Laryngeal cancer is the most common head and neck cancer in the world and its incidence is on the rise. However, the
molecular mechanism underlying laryngeal cancer pathogenesis is poorly understood. The goal of this study was to
develop a protein-protein interaction (PPI) network for laryngeal cancer to predict the biological pathways that underlie
the molecular complexes in the network. Genes involved in laryngeal cancer were extracted from the OMIM database
and their interaction partners were identified via text and data mining using Agilent Literature Search, STRING and
GeneMANIA. PPI network was then integrated and visualised using Cytoscape ver3.6.0. Molecular complexes in the
network were predicted by MCODE plugin and functional enrichment analyses of the molecular complexes were performed
using BiNGO. 28 laryngeal cancer-related genes were present in the OMIM database. The PPI network associated with
laryngeal cancer contained 161 nodes, 661 edges and five molecular complexes. Some of the complexes were related to
the biological behaviour of cancer, providing the foundation for further understanding of the mechanism of laryngeal
cancer development and progression.
Aliphatic glucosinolate is an important secondary metabolite responsible in plant defense mechanism and carcinogenic
activity. It plays a crucial role in plant adaptation towards changes in the environment such as salinity and drought.
However, in many plant genomes, there are thousands of genes encoding proteins still with putative functions and
incomplete annotations. Therefore, the genome of Arabidopsis thaliana was selected to be investigated further to identify
any putative genes that are potentially involved in the aliphatic glucosinolate biosynthesis pathway, most of its gene are
with incomplete annotation. Known genes for aliphatic glucosinolates were retrieved from KEGG and AraCyc databases.
Three co-expression databases i.e., ATTED-II, GeneMANIA and STRING were used to perform the co-expression network
analysis. The integrated co-expression network was then being clustered, annotated and visualized using Cytoscape plugin,
MCODE and ClueGO. Then, the regulatory network of A. thaliana from AtRegNet was mapped onto the co-expression
network to build the transcriptional regulatory network. This study showed that a total of 506 genes were co-expressed
with the 61 aliphatic glucosinolate biosynthesis genes. Five transcription factors have been predicted to be involved
in the biosynthetic pathway of aliphatic glucosinolate, namely SEPALLATA 3 (SEP3), PHYTOCHROME INTERACTING FACTOR
3-like 5 (AtbHLH15/PIL5), ELONGATED HYPOCOTYL 5 (HY5), AGAMOUS-like 15 (AGL15) and GLABRA 3 (GL3). Meanwhile,
three other genes with high potential to be involved in the aliphatic glucosinolates biosynthetic pathway were identified,
i.e., methylthioalkylmalate-like synthase 4 (MAML-4) and aspartate aminotransferase (ASP1 and ASP4). These findings
can be used to complete the aliphatic glucosinolate biosynthetic pathway in A. thaliana and to update the information
on the glucosinolate-related pathways in public metabolic databases.