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Proteogenomics analysis of non-coding region encoded peptides in normal tissues and five cancer types

Xiang, R.; Ma, L.; Yang, M.; Zheng, Z.; Chen, X.; Jia, F.; Xie, F.; Li, F.; Wu, K.; Zhu, Y.

2020-04-13 bioinformatics
10.1101/2020.04.10.029306 bioRxiv
Show abstract

Previous proteogenomics studies have identified peptides encoded by non-coding sequences such as pseudogenes and long non-coding RNAs (lncRNAs) in healthy human tissues as well as in cancers. However, these studies are either limited to analyze only healthy or cancerous tissues, lacking direct comparison between them. In this study, we used an established proteogenomics analysis workflow to analyze proteomics data from 926 cancer samples of five cancer types and 31 different healthy human tissues. We observed the protein level expression of pseudogenes can be classified as ubiquitous or lineage expression. The ubiquitously translated pseudogenes are homologous to house-keeping genes. Our results suggest there is common mechanism underlying the translation of pseudogenes in both normal and tumors. Moreover, we discovered several translated non-coding genes such as DGCR5 and RHOXF1P3 that were up-regulated in tumors compared to normal. These translated pseudogenes imply the biological function of pseudogenes extends to protein level yet to be studied. Further, from the non-coding region encoded peptides specifically detected in tumors we have predicted a large number of potential neoantigens which can be developed as cancer vaccine.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.