Non-Melanoma Skin Cancer

Skin cancer is divided into melanoma and non-melanoma categories. Non-melanoma skin cancer (NMSC) is a current public health problem worldwide. Non-melanoma skin cancer has two most common types, basal cell or basaloid carcinoma (BCC) and squamous cell or squamous cell carcinoma (SCC), with the former having a higher incidence than the latter. Other less common types of non-melanoma skin cancer are Merkel's carcinoma, Kaposi's sarcoma, and T-cell lymphoma. Microarray and RNA-seq data analyses have been performed on the publicly available datasets to reveal the common and uncommon patterns in these diseases.

non-melanoma-skin-cancer

Data Sources

EMBL's European Bioinformatics Institute (EMBL-EBI, https://www.ebi.ac.uk/)
Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)
The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/)
Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp)
Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/)
The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN, http://ualcan.path.uab.edu/)
Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/)

General Workflow

Perform integrated data analysis of microarray and RNA-seq.
Identify common DEGs that are proposed as potential biomarkers and therapeutic targets from differential expression analysis.
Perform pathway analysis to analyze the disrupted pathways and their possible relation with the disease.
Implement quantitative systems biology to analyze the results at a holistic level.
Model a pathway by using SimBiology application in Matlab.
Perform sensitivity analysis to analyze the response of the biological processes to model quantities (DEGs and genes).
Identify significant genes that might be proposed as potential biomarkers and therapeutic targets.

Data Quality Control

Perform the data QC by visualization and filtering techniques which include box plot, principal component analysis (PCA) plot, relative log expression (RLE) plot, and Heatmap clustering analysis. PCA and heatmap clustering analysis are performed and plotted to visualize the logarithmic transformations by using Bioconductor R packages.

CD ComputaBio utilizes microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.

Moreover, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.