Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Diagnostic and therapeutic challenges have been raised because of poor prognosis. Gene expression profiles of GBM and normal brain tissue samples from GSE68848, GSE16011, GSE7696, and The Cancer Genome Atlas (TCGA) were downloaded. We identified differentially expressed genes (DEGs) by differential expression analysis and obtained 3,800 intersected DEGs from all datasets. Enrichment analysis revealed that the intersected DEGs were involved in the MAPK and cAMP signaling pathways. We identified seven different modules and 2,856 module genes based on the co-expression analysis. Module genes were used to perform Cox and Kaplan-Meier analysis in TCGA to obtain 91 prognosis-related genes. Subsequently, we constructed a random survival forest model and a multivariate Cox model to identify seven hub genes (KDELR2, DLEU1, PTPRN, SRBD1, CRNDE, HPCAL1, and POLR1E). The seven hub genes were subjected to the risk score and survival analyses. Among these, CRNDE may be a key gene in GBM. A network of prognosis-related genes and the top three differentially expressed microRNAs with the largest fold-change was constructed. Moreover, we found a high infiltration of plasmacytoid dendritic cells and T helper 17 cells in GBM. In conclusion, the seven hub genes were speculated to be potential prognostic biomarkers for guiding immunotherapy and may have significant implications for the diagnosis and treatment of GBM.
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http://dx.doi.org/10.3389/fgene.2022.924802 | DOI Listing |
Bioinformatics
March 2025
Department of Statistics, Hunan University, Changsha, 410000, China.
Motivation: Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships.
View Article and Find Full Text PDFMed Oncol
March 2025
Centre for Biotechnology, Siksha O Anusandhan (Deemed to Be University), Bhubaneswar, Odisha, 751003, India.
Prostate cancer has garnered much importance in recent years due to its rising incidence and mortality among men worldwide. The ineffectiveness of existing therapies and adverse events associated with conventional treatment have led patients to turn towards traditional medicine for the management of prostate cancer. Cinnamomum zeylanicum bark essential oil (CZEO) possesses promising anticancer properties, yet the exact mechanism of action of CZEO for the management of prostate cancer remains unclear.
View Article and Find Full Text PDFMol Cell Biochem
March 2025
Department of Orthopaedic Surgery, Qingdao Municipal Hospital, Qingdao, China.
Chondrocytes in articular cartilage can secrete extracellular matrix to maintain cartilage homeostasis. It is well known that articular cartilage chondrocytes are sensitive to mechanical loading and that mechanical stimuli can be translated to biological processes. This study provides deep insight into the impact of mechanical loading on chondrocytes via single-cell RNA sequencing (scRNA-seq).
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
March 2025
Department of Respiratory and Critical Care Medicine, Deyang People's Hospital, Affiliated Hospital of Chengdu College of Medicine, Deyang, Sichuan Province, China.
Cancer-associated fibroblasts (CAFs) are related to drug resistance and prognosis of tumor patients. This study aimed to investigate the relationship between prognosis and drug treatment response in patients with CAF and lung adenocarcinoma (LUAD). The data pertaining to LUAD patients were obtained from The Cancer Genome Atlas-LUAD and GSE68465 datasets.
View Article and Find Full Text PDFFront Microbiol
February 2025
Department of Biophysics, University of Delhi South Campus, New Delhi, India.
Infections due to multidrug-resistant (MDR) are associated with severe morbidity and mortality, worldwide. Microbial drug resistance is a complex phenomenon which is conditioned by an interplay of several genomic, transcriptomic and proteomic factors. Here, we have conducted an integrated transcriptomics and proteomics analysis of MDR to identify genes which are differentially expressed at both mRNA and protein levels.
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