scholarly journals Corrigendum to “Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma Identified Based on Gene Expression Data and MicroRNA-mRNA Network Analysis”

2017 ◽  
Vol 2017 ◽  
pp. 1-1 ◽  
Author(s):  
Hui Zhang ◽  
Tangxin Li ◽  
Linqing Zheng ◽  
Xiangya Huang
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Zhang ◽  
Tangxin Li ◽  
Linqing Zheng ◽  
Xiangya Huang

Oral squamous cell carcinoma is one of the most malignant tumors with high mortality rate worldwide. Biomarker discovery is critical for early diagnosis and precision treatment of this disease. MicroRNAs are small noncoding RNA molecules which often regulate essential biological processes and are good candidates for biomarkers. By integrative analysis of both the cancer-associated gene expression data and microRNA-mRNA network, miR-148b-3p, miR-629-3p, miR-27a-3p, and miR-142-3p were screened as novel diagnostic biomarkers for oral squamous cell carcinoma based on their unique regulatory abilities in the network structure of the conditional microRNA-mRNA network and their important functions. These findings were confirmed by literature verification and functional enrichment analysis. Future experimental validation is expected for the further investigation of their molecular mechanisms.


2021 ◽  
Vol 11 ◽  
Author(s):  
Guanying Feng ◽  
Feifei Xue ◽  
Yingzheng He ◽  
Tianxiao Wang ◽  
Hua Yuan

ObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).ResultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.ConclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC.


2012 ◽  
Vol 32 (2) ◽  
pp. 81-89
Author(s):  
Tomohide ISOBE ◽  
Gou YAMAMOTO ◽  
Tarou IRIE ◽  
Tetuhiko TACHIKAWA ◽  
Kenji MISHIMA

PLoS ONE ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. e0222689
Author(s):  
Chi Thi Kim Nguyen ◽  
Wanlada Sawangarun ◽  
Masita Mandasari ◽  
Kei-ichi Morita ◽  
Hiroyuki Harada ◽  
...  

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