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. 2019 Oct 22;10(58):6168-6183.
doi: 10.18632/oncotarget.27249.

Differential gene expression analysis of HNSCC tumors deciphered tobacco dependent and independent molecular signatures

Inayatullah Shaikh  1 , Afzal Ansari  1 , Garima Ayachit  1 , Monika Gandhi  1 , Priyanka Sharma  1 , Shivarudrappa Bhairappanavar  1 , Chaitanya G Joshi  1 , Jayashankar Das  1
Affiliations

Differential gene expression analysis of HNSCC tumors deciphered tobacco dependent and independent molecular signatures

Inayatullah Shaikh et al. Oncotarget. .

Abstract

Head and neck cancer is the sixth most common cancer worldwide, with tobacco as the leading cause. However, it is increasing in non-tobacco users also, hence limiting our understanding of its underlying molecular mechanisms. RNA-seq analysis of cancers has proven as effective tool in understanding disease etiology. In the present study, RNA-Seq of 86 matched Tumor/Normal pairs, of tobacco smoking (TOB) and non-smokers (N-TOB) HNSCC samples analyzed, followed by validation on 375 similar datasets. Total 2194 and 2073 differentially expressed genes were identified in TOB and N-TOB tumors, respectively. GO analysis found muscle contraction as the most enriched biological process in both TOB and N-TOB tumors. Pathway analysis identified muscle contraction and salivary secretion pathways enriched in both categories, whereas calcium signaling and neuroactive ligand-receptor pathway was more enriched in TOB and N-TOB tumors respectively. Network analysis identified muscle development related genes as hub node i. e. ACTN2, MYL2 and TTN in both TOB and N-TOB tumors, whereas EGFR and MYH6, depicts specific role in TOB and N-TOB tumors. Additionally, we found enriched gene networks possibly be regulated by tumor suppressor miRNAs such as hsa-miR-29/a/b/c, hsa-miR-26b-5p etc., suggestive to be key riboswitches in regulatory cascade of HNSCC. Interestingly, three genes PKLR, CST1 and C17orf77 found to show opposite regulation in each category, hence suggested to be key genes in separating TOB from N-TOB tumors. Our investigation identified key genes involved in important pathways implicated in tobacco dependent and independent carcinogenesis hence may help in designing precise HNSCC diagnostics and therapeutics strategies.

Keywords: differentially expressed genes; head and neck squamous cell carcinoma (HNSCC); hub gene; miRNA; tobacco.

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Conflict of interest statement

CONFLICTS OF INTEREST The authors declare no competing interests.

Figures

Figure 1
Figure 1
(A) Volcano plot showing gene expression significantly (FDR<0.05; LogFC>2) altered (highlighted in red colour) in TOB sample. (B) Volcano plot showing genes significantly (FDR<0.05; LogFC>2) altered in expression, highlighted in red colour in N-TOB patients.
Figure 2
Figure 2. Top 20 DEGs identified in TOB and N-TOB tumors.
Genes were ranked based on p-values ≤ 0.05 and adjusted false discovery rate using the Benjamini–Hochberg procedure.
Figure 3
Figure 3. Venn diagram showing DEGs common and unique between Tobacco and non-Tobacco patients.
Genes highlighted in blue and pink red are showed altered expression in Tobacco and non-Tobacco patients only; and genes highlighted in dark red are observed to be altered in both Tobacco and non-Tobacco patients.
Figure 4
Figure 4. Gene Ontology Enrichment Analysis Results.
The figure contains interactive charts displaying the results of the Gene Ontology (GO) enrichment analysis generated using GOrilla. The x-axis indicates the enrichment score for each term. Significant terms are highlighted in bold.
Figure 5
Figure 5. Network representing enriched pathways integrated KEGG and Reactome pathways of both TOB and N-TOB tumors.
Highest significance of enriched pathway was obtained using advanced statistical settings such as Hyper-geometric (right-handed) enrichment distribution tests, p-value < 0.05, and Bonferroni adjustment. The size and colour represents number of DEGs involved and enrichment significance respectively- deeper the colour, the higher the enrichment significance.
Figure 6
Figure 6. Network based meta-analysis of hub genes.
Zero-order interaction network of DEGs obtained from RNA-seq data using force-directed algorithm with Fruchterman-Rengold layout; green nodes represents over-expressed and red nodes represents under-expressed genes.
Figure 7
Figure 7. Differential gene expression pattern of key genes in both TOB and N-TOB tumors.
Log2fold change values of genes showing opposite regulation of the same gene in between TOB and N-TOB tumors.
Figure 8
Figure 8. Detail workflow of the study.
Study workflow consists of three components i. e. 1) data set search and selection of relevant data; 2) mining of the selected data (RNA-seq read count) from TCGA; and 3) data analysis which includes differential gene expression (DEGs) analysis and annotations.
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