Articles
Journal of Supercomputing (15730484)81(5)
The importance of a node, known as centrality, can be defined and measured in various ways. The main challenge of these measurements is their extension to multilayer networks. In multilayer networks, the influence of inter-layer edges compared to intra-layer edges must be considered when calculating centrality measures. Here, the primary purpose is to provide a multilayer network-specific framework to measure the importance of nodes, with special consideration for inter-layer edges and intra-layer ones. First, we considered the different centrality measures offered for multilayer networks, as well as the associated tools and packages. Next, we implemented some more informative measures specific to multilayer networks. The functionality of implemented metrics is provided for some real networks using Python. We assessed these metrics as ranking criteria and then contrasted the ranking results using three methods: intersection similarity, rank differences, and Kendall’s tau. The findings demonstrated that incorporating information from various layers enhances the effectiveness of the criteria. The final product is a publicly available Python package called MultiNetPy, available at https://github.com/Multinetpy/Multinetpy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Data in Brief (23523409)61
Circular RNAs (circRNAs) are a type of RNAs that play crucial roles in various biological processes. Their outstanding properties such as tissue-specific expression and high resistance to exonuclease degradation make them attractive for research. However, a comprehensive analysis tool for analyzing circRNA data is still required. Here, we present CircPac, a newly developed web-based toolset that searches databases like circBase, circBank, and circRNADisease and organizes data to provide and visualize circRNAs information. Our toolset was created using the Python programming language and its libraries, such as pandas, seaborn, and the Django framework. CircPac enables users to unify the circRNA IDs and subsequently perform various bioinformatic analyses. These analyses include retrieving basic circRNA information, identifying target miRNAs, and analyzing circRNA expression changes in various diseases. Additionally, this toolset generates ready-to-publish figures of circRNA-miRNA interactions and circRNAs expression changes in diseases. CircPac is freely accessible (at https://www.circpac.ir) and offers a user-friendly platform for biologists to efficiently conduct and visualize circRNA data analyses in an appropriate format. © 2025 The Authors
Biochemistry and Biophysics Reports (24055808)41
Introduction: Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients. Materials and methods: GC RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to compute hypoxia and stemness scores using Gene Set Variation Analysis (GSVA) and the mRNA expression-based stemness index (mRNAsi). Hierarchical clustering identified clusters with distinct survival outcomes, and differentially expressed genes (DEGs) between clusters were identified. Weighted Gene Co-expression Network Analysis (WGCNA) identified modules and hub genes associated with clinical traits. Overlapping DEGs and hub genes underwent functional enrichment, protein-protein interaction (PPI) network analysis, and survival analysis. A prognostic decision tree was constructed using survival-associated shared genes. Results: Hierarchical clustering identified six clusters among 375 TCGA GC patients, with significant survival differences between cluster 1 (low hypoxia, high stemness) and cluster 4 (high hypoxia, high stemness). Validation in the GSE62254 dataset corroborated these findings. WGCNA revealed modules linked to clinical traits and survival, with functional enrichment highlighting pathways like cell adhesion and calcium signaling. The decision tree, based on genes such as AKAP6, GLRB, and RUNX1T1, achieved an AUC of 0.81 (training) and 0.67 (test), demonstrating the utility of combined scores in patient stratification. Conclusion: This study introduces a novel hypoxia-stemness-based prognostic decision tree for GC. The identified genes show promise as prognostic biomarkers, warranting further clinical validation. © 2024 The Authors
