Evidence map›Paper›PMID 42074110›Full record

ArticleInternational journal of molecular sciences2026

Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy.

Xiangzhe Yin, Lu Wang, Yanwu Sun, Shiyi Li, Wentong Yu, Siyao Wang, Zhichao Geng, Hongying Zhao, Li Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 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

9 authors.

Xiangzhe YinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Lu WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yanwu SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0009-0008-2005-4651
Shiyi LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Wentong YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Siyao WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Zhichao GengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Hongying ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Li WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0000-0002-1936-8513

Funding

National Natural Science Foundation of China 62372144National Natural Science Foundation of China 62572155National Natural Science Foundation of China 62573169Outstanding Youth Foundation of Heilongjiang Province YQ2023F004
6 · The paper itself

Abstract

With the rapid development of cancer treatment, immunotherapy has revolutionized renal cell carcinoma (RCC) treatment, yet patient responses remain heterogeneous. Here, a computational pipeline was constructed by integrating single-cell and bulk RNA sequencing data to identify immune-related candidate driver genes and characterize their impact on RCC immunotherapy. Based on gene regulatory networks (GRN), 25 immune-related candidate driver genes were identified, leading to the stratification of patients into three clusters (C1-C3). Compared to the C2/C3 cluster, the C1 cluster exhibited elevated immune infiltration, tumor mutation burden and checkpoint expression, which may represent immunotherapy responders. Dynamic analysis of GRNs revealed the critical role of candidate driver genes in predicting the efficacy of immunotherapy.

Indexed as

Carcinoma, Renal CellImmunotherapyKidney NeoplasmsChemokine CXCL9Gene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansInterferon Regulatory Factor-1STAT1 Transcription FactorTranscriptomeChemokine CXCL9CXCL9 protein, humanInterferon Regulatory Factor-1IRF1 protein, humanSTAT1 protein, humanSTAT1 Transcription Factorgene regulatory networkimmune-related candidate driver genesimmunotherapyrenal cell carcinomasingle-cell RNA sequencingtumor microenvironment

Identifiers

PMID42074110
PMCPMC13115843

What OpenQuestion holds

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Registered trials

None linked

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.