Multiple effect mechanisms of FLNC in dilated cardiomyopathy based on genetic variants, transcriptomics, and immune infiltration analysis

Multiple effect mechanisms of FLNC in dilated cardiomyopathy based on genetic variants, transcriptomics, and immune infiltration analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multiple effect mechanisms of FLNC in dilated cardiomyopathy based on genetic variants, transcriptomics, and immune infiltration analysis ChunYu Cai, Bin He, DianYou Yu, LiPing Quan, ChengBan Li, Yan Liu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2795537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: In recent years, the FLNC gene has been shown to participate in dilated cardiomyopathy (DCM) through different mechanisms, and its variants are a common cause of hereditary DCM. This study aimed to systematically evaluate multiple FLNC effect mechanisms in DCM and to expand the spectrum of FLNC gene variations. Methods: Based on five microarray expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, comprising DCM tissue and healthy control groups, the difference in FLNC gene expression levels between the two groups was evaluated. Subsequently, differentially expressed genes (DEGs) among 81 DCM tissues were identified based on FLNC grouping, and gene ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, correlation analysis, and protein–protein interaction (PPI) network construction were conducted for DEGs. Based on single-sample Gene Set Enrichment Analysis method, we then evaluated differences in immune infiltration levels between the two groups using ''student 's'' and the correlation between FLNC gene expression.and the immune cells we using '' Spearman's correlation '' methods. Then, we constructed a ce-RNA network of FLNC based on several databases.Finally,100 blood samples from DCM and non-DCM individuals from the Guangxi Zhuang population in China were selected for FLNC gene sequencing, case-specific newly discovered or rare FLNC gene mutation sites were screened, and the clinical information of patients with FLNC gene mutations and their families were collected for Sanger sequencing verification. Results: FLNC expression was significantly higher in the DCM group than in the control group. After grouping 81 DCM tissues according to median FLNC expression levels, 54 DEGs were identified. The enrichment analysis shows that downregulated DEGs were more associated with inflammation and immunity, while upregulated DEGs involved actin and mitogen-activated protein kinase signaling pathways. The correlation analysis of DEGs and the PPI network identified genes associated with FLNC . According to the immune infiltration analysis, the DCM group was more associated with immunity, and the infiltrating plasma cells had a strong correlation with the FLNC gene; we identified eight miRNAs and 29 lncRNAs that bind to the FLNC gene. The final gene sequencing results show that a total of eight FLNC -specific missense mutations were detected, among which FLNC T407N and FLNC R437L are rare mutations. Additionally, the mutation frequency and minimum allele frequencies determined by sequence comparison were higher than those of databases such as the 1,000Genomes database, and all were predicted to be harmful mutations by SIFT, PolyPhen-2, and Mutation Assessor software. FLNC R437L , FLNC T834M , FLNC G1264S , FLNC R1567Q , and FLNC L2538F mutations are highly conserved among different species and were verified as heterozygous mutations by Sanger sequencing, while FLNC V452M may be the pathogenic site of DCM. Conclusion: The data analysis of myocardial tissue samples and the mutation analysis of DCM serum samples provides a rich perspective for exploring the biological functions, molecular mechanisms, immune cell correlations, ceRNA networks, and pathogenic gene mutation sites connected to FLNC in DCM. FLNC dilated cardiomyopathy bioinformatics analysis gene sequencing gene variants Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Dilated cardiomyopathy (DCM) is a common life-threatening heterogeneous heart disease characterized by systolic dysfunction, increased ventricular volume, thinner ventricular walls, and prolonged cardiomyocytes [ 1 ]. The estimated prevalence rate of the disease among the global general population is approximately 1:250 to 400 [ 2 , 3 ], and the disease is more common among men and varies by race, with a progressive increase in prevalence over time due to expanded clinical screening [ 1 , 4 ]. Among these, familial DCM is defined as a Mendelian model of a single gene disease-inherited cardiomyopathy, and at least 50 single genes have been found to be associated with familial DCM, with the majority triggering disease with dominant mutations [ 5 , 6 ]. Approximately 30–50% of patients with DCM have gene mutations. Genetic diagnosis and risk stratification of patients with mutations are particularly important, and their prognosis determines the probability of heart failure and arrhythmias [ 1 , 7 ]. However, the mechanisms underlying DCM are complex and not yet fully understood. Studies have shown that DCM is the ultimate outcome of myocardial response to various genetic and environmental damages and can be caused by a variety of myocardial injuries, with a large proportion of DCM cases having an underlying genetic or inflammatory basis [ 8 ]. Most DCM-related mutations are specific to probands or families. To date, approximately 40% of genetic causes of DCM have been identified. More than 30 genes have been implicated in the etiology and risk of DCM and are found in a variety of cell ontologies and biochemical pathways [ 8 , 9 ]. In approximately 35% of patients with DCM, mutations can be detected in genes connected to cytoskeletal, sarcoplasmic, and nuclear membrane proteins [ 10 ]. The FLNC gene is a key regulator of cardiomyocyte ultrastructure and plays an important role in maintaining the contractile force transmission pathway, and its dysfunction may be the key to driving progressive DCM [ 11 ]. FLNC-associated DCM is linked with a high risk of a malignant clinical course and sudden cardiac death, and its clinical spectrum indicates different pathological mechanisms related to variant types and their positions in genes [ 7 ]. Mutations in the FLNC gene were first identified in myofibrillar myopathy and have since been identified in various forms of human cardiomyopathies, including DCM, hypertrophic cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic ventricular cardiomyopathy [ 12 , 13 ]. These results suggest that different types of FLNC mutations may lead to different pathogenic mechanisms. At present, there is still a lack of a systematic multi-mechanism evaluation of FLNC in DCM. Based on the sequencing of big data, we fully evaluated the possible mechanism of FLNC in DCM and the spectrum of gene variation, which deepened our understanding of the multiple effect mechanisms of FLNC in DCM and can provide guidance to future researchers. It is clear that a deep understanding of genetic variants may bring critical progress in navigating this dilemma and reversing the disease’s clinical burden. The development of next-generation sequencing technologies has opened a new era in clinical genetics and genomics, but the relationship between environmental and genetic factors in the pathogenesis of DCM is still rarely studied. To date, there has been no large-scale multicenter study published on systematic clinical screening of DCM and on families where exome or genome sequencing has been carried out to identify possible genetic causes. Therefore, based on the sequencing data of myocardial tissue and multiple databases, this paper firstly used a variety of bioinformatics analysis methods, including differential analysis, ROC curve, GO, KEGG enrichment analysis, PPI network construction, ssGSEA immune infiltration analysis. Spearman correlation analysis and ce-RNA network fully evaluated the various effector mechanisms that FLNC might be involved in DCM. Subsequently, gene sequencing analysis based on 200 serum samples revealed the genetic variation and novel pathogenic mutation sites of FLNC in DCM, in order to have a more comprehensive understanding of FLNC gene and its possible involvement in the disease development and development of DCM, expand the expression profile of FLNC gene, and provide potential targets for further research in the future. 2 Results 2.1 Evaluation of FLNC expression level and differentiation ability and identification of DEGs Box plots and ROC curves demonstrate the difference in FLNC expression levels and differentiation ability between the DCM and control groups (Fig. 1 A, B). The results show that the expression level of FLNC was significantly higher in the DCM group than in the control group, P < 0.05. FLNC has good differentiation ability between the two groups (Area under the ROC Curve [AUC] = 0.774). The results of the difference analysis based on the median value of FLNC expression after grouping are shown in ( Supplementary File 1 ). A volcano plot and heat map further demonstrate the results of the difference analysis (Fig. 1 C, D). In total, there are 54 DEGs, including 16 upregulated and 38 downregulated genes. 2.2 Enrichment analysis of DEGs GO and KEGG analyses were conducted on upregulated and downregulated DEGs, respectively ( Supplementary File 2 ). The results show that BPs and MFs of upregulated DEGs are enriched in protein folding and action fixation binding, respectively (Fig. 2 A). The GO analysis revealed that downregulated DEGs are mainly enriched in the activated immune system response (Fig. 2 B). The KEGG pathway of upregulated DEGs is mainly enriched in the mitogen-activated protein kinase (MAPK) signaling pathway (involving FLNC ) (Fig. 2 C). The KEGG pathway of downregulated DEGs is mainly enriched in inflammation-related pathways, such as Phagosome and Chemokine signaling pathways (Fig. 2 D).. 2.3 PPI network and gene module identification of DEGs To identify DEGs with close protein interactions with the FLNC gene, we constructed a PPI network and visualized it using 54 DEGs imported into the STRING database (Fig. 3 A). In this PPI network, FLNC only interacted with the upregulated DEG, XIRP1 . Subsequently, we individually imported FLNC genes into the STRING database to build a PPI network for FLNC , and the results show that 10 genes interacted with FLNC (Fig. 3 B). Finally, we analyzed the correlations of 40 of these DEGs (16 upregulated and 24 downregulated genes) (Fig. 3 C); FLNC was negatively correlated with downregulated genes and positively correlated with upregulated genes. Negative correlations were more pronounced with TLR3 , GPR34 , and MRC1 (correlation =-0.57, -0.51, and − 0.51, respectively), whereas positive correlations were more pronounced with TTC9 , PPFIA4 , DNAJB5 , XIRP1 , DUSP27 , and KIFC3 (0.57, 0.62, 0.64, 0.54, 0.62, 0.54, respectively) ( Supplementary Fig. 1 ). Combining the results of the PPI network of DEGs, the PPI network of FLNC, and the correlation analysis of DEGs, we found that FLNC only had protein interactions with the XIRP1 gene in the correlation analysis, whereas the interaction relationships with other genes that had significant negative and significant positive correlations still lacked evidence of correlation. 2.4 Immune infiltration analysis and correlation between FLNC and XIRP1 genes and type of immune cell infiltration To identify differences in the infiltration of different immune cell types between the DCM and non-DCM groups, 81 DCM myocardial tissue samples and 32 healthy myocardial tissue samples were used in this analysis, and the results are presented in ( Supplementary File 3 ). Violin plots show CD4 memory-activated T cells (P = 0.039), gamma delta T cells (P = 0.035), Macrophages M0 (P = 0.039) infiltrated at a higher level in the DCM group than in the control group, while naive B cells (P = 0.028) infiltrated at a lower level in the DCM group than in the control group (Fig. 4 A). The correlation heatmap demonstrates a correlation between FLNC and XIRP1 in the infiltrated immune cells. The results show that FLNC was positively correlated with plasma cells ( P < 0.01), whereas XIRP1 was negatively correlated with naive B cells ( P < 0.05) (Fig. 4 B). 2.5 Construction of ceRNA network of FLNC gene After searching three miRNA databases, a total of eight miRNAs were predicted to bind to FLNC (Table 1 ). The lncRNAs bound by miRNAs are shown in ( Supplementary File 4 ). Based on the predicted results, we constructed a ceRNA network (Fig. 5 A). The network contains 38 nodes (1 FLNC, eight miRNAs, and 29 lncRNAs) and 37 edges. Specifically, we found that 29 lncRNAs could competitively bind hsa-miR-558, hsa-miR-142-3p, hsa-miR-486-3p, and hsa-miR-1972 to regulate FLNC . Among these, there were 18 lncRNAs for hsa-miR-1972. In addition, seven lncRNAs targeted hsa-miR-558, three lncRNAs targeted hsa-miR-486-3p, and one lncRNA targeted hsa-miR-142-3p. Table 1 Eight miRNA bound to FLNC Gene miRNA Gene miRNA FLNC hsa-miR-558 FLNC hsa-miR-644a FLNC hsa-miR-142-3p FLNC hsa-miR-147a FLNC hsa-miR-19b-3p FLNC hsa-miR-486-3p FLNC hsa-miR-1972 FLNC hsa-miR-19a-3p 2.6 Comparison of general clinical data In this study, there were 100 cases in the DCM case group and 100 cases in the control group; the age of the case group was (53.57 ± 11.95) years and the age of the control group was (57.21 ± 12.96) years; the male to female ratio of the case group and the control group was 1:1.17 and 1:0.35, respectively, while comparing the age, height, gender, history of hypertension, history of smoking, and history of drinking between the DCM group and the control group, the differences were statistically significant( P 0.05), as shown in(Table 2 ). Table 2 Characteristics of included the DCM and the control groups Variables DCM(n = 100) Control(n = 100) t / χ ² P value Age (years) 53.57 ± 11.95 57.21 ± 12.96 -2.065 0.040 Weight (kg) 61.74 ± 10.85 59.19 ± 12.30 1.555 0.121 Height (cm) 161.39 ± 7.40 158.65 ± 7.23 2.642 0.009 Gender(n)(male/female) 81/19 46/54 24.426 0.000 Ethnicity, n (zhuang %) 73(73) 67(67%) 0.857 0.355 History of hypertension, n (%) 29(29) 52(52) 10.976 0.001 Smoking history, n (%) 46(46) 26(26) 8.681 0.003 Drinking history, n (%) 56(56) 26(26) 18.603 0.000 Statistical methods with student ' s or chi-square test. P < 0.05 means the difference is significant, P < 0.01 means the difference is statistically significant. 2.7 Screening FLNC gene mutations and bioinformatics analysis The FLNC gene was analyzed by gene sequencing and screened against dbSNP, 1000genomes, ExAC and GnomAD databases to find eight unique mutations: c.1220C > A (p.Thr407Asn), c.1310G > T (p.Arg437Leu), c.1354G > A (p.Val452Met). Val452Met), c.2501C > T (p.Thr834Met), c.3757G > A (p.Val1253Ile), c.3790G > A (p.Gly1264Ser), c.4700G > A (p.Arg1567Gln), c.7614G > T (p.Leu2538Phe). They are respectively referred to as FLNC T407N , FLNC R437L , FLNC V452M , FLNC T834M , FLNC V1253I , FLNC G1264S , FLNC R1567Q and FLNC L2538F . A total of 25 (25%) patients with missense DCM mutations in the FLNC gene coding region were screened. Sanger sequencing Further validation showed that all 8 mutations were heterozygous mutations. The results showed that two loci, FLNC T407N and FLNC R437L , were not reported in the 1000G, ExAC, and GnomAD-Exomes databases and were rare mutations (Table 3 ). Also describe the distribution of the 8sites variants of this gene and their protein-coding domains (Fig. 6 ). All three loci are in the ROD1 structural domain, and a search of public databases and related literature revealed that the above loci are de novo mutations, while the FLNC V452M locus is in the ROD2 region, which may lead to DCM pathogenesis due to protein dimerization and folding, not excluding that it may be the pathogenic mutation locus for hereditary cardiomyopathy. Table 3 Mutation of FLNC and bioinformatic Amino acid change dbSNP DCM probands (n = 100) Control (n = 100) 1000G ExAC gnomAD-Exomes Allele Allele MAF Allele Allele MAF ALL ESA Global Asian Global Asian p.Thr407Asn rs1421140939 1 200 0.005 0 200 0 - - - - - - p.Arg437Leu rs370138936 1 200 0.005 0 200 0 - - - - - - p.Val452Met rs192163925 1 200 0.005 0 200 0 0.000 0.002 0.000 0.000 0.000 0.001 p.Thr834Met rs75133741 6 200 0.03 5 200 0.025 0.024 0.036 0.009 0.039 0.008 0.038 p.Val1253Ile rs117366477 4 200 0.02 4 200 0.020 0.003 0.013 0.001 0.006 0.001 0.006 p.Gly1264Ser rs201335143 1 200 0.005 0 200 0 0.000 0.000 0.000 0.000 0.000 0.000 p.Arg1567Gln rs2291569 13 200 0.065 17 200 0.1 0.063 0.111 0.076 0.089 0.075 0.088 p.Leu2538Phe rs180834558 1 200 0.005 0 200 0 0.002 0.009 0.001 0.007 0.002 0.008 List of rare coding mutations identified in the FLNC gene.Frequency of rare coding mutations in the FLNC gene in the 1000G (1000 Genomes Project) ALL and ESA, ExAC (Exome Aggregation Consortium) Global and Asian, gnomAD(Genome Aggregation Database) Global and Asian populations.MAF(Minor Allele Frequency):Minimum allele frequency. ''-''stands for no cases of this locus in the population. 2.8 Results of amino acid hazard and conservativeness analyses of mutation sites The SIFT and PolyPhen-2 analysis software were used to analyze the harmfulness of the above 8 missense mutation amino acid sites, and predict whether the missense mutation (the mutation leading to the change of amino acid) would cause the protein structure or function. The results showed that the three sites were highly harmful, FLNC V452M was evaluated as Damaging (0.001) by the software SIFT; FLNC R2567Q is evaluated as Damaging (0.036) by software SIFT; FLNC L2538F is evaluated as Damaging (0.002) by software SIFT, and PolyPhen-2 is evaluated as Damaging (1.000) (Table 4 ). Table 4 Mutation of FLNC risk assessment Genomic position Amino acid change SIFTanalysis PolyPhen2 analysis Mutation Assessor analysis Mutation type Impact chr7:128478666 p.Thr407Asn 0.175(T) 0.002(B) 0.83(L) Missense Moderate chr7:128478756 p.Arg437Leu 0.074(T) 0.319(B) 2.31(M) Missense Moderate chr7:128478800 p.Val452Met 0.001(D) 0.886(P) 3.425(M) Missense Moderate chr7:128482959 p.Thr834Met 0.122(T) 0.044(B) 2.21(M) Missense Moderate chr7:128485276 p.Val1253Ile 0.549(T) 0.007(B) 1.37(L) Missense Moderate chr7:128485309 p.Gly1264Ser 0.055(T) 0.625(P) 3.095(M) Missense Moderate chr7:128488734 p.Arg1567Gln 0.036(D) 0.684(P) 3.005(M) Missense Moderate chr7:128497224 p.Leu2538Phe 0.002(D) 1.000(D) 3.285(M) Missense Moderate SIFT ≤ 0.05, PolyPhen-2 ≥ 0.909, Mutation Assessor ≥ 1.9, suggesting that the mutation is deleterious, and the closer the Polyphen-2 score is to 1.0, the greater the likelihood of damage, and the closer it is to 0, the less likely the damage. GERP + + and Jalview analysis software were used to analyze the conservatism of amino acid sites of the above eight missense mutations. FLNC T407N , FLNC R437L , FLNC V452M , FLNC T834M , FLNC V1253I , FLNC G1264S , FLNC R1567Q , and FLNC L2538F are conserved in Homo sapiens, Pan troglodyte, Macaca mulatta, Canis lupus familiaris, Bos taurus, Mus musculus, Rattus norvegicus, Danio rerio, and Xenopus tropicalis species (Fig. 7 ). Among them, FLNC R437L , FLNC T834M , FLNC G1264S , FLNC R1567Q , and FLNC L2538F are highly conserved among Homo sapiens, Pan troglodyte, Macaca mulatta, Canis lupus familiaris, Bos taurus, Mus musculus, and Xenopus tropicalis species. 2.9 Clinical characteristics and sequencing kurtosis results of patients carrying FLNC mutation after screening We filtered the data by dbSNP database, 1000genomes database, ExAC and GnomAD database to screen out known variants, and found that both FLNC T407N and FLNC R437L sites were not reported and belonged to rare mutations; among them, FLNC T407N was evaluated to be low risk and generally conserved, and was excluded for the time being. FLNC T834M , FLNC R1567Q and FLNC V1253I were found in both DCM and control groups and considered as polymorphic site, among which FLNC V1253I was poorly conserved and therefore excluded; finally, four FLNC variants were screened as FLNC R437L , FLNC V452M , FLNC G1264S and FLNC L2538F , which corresponded to sequencing numbers (53, 18, 103, 108), were all missense variants in DCM patients (Table 5 ). Table 5 Clinical information on patients with rare mutations in the FLNC gene Patient number Age Gender family history Age of onset Genotype Arrhythmia type ultrasonic cardiogram LAS(mm) LVD(mm) EF(%) 53 53 male none 49 p.Arg437Leu AF 49 62 34 18 38 male yes 35 p.Val452Met Sinus tachycardia 44 72 19 103 55 male none 55 p.Gly1264Ser AF 55 58 66 108 56 female none 51 p.Leu2538Phe Ventricular, atrial tachycardia 37 65 37 Left atrial end-systolic internal diameter (LAS), left ventricular end-diastolic internal diameter (LVD), ejection fraction (EF). Atrial fibrillation (AF), ultrasonic cardiogram (UCG). 2.10 Sequencing peak map and clinical data of proband and family members The FLNC R437L missense mutation of FLNC carried by proband 53 of DCM (change of arginine to leucine at site 437), which was considered as a new mutation of FLNC through database and literature query in the early stage, was predicted to be possibly pathogenic and highly conserved among species. The related patient was a man, who was hospitalized in our hospital in April 2018, and underwent an ECG that presented rapid atrial fibrillation, left ventricular high voltage, and abnormal TII, III, and augmented vector foot (aVF); the UCG showed an EF of 34% and a FS of 17%, and the patient was discharged after improvement with treatment. In May of the same year, he returned to the outpatient clinic for follow-up after recurrence of the disease and was reexamined: ECG rapid-type atrial fibrillation (AF) with T-wave changes; UCG: EF 23%, FS 11%. Later, the patient returned for follow-up in September 2018, and UCG was rechecked: EF 39% and FS 20%. He returned for follow-up in July 2019 and ECG findings were as follows: AF, RV5 > 2.5mV, and TII, III, and aVF abnormalities; UCG: EF 41% and FS 21%. DCM proband 103 carries the FLNC G1264S missense variation of FLNC (change of glycine to serine at site 1264), which was pre-analyzed by the GnomAD database as a rare variant (frequency 0.000012) and predicted to be deleterious and highly conservative. The related patient was a man, who was hospitalized in our hospital in April 2019, and underwent an ECG that presented tachyarrhythmic AF, premature ventricular beats, and T wave alterations; the UCG showed: EF 66% and FS 37%. The FLNC L2538F missense variation of DCM proband 108 carrying FLNC (change of leucine to phenylalanine at site 2538) was analyzed as a rare variation (frequency 0.000012 in GnomAD) in the early stage, and predicted to be harmful and highly conservative. The related patient was checked in during June 2019 and underwent an ECG that presented a sinus rhythm, left atrium abnormality, and T-wave change; the UCG showed: EF37% and FS18%. Patients with mutations at the above three sites had no family genetic history. All patients were 63 years of age and had no obvious symptoms of heart failure. The risk of sudden death was low. Symptoms improved after treatment. The FLNC V452M missense mutation of FLNC carried by proband 18 of DCM (change of valine to methionine at site 452), in the early stage, was predicted by database analysis to be highly conservative. The related patient was 35 years of age at the time of onset. From 2015 to 2016, the patient was hospitalized, diagnosed with DCM, and received drug treatment. After treatment, the symptoms of heart failure were slightly relieved, but recurred, and the condition worsened. In March 2017, the patient visited our hospital and was found to have an EF of 26% and a FS of 12% from UCG. At that time, the symptoms of heart failure were obvious, and liver and kidney functions were affected. The patient was treated with drugs and discharged after remission. Out-of-hospital symptoms recurred, and the patient returned to the hospital in March 2018 for inpatient treatment. The patient was admitted to the hospital citing panic attacks with shortness of breath and spontaneous shortness of breath at the slightest activity, and was given an ECG (Fig. 8 C) that namely suggested sinus tachycardia, left atrial abnormalities, Qr type VI, and T-wave changes. At the same time, the cardiac ultrasound long-axis view of the left ventricle, as shown in Fig. 8 D, suggested an LA anteroposterior diameter of 44 mm and LV anteroposterior diameter of 72 mm, with ultrasound features suggesting whole heart enlargement, diffusely diminished activity, myocardial lesions, and hyposystolic and diastolic left heart function with an EF of 19% and a FS of 9%. Considering the patient's clinical and other relevant examination findings,their condition was consistent with the diagnosis of DCM. Clinical symptoms were more severe in this patient than in other patients with mutations. The patient had aninfluential family history, and died at the follow-up in 2019, whose sequencing peak plot and clinical data are shown in Fig. 8 , suggesting that mutations in different FLNC loci can affect the cardiac phenotype and even increase the risk of sudden death in patients. Additionally, the combination of online bioinformatics software predictions and interspecies conservativeness analysis suggests that this locus is a deleterious mutation that may play a crucial effect on the protein. 3 Discussion DCM is a heterogeneous genetically-related disease that is characterized by enlarged ventricles and reduced function, often leading to heart failure and sudden cardiac death [ 14 , 15 ]. The prevalence of DCM may vary according to geographic and ethnic differences and diagnosis criteria. The disease progression and prognosis are driven by reverse remodeling within the heart, which makes timely and definitive heart failure treatment, which can involve drugs, devices, and procedures, essential for end-stage heart failure [ 16 ]. The heterogeneous etiology and clinical presentation of DCM makes a correct and timely diagnosis challenging, imposing a heavy burden on society and families. Patients with DCM benefit from current interventions and have a better clinical prognosis. However, there is still a lack of interventions that can reverse the outcome of DCM progression to heart failure. Filamin-C is an actin-binding protein encoded by the FLNC gene, which is located in chromosome band 7q32-q35, contains approximately 29.5 kb of genomic DNA and 48 coding exons [ 17 , 18 ] and 2,725 amino acids with two transcripts (GenBank isoform b:NP_001120959.1 and isoform b:NP_001449.3), of which the long transcript (isoform a) has a molecular weight of 291 kDa. A number of Z-disk-specific proteins interact with FLNC and are involved in maintaining the stability of myosin [ 19 , 20 ]. Additionally, FLNC consists of an N-terminal actin-binding domain and 24 immunoglobulin (Ig)-like repeat sequences (R1-24), which have two mediated calpain-sensitive hinges separating R15 and R16 (H1) and R23 and R24 (H2). FLNC subunits are dimerized by R24, and calpain cleaves the dimerization structural domain to regulate the migration of FLNC subunits. Due to the unique insertion of 82 amino acid residues in repeat sequence 20, FLNC is localized to the Z-disc and is required for healthy Z-disc formation that connects myofascicles [ 21 ]. The specialized cytoskeletal myogenic fibers of cardiac myocytes provide the structural basis for the continuous and correct contraction of cardiac myocytes to facilitate circulation. The combination of high-throughput sequencing technology and bioinformatic analysis has led to breakthroughs in disease research. To date, hundreds of unique FLNC variants have been associated with cardiomyopathy, including many arrhythmogenic cardiomyopathies and multiple FLNC truncating mutations in DCM patients [ 7 , 20 , 22 ]. Celeghin et al. [ 23 ] studied 270 patients with FLNC-associated cardiomyopathy and identified 12 novel FLNC variants and a high incidence of sudden cardiac death, mainly associated with left ventricular fibrosis. Familial cases are common, especially involving genes that encode cytoskeletal, myosin, and nuclear membrane proteins [ 12 ]. Zhao et al.[ 24 ] first reported the identification of 12 nonsynonymous mutations by NGS in 21 cases of DCM in Yunnan Province, China, with a high frequency of mutations in MYH6 and MYBPC3 myosin genes. In 2016, Reinstein et al. [ 25 ] used whole-exome sequencing to investigate the genetic etiology of a family with congenital DCM and found that double allelic variants in FLNC can lead to congenital DCM. In addition, Wang et al. [ 26 ] found that more than one-third of childhood DCM cases were caused by mutations in genes related to the structure or function of myonodules, bridging granules, or the cytoskeleton, and that most deaths occurring related to FLNC are connected to the structural integrity of myonodules and cellular signaling, and a better understanding of molecular alterations caused by FLNC variants can elucidate potential therapeutic targets. In this study, we evaluated the high expression of FLNC in DCM based on GSE3586 microarray expression profile data, and the ROC curve indicatesthe ability to differentiate between the two groups (AUC = 0.774). Earlier studies have shown that FLNC is mainly enhanced in cardiac and skeletal muscles, with a low level of expression in other tissues [ 18 , 27 ]. Cardiomyocytes express fewer filamin-C isoforms under basal conditions, but they are rapidly induced during cardiac stress [ 13 ]. The Human Protein Atlas database showing high FLNC expression in the heart similarly validates our results (Fig. 9 ). Due to the wide heterogeneity of the population, we integrated four datasets, and based on DCM tissue expression profile data, we identified 54 DEGs (obtained after FLNC-based grouping), including 16 upregulated and 38 downregulated genes. The analysis of 81 DCM myocardial tissues after FLNC-based grouping shows that upregulated DEGs are enriched in protein folding and actin filament binding. FLNC is known to be an actin-binding protein with a cytoskeletal structure consisting of myosin units in which thin and thick filaments cross each other to form distinct bands; many protein interactions are the molecular basis for the formation of these myosin-connecting devices [ 28 , 29 , 30 ]. The analysis of FLNC genes among the upregulated DEGs shows that they are mainly enriched in the MAPK signaling pathway. It has been shown that MAPK plays multiple roles in the regulation of heart failure and myocardial hypertrophy [ 31 ]. Hong et al. [ 32 ] found that silencing connective tissue growth factors inactivated the MAPK signaling pathway, thereby attenuating myocardial fibrosis and left ventricular hypertrophy in rats with DCM. In the present study, the MAPK signaling pathway may have had an involvement with DEGs, suggesting that the MAPK signaling pathway plays a key role in DCM. The downregulated DEGs were mainly enriched in inflammatory and immune-related pathways, such as Phagosome and Chemokine signaling pathways. Inflammation and immune involvement in the progression of cardiomyopathy have been confirmed by extensive studies, such as cytokines and chemokines secreted by infiltrative pro-inflammatory macrophages and lymphocytes, accelerating cardiomyocyte development of hypertrophy and progressive fibrotic responses, which in turn trigger extracellular matrix accumulation and fibrosis, leading to diabetic cardiogenesis [ 33 ]. Myocardial inflammation and fibrosis play important roles in the pathogenesis of almost all forms of myocardial injury [ 34 ]. The correlation analysis shows that the genes significantly and positively associated with FLNC were TTC9 , PPFIA4 , DNAJB5 , XIRP1 , DUSP27 , and KIFC3 , and those negatively associated with FLNC were TLR3 , GPR34 , and MRC1 . In combination with the PPI network, FLNC had protein interactions only with XIRP1 , a gene positively associated with closely related genes. Furthermore, cardiac intercalated discs (ICDs) are fundamental structures unique to the myocardium. FLNC is highly concentrated in cardiac ICDs, which maintain intercellular adhesion in cardiomyocytes, connect individual cardiomyocytes, and play a role in intercellular signaling [ 35 , 36 ]. In contrast, disruption of the ICDs structure and function is an important feature of many congenital and acquired heart diseases, including cardiomyopathy, arrhythmias, and heart failure [ 37 ]. XIRPs have been reported to be associated with the development of hypertrophic cardiomyopathy [ 38 , 39 ]. XIRPs, as muscle-specific proteins, co-localize with N-calmodulin, connexin 43, FLNC, and human vasodilator-activated phosphoprotein in adult cardiac ICDs and are involved in regulating the development and adaptive remodeling of the actin cytoskeleton in transverse myocytes, further emphasizing the importance of FLNC and XIRP2 in cardiac remodeling [ 40 , 41 ]. Other genes closely related to FLNC have not yet been reported and further exploration is needed. In conclusion, the study’s enrichment analyses and PPI constructs deepen our understanding of the biological functions, pathways, and proteins that interact with FLNC in DCM. The distribution and expression of FLNC gene transcriptome data in different tissues. Based on 81 cases of DCM and 32 controls, we identified differences in immune infiltration between the DCM and control groups, and the results show that CD4 memory-activated T cells, gamma delta T cells, and macrophage M0 infiltration levels were higher in the DCM group than in the control group, while naive B cells in the DCM group had a lower level of infiltration than those in the control group. It is well known that immune and inflammatory responses play important roles in cardiovascular disease, and inflammation is essential in the initial development and progression of many cardiovascular diseases involving innate and adaptive immune responses. The transport of immune cells to the heart and their interaction with cardiac cells are major determinants of cardiac dysfunction [ 42 ]. These imbalanced T and B cell pathways in the adaptive immune system are associated with tissue remodeling due to cardiomyocyte death and fibrosis, leading to cardiac dysfunction [ 43 ]. Different etiologies lead to cardiac inflammation mediated by common or different types of leukocytes, as well as pathways and recruitment of immune cells to the heart, and local inflammation leads to tissue fibrosis that hardens the heart and promotes the progression of dilatation and heart failure, which is likely to be present in most patients with advanced heart failure [ 44 ]. According to the characteristics of inflammatory cells/cytokines and etiology (such as infection and genetic background), the types and stages of inflammatory cardiomyopathy can be stratified [ 45 ]. By improving our understanding of the inherent and adaptive immune mechanisms of DCM, we can determine potential therapeutic targets in the future and apply precise treatment to myocardial diseases. Non-coding RNA also plays an important role in the occurrence and development of DCM [ 46 ]. At present, the evaluation of non-coding RNA that can bind to FLNC is still lacking, which would provide guidance for the epigenetic regulation and pathogenesis of DCM involving non-coding RNA through the FLNC gene. In the ce-RNA network of FLNC we constructed, eight FLNC-binding miRNAs were predicted, and the occurrence and development of many diseases are influenced by miRNAs. Among them, hsa-miR-558 and hsa-miR-1972 have been shown to regulate cancer progression. Studies have shown that hsa-miR-558 promotes tumorigenesis and invasiveness of gastric cancer cell lines in vitro and in vivo by reducing Smad4-mediated inhibition of heparanase expression [ 47 ]. Chen et al. showed that extracellular vesicles derived from fibroblast-like synoviocytes in rheumatoid arthritis accelerate endothelial angiogenesis through the p53/mTOR pathway via hsa-miR-1972 [ 48 ]. However, in the FLNC ce-RNA network constructed in this study, there are few reports on miRNAs and lncRNAs that play epigenetic roles in combination with FLNC genes. In summary, we conclude that FLNC is highly expressed in myocardial tissue, which suggests that FLNC expression is closely related to the integrity of healthy cardiac structure and function, and that its mutation leads to a series of pathological changes that result in myocardial damage and reduced cardiac function. Through differential analysis and PPI networks, we identified genes that are positively and negatively associated with FLNC genes that are highly expressed in the myocardium, and these genes with co-expression properties with FLNC can deepen our understanding of the position of FLNC in the heart and the functions in which it is involved. Functional enrichment studies have shown that the FLNC high expression group is more associated with actin folding, while FLNC is negatively associated with inflammatory immunity. Although numerous studies have now shown that inflammation and immunity are strongly associated with the progression of DCM, our study suggests that DCM due to FLNC mutations may involve less inflammation and immunity and more direct structural changes in the heart, leading to cardiac enlargement and reduced cardiac function. The immune infiltration analysis results in the present study further suggest that FLNC has little correlation with immunity and inflammation. Meanwhile, the correlation analysis of DEGs and the ce-RNA network of FLNC revealed that some genes and non-coding RNAs closely related to FLNC still need to be researched further, and more scholars investigatingthese genes and non-coding RNAs can deepen our knowledge of the pathogenic mechanism of FLNC . The focus of this study was to identify new deleterious mutation loci in FLNC through blood samples from 200 patients, which expands the variant profile of FLNC in DCM. Our subsequent studies will further evaluate the genes and non-coding RNAs that are closely related to FLNC to better understand the pathogenic mechanisms of FLNC in DCM and to guide drug development and treatment. Finally, based on serum samples, four DCM missense mutation sites were identified in this study. Among them, FLNC R437L , FLNC V452M , and FLNC G1264S are all in the ROD1 domain. The FLNC V452M and FLNC G1264S sites are predicted to be deleterious and may interfere with protein dimerization and folding or even cause cardiomyopathy. Previous studies have shown that few missense mutation sites occur in the ROD1 structural domain, but the new heterozygous mutation, FLNC V452M , and the observation of FLNC L2538F on Ig-like structural domain 23, both of which were found in this study, are missense mutations. Participants who carried the FLNC V452M mutation all exhibited varying degrees of malignant arrhythmias, with it pre-documented in both individuals and families, which eventually led to death. Thus, it can be considered as a possible causative mutation site for hereditary cardiomyopathy. An elevated level of myocardial fibrosis and conduction abnormalities observed on surface ECGs could explain the significantly increased risk of ventricular arrhythmias (> 80%) and sudden cardiac death in participants who carried an FLNC mutation [ 13 ]. Studies have shown that FLNC mutations are transmitted in an autosomal dominant manner, leading to a typical cardiomyopathy caused by FLNC truncating mutations as a result of clinical skeletal myopathy, and the phenotype manifests as an overlap between dilated and arrhythmogenic cardiomyopathy, characterized by varying degrees of left ventricular dilatation and systolic dysfunction, significant subepicardial or left ventricular intramyocardial fibrosis that leads to frequent ventricular arrhythmias, and sudden cardiac death [ 7 , 49 , 50 ]. Bains et al. [ 51 ] reported a case of arrhythmogenic bileaflet mitral valve prolapse syndrome in a 51-year-old man with a similar phenotype existing in the mother, aunt, and brother of this preexisting patient. A new truncated variant FLNC c.201G > A (p.Trp34- FLNC ) was identified in whole exome sequencing data, presumably with FLNC haploinsufficiency as a potential arrhythmogenic substrate, exacerbated by mitral valve prolapse. Another report described familial sudden cardiac death without signs of cardiomyopathy, again associated with a truncated variant of FLNC (p.Pro2513Glufs*12) [ 52 ]. Both cases highlight the arrhythmogenic potential associated with a truncated variant of FLNC . The present study further expanded the gene variant profile of FLNC in patients with cardiomyopathy from a minority of areas in Guangxi. In light of the small sample size of this study, future models can be established at the cellular and animal levels, if necessary, to further explore the pathogenesis of these mutations, with the aim of providing theoretical support for the early diagnosis and subsequent precise treatment of DCM. Additionally, further studies are needed to confirm our findings. Cardiomyopathies are life-threatening diseases in which DCM is the ultimate phenotype of multiple mutations in heterogeneous pathways, including components of the membrane scaffolding apparatus, myosin, nuclear membrane proteins, calcium-processing proteins, transcription factors, and RNA splicing to cellular energy production mechanisms. Although these mutations are highly diverse in the affected pathways, they share the common features of impaired contraction and insurmountable cellular damage, leading to cell death and fibrotic repair, and ultimately contributing to cardiac thinning and dilatation [ 11 , 53 ]. FLNC is considered a key factor in the progression of different types of cardiomyopathies; therefore, determining the genetic and physiological roles of FLNC is necessary to determine the onset and progression of the disease. A comprehensive analysis of the genetic and molecular causes of the disease can help with obtaining an accurate diagnosis, especially for diseases with unclear clinical findings or pathologies that overlap with other diseases. Recently, J. Haas et al. [ 54 ] dissected the genetic causes of DCM by using NGS methods and established a comprehensive approach to clinical genetic testing of all currently known disease genes, not only for clinically relevant DCM genes but also for genes causing other inherited cardiomyopathies, and to systematically test NGS as a new technology for analytical performance in a wide range of clinical applications. Meanwhile, with the development of DCM in ex vivo experiments over the years, Karakikes et al. [ 55 ] found that patient-specific human induced pluripotent stem cell (IPSC) lines can be successfully used as a research platform for cardiovascular disease, providing an excellent tool for in vitro modeling and elucidating potential disease mechanisms. However, the lack of isogenic IPSC DCM models limits our understanding of pathogenesis, and in 2018, Ma et al. [ 56 ] showed that pathogenicity of genomic variants of uncertain significance could be determined using CRISPR/Cas9 and IPSCs. This technique is one of the currently emerging methods and many studies have already identified patients carrying FLNC gene variants, based on the principle that CRISPR/Cas9 editing establishes homozygous human IPSC lines carrying FLNC mutations to mimic DCM, and the generated IPSC lines show normal karyotypes, express pluripotency markers, and exhibit trilineage differentiation potential in vitro [ 57 , 58 ]. As the FLNC mutation DCM modeling technology gradually matures and more loci are investigated, it will provide us with a deeper understanding of the mechanisms of FLNC , thus facilitating the development of precision medicine in this emerging field. While the exact mechanisms by which FLNC mutations cause different and partially overlapping cardiac phenotypic characteristics remain unclear, FLNC mutations are currently one of the most common causes of hereditary DCM and exist in and are related to various forms of human cardiomyopathy. A number of existing studies have shown that FLNC mutations can affect the heart phenotype and thus lead to an increased risk of disease, while individuals who are FLNC variant carriers have missense mutations, highlighting the potential necessity of genetic testing for individuals and families. Specifically, complex genetic heterogeneity, including haploid deficiency or functional loss variants produced thereby, will affect other phenotypic attributes, such as chamber hypertrophy and expansion, and ultimately increase the risk of arrhythmia and even sudden death, which will contribute to the practice of cardiovascular genetic medicine and our understanding of the prevalence and pathogenesis of DCM. 4 Conclusions In conclusion, we identified the biological functions, signaling pathways, immune correlations, and ce-RNA networks involved in the pathogenic mechanisms of DCM by sequencing myocardial tissue samples with big data analysis. By sequencing the FLNC gene in blood samples, we are able to discuss the characteristics of FLNC gene variants in cardiomyopathy patients, expand the regional cardiomyopathy FLNC gene variant profile, identify cardiomyopathy-associated variants in FLNC , and provide a brief overview of the mutational sites of these genes, finding that they are involved in the progression of FLNC-associated DCM mainly through different mechanisms. To improve our understanding of the genomic basis of DCM, more extensive genomic studies will need to be conducted on a wider scale, involving individuals with strict phenotypic pre-documentation or their families and to further mechanistically identify the pathways that FLNC may be involved in. This would further offer the prospect of genetic stratification for molecular therapy and precision medicine for patients with cardiomyopathy, which ultimately may provide potential new avenues for treatment. 5 Materials And Methods 5.1 Gene Expression Omnibus (GEO) dataset We downloaded five microarray expression profile datasets (GSE3586, GSE84796, GSE29819, GSE42955, and GSE120895) associated with DCM from the GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ). GSE3586, based on the GPL3050 platform, contained 13 DCM myocardial tissue samples and 15 controls ( Supplementary File5 ). It was used to assess differences in expression levels and the ability to differentiate the FLNC gene between the DCM and control groups. In addition, we obtained 81 DCM myocardial tissue samples and 32 healthy myocardial tissue samples from four datasets: GSE84796 (10 DCM and 7 controls), GSE29819 (12 DCM and 12 controls), GSE42955 (12 DCM and 5 controls), and GSE120895 (47 DCM and 8 controls) ( Supplementary File 6 ), based on the GPL14550 and GPL570 platforms. Non-DCM samples in two of the datasets (12 ischemic cardiomyopathy samples in GSE42955 and 12 arrhythmogenic right ventricular cardiomyopathy samples in GSE29819) were removed. 5.2 Data processing and recognition of differentially expressed genes (DEGs) The GSE3586 dataset, which was standardized using the R software "limma" package ( Supplementary File 7 ), was used to assess the expression level and differentiation ability of the FLNC gene. Box plots and the curve of ROCs were applied to present the results separately. Subsequently, the four datasets (GSE84796, GSE29819, GSE42955, and GSE120895) were merged, batch corrected, normalized, and analyzed for differences using the "sva" and "limma" packages of R software [ 59 ]. The details were as follows:1. Obtained the mean value of gene expression with the same gene symbol in different tissue samples. 2. The "ComBat" function was used to eliminate the batch effect between different tissue samples. 3. The "normalizeBetweenArrays" function was used to standardize data. Subsequently, in the four pretreated datasets, 32 healthy myocardial tissue samples were obtained ( Supplementary File 8 ), as well as 81 DCM myocardial tissue samples based on the median value of FLNC gene expression (low and high) of the two groups. Finally, based on the filter conditions of log fold change > 1 and adjusted P -value < 0.05, DEGs (including upregulated and downregulated genes) between the two groups were identified. The above results were further visualized by the "pheatmap" and "ggplot2" packages of R software. 5.3 Enrichment analysis of DEGs Upregulated and downregulated DEGs identified based on FLNC gene grouping were used to perform KEGG and GO enrichment analyses. Both enrichment methods were performed using the R software ''clusterProfiler'' and "enrichplot"packages [ 60 ]. According to the GO analysis, gene sets were involved in biological processes (BPs), cellular components, and molecular functions (MFs), and the KEGG analysis highlighted the signaling pathways in which gene sets are involved. Statistical significance was set at P < 0.05. 5.4 PPI network and gene module identification To identify proteins that interact closely with FLNC and are associated withDEGs, all DEGs and FLNC genes were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database ( https://string-db.org/ ) to construct two PPI networks. The filter condition was "Homo sapiens" and the "minimum required interaction score" was assigned to "medium confidence (0.40)". Cytoscape v3.8.2 software was used to visualize the PPI networks. Finally, correlations of 40 DEGs (including 16 upregulated and 24 downregulated genes) were evaluated and visualized using the "Spearman" correlation and "corrplot" package of R software. 5.5 Analysis of immune cell infiltration and its correlation with FLNC and hub genes After merging and preprocessing the four datasets, expression profile files (containing 81 DCM myocardial tissue samples and 32 healthy myocardial tissue samples) were used to perform the immune infiltration analysis. The analysis method and sample files for immune infiltration were based on the "CIBERSORT" package of R software and 22 immune cell types of the "LM22" file ( https://cibersort.stanford.edu/index.php ). Differences in infiltration levels of the 22 immune cell types in the DCM group and the non-DCM control group were demonstrated using violin plots. A P -value of < 0.05 was assumed to indicate a significant difference. Finally, the correlation between FLNC and XIRP1 genes and infiltrated immune cell types was demonstrated using correlation heatmaps based on the "Spearman" correlation and "corrplot" package of R software. 5.6 Prediction of ce-RNA network of FLNC gene The miRanda ( http://www.microRNA.org ), miRDB (MicroRNA Target Prediction Database), and TargetScan (TargetScanHuman 7.2) databases were used to predict FLNC gene mRNA–miRNA interactions. Among these, miRNAs that could be predicted in all three databases were retained. In addition, the spongeScan database ( http://spongescan.rc.ufl.edu/ ) was used to further predict lncRNAs bound by screened miRNAs. Subsequently, based on the results of the two-step prediction, mRNA-miRNA-lncRNA relationship files were collated for subsequent analysis. Finally, Cytoscape v3.8.2 software was used to visualize the three-way relationship/ce-RNA network. 5.7 Ethics and Consent to Participate Involved in this study were 100 patients with DCM from the Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi Province, and 100 patients comprising the non-DCM control group; the mean age of the DCM group was 53.57 ± a standard deviation of 11.95 and the mean age of the control group was 57.21 ± 12.96. Sample size was based on estimations by power analysis with a level of significance of 0.05 and a power of 0.97.All subjects were assigned to experimental groups using simple randomization.All subjects were unaware of the experimental grouping and outcome assessment.The study protocol and collection of peripheral venous blood were approved by the Medical Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (YYFY-LL-2016-001) and were in accordance with the principles of the Declaration of Helsinki. Participants and their families signed an informed consent form. 5.8 Sample Collection In this study, data from 100 DCM patients (mean age 53.57 ± 11.95, 81 men [81%] and 19 women [19%]) of the Zhuang population in Guangxi, China were collected. DCM diagnosis is based on DCM guidelines diagnostic criteria [ 12 ]. That is, clinical manifestations have objective evidence of ventricular enlargement and reduced myocardial systolic function: 1. Men and women with left ventricular end diastolic diameters > 5.0 cm and > 5.5 cm, respectively, or > 117% of the predicted value of age and body surface area, i.e. twice SD + 5% of the predicted value). 2. Left ventricular ejection fraction (EF) < 45% and fractional shortening (FS) < 25%.3. Other diseases causing myocardial damage, such as hypertension, coronary heart disease, valvular heart disease, congenital heart disease, alcoholic cardiomyopathy, tachycardic cardiomyopathy, pericardial disease, systemic disease, pulmonary heart disease, and neuromuscular disease, were excluded at the time of onset. In parallel, 100 healthy samples were collected as the control group (mean age 57.21 ± 12.96, 46 men [46%] and 54 women [54%]). Patients with severe organic heart disease and a family history of heart disease were also excluded. The general clinical information of all enrolled patients was collected, including sex, age of onset, genetic family history, and surgical history. ECG and chest radiography were routinely performed for auxiliary diagnoses, and transthoracic echocardiography (UCG) was used for clear diagnoses [ 3 ]. 5.9 Gene sequencing and bioinformatics analysis of FLNC gene clinical samples FLNC sequencing was performed by extracting peripheral venous blood genomic DNA from 100 DCM individuals and 100 non-DCM individuals. Single nucleotide variants (SNVs) were annotated using the hg38/GRCh38 (NM_001458) version as the reference sequence. All mutations obtained were compared with the Database for Single Nucleotide Polymorphisms and Other Classes of Minor Genetic Variation (dbSNP) and the 1,000 Genomes database, and DCM SNVs were compared with control SNVs to obtain case-specific newly identified or rare FLNC missense mutation loci. Mutation frequencies were detected using the following four public databases:dbSNP ( http://www.ncbi.nlm.nih.gov/projects/SNP ), 1,000 Genomes ( http://www.1000genomes.org ), the Exome Aggregation Consortium (ExAC, http://exac.broad-institute.org ), and the Genome Aggregation Database (GnomAD, http://gnomad.broadinstitute.org ). Mutation sites of corresponding samples were verified by Sanger sequencing. SIFT ( http://sift.jcvi.org/ ), PolyPhen-2 ( http://genetics.bwh.harvard.edu/pph/ ), and Mutation Assessor ( http://mutationassessor.org/r3/ ) software were used to predict missense mutations. Based on FLNC sequences in the UniProt database ( https://www.uniprot.org/align/A ), an analysis of the conservativeness of each locus took place using genomic evolutionary rate scoring (GERP++, http://mendel.standford.edu/SidowLab/downloads/gerp/ ), Clustal Omega ( https://www.ebi.ac.uk/Tools/msa/clustalo/ ), and Jalview ( http://www.omicshare.com/forum/thread-3141-1-1.html ) software. 5.10 Clinical characteristics and Sanger sequencing results of screening patients with FLNC gene mutation Sequencing data were screened for the presence of the causative mutation in all patients with clinical symptoms or corresponding abnormal ancillary tests in the family line, and the absence of the mutation in healthy individuals in the family line. This was followed by screening for the presence of variants in the causative gene associated with the FLNC gene, excluding the reported variants, and selecting the unreported loci as key candidate variants for causation. General data and imaging findings, including ECG, cardiac ultrasound, and sequencing peaks, were collected and collated from screened patients. 5.11 Statistics All statistical analyses in this study were performed using the SPSS 24.0 software package and R software 4.2.1. For measurement data, each mean and standard deviation were calculated using the chi-squared test. Bivariate statistics were used in all analyses, and differences were considered statistically significant at P < 0.05. Declarations Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary material . Conflicts of Interest The authors declares that there is no conflict of interest regarding the publication of this paper. Author Contributions C-YC was engaged to write and conceptualize the original draft. C-YC and BH were responsible for methodology. D-YY,L-PQ, C-BL, YL, Q-JW, L-FZ, J-JM and X-SP were responsible for software. J-JH and LL were responsible for reviewing and editing. The published version of the work has been reviewed and approved by all authors. Funding Statement This study was sponsored by funds from the Chinese National Natural Science Foundation of China (81560076),Guangxi Natural Science Foundation Youth Program (2018JJB140358), The First Batch of High-level Talent Scientific Research Projects of the Affiliated Hospital of Youjiang Medical University for Nationalities in 2019 (R20196316), Middle-aged and Young Teachers in Colleges and Universities in Guangxi Basic Ability Promotion Project (2021KY0534), Guangxi Postgraduate Education Innovation Program Project (YCSW2022455), General project of Guangxi Natural Science Foundation (2022JJA140070) and Training Program for Thousands of Young and Middle-aged Backbone Teachers in Guangxi Higher Education Institutions. 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Int J Mol Sci.(2021) 22. doi: 10.1016/j.ejmg.2018.08.006 . Haas J., Frese K. S., Peil B., Kloos W., Keller A., Nietsch R., et al. Atlas of the clinical genetics of human dilated cardiomyopathy[J]. European Heart Journal.(2015) 36:1123–1135.doi: 10.1093/eurheartj/ehu301 . Karakikes I, Ameen M, Termglinchan V, Wu JC. Human induced pluripotent stem cell-derived cardiomyocytes: insights into molecular, cellular, and functional phenotypes[J]. Circ Res.(2015) 117:80–88. Doi: 10.1161/CIRCRESAHA.117.305365 . Ma N, Zhang JZ, Itzhaki I, Zhang SL, Chen H, Haddad F, et al. Determining the Pathogenicity of a Genomic Variant of Uncertain Significance Using CRISPR/Cas9 and Human-Induced Pluripotent Stem Cells[J]. Circulation.(2018) 138:2666–2681. doi: 10.1161/CIRCULATIONAHA.117.032273 . Argenziano MA, Burgos Angulo M, Najari Beidokhti M, Yang J, Bertalovitz AC, McDonald TV. Generation of a heterozygous FLNC mutation-carrying human iPSC line, USFi002-A, for modeling dilated cardiomyopathy[J]. Stem Cell Res.(2021) 53:102394. doi: 10.1016/j.scr.2021.102394 . Rodina N, Khudiakov A, Perepelina K, Muravyev A, Boytsov A, Zlotina A, et al. Generation of iPSC line (FAMRCi009-A) from patient with familial progressive cardiac conduction disorder carrying genetic variant FLNC p.Val2264Met[J]. Stem Cell Res.(2021) 59:102640. doi: 10.1016/j.scr.2021.102640 . Ritchie M. E., Phipson B., Wu D., Hu Y., Law C. W., Shi W, et al.limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Research.(2015) 43: e47.doi: 10.1093/nar/gkv007 . Wu T., Hu E., Xu S., Chen M., Guo P., Dai Z., et al.clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. The Innovation.(2021) 2:100141. doi: 10.1016/j.xinn.2021.100141 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif Supplementaryfile1.xls Supplementaryfile2.xlsx Supplementaryfile3.xlsx Supplementaryfile4.xlsx Supplementaryfile5.xlsx Supplementaryfile6.xlsx Supplementaryfile7.xlsx Supplementaryfile8.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2795537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":190727702,"identity":"eb3a584e-b192-477c-b171-695891c6406b","order_by":0,"name":"ChunYu Cai","email":"","orcid":"","institution":"Graduate School of Youjiang Medical University for Nationalities","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ChunYu","middleName":"","lastName":"Cai","suffix":""},{"id":190727704,"identity":"b9f415a7-6b8d-47fd-8ca4-72467dca0841","order_by":1,"name":"Bin He","email":"","orcid":"","institution":"Graduate School 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14:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2795537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2795537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":35654488,"identity":"c7b8488a-202e-4478-833f-d2378af2de33","added_by":"auto","created_at":"2023-04-12 14:53:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":270937,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation and differential analysis of \u003cem\u003eFLNC\u003c/em\u003e gene. (A) The box graph of \u003cem\u003eFLNC\u003c/em\u003egene expression level in DCM group and control group, * represents \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. (B) ROC curve of\u003cem\u003eFLNC\u003c/em\u003e gene differentiation ability in DCM group and control group, AUC represents the area under the curve. (C) Based on 82 DCM samples after \u003cem\u003eFLNC\u003c/em\u003egene grouping, the volcano map of differential genes between the two groups. (D) The heat map of different genes between the two groups. Treat group represents the DCM group.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/dda8f67e324371f8a95188cb.png"},{"id":35655733,"identity":"82e15be0-6603-4b8d-b302-445bca404599","added_by":"auto","created_at":"2023-04-12 15:09:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":307777,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of up-regulated and down-regulated DEGs. (A-B) Bubble plots of GO analysis of up- and down-regulated DEGs. The size of the bubbles represents the number of genes involved and the color of the bubbles represents the significance of the enrichment. (C-D) Mesh bubble plots of KEGG analysis of up-and down-regulated DEGs, showing the top 5 significantly enriched signaling pathways and the genes involved.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/4962ad6a39d8dd98e68bed08.png"},{"id":35656073,"identity":"d870b6ec-9e13-40cd-bcbe-0a27ff40ba08","added_by":"auto","created_at":"2023-04-12 15:17:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":739045,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction network (PPI) and correlation analysis. (A) Protein interaction network map of DEGs. (B) Protein interaction network map of \u003cem\u003eFLNC\u003c/em\u003e genes. (C) Correlation heatmap of 16 up-regulated DEGs and 24 down-regulated DEGs. Red color indicates positive correlation and green color indicates negative correlation. The stronger the color, the more significant the correlation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/e244bbc2250f73f6463a4a65.png"},{"id":35655730,"identity":"4f4c027e-c0e2-47fd-8a1c-08cafeb1e2de","added_by":"auto","created_at":"2023-04-12 15:09:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":279740,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis and correlation analysis. (A) Violin plot demonstrating the differences between 22 different infiltrated immune cell types in the DCM group and control. (B) Heatmap of the correlation between \u003cem\u003eFLNC\u003c/em\u003e and \u003cem\u003eXIRP1\u003c/em\u003egenes and the 22 different immune cell types infiltrated, the redder the color, the more significant the correlation. \"*\",\"**\",\"***\" represent \u003cem\u003eP\u003c/em\u003e\u0026lt; (0.05, 0.01, 0.001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/af0450fb4f9f54c62b784306.png"},{"id":35655513,"identity":"91a788d0-0bc5-48a8-af03-738b5ec1e476","added_by":"auto","created_at":"2023-04-12 15:01:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117088,"visible":true,"origin":"","legend":"\u003cp\u003eThe ceRNA network of \u003cem\u003eFLNC\u003c/em\u003e gene. (A) Yellow ellipse represents \u003cem\u003eFLNC\u003c/em\u003e, green diamond represents miRNA bound to \u003cem\u003eFLNC\u003c/em\u003e, and red inverted triangle represents lncRNA bound to miRNA.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/85595db0b437e8e4d6b98390.png"},{"id":35655518,"identity":"d52f0b4c-44a2-42a0-b8de-ec4844d73ab0","added_by":"auto","created_at":"2023-04-12 15:01:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":167965,"visible":true,"origin":"","legend":"\u003cp\u003eStructure domains and variations of distribution of FLNC in the research. Schematic diagram of \u003cem\u003eFLNC\u003c/em\u003e gene variation distribution and its protein coding domain. The numbers in the box represent the Ig like domain of filamin C. The sketch map shows the mutation sites and variants of this study annotated at the protein level.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/eb654604035f4a66911e3482.png"},{"id":35654498,"identity":"b7117af1-6a7d-42dc-8713-10795bf96791","added_by":"auto","created_at":"2023-04-12 14:53:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":329344,"visible":true,"origin":"","legend":"\u003cp\u003eConservativeness analysis of \u003cem\u003eFLNC\u003c/em\u003egene mutation sites.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/ded990103d23bc6d84c33d80.png"},{"id":35654499,"identity":"eedd6fc9-de8d-4e5b-8e9e-22167e68f69e","added_by":"auto","created_at":"2023-04-12 14:53:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":311742,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/700418ceac13666292059045.png"},{"id":35655520,"identity":"463da153-b1c9-4ede-b4fc-22cf7a49c4c4","added_by":"auto","created_at":"2023-04-12 15:01:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":226563,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution and expression of\u003cem\u003e FLNC\u003c/em\u003e gene transcriptome data in different tissues.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-2795537/v1/4d13d4b37e5967b9d80c90fa.png"},{"id":35656075,"identity":"eeec1ac1-e4dd-470c-898e-83fe59391fbf","added_by":"auto","created_at":"2023-04-12 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Introduction","content":"\u003cp\u003eDilated cardiomyopathy (DCM) is a common life-threatening heterogeneous heart disease characterized by systolic dysfunction, increased ventricular volume, thinner ventricular walls, and prolonged cardiomyocytes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The estimated prevalence rate of the disease among the global general population is approximately 1:250 to 400 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the disease is more common among men and varies by race, with a progressive increase in prevalence over time due to expanded clinical screening [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, familial DCM is defined as a Mendelian model of a single gene disease-inherited cardiomyopathy, and at least 50 single genes have been found to be associated with familial DCM, with the majority triggering disease with dominant mutations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Approximately 30\u0026ndash;50% of patients with DCM have gene mutations. Genetic diagnosis and risk stratification of patients with mutations are particularly important, and their prognosis determines the probability of heart failure and arrhythmias [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the mechanisms underlying DCM are complex and not yet fully understood.\u003c/p\u003e \u003cp\u003eStudies have shown that DCM is the ultimate outcome of myocardial response to various genetic and environmental damages and can be caused by a variety of myocardial injuries, with a large proportion of DCM cases having an underlying genetic or inflammatory basis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Most DCM-related mutations are specific to probands or families. To date, approximately 40% of genetic causes of DCM have been identified. More than 30 genes have been implicated in the etiology and risk of DCM and are found in a variety of cell ontologies and biochemical pathways [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In approximately 35% of patients with DCM, mutations can be detected in genes connected to cytoskeletal, sarcoplasmic, and nuclear membrane proteins [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The \u003cem\u003eFLNC\u003c/em\u003e gene is a key regulator of cardiomyocyte ultrastructure and plays an important role in maintaining the contractile force transmission pathway, and its dysfunction may be the key to driving progressive DCM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. FLNC-associated DCM is linked with a high risk of a malignant clinical course and sudden cardiac death, and its clinical spectrum indicates different pathological mechanisms related to variant types and their positions in genes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mutations in the \u003cem\u003eFLNC\u003c/em\u003e gene were first identified in myofibrillar myopathy and have since been identified in various forms of human cardiomyopathies, including DCM, hypertrophic cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic ventricular cardiomyopathy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These results suggest that different types of \u003cem\u003eFLNC\u003c/em\u003e mutations may lead to different pathogenic mechanisms. At present, there is still a lack of a systematic multi-mechanism evaluation of FLNC in DCM. Based on the sequencing of big data, we fully evaluated the possible mechanism of FLNC in DCM and the spectrum of gene variation, which deepened our understanding of the multiple effect mechanisms of FLNC in DCM and can provide guidance to future researchers. It is clear that a deep understanding of genetic variants may bring critical progress in navigating this dilemma and reversing the disease\u0026rsquo;s clinical burden.\u003c/p\u003e \u003cp\u003eThe development of next-generation sequencing technologies has opened a new era in clinical genetics and genomics, but the relationship between environmental and genetic factors in the pathogenesis of DCM is still rarely studied. To date, there has been no large-scale multicenter study published on systematic clinical screening of DCM and on families where exome or genome sequencing has been carried out to identify possible genetic causes. Therefore, based on the sequencing data of myocardial tissue and multiple databases, this paper firstly used a variety of bioinformatics analysis methods, including differential analysis, ROC curve, GO, KEGG enrichment analysis, PPI network construction, ssGSEA immune infiltration analysis. Spearman correlation analysis and ce-RNA network fully evaluated the various effector mechanisms that \u003cem\u003eFLNC\u003c/em\u003e might be involved in DCM. Subsequently, gene sequencing analysis based on 200 serum samples revealed the genetic variation and novel pathogenic mutation sites of FLNC in DCM, in order to have a more comprehensive understanding of \u003cem\u003eFLNC\u003c/em\u003egene and its possible involvement in the disease development and development of DCM, expand the expression profile of \u003cem\u003eFLNC\u003c/em\u003e gene, and provide potential targets for further research in the future.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Evaluation of \u003cem\u003eFLNC\u003c/em\u003e expression level and differentiation ability and identification of DEGs\u003c/h2\u003e\n \u003cp\u003eBox plots and ROC curves demonstrate the difference in \u003cem\u003eFLNC\u003c/em\u003e expression levels and differentiation ability between the DCM and control groups (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). The results show that the expression level of \u003cem\u003eFLNC\u003c/em\u003e was significantly higher in the DCM group than in the control group, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. \u003cem\u003eFLNC\u003c/em\u003e has good differentiation ability between the two groups (Area under the ROC Curve [AUC]\u0026thinsp;=\u0026thinsp;0.774). The results of the difference analysis based on the median value of \u003cem\u003eFLNC\u003c/em\u003e expression after grouping are shown in (\u003cstrong\u003eSupplementary File 1\u003c/strong\u003e). A volcano plot and heat map further demonstrate the results of the difference analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). In total, there are 54 DEGs, including 16 upregulated and 38 downregulated genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Enrichment analysis of DEGs\u003c/h2\u003e\n \u003cp\u003eGO and KEGG analyses were conducted on upregulated and downregulated DEGs, respectively (\u003cstrong\u003eSupplementary File 2\u003c/strong\u003e). The results show that BPs and MFs of upregulated DEGs are enriched in protein folding and action fixation binding, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The GO analysis revealed that downregulated DEGs are mainly enriched in the activated immune system response (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The KEGG pathway of upregulated DEGs is mainly enriched in the mitogen-activated protein kinase (MAPK) signaling pathway (involving \u003cem\u003eFLNC\u003c/em\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The KEGG pathway of downregulated DEGs is mainly enriched in inflammation-related pathways, such as Phagosome and Chemokine signaling pathways (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD)..\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 PPI network and gene module identification of DEGs\u003c/h2\u003e\n \u003cp\u003eTo identify DEGs with close protein interactions with the \u003cem\u003eFLNC\u003c/em\u003e gene, we constructed a PPI network and visualized it using 54 DEGs imported into the STRING database (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). In this PPI network, \u003cem\u003eFLNC\u003c/em\u003e only interacted with the upregulated DEG, \u003cem\u003eXIRP1\u003c/em\u003e. Subsequently, we individually imported \u003cem\u003eFLNC\u003c/em\u003e genes into the STRING database to build a PPI network for \u003cem\u003eFLNC\u003c/em\u003e, and the results show that 10 genes interacted with \u003cem\u003eFLNC\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Finally, we analyzed the correlations of 40 of these DEGs (16 upregulated and 24 downregulated genes) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC); \u003cem\u003eFLNC\u003c/em\u003e was negatively correlated with downregulated genes and positively correlated with upregulated genes. Negative correlations were more pronounced with \u003cem\u003eTLR3\u003c/em\u003e, \u003cem\u003eGPR34\u003c/em\u003e, and \u003cem\u003eMRC1\u003c/em\u003e (correlation =-0.57, -0.51, and \u0026minus;\u0026thinsp;0.51, respectively), whereas positive correlations were more pronounced with \u003cem\u003eTTC9\u003c/em\u003e, \u003cem\u003ePPFIA4\u003c/em\u003e, \u003cem\u003eDNAJB5\u003c/em\u003e, \u003cem\u003eXIRP1\u003c/em\u003e, \u003cem\u003eDUSP27\u003c/em\u003e, and \u003cem\u003eKIFC3\u003c/em\u003e (0.57, 0.62, 0.64, 0.54, 0.62, 0.54, respectively) (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;1\u003c/strong\u003e). Combining the results of the PPI network of DEGs, the PPI network of FLNC, and the correlation analysis of DEGs, we found that \u003cem\u003eFLNC\u003c/em\u003e only had protein interactions with the \u003cem\u003eXIRP1\u003c/em\u003e gene in the correlation analysis, whereas the interaction relationships with other genes that had significant negative and significant positive correlations still lacked evidence of correlation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.4 Immune infiltration analysis and correlation between\u003c/strong\u003e \u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eFLNC\u003c/span\u003e \u003cstrong\u003eand\u003c/strong\u003e \u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eXIRP1\u003c/span\u003e \u003cstrong\u003egenes and type of immune cell infiltration\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo identify differences in the infiltration of different immune cell types between the DCM and non-DCM groups, 81 DCM myocardial tissue samples and 32 healthy myocardial tissue samples were used in this analysis, and the results are presented in (\u003cstrong\u003eSupplementary File 3\u003c/strong\u003e). Violin plots show CD4 memory-activated T cells (P\u0026thinsp;=\u0026thinsp;0.039), gamma delta T cells (P\u0026thinsp;=\u0026thinsp;0.035), Macrophages M0 (P\u0026thinsp;=\u0026thinsp;0.039) infiltrated at a higher level in the DCM group than in the control group, while naive B cells (P\u0026thinsp;=\u0026thinsp;0.028) infiltrated at a lower level in the DCM group than in the control group (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The correlation heatmap demonstrates a correlation between \u003cem\u003eFLNC\u003c/em\u003e and \u003cem\u003eXIRP1\u003c/em\u003e in the infiltrated immune cells. The results show that \u003cem\u003eFLNC\u003c/em\u003e was positively correlated with plasma cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas \u003cem\u003eXIRP1\u003c/em\u003e was negatively correlated with naive B cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.5 Construction of ceRNA network of \u003cem\u003eFLNC\u003c/em\u003e gene\u003c/h2\u003e\n \u003cp\u003eAfter searching three miRNA databases, a total of eight miRNAs were predicted to bind to \u003cem\u003eFLNC\u003c/em\u003e (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The lncRNAs bound by miRNAs are shown in (\u003cstrong\u003eSupplementary File 4\u003c/strong\u003e). Based on the predicted results, we constructed a ceRNA network (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The network contains 38 nodes (1 FLNC, eight miRNAs, and 29 lncRNAs) and 37 edges. Specifically, we found that 29 lncRNAs could competitively bind hsa-miR-558, hsa-miR-142-3p, hsa-miR-486-3p, and hsa-miR-1972 to regulate\u0026nbsp;\u003cem\u003eFLNC\u003c/em\u003e. Among these, there were 18 lncRNAs for hsa-miR-1972. In addition, seven lncRNAs targeted hsa-miR-558, three lncRNAs targeted hsa-miR-486-3p, and one lncRNA targeted hsa-miR-142-3p.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEight miRNA bound to \u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-644a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-142-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-147a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-19b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-486-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-1972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-19a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.6 Comparison of general clinical data\u003c/h2\u003e\n \u003cp\u003eIn this study, there were 100 cases in the DCM case group and 100 cases in the control group; the age of the case group was (53.57\u0026thinsp;\u0026plusmn;\u0026thinsp;11.95) years and the age of the control group was (57.21\u0026thinsp;\u0026plusmn;\u0026thinsp;12.96) years; the male to female ratio of the case group and the control group was 1:1.17 and 1:0.35, respectively, while comparing the age, height, gender, history of hypertension, history of smoking, and history of drinking between the DCM group and the control group, the differences were statistically significant(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the differences in weight and ethnicity were not statistically significant(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of included the DCM and the control groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDCM(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e/\u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.57\u0026thinsp;\u0026plusmn;\u0026thinsp;11.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.21\u0026thinsp;\u0026plusmn;\u0026thinsp;12.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.74\u0026thinsp;\u0026plusmn;\u0026thinsp;10.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender(n)(male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81/19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46/54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity, n (zhuang %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73(73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67(67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of hypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking history, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eStatistical methods with student \u0026apos; s or chi-square test. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 means the difference is significant, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 means the difference is statistically significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e2.7 Screening \u003cem\u003eFLNC\u003c/em\u003e gene mutations and bioinformatics analysis\u003c/h2\u003e\n \u003cp\u003eThe \u003cem\u003eFLNC\u003c/em\u003e gene was analyzed by gene sequencing and screened against dbSNP, 1000genomes, ExAC and GnomAD databases to find eight unique mutations: c.1220C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Thr407Asn), c.1310G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg437Leu), c.1354G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Val452Met). Val452Met), c.2501C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Thr834Met), c.3757G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Val1253Ile), c.3790G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Gly1264Ser), c.4700G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Arg1567Gln), c.7614G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Leu2538Phe). They are respectively referred to as \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT407N\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT834M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV1253I\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR1567Q\u003c/sup\u003e and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e. A total of 25 (25%) patients with missense DCM mutations in the \u003cem\u003eFLNC\u003c/em\u003e gene coding region were screened. Sanger sequencing Further validation showed that all 8 mutations were heterozygous mutations. The results showed that two loci, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT407N\u003c/sup\u003e and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e, were not reported in the 1000G, ExAC, and GnomAD-Exomes databases and were rare mutations (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Also describe the distribution of the 8sites variants of this gene and their protein-coding domains (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). All three loci are in the ROD1 structural domain, and a search of public databases and related literature revealed that the above loci are de novo mutations, while the \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e locus is in the ROD2 region, which may lead to DCM pathogenesis due to protein dimerization and folding, not excluding that it may be the pathogenic mutation locus for hereditary cardiomyopathy.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMutation of \u003cem\u003eFLNC\u003c/em\u003e and bioinformatic\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"14\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAmino acid change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003edbSNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDCM probands (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1000G\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eExAC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003egnomAD-Exomes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eESA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Thr407Asn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1421140939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg437Leu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers370138936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Val452Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers192163925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Thr834Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers75133741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Val1253Ile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers117366477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Gly1264Ser\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers201335143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg1567Gln\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2291569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Leu2538Phe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers180834558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eList of rare coding mutations identified in the \u003cem\u003eFLNC\u003c/em\u003e gene.Frequency of rare coding mutations in the\u003cem\u003eFLNC\u003c/em\u003e gene in the 1000G (1000 Genomes Project) ALL and ESA, ExAC (Exome Aggregation Consortium) Global and Asian, gnomAD(Genome Aggregation Database) Global and Asian populations.MAF(Minor Allele Frequency):Minimum allele frequency. \u0026apos;\u0026apos;-\u0026apos;\u0026apos;stands for no cases of this locus in the population.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e2.8 Results of amino acid hazard and conservativeness analyses of mutation sites\u003c/h2\u003e\n \u003cp\u003eThe SIFT and PolyPhen-2 analysis software were used to analyze the harmfulness of the above 8 missense mutation amino acid sites, and predict whether the missense mutation (the mutation leading to the change of amino acid) would cause the protein structure or function. The results showed that the three sites were highly harmful, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e was evaluated as Damaging (0.001) by the software SIFT; \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR2567Q\u003c/sup\u003e is evaluated as Damaging (0.036) by software SIFT; \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e is evaluated as Damaging (0.002) by software SIFT, and PolyPhen-2 is evaluated as Damaging (1.000) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMutation of \u003cem\u003eFLNC\u003c/em\u003e risk assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic position\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmino acid change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSIFTanalysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolyPhen2 analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMutation Assessor analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMutation type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpact\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128478666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Thr407Asn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175(T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83(L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128478756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg437Leu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074(T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.319(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31(M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128478800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Val452Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.886(P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.425(M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128482959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Thr834Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122(T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21(M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128485276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Val1253Ile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.549(T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37(L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128485309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Gly1264Ser\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055(T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.625(P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.095(M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128488734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg1567Gln\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.684(P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.005(M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr7:128497224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Leu2538Phe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.285(M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSIFT\u0026thinsp;\u0026le;\u0026thinsp;0.05, PolyPhen-2\u0026thinsp;\u0026ge;\u0026thinsp;0.909, Mutation Assessor\u0026thinsp;\u0026ge;\u0026thinsp;1.9, suggesting that the mutation is deleterious, and the closer the Polyphen-2 score is to 1.0, the greater the likelihood of damage, and the closer it is to 0, the less likely the damage.\u003c/p\u003e\n \u003cp\u003eGERP\u0026thinsp;+\u0026thinsp;+\u0026thinsp;and Jalview analysis software were used to analyze the conservatism of amino acid sites of the above eight missense mutations. \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT407N\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT834M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV1253I\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR1567Q\u003c/sup\u003e, and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e are conserved in Homo sapiens, Pan troglodyte, Macaca mulatta, Canis lupus familiaris, Bos taurus, Mus musculus, Rattus norvegicus, Danio rerio, and Xenopus tropicalis species (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Among them, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT834M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR1567Q\u003c/sup\u003e, and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e are highly conserved among Homo sapiens, Pan troglodyte, Macaca mulatta, Canis lupus familiaris, Bos taurus, Mus musculus, and Xenopus tropicalis species.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e2.9 Clinical characteristics and sequencing kurtosis results of patients carrying \u003cem\u003eFLNC\u003c/em\u003e mutation after screening\u003c/h2\u003e\n \u003cp\u003eWe filtered the data by dbSNP database, 1000genomes database, ExAC and GnomAD database to screen out known variants, and found that both \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT407N\u003c/sup\u003e and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003esites were not reported and belonged to rare mutations; among them, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT407N\u003c/sup\u003e was evaluated to be low risk and generally conserved, and was excluded for the time being. \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT834M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR1567Q\u003c/sup\u003e and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV1253I\u003c/sup\u003e were found in both DCM and control groups and considered as polymorphic site, among which \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV1253I\u003c/sup\u003e was poorly conserved and therefore excluded; finally, four \u003cem\u003eFLNC\u003c/em\u003e variants were screened as \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003e and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e, which corresponded to sequencing numbers (53, 18, 103, 108), were all missense variants in DCM patients (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical information on patients with rare mutations in the FLNC gene\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePatient\u003c/p\u003e\n \u003cp\u003enumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003efamily history\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge of onset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArrhythmia type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eultrasonic cardiogram\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLAS(mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLVD(mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEF(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Arg437Leu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e49 62 34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Val452Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSinus tachycardia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e44 72 19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Gly1264Ser\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e55 58 66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.Leu2538Phe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVentricular, atrial tachycardia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e37 65 37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eLeft atrial end-systolic internal diameter (LAS), left ventricular end-diastolic internal diameter (LVD), ejection fraction (EF). Atrial fibrillation (AF), ultrasonic cardiogram (UCG).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e2.10 Sequencing peak map and clinical data of proband and family members\u003c/h2\u003e\n \u003cp\u003eThe \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e missense mutation of \u003cem\u003eFLNC\u003c/em\u003e carried by proband 53 of DCM (change of arginine to leucine at site 437), which was considered as a new mutation of \u003cem\u003eFLNC\u003c/em\u003e through database and literature query in the early stage, was predicted to be possibly pathogenic and highly conserved among species. The related patient was a man, who was hospitalized in our hospital in April 2018, and underwent an ECG that presented rapid atrial fibrillation, left ventricular high voltage, and abnormal TII, III, and augmented vector foot (aVF); the UCG showed an EF of 34% and a FS of 17%, and the patient was discharged after improvement with treatment. In May of the same year, he returned to the outpatient clinic for follow-up after recurrence of the disease and was reexamined: ECG rapid-type atrial fibrillation (AF) with T-wave changes; UCG: EF 23%, FS 11%. Later, the patient returned for follow-up in September 2018, and UCG was rechecked: EF 39% and FS 20%. He returned for follow-up in July 2019 and ECG findings were as follows: AF, RV5\u0026thinsp;\u0026gt;\u0026thinsp;2.5mV, and TII, III, and aVF abnormalities; UCG: EF 41% and FS 21%. DCM proband 103 carries the \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003emissense variation of \u003cem\u003eFLNC\u003c/em\u003e (change of glycine to serine at site 1264), which was pre-analyzed by the GnomAD database as a rare variant (frequency 0.000012) and predicted to be deleterious and highly conservative. The related patient was a man, who was hospitalized in our hospital in April 2019, and underwent an ECG that presented tachyarrhythmic AF, premature ventricular beats, and T wave alterations; the UCG showed: EF 66% and FS 37%. The \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e missense variation of DCM proband 108 carrying \u003cem\u003eFLNC\u003c/em\u003e (change of leucine to phenylalanine at site 2538) was analyzed as a rare variation (frequency 0.000012 in GnomAD) in the early stage, and predicted to be harmful and highly conservative. The related patient was checked in during June 2019 and underwent an ECG that presented a sinus rhythm, left atrium abnormality, and T-wave change; the UCG showed: EF37% and FS18%. Patients with mutations at the above three sites had no family genetic history. All patients were 63 years of age and had no obvious symptoms of heart failure. The risk of sudden death was low. Symptoms improved after treatment.\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e missense mutation of \u003cem\u003eFLNC\u003c/em\u003e carried by proband 18 of DCM (change of valine to methionine at site 452), in the early stage, was predicted by database analysis to be highly conservative. The related patient was 35 years of age at the time of onset. From 2015 to 2016, the patient was hospitalized, diagnosed with DCM, and received drug treatment. After treatment, the symptoms of heart failure were slightly relieved, but recurred, and the condition worsened. In March 2017, the patient visited our hospital and was found to have an EF of 26% and a FS of 12% from UCG. At that time, the symptoms of heart failure were obvious, and liver and kidney functions were affected. The patient was treated with drugs and discharged after remission. Out-of-hospital symptoms recurred, and the patient returned to the hospital in March 2018 for inpatient treatment. The patient was admitted to the hospital citing panic attacks with shortness of breath and spontaneous shortness of breath at the slightest activity, and was given an ECG (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC) that namely suggested sinus tachycardia, left atrial abnormalities, Qr type VI, and T-wave changes. At the same time, the cardiac ultrasound long-axis view of the left ventricle, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD, suggested an LA anteroposterior diameter of 44 mm and LV anteroposterior diameter of 72 mm, with ultrasound features suggesting whole heart enlargement, diffusely diminished activity, myocardial lesions, and hyposystolic and diastolic left heart function with an EF of 19% and a FS of 9%. Considering the patient\u0026apos;s clinical and other relevant examination findings,their condition was consistent with the diagnosis of DCM. Clinical symptoms were more severe in this patient than in other patients with mutations. The patient had aninfluential family history, and died at the follow-up in 2019, whose sequencing peak plot and clinical data are shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, suggesting that mutations in different \u003cem\u003eFLNC\u003c/em\u003e loci can affect the cardiac phenotype and even increase the risk of sudden death in patients. Additionally, the combination of online bioinformatics software predictions and interspecies conservativeness analysis suggests that this locus is a deleterious mutation that may play a crucial effect on the protein.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eDCM is a heterogeneous genetically-related disease that is characterized by enlarged ventricles and reduced function, often leading to heart failure and sudden cardiac death [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The prevalence of DCM may vary according to geographic and ethnic differences and diagnosis criteria. The disease progression and prognosis are driven by reverse remodeling within the heart, which makes timely and definitive heart failure treatment, which can involve drugs, devices, and procedures, essential for end-stage heart failure [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The heterogeneous etiology and clinical presentation of DCM makes a correct and timely diagnosis challenging, imposing a heavy burden on society and families. Patients with DCM benefit from current interventions and have a better clinical prognosis. However, there is still a lack of interventions that can reverse the outcome of DCM progression to heart failure.\u003c/p\u003e \u003cp\u003eFilamin-C is an actin-binding protein encoded by the \u003cem\u003eFLNC\u003c/em\u003e gene, which is located in chromosome band 7q32-q35, contains approximately 29.5 kb of genomic DNA and 48 coding exons [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and 2,725 amino acids with two transcripts (GenBank isoform b:NP_001120959.1 and isoform b:NP_001449.3), of which the long transcript (isoform a) has a molecular weight of 291 kDa. A number of Z-disk-specific proteins interact with \u003cem\u003eFLNC\u003c/em\u003e and are involved in maintaining the stability of myosin [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, \u003cem\u003eFLNC\u003c/em\u003e consists of an N-terminal actin-binding domain and 24 immunoglobulin (Ig)-like repeat sequences (R1-24), which have two mediated calpain-sensitive hinges separating R15 and R16 (H1) and R23 and R24 (H2). \u003cem\u003eFLNC\u003c/em\u003e subunits are dimerized by R24, and calpain cleaves the dimerization structural domain to regulate the migration of FLNC subunits. Due to the unique insertion of 82 amino acid residues in repeat sequence 20, \u003cem\u003eFLNC\u003c/em\u003e is localized to the Z-disc and is required for healthy Z-disc formation that connects myofascicles [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The specialized cytoskeletal myogenic fibers of cardiac myocytes provide the structural basis for the continuous and correct contraction of cardiac myocytes to facilitate circulation.\u003c/p\u003e \u003cp\u003eThe combination of high-throughput sequencing technology and bioinformatic analysis has led to breakthroughs in disease research. To date, hundreds of unique \u003cem\u003eFLNC\u003c/em\u003e variants have been associated with cardiomyopathy, including many arrhythmogenic cardiomyopathies and multiple \u003cem\u003eFLNC\u003c/em\u003e truncating mutations in DCM patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Celeghin et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] studied 270 patients with FLNC-associated cardiomyopathy and identified 12 novel \u003cem\u003eFLNC\u003c/em\u003e variants and a high incidence of sudden cardiac death, mainly associated with left ventricular fibrosis. Familial cases are common, especially involving genes that encode cytoskeletal, myosin, and nuclear membrane proteins [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Zhao et al.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] first reported the identification of 12 nonsynonymous mutations by NGS in 21 cases of DCM in Yunnan Province, China, with a high frequency of mutations in \u003cem\u003eMYH6\u003c/em\u003e and \u003cem\u003eMYBPC3\u003c/em\u003e myosin genes. In 2016, Reinstein et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] used whole-exome sequencing to investigate the genetic etiology of a family with congenital DCM and found that double allelic variants in \u003cem\u003eFLNC\u003c/em\u003e can lead to congenital DCM. In addition, Wang et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] found that more than one-third of childhood DCM cases were caused by mutations in genes related to the structure or function of myonodules, bridging granules, or the cytoskeleton, and that most deaths occurring related to \u003cem\u003eFLNC\u003c/em\u003e are connected to the structural integrity of myonodules and cellular signaling, and a better understanding of molecular alterations caused by \u003cem\u003eFLNC\u003c/em\u003e variants can elucidate potential therapeutic targets.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated the high expression of \u003cem\u003eFLNC\u003c/em\u003e in DCM based on GSE3586 microarray expression profile data, and the ROC curve indicatesthe ability to differentiate between the two groups (AUC\u0026thinsp;=\u0026thinsp;0.774). Earlier studies have shown that FLNC is mainly enhanced in cardiac and skeletal muscles, with a low level of expression in other tissues [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Cardiomyocytes express fewer filamin-C isoforms under basal conditions, but they are rapidly induced during cardiac stress [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The Human Protein Atlas database showing high \u003cem\u003eFLNC\u003c/em\u003e expression in the heart similarly validates our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Due to the wide heterogeneity of the population, we integrated four datasets, and based on DCM tissue expression profile data, we identified 54 DEGs (obtained after FLNC-based grouping), including 16 upregulated and 38 downregulated genes. The analysis of 81 DCM myocardial tissues after FLNC-based grouping shows that upregulated DEGs are enriched in protein folding and actin filament binding. FLNC is known to be an actin-binding protein with a cytoskeletal structure consisting of myosin units in which thin and thick filaments cross each other to form distinct bands; many protein interactions are the molecular basis for the formation of these myosin-connecting devices [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The analysis of \u003cem\u003eFLNC\u003c/em\u003e genes among the upregulated DEGs shows that they are mainly enriched in the MAPK signaling pathway. It has been shown that MAPK plays multiple roles in the regulation of heart failure and myocardial hypertrophy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Hong et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] found that silencing connective tissue growth factors inactivated the MAPK signaling pathway, thereby attenuating myocardial fibrosis and left ventricular hypertrophy in rats with DCM. In the present study, the MAPK signaling pathway may have had an involvement with DEGs, suggesting that the MAPK signaling pathway plays a key role in DCM. The downregulated DEGs were mainly enriched in inflammatory and immune-related pathways, such as Phagosome and Chemokine signaling pathways. Inflammation and immune involvement in the progression of cardiomyopathy have been confirmed by extensive studies, such as cytokines and chemokines secreted by infiltrative pro-inflammatory macrophages and lymphocytes, accelerating cardiomyocyte development of hypertrophy and progressive fibrotic responses, which in turn trigger extracellular matrix accumulation and fibrosis, leading to diabetic cardiogenesis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Myocardial inflammation and fibrosis play important roles in the pathogenesis of almost all forms of myocardial injury [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The correlation analysis shows that the genes significantly and positively associated with \u003cem\u003eFLNC\u003c/em\u003e were \u003cem\u003eTTC9\u003c/em\u003e, \u003cem\u003ePPFIA4\u003c/em\u003e, \u003cem\u003eDNAJB5\u003c/em\u003e, \u003cem\u003eXIRP1\u003c/em\u003e, \u003cem\u003eDUSP27\u003c/em\u003e, and \u003cem\u003eKIFC3\u003c/em\u003e, and those negatively associated with \u003cem\u003eFLNC\u003c/em\u003e were \u003cem\u003eTLR3\u003c/em\u003e, \u003cem\u003eGPR34\u003c/em\u003e, and \u003cem\u003eMRC1\u003c/em\u003e. In combination with the PPI network, \u003cem\u003eFLNC\u003c/em\u003e had protein interactions only with \u003cem\u003eXIRP1\u003c/em\u003e, a gene positively associated with closely related genes. Furthermore, cardiac intercalated discs (ICDs) are fundamental structures unique to the myocardium. FLNC is highly concentrated in cardiac ICDs, which maintain intercellular adhesion in cardiomyocytes, connect individual cardiomyocytes, and play a role in intercellular signaling [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrast, disruption of the ICDs structure and function is an important feature of many congenital and acquired heart diseases, including cardiomyopathy, arrhythmias, and heart failure [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. XIRPs have been reported to be associated with the development of hypertrophic cardiomyopathy [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. XIRPs, as muscle-specific proteins, co-localize with N-calmodulin, connexin 43, FLNC, and human vasodilator-activated phosphoprotein in adult cardiac ICDs and are involved in regulating the development and adaptive remodeling of the actin cytoskeleton in transverse myocytes, further emphasizing the importance of \u003cem\u003eFLNC\u003c/em\u003e and \u003cem\u003eXIRP2\u003c/em\u003e in cardiac remodeling [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Other genes closely related to \u003cem\u003eFLNC\u003c/em\u003e have not yet been reported and further exploration is needed. In conclusion, the study\u0026rsquo;s enrichment analyses and PPI constructs deepen our understanding of the biological functions, pathways, and proteins that interact with FLNC in DCM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution and expression of \u003cem\u003eFLNC\u003c/em\u003e gene transcriptome data in different tissues.\u003c/p\u003e \u003cp\u003eBased on 81 cases of DCM and 32 controls, we identified differences in immune infiltration between the DCM and control groups, and the results show that CD4 memory-activated T cells, gamma delta T cells, and macrophage M0 infiltration levels were higher in the DCM group than in the control group, while naive B cells in the DCM group had a lower level of infiltration than those in the control group. It is well known that immune and inflammatory responses play important roles in cardiovascular disease, and inflammation is essential in the initial development and progression of many cardiovascular diseases involving innate and adaptive immune responses. The transport of immune cells to the heart and their interaction with cardiac cells are major determinants of cardiac dysfunction [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These imbalanced T and B cell pathways in the adaptive immune system are associated with tissue remodeling due to cardiomyocyte death and fibrosis, leading to cardiac dysfunction [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Different etiologies lead to cardiac inflammation mediated by common or different types of leukocytes, as well as pathways and recruitment of immune cells to the heart, and local inflammation leads to tissue fibrosis that hardens the heart and promotes the progression of dilatation and heart failure, which is likely to be present in most patients with advanced heart failure [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. According to the characteristics of inflammatory cells/cytokines and etiology (such as infection and genetic background), the types and stages of inflammatory cardiomyopathy can be stratified [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. By improving our understanding of the inherent and adaptive immune mechanisms of DCM, we can determine potential therapeutic targets in the future and apply precise treatment to myocardial diseases.\u003c/p\u003e \u003cp\u003eNon-coding RNA also plays an important role in the occurrence and development of DCM [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. At present, the evaluation of non-coding RNA that can bind to \u003cem\u003eFLNC\u003c/em\u003e is still lacking, which would provide guidance for the epigenetic regulation and pathogenesis of DCM involving non-coding RNA through the \u003cem\u003eFLNC\u003c/em\u003e gene. In the ce-RNA network of FLNC we constructed, eight FLNC-binding miRNAs were predicted, and the occurrence and development of many diseases are influenced by miRNAs. Among them, hsa-miR-558 and hsa-miR-1972 have been shown to regulate cancer progression. Studies have shown that hsa-miR-558 promotes tumorigenesis and invasiveness of gastric cancer cell lines in vitro and in vivo by reducing Smad4-mediated inhibition of heparanase expression [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Chen et al. showed that extracellular vesicles derived from fibroblast-like synoviocytes in rheumatoid arthritis accelerate endothelial angiogenesis through the p53/mTOR pathway via hsa-miR-1972 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, in the FLNC ce-RNA network constructed in this study, there are few reports on miRNAs and lncRNAs that play epigenetic roles in combination with \u003cem\u003eFLNC\u003c/em\u003e genes.\u003c/p\u003e \u003cp\u003eIn summary, we conclude that \u003cem\u003eFLNC\u003c/em\u003e is highly expressed in myocardial tissue, which suggests that \u003cem\u003eFLNC\u003c/em\u003e expression is closely related to the integrity of healthy cardiac structure and function, and that its mutation leads to a series of pathological changes that result in myocardial damage and reduced cardiac function. Through differential analysis and PPI networks, we identified genes that are positively and negatively associated with \u003cem\u003eFLNC\u003c/em\u003e genes that are highly expressed in the myocardium, and these genes with co-expression properties with \u003cem\u003eFLNC\u003c/em\u003e can deepen our understanding of the position of \u003cem\u003eFLNC\u003c/em\u003e in the heart and the functions in which it is involved. Functional enrichment studies have shown that the \u003cem\u003eFLNC\u003c/em\u003e high expression group is more associated with actin folding, while \u003cem\u003eFLNC\u003c/em\u003e is negatively associated with inflammatory immunity. Although numerous studies have now shown that inflammation and immunity are strongly associated with the progression of DCM, our study suggests that DCM due to \u003cem\u003eFLNC\u003c/em\u003e mutations may involve less inflammation and immunity and more direct structural changes in the heart, leading to cardiac enlargement and reduced cardiac function. The immune infiltration analysis results in the present study further suggest that \u003cem\u003eFLNC\u003c/em\u003e has little correlation with immunity and inflammation. Meanwhile, the correlation analysis of DEGs and the ce-RNA network of \u003cem\u003eFLNC\u003c/em\u003e revealed that some genes and non-coding RNAs closely related to \u003cem\u003eFLNC\u003c/em\u003e still need to be researched further, and more scholars investigatingthese genes and non-coding RNAs can deepen our knowledge of the pathogenic mechanism of \u003cem\u003eFLNC\u003c/em\u003e. The focus of this study was to identify new deleterious mutation loci in \u003cem\u003eFLNC\u003c/em\u003e through blood samples from 200 patients, which expands the variant profile of \u003cem\u003eFLNC\u003c/em\u003e in DCM. Our subsequent studies will further evaluate the genes and non-coding RNAs that are closely related to \u003cem\u003eFLNC\u003c/em\u003e to better understand the pathogenic mechanisms of \u003cem\u003eFLNC\u003c/em\u003e in DCM and to guide drug development and treatment.\u003c/p\u003e \u003cp\u003eFinally, based on serum samples, four DCM missense mutation sites were identified in this study. Among them, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e, and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003e are all in the ROD1 domain. The \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003e sites are predicted to be deleterious and may interfere with protein dimerization and folding or even cause cardiomyopathy. Previous studies have shown that few missense mutation sites occur in the ROD1 structural domain, but the new heterozygous mutation, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e, and the observation of \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e on Ig-like structural domain 23, both of which were found in this study, are missense mutations. Participants who carried the \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e mutation all exhibited varying degrees of malignant arrhythmias, with it pre-documented in both individuals and families, which eventually led to death. Thus, it can be considered as a possible causative mutation site for hereditary cardiomyopathy. An elevated level of myocardial fibrosis and conduction abnormalities observed on surface ECGs could explain the significantly increased risk of ventricular arrhythmias (\u0026gt;\u0026thinsp;80%) and sudden cardiac death in participants who carried an \u003cem\u003eFLNC\u003c/em\u003e mutation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have shown that \u003cem\u003eFLNC\u003c/em\u003e mutations are transmitted in an autosomal dominant manner, leading to a typical cardiomyopathy caused by \u003cem\u003eFLNC\u003c/em\u003e truncating mutations as a result of clinical skeletal myopathy, and the phenotype manifests as an overlap between dilated and arrhythmogenic cardiomyopathy, characterized by varying degrees of left ventricular dilatation and systolic dysfunction, significant subepicardial or left ventricular intramyocardial fibrosis that leads to frequent ventricular arrhythmias, and sudden cardiac death [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Bains et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] reported a case of arrhythmogenic bileaflet mitral valve prolapse syndrome in a 51-year-old man with a similar phenotype existing in the mother, aunt, and brother of this preexisting patient. A new truncated variant \u003cem\u003eFLNC\u003c/em\u003e c.201G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Trp34-\u003cem\u003eFLNC\u003c/em\u003e) was identified in whole exome sequencing data, presumably with FLNC haploinsufficiency as a potential arrhythmogenic substrate, exacerbated by mitral valve prolapse. Another report described familial sudden cardiac death without signs of cardiomyopathy, again associated with a truncated variant of \u003cem\u003eFLNC\u003c/em\u003e (p.Pro2513Glufs*12) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Both cases highlight the arrhythmogenic potential associated with a truncated variant of \u003cem\u003eFLNC\u003c/em\u003e. The present study further expanded the gene variant profile of FLNC in patients with cardiomyopathy from a minority of areas in Guangxi. In light of the small sample size of this study, future models can be established at the cellular and animal levels, if necessary, to further explore the pathogenesis of these mutations, with the aim of providing theoretical support for the early diagnosis and subsequent precise treatment of DCM. Additionally, further studies are needed to confirm our findings.\u003c/p\u003e \u003cp\u003eCardiomyopathies are life-threatening diseases in which DCM is the ultimate phenotype of multiple mutations in heterogeneous pathways, including components of the membrane scaffolding apparatus, myosin, nuclear membrane proteins, calcium-processing proteins, transcription factors, and RNA splicing to cellular energy production mechanisms. Although these mutations are highly diverse in the affected pathways, they share the common features of impaired contraction and insurmountable cellular damage, leading to cell death and fibrotic repair, and ultimately contributing to cardiac thinning and dilatation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. FLNC is considered a key factor in the progression of different types of cardiomyopathies; therefore, determining the genetic and physiological roles of FLNC is necessary to determine the onset and progression of the disease. A comprehensive analysis of the genetic and molecular causes of the disease can help with obtaining an accurate diagnosis, especially for diseases with unclear clinical findings or pathologies that overlap with other diseases. Recently, J. Haas et al. [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] dissected the genetic causes of DCM by using NGS methods and established a comprehensive approach to clinical genetic testing of all currently known disease genes, not only for clinically relevant DCM genes but also for genes causing other inherited cardiomyopathies, and to systematically test NGS as a new technology for analytical performance in a wide range of clinical applications. Meanwhile, with the development of DCM in ex vivo experiments over the years, Karakikes et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] found that patient-specific human induced pluripotent stem cell (IPSC) lines can be successfully used as a research platform for cardiovascular disease, providing an excellent tool for in vitro modeling and elucidating potential disease mechanisms. However, the lack of isogenic IPSC DCM models limits our understanding of pathogenesis, and in 2018, Ma et al. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] showed that pathogenicity of genomic variants of uncertain significance could be determined using CRISPR/Cas9 and IPSCs. This technique is one of the currently emerging methods and many studies have already identified patients carrying \u003cem\u003eFLNC\u003c/em\u003e gene variants, based on the principle that CRISPR/Cas9 editing establishes homozygous human IPSC lines carrying \u003cem\u003eFLNC\u003c/em\u003e mutations to mimic DCM, and the generated IPSC lines show normal karyotypes, express pluripotency markers, and exhibit trilineage differentiation potential in vitro [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. As the \u003cem\u003eFLNC\u003c/em\u003e mutation DCM modeling technology gradually matures and more loci are investigated, it will provide us with a deeper understanding of the mechanisms of \u003cem\u003eFLNC\u003c/em\u003e, thus facilitating the development of precision medicine in this emerging field.\u003c/p\u003e \u003cp\u003eWhile the exact mechanisms by which \u003cem\u003eFLNC\u003c/em\u003e mutations cause different and partially overlapping cardiac phenotypic characteristics remain unclear, \u003cem\u003eFLNC\u003c/em\u003e mutations are currently one of the most common causes of hereditary DCM and exist in and are related to various forms of human cardiomyopathy. A number of existing studies have shown that \u003cem\u003eFLNC\u003c/em\u003e mutations can affect the heart phenotype and thus lead to an increased risk of disease, while individuals who are \u003cem\u003eFLNC\u003c/em\u003e variant carriers have missense mutations, highlighting the potential necessity of genetic testing for individuals and families. Specifically, complex genetic heterogeneity, including haploid deficiency or functional loss variants produced thereby, will affect other phenotypic attributes, such as chamber hypertrophy and expansion, and ultimately increase the risk of arrhythmia and even sudden death, which will contribute to the practice of cardiovascular genetic medicine and our understanding of the prevalence and pathogenesis of DCM.\u003c/p\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eIn conclusion, we identified the biological functions, signaling pathways, immune correlations, and ce-RNA networks involved in the pathogenic mechanisms of DCM by sequencing myocardial tissue samples with big data analysis. By sequencing the \u003cem\u003eFLNC\u003c/em\u003e gene in blood samples, we are able to discuss the characteristics of \u003cem\u003eFLNC\u003c/em\u003e gene variants in cardiomyopathy patients, expand the regional cardiomyopathy \u003cem\u003eFLNC\u003c/em\u003e gene variant profile, identify cardiomyopathy-associated variants in \u003cem\u003eFLNC\u003c/em\u003e, and provide a brief overview of the mutational sites of these genes, finding that they are involved in the progression of FLNC-associated DCM mainly through different mechanisms. To improve our understanding of the genomic basis of DCM, more extensive genomic studies will need to be conducted on a wider scale, involving individuals with strict phenotypic pre-documentation or their families and to further mechanistically identify the pathways that FLNC may be involved in. This would further offer the prospect of genetic stratification for molecular therapy and precision medicine for patients with cardiomyopathy, which ultimately may provide potential new avenues for treatment.\u003c/p\u003e"},{"header":"5 Materials And Methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Gene Expression Omnibus (GEO) dataset\u003c/h2\u003e \u003cp\u003eWe downloaded five microarray expression profile datasets (GSE3586, GSE84796, GSE29819, GSE42955, and GSE120895) associated with DCM from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GSE3586, based on the GPL3050 platform, contained 13 DCM myocardial tissue samples and 15 controls (\u003cb\u003eSupplementary File5\u003c/b\u003e). It was used to assess differences in expression levels and the ability to differentiate the \u003cem\u003eFLNC\u003c/em\u003e gene between the DCM and control groups. In addition, we obtained 81 DCM myocardial tissue samples and 32 healthy myocardial tissue samples from four datasets: GSE84796 (10 DCM and 7 controls), GSE29819 (12 DCM and 12 controls), GSE42955 (12 DCM and 5 controls), and GSE120895 (47 DCM and 8 controls) (\u003cb\u003eSupplementary File 6\u003c/b\u003e), based on the GPL14550 and GPL570 platforms. Non-DCM samples in two of the datasets (12 ischemic cardiomyopathy samples in GSE42955 and 12 arrhythmogenic right ventricular cardiomyopathy samples in GSE29819) were removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Data processing and recognition of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eThe GSE3586 dataset, which was standardized using the R software \"limma\" package (\u003cb\u003eSupplementary File 7\u003c/b\u003e), was used to assess the expression level and differentiation ability of the \u003cem\u003eFLNC\u003c/em\u003e gene. Box plots and the curve of ROCs were applied to present the results separately. Subsequently, the four datasets (GSE84796, GSE29819, GSE42955, and GSE120895) were merged, batch corrected, normalized, and analyzed for differences using the \"sva\" and \"limma\" packages of R software [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The details were as follows:1. Obtained the mean value of gene expression with the same gene symbol in different tissue samples. 2. The \"ComBat\" function was used to eliminate the batch effect between different tissue samples. 3. The \"normalizeBetweenArrays\" function was used to standardize data. Subsequently, in the four pretreated datasets, 32 healthy myocardial tissue samples were obtained (\u003cb\u003eSupplementary File 8\u003c/b\u003e), as well as 81 DCM myocardial tissue samples based on the median value of \u003cem\u003eFLNC\u003c/em\u003e gene expression (low and high) of the two groups. Finally, based on the filter conditions of log fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 and adjusted \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, DEGs (including upregulated and downregulated genes) between the two groups were identified. The above results were further visualized by the \"pheatmap\" and \"ggplot2\" packages of R software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eUpregulated and downregulated DEGs identified based on \u003cem\u003eFLNC\u003c/em\u003e gene grouping were used to perform KEGG and GO enrichment analyses. Both enrichment methods were performed using the R software ''clusterProfiler'' and \"enrichplot\"packages [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. According to the GO analysis, gene sets were involved in biological processes (BPs), cellular components, and molecular functions (MFs), and the KEGG analysis highlighted the signaling pathways in which gene sets are involved. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 PPI network and gene module identification\u003c/h2\u003e \u003cp\u003eTo identify proteins that interact closely with \u003cem\u003eFLNC\u003c/em\u003e and are associated withDEGs, all DEGs and \u003cem\u003eFLNC\u003c/em\u003e genes were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to construct two PPI networks. The filter condition was \"Homo sapiens\" and the \"minimum required interaction score\" was assigned to \"medium confidence (0.40)\". Cytoscape v3.8.2 software was used to visualize the PPI networks. Finally, correlations of 40 DEGs (including 16 upregulated and 24 downregulated genes) were evaluated and visualized using the \"Spearman\" correlation and \"corrplot\" package of R software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Analysis of immune cell infiltration and its correlation with \u003cem\u003eFLNC\u003c/em\u003e and hub genes\u003c/h2\u003e \u003cp\u003eAfter merging and preprocessing the four datasets, expression profile files (containing 81 DCM myocardial tissue samples and 32 healthy myocardial tissue samples) were used to perform the immune infiltration analysis. The analysis method and sample files for immune infiltration were based on the \"CIBERSORT\" package of R software and 22 immune cell types of the \"LM22\" file (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/index.php\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differences in infiltration levels of the 22 immune cell types in the DCM group and the non-DCM control group were demonstrated using violin plots. A \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was assumed to indicate a significant difference. Finally, the correlation between \u003cem\u003eFLNC\u003c/em\u003e and \u003cem\u003eXIRP1\u003c/em\u003e genes and infiltrated immune cell types was demonstrated using correlation heatmaps based on the \"Spearman\" correlation and \"corrplot\" package of R software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Prediction of ce-RNA network of \u003cem\u003eFLNC\u003c/em\u003e gene\u003c/h2\u003e \u003cp\u003eThe miRanda (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.microRNA.org\u003c/span\u003e\u003cspan address=\"http://www.microRNA.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), miRDB (MicroRNA Target Prediction Database), and TargetScan (TargetScanHuman 7.2) databases were used to predict \u003cem\u003eFLNC\u003c/em\u003e gene mRNA\u0026ndash;miRNA interactions. Among these, miRNAs that could be predicted in all three databases were retained. In addition, the spongeScan database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://spongescan.rc.ufl.edu/\u003c/span\u003e\u003cspan address=\"http://spongescan.rc.ufl.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to further predict lncRNAs bound by screened miRNAs. Subsequently, based on the results of the two-step prediction, mRNA-miRNA-lncRNA relationship files were collated for subsequent analysis. Finally, Cytoscape v3.8.2 software was used to visualize the three-way relationship/ce-RNA network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.7 Ethics and Consent to Participate\u003c/h2\u003e \u003cp\u003eInvolved in this study were 100 patients with DCM from the Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi Province, and 100 patients comprising the non-DCM control group; the mean age of the DCM group was 53.57\u0026thinsp;\u0026plusmn;\u0026thinsp;a standard deviation of 11.95 and the mean age of the control group was 57.21\u0026thinsp;\u0026plusmn;\u0026thinsp;12.96. Sample size was based on estimations by power analysis with a level of significance of 0.05 and a power of 0.97.All subjects were assigned to experimental groups using simple randomization.All subjects were unaware of the experimental grouping and outcome assessment.The study protocol and collection of peripheral venous blood were approved by the Medical Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (YYFY-LL-2016-001) and were in accordance with the principles of the Declaration of Helsinki. Participants and their families signed an informed consent form.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.8 Sample Collection\u003c/h2\u003e \u003cp\u003eIn this study, data from 100 DCM patients (mean age 53.57\u0026thinsp;\u0026plusmn;\u0026thinsp;11.95, 81 men [81%] and 19 women [19%]) of the Zhuang population in Guangxi, China were collected. DCM diagnosis is based on DCM guidelines diagnostic criteria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. That is, clinical manifestations have objective evidence of ventricular enlargement and reduced myocardial systolic function: 1. Men and women with left ventricular end diastolic diameters\u0026thinsp;\u0026gt;\u0026thinsp;5.0 cm and \u0026gt;\u0026thinsp;5.5 cm, respectively, or \u0026gt;\u0026thinsp;117% of the predicted value of age and body surface area, i.e. twice SD\u0026thinsp;+\u0026thinsp;5% of the predicted value). 2. Left ventricular ejection fraction (EF)\u0026thinsp;\u0026lt;\u0026thinsp;45% and fractional shortening (FS)\u0026thinsp;\u0026lt;\u0026thinsp;25%.3. Other diseases causing myocardial damage, such as hypertension, coronary heart disease, valvular heart disease, congenital heart disease, alcoholic cardiomyopathy, tachycardic cardiomyopathy, pericardial disease, systemic disease, pulmonary heart disease, and neuromuscular disease, were excluded at the time of onset. In parallel, 100 healthy samples were collected as the control group (mean age 57.21\u0026thinsp;\u0026plusmn;\u0026thinsp;12.96, 46 men [46%] and 54 women [54%]). Patients with severe organic heart disease and a family history of heart disease were also excluded. The general clinical information of all enrolled patients was collected, including sex, age of onset, genetic family history, and surgical history. ECG and chest radiography were routinely performed for auxiliary diagnoses, and transthoracic echocardiography (UCG) was used for clear diagnoses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.9 Gene sequencing and bioinformatics analysis of \u003cem\u003eFLNC\u003c/em\u003e gene clinical samples\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFLNC\u003c/em\u003e sequencing was performed by extracting peripheral venous blood genomic DNA from 100 DCM individuals and 100 non-DCM individuals. Single nucleotide variants (SNVs) were annotated using the hg38/GRCh38 (NM_001458) version as the reference sequence. All mutations obtained were compared with the Database for Single Nucleotide Polymorphisms and Other Classes of Minor Genetic Variation (dbSNP) and the 1,000 Genomes database, and DCM SNVs were compared with control SNVs to obtain case-specific newly identified or rare \u003cem\u003eFLNC\u003c/em\u003e missense mutation loci. Mutation frequencies were detected using the following four public databases:dbSNP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/projects/SNP\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/projects/SNP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), 1,000 Genomes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.1000genomes.org\u003c/span\u003e\u003cspan address=\"http://www.1000genomes.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Exome Aggregation Consortium (ExAC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://exac.broad-institute.org\u003c/span\u003e\u003cspan address=\"http://exac.broad-institute.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Genome Aggregation Database (GnomAD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gnomad.broadinstitute.org\u003c/span\u003e\u003cspan address=\"http://gnomad.broadinstitute.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Mutation sites of corresponding samples were verified by Sanger sequencing. SIFT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sift.jcvi.org/\u003c/span\u003e\u003cspan address=\"http://sift.jcvi.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), PolyPhen-2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genetics.bwh.harvard.edu/pph/\u003c/span\u003e\u003cspan address=\"http://genetics.bwh.harvard.edu/pph/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Mutation Assessor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mutationassessor.org/r3/\u003c/span\u003e\u003cspan address=\"http://mutationassessor.org/r3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software were used to predict missense mutations. Based on \u003cem\u003eFLNC\u003c/em\u003e sequences in the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/align/A\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/align/A\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an analysis of the conservativeness of each locus took place using genomic evolutionary rate scoring (GERP++, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mendel.standford.edu/SidowLab/downloads/gerp/\u003c/span\u003e\u003cspan address=\"http://mendel.standford.edu/SidowLab/downloads/gerp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Clustal Omega (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/Tools/msa/clustalo/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/Tools/msa/clustalo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Jalview (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.omicshare.com/forum/thread-3141-1-1.html\u003c/span\u003e\u003cspan address=\"http://www.omicshare.com/forum/thread-3141-1-1.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.10 Clinical characteristics and Sanger sequencing results of screening patients with \u003cem\u003eFLNC\u003c/em\u003e gene mutation\u003c/h2\u003e \u003cp\u003eSequencing data were screened for the presence of the causative mutation in all patients with clinical symptoms or corresponding abnormal ancillary tests in the family line, and the absence of the mutation in healthy individuals in the family line. This was followed by screening for the presence of variants in the causative gene associated with the \u003cem\u003eFLNC\u003c/em\u003e gene, excluding the reported variants, and selecting the unreported loci as key candidate variants for causation. General data and imaging findings, including ECG, cardiac ultrasound, and sequencing peaks, were collected and collated from screened patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.11 Statistics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll statistical analyses in this study were performed using the SPSS 24.0 software package and R software 4.2.1. For measurement data, each mean and standard deviation were calculated using the chi-squared test. Bivariate statistics were used in all analyses, and differences were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/\u003cstrong\u003eSupplementary material\u003c/strong\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declares that there is no conflict of interest regarding the publication of this paper.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC-YC was engaged to write and conceptualize the original draft. C-YC and BH \u0026nbsp;were responsible for methodology. D-YY,L-PQ, C-BL, YL, Q-JW, L-FZ, J-JM and X-SP were responsible for software. J-JH and LL were responsible for reviewing and editing. The published version of the work has been reviewed and approved by all authors.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis study was sponsored by funds from the Chinese National Natural Science Foundation of China (81560076),Guangxi Natural Science Foundation Youth Program (2018JJB140358), The First Batch of High-level Talent Scientific Research Projects of the Affiliated Hospital of Youjiang Medical University for Nationalities in 2019 (R20196316), Middle-aged and Young Teachers in Colleges and Universities in Guangxi Basic Ability Promotion Project (2021KY0534), Guangxi Postgraduate Education Innovation Program Project (YCSW2022455), General project of Guangxi Natural Science Foundation\u0026nbsp;\u003c/em\u003e\u003cem\u003e(2022JJA140070) and Training Program for Thousands of Young and Middle-aged Backbone Teachers in Guangxi Higher Education Institutions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIt was the GEO network that helped make this studypossible. We are grateful for the freely available data from the GEO network.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Supplementary Material for this article can be found online at:https://dataview.ncbi.nlm.nih.gov/object/PRJNA902539?reviewer=r6di1on61d3kfb5l61gfi6gvq6.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eMcNally E. M, Mestroni L. Dilated Cardiomyopathy. Circulation Research. (2017) 121:731\u0026ndash;748. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCRESAHA.116.309396\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMcKenna WJ, Maron BJ, Thiene G. Classification, Epidemiology, Global Burden of Cardiomyopathies. Circ Res. 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The Innovation.(2021) 2:100141. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.xinn.2021.100141\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"FLNC, dilated cardiomyopathy, bioinformatics analysis, gene sequencing, gene variants","lastPublishedDoi":"10.21203/rs.3.rs-2795537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2795537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eIn recent years, the\u003cem\u003e FLNC \u003c/em\u003egene has been shown to participate in dilated cardiomyopathy (DCM) through different mechanisms, and its variants are a common cause of hereditary DCM. This study aimed to systematically evaluate multiple FLNC effect mechanisms in DCM and to expand the spectrum of \u003cem\u003eFLNC\u003c/em\u003e gene variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eBased on five microarray expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, comprising DCM tissue and healthy control groups, the difference in \u003cem\u003eFLNC\u003c/em\u003e gene expression levels between the two groups was evaluated. Subsequently, differentially expressed genes (DEGs) among 81 DCM tissues were identified based on \u003cem\u003eFLNC\u003c/em\u003e grouping, and gene ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, correlation analysis, and protein–protein interaction (PPI) network construction were conducted for DEGs. Based on single-sample Gene Set Enrichment Analysis method, we then evaluated differences in immune infiltration levels between the two groups using ''student 's'' and the correlation between \u003cem\u003eFLNC\u003c/em\u003e gene expression.and the immune cells we using '' Spearman's correlation '' methods. Then, we constructed a ce-RNA network of \u003cem\u003eFLNC\u003c/em\u003e based on several databases.Finally,100 blood samples from DCM and non-DCM individuals from the Guangxi Zhuang population in China were selected for \u003cem\u003eFLNC\u003c/em\u003e gene sequencing, case-specific newly discovered or rare \u003cem\u003eFLNC\u003c/em\u003e gene mutation sites were screened, and the clinical information of patients with \u003cem\u003eFLNC\u003c/em\u003e gene mutations and their families were collected for Sanger sequencing verification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u003cem\u003eFLNC\u003c/em\u003e expression was significantly higher in the DCM group than in the control group. After grouping 81 DCM tissues according to median \u003cem\u003eFLNC\u003c/em\u003e expression levels, 54 DEGs were identified. The enrichment analysis shows that downregulated DEGs were more associated with inflammation and immunity, while upregulated DEGs involved actin and mitogen-activated protein kinase signaling pathways. The correlation analysis of DEGs and the PPI network identified genes associated with \u003cem\u003eFLNC\u003c/em\u003e. According to the immune infiltration analysis, the DCM group was more associated with immunity, and the infiltrating plasma cells had a strong correlation with the \u003cem\u003eFLNC\u003c/em\u003e gene; we identified eight miRNAs and 29 lncRNAs that bind to the \u003cem\u003eFLNC\u003c/em\u003e gene. The final gene sequencing results show that a total of eight \u003cem\u003eFLNC\u003c/em\u003e-specific missense mutations were detected, among which \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT407N\u003c/sup\u003e and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e are rare mutations. Additionally, the mutation frequency and minimum allele frequencies determined by sequence comparison were higher than those of databases such as the 1,000Genomes database, and all were predicted to be harmful mutations by SIFT, PolyPhen-2, and Mutation Assessor software. \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR437L\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eT834M\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eG1264S\u003c/sup\u003e, \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eR1567Q\u003c/sup\u003e, and \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eL2538F\u003c/sup\u003e mutations are highly conserved among different species and were verified as heterozygous mutations by Sanger sequencing, while \u003cem\u003eFLNC\u003c/em\u003e\u003csup\u003eV452M\u003c/sup\u003e may be the pathogenic site of DCM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The data analysis of myocardial tissue samples and the mutation analysis of DCM serum samples provides a rich perspective for exploring the biological functions, molecular mechanisms, immune cell correlations, ceRNA networks, and pathogenic gene mutation sites connected to \u003cem\u003eFLNC\u003c/em\u003e in DCM.\u003c/p\u003e","manuscriptTitle":"Multiple effect mechanisms of FLNC in dilated cardiomyopathy based on genetic variants, transcriptomics, and immune infiltration analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-12 14:53:21","doi":"10.21203/rs.3.rs-2795537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"269a1321-2147-474b-992d-62c6f32b9daf","owner":[],"postedDate":"April 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-04-22T18:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-12 14:53:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2795537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2795537","identity":"rs-2795537","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}