ArticleCancer biomarkers : section A of Disease markers2021
Identification of immune cell infiltration pattern and related critical genes in metastatic castration-resistant prostate cancer by bioinformatics analysis.
Article in Cancer biomarkers : section A of Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 10 citations in OpenAlex.
- Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis.Discover oncology · 2024Article
- High Expression of PDLIM2 Predicts a Poor Prognosis in Prostate Cancer and Is Correlated with Epithelial-Mesenchymal Transition and Immune Cell Infiltration.Journal of immunology research · 2022Article
- The most effective but largely ignored target for prostate cancer early detection and intervention.Journal of Cancer · 2022Article
- Review
- Diagnostic Biomarkers and Immune Infiltration in Patients With T Cell-Mediated Rejection After Kidney Transplantation.Frontiers in immunology · 2021Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetastatic castration-resistant prostate cancer (mCRPC) is the lethal stage of prostate cancer and the main cause of morbidity and mortality, which is also a potential target for immunotherapy.
methodIn this study, using the Approximate Relative Subset of RNA Transcripts (CIBERSORT) online method, we analysed the immune cell abundance ratio of each sample in the mCRPC dataset. The EdgeR (an R package) was used to classify differentially expressed genes (DEGs). Using the Database for annotation, visualisation and interactive exploration (DAVID) online method, we performed functional enrichment analyses. STRING online database and Cytoscape tools have been used to analyse protein-protein interaction (PPI) and classify hub genes.
resultsThe profiles of immune infiltration in mCRPC showed that Macrophages M2, Macrophages M0, T cells CD4 memory resting, T cells CD8 and Plasma cells were the main infiltration cell types in mCRPC samples. Macrophage M0 and T cell CD4 memory resting abundance ratios were correlated with clinical outcomes. We identified 1102 differentially expressed genes (DEGs) associated with the above two immune cells to further explore the underlying mechanisms. Enrichment analysis found that DEGs were substantially enriched in immune response, cell metastasis, and metabolism related categories. We identified 20 hub genes by the protein-protein interaction network analysis. Further analysis showed that three critical hub genes, CCR5, COL1A1 and CXCR3, were significantly associated with prostate cancer prognosis.
conclusionOur findings revealed the pattern of immune cell infiltration in mCRPC, and identified the types and genes of immune cells correlated with clinical outcomes. A new theoretical basis for immunotherapy may be given by our results.
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