Literature DB >> 29520684

Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis.

W Liang1, F Sun2.   

Abstract

OBJECTIVE: To identify novel clinically relevant genes in papillary thyroid carcinoma from public databases.
METHODS: Four original microarray datasets, GSE3678, GSE3467, GSE33630 and GSE58545, were downloaded. Differentially expressed genes (DEGs) were filtered from integrated data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. The CentiScape pug-in was performed to scale degree. The genes at the top of the degree distribution (≥ 95% percentile) in the significantly perturbed networks were defined as central genes. UALCAN and The Cancer Genome Atlas Clinical Explorer were used to verify clinically relevant genes and perform survival analysis. RESULT: 225 commonly changed DEGs (111 up-regulated and 114 down-regulated) were identified. The DEGs were classified into three groups by GO terms. KEGG pathway enrichment analysis showed DEGs mainly enriched in the PI3K-Akt signaling pathway, pathways in cancer, focal adhesion and proteoglycans in cancer. DEGs' protein-protein interaction (PPI) network complex was developed; six central genes (BCL2, CCND1, FN1, IRS1, COL1A1, CXCL12) were identified. Among them, BCL2, CCND1 and COL1A1 were identified as clinically relevant genes.
CONCLUSION: BCL2, CCND1 and COL1A1 may be key genes for papillary thyroid carcinoma. Further molecular biological experiments are required to confirm the function of the identified genes.

Entities:  

Keywords:  Bioinformatics analysis; Key genes; Papillary thyroid cancer

Mesh:

Year:  2018        PMID: 29520684     DOI: 10.1007/s40618-018-0859-3

Source DB:  PubMed          Journal:  J Endocrinol Invest        ISSN: 0391-4097            Impact factor:   4.256


  30 in total

1.  DAVID: Database for Annotation, Visualization, and Integrated Discovery.

Authors:  Glynn Dennis; Brad T Sherman; Douglas A Hosack; Jun Yang; Wei Gao; H Clifford Lane; Richard A Lempicki
Journal:  Genome Biol       Date:  2003-04-03       Impact factor: 13.583

2.  affy--analysis of Affymetrix GeneChip data at the probe level.

Authors:  Laurent Gautier; Leslie Cope; Benjamin M Bolstad; Rafael A Irizarry
Journal:  Bioinformatics       Date:  2004-02-12       Impact factor: 6.937

3.  Concomitant BRAF(V600E) mutation and RET/PTC rearrangement is a frequent occurrence in papillary thyroid carcinoma.

Authors:  Anna Guerra; Pio Zeppa; Maurizio Bifulco; Mario Vitale
Journal:  Thyroid       Date:  2013-08-24       Impact factor: 6.568

4.  CCND1/CyclinD1 status in metastasizing bladder cancer: a prognosticator and predictor of chemotherapeutic response.

Authors:  Roland Seiler; George N Thalmann; Diana Rotzer; Aurel Perren; Achim Fleischmann
Journal:  Mod Pathol       Date:  2013-07-26       Impact factor: 7.842

5.  Integrated genomic characterization of papillary thyroid carcinoma.

Authors: 
Journal:  Cell       Date:  2014-10-23       Impact factor: 41.582

6.  Activating BRAF and PIK3CA mutations cooperate to promote anaplastic thyroid carcinogenesis.

Authors:  Roch-Philippe Charles; Jillian Silva; Gioia Iezza; Wayne A Phillips; Martin McMahon
Journal:  Mol Cancer Res       Date:  2014-04-25       Impact factor: 5.852

7.  BRAF mutation associated with other genetic events identifies a subset of aggressive papillary thyroid carcinoma.

Authors:  Angela M Costa; Agustín Herrero; Manuel F Fresno; Jonas Heymann; José Antonio Alvarez; Jose Cameselle-Teijeiro; Ginesa García-Rostán
Journal:  Clin Endocrinol (Oxf)       Date:  2007-12-05       Impact factor: 3.478

Review 8.  [BRAF gene mutation in the natural history of papillary thyroid carcinoma: diagnostic and prognostic implications].

Authors:  J Pedro Lopes; E Fonseca
Journal:  Acta Med Port       Date:  2011-12-31

9.  Identification of COL1A1 and COL1A2 as candidate prognostic factors in gastric cancer.

Authors:  Jun Li; Yuemin Ding; Aiqing Li
Journal:  World J Surg Oncol       Date:  2016-11-29       Impact factor: 2.754

10.  KEGG: new perspectives on genomes, pathways, diseases and drugs.

Authors:  Minoru Kanehisa; Miho Furumichi; Mao Tanabe; Yoko Sato; Kanae Morishima
Journal:  Nucleic Acids Res       Date:  2016-11-28       Impact factor: 16.971

View more
  15 in total

1.  Bioinformatics analysis of downstream circRNAs and miRNAs regulated by Runt-related transcription factor 1 in papillary thyroid carcinoma.

Authors:  Jiajie Xu; Guowan Zheng; Haiwei Guo; Kexin Meng; Wanchen Zhang; Ru He; Chuanming Zheng; Minghua Ge
Journal:  Gland Surg       Date:  2022-05

2.  Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis.

Authors:  Runrun Zhang; Xinpeng Zhou; Yehua Jin; Cen Chang; Rongsheng Wang; Jia Liu; Junyu Fan; Dongyi He
Journal:  Clin Rheumatol       Date:  2021-07-05       Impact factor: 2.980

3.  Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq.

Authors:  Yuling Shen; Yi Lai; Dong Xu; Le Xu; Lin Song; Jiaqing Zhou; Chengwen Song; Jiadong Wang
Journal:  Endocrine       Date:  2020-11-12       Impact factor: 3.633

4.  Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer.

Authors:  Qiang Li; Sheng Jiang; Tienan Feng; Tengteng Zhu; Biyun Qian
Journal:  Onco Targets Ther       Date:  2021-05-12       Impact factor: 4.147

5.  Identification of key biomarkers for thyroid cancer by integrative gene expression profiles.

Authors:  Jinyi Tian; Yizhou Bai; Anyang Liu; Bin Luo
Journal:  Exp Biol Med (Maywood)       Date:  2021-04-25

6.  Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis.

Authors:  Ying Wan; Xiaolian Zhang; Huilin Leng; Weihua Yin; Wenxing Zeng; Congling Zhang
Journal:  PeerJ       Date:  2020-07-09       Impact factor: 2.984

7.  Downregulation of miR‑193a‑3p via targeting cyclin D1 in thyroid cancer.

Authors:  Xiao-Jiao Li; Rong Wen; Dong-Yue Wen; Peng Lin; Deng-Hua Pan; Li-Jie Zhang; Yu He; Lin Shi; Yong-Ying Qin; Yun-Hui Lai; Jing-Ni Lai; Jun-Lin Yang; Qin-Qiao Lai; Jun Wang; Jun Ma; Hong Yang; Yu-Yan Pang
Journal:  Mol Med Rep       Date:  2020-07-08       Impact factor: 2.952

8.  Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer.

Authors:  Yiting Tian; Yang Xing; Zheng Zhang; Rui Peng; Luyu Zhang; Yan Sun
Journal:  Biomed Res Int       Date:  2020-08-22       Impact factor: 3.411

9.  Identification and analysis of genes associated with papillary thyroid carcinoma by bioinformatics methods.

Authors:  Shulong Zhang; Quan Wang; Qi Han; Huazhong Han; Pinxiang Lu
Journal:  Biosci Rep       Date:  2019-04-02       Impact factor: 3.840

10.  Bioinformatics analysis of C3 and CXCR4 demonstrates their potential as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC).

Authors:  Jing Quan; Yuchen Bai; Yunbei Yang; Er Lei Han; Hong Bai; Qi Zhang; Dahong Zhang
Journal:  BMC Cancer       Date:  2021-07-15       Impact factor: 4.430

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.