Metaproteomic analysis of human oral squamous cell carcinoma

Background: Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy, with an estimated 5-year survival rate of only 40-50%, largely due to late detection and diagnosis. While existing research has tried to overcome this by identifying early human biomarkers of disease, emerging evidence suggests that the human microbiome may in fact play a role in the pathogenesis of many diseases, including cancer. Studies of the oral microbiome have putatively identified bacteria that may have a role in oral carcinogenesis, although the impact of other microbial organisms such as fungi and viruses has largely been neglected.

Aim: The current project aims to develop a bioinformatic approach for analysis of viral, bacterial and fungal proteins from OSCC mass spectrometry (MS) data to identify enriched microbial proteins, infer species composition and evaluate their potential as diagnostic biomarkers.

Methodology: The Trans-Proteomic Pipeline (TPP) platform was employed for primary analysis of human OSCC MS data against microbial reference databases, followed by development of a computational pipeline in the R statistical programming language for secondary analysis to identify the viral, bacterial and fungal proteins enriched in OSCC patient tissue and infer their species’ identities.

Results: Overall viral, bacterial and fungal composition was inferred in healthy control and OSCC patient tissue from public proteomics data. A range of proteins were observed to be differentially enriched between healthy and OSCC conditions, with the fungal protein profile presenting the best potential discriminator of OSCC within the analysed dataset.

Significance and future directions: The current bioinformatic pipeline will aid in the identification of potential OSCC biomarkers to further assist detection and diagnosis of OSCC. While the current project sheds new light on the fungal and viral spheres of the oral microbiome in cancer in silico, further research will be required to validate these findings in an experimental setting.