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Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data.

ABSTRACT: Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

SUBMITTER: Jiang B

PROVIDER: S-EPMC5292384 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data.

Jiang Bing B Li Shuwen S Jiang Zhi Z Shao Ping P

BioMed research international 20170123

Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the pre ...[more]

PMID: 28232943

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Project description:Schizophrenia (SCZ) is a severe mental disorder that may result in hallucinations, delusions, and extremely disordered thinking. How each cell type in the brain contributes to SCZ occurrence is still unclear. Here, we leveraged the human dorsolateral prefrontal cortex bulk RNA-seq data, then used the RNA-seq deconvolution algorithm CIBERSORTx to generate SCZ brain single-cell RNA-seq data for a comprehensive analysis to understand SCZ-associated brain cell types and gene expression changes. Firstly, we observed that the proportions of brain cell types in SCZ differed from normal samples. Among these cell types, astrocyte, pericyte, and PAX6 cells were found to have a higher proportion in SCZ patients (astrocyte: SCZ = 0.163, control = 0.145, P.adj = 4.9 × 10-4, effect size = 0.478; pericyte: SCZ = 0.057, control = 0.066, P.adj = 1.1 × 10-4, effect size = 0.519; PAX6: SCZ = 0.014, control = 0.011, P.adj = 0.014, effect size = 0.377), while the L5/6_IT_CAR3 cells and LAMP5 cells are the exact opposite (L5/6_IT_Car3: SCZ = 0.102, control = 0.108, P.adj = 0.016, effect size = 0.369; LAMP5: SCZ = 0.057, control = 0.066, P.adj = 2.2 × 10-6, effect size = 0.617). Next, we investigated gene expression in cell types and functional pathways in SCZ. We observed chemical synaptic transmission dysregulation in two types of GABAergic neurons (PVALB and LAMP5), and immune reaction involvement in GABAergic neurons (SST) and non-neuronal cell types (endothelial and oligodendrocyte). Furthermore, we observed that some differential expression genes from bulk RNA-seq displayed cell-type-specific abnormalities in the expression of molecules in SCZ. Finally, the cell types with the SCZ-related transcriptomic changes could be considered to belong to the same module since we observed two major similar coordinated transcriptomic changes across these cell types. Together, our results offer novel insights into cellular heterogeneity and the molecular mechanisms underlying SCZ.

Project description:BackgroundCAFs regulate the signaling of GC cells by promoting their migration, invasion, and proliferation and the function of immune cells as well as their location and migration in the TME by remodeling the extracellular matrix (ECM). This study explored the understanding of the heterogeneity of CAFs in TME and laid the groundwork for GC biomarker and precision treatment development.MethodsThe scRNA-seq and bulk RNA-seq datasets were obtained from GEO and TCGA. The prognostic significance of various CAFs subtypes was investigated using ssGSEA combined with Kaplan-Meier analysis. POSTN expression in GC tissues and CAFs was detected using immunohistochemistry, immunofluorescence, and Western blotting. Differential expression analysis identified the differentially expressed genes (DEGs) between normal and tumor samples in TCGA-STAD. Pearson correlation analysis identified DEGs associated with adverse prognosis CAF subtype, and univariate Cox regression analysis determined prognostic genes associated with CAFs. LASSO regression analysis and Multivariate Cox regression were used to build a prognosis model for CAFs.ResultsWe identified five CAFs subtypes in GC, with the CAF_0 subtype associated with poor prognosis. The abundance of CAF_0 correlated with T stage, clinical stage, histological type, and immune cell infiltration levels. Periostin (POSTN) exhibited increased expression in both GC tissues and CAFs and was linked to poor prognosis in GC patients. Through LASSO and multivariate Cox regression analysis, three genes (CXCR4, MATN3, and KIF24) were selected to create the CAFs-score. We developed a nomogram to facilitate the clinical application of the CAFs-score. Notably, the CAFs signature showed significant correlations with immune cells, stromal components, and immunological scores, suggesting its pivotal role in the tumor microenvironment (TME). Furthermore, CAFs-score demonstrated prognostic value in assessing immunotherapy outcomes, highlighting its potential as a valuable biomarker to guide therapeutic decisions.ConclusionCAF_0 subtype in TME is the cause of poor prognosis in GC patients. Furthermore, CAFs-score constructed from the CAF_0 subtype can be used to determine the clinical prognosis, immune infiltration, clinicopathological characteristics, and assessment of personalized treatment of GC patients.

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Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data.

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Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data.

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