Dataset Information

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

SUBMITTER: Nouri N

PROVIDER: S-EPMC11228368 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.

Nouri Nima N Gaglia Giorgio G Mattoo Hamid H de Rinaldis Emanuele E Savova Virginia V

Cell reports methods 20240610 6

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene exp ...[more]

PMID: 38861988

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Project description:Background: Osteosarcoma (OS) is a common primary tumor with extensive heterogeneity. In this study, we used single-cell RNA sequencing (scRNA-seq) and network pharmacology to analyze effective targets for Osteosarcoma treatment. Methods: The cell heterogeneity of the Osteosarcoma single-cell dataset GSE162454 was analyzed using the Seurat package. The bulk-RNA transcriptome dataset GSE36001 was downloaded and analyzed using the CIBERSORT algorithm. The key targets for OS therapy were determined using Pearson's correlation analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on key targets. The DeepDR algorithm was used to predict potential drugs for Osteosarcoma treatment. Molecular docking analysis was performed to verify the binding abilities of the predicted drugs and key targets. qRT-PCR assay was used to detect the expression of key targets in osteoblasts and OS cells. Results: A total of 21 cell clusters were obtained based on the GSE162454 dataset, which were labeled as eight cell types by marker gene tagging. Four cell types (B cells, cancer-associated fibroblasts (CAFs), endothelial cells, and plasmocytes) were identified in Osteosarcoma and normal tissues, based on differences in cell abundance. In total, 17 key targets were identified by Pearson's correlation analysis. GO and KEGG analysis showed that these 17 genes were associated with immune regulation pathways. Molecular docking analysis showed that RUNX2, OMD, and CD4 all bound well to vincristine, dexamethasone, and vinblastine. The expression of CD4, OMD, and JUN was decreased in Osteosarcoma cells compared with osteoblasts, whereas RUNX2 and COL9A3 expression was increased. Conclusion: We identified five key targets (CD4, RUNX2, OMD, COL9A3, and JUN) that are associated with Osteosarcoma progression. Vincristine, dexamethasone, and vinblastine may form a promising drug-target pair with RUNX2, OMD, and CD4 for Osteosarcoma treatment.

Project description:Cervical cancer ranks first in female reproductive tract tumors in terms of morbidity and mortality. Yet the curative effect of patients with persistent, recurrent or metastatic cervical cancer remains unsatisfactory. Although antitumor angiogenic drugs have been recommended as the first-line treatment options for cervical cancer, there are no comprehensive prognostic indicators for cervical cancer based on angiogenic signature genes. In this study, we aimed to develop a model to assess the prognosis of cervical cancer based on angiogenesis-related (AG) signature genes, and to provide some reference for the comprehensive treatment of cervical cancer in the clinical setting. First we screened the AG gene set from GeneCard website, and then performed angiogenesis-related scores (AGS) per cell from single cell sequencing dataset GSE168652, followed by performing weighted gene co-expression network analysis (WGCNA) for cervical cancer patients according to angiogenesis phenotype. Thus, we established a prognostic model based on AGS by taking the intersection of WGCNA angiogenic module gene and differential gene (DEGs) of GSE168652. The GSE44001 was selected as an external validation set, followed by performing ROC curve analysis to assess its accuracy. The results showed that we successfully constructed a prognostic model related to the AG genes. Patients in the high-AGS group in both the train, test and the validation sets had a worse prognosis than those in the low-AGS group, had lower expression of most immune checkpoint-associated genes and lower tumor mutational burden as well. Patients in the low-AGS group were more sensitive to AMG.706, Bosutinib, and Lenalidomide while Imatinib, Pazopanib, and Sorafenib were more recommended to patients in the high-AGS group. Finally, TXNDC12 and ZC3H13, which have high hazard ratio and poor prognosis in the model, were highly expressed in cervical cancer cell lines and tissue. Meanwhile, the results showed that TXNDC12 promoted the migration of cervical cancer cells and the tubule-forming ability of endothelial cells. In conclusion, our model based on genes with AG features can effectively assess the prognosis of cervical cancer, and can also provide reference for clinicians to choose immune-related treatments.

Project description:BackgroundPrion diseases and prion-like disorders, including Alzheimer's disease and Parkinson's disease, are characterized by gliosis and accumulation of misfolded aggregated host proteins. Ablating microglia in prion-infected brain by treatment with the colony-stimulating factor-1 receptor (CSF-1R) inhibitor, PLX5622, increased accumulation of misfolded prion protein and decreased survival time.MethodsTo better understand the role of glia during neurodegeneration, we used RNA-seq technology, network analysis, and hierarchical cluster analysis to compare gene expression in brains of prion-infected versus mock-inoculated mice. Comparisons were also made between PLX5622-treated prion-infected mice and untreated prion-infected mice to assess mechanisms involved in disease acceleration in the absence of microglia.ResultsRNA-seq and network analysis suggested that microglia responded to prion infection through activation of integrin CD11c/18 and did not adopt the expression signature associated with other neurodegenerative disease models. Instead, microglia acquired an alternative molecular signature late in the disease process. Furthermore, astrocytes expressed a signature pattern of genes which appeared to be specific for prion diseases. Comparisons were also made with prion-infected mice treated with PLX5622 to assess the impact of microglia ablation on astrocyte gene expression during prion infection. In the presence of microglia, a unique mix of transcripts associated with A1- and A2-reactive astrocytes was increased in brains of prion-infected mice. After ablation of microglia, this reactive astrocyte expression pattern was enhanced. Thus, after prion infection, microglia appeared to decrease the overall A1/A2-astrocyte responses which might contribute to increased survival after infection.ConclusionsRNA-seq analysis indicated dysregulation of over 300 biological processes within the CNS during prion disease. Distinctive microglia- and astrocyte-associated expression signatures were identified during prion infection. Furthermore, astrogliosis and the unique astrocyte-associated expression signature were independent of microglial influences. Astrogliosis and the unique astrocyte-associated gene expression pattern were increased when microglia were ablated. Our findings emphasize the potential existence of alternative pathways for activating the A1/A2 paradigm in astrocytes during neurodegenerative disease.

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GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.

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GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.

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