Targeting tumor differentiation grade-related genes prognostic signature including COL5A1 based on single-cell RNA-seq in gastric cancer

doi:10.21203/rs.3.rs-3787159/v1

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Targeting tumor differentiation grade-related genes prognostic signature including COL5A1 based on single-cell RNA-seq in gastric cancer

https://doi.org/10.21203/rs.3.rs-3787159/v1

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Background

Tumor differentiation grade has been shown to be an independent prognostic factor in gastric cancer (GC). Here,we report a novel tumor differentiation grade-related genes signature to predict prognosis and provide new biomarkers in GC.

Methods

ScRNA-seq profiles of GC were analyzed by seurat package. Core modules and key genes related to tumor differentiation grade were identified through a weighted gene co-expression network analysis (WGCNA) from The Cancer Genome Atlas (TCGA) database. A prognostic signature associated with tumor differentiation grade module including COL5A1 was constructed in GC and validated.

Results

We identified the single-cell expression profiling and revealed the cell differentiation, cell clusters, marker genes in GC. Functional enrichment analysis revealed that common differentially expressed genes (DEGs) from cell transition trajectory were mainly enriched in neutrophil process. Integrating clinical factors in GC, WGCNA analysis indicated that tumor differentiation grade module was the most significant. We established and validated this signature based on ten tumor differentiation grade-related genes (TNFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13 and RGS2). Cox regression analysis showed that age, TNM stage and the risk score were significantly associated with prognosis. And then, these genes could predict prognosis in GC. Finally, the hub gene COL5A1 was a prognostic factor, and obviously correlated with B cells memory, dendritic cells activated, macrophages M0, macrophages M2, plasma cells, T cells follicular helper in GC.

Conclusions This study reveals a novel tumor differentiation grade-related genes signature predicting prognosis in GC, and COL5A1 represents a promising biomarker for GC immunotherapy.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

tumor differentiation grade

COL5A1

single-cell sequencing

gastric cancer

Gastric cancer (GC) is an aggressive tumor ranking fourth for mortality globally[1], and its incidence rate is increasing over the recent decades. Although treatments with surgery and chemotherapy lead to a satisfactory survival benefit[2, 3], the 5-year overall survival rate of GC is still low. Increasing biomarkers such as circular RNAs, microRNAs, and tumor microenvironment‑related genes have been identified to predict prognosis in GC[4–6]. Therefore, it is important to excavate the predictive biomarkers and find a novel strategy for GC.

With the increasing studies of cancer pathology, the tumor differentiation grade has been shown to be a prognostic factor in many cancers. Kristoffer derwinger et al. reported that the tumor differentiation grade was correlated significantly with the overall TNM stage and the risk of having lymph node metastasis[7]. A previous study has shown that apoptosis and cell proliferation correlated with tumor differentiation grade in patients with lung adenocarcinoma[8]. However, the prognostic risk model of tumor differentiation grade-related genes in GC was unexplored.

Single-cell RNA sequencing (scRNA-seq) is applied to explore the cellular components and gene expression at the single-cell level[9], and can provide a new insight into tumor heterogeneity. Moreover, several studies have elucidated the association between TME and intra-tumoral heterogeneity using scRNA-seq[10, 11]. ScRNA-seq is also a powerful way for identifying anti-cancer drug response[12, 13]. However, the potential roles of the tumor differentiation grade-related genes and prognostic signature based on scRNA-seq have not been elucidated yet.

The present study develops a novel tumor differentiation grade-related genes prognostic signature which included ten genes (TNFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13 and RGS2). These genes are also highly connected in the protein-protein interaction (PPI) network. The hub gene COL5A1 is supposed to be significantly associated with tumor infiltrating immune cells and can be potential targets for prognosis in GC. Our data could support a deeper understanding of tumor differentiation grade-related genes in GC to provide appropriate therapy strategies.

The study flowchart

The work flowchart is shown in Fig. 1A, revealing the process of tumor differentiation grade-related genes prognostic signature construction based on scRNA-seq in GC.

Data processing

ScRNA-seq data from six primary gastric cancers and four normal gastric tissues were downloaded from GSE112302 dataset. Gene expression data was downloaded from GEO database including gastric cancer and normal tissues in GSE84437 for validation cohort. The original gene expression profiles and clinical data of GC in training cohort were obtained from the TCGA data portal. Firstly, seurat package was used to control data quality, and principal component analysis (PCA) was performed to conducted linear dimensionality reduction. Moreover, the t-SNE algorithm was applied to perform the cluster classification analysis and selected out the marker genes. Finally, the marker genes were annotated by the cluster and cell categories based on the SingleR package (Version 0.99.13), and pseudotime analysis of cells was performed via the monocle package (Version 2.12.0).

Functional enrichment analysis

R package 'ggplot2', 'enrichplot', 'clusterProfiler' and 'GOplot' was applied to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) GO and KEGG enrichment analysis. Functional annotation with a P-value < 0.05 was considered statistically significant. Gene Set Enrichment Analysis (GSEA) is utilized for exploring signaling pathways in GC between the low and the high tumor differentiation grade risk.

The correlation of COL5A1 with tumor-infiltrating immune cells (TIICs)

CIBERSORT analysis tool is the deconvolution algorithm on basis of gene expression patterns for expressing tissue cell composition[14]. The “CIBERSORT.R” package was performed to generate a proportion matrix of TIICs. The correlation of COL5A1 with TIICs was analyzed in this study.

Development and validation of tumor differentiation grade-related genes prognostic signature

WGCNA analysis was used to identify the gene association patterns between different samples and highly coordinated gene sets[15]. The significant modules related to clinical traits were screened out using WGCNA. DEGs based on tumor differentiation grade module from WGCNA analysis were used to establish a multivariable Cox regression model. The riskscore formula was constructed according to the previous formula[16]. The riskscore formula was also used in training cohort and validation cohort.

Protein-protein interaction (PPI) and overall survival of tumor differentiation grade-related genes in GC

STRING database (https://cn.string-db.org/) is a functional protein association network, assembling all known and predicted proteins. The protein-protein interaction network of ten tumor differentiation grade-related genes was analyzed by STRING database (https://string-db.org/). The Kaplan Meier plotter is applied to evaluate the correlation between the expression of all genes (mRNA, miRNA, protein) and survival in 30k + samples from 21 tumor types[17]. The tool's primary purpose is the discovery and validation of survival biomarkers.

Statistical analysis

R software (version 4.0.0) was used to perform all statistical analyses in this study. Cox regression analysis, Kaplan-Meier curves with the log-rank test, Receiver operating characteristic (ROC) curve the corresponding area under the ROC curve (AUC) were conducted by the glmnet, survival, survivalROC packages. Statistical significance was set at P < 0.05.

scRNA-seq profiling of the tumor landscape in primary GCs and normal gastric tissues

With scRNA-seq, 401 cell samples were acquired from 6 patient-derived GC tissues and 4 gastric normal tissues. Firstly, the study of quality control and data filtering was performed. Subsequently, the variance analysis revealed CALD1, PI3, REG1B, C1QB, APOE, BPIFB1, GKN2, MUC6, GKN1 and LIPF were the top ten significant DEGs in 1500 genes (Fig. 1B). The principal component analysis (PCA) method was performed to screen out the significantly correlated genes in each component (Fig. 1C-E). We also used the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm successfully classified the samples into six clusters (Fig. 1F). The expression of tumor differentiation grade-related genes prognostic signature in clusters (TNFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13 and RGS2) was shown in Fig. 1G. Additionally, cell type was annotated for each cell sample, and all the cells in the six clusters were annotated as epithelial cells and macrophage (Fig. 1H). Cell transition trajectory analyses showed that marker genes expression patterns changed in three ways, and we divided the cells into three branches: branch 1, branch 2, and branch 3. Branch 2 mainly represented macrophages. (Fig. 1I-K).

Functional enrichment analysis of common DEGs from cell transition trajectory

To investigate the biological functional of common DEGs based on branch 1, branch 2 and branch 3, GO and KEGG were performed by R package “ggplot2”, “enrichplot”, “clusterProfiler” and “GOplot”. In this study, we found that neutrophil degranulation, neutrophil activation involved in immune response, neutrophil mediated immunity and neutrophil activation played an important role in biological processes (BP), cell components (CC), and molecular functions (MF) of GO terms (Fig. 2A-C). In addition, we observed that common DEGs from cell transition trajectory were mainly enriched in coronavirus disease- COVID-19, legionellosis, rheumatoid arthritis, complement and coagulation cascades, pertussis, malaria, lysosome, antigen processing and presentation (Fig. 2D-F). Above results showed that neutrophil could play an important role in GC.

Tumor differentiation grade-related genes identification

After data preprocessing, a total of 618 common DEGs from three branches were used to perform weighted gene co-expression network analysis (WGCNA). We also observed that 618 genes were also common in the expression matrix of GSE84437 and TCGA. The adjacency matrix and the topological overlap matrix were constructed (Fig. 3A). Next, four modules were identified based on average hierarchical clustering and dynamic tree clipping (Fig. 3B). Histological grading system revealed the extent of malignancy of GC. Jayanthi, V. et al. constructed grade-specific molecular interaction networks identified grade-specific biomarkers for breast cancer[18]. In this study, the blue module, brown module, grey module and turquoise module were highly related to pathological grades. Four modules were selected as a clinically important module for further analysis (Fig. 3C). Finally, a total of 190 differentially expressed genes were identified, as shown in Fig. 3D, 3E. We then integrated the clinical data from all patients of TCGA dataset, univariate analysis revealed that the expression of FCGR2A, TNFAIP2, MAGEA3, CXCR4, LAMB1, COL4A1, SPARC, COL4A2, BGN, COL1A1, COL1A2, THY1, TIMP1, FN1, INHBA, VCAN, PXDN, F2R, COL5A1, DUSP1, PPP1R14A, KLF5, MUC13, MYL9, PDLIM3, EHF, RGS2, GPX3, and AGT were distinctly associated with prognosis in GC patients (Fig. 3F).

Risk model construction of tumor differentiation grade-related genes and external data validation

We then selected ten genes based on the coefficients derived from the multivariate analysis to construct risk model. The coefficients of the ten prognostic genes are shown in Table 1. The study formula for the risk score was as follows[19]: Risk score = ( -0.209406939 × expression value of TNFAIP2) + ( 0.126113147 × expression value of MAGEA3) + ( 0.171457823 × expression value of CXCR4) + ( 0.58121411 × expression value of COL1A1)+( -0.2694724× expression value of FN1 ) + ( 0.345428991 × expression value of VCAN)+( -0.800354177 × expression value of COL5A1)+( -0.098651917 × expression value of MUC13) + ( 0.345428991 × expression value of PXDN) + ( 0.202535842 × expression value of RGS2). To investigate genes expression profiles in high-risk and low-risk GC groups, gene heat map, the risk score distribution and follow-up time were shown in Fig. 4A,4C,4E. The Kaplan-Meier plot revealed that patients in the high-risk group had a significantly poorer OS than those in the low-risk group in the train cohort (Fig. 4G). The prognostic signature had a good accuracy to predict OS in the train cohort with 1-year, 3-year and 5-year AUC of 0.570, 0.557, 0.580 (Fig. 4H).

The prognostic signature associated with grade module was validated using the same risk score formula and the same cut-off value. The gene expression pattern, risk distribution, survival status, the Kaplan-Meier plot and AUC were shown in Fig. 4D, 4F, 4I, 4J. Multivariate Cox regression analyses showed that age, stage and the risk score were found to be independent risk factors for prognosis in patients with gastric cancer (Fig. 4K-L). These analyses indicated that tumor differentiation grade-related genes and constructed risk prognostic models have good prognostic value.

Table 1

**The coefficients of the ten prognostic genes based on scRNA-seq in GC**
id	coef	HR	HR.95L	HR.95H	pvalue
TNFAIP2	-0.209	0.811	0.681	0.966	0.019
MAGEA3	0.126	1.134	1.043	1.234	0.003
CXCR4	0.171	1.187	0.993	1.419	0.060
COL1A1	0.581	1.788	1.160	2.758	0.009
FN1	-0.269	0.764	0.581	1.004	0.054
VCAN	0.267	1.306	0.941	1.814	0.110
PXDN	0.345	1.413	1.007	1.981	0.045
COL5A1	-0.800	0.449	0.241	0.838	0.012
MUC13	-0.099	0.906	0.813	1.010	0.075

GSEA enrichment and Kaplan-Meier Plotter analysis

In this study, GSEA software were applied to analyze the low-risk group and the high-risk group of tumor differentiation grade-related genes signature. We showed that grade-related genes in this signature are significantly enriched in the high-risk (Fig. 5A) and low-risk groups (Fig. 5B). The top five KEGG pathways in the high-risk group were neuroactive ligand receptor interaction, ECM receptor interaction, GAP junction, hypertrophic cardiomyopathy HCM, and focal adhesion. However, the top five KEGG pathways in the low-risk group were peroxisome, pyrimidine metabolism, glycolysis gluconeogenesis, alzheimers disease, and oxidative phosphorylation. Kaplan-Meier survival analysis suggested that the expression of TNFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13, and RGS2 had significantly worse prognosis (P < 0.05) (Fig. 5C-L).

COL5A1 was significant differentially expressed and associated with immune cells

Among tumor differentiation grade-related genes in the prognostic signature, collagen type V alpha 1 (COL5A1) is a key molecular node (Fig. 6A). Therefore, COL5A1, a risk factor of the risk model, was analyzed for subsequently experimental verification. We found that COL5A1 was highly expressed in the GC tissues compared to the adjacent normal tissues in the Human Protein Atalas webserver (Fig. 6B). Subsequently, we observed that the expression of COL5A1 was significantly associated with B cells memory (Fig. 6C), dendritic cells activated (Fig. 6D), macrophages M0 (Fig. 6E), macrophages M2 (Fig. 6F), plasma cells (Fig. 6G), T cells follicular helper (Fig. 6H). These data showed that COL5A1 could play an important role in tumor microenvironment in GC.

Morphological heterogeneity and genetic heterogeneity comprise tumor heterogeneity affecting diagnosis and therapy in cancer[20]. Gastric cancer is a highly heterogeneous malignant cancer with virous subtypes and clinical behaviors[21, 22]. Here, we explored tumor heterogeneity at the single cell level in GC using scRNA-seq.

Our result based on scRNA-seq analysis showed that tumors included virous cells, such as malignant cells, tumor infiltrating cells and stromal components. Cell transition trajectory analysis is widely used to explore different cell types at different stages of development and differentiation in several reports[23, 24]. We identified three branches in cell transition trajectory. In this study, common DEGs were analyzed in three branches. Our analysis revealed that these genes were enriched in Coronavirus disease 2019 (COVID-19). COVID-19 is defined as a respiratory tract infection caused by the severe acute respiratory syndrome (SARS) coronavirus (COV), also named SARS-CoV-2[25]. COVID-19 was first found in Central China (Wuhan, the capital of Hubei province) at the end of December 2019[26]. Many reports have explored the relationship of COVID-19 with cancer[27–29]. Hoang, T. et al reported that genetic susceptibility of ACE2 and TMPRSS2 in GC was associated with the susceptibility to COVID-19 infection[30].

The tumor microenvironment (TME) consists of a heterogenous cellular component affecting cancer cell behavior. Sathe, A. et al showed that gastric cancer TME was significantly enriched for stromal cells, macrophages, dendritic cells (DC), and Tregs[31]. Our study also found that cells cluster 5 was main macrophages. We investigated that common DEGs in three branches were associated with neutrophil degranulation, neutrophil activation involved in immune response, neutrophil mediated immunity and neutrophil activation. Increasing studies revealed that neutrophil played a critical role in tumor microenvironment[32–34].

In this study, we used ten tumor differentiation grade-related genes for the first time to establish the prognostic signature including TNFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13, and RGS2 based on single-cell RNA-seq. More importantly, age, stage and riskscore were independent prognostic factors. To our knowledge, this is the first study that directly revealed that a prognostic risk model based on tumor differentiation grade-related genes may predict prognosis in GC.

Many recent study showed that TNFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13, and RGS2 could predict overall survival as a single gene biomarker[35–43]. Here, we found that the expression level of TNFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13, and RGS2 is closely related to the prognosis of GC patients.

Several studies have used PPI network to study the interactome of protein and screen hub genes[44]. Our PPI network studies demonstrated that COL5A1 was a key molecular node. As the collagen family members, high level of COL5A1 is closely associated with the poor prognosis of multiple human tumors[45, 46]. Focused on the molecular role of COL5A1 in malignant cells, investigators have reported that NAT10 promoted GC metastasis via N4-acetylated COL5A1[47]. We also observed that COL5A1 was differentially expressed in GC tissues. A previous study shows that COL5A1 was a cancer-associated fibroblast gene signature as a poor prognostic factor and potential therapeutic target in GC[48]. Given that the correlations of COL5A1 with immune infiltrating cells, our results revealed that COL5A1 was significantly correlated with B cell memory, dendritic cells-activated, macrophage M0, macrophage M2, plasma cells, and T cells follicular helper, suggesting its role in regulating TME. However, this study has no enough clinical samples to validate this prognostic signature using our experimental data.

Together, we screened tumor differentiation grade-related genes prognostic signature including NFAIP2, MAGEA3, CXCR4, COL1A1, FN1, VCAN, PXDN, COL5A1, MUC13 and RGS2, and provided evidence of GC heterogeneity based on single-cell RNA-seq. Importantly, COL5A1 may be as a novel therapeutic target and biomarkers for GC.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

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

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the Youth Program of the National Natural Science Foundation of China (82003301) and the Tianjin Zhong Dian Xue Ke (ZDXK05202217) and (ZDXK05202201)

Authors' contributions

JW wrote the main manuscript text and XG prepared figures 1-6. All authors reviewed the manuscript.

Acknowledgements

We acknowledge TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets.

Authors' information

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No competing interests reported.

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Targeting tumor differentiation grade-related genes prognostic signature including COL5A1 based on single-cell RNA-seq in gastric cancer

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Abstract

Figures

Introduction

Materials and methods

The study flowchart

Data processing

Functional enrichment analysis

The correlation of COL5A1 with tumor-infiltrating immune cells (TIICs)

Development and validation of tumor differentiation grade-related genes prognostic signature

Protein-protein interaction (PPI) and overall survival of tumor differentiation grade-related genes in GC

Statistical analysis

Results

scRNA-seq profiling of the tumor landscape in primary GCs and normal gastric tissues

Functional enrichment analysis of common DEGs from cell transition trajectory

Tumor differentiation grade-related genes identification

Risk model construction of tumor differentiation grade-related genes and external data validation

GSEA enrichment and Kaplan-Meier Plotter analysis

COL5A1 was significant differentially expressed and associated with immune cells

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1