scholarly journals Prognostic factor identification by screening changes in differentially expressed genes in oral squamous cell carcinoma

Oral Diseases ◽  
2021 ◽  
Author(s):  
Boris Schminke ◽  
Orr Shomroni ◽  
Gabriela Salinas ◽  
Felix Bremmer ◽  
Philipp Kauffmann ◽  
...  
2012 ◽  
Vol 26 (S1) ◽  
Author(s):  
João Paulo Oliveira-Costa ◽  
Alex Fiorini Carvalho ◽  
Silvia Vanessa Lourenco ◽  
Luiz Paulo Kowalski ◽  
Dirce Maria Carraro ◽  
...  

2022 ◽  
Vol 11 ◽  
Author(s):  
Zhengqing Wan ◽  
Haofeng Xiong ◽  
Xian Tan ◽  
Tong Su ◽  
Kun Xia ◽  
...  

Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Due to the lack of early detection and treatment, the survival rate of OSCC remains poor and the incidence of OSCC has not decreased during the past decades. To explore potential biomarkers and therapeutic targets for OSCC, we analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation−regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining publicly available methylomic datasets together with our transcriptomic data. Protein−protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. Subsequently, expression and survival analyses of hub genes were performed using The Cancer Genome Atlas (TCGA) database and the Gene Expression Profiling Interactive Analysis (GEPIA) online tool. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the PPI networks constructed by those MeDEGs. Among them, the expression level of four hub genes (CTLA4, CDSN, ACTN2, and MYH11) were found to be significantly changed in the head and neck squamous carcinoma (HNSC) patients. Three hypomethylated hub genes (CTLA4, GPR29, and TNFSF11) and one hypermethylated hub gene (ISL1) were found to be significantly associated with overall survival (OS) of HNSC patients. Therefore, these hub genes may serve as potential DNA methylation biomarkers and therapeutic targets of OSCC.


The study's objective is to identify the non-linear relationship of differentially expressed genes that vary in terms of the tumour and normal tissue and correct for any variations among the RNA-Seq experiment focused on Oral squamous cell carcinoma samples from patients. A Laplacian Likelihood version of the Generalized Additive Model is proposed and compared with the regular GAM models in terms of the non-linear fitting. The Non-Linear machine learning approach of Laplacian Likelihood-based GAM could complement RNA-Seq Analysis mainly to interpret, validate, and prioritize the patient samples data of differentially expressed genes. The analysis eases the standard parametric presumption and helps discover complexity in the association between the dependent and the independent variable and parameter smoothing that might otherwise be neglected. Concurvity, standard error, deviance, and other statistical verification have been carried out to confirm Laplacian Likelihood-based GAM efficiency.


2005 ◽  
Vol 42 (2) ◽  
pp. 97-108 ◽  
Author(s):  
Shilpi Arora ◽  
Ajay Matta ◽  
Nootan Kumar Shukla ◽  
S.V.S. Deo ◽  
Ranju Ralhan

2011 ◽  
Vol 25 (1) ◽  
pp. 14-25 ◽  
Author(s):  
Satyendra Chandra Tripathi ◽  
Jatinder Kaur ◽  
Ajay Matta ◽  
Xin Gao ◽  
Bin Sun ◽  
...  

Oral Oncology ◽  
2022 ◽  
Vol 124 ◽  
pp. 105672
Author(s):  
Ramya Ramdoss ◽  
Monal Yuwanati ◽  
Abigail Viola E ◽  
Pratibha Ramani ◽  
M. Senthil Murugan

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