Artif Intell Med - Cancer survival classification using integrated data sets and intermediate information.


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JECTIVE: Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time.METHODS: FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS).RESULTS: In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS was 75.51% (RF), 87.76% (SVM) 85.71% (FSCOX_median), 85.71% (FSCOX_SVM). These results are higher than the results of using miRNA expression and mRNA expression alone. In addition we predict 16 hsa-miR-23b and hsa-miR-27b target genes in ovarian cancer data sets, obtained by SVM-based feature selection through integration of sequence information and gene expression profiles.CONCLUSION: Among the approaches used, the integrated miRNA and mRNA data set yielded better results than the individual data sets. The best performance was achieved using the FSCOX_SVM method with independent feature selection, which uses intermediate survival information between short-term and long-term survival time and the combination of the 2 different data sets. The results obtained using the combined data set suggest that there are some strong interactions between miRNA and mRNA features that are not detectable in the individual analyses.

Resumo Limpo

jectiv although numer studi relat cancer surviv publish increas predict accuraci surviv class still remain challeng integr differ data set microrna mirna mrna might increas accuraci surviv class predict therefor suggest machin learn ml approach integr differ data set develop novel method base featur select cox proport hazard regress model fscox improv predict cancer surviv timemethod fscox provid us intermedi surviv inform usual discard separ surviv group short longterm allow us perform surviv analysi use mlbase protocol featur select integr inform mirna mrna express profil featur level predict surviv phenotyp use follow classifi first exist ml method support vector machin svm random forest rf second new medianbas classifi use fscox fscoxmedian third svm classifi use fscox fscoxsvm compar method use type cancer tissu data set mirna express ii mrna express iii combin mirna mrna express latter data set includ featur select either combin mirnamrna profil independ mirna mrnas profil ifsresult ovarian data set accuraci surviv classif use combin mirnamrna profil if use rf use svm use fscoxmedian use fscoxsvm balanc shortterm longterm survivor data set accuraci higher use mirna alon rf svm fscoxmedian fscoxsvm mrna alon rf svm fscoxmedian fscoxsvm similar glioblastoma multiform data accuraci mirnamrna use if rf svm fscoxmedian fscoxsvm result higher result use mirna express mrna express alon addit predict hsamirb hsamirb target gene ovarian cancer data set obtain svmbase featur select integr sequenc inform gene express profilesconclus among approach use integr mirna mrna data set yield better result individu data set best perform achiev use fscoxsvm method independ featur select use intermedi surviv inform shortterm longterm surviv time combin differ data set result obtain use combin data set suggest strong interact mirna mrna featur detect individu analys

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