Data collection of lncRNACancer and how to use lncRNACancer:

1. Data generation of lncRNACancer database

    Cancer gene collection

    Calculating co-expression between cancer genes and lncRNA with matched samples for 11 TCGA cancer types

2. Information for cancer genes and lncRNAs

    General information and literature evidence

    Gene expression profile

    Co-expressing lncRNAs

    Gene regulation

    Mutation information

    Homologs

    Protein-protein interaction

    lncRNA information and expression

3. Query and search database

    Text search of cancer lncRNA gene

    Quick access information in database

    BLAST all cancer genes

4. Browse database

    By chromosome and cancer types

    By cancer gene set and KEGG pathway

5. Data download

6. Reference

Long non-coding RNA (lncRNA) refers as non-coding transcripts longer than 200 bases and are abundant in various normal and cancer tissues (Kung, Colognori et al. 2013). In general, lncRNAs have relatively low expression than protein-coding genes which more tissue-specificity (Derrien, Johnson et al. 2012). Rather than being regarded transcriptional , there is emerging evidence that lncRNAs play important roles in regulation of gene expression through influencing chromatin modification, transcriptional complexes targeting, mRNA splicing, protein translation (Rinn and Chang 2012, Roberts, Morris et al. 2014). In addition, recent accumulated evidences about dysregulation of lncRNA indicate their critical roles to activate different hallmark of human disease such as cancer by influencing gene expression in epigenetic, transcriptional and post-transcriptional level (Prensner and Chinnaiyan 2011).

The primary aim of the database is to support cancer lncRNA research by maintaining a high quality cancer lncRNA/coding-gene co-expression network that serves as a comprehensive and accurately gene regulatory network for cancer lncRNAs , with extensive cross-references and querying interfaces freely accessible to the scientific community.

Intuitively, protein-coding genes that are neighbors or overlap with the expressed lncRNAs are thought to be in close with lncRNA function with concordant expression patterns (Cabili, Trapnell et al. 2011, Spurlock, Tossberg et al. 2015). However, the expression levels of majority lncRNAs show more discordant expression pattern with their protein-coding gene neighbors than expected in various model organisms (Guttman and Rinn 2012, Nam and Bartel 2012, Pauli, Valen et al. 2012, Zhang, Liao et al. 2014). Therefore, the mere physical proximity of genes to a specific lncRNA is not useful to imply cellular functional clues. More importantly, thorough searches and analyses of the interactions between lncRNA and non-neighboring genes may help to infer potential biological function. However, these is rare data resource to provide the comprehensive co-expression pattern of lncRNAs and well-known cancer genes across multiple cancer types. This lncRNANet database contains the summarized billons of pre-computed co-expression pattern for 11 cancers from 2922 matched TCGA samples with both lncRNA and coding-gene expression. This resource will enable researcher to explore lncRNA expression pattern, their affected cancer genes and pathways, biological significance in the context of specific cancer types and other useful annotation related to particular kind of lncRNA-cancer gene interaction.

Exhaustive cancer gene collection:
The first step to explore the co-expression between lncRNAs and cancer genes is collecting a reliable cancer gene list. To this aim, we performed extensive data collection. The Human oncogene (Zhao, Sun et al. 2013) and experimentally characterized tumor suppressor (Zhao, Sun et al. 2013) were collected from our previous network analysis. A non-redundant list of 2102 human cancer genes were also downloaded from allOnco database. This gene list are integrated from 8 previous studies (Akagi, Suzuki et al. 2004, Futreal, Coin et al. 2004, Sjoblom, Jones et al. 2006, Huret, Ahmad et al. 2013, Vogelstein, Papadopoulos et al. 2013). For all the cancer genes, we reconciled them with current NCBI Entrez gene database where outdated synonyms were updated. In total, we have 9 cancer gene set as below:

Based on the expression profiles of cancer genes and lncRNAs, we first computed expression correlation scores, which represent the potential regulation between gene and lncRNA. We estimated the expression correlation among all the genes each collected cancer gene set and all lncRNA transcripts using the s correlation method that is implemented in the R language package (version 3.0.2) to calculate their expression correlation scores and corresponding P-values. For all the P-values in each type of cancer genes, a false discovery rate (FDR) was applied to correct the statistical significance of multiple testing. For all the pairs between cancer genes and lncRNSs, we required their absolute expression correlation scores greater than 0.3 and the FDR adjusted P-values be less than 0.01. According to these criteria, we have an appropriate number of pairs in each type of regulatory relationship.Spearman

For cancer genes, the annotattions are represented on seven different types of pages, including general information view, lncRNA view, gene expression view, gene regulation view, gene mutation view, gene homologs view, and gene interaction view.

The general information page is like the following:

In this page, users can find the general information about collected cancer genes from various data sources. It is easy to switch to other annotations by clicking the hyperlink at the top of the page.

User can find the details of the co-expressed lncRNAs in the lncRNA highlight page as below. The lncRNA transcription ID, Cancer Network, Correlation coefficient, Raw P-value, and FDR corrected P-values can be found in the page.

The gene expression page is as below:

In the page, users can find gene expression profiles from 184 human tumor samples and 84 normal tissue samples from BioGPS. It is easy to view all the sample information by clicking the hyperlink in the profile images. Some genes have multiple probes; to provide an unbiased view for users, we presented all the gene expressions from all probes without any modification.

User can obtain all the sample inforamtion by clicking on the expression images.

The gene regulation page appears as follows:

The transcription factor regulation and post-transcriptional modification information were integrated from the TRANSFAC and dbPTM databases. In addition, the methylation in promoter regions was annotated based on data from the DiseaseMeth database.

The gene mutation page appears as follows:

All the cancer related mutations were collected from the COSMIC database.

The gene homolog page appears as follows:

All the homologs from NCBI HomoloGene were collected from its public website data portal database.

The gene interaction page appears as follows:

All the related protein-protein interactions were collected from the PathwayCommon database; we further divided the interactions into three main types, including "Physical Interaction," "Metabolic Interaction," and "Signaling Interaction."

lncRNA information and expression:

User can access all the lncRNA information in lncRNA annotation page. We also downloaded the gene expression summary across multiple cancer and normal tissues from MItranscriptome.

All the cancer genes and their annotations in our database are searchable. The text search (Query) and sequence-based BLAST (BLAST) are provided.

Text search of various annotation in our database

Users can search against the lnCaNet by typing its name, accession IDs and its characteristics, including genomic location, interaction partner, mutation, biological pathway, and genetic disease. In total, we provided four different search forms for users, including "Gene General Information Search", "Literature Search", "Mutation Search", and "Other Annotation Search" allow users to access general information, literature-based information, mutation, and other annotation information respectively.

The search is performed by typing keywords into any field separately or into several fields simultaneously in the query forms. Generally, text search information in the each searching form mainly includes three steps. Take the basic information query as an example below

The lnCaNet database supports browsing cancer lncRNA genes using cancer types and curated organ and tissue types. In the cancer type page, users can explore the 288 cancer lncRNA types. In addition, to help users get a bird's eye view for specific topic of cancer lncRNA genes, the classified organ and tissue types were provided.

In addtion, lnCaNet also supports annotation-based browsing including chromosome.

Using different chromosomes

From the Browser page, users can browse the genes in lnCaNet by their chromosome location. Moreover, users can obtain the cancer lncRNA gene lists from different cancer type and organ tissue information.

Users can freely download all the pre-computed 110 cancer gene-lncRNA co-expression network in our lnCaNet for academic researchers, but not for profit purposes. Please access Download page.

If users have any suggestion to add new comment to records in current lnCaNet or to revise wrong information in current lnCaNet,please send us email directly.