ArticleOncology letters2018
Gene expression profile analysis of the bone microenvironment in patients with spinal metastases.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Artificial Intelligence for Non-Invasive Prediction of Molecular Signatures in Spinal Metastases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Game-changing insights on vertebral skeletal stem cells in bone metastasis and therapeutic horizons.Oncology research · 2023Article
- Construction and validation of web-based nomograms for detecting and prognosticating in prostate adenocarcinoma with bone metastasis.Scientific reports · 2022Article
- Review
- Genome-wide expression profiling of glioblastoma using a large combined cohort.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aimed to identify the underlying molecular mechanisms associated with spinal metastases. Gene expression profiles in cancellous bone samples from the spines of five patients with spinal metastases, with different primary cancers, and three normal control patients were measured using microarray analysis and subsequently compared. The differentially expressed genes (DEGs) identified were filtered using bioinformatics analyses followed by cluster analysis, gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, a protein-protein interaction network was constructed and analyzed. A total of 152 upregulated and 388 downregulated DEGs were identified. The cluster analysis demonstrated a marked difference between the gene expression profiles of samples from patients with spinal metastases and those from normal patients. The GO terms enriched in the upregulated DEGs were associated with cell death, and those enriched in the downregulated DEGs were associated with the cell cycle. The upregulated DEGs were enriched in signaling pathways associated with tight junctions, and the downregulated DEGs were enriched in signaling pathways associated with porphyrin metabolism. In the PPI network constructed, transcription factor AP-1 and proliferating cell nuclear antigen had the highest connectivity degrees with the upregulated and downregulated DEGs, respectively. The gene expression profile data from the present study provides new insights into the underlying molecular mechanisms of spinal metastases, and will aid in the development of novel anticancer treatments.
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Registered trials
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