4.6

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2.2

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
Liangyu Ge, Siyu Liu, Long Xie, Lei Sang, Changyan Ma, Hongwei Li. Differential mRNA expression profiling of oral squamous cell carcinoma by high-throughput RNA sequencing[J]. The Journal of Biomedical Research, 2015, 29(5): 397-404. DOI: 10.7555/JBR.29.20140088
Citation: Liangyu Ge, Siyu Liu, Long Xie, Lei Sang, Changyan Ma, Hongwei Li. Differential mRNA expression profiling of oral squamous cell carcinoma by high-throughput RNA sequencing[J]. The Journal of Biomedical Research, 2015, 29(5): 397-404. DOI: 10.7555/JBR.29.20140088

Differential mRNA expression profiling of oral squamous cell carcinoma by high-throughput RNA sequencing

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the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD, 2014-37)

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  • Received Date: June 21, 2014
  • Revised Date: August 11, 2014
  • Differentially expressed genes are thought to regulate the development and progression of oral squamous cell carcinomas (OSCC). The purpose of this study was to screen differentially expressed mRNAs in OSCC and matched paraneoplastic normal tissues, and to explore the intrinsic mechanism of OSCC development and progression. We obtained the differentially expressed mRNA expression profiles in 10 pairs of fresh-frozen OSCC tissue specimens and matched paraneoplastic normal tissue specimens by high-throughput RNA sequencing. By using Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, the functional significance of the differentially expressed genes were analyzed. We identified 1,120 significantly up-regulated mRNAs and 178 significantly down-regulated mRNAs in OSCC, compared to normal tissue. The differentially expressed mRNAs were involved in 20 biological processes and 68 signal pathways. Compared to adjacent normal tissue, the expression of MAGEA11 was up-regulated; TCHH was down-regulated. These findings were verified by real-time PCR. These differentially expressed mRNAs may function as oncogenes or tumor suppressors in the development and progression of OSCC. This study provides novel insights into OSCC. However, further work is needed to determine if these differentially expressed mRNAs have potential roles as diagnostic biomarkers and candidate therapeutic targets for OSCC.
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    Periodical cited type(11)

    1. Yu F, Zhao LX, Chu S. TCHH as a Novel Prognostic Biomarker for Patients with Gastric Cancer by Bioinformatics Analysis. Clin Exp Gastroenterol, 2024, 17: 61-74. DOI:10.2147/CEG.S451676
    2. Li K, Qiu Y, Liu X, et al. Biomimetic Nanosystems for the Synergistic Delivery of miR-144/451a for Oral Squamous Cell Carcinoma. Balkan Med J, 2022, 39(3): 178-186. DOI:10.4274/balkanmedj.galenos.2022.2021-11-1
    3. Yu CC, Chan MWY, Lin HY, et al. IRAK2, an IL1R/TLR Immune Mediator, Enhances Radiosensitivity via Modulating Caspase 8/3-Mediated Apoptosis in Oral Squamous Cell Carcinoma. Front Oncol, 2021, 11: 647175. DOI:10.3389/fonc.2021.647175
    4. Amiri-Dashatan N, Koushki M, Jalilian A, et al. Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma. Asian Pac J Cancer Prev, 2020, 21(6): 1841-1848. DOI:10.31557/APJCP.2020.21.6.1841
    5. Xu GQ, Li LH, Wei JN, et al. Identification and profiling of microRNAs expressed in oral buccal mucosa squamous cell carcinoma of Chinese hamster. Sci Rep, 2019, 9(1): 15616. DOI:10.1038/s41598-019-52197-3
    6. Li CY, Zhang WW, Xiang JL, et al. Integrated analysis highlights multiple long non‑coding RNAs and their potential roles in the progression of human esophageal squamous cell carcinoma. Oncol Rep, 2019, 42(6): 2583-2599. DOI:10.3892/or.2019.7377
    7. Zhong L, Liu Y, Wang K, et al. Biomarkers: paving stones on the road towards the personalized precision medicine for oral squamous cell carcinoma. BMC Cancer, 2018, 18(1): 911. DOI:10.1186/s12885-018-4806-7
    8. Willett CS, Wilson EM. Evolution of Melanoma Antigen-A11 (MAGEA11) During Primate Phylogeny. J Mol Evol, 2018, 86(3-4): 240-253. DOI:10.1007/s00239-018-9838-8
    9. Li J, Zhou D, Qiu W, et al. Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design. Sci Rep, 2018, 8(1): 622. DOI:10.1038/s41598-017-18705-z
    10. Mallik S, Zhao Z. ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis. Genes (Basel), 2017, 9(1): 7. DOI:10.3390/genes9010007
    11. Irimie AI, Braicu C, Pileczki V, et al. Knocking down of p53 triggers apoptosis and autophagy, concomitantly with inhibition of migration on SSC-4 oral squamous carcinoma cells. Mol Cell Biochem, 2016, 419(1-2): 75-82. DOI:10.1007/s11010-016-2751-9

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