Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

Abstract

Cardiorenal syndrome (CRS), defined as acute or chronic damage to the heart or kidney triggering impairment of another organ, has a poor prognosis. However, the molecular mechanisms underlying CRS remain largely unknown. The RNA-sequencing data of the left ventricle tissue isolated from the sham-operated and CRS model rats at different time points were downloaded from the Gene Expression Omnibus (GEO) database. Genomic differences, protein-protein interaction networks, and short time-series analyses, revealed fibronectin 1 (FN1) and periostin (POSTN) as hub genes associated with CRS progression. The transcriptome sequencing data of humans obtained from the GEO revealed that FN1 and POSTN were both significantly associated with many different heart and kidney diseases. Peripheral blood samples from 20 control and 20 CRS patients were collected from the local hospital, and the gene expression levels of FN1 and POSTN were detected by real-time quantitative polymerase chain reaction. FN1 (area under the curve [AUC] = 0.807) and POSTN (AUC = 0.767) could distinguish CRS in the local cohort with high efficacy and were positively correlated with renal and heart damage markers, such as left ventricular ejection fraction. To improve the diagnostic ability, diagnosis models comprising FN1 and POSTN were constructed by logistic regression (F-Score = 0.718), classification tree (F-Score = 0.812), and random forest (F-Score = 1.000). Overall, the transcriptome data of CRS rat models were systematically analyzed, revealing that FN1 and POSTN were hub genes, which were validated in different public datasets and the local cohort.

Causal relationship from heart failure to kidney function and CKD: A bidirectional two-sample mendelian randomization study

“…According to the findings of a multi-center study, approximately one-fifth to two-fifths of those afflicted with acute HF have renal dysfunction, while about four-fifths to six-tenths of those with chronic HF have CKD [ 10 ]. Research has revealed a correlation between HF and CKD, yet their causal relationship has not yet been examined in detail [ 11 , 12 ]. Therefore, it is still uncertain whether HF is a contributory factor in the onset of CKD.…”

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition

“…Time-series-based gene expression data have become one of the most fundamental methods for studying biological processes. Most biological processes are dynamic, and using time series data can characterize the function of specific genes, discover linkages and regulatory relationships between genes, and find their clinical applications [ 6 , 7 , 8 ]. Time series data can not only capture transient changes in gene expression, but also demonstrate the order of events that occur during biological processes and allow the study of the temporal patterns of gene expression [ 9 ].…”