The inability to halt or even delay the course of Alzheimer’s disease (AD) forces the development of new molecular signatures and therapeutic strategies. Insulin like growth factor 1 (IGF1) is a promising target for AD treatment, yet exact mechanisms of AD ascribed to IGF1 remain elusive. Herein, gene expression profiles of 195 samples were analyzed and 19,245 background genes were generated, among which 4,424 differentially expressed genes (DEGs) were overlapped between AD/control and IGF1-low/high groups. Based on such DEGs, seven co-expression modules were established by weight gene correlation network analysis (WGCNA). The turquoise module had the strongest correlation with AD and IGF1-low expression, the DEGs of which were enriched in GABAergic synapse, long-term potentiation, mitogen-activated protein kinase (MAPK), Ras, and forkhead box O (FoxO) signaling pathways. Furthermore, cross-talking pathways of IGF1, including MAPK, Ras, and FoxO signaling pathways were identified in the protein-protein interaction network. According to the area under the curve (AUC) analysis, down-regulation of IGF1 exhibited good diagnostic performance in AD prediction. Collectively, our findings highlight the involvement of low IGF1 in AD pathogenesis via MAPK, Ras, and FoxO signaling pathways, which might advance strategies for the prevention and therapy of AD based on IGF1 target.

Brain metastasis of a cancer is a malignant disease with high mortality, but the cause and the molecular mechanism remain largely unknown. Using the samples of primary tumors of 22 cancer types in the TCGA database, we have performed a computational study of their transcriptomic data to investigate the drivers of brain metastases at the basic physics and chemistry level. Our main discoveries are: (i) the physical characteristics, namely electric charge, molecular weight, and the hydrophobicity of the extracellular structures of the expressed transmembrane proteins largely affect a primary cancer cell's ability to cross the blood-brain barrier; and (ii) brain metastasis may require specific functions provided by the activated enzymes in the metastasizing primary cancer cells for survival in the brain micro-environment. Both predictions are supported by published experimental studies. Based on these findings, we have built a classifier to predict if a given primary cancer may have brain metastasis, achieving the accuracy level at AUC = 0.92 on large test sets.

Objective. To investigate the molecular function of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta (PIK3CB) underlying Alzheimer’s disease (AD). Methods. RNA sequencing data were used to filtrate differentially expressed genes (DEGs) in AD/nondementia control and PIK3CB-low/high groups. An unbiased coexpression network was established to evaluate module-trait relationships by using weight gene correlation network analysis (WGCNA). Global regulatory network was constructed to predict the protein-protein interaction. Further cross-talking pathways of PIK3CB were identified by functional enrichment analysis. Results. The mean expression of PIK3CB in AD patients was significantly lower than those in nondementia controls. We identified 2,385 DEGs from 16,790 background genes in AD/control and PIK3CB-low/high groups. Five coexpression modules were established using WGCNA, which participated in apoptosis, axon guidance, long-term potentiation (LTP), regulation of actin cytoskeleton, synaptic vesicle cycle, FoxO, mitogen-activated protein kinase (MAPK), and vascular endothelial growth factor (VEGF) signaling pathways. DEGs with strong relation to AD and low PIK3CB expression were extracted to construct a global regulatory network, in which cross-talking pathways of PIK3CB were identified, such as apoptosis, axon guidance, and FoxO signaling pathway. The occurrence of AD could be accurately predicted by low PIK3CB based on the area under the curve of 71.7%. Conclusions. These findings highlight downregulated PIK3CB as a potential causative factor of AD, possibly mediated via apoptosis, axon guidance, and FoxO signaling pathway.

Oxidative stress is known to be involved in and possibly a key driver of the development of numerous chronic diseases, including cancer. It is highly desired to have a capability to reliably estimate the level of intracellular oxidative stress as it can help to identify functional changes and disease phenotypes associated with such a stress, but the problem proves to be very challenging. We present a novel computational model for quantitatively estimating the level of oxidative stress in tissues and cells based on their transcriptomic data. The model consists of (i) three sets of marker genes found to be associated with the production of oxidizing molecules, the activated antioxidation programs and the intracellular stress attributed to oxidation, respectively; (ii) three polynomial functions defined over the expression levels of the three gene sets are developed aimed to capture the total oxidizing power, the activated antioxidation capacity and the oxidative stress level, respectively, with their detailed parameters estimated by solving an optimization problem and (iii) the optimization problem is so formulated to capture the relevant known insights such as the oxidative stress level generally goes up from normal to chronic diseases and then to cancer tissues. Systematic assessments on independent datasets indicate that the trained predictor is highly reliable and numerous insights are made based on its application results to samples in the TCGA, GTEx and GEO databases.

Alzheimer's disease (AD) and Huntington's disease (HD) are destructive worldwide diseases. Efforts have been made to elucidate the process of these two diseases, yet the pathogenesis remains elusive as it involves a combination of multiple factors, including genetic and environmental ones. To explore the potential role of forkhead box O1 (FOXO1) in the development of AD and HD, we identified 1,853 differentially expressed genes (DEGs) from 19,414 background genes in both the AD&HD/control and FOXO1-low/high groups. Four coexpression modules were predicted by the weighted gene coexpression network analysis (WGCNA), among which blue and turquoise modules had the strongest correlation with AD&HD and high expression of FOXO1. Functional enrichment analysis showed that DEGs in these modules were enriched in phagosome, cytokine-cytokine receptor interaction, cellular senescence, FOXO signaling pathway, pathways of neurodegeneration, GABAergic synapse, and AGE-RAGE signaling pathway in diabetic complications. Furthermore, the cross-talking pathways of FOXO1 in AD and HD were jointly determined in a global regulatory network, such as the FOXO signaling pathway, cellular senescence, and AGE-RAGE signaling pathway in diabetic complications. Based on the performance evaluation of the area under the curve of 85.6%, FOXO1 could accurately predict the onset of AD and HD. We then identified the cross-talking pathways of FOXO1 in AD and HD, respectively. More specifically, FOXO1 was involved in the FOXO signaling pathway and cellular senescence in AD; correspondingly, FOXO1 participated in insulin resistance, insulin, and the FOXO signaling pathways in HD. Next, we use GSEA to validate the biological processes in AD&HD and FOXO1 expression. In GSEA analysis, regulation of protein maturation and regulation of protein processing were both enriched in the AD&HD and FOXO1-high groups, suggesting that FOXO1 may have implications in onset and progression of these two diseases through protein synthesis. Consequently, a high expression of FOXO1 is a potential pathogenic factor in both AD and HD involving mechanisms of the FOXO signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and cellular senescence. Our findings provide a comprehensive perspective on the molecular function of FOXO1 in the pathogenesis of AD and HD.

Objective. The inability to intervene in Alzheimer’s disease (AD) forces the search for promising gene-targeted therapies. This study was aimed at exploring molecular signatures and mechanistic pathways to improve the diagnosis and treatment of AD. Methods. Microarray datasets were collected to filter differentially expressed genes (DEGs) between AD and nondementia controls. Weight gene correlation network analysis (WGCNA) was employed to analyze the correlation of coexpression modules with AD phenotype. A global regulatory network was established and then visualized using Cytoscape software to determine hub genes and their mechanistic pathways. Receiver operating characteristic (ROC) analysis was conducted to estimate the diagnostic performance of hub genes in AD prediction. Results. A total of 2,163 DEGs from 13,049 background genes were screened in AD relative to nondementia controls. Among the six coexpression modules constructed by WGCNA, DEGs of the key modules with the strongest correlation with AD were extracted to build a global regulatory network. According to the Maximal Clique Centrality (MCC) method, five hub genes associated with mitochondrial complexes were chosen. Further pathway enrichment analysis of hub genes, such as oxidative phosphorylation and retrograde endocannabinoid signaling, was identified. According to the area under the curve (AUC) of about 70%, each hub gene exhibited a good diagnostic performance in predicting AD. Conclusions. Our findings highlight the perturbation of mitochondrial complexes underlying AD onset, which is mediated by molecular signatures involved in oxidative phosphorylation (COX5A, NDUFAB1, SDHB, UQCRC2, and UQCRFS1) and retrograde endocannabinoid signaling (NDUFAB1) pathways.

OBJECTIVE:Alzheimer's disease (AD) is associated with cell cycle reentry of mature neurons that subsequently undergo degeneration. This study is aimed to identify key regulators of the cell cycle and their underlying pathways for developing optimal treatment of AD.METHODS:RNA sequencing data were profiled to screen for differentially expressed genes in the cell cycle. Correlation of created modules with AD phenotype was computed by weight gene correlation network analysis (WGCNA). Signature genes for trophic factor receptors were determined using Pearson correlation coefficient (PCC) analysis.RESULTS:Among the 13,679 background genes, 775 cell cycle genes and 77 trophic factor receptors were differentially expressed in AD versus nondementia controls. Four coexpression modules were constructed by WGCNA, among which the turquoise module had the strongest correlation with AD. According to PCC analysis, 10 signature trophic receptors most strongly interacting with cell cycle genes were filtered and subsequently displayed in the global regulatory network. Further cross-talking pathways of signature receptors, such as glutamatergic synapse, long-term potentiation, PI3K-Akt, and MAPK signaling pathways, were identified.CONCLUSIONS:Our findings highlighted the mechanistic pathways of signature trophic receptors in cell cycle perturbation underlying AD pathogenesis, thereby providing new molecular targets for therapeutic intervention in AD.

OBJECTIVE:The objective of this study was to investigate the potential molecular mechanisms of ATPase H+ transporting V1 subunit A (ATP6V1A) underlying Alzheimer's disease (AD).METHODS:Microarray expression data of human temporal cortex samples from the GSE118553 dataset were profiled to screen for differentially expressed genes (DEGs) between AD/control and ATP6V1A-low/high groups. Correlations of coexpression modules with AD and ATP6V1A were assessed by weight gene correlation network analysis (WGCNA). DEGs strongly interacting with ATP6V1A were extracted to construct global regulatory network. Further cross-talking pathways of ATP6V1A were identified by functional enrichment analysis. Diagnostic performance of ATP6V1A in AD prediction was evaluated using area under the curve (AUC) analysis.RESULTS:The mean expression of ATP6V1A was significantly downregulated in AD compared with nondementia controls. A total of 1,364 DEGs were overlapped from AD/control and ATP6V1A-low/high groups. Based on these DEGs, four coexpression modules were predicted by WGCNA. The blue, brown, and turquoise modules were significantly correlated with AD and low ATP6V1A, whose DEGs were enriched in phagosome, oxidative phosphorylation, synaptic vesicle cycle, focal adhesion, and gamma-aminobutyric acidergic (GABAergic) synapse. Global regulatory network was constructed to identify the cross-talking pathways of ATP6V1A, such as synaptic vesicle cycle, phagosome, and oxidative phosphorylation. According to the AUC value of 74.2%, low ATP6V1A expression accurately predicted the occurrence of AD.CONCLUSIONS:Our findings highlighted the pleiotropic roles of low ATP6V1A in AD pathogenesis, possibly mediated by synaptic vesicle cycle, phagosome, and oxidative phosphorylation.

Rationale: Protein arginine methyltransferase 5 (PRMT5) is an oncogene that promotes tumor cell proliferation, invasion and metastasis. However, the underlying mechanisms by which PRMT5 contributes to the progression of cervical cancer and especially the tumor microenvironment remain poorly understood. Methods: PRMT5 expression level was analyzed by Q-PCR, western blot, immunohistochemistry, and TCGA database. The role of PRMT5 in tumor growth was observed by transplanted tumor models, and the function of T cells in tumor microenvironment and in vitro co-culture system was investigated through flow cytometry. The transcriptional regulation of PRMT5 was analyzed using luciferase reporter and chromatin immunoprecipitation (ChIP) assay. The therapeutic effect of PRMT5 inhibitor was evaluated in a cervical cancer cell line transplanted tumor model. Results: We observed that the mRNA and protein expression levels of PRMT5 were increased in cervical cancer tissues, and the high expression of PRMT5 was associated with poor outcomes in cervical cancer patients. The absence of PRMT5 significantly inhibited tumor growth in a cervical cancer transplanted tumor model, and importantly, PRMT5 absence in tumors led to increase the number and enhance the function of tumor infiltrating T cells. Mechanistically, PRMT5 enhanced the transcription of STAT1 through symmetric dimethylation of histone H3R2 and thus promoted PD-L1 expression in cervical cancer cells. Moreover, in an in vitro co-culture system, knockdown of PRMT5 in tumor cells could directly enhance the expression of IFN-γ, TNF-α and granzyme B in T cells. These results suggested that PRMT5 promoted the development of cervical cancer by the crosstalk between tumor cells and T cells. Furthermore, the PRMT5 inhibitor EPZ015666 treatment could suppress tumor growth in a cervical cancer transplanted tumor model. Conclusion: Our results clarify a new mechanism which PRMT5 knockdown in cervical cancer cells drives an antitumor function via reprogramming T cell-mediated response and regulating PD-L1 expression. Thus, our study highlights that PRMT5 may be a potential target for cervical cancer therapy.

Alzheimer's disease (AD) is associated with abnormal calcium signaling, a pathway regulated by the calcium-dependent protein phosphatase. This study aimed to investigate the molecular function of protein phosphatase 3 regulatory subunit B (PPP3R1) underlying AD, which may provide novel insights for the predictive diagnostics, targeted prevention, and personalization of medical services in AD by targeting PPP3R1. A total of 1860 differentially expressed genes (DEGs) from 13,049 background genes were overlapped in AD/control and PPP3R1-low/high cohorts. Based on these DEGs, six co-expression modules were constructed by weight gene correlation network analysis (WGCNA). The turquoise module had the strongest correlation with AD and low PPP3R1, in which DEGs participated in axon guidance, glutamatergic synapse, long-term potentiation (LTP), mitogen-activated protein kinase (MAPK), Ras, and hypoxia-inducible factor 1 (HIF-1) signaling pathways. Furthermore, the cross-talking pathways of PPP3R1, such as axon guidance, glutamatergic synapse, LTP, and MAPK signaling pathways, were identified in the global regulatory network. The area under the curve (AUC) analysis showed that low PPP3R1 could accurately predict the onset of AD. Therefore, our findings highlight the involvement of PPP3R1 in the pathogenesis of AD via axon guidance, glutamatergic synapse, LTP, and MAPK signaling pathways, and identify downregulation of PPP3R1 as a potential biomarker for AD treatment in the context of 3P medicine—predictive diagnostics, targeted prevention, and personalization of medical services.