Identification of The Prognostic Genes For Early Basal-Like Breast Cancer With Weighted Gene Co-Expression Network Analysis

Abstract Background: Breast cancer (BC) has become the leading cause of death for women's malignancies and increasingly threatens the health of women worldwide. However, the basal-like BC is lack of effective targeted drugs. Therefore, biomarkers that related to the prognosis of early breast cancer need to be found.Methods: The RNA-seq data of 87 cases of early basal-like BC and 111 cases of normal breast tissue from The Cancer Genome Atlas (TCGA) were explored by Weighted Gene Co-Expression Network Analysis (WGCNA)method and Limma package. Then intersected genes (IGs) were identified and hub genes were selected by Maximal Clique Centrality method. The prognostic effect of the hub genes was also evaluated in early basal-like BC. Results: A total of 601 IGs were identified in this study. APPI network was constructed and top 10 hub genes were selected, namely cyclin B1 (CCNB1), cyclin A2 (CCNA2), cyclin dependent kinase 1 (CDK1), cell division cycle 20 (CDC20), DNA topoisomerase II alpha (TOP2A), BUB1 mitotic checkpoint serine/threonine kinase (BUB1), aurora kinase B (AURKB), cyclin B2 (CCNB2), kinesin family member 11 (KIF11), and assembly factor for spindle microtubules (ASPM). Only AURKB was found to be significant with the overall prognosis of early basal-like BC. The immune cells infiltration analysis displayed that the infiltration numbers of CD4+ T cell and naïve CD8+ T cell were positively correlated with AURKB expression level, while that of naïve B cell and macrophage M2 cell were negatively correlated with AURKB expression level in basal-like BC.Conclusion: AURKB might be a potential prognostic indicator in early basal-like BC.

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Hub Gene Identification and Prognostic Model Establishment for Patients with HBV-related Hepatocellular Carcinoma

10.21203/rs.3.rs-269454/v1 ◽

2021 ◽

Author(s):

Lianmei Wang ◽

Jing Liu ◽

Zhong Xian ◽

Jingzhuo Tian ◽

Chunying Li ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Expression Profiles ◽

Expression Patterns ◽

Cyclin B1 ◽

Mitotic Checkpoint ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Hub Genes

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~ 50% of HCC cases. Establishment of a prognostic model is pivotal for clinical therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. DNA topoisomerase II alpha (TOP2A), Disks large-associated protein 5 (DLGAP5), RAD51 associated protein 1 (RAD51AP1), ZW10 interactor (ZWINT), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Cyclin B2 (CCNB2), Aurora kinase A (AURKA), and Cyclin-dependent kinase 1 (CDK1) were identified as the top-ten hub genes. These hub-genes were verified by the Liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP) project, The Cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. FOXM1 and CDK1 were found to be prognostic-related molecules for HBV-HCC patients. The expression patterns of FOXM1 and CDK1were consistently in human and mouse. Furthermore, a nomogram model based on histology grade, pathology stage, sex and, expression of FOXM1 and CDK1 was built to predict the prognosis for HBV–HCC patients. The nomogram model could be used to predict the prognosis of HBV-HCC cases.

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Identification of Hub Genes and Pathways With Immune Cell Infiltration in Cholangiocarcinoma by Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-135007/v1 ◽

2020 ◽

Author(s):

Ye Yan ◽

Lihao Zhao ◽

Huafang Su ◽

Gang Li ◽

Sujing Jiang

Keyword(s):

T Cell ◽

Network Analysis ◽

Protein Interactions ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Cd4 T Cell ◽

Cell Infiltration ◽

Hub Genes ◽

Kegg Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most frequent tumor in biliary tract and the second most common primary tumor of the liver. However, the molecular biomarkers in tumorigenesis of CCA remain unclear. Therefore, we aim to explore the underlying mechanisms of progression and screen for novel prognostic biomarkers and therapeutic targets.Method: The genes expression sequencing of normal and CCA samples were selected from the Gene Expression Omnibus database (GEO) and the Cancer Genome Atlas (TCGA). The weighted gene co-expression network analysis (WGCNA) was used to build the co-expression network. Gene ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied for the selected genes. The protein-protein interactions of these modules are visualized using cytoscape. Furthermore, the significance of these genes was confirmed by survival analysis. The tumor immune estimation resource (TIMER) was presented to investigate assess the relationship between the hub genes and the immune cells infiltration.Results: Ten hub genes with CCA development were identified in this study containing CDC20, CCNA2, TOP2A, AURKA, CCNB2, UBE2C, NUSAP1, PRC1, PTTG1 and MCM4. Key genes of CCNB2 and PTTG1 might be potential prognostic biomarkers for CCA. GO analysis indicated the enrichment terms of nuclear division, collagen-containing extracellular matrix and cell adhesion molecule binding. KEGG analysis demonstrated that the cell cycle pathway was the significantly altered pathway. There was a negative correlation between TOP2A, AURKA, CCNB2, PRC1 expression and the infiltration of CD4+T cell, while MCM4 expression was positively associated with the infiltration of neutrophil cells. No significant association between CDC20 levels and CD4+T cell, CD8+T cell, B cell, neutrophil, macrophage, or dendritic cell infiltration in CCA, the same as CCNA2, UBE2C, NUSAP1, PTTG1 respectively.Conclusion: These candidate genes may involve in the development of CCA. Our results offer novel insights into the etiology, prognosis, and treatment of CCA.

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Gene and lncRNA co-expression network analysis reveals novel ceRNA network for triple-negative breast cancer

Scientific Reports ◽

10.1038/s41598-019-51626-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Kehao Le ◽

Hui Guo ◽

Qiulei Zhang ◽

Xiaojuan Huang ◽

Ming Xu ◽

...

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Triple Negative Breast Cancer ◽

Molecular Mechanisms ◽

Triple Negative ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Hub Genes ◽

Protein Coding ◽

Cerna Network

Abstract Breast cancer is the most frequently diagnosed malignancy among women, and triple-negative breast cancer (TNBC) is a highly aggressive subtype. Increasing evidence has shown that lncRNAs are involved in tumor growth, cell-cycle, and apoptosis through interactions with miRNAs or mRNAs. However, there is still limited data on ceRNAs involved in the molecular mechanisms underlying TNBC. In this study, we applied the weighted gene co-expression network analysis to the existing microarray mRNA and lncRNA expression data obtained from the breast tissues of TNBC patients to find the hub genes and lncRNAs involved in TNBC. Functional enrichment was performed on the module that correlated with Ki-67 status the most (Turquoise module). The hub genes in the Turquoise module were found to be associated with DNA repair, cell proliferation, and the p53 signaling pathway. We performed co-expression analysis of the protein-coding and lncRNA hub genes in the Turquoise module. Analysis of the RNA-seq data obtained from The Cancer Genome Atlas database revealed that the protein-coding genes and lncRNAs that were co-expressed were also differentially expressed in the TNBC tissues compared with the normal mammary tissues. On the basis of establishing the ceRNA network, two mRNAs (RAD51AP1 and TYMS) were found to be correlated with overall survival in TNBC. These results suggest that TNBC-specific mRNA and lncRNAs may participate in a complex ceRNA network, which represents a potential therapeutic target for the treatment of TNBC.

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Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Biology ◽

10.3390/biology10100957 ◽

2021 ◽

Vol 10 (10) ◽

pp. 957

Author(s):

Thong Ba Nguyen ◽

Duy Ngoc Do ◽

Tung Nguyen-Thanh ◽

Vinay Bharadwaj Tatipamula ◽

Ha Thi Nguyen

Keyword(s):

Liver Cancer ◽

Molecular Mechanisms ◽

Normal Liver ◽

Cyclin B1 ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Regulatory Subunit ◽

Hub Genes ◽

Topoisomerase Ii Alpha ◽

Liver Tissues

Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein–protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2, NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer.

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The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms22041820 ◽

2021 ◽

Vol 22 (4) ◽

pp. 1820

Author(s):

Anna Makuch-Kocka ◽

Janusz Kocki ◽

Anna Brzozowska ◽

Jacek Bogucki ◽

Przemysław Kołodziej ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Clinical Data ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Lymphatic Vessels ◽

The Cancer Genome Atlas ◽

Expression Level ◽

Cancer Tissue ◽

Expression Levels

The BIRC (baculoviral IAP repeat-containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

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Co‑expression Network Analysis by WGCNA and Identify Potential Prognostic Markers Associated with Lung Metastasis in Breast Cancer

10.21203/rs.3.rs-182567/v1 ◽

2021 ◽

Author(s):

xixun zhang

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Lung Metastasis ◽

Prognostic Markers ◽

Clinical Information ◽

Preventive Treatment ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Breast Cancer Patients ◽

Kaplan Meier

Abstract Backgroud: Breast cancer (BC) is an aggressive cancer with a high percentage recurrence and metastasis. As one of the most common distant metastasis organ in breast cancer, lung metastasis has a worse prognosis than that of liver and bone. Therefore, it’s important to explore some potential prognostic markers associated with the lung metastasis in breast cancer for preventive treatment. Methods: In our study, transcriptomic data and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Co-expression modules was built by Weighted gene co-expression network analysis (WGCNA) to find out the royalbule modules which is significantly associated with lung metastasis in breast cancer. Then, co-expression genes were analyzed for functional enrichment. Furthermore, the prognostic value of these genes was assessed by GEPIA Database and Kaplan-Meier Plotter. Results: Results showed that the hub genes, LMNB and CDC20, were up-regulated in breast cancer and indicated worse survival. Therefore, we speculate that these two genes play crucial roles in the process of lung metastasis in breast cancer, and can be used as potential prognostic markers in lung metastasis of breast cancer. Conclusion: Collectively, our study identified two potential key genes in the lung metastasis of breast cancer, which might be applied as the prognostic markers of the precise treatment in breast cancer with lung metastasis.

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Identification of novel mRNA-miRNA-lncRNA competing endogenous RNA network associated with prognosis of breast cancer

Epigenomics ◽

10.2217/epi-2019-0209 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1501-1518 ◽

Cited By ~ 6

Author(s):

Guansheng Zhong ◽

Weiyang Lou ◽

Minya Yao ◽

Chengyong Du ◽

Haiyan Wei ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Cerna Network ◽

Competing Endogenous Rna Network ◽

Cancer Genome Atlas ◽

Genome Atlas

Aim: To identify novel competing endogenous RNA (ceRNA) network related to patients prognosis in breast cancer. Materials & methods: Dysregulated mRNA based on intersection of three Gene Expression Omnibus and The Cancer Genome Atlas datasets were analyzed by bioinformatics. Results: In total 72 upregulated and 208 downregulated genes were identified. Functional analysis showed that some pathways related to cancer were significantly enriched. By means of stepwise reverse prediction and validation from mRNA to lncRNA, 19 hub genes, nine key miRNA and four key lncRNAs were identified by expression and survival analysis. Ultimately, the coexpression analysis identified RRM2-let-7a-5p- SNHG16/ MAL2 as key ceRNA subnetwork associated with prognosis of breast cancer. Conclusion: We successfully constructed a novel ceRNA network, among which each component was significantly associated with breast cancer prognosis.

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Abstract 4040: PD-L1 expression level and CD8+/FoxP3+T cell ratio in breast cancer and prognosis

10.1158/1538-7445.sabcs18-4040 ◽

2019 ◽

Author(s):

Masaya Hattori ◽

Galina Khramtsova ◽

Lise Sveen ◽

Toshio Yoshimatsu ◽

Dezheng Huo ◽

...

Keyword(s):

Breast Cancer ◽

T Cell ◽

Expression Level ◽

Cell Ratio

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SO004IDENTIFICATION OF HUB GENES, FOCUSED ON CHEMOKINES AND CHEMOKINE RECEPTORS, ASSOCIATED WITH THE DECLINE OF RENAL FUCTION OF DIABETIC KIDNEY DISEASE BY WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa139.so004 ◽

2020 ◽

Vol 35 (Supplement_3) ◽

Author(s):

Songtao Feng ◽

Bicheng Liu ◽

Linli Lv ◽

Gao Yueming ◽

Di Yin ◽

...

Keyword(s):

Renal Function ◽

Network Analysis ◽

Kidney Disease ◽

T Cell Activation ◽

Chemokine Receptors ◽

Diabetic Kidney Disease ◽

Expression Level ◽

Hub Genes ◽

Diabetic Kidney ◽

Gene Modules

Abstract Background and Aims The fact that activation of the innate immune system and chronic inflammation are closely involved in the pathogenesis of diabetic Kidney disease (DKD). Recent studies have suggested the inflammatory process plays a crucial role in the progression of DKD. Identifying novel inflammatory molecules closely related to the decline of renal function is of significance in diagnosing and predicting the progression of DKD. The weighted gene co-expression network analysis (WGCNA) algorithm represents a novel systems biology method that provide the approach of association between gene modules and clinical traits to find the genes involvement into the certain phenotypic trait. The goal of this study was to identify hub genes and their roles in DKD from the gene sets associated with the decline of renal function by WGCNA. Method The Gene Expression Omnibus (GEO) database and “Nephroseq” website were searched and transcriptome study from DN biopsies with well-established clinical phenotypic data were selected for analysis. Next, we constructed a weighted gene co-expression network and identified modules negatively correlated with eGFR by WGCNA in the data of glomerular tissue. Functional annotations of the genes in modules negatively correlated with eGFR were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Through protein-protein interaction (PPI) analysis and hub gene screening, the hub genes were obtained. Furthermore, we compared the expression level of hub genes between DKD and normal control and drew ROC curves to determine the diagnosis value to DKD of these genes. Results The microarray-based expression datasets GSE30528 were screened out for analysis, which included glomeruli tissue of 9 cases of DKD and 13 cases of control. This microarray platform represented the transcriptome profile of 12411 well-characterized genes. Using WGCNA, a total of 19 gene modules were identified. Then module eigengene were analyzed for correlation with clinical traits of age, sex, ethnicity and eGFR and the “MEhoneydew1” module showed negative associated with eGFR (r=-0.58). GO functional annotation showed that these 551 genes in the “MEhoneydew1” module mainly enriched in the T cell activation. KEGG annotation showed mainly enriched in chemokine signaling pathway. Except for C3, top 10 hub genes, CCR5, CXCR4, CCR7, CCL5, CXCL8, CCR2, CCR1, CX3CR1, C3AR1 and C3, are all members of chemokines or chemokine receptors. Furthermore, we compared the expression level of these 9 genes between DKD and control, and found that all of these 9 genes increased in the DKD group, and the differences of 6 genes, CCR5, CCR7, CCL5, CCR2, CCR1, C3AR1, were of statistical significance. Linear correlation analysis showed that the expression of these 6 genes was negatively correlated with eGFR, and the ROC curve showed that the area under the curve could reach 0.812∼1.0. Conclusion We identified a panel of 6 hub genes focused on chemokines and chemokine receptors critical for decline of renal function of DKD using WGCNA. These genes may serve as biomarkers for diagnosis/prognosis and as putative novel therapeutic targets for DKD.

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Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.628136 ◽

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.

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