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Author Notes:

Zhaohui S. Qin, zhaohui.qin@emory.edu

This project was conceived by Z.S.Q. K.S. implemented and improved the method and ran all the analyses. Q.Y. performed method comparison analysis. C.S.M., X.L., R.S., and S.-Y.S. made key suggestions to improve the method and the overall design of the study. All authors discussed the results and contributed to the writing of the manuscript.

We thank Dr. Ya Wang for helpful input. We thank Noah Rawlings for careful editing of the manuscript. This research was supported by NIH/NCI grant (U01CA217875) to C.M., and NIH/NCI grants (R01CA223220, R01CA245386 and UG1CA233259) to S.S. Z.S.Q. is partially supported by NIH/NHLBI (R01AI145231).

The authors declare no competing interests.

Subjects:

Keywords:

  • clinical prognosis
  • Pan-cancer

Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis

Tools:

Journal Title:

Cell Reports Methods

Volume:

Volume 1, Number 4

Publisher:

Type of Work:

Article | Final Publisher PDF

Abstract:

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.

Copyright information:

© 2021 The Authors

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/rdf).
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