scholarly journals Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis

Aging ◽  
2019 ◽  
Vol 11 (13) ◽  
pp. 4736-4756 ◽  
Author(s):  
Zhen-yu Song ◽  
Fan Chao ◽  
Zhiyuan Zhuo ◽  
Zhe Ma ◽  
Wenzhi Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Rank Aggregation ◽  
Hub Genes

scholarly journals Development and Validation of Novel Biomarkers Related to M2 Macrophages Infiltration by Weighted Gene Co-Expression Network Analysis in Prostate Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Ning Xu ◽  
Ru-Nan Dong ◽  
Ting-Ting Lin ◽  
Tian Lin ◽  
Yun-Zhi Lin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
Cancer Genomics ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Castration Resistance ◽  
Hub Genes

M2-tumor-associated macrophages (TAMs) work as a promoter in the processes of bone metastases, chemotherapy resistance, and castration resistance in prostate cancer (PCa), but how M2-TAMs affect PCa has not been fully understood. In this study, we analyzed the proportion of tumor-infiltrating immune cells using the CIBERSORT algorithm, based on samples from the Cancer Genome Atlas database. Then we performed weighted gene co-expression network analysis to examine the modules concerning infiltrated M2-TAMs. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was constructed. The International Cancer Genomics Consortium cohort was used as a validation cohort. The red module showed the most correlation with M2-TAMs in PCa. Biological processes and pathways were mainly associated with the immune-related processes, as revealed by functional annotation. Four hub genes were screened: ACSL1, DLGAP5, KIF23 and NCAPG. Further validation showed that the four hub genes had a higher expression level in tumor tissues than that in normal tissues, and they were good prognosis biomarkers for PCa. In conclusion, these findings contribute to understanding the underlying molecular mechanisms of how M2-TAMs affect PCa, and looking for the potential biomarkers and therapeutic targets for PCa patients.

scholarly journals Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Dechao Wei ◽  
Qiankun Li ◽  
Xiaobing Yang ◽  
Yili Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Single Gene ◽  
Characteristic Curve ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Hub Genes ◽  
Gene Set

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e–26) and tumor stage (r = 0.38, p = 2e–17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

scholarly journals CENPL, ISG20L2, LSM4 and MRPL3 Are Four Novel Hub Genes Involved in Breast Cancer Development and Progression

2020 ◽  
Author(s):  
Jinbao Yin ◽  
Chen Lin ◽  
Meng jiang ◽  
Xinbing Tang ◽  
Danlin Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Candidate Genes ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Rank Aggregation ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Ppi Networks ◽  
Kaplan Meier

Abstract BackgroundAs a highly prevalent tumor disease worldwide, Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remain an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil its mechanism and provide novel diagnostic and prognostic markers for breast cancer.MethodsIn this study, we systematically integrated multiple bioinformatic methods, including robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), ROC and Kaplan-Meier analyses, DNA methylation analyses and genomic mutation analyses, GSEA and GSVA, based on ten mRNA datasets to identify and investigate novel hub genes involved in breast cancer. In parallel, RNA in situ detection technology was applied to validate those novel hub gene.ResultsEZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4 and MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among those five hub genes, many studies on EZH2 gene in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These novel four hub genes were up-regulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. The preliminary analysis revealed those novel hub genes are four potentially candidate genes for further exploring the molecular mechanism of breast cancer.ConclusionWe identify four novel hub genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. Those hub genes are four potentially candidate genes served as promising candidate diagnostic biomarkers and prognosis predictors for breast cancer, and their exact functional mechanisms in breast cancer deserve further in-depth study.

scholarly journals Comprehensive Analysis of mRNA Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Zaizai Cao ◽  
Yinjie Ao ◽  
Yu Guo ◽  
Shuihong Zhou
Keyword(s):  
Network Analysis ◽  
Head And Neck ◽  
Cell Cancer ◽  
Rank Aggregation ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Head and neck squamous cell cancer (HNSCC) is the sixth most common cancer in the world; its pathogenic mechanism remains to be further clarified. Methods. Robust rank aggregation (RRA) analysis was utilized to identify the metasignature dysregulated genes, which were then used for potential drug prediction. Weighted gene coexpression network analysis (WGCNA) was performed on all metasignature genes to find hub genes. DNA methylation analysis, GSEA, functional annotation, and immunocyte infiltration analysis were then performed on hub genes to investigate their potential role in HNSCC. Result. A total of 862 metasignature genes were identified, and 6 potential drugs were selected based on these genes. Based on the result of WGCNA, six hub genes (ITM2A, GALNTL1, FAM107A, MFAP4, PGM5, and OGN) were selected ( GS > 0.1 , MM > 0.75 , GS p value < 0.05, and MM p value < 0.05). All six genes were downregulated in tumor tissue ( FDR < 0.01 ) and were related to the clinical stage and prognosis of HNSCC in different degrees. Methylation analysis showed that the dysregulation of ITM2A, GALNTL1, FAM107A, and MFAP4 may be caused by hypermethylation. Moreover, the expression level of all 6 hub genes was positively associated with immune cell infiltration, and the result of GSEA showed that all hub genes may be involved in the process of immunoregulation. Conclusion. All identified hub genes could be potential biomarkers for HNSCC and provide a new insight into the diagnosis and treatment of head and neck tumors.

scholarly journals Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

2020 ◽  
Vol 8 (21) ◽  
pp. 1348-1348
Author(s):  
Zetao Ma ◽  
Zhida Shen ◽  
Yingchao Gong ◽  
Jiaqi Zhou ◽  
Xiaoou Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Viral Myocarditis ◽  
Hub Genes

scholarly journals Novel Gene Signatures Predictive of Patient Recurrence-Free Survival and Castration Resistance in Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 917
Author(s):  
Jun A ◽  
Baotong Zhang ◽  
Zhiqian Zhang ◽  
Hailiang Hu ◽  
Jin-Tang Dong
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Treatment Decision ◽  
Rank Aggregation ◽  
Lymph Node Status ◽  
Calibration Plot ◽  
Recurrence Free Survival ◽  
Castration Resistance ◽  
Free Survival

Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. Here, we applied the Robust Rank Aggregation (RRA) method to PCa transcriptome profiles and identified 287 genes differentially expressed between localized castration-resistant PCa (CRPC) and hormone-sensitive PCa (HSPC). Least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses of the 287 genes developed a 6-gene signature predictive of RFS in PCa. This signature included NPEPL1, VWF, LMO7, ALDH2, NUAK1, and TPT1, and was named CRPC-derived prognosis signature (CRPCPS). Interestingly, three of these 6 genes constituted another signature capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and remained valid in patients stratified by tumor stage, Gleason score, and lymph node status. The signature also predicted overall survival and metastasis-free survival. The signature’s robustness was demonstrated by the C-index (0.55–0.74) and the calibration plot in all nine cohorts and the 3-, 5-, and 8-year area under the receiver operating characteristic curve (0.67–0.77) in three cohorts. The nomogram analyses demonstrated CRPCPS’ clinical applicability. The CRPCPS thus appears useful for RFS prediction in PCa.

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