Exploring the potential connection between myocardial cells and peripheral blood in patients with sepsis via bioinformatics method

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In our previous research, we revealed that some mitochondrial dysfunctions occur synchronously in the peripheral blood of sepsis patients and affect mortality with inflammatory and other related genes. However, these mitochondrial dysfunctions are not described in detail. Whether mitochondrial dysfunction affects the mortality of sepsis patients as an independent risk factor still needs to be further validated. Objects and methods In our study, we aimed to present the co-varied genes and pathways related to mitochondrial and aerobic respiratory function in myocardium and peripheral blood of sepsis patients, and to verify their effects regarding the mortality of sepsis. We applied weighted gene co-expression network analysis(WGCNA)to generate different modules from myocardium and blood datasets, and subsequent enrichment analysis was used to identify the mitochondrial-and aerobic respiratory-related modules. We obtained the co-varied differential expressed genes(DEGs)from the modules to separate sepsis patients into different subgroups and compare the survival rate between them. Machine learning algorithms were applied for mortality predictive model construction and validation. Results Blue and magenta modules in blood and blue modules in the myocardium were identified as being related to mitochondrial and aerobic respiratory function. There was a strong overlap in gene expression and pathways between these modules, and DEGs from them separated sepsis patients into two groups, but there was no statistical difference in mortality between the different groups(p-value=0.078). However, models generated from these DEGs performed well in mortality prediction. Conclusion Our research has found that some genes and pathways associated with mitochondrial aerobic respiratory dysfunction are generally altered in myocardium and peripheral blood, and the changes of these related genes can reflect the severity and mortality of sepsis. Therefore, we can expect the application prospect of these mitochondria-related genes as biomarkers of infectious cardiomyopathy. mitochondrial dysfunction aerobic respiratory septic cardiomyopathy gene expression profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Septic cardiomyopathy is a reversible suppressed condition of the myocardium caused by sepsis or septic shock.Approximately 30–60%of sepsis patients may suffer from myocardium damage to some extent, particularly newborns. Septic cardiomyopathy results in negative septic effects, with the mortality of sepsis increasing from 20%to 70–90%when cardiovascular complication occurs( 1 , 2 ). The main clinical manifestation of septic cardiomyopathy(SCM)consists of the suppression of myocardium contraction, a decrease in left ventricular ejection fraction(LVEF), and the dilation of peripheral vessels and ventricles[3]. The pathogenic mechanism of SCM is not fully understood, and infection-related inflammatory reactions (endotoxin, cytokines, nitric oxygen system) and mitochondrial dysfunction (mitochondrial and aerobic respiratory) are considered to be the main adverse factors causing this myocardium disorder ( 3 , 4 , 5 , 6 ). Our previous research attempted to present the pathways and genes commonly changed in both myocardium and peripheral blood ( 7 ). Multiple pathways relating to infection-related inflammatory reaction and mitochondrial dysfunction were discovered, and we constructed a gene model that was proven to be related to the mortality of sepsis patients.However, although we initially observed mitochondrial function-related genes, following machine learning screening, the genes ultimately included in the model were still mainly those related to inflammation. Thus, despite widespread changes in mitochondrial function–related genes reported in the myocardium ( 8 ), and we proved that the same change occurred in sepsis patients’peripheral blood. We were unable to demonstrate that the mitochondrial dysfunction that occurred in common had a profound impact on the mortality of sepsis or septic shock as septic cardiomyopathy. This result determines whether mitochondria-related markers have the potential to be biological markers and to predict the prognosis of septic cardiomyopathy. Mitochondrial dysfunction is widespread in the blood of patients with sepsis and may be associated with multiple adverse complications, such as acute respiratory distress syndrome (ARDS) ( 9 ). In this paper, bioinformatics and machine learning methods were applied,and we aimed to determine whether mitochondrial dysfunction in peripheral blood has potential effects on septic cardiomyopathy. Materials and methods Patients and datasets Three datasets (GSE79962,GSE65682,GSE54514) were obtained in our study, with all RNA sequencing data and clinical information coming from the Gene Expression Omnibus(GEO)database. GSE79962 contains 51 myocardium samples, comprising 20 samples of SCM, 11 samples of ischemic heart disease(IHD), 11 samples of dilated cardiomyopathy(DCM),and nine samples of non-failing heart(NFH). In the blood dataset GSE65682, we obtained 802 whole blood samples for sepsis patients(n = 760) and healthy controls (n = 142). In total, 468 sepsis samples with 28 days of survival data were studied as a training dataset and internal validation dataset for model construction (sepsis survivors[78.0%,365/468], sepsis non-survivors[22%,103/468]). The GSE54514 dataset, which included 163 PAX gene samples from sepsis survivors(n = 26), non-survivors(n = 9), and healthy controls(n = 18), was used as an external validation dataset. The whole-blood samples were collected daily for up to 5 days from patients admitted to the intensive care unit with sepsis. Differently expressed gene(DEG)identification and weighted gene co-expression network analysis(WGCNA) The Limma package in R was applied to finish the DEG identification( 10 ). Gene significance was defined as Student’s t -test statistic for testing differential expression between different groups. In the peripheral blood dataset, we compared the septic group to the healthy control. In the myocardium dataset, we compared the septic group to NFH group. We set p-value < 0.001 as a criterion for DEG selection in peripheral blood. In the myocardium dataset, we set the p-value < 0.05 cutoff to obtain more DEGs for the next step. DEGs obtained from myocardium and peripheral blood were stepped into WGCNA separately( 11 ). Modules were identified in the resulting dendrogram using the Dynamic Tree Cut algorithm. Modules with similar expression profiles were merged at the threshold of 0.25. While the modules were confirmed, we tested the correlation between modules and gene significance using Student’s t -test statistic, and modules with statistical significance were kept and incorporated into enrichment analysis. Enrichment analysis The Gene Omnibus(GO)database was used to finish enrichment analysis for the retained DEGs in modules and to determine the biological process, molecular function, and cellular component pathways that would be dysregulated in the blood and myocardium of septic shock patients, with a p-value < 0.05 and a q-value < 0.05 indicating a significantly enriched term. Mitochondrial-and aerobic respiratory-related modules were identified in this procedure. Gene set enrichment analysis(GSEA)was applied to the modules for the mitochondrial and aerobic respiratory function we were interested in, using the Reactome database. The clusterProfiler package in R and ClueGo in Cytoscape were applied for the enrichment analysis( 12 ). Crosstalk ofgenes andpathways between myocardium andperipheral blood Next, visualizations of multi-set were performed using the Upset package in R( 13 ). The crosstalk of both DEGs and pathways from enrichment analysis is presented in Fig. 2 , showcasing the intersection of genes and pathways among different modules. Among different modules,the crosstalk of the inflammatory reaction was much more complicated than that of mitochondrial and aerobic respiratory dysfunction. Despite focusing on mitochondrial and aerobic respiratory dysfunction in our study, we still applied CYBERSORT, an algorithm to assess the proportions of 22 immune cell subtypes based on the expression file, to present the immune filtration condition between the septic group and healthy control in both blood and myocardium. The perm parameter was set at 1,000. Samples with p < 0.05 in the CYBERSORT analysis result were used in this analysis( 14 ). K-means clustering analysis In the peripheral blood dataset, we combined the DEGs in the blue and magenta modules together and then intersected them with the DEGs in the blue module of myocardium. Finally, we obtained 135 genes and used them for k-means clustering analysis for the 468 sepsis patients with survival data in the GSE65682 dataset. The number of clusters was determined by the Elbow Method and the Average Silhouette Method.Afterward, Kaplan–Meier(K–M)survival analysis was performed between the two groups. A package of NbClust in R was used for the clustering, and a survival package was used for survival analysis( 15 , 16 ). Expression ofgenes and pathways related to mitochondrial and aerobic respiratory dysfunction in peripheral blood and myocardium In the deviation dataset GSE65682, we used Student’s t -test to compare the 135 DEGs between surviving patients and non-surviving patients by setting p-value < 0.05. A total of 51 genes were verified as related to mortality in the sepsis patient group. Heatmap of the 51 genes showcased the expression in different groups. Single sample gene set enrichment analysis(ssGSEA)was applied to the 29 pathways that changed synchronously in the three modules, with the score of the ssGSEA in the myocardium dataset and the blood dataset presented with heatmap. The R package clusterProfiler was used in these analyses( 12 ). Variable selection and model construction The GSE65682 dataset was divided into a training set and a validation set at a ratio of 7:3.LASSO and randomForest were also used to identify important classifier variables from the 135 DEGs in the training set. We used the best turningλsuggested by the algorithm to obtain the variables from LASSO. At the same time, the top 30 genes in either%lncMSE or lncNodePurity were obtained in randomForest. Genes at the intersection of LASSO and randomForest were kept for logistic regression. The glmnet and randomForest packages in R were used for the courses of LASSO and randomForest( 17 , 18 ). The top genes obtained from the machine learning methods were used for backward stepwise regression to mortality. Nested logistic regression models were generated by sequential deletion of the variables until all of the variables in the model were statistically meaningful with a p-value < 0.01. A chi-squared test was used to compare the predictive efficiency of different models. Finally, six genes were kept in the regression model.The glmnet package in R was used for the analysis( 17 ). Modelperformance in the internal and external verification dataset The gene model was first validated with the mortality predicting efficiency of the validation dataset generated in GSE65682 and compared with age using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.A nomograph was used to construct a scoring system to display the weight of each gene in the model, and a calibration curve was used to evaluate the effectiveness of the scoring model.We validated the ability of mortality prediction in GSE54514, and five genes in our model can be found in this external validation dataset. We trained the five genes for mortality with multivariable logistic regression and compared the prediction efficiency with age and appachII using the ROC curve( 19 ). Results DEG identification,WGCNA,and enrichment analysis for each module A total of 3,154 DEGs were identified in the myocardium dataset, and five modules were generated by WGCNA(Fig. 1 A and 1 C).In the blood dataset, 7,522 DEGs were obtained and 16 modules were clustered by WGCNA(Fig. 1 B and 1 D). There were 1,583 DEGs that were found in both the myocardium and blood datasets. The counts of genes at the intersection of different modules between myocardium and peripheral blood are presented in Fig. 2 A. Enrichment analysis revealed that the meaningful modular changes in myocardial tissue are primarily in myocardial structural development(module brown),mitochondrial and aerobic respiration(module blue), and immune inflammatory response(module yellow and turquoise). The meaningful module changes in blood were primarily focused on immune response(module green,salmon), metabolic response (module brown, grey, yellow), mitotic circle (module cyan), RNA metabolic process (module turquoise), and mitochondrial function and aerobic respiration (module blue, magenta). The intersections of the pathways between different modules are presented in Fig. 2 B. GO enrichment analysis revealed that the mitochondrial-and aerobic respiration-related modules in peripheral blood were blue and magenta, and blue in the myocardium module. There were 97 DEGs found in both the blue module of myocardium and the blue module of peripheral blood, and 38 DEGs in both the blue module of myocardium and the magenta module of peripheral blood. A total of 135 DEGs of mitochondrial and aerobic respiration were identified as commonly changed in both myocardium and peripheral blood. We also identified 29 pathways that commonly changed in the three modules. CYBERSORT The CYBERSORT estimated the score of 22 kinds of immune cells in different groups of myocardium and peripheral blood datasets, with the visualizations shown in Fig. 3 A and 3 B. GSEA and enrichment analysis for genes changed synchronously in myocardium and peripheral blood Enrichment analysis visualization of the three modules for mitochondrial and aerobic respiratory dysfunction is presented in Fig. 4 A– 4 C, with some overlap observed between the three modules. We performed GSEA with the Reactome database and obtained the following enrichment results, based on a p-value < 0.05 as a screening index: there were 58 statistically significant pathways in the blue module of blood and eight statistically significant pathways in the blue module of the myocardium, but there were no statistically significant pathways in the magenta module of blood. By comparison,Bloodblue and Heartblue had a total of six pathways that changed synchronously, with the results shown in Fig. 4 D and 4 E. From the results, some pathways identified changed synchronously in both myocardium and peripheral blood, but the sets of genes for the pathways and the details of the changes were not identical.To collect further information regarding the crosstalk between mitochondrial-related modules in myocardium and blood,we applied enrichment analysis for 97 DEGs in both the blue module in myocardium and the blue module in blood, and 38 DEGs in both the blue module in myocardium and the magenta module in blood. ClueGo in Cytoscape was used for the enrichment of the intersections. We found that 97 DEGs from both the blue module in the myocardium and the blue module in blood had been mainly enriched in the pathways for mitochondrial and aerobic respiratory dysfunction, with a small number for tRNA and ribosome (Fig. 5 A). The pathways enriched by the 38 DEGs from both the blue module in myocardium and the magenta module in blood are mainly aerobic respiratory function (oxidative phosphorylation, electron transfer activity, and cytochrome complex assembly) (Fig. 5 B). The network of the pathways is presented in Fig. 5 C and 5 D. ssGSEA and k-mean clustering analysis In the deviation dataset GSE65682, we used Student’s t -test to compare the 135 DEGs between surviving patients and non-surviving patients by setting a p-value < 0.05. A total of 51 genes were verified as related to mortality in the sepsis patient group. Heatmap of the 51 genes showcased the expression in different groups (Fig. 6 A and 6 B). ssGSEA was applied for the 29 pathways that changed commonly in the three modules, with a score of the ssGSEA in the myocardium dataset and the blood dataset also presented with heatmap (Fig. 6 C and 6 D). In the course of heatmap formation, we applied the k-mean clustering method to the samples in the myocardium and peripheral blood datasets. In both of these datasets, the sepsis group was generally separated from the control group and other disease groups. However, we also observed that the sepsis group was divided into different subgroups with different gene expression and ssGSEA score,especially in the blood dataset. In this case, we used k-mean clustering analysis to identify the subgroups in GSE65682 and compared the mortality between subgroups. The clustering analysis algorithm recommended dichotomous classification, and we compared the classified cases for mortality(Fig. 7 A). Despite there being different expression in DEGs and ssGSEA score of pathways related to mitochondrial and aerobic respiratory function, K–M survival analysis suggested an insignificant mortality difference between the two(p-value = 0.07)(Fig. 7 B). Model construction and validation The top 30 genes in either%lncMSE or lncNodePurity were obtained in randomForest, with a total of 51 genes kept (Fig. 7 C). In the course of LASSO, 35 variables stepped into the resulting model construction (Fig. 7 D and 7 E). At the intersection of LASSO and randomForest, 13 genes were kept for logistic regression. Finally, six genes were kept in the model ( PRRC2B , UQCRB , MRPS17 , PTRHD1 , COX6C , and NDUFS5 ). We tested the predictive potential of age on mortality in the produced dataset(GSE65682), determining that the AUC for age was 0.585(95%CI 0.247–0.911)and the AUC for the 6-gene model was 0.696(95%CI 0.434–0.931)(Fig. 8 A). The 6-gene model outperformed age(p < 0.001). Figure 8 C and 8 D show the nomograph and calibration curve, which were used to integrate genes to predict mortality. In the external validation set GSE54514, the same procedure was used. Because UQCRB was unavailable in the external validation set, we constructed a model with another five genes( PRRC2B , MRPS17 , PTRHD1 , COX6C , and NDUFS5 ). We retrained the five genes for mortality in GSE54514, with the results satisfied as in GSE65682. In GSE54514, we compared the 5-gene model to the age and appachII score system in terms of mortality prediction efficiency. The AUC of the 5-gene model was 0.857(95%CI 0.844–0.774), the AUC of age was 0.676(95%CI 0.427–0.968), and the AUC of appachII was 0.792.(95%CI 0.760–0.710)(Fig. 8 B). The 5-gene model’s death prediction effectiveness was greater than age and appachII(p < 0.001). DISCUSSION Sepsis cardiomyopathy is one of the most common causes of refractory septic shock and has a major impact on mortality. Diagnostic procedures of this myocardium disorder include echocardiography and a variety of laboratory tests,such as atrial brain natriuretic peptide, troponinT, and myocardial enzymes( 20 ). The specificity of these biochemical tests has not been satisfied in previous research( 21 , 22 ). Therefore, we applied numerous sepsis databases in our previous study and explored the genes in peripheral blood that changed synchronously with the genes in the myocardium to identify hubgenes related to inflammatory reaction and mitochondrial dysfunction proven to be related to the mortality of sepsis patients. We found that among the genes that were collectively altered,the majority that had an effect on mortality were still inflammatory factors.Because mitochondrial dysfunction has been reported to be a characteristic change in septic myocardium( 8 ), we further investigated the changes in mitochondrial function and aerobic respiration in blood and myocardium of patients with sepsis. We believe that this study will allow us to clarify the impact of mitochondrial function, which is specific to the myocardium of sepsis patients, on sepsis mortality, and enable us to obtain target genes from peripheral blood with higher myocardial specificity. In this study, we selected the mitochondrial function and aerobic respiratory-related modules in myocardium and peripheral blood in sepsis databases by applying WGCNA. We also obtained the genes and pathways that synchronously changed in these modules. When we removed the immune-related factors and chose mitochondrial-and aerobic respiratory-related genes to proceed with the clustering analysis, we observed that the sepsis group and the control group could be separated by those genes, but the subgroups of sepsis patients had no statistically significant effect on 28-day mortality. Hence, we determined that some genes and pathways related to mitochondrial and aerobic respiratory dysfunction were commonly changed in both myocardium and peripheral blood.However, the impact on sepsis mortality caused by these genes in peripheral blood is limited.It is proven that, as mentioned in the introduction, infection-related immune response remains the primary factor influencing sepsis severity and mortality. The influence of inflammatory reaction on sepsis patients is comprehensive, with not only the inflammatory factors reflecting the degree of systemic inflammation, but some infectious and inflammatory factors, such as lipopolysaccharide, interleukin-1, tumor necrosis factor, prostanoids, and the nitric oxide system, also working as depressant factors for the myocardium in severely septic patients( 3 , 23 , 24 , 25 ). However, the negative results of cluster analysis do not negate the potential of these genes as biological markers. A 6-gene-model ( PRRC2B , UQCRB , MRPS17 , PTRHD1 , COX6C , and NDUFS5 )was constructed via machine learning algorithm. The majority of the genes in our model are mitochondrial function and aerobic respiratory-related modules, and the model performed better in mortality prediction than appachII and age in validation datasets. These results suggest that mitochondrial function and aerobic respiratory disorder are still important factors reflecting the severity and mortality of the disease. At the same time,mitochondrial dysfunction has already been proven to be a systemic problem in sepsis patients,especially regarding myocardium( 26 , 27 , 28 ). It has been reported that in the myocardial tissue of sepsis patients, energy metabolism proteins such as mitochondria-related protein and myocardial contractility-related proteins are significantly downregulated( 8 , 29 ). Evidently, mitochondrial dysfunction has been observed in other organs and tissues taken from sepsis patients, including skeletal muscle( 30 , 31 , 32 ), platelets( 33 , 34 , 35 ), and peripheral blood mononuclear cells(PBMCs)( 36 ). In addition,plasma mitochondrial DNA level has been proven to be associated with the incidence of ARDS in trauma and sepsis patients( 9 ). Therefore, we can expect the application prospect of these mitochondria-related genes to be biological markers of septic cardiomyopathy. There are six genes contained in the model: PRRC2B , UQCRB , MRPS17 , PTRHD1 , COX6C , and NDUFS5 . PRRC2B and PTRHD1 are RNA binding and recycling related. UQCRB, MRPS17, COX6C, and NDUFS5 are mitochondrial and aerobic respiratory related. UQCRB encodes a subunit of the ubiquinol-cytochrome c oxidoreductase complex, which consists of one mitochondrial-encoded and 10 nuclear-encoded subunits. Mutations in this gene are associated with mitochondrial complex III deficiency. MRPS17 are encoded by nuclear genes and help in protein synthesis within the mitochondrion. COX6C(cytochrome c oxidase), the terminal enzyme of the mitochondrial respiratory chain, catalyzes the electron transfer from reduced cytochrome c to oxygen. NDUFS5 is a member of the nicotinamide-adenine dinucleotide (NADH) dehydrogenase (ubiquinone) iron–sulfur protein family. The encoded protein is a subunit of NADH:ubiquinone oxidoreductase(complex I), the first enzyme complex in the electron transport chain located in the inner mitochondrial membrane. All of the information regarding the genes mentioned above is available at https://www.ncbi.nlm.nih.gov/gene/ . According to PubMed search results, none of the genes in the model have been previously proven to be related to sepsis or septic cardiomyopathy, with only MRPS17 and NDUFS5 having been reported to be associated with coronary heart disease and non-ischemic heart failure( 37 , 38 ). Hence, further experiments and clinical research are necessary for validating the diagnosis and prognosis prediction ability of these genes. There are some limits to our research. We found it difficult to obtain a database with adequate findings from cardiac ultrasonography and other laboratory tests, as comparing our model with these standard procedures in terms of diagnostic efficiency and mortality prediction was problematic. Laboratory and clinical evidence on myocardial damage is necessary to validate our bioinformatic findings, as well as for an improved research design that would include a precise description of septic cardiomyopathy. Similarly, we would hope to obtain blood and heart tissue samples from the same patients to complete the design of a matched experiment; however, this may prove to be challenging. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Our study uses four datasets (GSE79962, GSE65682, GSE54514, and GSE134364), with all RNA sequencing data and clinical information coming from the Gene Expression Omnibus (GEO) database. Raw data and code can be available in https://github.com/longqi0302149. Competing interests The authors declare that they have no competing interests. Funding No funding was received. Authors’ contributions All authors contributed to the study conception and design. Material preparation, data collection were performed by Qiufen Dong,Yang Liu,Dan Li and Leilei Zhang. Qi Long and Yang Liu were contributed to statistical metod design and data analysis,The frst draf of the manuscript was written by Qiufen Dong and Qi Long, and all authors commented on previous versions of the manuscript. Qi Long and Gang Li took part in the manuscript revision. 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A.M.Japiassú, A.P.S.A.Santiago, J.D.C.P.d'Avila, L.F.Garcia-Souza, A.Galina, H.C.Castro Faria-Neto, et al(2011)., Bioenergetic failure of human peripheral blood monocytes in patients with septic shock is mediated by reduced F1Fo adenosine-5′-triphosphate synthase activity, Crit Care Med. 39:1056-1063. doi:10.1097/CCM.0b013e31820eda5c. G.Garrabou, C.Morén, S.López, E.Tobías, F.Cardellach, Ò.Miró, et al(2012)., The effects of sepsis on mitochondria, J Infect Dis.205:392–400. doi:10.1093/infdis/jir764. Yigang Z, Liuying C, Jingjing L, Yinghao Y, Qiang L, Kaimeng N et al(2021). Integration of summary data from GWAS and eQTL studies identified novel risk genes for coronary artery disease, Medicine(Baltimore).100(11): e24769. doi:10.1097/MD.0000000000024769. Togo I,Sho O, Masato K, Motohike O, Atsushi I, Goro M et al (2020). Novel myocardial markers GADD45G and NDUFS5 identified by RNA-sequencing predicts left ventricular reverse remodeling in advanced non-ischemic heart failure: a retrospective cohort study, BMC Caidiovasc Disord. 20(1):116. doi:10.1186/s12872-020-01396-2. Additional Declarations No competing interests reported. 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-4727561","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334804038,"identity":"39a64092-9bb8-4898-927a-69cc4d00c1f5","order_by":0,"name":"Qiufen Dong","email":"","orcid":"","institution":"Hubei Provincial Hospital of Tranditional Chinese Medicine,Affiliated Hospital of Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qiufen","middleName":"","lastName":"Dong","suffix":""},{"id":334804039,"identity":"0d3f281a-d06e-4a57-9baf-91b88641f3e1","order_by":1,"name":"Gang Li","email":"","orcid":"","institution":"Hubei Provincial Hospital of Tranditional Chinese Medicine,Affiliated Hospital of Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Li","suffix":""},{"id":334804040,"identity":"b232154d-e3aa-4efe-a2c8-00dc06a6a866","order_by":2,"name":"Yang Liu","email":"","orcid":"","institution":"Hubei Provincial Hospital of Tranditional Chinese Medicine,Affiliated Hospital of Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":334804041,"identity":"bfdf61e9-1604-4f61-9932-a2cdc5c6c58e","order_by":3,"name":"Dan Li","email":"","orcid":"","institution":"Hubei Provincial Hospital of Tranditional Chinese Medicine,Affiliated Hospital of Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Li","suffix":""},{"id":334804042,"identity":"40233534-557f-4f85-80c2-9e8946e5d84b","order_by":4,"name":"Leilei Zhang","email":"","orcid":"","institution":"Hubei Center For Medical Device Evaluation And Inspection: Health Commission of Hubei Province","correspondingAuthor":false,"prefix":"","firstName":"Leilei","middleName":"","lastName":"Zhang","suffix":""},{"id":334804043,"identity":"c8aa07cd-7388-43ac-bc67-07c67584eb17","order_by":5,"name":"Qi Long","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACPjBpIGHHxt7YYPChQkJOnpAWNogWm2R+nsMNhTPOWBgbNhClhSGNceaM9IbPvG0ViQwHCGlh7z384k3BYWaDA4mNm3nnSSQwNjA/fHQDnxaec2mWcwwO8xkcONhsOHebRB47A5uxcQ4+LRI5ZsY8BkBbDja2GbzdJlHM2MDDJk2MFsYNhxnbf/DOkUhsOEBYi/FjHgOg99sYGwx5G4jRwnPGjHEOOJCBWmYckzA2bCbgF372HuMPb/4Ao1L++QODDzV1cvLszQ8f49MCdhsPCp8Zv3Kwkg88hBWNglEwCkbBSAYAs+NKpqKYgfsAAAAASUVORK5CYII=","orcid":"","institution":"Hubei Provincial Hospital of Tranditional Chinese Medicine,Affiliated Hospital of Hubei University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Qi","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2024-07-12 03:07:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4727561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4727561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62220393,"identity":"8dc6646c-4383-4cd9-b3e3-b957381302ea","added_by":"auto","created_at":"2024-08-11 12:20:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2132480,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA of myocardium and peripheral blood dataset. (A)Dendrogram of DEGs of myocardium dataset clustered based on the measurement of dissimilarity. A total of 5 modules were identifified. (B)Dendrogram of DEGs of peripheral blood dataset clustered based on the measurement of dissimilarity. A total of 18 modules were identified by clustering analysis. (C)Heatmap presents the correlation index and p-value between modules and disease groups in myocardium dataset. (D)Heatmap presents the correlation index and p-value between modules and disease groups in peripheral blood dataset.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/064a764051d9fc1376f2dd39.png"},{"id":62220394,"identity":"8a167ee2-3086-49c4-aeb9-bfbff9f72c20","added_by":"auto","created_at":"2024-08-11 12:20:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1080695,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Multiset visualization presents the DEGs counts in the intersection between different modules in myocardium and peripheral datasets. (B)Multiset visualization presents the pathway counts in the intersection between myocardium and peripheral dataset after function enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/0ed24f800d6211b39640feb1.png"},{"id":62220392,"identity":"2f03f4e1-84e3-4e3b-ac0e-7dabb757271f","added_by":"auto","created_at":"2024-08-11 12:20:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1132469,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Immune infiltration analysis of sepsis peripheral blood by CYBERSORT; (B)Immune infiltration analysis of sepsis myocardium by CYBERSORT.\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/6ec21a07c9849a9ff690108e.png"},{"id":62220395,"identity":"20097bc0-3301-4e38-8a3b-6896ca515b86","added_by":"auto","created_at":"2024-08-11 12:20:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2958632,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment of blue and magenta module of peripheral blood,and blue module of myocardium,using Gene Omnibus database.Six pathways were identified that commonly changed in the 3 modules by GSEA,but no pathways were statistically significant changed in module blue of blood dataset adjusted with p-value. (A)Biological process(BP), cellular component(CC)and molecular function(MF) of blue module in peripheral blood. (B)BP, CC and MF of magenta module in peripheral blood.(C)BP, CC and MF of magenta module in myocardium. (D)GSEA of the 6 pathways in module blue in blood. (E)GSEA of the 6 pathways in in module magenta in blood.\u003c/p\u003e","description":"","filename":"Figure41.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/26c585d2e153c129308b702f.png"},{"id":62220398,"identity":"47ba63e6-d555-490c-b487-2b916a4222d9","added_by":"auto","created_at":"2024-08-11 12:20:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2055878,"visible":true,"origin":"","legend":"\u003cp\u003eClueGo in cytoscape was applied for gene enrichment analysis and pathway network. (A)The proportion of different pathways identified by gene enrichment analysis, using intersection of genes in blue module of myocardium and blue module of blood. (B)The proportion of different pathways identified by gene enrichment analysis, using intersection of genes in blue module of myocardium and magenta module of blood. (C)Pathways network identified with intersection of genes in blue module of myocardium and blue module of blood. (D)Pathways network identified with intersection of genes in blue module of myocardium and magenta module of blood.\u003c/p\u003e","description":"","filename":"Figure51.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/e1fb9b4f0ba47bd64538d177.png"},{"id":62220396,"identity":"1bf7a28e-1df4-4631-8d6c-3d35083b8b66","added_by":"auto","created_at":"2024-08-11 12:20:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3519567,"visible":true,"origin":"","legend":"\u003cp\u003eFigure5 We obtained135 DEGs and 29 pathways commonly changed in mitochondrial and aerobic respiratory related modules in myocardium and peripheral blood. Heatmaps were applied to present the expression of the genes and GSEA score of pathways. (A)Heatmap of the DEGs expression in peripheral blood;(B)Heatmap of the DEGs expression in myocardium; (C)Heatmap of GSEA score of the pathways in peripheral blood; (D)Heatmap of GSEA score of the pathways in myocardium.\u003c/p\u003e","description":"","filename":"Figure61.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/f1dbfae7488feaee70308ba3.png"},{"id":62220399,"identity":"95a009e3-f655-4f32-8481-70c2bf86cd56","added_by":"auto","created_at":"2024-08-11 12:20:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1536800,"visible":true,"origin":"","legend":"\u003cp\u003e(A)K-means clustering analysis recommended that the sepsis group be divided into two groups; (B)KM-survival analysis indicated that there is no statistically different between the two groups(p-value=0.078); (C)Top 30 genes suggested by randomforest method, using%IncMSE and IncNodePurity; (D)Progress variable selecting by LASSO regression; (E)The recommended area ofλand number of variables suggested to be obtained.\u003c/p\u003e","description":"","filename":"Figure71.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/1fd80d478938462be61bc231.png"},{"id":62221494,"identity":"bbcf75e0-d626-4766-8f11-bad15770aec2","added_by":"auto","created_at":"2024-08-11 12:28:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":928297,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Validation of the mortality prediction of the six-gene model and age in GSE65682. The AUC for the six-gene model was0.696(95 percent CI 0.434,0.931), the AUC for age was 0.585(95%CI 0.247–0.911). (B)Validation of the mortality prediction of the six-gene model and appach II score in GSE54514.The AUC of the six-gene model was 0.857(95 percent confidence interval:0.844,0.774), the AUC of appach II was 0.792(95%CI0.760–0.710), and the AUC of the age was 0.676(95 percent confidence interval:0.427-0.968). (C)Nomograph displaying the risk score of the five genes in the model in GSE65682, which contribute great weight to mortality. (D)Calibration curve of the regression model, which shows the good fit of our model. The mean absolute error is acceptable(mean absolute error=0.013).\u003c/p\u003e","description":"","filename":"Figure81.png","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/895fe3493f0208ecf2509885.png"},{"id":71465299,"identity":"d14a3e72-0423-4c74-831a-6ee5f4055f23","added_by":"auto","created_at":"2024-12-16 02:17:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16850555,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4727561/v1/0888d8f8-ca49-4110-a2f8-c7092d651c37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the potential connection between myocardial cells and peripheral blood in patients with sepsis via bioinformatics method","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSeptic cardiomyopathy is a reversible suppressed condition of the myocardium caused by sepsis or septic shock.Approximately 30\u0026ndash;60%of sepsis patients may suffer from myocardium damage to some extent, particularly newborns. Septic cardiomyopathy results in negative septic effects, with the mortality of sepsis increasing from 20%to 70\u0026ndash;90%when cardiovascular complication occurs(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The main clinical manifestation of septic cardiomyopathy(SCM)consists of the suppression of myocardium contraction, a decrease in left ventricular ejection fraction(LVEF), and the dilation of peripheral vessels and ventricles[3]. The pathogenic mechanism of SCM is not fully understood, and infection-related inflammatory reactions (endotoxin, cytokines, nitric oxygen system) and mitochondrial dysfunction (mitochondrial and aerobic respiratory) are considered to be the main adverse factors causing this myocardium disorder (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur previous research attempted to present the pathways and genes commonly changed in both myocardium and peripheral blood (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Multiple pathways relating to infection-related inflammatory reaction and mitochondrial dysfunction were discovered, and we constructed a gene model that was proven to be related to the mortality of sepsis patients.However, although we initially observed mitochondrial function-related genes, following machine learning screening, the genes ultimately included in the model were still mainly those related to inflammation. Thus, despite widespread changes in mitochondrial function\u0026ndash;related genes reported in the myocardium (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and we proved that the same change occurred in sepsis patients\u0026rsquo;peripheral blood. We were unable to demonstrate that the mitochondrial dysfunction that occurred in common had a profound impact on the mortality of sepsis or septic shock as septic cardiomyopathy. This result determines whether mitochondria-related markers have the potential to be biological markers and to predict the prognosis of septic cardiomyopathy.\u003c/p\u003e \u003cp\u003eMitochondrial dysfunction is widespread in the blood of patients with sepsis and may be associated with multiple adverse complications, such as acute respiratory distress syndrome (ARDS) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In this paper, bioinformatics and machine learning methods were applied,and we aimed to determine whether mitochondrial dysfunction in peripheral blood has potential effects on septic cardiomyopathy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003ePatients and datasets\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThree datasets (GSE79962,GSE65682,GSE54514) were obtained in our study, with all RNA sequencing data and clinical information coming from the Gene Expression Omnibus(GEO)database. GSE79962 contains 51 myocardium samples, comprising 20 samples of SCM, 11 samples of ischemic heart disease(IHD), 11 samples of dilated cardiomyopathy(DCM),and nine samples of non-failing heart(NFH). In the blood dataset GSE65682, we obtained 802 whole blood samples for sepsis patients(n\u0026thinsp;=\u0026thinsp;760) and healthy controls (n\u0026thinsp;=\u0026thinsp;142). In total, 468 sepsis samples with 28 days of survival data were studied as a training dataset and internal validation dataset for model construction (sepsis survivors[78.0%,365/468], sepsis non-survivors[22%,103/468]). The GSE54514 dataset, which included 163 PAX gene samples from sepsis survivors(n\u0026thinsp;=\u0026thinsp;26), non-survivors(n\u0026thinsp;=\u0026thinsp;9), and healthy controls(n\u0026thinsp;=\u0026thinsp;18), was used as an external validation dataset. The whole-blood samples were collected daily for up to 5 days from patients admitted to the intensive care unit with sepsis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eDifferently expressed gene(DEG)identification and weighted gene co-expression\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003enetwork analysis(WGCNA)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe Limma package in R was applied to finish the DEG identification(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Gene significance was defined as Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test statistic for testing differential expression between different groups. In the peripheral blood dataset, we compared the septic group to the healthy control. In the myocardium dataset, we compared the septic group to NFH group. We set p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 as a criterion for DEG selection in peripheral blood. In the myocardium dataset, we set the p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cutoff to obtain more DEGs for the next step. DEGs obtained from myocardium and peripheral blood were stepped into WGCNA separately(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Modules were identified in the resulting dendrogram using the Dynamic Tree Cut algorithm. Modules with similar expression profiles were merged at the threshold of 0.25. While the modules were confirmed, we tested the correlation between modules and gene significance using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test statistic, and modules with statistical significance were kept and incorporated into enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eEnrichment analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe Gene Omnibus(GO)database was used to finish enrichment analysis for the retained DEGs in modules and to determine the biological process, molecular function, and cellular component pathways that would be dysregulated in the blood and myocardium of septic shock patients, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating a significantly enriched term. Mitochondrial-and aerobic respiratory-related modules were identified in this procedure. Gene set enrichment analysis(GSEA)was applied to the modules for the mitochondrial and aerobic respiratory function we were interested in, using the Reactome database. The clusterProfiler package in R and ClueGo in Cytoscape were applied for the enrichment analysis(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eCrosstalk ofgenes andpathways between myocardium andperipheral blood\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eNext, visualizations of multi-set were performed using the Upset package in R(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The crosstalk of both DEGs and pathways from enrichment analysis is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, showcasing the intersection of genes and pathways among different modules. Among different modules,the crosstalk of the inflammatory reaction was much more complicated than that of mitochondrial and aerobic respiratory dysfunction. Despite focusing on mitochondrial and aerobic respiratory dysfunction in our study, we still applied CYBERSORT, an algorithm to assess the proportions of 22 immune cell subtypes based on the expression file, to present the immune filtration condition between the septic group and healthy control in both blood and myocardium. The perm parameter was set at 1,000. Samples with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the CYBERSORT analysis result were used in this analysis(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eK-means clustering analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the peripheral blood dataset, we combined the DEGs in the blue and magenta modules together and then intersected them with the DEGs in the blue module of myocardium. Finally, we obtained 135 genes and used them for k-means clustering analysis for the 468 sepsis patients with survival data in the GSE65682 dataset. The number of clusters was determined by the Elbow Method and the Average Silhouette Method.Afterward, Kaplan\u0026ndash;Meier(K\u0026ndash;M)survival analysis was performed between the two groups. A package of NbClust in R was used for the clustering, and a survival package was used for survival analysis(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpression ofgenes and pathways related to mitochondrial and aerobic respiratory dysfunction in peripheral blood and myocardium\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the deviation dataset GSE65682, we used Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test to compare the 135 DEGs between surviving patients and non-surviving patients by setting p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 51 genes were verified as related to mortality in the sepsis patient group. Heatmap of the 51 genes showcased the expression in different groups. Single sample gene set enrichment analysis(ssGSEA)was applied to the 29 pathways that changed synchronously in the three modules, with the score of the ssGSEA in the myocardium dataset and the blood dataset presented with heatmap. The R package clusterProfiler was used in these analyses(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eVariable selection and model construction\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe GSE65682 dataset was divided into a training set and a validation set at a ratio of 7:3.LASSO and randomForest were also used to identify important classifier variables from the 135 DEGs in the training set. We used the best turningλsuggested by the algorithm to obtain the variables from LASSO. At the same time, the top 30 genes in either%lncMSE or lncNodePurity were obtained in randomForest. Genes at the intersection of LASSO and randomForest were kept for logistic regression. The glmnet and randomForest packages in R were used for the courses of LASSO and randomForest(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe top genes obtained from the machine learning methods were used for backward stepwise regression to mortality. Nested logistic regression models were generated by sequential deletion of the variables until all of the variables in the model were statistically meaningful with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. A chi-squared test was used to compare the predictive efficiency of different models. Finally, six genes were kept in the regression model.The glmnet package in R was used for the analysis(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eModelperformance in the internal and external verification dataset\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe gene model was first validated with the mortality predicting efficiency of the validation dataset generated in GSE65682 and compared with age using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.A nomograph was used to construct a scoring system to display the weight of each gene in the model, and a calibration curve was used to evaluate the effectiveness of the scoring model.We validated the ability of mortality prediction in GSE54514, and five genes in our model can be found in this external validation dataset. We trained the five genes for mortality with multivariable logistic regression and compared the prediction efficiency with age and appachII using the ROC curve(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDEG identification,WGCNA,and enrichment analysis for each module\u003c/h2\u003e \u003cp\u003eA total of 3,154 DEGs were identified in the myocardium dataset, and five modules were generated by WGCNA(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).In the blood dataset, 7,522 DEGs were obtained and 16 modules were clustered by WGCNA(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). There were 1,583 DEGs that were found in both the myocardium and blood datasets. The counts of genes at the intersection of different modules between myocardium and peripheral blood are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. Enrichment analysis revealed that the meaningful modular changes in myocardial tissue are primarily in myocardial structural development(module brown),mitochondrial and aerobic respiration(module blue), and immune inflammatory response(module yellow and turquoise). The meaningful module changes in blood were primarily focused on immune response(module green,salmon), metabolic response (module brown, grey, yellow), mitotic circle (module cyan), RNA metabolic process (module turquoise), and mitochondrial function and aerobic respiration (module blue, magenta). The intersections of the pathways between different modules are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. GO enrichment analysis revealed that the mitochondrial-and aerobic respiration-related modules in peripheral blood were blue and magenta, and blue in the myocardium module. There were 97 DEGs found in both the blue module of myocardium and the blue module of peripheral blood, and 38 DEGs in both the blue module of myocardium and the magenta module of peripheral blood. A total of 135 DEGs of mitochondrial and aerobic respiration were identified as commonly changed in both myocardium and peripheral blood. We also identified 29 pathways that commonly changed in the three modules.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCYBERSORT\u003c/h2\u003e \u003cp\u003eThe CYBERSORT estimated the score of 22 kinds of immune cells in different groups of myocardium and peripheral blood datasets, with the visualizations shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGSEA and enrichment analysis for genes changed synchronously in myocardium and peripheral blood\u003c/h2\u003e \u003cp\u003eEnrichment analysis visualization of the three modules for mitochondrial and aerobic respiratory dysfunction is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, with some overlap observed between the three modules. We performed GSEA with the Reactome database and obtained the following enrichment results, based on a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as a screening index: there were 58 statistically significant pathways in the blue module of blood and eight statistically significant pathways in the blue module of the myocardium, but there were no statistically significant pathways in the magenta module of blood. By comparison,Bloodblue and Heartblue had a total of six pathways that changed synchronously, with the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE. From the results, some pathways identified changed synchronously in both myocardium and peripheral blood, but the sets of genes for the pathways and the details of the changes were not identical.To collect further information regarding the crosstalk between mitochondrial-related modules in myocardium and blood,we applied enrichment analysis for 97 DEGs in both the blue module in myocardium and the blue module in blood, and 38 DEGs in both the blue module in myocardium and the magenta module in blood. ClueGo in Cytoscape was used for the enrichment of the intersections. We found that 97 DEGs from both the blue module in the myocardium and the blue module in blood had been mainly enriched in the pathways for mitochondrial and aerobic respiratory dysfunction, with a small number for tRNA and ribosome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The pathways enriched by the 38 DEGs from both the blue module in myocardium and the magenta module in blood are mainly aerobic respiratory function (oxidative phosphorylation, electron transfer activity, and cytochrome complex assembly) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The network of the pathways is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003essGSEA and k-mean clustering analysis\u003c/h2\u003e \u003cp\u003eIn the deviation dataset GSE65682, we used Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test to compare the 135 DEGs between surviving patients and non-surviving patients by setting a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 51 genes were verified as related to mortality in the sepsis patient group. Heatmap of the 51 genes showcased the expression in different groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). ssGSEA was applied for the 29 pathways that changed commonly in the three modules, with a score of the ssGSEA in the myocardium dataset and the blood dataset also presented with heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In the course of heatmap formation, we applied the k-mean clustering method to the samples in the myocardium and peripheral blood datasets. In both of these datasets, the sepsis group was generally separated from the control group and other disease groups. However, we also observed that the sepsis group was divided into different subgroups with different gene expression and ssGSEA score,especially in the blood dataset. In this case, we used k-mean clustering analysis to identify the subgroups in GSE65682 and compared the mortality between subgroups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe clustering analysis algorithm recommended dichotomous classification, and we compared the classified cases for mortality(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Despite there being different expression in DEGs and ssGSEA score of pathways related to mitochondrial and aerobic respiratory function, K\u0026ndash;M survival analysis suggested an insignificant mortality difference between the two(p-value\u0026thinsp;=\u0026thinsp;0.07)(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eModel construction and validation\u003c/h2\u003e \u003cp\u003eThe top 30 genes in either%lncMSE or lncNodePurity were obtained in randomForest, with a total of 51 genes kept (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). In the course of LASSO, 35 variables stepped into the resulting model construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). At the intersection of LASSO and randomForest, 13 genes were kept for logistic regression. Finally, six genes were kept in the model (\u003cem\u003ePRRC2B\u003c/em\u003e, \u003cem\u003eUQCRB\u003c/em\u003e, \u003cem\u003eMRPS17\u003c/em\u003e, \u003cem\u003ePTRHD1\u003c/em\u003e, \u003cem\u003eCOX6C\u003c/em\u003e, and \u003cem\u003eNDUFS5\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eWe tested the predictive potential of age on mortality in the produced dataset(GSE65682), determining that the AUC for age was 0.585(95%CI 0.247\u0026ndash;0.911)and the AUC for the 6-gene model was 0.696(95%CI 0.434\u0026ndash;0.931)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The 6-gene model outperformed age(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD show the nomograph and calibration curve, which were used to integrate genes to predict mortality. In the external validation set GSE54514, the same procedure was used. Because UQCRB was unavailable in the external validation set, we constructed a model with another five genes(\u003cem\u003ePRRC2B\u003c/em\u003e, \u003cem\u003eMRPS17\u003c/em\u003e, \u003cem\u003ePTRHD1\u003c/em\u003e, \u003cem\u003eCOX6C\u003c/em\u003e, and \u003cem\u003eNDUFS5\u003c/em\u003e). We retrained the five genes for mortality in GSE54514, with the results satisfied as in GSE65682. In GSE54514, we compared the 5-gene model to the age and appachII score system in terms of mortality prediction efficiency. The AUC of the 5-gene model was 0.857(95%CI 0.844\u0026ndash;0.774), the AUC of age was 0.676(95%CI 0.427\u0026ndash;0.968), and the AUC of appachII was 0.792.(95%CI 0.760\u0026ndash;0.710)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The 5-gene model\u0026rsquo;s death prediction effectiveness was greater than age and appachII(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSepsis cardiomyopathy is one of the most common causes of refractory septic shock and has a major impact on mortality. Diagnostic procedures of this myocardium disorder include echocardiography and a variety of laboratory tests,such as atrial brain natriuretic peptide, troponinT, and myocardial enzymes(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The specificity of these biochemical tests has not been satisfied in previous research(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Therefore, we applied numerous sepsis databases in our previous study and explored the genes in peripheral blood that changed synchronously with the genes in the myocardium to identify hubgenes related to inflammatory reaction and mitochondrial dysfunction proven to be related to the mortality of sepsis patients. We found that among the genes that were collectively altered,the majority that had an effect on mortality were still inflammatory factors.Because mitochondrial dysfunction has been reported to be a characteristic change in septic myocardium(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), we further investigated the changes in mitochondrial function and aerobic respiration in blood and myocardium of patients with sepsis. We believe that this study will allow us to clarify the impact of mitochondrial function, which is specific to the myocardium of sepsis patients, on sepsis mortality, and enable us to obtain target genes from peripheral blood with higher myocardial specificity.\u003c/p\u003e \u003cp\u003eIn this study, we selected the mitochondrial function and aerobic respiratory-related modules in myocardium and peripheral blood in sepsis databases by applying WGCNA. We also obtained the genes and pathways that synchronously changed in these modules. When we removed the immune-related factors and chose mitochondrial-and aerobic respiratory-related genes to proceed with the clustering analysis, we observed that the sepsis group and the control group could be separated by those genes, but the subgroups of sepsis patients had no statistically significant effect on 28-day mortality. Hence, we determined that some genes and pathways related to mitochondrial and aerobic respiratory dysfunction were commonly changed in both myocardium and peripheral blood.However, the impact on sepsis mortality caused by these genes in peripheral blood is limited.It is proven that, as mentioned in the introduction, infection-related immune response remains the primary factor influencing sepsis severity and mortality. The influence of inflammatory reaction on sepsis patients is comprehensive, with not only the inflammatory factors reflecting the degree of systemic inflammation, but some infectious and inflammatory factors, such as lipopolysaccharide, interleukin-1, tumor necrosis factor, prostanoids, and the nitric oxide system, also working as depressant factors for the myocardium in severely septic patients(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the negative results of cluster analysis do not negate the potential of these genes as biological markers. A 6-gene-model (\u003cem\u003ePRRC2B\u003c/em\u003e, \u003cem\u003eUQCRB\u003c/em\u003e, \u003cem\u003eMRPS17\u003c/em\u003e, \u003cem\u003ePTRHD1\u003c/em\u003e, \u003cem\u003eCOX6C\u003c/em\u003e, and \u003cem\u003eNDUFS5\u003c/em\u003e)was constructed via machine learning algorithm. The majority of the genes in our model are mitochondrial function and aerobic respiratory-related modules, and the model performed better in mortality prediction than appachII and age in validation datasets. These results suggest that mitochondrial function and aerobic respiratory disorder are still important factors reflecting the severity and mortality of the disease. At the same time,mitochondrial dysfunction has already been proven to be a systemic problem in sepsis patients,especially regarding myocardium(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). It has been reported that in the myocardial tissue of sepsis patients, energy metabolism proteins such as mitochondria-related protein and myocardial contractility-related proteins are significantly downregulated(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Evidently, mitochondrial dysfunction has been observed in other organs and tissues taken from sepsis patients, including skeletal muscle(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), platelets(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), and peripheral blood mononuclear cells(PBMCs)(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In addition,plasma mitochondrial DNA level has been proven to be associated with the incidence of ARDS in trauma and sepsis patients(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Therefore, we can expect the application prospect of these mitochondria-related genes to be biological markers of septic cardiomyopathy.\u003c/p\u003e \u003cp\u003eThere are six genes contained in the model: \u003cem\u003ePRRC2B\u003c/em\u003e, \u003cem\u003eUQCRB\u003c/em\u003e, \u003cem\u003eMRPS17\u003c/em\u003e, \u003cem\u003ePTRHD1\u003c/em\u003e, \u003cem\u003eCOX6C\u003c/em\u003e, and \u003cem\u003eNDUFS5\u003c/em\u003e. PRRC2B and PTRHD1 are RNA binding and recycling related. UQCRB, MRPS17, COX6C, and NDUFS5 are mitochondrial and aerobic respiratory related. UQCRB encodes a subunit of the ubiquinol-cytochrome c oxidoreductase complex, which consists of one mitochondrial-encoded and 10 nuclear-encoded subunits. Mutations in this gene are associated with mitochondrial complex III deficiency. MRPS17 are encoded by nuclear genes and help in protein synthesis within the mitochondrion. COX6C(cytochrome c oxidase), the terminal enzyme of the mitochondrial respiratory chain, catalyzes the electron transfer from reduced cytochrome c to oxygen. NDUFS5 is a member of the nicotinamide-adenine dinucleotide (NADH) dehydrogenase (ubiquinone) iron\u0026ndash;sulfur protein family. The encoded protein is a subunit of NADH:ubiquinone oxidoreductase(complex I), the first enzyme complex in the electron transport chain located in the inner mitochondrial membrane. All of the information regarding the genes mentioned above is available at\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. According to PubMed search results, none of the genes in the model have been previously proven to be related to sepsis or septic cardiomyopathy, with only \u003cem\u003eMRPS17\u003c/em\u003e and \u003cem\u003eNDUFS5\u003c/em\u003e having been reported to be associated with coronary heart disease and non-ischemic heart failure(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Hence, further experiments and clinical research are necessary for validating the diagnosis and prognosis prediction ability of these genes.\u003c/p\u003e \u003cp\u003eThere are some limits to our research. We found it difficult to obtain a database with adequate findings from cardiac ultrasonography and other laboratory tests, as comparing our model with these standard procedures in terms of diagnostic efficiency and mortality prediction was problematic. Laboratory and clinical evidence on myocardial damage is necessary to validate our bioinformatic findings, as well as for an improved research design that would include a precise description of septic cardiomyopathy. Similarly, we would hope to obtain blood and heart tissue samples from the same patients to complete the design of a matched experiment; however, this may prove to be challenging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study uses four datasets (GSE79962, GSE65682, GSE54514, and GSE134364), with all RNA sequencing data and clinical information coming from the Gene Expression Omnibus (GEO) database. Raw data and code can be available in https://github.com/longqi0302149.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; \u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection were performed by Qiufen Dong,Yang Liu,Dan Li and Leilei Zhang. Qi Long and Yang Liu were contributed to statistical metod design and data analysis,The frst draf of the manuscript was written by Qiufen Dong and Qi Long, and all authors commented on previous versions of the manuscript. Qi Long and Gang Li took part in the manuscript revision. All authors read and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKakihana Y, Ito T, Nakahara M, Yamaguchi K and Yasuda T(2016). Sepsis-induced myocardial dysfunction: pathophysiology and management. J Intensive Care 4:22. doi: 10.1186/s40560-016-0148-1.eCollection 2016.\u003c/li\u003e\n\u003cli\u003eZanotti-Cavazzoni SL, Hollenberg SM(2009). Cardiac dysfunction in severe sepsis and septic shock. Curr Opin Crit Care 15: 392 - 397. doi: 10.1097/MCC.0b013e3283307a4e.\u003c/li\u003e\n\u003cli\u003eNidhruv Ravikumar, Mohammed Arbaaz Sayed, Chanaradh James Poosuph, Rijuvani Sehgal, Manasi Mahesh Shirke, Amer Harky (2021). Sepstic cardiomyopathy: From basis to management choices. Curr Probl Cardiol 46(4): 100767. doi: 10.1016/j.cpcardiol.2020.100767.\u003c/li\u003e\n\u003cli\u003eReines HD, Halushka PV, Cook JA, Wise WC, Rambo W.Plasma thromboxane concentrations are raised in patients dying with septic shock. Lancet 1982; 2: 174\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003ePreiser JC, Zhang H, Vray B, Hrabak A, Vincent JL. Time course of inducible nitric oxide synthase activity following endotoxin administration in dogs. Nitric Oxide 2001; 5: 208\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eBrealey, M.Brand, I.Hargreaves, S.Heales, J.Land, R.Smolenski, et al(2002). Association between mitochondrial dysfunction and severity and outcome of septic shock, The Lancet. 360:219\u0026ndash;223. doi: 10.1016/S0140-6736(02)09459-X.\u003c/li\u003e\n\u003cli\u003eLong Q, Li G, Dong Q, Wang M, Li J, Wang L. Exploration of the Shared Gene Signatures between Myocardium and Blood in Sepsis: Evidence from Bioinformatics Analysis.Biomed Res Int. 2022 Aug 6; 2022: 3690893. doi:10.1155/2022/3690893.PMID:35971449;PMCID:PMC9375705.\u003c/li\u003e\n\u003cli\u003eMatkovich SJ, Al Khiami B, Efimov IR, Evans S, Vader J, Jain Aet. Al (2017). Widespread Downregulation of Cardiac Mitochondrial and Sarcomeric Genes in Patients with Sepsis.Crit Care Med 45(3):407\u0026ndash;414. doi: 10.1097/CCM.0000000000002207.\u003c/li\u003e\n\u003cli\u003eHilary EF, John PR, Brian JA, Caroline AG, Caitlyn MF, Peggy Z et.al(2020). Plasma Mitochondrial DNA Levels Are Associated With ARDS in Trauma and Sepsis Patients, Chest.157:67-76. doi: 10.1016/j.chest.2019.09.028.\u003c/li\u003e\n\u003cli\u003eRitchie,M.E., Phipson,B., Wu,D., Hu,Y., Law,C.W., Shi,W., and Smyth,G.K.(2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. 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NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistical Software 2014,61(6),1-36.\u003c/li\u003e\n\u003cli\u003eTerry M. Therneau, Patricia M. Grambsch(2000). Modeling Survival Data: Extending the Cox Model. Springer, New York. ISBN 0-387-98784-3.\u003c/li\u003e\n\u003cli\u003eFriedman J, Hastie T, Tibshirani R (2010). \u0026ldquo;Regularization Paths for GeneralizedLinear Models via Coordinate Descent. \u0026rdquo; Journal of Statistical Software, 33(1), 1\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eA.Liaw and M. Wiener(2002). Classification and Regression by randomForest. R News 2(3), 18--22.\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J, M\u0026uuml;ller M(2011). pROC: an open-source package for R and S+to analyze and compare ROC curves. BMC Bioinformatics, 12,77. doi:10.1186/1471-2105-12-77.\u003c/li\u003e\n\u003cli\u003eLin H, Wang W, Lee M, Meng Q, Ren H. Current Status of Septic Cardiomyopathy: Basic Science and Clinical Progress. Front Pharmacol. 2020 Mar 3;11:210.doi:10.3389/fphar.2020.00210.\u003c/li\u003e\n\u003cli\u003eRosjo, H.et al. Circulating high sensitivity troponin T in severe sepsis and septic shock: distribution, associated factors, and relation to outcome. Intensive Care Med. 37, 77\u0026ndash;85(2011).\u003c/li\u003e\n\u003cli\u003ePapanikolaou, J.et al. New insights into the mechanisms involved in B-type natriuretic peptide elevation and its prognostic value in septic patients. Crit. Care 18,R94(2014).\u003c/li\u003e\n\u003cli\u003eL.Lorente, M.M.Mart\u0026iacute;n, E.L\u0026oacute;pez-Gallardo, R.Iceta, J.Sol\u0026eacute;-Viol\u0026aacute;n, J.Blanquer, et al (2011). Platelet cytochrome c oxidase activity and quantity in septic patients, Crit Care Med. 39 1289 \u0026ndash; 1294. doi: 10.1097/CCM.0b013e31820ee20c.\u003c/li\u003e\n\u003cli\u003eF.Sj\u0026ouml;vall, S.Morota, M.J.Hansson, H.Friberg, E.Gnaiger, E.Elm\u0026eacute;r(2010), Temporal increase of platelet mitochondrial respiration is negatively associated with clinical outcome in patients with sepsis, Crit Care.14R214.doi:10.1186/cc9337.\u003c/li\u003e\n\u003cli\u003eA.M.Japiass\u0026uacute;, A.P.S.A.Santiago, J.D.C.P.d\u0026apos;Avila, L.F.Garcia-Souza, A.Galina, H.C.Castro Faria-Neto, et al(2011)., Bioenergetic failure of human peripheral blood monocytes in patients with septic shock is mediated by reduced F1Fo adenosine-5\u0026prime;-triphosphate synthase activity, Crit Care Med.39:1056-1063. doi:10.1097/CCM.0b013e31820eda5c.\u003c/li\u003e\n\u003cli\u003eCimolai, MC, Alvarez, S,Bode, C,\u0026amp;Bugger, H(2015). Mitochondrial mechanisms in septic cardiomyopathy, 1776317778. https://doi.org/10.3390/ijms160817763.\u003c/li\u003e\n\u003cli\u003ePan P, Wang X, \u0026amp;Liu,D (2018). The potential mechanism of mitochondrial dysfunction in septic cardiomyopathy. https://doi.org/10.1177/0300060518765896.\u003c/li\u003e\n\u003cli\u003eTakasu O, Gaut,JP, Watanabe E, et al(2012). Mechanisms of cardiac and renal dysfunction in patients dying of sepsis. https://doi.org/10.1164/rccm.201211-1983OC.\u003c/li\u003e\n\u003cli\u003eBerit SB, Else ML, Bernt CH, Ingeborg LG, Jens PB, Ole KO et.al(2020). Extensive Changes in Transcriptomic \u0026ldquo;Fingerprints\u0026rdquo; and Immunological Cells in the Large Organs of Patients Dying of Acute Septic Shock and Multiple Organ Failure Caused by Neisseria meningitidis. 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Platelet cytochrome c oxidase activity and quantity in septic patients, Crit Care Med. 39 1289\u0026ndash;1294. doi:10.1097/CCM.0b013e31820ee20c.\u003c/li\u003e\n\u003cli\u003eF.Sj\u0026ouml;vall, S.Morota, M.J.Hansson, H.Friberg, E.Gnaiger, E.Elm\u0026eacute;r(2010), Temporal increase of platelet mitochondrial respiration is negatively associated with clinical outcome in patients with sepsis,Crit Care.14R214. doi:10.1186/cc9337.\u003c/li\u003e\n\u003cli\u003eA.M.Japiass\u0026uacute;, A.P.S.A.Santiago, J.D.C.P.d\u0026apos;Avila, L.F.Garcia-Souza, A.Galina, H.C.Castro Faria-Neto, et al(2011)., Bioenergetic failure of human peripheral blood monocytes in patients with septic shock is mediated by reduced F1Fo adenosine-5\u0026prime;-triphosphate synthase activity, Crit Care Med. 39:1056-1063. doi:10.1097/CCM.0b013e31820eda5c.\u003c/li\u003e\n\u003cli\u003eG.Garrabou, C.Mor\u0026eacute;n, S.L\u0026oacute;pez, E.Tob\u0026iacute;as, F.Cardellach, \u0026Ograve;.Mir\u0026oacute;, et al(2012)., The effects of sepsis on mitochondria, J Infect Dis.205:392\u0026ndash;400. doi:10.1093/infdis/jir764.\u003c/li\u003e\n\u003cli\u003eYigang Z, Liuying C, Jingjing L, Yinghao Y, Qiang L, Kaimeng N et al(2021). Integration of summary data from GWAS and eQTL studies identified novel risk genes for coronary artery disease, Medicine(Baltimore).100(11): e24769. doi:10.1097/MD.0000000000024769.\u003c/li\u003e\n\u003cli\u003eTogo I,Sho O, Masato K, Motohike O, Atsushi I, Goro M et al (2020). Novel myocardial markers GADD45G and NDUFS5 identified by RNA-sequencing predicts left ventricular reverse remodeling in advanced non-ischemic heart failure: a retrospective cohort study, BMC Caidiovasc Disord. 20(1):116. doi:10.1186/s12872-020-01396-2.\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":"mitochondrial dysfunction, aerobic respiratory, septic cardiomyopathy, gene expression profiling","lastPublishedDoi":"10.21203/rs.3.rs-4727561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4727561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Mitochondrial dysfunction has been proven to be a characteristic change in the myocardium of patients with sepsis. In our previous research, we revealed that some mitochondrial dysfunctions occur synchronously in the peripheral blood of sepsis patients and affect mortality with inflammatory and other related genes. However, these mitochondrial dysfunctions are not described in detail. Whether mitochondrial dysfunction affects the mortality of sepsis patients as an independent risk factor still needs to be further validated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjects\u003c/strong\u003e \u003cstrong\u003eand\u003c/strong\u003e \u003cstrong\u003emethods\u003c/strong\u003e In our study, we aimed to present the co-varied genes and pathways related to mitochondrial and aerobic respiratory function in myocardium and peripheral blood of sepsis patients, and to verify their effects regarding the mortality of sepsis. We applied weighted gene co-expression network analysis(WGCNA)to generate different modules from myocardium and blood datasets, and subsequent enrichment analysis was used to identify the mitochondrial-and aerobic respiratory-related modules. We obtained the co-varied differential expressed genes(DEGs)from the modules to separate sepsis patients into different subgroups and compare the survival rate between them. Machine learning algorithms were applied for mortality predictive model construction and validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Blue and magenta modules in blood and blue modules in the myocardium were identified as being related to mitochondrial and aerobic respiratory function. There was a strong overlap in gene expression and pathways between these modules, and DEGs from them separated sepsis patients into two groups, but there was no statistical difference in mortality between the different groups(p-value=0.078). However, models generated from these DEGs performed well in mortality prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Our research has found that some genes and pathways associated with mitochondrial aerobic respiratory dysfunction are generally altered in myocardium and peripheral blood, and the changes of these related genes can reflect the severity and mortality of sepsis. Therefore, we can expect the application prospect of these mitochondria-related genes as biomarkers of infectious cardiomyopathy.\u003c/p\u003e","manuscriptTitle":"Exploring the potential connection between myocardial cells and peripheral blood in patients with sepsis via bioinformatics method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:20:51","doi":"10.21203/rs.3.rs-4727561/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":"fa8215dd-438f-4bb4-a06d-6f19f54b9472","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-16T02:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-11 12:20:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4727561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4727561","identity":"rs-4727561","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}