Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids

Whole-transcriptome analysis of BLV-infected cows reveals downregulation of immune response genes in high proviral loads cows

Bovine leukemia virus (BLV) is a retrovirus that infects cattle, causing bovine enzootic leukosis, a chronic disease characterized by the proliferation of infected B cells. BLV proviral load (PVL) is a key determinant of disease progression and transmission risk. Cattle can exhibit distinct phenotypes of low PVL (LPVL) or high PVL (HPVL), which remain stable throughout their lifetime. Differential expression analysis revealed 1,908 differentially expressed genes (DEGs) between HPVL and LPVL animals, including 774 downregulated (DReg) and 1,134 upregulated (UReg) genes. Functional enrichment analysis revealed that DReg genes were associated primarily with immune response pathways. Conversely, the UReg genes were enriched in processes related to cell cycle regulation, mitotic division, and DNA biosynthesis. Protein-protein interaction analysis revealed six highly interconnected clusters. Interestingly, a cluster was enriched for sphingolipid metabolism, a process critical to enveloped virus infection and immune receptor signaling. These findings provide valuable insights into the molecular mechanisms of BLV infection, suggesting potential markers for disease monitoring and targets for therapeutic intervention.

Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates

Glioblastoma is an aggressive malignant tumor of the brain and spinal cord. Due to the blood-brain barrier, the accessibility of its treatments still remains significantly challenging. Unfortunately, the recurrence rates of glioblastoma upon surgery are very high too. Hence, understanding the molecular drivers of disease progression is valuable. In this study, we aimed to investigate the molecular drivers responsible for glioblastoma progression and identify valid biomarkers. Three microarray expression profiles GSE90604, GSE50601, and GSE134470 containing healthy and glioblastoma-affected samples revealed overlapping differentially expressed genes (DEGs). The interrelational pathway enrichment analysis elucidated the halt of cell cycle checkpoints and activation of signaling pathways and led to the identification of 6 predominant hub genes. Validation of hub genes in comparison with The Cancer Genome Atlas datasets identified the potential biomarkers of glioblastoma. The study evaluated two significantly upregulated genes, SPARC (secreted protein acidic and rich in cysteine) and VIM (vimentin) for glioblastoma. The genes CACNA1E (calcium voltage-gated channel subunit alpha1 e), SH3GL2 (SH3 domain-containing GRB2-like 2, endophilin A1), and DDN (dendrin) were identified as under-expressed genes as compared to the normal and pan-cancer tissues along with prominent putative prognostic biomarker potentials. The genes DDN and SH3GL2 were found to be upregulated in the proneural subtype, while CACNA1E in the mesenchymal subtype of glioblastoma exhibits good prognostic potential. The mutational analysis also revealed the benign, possibly, and probably damaging substitution mutations. The correlation between the DEG and survival in glioblastoma was evaluated using the Kaplan-Meier plots, and VIM had a greater life expectancy of 60.25 months. Overall, this study identified key candidate genes that might serve as predictive biomarkers for glioblastoma.

Analysis of Differentially Expressed Genes in the Dentate Gyrus and Anterior Cingulate Cortex in a Mouse Model of Depression

Major depressive disorder (MDD) is a prevalent, chronic, and relapse-prone psychiatric disease. However, the intermediate molecules resulting from stress and neurological impairment in different brain regions are still unclear. To clarify the pathological changes in the dentate gyrus (DG) and anterior cingulate cortex (ACC) regions of the MDD brain, which are the most closely related to the disease, we investigated the published microarray profile dataset GSE84183 to identify unpredictable chronic mild stress- (UCMS-) induced differentially expressed genes (DEGs) in the DG and ACC regions. Based on the DEG data, functional annotation, protein-protein interaction, and transcription factor (TF) analyses were performed. In this study, 1071 DEGs (679 upregulated and 392 downregulated) and 410 DEGs (222 upregulated and 188 downregulated) were identified in DG and ACC, respectively. The pathways and GO terms enriched by the DEGs in the DG, such as cell adhesion, proteolysis, ion transport, transmembrane transport, chemical synaptic transmission, immune system processes, response to lipopolysaccharide, and nervous system development, may reveal the molecular mechanism of MDD. However, the DEGs in the ACC involved metabolic processes, proteolysis, visual learning, DNA methylation, innate immune responses, cell migration, and circadian rhythm. Sixteen hub genes in the DG (Fn1, Col1a1, Anxa1, Penk, Ptgs2, Cdh1, Timp1, Vim, Rpl30, Rps21, Dntt, Ptk2b, Jun, Avp, Slit1, and Sema5a) were identified. Eight hub genes in the ACC (Prkcg, Grin1, Syngap1, Rrp9, Grwd1, Pik3r1, Hnrnpc, and Prpf40a) were identified. In addition, eleven TFs (Chd2, Zmiz1, Myb, Etv4, Rela, Tcf4, Tcf12, Chd1, Mef2a, Ubtf, and Mxi1) were predicted to regulate more than two of these hub genes. The expression levels of ten randomly selected hub genes that were specifically differentially expressed in the MDD-like animal model were verified in the corresponding regions in the human brain. These hub genes and TFs may be regarded as potential targets for future MDD treatment strategies, thus aiding in the development of new therapeutic approaches to MDD.

Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis

Globally, nearly 40 percent of all diabetic patients develop serious diabetic kidney disease (DKD). The identification of the potential early-stage biomarkers and elucidation of their underlying molecular mechanisms in DKD are required. In this study, we performed integrated bioinformatics analysis on the expression profiles GSE111154, GSE30528 and GSE30529 associated with early diabetic nephropathy (EDN), glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. A total of 1,241, 318 and 280 differentially expressed genes (DEGs) were identified for GSE30258, GSE30529, and GSE111154 respectively. Subsequently, 280 upregulated and 27 downregulated DEGs shared between the three GSE datasets were identified. Further analysis of the gene expression levels conducted on the hub genes revealed SPARC (Secreted Protein Acidic And Cysteine Rich), POSTN (periostin), LUM (Lumican), KNG1 (Kininogen 1), FN1 (Fibronectin 1), VCAN (Versican) and PTPRO (Protein Tyrosine Phosphatase Receptor Type O) having potential roles in DKD progression. FN1, LUM and VCAN were identified as upregulated genes for GDKD whereas the downregulation of PTPRO was associated with all three diseases. Both POSTN and SPARC were identified as the overexpressed putative biomarkers whereas KNG1 was found as downregulated in TDKD. Additionally, we also identified two drugs, namely pidorubicine, a topoisomerase inhibitor (LINCS ID- BRD-K04548931) and Polo-like kinase inhibitor (LINCS ID- BRD-K41652870) having the validated role in reversing the differential gene expression patterns observed in the three GSE datasets used. Collectively, this study aids in the understanding of the molecular drivers, critical genes and pathways that underlie DKD initiation and progression.

A Study of Gene Expression Changes in Human Spinal and Oculomotor Neurons; Identifying Potential Links to Sporadic ALS

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that causes compromised function of motor neurons and neuronal death. However, oculomotor neurons are largely spared from disease symptoms. The underlying causes for sporadic ALS as well as for the resistance of oculomotor neurons to disease symptoms remain poorly understood. In this bioinformatic-analysis, we compared the gene expression profiles of spinal and oculomotor tissue samples from control individuals and sporadic ALS patients. We show that the genes GAD2 and GABRE (involved in GABA signaling), and CALB1 (involved in intracellular Ca²⁺ ion buffering) are downregulated in the spinal tissues of ALS patients, but their endogenous levels are higher in oculomotor tissues relative to the spinal tissues. Our results suggest that the downregulation of these genes and processes in spinal tissues are related to sporadic ALS disease progression and their upregulation in oculomotor neurons confer upon them resistance to ALS symptoms. These results build upon prevailing models of excitotoxicity that are relevant to sporadic ALS disease progression and point out unique opportunities for better understanding the progression of neurodegenerative properties associated with sporadic ALS.