https://orcid.org/0000-0001-9467-2083
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Abstract
Aims: To identify a novel subtype with DNA driver methylation-transcriptomic multiomics and predict prognosis and therapy response in serous ovarian cancer (SOC). Methods: SOC cohorts with both mRNA and methylation were collected, and DNA driver methylation (DNAme) was identified with the MithSig method. A novel prognostic subtype was developed by integrating the information on DNAme and prognosis-regulated DNAme-associated mRNA by similarity network fusion. Results: 43 overlapped DNAme were identified in three independent cohorts. SOC patients were categorized into three distinct subtypes by integrated multiomics. There were differences in prognosis, tumor microenvironment and response to therapy among the subtypes. Conclusion: This study identified 43 DNAmes and proposes a novel subtype toward personalized chemotherapy and immunotherapy for SOC patients based on multiomics.
Plain language summary
Ovarian cancer is a highly malignant gynecological disease. The high heterogeneity of ovarian cancer may contribute to chemotherapy resistance and immunotherapy insensitivity. Gene alterations and aberrant methylation occur in the process of tumor initiation and progression, but not all alterations are drivers of tumor development. In this study, we aim to find the DNA driver methylation (DNAme) that plays a decisive role in ovarian cancer development and obtain a novel multiomics molecular subtype related to DNAme integrated by multiple omics information. We identified 43 overlapping DNAme in three cohorts. The multiomics subtype associated with DNAme could predict ovarian cancer prognosis and treatment response.
Tweetable abstract
A novel multiomics subtype based on DNA driver methylation provides insights into ovarian cancer prognostic prediction and precision medicine according to a new study by researchers at Harbin Medical University.
Supplementary data
To view the supplementary data that accompany this paper please visit the journal website at: www.tandfonline.com/doi/suppl/10.2217/epi-2022-0206
K Li and Y Hou designed the study; Z Xu and L Zhang wrote the manuscript; Z Xu, L Zhang, M Wang, Y Huang and M Zhang downloaded and analyzed the data; Sg Li and L Wang prepared all the figures and tables. All authors reviewed and approved the final manuscript.
Financial & competing interests disclosure
Y Hou received research funding from the National Natural Science Foundation of China (grant nos. 82173615). K Li has received research funding from the National Natural Science Foundation of China (grant nos. 81973149). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
Ethical conduct of research
The authors state that they have obtained verbal and written informed consent from the patient/patients for the inclusion of their medical and treatment history within this case report.
Data sharing statement
Genomic, transcriptomic, epigenomics and clinical data of ovarian cancer (OC) from The Cancer Genome Atlas Project were downloaded from FireBrowse (http://firebrowse.org) and the UCSC xena (https://xenabrowser.net). An additional two OC cohorts were obtained from The International Cancer Genome Consortium Data Portal (https://dcc.icgc.org) and the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo) database. Normal OC mRNA were downloaded from The Genotype-Tissue Expression in UCSC xena. Thirty-two OC cell lines were downloaded from Genomics of Drug Sensitivity in Cancer (www.cancerrxgene.org).