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. 2024 Dec 31;10(1):2380590.
doi: 10.1080/20565623.2024.2380590. Epub 2024 Aug 14.

A computational study of gene expression patterns in head and neck squamous cell carcinoma using TCGA data

Saqib Rauf  1   2 , Sami Ullah  2 , Muhammad Adil Abid  2 , Asad Ullah  3 , Gullzar Khan  2 , Ainee Urooj Khan  1 , Gulzar Ahmad  3 , Muhammad Ijaz  1 , Sidra Ahmad  3 , Sulaiman Faisal  1
Affiliations

A computational study of gene expression patterns in head and neck squamous cell carcinoma using TCGA data

Saqib Rauf et al. Future Sci OA. .

Abstract

Aim: Head and Neck squamous cell carcinoma (HNSCC) is the second most prevalent cancer in Pakistan. Methods: Gene expression data from TCGA and GETx for normal genes to analyze Differentially Expressed Genes (DEGs). Data was further investigated using the Enrichr tool to perform Gene Ontology (GO). Results: Our analysis identified most significantly differentially expressed genes and explored their established cellular functions as well as their potential involvement in tumor development. We found that the highly expressed Keratin family and S100A9 genes. The under-expressed genes KRT4 and KRT13 provide instructions for the production of keratin proteins. Conclusion: Our study suggests that factors such as poor oral hygiene and smokeless tobacco can result in oral stress and cellular damage and cause cancer.

Keywords: Big Data; DEG; Gene Ontology; HNSCC; KRT13; TCGA.

Plain language summary

The Cancer Genome Atlas (TCGA) holds vast cancer data processed with powerful computers and cloud tech. This sparks new bioinformatics for better cancer diagnosis, treatment, and prevention. In Southeast Asia, Head and Neck Squamous Cell Carcinoma (HNSCC) is prevalent. We used TCGA and GETx data to study gene expression. High-expression Keratin and S100A9 genes fight cellular damage under stress, while under-expressed KRT4 and KRT13 genes shape cell structure. Poor oral care and smokeless tobacco could induce cell damage, sparking cancer mutations. Unveiling HNSCC mechanisms may guide targeted treatments and preventive strategies.

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Conflict of interest statement

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
It show the code of python through which the data are processed.
Figure 2.
Figure 2.
Graphical comparison of both expression data's, on the left side tumor data is highly expressed, whereas on the right side of the graph normal data is highly expressed and in the middle the expression is similar.
Figure 3.
Figure 3.
The graphical representation of the genes shows overexpressed genes in the green box on the right side, with a 5–140-fold change. The left box contains under expressed genes, while the middle box includes genes with similar fold changes.
Figure 4.
Figure 4.
GO enrichment analysis of Group I overexpressed genes in tumor data. (A) GO biological process, (B) GO molecular function and (C) GO cellular component.
Figure 5.
Figure 5.
GO enrichment analysis of Group II overexpressed genes in tumor data. (A) GO biological process, (B) GO molecular function and (C) GO cellular component.
Figure 6.
Figure 6.
GO enrichment analysis of Group III overexpressed genes in tumor data. (A) GO biological process, (B) GO molecular function and (C) GO cellular component.
Figure 7.
Figure 7.
GO enrichment analysis of Group I under expressed genes in tumor data. (A) GO biological process and (B) GO cellular component.
Figure 8.
Figure 8.
GO enrichment analysis of Group II under expressed genes in tumor data. (A) GO biological process, (B) GO molecular function and (C) GO cellular component.
Figure 9.
Figure 9.
GO enrichment analysis of Group III under expressed genes in tumor data. (A) GO biological process, (B) GO molecular function and (C) GO cellular component.
Figure 10.
Figure 10.
Protein protein interaction (PPi) of the over expressed groups are generated with the help of STRING online tool, the ones with red color indicated the hubs of the interaction between these groups.
See this image and copyright information in PMC

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