Derivation and Comprehensive Analysis of Aging Patterns in Patients with Bladder Cancer

Identification and validation of a 7-genes prognostic signature for adult acute myeloid leukemia based on aging-related genes

To explore effects of aging-related genes (ARGs) on the prognosis of Acute Myeloid Leukemia (AML), a seven-ARGs signature was developed and validated in AML patients. The numbers of seven-ARG sequences were selected to construct the survival prognostic signature in TCGA-LAML cohort, and two GEO datasets were used independently to verify the prognostic values of signature. According to seven-ARGs signature, patients were categorized into two subgroups. Patients with high-risk prognostic score were defined as HRPS-group/high-risk group, while others were set as LRPS-group/low-risk group. HRPS-group presented adverse overall survival (OS) than LRPS-group in TCGA-AML cohort (HR=3.39, P<0.001). In validation, the results emphasized a satisfactory discrimination in different time points, and confirmed the poor OS of HRPS-group both in GSE37642 (HR=1.96, P=0.001) and GSE106291 (HR=1.88, P<0.001). Many signal pathways, including immune- and tumor-related processes, especially NF-κB signaling, were highly enriched in HRPS-group. Coupled with high immune-inflamed infiltration, the HRPS-group was highly associated with the driver gene and oncogenic signaling pathway of TP53. Prediction of blockade therapy targeting immune checkpoint indicated varied benefits base on the different ARGs signature score, and the results of predicted drug response suggested that Pevonedistat, an inhibitor of NEDD8-activating enzyme, targeting NF-κB signaling, may have potential therapeutic value for HRPS-group. Compared with clinical factors alone, the signature had an independent value and more predictive power of AML prognosis. The 7-ARGs signature may help to guide clinical-decision making to predict drug response, and survival in AML patients.

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Objective

Energy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.

Methods

Transcriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.

Results

A total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.

Conclusion

This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.