Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1987 ovarian cancer patients spanning 26 independent Gene Expression Omnibus cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust ten-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

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Publication Details

Journal
Journal of Biological Chemistry
Published
2024-08-22
DOI
https://doi.org/10.1016/j.jbc.2024.107710
Citations
1
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.23

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article

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives

Chunlong Zhang, Yunyi Peng, Ziyan Zhao, Fei Xue et al.
1 citations
Journal of Biological Chemistry
Single-cell and spatial transcriptomics
0.23
article

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives

Chunlong Zhang, Yunyi Peng, Ziyan Zhao, Fei Xue, Linan Xing, Qian Cheng, Guixiang Lv, Yongjian Zhang, Yuxi Zhang, Zhiyi Wu
article en
1 citations

Abstract

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1987 ovarian cancer patients spanning 26 independent Gene Expression Omnibus cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust ten-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

Journal of Biological ChemistryVol. 300(9)
Harbin Medical University (CN), Third Affiliated Hospital of Harbin Medical University (CN), First Affiliated Hospital Zhejiang University (CN)
Natural Science Foundation of Zhejiang Province
Zero hunger
Openalex Percentile: Top 43%
Single-cell and spatial transcriptomics
0.23
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