ArticleDiscover oncology2024
Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis.
Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Integrated Bulk and Single-Cell Transcriptomic Analysis Reveals Mitochondrial Transporter Gene Programs in Human Spermatogonial Stem Cells.Stem cell reviews and reports · 2026Article
- Identifying Mouse Undifferentiated To Differentiated Spermatogonia Stem Cells at the Single-Cell Level Using Machine Learning Approaches.Stem cell reviews and reports · 2026Article
- Machine learning, whole-transcriptome and integrative omics analysis reveals key regulatory networks governing human spermatogonial stem cells.Clinical and experimental medicine · 2026Article
- Analysis of microarray and single-cell RNA-seq finds gene co-expression, cell-cell communication, and tumor environment associated with cytoskeleton protein in epithelial-mesenchymal transition in ovarian cancer.Discover oncology · 2026Article
- Haplotype GWAS in Colorectal Cancer Patients with a Family History of Gastric or Prostate Cancer.International journal of molecular sciences · 2026Article
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- Analysis of Microarray and Single-Cell RNA-Seq Finds Gene Co-Expression and Tumor Environment Associated with Extracellular Matrix in Epithelial-Mesenchymal Transition in Prostate Cancer.International journal of molecular sciences · 2025Article
- Transcriptomic Analysis Identifies Oxidative Stress-Related Hub Genes and Key Pathways in Sperm Maturation.Antioxidants (Basel, Switzerland) · 2025Article
- Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment.Scientific reports · 2025Article
- Unique Biological Characteristics of Patients with High Gleason Score and Localized/Locally Advanced Prostate Cancer Using an In Silico Translational Approach.Current oncology (Toronto, Ont.) · 2025Article
- Role of Defense/Immunity Proteins in Non-Obstructive Azoospermia: Insights from Gene Expression and Single-Cell RNA Sequencing Analyses.Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
- Reduction of Prostate Cancer Risk: Role of Frequent Ejaculation-Associated Mechanisms.Cancers · 2025Review
- Identification of novel cytoskeleton protein involved in spermatogenic cells and sertoli cells of non-obstructive azoospermia based on microarray and bioinformatics analysis.BMC medical genomics · 2025Article
- Advances in the identification of novel cell signatures in benign prostatic hyperplasia and prostate cancer using single-cell RNA sequencing.Frontiers in immunology · 2025Review
- Association of systemic inflammatory biomarkers with prostate cancer risk: a population-based (NHANES) and clinical validation study.Frontiers in endocrinology · 2025Article
- Integrating microarray data and single-cell RNA-seq reveals correlation between kit and nmyc in mouse spermatogonia stem cell population.Frontiers in cell and developmental biology · 2025Article
- Integrating Microarray Data and Single-Cell RNA-Seq Reveals Key Gene Involved in Spermatogonia Stem Cell Aging.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways.
methodsIn order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA).
resultsA combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients.
conclusionsIn summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.
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