Background
As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive.
Methods
We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified
IGF2
as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay.
Results
Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and
IGF2
emerged as a key gene linked to prognosis. Experiments have proven that
IGF2
can promote the glycolysis pathway and the malignant biological progression of OC cells.
Conclusions
We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified
IGF2
as the marker gene. These insights provided a new avenue for exploring OC’s molecular mechanisms and personalized treatment approaches.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12885-024-12688-7.