Analysis of Tumor Suppressor Genes Based on Gene Ontology and the KEGG Pathway

Abstract

Cancer is a serious disease that causes many deaths every year. We urgently need to design effective treatments to cure this disease. Tumor suppressor genes (TSGs) are a type of gene that can protect cells from becoming cancerous. In view of this, correct identification of TSGs is an alternative method for identifying effective cancer therapies. In this study, we performed gene ontology (GO) and pathway enrichment analysis of the TSGs and non-TSGs. Some popular feature selection methods, including minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS), were employed to analyze the enrichment features. Accordingly, some GO terms and KEGG pathways, such as biological adhesion, cell cycle control, genomic stability maintenance and cell death regulation, were extracted, which are important factors for identifying TSGs. We hope these findings can help in building effective prediction methods for identifying TSGs and thereby, promoting the discovery of effective cancer treatments.

SNHG8 is identified as a key regulator of epstein-barr virus(EBV)-associated gastric cancer by an integrative analysis of lncRNA and mRNA expression

“…Enrichment between lncRNA and EBV genes can be represented by the hypergeometric test P value [87–89] of the target gene of lncRNA, L(i), and the target gene of EBV, V(j), which can be computed by: $\text{p}\left(\text{i,j}\right)=\text{p}\left(\text{L}\left(\text{i}\right),\text{V}\left(\text{j}\right)\right)={\displaystyle \sum _{k=m}^n\frac{\left(\begin{array}{c}M\\ m\end{array}\right)\left(\begin{array}{c}N-M\\ n-m\end{array}\right)}{\left(\begin{array}{c}N\\ n\end{array}\right)}}$ where N represents the total number of human genes, M and n represent the number of target genes of EBV gene j and the number of target genes of lncRNA i, respectively, and m represents the number of lncRNA target genes that also target genes of EBV gene j. The smaller the P value for a lncRNA and an EBV gene, the stronger the suggested association between them.…”

Cyclin D1 overexpression induces global transcriptional downregulation in lymphoid neoplasms

“…94% (n = 145) of the genes expressed in control cells were expressed at lower mRNA levels in JVM13-D1 T286A cells ( Figure 5D). To explore whether this mRNA downmodulation would include tumor suppressor genes (TSGs), we compiled a set of 583 genes defined as TSGs (32). We found that 30 of 33 (91%) expressed TSGs were downregulated in JVM13-D1 T286A cells.…”

Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

“…For a given GO term GO j and a gene g , let G 1 be a gene set consisting of genes annotated to GO j and G 2 be another gene set consisting of neighbor genes of g in the protein-protein interaction network reported in STRING (http://string-db.org/) [26], a well-known public database providing known and predicted protein-protein interactions. The GO enrichment score between GO j and g is defined as the −log 10 of the hypergeometric test P value [27–30] of G 1 and G 2 , which can be calculated by $\begin{array}{c}T1\mathrm{E}{\mathrm{S}}_{\mathrm{G}\mathrm{O}}\left(g,\mathrm{G}{\mathrm{O}}_j\right)=-{\log }_{\mathrm{10}}\left(\underset{k=m}{\overset{n}{{\displaystyle false\sum }}}\frac{\left(\begin{array}{c}M\\ k\end{array}\right)\left(\begin{array}{c}N-M\\ n-k\end{array}\right)}{\left(\begin{array}{c}N\\ n\end{array}\right)}\right),\end{array}$where N is the number of genes in human, M is the number of genes in G 1 , n is the number of genes in G 2 , and m is the number of common genes of G 1 and G 2 . A large enrichment score between GO j and g indicates close relationship between them.…”