Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma

Identification of critical transition signal (CTS) to characterize regulated stochasticity during ABA-induced growth-to-defense transition

BACKGROUND

Abscisic acid (ABA) plays a central role in regulating plant responses to abiotic stress. It orchestrates a complex regulatory network that facilitates the transition from growth to defense. Understanding the molecular mechanisms underlying this ABA-induced transition from growth to defense is essential for elucidating plant adaptive strategies under environmental stress conditions.

RESULTS

In this study, we used a refined dynamic network biomarker (DNB) approach to quantitatively identify the critical transition signal (CTS) and characterize the regulated stochasticity during the ABA-induced transition from growth to defense in Arabidopsis thaliana. By integrating high-resolution time-series RNA-seq data with dynamic network analysis, we identified a set of DNB genes that serve as key molecular regulators of this transition. The critical transition phase was identified precisely at the ninth time point (6 h after treatment), which marks the crucial switch from a growth-dominated to a defense -oriented state. Gene Ontology (GO) enrichment analysis revealed a significant overrepresentation of defense-related biological processes, while STRING network analysis revealed strong functional interactions between DNB genes and differentially expressed genes (DEGs) and highlighted key regulatory hubs. In particular, key hub genes such as PIF4, TPS8, NIA1, and HSP90-5 were identified as potential master regulators of ABA-mediated defense activation, highlighting their importance for plant stress adaptation.

CONCLUSIONS

By integrating a network-driven transcriptomic analysis, this study provides new insights into the molecular basis of ABA-induced transitions from growth to defense. The identification of CTS provides a new perspective on regulated stochasticity in plant stress responses and provides a conceptual framework for improving crop stress resistance. In addition, the establishment of a comprehensive database of ABA-responsive defense genes represents a valuable resource for future research on plant adaptation and resilience.

Global phase-space approach to rate-induced tipping: A brief review

In nonautonomous dynamical systems, rate-induced tipping (R-tipping) is a critical transition triggered by the rate of change of a time-varying parameter, rather than its absolute value. In recent years, there is a growing interest in R-tipping due to its relevance to significant problems of current interest, such as potential, catastrophic collapse of various ecosystems induced by climate change. This brief review provides an overview of the basic concept, theory, and real-world implications of R-tipping from a global phase-space point of view. The key quantity underlying the global approach is the probability of R-tipping defined with respect to initial conditions in the phase space. A recently discovered scaling law governing this probability and the rate of parameter change is introduced, which has so far been restricted to a class of high-dimensional, complex, and empirical ecological networks: pollinator-plant mutualistic networks. Issues such as prediction of tipping and protection of ecosystems from R-tipping are discussed.

Integration of Dynamical Network Biomarkers, Control Theory and Drosophila Model Identifies Vasa/DDX4 as the Potential Therapeutic Targets for Metabolic Syndrome

Metabolic syndrome (MetS) is a subclinical disease, resulting in increased risk of type 2 diabetes (T2D), cardiovascular diseases, cancer, and mortality. Dynamical network biomarkers (DNB) theory has been developed to provide early-warning signals of the disease state during a preclinical stage. To improve the efficiency of DNB analysis for the target genes discovery, the DNB intervention analysis based on the control theory has been proposed. However, its biological validation in a specific disease such as MetS remains unexplored. Herein, we identified eight candidate genes from adipose tissue of MetS model mice at the preclinical stage by the DNB intervention analysis. Using Drosophila, we conducted RNAi-mediated knockdown screening of these candidate genes and identified vasa (also known as DDX4), encoding a DEAD-box RNA helicase, as a fat metabolism-associated gene. Fat body-specific knockdown of vasa abrogated high-fat diet (HFD)-induced enhancement of starvation resistance through up-regulation of triglyceride lipase. We also confirmed that DDX4 expressing adipocytes are increased in HFD-fed mice and high BMI patients using the public datasets. These results prove the potential of the DNB intervention analysis to search the therapeutic targets for diseases at the preclinical stage.

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.

Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory

Raman spectroscopy is a non-destructive spectroscopic technique that provides complex molecular information. It is used to examine the physiological and pathological responses of living cells, such as differentiation, malignancy, and inflammation. The responses of two cellular states, initial and full-blown inflammation, have mainly been investigated using a comparative analysis with Raman spectra. However, the tipping point of the inflammatory state transition remains unclear. Therefore, the present study attempted to identify the tipping point of inflammation using a cell model. We stimulated RAW264.7 mouse macrophages with lipopolysaccharide (LPS) and continuously collected Raman spectra every 2 h for 24 h from the initial and full-blown inflammation states. A Partial Least Squares analysis and Principal Component Analysis-Linear Discriminant Analysis predicted the tipping point as 14 h after the LPS stimulation. In addition, a Dynamical Network Biomarker (DNB) analysis, identifying the tipping point of a state transition in various phenomena, indicated that the tipping point was 14 h and identified tryptophan as a biomarker. The results of a multivariate analysis and DNB analysis show the cellular tipping point.

Classification and spatiotemporal correlation of dominant fluctuations in complex dynamical systems

The behaviors of many complex systems, from nanostructured materials to animal colonies, are governed by local events/rearrangements that, while involving a restricted number of interacting units, may generate collective cascade phenomena. Tracking such local events and understanding their emergence and propagation in the system is often challenging. Common strategies consist, for example, in monitoring over time parameters (descriptors) that are designed ad hoc to analyze certain systems. However, such approaches typically require prior knowledge of the system's physics and are poorly transferable. Here, we present a general, transferable, and agnostic analysis approach that can reveal precious information on the physics of a variety of complex dynamical systems starting solely from the trajectories of their constitutive units. Built on a bivariate combination of two abstract descriptors, Local Environments and Neighbors Shuffling and TimeSmooth Overlap of Atomic Position, such approach allows to (i) detect the emergence of local fluctuations in simulation or experimentally acquired trajectories of multibody dynamical systems, (ii) classify fluctuations into categories, and (iii) correlate them in space and time. We demonstrate how this method, based on the abstract concepts of local fluctuations and their spatiotemporal correlations, may reveal precious insights on the emergence and propagation of local and collective phenomena in a variety of complex systems ranging from the atomic- to the macroscopic-scale. This provides a general data-driven approach that we expect will be particularly helpful to study and understand the behavior of systems whose physics is unknown a priori, as well as to revisit a variety of physical phenomena under a new perspective.

Understanding pesticide-induced tipping in plant-pollinator networks across geographical scales: Prioritizing richness and modularity over nestedness

Mutually beneficial interactions between plants and pollinators are crucial for biodiversity, ecosystem stability, and crop production. A threat to a mutualistic network is the occurrence of a tipping point at which the species abundances collapse to a near zero level. In modern agriculture, there is widespread use of pesticides. What are the effects of extensive pesticide use on mutualistic networks? We develop a plant-pollinator-pesticide model and study its dynamics using 123 mutualistic networks across the globe. We demonstrate that pesticide exposure can lead to a tipping point. Furthermore, while the network characteristics such as richness and modularity exhibit a strong association with pesticide-induced tipping, nestedness shows a weak association. A surprising finding is that the mutualistic networks in the African continent are less pesticide tolerant than those in Europe. We articulate and test a pragmatic intervention strategy through targeted management of pesticide levels within specific plant species to delay or avert the tipping point. Our study provides quantitative insights into the phenomenon of pesticide-induced tipping for safeguarding mutualistic networks that are fundamental to agriculture and ecosystems.

Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics

According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the most common types of cancer. Despite advancements in anticancer drugs and optimised chemotherapy regimens that have improved cure rates for malignant tumours, the presence of tumour heterogeneity has resulted in substantial variations among patients in terms of disease progression, clinical response, sensitivity to therapy, and prognosis, posing significant challenges in attaining optimal therapeutic outcomes for each patient. Here, we collected five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers and constructed multiple cancer blueprints of tumour cell heterogeneity. By integrating multiple bioinformatics analyses, we explored the biological differences underlying tumour cell heterogeneity at the single-cell level and identified tumour cell subcluster-specific biomarkers and potential therapeutic drugs for each subcluster. Interestingly, although tumour cell subpopulations exhibit dramatic differences within the same cancer type and between different cancers at both the genomic and transcriptomic levels, some demonstrate similar oncogenic pathway activities and phenotypes. Tumour cell subpopulations from the five cancers listed above were classified into three major groups corresponding to different treatment strategies. The findings of this study not only focus on the differences but also on the similarities among tumour cell subpopulations across different cancers, providing new insights for individualised therapy.

Time-dependent changes in genome-wide gene expression and post-transcriptional regulation across the post-death process in silkworm

Despite death marking the end of life, several gene expression and miRNA-mediated post-transcriptional regulation events may persist or be initiated. The silkworm (Bombyx mori) is a valuable model for exploring life processes, including death. In this study, we combined transcriptomics and miRNAomics analyses of young, old, and post-mortem silkworms across the entire process after death to unravel the dynamics of gene expression and miRNA-mediated post-transcriptional regulation. In total, 171 genes exhibited sustained differential expression in post-mortem silkworms compared to the pre-death state, which are primarily involved in nerve signalling, transport, and immune response. Post-mortem time-specific genes were associated with cell cycle regulation, thermogenesis, immunity, and zinc ion homeostasis. We found that the down-regulated expression of 36 genes related to transcription, epigenetic modification, and homeostasis resulted in a significant shift in global gene expression patterns at 2 h post-death. We also identified 5 mRNA-miRNA pairs (i.e. bmo-miR-2795-mhca, 2784-achi, 2762-oa1, 277-5p-creb, and 1000-tcb1) associated with stress hormone regulation, transcription activity, and signal transduction. The roles of these pairs were validated through in vivo experiments using miRNA mimics in silkworms. The findings provide valuable insights into the intricate mechanisms underlying the transcriptional and miRNA-mediated post-transcriptional regulation events in animals after death.

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.

Targeting SPP1-orchestrated neutrophil extracellular traps-dominant pre-metastatic niche reduced HCC lung metastasis

Background

The mechanisms by which tumor-derived factors remodel the microenvironment of target organs to facilitate cancer metastasis, especially organ-specific metastasis, remains obscure. Our previous studies have demonstrated that SPP1 plays a key role in promoting metastasis of hepatocellular carcinoma (HCC). However, the functional roles and mechanisms of tumor-derived SPP1 in shaping the pre-metastatic niche (PMN) and promoting lung-specific metastasis are unclear.

Methods

Orthotopic metastasis models, experimental metastasis models, CyTOF and flow cytometry were conducted to explore the function of SPP1 in shaping neutrophil-dominant PMN and promoting HCC lung metastasis. The main source of CXCL1 in lung tissues was investigated via fluorescence activated cell sorting and immunofluorescence staining. The expression of neutrophils and neutrophil extracellular traps (NETs) markers was detected in the lung metastatic lesions of HCC patients and mouse lung specimens. The therapeutic significance was explored via in vivo DNase I and CXCR2 inhibitor assays.

Results

SPP1 promoted HCC lung colonization and metastasis by modifying pulmonary PMN in various murine models, and plasma SPP1 levels were closely associated with lung metastasis in HCC patients. Mechanistically, SPP1 binded to CD44 on lung alveolar epithelial cells to produce CXCL1, thereby attracting and forming neutrophil-abundant PMN in the lung. The recruited neutrophils were activated by SPP1 and then formed NETs-dominant PMN to trap the disseminated tumor cells and promote metastatic colonization. Moreover, early intervention of SPP1-orchestrated PMN by co-targeting the CXCL1-CXCR2 axis and NETs formation could efficiently inhibit the lung metastasis of HCC.

Conclusions

Our study illustrates that HCC-lung host cell-neutrophil interactions play important roles in PMN formation and SPP1-induced HCC lung metastasis. Early intervention in SPP1-orchestrated PMN via CXCR2 inhibitor and DNase I is a potential therapeutic strategy to combat HCC lung metastasis.

Targeting SPP1-orchestrated neutrophil extracellular traps-dominant pre-metastatic niche reduced HCC lung metastasis

BACKGROUND

The mechanisms by which tumor-derived factors remodel the microenvironment of target organs to facilitate cancer metastasis, especially organ-specific metastasis, remains obscure. Our previous studies have demonstrated that SPP1 plays a key role in promoting metastasis of hepatocellular carcinoma (HCC). However, the functional roles and mechanisms of tumor-derived SPP1 in shaping the pre-metastatic niche (PMN) and promoting lung-specific metastasis are unclear.

METHODS

Orthotopic metastasis models, experimental metastasis models, CyTOF and flow cytometry were conducted to explore the function of SPP1 in shaping neutrophil-dominant PMN and promoting HCC lung metastasis. The main source of CXCL1 in lung tissues was investigated via fluorescence activated cell sorting and immunofluorescence staining. The expression of neutrophils and neutrophil extracellular traps (NETs) markers was detected in the lung metastatic lesions of HCC patients and mouse lung specimens. The therapeutic significance was explored via in vivo DNase I and CXCR2 inhibitor assays.

RESULTS

SPP1 promoted HCC lung colonization and metastasis by modifying pulmonary PMN in various murine models, and plasma SPP1 levels were closely associated with lung metastasis in HCC patients. Mechanistically, SPP1 binded to CD44 on lung alveolar epithelial cells to produce CXCL1, thereby attracting and forming neutrophil-abundant PMN in the lung. The recruited neutrophils were activated by SPP1 and then formed NETs-dominant PMN to trap the disseminated tumor cells and promote metastatic colonization. Moreover, early intervention of SPP1-orchestrated PMN by co-targeting the CXCL1-CXCR2 axis and NETs formation could efficiently inhibit the lung metastasis of HCC.

CONCLUSIONS

Our study illustrates that HCC-lung host cell-neutrophil interactions play important roles in PMN formation and SPP1-induced HCC lung metastasis. Early intervention in SPP1-orchestrated PMN via CXCR2 inhibitor and DNase I is a potential therapeutic strategy to combat HCC lung metastasis.

A photo-modulated nitric oxide delivering hydrogel for the accelerated healing of biofilm infected chronic wounds

Biofilm infection and impaired healing of chronic wounds are posing tremendous challenges in clinical practice. In this study, we presented a versatile antimicrobial hydrogel capable of delivering nitric oxide (NO) in a controllable manner to dissipate biofilms, eliminate microorganisms, and promote the healing of chronic wounds. This hydrogel was constructed by Schiff-base crosslinking of oxidized dextran and antimicrobial peptide ε-poly-lysine, further encapsulating photothermal nanoparticles bearing NO donor. This hydrogel could continuously and slowly release NO, effectively dissipating biofilms, and promoting the proliferation of mouse fibroblasts and the migration of endothelial cells. Upon exposure to NIR laser irradiation, the hydrogel generated hyperthermia and rapidly released NO, resulting in the efficient elimination of a broad spectrum of drug-resistant Gram-positive/negative bacterial and fungal biofilms through the synergistic effects of NO, photothermal therapy, and the antibacterial peptide. Notably, the hydrogel demonstrated exceptional in vivo therapeutic outcomes in accelerating the healing process of mice diabetic wounds infected with methicillin-resistant Staphylococcus aureus by successfully eliminating biofilm infection, regulating inflammation, and facilitating angiogenesis and collagen deposition. Overall, this proposed hydrogel shows great promise in accommodating the various demands of the complex repair process of chronic wounds infected with biofilms. STATEMENT OF SIGNIFICANCE: The presence of biofilm infections and underlying dysfunctions in the healing process made chronic wound become stuck in the inflammation stage and difficult to heal. This work developed a NIR laser-modulated three-stage NO-releasing versatile antimicrobial hydrogel (DEPN) exhibiting good therapeutic efficacy for chronic wound. This DEPN hydrogel could inherently and slowly released NO to disperse biofilm. Upon NIR laser irradiation, the DEPN hydrogel generated hyperthermia and induced a rapid burst release of NO effectively eliminating a broad spectrum of drug-resistant bacterial and fungal biofilms. Subsequently, the DEPN hydrogel continually release NO slowly to promote the tissue remolding. This DEPN hydrogel displays great potential in treatment of chronic wounds infected with biofilm.

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.

Alpha lipoic acid diminishes migration and invasion in hepatocellular carcinoma cells through an AMPK-p53 axis

Hepatocellular carcinoma (HCC) associated with viral or metabolic liver diseases is a growing cancer without effective therapy. AMPK is downregulated in HCC and its activation diminishes tumor growth. Alpha lipoic acid (ALA), an indirect AMPK activator that inhibits hepatic steatosis, shows antitumor effects in different cancers. We aimed to study its putative action in liver-cancer derived cell lines through AMPK signaling. We performed cytometric studies for apoptosis and cell cycle, and 2D and 3D migration analysis in HepG2/C3A and Hep3B cells. ALA led to significant inhibition of cell migration/invasion only in HepG2/C3A cells. We showed that these effects depended on AMPK, and ALA also increased the levels and nuclear compartmentalization of the AMPK target p53. The anti-invasive effect of ALA was abrogated in stable-silenced (shTP53) versus isogenic-TP53 HepG2/C3A cells. Furthermore, ALA inhibited epithelial-mesenchymal transition (EMT) in control HepG2/C3A but not in shTP53 nor in Hep3B cells. Besides, we spotted that in patients from the HCC-TCGA dataset some EMT genes showed different expression patterns or survival depending on TP53. ALA emerges as a potent activator of AMPK-p53 axis in HCC cells, and it decreases migration/invasion by reducing EMT which could mitigate the disease in wild-type TP53 patients.

Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems

Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions, and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.

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.

Interleukin-25 as a Potential Biomarker in Lung Metastasis of Hepatocellular Carcinoma with HBV History in Chinese Patients: A Single Center, Case-control Study

Background: Interleukin-25 (IL-25) has been proved to play a role in the pathogenesis and metastasis of Hepatocellular carcinoma (HCC), but the relationship between the level of IL-25 and the metastasis and prognosis of HCC is still not clear. This study aimed to investigate the expression of IL-25 and other potential biochemical indicators among healthy people, HBV-associated HCC patients without lung metastasis and HBV-associated HCC patients with lung metastasis. Methods: From September 2019 to November 2021, 33 HCC patients without lung metastasis, 37 HCC patients with lung metastasis and 29 healthy controls were included in the study. IL-25 and other commonly used biochemical markers were measured to establish predictors of overall survival (OS) and progression-free survival (PFS) after treatment. Results: The serum level of IL-25 was increased in HCC patients than healthy controls (p < 0.001) and HCC patients with lung metastasis had higher IL-25 level than HCC patients without metastasis (p = 0.035). Lung metastasis also indicated higher death rate (p < 0.001) by chi-square test, higher GGT level (p = 0.024) and higher AFP level (p = 0.049) by non-parametric test. Kaplan-Meier analysis demonstrated that IL-25 was negatively associated with PFS (p = 0.024). Multivariate Cox-regression analysis indicated IL-25 (p = 0.030) and GGT (p = 0.020) to be independent predictors of poorer PFS, while IL-25 showed no significant association with OS. Conclusion: The level of IL-25 was significantly associated with disease progression and lung metastasis of HBV-associated HCC. The high expression of IL-25 predicted high recurrence rate and death probability of HCC patients after treatment. Therefore, IL-25 may be an effective predictor of prognosis in HCC.

Exploring MiR-484 Regulation by Polyalthia longifolia: A Promising Biomarker and Therapeutic Target in Cervical Cancer through Integrated Bioinformatics and an In Vitro Analysis

BACKGROUND

MiR-484, implicated in various carcinomas, holds promise as a prognostic marker, yet its relevance to cervical cancer (CC) remains unclear. Our prior study demonstrated the Polyalthia longifolia downregulation of miR-484, inhibiting HeLa cells. This study investigates miR-484's potential as a biomarker and therapeutic target in CC through integrated bioinformatics and an in vitro analysis.

METHODS

MiR-484 levels were analyzed across cancers, including CC, from The Cancer Genome Atlas. The limma R package identified differentially expressed genes (DEGs) between high- and low-miR-484 CC cohorts. We assessed biological functions, tumor microenvironment (TME), immunotherapy, stemness, hypoxia, RNA methylation, and chemosensitivity differences. Prognostic genes relevant to miR-484 were identified through Cox regression and Kaplan-Meier analyses, and a prognostic model was captured via multivariate Cox regression. Single-cell RNA sequencing determined cell populations related to prognostic genes. qRT-PCR validated key genes, and the miR-484 effect on CC proliferation was assessed via an MTT assay.

RESULTS

MiR-484 was upregulated in most tumors, including CC, with DEGs enriched in skin development, PI3K signaling, and immune processes. High miR-484 expression correlated with specific immune cell infiltration, hypoxia, and drug sensitivity. Prognostic genes identified were predominantly epidermal and stratified patients with CC into risk groups, with the low-risk group showing enhanced survival and immunotherapeutic responses. qRT-PCR confirmed FGFR3 upregulation in CC cells, and an miR-484 mimic reversed the P. longifolia inhibitory effect on HeLa proliferation.

CONCLUSION

MiR-484 plays a crucial role in the CC progression and prognosis, suggesting its potential as a biomarker for targeted therapy.

Kinesin Family Member-18A (KIF18A) Promotes Cell Proliferation and Metastasis in Hepatocellular Carcinoma

BACKGROUND & AIMS

Kinesin family member 18A (KIF18A) is notable for its aberrant expression across various cancer types and its pivotal role is driving cancer progression. In this study, we aim to investigate the intricate molecular mechanisms underlying the impact of KIF18A on the progression of HCC.

METHODS

Western blotting assays, a quantitative real-time PCR and immunohistochemical analyses were performed to quantitatively assess KIF18A expression in HCC tissues. We then performed genetic manipulations within HCC cells by silencing endogenous KIF18A using short hairpin RNA (shRNA) and introducing exogenous plasmids to overexpress KIF18A. We monitored cell progression, analyzed cell cycle and cell apoptosis and assessed cell migration and invasion both in vitro and in vivo. Moreover, we conducted RNA-sequencing to explore KIF18A-related signaling pathways utilizing Reactome and KEGG enrichment methods and validated these critical mediators in these pathways.

RESULTS

Analysis of the TCGA-LIHC database revealed pronounced overexpression of KIF18A in HCC tissues, the finding was subsequently confirmed through the analysis of clinical samples obtained from HCC patients. Notably, silencing KIF18A in cells led to an obvious inhibition of cell proliferation, migration and invasion in vitro. Furthermore, in subcutaneous and orthotopic xenograft models, suppression of KIF18A sgnificantly redudce tumor weight and the number of lung metastatic nodules. Mechanistically, KIF18A appears to facilitate cell proliferation by upregulating MAD2 and CDK1/CyclinB1 expression levels, with the activation of SMAD2/3 signaling contributing to KIF18A-driven metastasis.

CONCLUSION

Our study elucidates the molecular mechanism by which KIF18A mediates proliferation and metastasis in HCC cells, offering new insights into potential therapeutic targets.

Dysfunction of CCT3-associated network signals for the critical state during progression of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and is a serious threat to human health; thus, early diagnosis and adequate treatment are essential. However, there are still great challenges in identifying the tipping point and detecting early warning signals of early HCC. In this study, we aimed to identify the tipping point (critical state) of and key molecules involved in hepatocarcinogenesis based on time series transcriptome expression data of HCC patients. The phase from veHCC (very early HCC) to eHCC (early HCC) was identified as the critical state in HCC progression, with 143 genes identified as key candidate molecules by combining the DDRTree (dimensionality reduction via graph structure learning) and DNB (dynamic network biomarker) methods. Then, we ranked the candidate genes to verify their mRNA levels using the diethylnitrosamine (DEN)-induced HCC mouse model and identified five early warning signals, namely, CCT3, DSTYK, EIF3E, IARS2 and TXNRD1; these signals can be regarded as the potential early warning signals for the critical state of HCC. We identified CCT3 as an independent prognostic factor for HCC, and functions of CCT3 involving in the "MYCtargets_V1" and "E2F-Targets" are closely related to the progression of HCC. The predictive method combining the DDRTree and DNB methods can not only identify the key critical state before cancer but also determine candidate molecules of critical state, thus providing new insight into the early diagnosis and preemptive treatment of HCC.

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.

Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis

Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages-834 NL, 711 MGUS, and 970 MM spectra-and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527-1530 cm-1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.

Rate-induced tipping in complex high-dimensional ecological networks

In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we find a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: Only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Identification and Characterization of CD8+ CD27+ CXCR3- T Cell Dysregulation and Progression-Associated Biomarkers in Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) etiopathogenesis highlights the contributions of overproduction of CD4+ T cells and loss of immune tolerance. However, the involvement of CD8+ T cells in SLE pathology and disease progression remains unclear. Here, the comprehensive immune cell dysregulation in total 263 clinical peripheral blood samples composed of active SLE (aSLE), remission SLE (rSLE) and healthy controls (HCs) is investigated via mass cytometry, flow cytometry and single-cell RNA sequencing. This is observed that CD8+ CD27+ CXCR3- T cells are increased in rSLE compare to aSLE. Meanwhile, the effector function of CD8+ CD27+ CXCR3- T cells are overactive in aSLE compare to HCs and rSLE, and are positively associated with clinical SLE activity. In addition, the response of peripheral blood mononuclear cells (PBMCs) is monitored to interleukin-2 stimulation in aSLE and rSLE to construct dynamic network biomarker (DNB) model. It is demonstrated that DNB score-related parameters can faithfully predict the remission of aSLE and the flares of rSLE. The abundance and functional dysregulation of CD8+ CD27+ CXCR3- T cells can be potential biomarkers for SLE prognosis and concomitant diagnosis. The DNB score with accurate prediction to SLE disease progression can provide clinical treatment suggestions especially for drug dosage determination.

Repurposing fluphenazine as an autophagy modulator for treating liver cancer

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system with a low early diagnosis rate. Owing to the side effects, tolerance, and patient contraindications of existing therapies, effective drug treatments for HCC remain a major clinical challenge. However, using approved or investigational drugs not initially intended for cancer therapy is a promising strategy for resolving this problem because their safety have been tested in clinic. Therefore, this study evaluated differentially expressed genes between liver cancer and normal tissues in a cohort of patients with HCC from The Cancer Genome Atlas and applied them to query a connectivity map to identify candidate anti-HCC drugs. As a result, fluphenazine was identified as a candidate for anti-HCC therapy in vitro and in vivo. Fluphenazine suppressed HCC cell proliferation and migration and induced cell cycle arrest and apoptosis, possibly owing to disrupted lysosomal function, blocking autophagy flux. Additionally, in vivo studies demonstrated that fluphenazine suppresses HCC subcutaneous xenografts growth without causing severe side effects. Strikingly, fluphenazine could be used as an analgesic to alleviate oxaliplatin-induced pain as well as pain related anxiety-like behavior. Therefore, fluphenazine could be a novel liver cancer treatment candidate.

Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.

Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation

Cassava storage roots (SR) are an important source of food energy and raw material for a wide range of applications. Understanding SR initiation and the associated regulation is critical to boosting tuber yield in cassava. Decades of transcriptome studies have identified key regulators relevant to SR formation, transcriptional regulation and sugar metabolism. However, there remain uncertainties over the roles of the regulators in modulating the onset of SR development owing to the limitation of the widely applied differential gene expression analysis. Here, we aimed to investigate the regulation underlying the transition from fibrous (FR) to SR based on Dynamic Network Biomarker (DNB) analysis. Gene expression analysis during cassava root initiation showed the transition period to SR happened in FR during 8 weeks after planting (FR8). Ninety-nine DNB genes associated with SR initiation and development were identified. Interestingly, the role of trehalose metabolism, especially trehalase1 (TRE1), in modulating metabolites abundance and coordinating regulatory signaling and carbon substrate availability via the connection of transcriptional regulation and sugar metabolism was highlighted. The results agree with the associated DNB characters of TRE1 reported in other transcriptome studies of cassava SR initiation and Attre1 loss of function in literature. The findings help fill the knowledge gap regarding the regulation underlying cassava SR initiation.

Review of T cell proliferation regulatory factors in treatment and prognostic prediction for solid tumors

T cell proliferation regulators (Tcprs), which are positive regulators that promote T cell function, have made great contributions to the development of therapies to improve T cell function. CAR (chimeric antigen receptor) -T cell therapy, a type of adoptive cell transfer therapy that targets tumor cells and enhances immune lethality, has led to significant progress in the treatment of hematologic tumors. However, the applications of CAR-T in solid tumor treatment remain limited. Therefore, in this review, we focus on the development of Tcprs for solid tumor therapy and prognostic prediction. We summarize potential strategies for targeting different Tcprs to enhance T cell proliferation and activation and inhibition of cancer progression, thereby improving the antitumor activity and persistence of CAR-T. In summary, we propose means of enhancing CAR-T cells by expressing different Tcprs, which may lead to the development of a new generation of cell therapies.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Dynamic gene regulatory networks improving spike fertility through regulation of floret primordia fate in wheat

The developmental process of spike is critical for spike fertility through affecting floret primordia fate in wheat; however, the genetic regulation of this dynamic and complex developmental process remains unclear. Here, we conducted a high temporal-resolution analysis of spike transcriptomes and monitored the number and morphology of floret primordia within spike. The development of all floret primordia in a spike was clearly separated into three distinct phases: differentiation, pre-dimorphism and dimorphism. Notably, we identified that floret primordia with meiosis ability at the pre-dimorphism phase usually develop into fertile floret primordia in the next dimorphism phase. Compared to control, increasing plant space treatment achieved the maximum increasement range (i.e., 50%) in number of fertile florets by accelerating spike development. The process of spike fertility improvement was directed by a continuous and dynamic regulatory network involved in transcription factor and genes interaction. This was based on the coordination of genes related to heat shock protein and jasmonic acid biosynthesis during differentiation phase, and genes related to lignin, anthocyanin and chlorophyll biosynthesis during dimorphism phase. The multi-dimensional association with high temporal-resolution approach reported here allows rapid identification of genetic resource for future breeding studies to realise the maximum spike fertility potential in more cereal crops.

A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study

BACKGROUND

The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients.

METHODS

Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC).

RESULTS

A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI.

DISCUSSION

The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Hypoxia-induced exosomes facilitate lung pre-metastatic niche formation in hepatocellular carcinoma through the miR-4508-RFX1-IL17A-p38 MAPK-NF-κB pathway

Background: Hypoxia plays an important role in the lung metastasis of hepatocellular carcinoma (HCC). However, the process by which hypoxia promotes the formation of a pre-metastatic niche (PMN) and its underlying mechanism remain unclear. Methods: Exosomes derived from normoxic and hypoxic HCC cells were collected to induce fibroblast activation in vitro and PMN formation in vivo. The micro RNA (miR) profiles of the exosomes were sequenced to identify differentially expressed miRNAs. Gain- and loss-of-function analyses were performed to investigate miR-4508 function. Dual-luciferase, western blotting, and real-time reverse transcription-PCR analyses were used to identify the direct targets of miR-4508 and its downstream signaling pathways. To demonstrate the roles of hypoxic tumor-derived exosomes (H-TDEs) and miR-4508 in the lung metastasis of liver cancer, H22 tumor cells were injected through the tail vein of mice. Blood plasma-derived exosomes from patients with HCC who underwent transarterial chemoembolization (TACE) were applied to determine clinical correlations. Results: We demonstrated that H-TDEs activated lung fibroblasts and facilitated PMN formation, thereby promoting lung metastasis in mice. Screening for upregulated exosomal miRNAs revealed that miR-4508 and its target, regulatory factor X1 (RFX1), were involved in H-TDE-induced lung PMN formation. Moreover, miR-4508 was significantly upregulated in plasma exosomes derived from patients with HCC after TACE. We confirmed that the p38 MAPK-NF-κB signaling pathway is involved in RFX1 knockdown-induced fibroblast activation and PMN formation. In addition, IL17A, a downstream target of RFX1, was identified as a link between RFX1 knockdown and p38 MAPK activation in fibroblasts. Conclusion: Hypoxia enhances the release of TDEs enriched with miR-4508, thereby promoting lung PMN formation by targeting the RFX1-IL17A-p38 MAPK-NF-κB pathway. These findings highlight a novel mechanism underlying hypoxia-induced pulmonary metastasis of HCC.

DA-SRN: Omics data analysis based on the sample network optimization for complex diseases

Effective biomarker identification and accurate sample label prediction are still challenging for complex diseases. Patient similarity network (PSN) analysis is a powerful tool in disease omics data analysis. The topology of PSN can reflect the discriminative ability of the corresponding feature space on which the sample network is built. In this study, a novel omics data analysis method based on the sample reference network (DA-SRN) is proposed to identify the potential biomarkers and predict the sample categories. DA-SRN defines the informative features and the sample reference network in optimizing the network structure by genetic algorithm. It labels the samples based on the graph neural network, the reference network and the selected informative features. DA-SRN was compared with nine efficient omics data analysis methods on the genomics, metabolomics and transcriptomics datasets to show its validation. The comparison results showed that it outperformed the other methods in area under receiver operating characteristic curve (AUROC), sensitivity, specificity and area under precision-recall curve (AUPRC) in most cases. Besides, the important metabolites identified by DA-SRN for the type 2 diabetes (T2D) metabolomics data were further examined. The pathway analysis revealed the close relationships between the identified metabolites and the critical metabolic pathways related to the occurrence and development of T2D. The experimental results illustrate that DA-SRN can extract the valuable information from the complex omics data by analyzing the sample relationship, and is promising in biomarker identification and sample discrimination for complex diseases.

Identification of CCDC115 as an adverse prognostic biomarker in liver cancer based on bioinformatics and experimental analyses

Liver hepatocellular carcinoma (LIHC) is a major malignant tumor of the digestive system with a high incidence rate and poor early diagnosis. Coiled-coil domain-containing protein 115 (CCDC115), an accessory component of vacuolar-ATPase with dramatically abnormal expression, is associated with survival outcomes of cancer patients. However, the role of CCDC115 in LIHC remains unclear. In this study, we aimed to determine the functional role of CCDC115 in LIHC by examining CCDC115 expression, and its influence on LIHC prognosis. Through extensive statistical analyses, using LIHC patient databases, we observed that CCDC115 expression significantly increased in tumor tissues of LIHC patients. In addition, CCDC115 expression correlated with the poor prognosis. Additionally, CCDC115 was found to be involved in several cancer-related pathways, specifically the PI3K-Akt pathway. The expression of CCDC115 was positively correlated with human leukocyte antigen molecules as well as with immune checkpoint molecules in LIHC patients. We performed in vitro experiments and confirmed that the expression of CCDC115 significantly affects the proliferation potential, metastasis and sorafenib resistance of liver cancer cells, as well as some key protein expression in PI3K-Akt pathway. These results indicate that CCDC115 could serve as a diagnostic and prognostic biomarker of LIHC, and targeting CCDC115 may provide a potential strategy to enhance the efficacy of liver cancer therapy.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development; therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Glucose-Responsive Antioxidant Hydrogel Accelerates Diabetic Wound Healing

Diabetic complications can be ameliorated by inhibiting excessive oxidative stress with antioxidants. To enhance therapeutic intervention, it is crucial to develop intelligent scaffolds for efficient delivery of antioxidants to diabetic wounds. This study introduces reversible boronic bonds to create an intelligent antioxidant hydrogel scaffold. This study modifies gelatin methacryloyl (GelMA) with 4-carboxyphenyboronic acid (CPBA) to synthesize a derivative of GelMA (GelMA-CPBA), and then photo cross-links GelMA-CPBA with (-)-epigallocatechin-3-gallate (EGCG) to form GelMA-CPBA/EGCG (GMPE) hydrogel. The GMPE hydrogel responds to changes in glucose levels, and more EGCG is released as glucose level increases due to the dissociation of boronic ester bonds. The GMPE hydrogel shows good biocompatibility and biodegradability, and its mechanical property is similar to that of the skin tissue. Both in vitro and in vivo results demonstrate that the GMPE hydrogel scaffolds effectively eliminate reactive oxygen species (ROS), reduce the inflammation, and promote angiogenesis, thereby improve collagen deposition and tissue remodeling during diabetic wound healing. This strategy offers new insight into glucose-responsive scaffolds, and this responsive antioxidan hydrogel scaffold holds great potential for the treatment of chronic diabetic wounds.

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Background

Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity.

Methods

We integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features.

Results

DNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice.

Conclusion

The integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation.

Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasets-based semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles: (1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning; (2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality; (3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas (TCGA) and the Gene Expression Comprehensive Database (GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

Personalised virtual brain models in epilepsy

Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.

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.

Dynamic network biomarker to determine the critical point of breast cancer stage progression

BACKGROUND

The discovery of early warning signs and biomarkers in patients with early breast cancer is crucial for the prevention and treatment of breast cancer. Dynamic Network Biomarker (DNB) is an approach based on nonlinear dynamics theory, which we exploited to identify a set of DNB members and their key genes as early warning signals during breast cancer staging progression.

METHODS

First, based on the gene expression profile of breast cancer in the TCGA database, the DNB algorithm was used to calculate the composite index (CI) of each gene cluster in the process of breast cancer anatomical staging. Then we calculated gene modules associated with the clinical phenotype stage based on weighted gene co-expression network analysis (WGCNA), combined with DNB membership to identify key genes in the network.

RESULTS

We identified a set of gene clusters with the highest CI in Stage II as DNBs, whose roles in related pathways indicate the emergence of a tipping point and impact on breast cancer development. In addition, analysis of the key gene GPRIN1 showed that high expression of GPRIN1 predicts poor prognosis, and related immune analysis showed that GPRIN1 is involved in the development of breast cancer through immune aspects.

CONCLUSION

The discovery of DNBs and the key gene GPRIN1 can provide potential biomarkers and therapeutic targets for breast cancer.

Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy

Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients' seizures. These key parameters together with their personalized model determine a given patient's EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non-seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.

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.

Dynamic network biomarker identifies cdkn1a-mediated bone mineralization in the triggering phase of osteoporosis

The identification of predictive markers to determine the triggering phase prior to the onset of osteoporosis is essential to mitigate further irrevocable deterioration. To determine the early warning signs before osteoporosis, we used the dynamic network biomarker (DNB) approach to analyze time-series gene expression data in a zebrafish osteoporosis model, which revealed that cyclin-dependent kinase inhibitor 1 A (cdkn1a) is a core DNB. We found that cdkn1a negatively regulates osteogenesis, as evidenced by loss-of-function and gain-of-function studies. Specifically, CRISPR/Cas9-mediated cdkn1a knockout in zebrafish significantly altered skeletal development and increased bone mineralization, whereas inducible cdkn1a expression significantly contributed to osteoclast differentiation. We also found several mechanistic clues that cdkn1a participates in osteoclast differentiation by regulating its upstream signaling cascades. To summarize, in this study, we provided new insights into the dynamic nature of osteoporosis and identified cdkn1a as an early-warning signal of osteoporosis onset.

Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation

The generation of ectoderm, mesoderm, and endoderm layers is the most critical biological process during the gastrulation of embryo development. Such a differentiation process in human embryonic stem cells (hESCs) is an inherently nonlinear multi-stage dynamical process which contain multiple tipping points playing crucial roles in the cell-fate decision. However, the tipping points of the process are largely unknown, letting alone the understanding of the molecular regulation on these critical events. Here by designing a module-based dynamic network biomarker (M-DNB) model, we quantitatively pinpointed two tipping points of the differentiation of hESCs toward definitive endoderm, which leads to the identification of M-DNB factors (FOS, HSF1, MYCN, TP53, and MYC) of this process. We demonstrate that before the tipping points, M-DNB factors are able to maintain the cell states and orchestrate cell-fate determination during hESC (ES)-to-ME and ME-to-DE differentiation processes, which not only leads to better understanding of endodermal specification of hESCs but also reveals the power of the M-DNB model to identify critical transition points with their key factors in diverse biological processes, including cell differentiation and transdifferentiation dynamics.

Construction of multifunctional hydrogel with metal-polyphenol capsules for infected full-thickness skin wound healing

Damaged skin cannot prevent harmful bacteria from invading tissues, causing infected wounds or even severe tissue damage. In this study, we developed a controlled-release antibacterial composite hydrogel system that can promote wound angiogenesis and inhibit inflammation by sustained releasing Cu-Epigallocatechin-3-gallate (Cu-EGCG) nano-capsules. The prepared SilMA/HAMA/Cu-EGCG hydrogel showed an obvious inhibitory effect on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). It could also promote the proliferation and migration of L929 fibroblasts. In vivo full-thickness infected wound healing experiments confirmed the angiogenesis and inflammation regulating effect. Accelerate collagen deposition and wound healing speed were also observed in the SilMA/HAMA/Cu-EGCG hydrogel treated group. The findings of this study show the great potential of this controlled-release antibacterial composite hydrogel in the application of chronic wound healing.