Welcome to ESCCEXPRESS

ESCCEXPRESS is a web tool constructed along with our study “Transcriptomics based multi-dimensional characterization and drug screen in esophageal squamous cell carcinoma”. It is designed to promote the accessibility of publicly available esophageal squamous cell carcinoma (ESCC) data and facilitate data mining. ESCC patients’ expression data, ESCC cell line related expression and drug screen data, as well as the RNA-seq and ATAC-seq data generated in our study are all curated in ESCCEXPRESS. Meanwhile, EXCCEXPRESS provides several key functions for users to explore and visualize above data.

Expression module

Users can check the expression alteration of the gene of interest across different ESCC datasets and identify consensus gene signatures.

Survival module

Users can explore whether the gene of interest is associated with the clinical outcomes of ESCC patients in different datasets.

Prognosis module

Users can establish gene signatures for ESCC prognosis assessment using LASSO Cox regression analysis and verify the markers in the validation set.

Correlation module

Users can check the correlation between any two given genes.

Cell Line module

Users can browse the baseline expression of the gene of interest in different ESCC cell lines and check the importance of the gene in the survival of ESCC cell lines.

Drug Sensitivity module

Users can explore the drug response data of different ESCC cell lines from CTRP, GDSC, and PRISM datasets.

Zs Patients module

Users can browse and download the RNA-seq and ATAC-seq data of ESCC patients generated in our study.

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Expression Alteration




This feature enables users to check the expression alteration of a given gene across 7 independent ESCC datasets based on GEO data repository. The horizontal dashed line represents adjusted p-value = 0.05.

  • Gene: Input a gene of interest.


    • Consensus Differentially Expressed Gene




    This feature enables users to browse consensus up-regulated or down-regulated genes across 7 independent ESCC datasets from GEO data repository.

  • Direction of alteration: Select consensus up-regulated or down-regulated genes.
  • Log2 (Fold change): Set custom Log2 fold change threshold. Default value is 0
  • Adjusted p-value: Set custom adjusted p-value threshold. Default value is 0.05.



    • Survival Curve




    This feature allows users to explore the surival curve of a given gene in GSE53625 or TCGA ESCC cohort, using Log-rank test for hypothesis test. Users can select a suitable threshold for splitting the high-expression and low-expression groups. The hazard ratio and the 95% confidence interval information based on the group cutoff will also be included in the survival plot.

  • Gene: Input a gene of interest.
  • Dataset: Select a ESCC dataset. Currently only GSE53625 and TCGA ESCC datasets contain survival information of patients.
  • Method: Select OS (Overall Survival), DFS (Disease Free Survival, only for TCGA), or PFS (Progression Free Survival, only for TCGA) for analysis.
  • Group Cutoff Method: Select a method for splitting the high-expression and low-expression groups. For maximal separation method, the cut-off point was determined based on the maximally selected log-rank statistics, meanwhile, each group will contain at least 30% of the total population to avoid assigning too few patients into a given group.
  • Cutoff-High(%): Samples with expression level higher than this cutoff are considered as the high-expression group.
  • Cutoff-Low(%): Samples with expression level lower than this cutoff are considered the low-expression group.
  • High Group Color: Set the color of high-expression group.
  • Low Group Color: Set the color of low-expression group.







    • Prognosis Assessment Gene Signatures Construction for ESCC




    This module allows users to construct prognosis (overall survival) assessment gene signatures using LASSO Cox regression analysis (10-fold cross validation). Users need to paste a list of potential prognosis related genes into the box and choose a dataset for signature construction, the other dataset will be used as the validation set. if no gene is assigned with a LASSO coefficient, the lambda value may need to be adjusted or the genes enrolled may not appropriate for prognosis prediction.

  • Gene List: Copy and paste a list of genes into the box.
  • Dataset: Select a dataset for model construction.
  • Lambda: Select a Lambda value for model construction. Lambda.min means minimum error, Lambda.1se means error is within 1 standard error of minimum error.




    • Correlation Analysis




    This feature allows users to perform pair-wise correlation analysis to explore the correlation between two genes in 3 independent ESCC datasets.

  • Gene A: Input a gene of interest.
  • Gene B: Input another gene of interest.
  • Dataset: Select a ESCC dataset. Note that GSE17351, GSE20347, GSE23400, GSE38129, GSE45670, and GSE77861, which are all based on the Affymetrix platform, were merge into a meta-cohort to obtain a larger dataset.
  • Correlation Coefficient: The method for calculating the correlation coefficient.




    • Cell Line Expression Analysis




    This feature enables users to check the expression of a specific gene across different ESCC cell lines based on CCLE and GDSC databases.

  • Gene Input a gene of interest.
  • Dataset Select of cancer cell line dataset.



    • Cell Line Dependency Analysis




    This feature enables users to check the dependency of a specific gene across different ESCC cell lines based on genome-wide CRISPR-Cas9 loss-of-function screening data provided by DepMap database. The value ranges from 0 to 1, and a higher value indicates a more important role in cell survival.

  • Gene Input a gene of interest.


    • Cell Line Drug Sensitivity Analysis




    This feature enables users to browse the drug response data of different ESCC cell lines based on CTRP, GDSC, and PRISM datasets. The value represents the area under the dose-response curve (AUC), with lower values suggesting higher sensitivities.

  • Dataset Select a drug sensitivity dataset.



    • Zs ESCC Patients




    Users can browse and download the RNA-seq and ATAC-seq data of ESCC Patients from Zhongshan Hospital generated in our study. N represents normal samples; T represents tumor samples.

  • Dataset Select RNA-seq or ATAC-seq data to download.