Exploring the role of Mir204/211 in HNSCC by the combination of bioinformatic analysis of ceRNA and transcription factor regulation
Introduction
Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer worldwide, with a high incidence of 500,000 newly diagnosed cases per year, a high rate of metastatic recurrence, and a five-year overall survival rate as low as approximately 50% [1], [2], [3], [4], [5]. Emerging targeted therapies, such as monoclonal antibodies against epidermal growth factor receptor (EGFR), tyrosine kinase inhibitors, the phosphoinositide 3-kinase (PI3K)/AKT/ mammalian target of rapamycin (mTOR) pathway pathway inhibitors, and immunotherapy agents such as anti-PD-1/PD-L1 antibodies and TLR-8 agonists, are under development, but with limited efficacy [6]. Therefore, an increased understanding of HNSCC pathogenesis at the molecular level is urgently needed to identify novel therapeutic strategies.
The gene expression network of cancer has been predicted to be intricate and fathomable, involving a large variety of players, such as transcription factors (TFs), microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and coding genes. Among all factors, miRNAs predominately play direct roles through translation inhibition and mRNA degradation [7], [8], while two or more miRNAs with similar sequences can frequently reside in clusters and function synergistically in the pathogenesis of various diseases [9], [10], [11]. Being responsible for oncogenesis, miRNAs possess great potential to be biomarkers in HNSCC and other cancers [8], [9], [12].
Several crosstalk mechanisms have gained ground. For example, miRNAs act as a bridge between lncRNAs and targeted mRNAs. Salmena et al. proposed the competing endogenous RNA (ceRNA) hypothesis: the same miRNA response elements (MRE) are shared among lncRNAs and mRNAs, enabling lncRNAs to interact with miRNAs as “sponges” and suppress their impact on mRNAs [13]. CeRNA networks have been verified in multiple cancers, including HNSCC [14], [15], [16], [17], [18]. Another factor at play is the impact of miRNAs on TFs. Compared with coding genes, a miRNA is twice as likely to choose a TF as its target [19], which entails indirect regulation on the transcriptional process besides its post-transcriptional role. Furthermore, a mathematical model quantified the maximal post-transcriptional regulatory power achievable by miRNA-mediated crosstalk in the case of ceRNA circuits. This model clarified that miRNA-mediated control could eclipse other regulation, such as direct transcriptional control via DNA-binding factors [20]. In conclusion, the current scenario indicates that besides its widely recognized noise-buffering role, miRNAs may indeed act as master regulators of gene expression.
Considering the intricate regulatory roles of miRNAs in affecting cell phenotype, a comprehensive and systematic analysis of their roles in lncRNA-miRNA-mRNA, miRNA-TF-mRNA triplet regulatory networks and related survival effects remains obscure. An integrated regulatory network centralized by miRNAs would contribute greatly to the understanding of the oncogenesis of HNSCC. Therefore, in our study, we explored The Cancer Genome Atlas database (TCGA) database and 3 related Gene Expression Omnibus (GEO) datasets to illustrate the pathogenesis of HNSCC. With multiple bioinformatic strategies, we established both a ceRNA network connecting lncRNAs, miRNAs, and mRNAs and a TF regulatory network connecting miRNAs, TFs and hub genes. We also conducted a closely related survival analysis illustrating the pathogenesis of HNSCC. As a result, we identified miR-204 and miR-211 as a promising cluster in the HNSCC pathological process, shedding light on new diagnostic and therapeutic approaches.
Section snippets
Microarray data acquisition
Expression data on HNSCC from the TCGA database (https://cancergenome.nih.gov/) were downloaded on June 8, 2018. The RNA-seq data include 502 HNSCC samples and 44 normal samples, while the miRNA-seq data include 525 HNSCC samples and 44 normal samples. Our study adhered to the TCGA publication guidelines and data access policies (http://cancergenome.nih.gov/publications/publicationguidelines).
Differentially expressed gene analysis
Based on the data from TCGA, the Empirical Analysis of Digital Gene Expression Data in R (edgeR)
Identification of differentially expressed mRNAs, miRNAs and lncRNAs
The workflow of our study is shown in Fig. 1. We collected HNSCC-related expression data from TCGA, including RNA-seq data with 502 HNSCC samples and 44 normal samples as well as miRNA-seq data with 525 HNSCC samples and 44 normal samples. Volcano plots were used to assess differentially expressed genes. In total, 3070 DE mRNAs were screened out, including 1650 downregulated and 1420 upregulated mRNAs in cancerous samples. Of the 2612 DE lncRNAs, 771 were downregulated, and 1841 were
Discussion
Multiple studies have revealed the complex gene expression pattern during oncogenesis. In some circumstances, miRNA-mediated control may indeed act as a master regulator of gene expression [20]. Clarification of such interactive regulatory networks in cancers could provide new therapeutic opportunities. However, a comprehensive analysis based on the centric role of miRNAs in HNSCC remains unavailable. Therefore, we aimed to explore the effects of miRNAs in HNSCC pathogenesis focusing on both
Conclusion
In summary, we established lncRNA-miRNA-mRNA and miRNA-TF-mRNA regulatory networks and then introduced survival analysis to reveal the possible role of miRNAs in HNSCC. On the one hand, the promising centric standing of miR-204/211 that we found may shed light on improved diagnosis or targeted treatment of HNSCC; on the other hand, a comprehensive approach to pathogenesis analysis of other diseases is provided.
Declaration of Competing Interest
Jingyi Cai, Yeke Yu, Yuzi Xu, Hao Liu, Jiawei Shou, Liangkun You, Hanliang Jiang, XuFeng Han, Binbin Xie, Weidong Han declare that: we have no proprietary, financial, professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled: Exploring the role of Mir204/211 in HNSCC by the combination of bioinformatic analysis of ceRNA and transcription factor
Acknowledgements
This work was supported by the Zhejiang Natural Sciences Foundation Grant (LQ18H160008, Q17H160042, LY17H160029, LY18H160007), the Ten Thousand Plan Youth Talent Support Program of Zhejiang Province, and the Zhejiang medical innovative discipline construction project-2016.
References (36)
MicroRNAs: target recognition and regulatory functions
Cell
(2009)- et al.
MicroRNAs as prognostic molecular signatures in renal cell carcinoma
Oral Oncol
(2015) - et al.
A ceRNA hypothesis: The rosetta stone of a hidden RNA language?
Cell
(2011) - et al.
Complex integrated analysis of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma
Oral Oncol
(2017) - et al.
Analysis of lncRNA-mediated ceRNA crosstalk and identification of prognostic signature in head and neck squamous cell carcinoma
Front Pharmacol
(2019) - et al.
The lncRNA NEAT1 facilitates cell growth and invasion via the miR-211/HMGA2 axis in breast cancer
Int J Biol Macromol
(2017) - et al.
SNP rs3202538 in 3’UTR region of ErbB3 regulated by miR-204 and miR-211 promote gastric cancer development in Chinese population
Cancer Cell Int
(2017) - et al.
miR-204-5p regulates cell proliferation and metastasis through inhibiting CXCR4 expression in OSCC
Biomed Pharmacother
(2016) - et al.
miR-204 inhibits angiogenesis and promotes sensitivity to cetuximab in head and neck squamous cell carcinoma cells by blocking JAK2-STAT3 signaling
Biomed Pharmacother
(2018) - et al.
MiR-211 promotes the progression of head and neck carcinomas by targeting TGFβRII
Cancer Lett
(2013)
Global cancer statistics, 2012
CA Cancer J Clin
Cancer statistics, 2015
CA Cancer J Clin
Comprehensive genomic characterization of head and neck squamous cell carcinomas
Nature
A feed-forward regulatory loop between HuR and the long noncoding RNA HOTAIR promotes head and neck squamous cell carcinoma progression and metastasis
Cell Physiol Biochem
The effects of GLUT1 on the survival of head and neck squamous cell carcinoma
Cell Physiol Biochem
Current treatment options for recurrent or metastatic head and neck squamous cell carcinoma
J Clin Oncol
MicroRNA-mediated regulatory circuits: outlook and perspectives
Phys Biol
Clustering and conservation patterns of human microRNAs
Nucl Acids Res
Cited by (5)
Comprehensive analysis of competitive endogenous RNAs network reveals potential prognostic lncRNAs in gastric cancer
2020, HeliyonCitation Excerpt :The detailed sequences of GC clinical samples can be found in Table S1. The edgeR package [13, 14] installed in R (version 3.6.1, www.r-project.org) was used to identify DE lncRNAs, DE miRNAs, and DE mRNAs in two separate comparisons: early-stage GC (stage I-II) versus normal tissue, and advanced GC (stage III-IV) versus normal tissue. The cutoff criteria for differential expression were |fold change|≥2 and FDR<0.01 [15].
Multiple microRNA signature panel as promising potential for diagnosis and prognosis of head and neck cancer
2022, Molecular Biology Reports