
==== Front
J Transl Med
J Transl Med
Journal of Translational Medicine
1479-5876
BioMed Central London

5628
10.1186/s12967-024-05628-3
Research
Overall survival prediction of gastric cancer using the gene signature of CT-detected extramural venous invasion combined with M2 macrophages infiltration
Yang Hao 1
Gou Xinyi 2
Feng Caizhen 2
Zhang Yuanyuan 3
Sun Boshi 4
Peng Peng 6
Wang Yi 2
Hong Nan 2
Ye Yingjiang 5
Cheng Jin chengjinpkuph@outlook.com

2
Gao Bo bo.gao@bjmu.edu.cn

6
1 https://ror.org/01f77gp95 grid.412651.5 0000 0004 1808 3502 Department of Oncology Surgery, Harbin Medical University Cancer Hospital, Harbin, China
2 https://ror.org/035adwg89 grid.411634.5 0000 0004 0632 4559 Department of Radiology, Peking University People’s Hospital, 11 Xizhimen South St, Beijing, 100044 China
3 https://ror.org/035adwg89 grid.411634.5 0000 0004 0632 4559 Department of Pathology, Peking University People’s Hospital, Beijing, China
4 https://ror.org/03s8txj32 grid.412463.6 0000 0004 1762 6325 Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
5 https://ror.org/035adwg89 grid.411634.5 0000 0004 0632 4559 Department of Gastrointestinal Surgery, Peking University People’s Hospital, Beijing, China
6 https://ror.org/035adwg89 grid.411634.5 0000 0004 0632 4559 Department of Hernia and Abdominal Wall Surgery, Peking University People’s Hospital, Beijing, 100044 China
9 9 2024
9 9 2024
2024
22 8291 3 2024
18 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

CT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach.

Method

We utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P < 0.01) to overlap the co-expression genes of WGCNA module and M2 macrophages related genes (from mRNA data of 371 GC patients in TCGA database), generating a total of 136 genes. An EMVI-M2-prognosis-related hub gene signature was constructed by COX and least absolute shrinkage and selection operator (LASSO) analysis from a training cohort TCGA database (n = 371) and validated it in a validation cohort from GEO database (n = 357). High- and low-risk groups were divided by hub gene (EGFLAM and GNG11) signature-derived risk scores. We assessed its predictive ability through Kaplan-Meier (K-M) curve and COX analysis. Furthermore, we utilized ESTIMATE to detect tumor mutation burden (TMB) and evaluate sensitivity to immune checkpoint inhibitors (ICIs). Expression of hub genes was tested using western blotting and immunohistochemistry (IHC) analysis.

Results

The overall survival (OS) was significantly reduced in the high-risk group (Training/Validation: AUC = 0.701/0.620; P < 0.001/0.003). Furthermore, the risk score was identified as an independent predictor of OS in multivariate COX regression analyses (Training/Validation: HR = 1.909/1.928; 95% CI: 1.225–2.974/1.308–2.844). The low-risk group exhibited significantly higher TMB levels (P = 1.6e− 07) and greater sensitivity to ICIs. Significant higher expression of hub-genes was identified on multiple GC cell lines and original samples. Hub-genes knockdown in gastric cancer cell lines inhibited their proliferation, metastatic and invasive capacity to varying degrees. In vivo experiments indicate that EGFLAM, as one of the hub genes, its high expression can serve as a biomarker for low response to immunotherapy.

Conclusion

Our study demonstrated EMVI-M2 gene signature could effectively predict the prognosis of GC tissue, reflecting the relationship between EMVI and M2 macrophages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-024-05628-3.

Keywords

M2 macrophages infiltration
Immune microenvironment
Gastric cancer
Radiogenomics
Extramural venous invasion
the National Natural Science Foundation of China81901819 Cheng Jin Peking University People’s Hospital Research and Development FundsRS2021-08 Gao Bo issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Gastric cancer (GC) is a highly heterogeneous disease and ranks among the leading causes of cancer-related death worldwide. The heterogeneous characteristics of GC result in significant variation in patient prognosis, even for patients with the same American Joint Committee on Cancer (AJCC) tumor, node, and metastasis (TNM) stage and similar treatment regimens [1]. Contrast enhanced computed tomography (CT), as the routine examination for GC patients, allows evaluation of the whole lesion characterization and assessment of disease progression. Among multiple macro imaging features derived from CT, extramural venous invasion (EMVI) has been identified as an independent poor prognostic factor in GC patients [2, 3]. Notably, it is a strong biomarker correlated with distant metastasis [4].

Tumor invasion and metastasis are related to the immune status of the tumor microenvironment (TME) [5–7]. There is evidence suggesting that immune responses may be blunted in EMVI-positive tumors [8]. In our previous study, we also found that the EMVI-positive group exhibited lower microsatellite instability (MSI), tumor mutation burden (TMB) and response rate to immune checkpoint inhibitors (ICIs), but paradoxically had a higher immune escape status [9]. Among the various cell types infiltrating the GC stroma, tumor-associated macrophages (TAMs) with an M2 phenotype have attract attention associating with cancer metastasis and worse prognosis in patients [10, 11]. M2 macrophages are known to promote tumor angiogenesis and contribute to the assembly of the intravasation sites [12], and It has been observed play a significant role in determining EMVI in colorectal cancer patients [8]. However, there is no evidence between M2 macrophages infiltration and the development of EMVI in GC.

In this study, we aimed to investigate the relationship between CT-detected EMVI and immune cell infiltration, particularly M2 macrophages, in GC patients by analyzing mRNA sequencing from GC specimens and the Cancer Genome Atlas (TCGA). Then to validate the findings using Gene Expression Omnibus (GEO) database.

Materials and methods

Research design

The retrospective analysis was supported by the institutional review board (Approval number: 2020PHB395-01), and the requirement for informed consent was waived. We included a total of 13 pathologically confirmed T4aN + M0 GC patients who underwent preoperative contrast-enhanced multidetector CT (ceMDCT), standard D2 gastrectomy, and adjuvant chemotherapies. Frozen tumor samples were stored in the institute’s biobank. The study’s flow chart is shown in Fig. 1. First, patients underwent preoperative abdominal ceMDCTs, and tissue samples were subjected to whole transcriptome sequencing. Second, CT-detected EMVI score-related genes were selected. Third, we utilized CIBERSORT and WGCNA analyses to identify M2 macrophage-related module and genes in all GC patients in TCGA database. In the fourth step, we identified the EMVI-M2-prognosis gene signature through the univariate COX regression and least absolute shrinkage and selection operator (LASSO) regression analyses. This gene signature was proposed using the TCGA database for training and validated externally using the GEO (GSE84433). In the fifth step, we compared gene mutation rate, immune cell infiltration status, ESTIMATE scores, and sensitivity to immune check point inhibitors (ICIs) treatment between high- and low-risk groups stratified by the EMVI-M2-prognosis gene signature. In the last step, we verified hub gene expression using western blotting and immunohistochemistry (IHC) on GC cell lines and original samples.

Fig. 1 Flow chart of the study

CT detected EMVI scoring

EMVI statuses were reviewed on the preoperative ceMDCT images. Although in previous studies, CT-detected EMVI was usually identified as negative and positive, however, similar with the EMVI scoring system in rectal cancer [13], CT detected EMVI of GC could be scored from 0 to 4. The scoring of EMVI was defined as follows: Score 0- tumor outline is not nodular without adjacent vessel; Score 1- tumor outline is irregular or nodular, without adjacent vessel; Score 2- Stranding demonstrates in the vicinity of extramural normal calibre vessel, without tumor attenuation within vessel lumen; Score 3: tumor attenuation apparent within extramural vessel, the calibre of the vessel is slightly expanded; Score 4: Irregular vessel contour or nodular expansion of extramural vessel by tumor attenuation.

mRNA sequencing and EMVI related gene selecting

Frozen tumor tissue samples from 13 patients were subjected to quality control followed by global genome sequencing using Illumina HiSeq 4000 (Platforms: GPL20301). RNA quantification and quality assurance were evaluated by NanoDrop ND-1000. The quality control process ensures that the samples are of high quality and suitable for sequencing. The sequencing data had been uploaded to the GEO database (GSE182831). Based on sequencing data, we calculated the correlation between gene expression and EMVI score using R language. EMVI (extramural venous invasion) score is a measure of how far cancer cells have spread into blood vessels outside the wall of the colon. A score of 1–2 indicates a negative result for EMVI, while a score of 3–4 indicates a positive result for EMVI. EMVI-related genes, the differentially expressed genes between the EMVI-positive and EMVI-negative groups, were identified when P < 0.01. The ESTIMATE scores and p-values of 396 EMVI-related genes are described in the additional file 1.

Coexpression gene selection of immune cell infiltration in WGCNA defined modules on TCGA database

We downloaded mRNA sequencing data and corresponding clinical information from all GC patients (n = 371) on the TCGA database using official download tool Genomic Data Commons (data through January, 2022, used). Using the CIBERSORT algorithm, the levels of 22 types of immune cells in every GC patients were identified. The screening criterion for CIBERSORT analysis was P < 0.05. Heatmap of immune related genes and the clinical data was generated.

Co-expression analysis was used to identify WGCNA modules related to immune cell infiltration and related gene expressions, with correlation coefficients and P values calculated for all 22 kinds of immune cells and each WGCNA module. From multiple immune-related WGCNA modules, only the midnightblue module and its module-related genes were positively related to M2 macrophages (P < 0.05). The midnightblue module was intersected with EMVI-related genes to obtain EMVI-M2 macrophage-related genes for subsequent analysis.

Construction and validation for EMVI-M2-prognosis related gene model

For construction for the EMVI-M2-prognosis related gene signature, the LASSO algorithm was used for variable selection and shrinkage with the “glmnet” R package. The independent variable in the regression was normalized expression matrix of candidate EMVI-M2-prognosis related genes, and the response variables were OS and status of patients in the training cohort of TCGA database. The penalty parameter (λ) for the model was determined using 10-fold cross-validation following the minimum criteria.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\text{R}\text{isk}\text{ }\text{score=}{\sum\:}_{\text{ }\text{i}\text{=1}}^{\text{ }\text{n}}\text{C}\text{oefi}\text{*xi}$$\end{document}

Patients were stratified into high-risk and low-risk groups based on the median value of the risk score. Kaplan-Meier curves were calculatedanalyses were also performed in training (TCGA) and validation set (GEO) to evaluate the impact of risk scores on the prognosis of gastric cancer patients. Independent prognostic parameters analysis was performed using univariate and multivariate COX regression analyses. The parameters included age, gender, tumor differentiation grade, T/N/disease stage, and risk score. The influence of the parameter on the outcome event was evaluated through computing the Hazard Ratio (HR) for this parameter. If the HR exceeds 1, it denotes that the variable acts as a risk factor, promoting mortality. An HR less than 1 indicates a protective role of the variable, hindering death. Lastly, an HR equal to 1 suggests that the variable exerts no impact on mortality.

Association of EMVI-M2-prognostic related signature with TMB, single mutation rate, immune cell infiltration and sensitivity to immune checkpoint inhibitors (ICIs) therapy

Based on the prognostic gene signature of EMVI-M2 macrophages, tumor mutation burden (TMB) was compared according to risk score and signature genes. Single-gene mutation rates were compared between high- and low-risk groups. K-M curves were compared according to EMVI-M2 macrophage-prognosis gene signature combined with TMB status. Immune cell infiltration and ESTIMATE analysis were compared according to risk score and signature gene expressions. Gene set variation analysis (GSVA) including KEGG and HALLAMRK were analyzed according to risk signature genes. Finally, sensitivity to ICIs according to risk score and signature genes were also analyzed.

Cell culture

GC cells (GES-1, AGS, HGC-27, KATO III, MKN-1, MKN-45 and MFC) were purchased from Procell Life Science & Technology (Wuhan, China), and the cells were cultured according to the manual instructions. The cell lines were cultured in RPMI-1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA) and 1% penicillin/streptomycin (Gibco, USA).

HGC-27, KATO III, MKN-45 and MFC cells (5 × 103) were cultured in 24-well plates and transfected with previously constructed RNA interference lentiviral vectors (Genechem, China) or a negative control (empty plasmid) for 24 h. Interference sequences of lentiviral are shown in additional file 2. The medium was changed to complete medium, and cell culture was continued for 1 week. The medium was then changed to complete medium containing puromycin. After 72 h, the fluorescence intensity was observed under a fluorescence microscope, and the visible fluorescence of the cells indicated that the transfection was successful. The lentivirus was resistant to puromycin, and the stable expression lentiviral cell lines were screened by adding puromycin in the medium. In the process of culture, the cells were overgrown in 24-well plates, and gradually passed into 12-well plates and 6-well plates.

Colony formation assay

HGC-27, KATO III and MKN-45 were seeded in a six-well plate with 1000 cells per plate, and colony formation was visible after 7 days of incubation. Microscopically counting more than 50 cell clones counts as a colony. After fixation in 4% paraformaldehyde for 30 min, cells were stained with 0.5% crystal violet for 30 min.

Wound-healing assay

HGC-27, KATO III and MKN-45 (5 × 106) were cultured in six-well plates until full confluence was achieved, and then were starved by adding serum-free medium for 24 h. Plates were scratched using a 200 µL pipette tip, removing a line of cells. Photographs were taken at 0, 12, and 24 h under a microscope to observe the degree of wound healing.

Transwell assay

The Transwell assay was performed according to the manufacturer’s instructions. HGC-27, KATO III and MKN-45 (2 × 104) were inoculated into a Transwell chamber containing 200 µL serum-free medium. The upper chamber surface of the Transwell chamber was coated with Matrigel mix to determine the invasion ability of cells. When testing the cell migration ability, the bottom of the chamber was not coated with Matrigel. Medium containing 10% FBS was added to the lower culture plate. After 24 h of incubation, the chamber was removed and stained with crystal violet for 30 min.

Western blotting

Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE, Epizyme, China) was used for western blotting of gastric cancer cells. After lysing cells, the lysate was subjected to electrophoresis, membrane transfer, and blocking of non-specific antigens. This was then incubated overnight at 4 °C with primary antibodies specific for anti-EGFLAM (ThermoFisher, USA), anti-GNG11 (Affinity, USA), and GAPDH (ImmunoWay, USA). The following day, the membrane was incubated with secondary antibodies for 1 h at room temperature. After visualization of protein bands, grayscale analysis was performed using the ImageJ software (version 1.8.0). The grayscale of the target protein was divided by the grayscale of Actin to obtain the relative amount of the target protein in each protein sample. Then GraphPad Prism 8.0 software was used for statistical analysis of target protein levels between samples.

Immunohistochemistry and immunofluorescence staining

Formalin-fixed tissue was processed, embedded in paraffin and sliced into 5 μm sections. Immunohistochemical (IHC) staining was performed using antibodies anti-EGFLAM and anti-GNG11. Tissue sections were deparaffinized in xylene and rehydrated in graded ethanol. Antigen retrieval was performed by heating sections in boiling sodium citrate buffer (Sigma-Aldrich, C-9999) for 20 min. After blocking with 3% hydrogen peroxide and bovine serum albumin (BSA), the tissues were incubated with the primary antibody at 4 °C overnight. After washing, the tissues were incubated with corresponding horseradish peroxidase (HRP)-conjugated secondary antibodies. The color was developed using diaminobenzidine (DAB) substrate (Sigma-Aldrich, D-7304) and slides were counterstained with hematoxylin. Images of three random areas from each section were captured at 100x magnification for evaluation. Immunofluorescence staining was performed using primary antibodies anti-EGFLAM and anti-GNG11. Corresponding Alexa Fluor dyes were used for fluorescent detection. DAPI was used for nuclear counter staining. Images were captured on the Zeiss LSM780 laser scanning confocal microscope. Quantitative analysis of immunohistochemistry and immunofluorescence images was performed using ImageJ software by measuring mean optical density values, and differences were analyzed using the t-test.

Subcutaneous tumor xenograft nude mouse model

Establishment of xenograft model was approved by Peking University People’s Hospital Ethics Committee. BALB/c mice were anesthetized with 2% isoflurane, and the axillary skin was disinfected using sterile cotton balls. MFC gastric cancer cells were adjusted to a density of 1 × 106/mL, and 100 µL of cell suspension was subcutaneously injected into the axilla using a 1 mL syringe. Mice were intraperitoneally administered anti-mouse PD-1 (Bio X Cell; BE0273; 3 mg/kg) or anti-mouse CTLA-4 (Bio X Cell; BE0131; 3 mg/kg) according to their grouping every 3 days. The tumor volume in each mouse was measured by vernier caliperevery 3 days. All mice were sacrificed after 18 days. Tumor tissues were harvested for measurement and weighing, the tumor volume was calculated, and growth curves were plotted. The tumor volume was calculated as volume (mm3) = 0.5 × long diameter × short diameter2 (mm2).

Statistics

Data were analyzed and visualized using the GraphPad Prism 8.0 software. The Student’s t-test was used to compare means between two groups, and one-way ANOVA was conducted to determine the significance of differences among multiple groups (> 2). Subcutaneous tumor growth curves were analyzed using two-way ANOVA. P < 0.05 was considered statistically significant.

Results

Co-expression gene selection of immune cell infiltration in WGCNA defined modules on TCGA database

The CIBERSORT algorithm was used to calculated the immune cell infiltration score of each GC patient on TCGA database (n = 371) (Fig. 2a-c). Co-expression analysis was used between WGCNA identifying gene modules and immune cell infiltration status (Fig. 2d-f). We screened for modules that showed positive and specific correlations with immune cells. The results indicated that Macrophages M2, Dendritic cells resting, Mast cells resting, and Eosinophils were significantly and positively correlated only with the midnight module, whereas Activated Dendritic cells and Activated Mast cells were significantly and positively correlated only with the cyan module (Fig. 2f). Given that among these immune cells, only M2 macrophages have a high association with gastric cancer progression, we chose the midnightblue module as the central module for this study’s focus. Further analysis was then conducted on the 3568 genes encompassed within this module. CT-detected EMVI scores and the clinical characteristics of thirteen included patients were detailed in our previous work [14]. Based on CT-detected EMVI scores, we selected 396 genes that were significantly associated with EMVI (P < 0.01) (additional file 1). By overlapping all EMVI related genes and M2 macrophages related genes of midnightblue module, and there were 136 genes selected for further analysis (Fig. 2g).

Fig. 2 Immune cell infiltration and WGCNA analysis in GC patients of TCGA. (a) Immune cell infiltration in each GC patients. (b) Correlation analysis of 22 types of immune cells in GC tissues. (c) Immune cell infiltration status of normal and cancerous gastric tissues. (d) Correlation of genes with each WGCNA module after clustering. Genes on the cluster tree are represented by different colors corresponding to distinct modules, where the color gray represents genes that could not be classified into any specific module. (e) Select the number of WGCNA modules according to the power value. (f) Co-expression of gene modules defined by WGCNA and immune cell infiltration. The vertical axis uses different colors to represent 13 functional modules, while the horizontal axis represents 22 types of immune infiltrating cells. (g) Gene overlapping of midnightblue module and EMVI related gene

Construction and validation for EMVI-M2-prognosis related gene model

We performed univariate COX analysis on the 136 target genes previously screened (with p < 0.001 as the screening condition), and we obtained prognostic-related genes EGFLAM and GNG11 (Fig. 3a, additional file 3). A LASSO regression model was then constructed based on EGFLAM and GNG11 (Fig. 3b, c). Then, we divided the Train Set cases and Test Set cases into high-risk group and low-risk group based on the median cutoff value. The high-risk group had significantly lower OS than the low-risk group (Fig. 3d, e). The risk curve diagram shows that as the sample risk score increases, the expression levels of the risk genes also rise. Patients in the high-risk group have shorter survival times compared to those in the low-risk group, indicating a poorer prognosis for the high-risk group (Fig. 3f, g). Univariate and multivariate COX regression analyses demonstrated that age (Train/Test HR = 1.034/1.023; 95% CI: 1.016–1.053/1.009–1.037) and risk score (Train/Test: HR = 1.909/1.928; 95% CI: 1.225–2.974/1.308–2.844) were independent predictors of OS (Fig. 3h, i) in training and validation cohort at the same time. The larger the area under the curve (AUC), the greater the accuracy of the model constructed, indicating a better performance of the predictive model. AUC of risk score were 0.701 and 0.620 in training and validation cohorts, respectively (Fig. 3j, k). AUC of Age were 0.606 and 0.543 in training and validation cohorts, respectively. This suggests that the risk score has greater specificity than age. Heat map of risk score and clinical characteristics was shown in additional file 4a. Furthermore, there were statistical differences in tumor differential grade, disease stage and T stage between high- and low-risk groups (additional file 4b).

Fig. 3 a. Univariate COX analysis was performed on 136 target genes to screen for genes associated with patient prognosis, and the results were displayed using a forest plot; b. A LASSO cross-validation plot was drawn, where the dotted line on the left side of the plot represents lambda.min, and the dotted line on the right side represents lambda.1se. c. The LASSO coefficient path plot shows how the coefficients of EGFLAM and GNG11 change as the Log Lambda parameter increases. Patients in the high-risk group had a significantly lower OS and death events than those in the low-risk group in training (d, f) and validation (e, g) cohorts. Univariate and multivariate Cox regression analyses demonstrated that age were independent predictors of OS (h, i) in training and validation cohort at the same time. AUC of risk scorewere 0.701 and 0.620 in training and validation cohorts, respectively (j, k)

Association of EMVI-M2-related signature with TMB, single-gene mutation rate, immune cell infiltration and sensitivity to immune therapy

Tumor mutation burden (TMB) was higher in high-risk group (Fig. 4a, b), and patients with high expression of EGFLAM and GNG11(Fig. 4c, d). Single-gene mutation (Fig. 4e, f) rates were also higher in low-risk group than in high-risk group. According to the median value of TMB rate, there was significant survival difference in TCGA cohort (Fig. 4g). Combined with the EMVI-related risk score and TMB rate, we found that the high-TMB/low-risk group had the best prognosis, while the low-TMB/high-risk group had the worst prognosis (Fig. 4h). The survival curves were cross between low-mutation/low risk and high-mutation/high risk.

Fig. 4 Box-plot (a) and scatter diagrams (b) show different tumor mutation burden (TMB) between low- and high-risk groups, as well as the different expression of EGFLAM (c) and GNG11 (d). Single mutations rate was different between low- (e, 91.8%) and high-risk (f, 84.36%) groups. K-M curves according to TMB status (g) and combined with risk score (h)

Immune cell infiltration was positively correlated (Correlation coefficient > 0) with risk score according to multiple algorithms (CIBERSORT-ABS, CIBERSORT, MCPCOUNTER, EPC, XCELL, TIMER, QUANTISEQ) (Fig. 5a). Associations between immune cells and two risk signature genes (EGFLM and GNG11) were shown in Fig. 5b and c. The two genes were positively correlated with M2 macrophages, fibroblasts and endothelial cells. ESTIMATE analysis shows that ImmuneScore, StromalScore and ESTIMATEScore were all significantly higher in high-risk groups than in low-risk groups (Fig. 6a). Low risk group was shown better response to immune check point inhibitors (ICIs) of Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) compared with high-risk groups, but there was no significant difference in response to programmed death 1 (PD-1) treatment (Fig. 6b). EMVI-M2-prognosis related gene signature had positive correlation with multiple ICIs related genes (additional file 4a). KEGG analysis showed that there were multiple oncology related pathways, such as WNT, VEGF, P53 and ERBB, et al., correlated with risk score and risk genes (additional file 4b). Furthermore, risk score and corresponding genes were also correlated with oncology related characteristics, by HALLMARK analysis, including angiogenesis, P53 (additional file 4c). We found that EGFLAM and GNG11 were highly expressed in tumors with high ImmuneScore, StromalScore and ESTIMATEScore by ESTIMATE analysis (Fig. 6c, f). Both genes were positively correlated with M2 macrophages by using CIBERSORT, with P values of 0.008 and 0.013, respectively (Fig. 6d, g). For ICIs therapy analysis, EGFLAM expression was a significant response factor (Fig. 6e), but not GNG11 (Fig. 6h). The results showed that patients with low expression of EGFLAM had a better response to anti-PD-1 or anti-CTLA-4 treatment compared to those with high expression. In contrast, there was no significant difference in treatment responsiveness between groups with high and low expression of GNG11. According to the pRRophetic algorithms, there were 12 ICIs drugs with higher sensitivity in the high-risk group (Fig. 7).

Fig. 5 Immune cell infiltration were positively correlated (Correlation coefficient > 0) with risk score according to multiple algorithms (CIBERSORT-ABS、CIBERSORT、MCPCOUNTER、EPC、XCELL、TIMER、QUANTISEQ) (a). Scatter diagrams show associations of immune cells and two risk signature compromising genes, EGFLM (b) and GNG11(c)

Fig. 6 Violin diagrams show that ImmuneScore, StromalS and ESTIMATEScore were all significantly higher in high-risk groups (a), high expression of EGFLAM (c), and GNG11 (f). Violin diagrams showed different responses to immune check point inhibitor (ICIs) according to risk-score (b), EGFLAM (e) and GNG11 (h) expressions. These two genes were both positive correlated with M2 macrophages (d, g)

Fig. 7 Box-plots show 12 kinds of ICI medicine more sensitive in high-risk groups

The expression analysis of EMVI-M2-related gene EGFLAM and GNG11

The result of IHC showed that the expression of EGFLAM and GNG11 in ceMDCT 3–4 EMVI score samples was significantly higher than that in ceMDCT 1–2 EMVI score samples (P < 0.0001) (Fig. 8a, additional file 5a). We detected the expression of the EMVI-M2-related gene EGFLAM and GNG11 in gastirc mucosal epithelial cell line (GES-1) and 5 GC cell lines (AGS, HGC-27, KATO III, MKN-1 and MKN-45) through western blotting. The results showed that the expression of EGFLAM and GNG11 in cancer cell lines was higher than gastric mucosal epithelial cell line. EGFLAM has the highest expression in HGC-27 (P < 0.01) and MKN-45 (P < 0.001) cell lines. GNG11 has the highest expression in HGC-27 (P < 0.01) and KATO III (P < 0.01) cell lines (Fig. 8b). The result of immunofluorescence was consistent with the result of western blotting (Fig. 8c). The knockdown of EGFLAM and GNG11 was performed in the HGC-27 cell line. Western Blot analysis revealed that the shRNA constructs shRNA-10,011 and shRNA-10,008 exhibited the most efficient knockdown (additional file 5b, c). Colony formation assays, scratch assays, and Transwell assays demonstrated that the knockdown of EGFLAM and GNG11 in gastric cancer cell lines (HGC-27, KATO III, and MKN-45) significantly inhibited their proliferation, migration, and invasion capabilities (Fig. 8d-f). Subcutaneous tumor volumes in mice were notably reduced by anti-PD-1 treatment in vivo experiments. The reduction in subcutaneous tumor volume was even more pronounced in the group where EGFLAM was knocked down compared to those treated with alone. It suggests that EGFLAM may attenuate the sensitivity to anti-PD-1 treatment (Fig. 8g-h). In contrast, there was no significant difference in subcutaneous tumor volume between the GNG11-KD group and the anti-PD-1 treatment group. Flow cytometry revealed that after anti-PD-1 treatment, BALB/c mice exhibited an increased population of CD8+ T cells, which contributes to the anti-tumor response in the mice. Notably, the EGFLAM-KD group had a higher number of sorted CD8+ T cells compared to the group treated with anti-PD-1 treatment alone (Fig. 8i). Using the same methodology, we also investigated the therapeutic effects of anti-CTLA-4 treatment. Our findings confirmed that CTLA-4 is capable of reducing the volume of subcutaneous tumors in BALB/c mice. Moreover, the group with EGFLAM knockdown displayed even smaller subcutaneous tumor volumes and a higher production of CD8+ T cells compared to those treated with CTLA-4 alone (Fig. 8j-l). The results are consistent with the previous bioinformatic analysis of immunotherapy responses, indicating that high expression of EGFLAM correlated with a lack of response to immunotherapy in gastric cancer. The knockdown of EGFLAM subsequently enhances the sensitivity to immunotherapy.

Fig. 8 EGFLAM and GNG11 augment the proliferation, migration, and invasiveness of gastric cancer cell lines. Furthermore, in in vivo experiments, EGFLAM attenuates the sensitivity of mice to anti-PD-1 and anti-CTLA-4 treatments. a. The result of IHC showed that the expression of EGFLAM and GNG11 in ceMDCT 3–4 EMVI score samples (n = 4) was significantly higher than that in ceMDCT 1–2 EMVI score samples (n = 4). b. Western blotting showed that EGFLAM has the highest expression in HGC-27 and MKN-45 cell lines. GNG11 has the highest expression in HGC-27 and KATO III cell lines. c. The result of immunofluorescence was consistent with the result of western blotting. d. EGFLAM or GNG11 knockdown in gastric cancer cell lines (HGC-27, KATO III, and MKN-45) inhibited their proliferative capacity. e, f. Scratch assays and Transwell assays indicated that after the knockout of EGFLAM and GNG11 in gastric cancer cell lines, there was a notable inhibition of both migratory and invasive abilities; g, j. A subcutaneous tumor model was established in BALB/C mice using MFC cells, followed by treatment with anti-PD-1 and anti-CTLA-4; h, k. Growth curves of subcutaneous tumors in BALB/C mice were monitored over time. i, l. Flow cytometry was performed on the subcutaneous tumors to quantify the presence of CD8 + T cells and CD4 + T cells, providing insights into the immune cell infiltration within the tumor microenvironment. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001

Discussion

In this radiogenomics-based study, we investigated the association of CT-detected EMVI related genes and M2 macrophages related genes from WGCNA constructed gene modules. Then we established a 2-gene (EGFLAM and GNG11) model which divided GC patients into low- and high-risk groups. These two genes were both positively correlated with M2 macrophages. The high-risk group was with lower TMB/single mutation rate and worse response to ICIs and poorer prognosis with significant differences.

Among multiple kinds of immune cells, M2 macrophages promote tumor angiogenesis and aid in intravasating migration [12], leading to poor prognosis [10, 11]. In Sonal et.al.’s study, EMVI positive colorectal cancer represented blunting immune response, demonstrating decreased expression PD-L1 positive macrophages. However, there was no significant difference in CD163, another M2 macrophages markers (PD-L1) between EMVI positive and negative groups [8]. They speculated that fibrosis and obliteration of the blood vessel caused by EMVI could inability of immune cells to reach inside the tumor and the “cold” environment elicits for tumorigenesis and subsequent generating EMVI. Different from colorectal cancer, GC is a characteristic inflammation related cancer but with high heterogeneity [15], and the tumor inflammatory microenvironment plays a crucial role in tumor progression and affecting the clinical benefit of ICIs therapy and prognosis [16, 17]. In this study, we found that immune cell infiltration status was higher in high-risk group, which confirmed the complexity of the tumor immune microenvironment.

In our study, we focused on the intersected EMVI-related genes that coexpressed with gene modules as defined by WGCNA. This systemic unit of coexpression among the EMVI-M2 macrophage-related genes derived from the WGCNA-defined model allows us to estimate the function of the gene network at its most comprehensive level, moving beyond a mere list of individual genes. Based on univariate Cox and LASSO analyses, we developed a CT-detected EMVI-M2 prognostic gene signature along with a corresponding risk score model. This model effectively distinguished survival differences between low- and high-risk groups in both training and validation cohorts for GC patients, showing satisfactory AUC values. These results are consistent with the previously reported prognostic predictive ability of CT-detected EMVI [2, 18].

Furthermore, these findings shed light on the development of EMVI, where cancer cells infiltrate and proliferate within the lumen of draining veins, potentially facilitated by the immune environment, including M2 macrophages and their associated cytokines [12]. Moreover, they enhance our understanding of the clinical significance of CT-detected EMVI, which serves as a critical predictor of distant metastasis and prognosis in GC patients [19]. According to the TCGA database, there was a significant difference in overall survival (OS) rates between high- and low-risk groups with respect to TMB. Previous literature has shown that patients with higher TMB exhibit significantly longer survival times, even among those with advanced disease stages [20, 21]. The K-M curves overlapped for patients with high-TMB/low-risk score and low-TMB/high-risk score. Noteworthy that the risk score was positively associated with T stage, disease stage and tumor differential grade, which is consistent with the characteristics of EMVI [22]. In order to avoid the bias of different clinical characteristics with EMVI presentation, all GC patients for mRNA sequencing were in T4aN + and had poorly differentiated adenocarcinoma.

Multiple oncologic-related gene pathways and functions were found to be correlated with the risk score. Among them, the VEGF pathway and angiogenesis characteristics could be used to explain the development of CT-detected EMVI. According to the hub genes of the EMVI-M2-prognosis related signature, EGFLAM has been shown to have functions related to cell proliferation, migration, and invasion, and is correlated with poor prognosis in glioblastoma patients [23]. Meanwhile, GNG11 (G protein subunit gamma 11) had also been identified as playing a crucial role in the biological process of ovarian cancer by the Extracellular Matrix (ECM) receptor mutation pathway, potentially affecting patient prognosis [24]. We observed a positive correlation between both genes and M2 macrophages, fibroblasts and endothelial cells, suggesting that EGFLAM and GNG11 are involved in the immune microenvironment. The upregulation of risk genes may facilitate the tumor microenvironment, which promotes tumor proliferation and migration.

Multiple clinical trials had proven that GC patients can benefit from immune therapy, primarily through the use of programmed cell death 1 (PD-1)/ programmed cell death ligand 1 (PD-L1) inhibitors [25, 26]. Additionally, several studies have demonstrated the anticancer effects of anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies in GC [27, 28]. In a previous study, targeting CTLA-4 could decrease M2 macrophages and promotes T cell activation [29], potentially explaining the significant difference in sensitivity to anti-CTLA-4 between low- and high-risk groups. Furthermore, in this study we found that risk score and corresponding genes were positively correlated with multiple immune checkpoint genes, suggesting the potential for targeted therapy of relevant ICIs in GC patients. Using the pRRophetic algorithm, we predicted sensitivities to 12 ICIs drugs and confirmed that the sensitivities of high-risk groups were significantly higher than those of low-risk groups. These new medicines could be suitable for high-risk or advanced GC patients. For example, PD0325901, as an ERK inhibitor could enhance the efficiency of PD-1 inhibitor in non-small cell lung carcinoma [30].

In recent years, TMB has received considerable attention in research related to immune checkpoint inhibitors (ICIs). Studies have shown that patients with high tumor mutational burdens are more likely to benefit from ICI treatments. For patients with TMB-high (TMB-H) tumors, their cancer cells produce neoantigens. Once these neoantigens are presented on the surface of tumor cells, they can be recognized by corresponding immune cells, leading to the activation of immune cell-mediated killing of the tumors [31]. Some studies have shown that TMB is associated with monotherapy or combination therapy using Immune Checkpoint Inhibitors (ICIs) for various tumors, and it has been proven to serve as a predictive biomarker for the efficacy of pan-cancer immunotherapy [32, 33]. Xishan Hao and colleagues found that the TMB-H subtype displayed a markedly immunologically active phenotype, as determined through transcriptomic analysis and further validated in the TCGA GC cohort. GC patients with TMB-H showed a significantly improved survival rate (P = 0.047) [34]. Similar research has found that in pancreatic cancer, patients with TMB-H tend to have longer survival times [35]. This is consistent with the findings of this article, where patients in the low-risk group were shown to have higher TMB status, alongside lower expression levels of EGFLAM and GNG11 in the same group. In the analysis of responsiveness to ICI treatments, the low EGFLAM group demonstrated improved responsiveness to PD-1 and CTLA-4 blockade. In vivo experiments showed that after knocking down EGFLAM, the volume of subcutaneous tumors in mice was significantly reduced.

The study has several limitations. First, the sample size was relatively small. However, our study had sufficient samples (6:7) for target genes mining. Secondly, most GC patients in TCGA database are white, African, or Latino; however, the model we constructed showed satisfactory predictive ability.

Conclusions

CT-detected EMVI-related genes were closely correlated with M2 macrophages infiltration in GC tissue, highlighting the relationship between EMVI and poor prognosis.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Supplementary Material 4

Supplementary Material 5

Acknowledgements

None.

Author contributions

HY: bioinformatics analysis, molecular biology experiment, manuscript writing and graphical visualization. XG: bioinformatics analysis and manuscript writing. CF: CT imaging analysis. YZ: molecular biology experiment and pathological analysis. BS: molecular biology experiment. PP: conceptual design and manuscript revision. NH: clinical data collection. YY: providing gastric cancer clinical samples. YW: CT imaging analysis, gene sequencing and study supervision. BG: conceptual design, bioinformatics analysis, manuscript revision and providing fund. JC: CT imaging analysis, gene sequencing, manuscript revision and providing fund. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China under grant number 81901819 and Peking University People’s Hospital Research and Development Funds under grant number RS2021-08.

Data availability

The original contributions presented in the study are publicly available. This data can be found in the GEO database, accession number: GSE182831.

Declarations

Ethics approval and consent to participate

This study was approved by the institutional review board under approval number: 2020PHB395-01, which waived the requirement for obtaining informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Abbreviations

GC Gastric cancer

TNM Tumor, node, and metastasis

CT Computed tomography

EMVI Extramural venous invasion

TME Tumor microenvironment

TMB Tumor mutation burden

ICIs Immune checkpoint inhibitors

TAMs Tumor-associated macrophages

TCGA The Cancer Genome Atlas

GEO Gene Expression Omnibus

ceMDCT Contrast-enhanced multidetector CT

LASSO Least absolute shrinkage and selection operator

IHC Immunohistochemistry

ROC Receiver operating curve

GSVA Gene set variation analysis

AUC Area under the curve

CTLA-4 Cytotoxic T-lymphocyte-associated antigen 4

PD-1 Programmed death 1

PD-L1 Programmed cell death ligand 1

OS Overall survival

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Hao Yang and Xinyi Gou contributed equally to this work and share first authorship.
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