ArticleDisease markers2022
Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Prostate Cancer.
Article in Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- RAD18 facilitates cancer progression and immunosuppression via the AKT/mTOR/c-Myc axis: a multi-omics analysis.NPJ precision oncology · 2026Article
- Applications and prospects of spatial transcriptomics in prostate cancer research: A narrative review.Current urology · 2025Article
- Research progress on the role of the NEIL family in cancer.Frontiers in cell and developmental biology · 2025Review
- Review
- Induction of senescence upon loss of the Ash2l core subunit of H3K4 methyltransferase complexes.Nucleic acids research · 2022Article
- Stemness-related gene signature for predicting therapeutic response in patients with esophageal cancer.Translational cancer research · 2022Article
Corrections and comments
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Authors and funding
18 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: We planned to uncover the cancer stemness-related genes (SRGs) in prostate cancer (PCa) and its underlying mechanism in PCa metastasis. Methods: We acquired the RNA-seq data of 406 patients with PCa from the TCGA database. Based on the mRNA stemness index (mRNAsi) calculated by one-class logistic regression (OCLR) algorithm, SRGs in PCa were extracted by WGCNA. Univariate and multivariate regression analyses were applied to uncover OS-associated SRGs. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Pearson's correlation analysis were performed to discover the possible mechanism of PCa metastasis. The significantly correlated transcription factors of OS-associated SRGs were also identified by Pearson's correlation analysis. ChIP-seq was applied to validate the binding relationship of TFs and OS-associated SRGs and spatial transcriptome and single-cell sequencing were performed to uncover the location of key biomarkers expression. Lastly, we explored the specific inhibitors for SRGs using CMap algorithm. Results: We identified 538 differentially expressed genes (DEGs) between non-metastatic and metastatic PCa. Furthermore, OS-associated SRGs were identified. The Pearson correlation analysis revealed that FOXM1 was significantly correlated with NEIL3 (correlation efficient =0.89, Conclusion: We constructed a regulatory network based on SRGs associated with PCa metastasis and explored possible mechanism.
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Registered trials
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.