PROGgene: gene expression based survival analysis web application for multiple cancers

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Abstract

Background

Identification of prognostic mRNA biomarkers has been done for various cancer types. The data that are published from such studies are archived in public repositories. There are hundreds of such datasets available for multiple cancer types in public repositories. Wealth of such data can be utilized to study prognostic implications of mRNA in different cancers as well as in different populations or subtypes of same cancer.

Description

We have created a web application that can be used for studying prognostic implications of mRNA biomarkers in a variety of cancers. We have compiled data from public repositories such as GEO, EBI Array Express and The Cancer Genome Atlas for creating this tool. With 64 patient series from 18 cancer types in our database, this tool provides the most comprehensive resource available for survival analysis to date. The tool is called PROGgene and it is available at http://www.compbio.iupui.edu/proggene.

Conclusions

We present this tool as a hypothesis generation tool for researchers to identify potential prognostic mRNA biomarkers to follow up with further research. For this reason, we have kept the web application very simple and straightforward. We believe this tool will be useful in accelerating biomarker discovery in cancer and quickly providing results that may indicate disease-specific prognostic value of specific biomarkers.

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PrognoScan: a new database for meta-analysis of the prognostic value of genes

Hideaki Mizuno, Kunio Kitada, Kenta Nakai … (2009)

Background In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform. Description To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets. Conclusion PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at .

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Prediction of venous metastases, recurrence, and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment.

Anuradha Budhu, Marshonna Forgues, Qing-Hai Ye … (2006)

Hepatocellular carcinoma (HCC) is an aggressive malignancy mainly due to metastases or postsurgical recurrence. We postulate that metastases are influenced by the liver microenvironment. Here, we show that a unique inflammation/immune response-related signature is associated with noncancerous hepatic tissues from metastatic HCC patients. This signature is principally different from that of the tumor. A global Th1/Th2-like cytokine shift in the venous metastasis-associated liver microenvironment coincides with elevated expression of macrophage colony-stimulating factor (CSF1). Moreover, a refined 17 gene signature was validated as a superior predictor of HCC venous metastases in an independent cohort, when compared to other clinical prognostic parameters. We suggest that a predominant humoral cytokine profile occurs in the metastatic liver milieu and that a shift toward anti-inflammatory/immune-suppressive responses may promote HCC metastases.

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Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer.

Marc Buyse, Sherene Loi, Laura Van't Veer … (2006)

A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients. Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups. The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively. The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.

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Author and article information

Contributors

Chirayu Pankaj Goswami

Harikrishna Nakshatri

Journal

Journal ID (nlm-ta): J Clin Bioinforma

Journal ID (iso-abbrev): J Clin Bioinforma

Title: Journal of Clinical Bioinformatics

Publisher: BioMed Central

ISSN (Electronic): 2043-9113

Publication date Collection: 2013

Publication date (Electronic): 28 October 2013

Volume: 3

Page: 22

Affiliations

[1 ]Thomas Jefferson University Hospitals, 117 S 11th Street, Suite 207, Philadelphia, PA 19107, USA

[2 ]Departments of Surgery, Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA

Article

Publisher ID: 2043-9113-3-22

DOI: 10.1186/2043-9113-3-22

PMC ID: 3875898

PubMed ID: 24165311

SO-VID: c166dd5f-ac92-4c5a-86a3-c6bbb08462f4

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PROGgene: gene expression based survival analysis web application for multiple cancers

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Abstract

Background

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Most cited references 14

PrognoScan: a new database for meta-analysis of the prognostic value of genes

Prediction of venous metastases, recurrence, and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment.

Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer.

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