We obtained a high throughput human sequencing data from the GEO database. The data were obtained from the accession number GSE116250 (HF = 37, control = 14) (18), the accession number GSE112087 (SLE = 31, control = 29) (19), the accession number GSE122459 (SLE = 20, control = 6) (20), and the accession number GSE196656 (HF = 3, control = 3) (21). GSE116250 and GSE112087 were used as training datasets. GSE122459 and GSE196656 were used as external validation datasets.

The DEGs from GSE116250 and GSE112087 were identified using the “limma” R package on normalized count data. The parameters |Log₂fold change| > 1 and adj. p < 0.05 were used as the screening criteria for DEGs in GSE116250. The parameters |Log₂fold change| > 2.5 and adj. p < 0.05 were used as the screening criteria for DEGs in GSE112087. Moreover, the heatmap and volcano plot of DEGs from the databases were constructed using “heatmap” and “ggplot2” R packages (22). The DEGs were imported to Metascape to perform functional analysis.

To uncover the possible biological roles and underlying mechanisms of genes, we employed the R package “clusterProfiler” to analyze the enrichment of KEGG terms pertaining to the target genes. Significance was determined based on KEGG pathways and GO terms encompassing biological processes (BPs), cellular components (CCs), and molecular functions (MFs), with an adj. P < 0.05 (5).

Using the CIBERSORT algorithm, we acquired the proportions of 22 different immune cell types from samples in both GSE116250 and GSE112087. To assess the variations in immune cell levels between disease and control samples, we employed the R package “vioplot” (23).

The R package “CIBERSORT” was employed to perform the analysis of immune infiltration. Spearman correlation analysis between shared hub genes and infiltrating immune cells was calculated using the R package “corrplot.” Lollipop diagrams were utilized to visualize the correlations between immune cells and hub genes (24).

We employed the R package “WGCNA” to construct the WGCNA. Initially, hierarchical clustering was executed on the study samples in order to identify any outliers and eliminate the abnormal samples. Next, β = 12 was chosen as the soft power parameter for establishing a scale-free network using the pick Soft Threshold function. Subsequently, the adjacency matrix was formulated and transformed into a topological overlap matrix (TOM), followed by the establishment of the gene dendrogram and module color based on the degree of dissimilarity. The “WGCNA” package was then utilized to calculate the correlations between modules and differentially infiltrating immune cells. Modules displaying strong correlation coefficients were regarded as potential candidates associated with differentially infiltrating immune cells and were selected for further analysis. Once the candidate module was chosen, we set the screening criteria for filtering key genes in the candidate module as |MM| (|Module membership|) > 0.8 and |GS| (|gene significance|) > 0.20 (5, 22).

The Interacting Genes Retrieval tools (STRING) database aided in constructing the PPI network (25). The PPI network was subsequently visualized using the Cytoscape software. Identifying significant gene clusters and obtaining hub genes was accomplished by utilizing the molecular complex detection (MCODE) plugins within the Cytoscape software (26).

The male C57BL/6 mice aged 10 weeks were obtained from Gempharmatech Co., Ltd. (Nanjing, China). Sham and left anterior descending branch (LAD) ligation surgery were performed as described in previously published protocols (27). Temporarily anesthetize mice by inhaling 2% isoflurane. Make a small skin incision on the left chest, expose the heart, and permanently ligate the LAD with 7–0 silk thread. The animals undergoing sham surgery underwent the same procedure, but without ligating the LAD. All animal experiments were performed in accordance with the United Kingdom Animals (Scientific Procedures) Act 1986 and the American Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of Animals (2020). Prior to the study, the research protocol was reviewed and approved by the Medical Ethics Committee of Union Hospital Affiliated to Huazhong University of Science and Technology. We affirmed that this study strictly followed the ARRIVE guidelines (https://arriveguidelines.org).

The total RNA from left ventricular tissues was extracted using TRIzol reagent. Subsequently, reverse transcription into cDNA was achieved by utilizing the PrimeScript RT Kit with gDNA Eraser. Afterwards, the mRNA levels of the targeted genes were determined via quantitative PCR using SYBR1 Premix Ex Taq II and normalized to the housekeeping gene 18S that served as an endogenous internal control. The sequences of mouse species primers used in our study are as follows:

Heat shock protein 90 alpha family class B member 1 (HSP90AB1): Forward: 5′-TGGCTGAGGACAAGGAGAACT AC-3′; Reverse: 5′-GAGAGGCGGCGTCGGTTAG-3′.

Ribosomal protein lateral stalk subunit P0 (RPLP0): Forward: 5′-AGCTGCTGCCACCACTGC-3′; Reverse: 5′-TCATCTGATTCC TCCGACTCTTCC-3′.

Ubiquitin C (UBC): Forward: 5′-CCACCAAGAAGGTCAAAC AGGAAG-3′; Reverse: 5′-TCACACCCAAGAACAAGCACAAG-3′.

Ubiquitin B (UBB): Forward: 5′-ACCTGGTCCTCCGCCTGAG-3′; Reverse: 5′- ATGCCCTCTTTATCCTGGATCTTGG-3′.

Neural precursor cell-expressed developmentally downregulated 8 (NEDD8): Forward: 5′-TGGCATCACATATCCTCTCACTCTC-3′; Reverse: 5′- CCCACCAGTAGACACACAAGATTG-3′.

18S: Forward: 5′-ACATCATCCCTGCATCCACT-3′; Reverse: 5′- GGGAGTTGCTGTTGAAGTCA-3′.

Bioinformatics statistical analysis was performed using R software v4.3.1 (http://www.r-project.org/). The expression of the hub genes was analyzed using the “Wilcox. Test,” adj. p < 0.05 was considered statistically significant. Molecular biology experimental data were presented as the mean ± standard error of the mean (SEM) of at least 3 independent experiments. Differences between the two groups were evaluated using the unpaired Student’s two-tailed t-test. Normal distribution of the data was analyzed using a Shapiro Wilk test. All data were analyzed using GraphPad Prism 8.3 (GraphPad Software, San Diego, CA). *p < 0.05; **p < 0.01; ns. indicates no significance between the 2 indicated groups.

Human training datasets (HF dataset GSE116250 and SLE dataset GSE116250) and human validation datasets (HF dataset GSE122459 and SLE dataset GSE196656) were retrieved and downloaded from the GEO database. The quality control of training datasets was performed through Principal Component Analysis (PCA) clustering analysis. It was observed that all samples from the HF and SLE datasets were well separated, suggesting high data quality (Supplemental Figure S1).

In order to identify significant genes in the HF dataset, we applied a screening criterion of |log₂FC| > 1 and adj. p < 0.05. Similarly, for the SLE dataset, we used a screening criterion of |log₂FC| > 2.5 and adj. p < 0.05. Volcano maps were subsequently created for the HF and SLE datasets. The HF dataset showed 1,538 upregulated genes and 2,510 downregulated genes, while the SLE dataset showed 5,606 upregulated genes and 1,981 downregulated genes (Figures 1A,B). Venn diagrams showed a total of 894 overlapping upregulated genes and 105 overlapping downregulated genes between the HF and SLE datasets (Figures 1C,D).

To investigate the functions and pathways associated with DEGs in SLE and HF datasets, functional enrichment analysis was performed using GO analysis. The results revealed that the DEGs in HF dataset were mainly enriched in pathways related to metabolism of RNA, cellular responses to stress, protein catabolic process, translation, and positive regulation of protein catabolic process (Figure 2A). Similarly, the DEGs in SLE dataset were prominently enriched in pathways related to metabolism of RNA, cellular responses to stress, regulation of protein stability, cellular macromolecule catabolic process, protein localization to organelle, and mitochondrial organization (Figure 2B). Furthermore, GO analysis of the common DEGs between HE and SLE datasets were predominantly enriched in pathways related to metabolism of RNA, cellular responses to stress, peptide metabolic process, cellular macromolecule catabolic process, positive regulation of catabolic process, and vesicle-mediated transport (Figure 3A). Concomitantly, KEGG analysis of the common DGEs between HE and SLE datasets were predominantly enriched in pathway related to Th17 cell differentiation, supporting the notion that immune system disorders might serve as a shared pathogenic factor for both HF and SLE (Figure 3B).

The common 999 DEGs between HF and SLE datasets were imported into the STRING database to construct a PPI network. Afterwords, the top 10 common DGEs with the highest ranking were identified based on 5 algorithms (Degree, DMNC, Eccentricity, Radiance, and Stress) from MCODE plugins in the Cytoscape software (Supplementary Table S1). Moreover, the 5 overlapping hub genes including NEDD8, UBC, HSP90AB1, UBB, and RPLP0, were identified using Venn plots (Figure 4A) and displayed in the PPI network (Figure 4B). Additionally, the co-expressed genes associated with 5 shared hub genes were further analyzed using GeneMANIA (28), among which the most significant genes with the highest ranking associated with these hub genes were adhesion regulating molecule 1 (ADRM1), proteasome 26S subunit, non-ATPase 4 (PSMD4), ubiquitin-conjugating enzyme E2M (UBE2M), mitochondrial ribosomal protein L7/L12 (MRPL12), and S-phase Kinase-Associated Protein 1 (SKP1) (Figure 4C).

In the subsequent analysis, we validated the expression levels of the 5 shared hub genes in patients with lupus and patients with HF using the human SLE dataset (GSE122459) and the human HF dataset (GSE196656) respectively. The results from the violin plots indicated an increase in the expression of 5 hub genes in both patients with HF (Figure 5A) and patients with lupus (Figure 5B) in comparison to those in normal individuals, suggesting that these 5 hub genes may be co-pathogenic hub genes for both HF and SLE patients.

The release of pro-inflammatory cytokines and infiltration of immune cells are characteristic features of HF (29, 30). Similarly, SLE is also characterized by the infiltration of various immune cells (31, 32). Therefore, we used CIBERSORT algorithm to investigate the extent of immune cell infiltration in these two distinct conditions based on HF dataset (GSE116250) and SLE dataset (GSE112087). The HF datasets showed a significant increase in monocytes and a decrease in dendritic cells in HF patients compared to those in control group (Figure 6), which was consistent with the findings in the SLE dataset regarding dendritic cells. Additionally, the SLE dataset revealed an increase in plasma cells and CD4+ naive cells, as well as a decrease in monocytes and NK cells (Figure 7). These findings suggested a potential shared pathogenesis based on immune cell infiltration between HF and SLE, particularly dendritic cells infiltration.

The similarity in immune cell composition constitutes solely one facet of the mutual pathogenesis shared by both HF and SLE. It remains imperative to confirm whether these 5 shared hub genes are involved in immune infiltration, identify the specific immune cells they are associated with, and determine their common characteristics. Consequently, we performed the Spearman correlation analysis to examine the correlation between these 5 shared hub genes and immune cell infiltration based on HF and SLE datasets. The results showed that HSP90AB1 and UBB were negatively correlated with monocytes and positively correlated with dendritic cells (Figures 8A,B), while NEDD8, RPLP0, and UBC showed a negative correlation with dendritic cells and a positive correlation with monocytes in the HF dataset (Figures 8C–E). On the other hand, HSP90AB1, NEDD8, RPLP0, and UBC were found to be positively correlated with both monocytes and dendritic cells (Figures 9A–C). While UBB showed a positive correlation with monocytes and a negative correlation with dendritic cells in the SLE dataset (Figures 9D,E). In general, a stable and positive correlation existed between hub genes (NEDD8, RPLP0, and UBC) and monocytes, as well as between HSP90AB1 and dendritic cells in the HF and SLE datasets.

To further verify the significance of hub genes in HF and SLE datasets, we initially created a gene co-expression network using the R package “WGCNA” based on the expression levels of genes in the GSE116250 dataset. Afterwards, cluster analysis was conducted for all the samples within the dataset except for the outlier sample dilated cardiomyopathy (DCM) 28 (Supplemental Figure S2). Additionally, a scale-first network with β = 12 as the soft threshold power was successfully established (Figure 10A). Next, the average linkage and Spearman correlation coefficients were calculated to construct a hierarchical clustering tree in which each leaf represented a specific gene and each branch represented a specific module that encompassed all genes exhibiting comparable expression levels (Figure 10B). Finally, we consolidated the functionally equivalent modules into a single large module, resulting in a total of 5 modules, among which the MEbrown module showed the highest correlation with HF (Figures 10C,D). Therefore, eigengenes within the MEbrown module were selected for the scatter map and imported into Cytoscape software to construct a protein interaction map (Figure 10E). Additionally, 5 shared hub genes were found to be located in the central part of this module, further confirming their significant role in the pathogenesis of HF (Figure 10F).

Considering the limited sample size of the validation dataset (GSE196656), a HF model was constructed by subjecting 10-week-old C57BL/6 wild type mice to MI surgery for 3 weeks to further validate the expression of hub genes in failing hearts in comparison to that in non-failing hearts. RT-PCR analysis confirmed a high expression level of HSP90AB1 and UBC in the hearts of MI mice compared to that in sham mice (Figures 11A,E), while the expression of UBB, RPLP0, and NEDD8 showed no obvious change between the two groups (Figures 11B–D).