The way for early diagnosis of sepsis: Read host immune response via pan-infection transcriptomics and transfer learning

Sepsis, a life-threatening condition resulting from an overactive immune response to infection, necessitates prompt diagnosis to prevent progression. Early detection remains a challenge due to its non-specific symptoms and the need for a thorough assessment to determine the infection’s source and severity. Although several studies explore host transcriptome as potential tools for sepsis diagnosis, they are limited by small data volumes or data integration methods for assembling datasets from different resources. Furthermore, infections trigger sepsis and pro-vide early-warning signal at the host immune response level. Therefore, we utilize infection transcriptome data as a source domain to enhance the performance of sepsis diagnosis as a target domain by leveraging deep transfer learning. A pan-infection dataset of 3,686 infectious samples is curated from 37 cohorts using the Pairwise Analysis of Gene Expression (PAGE) method. We train a deep neural network based on the pan-infection data to discriminate between infection and non-infection, and then fine-tune with five sepsis cohorts, con-structing a sepsis diagnosis model called sepSeek. sepSeek is validated in three external cohorts, which is superior to common machine learning algorithms and sepsis diagnosis methods. Our framework demonstrates potential for accurate sepsis diagnosis, offering a significant advancement in critical care medicine and cross-disease study.