RNA-seq and machine learning identifies hub genes for HFrEF

Heart failure (HF) is a posh medical syndrome characterised by excessive mortality and hospitalization charges. Despite ongoing developments within the prevention and analysis of cardiovascular ailments, the prevalence and mortality of HF stay excessive, making it a urgent public well being concern.1
HF is categorized into three varieties based mostly on left ventricular ejection fraction: HF with preserved ejection fraction (HFpEF), HF with mid-range ejection fraction (HFmrEF), and HF with lowered ejection fraction (HFrEF), amongst which HFrEF is essentially the most detrimental, demonstrating the very best mortality charge.2 Regarding pharmacologic therapies for HF, particular drugs have been acknowledged for their capability to have an effect on gene expression profiles, finally defending coronary heart operate. These therapeutic brokers, collectively termed epigenetic therapies, embrace hydralazine, metformin, statins, and sodium-glucose co-transporter 2 (SGLT2) inhibitors.3–5 Recently, SGLT2 inhibitors together with Empagliflozin, Canagliflozin, and Dapagliflozin have been integrated into the therapeutic choices for HFrEF as a brand new class of antidiabetic medication.6 Although massive randomized research, notably EMPEROR-Reduced and EMPA-REGOUTCOME, have confirmed the helpful results of Empagliflozin for treating HFrEF,7 the mechanism underlying Empagliflozin’s advantages for HFrEF sufferers stays elusive, making it essential to discover Empagliflozin’s mode of motion in HFrEF remedy.
With the arrival of second-generation sequencing, it has grow to be possible to analyze the mechanisms of drug motion on the molecular and genetic degree. For instance, RNA sequencing (RNA-seq) was utilized in researching the preventive results of japonica rice on lipopolysaccharide-induced acute lung damage,8 the protecting impact of Dendrobium officinale alkaloids on hepatic toxicity of carbon tetrachloride in mice,9 in addition to the cardiotoxicity of antipsychotic medication and the underlying mechanisms concerned.10,11 Additionally, single-cell RNA-seq evaluation was carried out to analyze the adversarial results of tamoxifen on grownup neurogenesis.12 In gentle of those developments, the target of this examine is to establish core gene targets related to HFrEF in response to Empagliflozin remedy by recruiting clinically identified sufferers and conducting comparative analyses of transcriptional gene adjustments earlier than and after Empagliflozin administration utilizing RNA-seq. This analysis endeavors to boost the comprehension of Empagliflozin’s mechanisms in treating HFrEF and present novel targets for bettering HFrEF remedy.
StrategiesPatients and Samples
This examine enrolled sufferers identified with HFrEF who visited our hospital’s cardiovascular division from February 2021 to February 2023. Initially, a complete of 11 sufferers had been strictly chosen based mostly on the diagnostic standards outlined within the Chinese Guidelines for the Diagnosis and Treatment of Heart Failure 2018. These sufferers had been subsequently divided into two teams: the “Before remedy” group (receiving commonplace remedy alone) and the “After remedy” group (receiving commonplace remedy plus Empagliflozin). Table 1 presents the fundamental data collected from these sufferers. During the follow-up, two sufferers had been excluded from the examine as a consequence of dying and non-compliance with medicine, leading to a remaining cohort of 9 sufferers. 10 mL of peripheral blood was extracted from the sufferers, and peripheral blood mononuclear cells (PBMCs) had been remoted earlier than and after Empagliflozin remedy.13
Table 1 General Information

According to the Chinese Guidelines for the Diagnosis and Treatment of Heart Failure 2018, anti-HF medication (ARNI/ACEI/ARB, β-blockers, MRA) and different standard therapies similar to cardiovascular secondary prevention, myocardial vitamin, improved myocardial perfusion are commonplace therapies. Empagliflozin, on this examine, was orally administered as soon as each day at a dose of 10 mg for two months.
This examine was authorized by the Ethics Committee of the Second Affiliated Hospital of Zhejiang Chinese Medical University (Approval No. 2022–045-IH01). Informed consent was obtained from all topics previous to their enrollment.
Inclusion and Exclusion Criteria
Inclusion standards: (1) Patients aged 18 years or above; (2) Patients identified with HFrEF together with different co-morbidities in non-acute exacerbation; (3) Patients who had been on standardized HF remedy drugs (ARNI/ACEI/ARB, β-blockers, MRA) for a minimal of 30 days.
Exclusion standards: (1) Patients with cardiogenic shock, malignant arrhythmia and different vital or doubtlessly life-threatening circumstances; (2) Patients with extreme renal insufficiency; (3) Patients with recognized allergy symptoms or intolerance to Empagliflozin; (4) Patients with a historical past of psychiatric sickness or impaired consciousness, rendering them unable to cooperate.
Extraction of PBMCs
In this examine, PBMCs had been extracted from the collected human entire blood utilizing the PBMC isolation answer (Solarbio P9010) inside 2 h of pattern assortment in response to the next steps: (1) The human entire blood was diluted with an equal quantity of phosphate-buffered saline (PBS); (2) An equal quantity of PBMC isolation answer was added right into a centrifuge tube, the diluted blood was layered above the liquid degree of the isolation answer, and centrifugation was carried out at room temperature with a horizontal rotor at 800 g for 30 min; (3) After centrifugation, clear stratification occurred, with the uppermost layer being the diluted plasma layer, the center being the clear isolate, and the white membrane layer between the plasma and the isolate containing PBMCs. Then, the PBMCs had been rigorously aspirated from the white membrane layer; (4) The obtained PBMCs had been washed twice with 10 mL of PBS, and centrifuged for 10 min with a horizontal rotor at 250 g; (5) The ensuing supernatant was discarded, leaving the precipitate, which had been PBMCs.13
RNA Extraction and RNA-Seq
Total RNA was extracted utilizing the Trizol reagent (Thermo Fisher, 15596018). In this examine, RNA from all samples was remoted and purified. The RNA integrity was subsequently assayed, making certain concentrations>50 ng/μL, RIN values>7.0, and complete RNA>1 μg, thus assembly the necessities for downstream experiments. To particularly seize mRNA with polyadenylate (polyA), two rounds of purification was carried out utilizing oligo (dT) magnetic beads (Dynabeads Oligo (dT), cat. 25–61005, Thermo Fisher, USA). The captured mRNA was fragmented at 94°C for 5–7 min, and subsequently served as a template for cDNA synthesis facilitated by reverse transcriptase (Invitrogen SuperScriptTM II Reverse Transcriptase, cat. 1896649, CA, USA). The obtained cDNA was subjected to PCR-pre-denaturation, involving a 3-min maintain at 95°C, adopted by denaturation at 98°C for a complete of 8 cycles, every lasting 15s, together with annealing for 15s in every cycle. Afterwards, the cDNAs had been amplified by means of PCR-pre-denaturation at 95°C for 3 min, adopted by denaturation at 98°C for a complete of 8 cycles, with every cycle lasting 15s, annealing at 60°C for 15s, extension at 72°C for 30s, and concluding with a remaining extension at 72°C for 5 min. This course of resulted in libraries with a fraction measurement of 300 bp ± 50 bp (strand-specific libraries). Finally, bipartite sequencing was carried out utilizing the Illumina NovaSeq™ 6000 following commonplace working procedures, with the sequencing mode of PE150.
Statistical Mapping of Gene Expression Value Distribution
The gene expression values for every pattern had been visualized utilizing FPKM field plots and violin plots. The abscissa represented the pattern title, and the ordinate represented log10 (FPKM). Each field plot corresponded to 5 statistical measures: the utmost worth, the higher quartile, the median worth, the decrease quartile, and the minimal worth.
Volcano Maps and Heat Maps
Differential expression evaluation was carried out on the expression recordsdata of each the “Before remedy” and “After remedy” teams utilizing the limma package deal in R (model 4.4.1). The threshold was set at |log2FC| > 1 and adj. Pvalue < 0.05. Visual evaluation of the differential expression outcomes was carried out utilizing the ggplot2 and heatmap packages, which resulted within the technology of corresponding volcano maps and heatmaps. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analyses Enrichment evaluation and visible illustration of the differentially expressed genes had been carried out utilizing the clusterProfiler, ggplot2, and GOplot packages in R. Immune Infiltration Analysis The CIBERSORT package deal (model 1.04) in R (model 4.2.2) was utilized for immune infiltration evaluation. Parameters perm=1000 and QN=FALSE had been set to filter the immune infiltration outcomes, and samples with p<0.05 were selected for subsequent analysis. Based on the immune infiltration results, a bar graph representing the relative proportions of immune-infiltrated cells in the samples was generated. Additionally, the corrplot package (version 0.92) was employed for immune cell correlation analysis. The vioplot package (version 0.3.7) was applied to analyze and create violin plots between the control and UC groups. Machine Learning Lasso analysis was performed using the glmnet package (version 4.1–4), and random forest analysis was conducted using the randomForest package (version 4.7–1.1) based on MeanDecreaseGini > 0.5. Support vector machine-recursive function elimination (SVM-RFE) evaluation was carried out utilizing the e1071 (model 1.7–11) and caret (model 6.0–92) packages. The intersection of outcomes from these three forms of machine learning analyses was visualized utilizing a Venn diagram.
Hub Gene Expression and ROC Analysis
The ggplot2 package deal (model 3.3.6) and ggpubr package deal (model 0.4.0) had been used to investigate hub gene expression between teams. P-values had been obtained utilizing the Wilcoxon methodology, and a gene expression field plot was generated. ROC evaluation was carried out utilizing the pROC package deal (model 1.18.0), and ROC curves had been plotted. Finally, a histogram illustrating hub gene expression was plotted for every affected person.
Correlation Analysis of Hub Genes and Immunity
The ggpubr package deal (model 0.4.0) and ggExtra package deal (model 0.10.0) had been used to investigate the correlation between genes and immune cells utilizing the Spearman methodology. Scatter plots had been generated for statistically vital correlations (p<0.05), and a lollipop diagram was plotted utilizing R, displaying the correlation between genes and immune cells. Correlation Analysis of Hub Genes and Clinical Indicators In this examine, we used the ggplot (v3.4.3) package deal and the ggExtra (v0.10) package deal to plot scatter plots of hub genes versus cardiac ultrasound indices and 6MWT. The fitted curves and equations had been plotted utilizing the stat_smooth operate in them. The correlation between hub genes and cardiac ultrasound indexes and 6MWT was analyzed utilizing the stat_cor operate within the ggpubr package deal (v0.4.0) (the correlation evaluation methodology was Pearson). Statistical Analysis In this examine, information had been preprocessed and analyzed utilizing SPSS 22.0 and R software program, together with ID conversion and duplicate elimination. Paired t-tests had been utilized to calculate parameters earlier than and after remedy, and P<0.05 was statistically significant between groups. Graphs were plotted using R software, GraphPad Prism, and online plotting tools to provide a comprehensive description of the statistical methods used throughout this study. ResultsGeneral Information All the nine patients enrolled in this study completed a two-month course of Empagliflozin treatment. There were seven male cases (77.78%) and two female cases (22.22%), with an average age of 62.11 ± 6.36 years. According to the New York Heart Association (NYHA) functional classification, the distribution of these patients was as follows: Grade IV (55.56%), Grade III (33.33%), and Grade II (11.11%). A history of smoking was reported in 44.44% of the patients, while 55.56% had a history of hypertension, and 66.67% had a history of coronary heart disease. These patients exhibited the following average clinical parameters: an ejection fraction (EF) of 35.44 ± 2.96%; a 6MWT of 345.56±23.55 m; interventricular septal thickness at end-diastole (IVSD) measuring 9.56 ± 1.13 mm, left ventricular internal diameter at end-diastole (LVDd) measuring 62.00 ± 7.30 mm, and left ventricular internal diameter at end-systole (LVDs) measuring 51.11 ± 6.58 mm; a B-type natriuretic peptide (BNP) level of 334.13 ± 332.73 pg/mL; a triglyceride (TG) level of 1.60 ± 1.11 mmol/L, a total cholesterol (TC) level of 4.69 ± 1.13 mmol/L, and a low-density lipoprotein (LDL) level of 2.78 ± 0.75 mmol/L; as well as a creatinine (Cr) level of 79.41 ± 12.00 μmol/L (Raw data can be found in Supplementary Table 1). Observation on the Efficacy of Empagliflozin In this study, we observed the indexes of cardiac ultrasound and 6MWT before and after two-month treatment with Empagliflozin. Compared with the “Before treatment” group, the cardiac ultrasound indexes improved in the “After treatment” group, with no significant difference (P>0.05) (Figure 1A-D), and the 6MWT elevated considerably (P<0.05), suggesting the effectiveness of the remedy (Figure 1E). In order to make clear whether or not Empagliflozin exerts results on cardiac ultrasound indexes, we recorded the indexes for an extra six months after remedy. As a end result, the cardiac ultrasound indexes together with EF, LVDd, LVDs and 6MWT all recommended Empagliflozin’s results in considerably bettering HFrEF (P < 0.01), except for IVSD, which showed no significant difference (P > 0.01) (Figure 1A-D). As anticipated, vital adjustments in 6MWT remained after six months of remedy (Figure 1E) (Raw information could be present in Supplementary Tables 2 and 3).
Figure 1 Observation on the efficacy of Empagliflozin. (A) Histogram evaluating EF earlier than remedy and after 2/6 months of remedy; (B) Histogram evaluating IVSD earlier than remedy and after 2/6 months of remedy; (C) Histogram evaluating LVDd earlier than remedy and after 2/6 months of remedy; (D) Histogram evaluating LVDs earlier than remedy and after 2/6 months of remedy; (E) Histogram evaluating 6MWT earlier than remedy and after 2/6 months of remedy (ns: P>0.05, *P<0.05, **P<0.01). Distribution Statistics of Gene Expression Values The FPKM field plot (Figure 2A) and violin plot (Figure 2B) had been generated based mostly on the gene expression values of every pattern. These plots revealed related gene expression values throughout the samples, indicating the absence of organic replicate samples. Figure 2 Distribution of gene expression values. (A) Box plot of FPKM values; (B) Violin map of FPKM values. Differential Gene Expression Analysis Differential gene expression evaluation was carried out on the peripheral blood gene expression information from each the “Before remedy” and “After remedy” teams. The rely of differentially expressed genes, each up-regulated (log2FC≥1 and q<0.05) and down-regulated (log2FC≤-1 and q<0.05), was determined for each comparison group. A total of 42 differentially expressed genes were identified, with six being up-regulated and 36 being down-regulated. This analysis resulted in the generation of a volcano plot (Figure 3A) a histogram (Figure 3B), and a heatmap displaying all differentially expressed genes (Figure 3C). Figure 3 Analysis of the differentially expressed genes. (A) Volcano map of the Before treatment VS After treatment groups (| log2FC | > 1, P < 0.05); (B) Gene expression adjustments within the Before remedy VS After remedy teams; (C) Heat map of the Before remedy VS After remedy teams. GO & KEGG Enrichment Analyses The 42 differentially expressed genes had been subjected to GO and KEGG enrichment analyses. A histogram illustrating the outcomes of GO enrichment evaluation was generated (Figure 4A), which concerned the classes of organic processes (BP), mobile parts (CC), and molecular capabilities (MF). Additionally, bubble charts had been drawn to visualise vital BP (Figure 4B). Specifically, GO enrichment evaluation revealed vital enrichment of those differentially expressed genes within the immune system, adaptive immune response, and innate immune response (BP); in addition to within the membrane and immunoglobulin complicated (CC), and in antigen binding and immunoglobulin receptor binding (MF). Furthermore, a bubble diagram displaying the outcomes of KEGG enrichment evaluation (Figure 4C) revealed enrichment of those differentially expressed genes in heparan sulfate/heparin and different associated pathways. Figure 4 GO & KEGG enrichment analyses. (A) GO and KEGG enrichment analyses of the differentially expressed genes; (B) Bubble diagram displaying the organic processes after GO enrichment evaluation; (C) Bubble Diagram displaying KEGG enrichment evaluation outcomes. Immune Infiltration Analysis CIBERSORT was employed to investigate the immune infiltration in each the “Before remedy” and “After remedy” teams. The outcomes of immune infiltration evaluation had been visualized by means of a bar graph, which illustrated the relative proportions of immune-infiltrating cells within the samples (Figure 5A). Furthermore, a correlation heatmap was created to exhibit the connection amongst totally different immune cells (Figure 5B). Subsequently, violin plots had been drawn to facilitate group comparisons (Figure 5C). According to the outcomes (P < 0.05), there was a significant difference in the proportion of plasma cells between the “After treatment” and “Before treatment” groups (P=0.011), while no significant differences were observed in other immune cells (P>0.05).
Figure 5 Immune correlation evaluation. (A) Bar chart displaying the immune infiltration evaluation leads to the Before remedy VS After remedy teams; (B) Violin chart displaying the immune infiltration evaluation leads to the Before remedy VS After remedy teams (Cells with p < 0.05 have proven crimson colour); (C) Heat map displaying the correlation between immune cells. Screening of Hub Genes by Machine Learning The outcomes of lasso evaluation on the 42 differentially expressed genes are proven in Figure 6A and B. Four genes (Figure 6C and D) had been recognized by means of random forest evaluation, and 34 had been obtained after SVM-RFE evaluation (Figure 6E). To visualize the intersection of the outcomes from these three machine learning analyses, a Venn diagram was drawn (Figure 6F). Ultimately, GTF2IP14 and MTLN had been recognized as hub genes. Figure 6 Machine learning. (A and B) Lasso evaluation of the differentially expressed genes; (C and D) Random forest evaluation of the differentially expressed genes; (E) SVM-RFE evaluation of the differentially expressed genes; (F) Venn diagrams of the intersected outcomes from lasso evaluation, random forest evaluation, and SVM-RFE evaluation. Original Dataset Verification The expression ranges of the hub genes (GTF2IP14 and MTLN) in each the “After remedy” and “Before remedy” teams was analyzed, and corresponding gene expression field plots had been generated (Figure 7A and B). Specifically, GTF2IP14 expression was up-regulated after remedy, whereas MTLN expression was down-regulated (Figure 7A and B). The P values for GTF2IP14 and MTLN had been 0.021 and 0.00048, respectively. Subsequently, receiver working attribute (ROC) evaluation was carried out, after which ROC curves had been plotted (Figure 7C and D). The space beneath the curve (AUC) for GTF2IP14 and MTLN was 0.604 and 0.977, respectively. Detailed data concerning GTF2IP14 and MTLN expression in particular person sufferers could be discovered within the Supplementary Materials, revealing a constant sample of upregulation for GTF2IP14 and downregulation for MTLN after remedy (Supplementary Figure 1A and B). Figure 7 Verification of the hub genes. (A) GTF2IP14 expression within the Before remedy VS After remedy teams; (B) MTLN expression within the Before remedy VS After remedy teams; (C) ROC curve of GTF2IP14; (D) ROC curve of MTLN. Analysis of the Correlation Between Hub Genes and Immunity The correlation between the hub genes (GTF2IP14 and MTLN) and immune cells was analyzed utilizing the Spearman methodology, and a lollipop chart was generated for example the evaluation outcomes (Figure 8A and B). GTF2IP14 was revealed to be considerably correlated with naive B cells, whereas MTLN exhibited vital correlations with regulatory T cells (Tregs) and resting reminiscence CD4+ T cells. To additional visualize these correlations, a scatter plot was created. GTF2IP14 was discovered to be negatively correlated with naive B cells (Figure 8C), whereas MTLN was positively correlated with Tregs in addition to resting reminiscence CD4+ T cells (Figure 8D and E). Figure 8 Correlation evaluation of hub genes and immunity. (A) Lollipop map of the correlation between GTF2IP14 and immune cells (Values with p < 0.05 have been marked in crimson); (B) Lollipop map of the correlation between MTLN and immune cells (Values with p < 0.05 have been marked in red); (C) Scatter plot of the correlation between GTF2IP14 and naive B cells; (D) Scatter plot of the correlation between MTLN and Tregs; (E) Scatter plot of the correlation between MTLN and resting memory CD4+ T cells. Analysis of the Correlation Between Hub Genes and Clinical Indicators In this study, GTF2IP14 was found to have no significant correlation with cardiac ultrasound indexes or the 6MWT (P > 0.05) (Supplementary Figure 2A, C, E, G and I), whereas MTLN demonstrated a correlation with the 6MWT (P < 0.05) however no correlation with cardiac ultrasound indexes (Supplementary Figure 2B, D, F, H and J). Discussion HF is a medical syndrome characterised by dyspnea or restricted motion as a consequence of ventricular filling and cardiac ejection dysfunction14 Although SGLT2 inhibitors like Empagliflozin have emerged as a novel remedy for HFrEF, the precise mechanism underlying their advantages stays elusive. Therefore, investigating the mechanism of Empagliflozin within the remedy of HFrEF is of nice significance. In this examine, we enrolled sufferers with HFrEF, and supplemented their current remedy with oral Empagliflozin (10 mgqd). The blood samples earlier than and after remedy had been collected for peripheral blood RNA-seq. Moreover, the information of post-treatment cardiac ultrasound in addition to the 6MWT had been collected to evaluate the efficacy of Empagliflozin in treating HFrEF. By performing bioinformatics evaluation on the RNA-seq outcomes, 42 differentially expressed genes had been obtained earlier than and after remedy, with six being up-regulated and 36 being down-regulated. To additional examine the position of those genes, GO & KEGG enrichment analyses had been respectively carried out. According to GO enrichment evaluation, these genes had been proven to be primarily enriched within the membrane and immunoglobulin complicated (BP); the immune system, adaptive immune response, and innate immune response (CC); and antigen binding and immunoglobulin receptor binding (MF). The KEGG pathway revealed enrichment of those genes in heparan sulfate/heparin and different associated pathways. Our enrichment evaluation outcomes indicated a detailed correlation between Empagliflozin’s mechanism of motion and the immune response. Patients with HF usually exhibit a systemic pro-inflammatory state, involving each innate and adaptive immunity15 Hence, irritation is a vital means of the immune response and displays the initiation and activation of this response. According to related cell experiments, Empagliflozin was revealed to concurrently scale back pro-inflammatory cytokines similar to IL-1β, IL-6 and chemokine by means of the IKK/NF-JATB, JAK2/STAT1/3, and MKK4/7-κ pathways, thereby inhibiting the inflammatory response of RAW264.7 macrophages16 Further animal experiments demonstrated Empagliflozin’s inhibitory results on the inflammatory response mediated by the IL-17/IL-23 axis, thus lowering liver damage in kind 2 diabetic mice with non-alcoholic fatty liver.17 Clinically, Empagliflozin has been noticed to inhibit the activation of NLRP3 inflammasomes and lower the secretion of IL1β by human macrophages, doubtlessly lowering the incidence of HF.18 Based on the enrichment evaluation outcomes, the connection between the differentially expressed genes and the distribution of twenty-two forms of immune cell subtypes was additional analyzed. A statistically vital distinction was present in one immune cell subtype (plasma cells) earlier than and after remedy. Plasma cells, also referred to as effector B cells, are a type of immune cells that may secrete antibodies, and differentiate and proliferate upon antigen stimulation. In a wholesome coronary heart, roughly 10% of B cells are concerned in regulating the transport of myocardial immune cells, in addition to the construction and operate of the left ventricle.19 Therefore, B cells play a vital position in coronary heart tissue. Current understanding of B cells’ position in HF is primarily centered in two features: (1) B cells secrete anti-myocardial antibodies, immediately inflicting myocardial harm;20 (2) B cells activate the complement system and overexpress TNF-α within the myocardium, resulting in cardiomyocyte hypertrophy, cardiac fibrosis, and apoptosis, that are all key components in cardiomyocyte damage in HF.20 As reported in a examine of myocardial tissue in end-stage coronary heart failure, it was discovered that immunoglobulin G deposits in as much as 70% of the center tissue.21 In HF, injury-associated molecular patterns are launched from broken cardiomyocytes. These patterns then work together with B cells, subsequently activating T cells and establishing a complete pro-inflammatory surroundings. This course of leads to poor ventricular transforming and impaired operate.21 Recent research have revealed Empagliflozin’s cardioprotective results in opposition to myocardial damage by regulating B cells, suggesting that B cells could also be considered one of its goal cells.22 In this examine, two hub genes: GTF2IP14 and MTLN had been recognized by performing lasso evaluation, random forest evaluation, and SVM-RFE evaluation. Then, to analyze whether or not GTF2IP14 and MTLN had been correlated with the medical information, a correlation evaluation was carried out. Neither GTF2IP14 nor MTLN confirmed vital correlations with cardiac ultrasound, and a potential correlation was revealed between MTLN and 6MWT. Expression verification was carried out within the authentic dataset and a corresponding ROC curve was plotted. GTF2IP14 is classed as a pseudogene of GTF2I, which is a DNA sequence within the genome resembling a traditional gene however not expressed.23 Traditionally, pseudogenes had been thought-about functionless. However, current research counsel that pseudogenes might play essential roles in numerous ailments by means of true genes. For occasion, pseudogene GTF2IP23 can inhibit the proliferation of breast most cancers cells by affecting the expression of GTF2I.24 Another pseudogene PTENP1 might influence the onset and development of prostate tumors by regulating the expression of tumor suppressor gene PTEN.25 GTF2I, the true gene of GTF2IP14, belongs to the household of basic transcription components. It resides within the cytoplasm, translocates to the nucleus throughout transcription, and is broadly expressed in human cells.26 GTF2I is concerned in numerous autoimmune ailments, similar to Sjogren’s syndrome, systemic lupus erythematosus, and rheumatoid arthritis, and is carefully related to heavy chain immunoglobulin transcription and T cell receptor signaling.26 MTLN encodes mitoregulatory proteins, that are extremely expressed in myocardial and skeletal muscle and localized within the mitochondrial intima. They are related to respiration, Ca2+ stability, and upkeep of mobile redox potential associated to ATP manufacturing.27 MTLN can affect mitochondrial respiration, Ca2+ buffering capability, in addition to the degrees of reactive oxygen species.28 Mitochondrial respiration and Ca2+ buffering are carefully associated to HF.29 After the onset of HF, the cardio oxidation capability of cardiomyocyte mitochondria is decreased, leading to inadequate power manufacturing of myocardial mitochondria respiration, ventricular transforming, lower of ejection fraction, and even the event of HFrEF.30 Additionally, cytoplasmic Ca2+ overload and decreased mitochondrial Ca2+ focus in HF cardiomyocytes continuously result in myocardial dysfunction.31 Therefore, Ca2+ homeostasis is carefully associated to cardiomyocyte contraction and HF. For instance, medullary development components mitigate HF attributable to stress overload by defending the Ca2+ biking capability.32 CXCR4 enhances β-adrenergic-mediated calcium mobilization of cardiomyocytes and regulates cardiomyocyte contractility.33 Regrettably, there's nonetheless an absence of research on researching the mechanism of GTF2IP14 and MTLN in HF, necessitating primary experiments to show the connection between GTF2IP14 and HF. Lastly, by means of correlation evaluation, GTF2IP14 was demonstrated to be considerably negatively correlated with naive B cells, and MTLN was considerably positively correlated with Tregs and resting reminiscence CD4+ T cells. However, there's a lack of complete research on the correlation between GTF2IP14 and naive B cells. Given the impact of pseudogenes on true genes, it’s value noting that GTF2I is carefully concerned within the transcription of heavy chain immunoglobulin, and antibodies secreted by B cells are vital immunoglobulins. Thus, it's speculated that GTF2IP14 might regulate the transcription degree of immunoglobulins by influencing the expression of GTF2I, and have an effect on the organic means of antibody secretion by naive B cells after maturation. Tregs grow to be a Th1-like proinflammatory subgroup and contribute to poor ventricular transforming in HF mice.34 Clinically, a decrease frequency of Tregs is positively related to the next danger of heart problems and can serve an unbiased predictor of worsening re-hospitalization in sufferers with HF.35 Unfortunately, there's inadequate analysis concerning the connection between MTLN and Tregs, in addition to resting reminiscence CD4+ T cells, necessitating additional primary experiments to confirm the correlation. In abstract, RNA-seq and bioinformatics evaluation had been utilized on this examine to achieve insights into the mechanism of Empagliflozin in treating HFrEF, revealing its potential affiliation with the immune inflammatory response. GTF2IP14 and MTLN had been finally recognized as hub genes that will work together with numerous immune cells. At the molecular and genetic ranges, this examine gives novel insights into Empagliflozin’s mechanism in treating HFrEF, which offer a brand new potential therapeutic goal for HFrEF. However, this examine has sure limitations. First, as a result of unavailability of cardiac tissue samples, this examine lacks specificity. Additionally, the outcomes haven't been verified by means of animal and cell experiments. Secondly, the comparatively small pattern measurement might influence the reliability of the outcomes, necessitating additional validation of the analysis findings. Conclusion In this examine, by means of RNA-seq and numerous bioinformatics analyses, GTF2IP14 and MTLN had been lastly recognized as hub genes for Empagliflozin-treated HFrEF. Based on our findings, it's proposed that the concerned mechanism of motion could also be related to the immune inflammatory response and numerous forms of immune cells. Ethics Approval and Informed Consent This examine was carried out in accordance with the Declaration of Helsinki and was authorized by the Ethics Committee of the Second Affiliated Hospital of Zhejiang Chinese Medical University (Approval No. 2022-045-IH01). Informed consent was obtained from all topics previous to enrollment. Funding This examine was supported by the Zhejiang Medicine and Health Science and Technology Plan Project (challenge quantity: 2020KY205), the Natural Science Foundation of Zhejiang Province (challenge quantity: LY20H020005) and the Zhejiang TCM Science and Technology Planning Project (challenge quantity: 2022ZA082). Disclosure The authors report no conflicts of curiosity on this work. References 1. Wang H, Chai Ok, Du M, et al. Prevalence and Incidence of Heart Failure Among Urban Patients in China: a National Population-Based Analysis. Circ Heart Fail. 2021;14(10):e008406. doi:10.1161/CIRCHEARTFAILURE.121.008406 2. Emmons-Bell S, Johnson C, Roth G. Prevalence, incidence and survival of coronary heart failure: a scientific assessment. Heart. 2022;108(17):1351–1360. doi:10.1136/heartjnl-2021-320131 3. Napoli C, Bontempo P, Palmieri V, et al. Epigenetic Therapies for Heart Failure: present Insights and Future Potential. Vasc Health Risk Manag. 2021;17:247–254. doi:10.2147/VHRM.S287082 4. Sarno F, Benincasa G, List M, et al. Clinical epigenetics settings for most cancers and cardiovascular ailments: real-life functions of community drugs on the bedside. Clin Epigenetics. 2021;13(1):66. doi:10.1186/s13148-021-01047-z 5. Gronda E, Lopaschuk GD, Arduini A, et al. Mechanisms of motion of SGLT2 inhibitors and their helpful results on the cardiorenal axis. Can J Physiol Pharmacol. 2022;100(2):93–106. doi:10.1139/cjpp-2021-0399 6. McDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the analysis and remedy of acute and persistent coronary heart failure. Eur Heart J. 2021;42(36):3599–3726. doi:10.1093/eurheartj/ehab368 7. Zannad F, Ferreira JP, Pocock SJ, et al. SGLT2 inhibitors in sufferers with coronary heart failure with lowered ejection fraction: a meta-analysis of the EMPEROR-Reduced and DAPA-HF trials. Lancet. 2020;396(10254):819–829. doi:10.1016/S0140-6736(20)31824-9 8. Liu C, Yin Z, Feng T, et al. An built-in community pharmacology and RNA-Seq strategy for exploring the preventive impact of Lonicerae japonicae flos on LPS-induced acute lung damage. J Ethnopharmacol. 2021;264(113364):113364. doi:10.1016/j.jep.2020.113364 9. Zhang Y, Zhou J, Liu J, et al. RNA-Seq evaluation of the safety by Dendrobium nobile alkaloids in opposition to carbon tetrachloride hepatotoxicity in mice. Biomed Pharmacother. 2021;137(111307):111307. doi:10.1016/j.biopha.2021.111307 10. Li L, Gao P, Tang X, et al. CB1R-stabilized NLRP3 inflammasome drives antipsychotics cardiotoxicity. Signal Transduct Target Ther. 2022;7(1):190. doi:10.1038/s41392-022-01018-7 11. Wang J, Li X, Liu Z, et al. Second-generation antipsychotics induce cardiotoxicity by disrupting spliceosome signaling: implications from proteomic and transcriptomic analyses. Pharmacol Res. 2021;170(105714):105714. doi:10.1016/j.phrs.2021.105714 12. Lee CM, Zhou L, Liu J, et al. Single-cell RNA-seq evaluation revealed long-lasting adversarial results of tamoxifen on neurogenesis in prenatal and grownup brains. Proc Natl Acad Sci U S A. 2020;117(32):19578–19589. doi:10.1073/pnas.1918883117 13. Benincasa G, Maron BA, Affinito O, et al. Association Between Circulating CD4(+) T Cell Methylation Signatures of Network-Oriented SOCS3 Gene and Hemodynamics in Patients Suffering Pulmonary Arterial Hypertension. J Cardiovasc Transl Res. 2023;16(1):17–30. doi:10.1007/s12265-022-10294-1 14. Triposkiadis F, Xanthopoulos A, Parissis J, et al. Pathogenesis of persistent coronary heart failure: cardiovascular getting older, danger components, comorbidities, and illness modifiers. Heart Fail Rev. 2022;27(1):337–344. doi:10.1007/s10741-020-09987-z 15. Paulus WJ, Zile MR. From Systemic Inflammation to Myocardial Fibrosis: the Heart Failure With Preserved Ejection Fraction Paradigm Revisited. Circ Res. 2021;128(10):1451–1467. doi:10.1161/CIRCRESAHA.121.318159 16. Lee N, Heo YJ, Choi SE, et al. Anti-inflammatory Effects of Empagliflozin and Gemigliptin on LPS-Stimulated Macrophage by way of the IKK/NF-κB, MKK7/JNK, and JAK2/STAT1 Signalling Pathways. J Immunol Res. 2021;2021(9944880):1–11. doi:10.1155/2021/9944880 17. Meng Z, Liu X, Li T, et al. The SGLT2 inhibitor empagliflozin negatively regulates IL-17/IL-23 axis-mediated inflammatory responses in T2DM with NAFLD by way of the AMPK/mTOR/autophagy pathway. Int Immunopharmacol. 2021;94(107492):107492. doi:10.1016/j.intimp.2021.107492 18. Kim SR, Lee SG, Kim SH, et al. SGLT2 inhibition modulates NLRP3 inflammasome exercise by way of ketones and insulin in diabetes with heart problems. Nat Commun. 2020;11(1):2127. doi:10.1038/s41467-020-15983-6 19. Sun Y, Pinto C, Camus S, et al. Splenic Marginal Zone B Lymphocytes Regulate Cardiac Remodeling After Acute Myocardial Infarction in Mice. J Am Coll Cardiol. 2022;79(7):632–647. doi:10.1016/j.jacc.2021.11.051 20. Bermea Ok, Bhalodia A, Huff A, et al. The Role of B Cells in Cardiomyopathy and Heart Failure. Curr Cardiol Rep. 2022;24(8):935–946. doi:10.1007/s11886-022-01722-4 21. van den Hoogen P, de Jager SCA, Huibers MMH, et al. Increased circulating IgG ranges, myocardial immune cells and IgG deposits assist a task for an immune response in pre- and end-stage coronary heart failure. J Cell Mol Med. 2019;23(11):7505–7516. doi:10.1111/jcmm.14619 22. Xu Y, Jiang Ok, Chen F, et al. Bone marrow-derived naïve B lymphocytes enhance coronary heart operate after myocardial infarction: a novel cardioprotective mechanism for empagliflozin. Basic Res Cardiol. 2022;117(1):47. doi:10.1007/s00395-022-00956-1 23. Jia L, Zhang Y, Pu F, et al. Pseudogene AK4P1 promotes pancreatic ductal adenocarcinoma development by means of relieving miR-375-mediated YAP1 degradation. Aging. 2022;14(4):1983–2003. doi:10.18632/getting older.203921 24. Zhou SW, Su BB, Feng YQ, et al. Expression of GTF2IP23 in breast most cancers and it mediated regulation of GTF2I. Zhonghua Zhong Liu Za Zhi. 2019;41(12):918–922. doi:10.3760/cma.j.issn.0253-3766.2019.12.007 25. Kerwin J, Khan I, Iorns E. Replication Study: a coding-independent operate of gene and pseudogene mRNAs regulates tumour biology. Elife. 2020;9. doi:10.7554/eLife.51019 26. Nathany S, Tripathi R, Mehta A. Gene of the month: GTF2I. J Clin Pathol. 2021;74(1):1–4. doi:10.1136/jclinpath-2020-207013 27. Friesen M, Warren CR, Yu H, et al. Mitoregulin Controls β-Oxidation in Human and Mouse Adipocytes. Stem Cell Reports. 2020;14(4):590–602. doi:10.1016/j.stemcr.2020.03.002 28. Averina OA, Permyakov OA, Emelianova MA, et al. Mitochondrial peptide Mtln contributes to oxidative metabolism in mice. Biochimie. 2023;204(136):136–139. doi:10.1016/j.biochi.2022.09.009 29. Choi M, Kang KW. Mitoregulin controls mitochondrial operate and stress-adaptation response throughout early section of endoplasmic reticulum stress in breast most cancers cells. Biochim Biophys Acta Mol Basis Dis. 2023;1869(1):166570. doi:10.1016/j.bbadis.2022.166570 30. Pereira GAR, Beck-da-Silva L. Iron Deficiency in Heart Failure with Reduced Ejection Fraction: pathophysiology, Diagnosis and Treatment. Arq Bras Cardiol. 2022;118(3):646–654. doi:10.36660/abc.20201257 31. Zhihao L, Jingyu N, Lan L, et al. SERCA2a: a key protein within the Ca(2+) cycle of the center failure. Heart Fail Rev. 2020;25(3):523–535. doi:10.1007/s10741-019-09873-3 32. Tanai E, Frantz S. Pathophysiology of Heart Failure. Compr Physiol. 2015;6(1):187–214. 33. Pyo RT, Sui J, Dhume A, et al. CXCR4 modulates contractility in grownup cardiac myocytes. J Mol Cell Cardiol. 2006;41(5):834–844. doi:10.1016/j.yjmcc.2006.08.008 34. Wei L. Immunological side of cardiac transforming: t lymphocyte subsets in inflammation-mediated cardiac fibrosis. Exp Mol Pathol. 2011;90(1):74–78. 35. Duerr GD, Heine A, Hamiko M, et al. Parameters predicting COVID-19-induced myocardial damage and mortality. Life Sci. 2020;260:118400. doi:10.1016/j.lfs.2020.118400

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