Correlation analysis of RasGRP4 gene expression with mast cell infiltration and clinical prognosis of lung adenocarcinoma

Correlation analysis of RasGRP4 gene expression with mast cell infiltration and clinical prognosis of lung adenocarcinoma | 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 Correlation analysis of RasGRP4 gene expression with mast cell infiltration and clinical prognosis of lung adenocarcinoma Yang Luo, Shu Zhang, Jian-Guo Zhang, Yi-Fei Liu, Qing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8318061/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Objective To explore the correlation between Ras guanyl nucleotide releasing protein 4 (RasGRP4) expression in lung adenocarcinoma (LUAD) and mast cells (MCs) infiltration, as well as its impact on clinical prognosis. Methods 1. The Cancer Genome Atlas (TCGA) database was utilized to analyze RasGRP4 expression in LUAD tissues and to assess its potential association with survival prognosis and clinicopathological features of patients. 2. The correlation between RasGRP4 and 22 tumor infiltrating immune cells (TIICs) was evaluated using the "CIBERSORT" R package. 3. Following a preliminary analysis of an online LUAD database, immunohistochemistry (IHC) experiments were conducted to validate the correlation between RasGRP4 expression and MCs infiltration in clinical samples, as well as its association with patient survival prognosis and clinicopathological parameters. Results 1. In the pooled TCGA database, RasGRP4 exhibited significantly low expression in various cancers, including LUAD. Patients with high RasGRP4 expression had a better survival prognosis. Multifactorial COX regression analysis indicated that tumor grade and RasGRP4 expression could serve as independent risk factors for LUAD prognosis. 2. A clear correlation was found between RasGRP4 expression and 10 types of immune cells. 3. IHC results confirmed the low expression of RasGRP4 in LUAD tissues, suggesting that its low expression may be associated with poor LUAD progression. 4. IHC results also demonstrated low MCs infiltration in LUAD tissues, which was significantly correlated with gender, tumor differentiation, tumor size, and T stage. 5. In LUAD, low RasGRP4 expression was positively correlated with low infiltration of MCs. Conclusions 1. RasGRP4 is downregulated in LUAD. 2. RasGRP4 may serve as an independent prognostic biomarker in LUAD patients.. 3. MCs show low infiltration in LUAD. 4. Low RasGRP4 expression in LUAD is positively correlated with low infiltration of MCs. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology RasGRP4 Mast cell Lung adenocarcinoma Prognosis Immune cell infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction In China, lung cancer is the the leading cause of both incidence and mortality among malignant tumors, with LUAD being the most prevalent subtype. [ 1 , 2 ] . As the significance of immunotherapy in cancer treatment continues to increase, researchers are intensifying their exploration of the role of tumor-infiltrating immune cells (TIICs) within the tumor microenvironment (TME) and their potential value in immunotherapy strategies.. RasGRP4, a member of the RasGEF superfamily, functions as a cation- and diacylglycerol (DAG)-regulated nucleotide exchange factor. It activates Ras by binding to GDP and exchanging it for GTP. [ 3 ] . Additionally, RasGRP4 is highly expressed in MCs, where it plays a crucial role in their development and function. [ 4 ] . Although RasGRP4 has been implicated in the pathogenesis of diffuse large B-cell lymphoma, [ 5 ] its association with lung adenocarcinoma (LUAD) and the underlying mechanisms remain to be elucidated. MCs, as TIICs, are widely distributed throughout various tissues. [ 6 ] .They exert both pro-tumor and anti-tumor effects. Regarding the impact of MCs on the prognosis of patients with LUAD, several conflicting studies have been published. Ma et al. [ 7 ] found that tumor-associated MCs indicate poor prognosis in human LUAD and advanced tumors. Research by Chen et al. [ 8 ] indicates that MCs with active KIT promote the formation, growth, and metastasis of Kras-mutant LUAD by providing interleukin-1, and are associated with the progression of LUAD. However, increased MCs abundance is associated with CCR2 + cytotoxic T cell enrichment and favorable prognosis in LUAD. [ 9 ] . The primary objective of this study is to elucidate the expression pattern of RasGRP4 in LUAD.Furthermore, this research explores the relationship between RasGRP4 expression and MCs infiltration in LUAD patients. By doing so, we aim to offer novel insights and therapeutic strategies for the diagnosis and treatment of LUAD, thereby advancing scientific development and practical application in the field of LUAD diagnosis and treatment. Methods 2.1 Public Bioinformatics Databases In this study, we retrieved expression data and corresponding clinical information for 551 LUAD samples, can be deposited in The Cancer Genome Atlas (TCGA) comprising 497 LUAD tissue samples and 54 adjacent tissue samples. Samples underwent rigorous screening, excluding those with insufficient or missing data. Consequently, a total of 458 samples with complete clinical information were retained for subsequent clinical-pathological analysis. 2.2 LUAD Tissue Specimens Tumor tissues from invasive LUAD resected at the Affiliated Hospital of Nantong University between January 2017 and December 2018 were collected, along with adjacent normal lung tissue samples obtained at least 2 cm from the tumor margins. A total of 345 LUAD patients were ultimately included in this study. Professional pathologists prepared routine tissue paraffin blocks and tissue microarrays from the specimens. Basic patient information was collected. Follow-up was conducted via telephone or electronic medical records. All patients were followed up until September 10, 2023, with a maximum follow-up duration of 80 months. This study protocol was approved by the Ethics Committee of the Affiliated Hospital of Nantong University. 2.3 TCGA Data Analysis The gene transcriptomic data and corresponding clinical information for the 551 LUAD samples utilized in this study were retrieved from the TCGA public database. Subsequently, all data were processed using R software (version 4.1.1) and Perl (version 5.30.0.1). The “beeswarm” R package was employed to investigate RasGRP4 expression across various tumor types and within LUAD. The “survival” and “survminer” R packages were utilized to examine the association between RasGRP4 expression and patient prognosis as well as clinical-pathological characteristics. In this study, P < 0.05 was considered statistically significant. 2.4 UALCAN Analysis The UALCAN platform ( https://ualcan.path.uab.edu/index.html ) offers extensive interactive bioinformatics studies utilizing RNA-seq and clinical data from 31 malignant tumors sourced from TCGA. This database facilitates comparisons of gene expression between tumor and healthy samples, across different tumor stages or subtypes, and in relation to other clinical and pathological characteristics. [ 10 ] . In this study, UALCAN was utilized to analyze the positive and negative expression of RasGRP4-associated genes in LUAD tissues. 2.5 GeneMANIA and STRING Analysis The GeneMANIA platform ( https://genemania.org/ ) integrates a variety of data sources encompassing gene-gene interactions, co-expression data, and pathway information. Meanwhile, the STRING database ( https://cn.string-db.org/ ) is a vital resource for collecting protein interaction information, enabling the prediction of RasGRP4's protein-protein interaction network. [ 11 , 12 ] . In this study, we utilized GeneMANIA and STRING to identify genes and proteins associated with RasGRP4. 2.6 Gene Enrichment Analysis In this study, GSEA (v3.0) was employed to conduct KEGG pathway enrichment analysis for RasGRP4. Within the TCGA cohort, a total of 248 samples were categorized into the high-risk group while 249 samples were assigned to the low-risk group. For the pathway enrichment analysis, results were deemed statistically significant if they met both criteria: a false discovery rate (FDR) < 0.05 and a P-value < 0.05. 2.7 TIMER Analysis The TIMER database ( https://compbio.cn/timer2/ ) is a public resource that investigates the relationship between immune cell infiltration and gene expression in cancer samples based on TCGA. [ 13 ] . In this study, the TIMER algorithm was employed to assess RasGRP4 expression across different cancer types. Additionally, a series of analyses were conducted to investigate the interplay between RasGRP4 expression and various immune cell infiltrations. 2.8 CIBERSORT Analysis CIBERSORT is a computational method to assess the relative abundance of different cell types in complex mixed tissue samples. The entire algorithm is based on linear support vector regression (SVR), specifically the deconvolution inference of support vector machines (SVMs). [ 14 ] . In this study, the CIBERSORT algorithm was employed to analyze the infiltration levels of 22 immune cell types and further evaluated their correlation with RasGRP4 expression in LUAD. 2.9 IHC Procedure The baked slides were dewaxed in xylene and graded ethanol. Antigen retrieval was then performed for 17 minutes. The primary antibodies: RasGRP4 (bs-19731R, 1:300, Beijing Bio-Ocean Biotech) and CD117 (ab32363, 1:400, Abcam), were added subsequently. The slides were incubated at room temperature for 3 hours. The secondary antibody, goat anti-rabbit, was then added and incubated at room temperature for 30 minutes. Finally, the slides were stained with hematoxylin. (1) Interpretation of RasGRP4 IHC Results Staining intensity was graded on a scale of 0 to 3, where 0 indicated negative staining, 1 indicated weak staining, 2 indicated moderate staining, and 3 indicated strong staining. Positive cell percentages were categorized into four groups: 1 for 0–25%, 2 for 26–50%, 3 for 51–75%, and 4 for 76–100%. The final score was calculated as the product of the staining intensity and the positive cell percentage. A final staining index of ≥ 6 was defined as high RasGRP4 expression, while a score of < 6 was defined as low expression. These sections were scored by two certified pathologists. (2) Interpretation of IHC Findings for MCs CD117 (c-kit) is utilized to label MCs, with CD117 localization in the MCs cytoplasm and cell membrane. MCs counting follows the Molin method: randomly observe 10 cancer tissue fields under high magnification, count the number of MCs in each field, and calculate the mean as the MCs count (MCC) for that case. [ 15 ] . The cutoff value was established at the median MCC of infiltrated cases in LUAD (1.3), dividing patients into high-MCs-infiltration and low-MCs-infiltration groups. MCs IHC results were also assessed using a double-blind method. 2.10 Results Analysis Data obtained from TCGA were processed and analyzed using R software (version 4.1.1) and Perl (version 5.30.0.1). The IHC results were statistically analyzed using SPSS Statistics (version 27.0). Graphs were subsequently generated using GraphPad Prism9. Paired t-tests were applied for paired variable data analysis. Survival analysis employed the Kaplan-Meier method and log-rank test. Chi-square tests assessed correlations between RasGRP4 expression and MCs infiltration with clinical pathological parameters. Pearson's correlation coefficient was calculated to determine the associations between RasGRP4 expression and MCs infiltration. In general, P < 0.05 was considered to indicate statistical significance. Result 3.1 Investigating the Correlation Between RasGRP4 and LUAD Using Bioinformatics Analysis 3.1.1 Expression of RasGRP4 in LUAD and Other Cancers This study comprehensively evaluated RasGRP4 expression across various cancer types using the TIMER algorithm. The results revealed significant underexpression of RasGRP4 in 12 cancer types, including LUAD, transitional cell carcinoma of the bladder, and colorectal cancer (Fig. 1A). Additionally, the study analyzed the mRNA expression differences of RasGRP4 between LUAD tissues and adjacent non-cancerous lung tissues in the TCGA database. Figure 1B and 1C visually present RasGRP4 expression data in non-tumorous lung tissue versus tumor tissue using scatter plots and paired scatter plots, respectively. The results demonstrate that RasGRP4 expression levels in tumor tissue are significantly lower than in the corresponding non-tumorous lung tissue. Figure 1 TCGA Analysis of RasGRP4 Expression in LUAD and Other Cancers Figure 1A: Expression of RasGRP4 in various types of cancer. Figure 1B: Expression of RasGRP4 in non-cancerous lung tissue and LUAD tissue. Figure 1C: Paired expression of RasGRP4 in non-cancerous lung tissue and LUAD tissue. 3.1.2 Impact of RasGRP4 on Survival Outcomes in Patients with LUAD Given the significant differences in RasGRP4 expression between LUAD tissues and adjacent normal lung tissues, this study further investigated the correlation between RasGRP4 expression and overall survival (OS). The cut-off value was set at the median level of RasGRP4 expression, thereby dividing all LUAD patients into high-expression and low-expression groups. The results indicate that RasGRP4 expression levels significantly impact survival in LUAD patients. Compared to patients with high RasGRP4 expression, those with low expression exhibited poorer prognosis survival, with this difference being statistically significant (P = 0.003) (Fig. 2). Figure 2 Survival Curves for Differential Expression of RasGRP4 in LUAD 3.1.3 Correlation between RasGRP4 Expression and Clinical-Pathological Parameters in LUAD Based on the results in Fig. 3A, this study observed a significant association between RasGRP4 expression and certain T stages. Specifically, statistically significant differences in RasGRP4 expression levels were observed between T1 and T2, as well as between T1 and T3 (P < 0.05). These findings suggest that LUAD patients with lower RasGRP4 expression may be more prone to developing tumors at more advanced T stages. This implies that RasGRP4 expression may be associated with the aggressiveness of LUAD, with reduced expression potentially correlating with disease progression and worsening. To explore the relationship between RasGRP4 expression and clinical-pathological factors in LUAD patients, this study conducted Cox proportional hazards model analysis on data downloaded from the TCGA database. This analysis provided further insights into the association between RasGRP4 expression and other clinical-pathological parameters, as well as patient survival outcomes. As shown in Fig. 3B, the results of univariate Cox regression analysis indicated that stage (HR = 1.65, P < 0.001), T stage (HR = 1.63, P < 0.001), N stage (HR = 1.79, P < 0.001), and RasGRP4 expression (HR = 0.54, P = 0.012) were significant prognostic risk factors for LUAD patients. Further, the results of multivariate Cox regression analysis, depicted in Fig. 3C, revealed that stage (HR = 1.85, P = 0.008) and RasGRP4 expression (HR = 0.52, P = 0.013) serve as independent prognostic risk factors for LUAD patients. Collectively, these findings indicate that RasGRP4 expression is significantly associated with prognosis in LUAD patients, thereby supporting the potential of RasGRP4 as a therapeutic target for LUAD. Figure 3 Relationship between RasGRP4 Expression in LUAD Tissue and Clinical Pathological Parameters and Survival Prognosis Figure 3A: Relationship between RasGRP4 expression and T stage. Figure 3B: Univariate Cox regression analysis. Figure 3C: Multivariate Cox regression analysis. 3.1.4 Co-expressed Genes Associated with RasGRP4 in LUAD This study investigates the molecular mechanisms by which RasGRP4 induces LUAD development through analyzing its co-expressed genes and associated proteins in LUAD. The UALCAN database was utilized to explore genes positively and negatively correlated with RasGRP4 in LUAD tissues, as shown in Figs. 4A and 4B, aiding in elucidating the potential role of RasGRP4 in LUAD. Additionally, protein interaction networks were constructed using GeneMANIA and STRING to evaluate potential interactors of differentially expressed RasGRP4. The results of this analysis are presented in Figs. 5A and 5B. This analysis helps reveal the position of RasGRP4 within the protein network and how it interacts with other proteins to influence biological processes in LUAD. Figure 4 UALCAN Database Analysis of Genes Associated with RasGRP4 in LUAD Tissue Figure 4A: Genes positively correlated with RasGRP4. Figure 4B: Genes negatively correlated with RasGRP4. Figure 5 Interactions between RasGRP4 and its associated proteins Figure 5A: GeneMANIA analysis of the protein interaction network for RasGRP4. Figure 5B: STRING analysis of the protein interaction network for RasGRP4. 3.1.5 RasGRP4-Associated Enrichment Analysis in LUAD This study utilized GSEA software to conduct KEGG pathway enrichment analysis, aiming to elucidate the biological patterns and functions associated with RasGRP4 expression in the progression of LUAD. As depicted in Fig. 6, the results indicate that high RasGRP4 expression may be positively correlated with pathways such as acute myeloid leukemia, apoptosis, B-cell receptor signaling pathways, chemokine receptor pathways, and mitogen-activated protein kinase (MAPK) signaling pathways. Conversely, it appears to be negatively correlated with pathways including basic transcription factors, cell cycle, RNA degradation, RNA polymerase, and spliceosome. Figure 6 GSEA Enriched Pathway Analysis 3.1.6 Relationship Between RasGRP4 Expression and TIICs in LUAD The characteristics of TIICs are significantly correlated with tumor initiation and progression. [16] . Therefore, to investigate whether RasGRP4 expression correlates with TIICs in LUAD tissues, this study divided 497 samples into 248 high-expression samples and 249 low-expression samples based on RasGRP4 expression. The “CIBERSORT” R software package was used to observe differences in the proportions of 22 immune cell types between the high and low-expression groups. Figure 7 shows the results for the 22 immune cell subpopulations, revealing that the differential expression of RasGRP4 was significantly associated with the following 10 types of immune cells: Immature B cells, plasma cells, CD8 + T cells, resting memory CD4 + T cells, follicular T helper cells, monocytes, M2 macrophages, resting dendritic cells, resting mast cells, and neutrophils (P < 0.001). Figure 7 Correlation between high and low expression groups of RasGRP4 and immune cells This study further explored the relationship between immune cell infiltration levels and RasGRP4 expression in LUAD tissues using the “limma” R package. As shown in Fig. 8A, RasGRP4 expression levels correlated with resting dendritic cells (r = 0.35, P = 1.7E − 14), M2 macrophages (r = 0.29, P = 2.3E − 10), and resting mast cells (r = 0.3, P = 5.6E − 11), monocytes (r = 0.36, P = 1.9E − 15), neutrophils (r = 0.24, P = 1.2E − 07), and resting memory CD4 + T cells (r = 0.18, P = 0.00017). In contrast, it negatively correlated with the infiltration levels of immature B cells (r = − 0.28, P = 9.4E − 10), plasma cells (r = − 0.29, P = 1.4E − 10), activated memory CD4 + T cells (r = − 0.11, P = 0.023), and follicular T helper cells (r = − 0.24, P = 2.2E − 07). In bioinformatics, Venn diagrams enable researchers to rapidly visualize information across experiments, conditions, and groups to explore relationships between datasets. [17] . As shown in Fig. 8B, plotting the differentially expressed immune cell results and immune cell correlation analysis results into a Venn diagram ultimately identified 10 immune cell types associated with RasGRP4. Notably, MCs infiltration exhibits a significant positive correlation with RasGRP4 expression. Figure 8 Correlation between RasGRP4 Expression and Immune Cells Figure 8A: Correlation between RasGRP4 expression and immune cell infiltration. Figure 8B: Venn diagram. 3.2 Internal Experimental Investigation of the Correlation Between RasGRP4 and MCs in LUAD 3.2.1 Expression of RasGRP4 in LUAD Tissue and Adjacent Normal Lung Tissue To further validate RasGRP4 expression in LUAD, IHC was performed on samples from 345 LUAD patients. The results demonstrated that the intensity of RasGRP4 staining was significantly lower in LUAD tissue compared to adjacent normal tissue (Fig. 9A,B), a finding that was statistically significant (Fig. 10A). Additionally, Fig. 9C-F illustrates RasGRP4 expression across different clinical pathological stages. These results demonstrate that RasGRP4 expression progressively decreases with increasing clinical pathological stage, reflecting heightened tumor invasiveness and malignancy. This suggests that RasGRP4 expression levels may correlate with the invasiveness and progression of LUAD. Figure 9 IHC staining results showing RasGRP4 expression in LUAD and adjacent non-cancerous lung tissue Figure 9A: Expression of RasGRP4 inLUAD tissue. Figure 9B: Expression of RasGRP4 in adjacent non-cancerous lung tissue. Figures 9C-F: Expression of RasGRP4 in LUAD tissue at stages T1, T2, T3, and T4, respectively. Magnification levels for the above images are 30× and 200×. 3.2.2 Correlation between RasGRP4 Expression and Prognosis as well as Clinical-Pathological Parameters in Patients with LUAD Based on IHC at the protein level, we investigated the relationship between RasGRP4 expression levels and prognosis as well as clinical-pathological parameters in LUAD patients. Patients with IHC scores ≥ 6 were classified into the RasGRP4 high-expression group, comprising 163 samples; those with scores < 6 were assigned to the RasGRP4 low-expression group, comprising 182 samples. Kaplan-Meier curves were plotted for patients with high or low RasGRP4 expression. Figure 10B demonstrated shorter survival times in the low-expression group (P = 0.0261), consistent with previous bioinformatics analysis results. This study further explored the correlation between RasGRP4 expression and clinical-pathological parameters. Table 3 − 1 summarizes the clinical-pathological characteristics of the internal cohort of 345 cases. Results indicate that RasGRP4 expression was significantly associated with tumor size (P = 0.028) and closely correlated with tumor TNM staging (P = 0.017). and tumor TNM staging (P = 0.017). No significant correlations were observed between RasGRP4 expression and other clinical-pathological features including age, gender, smoking status, tumor differentiation grade, lymph node metastasis, or T stage. Table 3-1 Correlation between RasGRP4 Expression and Clinical-Pathological Parameters in LUAD Patients Clinical Pathological Parameters Example count RasGRP4 P value Low-expression group High-expression group Total 345 182 163 Age 0.732 3 91 57 34 Tumor differentiation 0.256 Middle-High 214 118 96 Low 131 64 67 TNM stage 0.017 I + II 273 135 138 III + IV 72 47 25 Lymph node metastasis 0.273 Yes 116 66 50 No 229 116 113 T 0.060 I + II 326 168 158 III + IV 19 14 5 Table 3-2 Correlation between MCs Infiltration and Clinical-Pathological Parameters in Patients with LUAD Clinical Pathological Parameters Example count MCs P value Low-expression group High-expression group Total 345 177 168 Age 0.147 3 91 59 32 Tumor differentiation 0.030 Middle-High 214 100 114 Low 131 77 54 TNM Stage 0.108 I+II 273 134 139 III+IV 72 43 29 Lymph node metastasis 0.088 Yes 116 67 49 No 229 110 119 T 0.045 I+II 326 163 163 III+IV 19 14 5 Table 3-3 Relationship Between RasGRP4 Expression and MCs Infiltration Example count Low-infiltration group High-infiltration group P value r Total 345 177 168 0.022 0.123 Low-expression group 182 104 78 High-expression group 163 73 90 Figure 10 Expression of RasGRP4 in LUAD Tissue and Its Relationship with Prognosis Figure 10A: Statistical results of IHC staining for RasGRP4 expression in LUAD and adjacent non-cancerous lung tissue. Figure 10B: Kaplan-Meier survival curve. Table 3 − 1 Correlation between RasGRP4 Expression and Clinical-Pathological Parameters in LUAD Patients 3.2.3 MCs Infiltration in LUAD Tissue and Adjacent Normal Lung Tissue To elucidate the differences in MCs infiltration between LUAD tissue and adjacent normal lung tissue (at least 2 cm from tumor tissue), IHC analysis using the MCs marker CD117 (c-kit) was performed on 345 LUAD patient samples. The results demonstrated significantly lower MCC in LUAD tissue compared to adjacent normal tissue (Fig. 11A,B), with statistically significant differences (Fig. 12A). Figure 11 IHC staining results showing MCs infiltration in LUADand adjacent non-cancerous lung tissue Figure 11A: MCs infiltration in LUAD tissue. Figure 11B: MCs infiltration in adjacent non-cancerous lung tissue. Magnification levels for the above images are 30× and 200×, respectively. 3.2.4 Correlation Between MCs Infiltration and Prognosis as Well as Clinical-Pathological Parameters in Patients with LUAD Based on IHC analysis at the protein level, this study investigated the relationship between MCs infiltration levels and prognosis as well as clinical-pathological parameters in patients with LUAD. Patients were divided into a high MCs infiltration group (168 samples) and a low MCs infiltration group (177 samples) based on the median MCC (1.3) observed in LUAD cases. Kaplan-Meier survival curves were plotted for patients with high or low MCs infiltration. As shown in Fig. 12B, there was no significant correlation between MCs infiltration and prognosis in LUAD patients (P = 0.0584). This study further explored the correlation between MCs infiltration and clinical-pathological parameters. Table 3 − 2 summarizes the clinical-pathological parameters of the internal cohort of 345 cases. Results indicate that MCs infiltration was significantly associated with gender (P = 0.026), tumor size (P = 0.003), tumor differentiation (P = 0.030), and T stage (P = 0.045). No significant correlations were observed between MCs infiltration and other clinical-pathological features, such as age, tumor TNM stage, smoking status, or lymph node metastasis. Figure 12 Mast Cell Infiltration in LUAD Tissue and Its Relationship with Prognosis Figure 12A: Statistical results of IHC staining for MCs infiltration in LUAD and adjacent non-cancerous lung tissue. Figure 12B: Kaplan-Meier survival curve. Table 3 − 2 Correlation between MCs Infiltration and Clinical-Pathological Parameters in Patients with LUAD 3.2.5 Relationship Between RasGRP4 Expression and MCs Infiltration in Patients with LUAD This study revealed through IHC analysis that RasGRP4 exhibits low expression in LUAD tissues, while MCs demonstrate low infiltration levels in these tissues. Further analysis using the chi-square test confirmed a positive correlation between RasGRP4 expression levels and MCs infiltration. Tumors in the low RasGRP4 expression group exhibited significantly fewer MCs (P = 0.022, r = 0.123) (P = 0.022, r = 0.123) (Table 3-3). Table 3-3 Relationship Between RasGRP4 Expression and MCs Infiltration 3.2.6 Validation of RasGRP4 Expression Levels and MCs Infiltration in LUAD Patients This study selected 24 cases from 345 LUAD specimens. Paraffin blocks of LUAD and corresponding normal lung tissue were collected from these 24 patients, prepared into routine IHC sections, and used to further validated RasGRP4 expression and MCs infiltration in LUAD patients. As shown in Fig. 13A, the MCC in LUAD tissue was significantly lower than in adjacent non-cancerous tissue, with statistical significance. Figure 13B demonstrates that the intensity of RasGRP4 positive staining in LUAD tissue was significantly lower than in adjacent non-cancerous tissue, with statistical significance, consistent with previous IHC microarray results. Additionally, this study revealed a significant difference in MCs numbers between areas > 2 cm and ≤ 2 cm from the tumor margin in both LUAD and adjacent non-tumor tissues, with higher infiltration levels observed in the > 2 cm zone (Figs. 14A and B). This difference was statistically significant, as shown in Fig. 13C. Both RasGRP4 and CD117 (c-kit) were highly expressed in MCs. However, this study revealed that in LUAD tissues, RasGRP4 marked MCs at a significantly lower level than CD117 (Fig. 14C, D). Figure 13 MCs infiltration in LUAD tissue and RasGRP4 expression levels in conventional sections Figure 13A: IHC staining statistics for MCs infiltration in routine sections of LUAD and corresponding adjacent non-cancerous tissue (2cm away). Figure 13B: IHC staining statistics for RasGRP4 expression in routine sections of LUAD and corresponding adjacent non-cancerous tissue. Figure 13C: IHC staining statistics for MCs infiltration within and beyond 2cm of the tumor margin in routine sections of LUAD. Figure 14 MCs infiltration in peritumoral tissue and IHC staining results for two markers labeling MCs Figure 14A: MCs infiltration within 2cm of LUAD. Figure 14B: MCs infiltration in tissue beyond 2cm from the LUAD. Figure 14C: IHC staining of CD117-labeled MCs in LUAD. Figure 14D: IHC staining of RasGRP4-labeled MCs in LUAD. All images are magnified at 400×. Discussion Unlike other members of the RasGRP family, RasGRP4 is highly expressed in MCs and other myeloid cells, potentially playing a crucial role in MCs development and function. However, the specific role of RasGRP4 in MCs remains to be definitively established. The pathogenicity of RasGRP4 has been well-established in various diseases, including leukemia, rheumatoid arthritis, inflammatory bowel disease, and lymphoma. [ 18 , 19 ] . Patients with diffuse large B-cell lymphoma exhibit significantly elevated RasGRP4 levels. Downregulating RasGRP4 in tumor cells inhibits proliferation by reducing ERK expression and increasing JNK expression, thereby decreasing mitotic activity, promoting apoptosis, and elevating oxidative stress levels. [ 20 ] . This provides a research direction for investigating the relationship between RasGRP4 expression and tumors. The function and mechanism of action of RasGRP4 in LUAD require further exploration and investigation by researchers. MCs are important immune cells in the TME that influence tumor progression by regulating this microenvironment. Once infiltrating solid tumors, they are termed tumor-associated mast cells (TAMCs). These cells are recognized as distinct participants and coordinators of both pro-tumor and anti-tumor responses, representing one of the most controversial immune cell types in cancer. On one hand, they can promote various processes leading to tumor progression, such as angiogenesis, lymphangiogenesis, fibrosis, and metastasis. On the other hand, TAMCs can release mediators that induce the recruitment of other immune cells to the tumor, which can perform either pro- or anti-tumor functions. [ 21 ] . The impact of MCs on the prognosis of LUAD patients has also been extensively discussed. Studies indicate that lung cancer-derived exosomes mediate MCs activation, suggesting that exosomes may exert potential effects on cancer-associated coagulation dysfunction by altering the cytokine profile released by MCs. [ 22 ] . Additionally, tryptophanase released by mast cells promotes tumor cell metastasis via exosomes. [ 23 ] . However, another study indicates that high MCs abundance is associated with prolonged survival in patients with early-stage LUAD. [ 24 ] . Increased mast cell abundance correlates with the enrichment of CCR2 + cytotoxic T cells and favorable prognosis in LUAD. [ 9 ] . In summary, the results of this study reveal that RasGRP4 is downregulated in LUAD tissues and is closely associated with survival time in patients with LUAD. Therefore, RasGRP4 can be considered an independent prognostic factor for LUAD. This study also found that MCs show low infiltration in LUAD tissues and are significantly correlated with the downregulation of RasGRP4, suggesting that both RasGRP4 and MCs play crucial roles in the development and progression of LUAD. However, this study has several limitations. The expression of RasGRP4 in LUAD requires further validation through additional basic experiments. Furthermore, the specific mechanism linking RasGRP4 expression to MCs infiltration in LUAD remains unclear. Additionally, whether differences exist between RasGRP4 expression and MCs subtypes or MCs states requires further research analysis. Declarations Funding declaration This study was funded by grants from Basic Science Research Project in Nantong City, Jiangsu, China (JC2021029). Project Title: Investigation of the Molecular Mechanisms by Which the DDX46/FTO/BCL-2/Beclin1 Signaling Axis Regulates Autophagy and Proliferation in Lung Adenocarcinoma. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval Informed consent was obtained from all individual participants included in the study, giving their authorization to access their clinical information and tumor samples for research purpose. Author Contribution declaration Yang Luo: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Jianguo Zhang; Shu Zhang: Contributed reagents, materials, analysis tools or data. Qing Zhang; Yifei Liu: Conceived and designed the experiments. Availability of data and materials The datasets generated and analysed during the current study are available in the [https://portal.gdc.cancer.gov/]、[https://ualcan.path.uab.edu/index.html]、[https://genemania.org/]、[https://cn.string-db.org/]、[https://compbio.cn/timer2/] Ethics approval This study was approved by the Ethics committee of Affiliated Hospital of Nantong University. References Cao, M., Li, H., Sun, D. & Chen, W. Cancer burden of major cancers in China: A need for sustainable actions. Cancer Commun. (London England) . 40 (5), 205–210 (2020). Yang, D., Liu, Y., Bai, C., Wang, X. & Powell, C. A. 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The release of tryptase from mast cells promote tumor cell metastasis via exosomes. BMC cancer . 19 (1), 1015 (2019). Bao, X., Shi, R., Zhao, T. & Wang, Y. Mast cell-based molecular subtypes and signature associated with clinical outcome in early-stage lung adenocarcinoma. Mol. Oncol. 14 (5), 917–932 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 15 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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17:02:14","extension":"png","order_by":47,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":198080,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/b588903609d1584e307d2b81.png"},{"id":98387673,"identity":"5e32c59b-7c2b-4a45-add5-16a83e4ad043","added_by":"auto","created_at":"2025-12-17 08:57:14","extension":"xml","order_by":48,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93561,"visible":true,"origin":"","legend":"","description":"","filename":"1e46d63af6da48b0b947184fbd789cee1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/fba72da83f2df08a40342f1b.xml"},{"id":98387657,"identity":"34c7d2ed-d129-4cb1-8a15-0fbd6253ef03","added_by":"auto","created_at":"2025-12-17 08:57:14","extension":"html","order_by":49,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108377,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/b24d8d32fe52d1c8a5216ee2.html"},{"id":98441453,"identity":"4f2b4eb0-597d-4272-abc6-167bc099ca1a","added_by":"auto","created_at":"2025-12-17 17:05:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":299913,"visible":true,"origin":"","legend":"\u003cp\u003eTCGA Analysis of RasGRP4 Expression in LUAD and Other Cancers\u003c/p\u003e\n\u003cp\u003eA: Expression of RasGRP4 in various types of cancer. Figure 1B: Expression of RasGRP4 in non-cancerous lung tissue and LUAD tissue. Figure 1C: Paired expression of RasGRP4 in non-cancerous lung tissue and LUAD tissue.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/00337c1b846f90f9285a3cb9.png"},{"id":98387610,"identity":"5964e63e-83c7-4d86-a217-46d3b76c0fa8","added_by":"auto","created_at":"2025-12-17 08:57:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57661,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Curves for Differential Expression of RasGRP4 in LUAD\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/c3e6f0b1fe5cdc555b5c2b62.png"},{"id":98440389,"identity":"b4bb681d-13bc-4bf7-8776-972a93d999c5","added_by":"auto","created_at":"2025-12-17 17:03:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204067,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between RasGRP4 Expression in LUAD Tissue and Clinical Pathological Parameters and Survival Prognosis\u003c/p\u003e\n\u003cp\u003eA: Relationship between RasGRP4 expression and T stage. Figure 3B: Univariate Cox regression analysis. C: Multivariate Cox regression analysis.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/16dc746235c42bc81b73ff7f.png"},{"id":98440413,"identity":"f7505b1d-50ce-4107-ab49-d29c9f65dcdb","added_by":"auto","created_at":"2025-12-17 17:03:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161770,"visible":true,"origin":"","legend":"\u003cp\u003eUALCAN Database Analysis of Genes Associated with RasGRP4 in LUAD Tissue\u003c/p\u003e\n\u003cp\u003eA: Genes positively correlated with RasGRP4. B: Genes negatively correlated with RasGRP4.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/eec7cdde278cbc18b04f4193.png"},{"id":98440617,"identity":"41c10309-3cc0-4a3a-877c-17e56d679d13","added_by":"auto","created_at":"2025-12-17 17:04:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226874,"visible":true,"origin":"","legend":"\u003cp\u003eInteractions between RasGRP4 and its associated proteins\u003c/p\u003e\n\u003cp\u003eA: GeneMANIA analysis of the protein interaction network for RasGRP4. B: STRING analysis of the protein interaction network for RasGRP4.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/330a7f048f72778b16489fa7.png"},{"id":98441435,"identity":"fac8c74d-d983-4361-bd45-c297d5a5bc01","added_by":"auto","created_at":"2025-12-17 17:05:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":145361,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA Enriched Pathway Analysis\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/5edf74d8ad04c2f8ce1dc125.png"},{"id":98441289,"identity":"0e8e9591-6f79-4a2b-a905-a8e8e101e180","added_by":"auto","created_at":"2025-12-17 17:05:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":201809,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between high and low expression groups of RasGRP4 and immune cells\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/c733785a0dceaa551165eacc.png"},{"id":98440757,"identity":"bc7100be-892a-447a-89f4-fc317adc73f2","added_by":"auto","created_at":"2025-12-17 17:04:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":273047,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between RasGRP4 Expression and Immune Cells\u003c/p\u003e\n\u003cp\u003eA: Correlation between RasGRP4 expression and immune cell infiltration. B: Venn diagram.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/67b950b209b54032c3a11914.png"},{"id":98622745,"identity":"4bcc7924-cb95-415d-bd85-1e42ec12d774","added_by":"auto","created_at":"2025-12-19 17:01:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":831670,"visible":true,"origin":"","legend":"\u003cp\u003eIHC staining results showing RasGRP4 expression in LUAD and adjacent non-cancerous lung tissue\u003c/p\u003e\n\u003cp\u003eA: Expression of RasGRP4 inLUAD tissue. B: Expression of RasGRP4 in adjacent non-cancerous lung tissue. C-F: Expression of RasGRP4 in LUAD tissue at stages T1, T2, T3, and T4, respectively. Magnification levels for the above images are 30× and 200×.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/7b2f0239ab1a95715ac57470.png"},{"id":98441330,"identity":"87c524d0-692b-4242-86e9-016fe970be91","added_by":"auto","created_at":"2025-12-17 17:05:12","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":61461,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of RasGRP4 in LUAD Tissue and Its Relationship with Prognosis\u003c/p\u003e\n\u003cp\u003eA: Statistical results of IHC staining for RasGRP4 expression in LUAD and adjacent non-cancerous lung tissue. B: Kaplan-Meier survival curve.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/f89381e9972fcd852c9b798c.png"},{"id":98387632,"identity":"1921acd1-b675-44e6-bef4-be4325436d31","added_by":"auto","created_at":"2025-12-17 08:57:13","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":849384,"visible":true,"origin":"","legend":"\u003cp\u003eIHC staining results showing MCs infiltration in LUADand adjacent non-cancerous lung tissue\u003c/p\u003e\n\u003cp\u003eA: MCs infiltration in LUAD tissue. B: MCs infiltration in adjacent non-cancerous lung tissue. Magnification levels for the above images are 30× and 200×, respectively.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/7e205d27bf10e609d12fc4bf.png"},{"id":98441225,"identity":"ea0176b7-b20c-45aa-88fb-e8036e10cb9c","added_by":"auto","created_at":"2025-12-17 17:05:05","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":71409,"visible":true,"origin":"","legend":"\u003cp\u003eMast Cell Infiltration in LUAD Tissue and Its Relationship with Prognosis\u003c/p\u003e\n\u003cp\u003eA: Statistical results of IHC staining for MCs infiltration in LUAD and adjacent non-cancerous lung tissue. B: Kaplan-Meier survival curve.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/2d1175b81793594ea6490310.png"},{"id":98441426,"identity":"8c2e10de-788b-4864-b5d3-fb25716a90f1","added_by":"auto","created_at":"2025-12-17 17:05:22","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":83976,"visible":true,"origin":"","legend":"\u003cp\u003eMCs infiltration in LUAD tissue and RasGRP4 expression levels in conventional sections\u003c/p\u003e\n\u003cp\u003eA: IHC staining statistics for MCs infiltration in routine sections of LUAD and corresponding adjacent non-cancerous tissue (2cm away). B: IHC staining statistics for RasGRP4 expression in routine sections of LUAD and corresponding adjacent non-cancerous tissue. C: IHC staining statistics for MCs infiltration within and beyond 2cm of the tumor margin in routine sections of LUAD.\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/c9d9ef61e4d121ef221ffabe.png"},{"id":98387637,"identity":"da486fa8-6411-4c6a-83f2-8af63fcf796c","added_by":"auto","created_at":"2025-12-17 08:57:13","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":949868,"visible":true,"origin":"","legend":"\u003cp\u003eMCs infiltration in peritumoral tissue and IHC staining results for two markers labeling MCs\u003c/p\u003e\n\u003cp\u003eA: MCs infiltration within 2cm of LUAD. B: MCs infiltration in tissue beyond 2cm from the LUAD. Figure 14C: IHC staining of CD117-labeled MCs in LUAD. D: IHC staining of RasGRP4-labeled MCs in LUAD. All images are magnified at 400×.\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/1329443ce18656fc325b93b6.png"},{"id":98631308,"identity":"28838495-7e74-499f-a365-4e1c48e7fb22","added_by":"auto","created_at":"2025-12-19 17:19:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5624927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8318061/v1/e194f366-cf50-4cec-a3f2-ce5b991e4cb9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation analysis of RasGRP4 gene expression with mast cell infiltration and clinical prognosis of lung adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn China, lung cancer is the the leading cause of both incidence and mortality among malignant tumors, with LUAD being the most prevalent subtype.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. As the significance of immunotherapy in cancer treatment continues to increase, researchers are intensifying their exploration of the role of tumor-infiltrating immune cells (TIICs) within the tumor microenvironment (TME) and their potential value in immunotherapy strategies..\u003c/p\u003e \u003cp\u003eRasGRP4, a member of the RasGEF superfamily, functions as a cation- and diacylglycerol (DAG)-regulated nucleotide exchange factor. It activates Ras by binding to GDP and exchanging it for GTP. \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Additionally, RasGRP4 is highly expressed in MCs, where it plays a crucial role in their development and function.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Although RasGRP4 has been implicated in the pathogenesis of diffuse large B-cell lymphoma,\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e its association with lung adenocarcinoma (LUAD) and the underlying mechanisms remain to be elucidated.\u003c/p\u003e \u003cp\u003eMCs, as TIICs, are widely distributed throughout various tissues.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.They exert both pro-tumor and anti-tumor effects. Regarding the impact of MCs on the prognosis of patients with LUAD, several conflicting studies have been published. Ma et al.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e found that tumor-associated MCs indicate poor prognosis in human LUAD and advanced tumors. Research by Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e indicates that MCs with active KIT promote the formation, growth, and metastasis of Kras-mutant LUAD by providing interleukin-1, and are associated with the progression of LUAD. However, increased MCs abundance is associated with CCR2\u0026thinsp;+\u0026thinsp;cytotoxic T cell enrichment and favorable prognosis in LUAD.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary objective of this study is to elucidate the expression pattern of RasGRP4 in LUAD.Furthermore, this research explores the relationship between RasGRP4 expression and MCs infiltration in LUAD patients. By doing so, we aim to offer novel insights and therapeutic strategies for the diagnosis and treatment of LUAD, thereby advancing scientific development and practical application in the field of LUAD diagnosis and treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Public Bioinformatics Databases\u003c/h2\u003e \u003cp\u003eIn this study, we retrieved expression data and corresponding clinical information for 551 LUAD samples, can be deposited in The Cancer Genome Atlas (TCGA) comprising 497 LUAD tissue samples and 54 adjacent tissue samples. Samples underwent rigorous screening, excluding those with insufficient or missing data. Consequently, a total of 458 samples with complete clinical information were retained for subsequent clinical-pathological analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 LUAD Tissue Specimens\u003c/h2\u003e \u003cp\u003eTumor tissues from invasive LUAD resected at the Affiliated Hospital of Nantong University between January 2017 and December 2018 were collected, along with adjacent normal lung tissue samples obtained at least 2 cm from the tumor margins. A total of 345 LUAD patients were ultimately included in this study. Professional pathologists prepared routine tissue paraffin blocks and tissue microarrays from the specimens. Basic patient information was collected. Follow-up was conducted via telephone or electronic medical records. All patients were followed up until September 10, 2023, with a maximum follow-up duration of 80 months. This study protocol was approved by the Ethics Committee of the Affiliated Hospital of Nantong University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 TCGA Data Analysis\u003c/h2\u003e \u003cp\u003eThe gene transcriptomic data and corresponding clinical information for the 551 LUAD samples utilized in this study were retrieved from the TCGA public database. Subsequently, all data were processed using R software (version 4.1.1) and Perl (version 5.30.0.1). The \u0026ldquo;beeswarm\u0026rdquo; R package was employed to investigate RasGRP4 expression across various tumor types and within LUAD. The \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo; R packages were utilized to examine the association between RasGRP4 expression and patient prognosis as well as clinical-pathological characteristics. In this study, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 UALCAN Analysis\u003c/h2\u003e \u003cp\u003eThe UALCAN platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/index.html\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) offers extensive interactive bioinformatics studies utilizing RNA-seq and clinical data from 31 malignant tumors sourced from TCGA. This database facilitates comparisons of gene expression between tumor and healthy samples, across different tumor stages or subtypes, and in relation to other clinical and pathological characteristics.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In this study, UALCAN was utilized to analyze the positive and negative expression of RasGRP4-associated genes in LUAD tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 GeneMANIA and STRING Analysis\u003c/h2\u003e \u003cp\u003eThe GeneMANIA platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) integrates a variety of data sources encompassing gene-gene interactions, co-expression data, and pathway information. Meanwhile, the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a vital resource for collecting protein interaction information, enabling the prediction of RasGRP4's protein-protein interaction network.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In this study, we utilized GeneMANIA and STRING to identify genes and proteins associated with RasGRP4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gene Enrichment Analysis\u003c/h2\u003e \u003cp\u003eIn this study, GSEA (v3.0) was employed to conduct KEGG pathway enrichment analysis for RasGRP4. Within the TCGA cohort, a total of 248 samples were categorized into the high-risk group while 249 samples were assigned to the low-risk group. For the pathway enrichment analysis, results were deemed statistically significant if they met both criteria: a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 TIMER Analysis\u003c/h2\u003e \u003cp\u003eThe TIMER database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://compbio.cn/timer2/\u003c/span\u003e\u003cspan address=\"https://compbio.cn/timer2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a public resource that investigates the relationship between immune cell infiltration and gene expression in cancer samples based on TCGA.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In this study, the TIMER algorithm was employed to assess RasGRP4 expression across different cancer types. Additionally, a series of analyses were conducted to investigate the interplay between RasGRP4 expression and various immune cell infiltrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 CIBERSORT Analysis\u003c/h2\u003e \u003cp\u003eCIBERSORT is a computational method to assess the relative abundance of different cell types in complex mixed tissue samples. The entire algorithm is based on linear support vector regression (SVR), specifically the deconvolution inference of support vector machines (SVMs).\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In this study, the CIBERSORT algorithm was employed to analyze the infiltration levels of 22 immune cell types and further evaluated their correlation with RasGRP4 expression in LUAD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 IHC Procedure\u003c/h2\u003e \u003cp\u003eThe baked slides were dewaxed in xylene and graded ethanol. Antigen retrieval was then performed for 17 minutes. The primary antibodies: RasGRP4 (bs-19731R, 1:300, Beijing Bio-Ocean Biotech) and CD117 (ab32363, 1:400, Abcam), were added subsequently. The slides were incubated at room temperature for 3 hours. The secondary antibody, goat anti-rabbit, was then added and incubated at room temperature for 30 minutes. Finally, the slides were stained with hematoxylin.\u003c/p\u003e \u003cp\u003e(1) Interpretation of RasGRP4 IHC Results\u003c/p\u003e \u003cp\u003eStaining intensity was graded on a scale of 0 to 3, where 0 indicated negative staining, 1 indicated weak staining, 2 indicated moderate staining, and 3 indicated strong staining. Positive cell percentages were categorized into four groups: 1 for 0\u0026ndash;25%, 2 for 26\u0026ndash;50%, 3 for 51\u0026ndash;75%, and 4 for 76\u0026ndash;100%. The final score was calculated as the product of the staining intensity and the positive cell percentage. A final staining index of \u0026ge;\u0026thinsp;6 was defined as high RasGRP4 expression, while a score of \u0026lt;\u0026thinsp;6 was defined as low expression. These sections were scored by two certified pathologists.\u003c/p\u003e \u003cp\u003e(2) Interpretation of IHC Findings for MCs\u003c/p\u003e \u003cp\u003eCD117 (c-kit) is utilized to label MCs, with CD117 localization in the MCs cytoplasm and cell membrane. MCs counting follows the Molin method: randomly observe 10 cancer tissue fields under high magnification, count the number of MCs in each field, and calculate the mean as the MCs count (MCC) for that case.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The cutoff value was established at the median MCC of infiltrated cases in LUAD (1.3), dividing patients into high-MCs-infiltration and low-MCs-infiltration groups. MCs IHC results were also assessed using a double-blind method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Results Analysis\u003c/h2\u003e \u003cp\u003eData obtained from TCGA were processed and analyzed using R software (version 4.1.1) and Perl (version 5.30.0.1). The IHC results were statistically analyzed using SPSS Statistics (version 27.0). Graphs were subsequently generated using GraphPad Prism9. Paired t-tests were applied for paired variable data analysis. Survival analysis employed the Kaplan-Meier method and log-rank test. Chi-square tests assessed correlations between RasGRP4 expression and MCs infiltration with clinical pathological parameters. Pearson's correlation coefficient was calculated to determine the associations between RasGRP4 expression and MCs infiltration. In general, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.1 Investigating the Correlation Between RasGRP4 and LUAD Using Bioinformatics Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.1.1 Expression of RasGRP4 in LUAD and Other Cancers\u003c/h2\u003e\n \u003cp\u003eThis study comprehensively evaluated RasGRP4 expression across various cancer types using the TIMER algorithm. The results revealed significant underexpression of RasGRP4 in 12 cancer types, including LUAD, transitional cell carcinoma of the bladder, and colorectal cancer (Fig.\u0026nbsp;1A). Additionally, the study analyzed the mRNA expression differences of RasGRP4 between LUAD tissues and adjacent non-cancerous lung tissues in the TCGA database. Figure\u0026nbsp;1B and 1C visually present RasGRP4 expression data in non-tumorous lung tissue versus tumor tissue using scatter plots and paired scatter plots, respectively. The results demonstrate that RasGRP4 expression levels in tumor tissue are significantly lower than in the corresponding non-tumorous lung tissue.\u003c/p\u003e\n \u003cp\u003eFigure 1 TCGA Analysis of RasGRP4 Expression in LUAD and Other Cancers\u003c/p\u003e\n \u003cp\u003eFigure 1A: Expression of RasGRP4 in various types of cancer. Figure\u0026nbsp;1B: Expression of RasGRP4 in non-cancerous lung tissue and LUAD tissue. Figure\u0026nbsp;1C: Paired expression of RasGRP4 in non-cancerous lung tissue and LUAD tissue.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.1.2 Impact of RasGRP4 on Survival Outcomes in Patients with LUAD\u003c/h2\u003e\n \u003cp\u003eGiven the significant differences in RasGRP4 expression between LUAD tissues and adjacent normal lung tissues, this study further investigated the correlation between RasGRP4 expression and overall survival (OS). The cut-off value was set at the median level of RasGRP4 expression, thereby dividing all LUAD patients into high-expression and low-expression groups. The results indicate that RasGRP4 expression levels significantly impact survival in LUAD patients. Compared to patients with high RasGRP4 expression, those with low expression exhibited poorer prognosis survival, with this difference being statistically significant (P\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eFigure 2 Survival Curves for Differential Expression of RasGRP4 in LUAD\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.1.3 Correlation between RasGRP4 Expression and Clinical-Pathological Parameters in LUAD\u003c/h2\u003e\n \u003cp\u003eBased on the results in Fig.\u0026nbsp;3A, this study observed a significant association between RasGRP4 expression and certain T stages. Specifically, statistically significant differences in RasGRP4 expression levels were observed between T1 and T2, as well as between T1 and T3 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that LUAD patients with lower RasGRP4 expression may be more prone to developing tumors at more advanced T stages. This implies that RasGRP4 expression may be associated with the aggressiveness of LUAD, with reduced expression potentially correlating with disease progression and worsening.\u003c/p\u003e\n \u003cp\u003eTo explore the relationship between RasGRP4 expression and clinical-pathological factors in LUAD patients, this study conducted Cox proportional hazards model analysis on data downloaded from the TCGA database. This analysis provided further insights into the association between RasGRP4 expression and other clinical-pathological parameters, as well as patient survival outcomes. As shown in Fig.\u0026nbsp;3B, the results of univariate Cox regression analysis indicated that stage (HR\u0026thinsp;=\u0026thinsp;1.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T stage (HR\u0026thinsp;=\u0026thinsp;1.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), N stage (HR\u0026thinsp;=\u0026thinsp;1.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and RasGRP4 expression (HR\u0026thinsp;=\u0026thinsp;0.54, P\u0026thinsp;=\u0026thinsp;0.012) were significant prognostic risk factors for LUAD patients. Further, the results of multivariate Cox regression analysis, depicted in Fig.\u0026nbsp;3C, revealed that stage (HR\u0026thinsp;=\u0026thinsp;1.85, P\u0026thinsp;=\u0026thinsp;0.008) and RasGRP4 expression (HR\u0026thinsp;=\u0026thinsp;0.52, P\u0026thinsp;=\u0026thinsp;0.013) serve as independent prognostic risk factors for LUAD patients. Collectively, these findings indicate that RasGRP4 expression is significantly associated with prognosis in LUAD patients, thereby supporting the potential of RasGRP4 as a therapeutic target for LUAD.\u003c/p\u003e\n \u003cp\u003eFigure 3 Relationship between RasGRP4 Expression in LUAD Tissue and Clinical Pathological Parameters and Survival Prognosis\u003c/p\u003e\n \u003cp\u003eFigure 3A: Relationship between RasGRP4 expression and T stage. Figure\u0026nbsp;3B: Univariate Cox regression analysis. Figure\u0026nbsp;3C: Multivariate Cox regression analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e3.1.4 Co-expressed Genes Associated with RasGRP4 in LUAD\u003c/h2\u003e\n \u003cp\u003eThis study investigates the molecular mechanisms by which RasGRP4 induces LUAD development through analyzing its co-expressed genes and associated proteins in LUAD. The UALCAN database was utilized to explore genes positively and negatively correlated with RasGRP4 in LUAD tissues, as shown in Figs.\u0026nbsp;4A and 4B, aiding in elucidating the potential role of RasGRP4 in LUAD. Additionally, protein interaction networks were constructed using GeneMANIA and STRING to evaluate potential interactors of differentially expressed RasGRP4. The results of this analysis are presented in Figs.\u0026nbsp;5A and 5B. This analysis helps reveal the position of RasGRP4 within the protein network and how it interacts with other proteins to influence biological processes in LUAD.\u003c/p\u003e\n \u003cp\u003eFigure 4 UALCAN Database Analysis of Genes Associated with RasGRP4 in LUAD Tissue\u003c/p\u003e\n \u003cp\u003eFigure 4A: Genes positively correlated with RasGRP4. Figure\u0026nbsp;4B: Genes negatively correlated with RasGRP4.\u003c/p\u003e\n \u003cp\u003eFigure 5 Interactions between RasGRP4 and its associated proteins\u003c/p\u003e\n \u003cp\u003eFigure 5A: GeneMANIA analysis of the protein interaction network for RasGRP4. Figure\u0026nbsp;5B: STRING analysis of the protein interaction network for RasGRP4.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e3.1.5 RasGRP4-Associated Enrichment Analysis in LUAD\u003c/h2\u003e\n \u003cp\u003eThis study utilized GSEA software to conduct KEGG pathway enrichment analysis, aiming to elucidate the biological patterns and functions associated with RasGRP4 expression in the progression of LUAD. As depicted in Fig.\u0026nbsp;6, the results indicate that high RasGRP4 expression may be positively correlated with pathways such as acute myeloid leukemia, apoptosis, B-cell receptor signaling pathways, chemokine receptor pathways, and mitogen-activated protein kinase (MAPK) signaling pathways. Conversely, it appears to be negatively correlated with pathways including basic transcription factors, cell cycle, RNA degradation, RNA polymerase, and spliceosome.\u003c/p\u003e\n \u003cp\u003eFigure 6 GSEA Enriched Pathway Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e3.1.6 Relationship Between RasGRP4 Expression and TIICs in LUAD\u003c/h2\u003e\n \u003cp\u003eThe characteristics of TIICs are significantly correlated with tumor initiation and progression.\u003csup\u003e[16]\u003c/sup\u003e. Therefore, to investigate whether RasGRP4 expression correlates with TIICs in LUAD tissues, this study divided 497 samples into 248 high-expression samples and 249 low-expression samples based on RasGRP4 expression. The \u0026ldquo;CIBERSORT\u0026rdquo; R software package was used to observe differences in the proportions of 22 immune cell types between the high and low-expression groups. Figure\u0026nbsp;7 shows the results for the 22 immune cell subpopulations, revealing that the differential expression of RasGRP4 was significantly associated with the following 10 types of immune cells: Immature B cells, plasma cells, CD8\u0026thinsp;+\u0026thinsp;T cells, resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, follicular T helper cells, monocytes, M2 macrophages, resting dendritic cells, resting mast cells, and neutrophils (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eFigure 7 Correlation between high and low expression groups of RasGRP4 and immune cells\u003c/p\u003e\n \u003cp\u003eThis study further explored the relationship between immune cell infiltration levels and RasGRP4 expression in LUAD tissues using the \u0026ldquo;limma\u0026rdquo; R package. As shown in Fig.\u0026nbsp;8A, RasGRP4 expression levels correlated with resting dendritic cells (r\u0026thinsp;=\u0026thinsp;0.35, P\u0026thinsp;=\u0026thinsp;1.7E\u0026thinsp;\u0026minus;\u0026thinsp;14), M2 macrophages (r\u0026thinsp;=\u0026thinsp;0.29, P\u0026thinsp;=\u0026thinsp;2.3E\u0026thinsp;\u0026minus;\u0026thinsp;10), and resting mast cells (r\u0026thinsp;=\u0026thinsp;0.3, P\u0026thinsp;=\u0026thinsp;5.6E\u0026thinsp;\u0026minus;\u0026thinsp;11), monocytes (r\u0026thinsp;=\u0026thinsp;0.36, P\u0026thinsp;=\u0026thinsp;1.9E\u0026thinsp;\u0026minus;\u0026thinsp;15), neutrophils (r\u0026thinsp;=\u0026thinsp;0.24, P\u0026thinsp;=\u0026thinsp;1.2E\u0026thinsp;\u0026minus;\u0026thinsp;07), and resting memory CD4\u0026thinsp;+\u0026thinsp;T cells (r\u0026thinsp;=\u0026thinsp;0.18, P\u0026thinsp;=\u0026thinsp;0.00017). In contrast, it negatively correlated with the infiltration levels of immature B cells (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.28, P\u0026thinsp;=\u0026thinsp;9.4E\u0026thinsp;\u0026minus;\u0026thinsp;10), plasma cells (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.29, P\u0026thinsp;=\u0026thinsp;1.4E\u0026thinsp;\u0026minus;\u0026thinsp;10), activated memory CD4\u0026thinsp;+\u0026thinsp;T cells (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.11, P\u0026thinsp;=\u0026thinsp;0.023), and follicular T helper cells (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.24, P\u0026thinsp;=\u0026thinsp;2.2E\u0026thinsp;\u0026minus;\u0026thinsp;07).\u003c/p\u003e\n \u003cp\u003eIn bioinformatics, Venn diagrams enable researchers to rapidly visualize information across experiments, conditions, and groups to explore relationships between datasets. \u003csup\u003e[17]\u003c/sup\u003e. As shown in Fig.\u0026nbsp;8B, plotting the differentially expressed immune cell results and immune cell correlation analysis results into a Venn diagram ultimately identified 10 immune cell types associated with RasGRP4. Notably, MCs infiltration exhibits a significant positive correlation with RasGRP4 expression.\u003c/p\u003e\n \u003cp\u003eFigure 8 Correlation between RasGRP4 Expression and Immune Cells\u003c/p\u003e\n \u003cp\u003eFigure 8A: Correlation between RasGRP4 expression and immune cell infiltration. Figure\u0026nbsp;8B: Venn diagram.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003e3.2 Internal Experimental Investigation of the Correlation Between RasGRP4 and MCs in LUAD\u003c/h2\u003e\n \u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003e3.2.1 Expression of RasGRP4 in LUAD Tissue and Adjacent Normal Lung Tissue\u003c/h2\u003e\n \u003cp\u003eTo further validate RasGRP4 expression in LUAD, IHC was performed on samples from 345 LUAD patients. The results demonstrated that the intensity of RasGRP4 staining was significantly lower in LUAD tissue compared to adjacent normal tissue (Fig.\u0026nbsp;9A,B), a finding that was statistically significant (Fig.\u0026nbsp;10A). Additionally, Fig.\u0026nbsp;9C-F illustrates RasGRP4 expression across different clinical pathological stages. These results demonstrate that RasGRP4 expression progressively decreases with increasing clinical pathological stage, reflecting heightened tumor invasiveness and malignancy. This suggests that RasGRP4 expression levels may correlate with the invasiveness and progression of LUAD.\u003c/p\u003e\n \u003cp\u003eFigure 9 IHC staining results showing RasGRP4 expression in LUAD and adjacent non-cancerous lung tissue\u003c/p\u003e\n \u003cp\u003eFigure 9A: Expression of RasGRP4 inLUAD tissue. Figure\u0026nbsp;9B: Expression of RasGRP4 in adjacent non-cancerous lung tissue. Figures\u0026nbsp;9C-F: Expression of RasGRP4 in LUAD tissue at stages T1, T2, T3, and T4, respectively. Magnification levels for the above images are 30\u0026times; and 200\u0026times;.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003e3.2.2 Correlation between RasGRP4 Expression and Prognosis as well as Clinical-Pathological Parameters in Patients with LUAD\u003c/h2\u003e\n \u003cp\u003eBased on IHC at the protein level, we investigated the relationship between RasGRP4 expression levels and prognosis as well as clinical-pathological parameters in LUAD patients. Patients with IHC scores\u0026thinsp;\u0026ge;\u0026thinsp;6 were classified into the RasGRP4 high-expression group, comprising 163 samples; those with scores\u0026thinsp;\u0026lt;\u0026thinsp;6 were assigned to the RasGRP4 low-expression group, comprising 182 samples. Kaplan-Meier curves were plotted for patients with high or low RasGRP4 expression. Figure\u0026nbsp;10B demonstrated shorter survival times in the low-expression group (P\u0026thinsp;=\u0026thinsp;0.0261), consistent with previous bioinformatics analysis results.\u003c/p\u003e\n \u003cp\u003eThis study further explored the correlation between RasGRP4 expression and clinical-pathological parameters. Table 3\u0026thinsp;\u0026minus;\u0026thinsp;1 summarizes the clinical-pathological characteristics of the internal cohort of 345 cases. Results indicate that RasGRP4 expression was significantly associated with tumor size (P\u0026thinsp;=\u0026thinsp;0.028) and closely correlated with tumor TNM staging (P\u0026thinsp;=\u0026thinsp;0.017). and tumor TNM staging (P\u0026thinsp;=\u0026thinsp;0.017). No significant correlations were observed between RasGRP4 expression and other clinical-pathological features including age, gender, smoking status, tumor differentiation grade, lymph node metastasis, or T stage.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3-1 Correlation between RasGRP4 Expression and Clinical-Pathological Parameters in LUAD Patients\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClinical Pathological Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExample count\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRasGRP4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow-expression group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh-expression group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle-High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable 3-2 Correlation between MCs Infiltration and Clinical-Pathological Parameters in Patients with LUAD\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eClinical Pathological Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eExample count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow-expression group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-expression group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.026\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor size（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.030\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiddle-High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNM Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI+II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIII+IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.045\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI+II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIII+IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3-3 Relationship Between RasGRP4 Expression and MCs Infiltration\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExample count\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow-infiltration group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh-infiltration group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.123\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-expression group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-expression group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure 10 Expression of RasGRP4 in LUAD Tissue and Its Relationship with Prognosis\u003c/p\u003e\n \u003cp\u003eFigure 10A: Statistical results of IHC staining for RasGRP4 expression in LUAD and adjacent non-cancerous lung tissue. Figure\u0026nbsp;10B: Kaplan-Meier survival curve.\u003c/p\u003e\n \u003cp\u003eTable 3\u0026thinsp;\u0026minus;\u0026thinsp;1 Correlation between RasGRP4 Expression and Clinical-Pathological Parameters in LUAD Patients\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003e3.2.3 MCs Infiltration in LUAD Tissue and Adjacent Normal Lung Tissue\u003c/h2\u003e\n \u003cp\u003eTo elucidate the differences in MCs infiltration between LUAD tissue and adjacent normal lung tissue (at least 2 cm from tumor tissue), IHC analysis using the MCs marker CD117 (c-kit) was performed on 345 LUAD patient samples. The results demonstrated significantly lower MCC in LUAD tissue compared to adjacent normal tissue (Fig.\u0026nbsp;11A,B), with statistically significant differences (Fig.\u0026nbsp;12A).\u003c/p\u003e\n \u003cp\u003eFigure 11 IHC staining results showing MCs infiltration in LUADand adjacent non-cancerous lung tissue\u003c/p\u003e\n \u003cp\u003eFigure 11A: MCs infiltration in LUAD tissue. Figure\u0026nbsp;11B: MCs infiltration in adjacent non-cancerous lung tissue. Magnification levels for the above images are 30\u0026times; and 200\u0026times;, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003e3.2.4 Correlation Between MCs Infiltration and Prognosis as Well as Clinical-Pathological Parameters in Patients with LUAD\u003c/h2\u003e\n \u003cp\u003eBased on IHC analysis at the protein level, this study investigated the relationship between MCs infiltration levels and prognosis as well as clinical-pathological parameters in patients with LUAD. Patients were divided into a high MCs infiltration group (168 samples) and a low MCs infiltration group (177 samples) based on the median MCC (1.3) observed in LUAD cases. Kaplan-Meier survival curves were plotted for patients with high or low MCs infiltration. As shown in Fig.\u0026nbsp;12B, there was no significant correlation between MCs infiltration and prognosis in LUAD patients (P\u0026thinsp;=\u0026thinsp;0.0584).\u003c/p\u003e\n \u003cp\u003eThis study further explored the correlation between MCs infiltration and clinical-pathological parameters. Table 3\u0026thinsp;\u0026minus;\u0026thinsp;2 summarizes the clinical-pathological parameters of the internal cohort of 345 cases. Results indicate that MCs infiltration was significantly associated with gender (P\u0026thinsp;=\u0026thinsp;0.026), tumor size (P\u0026thinsp;=\u0026thinsp;0.003), tumor differentiation (P\u0026thinsp;=\u0026thinsp;0.030), and T stage (P\u0026thinsp;=\u0026thinsp;0.045). No significant correlations were observed between MCs infiltration and other clinical-pathological features, such as age, tumor TNM stage, smoking status, or lymph node metastasis.\u003c/p\u003e\n \u003cp\u003eFigure 12 Mast Cell Infiltration in LUAD Tissue and Its Relationship with Prognosis\u003c/p\u003e\n \u003cp\u003eFigure 12A: Statistical results of IHC staining for MCs infiltration in LUAD and adjacent non-cancerous lung tissue. Figure\u0026nbsp;12B: Kaplan-Meier survival curve.\u003c/p\u003e\n \u003cp\u003eTable 3\u0026thinsp;\u0026minus;\u0026thinsp;2 Correlation between MCs Infiltration and Clinical-Pathological Parameters in Patients with LUAD\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\"\u003e\n \u003ch2\u003e3.2.5 Relationship Between RasGRP4 Expression and MCs Infiltration in Patients with LUAD\u003c/h2\u003e\n \u003cp\u003eThis study revealed through IHC analysis that RasGRP4 exhibits low expression in LUAD tissues, while MCs demonstrate low infiltration levels in these tissues. Further analysis using the chi-square test confirmed a positive correlation between RasGRP4 expression levels and MCs infiltration. Tumors in the low RasGRP4 expression group exhibited significantly fewer MCs (P\u0026thinsp;=\u0026thinsp;0.022, r\u0026thinsp;=\u0026thinsp;0.123) (P = 0.022, r = 0.123) (Table 3-3).\u003c/p\u003e\n \u003cp\u003eTable 3-3 Relationship Between RasGRP4 Expression and MCs Infiltration\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\"\u003e\n \u003ch2\u003e3.2.6 Validation of RasGRP4 Expression Levels and MCs Infiltration in LUAD Patients\u003c/h2\u003e\n \u003cp\u003eThis study selected 24 cases from 345 LUAD specimens. Paraffin blocks of LUAD and corresponding normal lung tissue were collected from these 24 patients, prepared into routine IHC sections, and used to further validated RasGRP4 expression and MCs infiltration in LUAD patients. As shown in Fig.\u0026nbsp;13A, the MCC in LUAD tissue was significantly lower than in adjacent non-cancerous tissue, with statistical significance. Figure\u0026nbsp;13B demonstrates that the intensity of RasGRP4 positive staining in LUAD tissue was significantly lower than in adjacent non-cancerous tissue, with statistical significance, consistent with previous IHC microarray results.\u003c/p\u003e\n \u003cp\u003eAdditionally, this study revealed a significant difference in MCs numbers between areas\u0026thinsp;\u0026gt;\u0026thinsp;2 cm and \u0026le;\u0026thinsp;2 cm from the tumor margin in both LUAD and adjacent non-tumor tissues, with higher infiltration levels observed in the \u0026gt;\u0026thinsp;2 cm zone (Figs.\u0026nbsp;14A and B). This difference was statistically significant, as shown in Fig.\u0026nbsp;13C.\u003c/p\u003e\n \u003cp\u003eBoth RasGRP4 and CD117 (c-kit) were highly expressed in MCs. However, this study revealed that in LUAD tissues, RasGRP4 marked MCs at a significantly lower level than CD117 (Fig.\u0026nbsp;14C, D).\u003c/p\u003e\n \u003cp\u003eFigure 13 MCs infiltration in LUAD tissue and RasGRP4 expression levels in conventional sections\u003c/p\u003e\n \u003cp\u003eFigure 13A: IHC staining statistics for MCs infiltration in routine sections of LUAD and corresponding adjacent non-cancerous tissue (2cm away). Figure\u0026nbsp;13B: IHC staining statistics for RasGRP4 expression in routine sections of LUAD and corresponding adjacent non-cancerous tissue. Figure\u0026nbsp;13C: IHC staining statistics for MCs infiltration within and beyond 2cm of the tumor margin in routine sections of LUAD.\u003c/p\u003e\n \u003cp\u003eFigure 14 MCs infiltration in peritumoral tissue and IHC staining results for two markers labeling MCs\u003c/p\u003e\n \u003cp\u003eFigure 14A: MCs infiltration within 2cm of LUAD. Figure\u0026nbsp;14B: MCs infiltration in tissue beyond 2cm from the LUAD. Figure\u0026nbsp;14C: IHC staining of CD117-labeled MCs in LUAD. Figure\u0026nbsp;14D: IHC staining of RasGRP4-labeled MCs in LUAD. All images are magnified at 400\u0026times;.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnlike other members of the RasGRP family, RasGRP4 is highly expressed in MCs and other myeloid cells, potentially playing a crucial role in MCs development and function. However, the specific role of RasGRP4 in MCs remains to be definitively established. The pathogenicity of RasGRP4 has been well-established in various diseases, including leukemia, rheumatoid arthritis, inflammatory bowel disease, and lymphoma.\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Patients with diffuse large B-cell lymphoma exhibit significantly elevated RasGRP4 levels. Downregulating RasGRP4 in tumor cells inhibits proliferation by reducing ERK expression and increasing JNK expression, thereby decreasing mitotic activity, promoting apoptosis, and elevating oxidative stress levels.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This provides a research direction for investigating the relationship between RasGRP4 expression and tumors. The function and mechanism of action of RasGRP4 in LUAD require further exploration and investigation by researchers.\u003c/p\u003e \u003cp\u003eMCs are important immune cells in the TME that influence tumor progression by regulating this microenvironment. Once infiltrating solid tumors, they are termed tumor-associated mast cells (TAMCs). These cells are recognized as distinct participants and coordinators of both pro-tumor and anti-tumor responses, representing one of the most controversial immune cell types in cancer. On one hand, they can promote various processes leading to tumor progression, such as angiogenesis, lymphangiogenesis, fibrosis, and metastasis. On the other hand, TAMCs can release mediators that induce the recruitment of other immune cells to the tumor, which can perform either pro- or anti-tumor functions.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The impact of MCs on the prognosis of LUAD patients has also been extensively discussed. Studies indicate that lung cancer-derived exosomes mediate MCs activation, suggesting that exosomes may exert potential effects on cancer-associated coagulation dysfunction by altering the cytokine profile released by MCs.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Additionally, tryptophanase released by mast cells promotes tumor cell metastasis via exosomes.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. However, another study indicates that high MCs abundance is associated with prolonged survival in patients with early-stage LUAD.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Increased mast cell abundance correlates with the enrichment of CCR2\u0026thinsp;+\u0026thinsp;cytotoxic T cells and favorable prognosis in LUAD.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, the results of this study reveal that RasGRP4 is downregulated in LUAD tissues and is closely associated with survival time in patients with LUAD. Therefore, RasGRP4 can be considered an independent prognostic factor for LUAD. This study also found that MCs show low infiltration in LUAD tissues and are significantly correlated with the downregulation of RasGRP4, suggesting that both RasGRP4 and MCs play crucial roles in the development and progression of LUAD. However, this study has several limitations. The expression of RasGRP4 in LUAD requires further validation through additional basic experiments. Furthermore, the specific mechanism linking RasGRP4 expression to MCs infiltration in LUAD remains unclear. Additionally, whether differences exist between RasGRP4 expression and MCs subtypes or MCs states requires further research analysis.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by grants from Basic Science Research Project in Nantong City, Jiangsu, China (JC2021029). Project Title: Investigation of the Molecular Mechanisms by Which the DDX46/FTO/BCL-2/Beclin1 Signaling Axis Regulates Autophagy and Proliferation in Lung Adenocarcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study, giving their authorization to access their clinical information and tumor samples for research purpose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Luo: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.\u003c/p\u003e\n\u003cp\u003eJianguo Zhang; Shu Zhang: Contributed reagents, materials, analysis tools or data.\u003c/p\u003e\n\u003cp\u003eQing Zhang; Yifei Liu: Conceived and designed the experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the [https://portal.gdc.cancer.gov/]、[https://ualcan.path.uab.edu/index.html]、[https://genemania.org/]、[https://cn.string-db.org/]、[https://compbio.cn/timer2/]\u003c/p\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Ethics committee of Affiliated Hospital of Nantong University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCao, M., Li, H., Sun, D. \u0026amp; Chen, W. 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Ras guanine nucleotide-releasing protein-4 (RasGRP4) involvement in experimental arthritis and colitis. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e287\u003c/b\u003e (24), 20047\u0026ndash;20055 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenoit, A. et al. Mutated RAS-associating proteins and ERK activation in relapse/refractory diffuse large B cell lymphoma. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 779 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLigan, C., Ma, X. H., Zhao, S. L. \u0026amp; Zhao, W. The regulatory role and mechanism of mast cells in tumor microenvironment. \u003cem\u003eAm. J. cancer Res.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 1\u0026ndash;15 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen, S., Huang, X., Shi, Y., Xu, Z. \u0026amp; Xiao, H. Change in cytokine profiles released by mast cells mediated by lung cancer-derived exosome activation may contribute to cancer-associated coagulation disorders. \u003cem\u003eCell. communication signaling: CCS\u003c/em\u003e. \u003cb\u003e21\u003c/b\u003e (1), 97 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao, H. et al. The release of tryptase from mast cells promote tumor cell metastasis via exosomes. \u003cem\u003eBMC cancer\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e (1), 1015 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao, X., Shi, R., Zhao, T. \u0026amp; Wang, Y. Mast cell-based molecular subtypes and signature associated with clinical outcome in early-stage lung adenocarcinoma. \u003cem\u003eMol. Oncol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (5), 917\u0026ndash;932 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"RasGRP4, Mast cell, Lung adenocarcinoma, Prognosis, Immune cell infiltration","lastPublishedDoi":"10.21203/rs.3.rs-8318061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8318061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo explore the correlation between Ras guanyl nucleotide releasing protein 4 (RasGRP4) expression in lung adenocarcinoma (LUAD) and mast cells (MCs) infiltration, as well as its impact on clinical prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003e\u0026nbsp;1. The Cancer Genome Atlas (TCGA) database was utilized to analyze RasGRP4 expression in LUAD tissues and to assess its potential association with survival prognosis and clinicopathological features of patients. 2. The correlation between RasGRP4 and 22 tumor infiltrating immune cells (TIICs) was evaluated using the \"CIBERSORT\" R package. 3. Following a preliminary analysis of an online LUAD database, immunohistochemistry (IHC) experiments were conducted to validate the correlation between RasGRP4 expression and MCs infiltration in clinical samples, as well as its association with patient survival prognosis and clinicopathological parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003e1. In the pooled TCGA database, RasGRP4 exhibited significantly low expression in various cancers, including LUAD. Patients with high RasGRP4 expression had a better survival prognosis. Multifactorial COX regression analysis indicated that tumor grade and RasGRP4 expression could serve as independent risk factors for LUAD prognosis. 2. A clear correlation was found between RasGRP4 expression and 10 types of immune cells. 3. IHC results confirmed the low expression of RasGRP4 in LUAD tissues, suggesting that its low expression may be associated with poor LUAD progression. 4. IHC results also demonstrated low MCs infiltration in LUAD tissues, which was significantly correlated with gender, tumor differentiation, tumor size, and T stage. 5. In LUAD, low RasGRP4 expression was positively correlated with low infiltration of MCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003e1. RasGRP4 is downregulated in LUAD. 2. RasGRP4 may serve as an independent prognostic biomarker in LUAD patients.. 3. MCs show low infiltration in LUAD. 4. Low RasGRP4 expression in LUAD is positively correlated with low infiltration of MCs.\u003c/p\u003e","manuscriptTitle":"Correlation analysis of RasGRP4 gene expression with mast cell infiltration and clinical prognosis of lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 08:57:07","doi":"10.21203/rs.3.rs-8318061/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-16T16:40:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-16T15:18:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-15T14:11:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d8c57d4-fa92-4906-a7b5-8f684abebd8a","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59766505,"name":"Health sciences/Biomarkers"},{"id":59766506,"name":"Biological sciences/Cancer"},{"id":59766507,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59766508,"name":"Biological sciences/Immunology"},{"id":59766509,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-18T16:38:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 08:57:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8318061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8318061","identity":"rs-8318061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}