Evidence map›Paper›PMID 41048344›Full record

ArticleFrontiers in bioinformatics2025

Key genes associated with brain metastasis in non-small cell lung cancer: novel insights from bioinformatics analysis.

Shuang Zhao, He Zhang

Abstract read
In one paragraph

Article in Frontiers in bioinformatics, 2025. 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

2 authors.

Shuang Zhao *General Hospital of Northern Theater Command of the Chinese People's Liberation Army, Shenyang, China.
He Zhang *General Hospital of Northern Theater Command of the Chinese People's Liberation Army, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aims to investigate potential biomarkers associated with NSCLC-BM and elucidate their regulatory roles in critical pathways involved in cerebral metastatic dissemination. Methods: The identified DEGs were subjected to functional enrichment analysis. PPI networks were predicted using the STRING database and visualized with Cytoscape. Hub genes were subsequently screened from the PPI network to construct a transcription TF-miRNA regulatory network. Subsequent analyses included: survival analysis, immune infiltration assessment and comprehensive mutational profiling. Results: Among the 56 identified DEGs, 19 were upregulated while 37 were downregulated. GOntology enrichment analysis revealed significant enrichment in immune response, signaling receptor binding, and extracellular region. KEGG pathway analysis demonstrated predominant involvement in cytokine-cytokine receptor interaction and chemokine signaling pathway. Through Cytoscape-based screening, we identified 10 hub genes: CD19, CD27, IL7R, SELL, CCL5, CCR5, PRF1, GZMK, GZMA, and TIGIT. The TF-miRNA regulatory network analysis uncovered 6 transcription factors (STAT5A/B, NFKB1, EGR1, RELA, and CTCF) and 4 miRNAs(hsa-miR-204, hsa-miR-148b, hsa-miR-618, and hsa-miR-103) as critical transcriptional and post-transcriptional regulators of DEGs.Integrated analyses including Kaplan-Meier survival curves, immune infiltration profiling, and comprehensive mutational analysis demonstrated significant associations with brain metastatic progression in the studied cohort. Conclusion: This study provides novel biomarkers from a unique perspective for the diagnosis, prognosis, and development of molecular-targeted therapies or immunotherapies for brain metastasis in NSCLC.

Indexed as

biomarkerbrain metastasisgenenon-small-cell lung cancersignaling pathway

Identifiers

PMID41048344
PMCPMC12488587

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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.