Title:Comprehensive Pan-cancer Gene Signature Assessment through the
Implementation of a Cascade Machine Learning System
Volume: 18
Issue: 1
Author(s): Daniel Castillo-Secilla*, Juan Manuel Galvez, Francisco Carrillo-Perez, Juan Carlos Prieto-Prieto, Olga Valenzuela, Luis Javier Herrera and Ignacio Rojas
Affiliation:
- Fujitsu Technology Solutions S.A., CoE Data Intelligence, Camino del Cerro de los Gamos, 1, Pozuelo de Alarcón,
28224, Madrid, Spain
- Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Periodista
Rafael Gómez Montero, 2, 18014. Granada, Spain
Keywords:
Pan-cancer, RNA-seq, TCGA, gene expression, machine learning, feature selection, CDSS.
Abstract:
Background: Despite all the medical advances introduced for personalized patient treatment
and the research supported in search of genetic patterns inherent to the occurrence of its different manifestations
on the human being, the unequivocal and effective treatment of cancer, unfortunately, remains
as an unresolved challenge within the scientific panorama. Until a universal solution for its control is
achieved, early detection mechanisms for preventative diagnosis increasingly avoid treatments, resulting
in unreliable effectiveness. The discovery of unequivocal gene patterns allowing us to discern between
multiple pathological states could help shed light on patients suspected of an oncological disease but
with uncertainty in the histological and immunohistochemical results.
Methods: This study presents an approach for pan-cancer diagnosis based on gene expression analysis
that determines a reduced set of 12 genes, making it possible to distinguish between the main 14 cancer
diseases.
Results: Our cascade machine learning process has been robustly designed, obtaining a mean F1 score
of 92% and a mean AUC of 99.37% in the test set. Our study showed heterogeneous over-or underexpression
of the analyzed genes, which can act as oncogenes or tumor suppressor genes. Upregulation of
LPAR5 and PAX8 was demonstrated in thyroid cancer samples. KLF5 was highly expressed in the majority
of cancer types.
Conclusion: Our model constituted a useful tool for pan-cancer gene expression evaluation. In addition
to providing biological clues about a hypothetical common origin of cancer, the scalability of this study
promises to be very useful for future studies to reinforce, confirm, and extend the biological observations
presented here. Code availability and datasets are stored in the following GitHub repository to
aim for the research reproducibility: https://github.com/CasedUgr/PanCancerClassification.
