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Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on
Pengfei Liu1,2
1State Key Laboratory of Genetic Engineering, Collaborative Innovation Center For Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.
Abstract:
The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.
Insights
Aberrant DNA methylation (DNAm) in tumors can predict origin sites. This study identified 23 epi-driver genes and developed a classifier with >90% accuracy for metastatic cancer, aiding diagnosis and treatment.
Area of Science:
- Genomics
- Epigenetics
- Cancer Biology
Background:
- Metastatic cancer of unknown primary (CUP) is a major cause of cancer mortality with limited treatment options.
- Aberrant DNA methylation (DNAm) is a significant cancer risk factor with tissue-specific patterns, but its role in multi-omics regulatory networks is underexplored.
- Accurate prediction of tumor origin and understanding underlying mechanisms require further investigation.
Purpose of the Study:
- To investigate genome-wide DNA methylation mechanisms within multi-omics regulatory networks.
- To identify novel epi-driver genes associated with tumorigenesis.
- To develop a robust classifier for predicting the primary origin of metastatic cancers.
Main Methods:
- Integrative analysis of multi-omics data and molecular regulatory networks.
- Identification and enrichment analysis of 23 novel epi-driver genes.
- Development of a tissue-specific classifier using an ensemble algorithm based on six CpG sites in five epi-driver genes.
Main Results:
- Genome-wide DNA methylation mechanisms were uncovered, identifying 23 epi-driver genes.
- Aberrant methylation in gene bodies and intergenic regions, not just promoters, correlated with gene expression.
- The developed classifier achieved >95% accuracy on TCGA datasets and >90% on independent and GEO metastatic cancer datasets, demonstrating robustness.
Conclusions:
- Integrative omics analysis reveals complex DNA methylation regulation across cancer types and identifies key epi-driver genes in tumorigenesis.
- A novel, highly accurate classifier based on aberrant methylation in epi-driver genes can trace metastatic cancer origins.
- This multi-omics approach provides a comprehensive view of DNAm changes and holds potential for clinical applications in cancer diagnosis.
