Cervical Cancer

Cervical cancer (CC), classified as the second most prominent cancer, is one of the most recurrently diagnosed cancers in terms of prevalence and sources of cancer-related deaths in women worldwide . Despite effective vaccination and improved surgery and treatment, CC retains its fatality rate of about half of the infected population globally. The major screening biomarkers and therapeutic target identification have now become a global concern.

Our Advantages

• We can apply systems biology approaches to predict novel molecular oncogenes and gene signatures using existing gene expression profiles.
• We have retrieved the potential biomarkers and pathways from transcriptomic profiling. The involved pathways associated with these genes can be assessed through pathway enrichment.
• We are dedicated to exploring the differentially expressed genes (DEGs), gene network, pathways, and protein–protein interactions unique to CC to retrieve potential biomarkers and pathways of cervical carcinoma.

Methods

• Download the gene expression data of CC patients from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database.
• Screen out the core genes by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA).
• Construct the protein-protein interactions (PPI) network based on the STRING database.
• Use R software, the STRING online tool and Cytoscape software to screen out the hub genes.
• Conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the core genes to study their main functions and participation in signaling pathways.
• Use the public database, Gene Expression Profiling Interactive Analysis (GEPIA), to further verify the expression levels of the hub genes in normal tissues and tumour tissues.
• Determine the disease-free survival (DFS) rates of the hub genes using the GEPIA online tools.
• Identify the protein expression of the survival-related hub genes with the Human Protein Atlas (HPA) database.

Why Choose Us?

We mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways, utilizing deep sequencing technologies, advanced biostatistical approaches, and computational analysis methods. We are also interested in how the machine learning-based integration of different datasets can aid in the discovery of new cancer subgroups and biomarkers. In addition, 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.

References:

1. Tu S, Zhang H, Yang X, Wen W, Song K, Yu X, Qu X. Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis. Cancer Biomark. 2021;32(3):303-315.
2. Oany AR, Mia M, Pervin T, Alyami SA, Moni MA. Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer. J Pers Med. 2021;11(5):363.