Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis.

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality in the world, in which colon adenocarcinoma (COAD) is the most common histological subtype of CRC. In this study, our aim is to identify gene modules and representative candidate biomarkers for clinical prognosis of patients with COAD, and help to predict prognosis and reveal the mechanisms of cancer progression. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network and identify gene modules correlated with TNM clinical staging of COAD patients. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the module gene. Protein-protein interaction (PPI) network and hub gene identification were explored with Cytoscape software. Finally, the hub gene mRNA level was validated in Oncomine database. Five gene modules, related with the pathological TNM stage, were constructed, and the gene module was enriched in cell proliferation, invasion and migration related GO terms and metabolic related KEGG pathways. A total of top 10 hub genes was identified, and in which six of the hub genes show a significant up-regulation in COAD as compared to normal tissue, including IVL, KRT16, KRT6C, KRT6A, KRT78 and SBSN. In conclusion, we identified five gene modules and six candidate biomarkers correlated with the TNM staging of COAD patients. These findings may help us to understand the tumor progression of COAD and provide prognostic biomarkers as well as therapeutic targets.

Wang H ，Liu J ，Li J ，Zang D ，Wang X ，Chen Y ，Gu T ，Su W ，Song N ... -  《Cancer Genetics》

Identification of hub genes and construction of transcriptional regulatory network for the progression of colon adenocarcinoma hub genes and TF regulatory network of colon adenocarcinoma.

The aim of this study was to identify key genes related to the progression of colon adenocarcinoma (COAD), and to investigate the regulatory network of hub genes and transcription factors (TFs). Dataset GSE20916 including 44 normal colon, 55 adenoma, and 36 adenocarcinoma tissue samples was used to construct co-expression networks via weighted gene co-expression network. Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis for the objective module were performed using the online Database for Annotation, Visualization and Integrated Discovery. Hub genes were identified by taking the intersection of differentially expressed genes between dataset GSE20916 and GSE39582 and validated using The Cancer Genome Atlas (TCGA) database. The correlations between microRNA (miRNA) and hub genes were analyzed using the online website StarBase. Cytoscape was used to establish a regulatory network of TF-miRNA-target gene. We found that the orange module was a key module related to the tumor progression in COAD. In datasets GSE20916 and GSE39582, a total of eight genes (BGN, SULF1, COL1A1, FAP, THBS2, CTHRC1, COL5A2, and COL1A2) were selected, which were closely related with patients' survivals in TCGA database and dataset GSE20916. COAD patients with higher expressions of each hub gene had a worse prognosis than those with lower expressions. A regulatory network of TF-miRNA-target gene with 144 TFs, 26 miRNAs, and 7 hub genes was established, including model KLF11-miR149-BGN, TCEAL6-miR29B2-COL1A1, and TCEAL6-miR29B2-COL1A2. In conclusion, during the progression of COAD, eight core genes (BGN, SULF1, COL1A1, FAP, THBS2, CTHRC1, COL5A2, and COL1A2) play vital roles. Regulatory networks of TF-miRNA-target gene can help to understand the disease progression and optimize treatment strategy.

Wei S ，Chen J ，Huang Y ，Sun Q ，Wang H ，Liang X ，Hu Z ，Li X ... -  《-》

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma.

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA). From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built. Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability. The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Yang M ，He H ，Peng T ，Lu Y ，Yu J ... -  《-》

Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis.

Colon adenocarcinoma (COAD) is one of the most common cancers, and its carcinogenesis and progression is influenced by multiple long non-coding RNAs (lncRNA), especially through the miRNA sponge effect. In this study, more than 4000 lncRNAs were re-annotated from the microarray datasets through probe sequence mapping to obtain reliable lncRNA expression profiles. As a systems biology method for describing the correlation patterns among genes across microarray samples, weighted gene co-expression network analysis was conducted to identify lncRNA modules associated with the five stepwise stages from normal colonic samples to COAD (n = 94). In the most relevant module (R2  = -0.78, P = 4E-20), four hub lncRNAs were identified (CTD-2396E7.11, PCGF5, RP11-33O4.1, and RP11-164P12.5). Then, these four hub lncRNAs were validated using two other independent datasets including GSE20916 (n = 145) and GSE39582 (n = 552). The results indicated that all hub lncRNAs were significantly negatively correlated with the three-stage colonic carcinogenesis, as well as TNM stages in COAD (one-way analysis of variance P < 0.05). Kaplan-Meier survival curve showed that patients with higher expression of each hub lncRNA had a significantly higher overall survival rate and lower relapse risk (log-rank P < 0.05). In conclusion, through co-expression analysis, we identified and validated four key lncRNAs in association with the carcinogenesis and progression of COAD, and these lncRNAs might have important clinical implications for improving the risk stratification, therapeutic decision and prognosis prediction in COAD patients.

Jiang S ，Tan B ，Zhang X 《-》

Gene expression and methylation profiles identified CXCL3 and CXCL8 as key genes for diagnosis and prognosis of colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most common malignant tumors with high morbidity and mortality rates worldwide. Due to the poor clinical outcomes, it is indispensable to investigate novel biomarkers for the diagnosis and prognosis of COAD. The aim of this study is to explore key genes as potential biomarkers for the diagnosis and prognosis of COAD for clinical utility. Gene expression profiles (GSE44076 and GSE44861) and gene methylation profile (GSE29490) were analyzed to identify the aberrantly methylated-differentially expressed genes by R language and Perl software. Function enrichments were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Moreover, hub genes were identified through protein-protein interaction (PPI) network. Besides, key genes were found by the module analysis and The Cancer Genome Atlas (TCGA) survival analysis. Finally, TCGA data and quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the key genes involved in COAD. Our study found two hypomethylation-high-expression genes (CXCL3 and CXCL8) in COAD tissues compared with the adjacent normal tissues. These results were also confirmed by RT-qPCR with 25 pairs of COAD and adjacent normal tissues. Meanwhile, low expression of the two genes was associated with poor survival in patients with COAD. CXCL3 and CXCL8 may serve as key genes in the diagnosis and prognosis for COAD.

Zhao QQ ，Jiang C ，Gao Q ，Zhang YY ，Wang G ，Chen XP ，Wu SB ，Tang J ... -  《-》