Identification of key genes in prostate cancer gene expression profile by bioinformatics

Lü, Wei; Ding, Zhe

doi:10.1111/and.13169

Cited by 20 publications

(35 citation statements)

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“…The algorithm developed by Roethke et al was comparable in accuracy to human analyses, although this study used a small sample size and the older version of the PI-RADS scoring system [63]. Identification of new molecular drug targets is key to the development of effective treatments for advanced PCa [64]. A recent study combining four profiles from the Gene Expression Omnibus identified epithelial cell adhesion molecule (EPCAM), twist family basic helix-loop-helix transcription factor 1 (TWIST1), CD38, and vascular endothelial growth factor A (VEGFA) as hub genes which may be potential therapeutic targets in PCa.…”

Section: Future Directionsmentioning

confidence: 99%

“…A recent study combining four profiles from the Gene Expression Omnibus identified epithelial cell adhesion molecule (EPCAM), twist family basic helix-loop-helix transcription factor 1 (TWIST1), CD38, and vascular endothelial growth factor A (VEGFA) as hub genes which may be potential therapeutic targets in PCa. Promising results have been demonstrated with agents targeting CD38 and VEGFA in the treatment of myeloma and renal carcinoma, respectively [64]. These agents may also be applied for treatment of PCa, upon further validation of these targets and is worth investigating.…”

Section: Future Directionsmentioning

confidence: 99%

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Early Diagnosis of Prostate Cancer from the Perspective of Chinese Physicians

Zhou

2020

J. Cancer

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Prostate cancer (PCa) is the seventh most diagnosed cancer and the tenth leading cause of cancer mortality in China. Unlike the USA, both incidence and mortality continue to increase. In China, PCa is often diagnosed at a locally advanced or metastatic stage, resulting in a high mortality-to-incidence ratio. Implementing regular screening using a well-validated biomarker may result in the earlier diagnosis of localized disease. Furthermore, it is important to be able to distinguish between low-grade and high-grade disease, to avoid subjecting patients to unnecessary biopsies, undertreatment of significant disease, or overtreatment of indolent disease. While prostate-specific antigen (PSA) is commonly used in PCa screening around the world, its relationship to PCa is still unclear and results vary widely across different studies. New biomarkers, imaging techniques and risk predictive models have been developed in recent years to improve upon the accurate detection of high-grade PCa. Blood-and urine-based biomarkers, such as PSA isoforms, prostate cancer antigen 3, or mRNA transcripts, have been used to improve the detection of high-grade PCa. These markers have also been used to create risk predictive models, which can further improve PCa detection. Furthermore, multiparametric magnetic resonance imaging is becoming increasingly accessible for the detection of PCa. Because of ethnic variations, biomarkers and risk predictive models validated in Western populations cannot be directly applied to Chinese men. Validation of new biomarkers and risk predictive models in the Chinese population may improve PCa screening and reduce mortality of this disease in China.

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Section: Future Directionsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Early Diagnosis of Prostate Cancer from the Perspective of Chinese Physicians

Zhou

2020

J. Cancer

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“…As shown in Table 6, the literature search in Pubmed/GEO regarding gene expression profiles of PCa versus NP tissues yielded 10 relevant studies, 5 (Chen et al 2012;Endo et al 2009;Fan et al 2018;Fang et al 2017;He et al 2018) of which were based on a single GEO dataset whereas the other 5 studies (Lu 2019;Song et al 2019b;Tan et al 2019;Tong et al 2019;Zhao et al 2017) were integrated bioinformatic analyses based on multiple GEO datasets. The hub genes reported by the 10 eligible studies were extracted and compared with those identified in the present study.…”

Section: Validation Of Hub Genes Expression In Multiple Databasesmentioning

confidence: 99%

“…With the development of high-throughput sequencing technology and bioinformatic analysis methods, the Gene Expression Omnibus (GEO) online public database has been widely utilized to screen out differentially expressed genes (DEGs), to study molecular signals and their relations, and to aid in constructing gene regulatory networks (Clough & Barrett 2016). Up to now, by either analysis of a single dataset or integrated analysis of multiple datasets in GEO, several studies have dug out genes that exert important influence on the occurrence and progression of PCa, such as CDH1 (Fang et al 2017), CDCA8 (Zhao et al 2017), RPS21 (Fan et al 2018), PIK3R1 (He et al 2018), EPCAM (Lu 2019), LMNB1 (Song et al 2019b), IGF2 (Tan et al 2019), and IKZF1 (Tong et al 2019). However, the key genes detected by the above studies are largely different from each other and had little in common, and such discrepancy could be attributed to the fact that PCa is PeerJ reviewing PDF | (2019:06:38928:2:0:NEW 4 Sep 2019)…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

2019

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Background:Prostate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method. Methods: Differentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The UCSC Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts.Results: Totally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in 'oxidation-reduction process' and 'positive regulation of transcription from RNA polymerase II promoter'; KEGG pathway analysis enriched mostly in 'metabolic pathways' and 'protein digestion and absorption'. KLK3, CDH1, KLK2, FOXA1, and EPCAM were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant GEO datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients.Conclusions: This study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.

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“…Bioinformatics analysis based on high-throughput platform microarray technology has been extensively used to predict biomarkers of cancers over the last few decades (11)(12)(13). numerous gene expression microarrays have been used to identify potential target genes and their functions in Pca (14)(15)(16). However, the aforementioned studies focused on gene expression microarrays, the number of which is limited, preventing the accurate identification of target genes and their functions in Pca.…”

Section: Introductionmentioning

confidence: 99%

Identification of aberrantly methylated‑differentially expressed genes and gene ontology in prostate cancer

Wang

Wang²,

Quan

2019

Mol Med Report

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Prostate cancer (Pca) is the most frequent urological malignancy in men worldwide. dna methylation has an essential role in the etiology and pathogenesis of Pca. The purpose of the present study was to identify the aberrantly methylated-differentially expressed genes and to determine their potential roles in Pca. The important node genes identified were screened by integrated analysis. Gene expression microarrays and gene methylation microarrays were downloaded and aberrantly methylated-differentially expressed genes were obtained. enrichment analysis and protein-protein interactions (PPi) were obtained, their interactive and visual networks were created, and the node genes in the PPi network were validated. a total of 105 hypomethylation-high expression genes and 561 hypermethylation-low expression genes along with their biological processes were identified. The top 10 node genes obtained from the PPI network were identified for each of the two gene groups. The methylation and gene expression status of node genes in TCGA database, GEPIA tool, and the HPa database were generally consistent with those of our results. In conclusion, the present study identified 20 aberrantly methylated-differentially expressed genes in Pca by combining bioinformatics analyses of gene expression and gene methylation microarrays, and concurrently, the survival of these genes was analyzed. notably, methylation is a reversible biological process, which makes it of great biological significance for the diagnosis and treatment of prostate cancer using bioinformatics technology to determine abnormal methylation gene markers. The present study provided novel therapeutic targets for the treatment of Pca.

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Identification of key genes in prostate cancer gene expression profile by bioinformatics

Cited by 20 publications

References 37 publications

Early Diagnosis of Prostate Cancer from the Perspective of Chinese Physicians

Early Diagnosis of Prostate Cancer from the Perspective of Chinese Physicians

Peer Review #1 of "Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods (v0.1)"

Identification of aberrantly methylated‑differentially expressed genes and gene ontology in prostate cancer

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