Evidence map›Paper›PMID 42535370›Full record

ReviewInternational journal of molecular medicine2026

Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).

Jialin Jiang, Xinyu Tu, Yi Cao, Qun Yan, Buqing Sai, Jian Ma

Abstract readReview
In one paragraph

Review in International journal of molecular medicine, 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

6 authors.

Jialin Jiang *Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, P.R. China.
Xinyu Tu *Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, P.R. China.
Yi Cao *Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, P.R. China.
Qun YanDepartment of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China.
Buqing SaiDepartment of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming, Yunnan 650500, P.R. China.
Jian MaHunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tumor immune microenvironment plays a decisive role in tumor initiation, progression and therapeutic response. Spatial transcriptomics (ST) enables comprehensive analysis of the whole transcriptome or specific gene sets while preserving the original spatial location information within tissues, offering a revolutionary approach to unraveling the complexity of the tumor immune microenvironment in the spatial dimension. The present review aims to systematically elaborate on the role and application of ST in deciphering the tumor immune microenvironment. Focusing on tumor microenvironment niches at different stages of tumor progression, from precancerous lesions and locally advanced tumors to distant metastasis, recent advances in ST for mapping the spatial distribution and functional states of key cell subpopulations are summarized. The present review further highlights its potential for clinical translation in identifying spatially defined biomarkers and elucidating the mechanisms underlying therapeutic responses and analyze the key technical challenges that constrain the application of ST in clinical practice. In recent years, the rapid development of machine learning algorithms has provided new opportunities to overcome the technical limitations of ST. The deep integration of machine learning with ST is laying a solid theoretical foundation and technical support for precision medicine and early intervention.

Indexed as

NeoplasmsTranscriptomeTumor MicroenvironmentAnimalsBiomarkers, TumorGene Expression Regulation, NeoplasticHumansMachine LearningSpatial TranscriptomicsBiomarkers, Tumormachine learningmulti‑omics analysispersonalized therapyspatial transcriptomicstumor immune micro­environ­ment

Identifiers

PMID42535370
PMCPMC13446719

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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.