Evidence map›Paper›PMID 41679194›Full record

ArticleEBioMedicine2026

Identification and validation of an 11-kinase signature that predicts chemo- and radiosensitivity in gastric cancer.

Changyuan Hu, Sung-Young Shin, Yanan Wang, Chenbin Chen, Pei Liu, Rita A Busuttil, Yunjian Wu, Catriona A McLean, Hongying Shi, Terry Kwok and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 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

15 authors.

Changyuan HuWenzhou Medical University-Monash BDI Alliance in Clinical and Experimental Biomedicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia; Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia.
Sung-Young ShinCancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia; Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia; Computational Systems Oncology Program, South Australian ImmunoGENomics Cancer Institute (SAiGENCI), The University of Adelaide, Adelaide, SA, 5005, Australia.
Yanan WangBurning Rock Biotech, Guangzhou, Guangdong, China.
Chenbin ChenDepartment of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Zhejiang Province Key Laboratory for Intelligent+ Cancer Biomarker Research and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Pei LiuSchool of Big Data & Software Engineering, Chongqing University, Chongqing, China.
Rita A BusuttilSchool of Translational Medicine, Monash University, 99 Commercial Rd, Melbourne, VIC, 3004, Australia; Department of Gastroenterology, The Alfred Hospital, 55 Commercial Rd, Melbourne, VIC, 3004, Australia.
Yunjian WuOlivia Newton-John Cancer Research Institute, Heidelberg, VIC, Australia; School of Cancer Medicine, La Trobe University, Bundoora, VIC, Australia.
Catriona A McLeanAnatomical Pathology, Alfred Health and School of Translational Medicine, Monash University, Australia.
Hongying ShiDepartment of Epidemiology and Health Statistics, School of Public Health, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Terry KwokCancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia; Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia; Infection and Immunity Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, Australia; Department of Microbiology, Monash University, Clayton, Australia.
Lan K NguyenCancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia; Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia; Computational Systems Oncology Program, South Australian ImmunoGENomics Cancer Institute (SAiGENCI), The University of Adelaide, Adelaide, SA, 5005, Australia; Australian Research Council Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS), Australia.
Jiangning SongCancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia; Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia.
Alex BoussioutasSchool of Translational Medicine, Monash University, 99 Commercial Rd, Melbourne, VIC, 3004, Australia; Department of Gastroenterology, The Alfred Hospital, 55 Commercial Rd, Melbourne, VIC, 3004, Australia.
Xian ShenWenzhou Medical University-Monash BDI Alliance in Clinical and Experimental Biomedicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China; Zhejiang Province Key Laboratory for Intelligent+ Cancer Biomarker Research and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Roger J DalyWenzhou Medical University-Monash BDI Alliance in Clinical and Experimental Biomedicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia; Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia. Electronic address: roger.daly@monash.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost gastric cancer (GC) patients receive uniform treatment due to the lack of predictive biomarkers for chemotherapy or radiotherapy. We previously identified epithelial-mesenchymal transition (EMT) and metabolism subgroups in GC cell lines based on kinomic profiles, but their clinical relevance was unknown.

methodsWe developed an ensemble model using 37 kinases that differed between cell line subgroups to classify stage II-IV GC into EMT and metabolism subgroups. Survival differences between those who received chemotherapy or radiotherapy and those who did not were compared within each subgroup to validate the model effectiveness in predicting therapeutic response. An iteration approach was further applied to optimise and validate the feature set via multiple publicly-available datasets.

findingsAn 11-kinase signature stratified 893 patients into two subgroups. The metabolism subgroup showed significantly better survival with chemotherapy (HR

interpretationThis model holds promise for optimising chemotherapy and radiotherapy decisions for GC.

fundingBiomedicine Discovery Scholarship and Graduate Research Completion Award, Monash University; National Natural Science Foundation of China (81602165) (C.H.). Australian Research Council Centre of Excellence for the Mathematical Analysis of Cellular Systems (CE230100001) (L.K.N.).

Indexed as

Protein KinasesRadiation ToleranceStomach NeoplasmsBiomarkers, TumorCell Line, TumorEpithelial-Mesenchymal TransitionGene Expression ProfilingGene Expression Regulation, NeoplasticHumansNeoplasm StagingPrognosisBiomarkers, TumorProtein KinasesBiomarkerChemoresponseGastric cancerMachine learningRadioresponse

Identifiers

PMID41679194
PMCPMC12914827

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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.