Colorectal cancer (CRC) is a cancer of growing incidence that associates with a high mortality rate worldwide. There is a poor understanding of the heterogeneity of CRC with regard to causative genetic mutations and gene regulatory mechanisms. Previous studies have identified several susceptibility genes in small-scale experiments. However, the information has not been comprehensively and systematically compiled and interpreted. In this study, we constructed the gbCRC, the first literature-based gene resource for investigating CRC-related human genes. The features of our database include: (i) manual curation of experimentally-verified genes reported in the literature; (ii) comprehensive integration of five reliable data sources; and (iii) pre-computed regulatory patterns involving transcription factors, microRNAs and long non-coding RNAs. In total, 2067 genes associating with 2819 PubMed abstracts were compiled. Comprehensive functional annotations associated with all the genes, including gene expression profiles, homologous genes in other model species, protein-protein interactions, somatic mutations, and potential methylation sites. These comprehensive annotations and this pre-computed regulatory information highlighted the importance of the gbCRC with regard to the unexplored regulatory network of CRC. This information is available in a plain text format that is free to download.
Colorectal cancer (CRC) is the third most diagnosed cancer, resulting in high mortality rates worldwide1. In 2012, approximately 1.4 million individuals were diagnosed with CRC across the globe. The highest rates of incidence are in developed countries such as New Zealand, Australia, and Western Europe. Not surprisingly, the prognosis often decreases substantially for those individuals in which the disease has metastasized to other tissues and organs. Only 11% (colon cancer) and 12% (rectal cancer) of patients with distant metastases survive the first five years2. Therefore, the development of effective biomarkers to monitor CRC development is needed to improve the diagnosis rate at late stages.
Similar to other malignancies, CRC is caused by uncontrolled cell growth. However, it is remarkably heterogeneous at multiple cellular and molecular levels. For example, uncontrolled cell proliferation in CRC may result from the abnormal gene expression of tumor suppressors and oncogenes3, promoter methylation4, copy number alterations of cancer genes that control cell proliferation and death5, microRNA mutations and abnormal expression6, and long non-coding RNAs7. Thus, it is critical to prioritize the genetic and genomic suspects with an understanding of their tumorigenic mechanisms in future studies.
Before the development of high-throughput genome-wide sequencing, countless biochemical experimental and genetic association studies resulted in the accumulation of much data without systematic integration. To reuse and mine these published articles, we developed the first evidence-based gene resource for CRC by extensive literature curation and data integration at http://gbCRC.bioinfo-minzhao.org/. By extensive literature curation and data integration, it is expected that our gbCRC will be valuable for researchers to efficiently obtain functional and mutational data on CRC genes.
Gene collection pipeline
To address the genetic complexity of CRC, we conducted extensive data integration and literature curation. The final non-redundant 2309 relevant genes were stored in a publicly available database of human CRC-implicated genes. The CRC gene collection was mainly based on five data sources (Fig. 1A), including OMIM (Online Mendelian Inheritance in Man)8, GAD (The Genetic Association database)9, genes manually curated from GeneRif10, genome-wide association studies from GWASCatalog11 and meta-analyses results of genetic association studies from the CRCGene database (http://www.cphs.mvm.ed.ac.uk/projects/CRCgene/index.php). As the most authoritative compendium of human disease-related genes, OMIM did not include many genes. We obtained 25 genes associated with CRC from OMIM. The GAD database is an archive of published human genetic association studies, which contains curated information on each candidate gene. In total, we collected 279 unique human genes from GAD from 637 published studies. Additionally, 11 candidate genes were downloaded from three genome-wide association studies in the GWASCatalog database. From the CRCGene database, 95 unique human genes were collected. After combining data from these four publically-available genetic resources, we created a non-redundant gene list with 691 human genes.
To provide a detailed and precise regeneration gene resource with supporting literature evidence, we performed an extensive literature query of the GeneRif database on January 10, 2015 using the Perl regular expression to match those sentences with both “colorectal” and “cancer” keywords: [(colorectal OR colon OR rectal OR bowel) AND (cancer OR tumor OR carcinoma OR adenocarcinoma)]. In total, we retrieved 7581 relevant short descriptions related to CRC from 5378 PubMed abstracts. GeneRif (Gene Reference Into Function) is a collection of short descriptions on the function of different genes in the Entrez Gene database10. However, these short descriptions from GeneRif did not contain full abstracts for accurate data curation. Therefore, we extracted the 5378 abstracts with full text for manual checking. In general, literature curation had three steps: (i) grouping the 5378 extracted abstracts based on their computed similarity scores using the ELink function in Entrez; (ii) collecting contents related to CRC from grouped abstracts; and (iii) manually collecting gene names from the CRC-related sentences and mapping the gene names to Entrez gene IDs. These three curation steps provided high-quality data by cross-checking if and how the curated abstract was related to CRC. For a unified functional annotation, we regarded Entrez gene IDs as the unique key in all the data tables of our gbCRC database to link the same genes from different bioinformatics resources. To obtain accurate literature evidence, we collected the species information and the gene alias and manually mapped this information to the official HUGO gene symbol. For example, two negative results were described in the following sentences: “study does not support a role of COX2 and UGT1A6 genetic variations in the development of colon cancer”12 and “neither genetic variation nor allele-specific expression at TGFBR1 is likely to be a major colorectal cancer risk factor”13. In addition, in the first sentence, the gene name COX2 was one of the synonyms of PTGS2 in the current Entrez gene database. Thus, we deleted the associated relationship from the literature with the PTGS2 gene. As some of the studies were not conducted in the human, we mapped the collected genes to their corresponding human homologous groups using the NCBI HomoloGene database as described in previous studies14,15,16. In total, we pinpointed 1618 Entrez human homologous genes from 5515 PubMed abstracts. By integrating the 691 genes from other public databases, we consolidated 2309 human genes in Table S1.
All curated genes and relevant bioinformatics annotations were stored in a MySQL-based database on a Linux server. In the gbCRC, we provided two approaches to access our data: text-based query (Fig. 1B) and sequence similarity-based BLAST search. Users can retrieve a list of genes with their interesting annotations by using the text-based query. Users can also annotate unknown sequences via BLAST against all the DNA and protein sequences in the gbCRC. Lastly, users can search the data in a variety of ways, including the number of supporting references, the highlighted KEGG pathway, and genomic positions.
The annotations of a representative gbCRC gene were classified into eight categories by clicking the labels “expression”, “general information”, “homolog”, “literature”, “interaction”, “lncRNA”, “mutation”, and “regulation” (Fig. 1C). The keyword-marked references were provided on the “literature” page. The pre-computed co-expression correlation coefficients and the corrected statistical P-values between CRC-implicated genes and lncRNAs in matched cancer samples were presented on the “lncRNA” page. The “homolog” page provided the homologous genes from another 11 model species such as the mouse. On the “regulation” page layout, which listed the interactions with transcription factors, there was information on post-translational modifications and potential methylation sites.
Potential role of retinoic acid in colorectal cancer
To provide an overview of the data quality, we counted the number of supporting references for the collected 2,309 CRC-implicated genes. This preliminary statistical analysis not only highlighted the high tumor susceptibility of these well-studied genes, but also confirmed the heterogeneous genetic events of CRC. In total, there were 21 genes with more than 30 concordant positive supporting references. The majority of the genes (2172 genes, 94.06% of the total number of genes) had less than ten supporting references. These 21 genes were further classified according to their roles in cancer. We found six tumor suppressors and seven oncogenes (Fig. 2A). The remaining eight genes may also have contributed to cancer progression, but their roles in its suppression or progression were not immediately clear. Further functional enrichment analysis revealed that 17 of the 21 genes increased with tumor incidence and 15 of them regulated cell apoptosis. More importantly, these tumor suppressors and oncogenes were highly connected to each other in CRC (Fig. 2B).
A fundamental goal of biomedical research is to discover effective drugs to cure human diseases. To demonstrate the use of our gbCRC, we constructed a compound-gene interaction network using the STITCH database (http://stitch.embl.de/). Intriguingly, the compound-gene interaction enrichment module revealed that 16 of the 21 well-studied CRC-implicated genes were predicted to interact with retinoic acid (C20H28O2) (Fig. 2C). As the most active metabolite of vitamin A, retinoic acid can trigger programmed cell death in certain types of tumor cells17. Additionally, aberrant retinoic acid metabolism is implicated in tumorigenic events in cancer cell lines17. Furthermore, three metabolic enzymes for retinoic acid, CYP26A1, CYP26B1 and LRAT, are significantly overexpressed in CRC18. These enzymes were also significantly associated with poor prognosis in colorectal cohorts18. Our rudimentary analysis of a potential gene-compound interaction network allowed us to relate retinoic acid to CRC. Retinoic acid mediates the functions of vitamin A for individual growth and development, and retinoids can interact with estrogen signaling in breast cancer19. Although retinoid signaling is often compromised early in cancer progression19, its broader effect on CRC has been recognized recently20. Since a reduction in retinoid signaling may be required for cancer progression, retinoids can be used to induce differentiation and arrest proliferation in a clinical setting. Although it is ideal to treat cancer with retinoids, the delivery of retinoids to patients is hindered by their rapid metabolism. Regardless, our constructed gene-compound network may benefit cancer prevention strategies related to retinoids for CRC patients. Taken together, these results offer insight into the functional importance of several well-studied CRC-related genes with ample supporting evidence for their relevance to retinoid signaling. More extensive metabolic network analysis may provide more insight into these important compound in cancer progression21,22,23.
Abundant mutations within well-studied protein-coding tumor suppressors and oncogenes
A fundamental problem in cancer biology is discovering potential tumor suppressors and oncogenes, which may help us to understand the key genetic drivers for cancer progression. As shown in Figure S1, 13 well-studied tumor suppressors and oncogenes were highly mutated in CRC. Among 212 TCGA tumor samples, 206 of the individuals had at least one mutational event (mutational rate, 97%). However, the most abundantly mutated six genes covered 205 samples: APC (mutational rate, 76%), TP53 (52%), KRAS (42%), PIK3CA (20%), BRAF (10%) and CTNNB1 (5%). Notably, the majority of the mutations were enriched in a few important oncogenic pathways (Fig. 3A,B). A previous study showed that APC is truncated in the “mutation cluster region” close to codon 1,300. The mutations often result in the loss of C-terminal APC functions. The APC gene, located at 5q21-q22, encodes a critical Wnt signaling regulator (Fig. 3A)24. In the absence of the Wnt signal, APC can form a complex with AXIN1 and GSK3B to mediate degradation of β-catenin (CTNNB1) via phosphorylation. In this way, the abundance of β-catenin in cells is strictly controlled. Upon activation of Wnt signaling, however, the multi-protein complex (APC, AXIN1 and GSK3B) is disassembled, resulting in a high concentration of β-catenin. Upon translocation to the nucleus, β-catenin triggers HNF4A (hepatocyte nuclear factor 4) activity by displacing the suppressive effect of TLE (transduction enhancer-like protein). This triggers the expression of important genes associated with cell proliferation (MYC and CD1)24. Therefore, the truncated APC can induce higher levels of β-catenin (CTNNB1) and its downstream cell proliferation genes. In total, these two genes accounted for 165 samples (combined mutational rate, 77.83%). Additional analysis of other genes in the Wnt signaling pathway found that HNF4A had a mutational rate of 14% (Figure S2). Both GSK3B and AXIN1 had a mutational rate of 2%. For the 17 genes from the Wnt family, 14 were sporadically mutated with a rate less than 1%. The remaining three (WNT3, WNT5A, WNT10B) had a mutational rate of 2%. For the ten genes from the Wnt signaling receptor family (FZD genes), FZD3 had the highest mutational rate of 6%. Combining all the genes, 186 samples had at least one mutational event, accounting for 88% of the TCGA cohort. Mutations within TP53 are expected in CRC. By comparing the pan-cancer mutational pattern of APC and TP53, we found that the high mutational rate of APC is specific for CRC, placing it at the top of mutated cancer cohorts (Fig. 3C). On the contrary, TP53 is highly mutated across multiple cancers, not just in CRC (Figure S3). This specificity of the APC mutation may indicate that it is a top driver in CRC initiation. TP53 is more important at the late stage of cancer development.
The other most mutated oncogenes KRAS and BRAF belong to the RAS signaling pathway (Fig. 3B). Their downstream molecules include ZHX2, MAPK2K7 and MAPK1. In total, these five genes had a combinational mutation rate of 55% (Fig. 3D). Despite MAPK2K7 and MAPK1 having a mutational rate of only 1%, the direct downstream gene of KRAS (ZHXS) was mutated in 15 individuals.
Role of microRNAs in colorectal cancer
In recent years, an increasing number of miRNAs has been associated with CRC6. We collected 119 miRNAs with important roles in CRC. To assess their potential functions, we first used the TCGA mutational data to isolate those miRNAs with mutations in tumor samples. Because miRNAs are relatively short in terms of nucleotide sequence, single nucleotide variants (SNVs) may not be easily detected. Therefore, we investigated the meditate scale copy number variations (CNVs) for the miRNAs. In total, we found 34 miRNAs with CNVs in the 53 CRC patients from a cohort of 212 TCGA tumor samples (Fig. 4). For the top eight miRNAs with a mutational frequency of approximately 2%, only four miRNAs gained more copy number instead of losing copy number. To further explore the functional distribution of these four miRNAs with more copy number, we performed a target-based functional enrichment analysis using DIANA-miRPath (Table 1). Not surprisingly, the majority of the enriched pathways were related to key cancer pathways such as the cell cycle and p53 signaling pathways. For the cell cycle pathway, 42 genes were the targets of the five miRNAs (Figure S4, corrected P-value = 4.48E-20). The top two mutated miRNAs MIR1-1 and MIR133A2 had a copy number gain in approximately 7% of patients. Interestingly, these two miRNAs had a tendency to co-mutate in the same patients, which may be explained by their relative proximity to each other within the genome. MIR1-1 associates with MET-dependent proliferation in CRC25. However, these two miRNAs are down-regulated in CRC26,27, which conflicts with the consistent gene copy number gain. With more copy number, gene expression is often relatively high in agreement with the gene dosage effects. Further experimental validation is needed to test whether the down-regulation of these two miRNAs was caused by other epigenetic mechanisms such as DNA hypermethylation28. We also observed a similar scenario with two other miRNAs (MIR15A and MIR16). These two miRNAs cluster at the genomic region of 13q14.3, which is frequently deleted in chronic lymphocytic leukemia. MIR16 has also been reported to be down-regulated in CRC29. Furthermore, two additional miRNAs, MIR31 and MIR21, had more gene copies in the TCGA CRC cohort. Both MIR3130 and MIR2131 are up-regulated in CRC and are potential clinical biomarkers. In summary, the clinical significance of the MIR1-1/MIR133A2 and MIR15A/16 cluster in CRC has not been fully elucidated. Further tests should focus on their dysregulation with respect to their epigenetic effects.
Copy number mutations of highly connected genes
To improve the systems level understanding of CRC, we constructed a pathway-based protein-protein interaction map for CRC. To avoid the high level of noise, we only used the 114 genes with ten or more supporting references to build the network. In addition, we only utilized reliable human PPIs summarized in a few popular biological pathways such as the KEGG and Reactome pathway database32 to avoid a potentially highly skewed degree distribution of physical interaction-based PPI networks. Based on our previous module searching method33, a sub-network from the entire human pathway-based interactome was reconstructed containing 99 genes and 204 PPIs (Fig. 5A). Of the 99 nodes, 87 were from our curated 114 CRC-related genes with ten or more supporting references. The remaining 12 were the linker genes, which formed a completely connected cellular map. Further network topological analysis also indicated that most nodes in the reconstructed map were closely connected. There were only 31 nodes with one connection, which indicated that the remaining 68 nodes had two or more connections. The degrees of all nodes followed a power law distribution P(k) ~ k−b, where P(k) was the probability that a node had connections with other k nodes and b was an exponent with an estimated value of 1.149. Previous topological analysis on the human PPI network revealed that most of nodes are sparsely connected with exponent b as 2.934. Compared to the entire human PPI network, our network was more closely connected. This finding was also confirmed in the shortest path distribution, where approximately 55.98% of the communication between nodes was reached by only three steps (Fig. 5C).
With high modularity, the highly connected nodes may have critical roles in the transfer of cellular signals using the shortest paths. To test whether these genes are abnormally expressed or functionally relevant to CRC, we conducted a systematic examination of the genetic variants. Based on the single nucleotide variants (SNVs), copy number variations (CNVs) and abnormal expression in tumor samples, we found that 79 highly connected genes were altered in 211 of 212 CRC tumor samples. As shown in Figure S3, APC and TP53, the top two mutated genes, were mostly SNVs. Intriguingly, the other eight highly mutated genes (mutational frequency, >10%) had more copy number changes. To further explore these CNVs with potentially important roles in cancer progression, we mapped our 79 highly connected genes to the CNV data of the TCGA CRC dataset (Figure S5). The top eight CNV-based genes (BCL2L1, MMP9, SNAI1, SRC, MYC, CDX2, PTP4A3, and ERBB2) had a copy number gain, and not a copy number loss. Interestingly, these mutated genes have roles in tumorigenesis as reported in the literature. Taking BCL2L1 as an example, it can influence tumor cell apoptosis by regulating the opening of the outer mitochondrial membrane channel. After further mapping the gene expression data of the matched tumor samples in the TCGA CRC cohort, we found that there was a concordant copy number gain and an up-regulation of BCL2L1 (Figure S6). Our results may provide clues for the important role of BCL2L1 in the progression of CRC by copy number gain, which induces gene expression by increasing the gene dosage effect. In summary, our reconstructed CRC map not only identifies multiple pathways related to a few known signaling pathways, but it also provides a broader mutational landscape for previously overlooked highly connected genes. Hopefully, this information will be useful for users wishing to conduct further screening on CRC patients.
In conclusion, we developed a literature-based knowledge base of CRC genes with comprehensive annotations. The associated comprehensive annotations and pre-computed regulatory information highlighted the importance of the gbCRC in the unexplored field of CRC development. For advanced systems biology analysis, a complete downloadable gene list with functional features is available in a plain text format. Although we performed extensive literature curation and data integration, it is important to acknowledge the difficulty of performing an error-free search. Therefore, we provided a high confidence gene list with 162 genes from 2 or more data sources and with at least 5 supporting published articles.
Our systematic analysis of CRC-related genes from the literature resulted in numerous testable hypotheses regarding critical tumor suppressors, oncogenes, miRNAs, and oncogenic pathways. Most importantly, our integrative analyses of these genes between various annotations helped to uncover several critical biological events, as well as to explain their underlying interplay in cancer progression. In addition, the massive precomputed co-expressed lncRNAs will be helpful for the CRC-specific lncRNA identification and subsequent analyses. Moreover, the curated genes are useful for the design of target-sequencing experiments on CRC. Lastly, it will be also useful for the genetic mutation prioritization and filtering for whole genome sequencing and whole exome sequencing data analysis.
To collect additional references in the future, we implemented an automatic literature search by using NCBI Entrez programming utilities, which will retrieve PubMed references based on the matching keyword(s). The Entrez reference similarity will be used to cluster the newly available references with those curated references in our gbCRC. With regard to the bioinformatics annotation, we also implemented an automatic system to integrate functional information from public data sources. The web page will be updated accordingly on an annual basis.
To present a comprehensive functional view for each gene in the gbCRC, we provided the basic gene information, sequences and crosslinks to the NCBI Entrez gene and Homologene databases (downloaded on April 8, 2015)35. We also imported the BioGPS mRNA expression profiling data from both normal and tumorigenic tissues36. In the BioGPS database, the relative expression scores were generated from Affymetrix chips based on fluorescent intensity of different genes. For each gene, there were multiple probes on the microarray. The final intensity value for each gene was computed using the data processing algorithm GCRMA37. To provide a genetic mutational overview, we annotated all the genes using the mutational data from COSMIC (V72)38. To present a comprehensive biological pathway, we annotated the CRC-implicated genes using BioCyc39 and the KEGG pathway40. Additional regulatory information on post-translational modifications41, methylation sites42, and protein-protein interactions from Pathway Commons (V5)32 was also included. For advanced bioinformatics or systems biology-based analyses, we provided the downloadable gene list in a plain text format.
Co-expressed long non-coding RNAs from TCGA matched cancer samples
To explore the co-expressed long non-coding RNAs (lncRNAs) related to the CRC-implicated genes, we downloaded the human lncRNA expression profile from Mitranscriptome43. The Mitranscriptome is comprised of lncRNA expression data generated from the assembly of the RNAseq data from The Cancer Genome Atlas (TCGA) tumor samples. We computed the correlation coefficients between CRC-implicated genes and the 17,250 lncRNAs in the Mitranscriptome using Spearman’s correlation based on the matched TCGA CRC samples44. The statistical P-values were calculated using R (version 2.14.0), and the false discovery rate (FDR) was adjusted for multiple testing. For each CRC-implicated gene and lncRNA pair, we set the expression correlation score to greater than 0.3 and the FDR-adjusted P-value to less than 0.01.
To present a network view for the CRC-implicated genes, we used the online tools STRING (http://string-db.org/) and STITCH (http://stitch.embl.de/) to run the network analysis on a small number of genes of interest. For the larger gene list such as the 114 curated genes with ten or more supporting references, we adopted sub-network extraction as described in a previous study33. To reveal the potential function of each node, topological analyses were conducted using the NetworkAnalyzer plugin in Cytoscape 2.8 (Fig. 5B,C)45. The degree indicated the number of connections for each node in a network46. The short path was used to characterize the shortest route for each node to reach another46. The final network visualization was performed using Cytoscape 2.845. Throughout the study, the CRC-related mutational analyses were conducted using cBio portal47.
How to cite this article: Zhao, M. et al. A gene browser of colorectal cancer with literature evidence and pre-computed regulatory information to identify key tumor suppressors and oncogenes. Sci. Rep. 6, 30624; doi: 10.1038/srep30624 (2016).
Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136, E359–E386 (2015).
Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2015. CA Cancer J Clin 65, 5–29 (2015).
Chan, S. K., Griffith, O. L., Tai, I. T. & Jones, S. J. Meta-analysis of colorectal cancer gene expression profiling studies identifies consistently reported candidate biomarkers. Cancer Epidemiol Biomarkers Prev 17, 543–552 (2008).
Berman, B. P. et al. Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina-associated domains. Nat Genet 44, 40–46 (2012).
Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat Genet 45, 1134–1140 (2013).
Schmit, S. L. et al. MicroRNA polymorphisms and risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev 24, 65–72 (2015).
Xue, Y. et al. Genome-wide analysis of long noncoding RNA signature in human colorectal cancer. Gene 556, 227–234 (2015).
Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. McKusick’s Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res 37, D793–D796 (2009).
Becker, K. G., Barnes, K. C., Bright, T. J. & Wang, S. A. The genetic association database. Nat Genet 36, 431–432 (2004).
Aronson, A. R., Mork, J. G., Gay, C. W., Humphrey, S. M. & Rogers, W. J. The N. L. M. Indexing Initiative’s Medical Text Indexer. Stud Health Technol Inform 107, 268–272 (2004).
Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42, D1001–D1006 (2014).
Thompson, C. L. et al. No association between cyclooxygenase-2 and uridine diphosphate glucuronosyltransferase 1A6 genetic polymorphisms and colon cancer risk. World J Gastroenterol 15, 2240–2244 (2009).
Carvajal-Carmona, L. G. et al. Comprehensive assessment of variation at the transforming growth factor beta type 1 receptor locus and colorectal cancer predisposition. Proc Natl Acad Sci USA 107, 7858–7862 (2010).
Brown, G. R. et al. Gene: a gene-centered information resource at NCBI. Nucleic Acids Res 43, D36–D42 (2015).
Zhao, M., Sun, J. & Zhao, Z. TSGene: a web resource for tumor suppressor genes. Nucleic Acids Res 41, D970–D976 (2013).
Kong, L., Cheng, L., Fan, L. Y., Zhao, M. & Qu, H. IQdb: an intelligence quotient score-associated gene resource for human intelligence. Database (Oxford) 2013, bat063 (2013).
Osanai, M. & Petkovich, M. Expression of the retinoic acid-metabolizing enzyme CYP26A1 limits programmed cell death. Mol Pharmacol 67, 1808–1817 (2005).
Brown, G. T. et al. The expression and prognostic significance of retinoic acid metabolising enzymes in colorectal cancer. PLoS One 9, e90776 (2014).
Tang, X. H. & Gudas, L. J. Retinoids, retinoic acid receptors, and cancer. Annu Rev Pathol 6, 345–364 (2011).
Perraud, A. et al. Retinoid acid receptors in human colorectal cancer: An unexpected link with patient outcome. Exp Ther Med 2, 491–497 (2011).
Zhao, M. & Qu, H. Human liver rate-limiting enzymes influence metabolic flux via branch points and inhibitors. BMC Genomics 10 Suppl 3, S31 (2009).
Zhao, M. & Qu, H. PathLocdb: a comprehensive database for the subcellular localization of metabolic pathways and its application to multiple localization analysis. BMC Genomics 11 Suppl 4, S13 (2010).
Zhao, M. & Qu, H. High similarity of phylogenetic profiles of rate-limiting enzymes with inhibitory relation in Human, Mouse, Rat, budding Yeast and E. coli. BMC Genomics 12 Suppl 3, S10 (2011).
Fearon, E. R. Molecular genetics of colorectal cancer. Annu Rev Pathol 6, 479–507 (2011).
Reid, J. F. et al. miRNA profiling in colorectal cancer highlights miR-1 involvement in MET-dependent proliferation. Mol Cancer Res 10, 504–515 (2012).
Sarver, A. L. et al. Human colon cancer profiles show differential microRNA expression depending on mismatch repair status and are characteristic of undifferentiated proliferative states. BMC Cancer 9, 401 (2009).
Luo, X., Burwinkel, B., Tao, S. & Brenner, H. MicroRNA signatures: novel biomarker for colorectal cancer? Cancer Epidemiol Biomarkers Prev 20, 1272–1286 (2011).
Chen, W. S. et al. Silencing of miR-1-1 and miR-133a-2 cluster expression by DNA hypermethylation in colorectal cancer. Oncol Rep 28, 1069–1076 (2012).
Qian, J., Jiang, B., Li, M., Chen, J. & Fang, M. Prognostic significance of microRNA-16 expression in human colorectal cancer. World J Surg 37, 2944–2949 (2013).
Wang, C. J. et al. Clinicopathological significance of microRNA-31, -143 and -145 expression in colorectal cancer. Dis Markers 26, 27–34 (2009).
Toiyama, Y. et al. Serum miR-21 as a diagnostic and prognostic biomarker in colorectal cancer. J Natl Cancer Inst 105, 849–859 (2013).
Cerami, E. G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39, D685–D690 (2011).
Zhao, M., Li, X. & Qu, H. EDdb: a web resource for eating disorder and its application to identify an extended adipocytokine signaling pathway related to eating disorder. Sci China Life Sci 56, 1086–1096 (2013).
Jin, Y., Turaev, D., Weinmaier, T., Rattei, T. & Makse, H. A. The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks. PLoS One 8, e58134 (2013).
Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 39, D38–D51 (2011).
Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci USA 101, 6062–6067 (2004).
Gharaibeh, R. Z., Fodor, A. A. & Gibas, C. J. Background correction using dinucleotide affinities improves the performance of GCRMA. BMC Bioinformatics 9, 452 (2008).
Forbes, S. A. et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res 43, D805–D811 (2015).
Karp, P. D. et al. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 33, 6083–6089 (2005).
Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res 36, D480–D484 (2008).
Lee, T. Y. et al. dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res 34, D622–D627 (2006).
Lv, J. et al. DiseaseMeth: a human disease methylation database. Nucleic Acids Res 40, D1030–D1035 (2012).
Iyer, M. K. et al. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet 47, 199–208 (2015).
Cancer Genome Atlas N. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L. & Ideker, T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–432 (2011).
Barabasi, A. L. & Oltvai, Z. N. Network biology: understanding the cell’s functional organization. Nat Rev Genet 5, 101–113 (2004).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, pl1 (2013).
This work was supported by the National Natural Science Foundation of China (No. 31171270) and a research start-up fellowship from the University of the Sunshine Coast to MZ.
The authors declare no competing financial interests.
About this article
Cite this article
Zhao, M., Liu, Y., Huang, F. et al. A gene browser of colorectal cancer with literature evidence and pre-computed regulatory information to identify key tumor suppressors and oncogenes. Sci Rep 6, 30624 (2016). https://doi.org/10.1038/srep30624
Scientific Reports (2017)