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. 2020 Sep 4;10(1):14667.
doi: 10.1038/s41598-020-71346-7.

Systems-level differential gene expression analysis reveals new genetic variants of oral cancer

Syeda Zahra Abbas  1 , Muhammad Imran Qadir  1 , Syed Aun Muhammad  2
Affiliations

Systems-level differential gene expression analysis reveals new genetic variants of oral cancer

Syeda Zahra Abbas et al. Sci Rep. .

Abstract

Oral cancer (OC) ranked as eleventh malignancy worldwide, with the increasing incidence among young patients. Limited understanding of complications in cancer progression, its development system, and their interactions are major restrictions towards the progress of optimal and effective treatment strategies. The system-level approach has been designed to explore genetic complexity of the disease and to identify novel oral cancer related genes to detect genomic alterations at molecular level, through cDNA differential analysis. We analyzed 21 oral cancer-related cDNA datasets and listed 30 differentially expressed genes (DEGs). Among 30, we found 6 significant DEGs including CYP1A1, CYP1B1, ADCY2, C7, SERPINB5, and ANAPC13 and studied their functional role in OC. Our genomic and interactive analysis showed significant enrichment of xenobiotics metabolism, p53 signaling pathway and microRNA pathways, towards OC progression and development. We used human proteomic data for post-translational modifications to interpret disease mutations and inter-individual genetic variations. The mutational analysis revealed the sequence predicted disordered region of 14%, 12.5%, 10.5% for ADCY2, CYP1B1, and C7 respectively. The MiRNA target prediction showed functional molecular annotation including specific miRNA-targets hsa-miR-4282, hsa-miR-2052, hsa-miR-216a-3p, for CYP1B1, C7, and ADCY2 respectively associated with oral cancer. We constructed the system level network and found important gene signatures. The drug-gene interaction of OC source genes with seven FDA approved OC drugs help to design or identify new drug target or establishing novel biomedical linkages regarding disease pathophysiology. This investigation demonstrates the importance of system genetics for identifying 6 OC genes (CYP1A1, CYP1B1, ADCY2, C7, SERPINB5, and ANAPC13) as potential drugs targets. Our integrative network-based system-level approach would help to find the genetic variants of OC that can accelerate drug discovery outcomes to develop a better understanding regarding treatment strategies for many cancer types.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Normalization and differential analysis. Histogram (smoothed histograms) shows density estimate of the data. Typically, the distributions of the arrays have similar shapes and ranges. Arrays whose distributions are very different from the others are considered for possible problems. High levels of background shifted an array's distribution to the right. Lack of signal diminishes its right tail. A bulge at the upper end of the intensity range indicated the signal saturation.
Figure 2
Figure 2
RNA degradation plot. Side-by-side plot produced by plot AffyRNAdeg representing 5′–3′-trend indicating an assessment of the severity of RNA degradation and significance level.
Figure 3
Figure 3
The OC-related DEGs were curated using CTD (comparative toxicogenomics database), PubMed, OMIM, and MeSH databases.
Figure 4
Figure 4
Clinical phenotypes for oral cancer related DEGs using FunRich annotation tool.
Figure 5
Figure 5
Cluster analysis of 6 oral cancer-related DEGs with Euclidean distance (Binning method). Quantile lines indicate the boundaries of the clusters in the level of the tree.
Figure 6
Figure 6
Analysis and exploration of mutations affecting post-translational modification (PTM) sites in human genes/proteins using online ActiveDriverDB database. Needle plots indicate the PTM site mutations in our genes/proteins. The graph shows the outcomes based on the specific type of PTM, cancer type, and mutation subset (presented in legend color codes). Height (y-axis) represents the number of occurrences of the mutation while Horizontal position (x-axis) indicates the position of protein’s amino acid sequence. Pinhead color signifies the mutation impact and X-axis coloring shows the type of PTM associated with the mutation location. Mutational Impacts: Rewiring: mutation-induced gains and losses of kinase-bound sequence motifs (predicted by MIMP software); Distal: mutation affects an amino acid located 4–7 amino acids away from the PTM site; Direct: mutation affects the post transcriptionally modified amino acid; Proximal: mutation affects an amino acid located 1–3 amino acids away from PTM site; Sites: Amino acid sites/ regions enriched for mutations affecting post-translational modifications (PTMs).
Figure 7
Figure 7
Protein–protein interaction of OC genes. Red nodes represent DEGs interacting with Pink nodes (target genes/gene signatures). High-confidence interactions of HAPPI database were selected in this network (the five stars are equivalent to high score (0.90–1).
Figure 8
Figure 8
Pathway modeling for genome signaling and metabolic reconstruction revealed the pathological mechanism of oral cancer using KEGG and Wiki pathway databases.
Figure 9
Figure 9
Toxicogenomic analysis of differentially expressed genes carried out by a comparative toxicogenomic database (CTD) helps to study the chemical-genome to phenome relationships.
Figure 10
Figure 10
The drug–gene network was constructed between the FDA approved drugs and target genes. A broken line indicates the interaction between known drugs while solid line represents the novel drug targets. Anticancer drugs were retrieved from drug B+ ank.
Figure 11
Figure 11
The steps have been integrated in basic framework of our study.
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