Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Abstract

Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary.Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS).Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen–containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients.Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.

Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis

“…The RRA method, a recently emerging analysis method, has been widely used to integrate different datasets and produce a ranked list of the DEGs ( 40 ). For example, Ma et al utilized the RRA method to integrate four eligible DCM microarray datasets from the GEO and developed a 7-gene signature predictive model of DCM ( 11 ). While in the present study, using RUVSeq to substantially decrease batch effects, we integrated, for the first time, the different RNA-seq datasets of the GEO database to explore DEGs and hub genes associated with HF by using the RRA method.…”

“…The robust rank aggregation (RRA) method, first proposed in 2012 by Kolde et al, is a rigorous approach using probabilistic models to analyze the significant probability of all elements in different sequencing or microarray datasets ( 8 ). Recently, the RRA algorithm has been extensively used to integrate data in different microarray platforms to screen the differentially expressed genes (DEGs) in multiple diseases, including thyroid carcinoma ( 9 ), prostate cancer (PCa) ( 10 ), and DCM ( 11 ). For example, Song et al utilized the RRA method to integrate 10 eligible PCa microarray datasets from the GEO and identify four candidate biomarkers for prognosis of PCa ( 10 ).…”

“…For example, Song et al utilized the RRA method to integrate 10 eligible PCa microarray datasets from the GEO and identify four candidate biomarkers for prognosis of PCa ( 10 ). Ma et al integrated four eligible dilated cardiomyopathy (DCM) microarray datasets from the GEO database and developed a 7-gene signature predictive of DCM by utilizing the RRA method ( 11 ). However, due to the greater difficulty in integrating sequencing data, the application of the previous RRA algorithm was limited to microarray data, and the RRA analysis of RUVSeq was still rarely reported.…”

Utility of diffusion weighted imaging-based radiomics nomogram to predict pelvic lymph nodes metastasis in prostate cancer

“…PCC and ANOVA were used to reduce the dimensions of the feature matrix and to select the best features, respectively [ 34 ]. LASSO is a popular penalized regression method that minimizes the residual sum of squares and places a bound on the sum of the absolute value of the coefficients [ 35 ].…”

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments

“…With the popularization of gene chips and high-throughput sequencing, many disease databases have gradually been improved, and more and more effective data can be used to reveal the pathogenesis of diseases and new therapeutic targets. For example, Ma used a bioinformatic approach to analyze gene expression profiles and underlying functional networks in cardiac tissue from patients with dilated cardiomyopathy ( 13 ). Huang analyzed the correlation of serum 25-hydroxyvitamin D levels in the progression of proteinuria in DKD and its underlying mechanisms ( 14 ).…”