Lung cancer remains the leading cause of cancer related deaths worldwide. Patient mortality rates are failing to decrease, despite emerging therapies such as immune checkpoint inhibition. There is an immediate need for more effective treatment strategies to reduce patient mortality.
Dysregulated gene expression is common in many types of cancers, despite tightly regulated control at multiple stages. Genome-wide association studies of mutations have been conducted on many cancers; some genes such as TP53, RB1, EGFR, and KRAS are frequently mutated in varying cancers, whereas some mutations are rare or restricted to specific cancers. In recent years epigenetic marking of the genome has emerged as a means of regulating gene expression. Advancement in next-generation technologies, combined with epigenetic analysis tools, has improved our knowledge of non-genomic cancer-associated changes, which occur much faster than changes to the genome and can be reversible.
Histological classification defines two of the most common types of cancer as lung squamous cell carcinoma (LUSC) and adenocarcinoma (LUAD). However, defining the molecular mechanisms of cancer development is complicated by the presence of adjacent non-malignant stroma containing infiltrating inflammatory cells, neovascular cells and fibroblasts. Previous analysis of lung cancers using Next Generation Sequencing (NGS) and epigenetic profiling has involved the use of bulk tissue, which can sometimes mask the changes that occur in the cancer cells due the presence of the stromal cells. In this study, the authors developed a workflow for integrative RNA-sequencing (RNA‑Seq) and chromatin immunoprecipitation sequencing (ChIP‑Seq) using laser capture microdissected clinical samples of LUAD and LUSC.
RNA‑Seq was conducted on laser capture microdissected tumour cells, matched normal and tumour-associated stromal cells. Principal Component Analysis (PCA) demonstrated good clustering of the three types of tissue. For tumour samples, the two different types of tumours clustered separately, suggesting distinct transcriptomic profiles.
4,118 differentially expressed genes (DEGs) were identified between tumour and normal samples. When filtered by tumour subtype, 3,211 DEGs were identified for LUAD samples and 2,771 for LUSC.
In the lung adenocarcinoma samples, claudin 2 (CLDN2), secretoglobin 3A2 (SCGB3A2), and mucin 21 (MUC21) were highly expressed compared to control. These have been previously identified as potential biomarkers for its subtype. Similarly, keratin family members (KRT6A, KRT6B, KRT6C, KRT14, KRT16) have been reported as upregulated only in LUSC.
In KEGG pathway analysis, both LIAD and LUSC had the biosynthesis of amino acids and carbon metabolism in the upregulated pathways, whilst focal adhesion and cell adhesion molecules (CAMs) were enriched in downregulated pathways. Pathways included: Mucin-type O‑glycan biosynthesis (upregulated in LUAD), cell cycle (upregulated in LUSC), cGMP‑PKG signalling (downregulated in LUAD), and viral myocarditis (downregulated in LUSC).
Genes identified in this study were consistent with reference datasets for both LUAD and LUSC subtypes from the cancer genome atlas (TCGA).
ChIP‑Seq analysis identified 645 promoter binding sites that were differentially H3K4me3 methylated in NSCLC compared to normal controls. 351 promoters had higher methylation in the tumours, whilst 294 had lower levels of methylation.
444 promoters (approximately 70% of the 645) mapped to transcription start sites. 50% of promoter binding sites were associated with 310 genes that were differentially expressed in the RNA‑Seq dataset.
Levels of methylation at the promotor region were inversely related to levels of expression in the tumour cells. Pathway analysis demonstrated enrichment of genes involved in PI3K‑Akt signalling cell matrix adhesions in cancer: extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), and focal adhesion.
Downregulated tumour suppressor genes in the tumour regions included integrin alpha8 (ITGA8), Fms-like tyrosine kinase‑1 (FLT1), junctional adhesion molecule 2 (JAM2), calcium voltage-gated channel auxiliary subunit alpha2delta2 (CACNA2D2) and dystrophin (DMD).
Amongst the upregulated genes there were also oncogenes such as p‑cadherin (CDH3), claudin‑1 (CLDN1), desmoglein‑2 (DSG2), ephrin A3 (EFNA3), integrin subunit beta 4 (ITGB4), integrin subunit beta8 (ITGB8) and laminin gamma 2 (LAMC2).
31% of methylated regions did not overlap transcription start sites and were classed as enhancers. GeneHancer analysis revealed that 126 genes corresponding to these enhancers were present in the RNA‑Seq dataset, which were mostly transcription factors, including oncogenes such as high mobility group A2 (HMGA2), SRY‑Box transcription factor 2 (SOX2), forkhead box A1 (FOXA1) and potential tumour suppressors such as zinc finger protein 750 (ZNF750), GATA binding protein 2 (GATA2) and grainyhead-like transcription factor 1 (GRHL1).
Many of the differentially expressed genes were located in regions regulated by the PRC2 epigenetic regulator complex.
In this study, the authors used laser capture microdissection to isolate tumour and stromal cells from lung adenocarcinoma and squamous cell carcinoma patients. RNA‑Seq and ChIP‑Seq were then performed on these samples, free from the contamination commonly seen in bulk RNA‑Seq samples. RNA‑Seq and ChIP‑Seq correlated well, identifying the gain of potential oncogenes and loss of tumour suppressors. Different gene panels were observed in LUAD compared to LUSC, although in this pilot study there were too few samples for significant conclusions to be made.
Ong et al. Integrative RNA-Seq and H3 Trimethylation ChIP-Seq Analysis of Human Lung Cancer Cells Isolated by Laser-Microdissection. Cancers 2021, 13(7), 1719