B E S T

Biomarker Exploration of Solid Tumors


Step I: Select Your Cancer Type



ACC

10 datasets

508 samples


BLCA

13 datasets

1513 samples


BRCA

20 datasets

4332 samples


CESC

12 datasets

1331 samples


CHOL

3 datasets

123 samples


CRC

47 datasets

6222 samples


ESCA

7 datasets

732 samples


GBM

22 datasets

2277 samples


HNSC

8 datasets

1356 samples


KIRC

6 datasets

930 samples


LIHC

12 datasets

2181 samples


LGG

8 datasets

1846 samples


LUAD

22 datasets

3243 samples


LUSC

19 datasets

1842 samples


DLBC

17 datasets

3603 samples


MESO

6 datasets

310 samples


OV

23 datasets

3098 samples


PAAD

13 datasets

1971 samples


PCPG

5 datasets

351 samples


PRAD

11 datasets

2160 samples


SARC

15 datasets

1714 samples


SKCM

16 datasets

1539 samples


STAD

17 datasets

3126 samples


TGCT

2 datasets

193 samples


THYM

2 datasets

155 samples


THCA

6 datasets

897 samples


UCEC

5 datasets

998 samples





Step II: Determine Your Analysis Module



Single Gene

Users can enter a gene symbol or an Ensembl ID to explore a gene of interest.

Gene List

Users can enter a list of genes and pick a method to calculate the gene set score for each sample. The embedded methods include GSVA, ssGSEA, z-score, PLAGE, and the mean value.






News and updates

- 2022.4.1 version 1.0 released


Notes

- When a new cancer type or new gene is selected, you need to refresh the website page.




Dataset Summary

Detailed information about datasets in each cancer type.

 Number of datasets
Number of cases
RNAseq/Microarray



Term List


The summary table of abbreviations of clinlical terms is listed below.


Contact

Feedback can be sent to best_utopia@sina.com or by using the form below. We would like to receive feedback on how to improve this resource.


Address

No.1 Jianshe Dong Road, ErQi District, Zhengzhou, Henan province 450052, China.

Affliation
First Affiliated Hospital of Zhengzhou University

Email address
best_utopia@sina.com

Phone number

+86-16696137348

WeChat Official Account

Bilibili station
Welcome to our video blog

Comment and Feedback

Quality

About BEST

BEST provides a curated database and innovative analytical pipelines to explore cancer biomarkers at high resolution. It is an easy-to-use and time-saving web tool that allows users, especially clinicians and biologists without background knowledge of bioinformatics data mining, to comprehensively and systematically explore the clinical significance and biological function of cancer biomarkers. Below is a detailed tutorial of BEST.



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BEST Tutorial

BEST tutorial provides detailed instructional video for each step in the web server.

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Members

Meet Our Team Members

Engineer

Zaoqu Liu

Project sponsor & leader
Engineer

Xinwei Han

Project leader
Engineer

Long Liu

Project participant
Engineer

Chunguang Guo

Project participant
Engineer

Qin Dang

Project participant
Engineer

Libo Wang

Project participant
Engineer

Siyuan Weng

Project participant
Engineer

Zhe Xing

Project participant
Engineer

Hui Xu

Project participant
Engineer

Xiaoyong Ge

Project participant
Engineer

Yuqing Ren

Project participant
Engineer

Yuyuan Zhang

Project participant
Engineer

Youyang Zheng

Project participant
Engineer

Mengjie Duo

Project participant

Single Gene

This module is used to explore the clinical significance and biological functions of single gene in a specific cancer. Users can enter a gene symbol or an Ensembl ID to explore a gene of interest.


BEST exploration for single gene

Search a gene of interest




Analysis settings

Color palette

Color transparency


Select a clinical trait


Download PDF Download PNG

Analysis settings

Group cutoff method

Define percentage of cutoff value

Color palette

Color transparency


Analysis settings

Number of genes to analyze

Q-value cutoff

GO-plot width

KEGG-plot width




Red represents positive pathways and blue represents negative pathways.
Download PDF Download PNG Download Table


Red represents positive pathways and blue represents negative pathways.
Download PDF Download PNG Download Table

Before you click the START button, be aware of that there are several calculation steps to go, and it might take a few minutes. Please be patient.


Download PDF Download PNG Download Table

Select a GO term





Download PDF Download PNG Download Table

Select a KEGG pathway





Download PDF Download PNG Download Table

Select a Hallmark term



Click the START button to analyze immune cell infiltration status with multiple algorithms.


Download PDF Download PNG

Select a cohort

Select a cell type

Correlation method



Click the START button to analyze different type of immunomodulators.


Download PDF Download PNG

Select a cohort

Select an immunomodulator signature

Correlation method




Analysis settings

Color palette

Color transparency


Click the START button to evaluate the performance of clinical models for predicting immunotherapy response.

Analysis settings

Group cutoff method

Define percentage of cutoff value

Select a color palette

Set plot transparency

Analysis settings

Maximum number of agents to show

Select a database

Analyze different type of candidate agents.


Download PDF Download PNG Download Table

Select a cohort

Select an agent

Correlation method


Click the START button to explore SNVs and CNVs correlation with the target gene expression.

Gene List

This module is used to explore the clinical significance and biological functions of a gene list in a specific cancer. Users can enter a list of genes and pick a method to calculate the gene set score for each sample.


Input Gene List


Geneset Keywords