Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Guo, JJ; Gu, Yinzhong; Ma, Xiaoyu; Zhang, Lu; Li, Huimin; Yan, Zhongyi; Han, Yali; Xie, Longxiang; Guo, Xiangqian

doi:10.1111/jcmm.15102

Cited by 16 publications

(16 citation statements)

References 37 publications

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“…Secondly, carcinogenesis and progression of tumors are the result of multi-gene dysregulation and different genes can have various effects on tumor prognosis. Although the low-expression genes identified in ACC in the present study are involved in multiple key steps of tumorigenesis and progression, their effects on prognosis may be less than the effects of high-expression genes identified in previous studies (22,59). Compared with other urologic neoplasms, ACC has a low incidence (0.7-2/million), which may lead the insufficiency of datasets and samples.…”

Section: Discussionmentioning

confidence: 56%

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Zhang

Miao

et al. 2020

Exp Ther Med

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The molecular mechanisms of adrenocortical carcinoma (ACC) carcinogenesis and progression remain unclear. In the present study, three microarray datasets from the Gene Expression Omnibus database were screened, which identified a total of 96 differentially expressed genes (DEGs). A protein-protein interaction network (PPI) was established for these DEGs and module analysis was performed using STRING and Cytoscape. A total of eight hub genes were identified from the most significant module; namely, calponin 1 (CNN1), myosin light chain kinase (MYLK), cysteine and glycine rich protein 1 (CSRP1), myosin heavy chain 11 (MYH11), fibulin extracellular matrix protein 2 (EFEMP2), fibulin 1 (FBLN1), microfibril associated protein 4 (MFAP4) and fibulin 5 (FBLN5). The biological functions of these hub genes were analyzed using the DAVID online tool. Changes in the expression of hub genes did not affect overall survival; however, downregulated EFEMP2 decreased disease-free survival. CSRP1 and MFAP4 expression levels were associated with adverse clinicopathological features. In conclusion, although all eight hub genes were downregulated in ACC, they appeared to have important functions in ACC carcinogenesis and progression. Identification of these genes complements the genetic expression profile of ACC and provides insight for the diagnosis, treatment and prognosis of ACC.

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Section: Discussionmentioning

confidence: 56%

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Zhang

Miao

et al. 2020

Exp Ther Med

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“…Nevertheless, previous studies have not shown interactions between TOP2A and co-expression genes BUB1, CDK1, CENPF, RACGAP1, FBXO5 and BUB1B in CC. However, Guo et al (59) suggested that BUB1, CDK1, RACGAP1 and TOP2A may be involved in the tumorigenesis of adrenocortical carcinoma. Li et al (60) showed that lower expression levels of TOP2A and CENPF were associated with improved overall survival in patients with bladder cancer.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Zhao

Zhu

et al. 2020

Biomed Rep

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Cervical cancer (CC) is one of the most prevalent types of cancer affecting females worldwide. However, the molecular mechanisms underlying the development and progression of CC remains to be elucidated. Taking the high incidence and mortality rates amongst women into consideration, the identification of novel biomarkers to prevent CC is of great significance and required to improve diagnosis. Using three raw microarray datasets from the Gene Expression Omnibus database, 188 differentially expressed genes (DEGs) were identified. Gene Ontology and pathway analyses were performed on the DEGs. Through protein-protein interaction network construction and module analysis, eight hub genes [cell division cycle 6, cyclin-dependent kinase 1 (CDK1), cell division control protein 45, budding uninhibited by benzimidazoles 1 (BUB1), DNA topoisomerase II α (TOP2A) and minichromosome maintenance complex component 4, CCNB2 and CCNB1] were identified, but only TOP2A was considered a prognostic factor in survival analysis. There were strong positive correlations between TOP2A and BUB1 (P<0.0001, rs= 0.635), CDK1 (P<0.0001, rs= 0.511), centromere protein F (CENPF) (P<0.0001, rs= 0.677), Rac GTPase activating protein 1 (RACGAP1) (P<0.0001, rs= 0.612), F-box protein 5 (FBXO5) (P<0.0001, rs=0.585) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) (P<0.0001, rs= 0.584). Additionally, BUB1, CDK1, CENPF, RACGAP1, FBXO5 and BUB1B are all potentially suitable candidate targets for the diagnosis and treatment of CC. In conclusion, the present study identified TOP2A as a potential tumor oncogene and a biomarker for the prognosis of CC.

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“…The genes responsible for the analyzed functional pathways were selected from Kyoto Encyclopedia for Genes and Genomes (KEGG)[ 37 ]. However, KEGG, as any other specialized software (DAVID[ 38 ], Ingenuity[ 39 ], GeneMAPP[ 40 ] etc .) to ensemble genes into functional pathways, is a text miner from trustable peer-reviewed publications that explores gene and protein expression profiles .…”

Section: Introductionmentioning

confidence: 99%

Powerful quantifiers for cancer transcriptomics

Iacobaş

2020

WJCO

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Every day, investigators find a new link between a form of cancer and a particular alteration in the sequence or/and expression level of a key gene, awarding this gene the title of “biomarker”. The clinician may choose from numerous available panels to assess the type of cancer based on the mutation or expression regulation (“transcriptomic signature”) of “driver” genes. However, cancer is not a “one-gene show” and, together with the alleged biomarker, hundreds other genes are found as mutated or/and regulated in cancer samples. Regardless of the platform, a well-designed transcriptomic study produces three independent features for each gene: Average expression level, expression variability and coordination with expression of each other gene. While the average expression level is used in all studies to identify what genes were up-/down-regulated or turn on/off, the other two features are unfairly ignored. We use all three features to quantify the transcriptomic change during the progression of the disease and recovery in response to a treatment. Data from our published microarray experiments on cancer nodules and surrounding normal tissue from surgically removed tumors prove that the transcriptomic topologies are not only different in histopathologically distinct regions of a tumor but also dynamic and unique for each human being. We show also that the most influential genes in cancer nodules [the Gene Master Regulators (GMRs)] are significantly less influential in the normal tissue. As such, “smart” manipulation of the cancer GMRs expression may selectively kill cancer cells with little consequences on the normal ones. Therefore, we strongly recommend a really personalized approach of cancer medicine and present the experimental procedure and the mathematical algorithm to identify the most legitimate targets (GMRs) for gene therapy.

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Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Cited by 16 publications

References 37 publications

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Powerful quantifiers for cancer transcriptomics

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