Evidence map›Paper›PMID 42196432›Full record

ArticleInternational journal of molecular sciences2026

A Multi-Omics Framework Reveals Tumor Heterogeneity and Predicts Therapeutic Targets in Renal Cell Carcinoma.

Xiangzhe Yin, Zihe Zhou, Yunzhu Xue, Yangxinyue Zheng, Wentong Yu, Zhichao Geng, Yanwu Sun, Lu Wang, Zushun Chen, Siyao Wang and 2 more

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

12 authors.

Xiangzhe YinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Zihe ZhouCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yunzhu XueCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yangxinyue ZhengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Wentong YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Zhichao GengCollege 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
Lu WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Zushun ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Siyao WangCollege 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
Hongying ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Funding

National Natural Science Foundation of China 62372144National Natural Science Foundation of China 62572155National Natural Science Foundation of China 62573169Natural Science Foundation of Heilongjiang Province LH2023F012
6 · The paper itself

Abstract

Tumor cell heterogeneity and multicellular interactions critically influence drug resistance, recurrence, and prognosis. Here, CPcellsubpopulation, a computational framework integrating scRNA-seq, bulk RNA-seq, and clinical data was developed to identify cancer progression-associated cell subpopulations. Then, the integrated analyses of scRNA-seq and spatial transcriptomics were performed to predict potential interactions, identify critical transcription factors, and predict candidate anticancer drugs. Across nine cancers, we detected cancer progression-associated cell subpopulations significantly linked to prognosis, with consistent patterns across cancer types. In renal cell carcinoma (RCC), we identified conserved metabolic

Indexed as

Carcinoma, Renal CellKidney NeoplasmsGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMetabolic ReprogrammingMultiomicsPrognosisSingle-Cell Gene Expression AnalysisSpatial TranscriptomicsTumor Microenvironmentgene regulatory networksmetabolic reprogrammingmulti-omics analysisrenal cell carcinomaspatial transcriptomics

Identifiers

PMID42196432
PMCPMC13207066

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.