Identification of Parthanatos-related biomarkers in hemorrhagic shock and study of potential molecular mechanisms

Identification of Parthanatos-related biomarkers in hemorrhagic shock and study of potential molecular mechanisms | 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 Article Identification of Parthanatos-related biomarkers in hemorrhagic shock and study of potential molecular mechanisms Zhenqi XU, Qiulan YU, Wei YI, Xiaoling PENG, Yuehua GONG, Yifan RAO, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8740659/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Hemorrhagic shock (HS) remains a critical condition, yet the involvement of parthanatos is not well defined. This study aimed to identify parthanatos-related biomarkers in HS and elucidate their mechanisms. By analyzing HS datasets and parthanatos-related genes (PARGs), candidate genes were screened via differential expression and key modular analysis. Biomarkers were selected using machine learning, receiver operating characteristic (ROC) curves, and expression validation. Nomograms were constructed and evaluated. Functional mechanisms of biomarkers were explored through chromosomal localization, subcellular localization, enrichment analysis, immune infiltration, and regulatory networks. Expression of biomarkers was validated with reverse transcription quantitative polymerase chain reaction (RT-qPCR). Glycogenin-1 (GYG1) and Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D) were identified as parthanatos-related biomarkers in HS. Nomograms showed strong predictive potential for HS. Enrichment analysis revealed co-enrichment in oxidative phosphorylation, Parkinson’s disease, and proteasome pathways. Both biomarkers were correlated with various immune cells, and hsa-mir-181d-5p and hsa-mir-25-3p were identified as co-targeting GYG1 and PPP1R3D. Drug analysis revealed Digoxin as a potential therapeutic agent. RT-qPCR confirmed upregulation of GYG1 and PPP1R3D in HS samples. GYG1 and PPP1R3D were identified as biomarkers associated with parthanatos in HS, providing a reference for early diagnosis of HS and optimization of treatment options. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Hemorrhagic shock Parthanatos Biomarkers Immune infiltration Drug prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Hemorrhagic shock (HS) is a critical condition in which trauma or disease causes significant blood loss in the body, resulting in a decrease in effective circulating blood volume, insufficient tissue perfusion, and ultimately leading to cellular metabolic disorders and organ dysfunction [ 1 ] . HS is the leading cause of trauma related deaths, with approximately 1.5 million people worldwide dying from traumatic bleeding each year, of which over 60% of pre hospital deaths and over 40% of in-hospital deaths were caused by uncontrollable bleeding [ 2 ] . Mitigating organ dysfunction caused by HS is crucial for reducing long-term mortality in patients. But commonly used clinical treatments, including mechanical ventilation, extracorporeal membrane oxygenation, blood transfusion, anticoagulation, and immunomodulatory therapy, cannot reduce organ dysfunction [ 3 , 4 ] . Current research indicates that disruption of energy homeostasis and overactivation of the immune system are the main mechanisms by which HS induces organ dysfunction [ 5 ] . Some potential therapeutic drugs alleviate HS-induced organ damage by regulating immunity [ 6 ] , restoring energy metabolism homeostasis [ 7 ] , and inhibiting oxidative stress damage [ 8 ] . This provides a new possible approach for the treatment of HS. However, there are various factors that affect immune regulation, energy metabolism, and oxidative stress. Excessive activation or inhibition may both lead to adverse consequences [ 9 , 10 ] . Currently, most of these studies remain in the animal experiment stage. There is no clear treatment method or drug that can reduce the occurrence of organ dysfunction in HS patients. So finding new biomarkers for HS is urgently needed for exploring the potential pathogenesis of HS and obtaining potential therapeutic targets. Programmed cell death (PCD) refers to an active process of cell death, other than accidental death, that is defined by biochemical characteristics and programmed by internal mechanisms regulated by genes [ 11 ] . There are various types of PCD, such as apoptosis, programmed necrosis, pyroptosis, and parthanatos—a type recently discovered. Parthanatos is a form of cell death mediated by poly (ADP-ribose) polymerase 1 (PARP1), and its molecular mechanism mainly includes DNA damage, PARP1 overactivation, poly (ADP-ribose) (PAR), nicotinamide adenine dinucleotide (NAD+) and adenosine triphosphate (ATP) consumption, and apoptosis inducing factor (AIF) nuclear translocation [ 12 ] . Research has shown that the essence of HS is a sharp decrease in the effective circulating blood volume throughout the body, leading to severe tissue perfusion and oxygen supply disorders, thereby triggering systemic hypoxia and ischemia [ 13 ] . During this process, tissue hypoxia and ischemia can directly cause cellular DNA damage [ 14 ] . It is worth noting that DNA damage is a key trigger for activating PARP1, while excessive activation of PARP1 is the core initiating link of parthanatos programmed cell death pathway [ 12 ] . Previous studies have confirmed that parthanatos is one of the important lethal mechanisms of ischemia/hypoxia related tissue injury, especially in ischemia/hypoxia models, where inhibition of this pathway shows promising therapeutic potential [ 15 , 16 ] . However, there is still a lack of systematic research directly exploring the mechanism of parthanatos in HS systemic hypoxia stress. Therefore, elucidating whether and how parthanatos are involved in the progression of HS not only helps to reveal new mechanisms by which they cause organ damage, but also provides a possible research entry point for finding early diagnostic biomarkers and developing targeted intervention strategies. This study evaluated biomarkers related to parthanatos in HS based on transcriptome data and PARGs information from public databases. Subsequently, based on these identified biomarkers, the construction and evaluation of nomogram, enrichment analysis, immune infiltration analysis, and molecular docking were used to explore the potential mechanisms of action of these biomarkers in HS. The aim was to elucidate the molecular roles of these biomarkers in diseases and provide new references for early diagnosis and customized treatment plans for HS patients. 2. Materials and methods 2.1 Data source Accessed HS-related datasets (GSE64711 and GSE160905) from the GEO ( http://www.ncbi.nlm.nih.gov/geo/ ) database. GSE64711 (platform: GPL19607) was employed as a training set, including whole blood samples from 487 HS patients and 17 normal controls. GSE160905 (platform: GPL1261) was used as a validation set, including peripheral blood samples from 3 HS mice and 3 normal control mice. A total of 11 PARGs, including PARP1, AIFM1, ADPRS, MCOPDK8, RNF146, NAMPT, GPX4, SQSTM1, CAST, AIMP2 and RIPK1, were retrieved via the literature [ 17 ] . 2.2 Differential expression analysis Differentially expressed genes (DEGs) (HS and control samples) in the GSE64711 dataset were identified via the limma (v 3.54.0) package [ 18 ] , and the screening criteria were |log 2 FC| > 0.5, P < 0.05. Meanwhile, volcano plots and heatmaps of DEGs were generated through the ggplot2 (v 3.4.1) package [ 19 ] and ComplexHeatmap (v 2.14.0) package [ 20 ] , respectively. 2.3 Weighted gene co-expression network analysis (WGCNA) PARGs scores were computed for all samples in GSE64711 using single-sample gene set enrichment analysis (GSEA) in the gene set variation analysis (GSVA) (v 1.46.0) package [ 21 ] . Subsequently, differences in PARGs scores between HS and control samples were analysed via the Wilcoxon test ( P < 0.05). All samples in the GSE64711 were analysed via WGCNA package (v 1.71) [ 22 ] to identify the modular genes with the highest associations with PARGs scores. Initially, all samples were hierarchically clustered in GSE64711 through the Euclidean distance of the sample expression profiles and were used to recognize and exclude outliers. The optimal soft threshold (power) was chosen based on a scale-free fit index (signed R 2 ) greater than or equal to 0.80 and a mean connectivity approaching zero. Using the filtered expression matrix, we constructed the WGCNA network via the dynamic tree cutting algorithm, with parameters set as follows: a minimum of 30 genes per module and a mergeCutHeight of 0.25. Subsequently, co-expression modules were identified and hierarchical clustering trees were generated. The correlation matrix between PARGs scores and co-expression modules was calculated by Spearman correlation analysis using PARGs scores as the phenotypic traits (|correlation coefficients (cor)| > 0.50, P < 0.05). The positively and negatively correlated modules with the highest correlation with PARGs scores were chosen as key modules, and key module genes were recognized. 2.4 Identification and functional analysis of candidate genes The overlap of DEGs and key module genes was implemented utilizing the VennDiagram (v 1.7.1) package [ 23 ] with the aim of identifying DEGs associated with parthanatos in HS that were documented as candidate genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of the Genome (KEGG) enrichment analyses ( P < 0.05) were carried out on candidate genes with the help of the clusterProfiler (v 4.2.2) package [ 24 ] . The top5 enrichment results from GO and KEGG enrichment analyses were visualized using the enrichplot (v 1.18.3) package [ 25 ] . 2.5 Protein-protein interaction (PPI) network construction The PPI network (interaction score ≥ 0.40) was constructed using the Searching for Interacting Genes (STRING) database and the results were visualised via Cytoscape (v 3.10.1) software [ 26 ] . Afterwards, candidate genes with top 20 ClusteringCoefficient scores were screened for subsequent analyses using the cytohhub algorithm. 2.6 Biomarker identification and expression analysis Based on the top 20 genes identified above in the PPI network, LASSO regression analyses were conducted utilising the glmnet (v 4.1-4) package [ 27 ] , where genes that were not penalised to 0 were considered as feature genes. Subsequently, the ROC curves of feature genes in the GSE64711 dataset were plotted using pROC (v 1.18.0) software package [ 28 ] , and the characterised genes with area under curve (AUC) > 0.7 were recorded as candidate biomarkers. Finally, the expression of these candidate biomarkers in the GSE64711 and GSE160905 datasets were analysed separately, and candidate biomarkers that differed significantly ( P < 0.05) between the HS and normal groups and showed a consistent trend of expression in the two datasets were defined as the biomarkers for this study. 2.7 Construction and evaluation of nomogram To further analyze the reliability of biomarkers in predicting HS, a nomogram of biomarkers in GSE64711 was built through the rms (v 6.5-1) package [ 29 ] . Then, to determine the validity of the nomogram, calibration curve was graphed via the calibrate (v 1.7.7) package [ 30 ] ( P 0.7). Meanwhile, decision curve was charted through the ggDCA (v 1.2) package [ 31 ] , and Clinical Impact Curve (CIC) was plotted utilising the rmda (v 1.6) package [ 32 ] . 2.8 Enrichment analysis of biomarkers To probe the biological roles of biomarkers involved in the pathway of disease, GSEA was performed. The c2.cp.kegg.v11.0.symbols gene set was obtained from the Molecular Signatures Database (MSigDB) to serve as the background set for the analysis. Spearman correlations between the biomarkers and other genes were computed via the psych (v 2.1.6) package [ 33 ] in the GSE64711 dataset. Subsequently, GSEA for each biomarker was constructed using clusterProfiler package, with significance determined at adj. P 1. Besides, to further explore differences of KEGG pathways between HS and control group, GSVA was also completed utilizing the GSVA package based on the background gene set, C2: KEGG gene sets, where limma was applied to screen the signaling pathways that differed between different groups. Moreover, Predicting the genes related to biomarker functions and the functions involved by GeneMANIA database, and constructing gene-gene interaction (GGI) network. 2.9 Chromosomal localisation, subcellular localisation and correlation analysis of biomarkers Chromosomal localization analysis of genes was carried out via the RCircos software package (v 1.2.2) [ 34 ] . The subcellular localisation of the biomarkers was analysed via the Human Protein Atlas (HPA) database. Spearman correlation analysis between biomarkers by the psych (v 2.1.6) package (|cor| > 0.3). 2.10 Analysis of immune infiltration and cytokines expression To assess the level of infiltration of 22 immune cells [ 35 ] in HS and control samples from GSE64711, cell type identification was performed using the relative subset of estimated RNA transcripts (CIBERSORT) algorithm, which excludes samples with P > 0.05. The ggplot2 package was utilised to generate a stacked plot to show the proportionate distribution of the 22 immune cells in each sample. The Wilcoxon test was adopted to evaluate the types of infiltrating immune cells exhibiting significant differences between HS samples and control samples in the GSE64711 dataset ( P < 0.05). Subsequently, Spearman correlation analysis was applied via ggpubr (v 0.6.0) package [ 36 ] to probe the linkages among the differential immune cells, and between differential immune cells and biomarkers (|cor| > 0.30, P < 0.05), and correlation heatmaps were plotted to visualise the results using the ggcor (v 0.9.8) package [ 37 ] for correlation heatmaps to visualise the results. Lastly, to clarify the association of HS with cytokines in immune cells, the expression of 7 key cytokines [ 38 ] was compared between HS and control samples of GSE64711 using Wilcoxon test ( P < 0.05). 2.11 Construction of regulatory networks The VennDiagram package was used to overlap the miRWalk database and the PITA database predicted miRNAs from the pubs/mir07/mir07_data database, identifying key miRNAs targeting the biomarkers. Subsequently, key lncRNAs targeting key miRNAs were identified by overlaying lncRNAs obtained from the starBase database and the miRNet database using the same method. Ultimately, a comprehensive lncRNA-miRNA-mRNA network was established. 2.12 Drug prediction and molecular docking The enrichR (v 3.2) package [ 39 ] was utilized to predict drugs targeting biomarkers, and biomarker-drug networks were visualised by Cytoscape software. Subsequently, to understand the association between drugs, immune cells and biomarkers, the drug-biomarker-immune cell network was visualised using Sankey diagrams based on differential immune cells obtained from immune infiltration analyses, potential drugs and biomarkers targeting biomarkers for HS. Finally, based on the highest rated drugs and biomarkers among the potential drugs targeting biomarkers for HS, molecular docking was performed using Autodock2 software to explore the binding ability of the biomarkers to the drugs. The 3D structures of the biomarkers (acting as receptors) were extracted from the Protein Data Bank (PDB, https://www.rcsb.org/ ), while the 3D structures of the drugs (acting as ligands) were retrieved from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). Good binding capacity was defined as binding energies ≤ -5 kcal/mol. 2.13 Reverse transcription quantitative PCR (RT-qPCR) Ten whole blood samples were collected from five HS patients and five controls at the 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force. Approval was obtained from the hospital's ethics committee (Approval No. : 908YYLL2025009), and informed consent was obtained from all participants. Total RNA was abstracted from each sample using TRIzol reagent (Ambion, USA), and RNA concentration was quantitated using a NanoPhotometer N50 spectrophotometer. Reverse transcription was subsequently performed using the SureScript First-Strand cDNA Synthesis Kit. Primer sequences are listed in Supplementary Table S1 . Relative mRNA quantification was determined using the 2-ΔΔCT method, with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) serving as the housekeeping gene. RT-qPCR results were exported to Excel and analyzed statistically using GraphPad Prism 5 for visualization ( P < 0.05). 2.14 Statistical analysis Bioinformatic analyses were performed in the R (v 4.2.2). P < 0.05 was indicated to be significant. For RT-qPCR analysis, the t-test was applied for statistical comparisons. 3. Results 3.1 Altogether 1,516 DEGs and 1,909 key module genes were separately identified Totally 1,516 DEGs were screened between HS and control samples in GSE64711 dataset, with 580 up-regulated and 936 down-regulated DEGs. The top 10 up- and down-regulated DEGs were labeled in the volcano plot (Fig. 1 a) and the expression of DEGs in HS samples and control samples was shown in the heatmap (Fig. 1 b). Then, calculation of PARGs scores revealed that PARGs scores were significantly higher for HS samples in GSE64711 (Fig. 1 c). There were no abnormal samples in GSE64711 (Fig. 1 d). The optimal soft threshold (power) was established as 6 when the R 2 was greater than 0.8 and the mean connectivity was also near 0 (Fig. 1 e). Next, a co-expression matrix was established and 10 gene modules were determined (Fig. 1 f). Among them, Meyellow (cor = 0.55, P < 0.05) and Mepink (cor = -0.55, P < 0.05), which had the largest positive and negative correlation with PARGs scores, respectively, were considered as key modules (Fig. 1 g), which contained a total of 1,909 genes. 3.2 Candidate genes were associated with multiple pathways A total of 339 candidate genes were discovered after overlapping 1,516 DEGs and 1,909 key module genes. (Fig. 2 a). Besides, the enrichment analysis of 339 candidate genes showed they were enriched in 246 GO entries ( Supplementary Table S2 ) and 26 KEGG pathways ( Supplementary Table S3 ). In detail, the majority of the GO terms were detected to be related to immunity. (Fig. 2 b). Additionally, for KEGG analysis, candidate genes were notably enriched in signalling pathways such as NOD-like receptor and tumor necrosis factor (TNF) (Fig. 2 c). 3.3 GYG1 and PPP1R3D were deemed as biomarkers GYG1和PPP1R3D Initially, the PPI network was constructed, in which genes such as FBXW7 and PPIG were highly connected to other genes (Fig. 3 a). After that, the candidate genes with top 20 Clustering Coefficient scores were selected by the cytohhub algorithm for subsequent analyses ( Supplementary Table S4 ). Based on the 20 candidate genes screened by the PPI network, 7 feature genes were obtained by screening using LASSO regression analysis, including SYNE1, KIAA1012, C1orf58, VPS24, GYG1, PPP1R3D and IRAK3 (Figs. 3 b-c). AUC values of the 7 feature genes in the GSE64711 dataset were all greater than 0.9, which had good diagnostic ability for HS and could be used as candidate biomarkers in this study (Fig. 3 d). Moreover, the expression analysis of the candidate biomarkers in the GSE64711 and GSE160905 datasets showed that the expression trends of GYG1 and PPP1R3D were consistent in the 2 datasets and both were significantly up-regulated in the HS samples (Figs. 3 e-f). 3.4 The nomogram of biomarkers was built and assessed In order to further predict the risk of HS occurrence, the nomogram model was built based on biomarkers (Fig. 4 a). Subsequently, the calibration curve was evaluated, and the P-value of Hosmer-Lemeshow (HL) test was greater than 0.05 (Fig. 4 b). Meanwhile, in the ROC curve, the AUC value of the nomogram was 0.99 (Fig. 4 c). Afterthat, the decision curve analysis (DCA) revealed that the model's net utility surpassed that of any single factor (Fig. 4 d). Eventually, the CIC displayed the proportion of people at risk of HS for different threshold probabilities, and the CIC converged to the actual situation (Fig. 4 e). All these results suggested that the nomogram for biomarkers presented wonderful predictive performance for the occurrence of HS. 3.5 The function analysis was helpful for exploring the potential mechanism for HS To probe the biological functions of the biomarkers, GSEA was accomplished in GSE64711 dataset. 13 and 15 pathways were significantly enriched for GYG1 and PPP1R3D, respectively. Notably, Oxidative phosphorylation, Proteasome, Pathogenic Escherichia coli infection, and Parkinson's disease, which were significantly correlated with both GYG1 and PPP1R3D, were among the results of the enriched top 5 ( Supplementary Figures S5a-b ). The GSVA results of KEGG showed a total of 181 signaling pathways that were significantly different between HS and controls, with the top 20 results including reference cxcr4 gnaq plcb g calcineurin signaling pathway, reference glycolysis ( Supplementary Figure S5c ). Moreover, the GGI network was constructed and the top 20 genes associated with biomarker function (such as GYS1) were shown, which are associated with glycogen metabolism processes, among others ( Supplementary Figure S5d ). 3.6 Biomarkers localised in the cytoplasm but on different chromosomes The functions associated with biomarkers were subjected to further investigation. The results demonstrated that the GYG1 and PPP1R3D were localized to chromosomes 3 and 20, respectively (Fig. 5 a). Furthermore, GYG1 and PPP1R3D were predominantly localized within the cytoplasm (Fig. 5 b). Moreover, Spearman correlation analyses showed a positive correlation between GYG1 and PPP1R3D (|cor| > 0.3) (Fig. 5 c). 3.7 GYG1 and PPP1R3D were associated with immune infiltrating cells After excluding samples with P > 0.05 from the GSE64711 dataset, Fig. 6 a showed the infiltration levels of 22 immune cells in HS and control samples. Among them, the infiltration levels of 17 immune cells were significantly different between HS and control samples, including B cells memory (Fig. 6 b). In addition, correlations of differential immune cells with each other showed that Neutrophils had the most significant negative correlation with Monocytes (cor = -0.82), and that T cells CD4 memory activated and NK cells resting had the most significant positive correlation (cor = 0.73) (Fig. 6 c). Both GYG1 and PPP1R3D had the most significant negative correlation with B cells memory (cor − 0.39, -0.34, respectively) and the most significant positive linkage with T cells gamma delta (cor 0.41, 0.33, respectively) (Fig. 6 d). Finally, the differential expression of 7 key cytokines was compared between HS and control samples, and IL-10 was found to be markedly different between the 2 groups of samples ( P < 0.05) (Fig. 6 e). 3.8 Molecular regulatory networks probe regulatory mechanisms of biomarkers Initially, 752 and 72 miRNAs regulating two biomarkers (GYG1 and PPP1R3D) were predicted by the miRWalk and PITA databases, respectively. miRNAs predicted by the two databases were crossed, resulting in 18 key miRNAs, including hsa-mir-181d-5p (Fig. 7 a). The Starbase and miRNet databases predicted 262 and 356 lncRNAs upstream of key miRNAs, respectively, and the crossover yielded 92 key lncRNAs, such as LINC01343 and MIR181A1HG (Fig. 7 b). Accordingly, the lncRNA-miRNA-mRNA network containing two biomarkers (GYG1 and PPP1R3D), 18 key miRNAs, and 92 key lncRNAs was constructed (Fig. 7 c), indicating that GYG1 and PPP1R3D were regulated by multiple factors. Notably, hsa-mir-181d-5p and hsa-mir-25-3p co-targeted GYG1 and PPP1R3D. 3.9 Drug prediction and molecular docking suggested GYG1 and PPP1R3D as potential therapeutic targets for HS A total of 36 drugs were predicted to target 2 biomarkers, among which Digoxin, Ouabain, Lanatoside C, Digitoxigenin, Chlorzoxazone and Retinoic acid could target both GYG1 and PPP1R3D. A biomarker-drug network was thus constructed, such as GYG1-Chlorzoxazone, PPP1R3D-Digoxigenin ( Supplementary Figure S6a, Supplementary Table S7 ). After that, the drug-biomarker-differential immune cell network was constructed by combining differential immune cells ( Supplementary Figure S6b ). Digoxin scored the highest among the drugs targeting both GYG1 and PPP1R3D. Molecular docking was then performed. Specifically, GYG1 bound to Digoxin via hydrogen bonding with 8 binding sites and a binding energy of -9.3 kcal/mol ( Supplementary Figure S6c ). PPP1R3D bound to Digoxin via hydrogen bonding with 8 binding sites and a binding energy of -9.2 kcal/mol ( Supplementary Figure S6d ). Overall, these results elucidate the strong interaction of GYG1 and PPP1R3D with Digoxin, emphasizing their potential functional role in therapy. 3.10 The expression of biomarkers was verified Past studies have shown that GYG1 and PPP1R3D were significantly up-regulated in HS samples in the GSE64711 and GSE160905 datasets ( P < 0.05) (Figs. 3 e-f). This prompted the further utilization of RT-qPCR approaches to confirm the clinical expression levels of these biomarkers among HS patients. The RT-qPCR results demonstrated that GYG1 and PPP1R3D were significantly up-regulated in HS samples ( P < 0.05) (Figs. 8 a-b), which was consistent with the results in the GSE64711 and GSE160905 datasets, attesting the correctness of the bioinformatics analysis. 4. Discussion HS is the leading preventable cause of death, characterized by severe blood loss and inadequate tissue perfusion, and the reoxygenation of ischemic tissue can exacerbate organ damage through ischemia-reperfusion injury (IRI) [ 13 ] . Parthanatos is a type of PCD mediated by PARP1. Activated PARP induces PCD in a redox-dependent manner and plays an important role in liver injury and inflammation after IRI [ 40 ] . This study identified two highly correlated biomarkers (GYG1 and PPP1R3D) with HS and parthanatos through machine learning algorithms, and effectively validated them through nomogram construction and evaluation, enrichment analysis, regulatory network construction, and immune infiltration analysis. This provides novel insights into the potential pathogenesis of HS. This study identified GYG1 and PPP1R3D as potential biomarkers associated with parthanatos in HS for the first time. The GYG1 gene is located on human chromosome 3 and encodes glycoprotein-1. Glycogen synthesis begins with the self glucosylation of GYG1, followed by elongation of the glucose chain by glycogen synthase [ 41 ] . GYG1 is widely expressed in various tissues, especially in glycogen rich tissues such as muscles and liver. In addition, GYG1 deficiency can lead to impaired glycogen synthesis, resulting in glycogen storage disorders and muscle diseases [ 42 , 43 ] . PPP1R3D is located on chromosome 20 and serves as a glycogen targeting subunit of protein phosphatase 1. It can guide the catalytic subunit to the vicinity of glycogen granules and regulate the activity of key metabolic enzymes such as glycogen synthase and glycogen phosphorylase through dephosphorylation [ 46 ] . Its abnormal expression or functional impairment has been confirmed to be related to various pathological processes such as osteoarthritis and allergic reactions [ 47 , 48 ] . Both GYG1 and PPP1R3D are deeply involved in the regulation of glycogen metabolism, the core link of cellular energy reserve. Research has shown that maintaining liver glycogen during long-term fasting has a protective effect on energy homeostasis in mice [ 44 ] , The glycogen storage mediated by GYG1 plays a crucial energy buffering role in response to metabolic stress such as fasting and ischemia. In IRI, ATP depletion directly leads to a decrease in mitochondrial membrane potential and free radical generation, and insufficient glycogen reserves may further exacerbate such damage [ 45 ] . It is worth noting that glycogen metabolism disorders may cause mitochondrial dysfunction and reactive oxygen species bursts [ 49 , 50 ] , and ROS is one of the effective activators of PARP-1 [ 51 ] . The excessive activation of PARP-1 is the core event that triggers the parthanatos cascade reaction [ 12 ] . It is speculated that the dysregulation of GYG1 and PPP1R3D expression in ischemic, hypoxic, and energy stressed states caused by HS may disrupt glycogen metabolism homeostasis, affect intracellular redox balance, and promote ROS accumulation, potentially exacerbating the activation of PARP-1 dependent signaling pathways and promoting parthanatos' involvement in the HS disease process. Although GYG1 and PPP1R3D have been reported to play roles in shock related pathological processes such as oxidative stress suppression, inflammation relief, and immune regulation [ 52 , 53 ] , their roles in HS and their association with parthanatos have not been reported yet. This study firstly link these two key regulatory factors of glycogen metabolism with the parthanatos pathway in HS, providing a new perspective for understanding their complex molecular mechanisms. In terms of clinical diagnostic value, GYG1 and PPP1R3D also demonstrate outstanding potential. In the training dataset GSE64711, the areas under the working characteristic curves of subjects using both as biomarkers alone were as high as 0.995 and 0.901, respectively, demonstrating a strong ability to distinguish HS from normal samples. The column chart model constructed based on the two has better comprehensive discrimination performance, with a product under the curve of 0.99, and has been verified by calibration curves, decision curves, and clinical impact curves to have good reliability and clinical applicability. These results provide strong computational evidence for GYG1 and PPP1R3D as potential biomarkers for HS. In summary, this study demonstrates the clinical potential of GYG1 and PPP1R3D as early diagnostic biomarkers, providing new reference for elucidating the mechanisms and optimizing the diagnosis and treatment of HS. In the future, large-scale, multicenter clinical studies are still needed to further validate their expression stability, diagnostic efficacy, and clinical application value. Functional enrichment analysis in this study revealed that pathways such as oxidative phosphorylation and proteasome pathway are believed to be related to HS pathogenesis. Research has found that providing sufficient substrate to cells and utilizing oxidative phosphorylation at different stages to improve mitochondrial energy production efficiency alleviate multiple organ failure in HS [ 54 ] . Studies have also found that HS induces proteasome activity in intestinal tissue, and activated proteasomes play an important role in ischemic intestinal pathophysiology, thus acting as key triggers for systemic inflammatory response and multiple organ failure in HS [ 55 ] . Our research further found that GYG1 and PPP1R3D significantly enriched 13 and 15 pathways, respectively, with oxidative phosphorylation and proteasome being among the top 5 enriched pathways, confirming that oxidative phosphorylation, the proteasome, and other enriched pathways may play an important role in HS. Our study found significant differences in the infiltration levels of 17 immune cells, including neutrophils, NK cells, gamma delta T cells, etc., between HS and control samples. Vidaurre MDPH et al. found that neutrophil infiltration and organ damage can be observed in the lungs, kidneys, and other organs of rats with HS [ 56 ] . Manson et al. found that innate lymphocytes also directly participate in the hyperacute immune response of HS, with an increase in circulating NKT and NK cell numbers during the hyperacute phase and a sustained decrease in lymphocytes after 48 hours [ 57 ] . Studies have also found that T cells are critical to the pathogenesis of IRI, including not only CD4 + T cells, but also CD8 + and gamma delta T cells [ 58 ] . Our research further found that GYG1 and PPP1R3D are significantly correlated with neutrophils, NK cells, T cells, and other factors. This further suggests that GYG1 and PPP1R3D may be involved in the pathogenesis of HS by regulating the immune infiltration process. We constructed an lncRNA-miRNA-mRNA regulatory network containing two biomarkers (GYG1 and PPP1R3D), 18 key miRNAs, and 92 key lncRNAs, where hsa-mir-181d-5p and hsa-mir-25-3p jointly target GYG1 and PPP1R3D. Previous studies found that overexpression of miR-181d-5p significantly inhibited inflammatory mediators, reduced cell apoptosis, and further improved renal function after renal IRI [ 59 ] . Considering renal IRI is a common complication of HS, this suggests that hsa-mir-181d-5p may have a similar protective effect on kidney injury induced by HS. Li H et al. found that miR-25-3p, an extracellular vesicle of bone marrow mesenchymal stem cells, could regulate the p53 signaling pathway, inhibited cell apoptosis, and improved liver IRI [ 60 ] . This mechanism may also be applicable to HS related liver injury, as HS often triggers systemic IRI involving multiple organs. IRI is an important factor affecting organ function and prognosis after HS, while hsa-mir-181d-5p and hsa-mir-25-3p both have an impact on it. These two can jointly target GYG1 and PPP1R3D, explaining the molecular mechanism by which GYG1 and PPP1R3D affect the progression of HS, and confirming that these two miRNAs can serve as potential therapeutic targets for alleviating organ damage related to HS. Through prediction, we identified 36 drug targeted biomarkers. Among drugs targeting both GYG1 and PPP1R3D, Digoxin scored the highest. Through molecular docking, it was found that Digoxin exhibits low binding energy (-9.3 kcal/mol and − 9.2 kcal/mol ), which meant high binding affinity, with GYG1 and PPP1R3D. Digoxin is the oldest known cardiovascular drug and is still used to treat heart failure and atrial fibrillation today. Digoxin is a reversible sodium potassium adenosine triphosphatase inhibitor with variable and vagus nerve like properties, which can be used to treat refractory heart failure with reduced ejection fraction and control heart rate in atrial fibrillation [ 61 ] . Research has found that the severity of myocardial IRI varies with the day night cycle and is molecularly linked to components of the cellular clock, including the nuclear receptor REV-ERB α, a transcription inhibitory factor. Digoxin can increase the ubiquitination and proteasomal degradation of REV-ERB α, and has a cardioprotective effect on mice [ 62 ] . The effect of digoxin on myocardial IRI has been discovered in many studies, but its relationship with HS has not been reported yet. Our study has found for the first time that digoxin has the potential to treat HS. This study identified two biomarkers related to parthanatos in HS using bioinformatics methods: GYG1 and PPP1R3D, and validated their expression in clinical samples through RT-qPCR analysis. A nomogram model with good predictive performance was constructed based on these two biomarkers, and the biological pathways involved by biomarkers in HS were explored through functional enrichment analysis. Drug prediction and molecular docking have explored the binding ability of biomarkers with targeted drugs, providing new insights for the early diagnosis of HS and the development of new treatment strategies. However, there are still many aspects of this study that require further research. For example, further extensive experimental mechanism research and clinical application studies are needed to confirm the above relationship. We will also continue to monitor ongoing research and related progress in HS. Abbreviations AIF apoptosis inducing factor ATP adenosine triphosphate CIC Clinical Impact Curve DCA decision curve analysis DEGs differentially expressed genes GAPDH glyceraldehyde-3-phosphate dehydrogenase GGI gene-gene interaction GO Gene Ontology GSEA gene set enrichment analysis GSVA gene set variation analysis GYG1 Glycogenin-1 HL Hosmer-Lemeshow HPA Human Protein Atlas HS hemorrhagic shock IRI ischemia-reperfusion injury KEGG Kyoto Encyclopedia of the Genome NAD+ nicotinamide adenine dinucleotide PAR poly (ADP-ribose) PARGs parthanatos-related genes PARP1 poly (ADP-ribose) polymerase 1 PCD programmed cell death PDB Protein Data Bank PPI protein-protein interaction PPP1R3D Protein Phosphatase 1 Regulatory Subunit 3D ROC receiver operating characteristic RT-qPCR reverse transcription quantitative polymerase chain reaction TNF tumor necrosis factor WGCNA weighted gene co-expression network analysis Declarations Acknowledgements We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Zhenqi Xu, Qiulan Yu and Wei Yi. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the 908 th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force (protocol code 908YYLL2025009). Informed consent has been obtained from the patient. Consent for publication Not applicable. Availability of data and materials The datasets analyzed for this study can be found in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE64711 and GSE160905. Competing interests The authors declare that they have no competing interests. Funding Science and Technology Plan Project of Jiangxi Provincial Administration of Traditional Chinese Medicine (2024B0322). Author contributions Zhenqi XU and Qiulan YU wrote the main manuscript text, while Xiaoling PENG, Yuehua GONG, Yifan RAO, and Chunhua RUAN were responsible for organizing relevant data. Wei YI provided overall guidance and was responsible for the entire research and manuscript writing. All authors read and approved the final manuscript. References Zhang, Z. et al. New research direction of organ dysfunction caused by hemorrhagic shock: mechanisms of mitochondrial quality control. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue . 36 , 93–97 (2024). Rossaint, R. et al. The European guideline on management of major bleeding and coagulopathy following trauma: sixth edition. Crit. Care . 27 , 80 (2023). Gourd, N. M. & Nikitas, N. Multiple Organ Dysfunction Syndrome. J. Intensive Care Med. 35 , 1564–1575 (2020). Ramírez, M. Multiple organ dysfunction syndrome. Curr. Probl. 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Digoxin is still useful, but is still causing toxicity. Cleve Clin. J. Med. 91 , 489–499 (2024). Vinod, M. et al. Timed use of digoxin prevents heart ischemia-reperfusion injury through a REV-ERBα-UPS signaling pathway. Nat. Cardiovasc. Res. 1 , 990–1005 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Editor invited by journal 09 Feb, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 06 Feb, 2026 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. 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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-8740659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":591519131,"identity":"48f33cec-3d7d-4f02-b647-cc953c891eb6","order_by":0,"name":"Zhenqi XU","email":"","orcid":"","institution":"The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force","correspondingAuthor":false,"prefix":"","firstName":"Zhenqi","middleName":"","lastName":"XU","suffix":""},{"id":591519132,"identity":"88785d21-1456-413d-b322-1430d8ad322e","order_by":1,"name":"Qiulan YU","email":"","orcid":"","institution":"The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force","correspondingAuthor":false,"prefix":"","firstName":"Qiulan","middleName":"","lastName":"YU","suffix":""},{"id":591519133,"identity":"3d2446bc-bbc1-4017-b1fb-329325665d02","order_by":2,"name":"Wei YI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYNCCAgYGNvbGxocfiNdiANTCc7jZWIIkLQwS6W0CPEQpvt188OMXA5vEPsmHbQwSDHZyug2EtNw5liwtY5CW2Cad2PaggCHZ2OwAIS03cgykJQwOg7S0G0gwHEjcRlhL/uffEgb/E9skD7ZJ8BCnJYdN8oPBgcQ2CUYitUjeOWZmzWCQbNzGkwgMZAMi/MJ3u/nxzR8VdrLz248/fPihwk6OoBZguDIwA6PDsQHiTkLKoVoYfzAw2BOjdhSMglEwCkYoAAC9JUOU/5VFIQAAAABJRU5ErkJggg==","orcid":"","institution":"The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"YI","suffix":""},{"id":591519134,"identity":"6c1f469e-eac6-4ac0-bcd8-f34b3f5c18ae","order_by":3,"name":"Xiaoling PENG","email":"","orcid":"","institution":"The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"PENG","suffix":""},{"id":591519135,"identity":"886541e4-6354-440d-9ef9-4ec50554d77f","order_by":4,"name":"Yuehua GONG","email":"","orcid":"","institution":"The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force","correspondingAuthor":false,"prefix":"","firstName":"Yuehua","middleName":"","lastName":"GONG","suffix":""},{"id":591519136,"identity":"2c92ef50-f409-432e-b065-92c5df421931","order_by":5,"name":"Yifan RAO","email":"","orcid":"","institution":"The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"RAO","suffix":""},{"id":591519137,"identity":"60c71330-3842-4f5e-b481-2a432873508c","order_by":6,"name":"Chunhua RUAN","email":"","orcid":"","institution":"The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force","correspondingAuthor":false,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"RUAN","suffix":""}],"badges":[],"createdAt":"2026-01-30 11:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8740659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8740659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103050240,"identity":"e06c21df-d18b-418c-89f8-78bcc7594abf","added_by":"auto","created_at":"2026-02-20 07:48:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":372082,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEGs and WGCNA in HS. (a) Volcano plot of DEGs between HS and control tissues. The top 10 upregulated and downregulated genes are labeled. (b) Display heatmaps of DEGs expression in HS samples and control samples. (c) Comparison of PARG expression scores between HS samples and control samples in the GSE64711 dataset. *** indicators \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. (d) Sample clustering dendrogram and trait heatmap for the GSE64711 dataset. (e) The optimal soft threshold (power) when the R\u003csup\u003e2\u003c/sup\u003e was greater than 0.8 and the mean connectivity was also near 0. (f) Cluster dendrogram of genes grouped into 10 co-expression modules. (g) Pearson correlation coefficients and corresponding p-values are shown.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/b74ce26acead64d5b2f713b0.jpeg"},{"id":103018600,"identity":"43d10016-c406-46dc-874e-60f8ed5599fd","added_by":"auto","created_at":"2026-02-19 17:21:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249350,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of candidate genes. (a) Venn diagram showing the intersection between DEGs and genes from the key co-expression module identified by WGCNA. (b) Upregulation and downregulation of candidate genes in biological processes, cellular components, and molecular functions. (c) Upregulation and downregulation of candidate genes in the KEGG pathways.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/df71fcf7ec77b2982a753a8f.jpeg"},{"id":103018602,"identity":"dbdeacc2-0039-4ac0-b384-9e0ec2163c60","added_by":"auto","created_at":"2026-02-19 17:21:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":337524,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and validation of prognostic candidate genes. (a) Constructing PPI network with candidate genes. (b) LASSO regression analysis of candidate genes (cross validation curve). (c) LASSO regression analysis of candidate genes (LASSO coefficient pathway diagram for 7 risk factors). (d) Characteristic genes in the GSE64711 datasets (AUC value\u0026gt;0.9). (e) Expression of candidate biomarkers in the GSE64711 datasets (**** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001). (f) Expression of candidate biomarkers in the GSE160905 datasets (**\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/1ee23609347c2e91108d86f2.jpeg"},{"id":103018607,"identity":"37c72899-9831-4c40-a5b4-d8e0bd54fead","added_by":"auto","created_at":"2026-02-19 17:21:14","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198143,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment and validation of a predictive nomogram model based on candidate biomarkers. (a) The nomogram model of biomarkers. (b) Evaluation of calibration curve (\u003cem\u003eP\u003c/em\u003e\u0026gt; 0.05 for HL test). (c) The ROC curve of the nomogram model (AUC value was 0.99). (d) DCA of the nomogram Model. (e) CIC of the nomogram Model.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/8caf3efed91552d5b5de33bb.jpeg"},{"id":103050010,"identity":"48185235-d0a5-48f2-9429-6cd86f5db8c5","added_by":"auto","created_at":"2026-02-20 07:47:38","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":262282,"visible":true,"origin":"","legend":"\u003cp\u003eLocalization of biomarkers in chromosomes and cells. (a) Localization of GYG1 and PPP1R3D in chromosomes. (b) GYG1 and PPP1R3D are mainly located in the cytoplasm. (c) There is a positive correlation between GYG1 and PPP1R3D (| cor |\u0026gt;0.3).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/41f590654ac95ef4e9fee6e5.jpeg"},{"id":103018603,"identity":"6fd7bee5-906e-4e02-821f-2a5ba65bcb58","added_by":"auto","created_at":"2026-02-19 17:21:14","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":347274,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between biomarkers and immune infiltrating cells. (a) The infiltration level of 22 immune cells in HS and control samples. (b) There were significant differences in the infiltration levels of 17 immune cells between HS and control samples (* \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, **** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001). (c) The correlation between different immune cells. (d) The correlation among GYG1, PPP1R3D and immune cells. (e) Differential expression of 7 key cytokines between HS and control samples (**** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/4001aa3230d831f6cbde6a70.jpeg"},{"id":103018605,"identity":"43d505e6-b7cf-45db-a37c-f670dee61d86","added_by":"auto","created_at":"2026-02-19 17:21:14","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":464786,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular regulatory networks of biomarkers. (a) Regulating 18 key miRNAs of GYG1 and PPP1R3D. (b) Regulating 92 key lncRNAs of GYG1 and PPP1R3D. (c)The lncRNA miRNA mRNA network containing two biomarkers (GYG1 and PPP1R3D), 18 key miRNAs, and 92 key lncRNAs.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/bac113d4995467ff7d50a1bf.jpeg"},{"id":103050343,"identity":"dcbf3057-f3c6-4706-8593-127c9ebe410e","added_by":"auto","created_at":"2026-02-20 07:49:34","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":138287,"visible":true,"origin":"","legend":"\u003cp\u003eRT-qPCR expression validation of biomarkers. (a) The expression of GYG1 in HS and control group. (b) The expression of PPP1R3D in HS and control group.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/0097bbf89bae8d40f54f97fe.jpeg"},{"id":103051205,"identity":"2aea1b3f-6f0b-40bb-b494-0dd9a4ef552f","added_by":"auto","created_at":"2026-02-20 07:58:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3836222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8740659/v1/ec244442-1ded-4f80-bbc6-8cbcfed9262f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Parthanatos-related biomarkers in hemorrhagic shock and study of potential molecular mechanisms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHemorrhagic shock (HS) is a critical condition in which trauma or disease causes significant blood loss in the body, resulting in a decrease in effective circulating blood volume, insufficient tissue perfusion, and ultimately leading to cellular metabolic disorders and organ dysfunction \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. HS is the leading cause of trauma related deaths, with approximately 1.5\u0026nbsp;million people worldwide dying from traumatic bleeding each year, of which over 60% of pre hospital deaths and over 40% of in-hospital deaths were caused by uncontrollable bleeding \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Mitigating organ dysfunction caused by HS is crucial for reducing long-term mortality in patients. But commonly used clinical treatments, including mechanical ventilation, extracorporeal membrane oxygenation, blood transfusion, anticoagulation, and immunomodulatory therapy, cannot reduce organ dysfunction \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Current research indicates that disruption of energy homeostasis and overactivation of the immune system are the main mechanisms by which HS induces organ dysfunction \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Some potential therapeutic drugs alleviate HS-induced organ damage by regulating immunity \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, restoring energy metabolism homeostasis \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, and inhibiting oxidative stress damage \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This provides a new possible approach for the treatment of HS. However, there are various factors that affect immune regulation, energy metabolism, and oxidative stress. Excessive activation or inhibition may both lead to adverse consequences \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Currently, most of these studies remain in the animal experiment stage. There is no clear treatment method or drug that can reduce the occurrence of organ dysfunction in HS patients. So finding new biomarkers for HS is urgently needed for exploring the potential pathogenesis of HS and obtaining potential therapeutic targets.\u003c/p\u003e \u003cp\u003eProgrammed cell death (PCD) refers to an active process of cell death, other than accidental death, that is defined by biochemical characteristics and programmed by internal mechanisms regulated by genes \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. There are various types of PCD, such as apoptosis, programmed necrosis, pyroptosis, and parthanatos\u0026mdash;a type recently discovered. Parthanatos is a form of cell death mediated by poly (ADP-ribose) polymerase 1 (PARP1), and its molecular mechanism mainly includes DNA damage, PARP1 overactivation, poly (ADP-ribose) (PAR), nicotinamide adenine dinucleotide (NAD+) and adenosine triphosphate (ATP) consumption, and apoptosis inducing factor (AIF) nuclear translocation \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Research has shown that the essence of HS is a sharp decrease in the effective circulating blood volume throughout the body, leading to severe tissue perfusion and oxygen supply disorders, thereby triggering systemic hypoxia and ischemia \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. During this process, tissue hypoxia and ischemia can directly cause cellular DNA damage \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. It is worth noting that DNA damage is a key trigger for activating PARP1, while excessive activation of PARP1 is the core initiating link of parthanatos programmed cell death pathway \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Previous studies have confirmed that parthanatos is one of the important lethal mechanisms of ischemia/hypoxia related tissue injury, especially in ischemia/hypoxia models, where inhibition of this pathway shows promising therapeutic potential \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, there is still a lack of systematic research directly exploring the mechanism of parthanatos in HS systemic hypoxia stress. Therefore, elucidating whether and how parthanatos are involved in the progression of HS not only helps to reveal new mechanisms by which they cause organ damage, but also provides a possible research entry point for finding early diagnostic biomarkers and developing targeted intervention strategies.\u003c/p\u003e \u003cp\u003eThis study evaluated biomarkers related to parthanatos in HS based on transcriptome data and PARGs information from public databases. Subsequently, based on these identified biomarkers, the construction and evaluation of nomogram, enrichment analysis, immune infiltration analysis, and molecular docking were used to explore the potential mechanisms of action of these biomarkers in HS. The aim was to elucidate the molecular roles of these biomarkers in diseases and provide new references for early diagnosis and customized treatment plans for HS patients.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eAccessed HS-related datasets (GSE64711 and GSE160905) from the GEO (\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) database. GSE64711 (platform: GPL19607) was employed as a training set, including whole blood samples from 487 HS patients and 17 normal controls. GSE160905 (platform: GPL1261) was used as a validation set, including peripheral blood samples from 3 HS mice and 3 normal control mice. A total of 11 PARGs, including PARP1, AIFM1, ADPRS, MCOPDK8, RNF146, NAMPT, GPX4, SQSTM1, CAST, AIMP2 and RIPK1, were retrieved via the literature \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) (HS and control samples) in the GSE64711 dataset were identified via the limma (v 3.54.0) package \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and the screening criteria were |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Meanwhile, volcano plots and heatmaps of DEGs were generated through the ggplot2 (v 3.4.1) package \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e and ComplexHeatmap (v 2.14.0) package \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Weighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003ePARGs scores were computed for all samples in GSE64711 using single-sample gene set enrichment analysis (GSEA) in the gene set variation analysis (GSVA) (v 1.46.0) package \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Subsequently, differences in PARGs scores between HS and control samples were analysed via the Wilcoxon test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All samples in the GSE64711 were analysed via WGCNA package (v 1.71) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e to identify the modular genes with the highest associations with PARGs scores. Initially, all samples were hierarchically clustered in GSE64711 through the Euclidean distance of the sample expression profiles and were used to recognize and exclude outliers. The optimal soft threshold (power) was chosen based on a scale-free fit index (signed R\u003csup\u003e2\u003c/sup\u003e) greater than or equal to 0.80 and a mean connectivity approaching zero. Using the filtered expression matrix, we constructed the WGCNA network via the dynamic tree cutting algorithm, with parameters set as follows: a minimum of 30 genes per module and a mergeCutHeight of 0.25. Subsequently, co-expression modules were identified and hierarchical clustering trees were generated. The correlation matrix between PARGs scores and co-expression modules was calculated by Spearman correlation analysis using PARGs scores as the phenotypic traits (|correlation coefficients (cor)| \u0026gt; 0.50, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The positively and negatively correlated modules with the highest correlation with PARGs scores were chosen as key modules, and key module genes were recognized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification and functional analysis of candidate genes\u003c/h2\u003e \u003cp\u003eThe overlap of DEGs and key module genes was implemented utilizing the VennDiagram (v 1.7.1) package \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e with the aim of identifying DEGs associated with parthanatos in HS that were documented as candidate genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of the Genome (KEGG) enrichment analyses (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were carried out on candidate genes with the help of the clusterProfiler (v 4.2.2) package \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The top5 enrichment results from GO and KEGG enrichment analyses were visualized using the enrichplot (v 1.18.3) package \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein-protein interaction (PPI) network construction\u003c/h2\u003e \u003cp\u003eThe PPI network (interaction score\u0026thinsp;\u0026ge;\u0026thinsp;0.40) was constructed using the Searching for Interacting Genes (STRING) database and the results were visualised via Cytoscape (v 3.10.1) software \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Afterwards, candidate genes with top 20 ClusteringCoefficient scores were screened for subsequent analyses using the cytohhub algorithm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Biomarker identification and expression analysis\u003c/h2\u003e \u003cp\u003eBased on the top 20 genes identified above in the PPI network, LASSO regression analyses were conducted utilising the glmnet (v 4.1-4) package \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, where genes that were not penalised to 0 were considered as feature genes. Subsequently, the ROC curves of feature genes in the GSE64711 dataset were plotted using pROC (v 1.18.0) software package \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, and the characterised genes with area under curve (AUC)\u0026thinsp;\u0026gt;\u0026thinsp;0.7 were recorded as candidate biomarkers. Finally, the expression of these candidate biomarkers in the GSE64711 and GSE160905 datasets were analysed separately, and candidate biomarkers that differed significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the HS and normal groups and showed a consistent trend of expression in the two datasets were defined as the biomarkers for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction and evaluation of nomogram\u003c/h2\u003e \u003cp\u003eTo further analyze the reliability of biomarkers in predicting HS, a nomogram of biomarkers in GSE64711 was built through the rms (v 6.5-1) package \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Then, to determine the validity of the nomogram, calibration curve was graphed via the calibrate (v 1.7.7) package \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), ROC curve was plotted utilising the pROC (v 1.18.0) package, with AUC values calculated (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7). Meanwhile, decision curve was charted through the ggDCA (v 1.2) package \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, and Clinical Impact Curve (CIC) was plotted utilising the rmda (v 1.6) package \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.8 Enrichment analysis of biomarkers\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo probe the biological roles of biomarkers involved in the pathway of disease, GSEA was performed. The c2.cp.kegg.v11.0.symbols gene set was obtained from the Molecular Signatures Database (MSigDB) to serve as the background set for the analysis. Spearman correlations between the biomarkers and other genes were computed via the psych (v 2.1.6) package \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e in the GSE64711 dataset. Subsequently, GSEA for each biomarker was constructed using clusterProfiler package, with significance determined at adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |NES| \u0026gt; 1. Besides, to further explore differences of KEGG pathways between HS and control group, GSVA was also completed utilizing the GSVA package based on the background gene set, C2: KEGG gene sets, where limma was applied to screen the signaling pathways that differed between different groups. Moreover, Predicting the genes related to biomarker functions and the functions involved by GeneMANIA database, and constructing gene-gene interaction (GGI) network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Chromosomal localisation, subcellular localisation and correlation analysis of biomarkers\u003c/h2\u003e \u003cp\u003eChromosomal localization analysis of genes was carried out via the RCircos software package (v 1.2.2) \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The subcellular localisation of the biomarkers was analysed via the Human Protein Atlas (HPA) database. Spearman correlation analysis between biomarkers by the psych (v 2.1.6) package (|cor| \u0026gt; 0.3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Analysis of immune infiltration and cytokines expression\u003c/h2\u003e \u003cp\u003eTo assess the level of infiltration of 22 immune cells \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e in HS and control samples from GSE64711, cell type identification was performed using the relative subset of estimated RNA transcripts (CIBERSORT) algorithm, which excludes samples with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05. The ggplot2 package was utilised to generate a stacked plot to show the proportionate distribution of the 22 immune cells in each sample. The Wilcoxon test was adopted to evaluate the types of infiltrating immune cells exhibiting significant differences between HS samples and control samples in the GSE64711 dataset (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, Spearman correlation analysis was applied via ggpubr (v 0.6.0) package \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e to probe the linkages among the differential immune cells, and between differential immune cells and biomarkers (|cor| \u0026gt; 0.30, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and correlation heatmaps were plotted to visualise the results using the ggcor (v 0.9.8) package \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e for correlation heatmaps to visualise the results. Lastly, to clarify the association of HS with cytokines in immune cells, the expression of 7 key cytokines \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e was compared between HS and control samples of GSE64711 using Wilcoxon test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Construction of regulatory networks\u003c/h2\u003e \u003cp\u003eThe VennDiagram package was used to overlap the miRWalk database and the PITA database predicted miRNAs from the pubs/mir07/mir07_data database, identifying key miRNAs targeting the biomarkers. Subsequently, key lncRNAs targeting key miRNAs were identified by overlaying lncRNAs obtained from the starBase database and the miRNet database using the same method. Ultimately, a comprehensive lncRNA-miRNA-mRNA network was established.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Drug prediction and molecular docking\u003c/h2\u003e \u003cp\u003eThe enrichR (v 3.2) package \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e was utilized to predict drugs targeting biomarkers, and biomarker-drug networks were visualised by Cytoscape software. Subsequently, to understand the association between drugs, immune cells and biomarkers, the drug-biomarker-immune cell network was visualised using Sankey diagrams based on differential immune cells obtained from immune infiltration analyses, potential drugs and biomarkers targeting biomarkers for HS. Finally, based on the highest rated drugs and biomarkers among the potential drugs targeting biomarkers for HS, molecular docking was performed using Autodock2 software to explore the binding ability of the biomarkers to the drugs. The 3D structures of the biomarkers (acting as receptors) were extracted from the Protein Data Bank (PDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the 3D structures of the drugs (acting as ligands) were retrieved from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Good binding capacity was defined as binding energies \u0026le; -5 kcal/mol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Reverse transcription quantitative PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTen whole blood samples were collected from five HS patients and five controls at the 908th Hospital of Chinese People\u0026rsquo;s Liberation Army Joint Logistic Support Force. Approval was obtained from the hospital's ethics committee (Approval No. : 908YYLL2025009), and informed consent was obtained from all participants. Total RNA was abstracted from each sample using TRIzol reagent (Ambion, USA), and RNA concentration was quantitated using a NanoPhotometer N50 spectrophotometer. Reverse transcription was subsequently performed using the SureScript First-Strand cDNA Synthesis Kit. Primer sequences are listed in \u003cb\u003eSupplementary Table S1\u003c/b\u003e. Relative mRNA quantification was determined using the 2-ΔΔCT method, with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) serving as the housekeeping gene. RT-qPCR results were exported to Excel and analyzed statistically using GraphPad Prism 5 for visualization (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Statistical analysis\u003c/h2\u003e \u003cp\u003eBioinformatic analyses were performed in the R (v 4.2.2). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was indicated to be significant. For RT-qPCR analysis, the t-test was applied for statistical comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Altogether 1,516 DEGs and 1,909 key module genes were separately identified\u003c/h2\u003e \u003cp\u003eTotally 1,516 DEGs were screened between HS and control samples in GSE64711 dataset, with 580 up-regulated and 936 down-regulated DEGs. The top 10 up- and down-regulated DEGs were labeled in the volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and the expression of DEGs in HS samples and control samples was shown in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Then, calculation of PARGs scores revealed that PARGs scores were significantly higher for HS samples in GSE64711 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). There were no abnormal samples in GSE64711 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The optimal soft threshold (power) was established as 6 when the R\u003csup\u003e2\u003c/sup\u003e was greater than 0.8 and the mean connectivity was also near 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Next, a co-expression matrix was established and 10 gene modules were determined (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Among them, Meyellow (cor\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Mepink (cor = -0.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which had the largest positive and negative correlation with PARGs scores, respectively, were considered as key modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg), which contained a total of 1,909 genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Candidate genes were associated with multiple pathways\u003c/h2\u003e \u003cp\u003eA total of 339 candidate genes were discovered after overlapping 1,516 DEGs and 1,909 key module genes. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Besides, the enrichment analysis of 339 candidate genes showed they were enriched in 246 GO entries (\u003cb\u003eSupplementary Table S2\u003c/b\u003e) and 26 KEGG pathways (\u003cb\u003eSupplementary Table S3\u003c/b\u003e). In detail, the majority of the GO terms were detected to be related to immunity. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Additionally, for KEGG analysis, candidate genes were notably enriched in signalling pathways such as NOD-like receptor and tumor necrosis factor (TNF) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 GYG1 and PPP1R3D were deemed as biomarkers GYG1和PPP1R3D\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eInitially, the PPI network was constructed, in which genes such as FBXW7 and PPIG were highly connected to other genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). After that, the candidate genes with top 20 Clustering Coefficient scores were selected by the cytohhub algorithm for subsequent analyses (\u003cb\u003eSupplementary Table S4\u003c/b\u003e). Based on the 20 candidate genes screened by the PPI network, 7 feature genes were obtained by screening using LASSO regression analysis, including SYNE1, KIAA1012, C1orf58, VPS24, GYG1, PPP1R3D and IRAK3 (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c). AUC values of the 7 feature genes in the GSE64711 dataset were all greater than 0.9, which had good diagnostic ability for HS and could be used as candidate biomarkers in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Moreover, the expression analysis of the candidate biomarkers in the GSE64711 and GSE160905 datasets showed that the expression trends of GYG1 and PPP1R3D were consistent in the 2 datasets and both were significantly up-regulated in the HS samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The nomogram of biomarkers was built and assessed\u003c/h2\u003e \u003cp\u003eIn order to further predict the risk of HS occurrence, the nomogram model was built based on biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Subsequently, the calibration curve was evaluated, and the P-value of Hosmer-Lemeshow (HL) test was greater than 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Meanwhile, in the ROC curve, the AUC value of the nomogram was 0.99 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Afterthat, the decision curve analysis (DCA) revealed that the model's net utility surpassed that of any single factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Eventually, the CIC displayed the proportion of people at risk of HS for different threshold probabilities, and the CIC converged to the actual situation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). All these results suggested that the nomogram for biomarkers presented wonderful predictive performance for the occurrence of HS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The function analysis was helpful for exploring the potential mechanism for HS\u003c/h2\u003e \u003cp\u003eTo probe the biological functions of the biomarkers, GSEA was accomplished in GSE64711 dataset. 13 and 15 pathways were significantly enriched for GYG1 and PPP1R3D, respectively. Notably, Oxidative phosphorylation, Proteasome, Pathogenic Escherichia coli infection, and Parkinson's disease, which were significantly correlated with both GYG1 and PPP1R3D, were among the results of the enriched top 5 (\u003cb\u003eSupplementary Figures S5a-b\u003c/b\u003e). The GSVA results of KEGG showed a total of 181 signaling pathways that were significantly different between HS and controls, with the top 20 results including reference cxcr4 gnaq plcb g calcineurin signaling pathway, reference glycolysis (\u003cb\u003eSupplementary Figure S5c\u003c/b\u003e). Moreover, the GGI network was constructed and the top 20 genes associated with biomarker function (such as GYS1) were shown, which are associated with glycogen metabolism processes, among others (\u003cb\u003eSupplementary Figure S5d\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Biomarkers localised in the cytoplasm but on different chromosomes\u003c/h2\u003e \u003cp\u003eThe functions associated with biomarkers were subjected to further investigation. The results demonstrated that the GYG1 and PPP1R3D were localized to chromosomes 3 and 20, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Furthermore, GYG1 and PPP1R3D were predominantly localized within the cytoplasm (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Moreover, Spearman correlation analyses showed a positive correlation between GYG1 and PPP1R3D (|cor| \u0026gt; 0.3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7 GYG1 and PPP1R3D were associated with immune infiltrating cells\u003c/h2\u003e \u003cp\u003eAfter excluding samples with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 from the GSE64711 dataset, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea showed the infiltration levels of 22 immune cells in HS and control samples. Among them, the infiltration levels of 17 immune cells were significantly different between HS and control samples, including B cells memory (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). In addition, correlations of differential immune cells with each other showed that Neutrophils had the most significant negative correlation with Monocytes (cor = -0.82), and that T cells CD4 memory activated and NK cells resting had the most significant positive correlation (cor\u0026thinsp;=\u0026thinsp;0.73) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Both GYG1 and PPP1R3D had the most significant negative correlation with B cells memory (cor\u0026thinsp;\u0026minus;\u0026thinsp;0.39, -0.34, respectively) and the most significant positive linkage with T cells gamma delta (cor 0.41, 0.33, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Finally, the differential expression of 7 key cytokines was compared between HS and control samples, and IL-10 was found to be markedly different between the 2 groups of samples (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Molecular regulatory networks probe regulatory mechanisms of biomarkers\u003c/h2\u003e \u003cp\u003eInitially, 752 and 72 miRNAs regulating two biomarkers (GYG1 and PPP1R3D) were predicted by the miRWalk and PITA databases, respectively. miRNAs predicted by the two databases were crossed, resulting in 18 key miRNAs, including hsa-mir-181d-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The Starbase and miRNet databases predicted 262 and 356 lncRNAs upstream of key miRNAs, respectively, and the crossover yielded 92 key lncRNAs, such as LINC01343 and MIR181A1HG (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). Accordingly, the lncRNA-miRNA-mRNA network containing two biomarkers (GYG1 and PPP1R3D), 18 key miRNAs, and 92 key lncRNAs was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), indicating that GYG1 and PPP1R3D were regulated by multiple factors. Notably, hsa-mir-181d-5p and hsa-mir-25-3p co-targeted GYG1 and PPP1R3D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Drug prediction and molecular docking suggested GYG1 and PPP1R3D as potential therapeutic targets for HS\u003c/h2\u003e \u003cp\u003eA total of 36 drugs were predicted to target 2 biomarkers, among which Digoxin, Ouabain, Lanatoside C, Digitoxigenin, Chlorzoxazone and Retinoic acid could target both GYG1 and PPP1R3D. A biomarker-drug network was thus constructed, such as GYG1-Chlorzoxazone, PPP1R3D-Digoxigenin (\u003cb\u003eSupplementary Figure S6a, Supplementary Table S7\u003c/b\u003e). After that, the drug-biomarker-differential immune cell network was constructed by combining differential immune cells (\u003cb\u003eSupplementary Figure S6b\u003c/b\u003e). Digoxin scored the highest among the drugs targeting both GYG1 and PPP1R3D. Molecular docking was then performed. Specifically, GYG1 bound to Digoxin via hydrogen bonding with 8 binding sites and a binding energy of -9.3 kcal/mol (\u003cb\u003eSupplementary Figure S6c\u003c/b\u003e). PPP1R3D bound to Digoxin via hydrogen bonding with 8 binding sites and a binding energy of -9.2 kcal/mol (\u003cb\u003eSupplementary Figure S6d\u003c/b\u003e). Overall, these results elucidate the strong interaction of GYG1 and PPP1R3D with Digoxin, emphasizing their potential functional role in therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.10 The expression of biomarkers was verified\u003c/h2\u003e \u003cp\u003ePast studies have shown that GYG1 and PPP1R3D were significantly up-regulated in HS samples in the GSE64711 and GSE160905 datasets (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-f). This prompted the further utilization of RT-qPCR approaches to confirm the clinical expression levels of these biomarkers among HS patients. The RT-qPCR results demonstrated that GYG1 and PPP1R3D were significantly up-regulated in HS samples (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-b), which was consistent with the results in the GSE64711 and GSE160905 datasets, attesting the correctness of the bioinformatics analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHS is the leading preventable cause of death, characterized by severe blood loss and inadequate tissue perfusion, and the reoxygenation of ischemic tissue can exacerbate organ damage through ischemia-reperfusion injury (IRI) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Parthanatos is a type of PCD mediated by PARP1. Activated PARP induces PCD in a redox-dependent manner and plays an important role in liver injury and inflammation after IRI \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. This study identified two highly correlated biomarkers (GYG1 and PPP1R3D) with HS and parthanatos through machine learning algorithms, and effectively validated them through nomogram construction and evaluation, enrichment analysis, regulatory network construction, and immune infiltration analysis. This provides novel insights into the potential pathogenesis of HS.\u003c/p\u003e \u003cp\u003eThis study identified GYG1 and PPP1R3D as potential biomarkers associated with parthanatos in HS for the first time. The GYG1 gene is located on human chromosome 3 and encodes glycoprotein-1. Glycogen synthesis begins with the self glucosylation of GYG1, followed by elongation of the glucose chain by glycogen synthase \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. GYG1 is widely expressed in various tissues, especially in glycogen rich tissues such as muscles and liver. In addition, GYG1 deficiency can lead to impaired glycogen synthesis, resulting in glycogen storage disorders and muscle diseases \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. PPP1R3D is located on chromosome 20 and serves as a glycogen targeting subunit of protein phosphatase 1. It can guide the catalytic subunit to the vicinity of glycogen granules and regulate the activity of key metabolic enzymes such as glycogen synthase and glycogen phosphorylase through dephosphorylation \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Its abnormal expression or functional impairment has been confirmed to be related to various pathological processes such as osteoarthritis and allergic reactions \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Both GYG1 and PPP1R3D are deeply involved in the regulation of glycogen metabolism, the core link of cellular energy reserve. Research has shown that maintaining liver glycogen during long-term fasting has a protective effect on energy homeostasis in mice \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, The glycogen storage mediated by GYG1 plays a crucial energy buffering role in response to metabolic stress such as fasting and ischemia. In IRI, ATP depletion directly leads to a decrease in mitochondrial membrane potential and free radical generation, and insufficient glycogen reserves may further exacerbate such damage \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. It is worth noting that glycogen metabolism disorders may cause mitochondrial dysfunction and reactive oxygen species bursts \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, and ROS is one of the effective activators of PARP-1\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. The excessive activation of PARP-1 is the core event that triggers the parthanatos cascade reaction \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. It is speculated that the dysregulation of GYG1 and PPP1R3D expression in ischemic, hypoxic, and energy stressed states caused by HS may disrupt glycogen metabolism homeostasis, affect intracellular redox balance, and promote ROS accumulation, potentially exacerbating the activation of PARP-1 dependent signaling pathways and promoting parthanatos' involvement in the HS disease process. Although GYG1 and PPP1R3D have been reported to play roles in shock related pathological processes such as oxidative stress suppression, inflammation relief, and immune regulation \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e, their roles in HS and their association with parthanatos have not been reported yet. This study firstly link these two key regulatory factors of glycogen metabolism with the parthanatos pathway in HS, providing a new perspective for understanding their complex molecular mechanisms. In terms of clinical diagnostic value, GYG1 and PPP1R3D also demonstrate outstanding potential. In the training dataset GSE64711, the areas under the working characteristic curves of subjects using both as biomarkers alone were as high as 0.995 and 0.901, respectively, demonstrating a strong ability to distinguish HS from normal samples. The column chart model constructed based on the two has better comprehensive discrimination performance, with a product under the curve of 0.99, and has been verified by calibration curves, decision curves, and clinical impact curves to have good reliability and clinical applicability. These results provide strong computational evidence for GYG1 and PPP1R3D as potential biomarkers for HS. In summary, this study demonstrates the clinical potential of GYG1 and PPP1R3D as early diagnostic biomarkers, providing new reference for elucidating the mechanisms and optimizing the diagnosis and treatment of HS. In the future, large-scale, multicenter clinical studies are still needed to further validate their expression stability, diagnostic efficacy, and clinical application value.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis in this study revealed that pathways such as oxidative phosphorylation and proteasome pathway are believed to be related to HS pathogenesis. Research has found that providing sufficient substrate to cells and utilizing oxidative phosphorylation at different stages to improve mitochondrial energy production efficiency alleviate multiple organ failure in HS \u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Studies have also found that HS induces proteasome activity in intestinal tissue, and activated proteasomes play an important role in ischemic intestinal pathophysiology, thus acting as key triggers for systemic inflammatory response and multiple organ failure in HS \u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Our research further found that GYG1 and PPP1R3D significantly enriched 13 and 15 pathways, respectively, with oxidative phosphorylation and proteasome being among the top 5 enriched pathways, confirming that oxidative phosphorylation, the proteasome, and other enriched pathways may play an important role in HS.\u003c/p\u003e \u003cp\u003eOur study found significant differences in the infiltration levels of 17 immune cells, including neutrophils, NK cells, gamma delta T cells, etc., between HS and control samples. Vidaurre MDPH et al. found that neutrophil infiltration and organ damage can be observed in the lungs, kidneys, and other organs of rats with HS \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Manson et al. found that innate lymphocytes also directly participate in the hyperacute immune response of HS, with an increase in circulating NKT and NK cell numbers during the hyperacute phase and a sustained decrease in lymphocytes after 48 hours \u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Studies have also found that T cells are critical to the pathogenesis of IRI, including not only CD4\u0026thinsp;+\u0026thinsp;T cells, but also CD8\u003csup\u003e+\u003c/sup\u003e and gamma delta T cells \u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Our research further found that GYG1 and PPP1R3D are significantly correlated with neutrophils, NK cells, T cells, and other factors. This further suggests that GYG1 and PPP1R3D may be involved in the pathogenesis of HS by regulating the immune infiltration process.\u003c/p\u003e \u003cp\u003eWe constructed an lncRNA-miRNA-mRNA regulatory network containing two biomarkers (GYG1 and PPP1R3D), 18 key miRNAs, and 92 key lncRNAs, where hsa-mir-181d-5p and hsa-mir-25-3p jointly target GYG1 and PPP1R3D. Previous studies found that overexpression of miR-181d-5p significantly inhibited inflammatory mediators, reduced cell apoptosis, and further improved renal function after renal IRI \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Considering renal IRI is a common complication of HS, this suggests that hsa-mir-181d-5p may have a similar protective effect on kidney injury induced by HS. Li H et al. found that miR-25-3p, an extracellular vesicle of bone marrow mesenchymal stem cells, could regulate the p53 signaling pathway, inhibited cell apoptosis, and improved liver IRI \u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. This mechanism may also be applicable to HS related liver injury, as HS often triggers systemic IRI involving multiple organs. IRI is an important factor affecting organ function and prognosis after HS, while hsa-mir-181d-5p and hsa-mir-25-3p both have an impact on it. These two can jointly target GYG1 and PPP1R3D, explaining the molecular mechanism by which GYG1 and PPP1R3D affect the progression of HS, and confirming that these two miRNAs can serve as potential therapeutic targets for alleviating organ damage related to HS.\u003c/p\u003e \u003cp\u003eThrough prediction, we identified 36 drug targeted biomarkers. Among drugs targeting both GYG1 and PPP1R3D, Digoxin scored the highest. Through molecular docking, it was found that Digoxin exhibits low binding energy (-9.3 kcal/mol and \u0026minus;\u0026thinsp;9.2 kcal/mol ), which meant high binding affinity, with GYG1 and PPP1R3D. Digoxin is the oldest known cardiovascular drug and is still used to treat heart failure and atrial fibrillation today. Digoxin is a reversible sodium potassium adenosine triphosphatase inhibitor with variable and vagus nerve like properties, which can be used to treat refractory heart failure with reduced ejection fraction and control heart rate in atrial fibrillation \u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. Research has found that the severity of myocardial IRI varies with the day night cycle and is molecularly linked to components of the cellular clock, including the nuclear receptor REV-ERB α, a transcription inhibitory factor. Digoxin can increase the ubiquitination and proteasomal degradation of REV-ERB α, and has a cardioprotective effect on mice \u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. The effect of digoxin on myocardial IRI has been discovered in many studies, but its relationship with HS has not been reported yet. Our study has found for the first time that digoxin has the potential to treat HS.\u003c/p\u003e \u003cp\u003eThis study identified two biomarkers related to parthanatos in HS using bioinformatics methods: GYG1 and PPP1R3D, and validated their expression in clinical samples through RT-qPCR analysis. A nomogram model with good predictive performance was constructed based on these two biomarkers, and the biological pathways involved by biomarkers in HS were explored through functional enrichment analysis. Drug prediction and molecular docking have explored the binding ability of biomarkers with targeted drugs, providing new insights for the early diagnosis of HS and the development of new treatment strategies. However, there are still many aspects of this study that require further research. For example, further extensive experimental mechanism research and clinical application studies are needed to confirm the above relationship. We will also continue to monitor ongoing research and related progress in HS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eapoptosis inducing factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadenosine triphosphate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Impact Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAPDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene-gene interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene set enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene set variation analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGYG1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycogenin-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHosmer-Lemeshow\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Protein Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemorrhagic shock\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eischemia-reperfusion injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of the Genome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAD+\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enicotinamide adenine dinucleotide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoly (ADP-ribose)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePARGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eparthanatos-related genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePARP1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoly (ADP-ribose) polymerase 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogrammed cell death\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein Data Bank\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein-protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPP1R3D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein Phosphatase 1 Regulatory Subunit 3D\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT-qPCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereverse transcription quantitative polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrosis factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eweighted gene co-expression network analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors:\u0026nbsp;Zhenqi Xu, Qiulan Yu and Wei Yi.\u0026nbsp;In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of\u0026nbsp;the\u0026nbsp;908\u003csup\u003eth\u003c/sup\u003e Hospital of Chinese People’s Liberation Army Joint Logistic Support Force\u0026nbsp;(protocol code\u0026nbsp;908YYLL2025009). Informed consent has been obtained from the patient.\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\u003eThe datasets analyzed for this study can be found in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE64711 and GSE160905.\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\u003eScience and Technology Plan Project of Jiangxi Provincial Administration of Traditional Chinese Medicine (2024B0322).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhenqi XU and Qiulan YU wrote the main manuscript text, while Xiaoling PENG, Yuehua GONG, Yifan RAO, and Chunhua RUAN were responsible for organizing relevant data. Wei YI provided overall guidance and was responsible for the entire research and manuscript writing. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang, Z. et al. New research direction of organ dysfunction caused by hemorrhagic shock: mechanisms of mitochondrial quality control. \u003cem\u003eZhonghua Wei Zhong Bing Ji Jiu Yi Xue\u003c/em\u003e. \u003cb\u003e36\u003c/b\u003e, 93\u0026ndash;97 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossaint, R. et al. The European guideline on management of major bleeding and coagulopathy following trauma: sixth edition. \u003cem\u003eCrit. Care\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e, 80 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGourd, N. M. \u0026amp; Nikitas, N. Multiple Organ Dysfunction Syndrome. \u003cem\u003eJ. 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Timed use of digoxin prevents heart ischemia-reperfusion injury through a REV-ERBα-UPS signaling pathway. \u003cem\u003eNat. Cardiovasc. Res.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 990\u0026ndash;1005 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hemorrhagic shock, Parthanatos, Biomarkers, Immune infiltration, Drug prediction","lastPublishedDoi":"10.21203/rs.3.rs-8740659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8740659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHemorrhagic shock (HS) remains a critical condition, yet the involvement of parthanatos is not well defined. This study aimed to identify parthanatos-related biomarkers in HS and elucidate their mechanisms. By analyzing HS datasets and parthanatos-related genes (PARGs), candidate genes were screened via differential expression and key modular analysis. Biomarkers were selected using machine learning, receiver operating characteristic (ROC) curves, and expression validation. Nomograms were constructed and evaluated. Functional mechanisms of biomarkers were explored through chromosomal localization, subcellular localization, enrichment analysis, immune infiltration, and regulatory networks. Expression of biomarkers was validated with reverse transcription quantitative polymerase chain reaction (RT-qPCR). Glycogenin-1 (GYG1) and Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D) were identified as parthanatos-related biomarkers in HS. Nomograms showed strong predictive potential for HS. Enrichment analysis revealed co-enrichment in oxidative phosphorylation, Parkinson\u0026rsquo;s disease, and proteasome pathways. Both biomarkers were correlated with various immune cells, and hsa-mir-181d-5p and hsa-mir-25-3p were identified as co-targeting GYG1 and PPP1R3D. Drug analysis revealed Digoxin as a potential therapeutic agent. RT-qPCR confirmed upregulation of GYG1 and PPP1R3D in HS samples. GYG1 and PPP1R3D were identified as biomarkers associated with parthanatos in HS, providing a reference for early diagnosis of HS and optimization of treatment options.\u003c/p\u003e","manuscriptTitle":"Identification of Parthanatos-related biomarkers in hemorrhagic shock and study of potential molecular mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 17:21:04","doi":"10.21203/rs.3.rs-8740659/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-12T13:12:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T13:08:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-09T11:47:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T09:26:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-06T08:46:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9af18857-bbaf-4077-98fb-d66216320a9c","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62946352,"name":"Health sciences/Biomarkers"},{"id":62946353,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62946354,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-02-19T17:21:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 17:21:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8740659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8740659","identity":"rs-8740659","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}