Bioinformatics analysis identifies coagulation factor II receptor as a potential biomarker in stomach adenocarcinoma

Coagulation factor 2 thrombin receptor (F2R), a member of the G protein-coupled receptor family, plays an important role in regulating blood clotting through protein hydrolytic cleavage mediated receptor activation. However, the underlying biological mechanisms by which F2R affects the development of gastric adenocarcinoma are not fully understood. This study aimed to systematically analyze the role of F2R in gastric adenocarcinoma. Stomach adenocarcinoma (STAD)-related gene microarray data and corresponding clinicopathological information were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential expression genes (DEGs) associated with F2R were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and protein–protein interaction (PPI) networks. F2R mRNA expression data were utilized to estimate stromal cell and immune cell scores in gastric cancer tissue samples, including stromal score, immune score, and ESTIMATE score, derived from single-sample enrichment studies. Analysis of TCGA and GEO databases revealed significantly higher F2R expression in STAD tissues compared to normal tissues. Patients with high F2R expression had shorter survival times than those with low F2R expression. F2R expression was significantly correlated with tumor (T) stage, node (N) stage, histological grade and pathological stage. Enrichment analysis of F2R-related genes showed that GO terms were mainly related to circulation-mediated human immune response, immunoglobulin, cell recognition and phagocytosis. KEGG analysis indicated associations to extracellular matrix (ECM) receptor interactions, neuroactive ligand-receptor interactions, the phosphoinositide-3-kinase-protein kinase B/Akt (PI3K-AKT) signaling pathway, the Wnt signaling pathway and the transforming growth factor-beta (TGF-β) signaling pathway. GSEA revealed connections to DNA replication, the Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway and oxidative phosphorylation. Drug sensitivity analysis demonstrated positive correlations between F2R and several drugs, including BEZ235, CGP-60474, Dasatinib, HG-6-64-1, Aazopanib, Rapamycin, Sunitinib and TGX221, while negative correlation with CP724714, FH535, GSK1904529A, JNK-9L, LY317615, pyrimidine, rTRAIL and Vinorelbine. Knocking down F2R in GC cell lines resulted in slowed proliferation, migration, and invasion. All statistical analyses were performed using R software (version 4.2.1) and GraphPad Prism 9.0. p < 0.05 was considered statistically significant. In conclusion, this study underscores the significance of F2R as a potential biomarker in gastric adenocarcinoma, shedding light on its molecular mechanisms in tumorigenesis. F2R holds promise for aiding in the diagnosis, prognosis, and targeted therapy of STAD.

STAD samples from the TCGA database.The expression profiles of these genes were correlated with patients' clinical data.The RNA expression analysis data included 375 tumor samples and 32 nontumor samples.To further explore the potential biological functions of F2R in STAD, bioinformatics tools were employed to validate molecular mechanisms through GSEA and GO/KEGG enrichment analysis.We systematically screened miRNAs and lncRNAs upstream of F2R, constructing a ceRNA network.This network was investigated for its potential regulatory role in STAD development.In this study, we investigated the prognostic value of F2R in GC and comprehensively evaluated its potential value.Our findings contribute to the identification of novel prognostic biomarkers and provide insights into potential molecular mechanisms that influence GC prognosis.Finally, in vitro and in vivo experiments were performed to validate the expression and function of F2R in GC.

F2R expression in STAD
Our dataset, comprising 375 patients with STAD, was obtained from TCGA and included both clinical information and gene expression data.Various clinical characteristics, such as gender, age, histological grade, pathological grade, T stage, N stage, M stage and vital status, were collected (Table S1).The Perl programming language was used to match gene expression data with clinical information, eliminating samples of unknown or incomplete clinical data.The patient data were categorized into high and low F2R expression groups based on the median level of F2R gene expression.Table 1 provides a comprehensive overview of the differences in clinical data of GC patients in different F2R expression groups.Furthermore, the analysis of F2R expression across different tumor types using the UCSC Xena database revealed increased F2R expression in various tumors, including cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), rectum adenocarcinoma (READ), and STAD (Fig. 1A).Notably, based on TCGA data, F2R expression was significantly higher in STAD tissues compared to normal tissues (p = 2.158e−15) (Fig. 1B).Consistent with these findings, analysis of three GEO datasets demonstrated higher F2R expression in the STAD group (Fig. 1C-E).

Association between F2R gene expression and clinical characteristics in STAD patients
Kaplan-Meier survival curves revealed that patients with elevated F2R expression exhibited shorter survival times compared to those with low F2R expression (p = 0.029) (Fig. 2A).The relationship between F2R gene expression and clinicopathological variables in GC patients was further explored using the Wilcoxon signedrank test and logistic regression analysis.F2R mRNA expression was examined across patient groups categorized by age, gender, clinical grade, T stage, N stage, and M stage of the patients.The findings indicated a significant association between F2R overexpression and T stage (T2, T3, T4, and T1; p = 0.0014, 0.00016, 3.4e−05), N stage (N0 and N3; p = 0.026), histological grade (G1 and G3; p = 8.731e−06), and pathological stage (stage I and stage III; p = 3.485e−04).Notably, F2R gene expression was independent of clinical factors, including age, sex, and M stage (Fig. 2B).Patients with GC were stratified into two groups based on F2R expression levels, and clinically relevant thermography was employed to analyze patients' variables and the high/low expression of F2R.The results revealed a statistically significant correlation between patients' grade stage and F2R expression (p < 0.05) (Fig. 2C).The diagnostic value of F2R mRNA expression for predicting patient survival at 1, 3, and 5 years was assessed using ROC curves, yielding areas under the curve (AUCs) of 0.585, 0.547, and 0.758, respectively (Fig. 2D).Furthermore, univariate logistic regression analysis explored the relationship between F2R gene expression and the clinicopathological characteristics in GC patients.The results highlighted a significant association between high F2R gene expression and histological grade, pathological stage, T stage, and race.However, no significant differences were observed between F2R gene expression and other clinicopathological variables, including age, sex, N stage, M stage, tumor status and residual tumor (Table 2).

Development of a prognostic model integrating F2R expression and clinical factors
Univariate regression analysis unveiled that F2R gene expression was a high-risk factor for GC (HR 1.367; confidence interval, 1.110-1.683;p = 0.003) (Fig. 3A).In the subsequent multivariate analysis, F2R mRNA expression emerged as an independent risk factor for overall survival in GC patients (HR 1.294; confidence interval, 1.028-1.628;p = 0.028) (Fig. 3B).Both univariate and multifactorial Cox regression analyses consistently identified F2R gene expression as a high-risk factor for STAD (Table 3).Utilizing F2R expression and other clinical parameters, we developed a prediction model for overall survival prediction, integrating F2R as a biomarker for GC.A nomogram was established to visually represent this prediction model (Fig. 3C).A calibration curve evaluated the performance of the nomogram for F2R expression versus 1-year, 3-year, and 5-year survivals (Fig. 3D).

Construction of the lncRNA-miRNA-mRNA ceRNA regulatory network
Following the ceRNA hypothesis, where miRNAs negatively modulate gene expression by targeting mRNA, and lncRNAs can indirectly regulate this process by binding to these miRNAs, we aimed to establish a ceRNA network targeting F2R using bioinformatic methods.Using miRNA target prediction screening in the starBase database, 56 miRNAs associated with F2R were identified (Fig. 4A).A correlation table between F2R and these miRNAs is provided in Table S2, revealing strong negative correlations for hsa-miR-144-5p and hsa-miR-486-5p.These two miRNAs were chosen for further investigation due to their negative correlation with F2R expression and lower expression in GC tissues compared to normal tissues (Fig. 4B,C).Further details on the expression of other miRNAs in STAD and their correlation with F2R are presented in Supplementary Fig. S1A,B.To identify lncRNAs potentially interacting with hsa-miR-144-5p and hsa-miR-486-5p, the starBase database was utilized for further analysis.The detailed correlations between these miRNAs and lincRNA are presented in Tables S3 and  S4.Based on these findings, a ceRNA regulatory network was constructed using Cytoscape software (Fig. 4D,E).We identified upstream lncRNAs that exhibited a negative correlation with the expression of hsa-miR-144-5p and Vol:.( 1234567890 www.nature.com/scientificreports/hsa-miR-486-5p, while demonstrating a positive correlation with F2R expression.Specifically, lncRNA-FTX was found to regulate F2R expression in GC cells through the hsa-miR-144-5p/F2R axis, while lncZEB1-AS1 regulated F2R expression through the hsa-miR-486-5p/F2R axis, thereby modulating F2R expression in GC cells.Moreover, the expression levels of lncRNA FTX and lncZEB1-AS1 were significantly elevated in GC tissues compared to adjacent normal tissues (Fig. 4F-H).This upregulation suggests that these lncRNAs may be potential biomarkers for GC diagnosis or prognosis.

Enrichment analyses of F2R-related through GSEA, GO, and KEGG
Genes highly associated with F2R were screened in GC using differential expression analysis.The identified DEGs were then classified into two groups based on high or low expression of F2R (Fig. 5A).Eleven genes with significant differential expression were further characterized.PDGFRB, A2M, LRRC32, F2RL2, LTBP2, and PCDH18 exhibited a positive correlation with F2R expression.EFNA3, DCXR, SNORD104, NME1, and SLC25A10 showed a negative correlation with F2R expression (Fig. 5B).To delve deep into the relationships between F2R and these genes, a comprehensive correlation analysis was conducted (Table S5).Supplementary Figure S2A-R provide detailed information on the correlation between F2R expression and these identified DEGs.To understand the functional relevance of the identified F2R-associated genes, GO and KEGG pathway analyses were conducted.GO analysis revealed that F2R-related genes were primarily enriched in functional categories related to immune response and cell communication (Fig. 5C-D).Notably, these genes showed significant enrichment for terms such as "completion of the activation of the classical pathway", "circulating-mediated human immune response", "immunoglobulins", "and "cell recognition", suggesting potential roles in antibody-mediated immunity and cell-cell interactions.KEGG pathway analysis provided further insights, highlighting enrichment in various signaling pathways crucial for cell-cell communication and regulation.Genes associated with F2R were significantly enriched in pathways such as ECM receptor interactions, neuroactive ligand-receptor interactions, protein differentiation and uptake, cytokine-cyclin receptor interactions, the PI3K-Akt signaling pathway, the cAMP signaling pathway, the Wnt signaling pathway, the cGMP-dependent protein kinase (cGMP/PKG) signaling pathway, and the TGF-β signaling pathway (Fig. 5E,F).To explore the potential regulatory mechanisms of the F2R gene in GC, GSEA was performed using data from the TCGA database.The results showed associations of F2R mRNA expression with DNA replication, Huntington's disease, the JAK-STAT signaling pathway, the MAPK signaling pathway, and oxidative phosphorylation (Fig. 5G, Table S6).In addition, GSEA suggested possible involvement of F2R in the progression of GC and its association with the mammalian target of rapamycin (mTOR), p53 and T-cell receptor signaling pathways (Supplementary Fig. S3A-G).The PPI network also identified genes associated with F2R (Fig. 5H).

Correlation between F2R and immune cell infiltration
Tumor mutational burden (TMB) plays a pivotal role in tumorigenesis and progression.We investigated the correlation between TMB and F2R expression in STAD and explored the connection between F2R expression and mutations in STAD.Our findings indicated a significant correlation between increased F2R expression in STAD and decreased TMB (Fig. 6A).Furthermore, STAD patients were stratified into low and high F2R expression groups based on their F2R expression levels.Analysis of stromal and immune cell infiltration in these groups revealed significant differences in StromalScore, Immunoscore, and estimated core scores, with higher scores observed in the high F2R expression group (Fig. 6B).Stromal score refers to the number of stromal cells in the tumor tissue, immune score refers to the number of immune cells in the tumor tissue, and estimate score refers to the purity of the tumor.To analyze the association between F2R and immune cell infiltration, we calculated the correlation between F2R and 22 immune cells using CIBERSORT.The results highlighted variations in the abundance of immune cell subtypes between low and high F2R expression groups.Notably, CD4 cell memory activation, dendritic cell quiescence, eosinophils, M2 macrophages, and M1 macrophages exhibited increased abundance in the F2R high expression group, while natural killer (NK) cells were activated in the F2R low expression group (Fig. 6C).Analysis of the linear relationship between F2R and immunological checkpoint genes showed that F2R had a positive regulatory relationship with immunological checkpoint genes (Fig. 6D,E).Further exploration of the correlation between F2R and immune-related genes demonstrated its involvement in the immune cell infiltration of GC.Specifically, CD4 + cell memory-activated, M1 macrophages, M2 macrophages, eosinophils, and resting dendritic cells showed positive correlations with F2R, whereas NK cell activation displayed a negative correlation (Fig. 6F,G).A comprehensive analysis of F2R and immune cell gene markers revealed a significant positive correlation between F2R and the expression of 21 immune response markers across various immune cell types (Table S7).A linear plot of the correlation between F2R and immune cell genes is shown in Supplementary Fig. S4.The collective evidence suggested that F2R plays a crucial role in immune regulation and infiltration within GC tissues.www.nature.com/scientificreports/

Relationship between F2R expression and immune cell infiltration in GC tissues
To substantiate the regulatory role of F2R in immune cell infiltration within GC tissues, our analysis of F2R and immune cell gene markers revealed a significant positive correlation with the expression of six immune response markers on B cells, CD4 + T cells, CD8 + T cells, M2 macrophages, neutrophils and dendritic cells (Fig. 7A-F).
Additionally, we examined the association between F2R gene copy number and immune cells, and the results are shown in Supplementary Fig. S5A-E.The analysis consistently demonstrated a positive correlation between  www.nature.com/scientificreports/F2R and these immune cell types.Further investigating the relationship between F2R and immune checkpoints, we obtained consistent positive correlations between F2R expression and the immune checkpoints CD274, programmed cell death protein 1 (PD1), and cytotoxic T-lymphocyte associated protein 4 (CTLA4).These findings were validated by analyzing correlations using the TIMER and GEPIA databases (Fig. 7G-L).The relationship between F2R and other immune checkpoints is shown in Supplementary Fig. S6A,B.

Correlation analysis between risk group and drug sensitivity
Genes associated with sensitivity and resistance to anticancer drugs have been extensively studied.In this investigation, we examined drug sensitivity within two risk groups, exploring the relationship between risk scores and IC 50 values for various drugs potentially used to treat GC with high F2R expression.The prediction of the relationship between the F2R gene and chemical compound sensitivity was conducted using pRRophetic software.Our findings revealed that F2R was positively associated with BEZ235, CGP-60474, Dasatinib, HG-6-64-1, Pazopanib, Rapamycin, Sunitinib, and TGX221, while negatively associated with CP724714, FH535, GSK1904529A, JNK-9L LY317615, pyrimethamine, rTRAIL, and Vinorelbine (Fig. 9A,B).The sensitivity of the F2R gene to commonly used antitumour drugs was investigated using the CellMiner database.The relationship between gene expression and drug sensitivity was calculated, revealing a significant association of F2R expression with the sensitivity of 14 drugs (Fig. 10).Among them, F2R was positively associated with Simvastatin, Staurosporine and Procarbazine and negatively associated with Dexrazoxane, Fulvestrant and SR16157.Notably, some of these drugs, exhibiting sensitivity to F2R expression level, have yet to undergo extensive medical testing.Their potential as promising candidates for future therapeutic interventions warrants further exploration.

The potential of predicting immunotherapy efficacy based on F2R levels in tumors
To investigate the potential of utilizing F2R level as a predictive biomarker for immunotherapy efficacy in STAD, we conducted a comprehensive investigation leveraging established immunotherapy biomarkers' predictive capabilities.Our investigation focused on evaluating F2R's role in shaping the immunotherapeutic response in STAD.We examined the relationship between high and low expressions of F2R and immune scores during anti-PD1 and anti-CTLA4 treatment.Our results indicated no significant difference between the low and high F2R expression groups receiving anti-PD1 treatment.However, when subjected to anti-CTLA4 treatment, patients in the low F2R expression group exhibited higher immune scores, suggesting a more favorable outcome for individuals with low F2R expression when undergoing anti-CTLA4 treatment (Fig. 11A,B).

Impact of F2R on the proliferation, migration and invasion of STAD cells
To further validate F2R expression in gastric adenocarcinoma, we performed IHC staining on human GC tissue.This analysis revealed significantly upregulated F2R expression in five paired samples compared to their matched non-cancerous counterparts (Fig. 12A).qRT-PCR confirmed that F2R was significantly elevated in gastric cancer tissues, which was consistent with the IHC results (Fig. 12B).The intense staining observed in tumor tissues provides compelling evidence for F2R's heightened role in gastric cancer, paving the way for further investigation into its potential clinical implications.To verify the expression level of F2R in STAD cell lines, we assessed F2R expression in STAD cell lines (MGC-803 and SGC-7901) and normal gastric cells (HFE-145) using qPCR.F2R exhibited a significant upregulation (p < 0.05) in MGC-803 and SGC-7901 compared to the normal gastric cell line HFE-145 (Fig. 12C).Following F2R siRNA transfection, the downregulation of F2R in MGC-803 and SGC-7901 cell lines was confirmed through qPCR (Fig. 12D).We further investigated the impact of F2R on tumor progression.CCK-8 assays revealed a significant reduction in cell viability upon F2R silencing (Fig. 12E),

Discussion
Exploring biomarkers and molecular targets is imperative for early diagnosis and effective treatment of STAD 38 , a prevalent malignancy within the digestive system.Through robust bioinformatics analyses of the TCGA database, we identified genes associated with STAD pathogenesis and clinical prognosis.Our investigation revealed a significant upregulation of F2R in STAD, prompting compelling inquiries about its specific role in the development and progression of this cancer subtype.Compared to other gastrointestinal tumors, the elevated expression of F2R in STAD suggests its unique function within STAD biology.This finding has ignited a quest to decipher whether F2R's role in STAD is distinct from its functions in other cancers.This distinction is pivotal for unraveling the complexities of STAD and holds the potential to unveil novel therapeutic avenues tailored to its specific characteristics.
The prognostic significance of F2R expression in STAD provides new perspectives for patient stratification and treatment approaches.Our investigation revealed a strong association between high F2R expression and poorer survival outcomes, high F2R expression is associated with poor prognosis of GC, F2R gene is an oncogene, when it is highly expressed in tissues, it promotes the malignant progression of GC cells and shortens the survival time of patients, positioning F2R as a promising prognostic biomarker.Recognizing this correlation underscores the importance of considering F2R expression in therapeutic decision-making and emphasizes its potential as a guiding factor in the development of targeted therapies.In a thorough examination of F2R's impact on clinicopathological parameters in STAD patients, our findings indicated that F2R serves as an independent  prognostic factor influencing survival in GC patients.This aligns with existing literature, where F2R has been consistently linked to increased tumor aggressiveness and unfavorable prognosis across various cancer types.Incorporating F2R expression levels into established prognostic models has the potential to enhance the precision of survival prediction, offering valuable insights for personalized treatment planning in STAD patients.The high expression of F2R in STAD not only signifies its potential as a prognostic marker but also hints at its involvement in tumorigenesis and early progression.The correlation between F2R level and advanced disease  The correlation of F2R with key cancer-related signaling pathways suggests that F2R inhibition could disrupt these pathways, thereby inhibiting tumor proliferation and invasion.To explore this avenue further, the development of inhibitors or monoclonal antibodies against F2R could offer a novel therapeutic approach for STAD.The potential synergies between F2R-targeted therapies and existing treatment modalities such as chemotherapy and immunotherapy are worth exploring.This may lead to more effective combination therapies that enhance therapeutic efficacy while minimizing side effects.Furthermore, delving into the role of F2R within the tumor microenvironment (TME), including its interactions with immune cells and immune checkpoints, unveils opportunities for immunomodulatory strategies.Targeting F2R could potentially reshape the dynamics between tumor and immune system, thereby improving the efficacy of immunotherapy for STAD.As research progresses, the translation of these findings into clinical applications becomes critical.Investigating clinical treatment strategies for F2R-targeted therapies will provide essential evidence regarding efficacy and safety.This could pave the way for new treatment options for STAD patients.
The impact of immune infiltration on tumor behavior and prognosis underscores the potential of targeting the TME as a promising avenue for cancer treatment.In this study, we focused on understanding the role of F2R in immune infiltration, revealing a significant correlation between increased F2R expression and decreased TMB.As an immune-related gene, F2R is known to influence the behavior of immune cells, prompting a comprehensive analysis of its effects on the TME using the CIBERSORT algorithm.The CIBERSORT results demonstrated that high F2R expression in STAD is associated with the activation of memory CD4 + T cells, quiescent dendritic cells, eosinophils, M2 macrophages and M1 macrophages.Conversely, low F2R expression correlated with activated NK cells.The activation of CD4 + T cells, known for their role in regulating cytolytic mechanisms and enhancing B cell and CD8 + T cell responses 39,40 , suggests a complex interplay in STAD progression.The activation of macrophages and their subtypes in the high F2R expression group aligns with previous studies, implicating these cells in cancer development and metastasis 41 .
Inflammation can be divided into two categories-acute inflammation and chronic inflammation-depending on the duration of the disease.Acute inflammation is the initial response to a noxious stimulus and lasts for days or weeks.Most of the infiltrated inflammatory cells are granulocytes.If the pro-inflammatory stimulus is not eliminated during the acute inflammation, the result will be chronic inflammation.The main infiltrating immune cells at the site of chronic inflammation are macrophages and lymphocytes.Large numbers of immunosuppressive cells inhibit the killing function of T cells, leading to immune escape, which promotes tumour formation.There is evidence that up to 25% of cancers are associated with chronic inflammatory diseases, some of which are clinically considered precancerous, and our study suggests a potential link between F2R expression and the promotion of chronic inflammation in STAD.The persistence of inflammatory factors and immune cells, particularly macrophages, in the microenvironment may contribute to immune escape, fostering tumor formation and progression 42 .This suggests that increased F2R expression promotes the development of chronic inflammation, which in turn promotes increased immune pathway activity.Therefore, activation of immune pathway activity in GC patients is associated with poor prognosis in patients.
The association between F2R and immune pathways suggests its potential role as a key regulator in the immune response.In both in vivo and in vitro experiments, F2R was found to be significantly upregulated in gastric adenocarcinoma tissues.The high expression of the F2R gene promotes the progression of cancer, and the progression of cancer will activate tumor-related immune cells to produce inflammatory responses, resulting in poor prognosis in patients with high expression of the F2R gene 43 .Therefore, we speculate that F2R is a poor prognostic factor for patients with STAD and one of the key factors for regulating the immune response.In this study, F2R was proven to be related to the infiltration of immune cells in the STAD microenvironment.Moreover, knockdown of F2R resulted in a significant inhibition of cell proliferation, migration, and invasion in MGC-803 and SGC-7901 cells.Collectively, these bioinformatics analyses and experimental results position F2R as a potential biomarker and therapeutic target in STAD.
While our study enhances our understanding of the role of F2R in GC, certain limitations should be acknowledged.Firstly, the limited sample size and the absence of specific details on surgery, chemotherapy, and tumor size limit the completeness of our analysis.Secondly, reliance on public databases and published articles introduces potential biases, and data quality could influence results.Thirdly, the precision of the database and the choice of statistical methods can impact result interpretation.However, the consistency of our results across multiple databases and experimental validations reinforces the robustness of our conclusions.In the field of GC biomarker screening, global studies often encounter issues such as small sample sizes, reliance on single data analysis methods, and a lack of experimental verification.To address these challenges, our study employs a comprehensive approach, integrating various bioinformatics methods for an in-depth analysis of gene datasets from multiple platforms.By expanding the sample size and corroborating findings with in vitro cell experiments, we aim to enhance the scientific rigor of bioinformatics analysis.This endeavor provides a more accurate exploration of GC pathogenesis and therapeutic targets, offering a molecular biology foundation and suggesting new avenues for subsequent experimental studies.

Conclusion
We used bioinformatics to explore the relationship between high F2R gene expression and GC progression.We also evaluated the feasibility of F2R gene expression as a prognostic factor for GC and predicted potential signaling pathways through which F2R affects GC progression.Our findings provide novel insights into the prevention and prognosis of GC.The construction of a nomogram comprehensively visualized all factors in the model, www.nature.com/scientificreports/providing predictions that closely aligned with real-world scenarios.Calibration curves, assessing the probability of survival at 1, 3, and 5 years, confirmed the accuracy of the nomogram, showcasing strong agreement between observed and predicted OS in GC patients.These findings suggest that high F2R expression may independently contribute to a heightened risk of poor prognosis in GC.F2R's interaction with hsa-miR-144-5P and lncFTX, as well as with hsa-miR-486-5P and lncZEB1-AS1, forms a comprehensive lncRNA-miRNA-mRNA ceRNA network, implicating F2R in GC development.The analysis of this network provides initial insights into F2R's role in the onset and progression of GC.GSEA identified the signaling pathway associated with F2R mRNA expression in GC, revealing a significant association with tumor progression.The results suggest that suppressing F2R expression could potentially inhibit the progression of GC cells.The prediction of F2R's impact on GC through GSEA indicates its involvement in activating the MAPK and JAK-STAT pathways linked to tumor progression.Future therapeutic strategies may involve inhibiting these pathways to impede gastric cancer progression.This study underscores F2R's potential as a prognostic biomarker for GC, positioning it as a promising predictive biomarker and a potential target for immunotherapy.Subsequent experimental investigations are crucial to validate the biological mechanisms underlying the impact of elevated F2R expression on gastric cancer progression.

Gene expression analysis
GEPIA2 (Gene Expression Profiling Interactive Analysis) is a web tool based on TCGA and GTEx data that provides fast and customizable functionality.The GEPIA database was used to search and analyze F2R gene expression in GC and adjacent tissues, as well as OS data from different cancer datasets.The screening criteria |Log Change | > 1 and p < 0.01 were set as significance thresholds.Cancer type is selected as STAD, and we used log 2 (TPM+1) for logarithmic scaling.The Jitter Size was 0.4.Within the GEPIA online database, the STAD dataset was chosen for further investigation.We retrieved a survival graph illustrating the correlation between F2R expression and OS in the form of Kaplan-Meier curves.The median filter was applied with a truncation value, and hazard ratio was calculated based on the Cox Proportional-Hazards (PH) model, with 95% confidence intervals (CI) depicted as dotted lines.Axis units were specified in months.For PPI analysis, the top 20 genes displaying the most significant correlation with F2R were selected from the STAD dataset within the GEPIA database.Timer 2.0 (http:// timer.cistr ome.org/) analysis was used to explore the association between tumor immune cell infiltration and F2R expression levels.This included B cells, CD8 + T cells, CD4 + T cells, neutrophils, macrophages and dendritic cells.The TIMER database, a web server built on the R Shiny Web framework, was used for the exploration module.TCGA tumor data from TIMER allowed us to study the expression of F2R in the TCGA-STAD cohort, visualizing it through the visualization function of the website.In the Gene_DE module, the differential expression of F2R between tumor and adjacent normal tissues was studied across all TCGA tumors.The distribution of gene expression levels is shown using box plots.The statistical significance of Wilcoxon test calculations is annotated with stars (*p values < 0.05; **p value < 0.01; ***p value < 0.001).The gene module in TIMER focused on F2R, visualizing its correlation with immune cell infiltration in the STAD dataset.By clicking on different immune cells on the heatmap, the scatter plot illustrated the relationship between estimated immune cell infiltration and F2R gene expression.Partial Spearman correlations were used for association analysis, taking into account the confounding factor of tumor purity, with the purity adjustment option selected.

Data mining of the TCGA database
The analysis included RNA-Seq HTSeq count gene expression data and clinical information obtained from GC patients, using R software (version 4.2.1).The TCGA database was the source for GC samples, containing clinical information, mRNA, lncRNA and miRNA expression data.All original RNA sequence data, including lncRNA, miRNA, and mRNA, were obtained from the TCGA database.mRNA information from 375 GC tissues and 32 adjacent nontumor tissues was obtained from the TCGA database.High-quality mRNA information was extracted, followed by name conversion and logarithmic correction of RNA-seq data obtained from both TCGA and GEO.The R software packages ggplot2, limma and beeswarm were used to statistically analyze the expression of F2R in the two groups.Scatter plots and pairing plots were generated to visually display the results.Clinical data on GC patients, including age, gender, clinical grade, T stage, M stage, and N stage, were also acquired from the database.The Perl programming language was used to match gene expression information with clinical information.Unknown or incomplete clinical information was removed from the dataset.The integrated dataset, combining patient clinical information with F2R expression data, underwent further statistical analysis using the ggpubr package in R software.The survival package in R was used to analyze the survival status and gene expression.The significance level for all analyses was set at p < 0.05.

Logistic regression analysis
Clinical data of STAD patients were downloaded from the TCGA database, and the patients' clinical information was combined with F2R expression data.Logistic regression analysis was performed using R software to analyze the patients' clinical data.p < 0.05 was considered to be statistically significant.

Independent prognostic analysis
In our investigation into the impact of F2R expression on the prognosis of STAD patients, we employed a comprehensive approach.Univariate Cox regression analysis was initially applied to quantify the correlation between F2R expression levels and patient survival.Following this, multivariate analysis was conducted to determine whether F2R stands as an independent prognostic factor for survival in STAD patients.The patients' Cox data were statistically analyzed using the survival package within the R software, employing both univariate and multivariate methods to ascertain independent prognostic indicators.The results were visualized using forest www.nature.com/scientificreports/plots.Clinical data of STAD patients were downloaded from the TCGA database, and the patients' survival time and status were integrated with F2R expression data.The R software survival, survival, and time ROC packages were then used to conduct a rigorous time-dependent receiver operating characteristic (ROC) analysis.This analysis served to validate the predictive efficacy of the risk model at distinctive time points: 1, 3, and 5 years.The significance threshold was set at p < 0.05.

GSEA
The GSEA software, downloaded from the GSEA website, is a powerful platform for gene set enrichment analysis.The initial molecular signature database comprises 1325 gene sets, spanning various categories such as biological pathways, chromosomal locations, upstream cis-patterns, drug therapy responses, and expression profiles from microarray datasets.Additional gene sets can also be created through genetic and chemical perturbations, computational analysis of genomic information and other biological annotation.GSEA results yield a computational enrichment score (ES), indicating the extent to which a gene set S is overrepresented in the extreme (top or bottom) of the overall sorted list L. To enhance result interpretation, the ES is normalized to account for genome size, resulting in a normalized enrichment score (NES).To control for false positives, the false positive rate (FDR) corresponding to each NES is calculated.Patient data were stratified into high and low F2R gene expression groups based on the median level.These TCGA data were formatted in text files and imported into GSEA software.The grouping and expression matrix files were then imported into GSEA 4.3.2lineage enrichment analysis.The resulting data were exported to analyze significant functional and pathway differences between the high and low F2R expression groups.The genomic alignment was repeated 1000 times for each analysis.p values, FDR values, ES values, and NES values of GSEA were analyzed.In the gene enrichment analysis, expression differences were considered significant if they met the criteria of p < 0.05 and FDR < 25%.

UCSC Xena
The UCSC Cancer Genomics Browser (https:// genome-cancer.ucsc.edu/) is a web-based application that integrates relevant data, analysis, and visualization to facilitate the exploration and sharing of research observations.The platform can be used to investigate the relationship between genomic alterations and phenotypes by visualizing a variety of omics data, and phenotypic characteristics such as age, subtype classification, and genomic biomarkers.The Cancer Genomics Explorer currently contains 575 public datasets derived from genome-wide analyses of more than 227,000 samples.These datasets include information from TCGA, CCLE, Connectivity Map, and TARGET.For this study, data on the relationship between F2R gene expression levels and their copy number variations were downloaded from the Genome Data Public Center within the UCSC Xena browser.A total of 415 STAD and 35 matched normal samples were extracted for further analysis.To better understand the functions of F2R, its expression data across multiple cancer types (pancancer) were also extracted.The ggpubr package in R software was used to visualize the expression levels of F2R in tumor and noncancerous tissues across various cancers.The statistical significance of the analyses was determined at a significance level of p < 0.05.

Construction of the ceRNA network
The miRNA gene expression data from normal gastric tissue and GC tissue samples were downloaded from the TCGA website.Then, the mirbase (https:// www.mirba se.org/) database was utilized to obtain miRNA gene IDs, facilitating ID conversion for extracting miRNA expression levels in GC tissues and noncancerous tissues.StarBase v2.0 (https:// starb ase.sysu.edu.cn/ index.phpSt arBase) was used to build a comprehensive network of microRNA-lncRNA interactions.Utilizing a dataset of 108 CLIP-Seq generated from 37 independent studies, starBase v2.0 systematically identifies RNA-RNA and protein-RNA interaction networks.This database, enriched with CLIP-Seq experimental support, facilitated the identification of more than 10,000 ceRNA pairs from miRNA target sites.To predict the functions of miRNAs and other non-coding RNAs (ncRNAs), 13 functional genome annotations were incorporated, and miRFunction and ceRNAFunction web servers were developed.An interactive web implementation was also developed to facilitate visualization, analysis, and download of these large-scale datasets.Information on miRNAs and lncRNAs related to F2R was screened based on stringent criteria such as mammalian data, human genome version hg19, minimum CLIP data rigor (≥ 5), and presence or absence of degradome data.The STARBase database predicted interactions among lncRNA/miRNA pairs and miRNA/ mRNA pairs.Cytoscape software (version 3.6.1)was employed to construct a lncRNA-miRNA-miRNA-ceRNA mRNA regulation network, unraveling intricate lncRNA-miRNA and miRNA-mRNA relationships.To construct miRNAs network regulated by F2R, miRNAs targeted by F2R were identified using starBase v2.0.Leveraging the regulatory mechanisms of ceRNA networks, miRNAs expression differences between normal and tumor samples were analyzed.Spearman statistical analysis identified miRNAs negatively correlated with F2R mRNA expression.The STARBASE database was again utilized to screen upstream miRNAs of mRNA, and the selected miRNAs were analyzed for correlation, expression differences, and survival in R software.Spearman statistical analysis showed that there was a correlation between miRNA and mRNA.miRNAs were screened according to cor < − 0.2 and p < 0.001 screening criteria.lncRNA regulating miRNA and F2R was screened by STARBASE v2.0.According to the regulatory mechanism of the ceRNA network, lncRNA expression was different in normal samples and tumor samples, and lncRNAs were highly expressed in tumor tissues.Spearman statistically analysed whether lncRNAs were correlated with miRNAs and mRNAs.According to the screening criteria of COR = 0.2 and p = 0.001, lncRNAs were negatively correlated with miRNAs, and lncRNAs were positively correlated with mRNAs.This analytic process, involving multiple databases, computational tools, and statistical analyses, and the selected lncRNAs met the requirements of negative correlation with miRNA expression, positive correlation with mRNA expression, high expression in tumor tissues, and negative correlation with patient survival time.The above differences were statistically significant.The resulting lncRNA, miRNA and mRNA networks were imported into Cytoscape 3.8 to map the ceRNA networks.Led to the identification of key regulatory networks involving F2R, miRNAs, and lncRNAs.The resulting networks were visualized using Cytoscape 3.8, providing a comprehensive representation of the ceRNA interactions and shedding light on the intricate regulatory mechanisms surrounding F2R expression in GC.

Immunocorrelation analysis
The R software limma package and the ggpubr package were used to investigate the relationship between F2R expression and tumor-infiltrating immune cells.We leveraged the Tumor Immune Estimation Resource (TIMER2.0), a web-based interactive platform, to systematically analyze the immune infiltration in various malignant tumors.The TIMER2.0 database uses six advanced algorithms to deliver a rigorous assessment of tumor-infiltrating lymphocyte (TIL) levels.This platform is particularly valuable for analyzing TCGA or tumorrelated data and provides accurate estimates of tumor purity.Our investigation into the expression of F2R in GC involved an in-depth examination of its relationship with TILs using gene modules.In addition, we also analyzed the relationship between F2R expression and TIL gene markers through correlation modules.These gene markers encompassed various immune cell types, including CD8 + /CD4 + T cells, B cells, monocytes, NK cells, DCs, tumor-associated macrophages (TAMs), M1 macrophages, M2 macrophages, and neutrophils.Utilizing the correlation module, we generated a discrete map illustrating the expression patterns between a pair of custom genes specific to GC.The correlation and Spearman estimates were analyzed for statistical significance.The expression levels of the genes were reported in log 2 RSEM (RSEM: RNA-Seq by Expectation-Maximization).

Immune microenvironment landscape and drug sensitivity analysis
Estimates of stromal and immune cells infiltration were determined using the estimation algorithm, and the CIBERSORT algorithm was used to estimate the scores of 22 distinct immune cells types.Correlation analysis between F2R expression and cancer type, as well as immune/stromal score and immune cell type, was performed using the ggplot2 package, with statistical significance set at p < 0.0001.Correlations between F2R expression and TMB were analyzed and visualized using the limma package, ggplot2 package, ggpubr package, and ggExtra package in R software.Drug sensitivity, a critical metric represented by the IC 50 value, is an essential indicator for evaluating the efficacy of drug response.Based on the Genomics of Drug Sensitivity in Cancer (GDSC) database, the antitumour drug response of each GC sample was predicted by the R package pRRophetic, stratifying the analysis into low and high-risk categories.All statistical analyses were visualized by the ggplot2 R package.

GO and KEGG enrichment analyses
To elucidate the underlying biological processes and pathways associated with F2R, we used stringent criteria (|fold change| ≥ 2, p < 0.05) to identify genes correlated with F2R expression.Then, the R software packages, including clusterProfiler, org.hs.eg.db, enrich lot, ggplot2, circle, RColorBrewer, dplyr, and ComplexHeatmap, were used to conduct a comprehensive enrichment analysis and visually represent GO and KEGG [44][45][46] .

PPI network construction
PPI networks were extracted from the STRING database.The STRING database facilitates the collection of both direct (physical) and indirect (functional) interactions among proteins, providing a nuanced understanding of their relationships.To gain insights into the F2R-regulated protein interacting network, we constructed the F2R PPI network with a minimum interaction score of 0.1.The predictions regarding F2R-interacting proteins were predominantly derived from similar genes identified in the GEPIA database.By cross-referencing these databases, we aimed to bolster the robustness of our findings and provide a more comprehensive overview of the proteins potentially influenced by F2R.

DNA methylation analysis of CpG islands within the F2R gene
We explored the methylation status of the F2R gene using the MethSurv database (http:// biit.cs.ut.ee/ meths urv/).This web tool utilizes TCGA data to enable comprehensive survival analysis.Within the TCGA-STAD dataset, we specifically focused on the CpG loci of the F2R gene to analyze their methylation patterns.The association between the prognostic potential and OS of the CpG methylation status of F2R was analysed in TCGA-STAD samples.

Clinical sample collection
GC tissue samples and adjacent non-tumour tissues from 30 pairs of patients were collected at the Department of Gastrointestinal Surgery, Second Affiliated Hospital of Wannan Medical College (Wuhu, Anhui, China).The diagnosis of GC in the surgical specimens was independently determined by two senior pathologists.None of the patients had received any treatment prior to surgery.The study was approved by the Ethics Committee of the Second Affiliated Hospital of Wannan Medical College, and informed consent was obtained from all patients.
The study was conducted according to the Declaration of Helsinki.

Immunohistochemical (IHC) staining
GC tissue samples and adjacent non-tumour tissues were collected from 30 pairs of patients who underwent GC surgery at the Department of Gastrointestinal Surgery.Five pairs of tissues were randomly selected for immunohistochemical staining.The tissues were first fixed in 4% paraformaldehyde.They were then dehydrated.Sections of 4 mm thickness were then cut using a rotary slicer (RM2255, Leica, Germany).Immunohistochemistry for F2R protein was performed.Tissues were deparaffinised by immersion in xylene and rehydrated in reduced

Figure 1 .
Figure 1.The expression level of F2R in STAD samples was significantly increased.(A) The expression of F2R in different tumor tissues in the UCSC-xena database, and the expression of F2R in STAD increased.(B) The expression of F2R in STAD based on the TCGA data set increased.(C-E) Elevated F2R expression in STAD based on GEO datasets GSE19826, GSE65801 and GSE103236.

Figure 2 .
Figure 2. The expression of F2R is related to the prognosis and clinicopathological characteristics of patients with STAD.(A) Based on the TCGA data set, the total survival time of STAD patients with high F2R expression was significantly shorter than that of STAD patients with low F2R expression.(B) F2R expression was positively correlated with the clinical Histological grade, Pathological stage, T stage and N stage, but not with age, gender and M stage.(C) Clinical-related thermal imaging analysis in patients with STAD with high and low expression of F2R.(D) The survival work characteristic curve of patients with STAD showed that F2R had a good predictive value for 5-year survival (AUC = 0.758).

Figure 3 .
Figure 3. Clinical significance of F2R gene expression in the prognosis of STAD.(A) Univariate analysis showed that the expression levels of stage, age and F2R were independent predictors of survival time in patients with STAD.(B) Multivariate analysis showed that F2R expression was a reliable prognostic factor for patients with STAD.(C) A nomogram integrating F2R and other prognostic factors of STAD based on TCGA data.(D) The calibration curve of the nomogram.

Figure 4 .
Figure 4. Screening the upstream miRNA and lncRNA of F2R and constructing the ceRNA network diagram.(A) Network mapping of F2R-related miRNAs based on the Starbase database.(B) The expression of F2R based on the TCGA dataset was negatively correlated with the expression of hsa-miR-144-5p and hsa-miR-486-5p.(C) The expression of hsa-miR-144-5p and hsa-miR-486-5p in STAD tissues based on the TCGA data set decreased.(D) Mapping of hsa-miR-144-5p and hsa-miR-486-5p related lncRNA networks based on the Starbase database.(E) To construct an F2R-centered ceRNA network.(F) Based on the TCGA data set, the expression of hsa-miR-144-5p was negatively correlated with the expression of FTX, and the expression of has-miR-486-5p was negatively correlated with the expression of ZEB1-AS1.(G) The expression of FTX and ZEB1-AS1 in STAD based on the TCGA data set increased.(H) Based on the TCGA data set, F2R expression was positively correlated with FTX and ZEB1-AS1 expression.

Figure 5 .Figure 6 .Figure 7 .
Figure 5. Gene enrichment analysis to identify F2R-related pathways.(A) Screening genes that are positively or negatively correlated with F2R expression to draw a gene heat map.(B) Analysis of 11 genes with significantly different expression related to F2R. (C-D)The enrichment analysis of gene ontology (GO) showed that F2R-related genes were mainly related to the completion of the activation of classical pathways, circulating mediated human immune response, immunoglobulin, cell recognition and phagocytosis.(E,F) The enrichment analysis of the Kyoto Encyclopedia of Genes and Genome (KEGG) showed that F2R-related genes mainly interact with ECM receptor, neuroactive ligand receptor, protein differentiation and uptake, cytokine-cyclin receptor interaction, PI3K-Akt signal pathway, cAMP signal pathway, Wnt signal pathway, cGMP PKG signal pathway and TGF-β Signal path correlation.(G) Gene cluster enrichment analysis (GSEA) showed that the signal pathway related to the expression of F2R mRNA in gastric cancer.The results showed that DNA replication, Huntington's disease, JAK-STAT signal pathway, MAPK signal pathway and oxidative phosphorylation were related to the expression of F2R.(H) Protein-protein interaction (PPI) network was used to analyze genes related to F2R.

Figure 8 .
Figure 8. Methylation levels in the F2R gene are associated with the prognosis of STAD patients.The prognosis value of DNA methylation levels in the F2R gene and CpG islands in the F2R gene were analyzed using the MetSurv tool.

Figure 9 .Figure 10 .
Figure 9. Predicted drug sensitivity in risk groups.(A) Sensitive drugs in high-risk groups.(B) Sensitive drugs in low-risk groups.

Figure 11 .
Figure 11.Immunoscore of F2R expression with anti-pd1 and anti-ctla4 treatment.(A) High and low immune scores in the low and high F2R expression groups while on anti-pd1 therapy.(B) High and low immune scores in the low and high F2R expression groups while on anti-ctla4 therapy.

Table 1 .
STAD patient characteristics in the TCGA database.Significant values are in bold.

Table 2 .
F2R expression correlated with clinical pathological characteristics (logistic regression).Categorical dependent variable, greater or less than the median expression level.T topography distribution, N lymph node metastasis, M distant metastasis.Significant values are in bold.

Table 3 .
Univariate analysis and multivariate analyses of STAD patients overall survival in the TCGA database.T topography distribution, N lymph node metastasis, M distant metastasis.Significant values are in bold.
Vol.:(0123456789) Scientific Reports | (2024) 14:2468 | https://doi.org/10.1038/s41598-024-52397-6 staging in STAD suggests a role for F2R in driving tumor progression and metastasis.Investigating the role of F2R in tumor development and its potential as a biomarker for early detection could provide insights into the temporal changes in F2R expression from early to late stages of STAD.