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. 2025 Apr 9;16:497. doi: 10.1007/s12672-025-02210-1

Predicting immune status and gene mutations in stomach adenocarcinoma patients based on inflammatory response-related prognostic features

Huanjun Li 1, Jingtang Chen 1, Zhiliang Chen 2, Jingsheng Liao 1,✉
PMCID: PMC11982005  PMID: 40205166

Abstract

Background

Stomach adenocarcinoma (STAD) is an aggressive malignant tumor. Herein, we characterized the prognosis based on inflammatory response-related features and evaluated their potential impact on survival and immune status of STAD patients.

Methods

Inflammation-related genes obtained from public databases were used to analyze the inflammatory response scores of STAD samples. The differentially expressed genes (DEGs) between STAD and adjacent gastric tissue were then analyzed using the “limma” package. Genes associated with STAD prognosis were obtained from the intersection of inflammation-related genes and DEGs. The key genes screened by last absolute shrinkage and selection operator (LASSO) Cox and stepwise regression analyses were used to construct prognostic models and nomograms. The tumor immune dysfunction exclusion (TIDE) algorithm was used to assess potential differences in immunotherapy response between high- and low-risk groups and to explore gene mutation signatures using the R software maftools package. In addition, GSEA was used to predict pathway characteristics between different subgroups. Finally, scratch and transwell assays were performed to explore the role of SERPINE1 in STAD cells.

Results

We found that a high-inflammatory group was associated with poor prognosis in STAD patients. 14 inflammation-related DEGs out of 126 DEGs were identified to be associated with the prognosis of STAD patients, and the prognostic models and nomograms constructed from the subsequently identified key genes (SLC7A1, CD82, SERPINE1 ROS1 and SLC7A2) demonstrated a good predictive performance in terms of prognosis of STAD. Patients in the STAD high-risk group had higher StromalScore and TIDE scores. It was also found that patients in the STAD low-risk group may have a higher mutation burden. Enrichment analysis revealed significant enrichment of epithelial–mesenchymal transition, angiogenesis and KRAS pathways in the high-risk group. In-vitro experiments showed that down-regulation of SERPINE1 attenuated the migratory and invasive abilities of AGS cells.

Conclusion

This study provides new insights into prognostic prediction and immunotherapy for STAD from the perspective of the inflammatory response.

Keywords: Stomach adenocarcinoma, Inflammatory response, Prognostic model, Immune infiltration, Gene mutation signature

Introduction

Gastric cancer (GC) is the most common gastrointestinal tumor, with the fifth highest incidence and third highest mortality rate of malignant tumors worldwide [1–3]. One prevalent form of stomach histopathological cancer with a high fatality rate is stomach adenocarcinoma (STAD) [4]. In recent decades, substantial progress has been achieved in treating STAD, especially due to the introduction of cisplatin, which has somewhat prolonged the life expectancy of individuals diagnosed with gastric adenocarcinoma [5]. Nonetheless, the emergence of resistance to this drug has diminished its efficacy, resulting in localized tumor cell infiltration and metastases to distant sites. Consequently, the outlook for STAD remains bleak, with a 5-year survival rate falling below 30% [6]. Therefore, to maximize individualized treatment for patients with advanced disease, a more thorough risk assessment of STAD patients is necessary.

Inflammation is thought to be the seventh “hallmark” of cancer and has been linked to tumor invasion and metastasis [7]. It has also been revealed to be a primary promoter of tumor formation and progression in the hematological and tumor microenvironment. It is commonly acknowledged that the increase of cytokines in the inflammatory response reflects angiogenesis, DNA damage, and tumor invasion [8]. Accumulating research has recently shown that a poor prognosis for malignant tumors is tightly linked to the inflammatory response [9]. Patients with tumors such as esophagus cancer [4] and pancreatic cancer [10] have a favorable prognosis based on the inflammatory response index. Gastritis is thought to be the most prevalent gastrointestinal disease and is characterized by a continuous inflammation of the stomach mucosa without any particular clinical symptoms [11, 12]. Sequential stages of inflammation, chemotaxis, allogeneic hyperplasia and cancer have been demonstrated in a mature tumorigenic model of intestinal-type STAD caused by uncontrolled inflammation [13, 14]. There is compelling evidence that the inflammatory response worsens the course of STAD by impacting medication resistance, metastasis, and tumor growth [15]. The postoperative recovery of patients with STAD may be further impacted by the inflammatory response, according to mounting data in recent years [16]. In order to find possible indicators and treatment targets to enhance patient prognosis, it is imperative to comprehend how the inflammatory response and STAD prognosis interact.

In this study, we evaluated the correlation between inflammatory response scores and the prognosis of STAD patients. Differentially expressed genes (DEGs) between STAD samples and adjacent gastric tissue were analyzed, and genes characteristic of DEGs associated with inflammatory response in STAD patients were screened. After a risk-scoring method was developed, The Riskscore was displayed as a nomogram with various clinicopathological characteristics of STAD patients. Lastly, we investigated how various risk groups differed regarding immune infiltration, gene mutation signature, and pathway enrichment. All things considered, our study offers a straightforward, affordable, and trustworthy prediction methodology for STAD patients based on inflammatory response-associated DEGs, which could offer fresh perspectives on prognostic evaluation and STAD treatment approaches.

Methods

Data acquisition

As a training set, a combined cohort of mRNA sequencing data from STAD samples was obtained from the genotypic tissue expression (GTEx) and the cancer genome atlas (TCGA) databases on the UCSC Xena platform (https://xenabrowser.net/). Before additional analysis, samples with survival times longer than 30 days were kept, samples lacking clinical follow-up information and status were eliminated, data were converted to transcripts per million (TPM) values, and log 2 (TPM + 1) values were computed. A total of 210 normal gastric tissue samples and 385 samples of STAD were collected. The gene expression omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo) database was used to collect the test set of gene expression data and associated sample information for GSE66229 STAD patients. GSE66229 includes 300 tumor samples.

Single sample gene set enrichment analysis (ssGSEA)

The “GSVA” [17] and “GSEABase” packages in R were utilized to do the ssGSEA analysis, which quantified the inflammatory response score for every TCGA-STAD sample. The molecular signature database (MSigDB, http://software.broadinstitute.com) gave the gene profiles of the inflammatory response. The INFLAMMATORY RESPONSE gene collection was downloaded from the Molecular Signature Database (MSigDB, http://software.broadinstitute.org/gsea/msigdb) in order to characterize inflammatory response genes. A total of 200 genes were collected.

Differential gene screening and functional enrichment analysis

DEGs between cancers and control samples in TCGA-STAD were detected by the “limma” package [18], with the following characteristic values of fold change (FC): |log2FC|> 1 and adjusted p-value < 0.05 as the cut-off value. Functional enrichment of the screened DEGs was performed by the R package “clusterProfiler” [19], which calculates the score of each sample on the different biological pathways to obtain the ssGSEA score for each sample corresponding to each function, and analyses the differences in the biological processes among the DEGs. In addition, we performed ssGSEA on TCGA-STAD samples of different subtypes based on the DEGs of mutations between the high-risk and low-risk groups using the R package “GSVA” to calculate the scores of different functional pathways for each sample and to compare the differences of these pathways between the different risk subgroups [20].

Construction and validation of inflammatory response risk profiles

In order to successfully minimize the number of DEGs and lower the danger of overfitting, we first used the least absolute shrinkage and selection operator (LASSO) Cox regression analysis with the “glmnet” package [21] in R through the DEGs that had previously been screened. We determined each patient's Riskscore by identifying the important genes using stepwise regression analysis. The following is the formula: Riskscore = Σβi × Expi, where β is the corresponding gene Cox regression coefficient and i is the gene expression level. The training and validation cohort samples were split into two groups: high-risk (zscore > 0) and low-risk (zscore < 0). The Riskscore was standardized as zscore. Subsequently, survival analyses were performed between high-risk and low-risk groups using the R package “survminer” [22], and Kaplan–Meier survival curves were plotted for prognostic analyses. In addition, we used the R package “timeROC” [23] to display time-dependent subject work characteristics [24] curves and calculate the area under the curve (AUC) for 1-, 2-, 3-, 4- and 5- years.

Clinical characteristics of STAD and establishment and validation of the nomogram

By merging different clinical data (age, stage, gender, and grade), univariate and multivariate Cox regression analyses were conducted to verify whether Riskscore was an independent predictive factor for the survival of patients with STAD. Additionally, using multivariate Cox and stepwise regression analyses in the R software packages “rms” and “regplot” [25, 26] a nomogram was produced between Riskscore and the aforementioned clinical indicators. The R software’s “caret” package produced calibration curves, while the “rmda” package drew clinical decision curves [27].

Immune infiltration analysis

The R package “estimate” [28] was utilized to visualize the variations among three tumor immune microenvironment [29]-related metrics in TCGA-STAD, namely StromalScore, ImmuneScore, and ESTIMATEScore, based on the RNA expression patterns of the samples. The relative abundance of 22 types of immune cells in TCGA-STAD was determined using the CIBERSORT and ssGSEA techniques [30]. Using a gene set taken from earlier research, the scores of 28 types of tumor-infiltrating cells were determined using the “GSVA” package [31].

Immunotherapy response prediction

To evaluate any variations in treatment response between high- and low-risk groups, we imported expression matrices for the TCGA-STAD cohort into tumor immune dysfunction and exclusion (TIDE, http://tide.dfci.harvard.edu/) [32]. Patients with STAD who had a TIDE score greater than 0 were considered non-responders to immunotherapy. On the other hand, STAD patients were considered responders if their TIDE score was less than 0. Furthermore, we contrasted the two risk groups' Exclusion and Dysfunction scores. To predict the response to immunotherapy, the tumor mutational burden (TMB) profile was also used to evaluate the differences between the two risk groups. TMB was computed using the R package “maftools” using the TCGA cohort’s somatic mutation profile [33].

Copy number and DNA damage scoring

Numerous mutations frequently arise as a result of the genetic instability of tumor cells, and the manifestation of non-synonymous mutations can produce neoantigens, which are antigens unique to tumors. Since neoantigens are not expressed in healthy tissues, they are extremely immunogenic [34]. They then trigger an immunological response by activating CD4+ T and CD8+ T cells, which may make them novel targets for tumor immunotherapy. Among other things, neoantigen levels can be identified using single nucleotide variations (SNV) methods [35]. Tumor cells with homologous recombination defect (HRD) have been reported to be susceptible to inhibition of poly ADP ribose polymerase (PARP)-mediated DNA damage repair mechanisms [36]. Information on tumor genome stability in STAD patients, including single nucleotide variants (SNV), silent mutation rate, non-silent mutation rate, number of segments, fraction altered and HRD scores were obtained from published articles [37].

Cell culture and siRNA transfection

The Chinese Academy of Sciences Cell Bank (Shanghai, China) provided the human gastric adenocarcinoma cell line (AGS) and the human gastric mucosa cell line (GES-1). AGS cells were cultivated in the F-12 medium (Biological Industries, Shanghai, China), whilst GES-1 cells were cultivated in the RPMI-1640 medium. 10% fetal bovine serum and 1% penicillin/streptomycin (FBS; Gibco, Logan, UT, USA) were present in all media. These cells were kept in a humidified atmosphere with 5% CO2 at 37 °C. Lipofectamine 3000 transfection reagent (Thermo Fisher Scientific, China) was used to transfect the si-SERPINE1 (5′-CCCUAGAGAACCUGGGAAUUU-3′) and negative control si-RNA for transient transfection [38].

RNA extraction and quantitative real-time PCR

The RNA Extraction Kit (TRIzol, Invitrogen, USA) was used to extract total RNA from AGS and GES-1 cells by the manufacturer’s instructions. The HiScript II kit (Vazyme, China) was used to start the creation of cDNA templates after the purity and concentration of the total RNA of the resultant were assessed. The KAPA SYBR® FAST kit (Sigma Aldrich, San Luis, MO, USA) and particular primers were used to conduct quantitative real-time PCR (qRT-PCR). The 2−∆∆CT method was utilized to examine the data, and GAPDH was employed as an internal control [39]. Table 1 displays the primer sequences for the particular genes employed in this investigation. Experiments were performed in triplicate.

Table 1.

The sequences of primers for RT − qPCR used in this study

Gene Sequence
SLC7A1 F: GCGACTTGCTTCTATGCCTTCG
R: CCCAAAGTAGGCGATGAAGCAG
CD82 F: GGAAGAGGACAACAGCCTTTCTG
R: ATGATGCCCAGGTTCTCCTGCA
SERPINE1 F: CTCATCAGCCACTGGAAAGGCA
R: GACTCGTGAAGTCAGCCTGAAAC
ROS1 CCAGGACCATATGCTGACGTTG
ACGGCACTGATAACTCTTTCTGC
SLC7A2 GTAAGAGGCAGTCACCAGTTGC
CTGAGGATGAGAACACAGGCTG
GAPDH F: GTCTCCTCTGACTTCAACAGCG
R: ACCACCCTGTTGCTGTAGCCAA
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Cell migration and invasion assays

We then conducted scratch and transwell assays to evaluate the impact of SERPINE1 expression on the migratory and invasion capacity of AGS cells. A wound-healing assay was used to quantify collective cell migration for migration experiments. Six-well plates were injected with transfected cells (1 × 105 cells/well). A 10 μL plastic pipette tip was used to scrape the monolayer into a uniform wound once the cell density was between 60 and 80%. Following a PBS wash, the monolayers were cultured in a medium devoid of fetal bovine serum (FBS). Photographs were taken at 0 and 48 h to measure the wound edge lengths between the two edges of the migrating cell sheet. Every experiment was carried out three times. 5 × 104 cells were inoculated into the upper chamber, which was coated with 10% Matrigel (Corning), and the cells were cultured for 24 h in order to perform the invasion assay. A swab was used to remove any cells that were still in the upper chamber following the incubation time. After that, 4% paraformaldehyde was used to fix the cells on the filter’s lower side, and 0.1% crystal violet solution was used to stain them. Under a microscope, these invaded cells in the bottom chamber were counted using six distinct fields of view.

Statistical analyses

All statistical analyses were performed using GraphPad Prism 8 (GraphPad Software, San Diego, USA) and R software (version 3.6.0). The normality of continuous variables was assessed using the Shapiro–Wilk test. If the data did not follow a normal distribution, the Wilcoxon rank sum test was used for two-group comparisons, while the Kruskal–Wallis test was applied for comparisons involving more than two groups. Kaplan–Meier curves were used to analyze survival data, and the Spearman method was employed to compute correlations. A p-value of less than 0.05 was deemed statistically significant.

Result

Overall inflammatory response levels are upregulated in STAD and associated with tumor progression

We first examined the TCGA-STAD samples and used the ssGSEA method to determine the inflammatory response score for each sample in order to clarify the connection between inflammatory response and clinicopathological aspects in STAD. The findings demonstrated that STAD tissues had a considerably higher level of inflammatory response than para-carcinoma tissue (p ≤ 2e−16, Fig. 1A). The median inflammatory response score was then used to categorize the TCGA-STAD tumor samples into groups with high and low inflammatory responses. Patients in the strong inflammatory response group had a worse prognosis, according to Kaplan–Meier survival analysis (p = 0.0089, Fig. 1B). There was no significant difference between the inflammatory response scores and the clinical stages of Stage (p = 0.26), M stage (p = 0.44), and N stage (p = 0.34) in STAD patients when we compared the inflammatory response scores for clinicopathological features in the TCGA-STAD cohort (Fig. 1C–E). In addition, we observed significant differences in inflammatory response scores across Grade (p = 1.2e−05) and T Stage (p = 0.002) (Fig. 1F–G). These results reveal that the inflammatory response may play an important role in STAD progression.

Fig. 1.

Fig. 1

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Distribution of inflammatory response scores for different clinicopathological features. A Comparison of inflammatory response scores between tumor and paraneoplastic control samples in TCGA. B Association between inflammatory response level and prognosis in TCGA-STAD. C–G Association of inflammatory response scores with clinical grade progression in TCGA

Identification of DEGs between STAD and paraneoplastic samples

Then, using the “limma” software, we conducted a thorough DEG analysis of STAD and para-cancer samples based on the TCGA and GTEx dataset (Fig. 2A). The primary events of DEGs, according to the enrichment analysis of DEGs, were GOs linked to biological processes connected to the immune system, including leukocyte activation, T-cell activation, and lymphocyte activation regulation (Fig. 2B). The primary events of DEG were biological processes (Fig. 2B). 126 genes were then shown to be important DEGs influencing the inflammatory response in STAD patients by intersecting DEGs with genes linked to the inflammatory response (Fig. 2C). We then looked more closely at the relationship between inflammation-related DEGs and prognosis in STAD patients and found a significant correlation between 14 inflammation-related DEGs and patients’ prognosis (p < 0.05, Fig. 2D). Of these, 11 genes had a better positive correlation with prognosis than CD82, SLC7A1 and IRF1, which were linked to poor prognosis in STAD patients.

Fig. 2.

Fig. 2

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Identification of inflammatory response-related genes in STAD patients with prognostic significance. A Differential gene expression of STAD samples versus adjacent gastric tissuein the TCGA and GTEx datasets. B Functional enrichment analysis of biological processes of differential genes. C Intersection of DEG with inflammation-related genes. D Association of intersecting genes with STAD prognosis

Screening of key genes in STAD

Next, we used LASSO and stepwise regression analysis to further screen the screened 14 key genes related to inflammatory response in STAD (Fig. 3A). Five important genes were screened for further analysis (SLC7A1, CD82, SERPINE1, ROS1 and SLC7A2, Fig. 3B, C). Then, using the regression coefficients and the expression levels of the last important genes, we created characteristics to evaluate the prognosis of STAD patients, as explained below: Riskscore = (− 0.2*SLC7A1) + (− 0.22*CD82) + 0.197*SERPINE1 + 0.049*ROS1 + 0.067*SLC7A2. Therefore, STAD patients in the TCGA and GTEx training cohort were categorized into high- and low-risk groups based on median Riskscore. Additionally, the risk model was used to generate the Riskscore of STAD patients in the GSE66229 validation cohort. Similarly, the cut-off value of the training cohort Riskscore was used to classify patients in the validation cohort into high-risk and low-risk groups.

Fig. 3.

Fig. 3

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Identification of key genes associated with the prognosis of STAD patients. A In the process of λ value selection in the LASSO regression model, the left panel demonstrates the variation of gene coefficients with the L1 paradigm, and the right panel determines the optimal λ value by cross-validation. B Five screened genes and their coefficients. C Forest plot showing the risk ratios of these genes and their 95% confidence intervals. *p < 0.05; ***p < 0.001

Survival analysis and predictive ability assessment of risk models

The risk model’s performance was then evaluated using ROC analysis, which showed that the TCGA and GTEx training sets had respective 1-, 2-, 3- and 4-year AUC values of 0.68, 0.71, 0.68 and 0.68 (Fig. 4A). Patients in the high-risk group in the TCGA training set had a worse prognosis for survival than patients in the low-risk group, according to the Kaplan–Meier survival analysis (p = 0.00012, Fig. 4B). We used the GSE66229 validation set to evaluate the robustness of the model using a model and equivalence coefficients identical to those used in the training set used for the determination to verify the stability of the predictions of the inflammation-associated DEG clinical prognostic model. Similar results can be seen with 1-, 2-, 3-, 4- and 5-year AUC values of 0.71, 0.63, 0.62, 0.61 and 0.61 in the GSE66229 validation set cohort as shown in Fig. 4C. STAD patients in the low-risk group showed better prognostic outcomes than those in the high-risk group (p = 0.029, Fig. 4D). It indicates that prognostic characteristics are good predictors of prognosis for STAD patients.

Fig. 4.

Fig. 4

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Construction and validation of risk model for STAD patients. A, B TCGA cohort risk model 1-, 2-, 3- and 4-year time-dependent ROC curves, high and low-risk group KM survival analysis. C, D GSE66229 cohort risk model 1-, 2-, 3-, 4- and 5-year time-dependent ROC curves, high and low-risk group KM survival analysis

Nomogram construction and validation based on Riskscore and key clinical characteristics

We expanded our studies to incorporate four clinical metrics (Age, Gender, Stage, and Grade) in addition to the use of Riskscore to estimate the prognosis in STAD patients in order to reduce the unpredictability and potential bias of depending solely on Riskscore to do so. First off, Age, stage, and Riskscore were identified as important prognostic indicators of survival in STAD patients by both our univariate and multivariate Cox regression-based analyses. The results showed that hazard ratio (HR) values of Riskscore were 2.718 and 2.685, respectively (Fig. 5A, B). To better quantify the risk assessment and survival probability of STAD patients, we constructed a nomogram using Age, Stage, and Riskscore to estimate the OS of STAD patients at 1-, 3- and 5- years. The Riskscore had the greatest impact on the OS of STAD patients, as shown in Fig. 5C. The calibration curve for this nomogram showed a high degree of agreement between observed and predicted values (Fig. 5D). To evaluate the reliability of the model, the DCA was performed and confirmed that nomogram and Riskscore were most effective in predicting prognosis (Fig. 5E).

Fig. 5.

Fig. 5

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Nomogram was constructed for predicting the prognosis of STAD patients. A, B Riskscore with clinical characteristics for unifactorial and multifactorial results. C Riskscore combined with clinical characteristics for nomogram construction. D, E Calibration curves and decision curves for nomogram

Differences in tumor microenvironment characteristics and immunotherapy response among different risk subgroups of STAD patients

Subsequently, we evaluated the immune cell infiltration differences between patients in the low-risk and high-risk STAD scoring groups. We discovered that while there was no significant difference in the two groups’ overall ImmuneScore, the high-risk group had higher StromalScore and ESTIMATEScore (Fig. 6A). The CIBERSORT and ssGSEA algorithms were then used to analyze the relative abundance of various immune cell types in the high- and low-risk groups. The findings demonstrated that the high-risk group of STAD patients had higher levels of naive B cells, immature dendritic cells, macrophages, and nature killer T cells, as well as mast cells infiltration abundance. The low-risk group had higher levels of type 17 T helper cells and activated CD4 T cells (Fig. 6B, C).

Fig. 6.

Fig. 6

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Relationship between high and low-risk groups and immune characteristics in STAD patients. A Differences in ESTIMATE scores between risk subgroups in TCGA. B The difference in CIBERSORT score between risk subgroups in TCGA. C Differences in ssGSEA calculated immunity scores between risk subgroups in TCGA. D Difference in TMB between risk subgroups in TCGA. E Predicted immune response results between high and low-risk groups in TCGA. F Correlation of TIDE score with Riskscore in TCGA. G–H Dysfunction versus Exclusion differences between high and low-risk groups in TCGA. And ns, p > 0.05, not statistically significant; *p < 0.05; **p < 0.01; ***p < 0.001; p < 0.0001

Since TMB levels are linked to the effectiveness of PD1/PD-L1 antibodies, tumor cells with more TMB have a better chance of being recognized by the immune system [24]. Here, we compared the TMB distribution between the high- and low-risk groups of STAD patients. The findings indicated that the low-risk group of STAD patients had higher TMB (p = 1.2e−00, Fig. 6D). On the other hand, there was also a higher response to immunotherapy in high-risk patients relative to the low-risk group (Fig. 6E). After evaluating the association between the Riskscore and the TIDE score, we discovered a positive connection (Fig. 6F). We evaluated the possible clinical benefits of immunotherapy in high- and low-risk groups of STAD patients using TIDE software. The high-risk group had significantly higher levels of dysfunction and exclusion (Fig. 6G, H). From this, it is clear that immunological escape may occur primarily through immune cell malfunction and immune rejection in individuals in the high-risk group of STAD. When combined, these findings imply that high-risk STAD patients would be more vulnerable to immune escape and have a worse reaction to immune checkpoint blockade (ICB) therapy.

Analysis of gene mutation profiles and pathway enrichment among different risk subgroups of STAD

We then examined how patients in various STAD risk groups differed in their gene mutation profiles. According to the results, the number of SNV neoantigens was significantly higher in the low-risk group (p = 0.0094, Fig. 7A). This suggests that patients in the low-risk group may be more immunogenic and that they may have a higher mutation burden, which could result in more neoantigen generation. Furthermore, patients in the low-risk group of STAD had a considerably greater silent mutation rate (p = 1.6e−06) and non-silent mutation rate (p = 2.2e−06) than patients in the high-risk group (Fig. 7A), indicating that the low-risk group may have more mutated genes. In contrast, patients in the high-risk group had considerably higher HRD (p = 6.6e−05, Fig. 7A), suggesting that their ability to repair DNA may be compromised. We next used the GSEA technique to analyze the enrichment in several signaling pathways in the high-risk group. The epithelial-mesenchymal transition, angiogenesis, and KRAS signaling pathways of which are strongly linked to the development of cancer- were shown to be primarily enriched in the high-risk group (Fig. 7B).

Fig. 7.

Fig. 7

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Mutation characteristics and pathway enrichment analysis between different risk components in STAD. A Mutation characteristics between high and low-risk groups in TCGA. B GSEA shows the enrichment of high-risk groups on different signaling pathways

Downregulation of SERPINE1 impairs migration and invasion of STAD cells

To learn more about the roles and expression levels of important genes in STAD. First, we used qPCR to examine the mRNA expression levels of five important genes in AGS and GES-1 cells. According to qPCR results, AGS cells presented considerably greater levels of the genes SLC7A1, SERPINE1 and ROS1 than GES-1 cells, but the expression levels of CD82 and SLC7A2 were lower (Fig. 8A). In STAD cells, it has been observed that SERPINE1 facilitates the epithelial mesenchymal transition process [40]. Tumor cell invasion and metastasis are facilitated by epithelial–mesenchymal transition [41]. Thus, we investigated SERPINE1’s potential function in the development of STAD. Here, we used the wound healing test and the transwell assay to examine the impact of the SERPINE1 gene on AGS cell invasion and metastasis. The results showed that the reduction of SERPINE1 significantly inhibited AGS cell migration and metastasis (Fig. 8B–E).

Fig. 8.

Fig. 8

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Exploring the biological role of SERPINE1 in STAD. A The qPCR detection of expression levels of SLC7A1, CD82, SERPINE1, ROS1 and SLC7A2 in AGS and GES-1 cells. B, C Representative images and statistical analysis of wound healing assay in AGS cells after SERPINE1 knockdown. D, E Representative images of transwell assay of AGS cells after SERPINE1 knockdown and statistical analysis of invasive cell counts. Data are expressed as SD ± mean, *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001

Discussion

Since the majority of STAD patients are discovered at advanced stages with a poor prognosis and few available treatment options, STAD continues to be a major cause of cancer-related fatalities globally, resulting in high mortality rates [42]. Unfortunately, the sensitivity and specificity of the currently used biomarkers for STAD diagnosis and prognosis are limited. According to research, inflammation is a major contributor to the development of tumors and the spread of cancer since it can change epigenetics and trigger the synthesis of growth factors. As a result, methods to stop inflammation's initiation, spread, and reduction may stop or postpone the development of cancer [43]. In STAD, stromal-induced chemotherapeutic resistance may be addressed by focusing on inflammatory variables [44]. The effectiveness of tumor therapy can be considerably increased by certain pro-inflammatory cytokines or stimulants, which can encourage immune cell infiltration into infected tissues [45]. This suggests that inflammation is a “double-edged sword” and that reducing inflammation has become crucial for enhancing the effectiveness of cancer treatment. Therefore, there is an urgent need for research to identify additional biomarkers useful for inflammatory responses in cancer management that will help to improve the efficacy of personalized therapies targeting tumor-associated inflammation. In order to identify novel biomarkers for STAD treatment, we examined the significance of inflammation-related genes in STAD in this work.

In order to improve the clinical applicability, we combined DEGs between STAD samples and adjacent gastric tissue and analyzed 14 inflammation-related DEGs that were significantly associated with the prognosis of STAD patients. After LASSO Cox and stepwise regression analysis, we identified five genes (SLC7A1, CD82, SERPINE1, ROS1 and SLC7A2) as signature genes predicting the prognosis of STAD patients. By building prognostic models based on these genes, ROC curves, calibration curves, and forest plots confirmed that the constructed prognostic features could accurately predict OS in STAD patients. It is known that the biggest risk factor for STAD is chronic inflammation of the gastric mucosa caused by persistent Helicobacter pylori infection. Li et al. discovered that SLC7A1 mRNA levels were up-regulated in gastric cells following Helicobacter pylori infection [46]. CD82 was found to be significantly under-expressed in STAD tissues compared to normal gastric tissues, with a negative correlation between tumor grade, lymph node metastasis, depth of infiltration and T, N and M stage [47]. SERPINE1 expression has been shown to rise significantly with the progression of the T, N and M classifications of STAD, and it has been positively correlated with both immune cell infiltration and poor prognosis in patients with STAD [48]. Immunohistochemical detection of ROS1 was positive in STAD samples, and ROS1 inhibitors led to tumor shrinkage that was significantly promoted by ROS1 rearrangements [49]. In connection with STAD stromal activation and immunosuppression, Lin et al. researchers discovered that exosome genes, such as SLC7A2, independently and reliably predicted STAD prognosis [50]. These findings imply that DEGs linked to inflammation might affect immunotherapy in STAD patients. However, more experimental research is required to determine their involvement in relation to the inflammatory response in STAD patients as well as the molecular pathways.

The course of STAD and the prognosis of patients are significantly impacted when the balance between immunosuppressive and immune-activating signaling is upset [51]. Long-term chronic inflammatory manifestations in the presence of the STAD microenvironment decrease immunotherapeutic possibilities and accelerate tumor growth [52]. In TME, immune cells are essential. The production of memory CD8+ T cells and the penetration of activated immune cells into the tumor parenchyma, along with their anti-tumor properties, are the primary advantages of immunotherapy [53, 54]. To enable the immune system to eradicate tumor lesions, immunotherapy’s ultimate objective is to transform immunodominant TME into an immunostimulatory condition [53]. An immunological barrier against CD8+ T cell-mediated anti-tumor immune responses can be established by macrophage type 2 cells [55]. These established cellular and immunological mechanisms [56, 57] have served as the foundation for the development of numerous immune-related measures that may find use in therapeutic settings. Here, we discovered that the ESTIMATEScore and StromalScore were greater in the high-risk group. Patients in the STAD high-risk group exhibited a markedly higher number of T cells and were considerably enriched in naive B cells, immature dendritic cells, and macrophages. T cells and CD4 T cells were activated by CD8. The low-risk group had considerably higher levels of CD4 memory activated. All of these findings point to the presence of immunosuppressive cells, including macrophages, in the high-risk group of STAD patients. These cells create an immunosuppressive milieu that prevents CD8+ T cells from eliminating tumor cells.

Because PD-1/PD-L1 inhibitors can more easily identify tumor cells with greater TMB, they can change the immune surveillance status and aid in the therapy of some refractory tumors [58]. Based on the genetic characteristics of T-cell dysfunction and T-cell exclusion, Peng, J.’s group developed an algorithmic framework for TIDE [59] that enables the computation of T-cell dysfunction in TME and T-cell exclusion scores. The low-risk group of STAD patients in this study had a greater TMB than the high-risk group, which would suggest that the low-risk group is better suited for immunotherapy. Tumor immunotherapy response has been predicted using the TIDE score; patients with tumors with high TIDE scores might not react to immunotherapy, whereas those with low TIDE scores might benefit [32, 60]. We discovered that the high-risk group had significantly higher TIDE scores, dysfunctions, and exclusions than the low-risk group. These findings imply that patients with STAD who are in the high-risk group may experience immunological escape primarily through immune cell dysfunction and immune rejection.

In a range of tumor forms, tumor-specific neoantigens have been linked to enhanced OS and response to immune checkpoint inhibition, making them important targets for anti-tumor immunity [61]. Studies have confirmed that SNV biomarkers exhibit excellent sensitivity and specificity for the diagnostic ability of pre-cancerous colorectal adenomas, with high disease specificity for adenomas [62]. HRD scores were developed using genome-wide copy number data from arrays as a way to infer defects in homologous recombination DNA damage repair pathways in tumor samples [63]. It was found that triple-negative breast cancer patients with higher HRD scores had a higher response to platinum-based drug therapy [64]. In the current investigation, we discovered that the low-risk group had a much greater SNV, silent mutation rate, and non-silent mutation rate than the high-risk group. This suggests that there may be a greater load of mutations in the low-risk group, which could result in the production of more neoantigens with greater immunogenicity. On the other hand, the high-risk group’s HRD scores were greater, which would indicate that their DNA repair is poorer. It was noted that somatic changes can have an impact on the immunological response. KRAS mutations, for instance, change pathways that impact immune cells [37]. Numerous studies have demonstrated that the epithelial–mesenchymal transition limits the infiltration of immune cells with immunotoxicity function, which impacts the response to immunotherapy [65], and promotes tumor development and metastasis [66]. Additionally, using the GSEA algorithm, we discovered that the STAD high-risk group exhibited the highest levels of activity in pathways like angiogenesis, KRAS, and epithelial–mesenchymal transition. Combining these findings, we therefore concluded that low-risk STAD patients might be more responsive and a good fit for immunotherapy.

Conclusion

In patients with STAD, we created a prognostic model linked to the inflammatory response. There was a strong correlation between lower survival outcomes and higher Riskscore. In STAD patients, risk ratings showed a strong functional correlation with the immunotherapy response and tumor microenvironment features. We discovered that patients in the low-risk group would be more susceptible and appropriate for immunotherapy based on gene mutation and pathway enrichment. In summary, our prognostic model of inflammatory response-associated risk may be a potential biomarker for risk stratification and prediction of response to immunotherapy in STAD patients.

Limitation

This study still has several limitations. First off, this is a retrospective study, and the risk models developed in this study require validation from an independent prospective cohort. Second, this work had a weak validation component and mostly depended on datasets and computational predictions. Additional experimental research is required for validation in subsequent investigations. Lastly, it is mostly unknown and requires more research on how prognostic genes interact with the inflammatory response in STAD, as well as the oncogenic impact of prognostic genes in the model.

Acknowledgements

Not applicable.

Abbreviations

STAD

Stomach adenocarcinoma

DEGs

Differentially expressed genes

TMB

Tumor mutational burden

HRD

Homologous recombination defects

TCGA

The cancer genome atlas

GTEx

Genotypic tissue expression

MSigDB

Molecular signatures database

SNV

Single nucleotide variants

KM

Kaplan–Meier

AUC

Area under ROC curve

DCA

Decision curve analysis

GEO

Gene expression omnibus

LASSO

Least absolute shrinkage and selection operator

OS

Overall survival

ROC

Receiver operating characteristic analysis

GSVA

Gene set variant analysis

ssGSEA

Single-sample gene set enrichment analysis

TIDE

Tumor immune dysfunction and exclusion

Author contributions

All authors contributed to this present work: [HJL] and [JSL] concepted and designed the research. [JTC] and [ZLC] acquired the data, [JSL] and [HJL] analyzed and interpreted data, [HJL], [JTC] and [JSL] drafted the manuscript, [JSL], [HJL] and [ZLC] revised manuscript for important intellectual content. All authors read and approved the manuscript.

Funding

The study was supported by Dongguan Science and Technology of Social Development Program (20231800935802).

Data availability

The datasets generated and/or analyzed during the current study are available in the [GSE66229] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE66229].

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

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

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available in the [GSE66229] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE66229].

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