Prognostic Relevance of Surface α-Enolase (ENO1) in Patients With Lung Adenocarcinoma.

Background/Aim: α-Enolase (ENO1) is a glycolytic enzyme that also functions as a plasminogen receptor when translocated to the cell surface, where it promotes extracellular matrix degradation and invasion. The surface and cytosolic functions of ENO1 is therefore distinct, but whether they differ in prognostic value in lung adenocarcinoma (LUAD) is unknown. This study evaluated the prognostic value of surface and cytosolic ENO1 in LUAD. Patients and Methods: Surface and cytosolic ENO1 were assessed by immunohistochemistry in 174 LUAD patients and stratified as low or high according to the median H-score. The relationship between ENO1 expression and clinicopathological parameters was analyzed, and 5-year overall survival (OS) and progression-free survival (PFS) were evaluated. Results: =0.047). In univariate analysis, high surface ENO1 was associated with an increased risk of 5-year OS [hazard ratio (HR)=1.94, 95% confidence interval (CI)=1.02-3.71] and 5-year PFS (HR=1.62, 95%CI=1.00-2.63). In contrast, cytosolic ENO1 was not associated with any clinicopathological parameter or survival outcome. Conclusion: Surface, but not cytosolic, ENO1 is associated with adverse pathological features and poor survival in LUAD. These results suggest that the surface pool is the prognostically relevant fraction of ENO1 and support its evaluation as a biomarker and therapeutic target in LUAD.

Authors

Institutions

Publication Details

Journal
PubMed
Published
2026-11-01
DOI
https://doi.org/10.21873/cdp.10602
Primary Topic
Lung Cancer Research Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prognostic Relevance of Surface α-Enolase (ENO1) in Patients With Lung Adenocarcinoma.

Yuan‐Yao Tsai, K. S. Clifford Chao, Chia-Yu Tsai, Tao‐Wei Ke et al.
PubMed
Lung Cancer Research Studies
article

Prognostic Relevance of Surface α-Enolase (ENO1) in Patients With Lung Adenocarcinoma.

Yuan‐Yao Tsai, K. S. Clifford Chao, Chia-Yu Tsai, Tao‐Wei Ke, Chi-Hsien Huang, William Tzu-Liang Chen, Kevin Chih‐Yang Huang, Te-Hong Chen, Yu-Sen Lin
article en

Abstract

Background/Aim: α-Enolase (ENO1) is a glycolytic enzyme that also functions as a plasminogen receptor when translocated to the cell surface, where it promotes extracellular matrix degradation and invasion. The surface and cytosolic functions of ENO1 is therefore distinct, but whether they differ in prognostic value in lung adenocarcinoma (LUAD) is unknown. This study evaluated the prognostic value of surface and cytosolic ENO1 in LUAD. Patients and Methods: Surface and cytosolic ENO1 were assessed by immunohistochemistry in 174 LUAD patients and stratified as low or high according to the median H-score. The relationship between ENO1 expression and clinicopathological parameters was analyzed, and 5-year overall survival (OS) and progression-free survival (PFS) were evaluated. Results: =0.047). In univariate analysis, high surface ENO1 was associated with an increased risk of 5-year OS [hazard ratio (HR)=1.94, 95% confidence interval (CI)=1.02-3.71] and 5-year PFS (HR=1.62, 95%CI=1.00-2.63). In contrast, cytosolic ENO1 was not associated with any clinicopathological parameter or survival outcome. Conclusion: Surface, but not cytosolic, ENO1 is associated with adverse pathological features and poor survival in LUAD. These results suggest that the surface pool is the prognostically relevant fraction of ENO1 and support its evaluation as a biomarker and therapeutic target in LUAD.

PubMedVol. 6(6)
Asia University (TW), China Medical University (TW), China Medical University Hospital (TW)
Openalex Percentile: Top 16%
Lung Cancer Research Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon