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Get an overview of Lnc2Cancer 3.0 from the Home page.

Best experienced using the browsers Chrome Firefox Safari or IE version (9,10,11)

The Home page is displayed in Figure 1-1 and Figure 1-2:

1.Main functions of the database are provided in menu bar form (boxed in red).

2. Click this logo to hide the menu bar.

3. A quick way to get the e-mail of the publisher or turn to the download page and the help page.

4. The brief introduction of Lnc2cancer 3.0.

5. A quick search for lncRNAs, circRNAs and cancers.

6. Quick browse of the regulatory mechanism, function and the clinical application of lncRNAs and circRNAs.

Figure 1-1

1. Overview of the development trend of cancer-related lncRNAs and circRNAs in recent years.

2. New features of Lnc2Cancer 3.0 compared to Lnc2Cancer 2.0.

3. Overview of the images that Single Cell Web Tools can provide.

4. Overview of the images that RNA-seq Web Tools can provide.

Figure 1-2

How to use Browse page?

In the browse page, two different ways are provided to browse the lncRNAs and circRNAs. The Browse page is displayed in Figure 2-1:

1. Click the logos to browse the corresponding lncRNAs and cicRNAs according to different regulatory mechanisms.

2. Click the logos to browse the corresponding lncRNAs and cicRNAs according to different biological functions.

3. Click the logos to browse the corresponding lncRNAs and cicRNAs according to different clinical applications.

4. Click any of the tissue logos to get the information of related lncRNAs and circRNAs.

5. Quick browse of cancers, lncRNAs and circRNAs.

Figure 2-1

Use the Search page to enter a keyword and filter results.

Lnc2Cancer 3.0 provides general search and advanced search. The Search page is displayed in Figure 3-1:

1.Click to choose general search or advanced search.

2. Input your interested lncRNA, cicRNA or cancer for general search.

3. Input your interested lncRNA or cicRNA.

4. Input your interested cancer.

5. Dysregulation pattern including up-regualted, down-regulated and differential expression could filter the results.

6. Sample types including tissues, cell lines and blood could filter the results.

7. Users can choose to exclusively display lncRNA and circRNA, or to display all.

8. Different options of regulatory mechanism, biological function and clinical application could filter the results.

Figure 3-1

How to read Results?

Lnc2Cancer 3.0 results are organized in a data table, with a single association record on each line that contains lncRNA name, cancer name, methods, expression pattern, biomarker types and PubMed ID.

The result page is displayed in Figure 4-1:

1. Result for your search.

2. Click to download data.

3. Users can input keywords from any column to filter the results.

4. Click to check the literature of the entry.

5. Click to check the detail information of the entry.

Figure 4-1

How to read detail information of the entry?

Detailed information of a specific lncRNA-cancer association is displayed in Figure 5-1:

1. Users can get the basic information for the hit lncRNA.

2. Users can get the fuction for the hit lncRNA.

3. Users can get the information of cancer for the hit lncRNA.

4. Users can get the external links for the hit lncRNA.

5. Click to check the literature of the entry.

6. Users can search the external annotation information for the lncRNA by clicking on the corresponding hyperlink.

Figure 5-1

Use key interactive and customizable functions of single cell datasets in the Single Cell Web Tools.

The Single Cell Web Tools provides key interactive and customizable functions including general information, clustering, heatmap and differential expression analysis for lncRNAs based on 49 single cell datasets.

Introduce

Detailed information of the Single Cell Web Tools contained complex functions for mining single cell datasets is displayed in Figure 6-1:

1. Main functions of the Single Cell Web Tools are provided in menu bar form (boxed in red).

2. Detailed introduction of main functions of Single Cell Web Tools.

3. Detailed information of single cell datasets.

Figure 6-1

Cluster

This function allows users to perform cluster analysis based on lncRNA expression is displayed in Figure 6-2:

1. Select a sample for cluster.

2. Click the "Cluster" button: Cluster function will generate a cluster plot of cluster analysis based on lncRNA expression using high-dimensional reduction method tNSE and UMAP.

Figure 6-2

Heatmap

This function provides heatmap of differential expressed lncRNAs among diverse clusters is displayed in Figure 6-3:

1. Select a sample for cluster.

2. Click the "Heatmap" button: Heatmap function will generate a heatmap of cluster analysis based on selected single cell dataset.

Figure 6-3

DEA

This function allows users to obtain differential expression information and violin plot of lncRNAs is displayed in Figure 6-4:

1. Select a sample for differential analysis.

2. Select a threshold value of fold change for differential analysis.

3. Select a threshold value of p value for differential analysis.

4. Click the "DEG" button: DEA function will generate a list of differentially expressed lncRNAs based on input parameters.

5. Click the "Eye" button: DEA function will generate a violin plot of differentially expressed lncRNAs based on input parameters.

Figure 6-4

Use complex functions for mining cancer-related lncRNAs in the RNA-seq Web Tools.

The RNA-seq Web Tools contained complex functions for mining cancer-related lncRNAs including general information, differential expression analysis, box plotting, stage plotting, survival analysis, similar lncRNAs identification, correlation analysis, network construction and TF motif prediction.

Introduce

Detailed information of the RNA-seq Web Tools contained complex functions for mining cancer-related lncRNAs is displayed in Figure 7-1:

1. Main functions of the RNA-seq Web Tools are provided in menu bar form (boxed in red).

2. Detailed introduction of main functions for mining cancer-related lncRNAs.

Figure 7-1

General

This function provides general information is displayed in Figure 7-2:

1.Enter a specific lncRNA name in the "LncRNA" field, and click the "Plot" button to search for the lncRNA of interest.

2.Show basic information of the lncRNA of interest, including lncRNA name, Ensembl ID, alias, gene type, site, functional mechanism, biological process.

3. Show the number experimental report entry in the lncRNA of interest, including all, mechanism, function, clinical.

4. A global view of the lncRNA of interest statistics based on the human body map.

5. A barplot of the lncRNA of interest statistics based on the cancer type.

Figure 7-2

DEA

This function allows users to obtain differential expression analysis and heatmap for lncRNAs in a specific cancer is displayed in Figure 7-3:

1. Select a cancer for differential analysis.

2. Select a method for differential analysis.

3. Select a threshold value of fold change for differential analysis.

4. Select a threshold value of FDR for differential analysis.

5. Click the "List" button: DEA function will generate a list of differentially expressed lncRNAs based on input parameters.

6. Click the "Plot" button: DEA function will generate a heatmap of differentially expressed lncRNAs based on input parameters.

Figure 7-3

Boxplot

This function generates box plots for comparing expression of a specific lncRNA between cancer and normal samples is displayed in Figure 7-4:

1. Select a specific lncRNA for drawing the boxplot.

2. Select a cancer for drawing the boxplot.

3. Color Select: Select a color for drawing the boxplot.

4. Click the "Plot" button: Boxplot function will generate a boxplot for comparing expression in cancer and normal dataset based on input parameters.

Figure 7-4

Stage Plot

The functions generates expression violin plot for a specific lncRNA based on patient pathological stage is displayed in Figure 7-5:

1. Select a specific lncRNA for drawing the violin plot of cancer stages.

2. Select a color for drawing the violin plot of cancer stages.

3. Select a color for drawing the violin plot of cancer stages.

4. Select major pathological stage or detailed pathological stage for drawing the violin plot of cancer stages.

5. Click the "Plot" button: Stage Plot function will generate a violin plot for comparing expression in pathological stage based on input parameters.

Figure 7-5

Surival

This function performs overall survival or disease free survival analysis based on a specific lncRNA expression is displayed in Figure 7-6:

1. Select a specific lncRNA for drawing the survival curve.

2. Select a threshold value for drawing the survival curve.

3. Select a method for drawing the survival curve.

4. Select a cancer for drawing the survival curve.

5. Click the "Plot" button: Survival function will generate a survival curve for overall survival or disease free survival based on input parameters.

Figure 7-6

Similar

This function identifies a list of lncRNAs with similar expression pattern with an input lncRNA and selected datasets is displayed in Figure 7-7:

1. Select a specific lncRNA for defining similar lncRNAs.

2. Select a cancer for defining similar lncRNAs.

3. Select a method for defining similar lncRNAs.

4. Click the "List" button: Similar function will generate a list of lncRNAs with similar expression pattern with an input lncRNA and selected datasets.

Figure 7-7

Correlation

This function provides lncRNA expression correlation analysis for two interested lncRNAs in a cancer is displayed in Figure 7-8:

1. Select lncRNA A and B for drawing the scatter diagram.

2. Select a cancer for drawing the scatter diagram.

3. Select a color for drawing the scatter diagram.

4. Select a method for drawing the scatter diagram.

5. Click the "Plot" button: Correlation function will generate a scatter diagram of lncRNA A and B using correlation analysis based on input parameters.

Figure 7-8

Network

This function provides interacted and co-expressed miRNA-lncRNA and mRNA-lncRNA co-expressed networks is displayed in Figure 7-9:

1. Select a specific lncRNA for drawing the network.

2. Select a cancer for drawing the network.

3. Select a threshold value of P value for drawing the network.

4. Select a RNA type for drawing the network.

5. Click the "Plot" button: Network function will generate a co-expressed network of miRNA-lncRNA or mRNA-lncRNA based on input parameters.

Figure 7-9

TF motif

This function predicts TF motif for a specific lncRNA and provides TF motif sequence LOGO figure is displayed in Figure 7-10:

1. Select a specific lncRNA to acquire TF motif.

2. Select a q value to acquire TF motif.

3. Click the "List" button: TF motif function will generate a list of a specific lncRNA with its predicts TF motif based on input parameters.

c4. Click the "Eye" button: TF motif function will generate a TF motif sequence LOGO figure of selected TF motif.

Figure 7-10