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Label-free plasma proteomics for the identification of the putative biomarkers of oral squamous cell carcinoma
Mass spectrometry-based label-free proteomics is becoming the analytical tool of interest to identify and quantitate the biomarkers for cancer. The oral squamous cell carcinoma (OSCC) which is one of the leading cancers worldwide, lacks biomarkers for the early prognosis and diagnosis. The present study profiled plasma proteome of the Indian OSCC human patients using a label-free mass spectrometry proteomics approach. The study first time utilized the three most widely used data analysis software MaxQuant (MQ), Proteome discoverer (PD), and Trans proteomic pipeline (TPP) together for label-free quantitation of the proteins. The study identified 16 proteins which can be used as a signature protein panel for OSCC. The pathway analysis showed predominant involvement of the immune system, hemostasis as the major pathways that are indicative of the disease progression. The network analysis showed maximum interaction for the Fibronectin and C-reactive protein. The study demonstrates that plasma proteins contain signatures that can be used as putative biomarkers for OSCC. Based on the label-free quantitation and the mechanistic analysis C-reactive protein, Carbonic anhydrase-1, and Fibronectin are identified as putative biomarkers of OSCC. Once these findings are validated in the large cohorts these protein signatures can be used as a biomarker for OSCC. SIGNIFICANCE: The oral squamous cell carcinoma (OSCC) is the eighteenth most prevalent malignancy in the world and ranks second in India. There are no biomarkers that could be indicative of the diseased state. Various studies have been carried out on saliva and tumors of OSCC patients in India, but none of the studies have profiled the plasma. We utilized the label-free approach for the first time on the Indian population in generating the panel of plasma proteins which could be indicative of the diseased state. The study identified Carbonic anhydrase 1, C-reactive protein, and Fibronectin proteins as putative biomarkers for the OSCC. The study obtained the signature panel by utilizing the 3 most widely used software for the label-free quanatitation (LFQ) namely MaxQuant, Proteome Discoverer, and Trans proteomic pipeline. The utilization of 3 software for the LFQ reduced the bias and provided a comprehensive list of proteins. All the differential proteins were mechanistically studied for their biological relevance and the pathway and network analysis were carried out. The study helps us in increasing the understanding of the proteins which are involved in the progression of the diseases. Studying the panel of proteins from all biofluids along with plasma in large cohorts of the population will help in understanding the disease in greater depth and help in identifying the relevant biomarkers for the OSCC
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