{'fraction_non_alphanumeric': 0.030905360348807386, 'fraction_numerical': 0.019321193468410703, 'mean_word_length': 4.989503328213005, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 2, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 5, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
dbEMT: an epithelial-mesenchymal transition associated gene resource

As a cellular process that changes epithelial cells to mesenchymal cells, Epithelial-mesenchymal transition (EMT) plays important roles in development and cancer metastasis. Recent studies on cancer metastasis have identified many new susceptibility genes that control this transition. However, there is no comprehensive resource for EMT by integrating various genetic studies and the relationship between EMT and the risk of complex diseases such as cancer are still unclear. To investigate the cellular complexity of EMT, we have constructed dbEMT (http://dbemt.bioinfo-minzhao.org/), the first literature-based gene resource for exploring EMT-related human genes. We manually curated 377 experimentally verified genes from literature. Functional analyses highlighted the prominent role of proteoglycans in tumor metastatic cascades. In addition, the disease enrichment analysis provides a clue for the potential transformation in affected tissues or cells in Alzheimer’s disease and Type 2 Diabetes. Moreover, the global mutation pattern of EMT-related genes across multiple cancers may reveal common cancer metastasis mechanisms. Our further reconstruction of the EMT-related protein-protein interaction network uncovered a highly modular structure. These results illustrate the importance of dbEMT to our understanding of cell development and cancer metastasis, and also highlight the utility of dbEMT for elucidating the functions of EMT-related genes.


Results
Functional enrichment analysis revealed fundamental role of proteoglycans in EMT. Gene-set enrichment analyses have been widely used to help biologists in the analysis and interpretation of the results from any interesting gene list 9 . In this article, the implemented hypergeometric tests were adapted to characterize whether an input gene-set had a different frequency of annotation pairs unlikely by chance, given the background genes sizes. The expected frequency was further obtained for each of the functional terms. Using protein-coding genes in the human as background, we identified 62 statistically significant enriched pathways (Table 1) and 28 diseases (Table 2) associated with the 377 genes in dbEMT. The enriched functional pathways are mainly related to cancer pathways such as TGF-beta signalling, proteoglycans in cancer, colorectal cancer, and pancreatic cancer (Table 1). In addition to cancer-related pathways, the genes were also highly enriched in cell surface interaction such as non-integrin membrane-ECM interaction, extracellular matrix organization, focal adhesion, and adhesion junction. These results show that multiple membrane-related signaling events are related to EMT, especially proteoglycans in cancer. Specifically, there are 65 genes in our dbEMT that are reported to relate to proteoglycans in cancer with a corrected P-value of 5.95E-11. Proteoglycan content and distribution are markedly altered during cancer progression 10 . The specific structure of proteoglycan allows it to interact with both the ligands and receptors that regulate cancer pathogenesis. Therefore proteoglycan, as well as glycosaminoglycans, often have important function in the tumor metastatic cascade by modulating key downstream signaling mediators such as epidermal growth factor receptor, insulin growth factor receptor, estrogen receptors, and Wnt members 11 . In summary, the level of complexity of cell surface signaling systems involved in EMT arises from their fundamental roles in response to microenvironment regulation. Enrichment diseases on the 377 EMT-related genes. As a fundamental role on cell destination, it is not surprising that the EMT-related genes are consistently related to a number of complex diseases. However, the enrichment analysis of an EMT-related gene does not reveal if EMT has contributed to a specific disease. It does allow to test the risk factor of EMT in relevant diseases. The enrichment analysis on a few disease-related databases has confirmed that the 377 genes are related to a broad-spectrum of human cancers such as (Table 2). In total, 277 genes are related to various cancers. The cancers mainly occur in the digestive and urinary systems, including colorectal, pancreatic, endometrial, prostate, bladder, melanoma, renal cell, thyroid, oral, gastric, ovary, leukemia, cholangiocarcinoma, choriocarcinoma. Although many reports suggest that EMT is associated with cancer invasion and metastasis, there is no comparison of EMT across cancers. It is possible that the underlying common molecular mechanism between EMT and any cancers are the same. As shown in Fig. 1, there are 27 EMT-related genes in both colorectal cancer and pancreatic cancer. However, only 18 of these are common to both: SMAD2,  SMAD3, SMAD4, PIK3CA, MAP2K1, TP53, AKT1, BRAF, TGFBR1, RAF1, TGFB3, TGFB2, MAPK3,  MAPK1, MAPK8, KRAS, RAC1, TGFB1. It is interesting to note that the 337 EMT-related genes are over-represented in the Alzheimer's disease (AD) and type 2 diabetes (T2D). The possible association of EMT with Alzheimer's disease is also Scientific RepoRts | 5:11459 | DOi: 10.1038/srep11459 supported by AD-specific biomarkers, which were selectively detected only in the affected AD brain 12 . The potential role of EMT in T2D was also found recently 13 . Although the direct association of AD and T2D with EMT has not reported. The study in adult human exocrine pancreatic cells revealed that the EMT could impact on dedifferentiation of ductal cells during the regeneration of type 2 diabetic pancreatic tissues. Therefore, further data mining on our dbEMT may provide a clue about a potential transformation in affected tissues or cells, including changes of upstream signaling and other cell surface interactions during the AD/T2D disease.
Prioritizing the important genes in EMT and their mutational landscape in pan-cancer genomics data. The molecular basis that underlies EMT is unclear because of its high cellular complexity. Traditional characterization of candidate genes in individual studies often focuses on verifying specific genes functions related to EMT. However, the big map for the EMT and the relative importance for EMT-related genes have not been systematically evaluated. Using the gene ranking tool Endeavour, we obtained the top ten ranked genes involved inCTNNB1, SMAD3, HIF1A, TGFB1, ZEB2, EGFR, CDH1, ILK, ZEB1, TWIST1 (Table 3). Not surprisingly, majority of these top ranked genes are involved in the key pathway of EMT, such as the "Pathways in cancer", "Adherens junction", "Focal adhesion", and "TGF-beta signaling pathway". Although EMT-related genes have been demonstrated to have abnormal gene expression or other functional relevance to cancer, the systematic examination of the genetic variants has not been conducted. These are useful for users to identify potential cancer metastasis genes for further screening. As shown in Fig. 2 To further explore the common and distinct EMT-related somatic variants in different cancer types, we mapped the 377 EMT-related genes to the mutation data of TCGA pan-cancer 14 . To focus on the functional variants, we excluded non-sense and silent mutations. The remaining contains mis-senses, splicings, frameshift SNVs and INDELs. As shown in the Fig. 3, majority of EMT-related somatic variants   Table 3. The top 20 ranked EMT-related genes. 1 Represent the probability that a candidate gene would obtain these ranks by chance. 2 The global ranking ratio from Endeavour.   set whether the number of genes with meaning mutations was more than the actual number of mutated genes in our top 100 EMT related-genes. Using the number of randomly selected node sets with more mutated genes as input, we calculated the empirical P-values (See Methods section). As shown in Table  S2, all the 19 empirical P-values are all less than 0.01, which means the top 100 EMT related genes are highly mutated in the 19 pan-cancer datasets.
Highly expressed and mutated EMT genes during stage III to stage IV in ovarian cancer. As the EMT often occurs during the cancer metastasis, we only extracted those ovarian cancer samples related to stage III to stage IV, where metastasis transition occurs. The aim of this analysis was to identify those EMT-related genes whose expression is increasing between Stage III to stage IV. As shown in the Fig. 4, there are 35 mutated genes having comparatively high expression in average in ovarian cancer (student t test, P-values < 0.05). Multiple genes (MUC16 15 , POSTN 16 ) have been reported to be related to ovarian cancer metastasis or cell movement. Our analysis reveals a similar relationship between periostin (POSTN) and EMT of ovarian cancer. Some of genes (e.g. MMP7) were reported to have potential biomarker role in ovarian cancer, but not for EMT process 17 . Those genes might be used as biomarkers for the ovarian cancer EMT-related process, especially, the strong different expression of periostin from stage III to IV which may indicate the ovarian cancer EMT process.
The reconstructed EMT-related protein-protein interaction network exhibits a highly modular structure. In the past decade, the knowledge of the cellular pathway for metabolism and signaling transduction have been summarized in a few popular biological pathway data such as the KEGG pathway. Recently, the Pathway Commons database combined all the popular pathway databases to provide gene-gene functional interaction pairs and these are useful for further pathway reconstruction 18 . In our study, we utilized reliable public data sources and constructed a more comprehensive cellular map for EMT. The reconstructed EMT contains 371 genes and 2357 gene-gene interactions is based on current evidence from known biological pathways (Fig. 5A). Of the 371 nodes, 335 of them are from our curated 377 IQ-related genes. The remaining 36 are the novel genes that may potentially bridge the EMT-related gene to fully implement their cellular function. The majority of curated EMT-related genes are linked to each other in a highly modular structure. This not only support the accuracy of our data, but it also reveals the EMT-related genes are highly connected to each other and form a high density cellular modular.
Further network topological analysis also indicates that most molecules in our map were closely connected. Only 80 nodes were without in-degree and out-degree. This means the majority of nodes can communicate with each other in short steps. The degrees of all nodes in our reconstructed EMT map follow a power law distribution P(k) ~ k -b , where P(k) is the probability that a molecule has connections with other k molecules and b is an exponent with an estimated value of 0.998. This means that our EMT map is different from all the human PPI network where most of nodes were sparsely connected with exponent b as 2.9 19 . This feature made the shortest path distribution for the entire network was a smaller number (2 to 4), and means that about 53% of the node communication can be reached only by three steps (Fig. 5C). This observation is also confirmed from the relation between closeness and the number of neighbors. As shown in Fig. 5D, the nodes with higher closeness are more likely to have more neighbors and vice versa. With high modularity, the hub nodes in this network may have prominent roles as common connections to mediate information transduction in the short path. In total, there are 13 genes with at least 50 connections and these are: SRC (99), EGFR (69), PTK2 (65), PRKCA (64), MAPK1 (64), AKT1 (61), JAK2 (58), CTNNB1 (57), ERBB2 (57), FGFR1 (56), MAPK3 (53), TP53 (50), MAPK14 (50). All 13 genes are from our literature-based gene set; none are linker genes. In summary, our reconstructed map not only discovers multiple paths related to a few known signaling pathways but also provides a broader picture for the highly modular structure of the different previously unconnected signaling pathways.

Conclusion
dbEMT is constructed as a free database and analysis server to enable users to rapidly search and retrieve summarized EMT-related genes. The comprehensive functional enrichment analyses reveal that EMT-related genes are enriched in multiple signal events associated with development and cancers. Important questions should now be directed towards integration of various signaling pathways to initial EMT events. The dbEMT is freely available at http://dbemt.bioinfo-minzhao.org/. Abstract]))). Secondly, 1507 abstracts were retrieved and grouped by the "Related Articles" function in Entrez system. Thirdly, we then extracted the EMT-related description from the grouped abstracts. Those sentences related to EMT were manually read to extract the gene names and organism information with experimental evidence. Lastly, the extracted candidate gene name and organism information were manually checked. Finally, the 377 Entrez human Gene IDs with high confidence were collected as core EMT-related genes. This core gene list will be regularly updated based on newly published literature.

Biological functional annotations and database construction.
To present the biological function involved and over-represented in our 377 EMT-related genes, we retrieved comprehensive functional information from public resources. The basic gene information and sequences are included and crosslinked to the NCBI Entrez gene 20 , UniProt 21 , Ensembl 22 and Gene Ontology 23 . The mRNA expression profiling data from both normal and tumor tissues are imported from BioGPS 24 . To obtain comprehensive pathway-related information, we annotated the EMT-related genes using transporter substrate database 25 , BioCyc 26 , KEGG Pathway 27 , rate-limiting enzyme database 28 PANTHER 29 , PID Curated 30 , pathway localization database 31 , PID Reactome 32,33 . The involved diseases were incorporated from GAD (gene association database) 34 , KEGG Disease 35 , Fundo 36,37 , NHGIR 38 , as well as OMIM 20 . In addition, the original EMT-related literature references in the NCBI PubMed database are hyperlinked to each gene. A semi-automatic annotation pipeline is implemented to integrate functional information from Gene annotation 39 , Gene Ontology annotation, HPRD/BIND/BioGRID interaction annotation, KEGG LIGAND/BioCarta signaling event annotation 40,41 , and OMIM annotation using Perl Script and Swiss knife module [42][43][44][45] .
To obtain updated relevant publications in future, we constructed an automatic literature searching terms using My NCBI tool, which will return matched articles every two weeks. We will consider using the Entrez literature similarity to cluster the newly available articles to facilitate the curation. In addition, to keep pace with fast generation of cancer genomic data, we have implemented an automatic system to import functional information from a variety of public data sources, which can help us integrate more annotations quickly. Once the data content is updated, the web interface will be updated accordingly annually.
Gene set enrichment analysis. Throughout the paper, the functional enrichment analysis from KEGG, Reactome and other disease databases for each gene set were identified by KOBAS 46 . In these enrichment analyses, all the human protein-coding genes were set as background to calculate statistical significance. In addition, the Benjamini-Hochberg multiple testing corrected P-values for enriched annotations were adopted based on hypergeometric test by using KOBAS. Finally, the enriched human pathways with corrected P-values less than 0.01 were identified as over representative annotations for each gene set.
Gene ranking using Endeavour and cancer mutation landscape. We performed a gene prioritization using the Endeavour web server 47 to help the user prioritize all 377 genes in dbEMT. Endeavour utilizes multiple dimensional data to rank the input genes, including gene expression, regulatory information, functional annotations, sequence features, and literature mining data. It begins by extracting features about the genes that are known to play a role in the EMT. In this study, we compiled a training gene list that included 12 genes (TGFB1, SNAI1, CDH1, ZEB1, SNAI2, TWIST1, SMAD3, ILK, HIF1A, EGFR, ZEB2, and CTNNB1) with at least eight PubMed abstracts to train the ranking model. In the second stage, the ranking model was used to prioritize the remaining 365 genes using multiple genomic data. Finally, Endeavour combined all the rankings to a global ranking for all the candidate EMT-related genes using order statistics. In total, 209 valid human genes were ranked (Table S1). The top 100 ranking of EMT related-genes are input into the cBio portal to obtain a mutation pattern across multiple cancers 48 . Empirical resampling for evaluating the single nucleotide mutations of EMT related-genes across multiple cancers. To determine whether the SNV somatic mutation rate of top 100 EMT related-genes are relatively higher than expected, we conducted a total of 1000 resamplings. To this aim, we first downloaded all the SNV somatic mutations in MAF format across 19 TCGA tumor-types (https:// www.synapse.org/#!Synapse:syn1729383) 49 . During each resampling process, 100 human genes were randomly selected from 20059 human genes with SNV somatic mutations. To provide insight into biological meaning, we only focused on somatic mutations containing mis-senses, splicings, frameshift SNVs and INDELs instead of non-sense and silent mutations. We repeated this random process 1000 times.
Next, we counted the number of randomly selected node sets (N) whose number of mutated genes was more than the actual number of mutated genes in the top 100 EMT related-genes. Finally, we calculated the empirical P-value using N/1000 to represent the significance of how the top 100 EMT related-genes are highly mutated in certain cancers. Based on this empirical resampling approach, the empirical P-values for the relationship between the top 100 EMT related-gene set and cancers are all less than 0.01 (Table S2).

Gene expression analysis in ovarian cancer.
To explore the gene expression changes during cancer metastasis, we focused on a gene expression profile with 489 high-grade serous OVC samples produced by using three gene expression microarray platforms (Affymetrix Exon 1.0 array, Agilent 244 K whole genome expression array, and Affymetrix HT-HG-U133A array) 50 . To present a unified gene expression, all the three datasets were normalized and the expression values were calculated for each sample and gene on each platform separately. After subtracting the mean value across samples for the same gene, the expression values were divided by the standard deviation across samples to obtain the relative gene expression scores. Finally, the relative expression data from three platforms was integrated into a unified data set of 11,864 genes using a factor analysis model without batch effects [51][52][53] . The processed final gene expression data was downloaded from the TCGA website in a format of matrix, in which one row for each gene and one column for each of the sample (https://tcga-data.nci.nih.gov/docs/publications/ov_2011/).
From the prepared gene expression matrix of ovarian cancer, we extracted the expression values of the mutated EMT related-genes in stage III and IV. In total, there are 65 EMT related-genes with somatic mutations in ovarian cancer. Next, we extracted all the 65 gene expression profiles from 381 stage III samples and 79 stage IV samples of ovarian cancer. We determined the gene expression changes of the transition between stage III and IV using the student test. Finally, we found 35 genes with paired t-test P-values less than 0.05. All the statistic tests and heatmap generations were using R programing environment (R Development Core Team 2008). Reconstructing a protein-protein interaction network related to EMT genes. To present the underlying biological mechanisms related to EMT genes, we extracted a protein-protein interacting relation between 377 EMT-related genes with the remaining human genes. To this end, we collected a non-redundant human interactome based on all the known protein-protein interactions using PathCommons, containing 3629 nodes and 36034 protein-protein links. It is noteworthy that the collected protein-protein interactions are from pathway databases (HumanCyc, NCI signaling pathway database, Reactome, and KEGG), which have biological significance. The final interactome represents pathway-based gene-gene interaction links. To extract a sub-network related to the 377 EMT-related genes of interest, we used the similar approach implemented in our previous study 54 . In this algorithm, all input genes were mapped to the human interactome, which was used to produce a sub-network with input genes connected by their shortest path.
In spite of its high complexity, the biological network often follows a few simple rules that may relate to its function 55 . Generally, the topological characters may give more clues to reveal the potential function for a network. To decompose the reconstructed interactome from our 377 EMT-related genes, topological analyses were conducted using the NetworkAnalyzer plugin in Cytoscape 2.8 ( Fig. 5B-D) 56 . Often the amount of connections at each node was represented as degree in a network 55 . Finally, closeness centrality was used to reveal the shortest steps for one node to reach another 55 . The final network visualization was performed using Cytoscape 56 .

Web interface development.
The reliable open source relational database MySQL was used to store all the data and annotations in dbEMT on a Linux server. The CGI Web-based interface to the database is implemented using Perl. Using the Perl CGI module and JavaScript technology, web pages for each gene in the database are generated. In dbEMT, all the genes are annotated comprehensively with hyperlinks to their original data resources (Fig. 7A). Gene expression in various normal tissues and cancer samples are represented in a colored bar chart (Fig. 5A). In addition, the extensive literature evidence associated with EMT genes are also complied and highlighted with keywords related to EMT or diseases.
Our dbEMT provides a user-friendly web interface to perform text query (Fig. 7B), or to run a sequence similarity search (BLAST) against the nucleotide and protein sequences in dbEMT (Fig. 7C).
In the text-based query page, six different powerful input forms are provided for the Entrez Gene ID, pathway and disease information, genomic location, literature evidence, and gene expression range in normal/cancer samples. Additionally, a quick text search for GeneID, gene symbol, and gene alias is at the top right of each page, and is convenient for a user to obtain any data in the database, especially literature-based annotations. Furthermore, users can browse the data in dbEMT in a variety of ways, including significantly enriched pathway, related disease, reported linkage region, and chromosome number (Fig. 7D). For each related KEGG pathway, the marked chart is provided to highlight the entire related EMT-related gene. Finally, for any advanced study, dbEMT provides all downloadable gene annotation and sequence information in a plain text format for all the collected 377 genes related to EMT.