Discovery of Gene Expression Patterns across Multiple Cancer Types

AbstractClassifying pan-cancer samples using gene expression patterns is a crucial challenge for the accurate diagnosis and treatment of cancer patients. Machine learning algorithms have been considered proven tools to perform downstream analysis and capture the deviations in gene expression patterns across diversified diseases. In our present work, we have developed PC-RMTL, a pan-cancer classification model using regularized multi-task learning (RMTL) for classifying 21 cancer types and adjacent normal samples using RNASeq data obtained from TCGA. PC-RMTL is observed to outperform when compared with five state-of-the-art classification algorithms, viz. SVM with the linear kernel (SVM-Lin), SVM with radial basis function kernel (SVM-RBF), random forest (RF), k-nearest neighbours (kNN), and decision trees (DT). The PC-RMTL achieves 96.07% accuracy and 95.80% MCC score for a completely unknown independent test set. The only method that appears as the real competitor is SVM-Lin, which nearly equalizes the accuracy in prediction of PC-RMTL but only when complete feature sets are provided for training; otherwise, PC-RMTL outperformed all other classification models. To the best of our knowledge, this is a significant improvement over all the existing works in pan-cancer classification as they have failed to classify many cancer types from one another reliably. We have also compared gene expression patterns of the top discriminating genes across the cancers and performed their functional enrichment analysis that uncovers several interesting facts in distinguishing pan-cancer samples.

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Detecting Common Gene Expression Patterns in Multiple Cancer Outcome Entities

Biomedical Microdevices ◽

10.1007/s10544-005-3032-7 ◽

2005 ◽

Vol 7 (3) ◽

pp. 247-251 ◽

Cited By ~ 8

Author(s):

Xinan Yang ◽

Stefan Bentink ◽

Rainer Spang

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Multiple Cancer ◽

Common Gene ◽

Cancer Outcome

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Multi-Approach Bioinformatics Analysis of Curated Omics Data Provides a Gene Expression Panorama for Multiple Cancer Types

Frontiers in Genetics ◽

10.3389/fgene.2020.586602 ◽

2020 ◽

Vol 11 ◽

Author(s):

Bruno César Feltes ◽

Joice de Faria Poloni ◽

Itamar José Guimarães Nunes ◽

Sara Socorro Faria ◽

Marcio Dorn

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Patterns ◽

Gene Expression Omnibus ◽

Rna Seq ◽

Multiple Cancer ◽

Heterogeneous Nature ◽

Cancer Types ◽

Learning Analysis ◽

Overall Survival Analysis

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.

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