Laryngeal Cancer

Laryngeal cancer (LC) represents one of the most frequent tumors in the head and neck region. It forms in tissues of the larynx, the area of the throat that is used for breathing, swallowing, and talking. Most laryngeal cancers are squamous cell carcinomas (laryngeal squamous cell carcinoma, LSCC), which begin in cells lining the larynx. LSCC is the second most aggressive head and neck squamous cell carcinoma. There is an urgent need for the identification of specific molecular signatures that better predict the clinical outcomes and markers that serve as suitable therapeutic targets.

With the continuous advances in microarray technology and bioinformatics analysis, gene chip technology plays a significant role in exploring tumour gene expression profiles and identifying the differentially expressed genes (DEGs) and functional pathways associated to tumorigenesis and prognosis.

Data Sources

• 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/)

Computational Biology Approaches

• Obtain the microarray data of gene expression profiles.
• Identify DEGs between cancerous and noncancerous tissues.
• Deeper evaluation with Gene Ontology (GO) and KEGG pathway analyses.
• Construct a protein–protein interaction (PPI) network of DEGs.
• Use the CytoHubba plugin of Cytoscape to identify the hub genes.
• Confirm the overlapping gene expression between healthy and tumour tissues.

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.

Reference:

1. Ma J, Hu X, Dai B, Wang Q, Wang H. Bioinformatics analysis of laryngeal squamous cell carcinoma: seeking key candidate genes and pathways. PeerJ. 2021 Apr 14;9:e11259.