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Our goal of this study was to reconstruct a "genome-scale co-expression network" and find important modules in lung adenocarcinoma so that we could identify the genes involved in lung adenocarcinoma. We integrated gene mutation, GWAS, CGH, array-CGH and SNP array data in order to identify important genes and loci in genome-scale. Afterwards, on the basis of the identified genes a co-expression network was reconstructed from the co-expression data. The reconstructed network was named "genome-scale co-expression network". As the next step, 23 key modules were disclosed through clustering. In this study a number of genes have been identified for the first time to be implicated in lung adenocarcinoma by analyzing the modules. The genes EGFR, PIK3CA, TAF15, XIAP, VAPB, Appl1, Rab5a, ARF4, CLPTM1L, SP4, ZNF124, LPP, FOXP1, SOX18, MSX2, NFE2L2, SMARCC1, TRA2B, CBX3, PRPF6, ATP6V1C1, MYBBP1A, MACF1, GRM2, TBXA2R, PRKAR2A, PTK2, PGF and MYO10 are among the genes that belong to modules 1 and 22. All these genes, being implicated in at least one of the phenomena, namely cell survival, proliferation and metastasis, have an over-expression pattern similar to that of EGFR. In few modules, the genes such as CCNA2 (Cyclin A2), CCNB2 (Cyclin B2), CDK1, CDK5, CDC27, CDCA5, CDCA8, ASPM, BUB1, KIF15, KIF2C, NEK2, NUSAP1, PRC1, SMC4, SYCE2, TFDP1, CDC42 and ARHGEF9 are present that play a crucial role in cell cycle progression. In addition to the mentioned genes, there are some other genes (i.e. DLGAP5, BIRC5, PSMD2, Src, TTK, SENP2, PSMD2, DOK2, FUS and etc.) in the modules.
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PMID:Reconstruction of an integrated genome-scale co-expression network reveals key modules involved in lung adenocarcinoma. 2387 28

In recording the changes acquired in gene expression profile during culture of fresh bone marrow samples from patients with multiple myeloma or acute myeloid leukemia, the most remarkable finding in both instances was widespread downregulation of mitotic and transcriptional genes (e.g. MKI67, CCNB1, ASPM, SGOL1, DLGAP5, CENPF, BUB1, KIF23, KIF18a, KIF11, KIF14, KIF4, NUF2, KIF1, AE2FB, TOP2A, NCAPG, TTK, CDC20, and AURKB), which could account for the ensuing proliferation arrest. Many of these genes were also underexpressed in leukemic cells from the blood or myeloma cells from an extramedullary site compared with their expression in the aspirates. Taken together, our results exhibited mitotic and transcriptional gene subsets where their expression appears to be coordinated and niche dependent. In addition, the genes induced during culture specified a variety of angiogenic factors (e.g. interleukin-8 and CXCL-5) and extracellular matrix proteins (e.g. osteopontin and fibronectin) probably released by the tumor cells while generating their favored microenvironment.
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PMID:The proliferation arrest of primary tumor cells out-of-niche is associated with widespread downregulation of mitotic and transcriptional genes. 2407 79

Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. In this study, we constructed free-scale gene co-expression networks using weighted gene co-expression network analysis (WGCNA). The gene expression profiles of GSE25055 were downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers associated with breast cancer progression. GSE42568 was downloaded for validation. A total of 9 modules were established via the average linkage hierarchical clustering. We identified 3 hub genes (ASPM, CDC20, and TTK) in the significant module (R 2 = 0.52), which were significantly correlated with poor prognosis both in test and validation datasets. In the datasets GSE25055 and GSE42568, higher expression levels of ASPM, CDC20, and TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demonstrated that their protein levels were higher in tumor samples. According to gene set enrichment analysis, 4 commonly enriched pathways were identified: cell cycle pathway, DNA replication pathway, homologous recombination pathway, and P53 signaling pathway. In addition, strong correlations were found among their expression levels. In conclusion, our WGCNA analysis identified candidate prognostic biomarkers for further basic and clinical researches.
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PMID:Overexpression of ASPM, CDC20, and TTK Confer a Poorer Prognosis in Breast Cancer Identified by Gene Co-expression Network Analysis. 3110 47

One of the most common sites of extra-thoracic distant metastasis of nonsmall-cell lung cancer is the brain. Our study was performed to discover genes associated with postoperative brain metastasis in operable lung adenocarcinoma (LUAD). RNA seq was performed in specimens of primary LUAD from seven patients with brain metastases and 45 patients without recurrence. Immunohistochemical (IHC) assays of the differentially expressed genes were conducted in 272 surgical-resected LUAD specimens. LASSO Cox regression was used to filter genes related to brain metastasis and construct brain metastasis score (BMS). GSE31210 and GSE50081 were used as validation datasets of the model. Gene Set Enrichment Analysis was performed in patients stratified by risk of brain metastasis in the TCGA database. Through the initial screening, eight genes (CDK1, KPNA2, KIF11, ASPM, CEP55, HJURP, TYMS and TTK) were selected for IHC analyses. The BMS based on protein expression levels of five genes (TYMS, CDK1, HJURP, CEP55 and KIF11) was highly predictive of brain metastasis in our cohort (12-month AUC: 0.791, 36-month AUC: 0.766, 60-month AUC: 0.812). The validation of BMS on overall survival of GSE31210 and GSE50081 also showed excellent predictive value (GSE31210, 12-month AUC: 0.682, 36-month AUC: 0.713, 60-month AUC: 0.762; GSE50081, 12-month AUC: 0.706, 36-month AUC: 0.700, 60-month AUC: 0.724). Further analyses showed high BMS was associated with pathways of cell cycle and DNA repair. A five-gene predictive model exhibits potential clinical utility for the prediction of postoperative brain metastasis and the individual management of patients with LUAD after radical resection.
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PMID:Development and validation of a five-gene model to predict postoperative brain metastasis in operable lung adenocarcinoma. 3218 77

Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (TTK, HMMR, ASPM, CDCA8, KIF2C, CCNA2, CCNB2, and MKI67) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.
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PMID:Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma. 3318 19