Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

In an urban environment, traffic congestion, automobile emissions, and road safety are significant problems that lead to financial losses and environmental deterioration. Vehicle detection, counting, speed estimation, and pollution monitoring are frequently handled inconsistently by current traffic monitoring systems. This research closes this gap by applying the cutting-edge YOLOv8 deep learning architecture to create an extensive real-time vehicle recognition and counting system. In addition to calculating vehicle speeds and detecting five different types of vehicles and pedestrians, the system also estimates emission rates in real time using traffic data. After evaluation of two YOLOv8 variants (YOLOv8n and YOLOv8s), it was found that YOLOv8s performed better, with 0.936 precision, 0.822 recall, and 0.930 mAP50 for CNG automobiles. With an emission factor ranging from 0.6 to 0.8, real-time pollution monitoring was made possible by calculating vehicle emissions based on both type and speed. In addition, the system has a web application developed with the Flask framework and allows real-time traffic data display, including emission rates, vehicle counts, and speed calculations. The method is effective, as evidenced by the results, where YOLOv8s exceed YOLOv8n in essential metrics, including the miss-classification rate (as low as 0.112) and F1-score (0.875 for CNG). With its unique method of simultaneous vehicle identification, counting, speed estimation, and pollution monitoring, this research could lead to advancements in road safety, traffic management, and emission reduction. Doi: 10.28991/ESJ-2025-09-02-023 Full Text: PDF

Ovarian cancer (OC) is a common cause of death from cancer among women worldwide, so there is a pressing need to identify factors influencing mortality. Much OC patient clinical data is now publically accessible (including patient age, cancer site stage and subtype), as are large datasets of OC gene transcription profiles. These have enabled studies correlating OC patient survival with clinical variables and with gene expression but it is not well understood how these two aspects interact to influence mortality. To study this we integrated clinical and tissue transcriptome data from the same patients available from the Broad Institute Cancer Genome Atlas (TCGA) portal. We investigated OC mRNA expression levels (relative to normal patient tissue) of 26 genes already strongly implicated in OC, assessed how their expression in OC tissue predicts patient survival then employed Cox Proportional Hazard regression models to analyse both clinical factors and transcriptomic information to determine relative risk of death associated with each factor. Multivariate analysis of combined data (clinical and gene mRNA expression) found age, ovary tumour site and cancer stage IB significantly correlated with patient survival. Univariate analysis also confirmed significant differences in patient survival time when altered transcription levels of KLK6, CD36, MEF2C and SCGB2A1 were evident, while multivariate analysis that considered the 26 genes simultaneously revealed a significant relationship of mortality with KLK6, CD36 and E2F1 genes. However, analysis that considered all 26 genes with clinical variables together identified WFDC2, E2F1, BRCA1, KLK6, SCGB2A1 and SLPI genes as independently related to mortality in OC. This indicated that the latter genes affect OC patient survival, i.e., provided mechanistic and predictive information in addition to that of the clinical traits and provide strong evidence that these genes are critical markers of processes that underlie OC progression and mortality.

Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that individuals who smoke are more susceptible to COVID-19. In this study, we aimed to investigate the influence of smoking and COVID-19 on LC using bioinformatics and machine learning approaches. We compared the differentially expressed genes (DEGs) between LC, smoking, and COVID-19 datasets and identified 26 down-regulated and 37 up-regulated genes shared between LC and smoking, and 7 down-regulated and 6 up-regulated genes shared between LC and COVID-19. Integration of these datasets resulted in the identification of ten hub genes (SLC22A18, CHAC1, ROBO4, TEK, NOTCH4, CD24, CD34, SOX2, PITX2, and GMDS) from protein-protein interaction network analysis. The WGCNA R package was used to construct correlation network analyses for these shared genes, aiming to investigate the relationships among them. Furthermore, we also examined the correlation of these genes with patient outcomes through survival curve analyses. The gene ontology and pathway analyses were performed to find out the potential therapeutic targets for LC in smoking and COVID-19 patients. Moreover, machine learning algorithms were applied to the TCGA RNAseq data of LC to assess the performance of these common genes and ten hub genes, demonstrating high performances. The identified hub genes and molecular pathways can be utilized for the development of potential therapeutic targets for smoking and COVID-19-associated LC.

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors globally, significantly affecting liver functions, thus necessitating the identification of biomarkers and effective therapeutics to improve HCC-based disabilities. This study aimed to identify prognostic biomarkers, signaling cascades, and candidate drugs for the treatment of HCC through integrated bioinformatics approaches such as functional enrichment analysis, survival analysis, molecular docking, and simulation. Differential expression and functional enrichment analyses revealed 176 common differentially expressed genes from two microarray datasets, GSE29721 and GSE49515, significantly involved in HCC development and progression. Topological analyses revealed 12 hub genes exhibiting elevated expression in patients with higher tumor stages and grades. Survival analyses indicated that 11 hub genes (CCNB1, AURKA, RACGAP1, CEP55, SMC4, RRM2, PRC1, CKAP2, SMC2, UHRF1, and FANCI) and three transcription factors (E2F1, CREB1, and NFYA) are strongly linked to poor patient survival. Finally, molecular docking and simulation identified seven candidate drugs with stable complexes to their target proteins: tozasertib (−9.8 kcal/mol), tamatinib (−9.6 kcal/mol), ilorasertib (−9.5 kcal/mol), hesperidin (−9.5 kcal/mol), PF−562271 (−9.3 kcal/mol), coumestrol (−8.4 kcal/mol), and clofarabine (−7.7 kcal/mol). These findings suggest that the identified hub genes and TFs could serve as valuable prognostic biomarkers and therapeutic targets for HCC-based disabilities.

Pathogenic mechanisms that underlie malignant follicular thyroid carcinoma (FTC) development are poorly understood. To identify key genes and pathways driving malignant behaviour we employed a system biology-based integrative analyses comparing FTC transcriptomes with a similar but benign lesion, follicular thyroid adenoma (FTA). We identified differentially expressed genes (DEGs) in microarray gene expression datasets (n=52) of FTCs and FTA tissues. Pathway analyses of DEGs using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources revealed significant pathways, and pathway hub genes using protein-protein interactions (PPI). We identified 598 DEGs (relative to FTAs) in FTCs and 12 significant pathways with altered expression in FTC. 10 GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression. PPI analysis identified 12 potential hub genes based on degree and betweenness centrality. Moreover, 10 transcription factors (TFs) were identified that may underlie DEG expression as well as a number of microRNA (miRNAs). Thus, we identified DEGs, pathways, TFs and miRNAs that reflect molecular mechanisms differing between FTC and benign FTA. These may constitute biomarkers that distinguish these lesions and, given the similarities and common origin of the lesions, they may also be indicators of malignant progression potential.