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Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics.

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.

SUBMITTER: Liu P

PROVIDER: S-EPMC8770539 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMetastatic adenocarcinoma of unknown primary site (MACUP) is the most common cancer of unknown primary site, and shows worse prognosis. Prediction of its tumor site origin attracts a growing attention. However, the site determined by gene expression profiling does not have a significant impact on the survival. Some other special method might need to be found out.MethodsWe reviewed 1011 MACUP patients diagnosed by pathological examination and immunohistochemistry based on the Surveillance, Epidemiology, and End Results (SEER) database during 2010-2016. Kaplan-Meier curves and Cox proportional hazard model were analyzed to compare the survival. Logistic regression models and relevant nomograms were performed to predicting the probability of the primary site which including digestive system, respiratory system, and female breast. The validation and clinical utility of models were measured with relevant statistical approaches.ResultsAbout 324 (32.1%), 299 (29.6%), and 203 (20.1%) of MACUP patients were identified as the primary sites of digestive system, respiratory system, and female breast, respectively. Patients derived from digestive system and respiratory system showed poorer survival than these with other sites. Digestive system was significantly associated with liver (Odds ratio =13.21 [95% confidence interval =8.48-21.02]) or lung (2.36 [1.40-3.97]) metastasis, while respiratory system was linked to brain (11.68 [6.68-21.26]) or lymph node (3.39 [2.26-5.13]) metastasis. Patients identified as female breast were prone to occur bone metastasis (5.85 [3.68-9.45]). Logistic regression nomograms were developed to help clinicians intuitively predict the probabilities of tumor site origin with 0.867, 0.824, and 0.753 of the C-index, respectively. Decision curve analysis and clinical impact curves both revealed the clinical effectiveness.ConclusionsWe profiled different tumor site origin of MACUP patients and established prediction models. These features might be significant for clinicians to improve the probabilities of predicting the primary sites, and to decide subsequent treatment strategy.

Project description:BackgroundHepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure.MethodThe mutation data was analyzed by employing "maftools" package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package "limma". Gene ontology (GO) enrichment analysis was implemented with "clusterProfiler", "enrichplot" and "ggplot2" packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3.ResultsTMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction.ConclusionCollectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC.

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Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics.

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