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A Personalized Genomics Approach of the Prostate Cancer.
- Source :
-
Cells [Cells] 2021 Jun 30; Vol. 10 (7). Date of Electronic Publication: 2021 Jun 30. - Publication Year :
- 2021
-
Abstract
- Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary-"A", and two secondary-"B" & "C") cancer nodules and the surrounding normal tissue ("N") from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the "A", "B", "C", and "N" regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.
- Subjects :
- Aged
Apoptosis genetics
Cell Proliferation genetics
Cell Survival genetics
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Genes, Neoplasm
Genetic Therapy
Genomics
Humans
Male
Prostatic Neoplasms pathology
Prostatic Neoplasms therapy
Signal Transduction genetics
Tumor Suppressor Protein p53 metabolism
Precision Medicine
Prostatic Neoplasms genetics
Subjects
Details
- Language :
- English
- ISSN :
- 2073-4409
- Volume :
- 10
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Cells
- Publication Type :
- Academic Journal
- Accession number :
- 34209090
- Full Text :
- https://doi.org/10.3390/cells10071644