Background: COVID-19 has been a global pandemic affecting millions since March 2020. Both as a disease and as a syndrome, COVID19 mimics a number of other clinical entities. Also, current set of investigations such as RT-PCR and antibody based assays are not that efficient in diagnosis of the disease. This necessitates newer platforms such as proteomics to identify potential biomarker candidates that can assist in diagnosis as well as help to understand molecular eitio-pathogenesis. Methods: Proteins from Naso-oro-pharyngeal samples of healthy Control and COVID-19 subjects were trypsin digested and subjected to liquid chromatography assisted mass spectrometry analysis on Orbitrap Fusion Lumos (ThermoFisher). Proteomic analysis was carried using Proteome Discoverer 2.4 (ThermoFisher) using human and SARS-CoV-2 FASTA databases from UniProtKB. Proteins with fold Change ≤ 0.5, or ≥ 2 were designated as differentially expressed and taken up for interactomic analysis and using STRING and Cytoscape, and pathway analysis using KEGG, Reactome, and Wiki pathway. Area under curve for receiver operating characteristic was done in MetaboAnalyst (v6.0) to estimate diagnostic parameters of sensitivity and specificity. Results: Proteomic analysis of naso-oropharyngeal swabs from COVID-19 patients helped to identify 26 differentially expressed human proteins of which 11 are potential biomarker candidate proteins. In addition, 29 SARS-CoV-2 specific peptides from spike protein, nucleocapsid, membrane, ORF1ab, ORF6, ORF7a, ORF8 and ORF9b proteins have been identified of which 6 were mutated. These SARS-CoV-2 proteins make an array of interactions with the identified human proteins establishing their roles in pathogenesis of viral infection. Human biomarker candidate proteins are primarily associated with alterations in glycolytic pathways and immune dysregulation, which are known to facilitate viral invasion, replication, and contribute to disease severity. Additionally, identified human proteins directly interact with viral proteins, indicating a potential coordinated role in SARS-CoV-2 pathogenesis. Conclusions: Label-free proteomics is an ideal platform for identification of biomarker candidates and understand their possible roles in pathogenesis of COVID19. Identified biomarker candidates have a minimum of 70% and a maximum of 100%, sensitivity and specificity thereby having translational value in developing diagnostics for COVID-19.