A Pan-Cancer Analysis of the Prognostic Value and Expression of Adenylate Cyclase 7 (ADCY7) in Human Tumors.

BACKGROUND: The role of adenylate cyclase 7 (ADCY7) in cancer is still unclear. This study analyzed the interrelationship between the expression and immune function of ADCY7. METHODS: ADCY7 expression in multiple human cancers was analyzed using the databases of Genotype-Tissue Expression Project (GTEx), Cancer Cell Line Encyclopedia (CCLE), and The Cancer Genome Atlas (TCGA). Correlations among ADCY7 gene expression, mismatch repair (MMR) gene expression, and DNA methyltransferase (DNMT) expression were assessed using Spearman correlation analysis. Univariate survival analysis and Kaplan-Meier (KM) curve were used to examine the effect of ADCY7 expression on prognosis. The Tumor Immune Estimation Resource (TIMER) database was used to evaluate the relationship between ADCY7 gene expression and tumor immune invasion or immune checkpoint gene (ICG) expression. RESULTS: ADCY7 was abnormally expressed in multiple human cancers and was correlated with MMR genes and DNMT expression. Univariate survival analysis and KM curve showed that ADCY7 expression influences the overall survival (OS) of six types of cancer, disease-specific survival (DSS) of eight, and progression-free interval (PFI) of three. The high expression of ADCY7 in OS, DSS, and PFI was strongly associated with poor outcomes in patients with breast cancer and lung squamous cell carcinoma. ADCY7 expression was strongly associated with immune cell infiltration and ICG expression. CONCLUSION: The results of this study indicated that ADCY7 may be a prognostic biomarker of tumorigenesis. The study may also provide a new perspective on the role of ADCY7 in human cancers.

Screening of key biomarkers and immune infiltration in Pulmonary Arterial Hypertension via integrated bioinformatics analysis.

This study aimed to screen key biomarkers and investigate immune infiltration in pulmonary arterial hypertension (PAH) based on integrated bioinformatics analysis. The Gene Expression Omnibus (GEO) database was used to download three mRNA expression profiles comprising 91 PAH lung specimens and 49 normal lung specimens. Three mRNA expression datasets were combined, and differentially expressed genes (DEGs) were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the protein-protein interaction (PPI) network of DEGs were performed using the STRING and DAVID databases, respectively. The diagnostic value of hub gene expression in PAH was also analyzed. Finally, the infiltration of immune cells in PAH was analyzed using the CIBERSORT algorithm. Total 182 DEGs (117 upregulated and 65 downregulated) were identified, and 15 hub genes were screened. These 15 hub genes were significantly associated with immune system functions such as myeloid leukocyte migration, neutrophil migration, cell chemotaxis, Toll-like receptor signaling pathway, and NF-κB signaling pathway. A 7-gene-based model was constructed and had a better diagnostic value in identifying PAH tissues compared with normal controls. The immune infiltration profiles of the PAH and normal control samples were significantly different. High proportions of resting NK cells, activated mast cells, monocytes, and neutrophils were found in PAH samples, while high proportions of resting T cells CD4 memory and Macrophages M1 cell were found in normal control samples. Functional enrichment of DEGs and immune infiltration analysis between PAH and normal control samples might help to understand the pathogenesis of PAH.

Screening of Hub Genes Associated with Pulmonary Arterial Hypertension by Integrated Bioinformatic Analysis.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a disease or pathophysiological syndrome which has a low survival rate with abnormally elevated pulmonary artery pressure caused by known or unknown reasons. In addition, the pathogenesis of PAH is not fully understood. Therefore, it has become an urgent matter to search for clinical molecular markers of PAH, study the pathogenesis of PAH, and contribute to the development of new science-based PAH diagnosis and targeted treatment methods. METHODS: In this study, the Gene Expression Omnibus (GEO) database was used to downloaded a microarray dataset about PAH, and the differentially expressed genes (DEGs) between PAH and normal control were screened out. Moreover, we performed the functional enrichment analyses and protein-protein interaction (PPI) network analyses of the DEGs. In addition, the prediction of miRNA and transcriptional factor (TF) of hub genes and construction miRNA-TF-hub gene network were performed. Besides, the ROC curve was used to evaluate the diagnostic value of hub genes. Finally, the potential drug targets for the 5 identified hub genes were screened out. RESULTS: 69 DEGs were identified between PAH samples and normal samples. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in the inflammatory response and cytokine-cytokine receptor interaction, respectively. The miRNA-hub genes network was conducted subsequently with 131 miRNAs, 7 TFs, and 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) which screened out via constructing the PPI network. 17 drugs interacted with 5 hub genes were identified. CONCLUSIONS: Through bioinformatic analysis of microarray data sets, 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) were identified from DEGs between control samples and PAH samples. Studies showed that the five hub genes might play an important role in the development of PAH. These 5 hub genes might be potential biomarkers for diagnosis or targets for the treatment of PAH. In addition, our work also indicated that paying more attention on studies based on these 5 hub genes might help to understand the molecular mechanism of the development of PAH.

Analyses of expressions and prognostic values of Polo-like kinases in non-small cell lung cancer.

BACKGROUND: Despite great advances in its early diagnosis and treatment, lung cancer is still an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. The family of Polo-like kinases (PLKs) consists of five serine/threonine kinases, which have been reported to participate in various human diseases. However, the expression and prognostic value of each PLK in human lung cancer have not been fully understood. This study analyzed mRNA expression and prognostic value of different PLKs in human non-small cell lung cancer (NSCLC). METHODS: First, mRNA expression of PLKs in patients with NSCLC from the Oncomine and the Gene Expression Profiling Interactive Analysis (GEPIA) database was investigated. Then, a Kaplan-Meier plotter was employed for survival analysis. The sequence alteration for PLKs was analyzed using The Cancer Genome Atlas (TCGA) and the cBioPortal database. Additionally, we analyzed the association among different PLKs using the LinkedOmics database. Finally, the enrichment analysis of PLKs was achieved using the DAVID database. RESULTS: The mRNA expression levels of PLK1 and PLK4 were significantly overexpressed, while mRNA expression level of PLK3 was underexpressed in patients with NSCLC. mRNA expressions of PLK1 and PLK4 were significantly and positively related to the tumor stage of NSCLC. Increased expressions of PLK1, PLK4, and PLK5 and decreased expression of PLK2 were attributed to limited overall survival time in NSCLC. PLK1 was positively correlated with PLK4 via the LinkedOmics database. CONCLUSIONS: PLKs are relevant targets for NSCLC treatment, especially PLK1 and PLK4.

Screening of hub genes associated with prognosis in non-small cell lung cancer by integrated bioinformatics analysis.

BACKGROUND: Lung cancer is an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. This study conducted a bioinformatics analysis to identify critical genes associated with poor prognosis in non-small cell lung cancer (NSCLC). METHODS: We downloaded three datasets (GSE33532, GSE27262, and GSE18842) from the gene expression omnibus (GEO), and used the GEO2R online tools to identify the differentially expressed genes (DEGs). We then used the Search Tool for Retrieval of Interacting Genes (STRING) database to establish a protein-protein interaction (PPI) network and used the Cytoscape software to perform a module analysis of the PPI network. A Kaplan-Meier plotter was used to perform the overall survival (OS) analysis, and the Gene Expression Profiling Interactive Analysis (GEPIA) database was used for expression level analysis of hub genes. Further, the UALCAN database was used to validate the relationship between the gene expression level of each hub gene and clinical characteristics. RESULTS: We identified 254 DEGs, which were composed of 66 up-regulated genes and 188 down-regulated genes. Out of these, five DEGs were identified as hub genes (CDC20, BUB1, CCNB2, CCNB1, UBE2C) by constructing a PPI network. The use of a Kaplan-Meier plotter to generate patient survival curves suggested a strong relationship between the five hub genes with worse OS. Validation of the above results using the GEPIA database showed that all the hub genes were highly expressed in NSCLC tissues. Using the UALACN data mining platform, we found that the five hub genes are correlated with tumor stage and the status of node metastasis in NSCLC patients. CONCLUSIONS: We identified five hub DEGs that might provide perspectives in the explorations of pathogenesis and treatments for NSCLC.