Lung cancer causes more than 1.6 million deaths per year worldwide, despite current progress in treatment [1
]. The two major lung cancer types are non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The lung cancer mortality rate is driven by the high possibility of metastasis and problems in early diagnosis [2
]. Lung cancer is a complex disease, which involves both genetic and epigenetic alterations (reviewed in [4
The combined use of next-generation high-throughput sequencing (NGS) and ultra-sensitive mass spectrometry technologies has substantially improved our understanding of molecular epigenetic mechanisms, such as histone post-translational modifications (PTMs) and transcriptional regulation in normal and pathological conditions, especially in cancer. Histone PTMs are one of the most important mechanisms of epigenetic regulation of gene expression and chromatin organization. As such, histone lysine methylation is a key regulatory mechanism of chromatin organization. Histone lysine methylation status is regulated by histone lysine methyltransferases (KMTs) and lysine demethylases (KDMs). A large number of studies have substantiated the link between aberrant histone lysine methylation and malignancy, and the roles of KMTs in cancer metastasis [5
]. In particular, the methylation of histone 3 lysine 9 (H3K9), which is directly involved in heterochromatin formation and both gene repression and silencing [6
]. The main H3K9 KMTs, namely, G9A, GLP, SUV39H and SETDB1, are deregulated in many cancers, and variations in the global or local patterns of H3K9 methylations are found in tumor cells. For instance, abnormal H3K9 methylations have been associated with poor survival and higher risk of relapse [7
]. A loss of H3K9 dimethylation (H3K9me2) has been found in prostate, lung and kidney cancer patients [8
], and H3K9me3 is a diagnostic marker of metastasis in lung cancer patients [10
]. In addition, mutations in KMT genes or abnormal expression of KMTs are found in tumors [11
SETDB1 (also named KMT1E) is a major H3K9 KMT known to be required for mammalian development as it regulates pluripotency in the early embryo [12
], stem cell potential and terminal differentiation in many progenitor cell types [13
]. SETDB1 is central in embryonic stem cell (ESC) pluripotency and self-renewal [15
] and in many adult stem cells. Interestingly, tumors consist of heterogeneous cell populations and a subset of cells, so called cancer stem cells (CSCs), which express pluripotency markers and have the ability for self-renewal, such as ESCs. CSCs have been proposed as an origin for certain types of tumors, and the expression of pluripotency markers might hereby play a role [18
]. Thus, SETDB1 key roles in stemness regulation may provide a link between ESCs and CSCs biology. We participated in a study showing that the human SETDB1
gene is amplified in melanoma, in which SETDB1 accelerates tumorigenesis [19
Furthermore, SETDB1 is overexpressed in lung cancer [20
] and silences certain genes by direct interaction with the DNA methyltransferase DNMT3A [21
], and both are implicated in epithelial-to-mesenchymal transition (EMT) and metastasis [22
]. Most interestingly, an amplification of the SETDB1
gene was also described in lung cancer, in which SETDB1 is considered as a pro-oncogene able to increase tumor invasion [23
]. The SETDB1
gene was found to be amplified in lung cancer cell lines and primary tumors [23
]. The same study showed that the SETDB1
gene is amplified several times in human NSCLC and SCLC cell lines and in primary lung tumors. The authors observed an increase in SETDB1
copy number. Importantly, SETDB1 protein overexpression was associated with elevated cell growth rates and the invasive potential of cancer cells in nude mouse models [23
]. High levels of SETDB1
expression are also associated with poor prognosis in terms of overall survival of patients [24
]. SETDB1 hyperactivation affects various signaling pathways such as WNT, MAPK, Toll-like receptors (TLRs), focal adhesion, and JAK-STAT pathways in lung cancer cells [24
Here, we tested the clinical significance of SETDB1 expression in NSCLC, based on the analysis of large-scale transcriptomic datasets. To this end, we conducted different meta-analyses using 45 microarray datasets from the Gene Expression Omnibus (GEO) database. Furthermore, summary receiver operator characteristic curve (sROC) analysis was used to determine the discriminative yield of SETDB1 expression in NSCLC. In parallel, a systematic review of the literature was conducted up to April 2019 to provide information about the association of SETDB1 expression and NSCLC. Our results showed higher levels of SETDB1 mRNA in both ADC and SCC tissues compared to non-cancerous tissue controls. Interestingly, SETDB1 mRNA level was increased in former or current smoker NSCLC patients compared to non-smokers.
Our findings suggest that SETDB1 expression levels could be used as a diagnostic biomarker and/or potentially be used as a therapeutic target in NSCLC.
Lysine methylation is a key post-translational modification that regulates gene expression at different levels, ranging from transcriptional to post-transcriptional and translational. For instance, lysine methylation affects the stability, localization and activity of proteins, such as proteins involved in cell signaling pathways and in the transcriptional and post-transcriptional regulation of gene expression; TP53, NF-κB, YAP and STAT3 are some examples of important methylated proteins [58
]. Therefore, lysine methylation and its regulators have a key impact on normal cell fate and its deregulation in disease, such as in cancer.
Histone lysine methylation plays important roles in lung cancer development [10
]. Dynamic histone lysine methylation status is regulated by the interplay among histone methyltransferases (KMTs) or demethylases (KDMs). The genes coding for these enzymes may be subject to mutations, chromosomal deletions or amplifications, and these factors change the overall histone methylation/demethylation balance. For example, recent publications reported that SETDB1
is subject to gene amplification-associated activation in lung tumorigenesis [23
]. SETDB1 might impact the cancer phenotype by acting on different substrates. Indeed, in addition to its best-known target, namely, H3K9, it is also known that SETDB1 methylates many other non-histone substrates with high relevance to lung cancer. These include the tumor suppressor TP53 and the kinase AKT [50
]. Thus, SETDB1 overexpression in lung cancer cells could be crucial at different molecular levels, not only at the chromatin level.
Here, we asked whether SETDB1 overexpression was related to the clinical features of lung cancer patients with two major types of NSCLC, namely, adenocarcinoma and squamous cell carcinoma.
We analyzed 25 published gene transcriptomic datasets and found that SETDB1
mRNA level was significantly increased in NSCLC tissues compared to normal lung tissues. In many cases, the copy number gain or amplification of the SETDB1
gene locus in primary tumors was accompanied with elevated SETDB1
mRNA and protein levels [23
was also found to be amplified and/or upregulated in several NSCLC cell lines (NCI-H1437, NCI-H1395, A549, Calu-1, SK-MES-1, SK-LU-1, SW-900, and PC14) [20
Our subgroup analyses for ADC and SCC showed higher SETDB1
mRNA levels in ADC as compared to SCC, while SETDB1
expression in both cancer subtypes was still significantly higher than in normal lung tissues. This is consistent with lower levels of SETDB1
amplification in SCC compared to ADC [49
We observed no statistically significant correlation between the clinical stage of ADC or SCC and SETDB1
expression. Previously published studies differ on this issue. Indeed, Inoue et al. reported that SETDB1
amplification in ADC was associated with an advanced cancer stage [49
]. In contrast, Lafuente-Sanchis et al. showed that high SETDB1
expression in NSCLC was observed at the earliest cancer stages [51
]. In all cases, amplification and a high level of expression of SETDB1
were associated with a shorter disease-free survival [49
]. These discrepancies may be due to both the different selection criteria of the cases but also to a different number of patients included in these studies.
Several studies observed overexpression of SETDB1
in other types of tumors, like hepatocellular carcinoma and melanoma, which was associated with a poor prognosis [19
]. Importantly, the silencing of SETDB1 was shown to inhibit cell proliferation, cell invasion, tumor growth and metastasis in different types of cancer [65
]. In vitro and in vivo experiments showed that SETDB1
overexpression was associated with elevated cell growth rates and invasive potential of cancer cells in nude mouse models [20
]. SETDB1 hyperactivation affects various signaling pathways, such as the WNT, MAPK, Toll-like receptors (TLRs), focal adhesion, and JAK-STAT pathways in lung cancer cells [24
]. In particular, the WNT signaling pathway helps maintain cancer stem cells and correlates with an increased tumor growth and initial potential [67
]. The major (canonical) WNT pathway signaling occurs through β-catenin [68
]; abnormal expression of β-catenin is linked to the development of particular types of breast, colorectal, prostate and lung cancers [69
]. Wang et al. demonstrated that SETDB1-mediated AKT methylation correlates with AKT hyperactivation in NSCLC, promotes tumor development and predicts poor outcome [50
]. Chen et al. showed that SETDB1 negatively regulated the expression of TP53
]. Indeed, Lafuente-Sanchis et al., with multivariate analysis, confirmed the independent prognostic value of SETDB1 for patients with the early stage of NSCLC [51
As many other oncogenes, in certain conditions SETDB1 can participate in tumor suppression: the expression of SETDB1
was significantly decreased in highly metastatic sublines of the CL1 lung cancer cell line (adenocarcinoma) [52
], but at the same time SETDB1
mRNA was high in the primary tumor samples in the early stages of NSCLC compared to the advanced stages. Accordingly, Wu et al. reported not only a pro-oncogenic role of SETDB1, but also an anti-oncogenic role in different stages of lung carcinogenesis, which is probably related to the cellular model chosen [52
]. Thus, SETDB1 could play different roles in lung tumorigenesis. A strong correlation exists between high SETDB1
expression and the earliest stage of NSCLC, supporting the role of the gene at least in the first step of lung tumorigenesis. At later stages, SETDB1 becomes dispensable for tumor progression and its expression diminishes, though it remains high compared to normal lung epithelial cells. This behavior is found with many oncogenes [70
Our findings open up the possibility to use SETDB1 expression level as a marker for early detection of patients at early stages of NSCLC and as a potential drug target in these patients.
4. Materials and Methods
4.1. Search Strategy for Microarray Databases in the Gene Expression Omnibus (GEO) Repository
Available microarray datasets related to NSCLC were downloaded from the GEO repository (https://www.ncbi.nlm.nih.gov/gds
). The final date for inclusion was April 2019. The search strategy included the terms (“Carcinoma, Non-Small-Cell Lung” [Mesh]) AND (“Homo sapiens” [porgn:_txid9606]).
The inclusion criteria were the following: (1) enrolled data must be obtained from humans; (2) microarray datasets with information about SETDB1 expression; (3) the sample type is not cell lines; (4) sufficient information to calculate the standardized mean difference (SMD); (5) for association analyses between SETDB1 expression and NSCLC, two types of studies are included: (i) paired cancerous and adjacent non-cancerous tissues resected from NSCLC patients, (ii) cancerous specimens from NSCLC patients and normal specimens from a healthy control group. Importantly, the sample size must contain at least a ratio of 4:1 for cases and controls; (6) for clinical and pathological analyses, patients who had adenocarcinoma or squamous cell carcinoma with clinical information.
4.2. Data Extraction
Based on the inclusion criteria, the following detailed parameters were extracted: GEO accession number, PubMed identifier (PMID), sample type, cancer type (NSCLC, ADC or SCC), sample size, gender, age, cancer stage, smoking history and expression values of SETDB1, by using the tool GEO2R from the National Center for Biotechnology Information (NCBI).
4.3. Statistical Analysis
For each GEO dataset, the association between SETDB1 expression and NSCLC was assessed by a Student’s t-test or a Mann–Whitney unpaired test based on normality distribution. Furthermore, to generate individual receiver operator characteristic (ROC) curves, the true positive (TP), false positive (FP), false negative (FN), and true negative (TN) values were estimated. All aforementioned analyses were performed using the Statistical Package for Social Sciences (SPSS Version 25, Chicago, IL, USA).
For meta-analysis, standardized mean difference (SMD) with 95% confidence interval (95% CI) was used as a summary statistic, considering the fact that all studies measured the same outcome but at different scales. Heterogeneity was calculated by means of Cochran’s (Q) and Higgins’s (I2) tests. The I2 test was expressed as a ratio ranging from 0% to 100%. If I2 > 30% and p-value < 0.05, the random-effects model was selected. Otherwise, the fixed-effects model was selected. A significant Q-statistic (p < 0.10) indicated heterogeneity across studies. To further evaluate the probable sources of heterogeneity, subgroup analyses were carried out. The presence of publication bias was graphically examined using funnel plots and Egger’s regression asymmetry tests. Data were analyzed using the Comprehensive Meta-Analysis version 2 program (Biostat, Englewood, NJ, USA 2004).
For diagnostic study, a summarized receiver operator characteristic curve (sROC) was constructed and the area under the sROC curve (AUC) was recorded, as well as the sensitivity and specificity. These analyses were performed using the MetaDiSc 1.4 software.
4.4. Search Strategy for Peer-Reviewed Journals
A systematic review of electronic databases (Pubmed, EBI-EMBL, Web of Science, Embase, Bibliovie and Cochrane Library) was done independently by two experts. The final date for inclusion was April 2019. The search included publications about the association of SETDB1 and NSCLC. The search strategy used MeSH terms (“Carcinoma, Non-Small-Cell Lung”[Mesh]) AND (“SETDB1 protein, human” [Supplementary Concept]). Only manuscripts published in a peer-reviewed journal as a full paper were included. Summaries or abstracts were not accepted.