Identification of Candidate Biomarkers Associated with Mitochondrial Dysfunction and SUMOylation in Heart Failure Based on Bioinformatics Approaches

Summary

Using bioinformatics, machine learning, and qPCR validation, this study identified five candidate biomarkers associated with SUMOylation and mitochondrial dysfunction in heart failure. These findings improve understanding of heart failure mechanisms and suggest potential directions for future diagnostic research.

Abstract

Heart failure (HF) presents a persistent clinical challenge. While SUMOylation and mitochondrial function are vital for cardiomyocyte health, their combined influence on HF remains elusive. Two HF-related datasets were downloaded from GEO. The overlapping genes were obtained from all genes in the training set, SUMO-related genes, and mitochondrial-related genes. Three machine learning algorithms were applied to identify diagnostic key genes. Subsequently, diagnostic models were constructed and evaluated based on these genes. Besides, the immune microenvironment in HF versus healthy controls was assessed using CIBERSORT, MCP-counter, and ssGSEA. The differences in immune infiltration between HF and healthy controls were analyzed. Drug prediction and molecular docking were performed to identify potential drug candidates targeting these genes. Finally, qPCR was employed to validate gene expression levels in clinical samples. A total of 113 common genes with notable enrichment in mitochondrial regulation were identified. Five key genes, namely NFKB1, MYEF2, NSUN2, SQSTM1, and FKBP4, were identified by three machine learning algorithms. Functional enrichment analyses linked these genes to immune response, RNA processing, and cell cycle regulation. Moreover, immune infiltration profiling revealed that neutrophil infiltration contributes to dysregulated immune responses in HF. Molecular docking revealed that the small-molecule drug IMX-942 has a favorable binding affinity with SQSTM1 (-5.8 kcal/mol). qPCR validation supported the bioinformatics results. NFKB1, MYEF2, NSUN2, SQSTM1, and FKBP4 were identified as key genes linking SUMOylation and mitochondrial function in HF. These findings provide new insights into HF pathophysiology and may contribute to the development of novel diagnostic and therapeutic strategies.

Introduction

Heart failure (HF), the end-stage of various cardiovascular diseases, is characterized by impaired cardiac function that fails to meet the body's metabolic demands¹. This debilitating condition poses significant threats to patient health, leading to decreased quality of life and elevated mortality rates². Current diagnostic modalities for HF primarily include biochemical marker detection^3,4, echocardiography, and radiological imaging⁵. Although available treatments encompass pharmacological agents, device-based interventions, and surgi....

Protocol

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Third Hospital of Hebei Medical University (W2025-065-1) in November 2024. Informed consent was obtained from all subjects involved in the study.

Data source and preprocessing

RNA-seq data associated with HF were obtained, including two microarray datasets from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Two peripheral blood microarray datasets were selected: GSE59867 (34 HF samples and 30 controls) was used as the training dataset; GSE57338 (177 ....

Results

Identification and functional enrichment of intersecting genes

To identify genes involved in SUMOylation and mitochondrial function in HF, quality control was first performed on the training set GSE59867 (Supplementary Figure 1A). A three-way intersection analysis was conducted among all genes in the training set, SRGs, and MRGs, identifying 113 overlapping genes (Figure 1A). To explore the potential biological functions of these .......

Discussion

HF, a progressive and terminal stage of various cardiovascular diseases, is characterized by highly complex and multifactorial pathophysiological mechanisms^17,34. Although both SUMOylation and mitochondrial dysfunction have been individually implicated in HF, their potential synergistic roles remain insufficiently explored, particularly at the gene level. In the present study, we identified five key genes—NFKB1, MYEF2, NSUN2, SQSTM1, and FKBP4—and est.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Chloroform substitute	Servicebio	G3014-02	qPCR reagent
DEPC-treated water	Biosharp	BL510A	qPCR reagent
Fast First-Strand cDNA Synthesis Mix for RT (with dsDNase)	Albatross Biology	500-101	qPCR reagent
Fast Taq qPCR SYBR Green Mix	Albatross Biology	500-102	qPCR reagent
FKBP4 primers	Tsingke	N/A	Forward: 5’-GAAGGCGTGCTGAAGGTCAT-3’ Reverse: 5’-TGCCATCTAATAGCCAGCCAG-3’
Isopropanol	Hushi	80109218	qPCR reagent
MYEF2 primers	Tsingke	N/A	Forward: 5’-CAGCTCCAATGGCGTTAAAATG-3’ Reverse: 5’-TGGCCTTCTTACTTCCTGTAGAT-3’
NanoDrop spectrophotometer	Thermo Fisher Scientific	NanoDrop 2000C	qPCR reagent
NFKB1 primers	Tsingke	N/A	Forward: 5’-AACAGAGAGGATTTCGTTTCCG-3’ Reverse: 5’-TTTGACCTGAGGGTAAGACTTCT-3’
NSUN2 primers	Tsingke	N/A	Forward: 5’-GAACTTGCCTGGCACACAAAT-3’ Reverse: 5’-TGCTAACAGCTTCTTGACGACTA-3’
SQSTM1 primers	Tsingke	N/A	Forward: 5’-GCACCCCAATGTGATCTGC-3’ Reverse: 5’-CGCTACACAAGTCGTAGTCTGG-3’
TRIzol reagent	Vazyme	R401-01	qPCR reagent
β-actin primers	Tsingke	N/A	Forward: 5’-CATGTACGTTGCTATCCAGGC-3’ Reverse: 5’-CTCCTTAATGTCACGCACGAT-3’