Identification of PTPRR and JAG1 as key genes in castration-resistant prostate cancer by integrated bioinformatics methods^#

Identification of PTPRR and JAG1 as key genes in castration-resistant prostate cancer by integrated bioinformatics methods # 通过生物信息学方法鉴定 PTPRR 和 JAG1 为去势抵抗性前列腺癌(CRPC)的关键基因 1 2 first-author 1 2 1 2 corresp ‡Corresponding Author ‡Corresponding Author †E-mail:gpren2002@163.com †E-mail:gpren2002@163.com gpren2002@163.com Department of Pathology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China Department of Pathology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China Department of Pathology and Pathophysiology, School of Medicine, Zhejiang University, Hangzhou 310058, China Department of Pathology and Pathophysiology, School of Medicine, Zhejiang University, Hangzhou 310058, China To identify novel genes in castration-resistant prostate cancer (CRPC), we downloaded three microarray datasets containing CRPC and primary prostate cancer in Gene Expression Omnibus (GEO). R packages affy and limma were performed to identify differentially expressed genes (DEGs) between primary prostate cancer and CRPC. After that, we performed functional enrichment analysis including gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway. In addition, protein–protein interaction (PPI) analysis was used to search for hub genes. Finally, to validate the significance of these genes, we performed survival analysis. As a result, we identified 53 upregulated genes and 58 downregulated genes that changed in at least two datasets. Functional enrichment analysis showed significant changes in the positive regulation of osteoblast differentiation pathway and aldosterone-regulated sodium reabsorption pathway. PPI network identified hub genes like cortactin-binding protein 2 ( CTTNBP2 ), Rho family guanosine triphosphatase (GTPase) 3 ( RND3 ), protein tyrosine phosphatase receptor-type R ( PTPRR ), Jagged1 ( JAG1 ), and lumican ( LUM ). Based on PPI network analysis and functional enrichment analysis, we identified two genes ( PTPRR and JAG1 ) as key genes. Further survival analysis indicated a relationship between high expression of the two genes and poor prognosis of prostate cancer. In conclusion, PTPRR and JAG1 are key genes in the CRPC, which may serve as promising biomarkers of diagnosis and prognosis of CRPC. 目的 2 鉴定去势抵抗性前列腺癌(CRPC)的关键基因. 创新点 2 (1)结合多个数据库数据,运用生物信息学方法鉴定CRPC的关键基因;(2)首次报道 PTPRR 可能在CRPC里起关键作用. 方法 2 通过下载三个GEO数据库的mRNA微阵列数据,分析CRPC和激素敏感前列腺癌之间的基因差异,对筛选出的差异基因进行功能富集分析和蛋白质间相互作用分析,最终筛选出两个有重要功能的差异基因( PTPRR 和 JAG1 ).通过在多个其他数据库中进行表达量验证和生存分析,进一步证明这些基因的重要作用. 结论 2 PTPRR 和 JAG1 在CRPC中显著增高,并与预后差相关.因此,这两个基因有可能作为CRPC的诊断和预后的生物标志物. Bioinformatics Protein tyrosine phosphatase receptor-type R ( PTPRR ) Jagged1 ( JAG1 ) Differentially expressed genes (DEGs) Castration-resistant prostate cancer (CRPC) Functional enrichment 生物信息学 PTPRR JAG1 差异表达基因 激素抵抗前列腺癌 功能富集 Introduction 1 Prostate cancer (PCa) is one of the most commonly diagnosed cancers in the world. According to Cancer Statistics 2019, PCa became the first among estimated new cases of cancer in men and the second among estimated reasons behind deaths in men (Siegel et al., 2019 2019 ). Similarly, the incidence rates and mortality rates of PCa have been continuously rising in China (Chen et al., 2016 2016 ). Thus, PCa has received considerable critical attention. One of the main challenges of PCa is its treatment, especially the treatment of castration-resistant PCa (CRPC). CRPC has poor prognosis due to its aggressive behavior. It has been found that even PCa patients who initially responded well to the androgen deprivation therapy, they will eventually develop CRPC and the outcomes are not satisfactory (Heidenreich et al., 2014 2014 ). Thus, a key issue in PCa is the identification of biomarkers and treatment targets for CRPC. Recently, many mechanisms of castration resistance have been proposed, including de novo or cytochrome P450 family-induced synthesis of androgen, upregulation of androgen metabolism-related genes, and dysregulation of tumor microenvironment (Watson et al., 2015 2015 ). Despite acknowledgement of these mechanisms, treatment of CRPC remains a challenge (Heidenreich et al., 2014 2014 ). Thus, a better understanding of CRPC and identification of its biomarkers or targets, which may open a new window of opportunity for combating the disease, are urgently needed (Attard et al., 2016 2016 ). Previous studies have identified numerous biomarkers and targets in CRPC by microarrays. For example, D'Antonio et al. ( 2008 2008 ) compared androgen-independent PCa cells with androgen-dependent LNCaP cells, to identify pathways to androgen independence. Terada et al. ( 2010 2010 ) compared gene expression between a mouse xenograft model of PCa with its later CRPC and identified prostaglandin E2 receptor subtype 4 ( EP4 ) as a central gene of CRPC. These microarray data led us to think the possibility of using combined datasets and integrated bioinformatics to explore the molecular and cellular characteristics of CRPC. To our knowledge, there are few studies that have used bioinformatics to identify gene alterations between primary PCa and CRPC. In this study, we used three microarray datasets in Gene Expression Omnibus (GEO, https:// www.ncbi www.ncbi . nlm.nih.gov/geo) with the R packages affy and limma to identify differentially expressed genes (DEGs) between CRPC and primary PCa. Furthermore, functional enrichment analysis, protein–protein interaction (PPI) network, and survival analysis provided us with more information about CRPC. In brief, we identified protein tyrosine phosphatase receptor-type R ( PTPRR ) and Jagged1 ( JAG1 ) as key genes in CRPC, which may serve as promising biomarkers for diagnosis and prognosis, even as therapeutic targets of CRPC. Materials and methods 1 Microarray data 2 Three gene expression profiles (GSE8702, GSE21887, and GSE33316) were obtained from the GEO. These gene expression profiles include cell line (LNCaP from GSE8702) (D'Antonio et al., 2008 2008 ) and xenograft models (KUCaP-2 from GSE21887 and LuCaP35 from GSE33316) (Terada et al., 2010 2010 ; Sun et al., 2012 2012 ), which could mimic CRPC (Table 1 1 ). In addition, the platform of the three datasets was GPL570, which means that these microarrays detected the same gene pools, providing the comparability of these datasets. GEO: Gene Expression Omnibus; PMID: PubMed unique identifier; CRPC: castration-resistant prostate cancer Accession/ID PMID CRPC group Control group GSE8702 18302219 (D'Antonio et al., 2008 2008 ) Androgen deprived for 11 or 12 months Androgen deprived for three weeks or one month GSE21887 20145136 (Terada et al., 2010 2010 ) Castration resistance in xenograft mice Androgen-dependent xenograft mice GSE33316 22108827 (Sun et al., 2012 2012 ) Androgen-deprived LuCaP35 xenografts in castrated mice LuCaP35 xenografts from non-treated mice Identification of DEGs 2 R software (Version 3.5.1; https:// www.r-project www.r-project . org) was used for the identification of DEGs. We downloaded the raw data from GEO (GSE8702, GSE21887, and GSE33316), normalized them, and transformed them into expression values by the affy package of Bioconductor ( http://www.bioconductor.org http://www.bioconductor.org ) (Gautier et al., 2004 2004 ). Then we used empirical Bayes methods to identify DEGs between CRPC and primary androgen-dependent PCa with limma package (Ritchie et al., 2015 2015 ). The criteria of DEGs were false discovery rate (FDR) < 0.05 and |log 2 (fold change)| > 1 (Su et al., 2018 2018 ). Then we used Venn diagrams to find the common altered genes (including increased and decreased genes) in the three datasets. Functional enrichment analysis of the DEGs 2 The Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid http://www.david.niaid . nih.gov) (Dennis et al., 2003 2003 ) is a useful tool for functional annotation of DEGs via four web-based analysis modules including gene ontology (GO) charts (Gene Ontology Consortium, 2004 2004 ) and Kyoto encyclopedia of genes and genomes (KEGG) charts (Kanehisa and Goto, 2000 2000 ; Zhu et al., 2019 2019 ). We used the DAVID database to perform GO and KEGG pathway analyses on DEGs that were upregulated or downregulated in at least two datasets. PPI network construction and analysis 2 Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; https://string-db.org) is used to construct PPI network, which shows physical and functional interactions (Szklarczyk et al., 2017 2017 ; Lin et al., 2018 2018 ). In this study, the protein pairs with combined scores of > 0.15 were selected for the PPI network construction. Further, the Cytoscape software (Version 3.6.1) (Shannon et al., 2003 2003 ) was utilized to calculate the node degree by Network Analyzer app and draw the PPI network with different colors and sizes, which present the regulation (up or down) and node degree, respectively. Expression validation and survival analysis of the key genes 2 Expression validation was performed in another GEO dataset (GSE70770-GPL10558), which contains 13 patients with CRPC and 206 patients with primary PCa (the clinical and pathological characteristics were shown in Table S1 S1 ). Survival analysis was performed in Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/index.html http://gepia.cancer-pku.cn/index.html ) (Tang et al., 2017 2017 ), an interactive web application for gene expression and survival analysis based on The Cancer Genome Atlas (TCGA; http://www http://www . cancergenome.nih.gov) database (Weinstein et al., 2013 2013 ), which offers abundant clinical information from a huge PCa sample size (Guo et al., 2018 2018 ). Results 1 DEG identification 2 After performing DEG analysis with R package, we identified the DEGs in the CRPC samples compared with the primary PCa samples. The GSE8702 dataset harbors 616 upregulated genes and 1828 downregulated genes. The GSE21887 dataset identified 481 increased genes and 25 decreased genes, while the GSE33316 dataset found 149 genes with enhanced expression and 534 genes with suppressed expression. These DEGs are shown in volcano maps and heat maps (Figs. 1 1 and 2 2 ). Red dots represent genes with fold change≥2 and P < 0.05, green dots represent genes with fold change≤−2 and P < 0.05, and the other dots represent the rest of genes with no statistically significant change in expression. FC, fold change Red color represents upregulated genes (fold change≥2 and P < 0.05) and green color represents downregulated genes (fold change≤−2 and P < 0.05) Then, we performed Venn diagrams, which extracted 53 upregulated genes and 58 downregulated genes that changed in at least two datasets (Fig. 3 3 and Table 2 2 ). In addition, the expression of Rho family guanosine triphosphatase (GTPase) 3 ( RND3 ), neuroligin 4 X-linked ( NLGN4X ), and cyclic adenosine monophosphate (cAMP)-dependent protein kinase inhibitor α ( PKIA ) was enhanced, while only ephrin-B2 ( EFNB2 ) was decreased in all datasets. (a) Venn diagram of upregulated genes (fold change≥2 and P < 0.05). (b) Venn diagram of downregulated genes (fold change≤−2 and P < 0.05)  Alternation Genes Upregulated RND3 , NLGN4X , PKIA , FOXQ1 , CMPK2 , TMEM45A , C1orf168 , HLA-DRA , TPBG , MASTL , SPIN4 , SPDL1 , PSRC1 , SKA1 , LUM , RFWD3 , SACS , WBP5 , NUDT11 , SLC1A1 , ELOVL6 , ITM2A , NHS , SSTR5-AS1 , PVRL3 , CHAC2 , RHOBTB1 , DNAH5 , XDH , PTPRR , PTPRB , JAG1 , HAGLROS , TLR3 , NBEA , ZNF423 , MAP2 , SI , AGPAT9 , ADD3 , HOXA13 , DACH1 , FAM102B , CLUL1 , LINGO1 , SCGB1D2 , CTTNBP2 , RGS2 , CYP4X1 , APELA , ZBED9 , LOC286052 , FOXD1 Downregulated EFNB2 , TWIST1 , GUCY1A3 , CTBP1-AS , ADAMTS1 , CMTR2 , HSD11B2 , ABHD2 , FAM110C , SLC25A21 , PP14571 , CEBPD , SLC2A10 , HSPA2 , GCNT2 , SCNN1B , ACOT9 , NPNT , HIST1H2BE , TRIM6 , NR3C2 , LINC01139 , HHEX , CYB5B , KRT20 , ERO1LB , HIST1H4H , FZD3 , HIST1H1C , ZNF883 , PDLIM5 , RRN3 , CCNB3 , PFN2 , SLC16A9 , VLDLR , ZBTB16 , MAN2A1 , ADRB2 , GCHFR , RBPMS2 , KCTD9 , ELOVL2-AS1 , GADD45G , LOC100288675 , SLC16A6 , AIFM2 , ENDOD1 , ABAT , ZFYVE21 , GBA3 , BMPR1B , MAK , H2BFS , NANOS1 , EOGT , FXYD3 , CUX2 GO and KEGG pathway enrichment 2 To explore the functional enrichment of the identified DEGs, we submitted the upregulated genes and downregulated genes, as described in Table 2 2 , to the DAVID for functional pathway analysis including GO and KEGG, and the results are presented in Table 3 3 . GO-BP: gene ontology-biological process; GO-CC: gene ontology-cellular component; GO-MF: gene ontology-molecular function; KEGG: Kyoto encyclopedia of genes and genomes Pathway ID Pathway description Count P -value Genes GO-BP GO:0045669 Positive regulation of osteoblast differentiation 4 0.004 615 969 CEBPD , NPNT , JAG1 , BMPR1B GO:0001701 In utero embryonic development 5 0.020 901 560 MAN2A1 , HHEX , RRN3 , PTPRR , TWIST1 GO:0045892 Negative regulation of DNA-templated transcription 8 0.021 870 896 HHEX , CEBPD , TRIM6 , DACH1 , ZBTB16 , FOXD1 , TWIST1 , ZNF423 GO:0006334 Nucleosome assembly 4 0.029 188 620 H2BFS , HIST1H1C , HIST1H2BE , HIST1H4H GO:0060087 Relaxation of vascular smooth muscle 2 0.033 125 732 RGS2 , GUCY1A3 GO:0010389 Regulation of G2/M transition of mitotic cell cycle 2 0.049 279 109 CCNB3 , PKIA GO:2000737 Negative regulation of stem cell differentiation 2 0.049 279 109 TRIM6 , JAG1 GO-CC GO:0016324 Apical plasma membrane 7 0.004 174 384 ADRB2 , SI , FZD3 , JAG1 , SCNN1B , NHS , SLC1A1 GO:0043235 Receptor complex 5 0.004 393 425 PTPRB , ADRB2 , NR3C2 , BMPR1B , VLDLR GO:0005887 Integral component of plasma membrane 14 0.031 795 756 PTPRB , FXYD3 , SLC2A10 , EFNB2 , TLR3 , JAG1 , TPBG , ADRB2 , SLC16A6 , NLGN4X , SCNN1B , BMPR1B , SLC1A1 , HLA-DRA GO:0000940 Condensed chromosome outer kinetochore 2 0.040 957 971 SPDL1 , SKA1 GO-MF GO:0015293 Symporter activity 3 0.030 268 219 SLC16A6 , SLC16A9 , SLC1A1 GO:0042803 Protein homodimerization activity 9 0.040 796 847 RBPMS2 , XDH , HHEX , ADRB2 , CEBPD , NLGN4X , ABAT , ZBTB16 , TWIST1 KEGG hsa04960 Aldosterone-regulated sodium reabsorption 3 0.019 200 921 NR3C2 , HSD11B2 , SCNN1B The most significantly altered pathway was positive regulation of osteoblast differentiation pathway. The other significant pathways included: in utero embryonic development, negative regulation of stem cell differentiation, nucleosome assembly, regulation of G2/M transition of mitotic cell cycle, negative regulation of DNA-templated transcription (Table 3 3 ). In addition, the KEGG pathway analysis indicated that the aldosterone-regulated sodium reabsorption played an essential role in CRPC. Regarding molecular function, symporter activity and protein homodimerization activity were essential. Construction of PPI network 2 To explore the protein–protein interaction of the DEGs, we used STRING, a web-based analysis of protein interaction. Under the criteria as described in methods, a total of 82 DEGs among 111 commonly changed DEGs were filtered into the PPI network. The PPI network contained 82 nodes and 201 edges (Fig. 4 4 ), which was further analyzed in Cytoscape, with Network Analyzer app for calculating the node degree. The genes cortactin-binding protein 2 ( CTTNBP2 ), RND3 , lumican ( LUM ), Toll-like receptor 3 ( TLR3 ), zinc finger protein 423 ( ZNF423 ), Rho-related BTB domain containing protein 1 ( RhoBTB1 ), leucine-rich repeat and immunoglobulin domain-containing protein 1 ( LINGO1 ), trophoblast glycoprotein ( TPBG ), JAG1 , PTPRR , Nance-Horan Syndrome ( NHS ), protein tyrosine phosphatase receptor type β ( PTPRB ), zinc finger and BTB domain containing 16 ( ZBTB16 ), heat shock protein family A member 2 ( HSPA2 ), cyclin B3 ( CCNB3 ), PDZ and LIM domain protein 5 ( PDLIM5 ), twist homolog 1 ( TWIST1 ), histone cluster 1 H4 family member h ( HIST1H4H ), nuclear receptor subfamily 3 group C member 2 ( NR3C2 ), and guanylate cyclase 1 soluble subunit α3 ( GUCY1A3 ) were the most significant 20 node degree genes, which were regarded as the hub nodes, since they may be responsible for CRPC progression. Red circles are upregulated genes in PPI network, while green ones are downregulated genes Identification of key gene signatures in CRPC and survival analysis 2 Based on PPI network and GO pathway, we identified two genes ( PTPRR and JAG1 ) among the top 20 nodes in the PPI network as key genes (Fig. 4 4 ). Further, we applied expression validation and survival analysis on the two genes to evaluate their roles in prognosis. Both genes were upregulated in the CRPC compared with primary PCa (Fig. 5 5 ), and the high expression was associated with shorter disease-free survival (Fig. 6 6 ). Interestingly, the expression of PTPRR and JAG1 did not differ between primary PCa and normal prostate tissues (Fig. 7 7 ), which led us to hypothesize that these genes may have a role in the progression of PCa rather than in the initiation of PCa. (a–c) Expression of JAG1 mRNA in GSE8702, GSE33316, and GSE70770-GPL10558, respectively; (d–f) Expression of PTPRR mRNA in GSE8702, GSE33316, and GSE70770-GPL10558, respectively. Blue represents primary PCa and red represents CRPC. PCa: prostate cancer; JAG1 : Jagged1; PTPRR : protein tyrosine phosphatase receptor-type R; GEO: Gene Expression Omnibus; CRPC: castration-resistant prostate cancer (a) JAG1 , P =0.049; (b) PTPRR , P =0.0067. Red line represents cases with high expression of the genes; blue line represents cases with low expression of the genes. JAG1 : Jagged1; PTPRR : protein tyrosine phosphatase receptor-type R; TCGA: The Cancer Genome Atlas; TPM: transcripts per kilobase of exon model per million mapped reads; HR: hazard ratio (a) JAG1 ; (b) PTPRR . Red represents prostate cancer tissues and grey represents normal tissues. JAG1 : Jagged1; PTPRR : protein tyrosine phosphatase receptor-type R; TCGA: The Cancer Genome Atlas; GEPIA: Gene Expression Profiling Interactive Analysis; PRAD: prostate adenocarcinoma Discussion 1 In the present study, we identified two significant genes JAG1 and PTPRR , which participate in various pathways including positive regulation of osteoblast differentiation, stem cell differentiation, and embryonic development. The expression of these genes is robustly upregulated in CRPC rather than in primary PCa. Owing to the importance of these pathways in the progression and metastasis in PCa, we hypothesized that these two genes ( JAG1 and PTPRR ) play a critical role in the progression of PCa. In line with our finding, a number of seminal papers have reported that JAG1 has an essential role in cancers including PCa (Santagata et al., 2004 2004 ; Zhu et al., 2013 2013 ; Li et al., 2014 2014 ; Su et al., 2017 2017 ). JAG1 encodes the protein Jagged1, which is regarded as a major Notch ligand. Previous studies have revealed that Notch signaling pathway is an evolutionarily conserved pathway, which contributes to the regulation of tissue homeostasis and carcinogenesis as well as stem cell function, developmental cell fate decisions, and cellular differentiation (Artavanis-Tsakonas et al., 1999 1999 ). Consistent with its significant role in the development, the Notch signaling pathway has an active role in cancer development (Espinoza et al., 2013 2013 ). The Notch ligand Jagged1 has also served as a potential therapeutic of cancer (Li et al., 2014 2014 ). In this study, we found that JAG1 is upregulated in CRPC, and its high expression is related to poor outcome. This is not the only study demonstrating the important role of JAG1 . Some reports have shown that JAG1 is increased in PCa and is associated with metastasis and recurrence (Santagata et al., 2004 2004 ; Zhu et al., 2013 2013 ; Su et al., 2017 2017 ). Further, it has been shown that inhibition of Notch signaling weakens growth, proliferation, and migration in PCa (Shou et al., 2001 2001 ; Yong et al., 2011 2011 ; Kwon et al., 2016 2016 ), suggesting the critical role of JAG1 and Notch signaling in PCa. Mechanistically, Jagged1-mediated Notch activation can induce epithelial-mesenchymal transition (EMT), thus contributing to cancer cell motility (Sethi et al., 2011 2011 ). All in all, JAG1 and Notch signaling plays a critical role in PCa development. Given the role of JAG1 and Notch signaling in osteoblast differentiation and the frequent osteoblastic bone metastases (bone forming) in CRPC (Logothetis and Lin, 2005 2005 ), we hypothesized that JAG1 may be involved in the bone metastasis of CRPC. Thus, JAG1 might serve as a therapeutic target for bone metastasis in CRPC. One unanticipated finding was regarding the gene PTPRR , which is considered a tumor suppressor in several cancers. It was found to be overexpressed in CRPC and the high expression was related to poor prognosis. This is extremely striking because PTPRR belongs to the family of protein tyrosine phosphatases (PTPs), which regulates proliferation, differentiation, and function of cancer cells by dephosphorylating tyrosine (for example, tyrosine in mitogen-activated protein kinase (MAPK)) (Noordman et al., 2006 2006 ; Schmitt et al., 2009 2009 ). Several studies have shown that the expression of PTPRR is decreased in several cancers including colorectal carcinomas, cervical cancer, and oral squamous cell carcinoma due to high methylation (Laczmanska et al., 2013 2013 ; Su et al., 2013 2013 ; Chang et al., 2014 2014 ; Woźniak et al., 2014 2014 ), suggesting a tumor suppressive role for PTPRR (Menigatti et al., 2009 2009 ; Su et al., 2013 2013 ; Duś-Szachniewicz et al., 2015 2015 ). These in vitro studies have also shown that overexpression of PTPRR inhibits migration and invasion in these cancers. Further, a study has shown that PTPRR is suppressed by androgen in PCa, and negatively regulates the RAS/extracellular signal-regulated protein kinases 1 and 2 (ERK1/2) pathway, thus reducing cell proliferation in response to androgen (Munkley et al., 2015 2015 ). However, our results strongly indicate that patients with CRPC have higher expression of PTPRR and there is a relationship between higher expression of PTPRR and poorer outcomes, suggesting a survival advantage for cancer cells overexpressing PTPRR . Additionally, we found that the Grasso Prostate dataset also showed that PTPRR is upregulated in CRPC (Fig. S1 S1 ). There are two explanations for this difference. One explanation is that upregulation of PTPRR is a mechanism to combat cancers, which means that high expression of PTPRR suggests war against cancer cells, and thus the patients may have a poor prognosis. The other explanation is that PTPRR , in contrast to other PTP family members, serves as an oncogene. Thus, further study regarding PTPRR is necessary to identify the functions of this gene. In summary, PTPRR may serve as a prognosis biomarker for PCa. Although this study is first to identify the two significant genes ( JAG1 and PTPRR ) in CRPC by integrated bioinformatics analysis, we still need to recognize some limitations. Firstly, the sample we used to identify DEGs was from cell lines and xenograft models, and the sample size was small. Although we used the human sample tissue from another GEO dataset and TCGA to validate our results, the total sample size was still small due to the difficulty of obtaining CRPC sample. Secondly, our results have not been validated by experiments like quantitive real-time polymerase chain reaction (qRT-PCR), western blot, or functional in vitro experiments. Further studies on human tissue samples are needed to validate our results. Conclusions 1 We identified two essential genes ( JAG1 and PTPRR ) that contribute to CRPC possibly by regulating key pathways like positive regulation of osteoblast differentiation, stem cell differentiation, and embryonic development. 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This article does not contain any studies with human or animal subjects performed by any of the authors. 10.1631/jzus.B1900329 ,, 2019-06-20 2019-10-22 2022-01-05 Copyright © Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2020 2020 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 03 21