scholarly journals Potential Biomarkers of Colon Adenocarcinoma Based on Weighted Gene Co-Expression Network Analysis

2021 ◽  
Author(s):  
Zhongze Cui ◽  
Shuang He ◽  
Feifei Wen ◽  
Xiaoyang Xu ◽  
Yangyang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Colon Cancer ◽  
Network Analysis ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Future Research ◽  
Related Gene

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignancies worldwide. Although a large number of studies have elucidated the aetiology of colorectal cancer, the exact mechanism of colorectal cancer development remains to be determined.To identify key modules and prognostic genes that may be involved in the occurrence and development of COAD, weighted gene coexpression network analysis (WGCNA) and differential expression analysis were performed on datasets GSE41657 and GSE74602 from the Gene Expression Omnibus (GEO) database to screen for prognostic differentially expressed genes. Gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) database for verification.Results: Through WGCNA and DEGs analysis, 439 genes in key functional modules were obtained, and 26 prognostic related genes were finally obtained through prognostic analysis: (1) We screened 5 genes（RPP40, DUSP18, PPRC1, MFSD11 and PDCD11） that have not been studied in COAD.（2）We obtained the most critical module in the occurrence and development of colon cancer and obtained one prognosis-related gene, NUP85, from the most critical module.The relationship between it and tumor immune microenvironment was verified.（3） A prognostic model comprising four coexpressed differential genes was constructed; TIMP1, PMM2, E2F3 and MORC2 were selected as the key prognosis-related genes.Conclusions: （1）As new biomarkers，prognostic genes RPP40, DUSP18, PPRC1, MFSD11 and PDCD11 may be potential therapeutic targets for COAD, and provide new ideas for future research on the mechanism of COAD. （2）NUP85 may be an immune-related gene which was negatively correlated with CD4+ T cell and M2 macrophagesthat plays an important role in inhibiting the occurrence and development of colorectal adenocarcinoma. （3）A Cox proportional risk model based on gene expression can be used to predict the prognosis and survival time of patients with colon cancer.

scholarly journals Construction of a Pseudogene‐associated ceRNA Network and Identification of Prognostic Pseudogene Biomarkers for Colorectal Cancer

2021 ◽  
Author(s):  
Zhuoqi Li ◽  
Jing Zhou ◽  
Liankun Gu ◽  
Baozhen Zhang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Colorectal cancer (CRC) is one of the most common and deadly malignant carcinomas. Many long noncoding RNAs (lncRNA) have been reported to play an important role in the tumorigenesis of CRC by interacting with miRNAs and influencing the expression of some mRNAs through a competing endogenous RNA (ceRNA) network. Pseudogenes are one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks, but there are few studies about pseudogenes in CRC. In this study, total of 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma (COAD) obtained from The Cancer Genome Atlas (TCGA). And a ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis on the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. What’s more, the results were validated by the Gene Expression Omnibus (GEO) database, and quantitative real‐time PCR (qRT‐PCR) in 113 pairs of CRC tissues. In conclusion, this study provides a pseudogene-associated ceRNA network and 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful to predict the survival of CRC patients.

scholarly journals CLCA1 Has a Prognostic Indicator for the Colorectal Cancer by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Author(s):  
jiajng lin ◽  
lingzhi yang ◽  
suyong lin ◽  
zhihua chen ◽  
shaoqin chen
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Colon Cancer ◽  
Network Analysis ◽  
Mrna Expression ◽  
Disease Free Survival ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Hub Gene

Abstract Colorectal cancer (CRC) has become the second most common digestive tract tumor. Even though the means to treat colon cancer have improved, patients prognosis is low due to the lack of accurate molecular targets. Hence, it urgently demanded better biomarkers for prognosis and progression of colon cancer. This study explores the hub gene associated with the prognosis of colorectal cancer and further analyzes the hub gene function. In this study, all genes mRNA expression data were from the cancer genome atlas (TCGA) colon cancer database and the Gene Expression Omnibus (GEO). These databases were used to screen the differentially expressed co-genes between colon cancer tissue and normal tissue. Weighted Gene Co-expression Network Analysis screened out a total of 103 differential co-expression genes (WGCNA). According to the R cluster profile package annotation analysis, these genes biological functions mainly concentrate on energy metabolism. Moreover, in the protein-protein interaction (PPI) network, the CytoHubba plugin of Cytoscape was used to screen out ten genes (CLCA1, ZG16, GUCA2B, GUCA2A, CLCA4, SLC26A3, MS4A12, GCG, SI, and NR1H4). According to the survival analysis results, high expression of CLCA1has better overall survival and disease-free survival in patients with CRC. Simultaneously, the mRNA expression of CLCA1 in normal tissues was higher than that in CRC tissues. Besides, there were significant differences in the expression of CLCA1 in pathological stage, T stage, and M stage. By using a gene set enrichment analysis, we found several considerable enrichment pathways in the high-groups. CIBERSORT analysis for the proportion of TICs revealed that B-cell naive, dendritic cells, plasma cells, and CD4+ T cells were positively correlated with CLCA1 expression, suggesting that CLCA1 might be responsible for the preservation of immune-dominant status for TME. Finally, in the Human Protein Atlas (HPA) database, the protein level of CLCA1 in the colorectal cancer samples decreased, consistent with the down-regulation of the mRNA expression level CLCA1. To sum up, by integrating WGCNA with differential gene expression analysis, this research generated a significant survival correlative gene called CLCA1 that can predict prognosis prediction in colon cancer.

scholarly journals A network view of microRNA and gene interactions in different pathological stages of colon cancer

2019 ◽  
Vol 12 (S7) ◽  
Author(s):  
Jia Wen ◽  
Benika Hall ◽  
Xinghua Shi
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Gene Interactions ◽  
New Strategy ◽  
Pathological Stages

Abstract Background Colon cancer is one of the common cancers in human. Although the number of annual cases has decreased drastically, prognostic screening and translational methods can be improved. Hence, it is critical to understand the molecular mechanisms of disease progression and prognosis. Results In this study, we develop a new strategy for integrating microRNA and gene expression profiles together with clinical information toward understanding the regulation of colon cancer. Particularly, we use this approach to identify microRNA and gene expression networks that are specific to certain pathological stages. To demonstrate the application of our method, we apply this approach to identify microRNA and gene interactions that are specific to pathological stages of colon cancer in The Cancer Genome Atlas (TCGA) datasets. Conclusions Our results show that there are significant differences in network connections between miRNAs and genes in different pathological stages of colon cancer. These findings point to a hypothesis that these networks signify different roles of microRNA and gene regulation in the pathogenesis and tumorigenesis of colon cancer.

scholarly journals Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongfang Jia ◽  
Cheng Chen ◽  
Chen Chen ◽  
Fangfang Chen ◽  
Ningrui Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Gene Expression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Diagnostic Methods ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Hub Genes

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene expression profiles of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to classify BC samples and normal samples. The method proposed in this article triumphs over some of the shortcomings of traditional diagnostic methods and can conduct BC diagnosis more rapidly with high sensitivity and have no radiation. This study first selected the genes most relevant to cancer through weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA). Then it used the protein–protein interaction (PPI) network to screen 23 hub genes. Finally, it used the support vector machine (SVM), decision tree (DT), Bayesian network (BN), artificial neural network (ANN), convolutional neural network CNN-LeNet and CNN-AlexNet to process the expression levels of 23 hub genes. For gene expression profiles, the ANN model has the best performance in the classification of cancer samples. The ten-time average accuracy is 97.36% (±0.34%), the F1 value is 0.8535 (±0.0260), the sensitivity is 98.32% (±0.32%), the specificity is 89.59% (±3.53%) and the AUC is 0.99. In summary, this method effectively classifies cancer samples and normal samples and provides reasonable new ideas for the early diagnosis of cancer in the future.

scholarly journals Development and Validation of a Novel 13-Genes Prognostic Model for Colon Cancer

2021 ◽  
Author(s):  
Duo Yun ◽  
Zhirong Yang
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Prognostic Value ◽  
Risk Model ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment

Abstract Colon cancer is one of the most common malignant tumors in the world. The purpose of this study is to explore the prognostic value of genes in colon cancer. After analyzing gene expression profiles, differential expressed genes between 39 normal tissues and 398 tumor tissues were identified from The Cancer Genome Atlas database. We use Cox and lasso regression to find genes related to prognosis. Through analysis, 13 genes were found to predict the overall survival of colon cancer patients. In addition, the external comparing of gene expression and the single prognostic gene survival analysis were made. Finally, pathway enrichment and mutation status of each gene were also analyzed. After a series of bioinformatics analysis, we select 13 survival-related signature and established a prognostic risk model based on these genes. The prognostic risk model was developed to comprehensively predict the overall survival of colon cancer patients. The prognostic value of the 13-genes (CLDN23,HAND1,IL23A,KLHL35,SIX2,UPK2,HOXC11,KRT6B,SRCIN1,TNNI3,TYRO3,MIR6835,LINC02474) related risk score for each colon cancer patent was calculated to predict the survival. Furthermore, five genes (SIX2 MIR6835 LINC02474 CLDN23 HOXC11) were significantly associated with overall survival (OS). The KEGG pathway enrichment results suggested that most of the pathways are related to the occurrence, metabolism, proliferation and invasion of the tumor cells. It was found that the expression of 13-genes signature can be used as prognostic indicator for colon cancer patients. The 13-genes signature predictive model may help clinicians provide a prognosis and personalized treatment for colon cancer patients.

scholarly journals Identification of the miRNA signature and key genes in colorectal cancer lymph node metastasis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xi Wang ◽  
Guangyu Gao ◽  
Zhengrong Chen ◽  
Zhihao Chen ◽  
Mingxiao Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Expression Profiles ◽  
Bioinformatic Analysis ◽  
The Cancer Genome Atlas ◽  
Node Metastasis ◽  
Novel Mirna ◽  
Potential Biomarker

Abstract Background Because its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC. Methods GSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich (http://www.funrich.org). Furthermore, the mRNA–miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC. Results In total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA–mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples. Conclusion In summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA–mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.

scholarly journals A New Oxaliplatin Resistance-Related Gene Signature With Strong Predicting Ability in Colon Cancer Identified by Comprehensive Profiling

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiu Lin ◽  
Li Luo ◽  
Hua Wang
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Gene Signature ◽  
Single Individual ◽  
Related Gene ◽  
Specificity And Sensitivity

Numerous colon cancer cases are resistant to chemotherapy based on oxaliplatin and suffer from relapse. A number of survival- and prognosis-related biomarkers have been identified based on database mining for patients who develop drug resistance, but the single individual gene biomarker cannot attain high specificity and sensitivity in prognosis prediction. This work was conducted aiming to establish a new gene signature using oxaliplatin resistance-related genes to predict the prognosis for colon cancer. To this end, we downloaded gene expression profile data of cell lines that are resistant and not resistant to oxaliplatin from the Gene Expression Omnibus (GEO) database. Altogether, 495 oxaliplatin resistance-related genes were searched by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. As suggested by functional analysis, the above genes were mostly enriched into cell adhesion and immune processes. Besides, a signature was built based on four oxaliplatin resistance-related genes selected from the training set to predict the overall survival (OS) by stepwise regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. Relative to the low risk score group, the high risk score group had dismal OS (P < 0.0001). Moreover, the area under the curve (AUC) value regarding the 5-year OS was 0.72, indicating that the risk score was accurate in the prediction of OS for colon cancer patients (AUC >0.7). Additionally, multivariate Cox regression suggested that the signature constructed based on four oxaliplatin resistance-related genes predicted the prognosis for colon cancer cases [hazard ratio (HR), 2.77; 95% CI, 2.03–3.78; P < 0.001]. Finally, external test sets were utilized to further validate the stability and accuracy of oxaliplatin resistance-related gene signature for prognosis of colon cancer patients. To sum up, this study establishes a signature based on four oxaliplatin resistance-related genes for predicting the survival of colon cancer patients, which sheds more light on the mechanisms of oxaliplatin resistance and helps identify colon cancer cases with a dismal prognostic outcome.

scholarly journals SELE gene as a characteristic prognostic biomarker of colorectal cancer

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052110043
Author(s):  
Na Li ◽  
Honghe Xiao ◽  
Jiangli Shen ◽  
Ximin Qiao ◽  
Fenjuan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Expression Profiles ◽  
Immunohistochemical Analysis ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Cox Regression Analysis

Objective To investigate the expression and clinical value of the E-selectin gene ( SELE) in colorectal cancer (CRC). Methods Using gene expression profiles and clinicopathological data for patients with CRC from The Cancer Genome Atlas, and tumor and adjacent normal tissues from 31 patients with CRC from Xianyang Central Hospital, we studied the correlation between SELE gene expression and clinical parameters using Kaplan–Meier and Cox proportional hazards regression analyses. Results Higher expression of SELE was significantly associated with a poorer prognosis and shorter survival in patients with CRC. The median expression level of SELE was significantly higher in CRC tissues compared with healthy adjacent tissue. Cox regression analysis showed that the prognosis of CRC was significantly correlated with the expression of SELE. Immunohistochemical analysis also showed that positive expression of E-selectin increased significantly in line with increasing TNM stage. Conclusion: This study confirmed that SELE gene expression is an independent prognostic factor in patients with CRC.

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