The complex network patterns of human migration at different geographical scales: network science meets regression analysis

Applied Network Analysis is a reference book on the methodology of network analysis -- the study of the structure of relations between people, groups or formal organizations. Illustrations from real research show the problems that arise in network analysis -- and how to resolve or avoid them. Primarily written by Burt and Minor, the book has the cohesion of a text while still using work from other leading network analysts.

Zebrafish (Danio rerio) are a popular vertebrate model for high-throughput toxicity testing, serving as a model for embryonic development and disease etiology. However, standardized protocols using zebrafish tend to explore pathologies and behaviors at the organism level rather than at the organ-specific level. This study investigates the effects of chemical exposures on pancreatic function in whole-embryo zebrafish by integrating network analysis and machine learning, leveraging widely available datasets to probe an organ-specific effect. We compiled transcriptomics data for zebrafish exposed to 53 exposures from 25 unique chemicals, including halogenated organic compounds, pesticides/herbicides, endocrine-disrupting chemicals, pharmaceuticals, parabens, and solvents. All raw sequencing data were processed through a uniform bioinformatics pipeline for re-analysis and quality control, identifying differentially expressed genes and altered pathways related to pancreatic function and development. Clustering analysis revealed 5 distinct clusters of chemical exposures with similar impacts on pancreatic pathways, with gene co-expression network analysis identifying key driver genes within these clusters, providing insights into potential biomarkers of chemical-induced pancreatic toxicity. Machine learning was utilized to identify chemical properties that influence pancreatic pathway response, including average mass and biodegradation half-life. The random forest model achieved robust performance (4-fold cross-validation accuracy: 74%) over eXtreme Gradient Boosting, support vector machine, and multiclass logistic regression. This integrative approach enhances our understanding of the relationships between chemical properties and biological responses in a target organ, supporting the use of zebrafish whole embryos as a high-throughput vertebrate model. This computational workflow can be leveraged to investigate the complex effects of other exposures on organ-specific development.

Recent studies focused on the importance of adopting network analysis approaches such as social network analysis in the supply chain networks to better understand and manage the roles of organizations in inter-organizational relationships. The main aim of this research is to identify and integrate network analysis metrics in the existent literature in this realm which is applicable to characterize the position and role of organizations in the supply chain network context and their impact on the behavior and outcomes of organizations and the whole supply chain network. To this aim, we followed a systematic literature review process using Scopus database to identify high-quality papers through several screening stages. Our findings illustrate that there are two main sources of interfirm differences including atomistic properties and relational properties. With an emphasis on relational properties through the lens of network analysis metrics, we integrated influential characteristics on actor’s behavior and performance into three main categories of node level, tie level, and network level. Our findings are applicable to address any emergent phenomenon and the roles of actors based on their position in the network context such as supply chain network and study their behavior and performance.

Diabetic ulcer (DU) has been recognized as one of the most prevalent and serious complications of diabetes. However, the clinical efficacy of standard treatments for DU remains poor. Traditional Chinese medicine (TCM) shows a positive therapeutic effect on DU. Specifically, Zizhu ointment (ZZO) has been widely used to treat DU in long-term clinical practice, but the exact mechanism by which it promotes DU wound healing remains unknown. In this study, network analysis and high-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) were conducted to identify the active compounds of ZZO. We detected isovalerylshikonin (ISO), mandenol, daidzein, kaempferol, and formononetin in both network analysis and UPLC-HRMS. Moreover, ZZO could ameliorate DU by regulating the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT) and inflammation signaling pathways, according to the results of KEGG analysis. We established a DU mouse model with a high-fat diet and streptozotocin injection in vivo to evaluate the network analysis result. The experimental results showed that ZZO could inhibit inflammation, remodel fibrous tissue, and promote angiogenesis in the DU area, facilitating wound healing in DU mice. Moreover, the PI3K/AKT signaling pathway was indeed activated by ZZO treatment, promoting macrophage M2 polarization. In addition, we used molecular docking technology to evaluate the binding sites between ZZO and the PI3K/AKT pathway. The results showed that ISO has a good binding interaction with AKT. Moreover, ISO promoted M2 polarization in macrophages in a dose-dependent manner in vitro. Our study found that ZZO could promote DU wound healing by inhibiting inflammation, which was achieved by macrophage M2 polarization through activating the PI3K/AKT pathway. Further studies have demonstrated that ISO plays major role in the above process. These findings provide a theoretical basis for further preclinical evaluation and lay a foundation for nano-gel compound treatment with ZZO.

Gastric ulcer (GU) is one of the most prevalent digestive diseases that seriously affects people's health. Previous studies have demonstrated the anti-GU effect of Ruda-6 (RD-6), a classic formulae of traditional Mongolian medicine. However, the underlying mechanism of RD-6 against GU remains elusive. Thus, we conducted an integrative approach of network analysis, RNA-seq, and in vivo validation experiment to elucidate the therapeutic mechanisms of RD-6 in preventing GU. A network analysis was performed to predict the potential targets of RD-6. Rats were pretreated with RD-6 at different doses for 21days, followed by GU induction with indomethacin injection. The ulcer index and inhibition rates were calculated, and the levels of inflammatory related factors were determined by ELISA. The gastroprotective mechanism of RD-6 against ulceration was verified by RNA-seq and the key pathway was detected by in vivo validation. As the network analysis predicted, RD-6 exerts anti-GU effects by regulating 75 targets and 160 signaling pathways. Animal experiment results suggested that pretreatment with RD-6 significantly ameliorated the gastric mucosal injury and inflammatory response, as evidenced by a reduced ulcer index, decreased interleukin (IL)-1β, IL-6, and IL-17 levels, and increased prostaglandin E2 (PGE2) levels in the GU model rats induced by indomethacin. RNA-seq data identified four potential hub genes that were primarily involved in the IL-17 signaling pathway. Furthermore, in vivo validation experiment showed that RD-6 inhibited the IL-17 signaling pathway by down-regulating the expression of IL17RA, proto-oncogene C-Fos (FOS), IL1B and prostaglandin-endoperoxide synthase 2 (PTGS2). Taken together, the present study provides evidence that RD-6 could effectively protect against indomethacin-induced GU, which might be attributed to suppressed inflammation. The IL-17 signaling pathway may be one of the crucial mechanisms that mediates the effect of RD-6.

The analysis of cellular behavior and functionality is the most challenging aim of systems biology. The extensive analysis of the interactions between different classes of intra-cellular molecules reacting to genetic/environment changes can elucidate the mechanisms of regulation involved on different cellular processes. We propose a novel framework that enables the integrated analysis of metabolic and regulatory networks. The framework takes advantage on publicly available data repositories, sustaining the inference of knowledge from the integrated network. Since it is based on logic programming, it provides users with a powerful language to query information using both first and second order predicates. Also, it supports network topology analysis, motif finding and robustness evaluation. In this work, as an illustrative case study, our framework is used to build a model of the bacterium Escherichia coli K12.

The Costs and Returns of Human Migration

Ischemic stroke is a disease with high incidence. Astaxanthin is a functional foods with protective effects against ischemic stroke. However, the integral mechanism of astaxanthin protect ischemic stroke is not clear. The aim of this study was to investigate the mechanism of astaxanthin protect ischemic stroke by integrated network analysis. Middle cerebral artery occlusion model was used to establish ischemic stroke model, and ischemic stroke models were treated with 25, 45, 65mg/kg astaxanthin for 7 days. The rats were killed 24h after successful modeling. Integrated network analysis, molecular docking, molecular dynamics (MD) simulation, and Western blot were used to explore the astaxanthin and potential proteins related to inflammation and cell death. The results of integrated network analysis indicate that astaxanthin may protect ischemic stroke through Toll like receptor signaling pathway and apoptosis pathway. The main targets involved MMP9, IL1B, IL10, Bcl2 and among others. astaxanthin has a low binding score and compact complex with PARP1, AIF, Bax, IL10, MMP9, Bcl2. In addition, astaxanthin has reduced inflammation and cell death-related proteins such as PARP1, AIF, Bax, TLR4, MMP9, IL1β and increased anti-inflammation and anti-cell death-related proteins by Bcl2 and IL10. Astaxanthin can improve anti-inflammatory, anti-cell death ability after ischemic stroke. Our study provides a theoretical basis for the subsequent experimental and clinical application of astaxanthin in the treatment of ischemic stroke.

BackgroundRaw Moutan Cortex (RMC) has been used in China and other Asian countries for thousands of years. Its medical application is the treatment of cooling blood and promoting blood circulation. However, its therapeutic mechanism is still undefined.MethodsIn this study, the pharmacokinetics strategy that integrated network analysis was employed to explore the mechanism of RMC in blood-heat and blood stasis syndrome (BHS) model rats. Firstly, Ultra-High performance Liquid Chromatography coupled with Diode Array Detector (UHPLC-DAD) method was developed to determine nine absorbed compounds in rat serum in BHS and normal rats after oral administration of RMC extract respectively. Then the pharmacology network was established based on the relationship between nine compounds absorbed into the blood and BHS targets. Finally, the predicted hub targets were validated experimentally in human umbilical vein endothelial cells (HUVECs).ResultsPharmacokinetic study showed that the pharmacokinetic parameters of nine absorbed compounds had significant differences between BHS and normal groups (p < 0.05). Network analysis showed that 8 target genes, namely, F2, F10, F7, PLAU, MAPK14, MAPK10, AKT1, and NOS3 may be the primary targets regulated by RMC for the treatment of BHS. Among them, targets (F2, F10, F7 and MAPK14, MAPK10, AKT) and 4 active ingredients (paeonol, paeoniflorin, quercetin and oxypaeoniflorin) were selected for evaluating the reliability in vitro experiments, which revealed that the mechanism of RMC against BHS syndrome may inhibit inflammatory pathways and regulate coagulation cascades pathway for cooling and promoting blood circulation.ConclusionThe proposed pharmacokinetics study integrated network analysis strategy provides a combination method to explore the therapeutic mechanism of RMC on BHS.Graphical

Despite recent advances, overall survival for children with acute myeloid leukemia (AML) remains relatively low at 70 to 80%. Further refinement of risk stratification is paramount to guiding optimal therapy selection and improving outcomes. In this study, we leverage machine learning and network-based systems biology approaches that infer tumor protein activity and define key disease drivers - master regulator (MR) proteins - in pediatric AML to further refine existing patient risk stratification and identify potential therapeutic targets. Using the ARACNe and VIPER algorithms1, we reverse-engineered an AML gene regulatory network and inferred protein activity from enrichment of the proteins' transcriptional targets in gene expression signatures from a novel 1080-patient cohort treated on COG AAML1031. We integrated ensemble clustering with feed-forward feature selection on VIPER-inferred protein activity to identify groups of patients with unique protein activity signatures and correlated them to survival by Cox proportional-hazards regression analysis. We identified features predictive of each group by Monte Carlo cross-validation and binary classification. We found that the activity of 2-4 MR proteins was sufficient to predict patient stratification for each group with areas under the receiver-operator characteristic curve above 0.9. We validated our findings in three external test datasets: St-Jude Children's Research Hospital (n=249), TARGET (n=682) and Erasmus MC Sophia's Children's Hospital (n=237). We identified 10 groups with significant survival differences (P &amp;lt; 0.0001, overall survival range 42-85%). This includes a new high-risk group that is not associated with any known cytogenomic features, but shows high activity of NOTCH signaling pathway regulators HES1 and HEY2. Other groups show aberrant activity for known transcription regulators; for example RUNX1, KDM5B and POU4F1. In conclusion, we propose novel patient risk stratification based on VIPER-inferred MR protein activity in pediatric AML. We define 10 groups with differential survival, including refinement of a classically-characterized intermediate-risk cytogenomically normal group as high-risk. We classify patients into therapeutically-relevant risk groups that can guide treatment decision-making. We define the MR proteins driving these groups to offer mechanistic insights into AML tumorigenesis as well as suggest potential molecular targets for therapies. Reference: 1. Califano A, Alvarez M, The recurrent architecture of tumour initiation, progression and drug sensitivity, Nat Rev Cancer (2017), 17(2): 116-130 Citation Format: Alexandre P. Alloy, Jovana Pavisic, Rhonda E. Ries, Tanja Gruber, C Michel Zwaan, Monique L. den Boer, James R. Downing, Adolfo Ferrando, Soheil Meshinchi, Andrea Califano. Novel pediatric AML patient risk stratification by inferred protein activity through integrative network analysis and machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1.

Sutirtha Chatterjee) join the editor-in-chief (Robert Davison) to craft a position statement regarding the ISJ's view on theory. It is applicable, with sensitivity, to the empirical research articles that we consider for publication.

Multi-target mechanisms of Bojungikki-tang in psoriasis-like dermatitis: Integrative network and transcriptomic analysis.

BIRC5 (Survivin) is a crucial anti-apoptotic protein overexpressed in various cancers, promoting tumor growth and treatment resistance. This study investigates its expression across 33 cancer types and explores its diagnostic, prognostic, and immune-related significance. We analyzed RNA-seq data from TCGA and protein expression data from the Human Protein Atlas. Expression levels were compared between tumor and normal tissues. Correlations with molecular and immune subtypes were explored using TISIDB. Prognostic significance was evaluated through survival analysis, Cox regression, and ROC curve analysis. The PPI network was constructed using STRING. BIRC5 was significantly overexpressed in tumor tissues across 33 cancer types, with higher expression levels observed in tumors compared to normal tissues. The protein expression analysis revealed a similar trend. BIRC5 expression was significantly correlated with various molecular and immune subtypes in multiple cancer types. Survival analysis indicated that high BIRC5 expression was associated with poor prognosis across multiple cancers, including lung adenocarcinoma (LUAD) and kidney renal clear cell carcinoma (KIRC). ROC analysis showed that BIRC5 exhibited strong diagnostic potential, with high AUC values (>0.9) in several cancers. The PPI network analysis identified key interacting proteins involved in the cell cycle and tumor progression, further supporting BIRC5's role in cancer biology. Functional experiments in lung adenocarcinoma (LUAD) revealed that BIRC5 upregulation enhances cell proliferation, migration, and invasion, while its knockdown suppresses these activities. BIRC5 is a promising diagnostic and prognostic biomarker in multiple cancers. Its association with immune subtypes suggests a potential role in the tumor immune microenvironment. These findings support BIRC5 as a therapeutic target for cancer treatment.

BackgroundHematoporphyrin derivative (HPD) has a sensibilization effect in lung adenocarcinoma. This study was conducted to identify the target genes of HPD in lung adenocarcinoma.MethodsRNA sequencing was performed using the lung adenocarcinoma cell line A549 after no treatment or treatment with X-ray or X-ray + HPD. The differentially expressed genes (DEGs) were screened using Mfuzz package by noise-robust soft clustering analysis. Enrichment analysis was carried out using “BioCloud” online tool. Protein–protein interaction (PPI) network and module analyses were performed using Cytoscape software. Using WebGestalt tool and integrated transcription factor platform (ITFP), microRNA target and transcription factor (TF) target pairs were separately predicted. An integrated regulatory network was visualized with Cytoscape software.ResultsA total of 815 DEGs in the gene set G1 (continuously dysregulated genes along with changes in processing conditions [untreated—treated with X-ray—X-ray + treated with HPD]) and 464 DEGs in the gene set G2 (significantly dysregulated between X-ray + HPD-treated group and untreated/X-ray-treated group) were screened. The significant module identified from the PPI network for gene set G1 showed that ribosomal protein L3 (RPL3) gene could interact with heat shock protein 90 kDa alpha, class A member 1 (HSP90AA1). TFs AAA domain containing 2 (ATAD2) and protein inhibitor of activated STAT 1 (PIAS1) were separately predicted for the genes in gene set G1 and G2, respectively. In the integrated network for gene set G2, ubiquitin-specific peptidase 25 (USP25) was targeted by miR-200b, miR-200c, and miR-429.ConclusionRPL3, HSP90AA1, ATAD2, and PIAS1 as well as USP25, which is targeted by miR-200b, miR-200c, and miR-429, may be the potential targets of HPD in lung adenocarcinoma.

Esophageal cancer remains a significant global health concern with a persistently poor prognosis and high mortality rate (Sung et al., 2021). In 2024, it is estimated that approximately 22,370 new cases of esophageal cancer were diagnosed, and 16,130 deaths were attributed to the disease in the United States (Siegel et al., 2024). This Research Topic showcases a collection of studies employing innovative strategies to address critical challenges in esophageal cancer research, including (1) the development of advanced research model systems, (2) discovery of novel biomarkers, and (3) application of machine learning to enhance diagnostic accuracy.First, accurately recapitulating the complex biology of esophageal cancer in preclinical models is essential for advancing our understanding of disease pathogenesis and developing effective therapies. Two articles within this collection highlight advancements in modeling esophageal adenocarcinoma and its precursor lesion, Barrett’s esophagus. Traditional research models, such as cell lines and genetically engineered animal models, often fall short of capturing the inherent heterogeneity of esophageal adenocarcinoma (Article 1 [Esophageal adenocarcinoma models: a closer look] and Article 3 [Modelling esophageal adenocarcinoma and Barrett’s esophagus with patient-derived organoids]). Patient-derived organoids offer a promising alternative, providing a more representative model for drug screening and mechanistic studies. The successful culture of Barrett’s esophagus patient-derived organoids provides a unique opportunity to investigate the transition of this precursor lesion to esophageal adenocarcinoma, a process that remains poorly understood in the disease’s pathogenesis.Second, the identification of reliable biomarkers is critical for early detection and prognostication in esophageal cancer. The integration of network analysis with differential expression profiling revealed key genes in esophageal squamous cell carcinoma, such as CDK1, MAD2L1, PLK1, and TOP2A, which are implicated in cell survival and disease progression (Article 2 [Integrating network analysis with differential expression to uncover therapeutic and prognostic biomarkers in esophageal squamous cell carcinoma]). Moreover, MMP9 emerged as a potential prognostic marker, with elevated expression correlating with poor survival outcomes. Similarly, a pan-cancer analysis identified KCNN4 as a prognostic biomarker and a regulator of the tumor microenvironment, with potential implications for immunotherapy responsiveness (Article 7 [KCNN4 is a Potential Biomarker for Predicting Cancer Prognosis and an Essential Molecule that Remodels Various Components in the Tumor Microenvironment: A Pan-Cancer Study]). These findings collectively open the door for the development of targeted therapies and personalized treatments for patients.Furthermore, liquid biopsies offer a minimally invasive approach for cancer detection and monitoring. The identification of a panel of blood-derived microRNAs (miRNAs) with stable expression patterns across multiple cancer types holds promise as a universal diagnostic tool (Article 6 [A panel of blood-derived miRNAs with a stable expression pattern as a potential pan-cancer detection signature]). By addressing the variability introduced by sampling times, this study establishes a framework for utilizing miRNAs as pan-cancer biomarkers, potentially enabling earlier and more accurate disease detection.Third, the convergence of artificial intelligence and advanced technologies is rapidly transforming the landscape of medicine and research, offering unprecedented opportunities for precision diagnostics and personalized therapies. Machine learning, a powerful subset of AI, is playing a pivotal role in this revolution, particularly in the complex field of cancer diagnostics. This is clearly demonstrated by two studies within this collection, which showcase the potential of machine learning in esophageal cancer. One study developed and validated automated machine learning models for predicting lymph node metastasis (LNM) in Siewert type II T1 adenocarcinoma of the esophagogastric junction (Article 4 [The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction]). Among the five models evaluated, the deep learning model demonstrated superior predictive performance, suggesting its utility in guiding clinical decision-making. Another study leveraged deep learning techniques to classify esophageal cancer subtypes using high-resolution histopathology images (Article 5 [Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images]). The contributions to this Research Topic collectively underscore the importance of integrating advanced modeling systems, computational tools, and biomarker discovery in esophageal cancer research. The development of patient-derived organoids and deep learning models exemplifies how technological innovations are indeed bridging the existing gaps in our understanding of the different disease mechanisms and improving the diagnostic precision. Such findings should be validated in larger patient cohorts and translated into clinical applications. This collection represents a step forward, providing a foundation for ongoing efforts to combat esophageal cancer. We thank all authors, reviewers, and editors who contributed to this compilation of cutting-edge research. Together, these studies illuminate new pathways for understanding and managing esophageal cancer, fostering hope for improved patient care and survival outcomes.