IFI27 transcription is an early predictor for COVID-19 outcomes

Shojaei Maryam, Shamshirian Amir, Monkman James, Grice Laura, Tran Minh, Tan Chin Wee, Teo Siok Min, Rodrigues Rossi Gustavo, McCulloch Timothy R., Nalos Marek, Raei Maedeh, Razavi Alireza, Ghasemian Roya, Gheibi Mobina, Roozbeh Fatemeh, Sly Peter D., Spann Kirsten M., Chew Keng Yih, Zhu Yanshan, Xia Yao, Wells Timothy J., Senegaglia Alexandra Cristina, Kuniyoshi Carmen Lúcia, Franck Claudio Luciano, dos Santos Anna Flavia Ribeiro, Noronha Lucia de, Motamen Sepideh, Valadan Reza, Amjadi Omolbanin, Gogna Rajan, Madan Esha, Alizadeh-Navaei Reza, Lamperti Liliana, Zuñiga Felipe, Nova-Lamperti Estefania, Labarca Gonzalo, Knippenberg Ben, Herwanto Velma, Wang Ya, Phu Amy, Chew Tracy, Kwan Timothy, Kim Karan, Teoh Sally, Pelaia Tiana M., Kuan Win Sen, Jee Yvette, Iredell Jon, O’Byrne Ken, Fraser John F., Davis Melissa J., Belz Gabrielle T., Warkiani Majid E., Gallo Carlos Salomon, Souza-Fonseca-Guimaraes Fernando, Nguyen Quan, Mclean Anthony, Kulasinghe Arutha, Short Kirsty R., Tang Benjamin. Frontiers DOI: 10.3389/fimmu.2022.1060438

Purpose: Robust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.