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Record W3034036784 · doi:10.1002/ijc.33145

Elevated expression of <scp> <i>ACE2</i> </scp> in tumor‐adjacent normal tissues of cancer patients

2020· letter· en· W3034036784 on OpenAlexfundno aff
Tom Winkler, Uri Ben‐David

Bibliographic record

VenueInternational Journal of Cancer · 2020
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Cancer Association
KeywordsCancerMedicineAngiotensin-converting enzyme 2ChemotherapyLung cancerImmunologyInternal medicineCancer researchPathologyCoronavirus disease 2019 (COVID-19)Disease

Abstract

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Dear editor, The recent outbreak of a novel betacoronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised the concern that cancer patients might be particularly susceptible to infection by this virus.1-3 Importantly, the guidelines for cancer patients during the COVID-19 pandemic focus on lung cancer patients who are undergoing active chemotherapy or radical radiotherapy, and on patients with blood cancers.1 Intentional postponing of adjuvant chemotherapy or elective surgery for stable cancer has even been proposed to alleviate the risk.2 However, it is currently unknown whether patients with other epithelial solid tumors, or cancer patients not currently undergoing chemotherapy, are also more susceptible to COVID-19. SARS-CoV-2 requires the angiotensin-converting enzyme 2 (ACE2) to enter human cells.4, 5 Moreover, ACE2 gene expression levels in epithelial tissues corresponded to survival after SARS-CoV infection in transgenic mice6 and soluble human ACE2 inhibited SARS-CoV-2 infections in engineered human tissues.7 ACE2 mRNA levels are particularly high in the human kidney, testis, heart and intestinal tract.8, 9 Albeit not highly expressed in most cells of the normal lungs, ACE2 expression levels in the airway epithelia increase due to chronic exposure to cigarette smoke,8, 10 which was associated with infection susceptibility.11 ACE2 expression levels have also been suggested to underlie the increased susceptibility of patients with hypertension and diabetes to SARS-CoV-2 infection,12 and to be increased in patients with comorbidities associated with severe COVID-19.13 Therefore, ACE2 expression in epithelial tissues, and in particular in the airway epithelia, seem to have considerable effect on COVID-19 morbidity and mortality. We compared ACE2 mRNA levels between normal tissues (NT), primary tumors (PT) and normal tissues adjacent to tumors (NAT), using data from The Cancer Genome Atlas (TCGA) and GTEx14 (Supporting Information). Across multiple tissues, ACE2 mRNA levels in PT were significantly higher than in NT of the respective tissue (Figures 1A and S1). Surprisingly, ACE2 expression levels in NAT were also significantly higher than in NT across tissues, and in all cases were at least as high as in the respective PT (Figures 1A and S1). This result suggests that the NAT of cancer patients would likely be more susceptible to SARS-CoV-2 infection than the corresponding tissues of healthy individuals. Focusing on the lung due to its relevance in the disease etiology, we next queried the mRNA expression levels of ACE2 in two additional datasets of normal human tissues, the Human Protein Atlas15 and FANTOM5.16 In concordance with the GTEx data, the expression levels of ACE2 in whole-lung tissues from healthy donors were negligible (median of 0.7pTPM, 1.8pTPM and 2.6 scaled tags per million, in GTEx, HPA and FANTOM5, respectively). Next, we compared the relative expression levels of ACE2 between healthy and tumor-adjacent lung tissues, using six published gene expression microarray datasets17-20 (Supporting Information Methods). ACE2 expression levels in the tumor-adjacent normal lung samples were detected at discernible levels, and were significantly higher than those in the healthy normal lung samples (Figure 1B). This analysis confirmed that the mRNA levels of ACE2 are elevated in tumor-adjacent lung tissues of lung cancer patients. This observation raises the possibility that lung cancer patients may have an increased risk to SARS-CoV-2 infection, regardless of chemotherapy-induced immune suppression. Furthermore, patients with other types of cancer, such as renal or gastrointestinal cancers, may also have elevated infection risk. However, to determine whether this is indeed the case, two questions require urgent attention: (a) Is ACE2 expression level in non-lung epithelia associated with SARS-CoV-2 infection risk?; and (b) Is ACE2 upregulation limited to the tissue adjacent to the tumor (presumably due to the tumor microenvironment), or are ACE2 levels systemically elevated in cancer patients? In addition, although ACE2 mRNA levels are upregulated in NAT and PT compared to NT, we cannot rule out the possibility that ACE2 protein levels are not significantly different due to post-transcriptional regulation mechanisms. Until these questions are resolved, we propose that the discussion of cancer guidelines during the COVID-19 pandemic should expand beyond patients with treatment-induced immune suppression. Research in the Ben-David lab is supported by the Azrieli Foundation, the Richard Eimert Research Fund on Solid Tumors, the Tel-Aviv University Cancer Biology Research Center and the Israel Cancer Association (grant #20200111). We declare no conflict of interest. The data that support the findings of our study are available in Xena at https://xena.ucsc.edu/, in the Human Protein Atlas at https://www.proteinatlas.org/, and in GEO at https://www.ncbi.nlm.nih.gov/geo/ (accession numbers GSE14334, GSE14938, GSE5364, GSE19804, GSE32863 and GSE75037). Appendix S1. Supporting Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.377
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations10
Published2020
Admission routes1
Has abstractyes

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