Network Biomarkers Research Articles

Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs). Subsequently, receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways—tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis—were enriched, providing insight into the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as key components. In summary, this study highlights metabolic biomarkers that could potentially be used for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data offers a validated and in-depth understanding of TNBC metabolism.

Bioengineered Extracellular Vesicles Delivering siMDM2 Sensitize Oxaliplatin Therapy Efficacy in Colorectal Cancer.

Oxaliplatin (OXA) is the first-line drug for the treatment of colorectal cancer (CRC), and susceptibility to drug resistance affects patient prognosis. However, the exact underlying mechanisms remain unclear. Platinum-acquired resistance in CRC is a continuous transition process; though, current research has mainly focused on the end state of drug resistance, and the early events of drug resistance have been ignored. In this study, single-cell transcriptome sequencing is combined with a dynamic network biomarker (DNB), and found that the functional inhibition of the mitochondrial electron transport chain complex I occur early in the development of attained resistance to OXA in CRC cells, as evidenced by a decrease in the levels of subunit proteins, primarily NDUFB8. Specifically, the mouse double minute 2 homologue (MDM2) mediates the ubiquitination and degradation of NDUFB8, reducing intracellular reactive oxygen species (ROS) generation under chemotherapeutic stress, consequently contributing to drug resistance. Based on this, the study constructs engineered extracellular vesicles carrying siMDM2 by electroporation and validates the application of EV-siMDM2 to improve the efficacy of OXA-based chemotherapy by inhibiting the MDM2/NDUFB8/ROS signaling axis in patient-derived xenograft (PDX) and hepatic and pulmonary metastasis mouse models, thus providing new ideas and an experimental basis for the platinum-resistant treatment of CRC.

Transient destabilization of interhemispheric functional connectivity induced by spreading depolarization

ABSTRACT Cortical spreading depolarization (CSD), a slowly propagating wave of transient cellular depolarization, is a reliable cortical response to various brain insults (stroke, trauma, seizures) and underlying mechanism of migraine aura. Little is known about CSD effects on brain network activity. Using undirected (mutual information, MI) and directed (transfer entropy, TE) measures, we studied the dynamics of cross-hemispheric connectivity associated with the development of unilateral CSD in freely behaving rats and the involvement of inhibitory transmission in mechanisms of the coupling changes. We show that the development of CSD in the cortex of one hemisphere is followed by the transient loss of undirected functional connectivity (MI) between ipsilateral and contralateral cortical regions. The post-CSD functional disconnection of the hemispheres was accompanied by an increase in driving force from an unaffected contralateral cortex to an affected one (TE). Mild cortical disinhibition produced by pretreatment with an inhibitory receptor blocking agent (penthylenetetrazole) did not affect CSD but attenuated (MI) or eliminated (TE) the CSD-induced connectivity changes. The effects of CSD on functional connectivity in awake rodents were similar at the individual and group levels, suggesting that the described connectivity response may be a promising network biomarker of CSD occurrence in patients.

Unsupervised Learning with Network Biomarkers for Risk Stratification in IgA Nephropathy and Its Clinical Implications

Unsupervised Learning with Network Biomarkers for Risk Stratification in IgA Nephropathy and Its Clinical Implications

Functional activity, functional connectivity and complex network biomarkers of progressive hyposmia Parkinson's disease with no cognitive impairment: evidences from resting-state fMRI study.

Olfactory dysfunction stands as one of the most prevalent non-motor symptoms in the initial stage of Parkinson's disease (PD). Nevertheless, the intricate mechanisms underlying olfactory deficits in Parkinson's disease still remain elusive. This study collected rs-fMRI data from 30 PD patients [15 with severe hyposmia (PD-SH) and 15 with no/mild hyposmia (PD-N/MH)] and 15 healthy controls (HC). To investigate functional segregation, the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) were utilized. Functional connectivity (FC) analysis was performed to explore the functional integration across diverse brain regions. Additionally, the graph theory-based network analysis was employed to assess functional networks in PD patients. Furthermore, Pearson correlation analysis was conducted to delve deeper into the relationship between the severity of olfactory dysfunction and various functional metrics. We discovered pronounced variations in ALFF, ReHo, FC, and topological brain network attributes across the three groups, with several of these disparities exhibiting a correlation with olfactory scores. Using fMRI, our study analyzed brain function in PD-SH, PD-N/MH, and HC groups, revealing impaired segregation and integration in PD-SH and PD-N/MH. We hypothesize that changes in temporal, frontal, occipital, and cerebellar activities, along with aberrant cerebellum-insula connectivity and node degree and betweenness disparities, may be linked to olfactory dysfunction in PD patients.

Identifying Key Genes as Progression Indicators of Prostate Cancer with Castration Resistance Based on Dynamic Network Biomarker Algorithm and Weighted Gene Correlation Network Analysis.

Background: Androgen deprivation therapy (ADT) is the mainstay of treatment for prostate cancer, yet dynamic molecular changes from hormone-sensitive to castration-resistant states in patients treated with ADT remain unclear. Methods: In this study, we combined the dynamic network biomarker (DNB) method and the weighted gene co-expression network analysis (WGCNA) to identify key genes associated with the progression to a castration-resistant state in prostate cancer via the integration of single-cell and bulk RNA sequencing data. Based on the gene expression profiles of CRPC in the GEO dataset, the DNB method was used to clarify the condition of epithelial cells and find out the most significant transition signal DNB modules and genes included. Then, we calculated gene modules associated with the clinical phenotype stage based on the WGCNA. IHC was conducted to validate the expression of the key genes in CRPC and primary PCa patients Results:Nomograms, calibration plots, and ROC curves were applied to evaluate the good prognostic accuracy of the risk prediction model. Results: By combining single-cell RNA sequence data and bulk RNA sequence data, we identified a set of DNBs, whose roles involved in androgen-associated activities indicated the signals of a prostate cancer cell transition from an androgen-dependent state to a castration-resistant state. In addition, a risk prediction model including the risk score of four key genes (SCD, NARS2, ALDH1A1, and NFXL1) and other clinical-pathological characteristics was constructed and verified to be able to reasonably predict the prognosis of patients receiving ADT. Conclusions: In summary, four key genes from DNBs were identified as potential diagnostic markers for patients treated with ADT and a risk score-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions of CRPC.

Graph Representation Learning Based on Specific Subgraphs for Biomedical Interaction Prediction.

Discovering the novel associations of biomedical entities is of great significance and can facilitate not only the identification of network biomarkers of disease but also the search for putative drug targets.Graph representation learning (GRL) has incredible potential to efficiently predict the interactions from biomedical networks by modeling the robust representation for each node.> However, the current GRL-based methods learn the representation of nodes by aggregating the features of their neighbors with equal weights. Furthermore, they also fail to identify which features of higher-order neighbors are integrated into the representation of the central node. In this work, we propose a novel graph representation learning framework: a multi-order graph neural network based on reconstructed specific subgraphs (MGRS) for biomedical interaction prediction. In the MGRS, we apply the multi-order graph aggregation module (MOGA) to learn the wide-view representation by integrating the multi-hop neighbor features. Besides, we propose a subgraph selection module (SGSM) to reconstruct the specific subgraph with adaptive edge weights for each node. SGSM can clearly explore the dependency of the node representation on the neighbor features and learn the subgraph-based representation based on the reconstructed weighted subgraphs. Extensive experimental results on four public biomedical networks demonstrate that the MGRS performs better and is more robust than the latest baselines.

EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.

Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength. Multi-layer network analysis indicates that modularity is significantly higher for the non-alert state task than the alert state task in the alpha and beta bands. In the W4 time window (1 second before a collision), we identified significant features that can differentiate situations where a car collision is imminent from those where no collision occurs. Single-layer network analysis also revealed statistical differences in node strength and local efficiency for some EEG channels in the alpha and beta bands during the W4 and W5 time windows. Using these biomarkers to predict vehicle collision risk, the classification accuracy of a linear kernel SVM reached up to 87.5%, demonstrating the feasibility of predicting driving collisions through brain network biomarkers. These findings are important for the study of human intuition and the development of brain-computer interface-based intelligent driving hazard perception assistance systems.

CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes

BackgroundThere exists a critical transition or tipping point during the complex biological process. Such critical transition is usually accompanied by the catastrophic consequences. Therefore, hunting for the tipping point or critical state is of significant importance to prevent or delay the occurrence of catastrophic consequences. However, predicting critical state based on the high-dimensional small sample data is a difficult problem, especially for single-cell expression data.ResultsIn this study, we propose the comprehensive neighbourhood-based perturbed mutual information (CPMI) method to detect the critical states of complex biological processes. The CPMI method takes into account the relationship between genes and neighbours, so as to reduce the noise and enhance the robustness. This method is applied to a simulated dataset and six real datasets, including an influenza dataset, two single-cell expression datasets and three bulk datasets. The method can not only successfully detect the tipping points, but also identify their dynamic network biomarkers (DNBs). In addition, the discovery of transcription factors (TFs) which can regulate DNB genes and nondifferential ‘dark genes’ validates the effectiveness of our method. The numerical simulation verifies that the CPMI method is robust under different noise strengths and is superior to the existing methods on identifying the critical states.ConclusionsIn conclusion, we propose a robust computational method, i.e., CPMI, which is applicable in both the bulk and single cell datasets. The CPMI method holds great potential in providing the early warning signals for complex biological processes and enabling early disease diagnosis.

Comprehensive analysis of circRNA-miRNA-mRNA regulatory network and novel potential biomarkers in eutopic endometrium of adenomyosis

Adenomyosis (ADS) is a common gynecological disorder, and its pathogenesis remains unclear. This study explores the functions of circRNAs in the eutopic endometrium of ADS and their diagnostic efficacy for ADS. High-throughput RNA sequencing was performed on 12 eutopic endometrial samples from ADS patients and 3 control endometrial samples. Additionally, circRNAs were analyzed in conjunction with clinical features. A competitive endogenous RNA network was established based on bioinformatics analysis, comprising 3 circRNAs, 1 miRNA, and 13 mRNAs. In the ADS group, the expression levels of hsa_circ_0008959 and SLC15A4 were significantly reduced, while hsa-miR-124-3p expression was increased. SLC15A4 was associated with cell proliferation and invasion. Decreased expression of hsa_circ_0008959 and SLC15A4, along with high VAS scores and elevated hsa-miR-124-3p levels, were identified as risk factors for ADS development. The combination of hsa_circ_0008959 and VAS scores demonstrated the highest diagnostic value for ADS.

Dynamic network biomarker of treg cells differentiation critical state with neoadjuvant treatment in LUSC.

e20063 Background: Regulatory T cells (Tregs) suppress immune responses and can promote tumor growth and metastasis during cancer progression. Studies have shown that tumor-infiltrating Tregs upregulate inhibitory gene programs after immune checkpoint blockade therapy. Targeting Tregs is believed to be a valuable approach to enhance the effectiveness of immunotherapy. However, the precise role of Tregs in the tumor microenvironment, their connection to treatment resistance mechanisms, and how changes in their immune activity impact tumor treatment are still not well understood. Methods: Single-cell data and clinical information were obtained from the Gene Expression Omnibus database with the accession number GSE176021 for 4 patients diagnosed with lung squamous cell carcinoma (LUSC) who received neoadjuvant anti-PD-1 therapy, including 2 patients with major pathologic response (MPR) and 2 patients with non-MPR. BioTIP was employed to identify critical transition signals of the Treg cell differentiation trajectory constructed by Monocle2. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set variation analysis were performed to understand biological processes and pathways. Additionally, potential transcription factors were identified using Single-Cell rEgulatory Network Inference and Clustering, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Results: The study constructed a pseudo-temporal differentiation trajectory of Tregs in LUSC patients, and Treg subpopulations during the critical transition period were identified based on the dynamic network biomarker method. Notably, subpopulations of Treg cells clustering MPR or non-MPR samples were located at different ends of the differentiation trajectory, showing variations in Treg-related pathways and transcription factor regulation. The critical transition subpopulation exhibited high expression of glycolysis, TCA cycle, and natural killer cell receptors, along with upregulation of the CEBPB transcription factor activity and downregulation of ARNTL activity. SELPLG-SELL, CD99-CD99, and MHC-II-CD4 were identified as potential key signals that may affect therapeutic effect, primarily influencing cell adhesion, the immune suppressive activity of Treg cells, and T cell response. Conclusions: In conclusion, this study utilized single-cell data analysis to uncover the dynamic regulation of Treg cells in LUSC, identifying critical transition Treg subpopulations. Notably, our findings suggest the involvement of the transcription factors CEBPB and ARNTL in regulating Treg activity. These novel insights shed light on potential strategies for targeted therapies and provide valuable information regarding resistance mechanisms associated with Treg cells.

Detecting tipping points of complex diseases by network information entropy.

The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.

Association of Incident Stroke Risk With an IL-18-Centered Inflammatory Network Biomarker Composite.

A coordinated network of circulating inflammatory molecules centered on the pleotropic pro-atherogenic cytokine interleukin-18 (IL-18) is linked to cerebral small vessel disease. We sought to validate the association of this inflammatory biomarker network with incident stroke risk, cognitive impairment, and imaging metrics in a sample of the Framingham Offspring Cohort. Using available baseline measurements of serum levels of IL-18, GDF (growth and differentiation factor)-15, soluble form of receptor for advanced glycation end products, myeloperoxidase, and MCP-1 (monocyte chemoattractant protein-1) from Exam 7 of the Framingham Offspring Cohort (1998-2001), we constructed a population-normalized, equally weighted log-transformed mean Z-score value representing the average level of each serum analyte to create an inflammatory composite score (ICS5). Multivariable regression models were used to determine the association of ICS5 with incident stroke, brain magnetic resonance imaging features, and cognitive testing performance. We found a significant association between ICS5 score and increased risk for incident all-cause stroke (hazard ratio, 1.48 [95% CI, 1.05-2.08]; P=0.024) and ischemic stroke (hazard ratio, 1.51 [95% CI, 1.03-2.21]; P=0.033) in the Exam 7 cohort of 2201 subjects (mean age 62±9 years; 54% female) aged 45+ years with an all-cause incident stroke rate of 6.1% (135/2201) and ischemic stroke rate of 4.9% (108/2201). ICS5 and its component serum markers are all associated with the Framingham Stroke Risk Profile score (β±SE, 0.19±0.02; P<0.0001). In addition, we found a significant inverse association of ICS5 with a global cognitive score, derived from a principal components analysis of the neuropsychological battery used in the Framingham cohort (-0.08±0.03; P=0.019). No association of ICS5 with magnetic resonance imaging metrics of cerebral small vessel disease was observed. Circulating serum levels of inflammatory biomarkers centered on IL-18 are associated with an increased risk of stroke and cognitive impairment in the Framingham Offspring Cohort. Linking specific inflammatory pathways to cerebral small vessel disease may enhance individualized quantitative risk assessment for future stroke and vascular cognitive impairment.

Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems

BackgroundDisease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems.MethodsIn this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state.ResultsThe proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called “dark genes” that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis.ConclusionsThe proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

MNFE: microbiome network flow entropy for detecting pre-disease states of type 1 diabetes.

In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point or critical state in a reliable manner, thereby providing accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states. To address this problem, we proposed a new method - microbiome network flow entropy (mNFE) based on a single sample from each individual - for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identified some non-differential 'dark species' play important roles during the T1D progression. mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach not only for T1D pre-disease diagnosis or preventative treatment but also for preventative medicine of other diseases by gut microbiome.

Dynamic network biomarker C1QTNF1 regulates tumor formation at the tipping point of hepatocellular carcinoma.

Identifying the precise moment before the onset of hepatocellular carcinoma (HCC) remains a significant challenge in the medical field. The existing biomarkers fall short of pinpointing the critical point preceding HCC formation. This study aimed to determine the exact tipping point for the transition from cirrhosis to HCC, identify the core Dynamic Network Biomarker (DNB), and elucidate its regulatory effects on HCC. A spontaneous HCC mouse model was established to mimic HCC formation in patients with chronic hepatitis. Using the DNB method, C1q and tumor necrosis factor (TNF) related 1 (C1QTNF1) protein was identified as the key DNB at the crucial tipping time of spontaneous HCC development. Both in vitro and in vivo studies showed that C1QTNF1 could inhibit tumor growth. Overexpression of C1QTNF1 before the tipping point effectively prevented HCC occurrence. Patients with elevated C1QTNF1 expression demonstrated improved overall survival (OS) (P = 0.03) and disease-free survival (DFS) (P = 0.03). The diagnostic value of C1QTNF1 was comparable to that of alpha-fetoprotein (AFP) (area under the curve [AUC] = 0.84; sensitivity 85%; specificity 80%). Furthermore, our research indicated that platelet-expressed C1QTNF1 is involved in cancer-associated signaling pathways. Our findings introduce a novel perspective by highlighting C1QTNF1 as the pivotal biomarker at the tipping point of primary HCC formation using DNB. We propose C1QTNF1 as a prognostic biomarker for HCC, potentially influencing tumor development through a platelet-related cancer signaling pathway.

Personalized decision support system for tailoring IgA nephropathy treatment strategies

BackgroundThe ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. MethodsAnalyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80–20 split for training and testing purposes. ResultsIn the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. ConclusionOur system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.

Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes.

Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis athletes and identify specific brain network biomarkers. A total of 48 healthy right-handed college students participated in this study, including 24 table tennis athletes and 24 controls with no exercise experience. Electroencephalogram data were collected using a 64-conductive active electrode system during eyes-closed resting conditions. The analysis involved examining the average power spectral density and constructing brain functional networks using the weighted phase-lag index. Network topological characteristics were then calculated. The results revealed that table tennis athletes exhibited significantly higher average power spectral density in the α band compared to the control group. Moreover, athletes not only demonstrated stronger functional connections, but they also exhibited enhanced transmission efficiency in the brain network, particularly at the local level. Additionally, a lateralization effect was observed, with more potent interconnected hubs identified in the left hemisphere of the athletes' brain. Our findings imply that the α band may be uniquely associated with table tennis athletes and their motor skills. The brain network characteristics of athletes during the resting state are worth further attention to gain a better understanding of adaptability of and changes in their brains during training and competition.