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. 2006 Jan 1;34(Database issue):D607-12.
doi: 10.1093/nar/gkj082.

HPtaa database-potential target genes for clinical diagnosis and immunotherapy of human carcinoma

Xiaosong Wang  1 , Haitao Zhao, Qingwen Xu, Weibo Jin, Changning Liu, Huagang Zhang, Zhibin Huang, Xinyu Zhang, Yu Zhang, Dianqi Xin, Andrew J G Simpson, Lloyd J Old, Yanqun Na, Yi Zhao, Weifeng Chen
Affiliations

HPtaa database-potential target genes for clinical diagnosis and immunotherapy of human carcinoma

Xiaosong Wang et al. Nucleic Acids Res. .

Abstract

Tumor-associated antigens (TAAs) have been the most actively employed targets in the clinical diagnosis and treatment of human carcinoma, such as PSA in the diagnosis of prostate cancer and NY-ESO-1 in the immunotherapy of melanoma and other cancers. However, identification of TAAs has often been hampered by the complicated and laborsome laboratory procedures. In order to accelerate the process of tumor antigen discovery, and thereby improve diagnosis and treatment of human carcinoma, we have made an effort to establish a publicly available Human Potential Tumor Associated Antigen database (HPtaa) with potential TAAs identified by in silico computing (http://www.hptaa.org). Tumor specificity was chosen as the core of tumor antigen evaluation, together with other relevant clues. Various platforms of gene expression, including microarray, expressed sequence tag and SAGE data, were processed and integrated by several penalty algorithms. A total of 3518 potential TAAs have been included in the database, which is freely available to academic users. As far as we know, this database is the first one addressing human potential TAAs, and the first one integrating various kinds of expression platforms for one purpose.

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Figures

Figure 1
Figure 1
The flow chart of data procession of HPtaa database.
Figure 2
Figure 2
Mean differential expression ratio of PSA across various cancer types. When upregulated significantly in cancerous tissues, the value was computed as ‘cancer/normal’; when downregulated significantly the value was computed as ‘– (normal/cancer)’. The y-axis shows the names of the cancer datasets and source sequences of the probes in a given dataset. Red color represents upregulation and blue color downregulation.
See this image and copyright information in PMC

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