SETDB1 Overexpression Sets an Intertumoral Transcriptomic Divergence in Non-small Cell Lung Carcinoma

Abstract

An increasing volume of evidence suggests that SETDB1 plays a role in the tumorigenesis of various cancers, classifying SETDB1 as an oncoprotein. However, owing to its numerous protein partners and their global-scale effects, the molecular mechanism underlying SETDB1-involved oncogenesis remains ambiguous. In this study, using public transcriptome data of lung adenocarcinoma (ADC) and squamous-cell carcinoma (SCC), we compared tumors with high-level SETDB1 (SH) and those with low-level SETDB1 (comparable with normal samples; SL). The results of principal component analysis revealed a transcriptomic distinction and divergence between the SH and SL samples in both ADCs and SCCs. The results of gene set enrichment analysis indicated that genes involved in the “epithelial–mesenchymal transition,” “innate immune response,” and “autoimmunity” collections were significantly depleted in SH tumors, whereas those involved in “RNA interference” collections were enriched. Chromatin-modifying genes were highly expressed in SH tumors, and the variance in their expression was incomparably high in SCC-SH, which suggested greater heterogeneity within SCC tumors. DNA methyltransferase genes were also overrepresented in SH samples, and most differentially methylated CpGs (SH/SL) were undermethylated in a highly biased manner in ADCs. We identified interesting molecular signatures associated with the possible roles of SETDB1 in lung cancer. We expect these SETDB1-associated molecular signatures to facilitate the development of biologically relevant targeted therapies for particular types of lung cancer.

The Updating of Biological Functions of Methyltransferase SETDB1 and Its Relevance in Lung Cancer and Mesothelioma

“…NSCLC accounts for 85% of all lung cancers and is further classified into adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large-cell carcinoma (LCC). NSCLC, especially ADC and SCC, has relatively higher mutation rates than the mean rate of other lung cancer subtypes [ 121 ].…”

“…Recently, overexpression of SETDB1 mRNA was revealed in 1140 ADC and SCC patients compared to noncancerous tissues [ 2 ]. An additional report found that the SETDB1 copy number correlated with the SETDB1 expression level in both SCC and ADC and that amplification of the SETDB1 gene was more frequent in ADC samples than in SCC samples [ 121 ]. Furthermore, the overexpression of SETDB1 promotes the growth of lung cancer cells as well as the invasiveness potential of tumourous cells in vitro and in vivo in nude mouse models [ 12 , 13 , 38 ].…”

“…Epithelial–mesenchymal transition (EMT) plays a pivotal role in the metastasis of NSCLC cells [ 132 ]. Yong-Kook Kang et al showed that an inverse relationship exists between SETDB1 and EMT levels in SCC and ADC [ 121 ], which disagrees with the notion that SETDB1 overexpression promotes metastasis of liver [ 133 ] and breast cancers [ 134 , 135 ]. Overexpressed SETDB1 also leads to underrepresented genes related to EMT collection [ 121 ].…”

“…In addition, overexpressed SETDB1 also regulates the transcription of genes related to the host innate immune response, apoptosis, tumour growth and autoimmunity to inhibit growth and autoimmunity in ADCs and SCCs [ 121 ].…”

SETDB1 regulates microtubule dynamics

“…In recent years SETDB1 has been considered as an oncogene. Its genomic location is commonly amplified in melanoma (Ceol et al, 2011; Orouji et al, 2019) and its expression levels are increased in various types of cancer including melanoma (Ceol et al, 2011; Orouji et al, 2019), colorectal cancer (Chen et al, 2017; Ho et al, 2017; Yu et al, 2019), liver cancer (Fei et al, 2015; Wong et al, 2016; Zhang et al, 2018) and lung cancer (Cruz-Tapias et al, 2019; Kang and Min, 2020; Rodriguez-Paredes et al, 2014; Sun et al, 2015). SETDB1 oncogenic behavior supports cancer cell proliferation, migration and invasion (Chen et al, 2017; Fei et al, 2015; Gauchier et al, 2019; Guo et al, 2019; Orouji et al, 2019; Rodriguez-Paredes et al, 2014; Sun et al, 2015; Wang et al, 2019; Wong et al, 2016; Yu et al, 2019; Zhang et al, 2018).…”