Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE).

Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

Clinical Outcomes of Transaxillary Reverse-Sequence Endoscopic Nipple-Sparing Mastectomy and Direct-to-Implant Prepectoral Breast Reconstruction: A Prospective Study of Initial 68 Procedures.

BACKGROUND: Minimal access breast surgery improves cosmetic outcomes over conventional breast surgery but still faces barriers in becoming standard procedure for breast reconstruction. This report introduces a novel technique of transaxillary reverse-sequence endoscopic nipple-sparing mastectomy (R-E-NSM) followed by direct-to-implant prepectoral breast reconstruction (DTI-PBR) and describes its clinical outcomes. METHODS: This prospective study enrolled patients who underwent R-E-NSM and DTI-PBR from March 2021 to December 2021 at a single institution. Perioperative data, surgical complications, oncologic outcomes, and patient- and surgeon-reported cosmetic results were noted. RESULTS: The 60 patients in this study who underwent 68 R-E-NSM and DTI-PBR had a mean age was 40.4 ± 10.3 years. The average durations of uni- and bilateral operations were 156.5 ± 48.3 min and 191.3 ± 36.1 min, respectively. The overall surgical complication rate was 13.3%, including 10.0% of patients with minor complications and 3.3% of patients with major complications. The study had one case (1.7%) of implant loss and one case (1.7%) of skin flap necrosis treated by reoperation. During the median follow-up period of 24 months, one patient (1.7%) who discontinued chemotherapy for myelosuppression experienced liver metastases 5 months postoperatively, and one patient experienced new-onset contralateral ductal carcinoma in situ 24 months postoperatively. The preoperative and 18-month postoperative Breast-Q scores for satisfaction with breasts, psychosocial well-being, sexual well-being, and chest well-being did not differ significantly, and the Scar-Q was 81.2 ± 14.5 points. The good-to-excellent rate in surgeon-reported cosmetic results reached 90%. CONCLUSIONS: Transaxillary R-E-NSM followed by DTI-PBR is a safe and efficient technique with high cosmetic outcomes and reliable medium-term oncologic results.

SGAE: single-cell gene association entropy for revealing critical states of cell transitions during embryonic development.

The critical point or pivotal threshold of cell transition occurs in early embryonic development when cell differentiation culminates in its transition to specific cell fates, at which the cell population undergoes an abrupt and qualitative shift. Revealing such critical points of cell transitions can track cellular heterogeneity and shed light on the molecular mechanisms of cell differentiation. However, precise detection of critical state transitions proves challenging when relying on single-cell RNA sequencing data due to their inherent sparsity, noise, and heterogeneity. In this study, diverging from conventional methods like differential gene analysis or static techniques that emphasize classification of cell types, an innovative computational approach, single-cell gene association entropy (SGAE), is designed for the analysis of single-cell RNA-seq data and utilizes gene association information to reveal critical states of cell transitions. More specifically, through the translation of gene expression data into local SGAE scores, the proposed SGAE can serve as an index to quantitatively assess the resilience and critical properties of genetic regulatory networks, consequently detecting the signal of cell transitions. Analyses of five single-cell datasets for embryonic development demonstrate that the SGAE method achieves better performance in facilitating the characterization of a critical phase transition compared with other existing methods. Moreover, the SGAE value can effectively discriminate cellular heterogeneity over time and performs well in the temporal clustering of cells. Besides, biological functional analysis also indicates the effectiveness of the proposed approach.

Identifying the critical state of cancers by single-sample Markov flow entropy.

Background: The progression of complex diseases sometimes undergoes a drastic critical transition, at which the biological system abruptly shifts from a relatively healthy state (before-transition stage) to a disease state (after-transition stage). Searching for such a critical transition or critical state is crucial to provide timely and effective scientific treatment to patients. However, in most conditions where only a small sample size of clinical data is available, resulting in failure when detecting the critical states of complex diseases, particularly only single-sample data. Methods: In this study, different from traditional methods that require multiple samples at each time, a model-free computational method, single-sample Markov flow entropy (sMFE), provides a solution to the identification problem of critical states/pre-disease states of complex diseases, solely based on a single-sample. Our proposed method was employed to characterize the dynamic changes of complex diseases from the perspective of network entropy. Results: The proposed approach was verified by unmistakably identifying the critical state just before the occurrence of disease deterioration for four tumor datasets from The Cancer Genome Atlas (TCGA) database. In addition, two new prognostic biomarkers, optimistic sMFE (O-sMFE) and pessimistic sMFE (P-sMFE) biomarkers, were identified by our method and enable the prognosis evaluation of tumors. Conclusions: The proposed method has shown its capability to accurately detect pre-disease states of four cancers and provide two novel prognostic biomarkers, O-sMFE and P-sMFE biomarkers, to facilitate the personalized prognosis of patients. This is a remarkable achievement that could have a major impact on the diagnosis and treatment of complex diseases.

MAPKAPK2, a potential dynamic network biomarker of α-synuclein prior to its aggregation in PD patients.

One of the important pathological features of Parkinson's disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear. In this study, using the dynamic network biomarker (DNB) method, we first identified the critical time point when α-Syn undergoes pathological aggregation based on a SH-SY5Y cell model and found that DNB genes encode transcription factors that regulated target genes that were differentially expressed. Interestingly, we found that these DNB genes and their neighbouring genes were significantly enriched in the cellular senescence pathway and thus proposed that the DNB genes HSF1 and MAPKAPK2 regulate the expression of the neighbouring gene SERPINE1. Notably, in Gene Expression Omnibus (GEO) data obtained from substantia nigra, prefrontal cortex and peripheral blood samples, the expression level of MAPKAPK2 was significantly higher in PD patients than in healthy people, suggesting that MAPKAPK2 has potential as an early diagnostic biomarker of diseases related to pathological aggregation of α-Syn, such as PD. These findings provide new insights into the mechanisms underlying the pathological aggregation of α-Syn.

Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma.

BACKGROUND: Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear. METHODS: Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database. We used the recently proposed dynamic network biomarker (DNB) method to identify the critical stage of epithelial cell deterioration. Data analysis and visualization were performed using R and Cytoscape software. In addition, Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis was used to identify potential transcription factors, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analysis (GSVA) analyses were also performed. RESULTS: The trajectory of epithelial cell deterioration in adenoma to carcinoma progression was delineated, and the subpopulation of pre-deteriorated epithelial cells during colorectal cancer (CRC) initialization was identified at the single-cell level. Additionally, FOS/JUN were identified as biomarkers for pre-deteriorated epithelial cell subpopulations in CRC. Notably, FOS/JUN triggered low expression of P53-regulated downstream pro-apoptotic genes and high expression of anti-apoptotic genes through suppression of P53 expression, which in turn inhibited P53-induced apoptosis. Furthermore, malignant epithelial cells contributed to the progression of pre-deteriorated epithelial cells through the GDF signaling pathway. CONCLUSIONS: We demonstrated the trajectory of epithelial cell deterioration and used DNB to characterize pre-deteriorated epithelial cells at the single-cell level. The expression of DNB-neighboring genes and cellular communication were triggered by DNB genes, which may be involved in epithelial cell deterioration. The DNB genes FOS/JUN provide new insights into early intervention in CRC.

SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems.

Complex biological systems do not always develop smoothly but occasionally undergo a sharp transition; i.e. there exists a critical transition or tipping point at which a drastic qualitative shift occurs. Hunting for such a critical transition is important to prevent or delay the occurrence of catastrophic consequences, such as disease deterioration. However, the identification of the critical state for complex biological systems is still a challenging problem when using high-dimensional small sample data, especially where only a certain sample is available, which often leads to the failure of most traditional statistical approaches. In this study, a novel quantitative method, sample-perturbed network entropy (SPNE), is developed based on the sample-perturbed directed network to reveal the critical state of complex biological systems at the single-sample level. Specifically, the SPNE approach effectively quantifies the perturbation effect caused by a specific sample on the directed network in terms of network entropy and thus captures the criticality of biological systems. This model-free method was applied to both bulk and single-cell expression data. Our approach was validated by successfully detecting the early warning signals of the critical states for six real datasets, including four tumor datasets from The Cancer Genome Atlas (TCGA) and two single-cell datasets of cell differentiation. In addition, the functional analyses of signaling biomarkers demonstrated the effectiveness of the analytical and computational results.

Identifying the critical state of complex biological systems by the directed-network rank score method.

MOTIVATION: Catastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements. RESULTS: In this study, by exploring the dynamic changes in gene cooperative effects between the before-transition and critical states, we presented a model-free approach, the directed-network rank score (DNRS), to detect the early-warning signal of critical transition in complex biological systems. The proposed method is applicable to both bulk and single-cell RNA-sequencing (scRNA-seq) data. This computational method was validated by the successful identification of the critical or pre-transition state for both simulated and six real datasets, including three scRNA-seq datasets of embryonic development and three tumor datasets. In addition, the functional and pathway enrichment analyses suggested that the corresponding DNRS signaling biomarkers were involved in key biological processes. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/zhongjiayuan/DNRS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression.

The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration, at which a drastic and qualitative shift occurs. The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration, which allows the timely implementation of appropriate measures to prevent a catastrophic transition. However, identifying the pre-deterioration stage is a challenging task in clinical medicine, especially when only a single sample is available for most patients, which is responsible for the failure of most statistical methods. In this study, a novel computational method, called single-sample network module biomarkers (sNMB), is presented to predict the pre-deterioration stage or critical point using only a single sample. Specifically, the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples. Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets, including acute lung injury, stomach adenocarcinoma, esophageal carcinoma, and rectum adenocarcinoma. In addition, it provides signaling biomarkers for further practical application, which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.

TPD: a web tool for tipping-point detection based on dynamic network biomarker.

Tipping points or critical transitions widely exist during the progression of many biological processes. It is of great importance to detect the tipping point with the measured omics data, which may be a key to achieving predictive or preventive medicine. We present the tipping point detector (TPD), a web tool for the detection of the tipping point during the dynamic process of biological systems, and further its leading molecules or network, based on the input high-dimensional time series or stage course data. With the solid theoretical background of dynamic network biomarker (DNB) and a series of computational methods for DNB detection, TPD detects the potential tipping point/critical state from the input omics data and outputs multifarious visualized results, including a suggested tipping point with a statistically significant P value, the identified key genes and their functional biological information, the dynamic change in the DNB/leading network that may drive the critical transition and the survival analysis based on DNB scores that may help to identify 'dark' genes (nondifferential in terms of expression but differential in terms of DNB scores). TPD fits all current browsers, such as Chrome, Firefox, Edge, Opera, Safari and Internet Explorer. TPD is freely accessible at http://www.rpcomputationalbiology.cn/TPD.

Identifying the critical states and dynamic network biomarkers of cancers based on network entropy.

BACKGROUND: There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems. METHODS: In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this method effectively explores the key associations among biomolecules and captures their dynamic abnormalities. RESULTS: Based on LNE, the pre-disease states of ten cancers were successfully detected. Two types of new prognostic biomarkers, optimistic LNE (O-LNE) and pessimistic LNE (P-LNE) biomarkers, were identified, enabling identification of the pre-disease state and evaluation of prognosis. In addition, LNE helps to find "dark genes" with nondifferential gene expression but differential LNE values. CONCLUSIONS: The proposed method effectively identified the critical transition states of complex diseases at the single-sample level. Our study not only identified the critical transition states of ten cancers but also provides two types of new prognostic biomarkers, O-LNE and P-LNE biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Identifying the critical states of complex diseases by the dynamic change of multivariate distribution.

The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study, we propose a computational method, the direct interaction network-based divergence, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy.

During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

Single-cell transcriptomics reveal DHX9 in mature B cell as a dynamic network biomarker before lymph node metastasis in CRC.

Increasing evidence indicates that mature B cells in the adjacent tumor tissue, both as an intermediate state, are vital in advanced colorectal cancer (CRC), which is associated with a low survival rate. Developing predictive biomarkers that detect the tipping point of mature B cells before lymph node metastasis in CRC is critical to prevent irreversible deterioration. We analyzed B cells in the adjacent tissues of CRC samples from different stages using the dynamic network biomarker (DNB) method. Single-cell profiling of 725 CRC-derived B cells revealed the emergence of a mature B cell subtype. Using the DNB method, we identified stage II as a critical period before lymph node metastasis and that reversed difference genes triggered by DNBs were enriched in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway involving B cell immune capability. DHX9 (DEAH-box helicase 9) was a specific para-cancerous tissue DNB key gene. The dynamic expression levels of DHX9 and its proximate network genes involved in B cell-related pathways were reversed at the network level from stage I to III. In summary, DHX9 in mature B cells of CRC-adjacent tissues may serve as a predictable biomarker and a potential immune target in CRC progression.

Dynamic Network Biomarker of Pre-Exhausted CD8+ T Cells Contributed to T Cell Exhaustion in Colorectal Cancer.

Immunotherapy has achieved positive clinical responses in various cancers. However, in advanced colorectal cancer (CRC), immunotherapy is challenging because of the deterioration of T-cell exhaustion, the mechanism of which is still unclear. In this study, we depicted CD8+ T-cell developmental trajectories and characterized the pre-exhausted T cells isolated from CRC patients in the scRNA-seq data set using a dynamic network biomarker (DNB). Moreover, CCT6A identified by DNB was a biomarker for pre-exhausted T-cell subpopulation in CRC. Besides, TUBA1B expression was triggered by CCT6A as DNB core genes contributing to CD8+ T cell exhaustion, indicating that core genes serve as biomarkers in pre-exhausted T cells. Remarkably, both TUBA1B and CCT6A expressions were significantly associated with the overall survival of COAD patients in the TCGA database (p = 0.0082 and p = 0.026, respectively). We also observed that cellular communication between terminally differentiated exhausted T cells and pre-exhausted T cells contributes to exhaustion. These findings provide new insights into the mechanism of T-cell exhaustion and provide clue for targeted immunotherapy in CRC.

Hydrogen sulfide-loaded microbubbles combined with ultrasound mediate thrombolysis and simultaneously mitigate ischemia-reperfusion injury in a rat hindlimb model.

BACKGROUND: Thromboembolism and subsequent ischemia/reperfusion injury (IRI) remain major clinical challenges. OBJECTIVES: To investigate whether hydrogen sulfide (H2 S)-loaded microbubbles (hs-Mbs) combined with ultrasound (US) radiation (hs-Mbs+US) dissolve thrombi and simultaneously alleviate tissue IRI through local H2 S release. METHODS: hs-Mbs were manufactured and US-triggered H2 S release was recorded. White and red thromboembolisms were established ex vivo and in rats left iliac artery. All subjects randomly received control, US, Mbs+US, or hs-Mbs+US treatment for 30 minutes. RESULTS: H2 S was released from hs-Mbs+US both ex vivo and in vivo. Compared with control and US, hs-Mbs+US and Mbs+US showed comparable substantial decreases in thrombotic area, clot mass, and flow velocity increases for both ex vivo macrothrombi. In vivo, hs-Mbs+US and Mbs+US caused similarly increased recanalization rates, blood flow velocities, and hindlimb perfusion for both thrombi compared with the other treatments, with no obvious influence on hemodynamics, respiration, and macrophage vitality. More importantly, hs-Mbs+US substantially alleviated skeletal muscle IRI by reducing reactive oxygen species, cellular apoptosis, and proapoptotic Bax, caspase-3, and caspase-9 and increasing antiapoptotic Bcl-2 compared with other treatments. In vitro, hypoxia/reoxygenation-predisposed skeletal muscle cells and endothelial cells treated with normal saline solution exhibited similar trends, which were largely reversed by an H2 S scavenger or an inhibitor of Akt phosphorylation. CONCLUSION: hs-Mbs+US effectively dissolved both white and red macrothrombi and simultaneously alleviated skeletal muscle IRI through the US-triggered, organ-specific release of H2 S. This integrated therapeutic strategy holds promise for treating thromboembolic diseases and subsequent IRI.

Single-Sample Node Entropy for Molecular Transition in Pre-deterioration Stage of Cancer.

A complex disease, especially cancer, always has pre-deterioration stage during its progression, which is difficult to identify but crucial to drug research and clinical intervention. However, using a few samples to find mechanisms that propel cancer crossing the pre-deterioration stage is still a complex problem. In this study, we successfully developed a novel single-sample model based on node entropy with a priori established protein interaction network. Using this model, critical stages were successfully detected in simulation data and four TCGA datasets, indicating its sensitivity and robustness. Besides, compared with the results of the differential analysis, our results showed that most of dynamic network biomarkers identified by node entropy, such as NKD2 or DAAM1, located in upstream in many important cancer-related signaling pathways regulated intergenic signaling within pathways. We also identified some novel prognostic biomarkers such as PER2, TNFSF4, MMP13 and ENO4 using node entropy rather than expression level. More importantly, we found the switch of non-specific pathways related to DNA damage repairing was the main driven force for cancer progression. In conclusion, we have successfully developed a dynamic node entropy model based on single case data to find out tipping point and possible mechanism for cancer progression. These findings may provide new target genes in therapeutic intervention tactics.