Evergene: an interactive webtool for large-scale gene-centric analysis of primary tumours

Abstract

Motivation

The data sharing of large comprehensive cancer research projects, such as The Cancer Genome Atlas (TCGA), has improved the availability of high-quality data to research labs around the world. However, due to the volume and inherent complexity of high-throughput omics data, analysis of this is limited by the capacity for performing data processing through programming languages such as R or Python. Existing webtools lack functionality that supports large-scale analysis; typically, users can only input one gene, or a gene list condensed into a gene set, instead of individual gene-level analysis. Furthermore, analysis results are usually displayed without other sample-level molecular or clinical annotations. To address these gaps in the existing webtools, we have developed Evergene using R and Shiny.

Results

Evergene is a user-friendly webtool that utilizes RNA-sequencing data, alongside other sample and clinical annotation, for large-scale gene-centric analysis, including principal component analysis (PCA), survival analysis (SA), and correlation analysis (CA). Moreover, Evergene achieves in-depth analysis of cancer transcriptomic data which can be explored through dimensional reduction methods, relating gene expression with clinical events or other sample information, such as ethnicity, histological classification, and molecular indices. Lastly, users can upload custom data to Evergene for analysis.

Availability and implementation

Evergene webtool is available at https://bshihlab.shinyapps.io/evergene/. The source code and example user input dataset are available at https://github.com/bshihlab/evergene.

1 Introduction

In the era of open science where large cancer datasets are made publicly available via projects like The Cancer Genome Atlas (TCGA) Program (Chang et al. 2013), researchers can freely access valuable high-quality data, boosting their research capacity (Mangul et al. 2019). Complementary to this, several projects deliver pre-processed data from these publicly available databases through webtools that enable data visualization, further improving the accessibility to these resources for those lacking bioinformatics expertise (Cerami et al. 2012, Metsalu and Vilo 2015, Tang et al. 2019, Dwivedi et al. 2022).

When exploring gene expression in cancer datasets, principal component analysis (PCA) and survival analysis (SA) are two of the most commonly-used analysis methods. PCA is an unsupervised method for reducing the dimensionality of a dataset while retaining information on the overall variation (Hotelling 1933). SA encompasses a collection of methods used to analyse differential occurrence for specific events (such as death) between groups or for a given continuous variable; this has been applied to cancer samples with low or high expression of specific genes to infer their potential importance in cancers. Currently, there is no webtool available that provides integration between PCA, SA, and comprehensive sample annotations, such as all clinical survival outcomes, histology classification, race, age, and gender. Further, available molecular classifications from several TCGA pan-cancer projects, such as immune subtypes (Thorsson et al. 2018), molecular subtypes (Hoadley et al. 2018), and stemness index (Malta et al. 2018), are not so far integrated into existing webtools. Integration of these additional annotations would allow better stratification of results and provide further insight into gene expression patterns or clinical survival outcomes.

For survival outcome analytics, overall survival (OS) is a commonly-used endpoint and has the advantage of having minimal ambiguity for defining an OS event, as the event is registered as either alive or dead (Tolaney et al. 2021). However, OS does not distinguish non-cancer causes of death, nor does it necessarily reflect tumour aggressiveness. The use of other observed events, such as progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI), has the advantage of shorter minimum follow-up time and potentially closer representation of the tumour biology (Tolaney et al. 2021). Existing webtools that implemented TCGA data for SA are usually based on OS; existing tools have allowed for a limited number of clinical outcomes as registered events in SA. Moreover, Idogawa et al. (2021) have identified issues with the handling of survival data in several of the existing webtools. To address these, we incorporate OS, PFI, DSS, and DFI data from the standardized dataset published by the TCGA pan-cancer clinical data resource (TCGA-CDR) (Liu et al. 2018).

With the ever-increasing volume of data, there is a growing interest in high-throughput analysis. The number of gene queries is another limiting factor with existing TCGA data webtools, which are predominately designed for single-gene analysis. While some of the webtools for interrogating TCGA data have a multi-gene mode, they are limited in functionality. Even when multiple gene input is available, the plots and statistics tables often need to be individually downloaded and the plot data are often not available to the users, who may wish to analyse them further.

To address these issues, we have developed a webtool, Evergene, that supports integrated PCA and SA with multi-gene input capacity. Evergene incorporates OS, PFI, DSS, and DFI data from the standardized dataset published by TCGA-CDR (Liu et al. 2018).

2 Methods

2.1 Data processing

R (version 4.3.1) and RStudio (version 2023.06) were used for processing data. Harmonized TCGA RNA-sequencing (RNA-seq) data (Thorsson et al. 2018) and associated clinical information were downloaded from TCGA database through the R package TCGAbiolink (Colaprico et al. 2015) (version 2.28.3; downloaded on 27 July 2023). The harmonized TCGA RNA-seq data have been mapped to the human reference genome GRCh38 by Genomic Data Commons (v37.0). TCGA projects with 80 or more primary tumour samples were selected for downstream analysis. R package SummarizedExperiment was used for extracting the downloaded data; transcript per million (TPM) values were used for indicating gene expression levels in graphical outputs, and unstranded count values were used for PCA.

A total of 27 TCGA projects, each reflecting a cancer type or subtype, were found to have more than 80 samples (n = 80–1111) (Supplementary Table S1). Based on the TCGA-CDR recommendations, PFI is suitable to serve as an endpoint for clinical survival outcomes for all but 1 (Pheochromocytoma and Paraganglioma, PCPG) of the 27 TCGA projects, whereas OS is recommended to be used with caution for four of these projects (BRCA, LGG, PRAD, and READ). Therefore, PFI was chosen as the default option for SA in Evergene. As none of the endpoint measures were recommended for PCPG, the PCPG project was excluded, making a total of 26 TCGA projects included in Evergene.

PCA was performed independently for each cancer project with all samples and all genes detected in more than 20% of the samples. R package EdgeR (Robinson et al. 2009) (version 3.42.4) was used for performing TMM normalization in log-normalized count per million (CPM) values were used for PCA through R-base function prcomp using gene IDs as columns. PCA loadings for each gene were determined using the R package factoextra (version 1.0.7) for estimating the strength of contribution for each gene to each PC. The top 20 PCs were selected for visualization. The top 20 contributing genes for each PC were selected based on the strength of contribution irrespective of the direction, and the colour display indicates the multiplication between the gene contribution to an individual PC as well as the PC to the overall contribution data; a darker colour indicates a stronger contribution.

Correlation analyses were calculated with R base function corr using the Pearson correlation option. SA was performed using R base package survival (version 3.5–5) using one of the four possible clinical survival outcome, including OS, PFI, DSS, and DFI. Details of these four clinical survival outcomes were taken from the TCGA-CDR (Liu et al. 2018).

Additional sample annotation and clinical survival outcomes were derived from Liu et al. (2018). Stemness indexes, mRNAsi (based on mRNA expression), and EREG-mRNAsi (epigenetically regulated mRNAsi; based on both mRNA expression and DNA methylation), are derived from Malta et al. (2018), who utilized machine learning methods to identify stemness features associated with oncogenic dedifferentiation. Immune subtypes and molecular subtypes are derived from Thorsson et al. (2018) and Hoadley et al. (2018). Several annotations were shortened or combined for clearer visualization within Evergene (Supplementary Table S2). Values were treated as not available (NA) if missing or labelled as ‘not evaluated’, ‘unknown’, ‘discrepancy’, ‘not applicable’, or ‘not evaluated’.

2.2 Setup of the web platform

The source code for Evergene is written in the R programming language, and the interactive ShinyApp web-application was developed using the R base package shiny (version 1.7.4.1). Several additional R packages were used to customize the user interface, including shinythemes (version 1.2.0), shinyWidgets (version 0.7.6), and shinyBS (version 0.61.1). The final web platform was hosted on shinyapps.io and extensively tested on multiple operating systems (Linux, Mac, Windows) and web-browsers (Chrome, Firefox, Safari, Microsoft Edge). PCA and correlation plots were made with R package ggplot2 (version 3.4.2) and plotly (version 4.10.2), and coloured with R package viridis (version 0.6.4). The full source code for the Evergene ShinyApp and associated data pre-processing can be found on Github https://github.com/bshihlab/evergene.

3 Results

3.1 Overview of Evergene

Evergene is available at https://bshihlab.shinyapps.io/evergene. Within the webtool, users can provide their own data (Fig. 1a) or select from a list of TCGA cancer projects in addition to providing a list of input genes (Fig. 1b and c). For analysis using TCGA data, the gene inputs may be Ensembl gene IDs or Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbols. The maximum number of input gene–cancer combinations is limited to 100 due to computing constraints. This can be a combination of 10 genes in 10 cancers, or 100 genes in one cancer. Two example inputs are available, and each section contains explanatory notes under the question mark icons (Fig. 1d).

Figure 1.

Overview of Evergene. This screenshot shows the output page for ‘Example 1’ shown in (d) on Evergene (TP63, SOX2, GATA4, GATA6, and PHB for input gene and Esophageal Carcinoma for input cancer). Arrows indicate question marks that can be used to bring up help messages. (a) The switch toggles on options for users to upload their own data for PCA and CA. (b) Alternatively, cancer projects from TCGA can be selected. (c) Genes of interest can be inputted before using the ‘analyse’ button to submit inputs for analysis. (d) Example inputs are available for both cancer projects and user inputs, which can be downloaded from https://github.com/bshihlab/evergene. (e) The top panel is used to switch between the displayed outputs for different cancer projects, genes, and PCs. (f) The middle panel indicates the strength of contribution for the currently selected gene towards each PC. This can be used to identify the potential PCs of interest for a given gene. (g) The bottom panel is comprised of three tabs and each contains the results for a different type of analysis.

Three types of analyses are available in Evergene: PCA, SA, and CA. In the top panel, users can specify cancer projects and genes, along with two of the top 20 principal components (PCs) that they would like to display as the x and y axes in the PCA and SA plots (Fig. 1b). An overview of the contribution of the selected gene in each PC is indicated in the top panel (Fig. 1c). In the bottom panel (Fig. 1d), users can use the tab menu to switch between the results for PCA, SA, and CA.

Each analysis tab contains several plots with adjustable graphical inputs and buttons for downloading all plots and data. The main plots are Kaplan–Meier survival curve plots for SA, PCA scatter plots for the first 20 PCs, and scatter plots of selected variables for CA. Plots and data export are limited to the first 30 y-axis variables for CA and the first 100 gene-cancer combinations for SA and PCA.

3.2 Comparison to similar existing webtools

The two commonly-used PCA webtools, ClustVis (Metsalu and Vilo 2015) and GEPIA2 (Tang et al. 2019), both have different fundamental designs to Evergene (Table 1 and Supplementary Fig. S1). To our best knowledge, Evergene is the only webtool that performs whole-transcriptome PCA using TCGA data, as well as being the only webtool that annotates gene expression over PCA plots. ClustVis (Metsalu and Vilo 2015) can be used to perform PCA based on user input data or studies from the Array Express, but does not provide a list of cancer datasets. While GEPIA2 (Tang et al. 2019) has preloaded TCGA datasets, its PCA is performed on an input gene list, as opposed to Evergene which uses the full transcriptome. Unlike Evergene and ClustVis, GEPIA2 does not provide information on gene contribution towards each PC. Although ClustVis exports comprehensive PCA outputs as tables, it is not designed for interactive visualization and users need to plot graphs with external software. Lastly, PCA in Evergene is integrated with SA and CA, allowing users to explore potential relationships between PCs and other sample characteristics, such as survival, patient metadata, and molecular indices.

Table 1.

Comparison between Evergene and existing webtools for principal component analysis (PCA).

Functionality	Evergene	GEPIA2	ClustVis
User input	Yes	No	Yes
Preloaded data: number of projectsa; grouped cancer types	26; 26	33; 33	Studies of interest can be imported from Array Express
PCA method	Whole transcriptome	Selected genesb	Whole transcriptome, selected genes
Gene expression visualization	Yes	No	No
Sample annotation	Molecular (4), Clinical (up to 7)	Sample group (1)	Imported
Multiple plot export	Yes	No	No
Plot data export	Yes	No	Yes
Top contributing genes for each PC	Yes	No	Yes
Mix and match across multiple cancer/control studies	No	Yes	No
Gene contribution to each PC	Plot and data	No	Data
Number of principal components	Top 20	Top 10	Top 2 (plotted); all (data)
Interactive plots	Switch PCs. Sample annotation. Sample ID on hover	Switch PCs. Plot rotation (3D plot)	No

^aProjects refers to cancer projects from which the data are originated from.

^bAccepts up to approximately 250 genes; whole transcriptome cannot be selected.

There are several existing tools for SA, including Kaplan–Meier plotter [KM plotter] (Lánczky and Győrffy 2021), cBioPortal (Cerami et al. 2012, Gao et al. 2013, de Bruijn et al. 2023), Survival Genie (Dwivedi et al. 2022), GEPIA2 (Tang et al. 2019), and OncoLnc (Anaya 2016). The SA in Evergene has been compared and summarized in Supplementary Table S3 and Fig. S1. KM plotter is currently the only other webtool that accepts user input data; Survival Genie, GEPIA2, and OncoLnc are based on pre-loaded data. With the exception of GEPIA2 which has heatmap summaries on SA, these webtools are not designed for large numbers of individual gene queries; users need to perform and export the analysis on a gene-by-gene basis (Supplementary Table S3). Of the existing webtools, cBioPortal is the only one that shows comprehensive sample annotation for the groups of samples defined by high-/low-gene expression levels. Unlike cBioPortal, which shows summarized group information, Evergene plots sample annotation information for individual samples alongside SA and PCA.

Altogether, Evergene has the unique feature of integrating PCA, SA, and CA that allows for exploratory analysis on TCGA data with respect to their candidate genes, with interpretation aided by comprehensive sample metadata. Furthermore, Evergene is the only webtool which enables mass graph export when users have a large number of candidate genes or cancers of interest. Lastly, Evergene accepts in custom input data in the form of text files, allowing users to utilize all the above benefits with their own datasets. Example workflows are described in the Supplementary Fig. S2.

3.3 Case usage

For bladder urothelial carcinoma, the molecular subtypes BLCA.1–4, as defined by Hoadley et al. (2018), are separated by PC1 and PC3 (Fig. 2). Metascape pathway analysis has been performed using the top 100 genes contributing to PC1, which is significantly enriched in pathways related to inflammatory response (GO: 0006954. P < 1e−10) and regulation of immune cell activation (e.g. GO: 0050865 and GO: 0032944 for lymphocytes and mononuclear cells. P < 1e−10). Elevated systemic immune-inflammation index has been associated with poor survival outcomes for urothelial carcinoma (Liu et al. 2023). The second-highest contributing gene to PC1 is GNB4 (Fig. 2b), as shown in the final graph in the PCA tab. GNB4 is strongly correlated with PC1 (r = −0.83, P = 6.5E−104) and has higher expression in the BLCA.3 and BLCA.4, subtypes that are less common in those of Asian ancestry (Fig. 2d and e). Furthermore, the SA suggests higher GNB4 to be significantly (P < .01) associated with poorer progression-free interval, OS (Fig. 2f; P = .003 for log-rank test comparing top 33% to bottom 33%) and disease-specific survival; this is consistent with reports that elevated GNB4 protein levels is associated with poor prognosis in urothelial carcinoma (Chen et al. 2021). These observations suggest a potential difference in predisposition to inflammation-associated molecular subtypes in different ethnicities. On the other hand, PC3 is strongly correlated to the stemness index (mRNAsi) (Fig. 2g).

Figure 2.

Case usage: identification of key genes that can be used to explain variations seen in bladder urothelial carcinoma. (a) PC1 and PC3 separate bladder urothelial carcinoma into molecular subtypes indicated by Hoadley et al. (2018). (b) In PC1, the second-highest contributing gene is GNB4, (c) which shows a strong negative correlation with PC1 (r = −0.83, P = 6.5E−104). (d) Most individuals with Asian ancestry are on the right-hand side for the PC1 axis, akin to those of BLCA.1 in (a). Thus, (e) Asian individuals are more likely to have low GNB4 expression, a grouping that is associated with higher probability of overall survival (P = .003 for log-rank test comparing top 33% to bottom 33%). (g) On the other hand, stemness index is strongly positively correlated with PC3 (r = 0.73, P = 1.8E−68).

4 Limitations and future direction

There are functionalities available in other webtools that are not available in Evergene. For example, ClustVis can import Array Express datasets, Survival Genie correlates gene expression with immune cell compositions, and GEPIA2 can be used to find the genes with the highest differential survival (i.e. high expresser and low expresser have large difference in survival outcomes) for each cancer project as well as having summarized heatmap for SA. KM plotter, GEPIA2, and Survival Genie have additional parameters for restricting analysis to certain subsets of tumours or patient cohort. Evergene has restricted datasets to primary tumours; while this is to limit data variation to improve data stratification, some variations may be under-represented. Lastly, due to considerations around user accessibility and computational costs, most data processing steps are fixed, sample metadata does not include all available metadata from TCGA, and accepted inputs are restricted to 100 gene-cancer combinations. These areas can be further expanded in the future to reflect user needs.

5 Conclusion

In summary, Evergene provides a platform for gene-centric investigation of transcriptomic data across 26 cancer projects from TCGA, enabling the gene expression to be studied alongside clinical annotation and molecular classifications. The tool is designed for exploratory analysis, thus equipped with functionalities that enable a large number of gene-cancer inputs and download of many outputs. We have demonstrated the utility of Evergene in identifying potential biomarkers for histological subtypes and molecular subtypes that may reflect survival outcomes, such as TP63 and HID1 for differentiating squamous cell carcinoma from adenocarcinoma in multiple cancer types. The unsupervised analysis methods (PCA) in Evergene provide an overview of the transcriptomic variation across samples within cancer projects, and will therefore support the development of new molecular classification. We envisage Evergene will be a valuable exploratory tool for lay scientists in assisting the discovery of biomarkers in cancer.

Author contributions

Anna Kennedy (Data curation [equal], Formal analysis [equal], Software [equal]), Ella Richardson (Data curation [equal], Formal analysis [equal], Software [equal], Writing—original draft [equal]), Jonathan Higham (Formal analysis-Supporting, Validation [equal], Writing—original draft [equal], Writing—review & editing [equal]), Panagiotis Kotsantis (Conceptualization [equal], Validation [equal]), Richard Mort (Conceptualization [equal], Validation [equal]), and Barbara Shih (Conceptualization [lead], Methodology [lead], Project administration [lead], Software [equal], Visualization [lead], Writing—original draft [lead], Writing—review & editing [lead]).

Supplementary data

Supplementary data are available at Bioinformatics Advances online.

Conflict of interest

None declared.

Funding

This work has been supported by North West Cancer Research.

Data availability

The data underlying raw data used this article are available through the Genomic Data Commons Data Portal, at https://portal.gdc.cancer.gov.