Dataset Information

Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

ABSTRACT:

Background

Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets.

Methods

RNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared.

Results

The ability of the machine learning analysis to classify OSCC tumours was excellent. However, the tool exhibited poorer performance in discriminating histopathologically dissimilar cancers derived from the same type of tissue than in differentiating cancers of the same histopathologic type with different tissue origins, revealing that the differential gene expression pattern is a more important factor than the histopathologic features for differentiating cancer types.

Conclusion

The CNN-based diagnostic model and the visualisation methods using RNA sequencing data were useful for correctly categorising OSCC. The analysis showed differentially expressed genes in multiwise comparisons of various types of SCCs, such as KCNA10, FOSL2, and PRDM16, and extracted leader genes from pairwise comparisons were FGF20, DLC1, and ZNF705D.

SUBMITTER: Pratama R

PROVIDER: S-EPMC8164276 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Publications

Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

Pratama Rian R Hwang Jae Joon JJ Lee Ji Hye JH Song Giltae G Park Hae Ryoun HR

BMC oral health 20210529 1

<h4>Background</h4>Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets.<h4>Methods</h4>RNA sequencing data from SCC tissues from various sites, including oral, non-o ...[more]

PMID: 34051764

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Project description:Background: Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Although overall losses of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) have been previously observed, a genome-wide, single-base-resolution, and simultaneous mapping of 5mC and 5hmC in OSCC is still unaccomplished. Similarly, the mechanism of how 5mC and 5hmC collectively lead to abnormal gene expression in OSCC is largely unexplored. Using parallel whole-genome bisulfite sequencing (WGBS) and whole-genome oxidative bisulfite sequencing (oxWGBS), we characterized 5mC- and 5hmC-profiles at single-nucleotide resolution in paired primary OSCC samples and their normal adjacent tissues (NATs). We also analyzed the effect of 5mC- and 5hmC-modifications on differential gene expression in OSCC using multi-omics analysis. Results: An overall reduction of both 5mC and 5hmC in various genomic regions have been observed in OSCC samples. At promoter regions, a total of 6,921 differentially methylated regions and 1,024 differentially hydroxymethylated regions were identified in OSCC. Interestingly, compared to bidirectional modification with 5mC and 5hmC, unidirectional modification with 5mC and 5hmC at the promoters is associated with bigger change in the gene expression. Additionally, genes bearing unidirectional modification with 5mC and 5hmC at the promoters are enriched in signaling pathways like cell proliferation, cell differentiation, and receptor tyrosine kinase pathway that are essential for the tumorigenesis. Finally, the grouped expression signature of top 20 genes bearing promoter-unidirectional-modification with 5mC and 5hmC tends to correlate with the clinical outcome of certain subtypes of head and neck squamous cell carcinoma. Conclusion: Using parallel WGBS and oxWGBS analyses, we observed an overall reduction of 5mC- and 5hmC-modifications at various genomic regions in OSCC. Unidirectional modification with 5mC and 5hmC at the promoters is associated with enhanced changes in gene expression in OSCC tissues. Furthermore, such differentially expressed genes bearing unidirectional modifications with 5mC and 5hmC at the promoters might have clinical relevance to the outcome of OSCC.

Dataset Information

Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

Background

Methods

Results

Conclusion

Publications

Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

Similar Datasets

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