Differential gene expression identified in Uigur women cervical squamous cell carcinoma by suppression subtractive hybridization

Neoplasma ◽  
2010 ◽  
Vol 57 (2) ◽  
pp. 123-128 ◽  
Author(s):  
Z. Pan ◽  
S. Chen ◽  
X. Pan ◽  
Z. Wang ◽  
H. Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differential Gene Expression ◽  
Suppression Subtractive Hybridization ◽  
Squamous Cell ◽  
Subtractive Hybridization ◽  
Cervical Squamous Cell Carcinoma ◽  
Differential Gene

scholarly journals Mutations, Differential Gene Expression, and Chimeric Transcripts in Esophageal Squamous Cell Carcinoma Show High Heterogeneity

2018 ◽  
Vol 11 (6) ◽  
pp. 1283-1291 ◽  
Author(s):  
Paulo Thiago de Souza-Santos ◽  
Sheila Coelho Soares Lima ◽  
Pedro Nicolau-Neto ◽  
Mariana Boroni ◽  
Nathalia Meireles Da Costa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differential Gene Expression ◽  
Squamous Cell ◽  
Chimeric Transcripts ◽  
Differential Gene

scholarly journals Authentication of Differential Gene Expression in Oral Squamous Cell Carcinoma using Machine Learning Applications

2020 ◽  
Author(s):  
Rian Pratama ◽  
Jae Joon Hwang ◽  
Ji Hye Lee ◽  
Giltae Song ◽  
Hae Ryoun Park
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Differential Gene Expression ◽  
Sequencing Data ◽  
Machine Learning Applications ◽  
Differential Gene

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.

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