Identification of key classification features of early cervical squamous cell carcinoma |
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| Affiliation: | 1. Department of Chemistry and Molecular Biology, University of Gothenburg, Box 462, 40530 Gothenburg, Sweden;1. Department of Mechanical Engineering, Graduate University of Advanced Technology, Kerman, Iran;2. Department of Mechanical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran;3. Department of Medical Physics, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran;4. Istituto Italiano di Tecnologia, Graphene Labs, Genova, Italy;5. ISOF – Istituto per la Sintesi Organica e la Fotoreattività, Consiglio Nazionale delle Ricerche, Bologna, Italy;6. Laboratorio MIST.E-R Bologna, Bologna, Italy;7. Department of Materials Science, University of Patras, Rio Patras, Greece;8. Institute of Chemical Engineering Sciences, Foundation of Research and Technology-Hellas, Platani, Patras Acahaias, Greece;9. Laboratory of Bio-Inspired & Graphene Nanomechanics, Department of Civil, Environmental and Mechanical Engineering, Università di Trento, Trento, Italy;10. Center for Materials and Microsystems, Fondazione Bruno Kessler, Povo (Trento), Italy;11. School of Engineering & Materials Science, Queen Mary University of London, London, UK;1. Department of Obstetrics and Gynecology, University of Gaziantep, Gaziantep, Turkey;2. Department of Medical Services and Techniques, Vocational School of Health Services, Adiyaman University, Adiyaman, Turkey;3. Department of Pathology, University of Gaziantep, Gaziantep, Turkey |
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| Abstract: | Despite the tremendous progress in molecular analysis of pan-cancer, little is known regarding molecular classification of cervical squamous cell carcinoma. In this study, we adopted a multi-omics approach to identify potential key classification features of cervical squamous cell carcinoma. Specifically, we analyzed mRNA, and microRNA (miRNA) expression data, as well as DNA methylation and copy number variation in cervical squamous cell carcinoma cases, using datasets obtained from The Cancer Genome Atlas (TCGA). Moreover, we identified molecules in each dimension, as well as integrated and clustered filtered classification features, and used them to distinguish different subtypes. The resulting key classification features were used to establish a classification model for cervical squamous cell carcinoma. Our results revealed two cervical squamous cell carcinoma subtypes, with significant differences across clinical survival levels, as well as 8 key classification features of cervical squamous cell carcinomas. These findings are expected to provide important references for early classification of cervical squamous cell carcinoma and identification of classification markers. |
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| Keywords: | Multi-omics Classification markers Subtype classification Cervical squamous cell carcinoma Early stage of cancer |
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