Comprehensive analysis based on methylation microarrays and single-cell transcriptomics in esophageal squamous cell carcinoma

doi:10.21203/rs.3.rs-2237626/v1

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Comprehensive analysis based on methylation microarrays and single-cell transcriptomics in esophageal squamous cell carcinoma

https://doi.org/10.21203/rs.3.rs-2237626/v1

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Aberrant methylated genes (DMGs) play an important role in the etiology and pathogenesis of esophageal squamous cell carcinoma (ESCC). Single-cell transcriptome analysis of immune cells in tumors provides a way to comprehensively study these cells in a highly complex tumor microenvironment (TME). In this study, we combine methylation datasets and single cell datasets to ascertain aberrant methylated-differentially expressed genes and pathways associated with ESCC TME by comprehensive bioinformatics analysis. STRING and Cytoscape software were used to construct a protein-protein interaction (PPI) network. In total, 111 hypomethylated/high-expressed genes were identified at the screening step, and finally found six mostly changed hub genes including COL1A1, COL1A2, COL5A1, COL5A2, COL11A1 and THBS2. Pathway analysis indicated that aberrantly methylated DEGs mainly associated with extracellular matrix structural constituent and cytokine − cytokine receptor interaction. Analyzing the single cell data of ESCC, we confirmed the specific cell cluster highly expressing hub genes and predict the biological function of the cell cluster. These results can shed a light for screening and diagnosis of ESCC in future.

Methylation microarrays

Single-cell transcriptomics

Esophageal squamous cell carci-noma

Aberrant methylated genes

Esophageal squamous cell carcinoma (ESCC) is one of the most commonly diagnosed malignancies, especially in East Asia (Siegel et al. 2020). The research shows that the five-year survival rate of ESCC is less than 10%, making it a serious threat to human health (Ono et al. 2019). Esophageal squamous cell carcinoma is also the tumor types with the highest median mutation burden, higher than kidney, head and neck cancer (Alexandrov et al. 2013). Currently, finding key genes or biomarkers is one of the main directions to of ESCC occurrence and development research, especially to clarify its molecular mechanism (The Cancer Genome Atlas Research Network. 2017). Many characteristics of a highly complex tumor microenvironment (TME) in ESCC are also poor understood (Du et al. 2017). Therefore, the identification of genes related to ESCC, especially in TME are of great significance in clinical diagnosis, treatment and the immune status of ESCC analysis.

Epigenetics refers to the study of molecules and mechanisms that can perpetuate alternative gene activity states in the context of the same DNA sequence (Cavalli et al. 2019). A number of studies have confirmed the tight connection between DNA methylation and TME among several types of cancers, including lung adenocarcinoma (Zhang et al. 2020), gliomas (Binder et al. 2019), pancreatic ductal adenocarcinoma (Morris et al. 2019), and acute myeloid leukemia (Cimmino et al. 2017). These researches uncover how epigenetic characteristics in various processes of tumor development through influence on TME. However, such study have not been done in ESCC.

Combination of single cell data and methylation data provides a new thinking to identify and assess genes that mediate TME and biological processes associated with ESCC. In the present study, we analyzed datasets of gene expression and gene methylation to identify genes that were both abnormally methylated and differentially expressed between tumor tissues and normal tissues from patients with ESCC. The selected genes were then subjected to hierarchical clustering, and functional enrichment analyses of GO and KEGG pathways. Using STRING to create protein-protein interaction networks ,we identified hub genes that were key to these signaling events. In addition, we analyzed single cell data of immune cells from ESCC tumors. Through systematic analysis, we distinguished a specific cluster that highly expressed hub genes and conducted further analysis. Furthermore, we validated the results using GEPIA database and Oncomine database.

2.1 Data and resources

We used three different cohorts for this study. Expression data and single cell data were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) (Zheng et al. 2020). The first cohort was obtained from Affymetrix U133plus2 GEO ESCC Cohort (accession number GSE161533) consisted of 28 normal samples, 28 paratumor samples and 28 clinical ESCC samples. We selected normal samples and tumor samples for the research. The second cohort downloaded from GEO (accession number GSE145370) consisted of 7 surgically removed ESSC tumors and their matched adjacent normal tissues. We used one of total immune cells data of ESCC tumors. Gene expression profiles were obtained using the Illumina NovaSeq 6000 platform. Methylation data were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) (Xu et al. 2019). TCGA datasets contain information from cell lines derived from different tumor types; we used methylation data from ESCC tumors, resulting in 200 samples for this cohort. Profiles were obtained using the Illumina human methylation 450 platform.

2.2 Processing of expression datasets and methylation datasets

We identified differentially expressed genes (DEGs) by comparing the normalized expression data of ESCC samples to that of normal samples from GSE161533. Only DEGs with |log2FC|>1.5 and FDR < 0.05 were extracted using Limma package in R (Kassambara et al. 2019). + Identification of abnormally methylated genes

To identify both abnormally methylated and differentially expressed genes, Venn analysis was utilized to determine overlapping DMGs and DEGs among the GEO dataset and the TCGA dataset above. We selected hypomethylation-high expression genes for further analysis.

2.3 Functional enrichment analysis

We conducted pathway and protein-protein interaction enrichment analyses to predict the biological function of the aforementioned genes using clusterProfiler package in R (Yu et al. 2012). The p-value was set at < 0.01, indicating a statistically significant enrichment score of the GO or pathway terms.

2.4 Construction of PPI networks

We used the STRING database to construct a PPI network of hypomethylated/high-expression genes. An interaction score of 0.4 was regarded as the cut-off criterion and the PPI was visualized. The top 6 hub genes were selected by the CytoHubba app in Cytoscape software.

2.5 Validation of the hub genes

We used the Oncomine database to confirm the expression of hub genes in ESCC (Rhodes et al. 2004). The GEPIA database were used to identify prognostic value of hub genes in ESCC. The human Protein Atlas is a website that involves immunohistochemistry-base expression data for distribution and expression of 20 tumor tissues, 47 cell lines, 48 human normal tissues and 12 blood cells (Thul et al. 2017). In our study, direct contrast of protein expression of different hub genes between normal and HNSCC tissues was used by immunohistochemistry image.

2.6 Cell QC and normalization of single cell data

The single cell data was imported into the Seurat (v3) R toolkit for quality control and downstream analysis (Satija et al. 2015). Low-quality cells (< 200 genes/cell and > 25% mitochondrial genes) were excluded. Data was normalized to log scale using the ‘NormalizeData’ function with a default scale parameter of 10000. Highly variable genes were identified using the ‘FindVariableGenes’ function with selection method "vst". The effects of variation in sequencing depth were regressed out by including ‘nUMI’ as a parameter in the ‘ScaleData’ function.

2.7 Dimensionality reduction, clustering, and annotation

The data were scaled using ‘ScaleData’ and the first 20 principle components were adopted for auto-clustering analyses using ‘FindNeighbors’ and ‘FindClusters’ functions. We identified clusters setting the resolution parameter as 0.5, and the clustering results were visualized with the UMAP scatter plot. The marker genes of each cell cluster were identified using the ROC analysis function provided by the Seurat ‘FindMarkers’ function. Then we extract the specific cluster with high expressed aforementioned hub genes and perform biological process enrichment analysis.

3.1 Identification of abnormally methylated and differentially expressed genes in ESCC

The flowchart of our study is demonstrated in Fig. 1. For differentially expressed genes (DEGs) of the gene expression microarray, 267 overlapping high-expressed genes and 508 overlapping down-expressed genes were identified. For DMGs of the gene methylation microarray, 982 hypermethylated genes and 4323 hypomethylated genes were identified. Then, a total of 111 hypomethylated/high-expressed genes were obtained by overlapping 4323 hypomethylated genes and 267 upregulated genes; 26 hypomethylated/downregulated genes were obtained by overlapping 982 hypermethylated genes and 508 downregulated genes (Fig. 2).

3.2 Gene ontology and pathway functional enrichment analysis

Genes that were hypomethylated and highly expressed were enriched in extracellular structure organization, regulation of ERK1 and ERK2 cascade, endodermal cell differentiation, endoderm development, chemokine − mediated signaling pathway, response to chemokine, cellular response to chemokine, ossification, interleukin − 1 production and collagen metabolic process (Fig. 3A). Molecular Function indicated that hypomethylated highly-expressed genes were correlated with extracellular matrix structural constituent, glycosaminoglycan binding, heparin binding, sulfur compound binding, chemokine activity, metalloendopeptidase activity, endopeptidase activity, platelet − derived growth factor binding, cytokine activity and lipoprotein particle binding(Fig. 3B). For cellular component, hypomethylated/high-expression genes were enriched mainly in complex of collagen trimers, extracellular matrix component, collagen − containing extracellular matrix, collagen trimer, endoplasmic reticulum lumen, basement membrane and azurophil granule membrane (Fig. 3C). The pathway analysis revealed that hypomethylated highly-expressed genes were linked to cytokine − cytokine receptor interaction, protein digestion and absorption, amoebiasis, ECM − receptor interaction, IL − 17 signaling pathway, viral protein interaction with cytokine and cytokine receptor, TNF signaling pathway and Rheumatoid arthritis (Fig. 3D).

3.3 PPI networks analysis

The STRING database was applied for PPI network construction, with MCODE applied for module analysis (Fig. 4). Hub genes were identified using the cytoHubbaCytoscape software. The top three hub genes were COL1A1, COL1A2, COL5A1, COL5A2, COL11A1, THBS2.

3.4 Validation of the hub genes

We used the Oncomine database to confirm the expression of hub genes in ESCC (Fig. 5). The data are consistent with our outcomes. Then, we performed analysis of the protein expression patterns of the Hub gene in ESCC, by utilizing data available from the Human Protein Atlas (Fig. 6). The results showed that the COL1A1 protein gene and the COL5A1 protein gene were lowly expressed in normal esophageal tissues, whereas medium COL1A2 expression was observed in normal tissues. Medium expression of COL1A1 and COL1A2 was observed in ESCC tissue, while high COL5A1 gene expression was appreciated in tumor tissue. The expression of COL5A2, COL11A1 and THBS2 was not found in the database.

3.5 Clustering and subtype analyses

To define groups of cells with similar expression profiles, we performed unsupervised clustering analysis implemented in Seurat software. We grouped all cells into 20 subgroups using clustering analysis (Fig. 7A). Then we use a cut-off of p < 0.05 following Bonferroni correction to identify differentially expressed genes in greater than 25% of cells in a cluster and having a log fold change greater than 0.25. Differential gene expression for identifying markers of a cluster relative to all other clusters or compared to a specific cluster was determined using the ‘FindAllMarkers’ or ‘FindMarkers’ functions respectively and top10 genes were visualized (Fig. 7B). In addition, cell annotation was performed.

3.6 Identification of highly expressed hub genes cell cluster

We checked the expression of hub genes in each cluster and found a noticeable result that 19th cluster highly expressed all the hub genes. Then we conducted GO and pathway enrichment analyses to analysis the function of 19th cluster cell. Marker genes of cluster 19 were enriched in SRP − dependent cotranslational protein targeting to membrane, protein localization to endoplasmic reticulum, nuclear − transcribed mRNA catabolic process, nonsense − mediated decay, viral transcription, viral gene expression, translational initiation, neutrophil activation, neutrophil degranulation, neutrophil activation involved in immune response and neutrophil mediated immunity. The pathway analysis revealed that the marker genes were linked in Coronavirus disease − COVID − 19, Ribosome, Antigen processing and presentation, Phagosome, Graft-versus-host disease, Rheumatoid arthritis, Viral myocarditis, Type I diabetes mellitus, Allograft rejection and Th17 cell differentiation.

Although extensive research efforts have been focused on this field in past decades, the screening of ESCC and accurate prediction against complicated ESCC patients still remain an open issue. Recently, studies have found that the expression of certain genes was highly related to the occurrence and development of ESCC. Moreover, single-cell transcriptome analysis technology has been initially applied to ESCC. However, most works only focused on single cell or limited genes, and the predictive ability was barely narrow. Herein, we combined methylation, mRNA and single cell, to explore the role of specific genes in the potential development mechanism of ESCC and the immune microenvironment.

In the last years, the extracellular matrix (ECM) has been reported as playing a relevant role in ESCC development (Palumbo et al. 2020; Wang et al. 2017). The GO enrichment analysis revealed that the biological processes of hypomethylated/high-expression genes were involved in the regulation of extracellular structure organization, regulation of ERK1 and ERK2 cascade, endodermal cell differentiation, endoderm development, chemokine − mediated signaling pathway, response to chemokine, cellular response to chemokine, ossification, interleukin − 1 production and collagen metabolic process. Our results agree with the fact that carcinoma cell metastasis and invasion are closely associated with abnormal cell adhesion, endodermal cell division and nuclear division (Chai et al. 2019; Han et al. 2020; Zhang et al. 2019). Indeed, this is consistent with the research results that ECM remodeling creates a reorganized environment that promotes tumor progression by destabilizing cell polarity and cell–cell adhesions and by increasing growth factor signaling, which impact on tumor gene expression, differentiation, proliferation, migration (Paszek et al. 2005). Furthermore, the enriched KEGG pathways of hypomethylated highly expressed genes were mainly linked to Cytokine − cytokine receptor interaction, ECM − receptor interaction, IL − 17 signaling pathway, Viral protein interaction with cytokine and cytokine receptor, TNF signaling pathway. It has been demonstrated that ESCC TME is enriched for pro-inflammatory cytokines, chemokines, and growth factors, and their complex cross-talk with their receptors influences the development and progression of ESCC (Diakowska et al. 2013). The cytokines and chemokines secreted by the immune cells such as JAK/STAT, NF-κβ, NOTCH to mediate various properties of cancer hallmarks, mediate cancer-stromal interactions and activate several downstream effector pathways (Nisar et al. 2020). Furthermore, cancer cells secret Monocyte chemoattractant protein-1 (MCP-1/CCL2) into TME and product pro-angiogenic factors (Koide et al. 2004). The other immunosuppressive T cells, such as Tregs and Th17, are recruited by tumor cell-derived chemokines CCL17 and CCL22 and are known to promote ESCC pathogenesis (Xia et al. 2013). In particular, the presence of increased Tregs in the ESCC TME is associated with invasion, metastases, disease severity, decreased survival, and thus considered of prognostic significance (Jiao et al. 2012; Chen et al. 2012). Together, these data suggested that inflammatory cytokine play an essential role in ESCC proliferation, angiogenesis, and metastasis, and regulation of tumor immune microenvironment is helpful for prognosis.

The PPI network of hypomethylated/high-expression genes provides an approach to identifying the functional associations between them and the top six hub genes were selected from this: COL1A1, COL1A2, COL5A1, COL5A2, COL11A1, THBS2. Collagens, a family of 28 members, is the main structural protein of the extracellular matrix consisting of three subtypes, namely fibrillar collagens, non-fibril forming collagens, and fibril-associated collagens (Ricard-Blum 2011). COL1A1, COL1A2, COL5A1, COL5A2, COL11A1 are members of the collagen family, and the dysregulation may represent a basis for cancer invasion and migration. COL1A1 and COL1A2 are two types of type I COL, and the most ECM proteins are comprised of fibrillar collagens (Yamauchi et al. 2018). Recent studies have suggested that COL1A1 was up-regulated in several cancers, including colorectal cancer (Zhang et al. 2018), cervical cancer (Liu et al. 2017), oral squamous cancer and gastric cancer (He et al. 2018; Wang et al. 2018). In pancreatic cancer, COL1A2 was highly expressed (Wu et al. 2019). In ESCC, COL1A1 was negatively correlated with miR-133a-3p, which directly inhibit proliferation and metastasis, and promot the apoptosis of ESCC cells (Yin et al. 2019). It has been reported that PDGF receptor-beta increased the expression of COL1A2 through Akt/mTORC1 signaling pathway (Das et al. 2017). Collagen type V (COL5) is classified as a regulatory fibril-forming collagen, and is formed by the combinations of three different chains, namely COL5A1, COL5A2, and COL5A3 (Nardo et al. 2014). COL5 presents in most connective tissue matrices and plays a functional role in some cancers including breast cancer and pancreatic ductal adenocarcinoma (Chai et al. 2016). A study has revealed the peak role of COL11A1 and THBS2 in a metastasis-associated gene expressing signature (Liu et al. 2018). Notably, in patients with advanced pathological T (T3-T4) stage and lymph node metastases, high COL11A1 expression accounted for 69.1% and 72.5% respectively. Suggesting that up-regulated COL11A1 might take part in the process of invasiveness and regional migration (Zhang et al. 2018).

Alternatively, we leveraged the power of single-cell genomics to characterize the heterogenous cell types and states in the tumor microenvironment. We linked clusters to specific cell types including Effector memory CD8 T cells, Intermediate monocytes, Myeloid dendritic cells, Non − Vd2 gd T cells,Plasmablasts, Progenitor cells, Switched memory B cells, T regulatory cells. The 19th cluster cells highly expressing hub genes were annotated " SRP − dependent cotranslational protein targeting to membrane". The targeting of proteins to a membrane that occurs during translation and is dependent upon two key components, the signal-recognition particle (SRP) and the SRP receptor. SRP is a cytosolic particle that transiently binds to the endoplasmic reticulum (ER) signal sequence in a nascent protein, to the large ribosomal unit, and to the SRP receptor in the ER membrane (Costa et al. 2018). These results gave evidence for the role of 19th cluster cells in protein-targeting efficiency and specificity. KEGG analysis also revealed the connection between 19th cluster cells and ribosome and antigen processing and presentation. The MHC class I antigen presentation system enables T cell immunosurveillance of cancers and viruses. It's reported that 40S ribosomal protein S28 (RPS28) knockdown sensitizes tumor cells for T cell targeting by increasing DRiP synthesis, which degrade to A substantial fraction of the immunopeptidome (Wei et al. 2018).

In summary, our research offers a comprehensive bioinformatics analysis of aberrantly methylated DEGs associated with multiple signaling pathways that may be involved in the occurrence and development of ESCC.In addition, the six most modified hub genes were identified, including COL1A1, COL1A2, COL5A1, COL5A2, COL11A1 and THBS2. Then we performed a genomic analysis on the ESCC single cell datasets and clarified the function of cell clusters expressing hub genes specifically. These new discoveries may provide insights for unraveling the pathogenesis of ESCC, and these candidate genes may be optimal abnormal methylation-based biomarkers for the precise diagnosis and treatment of ESCC. Further molecular biological experiments are required to verify the function of the identified candidate genes in ESCC.

DMGs Aberrant methylated genes

ESCC Esophageal squamous cell carcinoma

TME Tumor microenvironment

PPI Protein-protein interaction

GEO Gene expression omnibus

TCGA The cancer genome atlas

DEGs differentially expressed genes

ECM Extracellular matrix

RPS28 ribosomal protein S28

Author contributions Conceptualization: all authors. Formal analysis and data curation: Qiyu Xiao, Qingguan Huang. Software and visualization: Qiyu Xiao. Writing—original draft preparation: Qiyu Xiao, Qingguan Huang. Writing—review and editing: all authors. Supervision: Qingguan Huang.

Funding This work was supported by the Hunan Provincial Health Commission Project Fund (Grant number: 200209043164)

Conflict of interest The authors have no relevant financial or non-financial interests to disclose

Ethical approval and Informed consent This study was approved by the Ethics Committee of the Huadu District People's Hospital of Guangzhou and Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, and all patients provided written informed consent.

Consent for publication Not applicable.

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Comprehensive analysis based on methylation microarrays and single-cell transcriptomics in esophageal squamous cell carcinoma

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Abstract

Figures

1. Introduction

2. Material And Methods

2.1 Data and resources

2.2 Processing of expression datasets and methylation datasets

2.3 Functional enrichment analysis

2.4 Construction of PPI networks

2.5 Validation of the hub genes

2.6 Cell QC and normalization of single cell data

2.7 Dimensionality reduction, clustering, and annotation

3. Results

3.1 Identification of abnormally methylated and differentially expressed genes in ESCC

3.2 Gene ontology and pathway functional enrichment analysis

3.3 PPI networks analysis

3.4 Validation of the hub genes

3.5 Clustering and subtype analyses

3.6 Identification of highly expressed hub genes cell cluster

4. Discussion

Abbreviations

Declarations

References

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