Keratin 17 and A2ML1 are negative prognostic biomarkers in non-small cell lung cancer.

A2ML1 Biomarker IHC Keratin 17 LSCC LUAD NSCLC TCGA

Journal

Pathology, research and practice
ISSN: 1618-0631
Titre abrégé: Pathol Res Pract
Pays: Germany
ID NLM: 7806109

Informations de publication

Date de publication:
03 Oct 2024
Historique:
received: 17 06 2024
revised: 02 10 2024
accepted: 02 10 2024
medline: 17 10 2024
pubmed: 17 10 2024
entrez: 16 10 2024
Statut: aheadofprint

Résumé

Although the overall prognosis for patients with non-small cell lung cancer (NSCLC) has improved over the past several decades, there are still survival differences that are not accurately defined by clinicopathological factors. Thus, there is an unmet clinical need to develop novel approaches to enhance prognostic accuracy for these patients. Keratin 17 (K17) is a negative prognostic biomarker in a wide range of cancer types, including pancreatic ductal adenocarcinoma, head and neck squamous cell carcinoma, and pulmonary adenocarcinoma (LUAD), but has yet to be investigated as a prognostic biomarker in primary lung squamous cell carcinoma (LSCC). Based on TCGA RNA-seq data, alpha-2-macroglobulin like 1 (A2ML1), a protease inhibitor, is highly correlated with K17 in other solid tumors, including pancreatic ductal adenocarcinoma and is also a prognostic biomarker for LSCC, although the prognostic accuracy of A2ML1 for LUAD has not been tested. Thus, we hypothesized that A2ML1 expression correlates with K17 expression and that K17/A2ML1 co-testing could provide complementary prognostic data for NSCLC. The aims of this study were to explore K17 and A2ML1 as dual prognostic biomarkers, using publicly available gene expression databases [The Cancer Genome Atlas (TCGA)] LSCC (n=266), LUAD (n=271)] and multiplexed immunohistochemistry (mIHC) on representative sections of LSCC (n=104) and LUAD (n=107) from two major academic medical centers. Our results suggest that using either mRNA or mIHC-based methods, combined K17 and A2ML1 testing provides information, independent of other clinicopathologic variables, that could impact treatment decisions for patients with NSCLC.

Identifiants

pubmed: 39413460
pii: S0344-0338(24)00554-5
doi: 10.1016/j.prp.2024.155643
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

155643

Informations de copyright

Copyright © 2024. Published by Elsevier GmbH.

Déclaration de conflit d'intérêts

Declaration of Competing Interest K.R.S and L.F.E.-H. are consultants to KDx Diagnostics Inc. All other authors report no competing interests with the current study.

Auteurs

Sruthi Babu (S)

Department of Pathology, Renaissance School of Medicine, Stony Brook, NY 11794, USA. Electronic address: sruthi.babu@stonybrookmedicine.edu.

Michael Horowitz (M)

Department of Pathology, Renaissance School of Medicine, Stony Brook, NY 11794, USA. Electronic address: mhorowit@buffalo.edu.

Lyanne A Delgado-Coka (LA)

Department of Pathology, Renaissance School of Medicine, Stony Brook, NY 11794, USA. Electronic address: loblein@stonybrookmedicine.edu.

Lucia Roa-Peña (L)

Department of Pathology, Renaissance School of Medicine, Stony Brook, NY 11794, USA; Department of Pathology, School of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia. Electronic address: lroap@unal.edu.co.

Ali Akalin (A)

Department of Pathology, University of Massachusetts Memorial Medical Center, Worcester, Worcester, MA 01655, USA. Electronic address: ali.akalin@umassmemorial.org.

Luisa F Escobar-Hoyos (LF)

Department of Pathology, Renaissance School of Medicine, Stony Brook, NY 11794, USA; Department of Therapeutic Radiology, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Division of Oncology, Medicine-Oncology, Yale University, New Haven, CT, USA. Electronic address: luisa.escobar-hoyos@yale.edu.

Kenneth R Shroyer (KR)

Department of Pathology, Renaissance School of Medicine, Stony Brook, NY 11794, USA. Electronic address: Kenneth.Shroyer@stonybrookmedicine.edu.

Classifications MeSH