SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

The consistent population growth is directly tied to the annual rise in livestock production, placing a substantial burden on the crop sector that supplies animal feed. The Danish government has been relying on importing soybeans and soybean meal to be used as animal feed. However, this sparked environmental concerns that require more environmentally friendly solutions, such as self-sufficiency in animal feed production. The rise of green biorefineries allows new avenues of animal proteinaceous feed production using green biomass to produce leaf protein concentrate (LPC) and utilize side-stream products, such as brown juice and press cake, for feed-quality products. This study evaluated the combination of grass-clover biorefinery and the power-to-X concept, including power-to-protein technology, for its environmental sustainability through a consequential life cycle assessment (CLCA). The production of protein concentrate from organic grass clover exhibits optimal environmental performance when press cake and brown juice are used for bioenergy recovery. The findings indicate that combining a green biorefinery with power-to-protein to fully valorize the carbon and nitrogen content of brown juice and press cake into feed-grade protein can increase the environmental benefits. Such an integration resulted in an avoided impact of -995.9 kg CO₂-eq/tonne of protein concentrate. The avoided impacts of climate change could be higher within the first 20 years due to a higher carbon sequestration rate. However, even after 20 years when a new carbon balance in the soil is reached, the environmental gain could be big enough to encourage the production and use of organic grass-clover protein concentrate.

Gliomas have displayed significant challenges in oncology due to their high degree of invasiveness, recurrence, and resistance to treatment strategies. In this work, the key hub genes mainly associated with different grades of glioma, which were represented by pilocytic astrocytoma (PA), oligodendroglioma (OG), anaplastic astrocytoma (AA), and glioblastoma multiforme (GBM), were identified through weighted gene co-expression network analysis (WGCNA) of microarray datasets retrieved from the Gene Expression Omnibus (GEO) database. Through this, four highly correlated modules were observed to be present across the PA (GSE50161), OG (GSE4290), AA (GSE43378), and GBM (GSE36245) datasets. The functional annotation and pathway enrichment analysis done through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) showed that the modules and hub genes identified were mainly involved in signal transduction, transcription regulation, and protein binding, which collectively deregulate several signaling pathways, mainly PI3K/Akt and metabolic pathways. The involvement of several hub genes primarily linked to other signaling pathways, including the cAMP, MAPK/ERK, Wnt/β-catenin, and calcium signaling pathways, indicates potential interconnectivity and influence on the PI3K/Akt pathway and, subsequently, glioma severity. The Drug Repurposing Encyclopedia (DRE) was used to screen for potential drugs based on the up- and downregulated hub genes, wherein the synthetic progestin hormones norgestimate and ethisterone were the top drug candidates. This shows the potential neuroprotective effect of progesterone against glioma due to its influence on EGFR expression and other signaling pathways. Aside from these, several experimental and approved drug candidates were also identified, which include an adrenergic receptor antagonist, a PPAR-γ receptor agonist, a CDK inhibitor, a sodium channel blocker, a bradykinin receptor antagonist, and a dopamine receptor agonist, which further highlights the gene network as a potential therapeutic avenue for glioma.

Non-small cell lung cancer (NSCLC), representing 85% of lung cancer cases, is characterized by its heterogeneity and progression through distinct stages. This study applied Weighted Gene Co-expression Network Analysis (WGCNA) to explore the molecular mechanisms of NSCLC and identify potential therapeutic targets. Gene expression data from the GEO database were analyzed across four NSCLC stages (NSCLC1, NSCLC2, NSCLC3, and NSCLC4), with the NSCLC2 dataset selected as the reference for module preservation analysis. WGCNA identified eight highly preserved modules—Cyan, Yellow, Red, Dark Turquoise, Turquoise, White, Purple, and Royal Blue—across datasets, which were enriched in key pathways such as “Cell cycle” and “Pathways in cancer”, involving processes like cell division and inflammatory responses. Hub genes, including PLK1, CDK1, and EGFR, emerged as critical regulators of tumor proliferation and immune responses. Estrogen receptor ESR1 was also highlighted, correlating with improved survival outcomes, suggesting its potential as a prognostic marker. Signature-based drug repurposing analysis identified promising therapeutic candidates, including GW-5074, which inhibits RAF and disrupts the EGFR–RAS–RAF–MEK–ERK signaling cascade, and olomoucine, a CDK1 inhibitor. Additional candidates like pinocembrin, which reduces NSCLC cell invasion by modulating epithelial-mesenchymal transition, and citalopram, an SSRI with anti-carcinogenic properties, were also identified. These findings provide valuable insights into the molecular underpinnings of NSCLC and suggest new directions for therapeutic strategies through drug repurposing.