Abstract
BackgroundRecently, 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.MethodsRNA 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.ResultsThe 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.ConclusionThe 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.
Highlights
The possibility of tumour classification based on genetic data has been investigated
We examined the diagnostic performance of machine learning applications using convolutional neural network (CNN) image classification for oral squamous cell carcinoma (OSCC) samples
Data procurement RNA sequencing data from samples of OSCC, nonoral head and neck SCC (HNSCC), oesophageal SCC, oesophageal adenocarcinoma, and cervical SCC were downloaded from the The Cancer Genome Atlas (TCGA) database
Summary
The possibility of tumour classification based on genetic data has been investigated. We examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets. Using RNA sequencing expression data, studies have attempted to find a diagnostic model that can efficiently and rapidly discriminate different tumours by simultaneously considering both genetic phenotypic features [15,16,17]. Deep learning methods exploiting image classification/recognition have been suggested, and these methods have displayed excellent performance as well as a low error rate [17, 20]. Using methods such as these, tumour classifications based on machine learning analysis of genetic data have been rapidly developed and tested. Research to establish a classification system to diagnose oral squamous cell carcinoma (OSCC) using genetic data is rare
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