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pmid:35372462
Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis.
Xiangdong Liang · Yanchao Wang · Long Pei · Xiaoliang Tan · Chunhui Dong
Frontiers in surgery · 2022 doi:10.3389/fsurg.2022.856446
Paper
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Experiment
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Data
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Analysis
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Execution
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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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Complete file
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Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.\nMethods: High-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.\nResults: A total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.\nConclusion: This new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.", "claims": [{"id": "claim-method", "statement": "The RNA expression profile was analyzed by the limma R package (30).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35372462", "locator": "sections[4] \"Identification of Differential Expression Genes (DEGs) in PCa\" paragraph 2", "quote": "The RNA expression profile was analyzed by the limma R package (30)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35372462", "locator": "sections[18] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The limma-based differential-expression analysis identified 841 DEGs, including 269 upregulated and 572 downregulated DE-pcRNAs, and yielded 385 common DEGs after intersection with TCGA DEGs.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Firstly, in order to find out the difference in genetic expression between PC tissue and normal tissue, DEGs were identified based on the RNA-Seq dataset (GSE46602) (p < 0.05 and log2 FC > 1.5)."}, "evidence": [{"asset": "source:pmid:35372462", "locator": "sections[11] \"Identification of Differentially Expressed Genes\" paragraph 2", "quote": "Firstly, in order to find out the difference in genetic expression between PC tissue and normal tissue, DEGs were identified based on the RNA-Seq dataset (GSE46602) (p < 0.05 and log2 FC > 1.5)."}, {"asset": "source:pmid:35372462", "locator": "sections[11] \"Identification of Differentially Expressed Genes\" paragraph 2", "quote": "As seen in Figure 1A, a total of 841 DE-pcRNAs were identified, which include 269 upregulated DEGs and 572 downregulated DE-pcRNAs."}, {"asset": "source:pmid:35372462", "locator": "sections[11] \"Identification of Differentially Expressed Genes\" paragraph 2", "quote": "Besides, we also screened out the TCGA database for DEGs in PCa and took the intersection with DEGs in the GEO database. 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