Head-and-neck squamous cell carcinoma (HNSCC) is a heterogeneous malignancy which accounts for approximately 300,000 deaths each year worldwide. Smoking, alcohol, and infections by high-risk human papillomavirus (HPV) are among the main risk factors for the development of the disease. The incidence of HPV-associated HNSCC is around 25% of the reported cases worldwide, with an even higher proportion in oropharyngeal cancer, and a predominance of infection by HPV-16 and HPV-18 types among those cases.

The TCGA (The Cancer Genome Atlas) is paramount for data collection of more than 30 cancer types. The diversity of omic layers, such as RNA-seq, methylation, miRNA, proteomic, clinical, copy number variation and mutation, can be analyzed in different aspects including mathematical models, statistics and machine learning.

The article can be read on Scientific Reports. DOI: 10.1038/s41598-018-33498-5.

The graph data model (Neo4j database) was chosen to better explore the connectivity among genes in the co-expressed networks. This model consists mainly of nodes, edges and properties.

.

Neo4j is a highly scalable, robust (fully ACID) native graph database. Neo4j is used in mission-critical apps by thousands of startups, enterprises, and governments around the world.

Visit the site: (https://neo4j.com/release-notes/neo4j-2-3-4/) and get the instructions on how to download, unzip, and install Neo4j.

The database directory hdscc.db must be moved to neo4j-community-xxx/data/.

You need to change the configuration.

• Open the file: /conf/neo4j-server.properties.
• Change the line: org.neo4j.server.database.location=data/graph.db to org.neo4j.server.database.location=data/hpv.db.
• Save and close the file.

• Start Neo4j: ./bin/neo4j start.
• Open the browser and access: http://localhost:7474.

There are many possibilities of queries in a graph database using Cypher, a declarative SQL-inspired language. Here are two Cypher query examples:

Show all co-expressed genes and PPI interactions:

MATCH (g:GENE)-[r:PPI_interaction]-(h:GENE)-[r2:Co_expression]-(g) 
RETURN count(g)

Find the gene connections among two modues via PPI:

MATCH (m:MODULE)-[r0:Belong]-(g:GENE)-[r:PPI_interaction]-(h:GENE)-[r2:Belong]-(mo:MODULE) 
WHERE m.name="YellowHPV+" AND mo.name= "BlueHPV+" 
RETURN DISTINCT g, m, h, mo

If you have any comments or questions about this work which can improve it, please let us know! Thanks ;-)

e-mail: [email protected].