Abstract Hypertrophic cardiomyopathy (HCM) is a common genetic heart disease, with macrophages playing a critical role in its pathological remodeling. Our study aims to investigate the molecular basis of HCM by analyzing macrophage-related gene expression at the single-cell level. Utilizing published scRNA-seq datasets (GSE181764 and GSE161921), we identified macrophages as the key cell cluster most associated with HCM. Integration with bulk RNA-seq data (GSE249925) and differential expression analysis revealed three hub genes: ASPN (asporin), F13A1 (Coagulation Factor XIII A Chain), and SORBS2 (Sorbin and SH3 domain-containing protein 2). Immune infiltration analysis showed significant decreases in multiple immune cell subsets in HCM patients, including neutrophil and macrophages. Intercellular communication analysis revealed an approximately 50% reduction in total interactions in HCM, accompanied by markedly weakened macrophage signaling reception and loss of regulatory pathways. Single-cell validation confirmed that F13A1 expression was predominantly restricted to macrophage clusters and significantly downregulated in HCM macrophages, demonstrating strong macrophage specificity and diagnostic potential. Furthermore, a LASSO-based diagnostic model incorporating three genes ( IGFBP4, FOS, CTSC) exhibited high predictive performance, with validated accuracy in both training and external validation sets. Collectively, our findings shed light on the mechanisms underlying macrophage dysfunction in HCM and offer novel insights into the cellular and molecular dynamics. 1. Introduction Notably, the pathological progression of HCM is far from being driven solely by cardiomyocyte-autonomous hypertrophy and dysfunction. Accumulating evidence indicates that dysregulation of the cardiac immune microenvironment, particularly the infiltration and activation of innate immune cells, plays a critical role in myocardial inflammation, fibrosis, and adverse remodeling [ 4]. Among these, macrophages, as a core population of resident and recruited immune cells in the heart, exert dual functions in maintaining tissue homeostasis, clearing apoptotic cells (i.e., efferocytosis), and coordinating repair and fibrotic responses through their remarkable phenotypic plasticity [ 5]. Previous studies have suggested that the phenotype of macrophages in the hearts of HCM patients may shift, for instance toward a pro-inflammatory phenotype, thereby influencing fibroblast activation through cytokine secretion and ultimately exacerbating myocardial fibrosis [ 6]. Moreover, deficiency of the macrophage-specific molecule NLRC5 has been shown to aggravate pressure overload-induced pathological cardiac remodeling by intensifying inflammatory responses [ 7]. Nevertheless, most of these findings are based on specific models or limited samples, and a systematic characterization of the overall immune landscape of the HCM heart at single-cell resolution—particularly the detailed alterations of macrophage subpopulations, their dynamic communication networks with other cardiac cells (e.g., cardiomyocytes and fibroblasts), and the precise functional implications of these changes in HCM pathogenesis—remains lacking. Therefore, the central aim of this study is to systematically reveal the remodeling of the cardiac immune microenvironment, particularly the macrophage-mediated intercellular communication network, in HCM by integrating single-cell and bulk transcriptomic data from human heart tissues. Specifically, we will first construct a high-resolution single-cell atlas of HCM and non-failing control hearts, quantifying and comparing the proportional changes to major cell types, especially macrophages. Second, through cross-analysis, we will identify macrophage-associated genes that are specifically altered in HCM. Third, using cell–cell communication analysis tools, we will deeply investigate the global alterations of the cardiac cell interaction network in HCM, with a focus on changes in the signaling sending and receiving capacities of macrophages. Finally, based on the identified key genes, we will employ advanced machine learning algorithms to construct and validate a robust and efficient diagnostic model for HCM. Different from prior bulk RNA-seq and immune deconvolution research on HCM, the present study makes three innovative advances: it combines scRNA-seq and bulk RNA-seq to screen macrophage-specific hub genes including ASPN, F13A1SORBS2, which possess independent diagnostic potential; it systematically constructs intercellular communication networks and for the first time reveals defective signal reception in macrophages under HCM conditions; and it additionally develops a macrophage-originated three-gene machine learning signature consisting of IGFBP4, FOSCTSC, with its diagnostic performance further verified via external datasets. Through this series of analyses, this study is expected not only to deepen the understanding of the immunopathological mechanisms underlying HCM and to reveal the potential role of macrophage dysfunction in driving disease progression, but also to provide a crucial theoretical foundation and data support for the development of novel non-invasive diagnostic biomarkers and potential immunomodulatory therapeutic targets. 3. Discussion This study investigated transcriptomic differences between HCM patients and non-failing controls to elucidate the molecular mechanisms underlying the disease. Our integrative analysis has uncovered three macrophage-associated genes ( ASPN, F13A1, and SORBS2) in HCM, with F13A1 showing selective downregulation within the macrophage population. The pronounced impairment of intercellular communication, coupled with the absence of several regulatory signaling pathways in HCM hearts, points to a compromised macrophage-mediated immune response. Additionally, a LASSO-derived diagnostic signature based on IGFBP4, FOS, and CTSC demonstrated strong predictive accuracy, underscoring its promise as a clinical tool for HCM detection. Validating the expression of target genes at the single-cell level, particularly confirming the specific high expression of F13A1 in macrophages and its marked downregulation in HCM, substantially enhances its credibility as a biomarker. F13A1 encodes the A chain of coagulation factor XIII, traditionally recognized for its role in the cross-linking and stabilization of fibrin during the terminal phase of coagulation. This gene has been implicated in various pathological conditions, including obesity [ 12], rheumatoid arthritis [ 13], tumor [ 14], and atherosclerosis [ 15]. Its high expression in tissue-resident macrophages suggests non-canonical functions, such as modulating extracellular matrix remodeling, inflammatory responses, and potentially influencing the tissue repair microenvironment [ 16]. Its downregulation may directly compromise the structural integrity of the extracellular matrix or indirectly impair macrophage-mediated inflammation resolution, thereby promoting the fibrotic pathology of HCM. In contrast, the upregulation of SORBS2 in HCM macrophages is an intriguing observation; this gene encodes a protein involved in cytoskeletal organization and focal adhesion formation, and its upregulation may be associated with altered mechanosensing and migratory capacity of macrophages adapting to the pathologically stiffened matrix [ 17]. Regarding ASPN, although its expression level is low across all cell types, its encoded protein asporin is an important component of the extracellular matrix and regulates TGF-β activity [ 18]. Thus, subtle changes in its expression may produce amplified effects through modulation of key pro-fibrotic pathways, explaining why it was selected as a critical diagnostic gene despite its low expression abundance. Notably, this study revealed a marked decrease in the proportion of macrophages in HCM cardiac tissues, a finding that contrasts with previous immune infiltration patterns inferred from bulk transcriptomic data [ 19]. Specifically, earlier studies using the CIBERSORT algorithm suggested higher abundances of macrophages (M0, M1, and M2 subtypes) in HCM samples compared with non-failing tissues [ 19], whereas our single-cell resolution direct counting revealed the opposite trend. This discrepancy likely arises from methodological limitations of computational approaches, as deconvolution-based algorithms may introduce bias when estimating cell proportions in complex tissues, thereby highlighting the advantage of direct single-cell sequencing for precise delineation of cellular composition. Further potential mechanisms underlying the reduced macrophage proportion include increased apoptosis, decreased recruitment from circulating monocytes, or phenotypic switching toward other cell types such as fibroblasts. A recent study in an HCM model identified an ENPP2-high, pro-inflammatory macrophage subset that activates fibroblasts through intercellular interactions, suggesting that macrophages may undergo functional reprogramming rather than merely a numerical reduction [ 1]. A complementary mechanistic study demonstrated that the activated fatty acid synthesis pathway in cardiac macrophages-via ACLY-mediated acetylation at the Krt17 promoter-drives the production of pro-fibrotic cytokines such as IL-33, thereby promoting the expansion of a pathogenic fibroblast subset highly expressing extracellular matrix genes after myocardial infarction [ 20]. This study also found that chronic inflammation downregulates SerpinB2 expression in tissue resident macrophages through the IFNγ pathway, leading to increased mitochondrial ROS, cytochrome c release, and macrophage apoptosis [ 21]. This results in increased insulin resistance and metabolic dysfunction—mechanisms that could also explain the reduced macrophage survival and disturbed immune homeostasis observed in patients with HCM. Moreover, another multi-omics study indicated that downregulation of endothelial-derived CX3CL1 may impair macrophage efferocytosis, representing a potential molecular pathway contributing to macrophage dysfunction and subsequent reduced clearance [ 5]. Beyond this, recent work has shown that RBPJ epigenetically controls efferocytosis by suppressing the repressive H3K9me3 mark on promoters of Stard13 and Arsg, thereby enhancing apoptotic cell clearance in macrophages [ 22]. Collectively, these findings suggest that both chemokine-mediated and epigenetic mechanisms may converge to regulate macrophage efferocytosis in HCM, though direct evidence in this disease context remains to be established. From the functional enrichment analysis, differentially expressed genes in HCM were significantly enriched in complement and coagulation cascades, as well as MAPK and TNF signaling pathways, confirming from a systems biology perspective the intricate intertwining of immune inflammation with fibrotic progression. This finding corroborates multiple studies; for instance, in Fabry disease-associated HCM, activation of the NF-κB pathway driven by inflammatory cytokines has been linked to myocardial hypertrophic remodeling [ 23]. Of particular interest is the enrichment of the complement and coagulation cascade pathway, which may not only relate to classical inflammatory amplification but also to myocardial microvascular pathology and local microthrombus formation in HCM critical pathological link leading to myocardial ischemia and fibrosis. Regarding the MAPK and TNF signaling pathways, they constitute a central bridge connecting immune cell activation and parenchymal cell responses. For example, NLRC5 in macrophages regulates the secretion of cytokines such as IL-6 by inhibiting the NF-κB pathway, thereby influencing cardiomyocyte hypertrophy and fibroblast activation [ 8]. The pathway results of this study suggest that macrophage dysfunction may persistently activate MAPK and other signals within cardiomyocytes and fibroblasts through the release of specific cytokines, driving pathological remodeling. This aligns with another study showing widespread activation of AP-1 transcription factors (e.g., FOS, JUN) in cardiomyocytes in early-stage HCM models [ 8], indicating coordinated signaling network activation across different cell types. The core genes of the machine learning-based HCM diagnostic model ( IGFBP4, FOS, CTSC) are not entirely consistent with the macrophage-associated genes identified through prior differential expression screening ( ASPN, F13A1, SORBS2). This discrepancy precisely reflects the distinct objectives of diagnostic model construction versus mechanistic exploration. ASPN, F13A1, and SORBS2 were identified through a stepwise multivariate regression aimed at selecting genes with independent diagnostic contribution for the purpose of mechanistic interpretation. These genes represent macrophage associated candidates that may directly participate in HCM pathogenesis. IGFBP4, FOS, CTSC were selected by the LASSO algorithm from the same pool of 86 macrophage related genes, but LASSO prioritizes predictive accuracy and parsimony through shrinkage, often choosing different features when multicollinearity exists. These three genes are not necessarily the most differentially expressed; rather, they form the most discriminative combination for distinguishing HCM from controls, making them suitable for a diagnostic model. Diagnostic models aim to select, through algorithmic approaches, the most informative combination of genes for discriminating between disease and control samples; these genes may serve as downstream or upstream hub nodes within disease-related pathways, rather than necessarily being the most significantly differentially expressed genes. For instance, FOS, a member of the AP-1 transcription factor complex, plays a central role in stress and growth factor signaling. Its activation in HCM cardiomyocytes has been documented in this study as well as in other work [ 26], and its expression changes may integrate inflammatory and hypertrophic signals originating from multiple cell types, including macrophages, thereby conferring strong diagnostic discriminatory power. IGFBP4 is specifically cleaved by the protease PAPP-A, and elevated levels of its fragments correlate with cardiovascular complications and increased mortality in coronary artery disease, acute coronary syndrome, and heart failure [ 27]. Combining such model-derived genes with existing clinical parameters (e.g., echocardiographic measures) holds promise for constructing a multi-dimensional diagnostic system that improves the detection of early-stage disease or atypical cases, analogous to recent explorations of machine learning for differentiating HCM from cardiac amyloidosis and other conditions [ 28]. The three-gene signature ( IGFBP4/FOS/CTSC) derived from macrophage-associated genes may support early detection and risk stratification of HCM. Future translational steps include: (1) prospective validation in independent multicenter clinical cohorts; (2) development of blood-based PCR or protein assays for noninvasive testing; (3) regulatory evaluation for clinical applicability. This study remains discovery-oriented; the identified genes are candidate biomarkers requiring further prospective verification before clinical use. Based on single-cell and batch transcriptome data and CellChat analysis, the findings regarding macrophage dysfunction, impaired signal reception, and their pro-fibrotic effects are correlation rather than direct causal evidence. Previous studies have confirmed that the dysfunction of macrophage signal reception can directly affect its survival and activation, thereby promoting the proliferation and collagen deposition of fibroblasts [ 1]. The reduced number or functional defect of macrophages will lead to persistent inflammation and insufficient clearance of apoptotic cells, and eventually aggravate myocardial fibrosis [ 5, 29]. Reduced macrophage–fibroblast communication in HCM is strongly associated with an imbalance in profibrotic pathways [ 7]. The results of this study are consistent with the conclusions of the above mechanistic studies, and provide a testable hypothesis for further exploration of the role of macrophages in the pathogenesis of HCM. Several limitations should be acknowledged. First, all findings are derived solely from computational analyses of public datasets without experimental confirmation via qRT-PCR, Western blot, immunohistochemistry, or functional assays in independent patient cohorts or animal models; therefore, the reliability of the identified genes ( ASPN, F13A1, SORBS2, IGFBP4, FOS, CTSC) as biomarkers or therapeutic targets for HCM remains to be determined by independent studies, and our results should be viewed as hypothesis-generating rather than conclusive. Large independent cohorts for subgroup analyses (age, sex, obstruction status) are currently unavailable. Further multicenter validation in stratified clinical subgroups is required to fully confirm the robustness of the identified genes and diagnostic model. Second, the scRNA-seq data include only 7 HCM and 6 non-failing hearts, which may not fully capture the heterogeneity of HCM across different genetic mutation subtypes or disease stages, and the number of macrophages in these data (4085 cells) is relatively modest, restricting our ability to perform fine-grained subclustering or to deeply explore the relationship between distinct macrophage functional states (e.g., M1/M2 polarization, efferocytosis) and HCM pathology. Moreover, the external validation set (GSE141910) is also small (only 11 HCM samples), which may lead to overestimation of the diagnostic model’s performance (AUC > 0.98), and multicenter, large-scale validation is needed before clinical translation. Finally, correlation does not imply causation: differential expression and cell–cell communication analyses reveal associations only, and they cannot distinguish whether macrophage dysfunction is a cause or a consequence of HCM. Despite these limitations, the integration of single-cell and bulk RNA-seq with rigorous machine learning provides biologically meaningful and clinically promising insights into macrophage-driven mechanisms in HCM. 4. Materials and Methods 4.1. Data Collection 4.2. scRNA-Seq Data Processing Quality control and preprocessing of the scRNA-seq data were performed using the Seurat package (version 5.0.1). Cells were filtered based on the following criteria: at least 200 detected genes, total gene counts >50, and mitochondrial gene content 0.58 and expression in at least 25% of cells within the population. Cell types were annotated based on marker genes from CellMarker 2.0 and relevant literature. Low-quality cells and genes were filtered using the preprocess_cds function. Nonlinear dimensionality reduction was conducted using UMAP, and cells were clustered based on UMAP coordinates with the cluster_cells function. All scRNA-seq analyses were performed using R (v4.3.2) with Seurat v5.0.1, DoubletFinder, Harmony, and CellChat v1.6.1. 4.3. Bulk RNA-Seq Differential Expression Analysis The bulk RNA-seq dataset GSE249925 was used to investigate the biological characteristics of HCM patients. Bulk RNA-seq dataset GSE249925 exclusively includes confirmed HCM patients; samples with dilated cardiomyopathy, restrictive cardiomyopathy, or heart failure of non-HCM etiology were excluded during data curation. Data processing included quality control, normalization, and differential expression analysis using the DESeq2 package (version 1.42.1). Low-expression genes-defined as those with counts 1 and false discovery rate (FDR) 1 and p.adj < 0.05. Enriched GO or KEGG items were identified by using the threshold of p.adj < 0.05. Spearman’s correlation analysis was utilized to explore the relationship between target gene and immune cell infiltration. 5. Conclusions In summary, by integrating single-cell and bulk transcriptomics, this study presents the first systematic delineation of macrophage-mediated immune microenvironment dysregulation and remodeling of cellular communication networks in HCM. Moreover, we established a machine learning-based diagnostic model exhibiting considerable potential for clinical translation. These findings not only provide novel insights into the immunopathological mechanisms underlying HCM but also establish a theoretical foundation for developing early diagnostic tools targeting macrophage-associated genes. Future research should validate the diagnostic performance of this model in prospective clinical cohorts and employ functional experiments to elucidate the precise molecular mechanisms by which key target genes contribute to myocardial hypertrophy and fibrosis. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27115102/s1. Author Contributions Conceptualization, Q.W. and J.Z.; methodology, J.Z.; software, X.S.; validation, X.S. and J.W.; formal analysis, Y.Q., C.L. (Chang Liu), R.L. and C.S.; investigation, Y.L. and C.S.; resources; data curation, Y.L.; writing—original draft preparation, C.L. (Chen Liang); visualization, J.Z. and X.S.; supervision, Q.W.; project administration, C.L. (Chen Liang); funding acquisition, C.L. (Chen Liang). All authors have read and agreed to the published version of the manuscript. Funding This work was funded by the Natural Science Foundation of Heilongjiang Province of China (No. PL2025H097 to C.L.) and the Distinguished Yong Scholars Fund (2024YQ10 to C.L.) from The First Affiliated Hospital of Harbin Medical University. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data is contained within the article or the Supplementary Materials. Conflicts of Interest The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: ASPN Asporin AUC Area under the curve DCA Decision curve analysis DEG Differentially expressed genes F13A1 Factor XIII A Chain FDR False discovery rate GBM Gradient Boosting Machine GO Gene Ontology HCM Hypertrophic cardiomyopathy KEGG Kyoto Encyclopedia of Genes and Genomes MAE Mean absolute error PCA Principal component analysis ROC Receiver operating characteristic scRNA-seq Single-cell RNA sequencing SORBS2 Sorbin and SH3 domain-containing protein 2 ssGSEA Single-sample gene set enrichment analysis SVM Support Vector Machine XGBoost Extreme Gradient Boosting References Huang, F.; Zhou, M.; Chen, Y.; Hua, S.; Han, Y.; Fan, Y.; Li, Q.; Sun, Z.; Yang, K.; Zhao, Q.; et al. Cross-dataset transcriptomic analyses identify a conserved ENPP2+ macrophage-fibroblast activation axis in hypertrophic cardiomyopathy. Brief. Bioinform. 2026, 27, bbag036. [] [ CrossRef] Captur, G.; Doykov, I.; Chung, S.C.; Field, E.; Barnes, A.; Zhang, E.; Heenan, I.; Norrish, G.; Moon, J.C.; Elliott, P.M.; et al. Novel Multiplexed Plasma Biomarker Panel Has Diagnostic and Prognostic Potential in Children with Hypertrophic Cardiomyopathy. Circ. Genom. Precis. Med. 2024, 17, e004448. [] [ CrossRef] Peng, W.L.; Zhang, T.J.; Shi, K.; Li, H.X.; Li, Y.; He, S.; Li, C.; Xia, D.; Xia, C.C.; Li, Z.L. Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy. Eur. Radiol. 2022, 32, 1044–1053. [] [ CrossRef] [ PubMed] Koenig, A.L.; Shchukina, I.; Amrute, J.; Andhey, P.S.; Zaitsev, K.; Lai, L.; Bajpai, G.; Bredemeyer, A.; Smith, G.; Jones, C.; et al. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat. Cardiovasc. Res. 2022, 1, 263–280. [] [ CrossRef] [ PubMed] Zhang, Y.; Yan, X.; Fu, X.L.; Yang, H.B.; Yan, Y.M.; He, H.F.; Yang, L. Impaired endothelial cell-derived CX3CL1-mediated macrophage efferocytosis in hypertrophic cardiomyopathy based on multi-omics analysis. Inflamm. Res. 2025, 74, 108. [] [ CrossRef] Kallikourdis, M.; Cochran, J.D.; Walsh, K.; Condorelli, G. Contributions of Noncardiac Organ-Heart Immune Crosstalk and Somatic Mosaicism to Heart Failure: Current Knowledge and Perspectives. Circ. Res. 2025, 136, 1208–1232. [] [ CrossRef] Yu, Q.; Ju, P.; Kou, W.; Zhai, M.; Zeng, Y.; Maimaitiaili, N.; Shi, Y.; Xu, X.; Zhao, Y.; Jian, W.; et al. Macrophage-Specific NLRC5 Protects from Cardiac Remodeling Through Interaction with HSPA8. JACC Basic Transl. Sci. 2023, 8, 479–496. [] [ CrossRef] Thottakara, T.; Padmanabhan, A.; Tanriverdi, T.; Thambidurai, T.; Diaz-Rg, J.A.; Amonkar, S.R.; Olgin, J.E.; Long, C.S.; Roselle Abraham, M. Single-nucleus RNA/ATAC-seq in early-stage HCM models predicts SWI/SNF-activation in mutant-myocytes, and allele-specific differences in fibroblasts. bioRxiv 2024. [] [ CrossRef] Tadros, H.J.; Turaga, D.; Zhao, Y.; Chang-Ru, T.; Adachi, I.A.; Li, X.; Martin, J.F. Activated fibroblasts drive cellular interactions in end-stage pediatric hypertrophic cardiomyopathy. bioRxiv 2024. [] [ CrossRef] [ PubMed] Khaliq, I.H.; Solanki, A.; Kinno, M.; Volgman, A.S.; Oktay, A.A. Challenges in the diagnosis and management of hypertrophic cardiomyopathy and the promise of artificial intelligence. Curr. Probl. Cardiol. 2026, 51, 103249. [] [ CrossRef] Li, R.; Wang, B.; Shan, B.; Sun, M.; Ta, S.; Zhao, J.; Kang, N.; Yang, F.; Yuan, Y.; Li, X.; et al. Application of untargeted plasma metabolomics and machine learning to construct a diagnostic model for hypertrophic cardiomyopathy: A case-control study. Metabolomics 2026, 22, 37. [] [ CrossRef] Myneni, V.D.; Mousa, A.; Kaartinen, M.T. Factor XIII-A transglutaminase deficient mice show signs of metabolically healthy obesity on high fat diet. Sci. Rep. 2016, 6, 35574. [] [ CrossRef] Mousa, A.; Cui, C.; Song, A.; Myneni, V.D.; Sun, H.; Li, J.J.; Murshed, M.; Melino, G.; Kaartinen, M.T. Transglutaminases factor XIII-A and TG2 regulate resorption, adipogenesis and plasma fibronectin homeostasis in bone and bone marrow. Cell Death Differ. 2017, 24, 844–854. [] [ CrossRef] [ PubMed] Porrello, A.; Leslie, P.L.; Harrison, E.B.; Gorentla, B.K.; Kattula, S.; Ghosh, S.K.; Azam, S.H.; Holtzhausen, A.; Chao, Y.L.; Hayward, M.C.; et al. Factor XIIIA-expressing inflammatory monocytes promote lung squamous cancer through fibrin cross-linking. Nat. Commun. 2018, 9, 1988. [] [ CrossRef] [ PubMed] Somodi, L.; Horvath, E.; Bardos, H.; Barath, B.; Petho, D.; Katona, E.; Balla, J.; Mutch, N.J.; Muszbek, L. Cellular FXIII in Human Macrophage-Derived Foam Cells. Int. J. Mol. Sci. 2023, 24, 4802. [] [ CrossRef] Shi, K.; Chen, X.; Zhao, Y.; Li, P.; Chai, J.; Qiu, J.; Shen, Z.; Guo, J.; Jie, W. Identification of potential therapeutic targets for nonischemic cardiomyopathy in European ancestry: An integrated multiomics analysis. Cardiovasc. Diabetol. 2024, 23, 338. [] [ CrossRef] Timmer, L.T.; den Hertog, E.; Versteeg, D.; Post, H.; Verdonschot, J.A.J.; Monshouwer-Kloots, J.; Kyriakopoulou, E.; Perini, I.; Koopmans, T.; van der Kraak, P.; et al. Cardiomyocyte SORBS2 expression increases in heart failure and regulates integrin interactions and extracellular matrix composition. Cardiovasc. Res. 2025, 121, 585–600. [] [ CrossRef] Hong, J.; Medzikovic, L.; Sun, W.; Wong, B.; Ruffenach, G.; Rhodes, C.J.; Brownstein, A.; Liang, L.L.; Aryan, L.; Li, M.; et al. Integrative Multiomics in the Lung Reveals a Protective Role of Asporin in Pulmonary Arterial Hypertension. Circulation 2024, 150, 1268–1287, Correction in Circulation 2025, 151, e921.. [] [ CrossRef] [ PubMed] Zheng, X.; Yang, Y.; Fu, C.H.; Huang, R. Identification and verification of promising diagnostic biomarkers in patients with hypertrophic cardiomyopathy associate with immune cell infiltration characteristics. Life Sci. 2021, 285, 119956. [] [ CrossRef] Sadaf, S.; Vasamsetti, S.B.; Jamal, I.; Johny, E.; Kubra, K.T.; Haque, S.; Mannan, A.; Razani, B.; Chaparala, S.; Okawa, S.; et al. Activated fatty acid synthesis pathway in macrophages propagates pathogenic fibroblast expansion after myocardial infarction. bioRxiv 2025. [] [ CrossRef] Vasamsetti, S.B.; Sadaf, S.; Uddin, M.A.; Shen, J.; Johny, E.; Mondal, A.; Florentin, J.; Lei, L.; Mannan, A.; Rao, K.S.; et al. Tissue-resident macrophage survival depends on mitochondrial function regulated by SerpinB2 in chronic inflammation. Nat. Commun. 2026, 17, 1493, Correction in Nat. Commun. 2026, 17, 3573.. [] [ CrossRef] Sadaf, S.; Zhang, X.; Lei, L.; Shen, J.; Jamal, I.; Modugu, G.; Dutta, P. Macrophage efferocytosis is controlled by epigenetic modifications mediated by RBPJ. bioRxiv 2025. [] [ CrossRef] Ivanova, M.M.; Dao, J.; Friedman, A.; Kasaci, N.; Goker-Alpan, O. Sex Differences in Circulating Inflammatory, Immune, and Tissue Growth Markers Associated with Fabry Disease-Related Cardiomyopathy. Cells 2025, 14, 322. [] [ CrossRef] Codden, C.J.; Larson, A.; Awata, J.; Perera, G.; Chin, M.T. Single Nucleus RNA-sequencing Reveals Altered Intercellular Communication and Dendritic Cell Activation in Nonobstructive Hypertrophic Cardiomyopathy. Cardiol. Cardiovasc. Med. 2022, 6, 398–415. [] [ CrossRef] [ PubMed] Tao, W.; Gong, M.; Ke, Z. Characterising Shared and Specific Cell-Cell Communication in Cardiomyopathy Subtypes from Single-Cell Transcriptomics Data. J. Cell Mol. Med. 2025, 29, e70554. [] [ CrossRef] [ PubMed] Zhuang, L.; Wang, Y.; Chen, Z.; Li, Z.; Wang, Z.; Jia, K.; Zhao, J.; Zhang, H.; Xie, H.; Lu, L.; et al. Global Characteristics and Dynamics of Single Immune Cells After Myocardial Infarction. J. Am. Heart Assoc. 2022, 11, e027228. [] [ CrossRef] [ PubMed] Adasheva, D.A.; Lebedeva, O.S.; Goliusova, D.V.; Postnikov, A.B.; Teriakova, M.V.; Kopylova, I.V.; Lagarkova, M.A.; Katrukha, A.G.; Serebryanaya, D.V. PAPP-A-Specific IGFBP-4 Proteolysis in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. Int. J. Mol. Sci. 2023, 24, 8420. [] [ CrossRef] Weberling, L.D.; Ochs, A.; Benovoy, M.; Aus dem Siepen, F.; Salatzki, J.; Giannitsis, E.; Duan, C.; Maresca, K.; Zhang, Y.; Moller, J.; et al. Machine Learning to Automatically Differentiate Hypertrophic Cardiomyopathy, Cardiac Light Chain, and Cardiac Transthyretin Amyloidosis: A Multicenter CMR Study. Circ. Cardiovasc. Imaging 2025, 18, e017761. [] [ CrossRef] Wang, Y.J.; Singh, K.; Lokman, A.B.; Deng, S.; Sunitha, B.; Coelho Lima, J., Jr.; Beglov, J.; Kelly, M.; Blease, A.; Fung, J.C.K.; et al. Regulatory T cells attenuate chronic inflammation and cardiac fibrosis in hypertrophic cardiomyopathy. Sci. Transl. Med. 2025, 17, eadq3516. [] [ CrossRef] Laird, J.; Perera, G.; Batorsky, R.; Wang, H.; Arkun, K.; Chin, M.T. Spatial Transcriptomic Analysis of Focal and Normal Areas of Myocyte Disarray in Human Hypertrophic Cardiomyopathy. Int. J. Mol. Sci. 2023, 24, 12625. [] [ CrossRef] Tian, P.; Xu, L.; Zou, F.; Jia, Q.; Liu, Y.; Chen, J.; Wang, P. Endothelial-specific genes TMTC1, RPS6KA2, and F8 are downregulated in hypertrophic cardiomyopathy. Eur. J. Med. Res. 2025, 30, 1305. [] [ CrossRef] [ PubMed] Zhang, X.Z.; Zhang, S.; Tang, T.T.; Cheng, X. Bioinformatics and Immune Infiltration Analyses Reveal the Key Pathway and Immune Cells in the Pathogenesis of Hypertrophic Cardiomyopathy. Front. Cardiovasc. Med. 2021, 8, 696321. [] [ CrossRef] [ PubMed] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Share and Cite MDPI and ACS Style Zhao, J.; Su, X.; Wu, J.; Qin, Y.; Song, C.; Li, Y.; Liu, C.; Li, R.; Wang, Q.; Liang, C. Integrative Transcriptomics and Machine Learning Identify Macrophage-Associated Biomarkers in Hypertrophic Cardiomyopathy. Int. J. Mol. Sci. 2026, 27, 5102. https://doi.org/10.3390/ijms27115102 AMA Style Zhao J, Su X, Wu J, Qin Y, Song C, Li Y, Liu C, Li R, Wang Q, Liang C. Integrative Transcriptomics and Machine Learning Identify Macrophage-Associated Biomarkers in Hypertrophic Cardiomyopathy. International Journal of Molecular Sciences. 2026; 27(11):5102. https://doi.org/10.3390/ijms27115102 Chicago/Turabian Style Zhao, Jianzhi, Ximiao Su, Jiali Wu, Yanan Qin, Chengyu Song, Yanli Li, Chang Liu, Ran Li, Qiushi Wang, and Chen Liang. 2026. "Integrative Transcriptomics and Machine Learning Identify Macrophage-Associated Biomarkers in Hypertrophic Cardiomyopathy" International Journal of Molecular Sciences 27, no. 11: 5102. https://doi.org/10.3390/ijms27115102 APA Style Zhao, J., Su, X., Wu, J., Qin, Y., Song, C., Li, Y., Liu, C., Li, R., Wang, Q., & Liang, C. (2026). Integrative Transcriptomics and Machine Learning Identify Macrophage-Associated Biomarkers in Hypertrophic Cardiomyopathy. 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