Abstract

Ectopically expressed olfactory receptors (ORs) have been linked with multiple clinically-relevant physiological processes. Previously used tissue-level expression estimation largely shadowed the potential role of ORs due to their overall low expression levels. Even after the introduction of the single-cell transcriptomics, a comprehensive delineation of expression dynamics of ORs in tumors remained unexplored. Our targeted investigation into single malignant cells revealed a complex landscape of combinatorial OR expression events. We observed differentiation-dependent decline in expressed OR counts per cell as well as their expression intensities in malignant cells. Further, we constructed expression signatures based on a large spectrum of ORs and tracked their enrichment in bulk expression profiles of tumor samples from The Cancer Genome Atlas (TCGA). TCGA tumor samples stratified based on OR-centric signatures exhibited divergent survival probabilities. In summary, our comprehensive analysis positions ORs at the cross-road of tumor cell differentiation status and cancer prognosis.

Through analyses of olfactory receptors in single-cell RNA sequencing dataset from different cancers, Kalra, Mittal et al. find that the number and expression levels of olfactory receptors vary by differentiation status. The study provides insights into an under-appreciated role for olfactory receptors in cancer pathogenesis.

Details

Title
Analysis of single-cell transcriptomes links enrichment of olfactory receptors with cancer cell differentiation status and prognosis
Author
Kalra Siddhant 1 ; Mittal Aayushi 1 ; Gupta, Krishan 2   VIAFID ORCID Logo  ; Singhal Vrinda 1 ; Gupta Anku 3 ; Mishra Tripti 4 ; Naidu Srivatsava 5 ; Sengupta Debarka 6   VIAFID ORCID Logo  ; Ahuja Gaurav 1   VIAFID ORCID Logo 

 Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India (GRID:grid.454294.a) (ISNI:0000 0004 1773 2689) 
 Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India (GRID:grid.454294.a) (ISNI:0000 0004 1773 2689); Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India (GRID:grid.454294.a) (ISNI:0000 0004 1773 2689) 
 Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India (GRID:grid.454294.a) (ISNI:0000 0004 1773 2689) 
 Pathfinder Research and Training Foundation, 30/7 and 8, Greater Noida, India (GRID:grid.454294.a) 
 Center for Biomedical Engineering, Indian Institute of Technology Ropar, Bara Phool, Rupnagar, India (GRID:grid.462391.b) (ISNI:0000 0004 1769 8011) 
 Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India (GRID:grid.454294.a) (ISNI:0000 0004 1773 2689); Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India (GRID:grid.454294.a) (ISNI:0000 0004 1773 2689); Centre for Artificial Intelligence, Indraprastha Institute of Information Technology, New Delhi, India (GRID:grid.454294.a) (ISNI:0000 0004 1773 2689); Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia (GRID:grid.1024.7) (ISNI:0000000089150953) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
23993642
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2441673398
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.