Evidence map›Paper›PMID 40084401›Full record

ArticleCancer medicine2025

Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms.

Yi Liu, Bingxian Bian, Shiyu Chen, Bingqian Zhou, Peng Zhang, Lisong Shen, Hui Chen

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  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

7 authors.

Yi LiuDepartment of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Bingxian BianDepartment of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shiyu ChenDepartment of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Bingqian ZhouDepartment of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Peng ZhangDepartment of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lisong ShenDepartment of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hui ChenDepartment of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID https://orcid.org/0000-0001-9115-6526

Funding

National Natural Science Foundation of China 81874152National Natural Science Foundation of China 81903038Shanghai Key Clinical Specialty Construction Project shslczdzk03304Shanghai Sailing Program 19YF1432700
6 · The paper itself

Abstract

backgroundGastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking.

methodsThis study conducted different analyses, including the identification of differentially expressed genes (DEGs), weighted correlation networks (WGCNA), single-cell RNA sequencing (scRNA-seq), mRNA expression-based stemness index (mRNAsi), and multiCox analysis, utilizing data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes. These findings were then validated through the receiver operating characteristic (ROC) curve and Kaplan-Meier analysis, and were experimentally confirmed in GC samples by reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA).

resultsIntegrated analysis of TCGA and GEO databases, coupled with LASSO regression and RF algorithms, allowed us to identify 18 hub genes encoding differentially expressed secreted proteins in GC. The results of RT-PCR and bioinformatics analysis revealed four promising biomarkers with optimal diagnostic and prognostic potential. ROC analysis and Kaplan-Meier curves highlighted CHI3L1, FCGBP, VSIG2, and TFF2 as promising biomarkers for GC, offering superior modeling accuracy. These findings were further confirmed by RT-PCR and ELISA, affirming the clinical utility of these four biomarkers. Additionally, CIBERSORT analysis indicated a potential correlation between the four biomarkers and the infiltration of B memory cells and Treg cells.

conclusionThis study unveiled four promising biomarkers present in the serum of patients with GC, which could serve as powerful indicators of GC and provide valuable insights for further research into GC pathogenesis.

Indexed as

Biomarkers, TumorMachine LearningStomach NeoplasmsAlgorithmsComputational BiologyFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisROC CurveBiomarkers, Tumordiagnostic and prognostic potentialgastric cancerintegrated bioinformatics analysispromising biomarkersRT‐PCR and ELISA

Identifiers

PMID40084401
PMCPMC11907202

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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.