Evidence map›Paper›PMID 42761353›Full record

ReviewFrontiers in oncology2026

Advances in protein microarray-based proteomics for gastric cancer applications in biomarker discovery, molecular subtyping, and precision oncology.

Panning Wang, Yu-Lin Xiao, Shuhong Luo, Hua Dong, Ruo-Pan Huang

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2026. 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

5 authors.

Panning WangDepartment of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.
Yu-Lin XiaoDepartment of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Shuhong LuoRaybiotech Co., Ltd., Guangzhou, Guangdong, China.
Hua DongDepartment of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.
Ruo-Pan HuangRaybiotech Co., Ltd., Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer remains a major cause of cancer-related mortality worldwide, largely due to late diagnosis and pronounced molecular heterogeneity. Although genomic and transcriptomic studies have advanced understanding of gastric tumor biology, they do not fully capture dynamic changes in protein expression, post-translational modifications, and signaling activity that directly regulate cellular function. High-throughput proteomic technologies, particularly protein microarrays, have emerged as powerful platforms for systematic protein profiling in complex biological samples. These technologies enable the simultaneous quantification of hundreds to thousands of proteins with high sensitivity and minimal sample requirements, supporting both discovery and translational research. Recent applications of protein microarrays in gastric cancer include biomarker discovery for early diagnosis, molecular subtyping, characterization of the tumor microenvironment, and assessment of therapeutic response. Multi-protein biomarker panels and integration with computational approaches have demonstrated improved diagnostic performance compared with conventional single-marker assays. In addition, proteomic profiling has provided insights into key signaling pathways and immune-related mechanisms underlying tumor progression and drug resistance. This review summarizes the principles and major formats of protein microarray technologies and highlights their applications in gastric cancer. Current challenges and future directions for clinical translation are also discussed.

Indexed as

biomarker discoveryearly diagnosisgastric cancermolecular subtypingprognosisprotein microarrayproteomics

Identifiers

PMID42761353
PMCPMC13587006

What OpenQuestion holds

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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.