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KERATIN 17 AND A2ML1 ARE NEGATIVE PROGNOSTIC BIOMARKERS IN NON-SMALL CELL LUNG CANCER

肺癌 医学 肿瘤科 角蛋白 内科学 生物标志物 病理 癌症研究 生物 遗传学
作者
Sruthi Babu,Michael Horowitz,Lyanne A. Delgado-Coka,Lucia Roa‐Peña,Ali Akalin,Luisa F. Escobar‐Hoyos,Kenneth R. Shroyer
出处
期刊:Pathology Research and Practice [Elsevier BV]
卷期号:: 155643-155643 被引量:3
标识
DOI:10.1016/j.prp.2024.155643
摘要

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.
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