<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>Srinivas Koduru</submitter><organism>Homo sapiens</organism><software>PartekFlow</software><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-14952</full_dataset_link><description>Prostate cancer (PCa) is a major health problem worldwide with variable incidence, progression and outcomes depending on genetic, environmental and socio-economic factors. This study compares gene expression profiles in PCa patients from South Africa (RSA) and the United States (USA) using RNA sequencing in whole blood and pathway analyses. Whole blood samples were collected in Wren RNA stabilization tubes from RSA-PCa (n=6), RSA-controls (n=6), USA-PCa (n=7) and USA-Controls (n=11). RNA sequencing revealed 1,627 differentially expressed genes (DEGs) in RSA-PCa vs. RSA-controls, and 2,193 DEGs in USA-PCa vs. USA-Controls. Pathway analyses identified geographical region-specific variations; RSA-PCa had upregulated myeloid suppressor cell pathways and immunosuppressive markers while USA-PCa samples exhibited upregulated cytokine signaling and inflammatory pathways. Comparative analysis of healthy controls revealed 2,280 DEGs, which indicated significant differences in molecular profile of the geographic locations. qRT-PCR undertaken on 27 biomarkers related to PCa in whole blood (PROSTest) identified that 26 (96%) of the marker genes were commonly expressed. RNAseq and normalized PCR gene expression of these markers were well-correlated (r=0.44, p=0.0012, n=30 pairs). The results of this study indicate that there are geographic differences in blood-based gene expression in both controls and individuals with PCa. Genes associated with a clinically validated molecular assay (PROSTest) were identified in both populations, but significant differences in gene expression relevant to tumor pathobiology were identified. These immune-associated signaling pathways suggest differences between these two cohorts in blood-based molecular architecture related to PCa. They also suggest the need to consider population-specific biomarkers to better understand this disease. Ultimately, optimizing blood-based molecular diagnostic and therapeutic approaches will require population-level studies.  Introduction</description><repository>biostudies-arrayexpress</repository><sample_protocol>Sample Collection - Whole blood was collected by prospect study in USA and RSA.</sample_protocol><sample_protocol>Library Construction - 50 ng of total RNA using oligo-dT beads and fragmented by incubation at 94C in the presence of Mg2+ (Kapa mRNA Hyper Prep).</sample_protocol><sample_protocol>Sequencing - Sample concentrations were normalized to 1.2 nM and loaded onto an Illumina NovaSeq flow cell at a density targeting 2 million passing filter clusters per sample. Sequencing was performed using 100 bp paired-end reads in accordance with Illumina protocols. A 10 bp unique dual index was used for sample identification. Positive control libraries (PhiX, 0.3%) were spiked in to monitor sequencing quality.</sample_protocol><sample_protocol>Nucleic Acid Extraction - Trizol method used to isolate total RNA from whole blood</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>QiaCube MDx</instrument_platform><instrument_platform>Illumina NovaSeq 6000</instrument_platform><instrument_platform>None</instrument_platform><study_type>RNA-seq of coding RNA</study_type><species>Homo sapiens</species><pubmed_authors>Srinivas Koduru</pubmed_authors></additional><is_claimable>false</is_claimable><name>Blood-based Prostate Cancer Transcriptomics: A Comparative Study Between South African and American Cohorts</name><description>Prostate cancer (PCa) is a major health problem worldwide with variable incidence, progression and outcomes depending on genetic, environmental and socio-economic factors. This study compares gene expression profiles in PCa patients from South Africa (RSA) and the United States (USA) using RNA sequencing in whole blood and pathway analyses. Whole blood samples were collected in Wren RNA stabilization tubes from RSA-PCa (n=6), RSA-controls (n=6), USA-PCa (n=7) and USA-Controls (n=11). RNA sequencing revealed 1,627 differentially expressed genes (DEGs) in RSA-PCa vs. RSA-controls, and 2,193 DEGs in USA-PCa vs. USA-Controls. Pathway analyses identified geographical region-specific variations; RSA-PCa had upregulated myeloid suppressor cell pathways and immunosuppressive markers while USA-PCa samples exhibited upregulated cytokine signaling and inflammatory pathways. Comparative analysis of healthy controls revealed 2,280 DEGs, which indicated significant differences in molecular profile of the geographic locations. qRT-PCR undertaken on 27 biomarkers related to PCa in whole blood (PROSTest) identified that 26 (96%) of the marker genes were commonly expressed. RNAseq and normalized PCR gene expression of these markers were well-correlated (r=0.44, p=0.0012, n=30 pairs). The results of this study indicate that there are geographic differences in blood-based gene expression in both controls and individuals with PCa. Genes associated with a clinically validated molecular assay (PROSTest) were identified in both populations, but significant differences in gene expression relevant to tumor pathobiology were identified. These immune-associated signaling pathways suggest differences between these two cohorts in blood-based molecular architecture related to PCa. They also suggest the need to consider population-specific biomarkers to better understand this disease. Ultimately, optimizing blood-based molecular diagnostic and therapeutic approaches will require population-level studies.  Introduction</description><dates><release>2025-05-01T00:00:00Z</release><modification>2025-03-20T12:00:32.166Z</modification><creation>2025-03-20T12:00:32.166Z</creation></dates><accession>E-MTAB-14952</accession><cross_references><ENA>ERP170603</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>