Extracellular Matrix Organization Genes Predict the Prognosis and Immune Microenvironment in Stomach Adenocarcinoma

doi:10.21203/rs.3.rs-2973037/v1

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Extracellular Matrix Organization Genes Predict the Prognosis and Immune Microenvironment in Stomach Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-2973037/v1

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Background

Extracellular matrix (ECM) dysregulation is crucial for malignant progression of human cancer. However, its effect on immune microenvironment of stomach adenocarcinoma (STAD) is unclarified.

Method

The Cancer Genome Atlas (TACG) database was utilized to retrieve STAD gene expression profiles and clinical information. Genes significantly associated with prognosis (P < 0.05) were assessed by univariate Cox regression. The Gene Ontology (GO) enrichment and protein-protein interaction (PPI) network were performed to identify the critical bioprocesses. Multivariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were utilized to identify independent prognostic signature. Differentiating between clusters according to ECM organization gene expression (ECMOGs) was achieved by unsupervised clustering analysis. The biological characteristics in the ECMOGs clusters were determined. The tumor microenvironment (TME) features, including immunization checkpoint block (ICB) response, immune checkpoint genes, and tumor-infiltrating lymphocytes, were predicted between the distinct clusters.

Results

A total of 948 genes were identified as STAD prognostic genes. ECM organization was found to be the most significant bioprocesses these prognostic genes enriched in. Twelve ECMOGs were considered to be linked to the development of STAD. Three ECMOGs (ADAMTS1, LOX, NFKB2) were determined as independent predictor signatures for risk scoring. Based on twelve ECMOGs expression, the 348 patients were divided into cluster 1 and 2. Patients in cluster 1 had the higher levels of the detected ECMOGs and the risk score, as well as the poor prognosis. Cluster 1 was distinguished by its higher ICB response as well as the infiltrated M2 macrophage.

Conclusion

This study demonstrated that ECMOGs were associated with a bad prognosis partly by affecting the immune microenvironment, which could be the potential biomarker for the diagnosis and prognosis, and therapeutic target in STAD.

stomach adenocarcinoma

extracellular matrix

immune microenvironment

biomarker

prognostic signature

Stomach cancer (SC) is a multifactorial high mortality malignancy worldwide [1], due to its aggressiveness and heterogeneity, it remains a global health concern [2]. STAD is the most common stomach cancer, accounting for 90%-95%, that arises from mucosal-forming cells [3]. Up to now, the combination of surgery and adjuvant radiation therapy or chemotherapy have been effective for STAD patients [4], however, the OS still remains poor with the median less than 12 months [5]. Besides, due to insidious clinical features associated with early-stage symptoms, many early stage patients are misdiagnosed with chronic gastritis or stomach ulcers and miss out opportunity for radical surgery, thus decreasing 5-year survival rate in STAD.[6]. Therefore, novel biomarkers for STAD prognosis diagnosis, and therapeutics of are still needed.

ECM is defined as a compilation of macromolecules secreted extracellularly by cells to support adjacent cells biochemically and structurally [7], that include elastin collagens, proteoglycans/glycosaminoglycans, laminins, fibronectin,, as well as other glycoproteins [8]. In addition, the ECM also has characteristics of its biophysical indices, including topography, tension molecular density, and stifness/rigidity. Therefore, ECM is extremely adaptable and is continuously remodeled [9]. The ECM of (tumor microenvironment) TME forms a scaffold in the tumor, essentially responsible for the promotion of a cancer malignant phenotype [10]. Frequently, interaction between tumor cells and TME results in ECM stiffening, resulting in abnormal mechanotransduction and subsequent malignant transformation [11]. ECM stiffness is also obstructive for drug penetration to the tumor site [10], thus enhances drug resistance. Beside stiffness, the alteration toward degradation of ECM is also contributed to tumor progression [10]. Understanding TME ECM dysregulation will help identify promising cancer therapeutic targets.

Recently, ECM genes have been reported to be linked to immune evasion and immunotherapy failure in cancer. For example, ECM transcriptional programme dyregulation is found to be correlated with immunosuppression in otherwise immunologically active tumours [12]. In vitro 3D ECM was verified to regulate the activity of cancer associated fibroblasts (CAFs) and T cells across breast cancer [13]. Moreover, a study about pan-cancer demonstrated that by analyzing data from TCGA, ECM regulator genes were considerably enriched and linked to mortality in numerous cancer types, associated to immune infiltration score and specifically with immunologically "cold" tumors [14]. However, ECM dysregulation effect on prognosis and STAD immune microenvironment remains unclear.

This study aimed to use TCGA cohort to evaluate the critical molecular factors affecting the prognosis in STAD by bioinformatics, and we found that 12 ECMOGs were significantly and differentially expressed and correlated to the OS among STAD patients. Subsequent analysis indicated that 12 ECMOGs were related to immune cell infiltrate and possible ICB response, which providing prospective novel biomarkers for STAD diagnosis, prognosis, and treatment.

Data Acquisition

The gene expression profiles (HTSEQ-Counts, HTSEQ-FPKM, N_disease=373, N_normal=32), clinical information for STAD were downloaded from TCGA Genomic Commons Data Portal GDC (available at: https://portal.gdc.cancer.gov/) of [15]. For LASSO regression analysis and nomogram cox model construction, a total of 348 STAD samples were considered after excluding those without clinical information. The Fragments per Kilobase Million (FPKM) values were utilized to perform the LASSO regression analysis.

Microarray-based data about gene expressions contained 432 SC patients acquired from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) dataset GSE26253[16], which was used for external validation. The data was obtained and preprocessed through R “GEOquery” package [17].

The Cox and nomogram model construction

Utilizing TCGA-STAD training set, univariate, LASSO, and multivariate Cox regression demonstrated independent prognostic signature. The correlation between gene expression and overall survival OS was calculated utilizing R "survival" package, and genes with P-value < 0.05 were maintained for subsequent LASSO regression analyses. The R "Glmnet" and "survival" packages were utilized for LASSO regression analysis to assess significant variables following univariate Cox regression. Multivariate Cox regression was finally utilized to screen independent prognostic signature (prognostic characteristic genes) following the LASSO regression. (exp-gene1*coef-gene1) + (exp-gene2*coef-gene2) +... (exp-gene n*coef-gene n) was the formula utilized to determine risk scoring level. The median risk scoring was utilized to classify patients as high- or low-risk.

Area under the ROC curve (AUC) was utilized to quantify prognostic and predictive accuracy, while time dependent ROC curves were utilized in order to evaluate survival prediction [18]. AUC > 0.6 was considered Cox model may predict actual value. R “Survcomp” package was utilized to calculate C-index analyses. The clinical indices (age, race, sex, and pTMN staging) in TCGA-STAD cohort were analysed for prognosis, and those with P-value < 0.05 were utilized to identify nomogram Cox model combining with prognostic signature factors and risk score. To evaluate predictions accuracy, a decision curve analysis (DCA) and calibration were conducted.

Identification of ECMORs pattern in LUAD patients

Unsupervised clustering analyses [19] were conducted to categorize diverse ECMORs patterns according to the expression of 12 ECMORs. A consensus clustering algorithm was used to evaluate the cluster numbers and robustness. R “ConsensuClusterPlus” package [20] was utilized for clustering analysis. PCA was conducted to validate distinct clusters. R “pheatmap” package was utilized to analyse 12 ECMORs expression patterns in different clusters.

DEGs determination and the functional enrichment analysis

Differentially expressed genes (DEGs) were determined utilizing R "limma" package across two clusters using HTSEQ-FPKM of TCGA-STAD [21]. Volcano and heatmap were utilized to visual DEGs drawn “Ggplot 2” and “pheatmap” R package. The threshold is fold change > 2 and P-value < 0.05.

Gene Ontology (GO) [22] and pathway Kyoto Encyclopedia of Genes and Genomes (KEGG) [23] enrichment were done by “ClusterProfiler” R package [24], to conduct the functional enrichment analyses. Bubble diagram was utilized to visual results drawn by R “Ggplot 2” package.

PPI network construction and the hub genes identification

STRING dataset (available at: https://cn.string-db.org/) [25] was utilized to analyse multiple protein-protein interaction (PPI) network, and Cytoscape (v3.7.2) [26] was used to visual result. For hub genes detection through PPI network, CytoHubba [27] was utilized to calculate rank of each gene, and 50 top hub genes were considered for subsequent analysis.

TME Estimate analysis

The TME's stromal, immune, ESTIMATE, and tumor purity scores were assessed utilizing "estimate" R package across STAD-TCGA cohort (Count, n = 348) [28]. The gene expression matrix data (FPKM, n = 348) were used for the CIBERSORT [29] algorithm to assess infiltrated abundance of 22 immune cells types in TME. The xCell [30] algorithm obtained by “immunedeconv” R package [31] was used to calculate the infiltrated abundance of 38 types of immune cells in the TME. R “pheatmap” package was utilized to plot heatmap to visual results. The correlation of the 12 ECMOGs with 22 types of immunocyte fraction calculated by CIBERSORT algorithm was assessed using Spearman correlation.

Based on RNA-sequencing expression profiles and associated clinical data for STAD, the tumor immune dysfunction and exclusion (TIDE) algorithm suggested a possible ICB response. The HLA genes and immune checkpoints components were evaluated according to related gene expression levels.

The chemotherapeutics forecast

The top 50 hub genes were considered to be the molecular basis for the difference in prognosis of between individuals in cluster 1 and cluster 2. Therefore, the small-molecule chemotherapeutics targeting these genes were identified utilizing mode of action (moa) connectivity module of mapping (CMap, https://clue.io/command) [32], which were considered to be more efficient for high-risk group.

Statistical analysis

A Wilcoxon rank sum test and an unpaired t-text were utilized for statistical analyses. All statistical analyses involved two groups. R software (v. 4.0.2) was utilized for visualization each statistical test. The specific statistical analysis was shown in the figure legend.

Extracellular matrix organization is involved in STAD development

To assess critical bioprocesses included in STAD development, prognostic genes were analysed in TCGA stomach cancer (TCGA-STAD) cohort. A total of 948 genes were found as prognostic following univariate cox regression (P < 0.05, Supplementary Table 1). The KEGG result showed these genes were enriched in the pathways of complement and coagulation cascades, and calcium signaling pathway (Supplementary Table 2, Fig. 1A). The GO enrichment results revealed that these genes belonged to biological processes (BP) that were associated with ECM structural organization, externally enveloped structural organization, and cell junction assembly; cellular components (CC) of collagen-containing ECM, platelet alpha granule, anchored component of membrane, and endoplasmic reticulum lumen; and heparin binding molecular function (MF), glycosaminoglycan binding, and collagen binding (Supplementary Table 3, Fig. 1B). The PPI network showed that the prognostic genes had close interactions (Fig. 1C). According to the rank calculated by Cytohubba, top 100 genes were selected (Supplementary Table 4). The PPI network showed that these genes belonged to three prominent subnetworks (Supplementary Fig. 1A). GO enrichment of the three subnetworks indicated that genes in the top one network were enriched in the BP of wound healing, ECM organization, structurally extracellular organization, externally enveloped structural organization, and regulation of coagulation; CC of collagen-containing ECM, platelet alpha granule, platelet alpha granule lumen, and endoplasmic reticulum lumen; and MFs of ECM structural component, heparin binding, glycosaminoglycan binding, and sulfur compound binding (Supplementary Fig. 1B). The genes in the second network were enriched in the BP of mitotic cell cycle phase transition, G2/M transition of mitotic cell cycle, cell cycle G2/M phase transition, and DNA conformation change; CC of chromosomal region, chromosome, telomeric region, replication fork, and nuclear chromosome; and the MFs of DNA helicase activity, catalytic activity, acting on DNA, ATP-dependent activity, acting on DNA, and helicase activity (Supplementary Fig. 1C). We found the biological process of ECM organization was highest significant enriched prognostic genes in STAD (P < 0.0001, Fig. 1B). We then selected 41 ECM organization-related genes from the term of ECM organization for the following analysis (Fig. 1D), we called them ECMOGs. A total of 12 ECMOGs were identified to be significantly and DEGs in GC, as ADAM metallopeptidase with thrombospondin type 1 motif 1 (ADAMTS1), metallopeptidase with thrombospondin type 1 motif 12 (ADAMTS12), angiotensinogen (AGT), ANTXR cell adhesion molecule (ANTXR1), collagen type V alpha 2 chain (COL5A2), collagen type X alpha 1 chain (COL10A1), fibroblast activation protein alpha (FAP), lysyl oxidase (LOX), lumican (LUM), olfactomedin like 2B (OLFML2B), periostin (POSTN), nuclear factor kappa B subunit 2 (NFKB2) (Fig. 1E, 1F, Supplementary Table 5). High expression of 11 genes (ADAMTS1, ADAMTS12, AGT, ANTXR1, COL5A2, COL10A1, FAP, LOX, LUM, OLFML2B, POSTN) were associated to worse GC outcome, while high NFKB2 expression was related to superior outcome (Fig. 1G, Supplementary Fig. 2). Moreover, ten ECMORs (ADAMTS1, ADAMTS12, ANTXR1, COL5A2, COL10A1, FAP, LOX, LUM, OLFML2B, POSTN) were positively correlated with the other ones in the expressional level in GC (Supplementary Fig. 3). These results indicated that these ECMOGs played critical role in STAD development.

Identification of independent prognostic ECMO gene signature in STAD

The twelve ECMOGs were then utilized to conduct LASSO regression across TCGA-STAD cohort (n = 348), and seven genes (ADAMTS1, AGT, COL10A1, LOX, OLFML2B, POSTN, NFKB2) were selected out, that were utilized to done multivariate Cox regression (Fig. 2A, 2B, Supplementary Table 6). After multivariate Cox regression, three genes (ADAMTS1, LOX, NFKB2) were identified as independent predictive signatures (Fig. 2C-2E, Supplementary Table 7, Supplementary Table 8). STAD samples were separated into high- and low-risk subgroups based on risk scoring derived from signature expression (Fig. 2C). The high-risk subgroup had more mortality than low-risk subgroup (Fig. 2D), and high-risk patients group had a significant worse outcome (P < 0.0001, Fig. 2F). The 1-, 3-, and 5-year AUC values of risk model was ranged from 0.649 to 0.783 (Fig. 2G), suggesting that the model might accurately predict STAD patients. Additionally, high NFKB2 expression was negatively related to risk scoring (Fig. 2E, Supplementary Fig. 4), while high expression of other 11 ECMOGs were strongly associated with risk scoring (Fig. 2E, Supplementary Fig. 4), indicating that high expression of the 11 ECMORs may also predict high-risk score patients.

Construction of nomogram model by the three-gene prognostic signature

To analyse risk model predictive value across STAD, clinical indices (Table 1, Supplementary Table 9) were incorporated, and the nomogram Cox model was constructed to predict STAD patients prognosis. We noticed that clinical indices had no significant value. The nomogram result showed that low NFKB2 expression predicted worse prognosis, while low ADAMTS1 and LOX expressions predicted superior prognosis of 1-, 3-, and 5-years (Fig. 3A). The findings of C-index, ROC, DCA and calibration analysis all suggested that nomogram Cox model accurately predicted GC patients' prognoses (Fig. 3A-3D). Unfortunately, the clinical indices of age, pTNM staging, and stage had no significant changes across two risk subgroups (Fig. 3E). We randomly sampled the TCGA-STAD cohort, and 132 samples were considered as the validation set to generate Cox model. The results revealed that high-risk patients had a lower OS than low-risk patients (Fig. 3F). The 1-, 3-, and 5-year AUC was ranging from 0.609 to 0.779 (Fig. 3G).

Table 1

The clinical information of TCGA-STAD
Characteristic	levels	Overall	High-risk	Low-risk
n (Dead/Alive)		348 (145/203)	174 (90/84)	174 (55/119)
Age, n (%)	>=65	195 (56.04%)	99 (56.89%)	96 (55.17%)
	< 65	153 (43.96%)	75 (43.11%)	78 (45.83%)
M stage, n (%)	M0	311 (89.36%)	155 (89.08%)	156 (89.66%)
	M1	22 (6.32%)	12 (6.89%)	10 (5.74%)
	MX	15 (4.32%)	7 (4.03%)	8 (4.60%)
N stage, n (%)	N0	103 (29.59%)	48 (27.58%)	55 (31.61%)
	N1	92 (26.43%)	46 (26.43%)	46 (26.44%)
	N2	71 (20.40%)	34 (19.54%)	37 (21.26%)
	N3	71 (20.40%)	40 (22.98%)	31 (17.82%)
	N/A	11 (3.18%)	6 (3.47%)	5 (2.87%)
T stage, n (%)	T1	16 (4.59%)	3 (1.72%)	13 (7.47%)
	T2	74 (21.26%)	36 (20.69%)	38 (21.84%)
	T3	159 (45.68%)	80 (45.98%)	79 (45.40%)
	T4	95 (27.29%)	51 (29.31%)	43 (24.71%)
	TX	4 (1.18%)	4 (2.30%)	1 (0.58%)
Pathologic stage, n (%)	Stage I	46 (13.21%)	20 (11.49%)	26 (14.94%)
	Stage II	110 (31.61%)	53 (30.46%)	57 (32.76%)
	Stage III	144 (41.38%)	74 (42.53%)	70 (40.23%)
	Stage IV	34 (9.77%)	19 (10.92%)	15 (8.62%)
	N/A	14 (4.69%)	8 (4.60%)	6 (3.45%)

Identification of different clusters mediated by the 12 ECMOGs in STAD

We then used the 12 ECMOGs to perform the unsupervised clustering analysis, and the STAD samples were redivided into cluster 1 and cluster 2 regarding 12 EXMOGs expression level (Fig. 4A-4D, Supplementary Table 10). Cluster 1 patients had a significant poor OS compared to in cluster 2 (Fig. 4E). Meanwhile, we found that 11 ECMOGs (ADAMTS1, ADAMTS12, AGT, ANTXR1, COL5A2, COL10A1, FAP, LOX, LUM, OLFML2B, POSTN) were all greatly overexpressed among cluster 1 (Fig. 4F, 4G). Notably, greater risk was associated with cluster 1 than cluster 2 (Fig. 4H), suggesting that these highly gene expressions predicted high risk clusters that were related to worse STAD outcome.

DEGs and biological characteristic analysis between the two ECMOGs clusters

To clarify distinct biological characteristics among the two ECMOGs clusters, DEGs of patients in cluster 1 vs. cluster 2 were identified, with P-value threshold < 0.05 and Fold change > 2. 506 genes were upregulated, while 8 genes were downregulated among cluster 1 patients compared to those in cluster 2 (Fig. 5A, Supplementary Fig. 5A, Supplementary Table 11). The subsequent KEGG pathways enrichment results showed that these DEGs enriched across PI3K-Akt signaling pathway, protein, ECM-receptor interaction, focal adhesion, along with digestion and absorption (Fig. 5B, Supplementary Table 12). The GO enrichment results also showed that these DEGs belonged to BP categories of extracellular structure organization, ECM organization, ossification, cell-substrate adhesion, and connective tissue development (Fig. 5C, Supplementary Table 12). Then, the PPI network was conducted by the DEGs, and the result showed that these proteins had close interactions (Supplementary Fig. 5B), indicating that the bioprocesses of these proteins were highly correlated. CytoHubba results further extracted 50 hub genes among these DEGs, and the top 15 were collagen type I alpha 1 chain (COL1A1), collagen type I alpha 2 chain (COL1A2), collagen type III alpha 1 chain (COL3A1), collagen type VI alpha 1 chain (COL6A1), collagen type V alpha 1 chain (COL5A1), periostin (POSTN), biglycan (BGN), matrix metallopeptidase 2 (MMP2), decorin (DCN), fibronectin 1 (FN1), secreted protein acidic and cysteine rich (SPARC), fibrillin 1 (FBN1), thrombospondin 1 (THBS1), lumican (LUM), lysyl oxidase (LOX) (Fig. 5D, Supplementary Table 13). The GO result demonstrated that hub genes were enriched across BP of ECM organization, blood vessel development, ossification, and connective tissue development etc. (Supplementary Fig. 5C). These implied that cluster 1 patients was characterized by the enhanced cell-substrate adhesion and ECM organization, and the potential more tumor cell infiltration.

Immune microenvironment characteristics in distinct ECMOGs clusters

The TME among two clusters was further estimated. The findings revealed that cluster 1 was featured by low tumor purity level, high level of stromal, immune, and estimate scoring (Fig. 6A-6D). The stromal score and estimate score were found to be strongly associated to risk scoring (Supplementary Fig. 6A, 6C), and tumor purity was inversely associated to risk scoring (Supplementary Fig. 6D). However, immune scoring was not significantly associated to risk scoring (Supplementary Fig. 6B). The immune TME cell infiltration was further assessed utilizing CIBERSORT and xCELL algorithm. The xCELL algorithm data revealed that proportion of infiltrated M0 macrophage, M1 macrophage, M2 macrophage, myeloid dendritic cell, activated myeloid dendritic cell, monocyte, hematopoietic stem cell, and endothelial cell were higher in cluster 1, and infiltrated proportion of CD4 + Th1 T cell, plasma B cell, naïve CD8 + T cell, and common lymphoid progenitor were greater among cluster 2 (Fig. 6E). Cluster 1 had a greater infiltration of M1 macrophages, M2 macrophages, and activated mast cells, as assessed by CIBERSORT algorithm, whereas abundances of infiltrated follicular helper T cell, plasma B cell, regulatory T cell (Tregs), and resting mast cell were higher among cluster 2 (Fig. 6F). These findings indicated that two ECMOGs clusters had the distinct TME of the differentially infiltrated immune cells.

The correlation analysis of immune cells infiltration and the 12 ECMOGs

The previous results showed that a different immune score and the distinct infiltrated abundance of lymphocyte subpopulation existed in the two ECMOGs-related clusters, indicating that the 12 ECMOGs might be associated with immune cells infiltrate among STAD. To verify this hypothesis, the correlation relationship between the infiltrated proportion of 22 immune cell types and the 12 ECMOGs expression levels were determined. The findings revealed that infiltrated abundance of M2 macrophage was eminently and positively correlated with nine ECMOGs (ADAMTS12, ANTXR1, COL5A2, COL10A1, FAP, LOX, LUM, OLFML2B, POSTN) in STAD (marked by red wireframe, Fig. 7A, Fig. 7B-7J), three ECMOGs (ADAMTS1, AGT, NFKB2) were not correlated with the M2 macrophage infiltration (Fig. 7A, Fig. 7K-7M). This was consistent with the previous results that M2 macrophage were highly filtrated in the TME of cluster 1 (Fig. 6E, 6F). Moreover, six ECMOGs (ADAMTS12, COL5A2, FAP, COL10A1, LOX, POSTN) were inversely associated to infiltrated abundance of B cells (Fig. 7A, Supplementary Fig. 7A-7L), among these genes, five genes (ADAMTS12, COL5A2, FAP, LOX, POSTN) were negatively correlated with the infiltrated abundance of Treg (Fig. 7A, Supplementary Fig. 7M-7R). We noticed that NFKB2 had a positive correlation with CD8 T cells, memory B cells, and Tregs, and a negative correlation with resting dendritic cell, eosinophils, and neutrophils, that were different with other ECMOGs (Fig. 7A, Supplementary Fig. 8A-F). These all suggested that the ECMOGs were involved in the M2 macrophage and CD4 + T regulatory cells infiltration in TME of STAD, which may further affect the own antitumour immune response.

Immunization checkpoint block of the two ECMOGs clusters

To assess potential impact of twelve ECMOGs on immunotherapy effect of STAD patients, the levels of ICB response, as immune checkpoint genes, TIDE, and HLA (major histocompatibility complex, class I and II (MHC I and II)) components, among two clusters were determined. The findings revealed that cluster 1 was featured by the high TIDE score (P = 3.6e-15, Fig. 8A). In addition, four immune checkpoint markers, which included programmed cell death-1-ligand-1 (PD 1L1) (CD274), hepatitis A virus cellular receptor 2 (HAVCR2), (PD-1-L2) (PDCD1LG2), and T cell immunoreceptor with Ig and ITIM domains (TIGIT), were expressed greater among cluster 1 than cluster 2 (Fig. 8B), indicating that cluster 1 patients had a potential bad immunotherapy effect.

The small-molecule chemotherapeutics forecast for high-risk patients

The CMap database's moa module predicted small-molecule chemotherapeutics for cluster 1 based on its highly expressed hub genes. The data revealed that 37 perturbagens were identified as possible chemotherapeutics for cluster 1, that targeted six genes: platelet derived growth factor receptor beta (PDGFRB), matrix metallopeptidase 2 (MMP2), matrix metallopeptidase 14 (MMP14), cellular communication network factor 2 (CTGF), elastin (ELN), and serpin family E member 1 (SERPINE1)) (Fig. 9). Among these perturbagens, the PDGFR receptor inhibitor, KIT inhibitor, FLT3 inhibitor, and VEGFR inhibitor, including dasatinib, sorafenib, sunitinib, tandutinib, and dovitinib were predicted as the outstanding ones targeting PDGFRB (marked in red, Fig. 9). In addition, three metalloproteinase inhibitors targeting MMP2 and MMP14 were also forecasted to be possible therapeutic schedule for those cluster 1 patients, such as ilomastat, PD-166793, and UK-356618 (marked in blue, Fig. 9).

Stomach cancer (SC) is 5th most prevalent malignancy and 3rd most common cause of cancer mortality globally [33], due to its bad incidence, prognosis, cellular and molecular heterogeneity [34]. Systemic chemotherapy, radiation, surgery, immunotherapy, and targeted therapy have all been shown to be effective in STAD treatment [35]. Identified biomarkers, such as programmed cell death ligand 1 (PD-L1), and microsatellite instability (MSI) that can be used to determine which patients will respond favorably to immunotherapy or targeted therapy [35]. However, these are not sufficient to all patients. Significant novel biomarkers remain to be developed to provide the opportunity for personalized therapy. ECM is a ubiquitous tissue component that is constantly being remodeled [36]. Dysregulation of ECM composition, structure, stiffness, and abundance are linked to neoplastic progression, like SC [37, 38]. A recent study has reported that ECM-related hubs genes have adverse effects on STAD prognosis [39]. However, the potential molecular mechanism and the predictive value of ECM in this malignancy remain unknown.

In our study, 12 ECMOGs were identified to predicted the prognosis and immune microenvironment in STAD. The prognosis-related genes in STAD were determined, and we found that ECM organization was the significant biological process with 41 genes enriched in. Among the 41 genes, 12 (ADAMTS1, ADAMTS12, AGT, ANTXR1, COL5A2, COL10A1, FAP, LOX, LUM, OLFML2B, POSTN, and NFKB2) were found to be differentially expressed in STAD, and three (NFKB2, LOX, ADAMTS1) were identified as independent prognostic signature after undergoing LASSO and multivariate cox regression analysis, indicating that the 12 ECMOGs may be involved in STAD development, and expression levels of three-gene independent prognostic signature might contribute to the prediction of the outcome. The protein encoded by NFKB2 is a subunit of transcription factor complex nuclear factor-kappa-B (NF-kB). NF-kB family components play a critical role as stressors and act as transcription factors in regulating essential regulatory genes expression, such as those involved in immunology, inflammation, cell death, and cell proliferation [40]. In the mouse model, Nfkb2 gene is needed for cancer initiation and progression in Kras-mutation driven models by regulating proliferation pathways [41]. In glioblastoma, diabetic neovascularization, osteogenic differentiation, bone matrix formation, ligament remodeling, polycystic ovarian syndrome, fetal membrane rupture, and tumor progression and metastasis, LOX plays a catalytic activity-related, primary role in assembly of ECM [42]. ADAMTS1 promotes the adhesion to ECM proteins, and increased abundance of the metalloprotease ADAMTS1 strongly correlated with metastasis in many kinds of human cancer [43–47]. In addition, other prognosis-related ECMOGs were studied to play important roles in ECM regulation and the further cancer progression. In SC, for instance, the tumor-promoting gene ADAMST12 is responsible of TME status and metabolic conversion of tumor energy [48]. ANTXR1 is predicted to serve as a valuable prognostic biomarker in stomach cancer, as well as a potential immunotherapeutic target and useful biomarker of sensitivity to chemotherapy [49]. COL5A2 is increased in several cancers, and the enhanced this gene is associated with cancer stages, recurrence, microsatellite instability [50]. High expression of COL5A2 revealed poor survival and aided cell migration in SC [51]. COL10A1 is studied to be a predictor for the poor prognosis in stomach cancer, and silencing this gene inhibited cell proliferation, migration, and invasion [52, 53]. LUM and OLFML2B are also predicted to be an oncogene in the progression of stomach cancer [54, 55]. Our founding here was not only consistent with these previous studies, but also illustrated that these 12 ECMOGs might be an independent prognostic signature to predict the OS in STAD.

According to the expressional levels of the 12 ECMOGs, the 348 STAD samples were divided into two diverse clusters by the unsupervised clustering analysis, and 11 ECMOGs were highly enriched in the subgroup of cluster 1, which was thus called as high ECMOGs subgroup. Moreover, patients in cluster 1 were featured by the poor outcome and the higher risk score, indicating that the population with high levels of the ECMOGs represented the high-risk subgroup in STAD. Increased ECM remodeling enzymes, recruited CAFs, immune cells, and other stromal cells, secreted several growth factors, and stimulated collagen deposition are all known to participate to ECM remodeling at tumor site, which in turn leads to elevated ECM stiffness, abnormal cell-cell adhesion, up-regulation of integrin signaling, and subsequent activation of downstream cascades, and hence stimulate tumor development and progression [38]. Here, we found that the 12 ECMOGs could act as a gene signature to expect prognosis in STAD, overexpression of these ECMOGs may promote the progression of STAD.

A total of 514 genes were identified as DEGs between two ECMOGs-related clusters, and among these, 506 genes were highly expressed in cluster 1. The subsequent GO enrichment analysis revealed that these ECMOGs-related DEGs were associated with biological processes of extracellular structure organization, extracellular matrix organization, ossification, cell-substrate adhesion, and connective tissue development, as well as KEGG pathways of PI3K-Akt signaling pathway, focal adhesion, protein digestion and absorption, and ECM-receptor interaction. These showed that two ECMOGs-related populations may have distinct biological characteristics that may thus possess discrepant outcome. Obviously, the main different pathways enrich in the two cluster were PI3K-Akt signaling pathway, and focal adhesion. Phosphoinositide 3-kinases (PI3Ks) are a broad family of lipid enzymes that can phosphorylate 3'-OH group of phosphatidylinositols (PtdIns) in plasma membrane. PI3K pathway is one of the most commonly activated signal transduction pathways in human cancer [56]. PI3K/AKT signaling is essential for chemoresistance and contributes to epithelial mesenchymal transition (EMT) in drug-resistant and metastatic human cancer cells [57]. Additionally, the hub genes identified by the PPI network, such as COL1A2, MMP2, COL6A1, COL1A1, COL3A1, COL5A1, THBS1, were ECM organization-related genes. These all demonstrated that elevated the ECMOGs expression caused the dysregulated ECM, and activated PI3K/AKT signaling and focal adhesion, that further promoted cancer progression in STAD.

The tumor immune microenvironment is environment in which anticancer immunological response of the host occurs. [58]. Important contributions to tumor immune microenvironment are tumor-infiltrating immune cells, such as neutrophils, macrophages, dendritic cells, and various types of T cells. [59]. Previous studies have demonstrated that tumor-infiltrating lymphocytes proportion were linked to patients survival with SC [60]. In SC, for instance, infiltrating macrophages have been implicated as facilitators of metastasis formation by acting as alternative regulators of tumor cell proliferation, angiogenesis, as well as tissue remodelling [61]. In this study, we found that the immune score was higher in the ECMOGs-related cluster 1, and different pattern of tumor-infiltrating lymphocytes was also existed between the two subgroups. What stands out is that, macrophages (M1 and M2) were infiltrated higher in the population of cluster 1 than in those of cluster 2. What’s more, nine of the twelve ECMOGs we detected were positively associated with infiltrated abundance of M2 macrophages, and five ECMOGs were correlated with M1 macrophages infiltration. These all indicated that high expression of detected ECMOGs could stimulate M1 and M2 macrophages infiltration during STAD progression. Tumor-associated macrophages (TAMs) principal population of tumor-invading immune cells, which typically support tumor immune evasion, angiogenesis, tumor development, and metastasis. Macrophages are always polarized into different subtypes (M1 macrophages and M2 macrophages) by the specific factors according to changes in their environment [62]. In contrast to M1 macrophages, which are largely anti-tumor and immune-promoting, M2 macrophages have a similar profile to TAMs, that stimulate tumor development and metastasis [62]. Our results showed that both infiltrated M1 and M2 macrophages were higher in ECMOGs-enriched cluster 1, and this seems contradictory. That might be attributed to the basic function of the macrophages. Tissue damage induced by the pro-inflammation of M1 macrophages may promote that M2 macrophages polarization to repair the damage tissue, and thus tumor-associated M1 and M2 macrophages may be in a state of dynamic equilibrium. However, our results showed a more prominent correlation between the ECMOGs and M2 macrophages infiltration in STAD, which further demonstrated that highly expressed ECMOGs have the potential to promote tumor development by enhancing M2 macrophages invasion.

Moreover, the potential immunotherapy effect of STAD patients in the two ECMOGs-related clusters were assessed. We found that cluster 1patients had a greater TIDE score, as well as higher levels of four immune checkpoint genes (CD274, HAVCR2, PDCD1LG2, and TIGIT). It is clear that certain immune checkpoint pathways play a crucial role in immunological resistance, notably versus T cells which are tumor specific antigens [63]. The most studied and identified inhibitory checkpoint mechanisms are cytotoxic T lymphocyte-associated molecule-4 (CTLA-4), (PD-1), and (PD-L1 (CD274)) [64]. PD-1 signaling inversely modulates T cell-mediated immune responses and facilitates tumor evasion of an antigen-specific T cell immunologic response. It contributes to tumorigenesis and progression through tumor survival boosting [65]. HAVCR2 (T cell immunoglobulin and mucin domain 3 (TIM3)) additionally serves as a tumor-infiltrating lymphocyte-expressed inhibitory checkpoint protein. TIM3 and Gal9 interaction inhibit innate and adaptive immune cell anti-tumor immunity [66]. TIGIT is an inhibitory receptor primarily expressed on NK, CD8 + T, CD4 + T, and T regulatory (Treg) cells. Interaction of CD155 and TIGIT always inhibits the function of T and NK cells. In addition, TIGIT blockade was supported to be used combining with other immune checkpoint inhibitors to treat advanced solid malignant tumors [67]. ICB, which blocks inhibitory pathways of T cells activation, has shown tremendous success in treating cancer [68]. Our result that the TIDE score was higher in cluster 1 further indicated that these patients may had a poor ICB response and the potential immunotherapeutic resistance. These all demonstrated that the detected ECMOGs may be correlated with a higher immune checkpoint and the poor anti-tumor immune response.

The patients in the subgroup of ECMOGs high expression were identified as high-risk population, who had worse prognosis. To predict possible chemotherapeutics suitable for the population of cluster 1, we used (moa) module of CMap dataset to determine small-molecule chemotherapeutics targeting 50 hub genes that were highly expressed among cluster 1. Cluster 1 was shown to have 37 possible chemotherapeutic perturbagens. Among these, dasatinib, sorafenib, sunitinib, and dovitinib were predicted as the prominent ones targeting PDGFRB, that had the largest moa modules, such as PDGFR receptor inhibitor, and VEGFR inhibitor. Dasatinib, a short-acting oral multiple tyrosine kinase inhibitor, approved to treat chronic myeloid leukemia. [69]. Besides, Dasatinib is a promising safe medication for treating refractory metastatic solid cancers [70]. Oral kinase inhibitor sorafenib suppresses tumor cell growth and angiogenesis as well as promotes cancer apoptosis [71]. From 2005 to 2013, sorafenib was the first targeted therapy authorized for renal cell carcinoma, hepatocellular carcinoma, and thyroid cancer patients [72]. Sunitinib is an orally bioavailable, multi-target tyrosine kinase inhibitor that has been shown to be effective against imatinib-resistant gastrointestinal stromal tumors (GISTs), renal carcinomas, and pancreatic neuroendocrine tumors. [73]. Dovitinib is a kind of FGFR3 tyrosine kinase inhibitors [74], and it has been employed extensively in clinical practice and FDA-approved for cancer treatment. [75]. In addition, three metalloproteinase inhibitors targeting MMP2 and MMP14 were also forecasted to be possible therapeutic schedule for those cluster 1 patients, such as ilomastat, PD-166793, and UK-356618. For example, ilomastat is one kind of derivatives of batimastat that are in clinical or experimental usage for anti-cancer therapy through inhibiting neutrophil inflammatory responses [76]. These inhibitors were all anticipated to be beneficial for STAD patients in cluster 1 of high risk, however, more experiment in vitro and in vivo should be conducted for the subsequent analysis.

In this paper, we provided the systematic analysis of prognostic genes in STAD using public database data and by bioinformatics. According to the GO enrichment and PPI results, ECM organization was identified to be the significant biological process with 41 genes enriched in. Therewith, we systematically evaluated immune microenvironment and prognosis in STAD patients with different ECMOGs expression subgroups. This is the first study to illustrate the association between ECM organization dysregulation and immune microenvironment changes, and to provide a novel biological target for the targeted therapy of STAD. However, there are still some deficiencies in this current paper. First of all, although some correlation prediction analysis can be performed by using bioinformatics techniques, simple analysis is not enough to explain the molecular mechanism of ECMOGs affecting poor prognosis of STAD. Therefore, large numbers of experimental assays both in vitro and in vivo should be carried out to subsequent study the molecular mechanism of these ECMOGs in promoting the development of STAD. Second, the clinical samples with STAD should be collected for the high-throughput sequencing to further validate the correlation of the ECMOGs and immune microenvironment. Besides, the protein expressional levels of these detected ECMOGs in the clinical STAD samples also should be detected. Finally, the predicted chemotherapeutics in this study should be evaluated by the experimental assays.

To sum up, this paper demonstrated that dysregulation of ECM organization was associated to worse STAD prognosis, and 12 ECMOGs were identified to be implicated in STAD development and progression, which could predict the immune microenvironment and thus the potential immunotherapy effect in STAD.

Acknowledgement

This research was supported by the Medical and Health Science and Technology Development Project of Shandong Province (No.202203030218).

Author Contributions

Conceptualization, Qun Liu; Data curation, Qun Liu ; Formal analysis, Qun Liu; Investigation, Yan Wang, Kuo Wang and Zhaoshui Li; Methodology, Qun Liu, Yan Wang, Kuo Wang and Zhaoshui Li; Project administration, Quanjiang Dong; Supervision, Quanjiang Dong; Validation, Qun Liu and Quanjiang Dong; Visualization, Qun Liu; Writing – original draft, Qun Liu; Writing – review & editing, Quanjiang Dong.

Ethical Approval

Ethical approval was not required for this article.

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Extracellular Matrix Organization Genes Predict the Prognosis and Immune Microenvironment in Stomach Adenocarcinoma

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Abstract

Background

Method

Results

Conclusion

Figures

Introduction

Materials and Methods

Data Acquisition

The Cox and nomogram model construction

Identification of ECMORs pattern in LUAD patients

DEGs determination and the functional enrichment analysis

PPI network construction and the hub genes identification

TME Estimate analysis

The chemotherapeutics forecast

Statistical analysis

Results

Extracellular matrix organization is involved in STAD development

Identification of independent prognostic ECMO gene signature in STAD

Construction of nomogram model by the three-gene prognostic signature

Identification of different clusters mediated by the 12 ECMOGs in STAD

DEGs and biological characteristic analysis between the two ECMOGs clusters

Immune microenvironment characteristics in distinct ECMOGs clusters

The correlation analysis of immune cells infiltration and the 12 ECMOGs

Immunization checkpoint block of the two ECMOGs clusters

The small-molecule chemotherapeutics forecast for high-risk patients

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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