Evidence map›Paper›PMID 42454031›Full record

ArticleFrontiers in immunology2026

Tumor-intrinsic immune-genetic dynamics identify RNASE1 as an immune-evasion-associated biomarker and predictor of checkpoint blockade response in gastric adenocarcinoma.

Haoyun Xia, Tianxing Guo, Nan Zhang, Dajia Lin, Guodong Guo, Yue Tian, Chenshen Huang, Shen Guan, Fangqin Xue, Liqi Chen

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Haoyun Xia *Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
Tianxing Guo *Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
Nan Zhang *College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Dajia Lin *Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, China.
Guodong GuoShengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
Yue TianFujian Medical University Union Hospital, Fuzhou, China.
Chenshen HuangFuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, China.
Shen GuanDepartment of Colorectal Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
Fangqin XueShengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
Liqi ChenShengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although immune checkpoint blockade (ICB) has changed treatment strategies for gastric adenocarcinoma (STAD), many patients show primary resistance. Tumor-intrinsic transcriptional heterogeneity may contribute to immune escape, but the malignant-cell programs and candidate biomarkers linked to this process remain incompletely defined. Here, we integrated single-cell transcriptomic profiles of 162,116 cells from 35 STAD samples with multiple bulk transcriptomic cohorts to characterize malignant-cell regulatory programs. Non-negative matrix factorization identified meta-program 2 (MP2), a malignant-cell program closely associated with immune-escape signatures. Through integrated co-expression analysis and machine-learning-based prioritization, RNASE1 was identified as an MP2-related candidate biomarker. High RNASE1 expression was associated with poor survival, higher immune and stromal scores, increased immune-checkpoint expression, and enrichment of ICB-response-related transcriptomic signatures.

Indexed as

AdenocarcinomaBiomarkers, TumorImmune Checkpoint InhibitorsRibonuclease, PancreaticStomach NeoplasmsTumor EscapeCarrier ProteinsCell Line, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisTranscriptomeBiomarkers, TumorCarrier ProteinsImmune Checkpoint InhibitorsRibonuclease, PancreaticRNH1 protein, humanartificial intelligencegastric cancerimmune evasionmachine learning (ML)NMF (nonnegative matrix factorization)RNASE1

Identifiers

PMID42454031
PMCPMC13364911

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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.