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Objectives: Technological advances in identifying gene expression profiles are being applied to study an array of cancers. The goal of this study was to identify differentially expressed genes in pancreatic ductal adenocarcinoma (PDAC) and examine their potential role in tumorigenesis and metastasis.
Methods: The transcriptomic profiles of PDAC and non-tumorous tissue samples were derived from the gene expression omnibus (GEO), which is a public repository. The GEO2R tool was used to further derive differentially expressed genes from those profiles.
Results: In this study, a total of 68 genes were derived from upregulated PDAC genes in three or more transcriptomic profiles and were considered PDAC gene sets. The identified PDAC gene sets were examined in the molecular signatures database (MSigDB) for ontological investigation, which revealed that these genes were involved in the extracellular matrix and associated with the cell adhesion process in PDAC tumorigenesis. The gene set enrichment analysis showed greater enrichment scores for the gene sets. Moreover, the identified gene sets were examined for protein-protein interaction using the STRING database. Based on functional k-means clustering, the following three functional cluster groups were identified in this study: extracellular matrix/cell adhesion, metabolic, and mucus secretion-related protein groups. The receiver operating characteristic (ROC) curve revealed greater specificity and sensitivity for these cluster genes in predicting PDAC tumorigenesis and metastases. In addition, the expression of the cluster genes affects the overall survival rate of PDAC patients. Using the cancer genome atlas (TCGA) database, the associations between expression levels and clinicopathological features were validated.
Conclusions: Overall, the genes identified in this study appear to be critical in PDAC development and can serve as potential diagnostic and prognostic targets for pancreatic cancer treatment.
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http://dx.doi.org/10.3390/cancers16234049 | DOI Listing |
Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer and is associated with poor prognosis in advanced stages. This study aims to develop a prognostic model for patients with ccRCC based on a lysosome-related gene signature.
Methods: The clinical and transcriptomic data of Kidney Renal Clear Cell Carcinoma (KIRC) patients were downloaded from TCGA, cBioportal and GEO databases, and lysosome-related gene sets were acquired in the previous study.
Bioinformatics
September 2025
Centre National de Recherche en Génomique Humaine, Institut François Jacob CEA Université Paris-Saclay.
Motivation: Graph Neural Network (GNN) models have emerged in many fields and notably for biological networks constituted by genes or proteins and their interactions. The majority of enrichment study methods apply over-representation analysis and gene/protein set scores according to the existing overlap between pathways. Such methods neglect knowledges coming from the interactions between the gene/protein sets.
View Article and Find Full Text PDFmSphere
September 2025
Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada.
Through horizontal gene transfer, closely related bacterial strains assimilate distinct sets of genes, resulting in significantly varied lifestyles. However, it remains unclear how strains properly regulate horizontally transferred virulence genes. We hypothesized that strains may use components of the core genome to regulate diverse horizontally acquired genes.
View Article and Find Full Text PDFBrief Bioinform
August 2025
School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang Road, Shushan District, Hefei, Anhui 230036, China.
Protein-nucleic acid binding sites play a crucial role in biological processes such as gene expression, signal transduction, replication, and transcription. In recent years, with the development of artificial intelligence, protein language models, graph neural networks, and transformer architectures have been adopted to develop both structure-based and sequence-based predictive models. Structure-based methods benefit from the spatial relationship between residues and have shown promising performance.
View Article and Find Full Text PDFOncol Res
September 2025
Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Studies have reported the special value of PANoptosis in cancer, but there is no study on the prognostic and therapeutic effects of PANoptosis in bladder cancer (BLCA). This study aimed to explore the role of PANoptosis in BLCA heterogeneity and its impact on clinical outcomes and immunotherapy response while establishing a robust prognostic model based on PANoptosis-related features. Gene expression profiles and clinical data were collected from public databases.
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