Open Access

Role of downregulated ADARB1 in lung squamous cell carcinoma

  • Authors:
    • Xiang Wang
    • Xinxin Ren
    • Wanli Liu
    • Xi Chen
    • Jie Wei
    • Zhicheng Gong
    • Yuanliang Yan
    • Zhijie Xu
  • View Affiliations

  • Published online on: January 23, 2020     https://doi.org/10.3892/mmr.2020.10958
  • Pages: 1517-1526
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Non‑small cell lung cancer (NSCLC) is prevalent worldwide. Lung squamous cell carcinoma (LUSC) is one of the main subtypes of NSCLC yet, currently, few biomarkers are available for the diagnosis of LUSC. The present study aimed to investigate the expression and role of adenosine deaminase RNA specific B1 (ADARB1) in lung squamous cell carcinoma (LUSC). Integrative bioinformatics analysis was used to identify the effects of ADARB1 expression on the occurrence and prognosis of LUSC. The expression of ADARB1 was further examined by immunohistochemistry (IHC). Bioinformatics analysis suggested that ADARB1 was downregulated in LUSC, serving as a potential tumor suppressor, and these results were verified by IHC performed on a lung cancer tissue array. Clinical studies suggested that ADARB1 expression and methylation levels were significantly associated with patient characteristics in LUSC. Moreover, ADARB1 global methylation levels were upregulated in LUSC tissues compared with normal lung tissues. Higher methylation levels of cg24063645 were associated with shorter overall survival time of patients with LUSC. A negative correlation was identified between ADARB1 and epidermal growth factor receptor (EGFR) expression in LUSC. Using the Gene Expression Omnibus database, it was suggested that the expression of ADARB1 in LUSC was significantly different compared with that in lung adenocarcinoma. Furthermore, protein‑protein interactions were studied and a biological process annotation analysis was conducted. The present study suggested that ADARB1 was downregulated in LUSC; therefore, ADARB1 may serve as a specific biomarker and a potential therapeutic target for LUSC.

Introduction

Lung cancer was the leading cause of cancer-related death worldwide in 2015 and non-small cell lung cancer (NSCLC) accounts for ~85% of lung cancer cases (1). Lung squamous cell carcinoma (LUSC) is one of the main subtypes of NSCLC. A previous study indicated that the 5-year overall survival (OS) rate for patients with LUSC is 17.7% due to late diagnosis (2). In addition, there have been limited opinions for LUSC treatment compared to lung adenocarcinoma (LUAD) although progress has been made in the treatment of NSCLC (35). The reason is partly because there are more molecular abnormalities for LUSC in the discoidin domain receptor tyrosine kinase 2 and fibroblast growth factor receptor 1 genes (6). Early identification of the disease would reduce the mortality rate of patients with LUSC who could benefit most from effective therapies (7). However, few biomarkers are currently available for the effective diagnosis of LUSC, which would enable patients to receive timely treatment (8). Thus, identifying novel target molecules is essential for improving the diagnosis and treatment of LUSC.

Adenosine deaminase RNA specific B1 (ADARB1) is a member of the adenosine deaminases acting on RNA (ADAR) family, which exhibit enzymatic editing activity in a process known as adenosine-to-inosine (A-to-I) editing (9). Recently, research has indicated an association between ADARB1 and cancer. A previous study reported that ADARB1, an indispensable marker in brain development and function, could inhibit the proliferation and progression of glioblastoma (10). In terms of esophageal squamous cell carcinoma, Chen et al (11) reported that ADARB1 induces apoptosis and suppresses tumor growth by editing insulin-like growth factor binding protein 7 mRNA. Additionally, ADARB1 can regulate exon 12A, an exon from the heterogeneous nuclear ribonucleoprotein L like transcript, to promote cancer cell survival (12). However, the relationship between ADARB1 and LUSC has not been investigated.

Therefore, the present study aimed to investigate the role of ADARB1 in LUSC. The results suggested that ADARB1 was downregulated in LUSC tissues and cell lines. The expression and methylation status of ADARB1 were significantly associated with clinical characteristics and prognosis. Additionally, the GSE10245 dataset (13), acquired from the Gene Expression Omnibus (GEO) (14) database, suggested that ADARB1 expression levels in LUSC were lower than in LUAD. Finally, functional enrichment analysis of ADARB1-associated co-expression genes was conducted and a protein-protein interaction (PPI) network was established. The results of the Gene Ontology (GO) analysis suggested that the main biological processes (‘metabolic process’ and ‘biological regulation’), cellular components (‘nucleus’) and molecular functions (‘protein binding’) were associated with ADARB1. In addition, ADARB1 expression was significantly related to the Kyoto Encyclopedia of Genes and Genomes (KEGG) terms ‘ubiquitin mediated proteolysis’ and ‘Wnt signaling pathway’.

Materials and methods

Data acquisition and reanalysis using bioinformatics methods

Several bioinformatics web resources were used to reanalyze data concerning ADARB1 (Table SI). The Genome Mining (GE-mini) database was employed to examine gene expression profiles over a number of tissue types, including tumors (15). The Cancer Cell Line Encyclopedia (CCLE) project is a public database containing detailed genetic and pharmacological characterization of numerous cell lines (16). The GE-mini and CCLE databases were used to identify the expression profiles of ADARB1 in LUSC tissues and cell lines. Oncomine (17), University of Alabama Cancer Database (UALCAN) (18) and Gene Expression Profiling Interactive Analysis (GEPIA) (19) were used to verify the results. The GEPIA database was also used to identify the Pearson correlation between ADARB1 and epidermal growth factor receptor (EGFR) in LUSC.

Wanderer is an interactive platform, which provides human cancer gene expression and DNA methylation data. This platform was used to evaluate the relationship between ADARB1 levels and patient clinical characteristics (20). DiseaseMeth (version 2.0) is a human disease methylation database, which was used to analyze ADARB1 global methylation levels in LUSC (21). The MethSurv tool was used to analyze the association between ADARB1 methylation and LUSC prognosis by using the mean value to dichotomize methylation profiles of patients (22). The GSE10245 dataset, downloaded from the GEO database, provided ADARB1 expression profiles in LUAD and LUSC samples.

A dataset LUSC (TCGA, Nature 2012) (23) from the cBioportal (24) web tool was used to obtain gene co-expression data for ADARB1 in LUSC. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (25) and Cytoscape software (26) were then used to build a PPI network of the co-expression genes. Moreover, GO and KEGG (27) pathway analyses were conducted, using the Web-based Gene Set Analysis Toolkit (WebGestalt) (28) and the Database for Annotation, Visualization and Integrated Discovery (DAVID) (29) bioinformatics resources, respectively.

Immunohistochemistry (IHC) analysis of a lung cancer tissue array

The LUC481 lung cancer tissue array and information regarding the cancer subtypes of the array were obtained from Fanpu Biotech, Inc. IHC was performed using the Histomouse SP Broad Spectrum DAB kit (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. Following antigen retrieval which was performed by microwaving in 10 mM citrate buffer (pH 6.0), paraffin-embedded sections were immunostained using a streptavidin peroxidase procedure. After blocking the samples with 3% bovine serum albumin (100 ml, SW3015, Beijing Solarbio Science & Technology Co., Ltd.) for 15 min at room temperature, the primary antibody against ADARB1 (cat. no. sc-73409, Santa Cruz Biotechnology, Inc., 1:100 dilution) was added and samples were incubated at 4°C overnight. Following washing with PBS, the horseradish peroxidase (HRP)-conjugated polymer second antibody (PV-6000, OriGene Technologies, Inc.) was added and samples were incubated for 30 min at room temperature. The signal was examined using a 3,3′-diaminobenzidine solution. Subsequently, four fields of view of the stained sections were independently observed in a light microscope at magnifications of ×4 and ×20 by two pathologists. The staining intensity of each protein was divided into four grades (intensity scores): 0 (negative), 1 (weak brown), 2 (moderate brown) and 3 (strong brown). The percentage of positive cells was scored as 0 (≤10%), 1 (11–25%), 2 (26–50%), 3 (51–75%) and 4 (>75%) (30). The final staining score was calculated using the following formula: Intensity score × percentage score (1). A final score >1 was defined as high expression and final scores ≤1 were defined as low expression.

Statistical analysis

Statistical analyses were performed using SPSS software (version 12.0; SPSS, Inc.). The data are presented as the mean ± SD. A paired-sample t-test was used for normally distributed and continuous variables were statistically analyzed by one-way analysis of variance with LSD post hoc test. Multivariate analysis was accomplished utilizing the Cox regression model to identify independence of accepted clinical parameters and the ordinal variables were tested with Kruskall-Wallis test followed by Dunn's post-hoc test. The Kaplan-Meier analysis was statistically tested by the log-rank method. P<0.05 was considered to indicate a statistically significant difference.

Results

ADARB1 is downregulated in LUSC tissues and cell lines

By analyzing several databases, the transcriptional levels of ADARB1 in LUSC tissues and cell lines were compared with those observed in normal lung tissue and cells. GE-mini analysis revealed that ADARB1 mRNA expression was significantly decreased in LUSC tissues (Fig. 1A). The CCLE database suggested that ADARB1 mRNA expression was significantly downregulated in ~10 LUSC cell lines compared with immortalized lung epithelial cell lines (P=0.001; Fig. 1B). To further investigate these observations, the Oncomine, UALCAN and GEPIA databases were employed. Analysis with all three databases suggested that ADARB1 was expressed at significantly lower levels in LUSC tissues compared with in normal lung tissues (Fig. 1C-E). Furthermore, IHC analysis, based on a lung cancer array containing 16 normal samples, 12 LUSC samples and 10 LUAD samples, suggested that ADARB1 was significantly downregulated in LUSC samples compared with normal lung samples (P=1.58×10−8; Fig. 2A and B). In summary, the downregulation of ADARB1 expression levels in LUSC tissues and cell lines suggested that ADARB1 may be a promising anti-oncogene in LUSC.

ADARB1 could be used for the identification of LUSC and LUAD

The results of the IHC analysis further suggested that ADARB1 was downregulated in LUAD tissues compared with normal lung tissues, which was consistent with our previous study (P=0.024; Fig. 2A and B) (31). Furthermore, ADARB1 expression levels were significantly lower in LUSC tissues than in LUAD tissues (P=0.025; Fig. 2A and B). Subsequently, the GSE10245 dataset, obtained from GEO, suggested that ADARB1 was expressed at a lower level in LUSC compared with LUAD (P=0.031; Fig. 3A). Furthermore, this significant difference was verified by the CCLE database (P=0.008; Fig. 3B). Therefore, these results suggested that ADARB1 expression profiles may serve as indicators for the clinical diagnosis of LUSC and LUAD.

Relationship between ADARB1 expression and the clinical characteristics of patients with LUSC

The effects of ADARB1 expression on the clinical characteristics of patients with LUSC were examined. Clinical data were downloaded from the Wanderer database, and the clinical characteristic parameters are summarized in Table I. The expression of ADARB1 was significantly associated with the pathologic N stage (P=0.045), pathologic M stage (P=0.012) and tobacco smoking history indicator (P=0.008). Furthermore, multivariate analysis of the clinical data suggested that the pathologic N stage (P=0.020), pathologic M stage (P=0.003) and tobacco smoking history indicator (P=0.017) were independently associated with ADARB1 expression in patients with LUSC (Table II).

Table I.

Association between clinical characteristic parameters and the expression of adenosine deaminase RNA specific B1 in lung squamous cell carcinoma.

Table I.

Association between clinical characteristic parameters and the expression of adenosine deaminase RNA specific B1 in lung squamous cell carcinoma.

VariablenMean ± SDP-value
Pathologic T 0.433
  T1/T1a/T1b1008.148±0.924
  T2/T2a/T2b2528.103±1.035
  T3488.367±0.967
  T4198.155±1.232
Pathologic N 0.045
  N02628.134±1.008
  N11118.236±0.955
  N257.533±1.031
  N369.050±0.584
Pathologic M 0.012
  M03578.113±0.996
  M1/M1a67.576±0.607
  MX508.511±1.081
Pathologic tumor stage 0.078
  I/IA/IB2118.116±0.985
  IIA/IIB1238.315±0.981
  IIIA/IIIB768.013±1.122
  IV67.576±0.607
Ethnicity 0.133
  White2978.190±1.033
  Black/African American168.208±0.809
  Asian98.888±1.028
Tobacco smoking history indicator 0.008
  Current reformed smoker for >15 years718.404±0.991
  Current reformed smoker for ≤15 years2118.018±1.007
  Current reformed smoker, duration not specified19.922±0.000
  Lifelong non-smoker178.533±1.042
  Current smoker1128.137±0.977
Age 0.857
  ≤60 years898.133±1.093
  >60 years3228.155±0.994
Karnofsky score 0.236
  0-70477.939±1.233
  80147.910±0.884
  90228.173±0.948
  100108.687±0.957
Eastern Cooperative Oncology Group score 0.079
  0608.470±1.099
  1718.208±0.978
  2208.007±1.061
  337.211±0.597

[i] Values in bold are significant.

Table II.

Multivariate analysis of clinical data related to adenosine deaminase RNA specific B1 expression in lung squamous cell carcinoma.

Table II.

Multivariate analysis of clinical data related to adenosine deaminase RNA specific B1 expression in lung squamous cell carcinoma.

CharacteristicType III sum of squaresDegrees of freedomMean squareF-valueP-value
Pathologic N11.3242.832.960.020
Pathologic M11.5025.756.020.003
Tobacco smoking history indicator   9.8833.303.450.017
ADARB1 methylation is related to the clinical characteristics of patients with LUSC

The ADARB1 global methylation levels in LUSC were evaluated using the DiseaseMeth database. ADARB1 displayed significantly higher levels of methylation in LUSC samples compared with in normal lung samples (P=1.62×10−12; Fig. 4A). The highest methylation value of cg24063645 in ADARB1 was identified using the Wanderer and MethSurv databases (P=3.60×10−19; Fig. 4B; Table SII). Subsequently, the association between cg24063645 methylation and the clinical characteristics of patients with LUSC was analyzed. The methylation of cg24063645 of ADARB1 was significantly associated with the Karnofsky score of patients with LUSC (P=0.025; Table III). Moreover, higher methylation levels of cg24063645 of ADARB1 were associated with shorter OS of patients with LUSC (P=0.044; Fig. 4C). Therefore, the results suggested that ADARB1 methylation levels may serve a role in the prognosis of patients with LUSC.

Table III.

Association between the clinical characteristics and methylation site value of cg24063645 of adenosine deaminase RNA specific B1 in patients with lung squamous cell carcinoma.

Table III.

Association between the clinical characteristics and methylation site value of cg24063645 of adenosine deaminase RNA specific B1 in patients with lung squamous cell carcinoma.

VariablenMean ± SDP-value
Sex 0.717
  Male2150.983±0.042
  Female  730.985±0.021
KRAS gene analysis indicator 0.760
  Yes  60.988±0.002
  No1970.983±0.038
EGFR mutation status 0.682
  Yes  150.987±0.002
  No1740.983±0.040
Treatment outcome: First course 0.718
  Complete remission/response  550.983±0.022
  Stable disease  20.989±0.001
Pathologic T 0.562
  T1/T1a/T1b  760.986±0.009
  T2/T2a/T2b1660.980±0.049
  T3  370.987±0.004
  T4  90.988±0.002
Pathologic N 0.117
  N01790.984±0.029
  N1  790.985±0.014
  N2  240.967±0.099
  NX  60.984±0.004
Pathologic M 0.861
  M02330.984±0.035
  M1/M1a  30.988±0.003
  MX  500.986±0.004
Tumor stage 0.919
  I/IA/IB1410.986±0.015
  IIA/IIB  960.986±0.013
  IIIA/IIIB  460.976±0.072
  IV  30.988±0.003
Ethnicity 0.878
  White2240.984±0.028
  Black/African American  100.988±0.002
  Asian  70.988±0.003
Karnofsky score 0.025
  0-70  290.988±0.002
  80  140.987±0.002
  90  180.985±0.004
  100  90.987±0.002
Age 0.341
  ≤60 years  590.987±0.003
  >60 years2210.982±0.043

[i] Values in bold are significant. EGFR, epidermal growth factor receptor.

ADARB1 expression is negatively correlated with EGFR expression in LUSC tissues

EGFR is an oncogenic driver that contributes to the activation and development of lung cancer (7). Previous studies have indicated a poor correlation between EGFR expression and the overall response rate in lung cancer, particularly in LUSC (7,32,33). The GEPIA database was employed to evaluate the relationship between ADARB1 and EGFR. A negative correlation between ADARB1 and EGFR transcript levels in tissues of patients with LUSC was identified (P=7.80×10−6; R=−0.16; Fig. 5), which suggested that ADARB1 acted as an anti-cancer gene and might be linked to EGFR.

Network analysis of genes co-expressed with ADARB1

To further investigate the biological function of ADARB1, a functional enrichment analysis of its associated co-expressed genes was conducted. Firstly, 14,102 genes co-expressed with ADARB1 in LUSC were identified using the cBioPortal database, and a volcano plot was generated to display the association between groups with altered and unaltered ADARB1 expression (Fig. 6A). Based on the criteria of |log ratio|>0.3 and P<0.05, 158 ADARB1-associated, co-differentially expressed genes (co-DEGs) were identified (Table SIII). A protein-protein interaction network was generated using the STRING database and Cytoscape software (Fig. 6B). Moreover, WebGestalt was used to analyze GO terms (Fig. 6C) and indicated that the main biological processes of the co-DEGs were primarily enriched in ‘metabolic process’ and ‘biological regulation’. Moreover, Gan et al (34) reported that ADARB1 participated in the regulation of pancreatic islet and β-cell function. Furthermore, deficient or hyperactive ADARB1 expression is associated with a number of human diseases, including immunological disorders, amyotrophic lateral sclerosis, epilepsy and cancer (35). Therefore, it was speculated that ADARB1 might serve a role in LUSC via metabolic processes and biological regulation. In addition, for cellular components and molecular functions, the co-DEGs were primarily enriched in ‘nucleus’ and ‘protein binding’, respectively. Finally, KEGG pathway enrichment was analyzed using the DAVID database, and ‘ubiquitin mediated proteolysis’ and ‘Wnt signaling pathway’ were identified as significantly enriched pathways related to ADARB1 expression (Table SIV).

Discussion

The present study was conducted to evaluate the use of ADARB1 as a potential biomarker for patients with LUSC. To the best of our knowledge, the present study was the first to use bioinformatics methods to suggest that ADARB1 was downregulated in LUSC tissues and cell lines. Moreover, the expression of ADARB1 in LUSC was significantly different to that in LUAD. In addition, the global methylation levels of ADARB1 were upregulated in LUSC and a higher methylation value of cg24063645 was related to a shorter OS in patients with LUSC.

A-to-I RNA editing is a post-transcriptional modification mediated by the ADAR family, which is comprised of three members, ADAR1, ADARB1 and ADAR3 (36,37). The expression levels of ADARB1 determine the RNA editing activity at a number of A-to-I sites, which are involved in several diseases, including amyotrophic lateral sclerosis, autism and Fragile-X Syndrome (38). Recently, several studies have indicated that ADARB1 participates in tumorigenesis as a cancer suppressor gene, which is consistent with the results of the present study. By analyzing the influence of A-to-I RNA modification on cancer progression, Fritzell et al (39) reported that ADARB1 was expressed at low levels in glioblastoma, indicating that ADARB1 downregulation could induce malignant phenotypes. Additionally, using next-generation sequencing transcriptomics, Chan et al (40) reported that ADARB1 contributed to inhibitory mechanisms in gastric cancer via its catalytic deaminase. However, Altadill et al (41) demonstrated that ADARB1 was upregulated in endometrial cancer, and the increased expression was positively associated with disease aggression, suggesting that ADARB1 functions as an oncogene in endometrial cancer. The inconsistency between the observations of these aforementioned studies and the present study may be due to the diverse pathological states of different diseases; therefore, further investigation is required.

ADARB1 is a therapeutic target in a number of human diseases. Uchida et al (42) reported a significant increase in ADAR2-dependent A-to-I editing at the glutamate ionotropic receptor AMPA type subunit R/G, 5-hydroxytryptamine 2c receptor D and COPI coat complex-α I/V sites, which suggested that ADARB1 was a regulator of neuropathic pain after peripheral nerve injury and could be used as a target for the treatment of neuropathic pain. In addition, Galeano et al (43) reported that ADARB1 could inhibit glioblastoma growth by the regulation of the cell division cycle 14B/S-phase kinase associated protein 2/p21/p27 axis; therefore, ADARB1 or its substrates represented suitable targets for the treatment of glioblastoma. In the present study, ADARB1 was significantly downregulated in LUSC and was associated with clinical characteristics of patients with LUSC. Furthermore, the hypermethylation of ADARB1 was related to the poor prognosis of patients with LUSC. Therefore, it was speculated that ADARB1 may represent a potential target for the treatment of LUSC; however, this requires further investigation.

LUSC and LUAD are the two main subtypes of NSCLC, and the accurate differentiation of LUSC and LUAD is important for therapy stratification (44). Although p40 is regarded as the gold standard for the differentiation of LUSC and LUAD by the World Health Organization and the European Society for Medical Oncology (4547), the identification of further biomarkers is required for distinguishing LUSC from LUAD. At present, numerous immune markers have been reported for the differentiation of LUSC and LUAD, including NK2 homeobox 1, napsin A, keratin 5 and p63. Kriegsmann et al and van Zyl et al (48,49) reported that all four aforementioned markers were differentially expressed in LUSC and LUAD, and could therefore be used as biomarkers for the two cancer subtypes. By immunohistochemistry and western blotting, Wang et al (50) reported that the expression of the transcriptional co-factor tafazzin was associated with subtypes of LUSC and LUAD. Additionally, spermatogenesis associated serine rich 2, which is highly expressed in both LUSC and LUAD, displayed significantly higher expression in LUSC than LUAD, and thus, could be used as a marker to discriminate between the two subtypes (51,52). Similarly, in the present study, ADARB1 expression was significantly higher in LUAD tissues and cell lines compared with in LUSC tissues. Therefore, the present study suggested that ADARB1 might be used to distinguish between LUSC and LUAD.

EGFR has been demonstrated to be a driving factor in various types of cancer. A previous study indicated that, following phosphorylation on a regulatory tyrosine, EGFR can be activated in a ligand-dependent or -independent manner to induce tumor progression (53). Additionally, EGFR has been reported to be upregulated in a number of different types of cancer and has been associated with cancer metastasis, drug resistance, poor prognosis and lower survival rates (54). Moreover, the combination of aberrantly expressed EGFR and transforming growth factor (TGF)-α secretion by NSCLC cells can form a TGF-α-EGFR autocrine ring, which promotes tumor development (55,56). EGFR can also activate the downstream PI3K/Akt/mTOR signaling pathway, leading to cell growth and proliferation during NSCLC (57). The present study identified a negative correlation between ADARB1 and EGFR expression in LUSC. Thus, it was hypothesized that ADARB1 could disturb the EGFR carcinogenic effect via metabolic processes and biological regulation in LUSC. This observation suggested that ADARB1 might act as an anti-oncogene by influencing EGFR expression; therefore, ADARB1 may be a useful molecular target for LUSC therapeutics.

In summary, the expression and role of ADARB1 in LUSC was investigated using a novel bioinformatics method. The results suggested that ADARB1 may be an anti-oncogene, and that it could be a promising biomarker in the tumorigenesis of LUSC and development of novel therapeutics for the disease. Therefore, understanding the function and mechanisms of ADARB1 in LUSC would provide a useful basis for the diagnosis and treatment of LUSC.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

The present study was supported by grants from the National Natural Science Foundation of China (grant nos. 81703036, 81803035 and 81572946), the Fundamental Research Funds for the Central Universities of Central South University (grant no. 2019zzts345), the China Postdoctoral Science Foundation (grant no. 2017M610510) and the Youth Fund of Xiangya Hospital (grant no. 2017Q17).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

ZX, YY and ZG conceived and designed the study. XW, YY and ZX developed the methodology. XR, XW and WL acquired the data. ZX, XW, YY, XC and JW analyzed and interpreted the data. XW, XR and ZX wrote, reviewed and revised the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Yan Y, Xu Z, Qian L, Zeng S, Zhou Y, Chen X, Wei J and Gong Z: Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma. Am J Physiol Lung Cell Mol Physiol. 316:L630–L643. 2019. View Article : Google Scholar : PubMed/NCBI

2 

Zheng H, Zhan Y, Liu S, Lu J, Luo J, Feng J and Fan S: The roles of tumor-derived exosomes in non-small cell lung cancer and their clinical implications. J Exp Clin Cancer Res. 37:2262018. View Article : Google Scholar : PubMed/NCBI

3 

Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WE, Poddubskaya E, Antonia S, Pluzanski A, Vokes EE, Holgado E, et al: Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med. 373:123–135. 2015. View Article : Google Scholar : PubMed/NCBI

4 

Herbst RS, Baas P, Kim DW, Felip E, Pérez-Gracia JL, Han JY, Molina J, Kim JH, Arvis CD, Ahn MJ, et al: Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): A randomised controlled trial. Lancet. 387:1540–1550. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Zugazagoitia J, Ponce S and Paz-Ares L: Necitumumab for first-line treatment of advanced, squamous, non-small-cell lung cancer: A relevant step forward? Transl Lung Cancer Res. 5:95–97. 2016.PubMed/NCBI

6 

Drilon A, Rekhtman N, Ladanyi M and Paik P: Squamous-cell carcinomas of the lung: Emerging biology, controversies and the promise of targeted therapy. Lancet Oncol. 13:e418–e426. 2012. View Article : Google Scholar : PubMed/NCBI

7 

Yan Y, Xu Z, Hu X, Qian L, Li Z, Zhou Y, Dai S, Zeng S and Gong Z: SNCA is a functionally low-expressed gene in lung adenocarcinoma. Genes. 9(pii): E162018. View Article : Google Scholar : PubMed/NCBI

8 

Tan WL, Jain A, Takano A, Newell EW, Iyer NG, Lim WT, Tan EH, Zhai W, Hillmer AM, Tam WL and Tan DSW: Novel therapeutic targets on the horizon for lung cancer. Lancet Oncol. 17:e347–e362. 2016. View Article : Google Scholar : PubMed/NCBI

9 

Wang C, Zou J, Ma X, Wang E and Peng G: Mechanisms and implications of ADAR-mediated RNA editing in cancer. Cancer Lett. 411:27–34. 2017. View Article : Google Scholar : PubMed/NCBI

10 

Peng L, Zhang H, Su Y, Shen Q, Du C, Xie H, Li H, Yan J, Shen Z, Jiang W, et al: Lipopolysaccharide enhances ADAR2 which drives Hirschsprung's disease by impairing miR-142-3p biogenesis. J Cell Mol Med. 22:4045–4055. 2018. View Article : Google Scholar : PubMed/NCBI

11 

Chen YB, Liao XY, Zhang JB, Wang F, Qin HD, Zhang L, Shugart YY, Zeng YX and Jia WH: ADAR2 functions as a tumor suppressor via editing IGFBP7 in esophageal squamous cell carcinoma. Int J Oncol. 50:622–630. 2017. View Article : Google Scholar : PubMed/NCBI

12 

Chen YT, Chang IY, Liu H, Ma CP, Kuo YP, Shih CT, Shih YH, Kang L and Tan BC: Tumor-associated intronic editing of HNRPLL generates a novel splicing variant linked to cell proliferation. J Biol Chem. 293:10158–10171. 2018. View Article : Google Scholar : PubMed/NCBI

13 

Kuner R, Muley T, Meister M, Ruschhaupt M, Buness A, Xu EC, Schnabel P, Warth A, Poustka A, Sültmann H and Hoffmann H: Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes. Lung Cancer. 63:32–38. 2009. View Article : Google Scholar : PubMed/NCBI

14 

Barrett T and Edgar R: Reannotation of array probes at NCBI's GEO database. Nat Methods. 5:1172008. View Article : Google Scholar : PubMed/NCBI

15 

Tang Z, Li C, Zhang K, Yang M and Hu X: GE-mini: A mobile APP for large-scale gene expression visualization. Bioinformatics. 33:941–943. 2017.PubMed/NCBI

16 

Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, et al: The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 483:603–607. 2012. View Article : Google Scholar : PubMed/NCBI

17 

Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A and Chinnaiyan AM: ONCOMINE: A cancer microarray database and integrated data-mining platform. Neoplasia. 6:1–6. 2004. View Article : Google Scholar : PubMed/NCBI

18 

Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI

19 

Tang Z, Li C, Kang B, Gao G, Li C and Zhang Z: GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 45:W98–W102. 2017. View Article : Google Scholar : PubMed/NCBI

20 

Diez-Villanueva A, Mallona I and Peinado MA: Wanderer, an interactive viewer to explore DNA methylation and gene expression data in human cancer. Epigenetics Chromatin. 8:222015. View Article : Google Scholar : PubMed/NCBI

21 

Xiong Y, Wei Y, Gu Y, Zhang S, Lyu J, Zhang B, Chen C, Zhu J, Wang Y, Liu H and Zhang Y: DiseaseMeth version 2.0: A major expansion and update of the human disease methylation database. Nucleic Acids Res. 45:D888–D895. 2017. View Article : Google Scholar : PubMed/NCBI

22 

Modhukur V, Iljasenko T, Metsalu T, Lokk K, Laisk-Podar T and Vilo J: MethSurv: A web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics. 10:277–288. 2018. View Article : Google Scholar : PubMed/NCBI

23 

Cancer Genome Atlas Research Network, . Comprehensive genomic characterization of squamous cell lung cancers. Nature. 489:519–525. 2012. View Article : Google Scholar : PubMed/NCBI

24 

Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI

25 

Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI

26 

Reimand J, Isserlin R, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, Wadi L, Meyer M, Wong J, Xu C, et al: Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc. 14:482–517. 2019. View Article : Google Scholar : PubMed/NCBI

27 

Kanehisa M, Furumichi M, Tanabe M, Sato Y and Morishima K: KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45:D353–D361. 2017. View Article : Google Scholar : PubMed/NCBI

28 

Wang J, Vasaikar S, Shi Z, Greer M and Zhang B: WebGestalt 2017: A more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45:W130–W137. 2017. View Article : Google Scholar : PubMed/NCBI

29 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

30 

Yang L, Liu L, Xu Z, Liao W, Feng D, Dong X, Xu S, Xiao L, Lu J, Luo X, et al: EBV-LMP1 targeted DNAzyme enhances radiosensitivity by inhibiting tumor angiogenesis via the JNKs/HIF-1 pathway in nasopharyngeal carcinoma. Oncotarget. 6:5804–5817. 2015.PubMed/NCBI

31 

Wang X, Xu Z, Ren X, Chen X, Wei J, Lin W, Li Z, Ou C, Gong Z and Yan Y: Function of low ADARB1 expression in lung adenocarcinoma. PLoS One. 14:e02222982019. View Article : Google Scholar : PubMed/NCBI

32 

Zhou S, Yan Y, Chen X, Wang X, Zeng S, Qian L, Wei J, Yang X, Zhou Y, Gong Z and Xu Z: Roles of highly expressed PAICS in lung adenocarcinoma. Gene. 692:1–8. 2019. View Article : Google Scholar : PubMed/NCBI

33 

Xiao D, Lu C, Zhu W, He Q, Li Y, Fu C, Zhou J, Liu S and Tao Y: Comparison of small biopsy specimens and surgical specimens for the detection of EGFR mutations and EML4-ALK in non-small-cell lung cancer. Oncotarget. 7:59049–59057. 2016. View Article : Google Scholar : PubMed/NCBI

34 

Gan Z, Zhao L, Yang L, Huang P, Zhao F, Li W and Liu Y: RNA editing by ADAR2 is metabolically regulated in pancreatic islets and beta-cells. J Biol Chem. 281:33386–33394. 2006. View Article : Google Scholar : PubMed/NCBI

35 

Li Z, Tian Y, Tian N, Zhao X, Du C, Han L and Zhang H: Aberrant alternative splicing pattern of ADAR2 downregulates adenosine-to-inosine editing in glioma. Oncol Rep. 33:2845–2852. 2015. View Article : Google Scholar : PubMed/NCBI

36 

Merkle T, Merz S, Reautschnig P, Blaha A, Li Q, Vogel P, Wettengel J, Li JB and Stafforst T: Precise RNA editing by recruiting endogenous ADARs with antisense oligonucleotides. Nat Biotechnol. 37:133–138. 2019. View Article : Google Scholar : PubMed/NCBI

37 

Kuang L, Lv G, Wang B, Li L, Dai Y and Li Y: Overexpression of adenosine deaminase acting on RNA 1 in chordoma tissues is associated with chordoma pathogenesis by reducing miR125a and miR10a expression. Mol Med Rep. 12:93–98. 2015. View Article : Google Scholar : PubMed/NCBI

38 

Hosaka T, Yamashita T, Teramoto S, Hirose N, Tamaoka A and Kwak S: ADAR2-dependent A-to-I RNA editing in the extracellular linear and circular RNAs. Neurosci Res. 147:48–57. 2019. View Article : Google Scholar : PubMed/NCBI

39 

Fritzell K, Xu LD, Lagergren J and Öhman M: ADARs and editing: The role of A-to-I RNA modification in cancer progression. Semin Cell Dev Biology. 79:123–130. 2018. View Article : Google Scholar

40 

Chan TH, Qamra A, Tan KT, Guo J, Yang H, Qi L, Lin JS, Ng VH, Song Y, Hong H, et al: ADAR-Mediated RNA editing predicts progression and prognosis of gastric cancer. Gastroenterology. 151:637–650.e10. 2016. View Article : Google Scholar : PubMed/NCBI

41 

Altadill T, Dowdy TM, Gill K, Reques A, Menon SS, Moiola CP, Lopez-Gil C, Coll E, Matias-Guiu X, Cabrera S, et al: Metabolomic and Lipidomic profiling identifies the role of the RNA editing pathway in endometrial carcinogenesis. Sci Rep. 7:88032017. View Article : Google Scholar : PubMed/NCBI

42 

Uchida H, Matsumura S, Okada S, Suzuki T, Minami T and Ito S: RNA editing enzyme ADAR2 is a mediator of neuropathic pain after peripheral nerve injury. FASEB J. 31:1847–1855. 2017. View Article : Google Scholar : PubMed/NCBI

43 

Galeano F, Rossetti C, Tomaselli S, Cifaldi L, Lezzerini M, Pezzullo M, Boldrini R, Massimi L, Di Rocco CM, Locatelli F and Gallo A: ADAR2-editing activity inhibits glioblastoma growth through the modulation of the CDC14B/Skp2/p21/p27 axis. Oncogene. 32:998–1009. 2013. View Article : Google Scholar : PubMed/NCBI

44 

Chen M, Liu X, Du J, Wang XJ and Xia L: Differentiated regulation of immune-response related genes between LUAD and LUSC subtypes of lung cancers. Oncotarget. 8:133–144. 2017.PubMed/NCBI

45 

Planchard D, Popat S, Kerr K, Novello S, Smit EF, Faivre-Finn C, Mok TS, Reck M, Van Schil PE, Hellmann MD and Peters S: Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 30:863–870. 2019. View Article : Google Scholar

46 

Bishop JA, Teruya-Feldstein J, Westra WH, Pelosi G, Travis WD and Rekhtman N: p40 (ΔNp63) is superior to p63 for the diagnosis of pulmonary squamous cell carcinoma. Mod Pathol. 25:405–415. 2012. View Article : Google Scholar : PubMed/NCBI

47 

Travis WD, Brambilla E, Burke AP, Marx A and Nicholson AG: Introduction to The 2015 World Health Organization classification of tumors of the lung, pleura, thymus, and heart. J Thorac Oncol. 10:1240–1242. 2015. View Article : Google Scholar : PubMed/NCBI

48 

Kriegsmann K, Cremer M, Zgorzelski C, Harms A, Muley T, Winter H, Kazdal D, Warth A and Kriegsmann M: Agreement of CK5/6, p40, and p63 immunoreactivity in non-small cell lung cancer. Pathology. 51:240–245. 2019. View Article : Google Scholar : PubMed/NCBI

49 

van Zyl A, Schubert PT and Koegelenberg CFN: The utility of TTF-1, napsin A, CK5 and p63 staining in the sub-classification of non-small cell carcinoma of the lung. Cytopathology. 30:586–591. 2019. View Article : Google Scholar : PubMed/NCBI

50 

Wang Y, Han Y, Guo Z, Yang Y and Ren T: Nuclear TAZ activity distinctly associates with subtypes of non-small cell lung cancer. Biochem Biophys Res Commun. 509:828–832. 2019. View Article : Google Scholar : PubMed/NCBI

51 

Hou J, Aerts J, den Hamer B, van Ijcken W, den Bakker M, Riegman P, van der Leest C, van der Spek P, Foekens JA, Hoogsteden HC, et al: Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PLoS One. 5:e103122010. View Article : Google Scholar : PubMed/NCBI

52 

Takamochi K, Ohmiya H, Itoh M, Mogushi K, Saito T, Hara K, Mitani K, Kogo Y, Yamanaka Y, Kawai J, et al: Novel biomarkers that assist in accurate discrimination of squamous cell carcinoma from adenocarcinoma of the lung. BMC Cancer. 16:7602016. View Article : Google Scholar : PubMed/NCBI

53 

Lipsick J: A history of cancer research: Tyrosine kinases. Cold Spring Harb Perspect Biol. 11(pii): a0355922019. View Article : Google Scholar : PubMed/NCBI

54 

Martinez P, Peters S, Stammers T and Soria JC: Immunotherapy for the First-line treatment of patients with metastatic non-small cell lung cancer. Clin Cancer Res. 25:2691–2698. 2019. View Article : Google Scholar : PubMed/NCBI

55 

Liu Y, Jiang H, Zhou H, Ying X, Wang Z, Yang Y, Xu W, He X and Li Y: Lentivirus-mediated silencing of HOTAIR lncRNA restores gefitinib sensitivity by activating Bax/Caspase-3 and suppressing TGF-α/EGFR signaling in lung adenocarcinoma. Oncol Lett. 15:2829–2838. 2018.PubMed/NCBI

56 

Romero-Ventosa EY, Blanco-Prieto S, Gonzalez-Pineiro AL, Rodriguez-Berrocal FJ, Pineiro-Corrales G and Paez de la Cadena M: Pretreatment levels of the serum biomarkers CEA, CYFRA 21-1, SCC and the soluble EGFR and its ligands EGF, TGF-alpha, HB-EGF in the prediction of outcome in erlotinib treated non-small-cell lung cancer patients. Springerplus. 4:1712015. View Article : Google Scholar : PubMed/NCBI

57 

Gao J, Qiu X, Xi G, Liu H, Zhang F, Lv T and Song Y: Downregulation of GSDMD attenuates tumor proliferation via the intrinsic mitochondrial apoptotic pathway and inhibition of EGFR/Akt signaling and predicts a good prognosis in non-small cell lung cancer. Oncol Rep. 40:1971–1984. 2018.PubMed/NCBI

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Volume 21 Issue 3

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APA
Wang, X., Ren, X., Liu, W., Chen, X., Wei, J., Gong, Z. ... Xu, Z. (2020). Role of downregulated ADARB1 in lung squamous cell carcinoma. Molecular Medicine Reports, 21, 1517-1526. https://doi.org/10.3892/mmr.2020.10958
MLA
Wang, X., Ren, X., Liu, W., Chen, X., Wei, J., Gong, Z., Yan, Y., Xu, Z."Role of downregulated ADARB1 in lung squamous cell carcinoma". Molecular Medicine Reports 21.3 (2020): 1517-1526.
Chicago
Wang, X., Ren, X., Liu, W., Chen, X., Wei, J., Gong, Z., Yan, Y., Xu, Z."Role of downregulated ADARB1 in lung squamous cell carcinoma". Molecular Medicine Reports 21, no. 3 (2020): 1517-1526. https://doi.org/10.3892/mmr.2020.10958