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. 2020 Jan 1:2020:baaa052.
doi: 10.1093/database/baaa052.

Autophagy and Tumor Database: ATdb, a novel database connecting autophagy and tumor

Kelie Chen  1 , Dexin Yang  1 , Fan Zhao  1 , Shengchao Wang  2 , Yao Ye  3 , Wenjie Sun  4 , Haohua Lu  1 , Zhi Ruan  5 , Jinming Xu  6 , Tianru Wang  7 , Guang Lu  8 , Liming Wang  8 , Yu Shi  9 , Honghe Zhang  4 , Han Wu  10 , Weiguo Lu  2 , Han-Ming Shen  8   11 , Dajing Xia  1 , Yihua Wu  1
Affiliations

Autophagy and Tumor Database: ATdb, a novel database connecting autophagy and tumor

Kelie Chen et al. Database (Oxford). .

Abstract

Autophagy is an essential cellular process that is closely implicated in diverse pathophysiological processes and a variety of human diseases, especially tumors. Autophagy is regarded as not only an anti-cancer process in tumorigenesis but also a pro-tumor process in progression and metastasis according to current research. It means the role of autophagy in tumor is considered to be complex, controversial and context dependent. Hence, a comprehensive database is of great significance to obtain an in-depth understanding of such complex correlations between autophagy and tumor. To achieve this objective, here we developed the Autophagy and Tumor Database (named as ATdb, http://www.bigzju.com/ATdb/#/) to compile the published information concerning autophagy and tumor research. ATdb connected 25 types of tumors with 137 genes required for autophagy-related pathways, containing 219 population filters, 2650 hazard ratio trend plots, 658 interacting microRNAs, 266 interacting long non-coding RNAs, 155 post-translational modifications, 298 DNA methylation records, 331 animal models and 70 clinical trials. ATdb could enable users to search, browse, download and carry out efficient online analysis. For instance, users can make prediction of autophagy gene regulators in a context-dependent manner and in a precise subpopulation and tumor subtypes. Also, it is feasible in ATdb to cluster tumors into distinguished groups based on the gene-related long non-coding RNAs to gain novel insights into their potential functional implications. Thus, ATdb offers a powerful online database for the autophagy community to explore the complex world of autophagy and tumor. Database URL: http://www.bigzju.com/ATdb/#/.

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Figures

Figure 1
Figure 1
The flow diagram of the database construction. Gene-specific information was collected from Human Protein Atlas, HGNC, UniProt and Ensembl. Experiments identified regulators was searched in the PubMed. RNA sequencing data from TCGA were used to carry out correlation analysis and differential expression analysis. TCGA survival data and phenotype data were used to customize survival analysis.
Figure 2
Figure 2
Browse entrance. (A) Search in the homepage; (B) click on the autophagy pathway illustration; (C) select from the dropdown list of genes; (D) click on the anatomy illustration; (E) select from the dropdown list of tumors.
Figure 3
Figure 3
Screenshots from the Autophagy in Tumors. (A) Differential expression analysis; (B) box plot; (C) heat plot; (D) population filter for customized survival analysis; (E) interactive Kaplan–Meier plotter interface; (F) clinical trials.
Figure 4
Figure 4
Screenshots from the genes section. (A) Expression profiles; (B) intracellular location; (C) heat plot; (D) scatter plot; (E) manual curated regulators; (F) DNA methylation; (G) animal models.
Figure 5
Figure 5
Example for ATdb application. Kaplan–Meier plot for (A) breast cancer (TCGA-BRCA); (B) endometrioid cancer (TCGA-UCEC); (C) lung adenocarcinoma (TCGA-LUAD); (D) lung squamous cell carcinoma (TCGA-LUSC).
Figure 6
Figure 6
Circus plot between the most connected lncRNAs and genes. The top five most connected lncRNAs are connected with a wide range of genes.
See this image and copyright information in PMC

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