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Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression.

ABSTRACT: In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classification for disease samples from controls. Here, we comprehensively summarize the regularized logistic regression with seven effective penalties developed for the selection of strongly indicative genes of SPTB from microarray data. We compare their properties and assess their classification performances in multiple datasets. It shows that elastic net, lasso, L1/2 and SCAD penalties get the better performance than others and can be successfully used to identify biomarkers of SPTB. Particularly, we make a functional enrichment analysis on these biomarkers and construct a logistic regression classifier based on them. The classifier generates an indicator of preterm risk score (PRS) for predicting SPTB. Based on the trained predictor, we verify the identified biomarkers on an independent dataset. The biomarkers achieve the AUC value of 0.933 in the SPTB classification. The results demonstrate the effectiveness and efficiency of the built-up strategy of biomarker discovery with regularized logistic regression. Obviously, the proposed method of discovering biomarkers for SPTB can be easily extended for other complex diseases.

SUBMITTER: Li L

PROVIDER: S-EPMC7689379 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression.

Li Lingyu L Liu Zhi-Ping ZP

Computational and structural biotechnology journal 20201110

In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classi ...[more]

PMID: 33294138

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Project description:To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility.In this article, we propose a regularized logistic regression model. Different from previous methods which focus on individual genes or modules, our model takes gene pairs, which are connected in a protein-protein interaction network, into account. A line graph is constructed to represent the adjacencies between pairwise interactions. Based on this line graph, we incorporate the degree information in the model via an adaptive elastic net, which makes our model less dependent on the expression data. Experimental results on six publicly available breast cancer datasets show that our method can not only achieve competitive performance in classification, but also retain great stability in variable selection. Therefore, our model is able to identify the diagnostic and prognostic biomarkers in a more robust way. Moreover, most of the biomarkers discovered by our model have been verified in biochemical or biomedical researches.The proposed method shows promise in the diagnosis of disease pathogenesis with different clinical characteristics. These advances lead to more accurate and stable biomarker discovery, which can monitor the functional changes that are perturbed by diseases. Based on these predictions, researchers may be able to provide suggestions for new therapeutic approaches.

Project description:ObjectiveTo predict preterm birth in nulliparous women using logistic regression and machine learning.DesignPopulation-based retrospective cohort.ParticipantsNulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014.MethodsWe used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample.ResultsDuring the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters.ConclusionAlthough artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

Project description:Evidence from family and twin-based studies provide strong support for a significant contribution of maternal and fetal genetics to the timing of parturition and spontaneous preterm birth. However, there has been only modest success in the discovery of genes predisposing to preterm birth, despite increasing sophistication of genetic and genomic technology. In contrast, DNA variants associated with other traits/diseases have been identified. For example, there is overwhelming evidence that suggests that the nature and intensity of an inflammatory response in adults and children are under genetic control. Because inflammation is often invoked as an etiologic factor in spontaneous preterm birth, the question of whether spontaneous preterm birth has a genetic predisposition in the case of pathologic inflammation has been of long-standing interest to investigators. Here, we review various genetic approaches used for the discovery of preterm birth genetic variants in the context of inflammation-associated spontaneous preterm birth. Candidate gene studies have sought genetic variants that regulate inflammation in the mother and fetus; however, the promising findings have often not been replicated. Genome-wide association studies, an approach to the identification of chromosomal loci responsible for complex traits, have also not yielded compelling evidence for DNA variants predisposing to preterm birth. A recent genome-wide association study that included a large number of White women (>40,000) revealed that maternal loci contribute to preterm birth. Although none of these loci harbored genes directly related to innate immunity, the results were replicated. Another approach to identify DNA variants predisposing to preterm birth is whole exome sequencing, which examines the DNA sequence of protein-coding regions of the genome. A recent whole exome sequencing study identified rare mutations in genes encoding for proteins involved in the negative regulation (dampening) of the innate immune response (eg, CARD6, CARD8, NLRP10, NLRP12, NOD2, TLR10) and antimicrobial peptide/proteins (eg, DEFB1, MBL2). These findings support the concept that preterm labor, at least in part, has an inflammatory etiology, which can be induced by pathogens (ie, intraamniotic infection) or "danger signals" (alarmins) released during cellular stress or necrosis (ie, sterile intraamniotic inflammation). These findings support the notion that preterm birth has a polygenic basis that involves rare mutations or damaging variants in multiple genes involved in innate immunity and host defense mechanisms against microbes and their noxious products. An overlap among the whole exome sequencing-identified genes and other inflammatory conditions associated with preterm birth, such as periodontal disease and inflammatory bowel disease, was observed, which suggests a shared genetic substrate for these conditions. We propose that whole exome sequencing, as well as whole genome sequencing, is the most promising approach for the identification of functionally significant genetic variants responsible for spontaneous preterm birth, at least in the context of pathologic inflammation. The identification of genes that contribute to preterm birth by whole exome sequencing, or whole genome sequencing, promises to yield valuable population-specific biomarkers to identify the risk for spontaneous preterm birth and potential strategies to mitigate such a risk.

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Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression.

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Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression.

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