PT  - JOURNAL ARTICLE
AU  - Lucas Cardoso Lazari
AU  - Fabio De Rose Ghilardi
AU  - Livia Rosa-Fernandes
AU  - Diego M Assis
AU  - José Carlos Nicolau
AU  - Veronica Feijoli Santiago
AU  - Talia Falcão Dalçóquio
AU  - Claudia B Angeli
AU  - Adriadne Justi Bertolin
AU  - Claudio RF Marinho
AU  - Carsten Wrenger
AU  - Edison Luiz Durigon
AU  - Rinaldo Focaccia Siciliano
AU  - Giuseppe Palmisano
TI  - Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19
AID  - 10.26508/lsa.202000946
DP  - 2021 Aug 01
TA  - Life Science Alliance
PG  - e202000946
VI  - 4
IP  - 8
4099  - https://www.life-science-alliance.org/content/4/8/e202000946.short
4100  - https://www.life-science-alliance.org/content/4/8/e202000946.full
SO  - Life Sci. Alliance2021 Aug 01; 4
AB  - SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS–PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.