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Primary research
Shi Jin
First Affiliated Hospital of Dalian Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3113-3186
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: DNA methylation is one of most frequently occurring molecular behaviours that occurs early during the complicated carcinogenesis process of hepatocellular carcinoma (HCC).
Methods: In this study, 348 samples were collected from The Cancer Genome Atlas (TCGA) database and analysed to mine the specific DNA methylation sites that affect the prognosis of HCC patients.
Results: The 10699 selected CpG sites (CpGs) that were markedly correlated with patient prognosis were used for consistent clustering of the samples into 7 subgroups, and the samples in each subgroup varied in terms of race, age, tumour stage, receptor status, histological type, metastasis status and patient prognosis. In addition, the level of methylation sites in each subgroup were calculated, and 119 methylation sites (corresponding to 105 genes) were screened as the intra-subgroup-specific methylation sites. Moreover, genes in the corresponding promoter regions in which the above specific methylation sites were located were subjected to signalling pathway enrichment analysis, and it was discovered that these genes were enriched in the bio-logical pathways that were reported to be closely correlated with HCC. Additionally, the subsequent transcription factor enrichment analysis revealed that these genes were mainly enriched in the transcription factor KROX. A prognosis prediction mod-el for HCC patients was constructed using the naive Bayesian classification model, and the training and test datasets were used for independent verification and testing.
Conclusions: This classification method based on specific DNA methylation sites can well reflect the heterogeneity of HCC tissues and contributes to developing individualized treatments and accurately predicting patient prognosis.
Keywords
DNA Methylation, Molecular Subtype Classification, TCGA database, Hepatocellular carcinoma
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Primary research
Shi Jin
First Affiliated Hospital of Dalian Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3113-3186
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 09 Apr, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: DNA methylation is one of most frequently occurring molecular behaviours that occurs early during the complicated carcinogenesis process of hepatocellular carcinoma (HCC).
Methods: In this study, 348 samples were collected from The Cancer Genome Atlas (TCGA) database and analysed to mine the specific DNA methylation sites that affect the prognosis of HCC patients.
Results: The 10699 selected CpG sites (CpGs) that were markedly correlated with patient prognosis were used for consistent clustering of the samples into 7 subgroups, and the samples in each subgroup varied in terms of race, age, tumour stage, receptor status, histological type, metastasis status and patient prognosis. In addition, the level of methylation sites in each subgroup were calculated, and 119 methylation sites (corresponding to 105 genes) were screened as the intra-subgroup-specific methylation sites. Moreover, genes in the corresponding promoter regions in which the above specific methylation sites were located were subjected to signalling pathway enrichment analysis, and it was discovered that these genes were enriched in the bio-logical pathways that were reported to be closely correlated with HCC. Additionally, the subsequent transcription factor enrichment analysis revealed that these genes were mainly enriched in the transcription factor KROX. A prognosis prediction mod-el for HCC patients was constructed using the naive Bayesian classification model, and the training and test datasets were used for independent verification and testing.
Conclusions: This classification method based on specific DNA methylation sites can well reflect the heterogeneity of HCC tissues and contributes to developing individualized treatments and accurately predicting patient prognosis.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
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