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Front. Cell Dev. Biol. | doi: 10.3389/fcell.2019.00299

Comprehensive identification and characterization of human secretome based on integrative proteomic and transcriptomic data

 Geng Chen1,  Jiwei Chen1, Huanlong Liu1, Shuangguan Chen1, Yang Zhang1, Peng Li1,  Danielle Thierry-Mieg2, Jean Thierry-Mieg2,  William Mattes3,  Baitang Ning3 and  Tieliu Shi1*
  • 1School of Life Sciences, East China Normal University, China
  • 2National Center for Biotechnology Information (NLM), United States
  • 3Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, United States

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients’ survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches.

Keywords: secreted proteins, Proteome, Transcriptome, RNA-Seq, human secretome

Received: 27 Aug 2019; Accepted: 07 Nov 2019.

Copyright: © 2019 Chen, Chen, Liu, Chen, Zhang, Li, Thierry-Mieg, Thierry-Mieg, Mattes, Ning and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Tieliu Shi, School of Life Sciences, East China Normal University, Shanghai, 200241, Shanghai Municipality, China, tlshi@bio.ecnu.edu.cn