Evidence map›Paper›PMID 41514850›Full record

ArticleBiology2025

LYZ Gene as a Novel Therapeutic Target and Diagnostic Biomarker in Glioblastoma: Insights from Multi-Omics Analysis and Functional Validation.

Nuoyan Zhu, Jiahui Wang, Liangliang Cai

Abstract read
In one paragraph

Article in Biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Nuoyan ZhuThe First Clinical Medical College, Faculty of Medicine, Yangzhou University, Yangzhou 225000, China.ORCID 0009-0001-6706-7491
Jiahui WangSchool of Basic Medical Sciences and School of Public Health, Faculty of Medicine, Yangzhou University, Yangzhou 225000, China.
Liangliang CaiSchool of Basic Medical Sciences and School of Public Health, Faculty of Medicine, Yangzhou University, Yangzhou 225000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune checkpoint blockade is one of the current treatments for glioblastoma (GBM), which is still a very aggressive and treatment-resistant tumor of the central nervous system. This study focused on the LYZ gene to find new therapeutic targets. We performed a thorough screening of differential gene expressions between GBM and normal samples using many databases (TCGA, GTEx, GEO, and CGGA). Because LYZ is significantly upregulated in GBM tissues and is associated with shorter patient survival periods, we identified it as a gene of interest. LYZ's position on the exterior side of the plasma membrane and its participation in leukocyte-mediated immunity were identified by functional enrichment analysis, indicating a role in cell surface immune responses. Significant associations between LYZ expression and particular immune cell types were found using immune infiltration analysis, suggesting that LYZ may have an impact on the tumor microenvironment. Within GBM, single-cell research verified LYZ expression in macrophages and monocytes. LYZ was shown to express differently in GBM cell lines than in normal glial cells, according to cellular experimental verification. The LYZ gene's functional importance in the pathophysiology of GBM was highlighted by the dramatic reduction in cell proliferation, motility, and invasion that resulted from its knockout. These results suggest that LYZ is a viable therapeutic target and possible GBM diagnostic biomarker, which calls for more research into its mechanisms of action and potential clinical use.

Indexed as

diagnostic biomarkerglioblastoma (GBM)LYZ genemulti-omics analysistherapeutic target

Identifiers

PMID41514850
PMCPMC12784846

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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.