Evidence map›Paper›PMID 41660849›Full record

ArticleMicrobiology spectrum2026

Integrative analysis of immune and microbial subtypes predicts immunotherapy response in stomach adenocarcinoma.

Yumeng Zhang, Huakai Wen, Xianfang Tang, Yuhua Yao

Abstract read
In one paragraph

Article in Microbiology spectrum, 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

4 authors.

Yumeng ZhangSchool of Mathematics and Statistics, Hainan Normal University, Haikou, China.ORCID 0009-0000-4050-0163
Huakai WenSchool of Mathematics and Statistics, Hainan Normal University, Haikou, China.
Xianfang TangSchool of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China.
Yuhua YaoSchool of Mathematics and Statistics, Hainan Normal University, Haikou, China.ORCID 0009-0007-6191-8770

Funding

Hainan Province Science and Technology Special Fund ZDYF2026GXJS006National Natural Science Foundation of China 62162025
6 · The paper itself

Abstract

The tumor immune microenvironment and intratumoral microbiota play critical roles in cancer progression and immunotherapy response, yet their integrated functions in stomach adenocarcinoma (STAD) are not well understood. This study conducted a multi-omics analysis of transcriptomic and microbiome data from 348 patients with STAD. Using the ImmuCellAI algorithm, immune cell infiltration (ICI) was estimated, and non-negative matrix factorization classified samples into three immune subtypes (INC-1, INC-2, and INC-3). Differential expression analysis identified immune-related signature genes enriched in immune signaling pathways. Tumor mutational burden, microsatellite instability, immune checkpoint gene expression, and drug sensitivity were compared across subtypes. Microbiome clustering identified three subtypes (MC-1, MC-2, and MC-3), with associations to immune infiltration and microbial composition. The immune subtypes showed distinct patterns of ICI, clinical stage, and gene expression, with differentially expressed genes enriched in immune and tumor-related pathways. Microbiome subtypes exhibited unique diversity metrics and associations with the immune microenvironment. Integration of immune and microbial data improved immune checkpoint blockade (ICB) prediction, with genera like

Indexed as

AdenocarcinomaImmunotherapyStomach NeoplasmsHumansMultiomicsTranscriptomeTumor Microenvironmentimmune checkpoint blockadeintratumoral microbiotamachine learningstomach adenocarcinomatumor immune microenvironment

Identifiers

PMID41660849
PMCPMC12955421

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.