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Enregistrement 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 sur OpenAlexfundno aff
Tom Winkler, Uri Ben‐David

Notice bibliographique

RevueInternational Journal of Cancer · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensnon disponible
Organismes subventionnairesAzrieli FoundationIsrael Cancer Association
Mots-clésCancerMedicineAngiotensin-converting enzyme 2ChemotherapyLung cancerImmunologyInternal medicineCancer researchPathologyCoronavirus disease 2019 (COVID-19)Disease

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,377
Écart entre enseignants0,345 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2020
Routes d'admission1
Résumé présentoui

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