Evidence map›Paper›PMID 42196229›Full record

ArticleInternational journal of molecular sciences2026

Identification and Characterization of Cancer-Related Risk Metabolic Subpathways Reveal Their Functional Significance in Cancer.

Hongying Zhao, Jinxing Yan, Ming Wu, Shiyi Li, Weiming He, Xiangzhe Yin, Wangyang Liu, Ying Liu, Meiting Fei, Wan Li and 3 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
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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

13 authors.

Hongying ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Jinxing YanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Ming WuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Shiyi LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Weiming HeInstitute of Opto-Electronics, Harbin Institute of Technology, Harbin 150000, China.
Xiangzhe YinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Wangyang LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Ying LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Meiting FeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0000-0002-9797-0315
Junjie LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Lina ChenCollege 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 62573169Natural Science Foundation of Heilongjiang Province of China LH2023F012
6 · The paper itself

Abstract

Cancer progression is accompanied by significant metabolic alterations. We developed a novel computational approach to identify cancer-related risk metabolic subpathways (CMSubpathway). By leveraging the topology of large-scale metabolic pathway gene networks, we initially identified metabolic subpathways and then refined them by taking into account pathway activity dysregulation, prognostic efficacy, and classification performance. We employed the CMSubpathway to extensively identify cancer-related metabolic subpathways across 21 cancer types. Ultimately, 12 risk metabolic subpathways were identified in six cancer types. Subsequently, the 12 overlapping genes of risk metabolic subpathways were identified as the core metabolic module genes. Utilizing the public CRISPR knockout screening datasets sourced from DepMap, we further supported our hypothesis that the essential roles of ADH5, ALDH1B1, and ALDH7A1 in breast cancer cell growth and development. The core metabolic module and its associated genes exhibited significant down-regulation at both the transcriptome and proteome levels based on data from tissues, blood, and single cells. The activity of this core metabolic module was associated with the immune infiltration levels of multiple immune cells, especially T cells. Notably, an abnormal core metabolic module was observed in CD8 T cell subtypes, with the stem-like CD8 T cell subtype showing high metabolic activity and exhaustion markers. Thus, we established a method for identifying risk metabolic subpathways in cancers, which helps to identify more precise biomarkers for cancer patients.

Indexed as

Metabolic Networks and PathwaysNeoplasmsBiomarkers, TumorComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansTranscriptomeBiomarkers, Tumorbiomarkersmetabolismsubpathways

Identifiers

PMID42196229
PMCPMC13207224

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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.