Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Yuan, Lushun; Chen, Liang; Qian, Kaiyu; Qian, Guofeng; Wu, Chin‐Lee; Wang, Xinghuan; Xiao, Yu

doi:10.1016/j.gdata.2017.10.006

Cited by 85 publications

(70 citation statements)

References 31 publications

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“…Similar to IPI score, these prognostic models also did not include tumor-specific biological factors, and can therefore not be used for targeted therapies (31,34). nm23, TOP2A, MUM-1 and VEGF are very common markers used to estimate the progression, infiltration and metastasis ability of many malignant tumors, particularly carcinomas (14,(40)(41)(42)(43)(44). Although their prognostic role in various carcinomas has been widely investigated over time, such as breast (45), pancreatic (12) and renal cell carcinoma (42), to the best of our knowledge, a limited number of have focused on their value in lymphomas.…”

Section: Discussionmentioning

confidence: 99%

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T‑cell lymphoma, not otherwise specified

et al. 2019

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Section: Discussionmentioning

confidence: 99%

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T‑cell lymphoma, not otherwise specified

et al. 2019

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“…As the method used by previous studies [11,12], based on mRNA expression, the scale-free gene modules of co-expression were constructed by WGCNA. To ensure the reliability of the co-expression network, hierarchical clustering was performed based on Euclidean distance and 2 outlier samples were removed.…”

Section: Construction Of Co-expression Module Networkmentioning

confidence: 99%

“…As the method used by previous studies [12,13], the aberrantly expressed mRNAs in the key gene modules were selected to perform GO function analysis and KEGG pathway analysis. For GO analysis, the corresponding genes were divided into by biological process (BPs) analysis.…”

Section: Enrichment Of Key Co-expression Modules Analysismentioning

confidence: 99%

A key genomic signature associated with lymphovascular invasion in head and neck squamous cell carcinoma

Zhang

Jiang

Xie

et al. 2020

Preprint

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Objective: Lymphovascular invasion (LOI), a key pathological feature of head and neck squamous cell carcinoma (HNSCC), predicts poor survival. However, the associated clinical characteristics remain uncertain, and the molecular mechanisms are largely unknown. Methods: Weighted gene co-expression network analysis was performed to construct gene co-expression networks and investigate the relationship between modules and LOI clinical trait. Functional enrichment and KEGG pathway enrichment analysis were performed for differentially expressed genes using DAVID database. The protein-protein interaction network was constructed using Cytoscape software, and module analysis was performed using MCODE. Prognosis role and expression analysis was further validated by survival analysis, GEPIA analysis and HPA database. Multivariable Cox regression analysis was used to establish a prognostic risk formula and the areas under the receiver operating characteristic curve (AUCs) were used to evaluate prediction efficiency. And the potential targeted LOI molecular agents were identiﬁed with DrugBank. Results: 10 co-expression modules in two key modules (turquoise and pink) associated with tumor LOI were identiﬁed. Functional enrichment and KEGG analysis identified turquoise and pink modules played signiﬁcant roles in the progression of HNSCC. The seven hub genes (CNFN, KIF18B, KIF23, PRC1, CCNA2, DEPDC1 and TTK) in two modules were identiﬁed and validated by survival analysis and expression analysis. Multivariable Cox regression analysis was used to establish a prognostic risk formula (risk score = EXP DEPDC1 * 0.32636 + EXP CNFN * (-0.07544). The low-risk group had a better OS than the high-risk group ( P <0.001), and the areas under the receiver operating characteristic curve (AUCs) of 1-, 3- and 5-year OS were 0.582, 0.634 and 0.636, respectively. Eight small molecular agents, including XL844, AT7519, AT9283, Alvocidib, Nelarabine, Benzamidine, L-Glutamine, and Zinc, may be a candidate drug for treating LOI ( P < 0.05). Conclusions: Our research revealed the two-mRNA signature could serve as an independent biomarker to predict LOI risk, which provide some new insights into LOI of HNSCC. Additionally, the small molecular agents may be a candidate drug for treating LOI.

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“…For example, Li et al used RNA‐Seq data and compared gene expressions among cancer samples considering smoking and nonsmoking histories. More recently, systems biology approaches have been used to analyze the RNA‐Seq data, to extract coexpressed genes and explore relationships between gene sets and clinical features . To our knowledge, the number of research are limited focusing on the coexpression network analysis of the NSCLC considering clinical information in relation to the smoking history and the cancer stages.…”

Section: Introductionmentioning

confidence: 99%

Considering smoking status, coexpression network analysis of non–small cell lung cancer at different cancer stages, exhibits important genes and pathways

Mortezaei

Tavallaei

Hosseini

2019

J of Cellular Biochemistry

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Non–small cell lung cancer (NSCLC) is the most common subtype of lung cancer among smokers, nonsmokers, women, and young individuals. Tobacco smoking and different stages of the NSCLC have important roles in cancer evolution and require different treatments. Existence of poorly effective therapeutic options for the NSCLC brings special attention to targeted therapies by considering genetic alterations. In this study, we used RNA‐Seq data to compare expression levels of RefSeq genes and to find some genes with similar expression levels. We utilized the “Weighted Gene Co‐expression Network Analysis” method for three different datasets to create coexpressed genetic modules having relations with the smoking status and different stages of the NSCLC. Our results indicate seven important genetic modules having important associations with the smoking status and cancer stages. Based on investigated genetic modules and their biological explanation, we then identified 13 newly candidate genes and 7 novel transcription factors in association with the NSCLC, the smoking status, and cancer stages. We then examined those results using other datasets and explained our results biologically to illustrate some important genes in relation with the smoking status and metastatic stage of the NSCLC that can bring some crucial information about cancer evolution. Our genetic findings also can be used as some therapeutic targets for different clinical conditions of the NSCLC.

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Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Cited by 85 publications

References 31 publications

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T‑cell lymphoma, not otherwise specified

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T‑cell lymphoma, not otherwise specified

A key genomic signature associated with lymphovascular invasion in head and neck squamous cell carcinoma

Considering smoking status, coexpression network analysis of non–small cell lung cancer at different cancer stages, exhibits important genes and pathways

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