Integrative Multi-Omics and Machine Learning Approach Reveals Tumor Microenvironment-Associated Prognostic Biomarkers in Ovarian Cancer

Abstract

Purpose The main purpose of this study is to dissect the intricacies of the Tumor Microenvironment (TME) in Ovarian Cancer (OV) by analyzing its immune cell composition and gene expression profiles. We aim to investigate how TME elements influence ovarian cancer prognosis, particularly their impact on the responsiveness to immune therapy. Our goal is to enhance understanding of immune interactions in OV TME, contributing to the development of precise, personalized therapeutic strategies and potentially improving clinical outcomes for OV patients. Methods Single-cell RNA sequencing (scRNA-seq) data from the GEO database (GSE184880) for normal and OV cases were analyzed using the Seurat package, identifying 700 TME-related genes. A prognostic model based on these genes was developed using LASSO regression and validated with an independent dataset (GSE140082). Differential gene expression and gene function analyses were conducted using the TCGA-OV cohort, with a focus on immune infiltration assessed by the xCell algorithm. Results The study uncovered distinct immune cell infiltrates and associated genes within the OV TME. We developed a prognostic model that incorporates immune cell subgroup markers, showing its relevance in predicting patient outcomes. This model was also correlated with responses to immune therapy and drug sensitivity. Our analyses of T cell subgroups and trajectories provided insights into the dynamic nature of TME and its impact on patient prognosis. Conclusion This research offers a detailed characterization of the TME in OV, underlining the prognostic importance of TME-related gene signatures. Concentrating on the immune component, including gene expression and pathways related to immune cell infiltration and T cell dynamics, the findings clarify the response of OV to immune therapy. These insights not only deepen our comprehension of the complexity of TME but also pave the way for new, individualized treatment methods, potentially enhancing patient outcomes and aiding in the development of more effective therapeutic interventions.