenvironment, and the cross-sectional data used in this study do not account for dynamic changes in immune responses or tumor evolution over time. To address these limitations, future studies should incorporate longitudinal data and prospective cohorts to validate the model in diverse clinical settings, leveraging high-resolution technologies like single-cell sequencing and multiplex imaging to refine immune-related analyses. Additionally, expanding the model to include multi-omics data, such as proteomics and metabolomics, could enhance its predictive power and biological relevance. To ensure clinical applicability, a validation pathway should include external validation in independent cohorts, followed by prospective trials to evaluate its utility in stratifying patients for personalized therapies, such as immune checkpoint inhibitors or chemotherapy. Ultimately, integrating the model into routine practice would require the development of accessible tools, such as web-based platforms or clinical decision-support systems, to facilitate its adoption and enhance its impact on patient care.

In conclusion, our study contributes to the evolving field of personalized medicine in ovarian cancer by providing a comprehensive prognostic model that integrates genetic, clinical, and immunological data. By offering insights into the complex interplay between the tumor and its microenvironment, our model holds the potential to guide more personalized and effective treatment strategies, ultimately improving the prognosis and quality of life for ovarian cancer patients.