Li AChen J.-YCHIA-LANG HSUOyang, Yen-JenYen-JenOyangHuang H.-CHSUEH-FEN JUAN2022-11-182022-11-18202219994923https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129905634&doi=10.3390%2fpharmaceutics14050971&partnerID=40&md5=d9a8a757320bb5faeba4cb75026fb589https://scholars.lib.ntu.edu.tw/handle/123456789/625693Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients’ transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interac-tome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.biological networks; COVID-19; drug repurposing; pulmonary fibrosis; single-cell RNA sequencing[SDGs]SDG3Article; biological functions; cohort analysis; controlled study; coronavirus disease 2019; disease association; drug repositioning; gene expression; genetic analysis; genetic association; human; lung fibrosis; major clinical study; mathematical computing; molecular diagnosis; molecular interaction; network analysis; quantitative analysis; single cell RNA seq; transcriptomicsjournal article10.3390/pharmaceutics140509712-s2.0-85129905634