CPE), LIM, SH3 protein 1 (LASP1), Pyruvate Dehydrogenase Kinase 1 (PDK1) and Serine beta-lactamase-like protein (LACTB) were finally identified. The results showed that the gene signature could reliably predict the prognostic status and immune status of osteosarcoma patients.Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs  affect key features of osteosarcoma progression, such as proliferation, migration, invasion, and drug resistance. Comprehensive multi-omics analysis is critical to understand its chemoresistant and pathogenic mechanisms. Currently, the network analysis-based non-negative matrix factorization (netNMF) method is widely used for multi-omics data fusion analysis. However, the effects of data noise and inflexible settings of regularization parameters affect its performance, while integrating and processing different types of genetic data is also a challenge. In this study, we introduced a novel adaptive total variation netNMF (ATV-netNMF) method to identify feature modules and characteristic genes by integrating methylation and gene expression data, which can adaptively choose an anisotropic smoothing scheme to denoise or preserve feature details based on the gradient information of the data by introducing an adaptive total variation constraint in netNMF. By comparing with other similar methods, the results showed that the proposed method could extract multi-omics fusion features more effectively. Furthermore, by combining the mRNA and miRNA data of methotrexate (MTX) resistance with the extracted feature genes, four genes, Carboxypeptidase E ( Osteosarcoma is one of the most common malignant bone cancers, accounting for approximately 30% of all osteosarcomas and mainly affecting children and adolescents, with a peak incidence at age 18 . NeoadjuMalignant osteosarcoma cells are strongly associated with chemoresistance, recurrence, and metastatic processes , and ostCurrently, non-negative matrix factorization (NMF) and its various improvements are widely used in a single type of genetic data analysis. For example, Lei et al. applied NMF to osteosarcoma gene data analysis and identified molecular subgroups with different Ferroptosis-related gene expression patterns . Jiao etIn this study, we proposed an improved NMF network analysis method (ATV-netNMF) to integrate DNA methylation and gene expression data. On this basis, combined with the miRNA and mRNA data of MTX resistance, we built a signature of MTXDEGs that predicted the prognosis of osteosarcoma, and the results revealed that the high-risk group had fewer immune cells and a lower degree of immune infiltration, which could lead to a poor prognosis.This study is mainly divided into three stages, in the first stage, to efficiently fuse methylation and gene expression data, the proposed ATV-netNMF was applied to two types of genetic data to identify co-expression networks and core gene modules that are strongly associated with variation in both data. Furthermore, the core module was analyzed by KEGG and GO enrichment to compare with other methods based on the number of pathways enriched and pathway significance . In the https://portal.gdc.cancer.gov/) as the training cohort, and 53 osteosarcoma patients with RNA-seq and clinical characteristics from GSE21257 in the GEO database (https://www.ncbi.nlm.nih.gov/geo) as the validation cohort. GSE16089  , if V , netNMF  is defink is a prespecified dimensionality reduction parameter; L is the graph Laplacian matrix; NMFNA  applies G, In order to better remove data noise and retain key feature details, we propose the ATV-netNMF method, which can improve the tolerance of the algorithm to noise and improve the performance of the algorithm by introducing adaptive total-variation constraint on NMFNA. Adaptive total variation  can be aBased on the NMFNA and the adaptive total variation constraint, the objective function of the ATV-netNMF is defined as,Where This study uses the multiplicative iterative update algorithm to minimize the objective function of ATV-netNMF. Suppose Thus, the partial derivatives of Let Where ve noise . ATV canve noise . Firstlyand G2 , z-scoreThe genes with each column weight greater than or equal to the threshold are used as module members, the threshold is set to 2, and the module with the most genes called the core module according to the previous study .p < 0.05.The R package “limma”  was usedhttp://www.targetscan.org/) and miRDB (http://mirdb.org/miRDB/) online databases, and the screening threshold was set as the cut-off parameter for TargetScan was the total contex++ score < - 0.05, while miRdb score >50.The target genes of upregulated miRNAs were predicted by TargetScan (p < 0.05) MTXDEGs associated with OS prognosis were obtained by univariate COX regression analysis using the survival package in R. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed on prognosis-related MTXDEGs using the glmnet package in R to reduce the dimensionality of genes in the model. Subsequently, independent prognostic genes were screened using multivariate Cox regression analysis, and regression coefficients for the corresponding genes were generated. A linear combination of gene expression levels and regression coefficients created a signature with the following formula for the risk score.Statistically, significant  curves were used to assess the predictive performance of prognostic signatures on overall survival. GSE21257 was used as a validation set to verify the predictive performance of the signature.p < 0.05 was considered statistically significant. To predict the prognosis of patients with osteosarcoma, a nomogram integrating riskscore and clinical characteristics was constructed, and calibration curves were used to evaluate the predictive accuracy of the nomogram, which was constructed from the “rms” R package , univariate and multivariate COX analyses were performed to determine the independence of our established gene signature in predicting overall survival, and  package . To estihttps://david.ncifcrf.gov). The threshold for significantly enriched pathways was set to p-value <0.05, and the top 20 most significant pathways were selected.The R package limma was used to obtain differential genes between high-risk and low-risk groups, which were analyzed for GO and KEGG pathway enrichment using David  was performed to identify pathways enriched in the high-risk and low-risk groups to explore the relationship between riskscore and biological function, with the threshold of A single sample gene set enrichment analysis (ssGSEA)  method win vivo drug response in cancer patients, including half-maximal inhibitory concentration (IC50) values for 189 drugs corresponding to cell lines and a normalized gene expression matrix for 809 tumor cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database. IC50 values for the TARGET-OS cohort were predicted using the oncoPredict method with a significance threshold set at p < 0.001.The OncoPredict  R packagk generally is at most one-tenth of the minimum number of samples or features of the network modules. Therefore, in this study, k is set to 8, and the number of iterations is set to 200. By running the The methylation and gene data of the TARGET-OS cohort had the same 82 osteosarcoma samples but with different features. Methylated genes with expression mean values less than 0.25 were filtered out from ME data, 15,819 methylated genes were retained, and 23,683 mRNAs were filtered out in GE data. Three co-expression networks were created from these data: ME network us study , the valmilarity  is definThe top 10 enrichment terms of the core GE modules identified by ATV-netNMF are shown in The flowchart for the identification of MTXDEGs is shown in p < 0.05)  . LASSO r05) (λ . Based op = 0.00055) (p = 0.015)  . The AUC0.00055) . Figure 0.00055) . The abo= 0.015) , with AU= 0.015) , respect= 0.015) , indicatp = 0.003) and Riskscore (p < 0.001) were independent prognostic factors for osteosarcoma  for analysis. Specifically, we calculated the Pearson correlation coefficients between the reconstructed matrices To further evaluate the applicability of ATV-netNMF and the contribution between individual modules, we downloaded gene expression data and miRNA data from cancer samples in the High-Risk Wilms Tumor (TARGET-WT), Breast Invasive Carcinoma (TCGA-BRCA), and Lung Adenocarcinoma (TCGA-LUAD) datasets from the UCSC Xena database  and two low-risk MTXDEGs . We can categorize patients into high-risk and low-risk subgroups based on the risk scores derived from this predictive model. ll lines , and thema cells . PDK1 isma cells , consistt cancer . LASP1 ic cancer . After tma cells . LACTB ind liver . In breapatients . Howeverferation .Based on these four genetic features in the risk model, we performed a comprehensive analysis and assessment of the two subgroups, which showed a significant difference in survival time between patients in the high-risk and low-risk groups. In addition, our KEGG and GO enrichment analyses showed that many immune and tumor-related pathways were enriched. We further performed GSEA analysis and found that metabolism-related pathways such as ascorbate and aldate metabolism, cytochrome P450 in drug metabolism to xenobiotics, adolescent diabetes mellitus, glucose metabolism, etc., were enriched in the high-risk group. In the low-risk group, pathways related to immune function, such as cell adhesion molecules, hematopoietic factor signaling pathways, cytokine-receptor interactions, natural killer cell-mediated cytotoxicity, and hematopoietic cell lines, were enriched.We further analyzed the infiltration status of various immune cells using ESTIMATE and ssGSEA methods to investigate the immune infiltration differences between the two subgroups. The findings showed that the immune, stromal, and estimate scores were significantly lower in the high-risk group than in the low-risk group. A significant negative correlation existed between our calculated risk scores and the three immune scores. In addition, the infiltration abundance of all 28 immune cells was lower in the high-risk group, with significant differences in the infiltration abundance of 23 immune cells. Riskscore showed a significant positive correlation with 24 immune cells. Osteosarcoma is considered a “cold tumor” in terms of immunogenicity. In the high-risk group, the infiltration of immune cells was significantly lower. The lower number of immune cells in the tumor immune microenvironment may lead to a worse prognosis. The prognosis of high-risk patients may be improved by increasing the immune reactivity.The need for high iron is an important feature of many cancer cells , and manSurvival of patients in the high-risk and low-risk groups was significantly correlated with their sensitivity to chemotherapy, and changes in therapeutic strategies are necessary to improve outcomes in patients who are insensitive to chemotherapeutic agents. Therefore, we analyzed the sensitivity of patients in both groups to commonly used chemotherapeutic and targeted drugs. The results showed that the high-risk group resisted most drugs, and BI-2536 may be considered a therapeutic candidate for the high-risk group.This study has some limitations. Although this study is based on multiple datasets and multi-omics data, further experimental validation still needs to be improved. In subsequent studies, we need to conduct more experiments to clarify the underlying molecular mechanisms of MTXDEGs.We proposed an adaptive total variant constrained-based netNMF multi-omics analysis method that integrates and efficiently identifies co-expression modules and characteristic genes in osteosarcoma methylation and gene expression data. Combined with the methotrexate-resistant multi-omics data, we identified a four-gene-based prognostic model with predictive solid ability for patient survival, immune microenvironment, and immunotherapeutic efficacy, which provides direction for new therapeutic strategies. In conclusion, the MTX resistance-associated model based on ATV-netNMF offers new targets for researchers to explore the mechanism of action of chemoresistance in osteosarcoma.


This systematic review and meta-analysis aimed to determine whether hematogones in patients with hematopoietic disorders after allogeneic hematopoietic stem cell transplantation  are associated with clinical outcomes.ClinicalTrials.gov, and World Health Organization International Clinical Trials Registry Platform databases from their inception to March 2023. The primary outcome in the summary of findings was three-year relapse-free survival (RFS), and secondary outcomes in the summary of findings included three-year relapse, non-relapse mortality (NRM), overall survival (OS), acute and chronic graft-versus-host disease (GVHD), and infection. The certainty of evidence was determined using the grading of recommendation assessment, development, and evaluation approaches. A systematic review and meta-analysis of outcome measures were conducted using a random-effects model. This study protocol was registered in the Open Science Framework.We searched the MEDLINE, Embase, Cochrane Central Register of Controlled Trials, A total of six studies (including 888 patients) were included in the meta-analysis. Hematogones were related to favorable three-year RFS (risk ratio (RR) = 1.84; 95% confidence interval (CI) = 1.01 to 3.34) and favorable NRM , OS , and acute GVHD . The certainty of the evidence was low for RFS, NRM, OS, and acute GVHD. Evidence regarding the association between hematogones, relapse, and infections is uncertain. Hematogones may be a prognostic factor for long-term prognosis and acute adverse events in patients with hematopoietic disorders after allo-HSCT. Further studies are required to address the long-term life-threatening events. Allogeneic hematopoietic stem cell transplantation  is an important and potentially curative treatment for many malignant and non-malignant hematopoietic bone marrow disorders, such as aplastic anemia . HoweverHematogones, which denote benign B-lymphocyte precursors, appear during bone marrow function recovery after induction therapy, consolidation therapy, or allo-HSCT ,10. HemaTo investigate the prognostic value of hematogones in the clinical course of allo-HSCT, this systematic review and meta-analysis estimated an average effect across studies and assessed each study’s limitations as well as gaps in the entire published literature. This study also provided information to guide a further study in the context of study design.MethodologyCompliance with Reporting GuidelinesUsing a pre-specified protocol (Open Science Frame Registry ID: CRD42018105511), we conducted a systematic review of the relevant literature in agreement with the recommendations of the Cochrane Handbook  and the Eligibility CriteriaThe target population comprised patients with hematological disorders who underwent allo-HSCT. Hematogones in bone marrow aspiration or biopsy smear are defined as lymphoid precursor cells with diameters varying from 10 to 20 µm, scant cytoplasm, and densely homogeneous nuclear chromatin . The bonThe primary outcome was the three-year RFS. The secondary outcomes were the proportion of one-, three-, and five-year relapses; non-relapse mortality (NRM); overall survival (OS); incidence of grade Ⅱ-IV acute GVHD at one year; moderate-to-severe chronic GVHD at three years; total infections at one year; bacterial, viral, and fungal infections at one year; and serum immunoglobulin (Ig)G levels at three months.We included prospective and retrospective cohort studies that assessed the clinical predictive value of hematogones. No restrictions were applied based on the language of publication, country, observation period, or year of publication. Published articles were included, whereas conference abstracts were excluded.We excluded patients presenting with the following results from the bone marrow samples: (1) recurrence, (2) residual disease, or (3) incomplete chimerism. The definitions of these were adopted from the respective studies. No exclusion was made based on donor source or disease status at the time of allo-HSCT.Search Strategy and Selection of StudiesClinicalTrial.gov. via search portals. The search was performed on March 15, 2023, using a set of suitable search terms, including “allo-HSCT,” “hematogones,” “B-lymphoid precursor cells,” and related terms  independently extracted study-level data using pre-specified forms. Disagreements regarding data extraction were resolved through discussion. Where necessary, we contacted the authors of the studies for which sufficient information was not available. The risk of bias of each study was assessed independently by two reviewers (Hirotaka Mori and Daisuke Koyama) using the Quality in Prognostic Studies (QUIPS) tool . We asseData AnalysisFor three-year RFS; three-year, one-year, and five-year relapses; NRM; OS; incidence of acute grade II-IV GVHD at one year; moderate-to-severe chronic GVHD at three years; total infections at one year; and bacterial, viral, and fungal infections as dichotomous variables and pooled risk ratios (RR) with 95% confidence interval (CI) were provided. The mean differences with 95% CI were calculated for serum IgG levels as continuous variables.http://www.digitizeit.de/) and applied established algorithms [To calculate the number of events, we multiplied the percentage of events at a prespecified time point using the Kaplan-Meier method  or Gray’gorithms ,22. Secogorithms . The effWe created a table of summary of findings including the overall grading of evidence certainty for each of the main outcomes, evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach .All analyses were conducted using the Stata software version 17 .ResultsStudy SelectionFigure Study CharacteristicsTable Risk of Bias in the Included StudiesThe QUIPS tool was used to assess the risk of bias of the included studies . Figure Certainty of Evidence2 = 86.0%; n = 353)       Figure ; the cerSubgroup AnalysisThe prespecified subgroup analysis for the primary outcome of three-year RFS revealed a similar direction of effect to the overall group . We obseAdditional Outcomes2 = 0%; n = 616)   Figure . Data on) Figure .2 = 20.2%; n = 679)   Figure . Data on2 = 78.2%; n = 662)   Figure . Data on) Figure .2 = 0%; n = 490)     Figure . Data on) Figure . Data on) Figure . Data on) Figure .Differences Between the Protocol and the ReviewAs the largest number of integrated studies was four, we could not assess publication bias using funnel plots and Egger tests. Owing to the small sample size, we could not conduct planned sensitivity analyses for the primary outcomes. (i) exclusion of studies with a high risk in QUIPS domains, (ii) exclusion of the prognostic values of hematogones evaluated from 100 median days after allo-HSCT, and (iii) when the meta-analytic result of the primary outcome showed significant heterogeneity. We could not perform a meta-regression of the cutoff levels for positive or negative hematogones because of the small number of included studies. We also searched for case-control studies but found none that met the eligibility criteria.DiscussionThis systematic review included six studies evaluating the prognostic value of hematogones in 888 patients with hematopoietic disorders after allo-HSCT. The presence of hematogones was related to lower cumulative incidence estimates of three-year NRM, and, as a result, demonstrated an improvement in three-year RFS. Subgroup analysis by donor source and disease category consistently demonstrated a favorable three-year RFS. Acute GVHD and infections were the major causes of NRM in the allo-HSCT setting ,28, and Our results imply that hematogones are a prognostic factor for survival and acute GVHD in patients with hematopoietic disorders after allo-HSCT. Currently, the useful prognostic factors for patients in complete remission after allo-HSCT are limited. A cohort study showed that the prognostic values of neutrophil and platelet engraftment times for the three-year OS rate were almost the same in patients with engraftment before day 28 and those with engraftment between days 28 and 42 . A meta-The evidence of hematogones is uncertain regarding long-term adverse events, such as relapse and infection, and there is no information about chronic GVHD. In particular, results regarding relapse, in which the confidence interval crosses the line of null effects, should be interpreted with caution, although this does not mean that hematogones have no prognostic value . The sigThis review has several strengths. The results of this review are based on the best available evidence after a comprehensive search. Additionally, we used a systematic methodology that followed a written a priori protocol developed according to the PRISMA statement . We usedThere are some potential limitations to the risk of bias and study methods. First, attrition bias was judged to be high in all studies. The included studies did not report the number of participants lost to follow-up or the reasons, which could have systematically biased the association between the prognostic factors and prognosis . AdditioHematologists may use hematogones as prognostic factors for long-term prognosis and acute unfavorable events in patients with hematopoietic disorders following allo-HSCT. There is still room for improvement in methods of primary research to lower the risk of bias. Further cohort studies are warranted to address the methodological issues and the uncertainty regarding long-term life-threatening events.


The concentrations of apelin-13 and CRH had strong positive correlations  and were significantly higher in surviving non-septic patients (Apelin-13 (pg/mL): 2286 (790–3330) vs. 818 (574–2732) p < 0.05; CRH (pg/mL) 201 (84–317) vs. 89 (74–233) p < 0.05). Apelin-13 and free cortisol were independent determinants of survival in the multivariate Cox regression analysis, while copeptin, CRH, or aldosterone were not. Conclusions: Beyond free cortisol, serum apelin-13 may also help refine prognostic predictions in the early phase of critical illness, especially in non-septic patients.Background: The adrenocortical system and copeptin as prognostic markers were intensively investigated in critical illness. The potential predictive power of apelin-13 as a biomarker is largely unknown. We aimed to investigate the prognostic role of apelin-13 in relation to free cortisol, aldosterone, CRH, and copeptin in critically ill patients. Methods: In this prospective observational study, 124 critically ill patients  years) were consecutively enrolled at the time of admission. All routinely available clinical and laboratory parameters were evaluated and correlated to hormonal changes. Results: Serum apelin-13 was 1161 (617–2967) pg/mL in non-survivors vs. 2477 (800–3531) pg/mL in survivors ( The mortality of patients requiring critical care is high and partly unpredictable. Complex prognostic scores have been created to improve the prediction of outcomes. However, despite intensive efforts, the predictive power of prognostic biomarkers is not optimal. Looking for new biomarkers would be essential not just to improve the prognostic role but for a better understanding of the pathomechanisms of critical conditions as well ,2.The prognostic roles of cortisol and arginine vasopressin (AVP) have been intensively studied in the literature.Neuro-hormonal responses, including elevating cortisol levels, play a vital role in stress. Free cortisol concentration is typically high in critically ill patients due to the early activation of the hypothalamus-pituitary–adrenocortical axis (HPA) and decreased cortisol metabolism ,4. PreviArginine vasopressin, or anti-diuretic hormone (ADH), is released from the posterior pituitary by increasing plasma osmolality and upon various stressors, such as hypovolemia, hypoxia, acidosis, and severe infections. The copeptin level is a reliable surrogate parameter for the AVP effect in healthy and critically ill patients . CopeptiApelin, a 36-aminoacid peptide hormone, is particularly abundant in the supraoptic and paraventricular nuclei, co-localizing with AVP in magnocellular neurons ,16. ApelIn animal models, apelin’s in vivo effects on HPA function have been mediated by CRH and AVP-dependent mechanisms . Apelin Apelin-13 has been identified as a significant apelin isoform in human plasma .Corticotropin-releasing hormone (CRH) is a central regulator of the hormonal stress response, stimulating both corticotropin (ACTH) and AVP secretion . The parOur present study aimed to investigate a more complex interplay of the hypothalamic and adrenocortical systems in a mixed population of patients with critical illness. Based on the abovementioned data, the following hormones were selected for analysis as biomarkers: apelin-13, copeptin, CRH, free cortisol, and aldosterone.A prospective cohort study was carried out on critically ill patients admitted to the Intensive Care Unit of either Department of Emergency Medicine or 1st Department of Medicine, Clinical Center, the University of Pécs. Recruitment was achieved in two periods, between June and October 2012 and May 2019 and June 2020. Vital signs, clinical status, and routine laboratory parameters were monitored. The treatment of patients was thoroughly evaluated, and blood samples disturbed by glucocorticoid treatment were excluded from further analysis. The severity of the diseases was scored according to the SAPS II and the APACHE II scoring systems.It was a mixed population of patients with medical emergencies. The patients were consecutively enrolled in two periods requiring intensive care due to vital organ dysfunction .Patients with COVID-19, surgical procedure, or trauma were excluded. None of the patients received etomidate, ketoconazole, or any other drug influencing steroid metabolism. Eighteen patients needed complete cardiopulmonary resuscitation, and eight were defibrillated before admission.Our study was performed by the ethical guidelines of the 2003 Declaration of Helsinki, and we obtained the permission of the Regional Research Ethical Committee of the University of Pécs. Written informed consent was obtained from the participants or the participants’ parent/legal guardian/next of kin to participate in the study.At the time of admission, the chemistry panel and fully automated blood picture tests were determined using the standard laboratory diagnostic kits and automated instrumentation of the Department of Laboratory Medicine, University of Pécs (accreditation number: NAH-1-1553/2016).At admission, blood samples were taken to measure the free cortisol, apelin-13, copeptin, CRH, and aldosterone levels. They were collected in plastic tubes in an anticoagulant-free Vacutainer . After centrifuging the collected blood samples at 2200× g for 10 min, serum was separated into aliquots in Eppendorf tubes and frozen under −80 °C. Sample preparation and the measurements for free cortisol analysis were performed according to the validated method using high-performance liquid chromatography coupled with high-resolution ESI-TOF mass spectrometry described by Montsko et al. .Serum apelin-13, copeptin, and CRH levels were measured with the ELISA method using Human Apelin-13 ELISA kit , Human Copeptin (CT-proAVP) ELISA kit , and Human Corticotropin-Releasing Hormone (CRH) ELISA Kit  according to the manufacturer’s instructions on a BioTek Synergy HT plate reader at 450 nm. Serum aldosterone was measured using the radioimmunoassay method .p-value of <0.1 was deemed significant. A p-value of <0.05 was regarded as significant, except for multiple logistic regression analyses; if linear methods with backward selection were used, a p-value of <0.1 was determined as significant. The beta value reflects the direction and the power of association. Cox regression analysis with backward selection was used to describe survival time; a p-value of <0.1 was significant.Statistical analyses were carried out using SPSS 22.0 software. The Shapiro-Wilk test was used to check normal distribution; data are presented as mean ± SD in parameters with normal distribution, while median and interquartile values are in the non-normal distribution. To determine the relationship between parameters, Spearman’s correlation was used. Comparisons of two subgroups were made with Mann-Whitney U tests. The backward selection was used for binary logistic regression analyses, and a A total of 124 patients were recruited for the study. Patients represented a mixed population with a demand for critical care.The key parameters of the patient population are shown in SAPS II score and hormonal parameters according to 30-day survival are shown in The median and interquartile of the investigated hormonal parameters are separately demonstrated according to the main diagnosis of admission in Correlations of hormonal parameters with hormonal levels, 30-day mortality, severity score, and clinical parameters are demonstrated in p < 0.001), copeptin , aldosterone , CRH , and SAPS II , but not with apelin-13 or 30-day mortality.Fifty-five patients (44%) had hypotension at admission. Significant correlations were found between hypotension and free cortisol . Using the same model, the independent predictors of serum CRH level were apelin-13 level and kidney injury  and creatinine (p = 0.005) were significantly elevated.Hormone levels according to the septic state are shown in SAPS II score and hormonal parameters according to 30-day survival in the non-septic subgroup  are shown in Free cortisol and apelin-13 were significant independent predictors of mortality of the investigated hormonal parameters. In the whole population, free cortisol was the strongest predictor, while in the non-septic subgroup, apelin-13 became a stronger predictor of mortality than free cortisol .. SurvivOur current study investigated various hypothalamic and adrenal hormones in a population with a mixed critical illness. According to previous works, the free cortisol and copeptin serum concentrations were significantly higher among non-survivors ,10,11. AApelin has been found to be an essential biomarker for heart failure . MoreoveThe biological efficacy of the apelin system is compromised under some environmental pressure. For example, in human sepsis, endogenous apelinergic levels rise early, and specific enzymatic breakdown activities potentially threaten endogenous apelin system reactivity and negatively impact the outcome . FurtherTo the best of our knowledge, this is also the first study to investigate serum CRH concentrations in a critically ill population. Compared to the reference range, serum CRH was highly elevated in these patients, especially in septic ones, and positively correlated with apelin-13 and copeptin; however, interestingly, not with free cortisol. Furthermore, serum CRH has strongly determined apelin-13 levels among these patients. Moreover, similar to the apelin-13, serum CRH was significantly higher in surviving non-septic patients than in those deceased within 30 days. No previous human data are available regarding the strong correlation between CRH and apelin-13, so the explanation is only hypothetical; extreme stress reaction may be responsible for the elevation of both hormones. Our knowledge about the degradation of CRH and apelin-13 is also incomplete. It is well known that the elevation of cortisol levels in critical illness is partly due to the decreased degradation process . It may Although both apelin-13 and CRH are basically expressed in the hypothalamus, their serum concentrations are determined by other sources and have weak relationships with their local effects on the central nervous system . More coIt has been previously demonstrated that the stress response of early admitted ICU patients is different in septic vs. non-septic conditions . The sepSerum aldosterone correlated to the free cortisol and SAPS II severity score; otherwise, aldosterone did not show any remarkable connections with the other parameters.Many attempts have been made to improve survival in critical illness and to determine a more accurate prognostic score. There is a lack of convincing disease-modifying therapy in multi-organ failure. The investigation of the role of apelin-13 may have three benefits: to better understand the pathophysiological process and the complex regulation of this hormonal system, to find a prognostic marker that may be simpler than the currently used prognostic scores (which contains seventeen parameters), and to identify a potential therapeutic target.The apelin system is obviously modified by critical illness, and these changes seem to be different in septic and non-septic conditions. In sepsis, the apelin-13 is extremely high; survival is better in those patients who have below the mean apelin-13 level, therefore, in this patient population, the elevation of apelin-13 may be a marker of severity, and a further increase of apelin-13 by the administration of exogenous apelin-13 is questionable. In the non-septic patient population, only apelin-13 remained a significant determinant of survival in multivariate Cox regression analysis. In non-septic patients with circulatory failure and low apelin-13 levels, exogenous apelin-13 administration might improve the prognosis.There may be some possible limitations of the study. The sample size of the investigated population was relatively limited. This may explain why the serum apelin-13 elevation did not reach significance in the patients that survived compared to the non-survived subgroup of the whole population. However, the study’s limited size does not seem to influence our basic observations. Furthermore, no normal controls were included. However, this might not affect the observations related to the potential prognostic roles of the evaluated hormonal parameters at admission to the ICU. Moreover, other relevant humoral factors with potential significance, e.g., ACTH, could have also been tested that might have revealed other relevant hormonal interrelations.Serum apelin-13 showed a decreased level in more severe cases of the mixed population with a critical illness. It appears that there is a considerable difference between the septic and non-septic populations with respect to apelin-13 levels. Moreover, the type of vital organ dysfunction markedly influenced not just the apelin-13 but all the other investigated hormones. The concentrations of apelin-13 and CRH had a strong positive correlation and both hormone levels were significantly higher in surviving non-septic patients. In the multivariate Cox regression analysis of the investigated hormonal parameters, apelin-13 and free cortisol were independent determinants of survival, while copeptin, CRH, or aldosterone were not.


Immunotherapy has revolutionized the treatment of melanoma, but its limitations due to resistance and variable patient responses have become apparent. The microbiota, which refers to the complex ecosystem of microorganisms that inhabit the human body, has emerged as a promising area of research for its potential role in melanoma development and treatment response. Recent studies have highlighted the role of microbiota in influencing the immune system and its response to melanoma, as well as its influence on the development of immune-related adverse events associated with immunotherapy. In this article, we discuss the complex multifactorial mechanisms through which skin and gut microbiota can affect the development of melanoma including microbial metabolites, intra-tumor microbes, UV light, and the immune system. In addition, we will discuss the pre-clinical and clinical studies that have demonstrated the influence of different microbial profiles on response to immunotherapy. Additionally, we will explore the role of microbiota in the development of immune-mediated adverse events. Skin cancer is a major public health concern with increasing incidence rates worldwide. It has been estimated that melanoma alone will account for around 100,000 new cancer diagnosis cases in 2022 . Imm. Imm8]. Akkermansia muciniphilia restored the responsiveness of melanoma-bearing hosts to ICIs, a response that had been previously inhibited with the use of antibiotics [Bacteroides fragilis [B. fragilis in germ-free mice restored the therapeutic response of anti-CTLA-4 via the induction of T helper 1 (TH1) immune responses in tumor-draining lymph nodes (LN) and the promotion of the maturation of intra-tumoral dendritic cells (DC) [B. fragilis [In a study by Routy et al., the therapeutic efficacy of anti-PD-1 alone or in combination with anti-CTLA-4 was compared between antibiotic-treated (germ-free) or untreated mice with melanoma . The admibiotics . Similaribiotics . Vetizoufragilis . Oral sulls (DC) . In addifragilis .Clostridiales order, the Ruminococcaceae family and the Faecalibacterium genus, whereas those of non-responding patients were enriched with Bacteroidales [Faecalibacterium had longer progression-free survival versus patients whose gut microbiota was enriched for Bacteroidale [Bacteroidales was associated with better response to anti-CTLA-4 [Faecalibacterium in their gut microbiota [A study by Gopalakrishnan et al. examined the feces of 112 patients with melanoma treated with anti-PD-1 therapy. The patients’ gut microbiota was examined pre- and post-treatment via 16S sequencing and metagenomic whole genome shotgun sequencing. Patients with a more diverse gut microbiome had a better response to anti-PD-1 therapy compared to patients with less diverse gut microbiome. The microbiota of responding patients were enriched with the roidales . Similareroidale . Howeveri-CTLA-4 . Gopalakcrobiota .Bacteroides stercoris, Parabacteroides distasonis, and Fournierella massiliensis. However, non-responding patients were more likely to have a microbial composition rich in Klebsiella aerogenes and Lactobacillus rogosae [Actinobacteria phylum and the Lachnospiraceae/Ruminococcaceae families of Firmicutes were associated with a favorable response, whereas Gram-negative bacteria were associated with an inflammatory intestinal gene signature, increased blood neutrophil-to-lymphocyte ratio, and unfavorable outcome [Lachnospiraceae spp. and the other for Streptococcaceae spp., were linked with favorable and unfavorable clinical response, respectively, and with distinct immune-related adverse effects [Finally, by studying the response of 38 patients with metastatic melanoma to anti-PD-1 and anti-CTLA-4, Andrews et al. showed that responders had different gut microbial composition compared to non-responders. Through 16S rRNA gene sequencing and shotgun metagenome sequencing of fecal samples, they showed that patients who were more likely to respond had microbiome rich in  rogosae . In thei rogosae . When th outcome . Two mic effects . Despite effects . In summScience investigating FMT from immunotherapy responders combined with anti-PD-1 antibodies as a strategy to overcome anti-PD-1 resistance in patients with metastatic melanoma [Bifidobacterium adolescentis, whereas the recipients from Donor #2 had a higher abundance of taxa like Ruminococcus bromii. Further analysis found that responders had a higher relative abundance of Enterococcaceae, Enterococcus, and Streptococcus australis and a lower relative abundance of Veillonella atypica. Interestingly, only recipients from donor #1 upregulated some additional gene sets related to APCs activity, innate immunity, and interleukin-12 (IL-12), with the responding patients increasing the CD8+ T cell infiltration into the tumors [Lachnospiraceae and Ruminococcaceae families) and Actinobacteria (Bifidobacteriaceae and Coriobacteriaceae families) taxa that were previously linked to the response to anti-PD-1, as well as increased activation of CD8+ T cells and a decrease in the frequency of myeloid cells expressing interleukin-8 [Complete response to anti-PD-1 antibodies occurs in 10–20% of patients with metastatic melanoma, and the majority of patients who receive anti-PD-1 antibodies for metastatic melanoma will ultimately develop resistance . There imelanoma ,44. Barumelanoma . Two FMTmelanoma . There we tumors . In addiAkkermansia muciniphilia, Bacteroide fragilis, and Fecalibacterium tend to be usually associated with a positive outcome, unlike Bacteroidales which usually negatively impact the response of melanoma patients to ICIs. It is worth noting, however, that microbial diversity remains the only consistent finding associated with positive response outcomes along the different studies.While it can be difficult to draw definite conclusions regarding the effect of specific microbial species on the response to ICIs given the inconsistencies between different studies, it has been noticed that The mechanisms through which the microbiota influences the response to ICIs have been extensively studied. Some of those potential mechanisms are summarized in Clostridiales order, the Ruminococcaceae family, and the Faecalibacterium genus, and it was non-significantly but negatively correlated with Bacteroidales. In addition, a higher level of systemically circulating effector CD4+ and CD8+ T cells with a preserved cytokine response to anti-PD-1 therapy was associated with Clostridiales order, the Ruminococcaceae family, and the Faecalibacterium. On the other hand, gut microbiota enriched with Bacteroidales was associated with higher levels of Treg cells and myeloid-derived suppressor cells in the systemic circulation with a blunted cytokine response to anti-PD-1 therapy [The impact of the microbiota on anti-tumor immune cell infiltration is the most studied mechanism of interaction between the microbiota and the response to ICIs. At the level of the tumor microenvironment, a higher density of CD8+ T cells were observed in responding patients as compared to non-responding patients. This CD8+ T cell infiltration was positively correlated with the  therapy . Moreove therapy . Additio therapy . This, i therapy . Similar therapy . Another therapy . Increas therapy . Therefo therapy . It is iFaecalibacterium prausnitzii is considered a major contributor to butyrate formation. SCFA formation has been correlated with improvement in response to anti-PD-1 therapy [Another proposed mechanism includes immune modulation by bacterial metabolites. Bacterial fermentation of carbohydrates into short chain fatty acids (SCFAs) has been correlated with the host immunity to ICIs . The eff therapy ,47,48. H therapy .In addition to the production of SCFA, gut microbiota may also induce molecular mimicry to host cell antigens. Self-reactive T cells are mostly eliminated during development; however, some are able to escape . These mOne suggested mechanism also involves the formation of anabolic amino acids by the gut microbiota. These were found to be predominant in responding patients, while catabolic amino acids were mostly predominant in non-responding patients . In factBifidobacterium and Akermansia muciniphilia. It has been shown to enhance TH1 differentiation and adenosine A2A receptor expressing naïve T cell function. Inosine modulates the response to ICIs by inhibiting the immunosuppressive activity of adenosine, which is a naturally occurring molecule that suppresses the immune response. In contrast to adenosine, inosine has pro-inflammatory effects on the adenosine A2A receptor, supporting the TH1 and its anti-tumor effects in the mice [Furthermore, inosine, a purine riboside, has been shown to be correlated with response to ICIs in mice. Inosine is an intestinal metabolite produced by the mice . Inosinethe mice .Although use of ICIs has transformed the landscape of cancer treatment by facilitating the harnessing of the immune system to generate an anti-tumor immune response, there exist several limitations to using these drugs. First, not all patients respond to ICI, and response rates vary by tumor type . In addiStaphylococcus epidermidis [S. epidermidis and systemic anti-CTLA-4, skin inflammation developed on days 6 to 8 of treatment [Cutaneous irAEs can range from mild pruritus to life-threatening epidermal necrolysis ,57,58. Hdermidis . Howeverreatment . The infreatment . This inreatment . These fBacteroidetes phylum has been associated with increased resistance to colitis. Dubin et al. analyzed the composition of the intestinal microbiota in 34 patients with metastatic melanoma being treated with anti-CTLA-4. Patients who were not diagnosed with gastrointestinal inflammation between 13 and 59 days of treatment were found to have a higher abundance of Bacteroidaceae, Rikenellaceae, and Barnesiellaceae [Bacteroidetes phylum stimulates Treg cell differentiation, which may play a role in certain patients’ resistance to colitis [Bacteroidetes phylum was associated with resistance to colitis. In addition, patients with Firmicutes-rich microbiota were more likely to develop colitis. Interestingly, it was also found that decreased bacterial diversity was also associated with gastrointestinal inflammation [Colitis is a common irAE seen with both anti-CTLA-4 and anti-PD-1 antibodies and may be severe or life-threatening ,61. The ellaceae . It is t colitis ,62,63. A colitis . Abundanammation .Firmicutes, while patients prone to colitis had a higher abundance of Bacteroidetes [Bacteroidetes-rich profiles and that developed colitis were found to upregulate IL-1ß. This was confirmed via treatment with IL-1 receptor antagonist (anakinra), along with the dual ICIs, resulting in less inflammation. In addition, via transcriptional profiling, a prompt and selective transcriptional upregulation of Il1b was also found [However, opposing results were found in patients treated with dual ICIs (anti-CTLA-4 and anti-PD-1) . In factroidetes . Patientso found .Streptococcus, Paecalibacterium, and Stenotrophomonas. However, patients with mild irAE had gut microbiota enriched for Faecalibacterium. Patients experiencing each irAE were analyzed and compared to patients with no irAE. The microbiota in patients experiencing pruritis and rash had no significant difference when compared to those without irAE. However, patients with no irAE had a higher abundance of Bacteroides and Lactobacillus as compared to patients experiencing thyroid dysfunction, who had abundant Paecalibacterium. It was also found that patients with severe diarrhea had a higher presence of Stenotrophomonas and Streptococcus, whereas patients without irAEs or with mild diarrhea had higher levels of Faecalibacterium and Bacteroides [Liu et al. found a link between the composition of a patient’s gut microbiome and their likelihood of developing irAE from anti-PD-1 antibodies . Patientteroides .In summary, these studies highlight the interplay between the gut microbiota and development of irAE. A retrospective analysis of 327 cancer patients treated with ICI for multiple tumor types found that patients who developed diarrhea or colitis had improved overall survival compared to those who did not develop diarrhea or colitis . The mecIn conclusion, emerging evidence suggests that the composition and diversity of the skin and gut microbiota play a critical role in modulating the efficacy of immune checkpoint inhibitors in the treatment of skin cancers. Despite the progress made in the field, several challenges remain in harnessing the potential of microbiota-based therapies to optimize immune checkpoint inhibitor efficacy. Future research efforts should aim to identify specific microbial profiles that predict the response to therapy and elucidate the molecular mechanisms underlying their effects on tumor immunity. This knowledge may enable the development of microbiota-based interventions, such as fecal microbiota transplantation or probiotics, to enhance the clinical efficacy and safety of immune checkpoint inhibitors in patients with skin cancers.


A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts. Large-scale disease-association data are widely used for pathomechanism mining, even if disease definitions used for annotation are mostly phenotype-based. Here, the authors show that this bias can lead to a blurred view on disease mechanisms, highlighting the need for close-up studies based on molecular data for well-characterized patient cohorts. One of the network medicine field’s long-term objectives is to replace our current mainly phenotype-based disease classification systems by a mechanistically grounded disease vocabulary7. In such a vocabulary, phenotype-based disease definitions are replaced by so-called endotypes, i.e., distinct molecular mechanisms underlying the disease phenotypes. Once properly disentangled into disjoint, individually targetable endotypes5, disease-modifying treatment strategies might become available for diseases which, at the moment, can be treated only symptomatically.Since the seminal articles by Goh et al. 8, Lötvall et al. 9, and Nogales et al. 5 – i.e., the underlying molecular mechanisms driving disease phenotypes. There are other works where the term “endo(patho)phenotype” denotes common intermediate phenotypes6 such as inflammation, fibrosis, or thrombosis which drive phenotypic disease manifestations11. Secondly, we would like to stress that compiling a endotype-based disease vocabulary is a genuinely biomedical rather than a semantic endeavor: It does not consist in redefining semantic relationships between existing disease terms but in uncovering currently unknown molecular disease mechanisms and dissecting umbrella diseases such as Alzheimer’s disease or coronary artery disease into endotypes which are clearly characterized at a molecular level5.Two clarifications are required to define the scope of this paper: Firstly, we use the term “endotype” to denote molecular endotypes as explained by Anderson12, liver fibrosis13, pulmonary arterial hypertension14, asthma15, hypertrophic cardiomyopathy16, pre-eclampsia17, chronic obstructive pulmonary disease, and idiopathic pulmonary fibrosis18.In order to reach the objective of an endotype-based disease vocabulary, network medicine approaches aim at uncovering pathomechanisms driving diseases. Here, we broadly distinguish between close-up and bird’s-eye-view (BEV) network medicine approaches, depending on the data used as primary input towards this task . Close-up network medicine studies focus on a specific disease and start their analyses with molecular data for well-characterized patient cohorts. Such studies are typically carried out as close collaborations between bioinformaticians and domain experts from the biomedical sciences. They tend to be time- and labor-intensive and often involve the development or customization of data analysis methods for specific datasets. The most impressive translational results of the network medicine field have been reached via such close-up studies. For instance, close-up studies have led to novel mechanistic insights into type 2 diabetes19 demonstrated that disease-associated genes form so-called disease modules, i.e., highly connected subnetworks within protein-protein interaction (PPI) networks, and that biological and clinical similarity of two diseases results in significant topological proximity of these modules. In a similar vein, Iida et al. 20 showed that shared therapeutic targets or shared drug indications are correlated with high topological module proximity. Guney et al. 21 and Cheng et al. 22 showed that the network-based separation between drug targets and disease modules is indicative of drug efficacy. Cheng et al. 23 and Zhou et al. 24 found that FDA-approved drug combinations are proximal to each other and to the modules of the targeted diseases in the interactome.In contrast to that, BEV approaches use large-scale disease association data that are typically gathered from several data sources. Various studies have generated evidence for the validity of this overall approach: For instance, Menche et al. 19 have studied the effect of incompleteness of disease-gene association and protein-protein interaction (PPI) data on network medicine. Schaefer et al. 25 have shown that the previously observed28 high node degree of cancer-associated proteins in PPI networks can largely be explained by the fact that cancer-associated proteins are tested more often for interaction than others. Lazareva et al. 29 found that widely used methods to mine PPI networks for pathomechanisms inherit this bias in that they mainly learn from the node degrees instead of exploiting the biological knowledge encoded in the edges of the PPI networks. Haynes et al. 30 showed that study bias also distorts functional gene annotation resources such as the Gene Ontology (GO)31. Kustatcher et al. 32 made a similar point for functional protein annotations and sketched a roadmap for systematically exploring the understudied part of the proteome. Stoeger et al. 33 and Rodriguez-Esteban34 looked into reasons that might lead to the emergence of gene study bias and identified, respectively, a limited number of biological characteristics33 and speed of information propagation between scientific communities as potential drivers34.Despite the promising findings summarized above, several studies have pointed out important biases in the data used by BEV approaches. Menche et al. While the aforementioned studies have analyzed the impact of various types of data biases related to genes and proteins , the disease part of disease-gene and other disease association data introduces another, so far unstudied type of data bias: In currently available large-scale disease association data, diseases are annotated with the very phenotype-based disease definitions the network medicine field aims to overcome. BEV approaches hence risk to systematically reproduce the biases introduced by these disease definitions. Consequently, BEV approaches make the implicit assumption that the biases introduced by phenotype-based disease definitions even out and that, despite those biases, disease association data using these definitions still contain useful information about the pathomechanism that are to be uncovered.In this work, we quantify to which extent this implicit assumption is indeed backed by data. Towards this end, we construct disease-disease networks  based on (1) disease-gene associations, (2) disease-variant associations, (3) comorbidity data, (4) symptom data, and (5) drug-indication data, as well as drug-disease and drug-drug networks  based on drug-indication and drug-target data. We then formulate two testable hypotheses that follow from the implicit assumption of BEV network medicine: The global-scale hypothesis states that, globally, networks constructed from two different types of association data that both contain useful information about endotypes should be pairwise more similar than expected by chance. The local-scale hypothesis states that this should hold not only globally but also for the neighborhoods of the individual diseases and drugs represented by nodes in the constructed networks.24, our analyses provide solid evidence for the global-scale hypothesis. However, they only partially support the local-scale hypothesis. Figuratively speaking, BEV network medicine hence only allows a distal view at the endotypes that are to be discovered. When zooming in on individual diseases, the picture becomes blurred and less reliable . While only two local empirical P-values are significant at 0.05 level, the cluster-level and global empirical P-values are significant at levels 0.01 and 0.001, respectively.Before presenting the comprehensive results of our analyses, we visualize the phenomenon of local blurriness in BEV network medicine with a small example. We compiled a list of diseases that fall under the parent term “neurodegenerative disease” in the MONDO disease hierarchy. From those, we kept diseases for which we have nodes in the aligned gene- and drug-based diseasomes. This led to a cluster of seven neurodegenerative diseases which are highly connected in both diseasomes. Figure 35. For instance, we would expect that the diseases 36 and DrugCentral37), diseasomes Global-scale hypothesis: For all disease association data Local-scale hypothesis: For all disease association data Let 39, we then compared these networks in a pairwise manner both on a local scale, i.e. zoomed-in on individual disease or drug nodes, and on a global scale. More precisely, we generated 1000 permuted networks as randomized counterparts for each network. Subsequently, we compared the distributions of local and global GEDs obtained for the original networks to GED distributions obtained for randomized counterparts. Network randomization and computation of local and global GED are illustrated in Fig. To test these two hypotheses, we constructed various diseasomes, drugomes, and drug-disease networks based on different data types. An overview of the used data types and derived networks is shown in Fig. 40 and UMLS CUIs41 (finer granularity) and ICD-1042 three-character codes (coarser granularity) as node IDs in the constructed networks, respectively. To this end, where possible, we constructed the networks in MONDO, UMLS CUI, and in ICD-10 vocabulary (using three-character level codes). Note that analyses involving comorbidity data were carried out only in ICD-10 and the comparison between target- and indication-based drugomes only in MONDO vocabulary (see Methods for an explanation). Moreover, neither the semantic layers of the MONDO disease ontology nor the hierarchy of the UMLS CUI and ICD-10 classification system were used to add edges to our diseasomes. MONDO, UMLS CUI, and ICD-10 were only used as vocabularies, i.e., to provide the node IDs in our networks. Whether two disease nodes are connected by an edge exclusively depends on the primary databases containing the association data . For instance, two diseases are connected in the gene-based diseasome in MONDO vocabulary if the intersection of the sets of genes associated with their MONDO IDs is non-empty, where disease-gene associations were obtained from OMIM43 and DisGeNET36.We also evaluated how annotating the data using disease vocabularies of different granularity affect the results, by carrying out the analyses using MONDO IDsGED quantifies the dissimilarity between two networks as the minimum cost of an edit path transforming one network into the other. Edit paths are sequences of elementary edit operations , all of which come with associated edit costs. Hence, the GED is a distance measure between two networks. We computed three different versions of GED using uniform, weight-based, and rank-based edge editing costs, respectively. Uniform edit costs discard the association strengths of the edges in the compared networks; weight- and rank-based edit costs incorporate them by making it more expensive to delete or insert edges with strong associations or to substitute them by edges with weak associations. Corroborating the robustness of our analysis method, we obtained similar results for all three versions of GED. In the following, only the results of uniform edit costs are reported. Results for rank- and weight-based edit costs can be found in Supplementary Figs. P-values for each pair of networks based on global GEDs  test  tP-values using the one-sided MWU test based on local GEDs to evaluate whether the local distances for the original networks are significantly smaller than the local distances for the permuted counterparts  against disease nodes (0 out of 3 comparisons significant at 0.05 level) obtained from local-similarity analyses of indication- versus protein-based drug-disease network as well as P-values obtained from target- and indication-based drugome (significant at 0.001 level), we discovered that, in general, drug neighborhoods are better preserved across the compared networks than disease neighborhoods  than in MONDO vocabulary (2 out of 6 significant at 0.05 level) or UMLS CUI vocabulary (1 out of 6 significant at 0.05 level). The results also suggest that variant-based diseasomes have higher similarities with other diseasomes (7 out of 10 comparisons significant at 0.05 level) than gene-based diseasomes (5 out of 10 comparisons significant at 0.05 level), considering all three vocabularies. By inspecting the P-values individually for nodes based on local GEDs  Python package, which is freely available on GitHub .In order to make our results explorable and actionable, we developed the GraphSimViz  web interface, which is freely available at 24, provide solid evidence for the overall validity of the BEV network medicine paradigm. However, they also indicate that results generated via BEV network medicine approaches become less reliable when zooming-in on individual diseases. Our results hence confirm that it is problematic to exclusively rely on data annotated with phenotype-based definitions if the objective is to uncover molecular pathomechanisms. As long as phenotype-based disease definitions have not been replaced by endotypes, large-scale disease association databases should therefore be used with care in network medicine and should be combined with additional layers of disease-specific omics data. In the following, we further speculate on issues that might play a role in the local blurriness of BEV network medicine and sketch a roadmap to overcome this problem.Our results strongly support the global-scale hypothesis and, in line with previous studiesWhile there are vast amounts of datasets online that contain useful information about diseases such as genetic associations, comorbidities, and symptoms, each of these datasets may use different disease vocabularies to describe their associations. The vocabularies have different degrees of granularity and are generated in different ways and for different purposes. However, for downstream (BEV) network medicine analyses, in order to jointly leverage the disease association from various data sources that use disease terms from different vocabularies as disease identifiers, we have to map data to a joint target vocabulary. This is a mammoth task that inevitably involves losing some data due to unmappable terms , areas in the body that are affected  or symptoms of the disease . ICD-10 codes are considered inadequate due to their overly inclusive designations, ranging from symptoms  over syndromes  to true endotypes with definable molecular determinants . This leads to data that is blurred, as diseases with distinct pathomechanisms are being aggregated together, e.g., due to symptom or organ commonality. This blurriness not only has severe clinical consequences , but also makes it very challenging to mine disease-associated data for pathomechanisms via BEV network medicine approachesP-values , around 50% of all computed MWU P-values are significant at 0.001 level. When comparing the entire diseasomes via global GEDs, all empirical P-values are significant.The results presented in this study, where drugome comparisons have led to more significant results on a local level than diseasome comparisons, are evidence that network-based analyses yield more targeted and reliable results when the underlying annotations are well-defined (such as in drug vocabularies). Comparing the results of the GED-based analyses for full diseasomes  with those obtained for analyses based on local GEDs in diseasomes with ICD-10 three-character codes, UMLS CUIs, and MONDO terms as nodes, respectively, further highlights the detrimental effect of local blurriness in currently used disease definitions: The higher the resolution of the analysis, the less significant the obtained see Fig. . When usP-values computed based on local GEDs may be a consequence of some of the current phenotype-based disease entities already corresponding to true endotypes. We speculate that, for diseases where our current definitions already have a one-to-one mapping to true endotypes, the local-scale hypothesis holds.The fact that we could not identify any clear patterns among diseases with small or large empirical Even though we expected to obtain similar results for variant-based and gene-based diseasomes, the local-similarity analyses show that variant-based diseasomes have higher similarities with other diseasomes compared to gene-based diseasomes. This indicates that the disease-gene associations underlying the gene-based diseasomes contain less targeted information than the disease-variant associations underlying the variant-based diseasomes. Hence, using disease-variant data might yield more reliable results in the context of BEV network medicine applications.46, disease-gene associations were taken from OMIM and DisGeNET curated databases. The latter collates disease-gene associations from different databases: UniProt47, CTD48, Orphanet49, ClinGen50, Genomics England51, CGI52, and PsyGeNET53. These constituent databases comprise multiple types of disease-gene associations such as causal mutations (mutations known to cause the disease), modifying mutations , or merely statistical associations without evidence of causality. Disease-variant associations used in our study were extracted from DisGeNET, which itself integrates various databases: GWASdb54, ClinVar55, GWAS Catalog56, UniProt, and BeFree57. Like for disease-gene associations, there are different types of disease-variant associations, ranging from known causal variants to variants with merely statistical evidence. However, the heterogeneity of the association types is higher for disease-gene associations than for disease-variant associations. Moreover, the genetic variation data from the constituent disease-variant databases of DisGeNET is mainly taken from genome-wide association studies (GWAS), which identify associations between common genetic variants and phenotypic traits via hypothesis-free, genome-wide scans. In contrast, in the disease-gene databases used by DisGeNET, parts of the data are curated from studies where evidence for disease-gene associations stems from a very limited number of patients or where hypothesis-driven approaches were used .To seek a possible explanation for this difference, we had a closer look at the associations underlying these two types of diseasomes. In our study, as well as in many other network medicine studies58. Finally, the difference in results between gene- and variant-based diseasomes may also partly be due to loss of information introduced when aggregating P-values for disease-variant associations at gene level59.Another reason for the difference in results between gene-based and variant-based diseasomes may consist in the loss of detail resulting from mapping variants to genes. Distinct mutations in one gene may cause different phenotypes, but this information cannot be captured at the level of disease-gene associations and is better conserved at disease-variant level. A very good example is the LMNA gene, where different mutations can cause 13 different diseases such as Hutchinson-Gilford progeria syndrome and the Dunnigan-type familial partial lipodystrophyP-values in Supplementary Figs. A limitation of our study is that our results do not rule out the possibility that confounders other than mechanistically inadequate disease definitions lead to the observed local blurriness of BEV network medicine. For instance, off-target effects might introduce biases in our analyses using drug association data, while the known biases in gene association data discussed above might explain the results obtained for analyses involving gene association data. However, we would like to stress that the obtained results are remarkably stable across all employed data modalities  and expert knowledge of the clinicians, and finally validated in preclinical studies . As mentioned above, such approaches have already led to various important insights into specific disease mechanisms.62, most existing pathomechanism mining methods still rely on phenotypic case-versus-control annotations64 or lists of genes associated with a  disease term67.Secondly, unsupervised network medicine methods are needed, which not only return candidate pathomechanisms but at the same time de novo stratify patients into mechanistically distinct subgroups and hence do not rely on potentially misleading priorly available phenotypically defined subtype annotations. While few such approaches existFinally, we would like to point out that the current lack of mechanistic disease definitions not only hampers progress in (BEV) network medicine, but also has a detrimental effect on virtually all other data-centric approaches to, e.g., treatment design or diagnosis which rely on disease association data that utilize phenotype-based disease definitions. For instance, an artificial intelligence model for diagnosis assistance trained on genetic disease signatures will systematically produce unreliable results if the disease annotations used for training do not correspond to true endotypes. While we here quantified the effect of this problem in the context of BEV medicine, overcoming it would hence be beneficial for a large fraction of the biomedical research community.Our research complies with all relevant ethical regulations. The only non-public data used for this study is the comorbidity data we obtained from the Estonian Biobank. The Estonian Biobank is a population-based biobank managed by the Institute of Genomics at the University of Tartu. All participants have signed a broad consent upon joining the biobank, allowing their sample and data to be used for further research. ICD-10 diagnoses are obtained from epicrises, prescriptions and bills to the Health Insurance Fund. The work in this article was covered by the ethics approval “234T-12 Omics for Health”  by the Estonian Committee of Bioethics and Human Research. Data was released by the Estonian Biobank .68: First, MONDO contains mappings between its own disease vocabulary and various other vocabularies, including OMIM, MeSH69, and ICD-10. Then, mappings between several vocabularies and ICD-10 could be achieved by mapping disease terms to MONDO, followed by mapping MONDO to ICD-10. Mapping to UMLS CUI was carried out using the mappings provided in the UMLS Metathesaurus 2022AA full release. For all pairwise analyses, the two compared networks were aligned before computing GEDs, i.e., only the nodes contained in both of them were taken into account.As shown in Table The comorbidity data was obtained from the Estonian Biobank, which uses originally ICD-10 codes. In order to carry out analyses involving comorbidity data in MONDO or UMLS CUI vocabulary, the comorbidity data needed to be mapped from a coarser-grained (ICD-10) to a finer-grained disease vocabulary (MONDO and UMLS CUI). Although this is possible from a technical point of view, it would have introduced a lot of noise in the obtained comorbidity networks. To avoid overshadowing all other effects by the introduced noise, we decided to carry out analyses involving comorbidity data only in ICD-10 vocabulary. Consequently, all analyses involving comorbidity data were carried out only in ICD-10 vocabulary. On the other hand, the comparison between the target- and the indication-based drugomes was carried out only in MONDO vocabulary. In these networks, nodes are drugs and not diseases and using different disease vocabularies leaves the nodes of the networks unchanged. In the indication-based drogomes, the choice of the disease vocabulary can change the edges of the networks, but, in practice, we observed that the differences are small. Target-based drugomes are not affected at all by the choice of the disease ontology. Therefore, we only use MONDO for the comparison of drugomes.Additionally, further data harmonization steps were carried out: Since HPO contains both general and specific terms, we pruned the data by removing very general symptom terms, using the existing hierarchy in HPO. More specifically, we decomposed the generated hierarchical phenotype network into its levels and removed the terms from the top three levels.The diagnoses in around 140 K patients records available in the Estonian Biobank (April 2020 version used for this study) are encoded in ICD-10 vocabulary, and the records contain both three- and four-character ICD-10 codes. In order to generate uniform data, we therefore truncated all four-character codes to three-character level. Moreover, we removed diseases with incidence below five from the data, as well as the codes from the ICD-10 chapters XV , XVI , XVIII , XIX , XX , XXI , and XXII .68. Disease-variant and disease-symptom associations were directly obtained from DisGeNET and HPO, respectively.For network construction, some part of the data such as disease-gene, drug-indication, drug-target, gene-encoding-protein, and PPI data were obtained from the databases shown in Table Supplementary Table The indication- and target-based drugomes as well as the gene-, variant-, symptom-, and indication-based diseasomes were constructed based on the Jaccard index of the respective annotations. The bipartite indication-based drug-disease network was directly constructed from the data source, i.e., we connected a disease 70. It is defined as the minimum cost of transforming a source graph 71.GED is a widely used and generically applicable distance measure for attributed graphsWe used a customized version of GED to compare the different diseasomes, drugomes, and drug-disease networks constructed as detailed in the previous section as well as their randomized counterparts. Since the networks were aligned before all pairwise comparisons, we had Based on the local distances, we also computed cluster-level distances for a cluster of nodes We used three types of edge edit cost functions, namely, uniform costs and costs based on normalized edge ranks or normalized edge weights. The uniform costs are defined by simply setting 73. For each node Using GED, we tested the local- and the global-scale hypotheses as follows: For each pair P-values asP-values asP-values asTo test the global-scale hypothesis, we computed one-sided empirical P-values for multiple testing. The reason for this choice is that the relevance of our results stems from the non-significance of a large fraction of the obtained P-values. If we had corrected for multiple testing, we would have inflated this fraction.Note that we consciously refrained from adjusting 75, kernel-based76, and message-passing-based78 approaches. We decided to use GED because, to the best of our knowledge, it is the only distance measure satisfying the following requirements necessary for our analyses:To allow testing both the global- and the local-scale hypothesis, we need a graph distance measure The local node distances Since a node alignment between the compared networks is given , both the global network distance The distances need to be computable in linear time w.r.t. the size of the networks in order to enable our large-scale permutation tests.In addition to our version of GED, there are various other network dissimilarity measures–most notably, embedding-based74 with subsequent distance computation in embedding space) typically do not satisfy requirements 2 through 4. Exceptions we are aware of are DeltaCon79  and the graphlet degree signature80 (which satisfies requirements 1 and 2 but not requirements 3 and 4). Highly successful techniques in graph learning follow a message passing concept78. When restricted to a single hop (as needed to satisfy requirement 2), these methods define node 77 mapping sets to vectors . Here, using unique node labels renders the method node-identity-aware and allows to drop While most of the kernel-based methods already fall short of requirement 1, popular node-embedding-based approaches  all disease-disease pairs in a disease-gene-gene-disease network, (2) all drug-drug pairs in a drug-protein-protein-drug network, and (3) all disease-drug pairs in a disease-protein-protein-drug network. For each network, we split the multi-set of obtained distances into multi-sets Recall that we have introduced BEV network medicine as the subfield of network medicine which aims at uncovering disease mechanisms by mining large-scale disease-association data. Let 81 for network handling and Scipy82 for carrying out statistical tests and comes with all networks and scripts to reproduce the results reported in this paper. Moreover, GraphSimQT can be used to compare user-provided networks, using the techniques presented in this paper. Significance of comorbidity associations was evaluated using the Scipy implementation of Fisher’s exact test and the statsmodels83 implementation of Benjamini-Hochberg multiple testing correction. The GraphSimViz web tool (https://graphsimviz.net) was implemented using Vue.js as a frontend framework, the Drugst.One (https://drugst.one) plugin as network explorer and a Django backend with a PostgreSQL database.We have implemented all network analysis approaches underlying this article in a Python package called GraphSimQT. GraphSimQT uses graph-toolFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary