Search Results (135)

Background: Gastric cancer is a leading cause of cancer-related mortality worldwide, with approximately one million new cases diagnosed annually. While Helicobacter pylori infection remains a primary etiological factor, mounting evidence implicates obesity and lipid metabolic dysregulation, particularly in hypercholesterolemia, as emerging drivers of gastric tumorigenesis. This study investigates the molecular intersections between gastric cancer, obesity, and hypercholesterolemia through a comprehensive multi-omics and systems biology approach. Methods: We conducted integrative transcriptomic analysis of gastric adenocarcinoma using The Cancer Genome Atlas (TCGA) RNA-sequencing dataset (n = 623, 8863 genes), matched with standardized clinical metadata (n = 413). Differential gene expression between survival groups was assessed using Welch’s t-test with Benjamini–Hochberg correction (FDR < 0.05, |log₂FC| ≥ 1). High-confidence gene sets for obesity (n = 128) and hypercholesterolemia (n = 97) were curated from the OMIM, STRING (confidence ≥ 0.7), and KEGG databases using hierarchical evidence-based prioritization. Overlapping gene signatures were identified, followed by pathway enrichment via Enrichr (KEGG 2021 Human) and protein–protein interaction (PPI) analysis using STRING v11.5 and Cytoscape v3.9.0. CD36’s prognostic value was evaluated via Kaplan–Meier and log-rank testing alongside clinicopathological correlations. Results: We identified 36 genes shared between obesity and gastric cancer, and 31 genes shared between hypercholesterolemia and gastric cancer. CD36 emerged as the only gene intersecting all three conditions, marking it as a unique molecular integrator. Enrichment analyses implicated dysregulated fatty acid uptake, adipocytokine signaling, cholesterol metabolism, and NF-κB-mediated inflammation as key pathways. Elevated CD36 expression was significantly correlated with higher tumor stage (p = 0.016), reduced overall survival (p = 0.001), and race-specific expression differences (p = 0.007). No sex-based differences in CD36 expression or survival were observed. Conclusions: CD36 is a central metabolic–oncogenic node linking obesity, hypercholesterolemia, and gastric cancer. It functions as both a mechanistic driver of tumor progression and a clinically actionable biomarker, particularly in metabolically comorbid patients. These findings provide a rationale for targeting CD36-driven pathways as part of a precision oncology strategy and highlight the need to incorporate metabolic profiling into gastric cancer risk assessment and treatment paradigms. Full article

Lung cancer is the leading cause of cancer-related death worldwide, ranking first among men and second among women for both incidence and mortality. Surgery remains the primary treatment for early-stage, resectable non-small cell lung cancer (NSCLC), encompassing stages I and selected cases of stage IIIB. For patients with stage II to III disease, as well as some stage IB tumors, neoadjuvant or adjuvant systemic therapies are recommended. It is well recognized that specific driver gene mutations play a critical role in tumor progression and aggressiveness, and patients with these genetic alterations may benefit from targeted treatment approaches. These alterations are referred to as “druggable”, “targetable”, or “actionable”, representing specific targets for personalized treatments. Tyrosine kinase inhibitors (TKIs) are now the preferred first-line treatment for patients harboring mutations in EGFR, ALK, ROS1, and BRAF. Additionally, targeted therapies exist for patients with alterations in RET, ERBB2, KRAS, MET, and NTRK, either for those who have received prior treatments or as part of ongoing clinical trials. The success of targeted therapies is reshaping treatment approaches for NSCLC with targetable driver gene alterations, both in early-stage and locally advanced settings. This review focuses on current therapeutic strategies that combine targeted therapies with surgical resection in patients with resectable non-small cell lung cancer (NSCLC) harboring actionable driver gene alterations. Full article

Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication of sepsis, characterized by high mortality and prolonged hospitalization. Early diagnosis and effective therapy remain difficult despite extensive investigation. To address this, we developed an AI-driven integrative framework that combines a Transformer-based deep learning model with established machine learning techniques (LASSO, SVM-RFE, Random Forest and neural networks) to uncover complex, nonlinear interactions among gene-expression biomarkers. Analysis of normalized microarray data from GEO (GSE95233 and GSE69063) identified differentially expressed genes (DEGs), and KEGG/GO enrichment via clusterProfiler revealed key pathways in immune response, protein synthesis, and antigen presentation. By integrating multiple transcriptomic cohorts, we pinpointed 617 SA-AKI-associated DEGs—21 of which overlapped between sepsis and AKI datasets. Our Transformer-based classifier ranked five genes (MYL12B, RPL10, PTBP1, PPIA, and TOMM7) as top diagnostic markers, with AUC values ranging from 0.9395 to 0.9996 (MYL12B yielding 0.9996). Drug–gene interaction mining using DGIdb (FDR < 0.05) nominated 19 candidate therapeutics for SA-AKI. Together, these findings demonstrate that melding deep learning with classical machine learning not only sharpens early SA-AKI detection but also systematically uncovers actionable drug targets, laying groundwork for precision intervention in critical care settings. Full article

(1) Differential co-expression analysis between two phenotypes with a known gene set helps to uncover gene regulation alterations. (2) GSNCASCR uses CSCORE to estimate the gene pair correlations for network reconstruction and GSNCA to quantify the structure changes of co-expression networks of the predefined gene sets. It also ranks genes based on their “importance” in the weighted network. The method is implemented with free R software (version 0.1.0, available on GitHub), allowing users to analyze their data with the help of demo vignettes included in the package. (3) With analysis of both simulated and real datasets, we demonstrate that the statistical tests performed with GSNCASCR are able to identify differentially co-expressed gene sets with higher precision than tests with Gene Set Co-Expression Analysis (GSCA, version 1.1.1) and Gene Sets Net Correlations Analysis (GSNCA, version 1.42.0). Specifically, GSNCASCR achieved an AUC value of 0.985, while GSNCA and GSCA achieved 0.817 and 0.893, respectively, when positive and negative pathways are defined as having more than 40% and less than 20% co-expressed gene pairs in the simulated data, respectively. Furthermore, across simulated data with varying noise levels, pathway sizes, and positive/negative pathway definitions, GSNCASCR consistently performs best in over 90% of scenarios, as evaluated by AUC values. With an available COVID-19 dataset, we show CD4⁺ T cell dysfunction in severe COVID-19 as TNF-α/TNF receptor 1-dependent immune pathways. In the weighted network of a gene set of IFN-γ, IFITM3 was identified as a hub gene, which has been evidenced by a genome-wide association study and functional studies. (4) We developed a bioinformatics tool, GSNCASCR, that analyzes differentially co-expressed pathways with single-cell RNA-sequencing data and also evaluates the importance of the genes within pathways. This tool combines the advantages of two algorithms, enabling the quantification and examination of cell type-specific co-expression changes within pathways. The package allows for the analysis of shared and unique disease-affected pathways across different cell types. Full article

The psoas major muscle (PMM) is a piece of pork with good tenderness and high value. Intramuscular fat (IMF) content, serving as a pivotal indicator of pork quality, varies greatly among pigs within the same breed. However, there is a paucity of studies focusing on investigating the molecular mechanism of PMM IMF deposition in the same pig breed. The present study aimed to identify the potential genes related to the IMF content of PMM in low- and high-IMF Saba pigs based on transcriptome data analysis. The data used in this study were the RNA sequences of PMM from 12 Saba pigs (PRJNA1223630, from our laboratory) and gene expression profiles (GSE207279) acquired from the NCBI Sequence Read Archive database and the GEO database, respectively, together with data on the fatty acid and amino acid composition of the 12 Saba pigs’ PMM. It was found that the high-IMF pigs exhibited significantly elevated levels of saturated fatty acids and (mono)unsaturated fatty acids, especially C14:0, C16:0, C20:0, C16:1, C18:1n9c, and C20:2, compared with those in the low-IMF pigs (p < 0.05 or p < 0.01). A total of 370 differentially expressed genes (DEGs) (221 up- and 149 down-regulated) were identified based on PRJNA1223630. Then, 20 hub genes were identified through protein–protein interaction (PPI) network analysis. Four potential fat-deposition-related genes (DGAT2, PCK1, MELK, and FASN) were further screened via the intersection of the candidate genes identified by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and the top five genes ranked by the Random Forest (RF) method based on the 20 hub genes and were validated in the test gene set (GSE207279). The constructed mRNA (gene)–miRNA–lncRNA network, involving miRNAs (miR-103a-3p, miR-107, and miR-485-5p), lncRNAs (XIST, NEAT1, and KCNQ1OT1), and FASN, might be crucial for IMF deposition in pigs. These findings might delineate valuable regulatory molecular mechanisms coordinating IMF deposition and could serve as a beneficial foundation for the genetic improvement of pork quality. Full article

Liver cancer is one of the most common malignancies and the second leading cause of cancer-related deaths worldwide, particularly in developing countries, where it poses a significant financial burden. Early detection and timely treatment remain challenging due to the complex mechanisms underlying the initiation and progression of liver cancer. This study aims to uncover key genomic features, analyze their functional roles, and propose potential therapeutic drugs identified through molecular docking, utilizing single-cell RNA sequencing (scRNA-seq) data from liver cancer studies. We applied two advanced hybrid methods known for their robust identification of differentially expressed genes (DEGs) regardless of sample size, along with four top-performing individual methods. These approaches were used to analyze four scRNA-seq datasets, leading to the identification of essential DEGs. Through a protein−protein-interaction (PPI) network, we identified 25 hub-of-hub genes (hHubGs) and 20 additional hHubGs from two naturally occurring gene clusters, ultimately validating a total of 36 hHubGs. Functional, pathway, and survival analyses revealed that these hHubGs are strongly linked to liver cancer. Based on molecular docking and binding-affinity scores with 36 receptor proteins, we proposed 10 potential therapeutic drugs, which we selected from a pool of 300 cancer meta-drugs. The choice of these drugs was further validated using 14 top-ranked published receptor proteins from a set of 42. The proposed candidates include Adozelesin, Tivozanib, NVP-BHG712, Nilotinib, Entrectinib, Irinotecan, Ponatinib, and YM201636. This study provides critical insights into the genomic landscape of liver cancer and identifies promising therapeutic candidates, serving as a valuable resource for advancing liver cancer research and treatment strategies. Full article

Background/Objectives: Drug development for complex diseases such as NAFLD is often lengthy and expensive. Drug repurposing, the process of finding new therapeutic uses for existing drugs, presents a promising alternative to traditional approaches. This study aims to identify potential repurposed drugs for NAFLD by leveraging disease–disease relationships and drug–target data from the BioSNAP database. Methods: A bipartite network was constructed between drugs and their target genes, followed by the application of the BiClusO bi-clustering algorithm to identify high-density clusters. Clusters with significant associations with NAFLD risk genes were considered to predict potential drug candidates. Another set of candidates was determined based on disease similarity. Results: A novel ranking methodology was developed to evaluate and prioritize these candidates, supported by a comprehensive literature review of their effectiveness in NAFLD treatment. Conclusions: This research demonstrates the potential of drug repurposing to accelerate the development of therapies for NAFLD, offering valuable insights into novel treatment strategies for complex diseases. Full article

Royal jelly (RJ) has long been considered a crucial dietary component in dictating caste differentiation in honeybees. As a nutritional additive, royal jelly imparts a broad range of benefits to mammals and humans; however, its precise impact on the social hierarchy of these advanced animals is not yet fully understood. This study aims to determine whether the benefits of royal jelly can be transferred to rats to alter their social ranks and uncover the underlying mechanisms. A submissive model was established by inducing dysbiosis in rats, via the persistent exposure of vancomycin. Royal jelly at a dose of 2.5 g/kg was daily administered to the subject rats during postnatal weeks (PNW) 6 and 7. At the end of the intervention, animals were subjected to agonistic, water and tube competition tests, in order to assess their dominance status. As revealed by the results, the RJ treatment significantly improved the social rank of the dysbiotic rats, demonstrating that RJ can elicit positive effect on the social behaviors (caused by dysbiosis) of rats. All behavioral paradigms yielded consistent results, with no notable differences in body weight or anxiety levels. Regarding gut microbiome, vancomycin exposure caused the dysbiosis of the subject rats, which was partially reversed by treatment with royal jelly. Specifically, the intestinal presence of Proteobacteria was profoundly attenuated by the RJ supplementation, resulting in a comparable level with the intact/dominant rats. At the genus level, both Escherichia and Clostridium displayed similar dynamics in relation to Proteobacteria, implying their involvement with the RJ-mediated dominance switching. Transcriptomic analysis in the medial prefrontal context showed that the expression of a broad range of genes was influenced by RJ intake, embodying various pathways related to neuronal transmission such as neuroactive ligan–receptor interaction, the synaptic vesicle cycle, etc. By virtue of correlation analysis, Escherichia, Akkermansia and Clostridium were strongly associated with a set of gene modules around gastrin releasing peptide (Grp) and signaling pathways around Rps6ka3, establishing an intrinsic gut–brain communication. Furthermore, the infection trials of Escherichia significantly degraded the social ranks of the RJ-remedied rats in tube tests, while a series of cerebral genes like Grpr and Grpel1, as well as prefrontal spine density, were concordantly altered, underscoring the critical role of the gut–brain link in deciding the outcomes of the dyadic contests. In summary, this is an intriguing example of how royal jelly can influence the social ranks of mammals, emphasizing the importance of microbe–host interaction in mediating this species-spanning function of royal jelly in shaping social hierarchy. Full article

The selection of a stably expressed reference gene is a critical step for the quantitation of gene expression by qRT-PCR. We tested the stability of expression of nine putative reference genes in normoxia and hypoxia in four different human cell types: coronary (HCAECs) and pulmonary endothelial cells (HPAECs), EA.hy926 endothelial cells, and A549 alveolar epithelial cells. Cells were cultured in normoxic and hypoxic conditions for up to 72 h. Total RNA was isolated and used for qRT-PCR. Stability of expression was assessed by calculating the coefficient of variation of the cycle threshold (Ct CV) by pairwise comparison of ΔCt values, and by the NormFinder algorithm. A final rank was calculated for each gene. Finally, we analyzed VEGFA expression by using GAPDH or the optimal candidate reference gene found in this study. Gene expression was variable between cell lines and experimental conditions. The most stable reference gene across all cell lines was TBP, followed by RPLP1 and RPL13A. VEGFA expression was significantly upregulated by 4-fold in hypoxia when using TBP as reference, whilst this result was insignificant when GAPDH was used. The selection of a stably expressed reference gene is a critical step for the generation of reliable and reproducible data in gene expression studies. The most appropriate reference gene may vary in different cell lines and experimental conditions; it should be chosen individually for each experimental set-up. Full article

Biodiversity, from genes to entire ecosystems, is crucial for a healthy planet. However, human activities, including business practices, are causing rapid biodiversity loss. This study focuses on selecting and integrating biodiversity protection practices into the supply chain, offering a chance to make positive changes for the environment and future generations. A new hybrid grey multi-criteria decision-making (MCDM) model is proposed in this paper, which combines the grey Best–Worst Method (BWM) for obtaining criteria weights and the grey Axial Distance-based Aggregated Measurement (ADAM) method for ranking alternatives (practices). The applicability of the proposed model for solving the defined problem was demonstrated by ranking nine practices according to seven criteria. The most effective supply chain management practices in the context of biodiversity conservation were supply chain policies (with a score of 0.044), biodiversity goal setting, monitoring, reporting, and transparency (0.039), and education and awareness raising (0.037). These practices are the best because they combine clear frameworks, measurable goals, and long-term cultural change for effective biodiversity conservation. The lowest ranked practice is compliance with legislation (0.006) since it represents a baseline, reactive approach rather than a proactive or innovative strategy for biodiversity conservation. This study provides a comprehensive framework and hybrid MCDM model that enhances theoretical knowledge and can serve as a basis for developing a practical tool for integrating, assessing, and prioritizing biodiversity-focused practices in supply chains. The main novelties of this paper are the extension of the ADAM method in the grey environment, the development of a new hybrid MCDM model that combines the grey BWM and grey ADAM method, the identification of biodiversity-oriented business strategies in supply chains and the criteria for their evaluation, and a framework for practice evaluation and selection. Full article

Bacillus cereus is a Gram-positive pathogen associated with foodborne illnesses and severe non-gastrointestinal infections. Robust tools for accurate gene transcription analysis are essential for studying toxin gene expression dynamics and deciphering the complex regulatory networks orchestrating the expression of toxin and virulence factors. This study aimed to identify reliable reference genes for normalizing reverse transcription quantitative PCR (RT-qPCR) data in toxigenic B. cereus. An emetic and an enteropathogenic strain were used as model organisms to establish a suitable reference gene set to monitor the dynamics of toxin gene transcription. Ten candidate reference genes were evaluated for their expression stability using geNorm, NormFinder, BestKeeper and the ΔCq method, with the final rankings integrated via RefFinder. Among the tested genes, rho, rpoD and recA were identified as the most stable expressed reference genes across all tested conditions. As shown in this proof-of-principle study, the established reference gene set provides a suitable tool to investigate the influence of extrinsic and intrinsic factors on toxin gene transcription. In conclusion, our newly established reference gene set provides a robust basis for studying toxin gene expression in B. cereus and contributes to a better understanding of its pathogenicity and potential strategies to mitigate its harmful effects. Full article

Background/Objectives: Transcriptional promoters play an essential role in regulating protein expression. Promoters with weak activity generally lead to low levels of expression, resulting in fewer proteins being produced. At the same time, strong promoters are commonly used in studies using transgenic organisms as model systems. This approach can have various negative consequences for the organism, as many regulatory proteins need to be expressed in small quantities, and excessive expression can have harmful effects on cells and organisms. Therefore, it is important to select the right promoter when creating transgenic organisms for research and practical applications. Methods: In this study, we used the Drosophila melanogaster genome as a source of natural promoter sequences for RNA polymerase II. These sequences were extracted and used to create a set of promoters that are suitable for practical application. The promoters were tested in a model system using fluorescent reporter genes in S2 cells and transgenic lines of Drosophila. Results: We assessed the expression levels of fluorescent reporter genes to rank the tested promoters from strongest to weakest. Six individual promoters of different sizes were established and compared. Additionally, we designed and tested three pairs of bidirectional promoters that could be used to simultaneously express two proteins. Conclusions: Based on our findings, we grouped the tested promoters into three categories: strong, moderate, and weak. These promoters can be utilized in transgenic model systems for protein production at different levels, from high to low. Bidirectional promoters, constructed “head-to-head”, meaning oppositely directed with the minimum distance between them, represent a novel tool for the co-expression of proteins. Full article

FOLFIRI (5-FU, leucovorin, irinotecan) is the first-line chemotherapy for metastatic colorectal cancer (mCRC), but response rates are under 50%. This study aimed to develop a predictive signature for FOLFIRI response in mCRC patients. Firstly, Spearman’s rank correlation and Wilcoxon rank-sum test were used to select chemotherapy response genes and gene pairs, respectively. Then, an optimization procedure was used to determine the final signature. A predictive signature consisting of three gene pairs (3-GPS) was identified. In the training set, 3-GPS achieved an accuracy of 0.94. In a validation set of 60 samples, predicted responders had significantly better progression-free survival than the predicted non-responders (HR = 0.47, p = 0.01). A comparable result was observed in an additional validation set of 27 samples (HR = 0.06, p = 0.02). The co-expressed genes of the signature were enriched in pathways associated with the immunotherapy response, and they interacted extensively with FOLFIRI-related genes. Notably, the expression of signature genes significantly correlated with various immune cell types, including plasma cells and memory-resting CD4+ T cells. In conclusion, the REO-based signature effectively identifies mCRC patients likely to benefit from FOLFIRI. Furthermore, these signature genes may play a crucial role in the chemotherapy. Full article

Pancreatic adenocarcinoma is one of the most aggressive types of cancer. Among different mechanisms generally believed to be important for the development of cancer, aberrant regulation of the p53 protein is a well-known and common feature for many cancer entities. Our work aims to analyze the impact of p53 deregulation and proteins encoded by p53 target genes on the survival of patients suffering from pancreatic adenocarcinoma. We, therefore, focused on the analysis of the selected collective for the TP53 mutation status, the p53 protein level, their correlation, and possible impacts on the prognosis/survival. We compared and analyzed a set of 123 patients. We have extracted information regarding the TP53 mutation status, p53 protein levels, the level of proteins encoded by prominent p53 target genes, and information on the overall survival. Survival analyses were displayed by Kaplan–Meier plots, using the log-rank test, in order to check for statistical significance. Protein levels were compared using the Mann–Whitney Test. We did not find any statistically significant correlation between the TP53 mutation status and the survival of the patients. Moreover, we have not found any significant correlation between the protein amount of prominent p53 target genes and the patients’ survival. However, we see a significant correlation between the p53 protein level in cancer samples and the overall survival of pancreatic adenocarcinoma patients: patients having tumors with a p53 protein level within the upper quartile of all measured cases show a significantly reduced survival compared to the rest of the patients. Thus, in pancreatic adenocarcinoma, the p53 protein level is a relevant marker for prognosis, and cancers having a high p53 protein amount show a shortened patients’ survival. In contrast, for this cancer entity, the TP53 mutation status or the protein amount of prominent p53 target genes on their own seems not to have a significant impact on survival. Full article

Background/Objectives: Neutrophils have recently gained significant attention due to their heterogeneity in tumor settings. The gene expression profiles of neutrophils from different tumor types are of great interest. Murine splenic neutrophils reflect the immune status of the organism and could be a source of tumor-associated neutrophils in tumor-bearing mice. However, information about appropriate reference genes for RT-qPCR analysis of murine neutrophils in the literature is lacking. The aim of this study was to identify stably expressed reference genes in murine splenic neutrophils. Methods: Bone marrow- and spleen-derived neutrophils were isolated from healthy C57Bl/6 and CBA/LacSto mice. Spleen-derived neutrophils were isolated from mice with Lewis lung carcinoma (LLC) and drug-resistant lymphosarcoma (RLS₄₀). RNA was isolated and used for RT-qPCR analysis of 10 selected reference genes. Analysis of reference gene stability was performed using four different algorithms (BestKeeper, NormFinder, geNorm, ΔCt method), and comprehensive ranking was constructed using RefFinder. Results: The Ct values for the reference genes were in the range of 16.73–30.83 with the highest expression levels observed for B2m and the lowest for Sdha. Differences in the stability ranking performed by different algorithms were observed; however, the overall ranking of the studied reference genes was as follows, from most to least stably expressed: Tbp, Hprt1, Ywhaz, B2m, Gapdh, Actb, Sdha, Eef2, Rack1, and Rpl13a. Using Tbp or Rpl13a for RT-qPCR data normalization significantly affected the interpretation of target gene expression. Conclusions: Tbp and Hprt1 are recommended reference genes for murine splenic neutrophils regardless of their activation status. Full article