Evidence map›Paper›PMID 41158224›Full record

ReviewTranslational cancer research2025

Prospective proteomics for discovering biomarkers in lung adenocarcinoma: a literature review.

Yichen Wang, Mingyue Xu, Xiaoyu Wei, Haitao Huang, Qi Chen, Baofu Chen, Xiaohong Bao, Jicheng Li

Abstract readReview
In one paragraph

Review in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yichen WangMajor Disease Biomarker Research Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China.
Mingyue XuMajor Disease Biomarker Research Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China.
Xiaoyu WeiMajor Disease Biomarker Research Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China.
Haitao HuangThoracic Surgery, Taizhou Central Hospital (Taizhou University Hospital), School of Medicine, Taizhou University, Taizhou, China.
Qi ChenThoracic Surgery, Taizhou Central Hospital (Taizhou University Hospital), School of Medicine, Taizhou University, Taizhou, China.
Baofu ChenThoracic Surgery, Taizhou Central Hospital (Taizhou University Hospital), School of Medicine, Taizhou University, Taizhou, China.
Xiaohong BaoThoracic Surgery, Taizhou Central Hospital (Taizhou University Hospital), School of Medicine, Taizhou University, Taizhou, China.ORCID https://orcid.org/0000-0003-2477-8100
Jicheng LiMajor Disease Biomarker Research Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China.ORCID https://orcid.org/0009-0009-0493-9289

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: Lung adenocarcinoma (LUAD), as the main subtype of non-small cell lung cancer (NSCLC), faces clinical challenges including molecular heterogeneity, late diagnosis, and aggressive growth, leading to a low 5-year survival rate. Biomarkers are critical for early detection, accurate differentiation of benign/malignant lesions, and guiding personalized treatment strategies. Proteomic technologies using liquid biopsy show potential by analyzing protein changes and post-translational modifications (PTMs) to identify novel biomarkers and unravel cancer mechanisms. This review examines proteomic advances in LUAD, compares platform strengths, lists validated protein markers, and discusses challenges like specificity and regulations. It aims to develop a precision medicine framework by integrating multi-omics data for improved diagnosis and treatment. Methods: This study conducted a literature review by searching the PubMed and Web of Science databases for original articles written in English from 2002 to 2025, using the keywords "lung adenocarcinoma" OR "LUAD" AND "biomarkers" AND "proteomics" OR "SomaScan" OR "spatial proteomics" to identify the latest research findings in the field of proteomics technology and LUAD biomarkers. The included studies mainly focused on the current landscape of biomarkers in the diagnosis, treatment, and prognosis of LUAD. Key Content and Findings: This review discusses high-throughput methods for comprehensive protein profiling in accessible biospecimens (tissues, blood, urine) to identify biomarkers for LUAD. We systematically evaluate emerging proteomic strategies, including mass spectrometry (MS), proximity extension assays (PEAs), spatial proteomics techniques, and SomaScan platforms-coupled with innovative computational frameworks have revolutionized biomarkers discovery and their translational potential in developing precision diagnostics and targeted therapies. Additionally, the review addresses challenges in integrating proteomics with genomics, transcriptomics, and metabolomics, offering new methodologies and expanding research in life sciences. As technological advancements continue, it is anticipated that more potential biomarkers will be conducted to validate the broader application in LUAD treatment, addressing early-stage disease complexities and aiding in selecting more effective treatment strategies. Conclusions: By synthesizing cutting-edge evidence on proteome-driven LUAD biomarkers, this review elucidates actionable strategies to refine early detection protocols and mechanism-informed personalized treatment frameworks, directly advancing precision oncology initiatives for this prevalent malignancy through biomarker-guided clinical decision-making and multi-omics integration.

Indexed as

biomarkersLung adenocarcinoma (LUAD)mass spectrometry (MS)precision diagnosisproteomics

Identifiers

PMID41158224
PMCPMC12554480

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.