A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney,...

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Bibliographic Details
Published in:Cancers 2022-05, Vol.14 (9), p.2325
Main Authors: Barbosa-Silva, Adriano, Magalhães, Milena, Da Silva, Gilberto Ferreira, Da Silva, Fabricio Alves Barbosa, Carneiro, Flávia Raquel Gonçalves, Carels, Nicolas
Format: Article
Language:English
Subjects:
Apoptosis
Biology
Breast cancer
Cancer therapies
Cell death
Cell division
Cell signaling
Chemotherapy
Connectivity
Data science
Gene expression
Gene mapping
Grb2 protein
Liver cancer
Lung cancer
Medical research
Notch1 protein
Oncology
Patients
Phenotypes
Prostate
Protein interaction
Proteins
Signal transduction
Statistical analysis
Thyroid
Thyroid gland
Tumors
Wnt protein
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Summary:The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found , , , , , , , , and . Except for , the RFC analysis showed a different list, which was , , , , , , , , , and . Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers14092325