Abstract
Aim: To explore the mechanism of cancer by employing a comprehensive analysis of DNA methylation patterns and variations among pan-cancer cohorts. Materials & methods: This research focused on the discovery of universally specific or common biomarkers by mathematical statistics and machine learning methods in The Cancer Genome Atlas. Results: We found 138 differently methylated CpGs (DMCs) with a common methylation trend and eight common differently methylated regions in different cancer cohorts. Additionally, we found 99 DMCs to distinguish 32 different cancer cohorts in random forest analysis because of the specificity mechanism, but each DMC still had high instability. Conclusion: Our results could facilitate the development of biomarkers that are universally specific and common features across pan-cancer cohorts.
Supplementary data
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Acknowledgments
The authors thank the State Key Laboratory of Agrobiotechnology for their support and the use of their high-performance computing platform.
Financial & competing interests disclosure
The project was supported by the National Special Foundation for Transgenic Species of China (2016ZX08009003-006), the Key-Area Research and Development Program of Guangdong Province (2018B020203001) and the Plan 111 (B12008). 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 appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.