Evidence map›Paper›PMID 40673083›Full record

ArticleTranslational lung cancer research2025

Fructose-diphosphate aldolase C as a novel diagnostic biomarker for early-stage non-small cell lung cancer: a low-abundance proteomics study.

Changsen Bai, Qianhui Hao, Yunxiang Chen, Jiayi Wang, Jiawei Xiao, Da Hyun Kang, Li Ren

Abstract read
In one paragraph

Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Changsen Bai *Department of Clinical Laboratory, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Qianhui Hao *Department of Clinical Laboratory, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Yunxiang ChenDepartment of Clinical Laboratory, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Jiayi WangDepartment of Clinical Laboratory, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Jiawei XiaoDepartment of Clinical Laboratory, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Da Hyun KangDepartment of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, Korea.
Li RenDepartment of Clinical Laboratory, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung cancer remains one of the leading causes of cancer-related deaths worldwide. Early detection is crucial for improving prognosis and survival rates. This study aimed to identify the low-abundance plasma proteins as potential diagnostic biomarkers for early-stage non-small cell lung cancer (NSCLC) and to distinguish malignant from benign lung nodules. Methods: Using a sodium-type Y zeolite-polymer polyanionic complex (NaY-PPC)-based low-abundance proteomics, we analyzed 181 plasma samples from healthy controls (HC; n=65), patients with benign lung nodules (BNs; n=21), and patients with early-stage NSCLC (n=95). Principal component analysis (PCA) and heatmap visualization were employed for differential analysis. The diagnostic performance of candidate biomarkers was evaluated using receiver operating characteristic (ROC) curves, and enzyme-linked immunosorbent assay (ELISA) was used for validation. Functional studies, including fructose-bisphosphate aldolase C (ALDOC) knockdown, were conducted to assess the role of ALDOC in NSCLC progression. Results: We identified 23 significantly differentially expressed proteins, with ALDOC showing the most promising diagnostic potential. ALDOC could effectively distinguish NSCLC patients from HCs [area under the curve (AUC) =0.994] and from those with BNs (AUC =0.720). Combining ALDOC with the traditional biomarkers carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and cytokeratin fragment 21-1 (CYFRA21-1) improved the differentiation between NSCLC and BN (AUC =0.824). ELISA validation confirmed the findings from the proteomics analysis. Additionally, ALDOC was upregulated in NSCLC tissues, and its high expression correlated with poor overall survival. Knockdown of ALDOC significantly reduced NSCLC cell growth and motility, suggesting its tumor-promoting role. Conclusions: ALDOC is a promising diagnostic biomarker for early-stage NSCLC, with potential clinical utility in distinguishing malignant lung nodules from BNs. This study highlights the value of low-abundance proteomics in identifying novel biomarkers for lung cancer detection and risk assessment.

Indexed as

diagnostic biomarkerfructose-bisphosphate aldolase C (ALDOC)low-abundance proteomicsNon-small cell lung cancer (NSCLC)

Identifiers

PMID40673083
PMCPMC12261238

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.