Immune infiltration and clinical significance of the prognostic and immune-related gene signature in gastric cancer

Immune infiltration and clinical significance of the prognostic and immune-related gene signature in gastric cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Immune infiltration and clinical significance of the prognostic and immune-related gene signature in gastric cancer Hang Yang, Huihan Ai, Guanglong Chen, Weijie Zhao, Zikun Wu, Chai Lv, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5307766/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gastric cancer is a prevalent digestive system tumor. However, its heterogeneity and poor prognosis pose challenges to patient treatment. Therefore, there is a need to improve patient outcomes and guide treatment through patient stratification and immune prognostic models. Methods We analyzed gene expression in the The Cancer Genome Atlas dataset using statistical tests and developed a 24-gene risk signature called Prognostic and Immune-Related Gene Signature (PIRGS) using LASSO Cox regression. The Asian Cancer Research Group database was used to validate the model's accuracy. Based on the PIRGS signature, we categorized gastric cancer patients into high-risk and low-risk groups. Further analysis was conducted to explore immune infiltration, signaling pathways, and drug sensitivity differences between two groups. We also developed a nomogram combining the PIRGS signature and clinical variables for prognostic assessment. Key genes in the model were validated at tissue and cellular levels. Results The PIRGS signature, consisting of 24 genes, accurately predicted 1-year, 3-year, and 5-year survival rates in gastric cancer patients. The PIRGS score classified patients into PIRGS-High risk and PIRGS-Low risk groups. The PIRGS-High risk group showed upregulation of the TGF-β signaling pathway and increased type II interferon response, along with unfavorable prognosis and elevated monocyte levels. PD-L1 immune therapy appeared more effective in low-risk patients. Several potential therapeutic compounds were identified, particularly for PIRGS-High risk patients. CD14 and TGFB1/2/3 were expressed at higher levels in the PIRGS-High risk subgroup. Investigation of APOD as a potential target showed its association with unfavorable prognosis, and knockdown inhibited gastric cancer cell growth. Conclusions The PIRGS is a potent prognostic factor in gastric cancer and accurately predicts survival rates. It provides insights into immune infiltration characteristics, correlating with immune therapy, chemotherapy, and targeted inhibitors. This knowledge facilitates patient stratification and personalized treatment strategies. Biological sciences/Cancer/Cancer models Biological sciences/Cancer Biological sciences/Cancer/Gastrointestinal cancer/Gastric cancer Gene Signature Gastric Cancer Immunology Treatment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction According to estimates from GLOBOCAN in 2020, gastric cancer (GC) was responsible for approximately 1 million new cases and resulted in 770,000 deaths worldwide each year. These figures underscore the substantial impact of GC, ranking it as the fifth most common cancer and the fourth leading cause of cancer-related deaths globally [ 1 ] . In order to confront this challenge, the current clinical approach for gastric cancer incorporates a combination of neoadjuvant therapy, surgical resection, and immunotherapy. Eligibility for immunotherapy is determined based on the results of genetic testing [ 2 ] . Nevertheless, the heterogeneity of GC presents a significant hurdle, leading to diverse treatment responses among patients. Moreover, postoperative GC patients confront a grim prognosis with a relatively low 5-year survival rate [ 3 ] . Consequently, it is of utmost importance to enhance prognostic outcomes for these individuals through stratification and the creation of innovative immune prognostic models that align more accurately with their distinct prognoses. This approach holds tremendous potential in enabling personalized treatment strategies customized to meet the specific needs of each GC patient. In the current research landscape, numerous researchers have made remarkable advancements in creating gene models designed to stratify patients with GC according to their prognosis. These models utilize genetic information to identify high-risk and low-risk groups, facilitating more accurate prognostic assessments and personalized treatment approaches for GC patients. [ 4 – 7 ] . However, there is a lack of comprehensive reports that integrate both prognostic genes and immune genes to construct tailored prognostic models specifically for GC patients. This study would be valuable in better understanding the complex interplay between tumor biology, immune response, and prognosis in GC, ultimately leading to the development of more effective prognostic models and personalized treatment strategies. To bridge these knowledge gaps, it is crucial to establish robust prognostic models for GC that integrate both prognostic genes and immune genes. This integration has immense potential in advancing precision medicine approaches, empowering clinicians to administer personalized treatments to patients based on their specific genetic and immune profiles. By implementing these innovative prognostic models, clinicians can effectively enhance patient outcomes and optimize treatment decisions for gastric cancer. In our study, we conducted an extensive screening and identification of immune-related candidate genes using a variety of biological techniques to develop a comprehensive immune prognosis model specifically for GC. This model, known as the Prognostic and Immune-Related Gene Signature (PIRGS), allows for precise evaluation of immune infiltration patterns across different subgroups within GC patients. To enhance clinical decision-making and enable personalized treatment approaches, we incorporated nomograms, which visually represent complex data, aiding clinicians in making informed decisions based on individual patient characteristics. Moreover, our research has uncovered compelling associations between the proposed model and immune infiltration patterns, highlighting their significance in the context of immune therapy and chemotherapy. As a result, this model demonstrates its potential in providing valuable insights for individualized treatment strategies in GC. 2.Materials and methods 2.1 Sample Collection Fresh clinical samples were procured from the Affiliated Tumor Hospital of Zhengzhou University, and the necessary ethical approval (2021-499-002) was obtained from the relevant committee. 2.2 Cell Culturing The immortalized gastric epithelial cells (GES-1) and four gastric cancer cell lines (AGS, MKN-45, NCI-N87, HGC-27) were obtained from the Institute of Medical Biotechnology, Chinese Academy of Medical Sciences. These cell lines were cultured in RPMI1640 medium supplemented with 10% serum. The incubation process occurred at a temperature of 37 ℃, within a dedicated incubator set at a 5% CO 2 environment. 2.3 Data Collection Transcriptome data for stomach adenocarcinoma (STAD), which included 407 gastric cancer tissues and 32 adjacent non-cancerous tissues, was obtained from the The Cancer Genome Atlas (TCGA) database (www.cancer.gov/ccg/research/genome-sequencing/tcga). The clinical information of gastric cancer patients, such as gender, age, TNM staging, and overall survival (OS), was also available. Patients with incomplete clinical data were excluded from the analyses related to survival. Additionally, for model validation purposes, the Asian Cancer Research Group (ACRG) dataset was downloaded from the gene expression omnibus (GEO) database (GSE66229, www.ncbi.nlm.nih.gov/geo). To ensure the comparability of diverse datasets, data standardization was conducted using z-scores to normalize the data [8] . 2.4 Enrichment Analysis of Prognostic and Immune-Related Genes To observe the differential gene expression between gastric cancer tissues and adjacent non-cancerous tissues, we employed the Wilcoxon rank-sum test. The methods demonstrated comparable or superior performance when compared to three parametric tests (DESeq2, edgeR and limma-voom), as well as other non-parametric tests, particularly when the sample size exceeded 8 for each condition [9] . Immune-related gene sets were obtained from the ImmPort database (www.immport.org) and intersected with the differentially expressed genes in cancer and adjacent tissues. Clinical data obtained from TCGA was utilized for conducting Kaplan-Meier (KM) plotter analysis and single-factor Cox analysis. The primary aim of this analysis was to identify immune prognostic candidate genes that hold specificity to gastric cancer, which would subsequently serve as the basis for constructing subsequent models. 2.5 Construction of Prognostic and Immune-Related Gene Signature Lasso is a powerful data dimensionality reduction method applicable to both linear and non-linear cases [10] . By imposing a penalty on the sample data, Lasso performs variable selection and effectively identifies and discards non-significant variables by compressing and shrinking their coefficients towards zero. In order to identify the most representative immune prognostic genes for gastric cancer, we integrated the expression data of candidate genes into Lasso regression using the R package “glmnet”. Subsequently, the R package “survival” was used for multivariate Cox regression analysis to determine the coefficients of each gene that impact patient OS. The retained genes in the model were then used to construct a gastric cancer immune prognosis model. To calculate the prognostic and immune-related gene signature (PIRGS) for each sample, we utilized the formula: PIRGS = ∑ (gene coefficient × gene expression). The R package “survival” was employed to identify the optimum cutoff value for PIRGS (cutoff = 0.02), and based on this value, the samples were divided into PIRGS-High and PIRGS-Low groups. To visualize the comparison of PIRGS score, overall survival status and time, as well as the risk heatmap of gene expression, we utilized the R package “pheatmap”. Moreover, Kaplan-Meier survival analysis and log-rank test were performed using the “survminer” package in R to evaluate survival differences among PIRGS subgroups in various cohorts. To assess the prognostic ability of PIRGS, we employed the “survivalROC” package for time-dependent receiver operating characteristic (ROC) curve analysis and calculated the area under the curve (AUC) for 1, 3, and 5 years. Additionally, multivariate Cox proportional hazards regression analysis was conducted. 2.6 Functional enrichment analysis To explore the biological functions of differentially expressed pathways in PIRGS-High and -Low subgroups, we divided patients into high and low groups based on a predefined threshold. We conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the “clusterProfiler” package in R software. Gene Set Enrichment Analysis (GSEA) is a computational technique used to assess significant and consistent differences in a predetermined set of genes between two biological conditions [11] . In this study, we performed gene set enrichment analysis using hallmark gene sets from MSigDB to understand the underlying biological mechanisms that distinguish the high and low subgroups (www.gsea-msigdb.org/gsea/msigdb). Pathways with a P value ≤ 0.05 were considered statistically significant. 2.7 Immunity analysis We utilized the ESTIMATE and TIMER to compare the cellular composition and immune response between the high-risk and low-risk groups. The ESTIMATE algorithm conducts gene set enrichment analysis on individual samples and generates three scores: the Stromal Score, which indicates the presence of stromal cells in tumor tissue; the Immune Score, which reflects the infiltration of immune cells in tumor tissue; and the Estimate Score, which estimates tumor purity [12] . To assess the infiltration of major immune cells and the status of immune-related pathways, we conducted single-sample gene set enrichment analysis (ssGSEA) using the “gsva” package and calculated scores for each immune-related pathway. Additionally, we employed the CIBERSORT algorithm to quantitatively analyze the abundance of 22 immune cell subtypes in individual samples obtained from TCGA. This algorithm analyzes transcriptome features and utilizes standardized gene expression data to clarify the differences in infiltration within the risk group for each immune cell subtype [13] . Furthermore, KM survival analysis was used to investigate the prognostic significance of each immune cell and immune-related pathway. 2.8 Drug sensitivity In order to predict the treatment susceptibility of PIRGS, we employed the “pRRophetic” algorithm to predict IC50 values for drugs. This innovative approach leverages the expression profile of genomics of drug sensitivity in cancer (GDSC) cell lines as a comprehensive training set. Based on this training set, we constructed a ridge regression model capable of predicting both the Chemotherapeutic Response of clinical samples in the TCGA cohort and the IC50 values of drugs for both PIRGS-High and -Low groups. 2.9 The predictive nomogram To provide valuable insights for clinicians in assessing the overall survival of GC patients over a period of 1 year, 3 years, and 5 years, we utilized the powerful R software packages “regplot” and “rms”. These packages facilitated the establishment and validation of column line plots that seamlessly integrated multiple clinical features with prognostic models. In an academic context, we evaluated the discriminative ability of these column line plots using well-established metrics such as the concordance index (C-index) and calibration plot. The C-index, widely acknowledged for its effectiveness in survival analysis, allows us to measure the differentiation between predicted values derived from Cox models and the actual observed values. Additionally, we employed the “timeROC” R package to calculate the area under the ROC curve — a robust metric that enables us to assess the efficacy of the ROC curves. 2.10 Quantitative real-time PCR Quantitative real-time PCR (qRT-PCR) was performed in accordance with established protocols. Total RNA was extracted and purified from patient tissues using Novozyme RNA extraction reagent (#R323-01). Subsequently, the purified RNA underwent reverse transcription to generate complementary DNA (cDNA). The qRT-PCR analysis was conducted employing the Novozyme SYBR Green PCR Kit (#Q711-02/03). To ensure accurate normalization of mRNA expression, the expression levels were standardized against the reference gene GAPDH. The relative mRNA levels in the experimental group were then compared to those in the control group. The primer sequences used in this study are detailed in the Table 1 . The siRNAs duplexes were synthesized by RiboBio Co., Ltd. (China). The sequences of siRNAs for APOD were as follows: si-APOD#2: 5’-GAAGATGGTACGAAATTGA-3’, si-APOD#3: 5’- CTGCCAAGCTGGAAGTTAA-3’. Table 1: The primer sequences Gene Forward sequence Reverse sequence GAPDH 5'- CATGAGAAGTATGACAACAGCCT -3' 5'- AGTCCTTCCACGATACCAAAGT -3' NRP1 5'- CAACAACGGCTCGGACTGGAAG -3' 5'- GCCTGGTCGTCATCACATTCATCC -3' APOD 5'- ACCTTTGAGAATGGACGCTGCATC -3' 5'- CTGGAGGGAGATTAGGGTTTCTTGC -3' MAPK14 5'- CTGGCTCGGCACACAGATGATG -3' 5'- GGGTGTTCCTGTCAGACGCATAATC -3' ITGAV 5'- CAGGAGTTCCAAGAGCAGCAAGG -3' 5'- GCCATCAGAGCCACGATCCATG -3' GLP2R 5'- GACCAGTCCTCTCTCCTTCCACAG -3' 5'- GCGTTCTCTATCGTCTGCCAAGTC -3' CD14 5'- AGCCTAGACCTCAGCCACAACTC -3' 5'- TCCCGTCCAGTGTCAGGTTATCC -3' TGFB1 5'- ACTACTACGCCAAGGAGGTCACC -3' 5'- TGAGGTATCGCCAGGAATTGTTGC -3' TGFB2 5'- ACTTGAGTCACAACAGACCAACCG -3' 5'- ACGCAGCAAGGAGAAGCAGATG -3' TGFB3 5'- CTGTGCGTGAGTGGCTGTTGAG -3' 5'- GGGTTGTGGTGATCCTTCTGCTTC -3' 2.11 Statistical analysis Statistical analyses were conducted using R version 4.1.0 software. Survival differences were assessed through Kaplan-Meier analysis. Data comparisons between two groups were performed using either Student's t-test or Wilcoxon rank-sum test, depending on the nature of the data. Statistical significance was determined at a predetermined threshold of P < 0.05. We employed the following notations to denote various levels of significance: *** P < 0.001, ** P < 0.01, * P < 0.05. 3. Results 3.1 Identification of differentially expressed prognostic and immune-related genes To conduct the differential expression gene analysis, we employed the TCGA-STAD database, which encompasses a comprehensive collection of 407 tumor samples, alongside 32 adjacent non-tumor samples. The comprehensive statistical methods (Wilcoxon test, FDR ≤ 0.05) were rigorously applied, leading us to identify a total of 19,684 genes that displayed significant differential expression, as exemplified in Fig. S1 . In order to concentrate specifically on immune-related aspects, we procured immune-related gene sets from the Immport Portal database. By comparing these gene sets with the differentially expressed genes, we successfully pinpointed 775 differentially expressed immune-related genes (IRGs). Prognostic implications of these IRGs were investigated using Kaplan-Meier (KM) analysis, which remarkably revealed that 398 IRGs exhibited a significant association with overall survival (OS). To streamline our analysis and establish a robust predictive model, univariate Cox analysis was employed to narrow down the gene selection. Consequently, we identified 100 genes that showcased considerable prognostic potential. Subsequently, we incorporated these genes into the prognostic and immune-related genes model to enhance its effectiveness. 3.2 Construction and validation of the prognostic and immune-related genes signature A prognostic signature incorporating immune-related genes was constructed for GC by employing LASSO regression analysis on a carefully curated set of 100 genes. The implementation of LASSO regression, a widely acclaimed statistical modeling approach, facilitated the identification of an optimal gene combination that exhibited superior predictive capability for patient prognosis. By means of meticulous analysis, we ascertained that the most favorable model fit was achieved through the inclusion of 24 genes, yielding a lambda.min value of 0.032 (Fig. 1 A). This predictive model, known as the Prognostic and Immune-Related Genes Signature (PIRGS), was constructed by fitting the coefficients of these 24 genes, as shown in Fig. 1 B. The PIRGS score is calculated as follows: PIRGS score = (NRP1 × 0.11) + (APOD × 0.01) + (CXCR4 × 0.15) + (GLP2R × 0.01) + (ITGAV × 0.08) + (STC1 × 0.04) + (TMSB15A × 0.09) + (ALB × 0.05) + (FCN2 × 0.01) + (IFNA14 × 0.12) + (LBP × 0.07) + (LCN1 × 0.12) + (MAPK14 × 0.07) + (PDYN × 0.09) + (PROC × 0.1) + (STAB2 × 0.07) + (GH2 × 0.11) - (HDGF × 0.01) - (CTLA4 × 0.16) - (SH3BP2 × 0.01) - (RFX5 × 0.04) - (OBP2B × 0.1) - (PMCH × 0.15) - (TYK2 × 0.03). To evaluate the predictive ability of the model, we calculated the scores for all patients and determined a cutoff value of 0.02 based on the relationship between the scores and prognosis (Fig. 1 C). Importantly, as shown in Fig. 1 D, the high-risk group (score > 0.02, n = 158) exhibited significantly worse prognosis compared to the low-risk group (score < 0.02, n = 179), with a hazard ratio of 4.6 (95% CI: 3.3–6.3). Furthermore, analysis of the receiver operating characteristic (ROC) curve revealed an increasing area under the curve (AUC) over time at 1 year, 3 years, and 5 years. The AUC values were 0.747, 0.761, and 0.788, respectively, indicating improved accuracy and sensitivity of the model as time progressed (Fig. 1 E). To validate the applicability of our model, we utilized the ACRG database, which includes a large cohort of 300 patients. Consistently, the high-risk group exhibited lower survival rates compared to the low-risk group using the same cutoff value of 0.02 ( Fig. S2A, S2B ). The AUC values for our model at 1 year, 3 years, and 5 years were 0.581, 0.597, and 0.608, respectively, showing an upward trend ( Fig. S2C ). In summary, our newly developed PIRGS for gastric cancer demonstrates significant potential in accurately predicting patient prognosis. The model has been validated and exhibits robustness and applicability. The increasing AUC values over time further emphasize the improved accuracy and sensitivity of the model. This model may have important implications for clinical decision-making and personalized treatment strategies in gastric cancer patients. 3.3 Molecular characteristics of PIRGS-High and -Low subgroup To distinguish between patients at high risk and low risk, we conducted score value calculations for all individuals. Patients exceeding a score value threshold of 0.02 were categorized as the high-risk group, denoted as PIRGS-High, while those below this threshold were assigned to the low-risk group, referred to as PIRGS-Low. By meticulously examining the differential pathways in these two groups, we can attain a more comprehensive understanding of the potential impact exerted by these pathways on patient prognosis. Employing KEGG enrichment analysis, we identified notable alterations in signaling pathways associated with cancer development and immunity, including TCF-β and PI3K-AKT (Fig. 2 A ) . Subsequent GSEA analysis unveiled an upregulation of the TGF-β signaling pathway specifically in the PIRGS-High subgroup (Fig. 2 B, 2 C). Furthermore, this upregulated expression pattern was independently confirmed using a separate dataset procured from ACRG ( Fig. S3A, 3B ). 3.4 The immune landscape The tumor microenvironment, which encompasses the intricate interaction between immune cells and stromal cells, plays a crucial role in tumor progression, metastasis, recurrence, and the development of drug resistance. Understanding these dynamics is of paramount importance. Therefore, we conducted an analysis to explore the correlation between risk scores and various measures of immune infiltration, stromal composition, and overall tumor purity as represented by ESTIMATE scores. Our findings unveil a statistically significant correlation between risk scores and stromal scores (P < 0.001), as well as ESTIMATE scores ( P < 0.001, Fig. S4) . These results shed light on the complex relationship between the risk profile and the tumor microenvironment, emphasizing further the potential influence of immune response and stromal cell activity on disease progression. Next, our evaluation focused on assessing the differences in immune response between the PIRGS-High and PIRGS-Low groups. Initially, we employed the ssGSEA algorithm to calculate the variances in the levels of infiltration of 16 immune cells and 13 immune-related pathways across the two groups. We observed a general reduction in the infiltration of CD8 + T cells, Mast cells, Th1 cells, and Th2 cells in the PIRGS-High subgroup compared to the PIRGS-Low subgroup (Fig. 3 A, Fig. S5A ). Additionally, immune-related pathways such as checkpoint, cytolytic activity, MHC class Ⅰ, and T cell co-inhibition were significantly downregulated in the PIRGS-High subgroup, while the type-II interferon (IFN) response showed an increase (Fig. 3 A, Fig. S5A ). Moreover, our survival analysis based on immune cell infiltration indicated that a lower infiltration of Th1 cells and Th2 cells was associated with a worse prognosis (Fig. 3 B, 3 C, and Fig. S5B ). Conversely, an upregulation of cytolytic activity, inflammation-promoting factors, and MHC class Ⅰ pathways was associated with a better prognosis (Fig. 3 D, 3 E, 3 F, and Fig. S5B ). However, an upregulation of type-II interferon pathways was associated with a worse prognosis (Fig. 3 G and Fig. S5B ). To further analyze the subtypes of immune cells, we utilized the CIBERSORT algorithm (Fig. 4 A- 4 D, Fig. S6A-6B ). The results revealed that monocytes were more abundant in the PIRGS-High subgroup, and their infiltration was associated with a worse prognosis (Fig. 4 B, 4 C, and Fig. S6B ). Additionally, T cell CD4 memory activated cells were significantly reduced in the PIRGS-High subgroup, which was also associated with a worse outcome (Fig. 4 B, 4 D, and Fig. S6B ). 3.5 PIRGS for the prognostic prediction of GC To further validate the clinical prognostic value of the model, we developed a nomogram as a clinical reference tool. This nomogram integrates variables such as Age, Gender, Stage, Grade, T, N, M staging, and RiskScore to predict gastric cancer patients' survival period at 1 year, 3 years, and 5 years (Fig. 5 A). We generated a calibration curve to assess the accuracy of the nomogram's predictions, and the results showed that the predicted outcomes (dotted line) closely aligned with the actual outcomes (colored line) on the calibration chart (Fig. 5 B). This indicates that the nomogram's predictions were highly accurate and of high quality. Notably, our model demonstrates remarkable accuracy when compared to traditional TNM staging. The Area Under the Curve (AUC) of our model surpasses that of T staging (AUC = 0.573), N staging (AUC = 0.647), and M staging (AUC = 0.548), as illustrated in Fig. 5 C. This highlights the superiority of our model's predictive performance compared to conventional staging methods. 3.6 Treatment benefit Subsequently, we evaluated the therapeutic implications of the PIRGS model in gastric cancer treatment, focusing on immune therapy and commonly used chemotherapy drugs. Firstly, we compared the expression levels of immune checkpoint molecules between the PIRGS-High and PIRGS-Low groups. The results showed significantly higher expression of these checkpoint molecules in the PIRGS-High group (Fig. 6 A), indicating that patients in the PIRGS-Low group may benefit more from immune therapy. We further validated this by analyzing a dataset of urothelial carcinoma patients who received anti-PD-L1 treatment [ 14 ] . The results revealed that responders had lower PIRGS scores compared to non-responders (Fig. 6 B). Moreover, within the PIRGS-Low group, a higher percentage of patients showed complete or partial response (Fig. 6 C). By stratifying patients into high and low PIRGS subtypes, we found that the low PIRGS subtype had significantly longer overall survival (Fig. 6 D). These findings suggest that the low PIRGS group exhibits better responses to immune therapy. In addition to immune therapies, neoadjuvant chemotherapy is commonly used to reduce tumor size prior to surgery. We compared the sensitivity to 5-Fu and cisplatin between the high and low PIRGS groups based on IC50 values. The results showed that the PIRGS-Low group was more sensitive to 5-Fu treatment but less sensitive to platinum-based drugs compared to the PIRGS-High group ( Fig. S7A, S7B ). Furthermore, we evaluated the sensitivity to various targeted drugs between the two risk groups. Patients in the PIRGS-High group showed increased sensitivity to PFI (BET inhibitor), BIBR-0796 (known alternatively as Doramapimod, represents a highly potent inhibitor of p38 MAPK), DMOG (an agonist of α-ketoglutarate co-factor and the inhibitor of HIF prolylhydroxylase), and AMG-706 which is a potent ATP-competitive inhibitor, has demonstrated significant efficacy in targeting VEGFR1/2/3 (Fig. 6 F- 6 I), while patients in the PIRGS-Low group demonstrated increased sensitivity to VX-11e (ERK2 inhibitor) (Fig. 6 E). These findings provide valuable insights for treatment decision-making using the PIRGS model. Overall, the PIRGS model has potential therapeutic advantages in guiding immune therapy and chemotherapy drug selection for gastric cancer patients. However, further clinical studies are needed to validate and optimize its applications in personalized treatment strategies 3.7 Individual PIRGS genes evaluation and experimental validation. A comprehensive analysis was conducted to investigate the differential expression of twenty-four genes in both the PIRGS-High and PIRGS-Low groups using the TCGA database. Fourteen upregulated genes were identified as oncogenes (Fig. 7 A ) . Importantly, further characterization through immune infiltration analysis revealed that NRP1, MAPK14, ITGAV, GLP2R, and APOD were strongly correlated with increased monocyte infiltration, suggesting their role as oncogenes (Fig. 7 B). Consequently, these five oncogenes were selected as the primary signature for subsequent validation studies. To assess their differential expression, a quantitative polymerase chain reaction (qPCR) experiment was performed, demonstrating a significant upregulation of NRP1, APOD, MAPK14, ITGAV, and GLP2R gene expression in T1 stage GC patients (representing the better prognosis, PIRGS-Low group) compared to T4 stage GC patients (representing the poorer prognosis, PIRGS-High group) (Fig. 7 C). Moreover, consistent with previous indications, the PIRGS-High cohort exhibited a significant upregulation of the TGF-β signaling pathway and increased infiltration of monocytes. Building upon these findings, additional experiments revealed elevated expression levels of CD14, a widely recognized monocyte marker, as well as TGFB1, TGFB2, and TGFB3, which act as markers for the TGF-β pathway, in T4 stage GC patients representing the PIRGS-High subgroup (Fig. 7 C). These collective observations suggest an augmented monocyte infiltration and activation of the TGF-β signaling pathway, thereby reinforcing earlier analyses and enhancing the credibility of our model. Furthermore, by conducting a systematic literature review, APOD emerged as a promising candidate for in-depth exploration due to its frequent mention in various studies relating to model construction, coupled with a lack of comprehensive investigations into its biological functions [ 15 – 18 ] . Its potential role in carcinogenesis was substantiated by observing its increased expression in four gastric cancer cell lines compared to the normal gastric epithelial cell line, GES-1 (Fig. 7 D). Through the utilization of TCGA and KM databases, this study unveiled a correlation between elevated APOD expression and an unfavorable prognosis among patients (Fig. 7 E, 7 F). To delve deeper into the underlying biological function associated with APOD, siRNA was employed to suppress APOD, resulting in a noteworthy inhibition of gastric cancer cell clonogenicity (Fig. 7 G, 7 H). 4. Discussion Gastric cancer, a prevalent form of cancer worldwide, is characterized by its low early detection rate and high recurrence rate in advanced stages, resulting in an unfavorable prognosis [ 19 ] . The efficacy of surgical resection, radiotherapy, or chemotherapy differs among patients due to the presence of tumor heterogeneity. As a consequence, clinical outcomes for individuals with gastric cancer exhibit substantial heterogeneity, with survival periods ranging from less than 5 months [ 20 ] . In this study, we have successfully identified immune prognostic genes that exhibit abnormal expression in gastric cancer tissues. By employing LASSO regression, we constructed a prognostic and immune-related gene signature (PIRGS) comprising 24 genes. Subsequently, we calculated the PIRGS score and employed it to classify patients into two groups: PIRGS-High risk and PIRGS-Low risk, based on the optimal cutoff value. Notably, patients within the high-risk group displayed a significantly worse prognosis, as evidenced by the survival analysis (HR = 4.6, 95% CI = 3.3–6.3, P < 0.001) (Fig. 1 D). To assess the predictive accuracy of PIRGS for estimating the 1-year, 3-year, and 5-year survival rates in gastric cancer patients, we performed ROC curve analysis. Encouragingly, the results indicated that PIRGS exhibited higher diagnostic accuracy in comparison to existing methods [ 6 ] . Specifically, the AUC values for these predictions were 0.747, 0.761, and 0.788, respectively (Fig. 1 E). Furthermore, we sought to validate the adverse prognostic outcome associated with high PIRGS scores by employing an external dataset. The findings from this validation process further confirmed the correlation between high PIRGS scores and unfavorable prognostic outcomes ( Fig. S2 ). In order to investigate the potential biological functions of PIRGS, it was categorized into high and low-risk groups using a predefined cutoff value. Through comprehensive biological analysis, several associations were identified between PIRGS and various crucial biological processes, including neuroactive ligand receptor interaction and cAMP signaling pathway. Remarkably, detailed analysis of GSEA revealed a significant upregulation of the TGF-β signaling pathway in PIRGS-High group (Fig. 2 ). Considering the well-known role of TGF-β in gastric cancer [ 21 , 22 ], these findings strongly suggest that the TGF-β signaling pathway may contribute to a poorer prognosis in the PIRGS-High subgroup. Moreover, we observed a significant augmentation in the type II interferon (IFN) response pathway concomitant with heightened levels of monocytes, which correlated with an unfavorable prognosis (Fig. 3 A, 3 G, 4 B, 4 C). The 24-gene model incorporates a comprehensive array of genes implicated in the initiation and progression of cancer. Notably, among these genes, 14 exhibited high expression levels in the PIRGS-High subgroup, with 5 demonstrating a positive correlation with mononuclear cell infiltration, which aligns with our experimental findings (Fig. 7 C). Subsequently, we focused on APOD as a target for further investigation. Our analysis revealed that APOD exhibited heightened expression in gastric cancer cell lines when compared to normal gastric epithelial cells. Additionally, it was associated with an unfavorable prognosis in gastric cancer patients. Remarkably, knockdown of APOD resulted in the inhibition of gastric cancer cell growth, indicating a potential oncogenic role for APOD (Fig. 7 D- 7 H). Further exploration is warranted to investigate the impact of other genes within the model on the occurrence and progression of gastric cancer. Recent advancements in cancer treatment have highlighted the growing significance of immune therapy, emphasizing the critical role of immune cell infiltration. Therefore, this study investigates the association between risk scoring and levels of immune cell infiltration. Our findings reveal a notable decrease in the infiltration of helper T cells, including Th1 and Th2 cells, among high-risk individuals, as determined through immune landscape analysis. Conversely, an increase in monocyte infiltration was observed. Survival analysis demonstrates that these cell infiltrations significantly influence patient prognosis (Fig. 3 , 4 ). Furthermore, our investigation into immune-related pathways reveals that the “Type_II_IFN_Response” is upregulated to a significant extent in PIRGS-High subgroup. Conversely, pathways such as “Cytolytic_activity”, “Inflammation-promoting”, “MHC_class_I” and “T_cell_co-inhibition” exhibit significant downregulation (Fig. 3 ). Additionally, we identified a correlation between risk scoring and the overall response to PD-L1 immune therapy. Patients with lower risk scores displayed greater sensitivity to PD-L1 immune therapy compared to those with higher risk scores. Moreover, within the high-risk group, the expression level of immune checkpoint genes was substantially lower than that observed in the low-risk group (Fig. 6 ). These findings imply that low-risk gastric cancer patients are more likely to experience a favorable response to PD-L1 immune therapy. Hence, this characteristic can serve as a predictive factor for the response to immune therapy. In clinical practice, the predictive capacity of existing staging systems in isolation, for determining the potential benefits of adjuvant chemotherapy in stage II or III patients, is limited [ 23 ] . Our data highlights an independent risk factor for GC, underscoring the significance of risk scoring in this context. By incorporating patient age, sex, stage, grade, and TNM staging, it becomes possible to generate personalized predictions for one-year, three-year, or five-year survival rates (Fig. 5 ). Additionally, this signature enables the assessment of chemotherapy drug sensitivity. Specifically, gastric cancer patients with high-risk scores are more likely to benefit from platinum-based adjuvant chemotherapy ( Fig. S7 ). Furthermore, there are several potential small molecule compounds, including PFI-1, BIBR-0796, DMOG, and AMG-706, that demonstrate therapeutic potential worthy of exploration in PIRGS-high patients (Fig. 6 ). To summarize, PIRGS has been developed to discern the molecular and immune characteristics of individuals with gastric cancer, thereby providing valuable insights for prognosticating patient outcomes and determining clinical drug sensitivity. 5. Conclusions Our research indicates that PIRGS represents a robust prognostic factor in gastric cancer. This novel finding not only brings clarity to the prognosis of GC patients but also positions PIRGS as a potent tool for predicting survival rates in this population. Moreover, our study has unveiled associations between PIRGS and immune infiltration characteristics, as well as their relevance to immune therapy and chemotherapy. This newfound knowledge enables the stratification of GC patients who would benefit from anti-tumor immune therapy, chemotherapy, and the prediction of efficacy of specific small molecule targeted inhibitors with heightened sensitivity. As a result, PIRGS provides valuable guidance for the treatment of gastric cancer patients. In summary, our systematic investigation has successfully elucidated the role and value of PIRGS in gastric cancer, thereby establishing the foundation for further comprehension and exploration in this field. Abbreviations ACRG Asian Cancer Research Group AUC Area Under the Curve CIBERSORT Cell-type Identification by Estimating Relative Subsets of RNA Transcripts ESTIMATE Estimation of Stromal and Immune cells in Malignant Tumors using Expression Data GC Gastric Cancer GDSC Genomics of Drug Sensitivity in Cancer GEO Gene Expression Omnibus GSEA Gene Set Enrichment Analysis KEGG Kyoto Encyclopedia of Genes and Genomes KM Kaplan-Meier OS Overall Survival PIRGS Prognostic and Immune-related Gene Signature qRT-PCR Quantitative real-time PCR ROC Receiver Operating Characteristic ssGSEA single-sample Gene Set Enrichment Analysis TCGA The Cancer Genome Atlas Declarations Ethics approval and consent to participate The study was approved by the ethics committee of the Affiliated Tumor Hospital of Zhengzhou University and signed informed consent was obtained from patient. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Consent for publication Not applicable Availability of data and materials The datasets used for the current study are available from the corresponding authors upon reasonable request Competing interests The authors declare that they have no competing interests Funding This study was supported by the Medical science and technology Project of Henan Province (Grant Nos. 232102311077, LHGJ20240108, LHGJ20220198, China), and National Key Clinical Discipline Construction Project. Authors' contributions HY, and HA contributed equally to this work. All authors have made significant contributions to the conception, supervision, and final approval of the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5307766","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379458349,"identity":"0b2460aa-dc06-427f-a7ae-c850c34e3060","order_by":0,"name":"Hang Yang","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Yang","suffix":""},{"id":379458351,"identity":"e548c6bc-44ca-4dc1-bf9c-ee42109fce26","order_by":1,"name":"Huihan Ai","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huihan","middleName":"","lastName":"Ai","suffix":""},{"id":379458353,"identity":"a9251349-52e6-4dc2-b1ec-b3b6b719147c","order_by":2,"name":"Guanglong Chen","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guanglong","middleName":"","lastName":"Chen","suffix":""},{"id":379458354,"identity":"6fa9a379-589d-4238-8682-bc3e112193d3","order_by":3,"name":"Weijie Zhao","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Zhao","suffix":""},{"id":379458355,"identity":"a61ecf00-e948-4a7d-a807-31c392f760e2","order_by":4,"name":"Zikun Wu","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zikun","middleName":"","lastName":"Wu","suffix":""},{"id":379458356,"identity":"2f5fdeb3-8f2e-46e5-9808-a1720ed0d2b1","order_by":5,"name":"Chai Lv","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chai","middleName":"","lastName":"Lv","suffix":""},{"id":379458357,"identity":"c287dfdc-e70f-4b88-bcd7-272c0870dd67","order_by":6,"name":"Zhi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3RsUoDMRjA8a8Evih8rWvEoa/wQaUoQvsqCbcX5ZYOchQOzqUP0ELRZ3DpnCOgiw9QcFFcHW5ycjDpbYUcHR3yJ1mS/EggAKnUf0z6+cGgUUhrm3m7qDqJ8FMHIpemXr0dTcIgO3Kn1RFkWNJY6dtiNlBau95jMWUr6neCySxGenvCmKMy1t1tUbDF7IYgy6PvEntCpgq3rLeEbGl8QWDNIkKwJSoQdv2NIrZnP52EWsKmIuvJgpW/BTuJEphfadamkktdr140nzscXW84i5LhQ/m8a34L81RK1zT3xXTwWn7uvueTKAE4uTz4hfBTwNHzPvnVdG2nUqlUCv4AcapPuV6D6UkAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-22 02:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5307766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5307766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70579606,"identity":"502207ce-4b9e-4a19-8561-21325c314a0f","added_by":"auto","created_at":"2024-12-04 14:58:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":622988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe construction of the prognostic and immune-related gene signature (PIRGS) using various analyses.\u003c/strong\u003e \u003cstrong\u003eA, B \u003c/strong\u003eLASSO regression analysis with 10-fold cross-validation was performed to construct PIRGS. LASSO regression is a method that helps select relevant features by shrinking coefficients of less informative variables towards zero. The 10-fold cross-validation helps assess the performance and generalizability of the model. \u003cstrong\u003eC \u003c/strong\u003eThe risk scores for patients were ranked, showing the distribution of patient scores in the upper panel. The survival status distribution of patients is depicted in the median panel, indicating the number of patients who survived or experienced an event (such as death). The lower panel presents a heatmap displaying the expression levels of the 24 prognostic and immune-related genes within the PIRGS. \u003cstrong\u003eD \u003c/strong\u003eKaplan-Meier curves were generated to visualize the overall survival of patients stratified into PIRGS-High and PIRGS-Low groups. This analysis helps evaluate the prognostic significance of the PIRGS, showing potential differences in survival outcomes between the two groups. \u003cstrong\u003eE\u003c/strong\u003e Time-dependent ROC (Receiver Operating Characteristic) analyses were conducted to assess the predictive accuracy of the PIRGS for survival status at different time points, including one, three, and five years. These analyses provide information about the performance of the PIRGS as a prognostic tool, indicating its ability to distinguish between patients with different survival outcomes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/3c8434f962ab5945e2b7113b.png"},{"id":70578520,"identity":"2a985e91-d2cd-4a08-952f-513ecd665f85","added_by":"auto","created_at":"2024-12-04 14:50:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular characteristics of the PIRGS-high and PIRGS-low subgroups.\u003c/strong\u003e \u003cstrong\u003eA \u003c/strong\u003eKEGG enrichment analysis was conducted to identify the most enriched signaling pathways between the PIRGS-high and PIRGS-low subgroups. \u0026nbsp;\u003cstrong\u003eB\u003c/strong\u003eEnriched signaling pathways in the PIRGS-high and PIRGS-low subgroups were determined using GSEA. \u0026nbsp;\u003cstrong\u003eC\u003c/strong\u003e GSEA specifically reveals the upregulated TGF-β (Transforming Growth Factor-beta) signaling pathway in the PIRGS-high subgroup. The normalized enrichment score (NES) of 1.34 indicates the extent to which this pathway is activated in the PIRGS-high subgroup compared to the PIRGS-low subgroup. The p-value (\u0026lt; 0.05) suggests statistical significance, indicating that the differential regulation of the TGF-β signaling pathway is likely to be biologically relevant in the context of the PIRGS.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/e007494298084f1892b3ada8.png"},{"id":70578516,"identity":"0bab8a66-35ed-42f7-9513-9389bf024bbd","added_by":"auto","created_at":"2024-12-04 14:50:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98954,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe immune landscape of PIRGS based on ssGSEA scores.\u003c/strong\u003e \u003cstrong\u003eA \u003c/strong\u003eDifferential infiltration levels of 16 types of immune cells and 13 immune-related pathways in PRIGS-High and PIRGS-Low subgroups. \u003cstrong\u003eB-G\u003c/strong\u003e Kaplan-Meier plots are displayed to assess the survival difference between TCGA patients with high and low infiltration of immune cells.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/4247dd85d801a807dc64ebf5.png"},{"id":70578521,"identity":"260078f9-7ce5-4e77-bb22-1a38c7b8c555","added_by":"auto","created_at":"2024-12-04 14:50:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":186426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe immune landscape of the PIRGS using the CIBERSORT algorithm. A \u003c/strong\u003eThe relative proportion of abundance of tumor microenvironment (TME) cells in patients is shown. \u003cstrong\u003eB \u003c/strong\u003eA bar plot displays the proportions of 22 subtypes of immune cells in the PIRGS-high and PIRGS-low subgroups. \u003cstrong\u003eC\u003c/strong\u003e Kaplan-Meier curves are presented to evaluate the survival difference between TCGA patients with high and low infiltration of immune cells.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/3f4278ac729f021a8d927f25.png"},{"id":70578517,"identity":"084e0c75-04e0-4bea-99a1-831c2fd609f5","added_by":"auto","created_at":"2024-12-04 14:50:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePIRGS for the prognostic prediction of GC. A \u003c/strong\u003eThe comprehensive nomogram that combines the risk score derived from the PIRGS with various pathologic features to predict the 1-, 3-, and 5-year OS of GC patients. \u003cstrong\u003eB\u003c/strong\u003e Calibration plots are shown, which assess the performance of the predictive model by comparing the predicted probabilities with the actual observed outcomes. \u003cstrong\u003eC\u003c/strong\u003e ROC curves are presented to evaluate the ability of the risk score derived from the PIRGS, along with other variables such as age, gender, stage, grade, T stage, and N stage, to predict patient survival.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/62c90618b02d23e122389c0c.png"},{"id":70578527,"identity":"84d73fd8-7a9e-42dc-8247-45718b5aac8c","added_by":"auto","created_at":"2024-12-04 14:50:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":283978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe\u003c/strong\u003e \u003cstrong\u003etreatment benefit associated with the PIRGS in GC patients. A \u003c/strong\u003ethe expression levels of five immune checkpoint genes (PD-L1, CTLA4, HAVCR2, LAG3, and PD-1) in the PIRGS-High and PIRGS-Low subgroups. \u003cstrong\u003eB\u003c/strong\u003e A boxplot illustrates the differential clinical response to anti-PD-L1 immunotherapy in the PIRGS-High and PIRGS-Low subgroups, using data from the IMvigor210 study. \u003cstrong\u003eC\u003c/strong\u003eThe rate of clinical response to anti-PD-L1 immunotherapy in the high or low PIRGS score groups in the IMvigor210 study. CR: complete response, PR: partial response, SD: stable disease, PD: progressive disease. \u003cstrong\u003eD\u003c/strong\u003e A Kaplan-Meier curve illustrates the overall survival (OS) difference between patients classified as PIRGS-High and PIRGS-Low in the IMvigor210 study. \u003cstrong\u003eE-I\u003c/strong\u003e The chemotherapy response and 3D structure of six candidate small-molecule drugs.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/7e762643e6eca1f983bafc89.png"},{"id":70578519,"identity":"1a555e19-69b7-4007-8949-1e029611a009","added_by":"auto","created_at":"2024-12-04 14:50:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":450133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe individual PIRGS genes evaluation and experimental validation in gastric cancer. A \u003c/strong\u003eThe expression levels of 14 PIRGS oncogenes in High and Low-PIRGS groups. \u0026nbsp;\u003cstrong\u003eB\u003c/strong\u003e Spearman's correlation analysis reveals that five PIRGS genes are positively correlated with the infiltration of monocytes in the TIMER database. \u003cstrong\u003eC\u003c/strong\u003e The mRNA expression levels of selected PIRGS oncogenes (NRP1, APOD, MAPK14, ITGAV, GLP2R), monocyte marker (CD14), and TGF-β signaling markers (TGFB1, TGFB2, and TGFB3) were evaluated in T1 and T4 stage gastric samples. T1 refers to higher overall survival (OS), while T4 refers to lower OS. \u003cstrong\u003eD\u003c/strong\u003e The mRNA expression level of APOD was evaluated in several gastric cancer cell lines (AGS, MKN-45, NCI-N87, HGC-27) compared to normal gastric epithelial cells (GES-1). \u003cstrong\u003eE\u003c/strong\u003e Kaplan-Meier survival analysis was conducted to evaluate the prognostic value of APOD expression in gastric cancer patients. The optimal cut-off for APOD expression was calculated by the surv_cutpoint algorithm. \u003cstrong\u003eF\u003c/strong\u003e Kaplan-Meier curve to predict OS according to APOD expression level from KM database. \u003cstrong\u003eG, H\u003c/strong\u003e APOD knockdown efficiency was assessed by siRNA transfection in AGS and HGC-27 cells, followed by colony formation assay.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/1926d81a101382c76fdf7066.png"},{"id":75539720,"identity":"ac136e95-234f-49e8-a5e1-663f95335b4b","added_by":"auto","created_at":"2025-02-05 15:48:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2945508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/5da82a6e-9164-4300-adf1-656a17f24f4c.pdf"},{"id":70578525,"identity":"fa02aa32-bc89-4cb6-aae5-4bf2876c501e","added_by":"auto","created_at":"2024-12-04 14:50:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7719055,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5307766/v1/34624cf1c33b7aa319f8f6e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immune infiltration and clinical significance of the prognostic and immune-related gene signature in gastric cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to estimates from GLOBOCAN in 2020, gastric cancer (GC) was responsible for approximately 1\u0026nbsp;million new cases and resulted in 770,000 deaths worldwide each year. These figures underscore the substantial impact of GC, ranking it as the fifth most common cancer and the fourth leading cause of cancer-related deaths globally\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In order to confront this challenge, the current clinical approach for gastric cancer incorporates a combination of neoadjuvant therapy, surgical resection, and immunotherapy. Eligibility for immunotherapy is determined based on the results of genetic testing\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the heterogeneity of GC presents a significant hurdle, leading to diverse treatment responses among patients. Moreover, postoperative GC patients confront a grim prognosis with a relatively low 5-year survival rate\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Consequently, it is of utmost importance to enhance prognostic outcomes for these individuals through stratification and the creation of innovative immune prognostic models that align more accurately with their distinct prognoses. This approach holds tremendous potential in enabling personalized treatment strategies customized to meet the specific needs of each GC patient.\u003c/p\u003e \u003cp\u003eIn the current research landscape, numerous researchers have made remarkable advancements in creating gene models designed to stratify patients with GC according to their prognosis. These models utilize genetic information to identify high-risk and low-risk groups, facilitating more accurate prognostic assessments and personalized treatment approaches for GC patients. \u003csup\u003e[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, there is a lack of comprehensive reports that integrate both prognostic genes and immune genes to construct tailored prognostic models specifically for GC patients. This study would be valuable in better understanding the complex interplay between tumor biology, immune response, and prognosis in GC, ultimately leading to the development of more effective prognostic models and personalized treatment strategies. To bridge these knowledge gaps, it is crucial to establish robust prognostic models for GC that integrate both prognostic genes and immune genes. This integration has immense potential in advancing precision medicine approaches, empowering clinicians to administer personalized treatments to patients based on their specific genetic and immune profiles. By implementing these innovative prognostic models, clinicians can effectively enhance patient outcomes and optimize treatment decisions for gastric cancer.\u003c/p\u003e \u003cp\u003eIn our study, we conducted an extensive screening and identification of immune-related candidate genes using a variety of biological techniques to develop a comprehensive immune prognosis model specifically for GC. This model, known as the Prognostic and Immune-Related Gene Signature (PIRGS), allows for precise evaluation of immune infiltration patterns across different subgroups within GC patients. To enhance clinical decision-making and enable personalized treatment approaches, we incorporated nomograms, which visually represent complex data, aiding clinicians in making informed decisions based on individual patient characteristics. Moreover, our research has uncovered compelling associations between the proposed model and immune infiltration patterns, highlighting their significance in the context of immune therapy and chemotherapy. As a result, this model demonstrates its potential in providing valuable insights for individualized treatment strategies in GC.\u003c/p\u003e"},{"header":"2.Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh clinical samples were procured from the Affiliated Tumor Hospital of Zhengzhou University, and the necessary ethical approval (2021-499-002) was obtained from the relevant committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Cell Culturing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immortalized gastric epithelial cells (GES-1) and four gastric cancer cell lines (AGS, MKN-45, NCI-N87, HGC-27) were obtained from the Institute of Medical Biotechnology, Chinese Academy of Medical Sciences. These cell lines were cultured in RPMI1640 medium supplemented with 10% serum. The incubation process occurred at a temperature of 37 ℃, within a dedicated incubator set at a 5% CO\u003csub\u003e2\u003c/sub\u003e environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptome data for stomach adenocarcinoma (STAD), which included 407 gastric cancer tissues and 32 adjacent non-cancerous tissues, was obtained from the The Cancer Genome Atlas (TCGA) database (www.cancer.gov/ccg/research/genome-sequencing/tcga). The clinical information of gastric cancer patients, such as gender, age, TNM staging, and overall survival (OS), was also available. Patients with incomplete clinical data were excluded from the analyses related to survival. Additionally, for model validation purposes, the Asian Cancer Research Group (ACRG) dataset was downloaded from the gene expression omnibus (GEO) database (GSE66229, www.ncbi.nlm.nih.gov/geo). To ensure the comparability of diverse datasets, data standardization was conducted using z-scores to normalize the data\u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Enrichment Analysis of Prognostic and Immune-Related Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo observe the differential gene expression between gastric cancer tissues and adjacent non-cancerous tissues, we employed the Wilcoxon rank-sum test. The methods demonstrated comparable or superior performance when compared to three parametric tests (DESeq2, edgeR and limma-voom), as well as other non-parametric tests, particularly when the sample size exceeded 8 for each condition\u003csup\u003e[9]\u003c/sup\u003e. Immune-related gene sets were obtained from the ImmPort database (www.immport.org) and intersected with the differentially expressed genes in cancer and adjacent tissues. Clinical data obtained from TCGA was utilized for conducting Kaplan-Meier (KM) plotter analysis and single-factor Cox analysis. The primary aim of this analysis was to identify immune prognostic candidate genes that hold specificity to gastric cancer, which would subsequently serve as the basis for constructing subsequent models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Construction of Prognostic and Immune-Related Gene Signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLasso is a powerful data dimensionality reduction method applicable to both linear and non-linear cases\u003csup\u003e[10]\u003c/sup\u003e. By imposing a penalty on the sample data, Lasso performs variable selection and effectively identifies and discards non-significant variables by compressing and shrinking their coefficients towards zero. In order to identify the most representative immune prognostic genes for gastric cancer, we integrated the expression data of candidate genes into Lasso regression using the R package \u0026ldquo;glmnet\u0026rdquo;. Subsequently, the R package \u0026ldquo;survival\u0026rdquo; was used for multivariate Cox regression analysis to determine the coefficients of each gene that impact patient OS. The retained genes in the model were then used to construct a gastric cancer immune prognosis model.\u003c/p\u003e\n\u003cp\u003eTo calculate the prognostic and immune-related gene signature (PIRGS) for each sample, we utilized the formula: PIRGS = \u0026sum; (gene coefficient \u0026times; gene expression). The R package \u0026ldquo;survival\u0026rdquo; was employed to identify the optimum cutoff value for PIRGS (cutoff = 0.02), and based on this value, the samples were divided into PIRGS-High and PIRGS-Low groups. To visualize the comparison of PIRGS score, overall survival status and time, as well as the risk heatmap of gene expression, we utilized the R package \u0026ldquo;pheatmap\u0026rdquo;. Moreover, Kaplan-Meier survival analysis and log-rank test were performed using the \u0026ldquo;survminer\u0026rdquo; package in R to evaluate survival differences among PIRGS subgroups in various cohorts. To assess the prognostic ability of PIRGS, we employed the \u0026ldquo;survivalROC\u0026rdquo; package for time-dependent receiver operating characteristic (ROC) curve analysis and calculated the area under the curve (AUC) for 1, 3, and 5 years. Additionally, multivariate Cox proportional hazards regression analysis was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the biological functions of differentially expressed pathways in PIRGS-High and -Low subgroups, we divided patients into high and low groups based on a predefined threshold. We conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the \u0026ldquo;clusterProfiler\u0026rdquo; package in R software. Gene Set Enrichment Analysis (GSEA) is a computational technique used to assess significant and consistent differences in a predetermined set of genes between two biological conditions\u003csup\u003e[11]\u003c/sup\u003e. In this study, we performed gene set enrichment analysis using hallmark gene sets from MSigDB to understand the underlying biological mechanisms that distinguish the high and low subgroups (www.gsea-msigdb.org/gsea/msigdb). Pathways with a \u003cem\u003eP\u003c/em\u003e value \u0026le; 0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Immunity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the ESTIMATE and TIMER to compare the cellular composition and immune response between the high-risk and low-risk groups. The ESTIMATE algorithm conducts gene set enrichment analysis on individual samples and generates three scores: the Stromal Score, which indicates the presence of stromal cells in tumor tissue; the Immune Score, which reflects the infiltration of immune cells in tumor tissue; and the Estimate Score, which estimates tumor purity\u003csup\u003e[12]\u003c/sup\u003e. To assess the infiltration of major immune cells and the status of immune-related pathways, we conducted single-sample gene set enrichment analysis (ssGSEA) using the\u0026nbsp;\u0026ldquo;gsva\u0026rdquo;\u0026nbsp;package and calculated scores for each immune-related pathway. Additionally, we employed the CIBERSORT algorithm to quantitatively analyze the abundance of 22 immune cell subtypes in individual samples obtained from TCGA. This algorithm analyzes transcriptome features and utilizes standardized gene expression data to clarify the differences in infiltration within the risk group for each immune cell subtype\u003csup\u003e[13]\u003c/sup\u003e. Furthermore, KM survival analysis was used to investigate the prognostic significance of each immune cell and immune-related pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Drug sensitivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to predict the treatment susceptibility of PIRGS, we employed the \u0026ldquo;pRRophetic\u0026rdquo; algorithm to predict IC50 values for drugs. This innovative approach leverages the expression profile of genomics of drug sensitivity in cancer (GDSC) cell lines as a comprehensive training set. Based on this training set, we constructed a ridge regression model capable of predicting both the Chemotherapeutic Response of clinical samples in the TCGA cohort and the IC50 values of drugs for both PIRGS-High and -Low groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 The predictive nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo provide valuable insights for clinicians in assessing the overall survival of GC patients over a period of 1 year, 3 years, and 5 years, we utilized the powerful R software packages \u0026ldquo;regplot\u0026rdquo; and \u0026ldquo;rms\u0026rdquo;. These packages facilitated the establishment and validation of column line plots that seamlessly integrated multiple clinical features with prognostic models. In an academic context, we evaluated the discriminative ability of these column line plots using well-established metrics such as the concordance index (C-index) and calibration plot. The C-index, widely acknowledged for its effectiveness in survival analysis, allows us to measure the differentiation between predicted values derived from Cox models and the actual observed values. Additionally, we employed the \u0026ldquo;timeROC\u0026rdquo; R package to calculate the area under the ROC curve \u0026mdash; a robust metric that enables us to assess the efficacy of the ROC curves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Quantitative real-time PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative real-time PCR (qRT-PCR) was performed in accordance with established protocols. Total RNA was extracted and purified from patient tissues using Novozyme RNA extraction reagent (#R323-01). Subsequently, the purified RNA underwent reverse transcription to generate complementary DNA (cDNA). The qRT-PCR analysis was conducted employing the Novozyme SYBR Green PCR Kit (#Q711-02/03). To ensure accurate normalization of mRNA expression, the expression levels were standardized against the reference gene GAPDH. The relative mRNA levels in the experimental group were then compared to those in the control group. The primer sequences used in this study are detailed in the \u003cstrong\u003eTable 1\u003c/strong\u003e. The siRNAs duplexes were synthesized by RiboBio Co., Ltd. (China). The sequences of siRNAs for APOD were as follows: si-APOD#2: 5\u0026rsquo;-GAAGATGGTACGAAATTGA-3\u0026rsquo;, si-APOD#3: 5\u0026rsquo;- CTGCCAAGCTGGAAGTTAA-3\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e \u003cstrong\u003eThe primer sequences\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGene\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- CATGAGAAGTATGACAACAGCCT -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- AGTCCTTCCACGATACCAAAGT -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eNRP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- CAACAACGGCTCGGACTGGAAG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- GCCTGGTCGTCATCACATTCATCC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eAPOD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- ACCTTTGAGAATGGACGCTGCATC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- CTGGAGGGAGATTAGGGTTTCTTGC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eMAPK14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- CTGGCTCGGCACACAGATGATG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- GGGTGTTCCTGTCAGACGCATAATC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eITGAV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- CAGGAGTTCCAAGAGCAGCAAGG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- GCCATCAGAGCCACGATCCATG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eGLP2R\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- GACCAGTCCTCTCTCCTTCCACAG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- GCGTTCTCTATCGTCTGCCAAGTC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eCD14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- AGCCTAGACCTCAGCCACAACTC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- TCCCGTCCAGTGTCAGGTTATCC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eTGFB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- ACTACTACGCCAAGGAGGTCACC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- TGAGGTATCGCCAGGAATTGTTGC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eTGFB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- ACTTGAGTCACAACAGACCAACCG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- ACGCAGCAAGGAGAAGCAGATG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.828%;\"\u003e\n \u003cp\u003e\u003cem\u003eTGFB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- CTGTGCGTGAGTGGCTGTTGAG -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.086%;\"\u003e\n \u003cp\u003e5\u0026apos;- GGGTTGTGGTGATCCTTCTGCTTC -3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using R version 4.1.0 software. Survival differences were assessed through Kaplan-Meier analysis. Data comparisons between two groups were performed using either Student\u0026apos;s t-test or Wilcoxon rank-sum test, depending on the nature of the data. Statistical significance was determined at a predetermined threshold of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. We employed the following notations to denote various levels of significance: ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of differentially expressed prognostic and immune-related genes\u003c/h2\u003e \u003cp\u003eTo conduct the differential expression gene analysis, we employed the TCGA-STAD database, which encompasses a comprehensive collection of 407 tumor samples, alongside 32 adjacent non-tumor samples. The comprehensive statistical methods (Wilcoxon test, FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05) were rigorously applied, leading us to identify a total of 19,684 genes that displayed significant differential expression, as exemplified in \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. In order to concentrate specifically on immune-related aspects, we procured immune-related gene sets from the Immport Portal database. By comparing these gene sets with the differentially expressed genes, we successfully pinpointed 775 differentially expressed immune-related genes (IRGs). Prognostic implications of these IRGs were investigated using Kaplan-Meier (KM) analysis, which remarkably revealed that 398 IRGs exhibited a significant association with overall survival (OS). To streamline our analysis and establish a robust predictive model, univariate Cox analysis was employed to narrow down the gene selection. Consequently, we identified 100 genes that showcased considerable prognostic potential. Subsequently, we incorporated these genes into the prognostic and immune-related genes model to enhance its effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Construction and validation of the prognostic and immune-related genes signature\u003c/h2\u003e \u003cp\u003eA prognostic signature incorporating immune-related genes was constructed for GC by employing LASSO regression analysis on a carefully curated set of 100 genes. The implementation of LASSO regression, a widely acclaimed statistical modeling approach, facilitated the identification of an optimal gene combination that exhibited superior predictive capability for patient prognosis. By means of meticulous analysis, we ascertained that the most favorable model fit was achieved through the inclusion of 24 genes, yielding a lambda.min value of 0.032 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This predictive model, known as the Prognostic and Immune-Related Genes Signature (PIRGS), was constructed by fitting the coefficients of these 24 genes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. The PIRGS score is calculated as follows: PIRGS score = (NRP1 \u0026times; 0.11) + (APOD \u0026times; 0.01) + (CXCR4 \u0026times; 0.15) + (GLP2R \u0026times; 0.01) + (ITGAV \u0026times; 0.08) + (STC1 \u0026times; 0.04) + (TMSB15A \u0026times; 0.09) + (ALB \u0026times; 0.05) + (FCN2 \u0026times; 0.01) + (IFNA14 \u0026times; 0.12) + (LBP \u0026times; 0.07) + (LCN1 \u0026times; 0.12) + (MAPK14 \u0026times; 0.07) + (PDYN \u0026times; 0.09) + (PROC \u0026times; 0.1) + (STAB2 \u0026times; 0.07) + (GH2 \u0026times; 0.11) - (HDGF \u0026times; 0.01) - (CTLA4 \u0026times; 0.16) - (SH3BP2 \u0026times; 0.01) - (RFX5 \u0026times; 0.04) - (OBP2B \u0026times; 0.1) - (PMCH \u0026times; 0.15) - (TYK2 \u0026times; 0.03). To evaluate the predictive ability of the model, we calculated the scores for all patients and determined a cutoff value of 0.02 based on the relationship between the scores and prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Importantly, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, the high-risk group (score\u0026thinsp;\u0026gt;\u0026thinsp;0.02, n\u0026thinsp;=\u0026thinsp;158) exhibited significantly worse prognosis compared to the low-risk group (score\u0026thinsp;\u0026lt;\u0026thinsp;0.02, n\u0026thinsp;=\u0026thinsp;179), with a hazard ratio of 4.6 (95% CI: 3.3\u0026ndash;6.3). Furthermore, analysis of the receiver operating characteristic (ROC) curve revealed an increasing area under the curve (AUC) over time at 1 year, 3 years, and 5 years. The AUC values were 0.747, 0.761, and 0.788, respectively, indicating improved accuracy and sensitivity of the model as time progressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). To validate the applicability of our model, we utilized the ACRG database, which includes a large cohort of 300 patients. Consistently, the high-risk group exhibited lower survival rates compared to the low-risk group using the same cutoff value of 0.02 (\u003cb\u003eFig. S2A, S2B\u003c/b\u003e). The AUC values for our model at 1 year, 3 years, and 5 years were 0.581, 0.597, and 0.608, respectively, showing an upward trend (\u003cb\u003eFig. S2C\u003c/b\u003e). In summary, our newly developed PIRGS for gastric cancer demonstrates significant potential in accurately predicting patient prognosis. The model has been validated and exhibits robustness and applicability. The increasing AUC values over time further emphasize the improved accuracy and sensitivity of the model. This model may have important implications for clinical decision-making and personalized treatment strategies in gastric cancer patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Molecular characteristics of PIRGS-High and -Low subgroup\u003c/h2\u003e \u003cp\u003eTo distinguish between patients at high risk and low risk, we conducted score value calculations for all individuals. Patients exceeding a score value threshold of 0.02 were categorized as the high-risk group, denoted as PIRGS-High, while those below this threshold were assigned to the low-risk group, referred to as PIRGS-Low. By meticulously examining the differential pathways in these two groups, we can attain a more comprehensive understanding of the potential impact exerted by these pathways on patient prognosis. Employing KEGG enrichment analysis, we identified notable alterations in signaling pathways associated with cancer development and immunity, including TCF-β and PI3K-AKT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Subsequent GSEA analysis unveiled an upregulation of the TGF-β signaling pathway specifically in the PIRGS-High subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Furthermore, this upregulated expression pattern was independently confirmed using a separate dataset procured from ACRG (\u003cb\u003eFig. S3A, 3B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The immune landscape\u003c/h2\u003e \u003cp\u003eThe tumor microenvironment, which encompasses the intricate interaction between immune cells and stromal cells, plays a crucial role in tumor progression, metastasis, recurrence, and the development of drug resistance. Understanding these dynamics is of paramount importance. Therefore, we conducted an analysis to explore the correlation between risk scores and various measures of immune infiltration, stromal composition, and overall tumor purity as represented by ESTIMATE scores. Our findings unveil a statistically significant correlation between risk scores and stromal scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as ESTIMATE scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cb\u003eFig. S4)\u003c/b\u003e. These results shed light on the complex relationship between the risk profile and the tumor microenvironment, emphasizing further the potential influence of immune response and stromal cell activity on disease progression. Next, our evaluation focused on assessing the differences in immune response between the PIRGS-High and PIRGS-Low groups. Initially, we employed the ssGSEA algorithm to calculate the variances in the levels of infiltration of 16 immune cells and 13 immune-related pathways across the two groups. We observed a general reduction in the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells, Mast cells, Th1 cells, and Th2 cells in the PIRGS-High subgroup compared to the PIRGS-Low subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003eFig. S5A\u003c/b\u003e). Additionally, immune-related pathways such as checkpoint, cytolytic activity, MHC class Ⅰ, and T cell co-inhibition were significantly downregulated in the PIRGS-High subgroup, while the type-II interferon (IFN) response showed an increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003eFig. S5A\u003c/b\u003e). Moreover, our survival analysis based on immune cell infiltration indicated that a lower infiltration of Th1 cells and Th2 cells was associated with a worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cb\u003eand Fig. S5B\u003c/b\u003e). Conversely, an upregulation of cytolytic activity, inflammation-promoting factors, and MHC class Ⅰ pathways was associated with a better prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, \u003cb\u003eand Fig. S5B\u003c/b\u003e). However, an upregulation of type-II interferon pathways was associated with a worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG \u003cb\u003eand Fig. S5B\u003c/b\u003e). To further analyze the subtypes of immune cells, we utilized the CIBERSORT algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cb\u003eFig. S6A-6B\u003c/b\u003e). The results revealed that monocytes were more abundant in the PIRGS-High subgroup, and their infiltration was associated with a worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cb\u003eand Fig. S6B\u003c/b\u003e). Additionally, T cell CD4 memory activated cells were significantly reduced in the PIRGS-High subgroup, which was also associated with a worse outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cb\u003eand Fig. S6B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 PIRGS for the prognostic prediction of GC\u003c/h2\u003e \u003cp\u003eTo further validate the clinical prognostic value of the model, we developed a nomogram as a clinical reference tool. This nomogram integrates variables such as Age, Gender, Stage, Grade, T, N, M staging, and RiskScore to predict gastric cancer patients' survival period at 1 year, 3 years, and 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We generated a calibration curve to assess the accuracy of the nomogram's predictions, and the results showed that the predicted outcomes (dotted line) closely aligned with the actual outcomes (colored line) on the calibration chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This indicates that the nomogram's predictions were highly accurate and of high quality. Notably, our model demonstrates remarkable accuracy when compared to traditional TNM staging. The Area Under the Curve (AUC) of our model surpasses that of T staging (AUC\u0026thinsp;=\u0026thinsp;0.573), N staging (AUC\u0026thinsp;=\u0026thinsp;0.647), and M staging (AUC\u0026thinsp;=\u0026thinsp;0.548), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. This highlights the superiority of our model's predictive performance compared to conventional staging methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Treatment benefit\u003c/h2\u003e \u003cp\u003eSubsequently, we evaluated the therapeutic implications of the PIRGS model in gastric cancer treatment, focusing on immune therapy and commonly used chemotherapy drugs. Firstly, we compared the expression levels of immune checkpoint molecules between the PIRGS-High and PIRGS-Low groups. The results showed significantly higher expression of these checkpoint molecules in the PIRGS-High group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), indicating that patients in the PIRGS-Low group may benefit more from immune therapy. We further validated this by analyzing a dataset of urothelial carcinoma patients who received anti-PD-L1 treatment\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The results revealed that responders had lower PIRGS scores compared to non-responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Moreover, within the PIRGS-Low group, a higher percentage of patients showed complete or partial response (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). By stratifying patients into high and low PIRGS subtypes, we found that the low PIRGS subtype had significantly longer overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These findings suggest that the low PIRGS group exhibits better responses to immune therapy. In addition to immune therapies, neoadjuvant chemotherapy is commonly used to reduce tumor size prior to surgery. We compared the sensitivity to 5-Fu and cisplatin between the high and low PIRGS groups based on IC50 values. The results showed that the PIRGS-Low group was more sensitive to 5-Fu treatment but less sensitive to platinum-based drugs compared to the PIRGS-High group (\u003cb\u003eFig. S7A, S7B\u003c/b\u003e). Furthermore, we evaluated the sensitivity to various targeted drugs between the two risk groups. Patients in the PIRGS-High group showed increased sensitivity to PFI (BET inhibitor), BIBR-0796 (known alternatively as Doramapimod, represents a highly potent inhibitor of p38 MAPK), DMOG (an agonist of α-ketoglutarate co-factor and the inhibitor of HIF prolylhydroxylase), and AMG-706 which is a potent ATP-competitive inhibitor, has demonstrated significant efficacy in targeting VEGFR1/2/3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eI), while patients in the PIRGS-Low group demonstrated increased sensitivity to VX-11e (ERK2 inhibitor) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These findings provide valuable insights for treatment decision-making using the PIRGS model. Overall, the PIRGS model has potential therapeutic advantages in guiding immune therapy and chemotherapy drug selection for gastric cancer patients. However, further clinical studies are needed to validate and optimize its applications in personalized treatment strategies\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Individual PIRGS genes evaluation and experimental validation.\u003c/h2\u003e \u003cp\u003eA comprehensive analysis was conducted to investigate the differential expression of twenty-four genes in both the PIRGS-High and PIRGS-Low groups using the TCGA database. Fourteen upregulated genes were identified as oncogenes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Importantly, further characterization through immune infiltration analysis revealed that NRP1, MAPK14, ITGAV, GLP2R, and APOD were strongly correlated with increased monocyte infiltration, suggesting their role as oncogenes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Consequently, these five oncogenes were selected as the primary signature for subsequent validation studies. To assess their differential expression, a quantitative polymerase chain reaction (qPCR) experiment was performed, demonstrating a significant upregulation of NRP1, APOD, MAPK14, ITGAV, and GLP2R gene expression in T1 stage GC patients (representing the better prognosis, PIRGS-Low group) compared to T4 stage GC patients (representing the poorer prognosis, PIRGS-High group) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Moreover, consistent with previous indications, the PIRGS-High cohort exhibited a significant upregulation of the TGF-β signaling pathway and increased infiltration of monocytes. Building upon these findings, additional experiments revealed elevated expression levels of CD14, a widely recognized monocyte marker, as well as TGFB1, TGFB2, and TGFB3, which act as markers for the TGF-β pathway, in T4 stage GC patients representing the PIRGS-High subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). These collective observations suggest an augmented monocyte infiltration and activation of the TGF-β signaling pathway, thereby reinforcing earlier analyses and enhancing the credibility of our model. Furthermore, by conducting a systematic literature review, APOD emerged as a promising candidate for in-depth exploration due to its frequent mention in various studies relating to model construction, coupled with a lack of comprehensive investigations into its biological functions\u003csup\u003e[\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Its potential role in carcinogenesis was substantiated by observing its increased expression in four gastric cancer cell lines compared to the normal gastric epithelial cell line, GES-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Through the utilization of TCGA and KM databases, this study unveiled a correlation between elevated APOD expression and an unfavorable prognosis among patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). To delve deeper into the underlying biological function associated with APOD, siRNA was employed to suppress APOD, resulting in a noteworthy inhibition of gastric cancer cell clonogenicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGastric cancer, a prevalent form of cancer worldwide, is characterized by its low early detection rate and high recurrence rate in advanced stages, resulting in an unfavorable prognosis\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The efficacy of surgical resection, radiotherapy, or chemotherapy differs among patients due to the presence of tumor heterogeneity. As a consequence, clinical outcomes for individuals with gastric cancer exhibit substantial heterogeneity, with survival periods ranging from less than 5 months\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we have successfully identified immune prognostic genes that exhibit abnormal expression in gastric cancer tissues. By employing LASSO regression, we constructed a prognostic and immune-related gene signature (PIRGS) comprising 24 genes. Subsequently, we calculated the PIRGS score and employed it to classify patients into two groups: PIRGS-High risk and PIRGS-Low risk, based on the optimal cutoff value. Notably, patients within the high-risk group displayed a significantly worse prognosis, as evidenced by the survival analysis (HR\u0026thinsp;=\u0026thinsp;4.6, 95% CI\u0026thinsp;=\u0026thinsp;3.3\u0026ndash;6.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). To assess the predictive accuracy of PIRGS for estimating the 1-year, 3-year, and 5-year survival rates in gastric cancer patients, we performed ROC curve analysis. Encouragingly, the results indicated that PIRGS exhibited higher diagnostic accuracy in comparison to existing methods\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Specifically, the AUC values for these predictions were 0.747, 0.761, and 0.788, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, we sought to validate the adverse prognostic outcome associated with high PIRGS scores by employing an external dataset. The findings from this validation process further confirmed the correlation between high PIRGS scores and unfavorable prognostic outcomes (\u003cb\u003eFig. S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn order to investigate the potential biological functions of PIRGS, it was categorized into high and low-risk groups using a predefined cutoff value. Through comprehensive biological analysis, several associations were identified between PIRGS and various crucial biological processes, including neuroactive ligand receptor interaction and cAMP signaling pathway. Remarkably, detailed analysis of GSEA revealed a significant upregulation of the TGF-β signaling pathway in PIRGS-High group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Considering the well-known role of TGF-β in gastric cancer\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],\u003c/sup\u003e these findings strongly suggest that the TGF-β signaling pathway may contribute to a poorer prognosis in the PIRGS-High subgroup. Moreover, we observed a significant augmentation in the type II interferon (IFN) response pathway concomitant with heightened levels of monocytes, which correlated with an unfavorable prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThe 24-gene model incorporates a comprehensive array of genes implicated in the initiation and progression of cancer. Notably, among these genes, 14 exhibited high expression levels in the PIRGS-High subgroup, with 5 demonstrating a positive correlation with mononuclear cell infiltration, which aligns with our experimental findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Subsequently, we focused on APOD as a target for further investigation. Our analysis revealed that APOD exhibited heightened expression in gastric cancer cell lines when compared to normal gastric epithelial cells. Additionally, it was associated with an unfavorable prognosis in gastric cancer patients. Remarkably, knockdown of APOD resulted in the inhibition of gastric cancer cell growth, indicating a potential oncogenic role for APOD (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). Further exploration is warranted to investigate the impact of other genes within the model on the occurrence and progression of gastric cancer.\u003c/p\u003e \u003cp\u003eRecent advancements in cancer treatment have highlighted the growing significance of immune therapy, emphasizing the critical role of immune cell infiltration. Therefore, this study investigates the association between risk scoring and levels of immune cell infiltration. Our findings reveal a notable decrease in the infiltration of helper T cells, including Th1 and Th2 cells, among high-risk individuals, as determined through immune landscape analysis. Conversely, an increase in monocyte infiltration was observed. Survival analysis demonstrates that these cell infiltrations significantly influence patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, our investigation into immune-related pathways reveals that the \u0026ldquo;Type_II_IFN_Response\u0026rdquo; is upregulated to a significant extent in PIRGS-High subgroup. Conversely, pathways such as \u0026ldquo;Cytolytic_activity\u0026rdquo;, \u0026ldquo;Inflammation-promoting\u0026rdquo;, \u0026ldquo;MHC_class_I\u0026rdquo; and \u0026ldquo;T_cell_co-inhibition\u0026rdquo; exhibit significant downregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, we identified a correlation between risk scoring and the overall response to PD-L1 immune therapy. Patients with lower risk scores displayed greater sensitivity to PD-L1 immune therapy compared to those with higher risk scores. Moreover, within the high-risk group, the expression level of immune checkpoint genes was substantially lower than that observed in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These findings imply that low-risk gastric cancer patients are more likely to experience a favorable response to PD-L1 immune therapy. Hence, this characteristic can serve as a predictive factor for the response to immune therapy.\u003c/p\u003e \u003cp\u003eIn clinical practice, the predictive capacity of existing staging systems in isolation, for determining the potential benefits of adjuvant chemotherapy in stage II or III patients, is limited\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Our data highlights an independent risk factor for GC, underscoring the significance of risk scoring in this context. By incorporating patient age, sex, stage, grade, and TNM staging, it becomes possible to generate personalized predictions for one-year, three-year, or five-year survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, this signature enables the assessment of chemotherapy drug sensitivity. Specifically, gastric cancer patients with high-risk scores are more likely to benefit from platinum-based adjuvant chemotherapy (\u003cb\u003eFig. S7\u003c/b\u003e). Furthermore, there are several potential small molecule compounds, including PFI-1, BIBR-0796, DMOG, and AMG-706, that demonstrate therapeutic potential worthy of exploration in PIRGS-high patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo summarize, PIRGS has been developed to discern the molecular and immune characteristics of individuals with gastric cancer, thereby providing valuable insights for prognosticating patient outcomes and determining clinical drug sensitivity.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur research indicates that PIRGS represents a robust prognostic factor in gastric cancer. This novel finding not only brings clarity to the prognosis of GC patients but also positions PIRGS as a potent tool for predicting survival rates in this population. Moreover, our study has unveiled associations between PIRGS and immune infiltration characteristics, as well as their relevance to immune therapy and chemotherapy. This newfound knowledge enables the stratification of GC patients who would benefit from anti-tumor immune therapy, chemotherapy, and the prediction of efficacy of specific small molecule targeted inhibitors with heightened sensitivity. As a result, PIRGS provides valuable guidance for the treatment of gastric cancer patients. In summary, our systematic investigation has successfully elucidated the role and value of PIRGS in gastric cancer, thereby establishing the foundation for further comprehension and exploration in this field.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACRG Asian Cancer Research Group\u003c/p\u003e\n\u003cp\u003eAUC Area Under the Curve\u003c/p\u003e\n\u003cp\u003eCIBERSORT Cell-type Identification by Estimating Relative Subsets of RNA Transcripts\u003c/p\u003e\n\u003cp\u003eESTIMATE Estimation of Stromal and Immune cells in Malignant Tumors using Expression Data\u003c/p\u003e\n\u003cp\u003eGC Gastric Cancer\u003c/p\u003e\n\u003cp\u003eGDSC Genomics of Drug Sensitivity in Cancer\u003c/p\u003e\n\u003cp\u003eGEO Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eGSEA Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eKEGG Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eKM Kaplan-Meier\u003c/p\u003e\n\u003cp\u003eOS Overall Survival\u003c/p\u003e\n\u003cp\u003ePIRGS Prognostic and Immune-related Gene Signature\u003c/p\u003e\n\u003cp\u003eqRT-PCR Quantitative real-time PCR\u003c/p\u003e\n\u003cp\u003eROC Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003essGSEA single-sample Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eTCGA The Cancer Genome Atlas\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of the Affiliated Tumor Hospital of Zhengzhou University and signed informed consent was obtained from patient. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used for the current study are available from the corresponding authors upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Medical science and technology Project of Henan Province (Grant Nos. 232102311077, LHGJ20240108, LHGJ20220198, China), and National Key Clinical Discipline Construction Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHY, and HA contributed equally to this work. All authors have made significant contributions to the conception, supervision, and final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge and appreciate our colleagues for their suggestions and assistance for this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eH. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, F. Bray, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA. Cancer J. Clin. 71 (2021) 209\u0026ndash;249. https://doi.org/10.3322/caac.21660.\u003c/li\u003e\n\u003cli\u003eA. Gornowicz, W. Szymanowski, K. Bielawski, Z. Kałuża, O. Michalak, A. Bielawska, Mucin 1 as a Molecular Target of a Novel Diisoquinoline Derivative Combined with Anti-MUC1 Antibody in AGS Gastric Cancer Cells, Mol. 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Res. 26 (2012) 303\u0026ndash;306. https://doi.org/10.7555/JBR.26.20110056.\u003c/li\u003e\n\u003cli\u003eX. Wang, X. Shi, H. Lu, C. Zhang, X. Li, T. Zhang, J. Shen, J. Wen, Succinylation Inhibits the Enzymatic Hydrolysis of the Extracellular Matrix Protein Fibrillin 1 and Promotes Gastric Cancer Progression, Adv. Sci. Weinh. Baden-Wurtt. Ger. 9 (2022) e2200546. https://doi.org/10.1002/advs.202200546.\u003c/li\u003e\n\u003cli\u003eX. Xia, Z. Zhang, C. Zhu, B. Ni, S. Wang, S. Yang, F. Yu, E. Zhao, Q. Li, G. Zhao, Neutrophil extracellular traps promote metastasis in gastric cancer patients with postoperative abdominal infectious complications, Nat. Commun. 13 (2022) 1017. https://doi.org/10.1038/s41467-022-28492-5.\u003c/li\u003e\n\u003cli\u003eD. Chen, H. Chen, L. Chi, M. Fu, G. Wang, Z. Wu, S. Xu, C. Sun, X. Xu, L. Lin, J. Cheng, W. Jiang, X. Dong, J. Lu, J. Zheng, G. Chen, G. Li, S. Zhuo, J. Yan, Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer, JAMA Netw. Open. 4 (2021) e2136388. https://doi.org/10.1001/jamanetworkopen.2021.36388.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gene Signature, Gastric Cancer, Immunology, Treatment","lastPublishedDoi":"10.21203/rs.3.rs-5307766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5307766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGastric cancer is a prevalent digestive system tumor. However, its heterogeneity and poor prognosis pose challenges to patient treatment. Therefore, there is a need to improve patient outcomes and guide treatment through patient stratification and immune prognostic models.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed gene expression in the The Cancer Genome Atlas dataset using statistical tests and developed a 24-gene risk signature called Prognostic and Immune-Related Gene Signature (PIRGS) using LASSO Cox regression. The Asian Cancer Research Group database was used to validate the model's accuracy. Based on the PIRGS signature, we categorized gastric cancer patients into high-risk and low-risk groups. Further analysis was conducted to explore immune infiltration, signaling pathways, and drug sensitivity differences between two groups. We also developed a nomogram combining the PIRGS signature and clinical variables for prognostic assessment. Key genes in the model were validated at tissue and cellular levels.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe PIRGS signature, consisting of 24 genes, accurately predicted 1-year, 3-year, and 5-year survival rates in gastric cancer patients. The PIRGS score classified patients into PIRGS-High risk and PIRGS-Low risk groups. The PIRGS-High risk group showed upregulation of the TGF-β signaling pathway and increased type II interferon response, along with unfavorable prognosis and elevated monocyte levels. PD-L1 immune therapy appeared more effective in low-risk patients. Several potential therapeutic compounds were identified, particularly for PIRGS-High risk patients. CD14 and TGFB1/2/3 were expressed at higher levels in the PIRGS-High risk subgroup. Investigation of APOD as a potential target showed its association with unfavorable prognosis, and knockdown inhibited gastric cancer cell growth.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe PIRGS is a potent prognostic factor in gastric cancer and accurately predicts survival rates. It provides insights into immune infiltration characteristics, correlating with immune therapy, chemotherapy, and targeted inhibitors. This knowledge facilitates patient stratification and personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Immune infiltration and clinical significance of the prognostic and immune-related gene signature in gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-04 14:50:07","doi":"10.21203/rs.3.rs-5307766/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7438c64-b6b0-48ef-aa1f-c87fda2772c6","owner":[],"postedDate":"December 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40405671,"name":"Biological sciences/Cancer/Cancer models"},{"id":40405672,"name":"Biological sciences/Cancer"},{"id":40405673,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Gastric cancer"}],"tags":[],"updatedAt":"2025-02-05T15:39:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-04 14:50:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5307766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5307766","identity":"rs-5307766","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}