Dataset Information

Estimation of Hub Genes and Infiltrating Immune Cells in Non-Smoking Females with Lung Adenocarcinoma by Integrated Bioinformatic Analysis.

ABSTRACT: BACKGROUND In recent years, the morbidity and mortality rates of lung adenocarcinoma in non-smoking females have been increasing dramatically. Although much research has been done with some progress, the molecular mechanism remains unclear. In this study we aimed to estimate hub genes and infiltrating immune cells in non-smoking females with lung adenocarcinoma. MATERIAL AND METHODS Firstly, we obtained differentially expressed genes (DEGs) by GEO2R analysis based on 3 independent mRNA microarray datasets of GSE10072, GSE31547, and GSE32863. The DAVID database was utilized for functional enrichment analysis of DEGs. Moreover, we identified hub genes with prognostic value by STRING, Cytoscape, and Kaplan Meier plotter. Subsequently, these genes were further analyzed by Gene Expression Profiling Interactive Analysis, Oncomine, Tumor Immune Estimation Resource, and Human Protein Atlas. Finally, the immune infiltration analysis was performed by CIBERSORT and The Cancer Genome Atlas with R packages. RESULTS We found 315 DEGs enriching in the extracellular matrix organization, cell adhesion, integrin binding, angiogenesis, and hypoxic response. And among these DEGs, we identified 10 hub genes (SPP1, ENG, ATF3, TOP2A, COL1A1, PAICS, CAV1, CAT, TGFBR2, and ANGPT1) of significant prognostic value. Simultaneously, we illustrated the distribution and differential expressions of 22 immune cell subtypes. and dendritic cells resting and macrophages M1 were identified with prognostic significance. CONCLUSIONS The results indicated that 10 hub genes and 2 immune cell subtypes might be promising biomarkers for lung adenocarcinoma in non-smoking females. This finding needs to be further evaluated.

SUBMITTER: Li J

PROVIDER: S-EPMC7384333 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Estimation of Hub Genes and Infiltrating Immune Cells in Non-Smoking Females with Lung Adenocarcinoma by Integrated Bioinformatic Analysis.

Li Jie J Wang Ben B Li Xin X Zhu Yuxi Y

Medical science monitor : international medical journal of experimental and clinical research 20200716

BACKGROUND In recent years, the morbidity and mortality rates of lung adenocarcinoma in non-smoking females have been increasing dramatically. Although much research has been done with some progress, the molecular mechanism remains unclear. In this study we aimed to estimate hub genes and infiltrating immune cells in non-smoking females with lung adenocarcinoma. MATERIAL AND METHODS Firstly, we obtained differentially expressed genes (DEGs) by GEO2R analysis based on 3 independent mRNA microarra ...[more]

PMID: 32669531

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Project description:The present study aimed to explore important estrogen receptor-associated genes and to determine the potential pathogenic and prognostic factors for lung adenocarcinoma in non-smoking females. The gene expression profiles of the two datasets (GSE32863 and GSE75037) were downloaded from the Gene Expression Omnibus (GEO) database. Data for non-smoking female patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database were also downloaded. The Linear Models for Microarray Data package in R was used to explore the differentially expressed genes (DEGs) between samples from non-smoking female patients with lung adenocarcinoma and samples of adjacent non-cancerous lung tissue. The Database for Annotation, Visualization and Integrated Discovery was used for functional enrichment of the DEGs. The Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape software were used to obtain a protein-protein interaction (PPI) network and to identify the hub genes. In addition, the network between the estrogen receptor and the DEGs was constructed. A Kaplan-Meier survival plot was used to analyze the overall survival (OS). In total, 248 DEGs were identified in the GEO database, and 2,362 DEGs were identified in TCGA database. The intersection of the two datasets (DEGs in GEO and TCGA) revealed 170 DEGs, and these were selected for further investigation. Gene Ontology was used to group the 170 DEGs into biological process, molecular function and cellular component categories. Kyoto Encyclopedia of Genes and Genomes pathway analysis was subsequently performed. A total of 27 hub genes, including caveolin 1 (CAV1), matrix metallopeptidase 9 (MMP9), secreted phosphoprotein 1 (SPP1) and collagen type I ? 1 chain (COL1A1), were closely associated with the estrogen receptor. CAV1 and SPP1 were associated with the OS. However, MMP9 and COL1A1 did not have any significant effect on OS. In summary, the identification of CAV1, MMP9, SPP1 and COL1A1 may provide novel insights into the molecular mechanism of lung adenocarcinoma in non-smoking female patients, and the results obtained in the current study may guide future clinical studies.

Project description:The magnitude of the immune response is closely associated with clinical outcome in patients with cancer. However, finding potential therapeutic targets for lung cancer in the immune system remains challenging. Here, we constructed a vital immune-prognosis genes (VIPGs) based cluster of lung adenocarcinoma (LUAD) from IMMPORT databases and The Cancer Genome Atlas. A transcription factor regulatory network for the VIPGs was also established. The tumor microenvironment of LUAD was analyzed using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and single-sample Gene Set Enrichment Analysis. The immune checkpoints and genomic alterations were explored in the different immune clusters. We identified 15 VIPGs for patients with LUAD and clustered the patients into low-immunity and high-immunity subtypes. The immune score, stromal score and ESTIMATE score were significantly higher in the high-immunity subtype, whereas tumor purity was higher in the low-immunity subtype. In addition, the immune checkpoints cytotoxic T lymphocyte associate protein-4(CTLA4), programmed cell death protein-1 and programmed death-ligand were elevated in the low-immunity subtype. The genomic results also showed that the tumor mutation burden was higher in the high-immunity subtype. Finally, Gene Set Enrichment Analysis showed that several immune-related gene sets, including interleukin-2/STAT5 signaling, inflammatory response, interleukin-6/Janus kinase(JAK)/signal transducer and activator of transcription 3 (STAT3) signaling, interferon-gamma response and allograft rejection, were elevated in the high-immunity subtype. Finally, high-immunity patients exhibited greater overall and disease-specific survival outcome compared with low-immunity patients (log rank P = 0.013 and P = 0.0097). Altogether, here we have identified 15 immune-prognosis genes and a potential immune subtype for patients with LUAD, which may provide new insights into the prognosis and treatment of LUAD.

Project description:ObjectivesLung adenocarcinoma (LUAD) is recognized as one of the most prevalent and deadliest malignancies around the globe. The molecular mechanisms behind LUAD have not been fully elucidated. This study was launched to explore LUAD-associated hub genes and their enriched pathways using bioinformatics methods.MethodsInformation on GSE10072 was retrieved from the Gene Expression Omnibus (GEO) database and analyzed via the Limma package-based GEO2R tool to obtain the top 100 differentially expressed genes (DEGs) in LUAD. The protein-protein interaction (PPI) network of the DEGs was drawn using the STRING website and was shifted into Cytoscape to screen the top 6 hub genes via the CytoHubba application. Furthermore, the expression analysis and validation of hub genes in LUAD samples and cell lines were done using UALCAN, OncoDB, and GENT2 databases. Moreover, OncoDB was also used for analyzing hub gene DNA methylation levels. In addition, cBioPortal, GSEA tool, Kaplan-Meier (KM) plotter, Enrichr, CancerSEA, and DGIdb were performed to explore some other important aspects of hub genes in LUAD.ResultsWe identified Interleukin 6 (IL6), Collagen, type I, alpha 1 (COL1A1), TIMP metallopeptidase inhibitor 1 (TIMP1), CD34 molecule (CD34), Decorin (DCN), and Secreted Phosphoprotein 1 (SPP1) genes as the hub genes in LUAD, out of which IL6, CD34, and DCN were significantly down-regulated while COL1A1, TIMP1, and SPP1 were significantly up-regulated in LUAD cell lines and samples of diverse clinical variables. In this study, we also documented some important correlations between hub genes and other parameters such as DNA methylation, genetic alterations, Overall Survival (OS), and 14 important states at the single cell level. Lastly, we also identified hub genes associated with the ceRNA network and 11 important chemotherapeutic drugs.ConclusionWe identified 6 hub genes involved in the development and progression of LUAD. These hub genes can also be helpful in the accurate detection of LUAD and provide novel ideas for treatment.

Project description:Targeting the tumor microenvironment is increasingly recognized as an effective treatment of advanced lung adenocarcinoma (LUAD). However, few studies have addressed the efficacy of immunotherapy for LUAD. Here, a novel method for predicting immunotherapy efficacy has been proposed, which combines single-cell and bulk sequencing to characterize the immune microenvironment and metabolic profile of LUAD. TCGA bulk dataset was used to cluster two immune subtypes: C1 with "cold" tumor characteristics and C2 with "hot" tumor characteristics, with different prognosis. The Scissor algorithm, which is based on these two immune subtypes, identified GSE131907 single cell dataset into two groups of epithelial cells, labeled as Scissor_C1 and Scissor_C2. The enrichment revealed that Scissor_C1 was characterized by hypoxia, and a hypoxic microenvironment is a potential inducing factor for tumor invasion, metastasis, and immune therapy non-response. Furthermore, single cell analysis was performed to investigate the molecular mechanism of hypoxic microenvironment-induced invasion, metastasis, and immune therapy non-response in LUAD. Notably, Scissor_C1 cells significantly interacted with T cells and cancer-associated fibroblasts (CAF), and exhibited epithelial-mesenchymal transition and immunosuppressive features. CellChat analysis revealed that a hypoxic microenvironment in Scissor_C1elevated TGFβ signaling and induced ANGPTL4 and SEMA3C secretion. Interaction with endothelial cells with ANGPTL4, which increases vascular permeability and achieves distant metastasis across the vascular endothelium. Additionally, interaction of tumor-associated macrophages (TAM) and Scissor_C1 via the EREG/EFGR pathway induces tyrosine kinase inhibitor drug-resistance in patients with LAUD. Thereafter, a subgroup of CAF cells that exhibited same features as those of Scissor_C1 that exert immunosuppressive functions in the tumor microenvironment were identified. Moreover, the key genes (EPHB2 and COL1A1) in the Scissor_C1 gene network were explored and their expressions were verified using immunohistochemistry. Finally, the metabolism dysfunction in cells crosstalk was determined, which is characterized by glutamine secretion by TAM and uptake by Scissor_C1 via SLC38A2 transporter, which may induce glutamine addiction in LUAD cells. Overall, single-cell sequencing clarifies how the tumor microenvironment affects immunotherapy efficacy via molecular mechanisms and biological processes, whereas bulk sequencing explains immunotherapy efficacy based on clinical information.

Project description:Background: Butyrophilin subfamily 3 member A2 (BTN3A2) is an important mediator in immune activation, and it is reported to be linked to many cancer progresses. However, the relation with infiltrating immune and prognosis of BTN3A2 in lung adenocarcinoma are not clear. Methods: In our study, we checked the mRNA expression and protein expression profile of BTN3A2 in lung adenocarcinoma (LUAD) and its relation to clinical outcomes using TIMER and UALCAN databases. In addition, we analyzed the survival of BTN3A2 in LUAD using the Kaplan-Meier Plotter database and PrognoScan database. Moreover, we analyzed gene set enrichment analysis (GSEA) of the BTN3A2. Next, we explored the relation of BTN3A2 expression with the immune infiltration by TIMER. At last, in order to enrich the regulatory mechanism of BTN3A2, we used miRarbase, starbase, and miRDB databases to look for miRNA targets of BTN3A2. Results: The mRNA along with the protein expression of BTN3A2 in the LUAD group was lower than that in the normal group. In addition, high BTN3A2 expression was connected with good first progression (FP) and overall survival (OS) in LUAD. Then, the GSEA analysis demonstrated that T-cell receptor signaling cascade, B-cell receptor signaling cascade, natural killer cell-mediated cytotoxicity, immune receptor activity, immunological synapse, and T-cell activation were enriched differentially in the BTN3A2 high expression phenotype of LUAD. Moreover, BTN3A2 expression is a remarkable positive correlation with invading levels of tumor purity, B cells, neutrophils, CD4+ T cells, dendritic cells, macrophages, and CD8+ T cells in LUAD, and B cells and dendritic cells were linked with a good prognosis of LUAD. To further enrich the possible regulatory mechanisms of BTN3A2, we analyzed the miRNA targets. The results showed that hsa-miR-17-5p may be miRNA targets of BTN3A2. Conclusion: Taking together, we provide evidence of BTN3A2 as possible prognosis biomarkers of LUAD. In addition, high BTN3A2 expression in LUAD may influence the prognosis because of immune invasion. Moreover, our findings provide a potential mechanism that hsa-miR-17-5p may be miRNA targets of BTN3A2.

Dataset Information

Estimation of Hub Genes and Infiltrating Immune Cells in Non-Smoking Females with Lung Adenocarcinoma by Integrated Bioinformatic Analysis.

Publications

Estimation of Hub Genes and Infiltrating Immune Cells in Non-Smoking Females with Lung Adenocarcinoma by Integrated Bioinformatic Analysis.

Similar Datasets

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