Identification of potential key genes and high-frequency mutant genes in prostate cancer by using RNA-Seq data

Zhang, Ze; He, Wei; Zhou, Hong; Gu, Yue; Bai, Yufeng; Yu, Song; An, Ruihua; Qi, Jiping

doi:10.3892/ol.2018.7846

Cited by 6 publications

(6 citation statements)

References 41 publications

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“…Studies suggest FOLH1B may play an important role in the development and progression of prostate cancer [47]. Interestingly, FOLH1B is expressed in kidney and liver, but not in any other normal tissue, including prostate [48].…”

Section: The Discovered Age Markers For Semenmentioning

confidence: 99%

Epigenetic age prediction in semen – marker selection and model development

Pisarek

Pośpiech

Heidegger

et al. 2021

Aging

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DNA methylation analysis is becoming increasingly useful in biomedical research and forensic practice. The discovery of differentially methylated sites (DMSs) that continuously change over an individual's lifetime has led to breakthroughs in molecular age estimation. Although semen samples are often used in forensic DNA analysis, previous epigenetic age prediction studies mainly focused on somatic cell types. Here, Infinium MethylationEPIC BeadChip arrays were applied to semen-derived DNA samples, which identified numerous novel DMSs moderately correlated with age. Validation of the ten most age-correlated novel DMSs and three previously known sites in an independent set of semen-derived DNA samples using targeted bisulfite massively parallel sequencing, confirmed age-correlation for nine new and three previously known markers. Prediction modelling revealed the best model for semen, based on 6 CpGs from newly identified genes SH2B2, EXOC3, IFITM2, and GALR2 as well as the previously known FOLH1B gene, which predict age with a mean absolute error of 5.1 years in an independent test set. Further increases in the accuracy of age prediction from semen DNA will require technological progress to allow sensitive, simultaneous analysis of a much larger number of age correlated DMSs from the compromised DNA typical of forensic semen stains.

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Section: The Discovered Age Markers For Semenmentioning

confidence: 99%

Epigenetic age prediction in semen – marker selection and model development

Pisarek

Pośpiech

Heidegger

et al. 2021

Aging

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show abstract

“…For example, among the top 10 up-regulated genes, KLK3 and KLK2, are highly enriched in prostate cancer, which are taken as effective biomarkers for diagnose and prognostic monitoring of prostate cancer ( 23 ). GOLM1 ( 24 ), FOLH1B ( 25 ), STEAP1 ( 26 ) and PLPP1 ( 27 ) are also identified as a candidate biomarker for prostate cancer. AGR2 expresses strongly in prostate tissue and show increased expression in prostate cancer ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Gene Signatures Associated With Bone Metastasis in Castration-Resistant Prostate Cancer Using Co-Expression Analysis

Zou²,

Wang

et al. 2021

Front. Oncol.

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About 80–90% of castration-resistant prostate cancer (CRPC) patients would develop bone metastasis. However, the molecular mechanisms of bone metastasis are still not clear. This study aimed to detect the differences between the tumor and normal samples in bone after metastatic colonization. Four transcriptional datasets (GSE32269, GSE101607, GSE29650, and GSE74685) were obtained from the GEO database. 1983 differentially expressed genes (DEGs) were first identified between tumor and normal marrow samples in GSE32269. Most of the top 10 up-regulated DEGs are related with prostate cancer, and the top 10 down-regulated DEGs are mainly related with bone development. Seven co-expression modules were then detected based on the 1469 DEGs shared by the four datasets. Three of them were found highly preserved among the four datasets. Enrichment analysis showed that the three modules were respectively enriched in Cell adhesion molecules (CAMs), Leukocyte transendothelial migration and cell cycle, which might play significantly important roles in the tumor development in bone marrow. Ten, 17, and 99 hub genes for each module were then identified. And four genes (C3AR1, IL10RA, LY86, and MS4A6A) were detect to be tightly related to progression of bone metastatic CRPC. ROC curve was plotted and AUC was calculated to distinguish tumor and normal bone marrow samples as well as bone and non-bone metastatic CRPCs. The present study identified key genes and modules involved in bone metastatic CRPCs, which may provide new insights and biomarkers for understanding of the molecular mechanisms of bone metastatic CRPC.

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“…For example, among the top 10 up-regulated genes, KLK3 and KLK2, are highly enriched in prostate cancer, which are taken as effective biomarkers for diagnose and prognostic monitoring of prostate cancer [16]. GOLM1 [17], FOLH1B [18], STEAP1 [19] and PLPP1 [20] are also identified as a candidate biomarker for prostate cancer. AGR2 expressed strongly in prostate tissue and show increased expression in prostate cancer [21].…”

Section: Discussionmentioning

confidence: 99%

Candidate biomarkers and gene modules investigation for bone tumor samples derived from castration-resistant prostate cancer bone metastasis patients using WGCNA

Yu¹,

Zou²,

Wang³

et al. 2019

Preprint

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Background About 80-90% of castration-resistant prostate cancer (CRPC) patients would develop bone metastasis, which leads to the disorder of bone metabolism and induces skeletal related events. However, except for the few approved radiotherapeutic and chemotherapy drugs, like radium-223 and denosumab, there is still lack of effective treatment targeting the bone metastatic tumor. It is necessary and significant to explore the mechanisms of bone metastasis and tumorigenesis, especially the differences between the tumor and normal cells in bone after metastatic colonization, which will provide a set of candidate genes for the screening of novel bone targeting agents.Results 4 datasets (GSE32269, GSE101607, GSE29650 and GSE74685) were obtained from the GEO database. 1983 differentially expressed genes (DEGs) were first identified between bone marrow tumor samples and normal marrow samples in GSE32269, followed by the weighted gene co-expression analysis. Most of the top 10 DEGs are found to be related with prostate cancer. 7 co-expression modules were then detected based on the 1469 DEGs shared by the 4 datasets, and 3 of them were found highly preserved among the other three datasets. The top 30 hub genes of the 3 modules were extracted. Among the enriched pathways of preserved modules, Cell adhesion molecules (CAMs) and Leukocyte transendothelial migration might play significant important roles in the tumor development in bone marrow. Literature searches further showed that a set of DEGs and hub genes might also contribute to the development of tumor in bone.Conclusions Together, our findings not only provide outline of expression profile in CRPC bone metastasis, but also screen a set of genes associated with CPRC tumor cell colonization and development of bone tumor, which could be helpful for novel bone targeting agents screening.

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Identification of potential key genes and high-frequency mutant genes in prostate cancer by using RNA-Seq data

Cited by 6 publications

References 41 publications

Epigenetic age prediction in semen – marker selection and model development

Epigenetic age prediction in semen – marker selection and model development

Identification of Key Gene Signatures Associated With Bone Metastasis in Castration-Resistant Prostate Cancer Using Co-Expression Analysis

Candidate biomarkers and gene modules investigation for bone tumor samples derived from castration-resistant prostate cancer bone metastasis patients using WGCNA

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