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Gene expression-based identification of prognostic markers in lung adenocarcinoma

Salomonsson, Annette LU ; Ehinger, Daniel LU orcid ; Jönsson, Mats LU ; Botling, Johan ; Micke, Patrick ; Brunnström, Hans LU orcid ; Staaf, Johan LU orcid and Planck, Maria LU (2025) In PLoS ONE 20(5 May).
Abstract

Introduction Many studies have aimed at identifying additional prognostic tools to guide treatment choices and patient surveillance in lung cancer by assessing the expression of individual proteins through immunohistochemistry (IHC) or, more recently, through gene expression-based signatures. As a proof-of-concept, we used a multi-cohort, gene expression-based discovery and validation strategy to identify genes with prognostic potential in lung adenocarcinoma. The clinical applicability of this strategy was further assessed by evaluating a selection of the markers by IHC. Materials and methods Publicly available gene expression data sets from six microarray-based studies were divided into four discovery and two validation data sets.... (More)

Introduction Many studies have aimed at identifying additional prognostic tools to guide treatment choices and patient surveillance in lung cancer by assessing the expression of individual proteins through immunohistochemistry (IHC) or, more recently, through gene expression-based signatures. As a proof-of-concept, we used a multi-cohort, gene expression-based discovery and validation strategy to identify genes with prognostic potential in lung adenocarcinoma. The clinical applicability of this strategy was further assessed by evaluating a selection of the markers by IHC. Materials and methods Publicly available gene expression data sets from six microarray-based studies were divided into four discovery and two validation data sets. First, genes associated with overall survival (OS) in all four discovery data sets were identified. The prognostic potential of each identified gene was then assessed in the two validation data sets, and genes associated with OS in both data sets were considered as potential prognostic markers. Finally, IHC for selected potential prognostic markers was performed in two independent and clinically well-characterized lung cancer cohorts. Results and conclusions The gene expression-based strategy identified 19 genes with correlation to OS in all six data sets. Out of these genes, we selected Ki67, MCM4 and TYMS for further assessment with IHC. Although an independent prognostic ability of the selected markers could not be confirmed by IHC, this proof-of-concept study demonstrates that by employing a gene expression-based discovery and validation strategy, potential prognostic markers can be identified and further assessed by a technique universally applicable in the clinical practice. The concept of studying potential prognostic markers through gene expression-based strategies, with a subsequent evaluation of the clinical utility, warrants further exploration.

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organization
publishing date
type
Contribution to journal
publication status
published
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in
PLoS ONE
volume
20
issue
5 May
article number
e0310232
publisher
Public Library of Science (PLoS)
external identifiers
  • scopus:105004780745
  • pmid:40333815
ISSN
1932-6203
DOI
10.1371/journal.pone.0310232
language
English
LU publication?
yes
id
5038d47d-d545-469e-84ad-c41cab829a4e
date added to LUP
2025-08-05 11:33:37
date last changed
2025-08-06 03:00:02
@article{5038d47d-d545-469e-84ad-c41cab829a4e,
  abstract     = {{<p>Introduction Many studies have aimed at identifying additional prognostic tools to guide treatment choices and patient surveillance in lung cancer by assessing the expression of individual proteins through immunohistochemistry (IHC) or, more recently, through gene expression-based signatures. As a proof-of-concept, we used a multi-cohort, gene expression-based discovery and validation strategy to identify genes with prognostic potential in lung adenocarcinoma. The clinical applicability of this strategy was further assessed by evaluating a selection of the markers by IHC. Materials and methods Publicly available gene expression data sets from six microarray-based studies were divided into four discovery and two validation data sets. First, genes associated with overall survival (OS) in all four discovery data sets were identified. The prognostic potential of each identified gene was then assessed in the two validation data sets, and genes associated with OS in both data sets were considered as potential prognostic markers. Finally, IHC for selected potential prognostic markers was performed in two independent and clinically well-characterized lung cancer cohorts. Results and conclusions The gene expression-based strategy identified 19 genes with correlation to OS in all six data sets. Out of these genes, we selected Ki67, MCM4 and TYMS for further assessment with IHC. Although an independent prognostic ability of the selected markers could not be confirmed by IHC, this proof-of-concept study demonstrates that by employing a gene expression-based discovery and validation strategy, potential prognostic markers can be identified and further assessed by a technique universally applicable in the clinical practice. The concept of studying potential prognostic markers through gene expression-based strategies, with a subsequent evaluation of the clinical utility, warrants further exploration.</p>}},
  author       = {{Salomonsson, Annette and Ehinger, Daniel and Jönsson, Mats and Botling, Johan and Micke, Patrick and Brunnström, Hans and Staaf, Johan and Planck, Maria}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{5 May}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Gene expression-based identification of prognostic markers in lung adenocarcinoma}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0310232}},
  doi          = {{10.1371/journal.pone.0310232}},
  volume       = {{20}},
  year         = {{2025}},
}