Related Experiment Video
Updated: Feb 15, 2026

Development of Compendium for Esophageal Squamous Cell Carcinoma
Published on: April 12, 2024
Identification of Biomarker for Cutaneous Squamous Cell Carcinoma Using Microarray Data Analysis
1Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Abstract:
Cutaneous squamous cell carcinoma (CSCC) is one of the most malignant tumors worldwide. We aimed to explore the molecular mechanism of this CSCC and screen feature genes that can function as the biomarker of CSCC and thus provide a theoretical basis for the pathogenesis research and development of medicine. The method of microarray data analysis was used in this study to explore the differentially expressed genes between tissues of normal specimens and tissues of patients with CSCC. Besides, functional enrichment analysis and signal pathway were performed on these genes to screen the feature genes that are closely associated with CSCC can function as the potential biomarkers of CSCC.A total of 53 samples from two datasets, GSE45216 and GSE45164, were used in the differentially expressed analysis. And as a result, a total of 833 genes were screened out, including 465 up-regulated genes and 215 down-regulated genes. Candidate genes, including up-regulated genes like S100A12, MMP1, DEFB4B/DEFB4A, KRT16 and PI3, and down-regulated genes like EGR3, LRP4, C14orf132, PAMR1, CCL27, and KRT2 were screened out. All these genes were testified in the dataset of GSE66359. The result showed that only three genes, KRT16, PI3 and EGR3, were mostly differentially expressed and only EGR3 had the same expression pattern with both datasets, GSE45216 and GSE45164.Of note, EGR3 gene was found to be the most differentially expressed gene in cutaneous squamous cell carcinoma, which had the potential to function as the candidate genes and help in the diagnosis and prognostic treatments of CSCC.
Related Concept Videos
Analysis of Population Pharmacokinetic Data
Overview of Microsoft Excel as a Data Analysis Tool
DNA Microarrays
Performing a Simple Data Analysis using MS-Excel Function
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
Statistical Software for Data Analysis and Clinical Trials
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...

