scholarly journals A Novel circRNA–miRNA–mRNA Hub Regulatory Network in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Haiwei Zuo ◽  
Xia Li ◽  
Xixi Zheng ◽  
Qiuwen Sun ◽  
Qianqian Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Wnt Signaling Pathway ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Expression Data ◽  
Functional Mechanism

The growing evidence suggests that circular RNAs (circRNAs) have significant associations with tumor occurrence and progression, yet the regulatory mechanism of circRNAs in lung adenocarcinoma (LUAD) remains unclear. This study clarified the potentially regulatory network and functional mechanism of circRNAs in LUAD. The expression data of circRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were obtained from the Gene Expression Omnibus (GEO) database. Relying on GSE101586, GSE101684, and GSE112214, we identified differentially expressed circRNAs (DEcircRNAs). Depending on GSE135918 and GSE32863, we screened out differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs), respectively. Then, a novel competing endogenous RNA (ceRNA) regulatory network related to LUAD was constructed. We also revealed biological processes and signal pathways regulated by these DEcircRNAs. Based on gene expression data and survival information of LUAD patients in The Cancer Genome Atlas (TCGA) and GEO, we implemented survival analysis to select DEmRNAs related to prognosis and build a novel circRNA–miRNA–mRNA hub regulatory network. Meanwhile, quantitative real-time PCR (qRT-PCR) was utilized to validate DEcircRNAs in the ceRNA hub regulatory network. As a result, a total of 8 DEcircRNAs, 19 DEmiRNAs, and 85 DEmRNAs were identified. The novel ceRNA regulatory network included 5 circRNAs, 8 miRNAs, and 22 mRNAs. The final ceRNA hub regulatory network contained two circRNAs, two miRNAs, and two mRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the five DEcircRNAs may affect LUAD onset and progression through Wnt signaling pathway and Hippo signaling pathway. All in all, this study revealed the regulatory network and functional mechanism of circRNA-related ceRNAs in LUAD.

Application of a Deep Matrix Factorization Model on Integrated Gene Expression Data

2020 ◽  
Vol 15 (4) ◽  
pp. 359-367
Author(s):  
Yong-Jing Hao ◽  
Mi-Xiao Hou ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Xiang-Zhen Kong
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Matrix Factorization ◽  
Coefficient Matrix ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Complex Data ◽  
Expression Data ◽  
Factorization Model

Background: Non-negative Matrix Factorization (NMF) has been extensively used in gene expression data. However, most NMF-based methods have single-layer structures, which may achieve poor performance for complex data. Deep learning, with its carefully designed hierarchical structure, has shown significant advantages in learning data features. Objective: In bioinformatics, on the one hand, to discover differentially expressed genes in gene expression data; on the other hand, to obtain higher sample clustering results. It can provide the reference value for the prevention and treatment of cancer. Method: In this paper, we apply a deep NMF method called Deep Semi-NMF on the integrated gene expression data. In each layer, the coefficient matrix is directly decomposed into the basic and coefficient matrix of the next layer. We apply this factorization model on The Cancer Genome Atlas (TCGA) genomic data. Results: The experimental results demonstrate the superiority of Deep Semi-NMF method in identifying differentially expressed genes and clustering samples. Conclusion: The Deep Semi-NMF model decomposes a matrix into multiple matrices and multiplies them to form a matrix. It can also improve the clustering performance of samples while digging out more accurate key genes for disease treatment.

scholarly journals Identification of LINC02310 as an enhancer in lung adenocarcinoma and investigation of its regulatory network via comprehensive analyses

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Wenyuan Zhao ◽  
Jun Wang ◽  
Qingxi Luo ◽  
Wei Peng ◽  
Bin Li ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Regulatory Network ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Feature Analysis ◽  
Clinical Feature ◽  
Ppi Network ◽  
Network Construction

Abstract Background Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer and has one of the highest mortality rates. An increasing number of long non-coding RNAs (LncRNAs) were reported to be associated with the occurrence and progression of LADC. Thus, it is necessary and reasonable to find new prognostic biomarkers for LADC among LncRNAs. Methods Differential expression analysis, survival analysis, PCR experiments and clinical feature analysis were performed to screen out the LncRNA which was significantly related to LADC. Its role in LADC was verified by CCK-8 assay and colony. Furthermore, competing endogenous RNA (ceRNA) regulatory network construction, enrichment analysis and protein–protein interaction (PPI) network construction were performed to investigate the downstream regulatory network of the selected LncRNA. Results A total of 2431 differentially expressed LncRNAs (DELncRNAs) and 2227 differentially expressed mRNAs (DEmRNAs) were from The Cancer Genome Atlas database. Survival analysis results indicated that lnc-YARS2-5, lnc-NPR3-2 and LINC02310 were significantly related to overall survival. Their overexpression indicated poor prognostic. PCR experiments and clinical feature analysis suggested that LINC02310 was significantly correlated with TNM-stage and T-stage. CCK-8 assay and colony formation assay demonstrated that LINC02310 acted as an enhancer in LADC. In addition, 3 targeted miRNAs of LINC02310 and 414 downstream DEmRNAs were predicted. The downstream DEmRNAs were then enriched in 405 Gene Ontology terms and 11 Kyoto Encyclopedia of Genes and Genomes pathways, which revealed their potential functions and mechanisms. The PPI network showed the interactions among the downstream DEmRNAs. Conclusions This study verified LINC02310 as an enhancer in LADC and performed comprehensive analyses on its downstream regulatory network, which might benefit LADC prognoses and therapies.

scholarly journals Identification of LINC02310 as an enhancer in lung adenocarcinoma and investigation of its regulatory network via comprehensive analyses

2020 ◽  
Author(s):  
Wenyuan Zhao ◽  
Jun Wang ◽  
Qingxi Luo ◽  
Wei Peng ◽  
Bin Li ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Regulatory Network ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Feature Analysis ◽  
Clinical Feature ◽  
Ppi Network ◽  
Network Construction

Abstract Background: Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer (NSCLC) and has one of the highest mortality rates. An increasing number of long non-coding RNAs (LncRNAs) were reported to be associated with the occurrence and progression of LADC. Thus, tt is necessary and reasonable to find new prognostic biomarkers for LADC among LncRNAs.Methods: Differential expression analysis, survival analysis, PCR experiments and clinical feature analysis were performed to screen out the LncRNA which was significantly related to LADC. Its role in LADC was verified by CCK-8 assay and colony. Furthermore, competing endogenous RNA (ceRNA) regulatory network construction, enrichment analysis and protein-protein interaction (PPI) network construction were performed to investigate the downstream regulatory network of the selected LncRNA.Results: A total of 2431 differentially expressed LncRNAs (DELncRNAs) and 2227 differentially expressed mRNAs (DEmRNAs) were from The Cancer Genome Atlas (TCGA) database. Survival analysis results indicated that lnc-YARS2-5, lnc-NPR3-2 and LINC02310 were significantly related to overall survival. Their overexpression indicated poor prognostic. PCR experiments and clinical feature analysis suggested that LINC02310 was significantly correlated with TNM-stage and T-stage. CCK-8 assay and colony formation assay demonstrated that LINC02310 acted as an enhancer in LADC. In addition, 3 targeted miRNAs of LINC02310 and 414 downstream DEmRNAs were predicted. The downstream DEmRNAs were then enriched in 405 Gene Ontology (GO) terms and 11 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, which revealed their potential functions and mechanisms. The PPI network showed the interactions among the downstream DEmRNAs. Conclusions: This study verified LINC02310 as an enhancer in LADC and performed comprehensive analyses on its downstream regulatory network, which might benefit LADC prognoses and therapies.

scholarly journals Identification of LINC02310 as an enhancer in lung adenocarcinoma and investigation of its regulatory network via comprehensive analyses

2020 ◽  
Author(s):  
Wenyuan Zhao ◽  
Jun Wang ◽  
Qingxi Luo ◽  
Wei Peng ◽  
Bin Li ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Regulatory Network ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Feature Analysis ◽  
Clinical Feature ◽  
Ppi Network ◽  
Network Construction

Abstract Background: Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer (NSCLC) and has one of the highest mortality rates. An increasing number of long non-coding RNAs (LncRNAs) were reported to be associated with the occurrence and progression of LADC. Thus, tt is necessary and reasonable to find new prognostic biomarkers for LADC among LncRNAs. Methods: Differential expression analysis, survival analysis, PCR experiments and clinical feature analysis were performed to screen out the LncRNA which was significantly related to LADC. Its role in LADC was verified by CCK-8 assay and colony. Furthermore, competing endogenous RNA (ceRNA) regulatory network construction, enrichment analysis and protein-protein interaction (PPI) network construction were performed to investigate the downstream regulatory network of the selected LncRNA. Results: A total of 2431 differentially expressed LncRNAs (DELncRNAs) and 2227 differentially expressed mRNAs (DEmRNAs) were from The Cancer Genome Atlas (TCGA) database. Survival analysis results indicated that lnc-YARS2-5, lnc-NPR3-2 and LINC02310 were significantly related to overall survival. Their overexpression indicated poor prognostic. PCR experiments and clinical feature analysis suggested that LINC02310 was significantly correlated with TNM-stage and T-stage. CCK-8 assay and colony formation assay demonstrated that LINC02310 acted as an enhancer in LADC. In addition, 3 targeted miRNAs of LINC02310 and 414 downstream DEmRNAs were predicted. The downstream DEmRNAs were then enriched in 405 Gene Ontology (GO) terms and 11 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, which revealed their potential functions and mechanisms. The PPI network showed the interactions among the downstream DEmRNAs. Conclusions: This study verified LINC02310 as an enhancer in LADC and performed comprehensive analyses on its downstream regulatory network, which might benefit LADC prognoses and therapies.

scholarly journals The Construction and Analysis of ceRNA Network in H pylori infection atrophic gastritis

2021 ◽  
Author(s):  
Wen-jun BAI ◽  
Jun-wei LIANG ◽  
Xiao-yan WANG
Keyword(s):  
Gastric Cancer ◽  
Atrophic Gastritis ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Interaction Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
H Pylori

Abstract Background：Chronic atrophic gastritis (CAG) is an established pre-cancerous lesion of intestinal type gastric cancer(GC),H pylori infection is the main pathogenic cause,this study intends to study the pathogenesis of atrophic gastritis(Hp+) from the lncRNA-miRNA-mRNA ceRNA regulatory network, in order to provide the oretical basis and data support for the treatment of atrophic gastritis.Results:GSE111762 downloaded from GEO database was used to analyze the differentially expressed lncRNAs and mRNAs(DEGs).A total of 395 differentially expressed lncRNA (225 upregulated,170 downregulated) and 1093 DEGs ( 674 upregulated, 419 downregulated) are obtained. Through the cross-mapping of miRcode, starBase, Sponescan,miRTarBase and miRBase databases,16 miRNAs were predicted,and the lncRNA-miRNA-mRNA ceRNA regulatory network consisting of 71 IncRNAs,16 miRNAs and 597 mRNAs was constructed.597 DEGs were analyzed by David database for functional enrichment. A total of 250 GO enrichment items were obtained, including 160 BP entries, 48 CC entries and 42 MF entries,29 signal pathways were obtained by enrichment analysis of KEGG pathways, mainly p53 signaling pathway, PI3K-Akt signaling pathway and MAPK signaling pathway. Using Cytoscape plug-in CytoHubba to filter 597 DEGs with "MCC, MNC, Degree" top20 as screening conditions, Eleven key hub targets are obtained from the intersection of jvenn.Protein interaction analysis of key hub targets through Cytoscape plug-in GeneMania, it was found that 87.65% displayed similar co-expression characteristics.Construct ceRNA regulatory network of the key hub targets,11 mRNAs(such as BRCA1, RAD54L),12 miRNAs(such as hsa-miR-340-5p,hsa-miR-182-5p) and 58 lncRNAs(such as PCGEM1,FTX) were predicted. Conclusions:Clarify the complex reticular regulation of atrophicgastritis with multi-targets, multi-pathways and multi-pathways.Which provides a new idea for the study of the mechanism of action of atrophicgastritis (Hp+) and a potential target for its treatment,thus to further early diagnosis and reversal of gastric cancer.

scholarly journals Identification of a Potentially Functional microRNA–mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiao-Jun Wang ◽  
Jing Gao ◽  
Zhuo Wang ◽  
Qin Yu
Keyword(s):  
Lung Adenocarcinoma ◽  
Regulatory Network ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Messenger Rnas ◽  
Ppi Network ◽  
Tissue Samples ◽  
Go Analysis ◽  
Kegg Pathways

BackgroundLung adenocarcinoma (LUAD) is a common lung cancer with a high mortality, for which microRNAs (miRNAs) play a vital role in its regulation. Multiple messenger RNAs (mRNAs) may be regulated by miRNAs, involved in LUAD tumorigenesis and progression. However, the miRNA–mRNA regulatory network involved in LUAD has not been fully elucidated.MethodsDifferentially expressed miRNAs and mRNA were derived from the Cancer Genome Atlas (TCGA) dataset in tissue samples and from our microarray data in plasma (GSE151963). Then, common differentially expressed (Co-DE) miRNAs were obtained through intersected analyses between the above two datasets. An overlap was applied to confirm the Co-DEmRNAs identified both in targeted mRNAs and DEmRNAs in TCGA. A miRNA–mRNA regulatory network was constructed using Cytoscape. The top five miRNA were identified as hub miRNA by degrees in the network. The functions and signaling pathways associated with the hub miRNA-targeted genes were revealed through Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The key mRNAs in the protein–protein interaction (PPI) network were identified using the STRING database and CytoHubba. Survival analyses were performed using Gene Expression Profiling Interactive Analysis (GEPIA).ResultsThe miRNA–mRNA regulatory network consists of 19 Co-DEmiRNAs and 760 Co-DEmRNAs. The five miRNAs (miR-539-5p, miR-656-3p, miR-2110, let-7b-5p, and miR-92b-3p) in the network were identified as hub miRNAs by degrees (>100). The 677 Co-DEmRNAs were targeted mRNAs from the five hub miRNAs, showing the roles in the functional analyses of the GO analysis and KEGG pathways (inclusion criteria: 836 and 48, respectively). The PPI network and Cytoscape analyses revealed that the top ten key mRNAs were NOTCH1, MMP2, IGF1, KDR, SPP1, FLT1, HGF, TEK, ANGPT1, and PDGFB. SPP1 and HGF emerged as hub genes through survival analysis. A high SPP1 expression indicated a poor survival, whereas HGF positively associated with survival outcomes in LUAD.ConclusionThis study investigated a miRNA–mRNA regulatory network associated with LUAD, exploring the hub miRNAs and potential functions of mRNA in the network. These findings contribute to identify new prognostic markers and therapeutic targets for LUAD patients in clinical settings.

scholarly journals Comprehensive Analysis of the Functions and Prognostic Value of RNA-Binding Proteins in Thyroid Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yue Ma ◽  
Shi Yin ◽  
Xiao-feng Liu ◽  
Jing Hu ◽  
Ning Cai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Gene Expression Data ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Expression Data

RNA binding proteins (RBPs) have been proved to play pivotal roles in a variety types of tumors. However, there is no convincible evidence disclosing the functions of RBPs in thyroid cancer (THCA) thoroughly and systematically. Integrated analysis of the functional and prognostic effect of RBPs help better understanding tumorigenesis and development in thyroid and may provide a novel therapeutic method for THCA. In this study, we obtained a list of human RBPs from Gerstberger database, which covered 1,542 genes encoding RBPs. Gene expression data of THCA was downloaded from The Cancer Genome Atlas (TCGA, n = 567), from which we extracted 1,491 RBPs’ gene expression data. We analyzed differentially expressed RBPs using R package “limma”. Based on differentially expressed RBPs, we constructed protein-protein interaction network and the GO and KEGG pathway enrichment analyses were carried out. We found six RBPs (AZGP1, IGF2BP2, MEX3A, NUDT16, NUP153, USB1) independently associated with prognosis of patients with thyroid cancer according to univariate and multivariate Cox proportional hazards regression models. The survival analysis and risk score analysis achieved good performances from this six-gene prognostic model. Nomogram was constructed to guide clinical decision in practice. Finally, biological experiments disclosed that NUP153 and USB1 can significantly impact cancer cell proliferation and migration. In conclusion, our research provided a new insight of thyroid tumorigenesis and development based on analyses of RBPs. More importantly, the six-gene model may play an important role in clinical practice in the future.

scholarly journals Comprehensive analyses to identify LINC02310 as an enhancer in lung adenocarcinoma

2020 ◽  
Author(s):  
Wenyuan Zhao ◽  
Jun Wang ◽  
Qingxi Luo ◽  
Wei Peng ◽  
Bin Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Clinical Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Expression Data ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Cancer Genome Atlas ◽  
Non Coding Rnas ◽  
Go Terms

Abstract Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer (NSCLC) and has one of the highest mortality rates. An increasing number of long non-coding RNAs (lncRNAs) were reported to be associated with the occurrence and progression of LADC. In order to find potential biomarkers for LADC therapies among lncRNAs, 2431 differentially expressed lncRNAs (DElncRNAs) over LADC and adjacent normal samples were identified from The Cancer Genome Atlas (TCGA) expression data. Lnc-YARS2-5, lnc-NPR3-2 and LINC02310, which were the top-3 significant DElncRNAs related to overall survival, were selected for further analysis. Their overexpression indicated poor prognostic. PCR experimental results also showed that they were mainly up-regulated in the LADC tissues from Xiangya Hospital. Clinical analysis of these cases suggested that LINC02310 was significantly correlated with TNM-stage and T-stage, which was further validated by the clinical analysis based on TCGA. Reasonably, we assumed that LINC02310 acted as an enhancer in LADC, which was demonstrated by CCK-8 assay and colony formation assay. Moreover, 3 targeted miRNAs of LINC02310 and 414 downstream differentially expressed mRNAs (DEmRNAs) were predicted. The competing endogenous RNA (ceRNA) regulatory network was constructed with LINC02310, 3 miRNAs and 113 DEmRNAs involved. The downstream mRNAs were then enriched in 405 GO terms and 11 KEGG pathways, which revealed their potential functions and mechanisms. The protein-protein interaction (PPI) network showed the relationships between the downstream mRNAs. In conclusion, this study verified LINC02310 as an enhancer in LADC and conducted comprehensive analyses on its target miRNAs and downstream mRNAs.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Mining The Cancer Genome Atlas gene expression data for lineage markers in distinguishing bladder urothelial carcinoma and prostate adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ewe Seng Ch’ng
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
The Cancer Genome Atlas ◽  
Relative Importance ◽  
Expression Data ◽  
Gene Expressions ◽  
Urothelial Carcinomas ◽  
Cancer Genome Atlas ◽  
Lineage Markers ◽  
Genome Atlas

AbstractDistinguishing bladder urothelial carcinomas from prostate adenocarcinomas for poorly differentiated carcinomas derived from the bladder neck entails the use of a panel of lineage markers to help make this distinction. Publicly available The Cancer Genome Atlas (TCGA) gene expression data provides an avenue to examine utilities of these markers. This study aimed to verify expressions of urothelial and prostate lineage markers in the respective carcinomas and to seek the relative importance of these markers in making this distinction. Gene expressions of these markers were downloaded from TCGA Pan-Cancer database for bladder and prostate carcinomas. Differential gene expressions of these markers were analyzed. Standard linear discriminant analyses were applied to establish the relative importance of these markers in lineage determination and to construct the model best in making the distinction. This study shows that all urothelial lineage genes except for the gene for uroplakin III were significantly expressed in bladder urothelial carcinomas (p < 0.001). In descending order of importance to distinguish from prostate adenocarcinomas, genes for uroplakin II, S100P, GATA3 and thrombomodulin had high discriminant loadings (> 0.3). All prostate lineage genes were significantly expressed in prostate adenocarcinomas(p < 0.001). In descending order of importance to distinguish from bladder urothelial carcinomas, genes for NKX3.1, prostate specific antigen (PSA), prostate-specific acid phosphatase, prostein, and prostate-specific membrane antigen had high discriminant loadings (> 0.3). Combination of gene expressions for uroplakin II, S100P, NKX3.1 and PSA approached 100% accuracy in tumor classification both in the training and validation sets. Mining gene expression data, a combination of four lineage markers helps distinguish between bladder urothelial carcinomas and prostate adenocarcinomas.

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