Aberrant FGF19 Expression Is Associated with Prognosis and Radiosensitivity in Gastric Cancer

Aberrant FGF19 Expression Is Associated with Prognosis and Radiosensitivity in Gastric Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Aberrant FGF19 Expression Is Associated with Prognosis and Radiosensitivity in Gastric Cancer Zhong Fan, Mengyu Liu, Haowei Wu, Hong Zhu, Yang Ding, Junjie Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8383155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective. To evaluate fibroblast growth factor 19 (FGF19) expression in gastric cancer and its association with clinicopathological features, prognosis, and radiosensitivity. Methods. FGF19 expression patterns and prognostic significance were analyzed using the TCGA-STAD dataset. Immunohistochemistry was performed on tumor tissues and matched adjacent tissues obtained from 115 surgical patients and 48 patients undergoing radiotherapy at the Second Affiliated Hospital of Anhui Medical University between 2021 and 2025. Paired chi-square tests, regression analyses, Kaplan–Meier survival estimates, and logistic regression models were used to assess clinical correlations and radiosensitivity. Results. FGF19 expression was significantly higher in gastric cancer tissues than in adjacent tissues in both the TCGA cohort and the clinical cohort (p < 0.001). High FGF19 expression was associated with shorter recurrence-free survival but not overall survival. Subgroup analyses revealed poorer recurrence-free survival among female and Caucasian patients with high FGF19 levels. Immunohistochemical staining confirmed marked overexpression in tumor tissues (p < 0.001). Elevated FGF19 expression was also associated with reduced radiosensitivity (p = 0.013; OR = 7.014). Conclusion. FGF19 is upregulated in gastric cancer and is associated with increased recurrence risk and diminished radiosensitivity. These findings suggest that FGF19 may serve as a useful biomarker for predicting recurrence and radiotherapy response. gastric cancer FGF19 radiotherapy sensitivity prognosis biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Gastric cancer (GC) is a common digestive tract malignancy in China and remains a major public health concern due to its high incidence and mortality. In 2022, approximately 358,700 new GC cases and 260,400 GC-related deaths were reported nationwide, placing its mortality rate third among all malignant tumors[ 1 ]. Despite advances in surgery, chemotherapy, and radiotherapy, the 5-year survival rate for patients with advanced stomach cancer (STAD) remains below 30 percent[ 2 ], substantially affecting survival and quality of life. For patients with positive surgical margins or locally advanced disease who are not candidates for surgery, radiotherapy is an important therapeutic option. However, considerable variability in radiotherapy response is observed in clinical practice: some patients achieve substantial clinical benefit, whereas others develop radioresistance, resulting in progression or recurrence. This interindividual heterogeneity significantly limits further improvements in radiotherapy outcomes. At present, no reliable molecular biomarkers are available to predict radiosensitivity. This lack of predictive tools not only impedes the implementation of precision radiotherapy but also increases the risk of ineffective or excessive treatment. Therefore, identifying and validating novel biomarkers capable of predicting radiosensitivity is of great clinical significance for guiding individualized treatment, optimizing radiotherapy strategies, and improving patient prognosis. In recent years, targeted therapy and immunotherapy have emerged as important therapeutic strategies for gastric cancer, with well-established targets such as HER2, PD-L1, and Claudin 18.2[ 3 ]. Nevertheless, compared with these therapeutic advances, molecular studies on radiosensitivity remain relatively underdeveloped, and no validated predictive model is currently available. Previous studies have shown that DNA damage repair capacity, apoptotic signaling, and oxidative stress responses are closely associated with radiosensitivity. These observations suggest that radioresponse may be driven by dysregulation of specific molecular pathways. Fibroblast growth factor 19 (FGF19) is a secreted endocrine protein and the specific ligand of fibroblast growth factor receptor 4 (FGFR4). Aberrant overexpression of FGF19 has been reported in multiple malignancies. After synthesis, FGF19 is secreted extracellularly, leading to its presence in both intra- and extracellular compartments. This dual distribution enables FGF19 to influence the tumor microenvironment through paracrine or autocrine signaling while also contributing to intracellular signaling and metabolic regulation. Studies have demonstrated that FGF19 promotes cell proliferation, invasion, and metastasis in hepatocellular carcinoma, colorectal cancer, breast cancer, and lung cancer, and its overexpression is consistently associated with poor prognosis[ 4 – 6 ]. Mechanistically, the FGF19–FGFR4 axis activates signaling pathways such as MAPK and PI3K/AKT, regulating tumor cell proliferation and resistance to apoptosis[ 7 ]. These pathways have also been implicated in radioresistance in other cancer types[ 8 ], suggesting that aberrant FGF19 overexpression may contribute to reduced radiosensitivity in gastric cancer. Previous research has mainly focused on the roles of FGF19 in tumorigenesis and drug resistance, underscoring its biological importance across a variety of malignancies. However, whether FGF19 can serve as a biomarker for predicting radiotherapy response in gastric cancer remains largely unexplored, and both clinical evidence and underlying mechanisms are insufficiently defined. Based on this knowledge gap, the present study integrates bioinformatics analysis of The Cancer Genome Atlas (TCGA) with immunohistochemical data from gastric cancer patients treated at our center to comprehensively evaluate FGF19 expression and its association with clinicopathological features, prognosis, and radiosensitivity. Through combined bioinformatic and clinical validation, this study provides new evidence supporting FGF19 as a potential biomarker for predicting prognosis and radiotherapy response in gastric cancer and explores its possible molecular mechanisms. 2. Materials and Methods 2.1 Bioinformatic Analysis 2.1.1 Expression of FGF19 in Tumor and Adjacent Normal Tissues of GC Transcriptomic and clinical data from 33 cancer types were obtained from The Cancer Genome Atlas (TCGA). These data were automatically transformed using log₂(TPM + 1) by the Tumor Immune Estimation Resource (TIMER) platform to minimize technical variability and satisfy the assumptions of normal distribution[ 9 ]. Using the Gene DE module in TIMER 2.0, FGF19 expression in stomach adenocarcinoma (STAD) tissues was compared with matched adjacent normal tissues from the TCGA dataset. A paired Wilcoxon signed-rank test was applied, with p < 0.05 considered statistically significant. 2.1.2 Association Between FGF19 Expression and Prognosis in Gastric Cancer GEPIA2 ( http://gepia2.cancer-pku.cn/ ) is an online tool that analyzes RNA sequencing data from TCGA and the Genotype–Tissue Expression (GTEx) project[ 10 ]. It was used to assess the prognostic significance of FGF19 expression in gastric cancer, including disease-free survival (DFS), overall survival (OS), and recurrence-free survival (RFS). Samples were stratified into low-expression ( 75th percentile) groups; intermediate expression levels were excluded. Kaplan–Meier survival curves were generated and compared using the log-rank test, and hazard ratios (HRs) with 95 percent confidence intervals (CIs) were computed using the Cox proportional hazards model. The Kaplan–Meier Plotter database ( https://kmplot.com/analysis/ ), which includes survival data on 54,675 genes across 21 cancer types, was also used[ 11 ]. In the pan-cancer STAD cohort, the mRNA RNA-seq module was selected. After entering the target gene FGF19, patients were divided into high- and low-expression groups using the median expression level as the cutoff. Kaplan–Meier survival curves were generated and differences were compared using the log-rank test. HRs and 95 percent CIs were automatically calculated using the Cox model, with p < 0.05 considered statistically significant. All survival plots were generated and exported directly from the platform. 2.1.3 Prognostic Significance of FGF19 in Clinical Subgroups Using the Kaplan–Meier Plotter database, subgroup analyses were performed for FGF19 based on sex (male, female), race (Caucasian, Asian), histological grade (moderately differentiated G2, poorly differentiated G3), and mutation burden (high, low). Survival differences between high- and low-expression groups were evaluated using the log-rank test. HRs and 95 percent CIs were calculated, with p < 0.05 considered statistically significant. 2.2 Immunohistochemistry Analysis 2.2.1 Patients and General Information A total of 115 gastric cancer patients who underwent surgery and 48 patients who received radiotherapy at the Second Affiliated Hospital of Anhui Medical University between October 2021 and April 2025 were included. Inclusion criteria were: (1) histologically confirmed gastric cancer; (2) receipt of radical gastrectomy or radiotherapy; (3) availability of complete clinical, pathological, and imaging data. Exclusion criteria were: (1) history of other malignancies; (2) severe organ dysfunction; (3) significant cardiovascular or cerebrovascular disease. 2.2.2 Collection of Clinical Data and Pathological Specimens Clinical data and pathological specimens were collected. Clinical variables included age, sex, maximum tumor diameter, treatment regimen, TNM stage, presence of vascular tumor thrombus, neural invasion, and tumor location. Pathological specimens—including surgical samples, endoscopic biopsies, and lymph node punctures—were processed into paraffin-embedded blocks in the Pathology Department of the Second Affiliated Hospital of Anhui Medical University. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No.: SL-YX2024-279). 2.2.3 Immunohistochemistry Tissue sections were prepared and baked at 65°C for 1 hour. Sections were deparaffinized with xylene, rehydrated through graded ethanol, and endogenous peroxidase activity was blocked with hydrogen peroxide. Antigen retrieval was performed in citrate buffer (pH 6.0) under high pressure. Sections were incubated overnight at 4°C with primary antibody (rabbit anti-human FGF19, 1:200, Affinity, China; DF2651), followed by incubation with a horseradish peroxidase (HRP)–conjugated secondary antibody (Chengdu Zhengneng Biotechnology, Cat. No. 511203). Visualization was achieved using DAB substrate, and nuclei were counterstained with hematoxylin. Sections were then dehydrated, cleared, and mounted. Phosphate-buffered saline (PBS) served as a negative control in place of the primary antibody. 2.2.4 Evaluation Criteria FGF19 expression in GC tissues was evaluated using the H-score method: H-score = staining intensity × (number of positive cells / total epithelial cells) × 100%[ 12 ].Staining intensity was scored as follows: strong positive (dark brown), 3; moderate positive (brown), 2; weak positive (light yellow), 1; negative (no cytoplasmic yellow granular staining), 0. The proportion of positive epithelial cells was scored as: 0–5%, 0; 6–24%, 1; 25–49%, 2; 50–74%, 3; ≥75%, 4, producing a total possible score of 0–12. 2.2.5 Radiotherapy Regimen and Response Assessment Patients receiving radiotherapy underwent intensity-modulated radiation therapy (IMRT) with a total dose of 35–56 Gy, delivered in 1.8–3.0 Gy fractions over 14–28 sessions, five sessions per week. Four weeks after completing radiotherapy, imaging assessments were performed, and response was evaluated according to RECIST version 1.0[ 13 ]. Patients achieving complete remission (CR; disappearance of all target lesions) or partial remission (PR; ≥30% decrease in the sum of longest diameters of target lesions) were classified as the sensitive group. Patients with progressive disease (PD; ≥20% increase in the sum of longest diameters of target lesions or appearance of new lesions) or stable disease (SD; changes between PR and PD) were classified as the resistant group. 2.2.6 Statistical Analysis Statistical analyses were conducted using SPSS version 22.0. IHC data were treated as ordinal variables. Paired chi-square tests were used to compare FGF19 expression between tumor and adjacent tissues. Associations between clinicopathological characteristics and IHC scores were assessed using multiple linear regression. Survival curves were generated using the Kaplan–Meier method and compared with the log-rank test.[ 14 ] For the radiotherapy cohort, group comparisons were performed using Fisher's exact test, chi-square test, and binary logistic regression. Statistical significance was set at P < 0.05. 3. Results 3.1 Bioinformatic Analysis of FGF19 in STAD 3.1.1 Expression of FGF19 in Gastric Cancer and Pan-Cancer Analysis FGF19 expression was evaluated across 33 tumor types using data from The Cancer Genome Atlas (TCGA). FGF19 was significantly overexpressed in several malignancies, including stomach cancer (STAD, P < 0.001), colon adenocarcinoma (COAD, P < 0.001), esophageal carcinoma (ESCA, P < 0.001), breast invasive carcinoma (BRCA, P < 0.001), lung adenocarcinoma and squamous cell carcinoma (LUAD and LUSC, P < 0.001), among others (Fig. 1 A). 3.1.2 Association Between FGF19 Expression and Prognosis in Gastric Cancer Survival analysis using the GEPIA2 platform showed that FGF19 expression was not significantly associated with overall survival (OS) in STAD ( P > 0.05). However, high FGF19 expression was significantly correlated with shorter disease-free survival (DFS; P = 0.0017), indicating that elevated FGF19 may be linked to a poorer prognosis (Fig. 1 B–C). Consistent with these findings, analysis using the Kaplan–Meier Plotter database demonstrated a significant association between high FGF19 expression and shorter recurrence-free survival (RFS, P = 0.024; Fig. 1 D). Patients with low FGF19 expression had a median RFS of 55.87 months, compared with 18.63 months in the high-expression group. Multivariate Cox analysis further confirmed that high FGF19 expression increased the risk of recurrence ( HR = 2.12; 95% CI : 1.09–4.12). 3.1.3 Prognostic significance of FGF19 in clinical subgroups of STAD Stratified survival analysis of STAD patients was performed using the Kaplan–Meier Plotter database according to clinical subgroups, including sex (male, female), race (Caucasian, Asian), tumor differentiation (moderately differentiated, G2; poorly differentiated, G3), and tumor mutation burden (TMB, high vs. low, Fig. 2 ). In female patients with STAD, high FGF19 expression was significantly associated with shorter recurrence-free survival (RFS) ( P = 0.024, HR = 5.70, 95% CI : 1.20–26.98), whereas no significant difference was observed in male patients ( P = 0.11). Among racial subgroups, Caucasian patients with high FGF19 expression had significantly shorter RFS compared with the low-expression group ( P = 0.0037, HR = 3.93, 95% CI : 1.45–10.66). Regarding tumor differentiation, high FGF19 expression predicted poorer RFS in poorly differentiated (G3) STAD patients: G2 subgroup, P = 0.89, HR = 1.10, 95% CI : 0.29–4.10; G3 subgroup, p = 0.00046, HR = 4.11, 95% CI : 1.74–9.69. Moreover, regardless of TMB levels, high FGF19 expression was associated with worse RFS. In patients with high TMB, high FGF19 expression indicated significantly poorer prognosis ( P = 0.021, HR = 3.47, 95% CI : 1.12–10.70), while a similar trend was observed in patients with low TMB ( P = 0.048, HR = 2.57, 95% CI : 0.97–6.76). 3.2 FGF19 expression and its correlates in surgically resected gastric cancer 3.2.1 Comparison of FGF19 expression between tumor and adjacent tissues in surgically treated patients Immunohistochemical (IHC) analysis showed strong cytoplasmic expression of FGF19 in tumor cells, presenting as brownish-yellow granular staining. Tumor tissues exhibited more extensive and intense staining than adjacent normal tissues from the same patients (Fig. 3 A–B). Using an IHC score of 8 as the cutoff, specimens were classified into high- and low-expression groups. Paired chi-square analysis demonstrated that high FGF19 expression was significantly more frequent in tumor tissues than in adjacent tissues ( χ² = 10.721; P = 0.001; Table 1 ; Fig. 3 C). Table 1 Comparison of FGF19 expression between tumor and adjacent tissues in gastric cancer Group Tumor tissue χ 2 P High expression Low expression Adjacent tissue High expression 8 20 10.721 0.001 Low expression 48 39 3.2.2 Correlation between FGF19 expression and clinicopathological characteristics Multiple linear regression analysis was performed to evaluate the association between FGF19 expression and clinicopathological factors, including age, sex, T stage, N stage, TNM stage, perineural invasion, vascular invasion, tumor size, tumor location, and FGF19 expression in adjacent tissues (Table 2 ). Age was positively correlated with FGF19 expression ( B = 0.049; P = 0.028). No significant associations were observed for the remaining variables, including sex ( P = 0.841), T stage ( P = 0.579), N stage ( P = 0.625), TNM stage ( P = 0.341), perineural invasion ( P = 0.902), vascular invasion ( P = 0.554), or adjacent-tissue expression ( P = 0.071). To further exclude potential multicollinearity among variables, chi-square tests were performed to assess the association between FGF19 immunohistochemical scores and the above clinicopathological parameters. FGF19 expression was significantly correlated with age ( χ² = 4.081, P = 0.028) and perineural invasion ( χ² = 6.656, P = 0.01), whereas no statistically significant associations were observed with the remaining clinicopathological features (Table S1 ). Table 2 Multivariate linear regression analysis of the association between FGF19 expression and clinicopathological characteristics Variable B (Coefficient) 95% CI P Lower 95% CI Upper 95% CI Constant 3.365 -0.417 7.147 0.080 Sex -0.084 -0.918 0.749 0.841 Age 0.049 0.006 0.093 0.028 T stage -0.229 -1.047 0.589 0.579 N stage -0.145 -0.733 0.443 0.625 TNM stage 0.737 -0.796 2.270 0.341 Perineural invasion -0.095 -1.630 1.440 0.902 Vascular invasion -0.396 -1.725 0.932 0.554 Maximum tumor diameter 0.189 -0.724 1.103 0.681 Tumor location 0.151 -0.341 0.644 0.543 Adjacent non-tumor FGF19 expression -0.114 -0.239 0.010 0.071 3.2.3 Prognostic analysis of surgically treated gastric patients A total of 115 patients with gastric cancer who underwent curative resection were included in the analysis. The last follow-up date was May 30, 2025, with a median follow-up of 7.1 months. Six patients were lost to follow-up (5.2%). The cohort’s median RFS was 6.3 months (range, 0.5–19.3 months). Using recurrence or death as the event variable, survival analysis showed that patients with high FGF19 expression had significantly shorter RFS than those with low expression ( P = 0.024; Table 3 ; Fig. 3 d). This result was consistent with the findings from bioinformatic analyses. Further log-rank analyses incorporating clinicopathological factors—including tumor FGF19 expression, T stage, N stage, TNM stage, perineural invasion, vascular invasion, tumor size, tumor location, and FGF19 expression in adjacent tissues—revealed that several variables were significantly associated with shorter RFS(Table 3 ). These included high tumor FGF19 expression ( p = 0.024), advanced T stage ( p = 0.009), lymph node metastasis ( P = 0.013), higher TNM stage ( p = 0.021), vascular invasion ( P = 0.013), tumor size ≥ 5 cm ( P = 0.005), and tumor location in the upper stomach ( P = 0.041). Table 3 Univariate analysis of factors associated with recurrence-free survival in patients with GC Variable χ² P value FGF19 expression in tumor tissue 5.097 0.024 T stage 6.836 0.009 N stage 10.821 0.013 TNM stage 7.753 0.021 Age 0.886 0.346 Sex 2.723 0.099 Perineural invasion 1.767 0.186 Vascular invasion 6.124 0.013 Maximum tumor diameter 7.824 0.005 Tumor location 6.385 0.041 Notes: Patients were divided into high and low FGF19 expression groups using a cutoff score of 8. T, N, and TNM stages were classified according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. Statistical significance was determined by the log-rank test, with p < 0.05 considered statistically significant 3.3 Association between tumor FGF19 expression and radiotherapy sensitivity in GC patients A total of 48 patients with gastric cancer who received radiotherapy were included in the analysis. Based on treatment response, 16 patients who achieved a partial response (PR) were categorized as the radiotherapy-sensitive group, whereas 32 patients with stable disease (SD, n = 25) or progressive disease (PD, n = 7) were classified as the radiotherapy-resistant group. Comparisons of clinicopathological characteristics between the two groups are summarized in Table 4 . No significant differences were observed in sex, age, or TNM stage (all P > 0.05). However, FGF19 expression differed significantly between the groups ( P = 0.013). High FGF19 expression was detected in only 12.5% (2/16) of the radiotherapy-sensitive patients, compared with 50.0% (16/32) in the radiotherapy-resistant group. To further assess the independent association between FGF19 expression and radiotherapy response, binary logistic regression was performed with sex, age, TNM stage, and FGF19 expression included as covariates. Variable assignments were as follows: radiotherapy response (sensitive = 0, resistant = 1); sex (female = 0, male = 1); TNM stage (I–II = 0, III–IV = 1); FGF19 expression (IHC score < 8 = 0, ≥ 8 = 1); and age was treated as a continuous variable. After adjustment for sex, age, and TNM stage, high FGF19 expression remained an independent predictor of radiotherapy resistance (OR = 7.014; 95% CI, 1.322–37.197; P = 0.022). In contrast, age (OR = 0.990; P = 0.772), sex (OR = 1.601; P = 0.571), and TNM stage (OR = 3.014; P = 0.299) were not significantly associated with radiotherapy sensitivity (Table 5 ). Table 4 Comparison of Clinicopathological Characteristics Between Radiotherapy-Sensitive and -Resistant Groups in Patients with GC Variable Cases (n) Radiotherapy-sensitive(n = 16) Radiotherapy-resistant(n = 32) P value Sex 1.000 Male 37 12 (32.4%) 25 (67.6%) Female 11 4 (36.4%) 7 (63.6%) Age 0.683 < 67years 23 7 (30.4%) 16 (69.6%) ≥ 67 years 25 9 (36.0%) 16 (64.0%) TNM stage 0.316 Ⅰ + Ⅱ 5 3 (60.0%) 2 (40.0%) Ⅲ + Ⅳ 43 13 (30.2%) 30 (69.8%) FGF19 expression 0.013 Low 30 14 (46.7%) 16 (53.3%) High 18 2 (11.1%) 16 (88.9%) Note: The median age of the patients in the cohort is 67 years. Table 5 Binary Logistic Regression Analysis of Factors Associated with Radiotherapy Sensitivity in Patients with GC Variable B SE OR 95% CI P value FGF19 expression 1.948 0.851 7.014 1.322–37.197 0.022 Age -0.010 0.035 0.990 0.925–1.059 0.772 Sex 0.471 0.831 1.601 0.314–8.161 0.571 TNM stage 1.103 1.063 3.014 0.375–24.219 0.299 Constant -0.539 2.380 0.584 – 0.821 Note: Binary logistic regression analysis was performed with radiotherapy sensitivity as the dependent variable (0 = sensitive, 1 = resistant). High FGF19 expression (≥ 8 IHC score) was significantly associated with an increased risk of radiotherapy resistance after adjustment for age, sex, and TNM stage 4. Discussion In this study, we comprehensively evaluated the expression pattern and clinical relevance of FGF19 in gastric cancer by integrating TCGA-based bioinformatic analyses with immunohistochemical data from an independent institutional cohort. Our results demonstrate that FGF19 is significantly overexpressed in gastric cancer tissues and is associated with shorter recurrence-free survival. Importantly, elevated FGF19 expression was correlated with reduced responsiveness to radiotherapy, suggesting a potential role in treatment resistance. FGF19 is an endocrine fibroblast growth factor that signals primarily through FGFR4 and the co-receptor β-Klotho, activating downstream MAPK and PI3K/AKT pathways that promote tumorigenesis [ 7 , 15 ]. The oncogenic role of the FGF19–FGFR4 axis has been well documented in hepatocellular, colorectal, and breast cancers [ 16 , 17 ]. Consistent with these findings, our data indicate that high FGF19 expression predicts unfavorable recurrence-free survival in gastric cancer [ 4 , 18 ]. Notably, few prior studies have explored the relationship between FGF19 and radiotherapy outcomes, and our findings therefore extend the clinical relevance of this pathway by implicating it in radioresistance. Mechanistically, radioresistance is thought to arise from enhanced DNA damage repair, attenuation of oxidative stress, and sustained activation of pro-survival signaling pathways [ 19 ]. Activation of PI3K/AKT signaling downstream of FGFR4 can stimulate DNA-PKcs activity, thereby promoting non-homologous end-joining repair of radiation-induced DNA double-strand breaks [ 20 – 21 ]. In parallel, PI3K/AKT signaling may reduce intracellular reactive oxygen species and suppress radiation-induced apoptosis through antioxidant and survival pathways [ 22 – 24 ]. Together, these mechanisms provide a biologically plausible explanation for the reduced radiotherapy sensitivity observed in tumors with high FGF19 expression. Beyond tumor-intrinsic effects, emerging evidence suggests that the FGF19–FGFR4 axis may also influence the tumor immune microenvironment by promoting immunosuppressive cell infiltration and impairing cytotoxic T-cell activity, potentially limiting the efficacy of immune checkpoint inhibitors (Fig. 4 ) [ 25 ]. In this context, preclinical studies reporting synergistic antitumor effects of combined FGFR4 inhibition and immunotherapy are of particular interest [ 26 ]. These observations raise the possibility that therapeutic strategies integrating radiotherapy, FGFR4-targeted agents, and immunomodulatory approaches may be especially relevant for patients with high FGF19 expression. From a clinical perspective, FGF19 expression was associated with recurrence-free survival but not overall survival, suggesting that it may be more informative as a marker of early recurrence risk and treatment responsiveness rather than long-term prognosis [ 27 ]. Incorporating FGF19 into risk stratification frameworks could therefore assist in identifying patients who may benefit from treatment intensification, alternative radiotherapy strategies, or combination regimens. Although FGFR4-targeted therapies, including selective inhibitors and FGF19-neutralizing antibodies, have shown encouraging activity in hepatocellular carcinoma and preclinical models [ 28 – 29 ], their efficacy in gastric cancer may be influenced by tumor heterogeneity, compensatory signaling pathways, and variable β-Klotho expression [ 30 ]. Consequently, careful patient selection and mechanistically informed clinical trial design will be critical. Taken together, our findings indicate that FGF19 overexpression is linked to increased recurrence risk and reduced radiotherapy sensitivity in gastric cancer, supporting a role for the FGF19-FGFR4 axis as both a prognostic and predictive biomarker. Further mechanistic studies and prospective clinical investigations are warranted to validate these observations and to determine whether targeting this pathway can improve therapeutic outcomes in selected patient populations. Declarations Ethical statement The study was approved by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No.: YX2024-279). The studies were conducted in accordance with the local legislation and institutional requirements. Author contributions Study conception and design were contributed by Zhong Fan and Min Liu. Material preparation, data collection, and analysis were performed by Haowei Wu, Hong Zhu, Yang Ding, Junjie Liu, Rui Luo, and Xiaonan Pang. Project administration was coordinated by Bailong Liu. The first draft of the manuscript was written by Zhong Fan and Mengyu Liu, and all authors commented on and revised previous versions of the manuscript. Figures and tables were prepared by Zhong Fan. All authors read and approved the final manuscript. Consent to Participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University. Written informed consent to participate in the study was obtained from all patients prior to the collection of gastric cancer tissue samples. No participants under the age of 16 were included in this study. Consent to publish Informed consent for publication was obtained from all participants included in this study. Clinical trial number Not applicable Funding This work was supported by the Scientific Research Projects of Anhui Provincial Health Commission , the Natural Science Foundation of Anhui Province , the Anhui Medical University Research Foundation . References Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4(1):47–53. 10.1016/j.jncc.2023.11.003 . He J, Chen W, Li Z, et al. 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PI3Kα inhibitors sensitize esophageal squamous cell carcinoma to radiation by abrogating survival signals in tumor cells and tumor microenvironment. Cancer Lett. 2019;459:145–55. 10.1016/j.canlet.2019.05.040 . Kang BW, Chau I. Molecular target: pan-AKT in gastric cancer. ESMO Open. 2020;5(5):e000728. 10.1136/esmoopen-2020-000728 . Liu Y, Cao M, Li X, Zhao C, Cui R. Dissecting the role of the FGF19–FGFR4 signaling pathway in cancer development and progression. Front Cell Dev Biol. 2020;8:80. 10.3389/fcell.2020.00080 . Ruan R, Li L, Li X, et al. Unleashing the potential of combining FGFR inhibitor and immune checkpoint blockade for FGF/FGFR signaling in tumor microenvironment. Mol Cancer. 2023;22:60. 10.1186/s12943-023-01703-9 . Harada K, Baba Y, Shigaki H, et al. Prognostic and clinical impact of PIK3CA mutation in gastric cancer: pyrosequencing technology and literature review. BMC Cancer. 2016;16:400. 10.1186/s12885-016-2424-9 . French DM, Lin BC, Wang M, et al. Targeting FGFR4 inhibits hepatocellular carcinoma in preclinical mouse models. PLoS ONE. 2012;7(5):e36713. 10.1371/journal.pone.0036713 . Gao L, Xie J, Wu J, et al. FGF19/FGFR4 signaling contributes to the resistance of hepatocellular carcinoma to sorafenib. J Exp Clin Cancer Res. 2017;36:106. 10.1186/s13046-017-0570-8 . Brooks AN, Kilgour E, Smith PD. Molecular pathways: fibroblast growth factor signaling: a new therapeutic opportunity in cancer. Clin Cancer Res. 2012;18(7):1855–62. 10.1158/1078-0432.CCR-11-0699 . Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8383155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574531348,"identity":"df4eb852-6a95-434f-ac72-41f2440a83d7","order_by":0,"name":"Zhong Fan","email":"","orcid":"","institution":"Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Fan","suffix":""},{"id":574531349,"identity":"6dc5e3b6-b609-4090-9384-48484ad209fe","order_by":1,"name":"Mengyu Liu","email":"","orcid":"","institution":"Second Hospital of Anhui Medical 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13:45:04","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13294408,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/ac1036cae583dcf3119ab864.tif"},{"id":100567768,"identity":"821c75a5-1e81-4202-a7a8-fdfea3d8137c","added_by":"auto","created_at":"2026-01-19 09:14:06","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108090,"visible":true,"origin":"","legend":"","description":"","filename":"305c5fa85dc049b1b2d7bd9e0d279ad51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/353888ef28856bb248900e99.xml"},{"id":100567769,"identity":"ff50dd5e-ca9c-4ac9-ba6b-d0f14c015299","added_by":"auto","created_at":"2026-01-19 09:14:07","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122144,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/62fe6915b9b868d6e13a3861.html"},{"id":100567770,"identity":"fd9cdc8a-549e-4edd-8b5b-16aec547cde9","added_by":"auto","created_at":"2026-01-19 09:14:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4563934,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and clinical significance of FGF19 in STAD. (A) Differential expression of FGF19 between tumor and adjacent normal tissues across 33 cancer types. Red indicates tumor tissues and blue indicates normal tissues; the red box highlights STAD. *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***: \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001. (B-C) Kaplan-Meier curves comparing overall survival (OS) and disease-free survival (DFS) between patients with high and low FGF19 expression based on GEPIA2 analysis. (D) Recurrence-free survival of STAD patients in the FGF19 high- and low-expression groups from the KM Plotter database\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/f85a15bab10ca1013e314372.png"},{"id":100567764,"identity":"50167fda-4f4c-4df1-b6e0-074225da08d8","added_by":"auto","created_at":"2026-01-19 09:14:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":609669,"visible":true,"origin":"","legend":"\u003cp\u003eFGF19 expression and prognosis in STAD clinical subgroups. (A–H) Stratified RFS analysis by sex, race, the tumor grade, and the tumor mutation burden (TMB).\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/355e5c0eb3a4e66922b6b2dc.png"},{"id":100567743,"identity":"9a0aafaa-e6bb-41e8-96fa-78e708a278df","added_by":"auto","created_at":"2026-01-19 09:14:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13887657,"visible":true,"origin":"","legend":"\u003cp\u003eFGF19 expression and its association with recurrence-free survival in gastric cancer. (A-B) Representative immunohistochemical images showing cytoplasmic FGF19 expression as brownish-yellow granules in gastric cancer tissues and adjacent normal tissues (100× magnification). Red arrows indicate positive granular staining. Scale bars: 100 μm. (C) Box-and-whisker plot comparing FGF19 expression levels between gastric cancer tissues and adjacent normal tissues. o³: three discrete outlier values. (D) Recurrence-free survival (RFS) in surgical gastric cancer patients stratified by FGF19 expression.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/ee06693ebfcd7c073714e746.png"},{"id":100567741,"identity":"19fa7aa4-04e6-4eae-9ce3-013057642322","added_by":"auto","created_at":"2026-01-19 09:14:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":528139,"visible":true,"origin":"","legend":"\u003cp\u003ePotential molecular mechanism of FGF19-mediated radioresistance. PD 98059: ERK1/2 inhibitor. The figure was created by the authors using \u003ca href=\"http://www.biorender.com/\"\u003ehttp://www.biorender.com\u003c/a\u003e, with appropriate permissions for publication\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/a9785d786451ea3ce037efe8.png"},{"id":104779207,"identity":"109d5204-323e-4ed5-8efb-86ab7469998b","added_by":"auto","created_at":"2026-03-17 07:36:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5089984,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/ffc4b1ef-bd1f-40e0-a8c1-18b7e654f66e.pdf"},{"id":100596010,"identity":"d7e19f2b-2fd5-4135-abe5-1a92a4d26807","added_by":"auto","created_at":"2026-01-19 13:50:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20220,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8383155/v1/0600898abb70b09d4a8837d3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aberrant FGF19 Expression Is Associated with Prognosis and Radiosensitivity in Gastric Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) is a common digestive tract malignancy in China and remains a major public health concern due to its high incidence and mortality. In 2022, approximately 358,700 new GC cases and 260,400 GC-related deaths were reported nationwide, placing its mortality rate third among all malignant tumors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in surgery, chemotherapy, and radiotherapy, the 5-year survival rate for patients with advanced stomach cancer (STAD) remains below 30 percent[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], substantially affecting survival and quality of life.\u003c/p\u003e \u003cp\u003eFor patients with positive surgical margins or locally advanced disease who are not candidates for surgery, radiotherapy is an important therapeutic option. However, considerable variability in radiotherapy response is observed in clinical practice: some patients achieve substantial clinical benefit, whereas others develop radioresistance, resulting in progression or recurrence. This interindividual heterogeneity significantly limits further improvements in radiotherapy outcomes. At present, no reliable molecular biomarkers are available to predict radiosensitivity. This lack of predictive tools not only impedes the implementation of precision radiotherapy but also increases the risk of ineffective or excessive treatment. Therefore, identifying and validating novel biomarkers capable of predicting radiosensitivity is of great clinical significance for guiding individualized treatment, optimizing radiotherapy strategies, and improving patient prognosis.\u003c/p\u003e \u003cp\u003eIn recent years, targeted therapy and immunotherapy have emerged as important therapeutic strategies for gastric cancer, with well-established targets such as HER2, PD-L1, and Claudin 18.2[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Nevertheless, compared with these therapeutic advances, molecular studies on radiosensitivity remain relatively underdeveloped, and no validated predictive model is currently available. Previous studies have shown that DNA damage repair capacity, apoptotic signaling, and oxidative stress responses are closely associated with radiosensitivity. These observations suggest that radioresponse may be driven by dysregulation of specific molecular pathways.\u003c/p\u003e \u003cp\u003eFibroblast growth factor 19 (FGF19) is a secreted endocrine protein and the specific ligand of fibroblast growth factor receptor 4 (FGFR4). Aberrant overexpression of FGF19 has been reported in multiple malignancies. After synthesis, FGF19 is secreted extracellularly, leading to its presence in both intra- and extracellular compartments. This dual distribution enables FGF19 to influence the tumor microenvironment through paracrine or autocrine signaling while also contributing to intracellular signaling and metabolic regulation. Studies have demonstrated that FGF19 promotes cell proliferation, invasion, and metastasis in hepatocellular carcinoma, colorectal cancer, breast cancer, and lung cancer, and its overexpression is consistently associated with poor prognosis[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mechanistically, the FGF19\u0026ndash;FGFR4 axis activates signaling pathways such as MAPK and PI3K/AKT, regulating tumor cell proliferation and resistance to apoptosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These pathways have also been implicated in radioresistance in other cancer types[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], suggesting that aberrant FGF19 overexpression may contribute to reduced radiosensitivity in gastric cancer.\u003c/p\u003e \u003cp\u003ePrevious research has mainly focused on the roles of FGF19 in tumorigenesis and drug resistance, underscoring its biological importance across a variety of malignancies. However, whether FGF19 can serve as a biomarker for predicting radiotherapy response in gastric cancer remains largely unexplored, and both clinical evidence and underlying mechanisms are insufficiently defined.\u003c/p\u003e \u003cp\u003eBased on this knowledge gap, the present study integrates bioinformatics analysis of The Cancer Genome Atlas (TCGA) with immunohistochemical data from gastric cancer patients treated at our center to comprehensively evaluate FGF19 expression and its association with clinicopathological features, prognosis, and radiosensitivity. Through combined bioinformatic and clinical validation, this study provides new evidence supporting FGF19 as a potential biomarker for predicting prognosis and radiotherapy response in gastric cancer and explores its possible molecular mechanisms.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Bioinformatic Analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Expression of FGF19 in Tumor and Adjacent Normal Tissues of GC\u003c/h2\u003e \u003cp\u003eTranscriptomic and clinical data from 33 cancer types were obtained from The Cancer Genome Atlas (TCGA). These data were automatically transformed using log₂(TPM\u0026thinsp;+\u0026thinsp;1) by the Tumor Immune Estimation Resource (TIMER) platform to minimize technical variability and satisfy the assumptions of normal distribution[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Using the Gene DE module in TIMER 2.0, FGF19 expression in stomach adenocarcinoma (STAD) tissues was compared with matched adjacent normal tissues from the TCGA dataset. A paired Wilcoxon signed-rank test was applied, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Association Between FGF19 Expression and Prognosis in Gastric Cancer\u003c/h2\u003e \u003cp\u003eGEPIA2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an online tool that analyzes RNA sequencing data from TCGA and the Genotype\u0026ndash;Tissue Expression (GTEx) project[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It was used to assess the prognostic significance of FGF19 expression in gastric cancer, including disease-free survival (DFS), overall survival (OS), and recurrence-free survival (RFS). Samples were stratified into low-expression (\u0026lt;\u0026thinsp;25th percentile) and high-expression (\u0026gt;\u0026thinsp;75th percentile) groups; intermediate expression levels were excluded. Kaplan\u0026ndash;Meier survival curves were generated and compared using the log-rank test, and hazard ratios (HRs) with 95 percent confidence intervals (CIs) were computed using the Cox proportional hazards model.\u003c/p\u003e \u003cp\u003eThe Kaplan\u0026ndash;Meier Plotter database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"https://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which includes survival data on 54,675 genes across 21 cancer types, was also used[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the pan-cancer STAD cohort, the mRNA RNA-seq module was selected. After entering the target gene FGF19, patients were divided into high- and low-expression groups using the median expression level as the cutoff. Kaplan\u0026ndash;Meier survival curves were generated and differences were compared using the log-rank test. HRs and 95 percent CIs were automatically calculated using the Cox model, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. All survival plots were generated and exported directly from the platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Prognostic Significance of FGF19 in Clinical Subgroups\u003c/h2\u003e \u003cp\u003eUsing the Kaplan\u0026ndash;Meier Plotter database, subgroup analyses were performed for FGF19 based on sex (male, female), race (Caucasian, Asian), histological grade (moderately differentiated G2, poorly differentiated G3), and mutation burden (high, low). Survival differences between high- and low-expression groups were evaluated using the log-rank test. HRs and 95 percent CIs were calculated, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Immunohistochemistry Analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Patients and General Information\u003c/h2\u003e \u003cp\u003eA total of 115 gastric cancer patients who underwent surgery and 48 patients who received radiotherapy at the Second Affiliated Hospital of Anhui Medical University between October 2021 and April 2025 were included. Inclusion criteria were: (1) histologically confirmed gastric cancer; (2) receipt of radical gastrectomy or radiotherapy; (3) availability of complete clinical, pathological, and imaging data. Exclusion criteria were: (1) history of other malignancies; (2) severe organ dysfunction; (3) significant cardiovascular or cerebrovascular disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Collection of Clinical Data and Pathological Specimens\u003c/h2\u003e \u003cp\u003eClinical data and pathological specimens were collected. Clinical variables included age, sex, maximum tumor diameter, treatment regimen, TNM stage, presence of vascular tumor thrombus, neural invasion, and tumor location. Pathological specimens\u0026mdash;including surgical samples, endoscopic biopsies, and lymph node punctures\u0026mdash;were processed into paraffin-embedded blocks in the Pathology Department of the Second Affiliated Hospital of Anhui Medical University. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No.: SL-YX2024-279).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Immunohistochemistry\u003c/h2\u003e \u003cp\u003eTissue sections were prepared and baked at 65\u0026deg;C for 1 hour. Sections were deparaffinized with xylene, rehydrated through graded ethanol, and endogenous peroxidase activity was blocked with hydrogen peroxide. Antigen retrieval was performed in citrate buffer (pH 6.0) under high pressure. Sections were incubated overnight at 4\u0026deg;C with primary antibody (rabbit anti-human FGF19, 1:200, Affinity, China; DF2651), followed by incubation with a horseradish peroxidase (HRP)\u0026ndash;conjugated secondary antibody (Chengdu Zhengneng Biotechnology, Cat. No. 511203). Visualization was achieved using DAB substrate, and nuclei were counterstained with hematoxylin. Sections were then dehydrated, cleared, and mounted. Phosphate-buffered saline (PBS) served as a negative control in place of the primary antibody.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Evaluation Criteria\u003c/h2\u003e \u003cp\u003eFGF19 expression in GC tissues was evaluated using the H-score method: H-score\u0026thinsp;=\u0026thinsp;staining intensity \u0026times; (number of positive cells / total epithelial cells) \u0026times; 100%[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].Staining intensity was scored as follows: strong positive (dark brown), 3; moderate positive (brown), 2; weak positive (light yellow), 1; negative (no cytoplasmic yellow granular staining), 0. The proportion of positive epithelial cells was scored as: 0\u0026ndash;5%, 0; 6\u0026ndash;24%, 1; 25\u0026ndash;49%, 2; 50\u0026ndash;74%, 3; \u0026ge;75%, 4, producing a total possible score of 0\u0026ndash;12.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Radiotherapy Regimen and Response Assessment\u003c/h2\u003e \u003cp\u003ePatients receiving radiotherapy underwent intensity-modulated radiation therapy (IMRT) with a total dose of 35\u0026ndash;56 Gy, delivered in 1.8\u0026ndash;3.0 Gy fractions over 14\u0026ndash;28 sessions, five sessions per week. Four weeks after completing radiotherapy, imaging assessments were performed, and response was evaluated according to RECIST version 1.0[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Patients achieving complete remission (CR; disappearance of all target lesions) or partial remission (PR; \u0026ge;30% decrease in the sum of longest diameters of target lesions) were classified as the sensitive group. Patients with progressive disease (PD; \u0026ge;20% increase in the sum of longest diameters of target lesions or appearance of new lesions) or stable disease (SD; changes between PR and PD) were classified as the resistant group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SPSS version 22.0. IHC data were treated as ordinal variables. Paired chi-square tests were used to compare FGF19 expression between tumor and adjacent tissues. Associations between clinicopathological characteristics and IHC scores were assessed using multiple linear regression. Survival curves were generated using the Kaplan\u0026ndash;Meier method and compared with the log-rank test.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] For the radiotherapy cohort, group comparisons were performed using Fisher's exact test, chi-square test, and binary logistic regression. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Bioinformatic Analysis of FGF19 in STAD\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Expression of FGF19 in Gastric Cancer and Pan-Cancer Analysis\u003c/h2\u003e \u003cp\u003eFGF19 expression was evaluated across 33 tumor types using data from The Cancer Genome Atlas (TCGA). FGF19 was significantly overexpressed in several malignancies, including stomach cancer (STAD, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), colon adenocarcinoma (COAD, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), esophageal carcinoma (ESCA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), breast invasive carcinoma (BRCA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lung adenocarcinoma and squamous cell carcinoma (LUAD and LUSC, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Association Between FGF19 Expression and Prognosis in Gastric Cancer\u003c/h2\u003e \u003cp\u003eSurvival analysis using the GEPIA2 platform showed that FGF19 expression was not significantly associated with overall survival (OS) in STAD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, high FGF19 expression was significantly correlated with shorter disease-free survival (DFS; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017), indicating that elevated FGF19 may be linked to a poorer prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026ndash;C).\u003c/p\u003e \u003cp\u003eConsistent with these findings, analysis using the Kaplan\u0026ndash;Meier Plotter database demonstrated a significant association between high FGF19 expression and shorter recurrence-free survival (RFS, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Patients with low FGF19 expression had a median RFS of 55.87 months, compared with 18.63 months in the high-expression group. Multivariate Cox analysis further confirmed that high FGF19 expression increased the risk of recurrence (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.12; 95% \u003cem\u003eCI\u003c/em\u003e: 1.09\u0026ndash;4.12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Prognostic significance of FGF19 in clinical subgroups of STAD\u003c/h2\u003e \u003cp\u003eStratified survival analysis of STAD patients was performed using the Kaplan\u0026ndash;Meier Plotter database according to clinical subgroups, including sex (male, female), race (Caucasian, Asian), tumor differentiation (moderately differentiated, G2; poorly differentiated, G3), and tumor mutation burden (TMB, high vs. low, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn female patients with STAD, high FGF19 expression was significantly associated with shorter recurrence-free survival (RFS) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024, \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.70, 95% \u003cem\u003eCI\u003c/em\u003e: 1.20\u0026ndash;26.98), whereas no significant difference was observed in male patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11). Among racial subgroups, Caucasian patients with high FGF19 expression had significantly shorter RFS compared with the low-expression group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0037, \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.93, 95% \u003cem\u003eCI\u003c/em\u003e: 1.45\u0026ndash;10.66).\u003c/p\u003e \u003cp\u003eRegarding tumor differentiation, high FGF19 expression predicted poorer RFS in poorly differentiated (G3) STAD patients: G2 subgroup, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.89, \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.10, 95% \u003cem\u003eCI\u003c/em\u003e: 0.29\u0026ndash;4.10; G3 subgroup, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00046, \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.11, 95% \u003cem\u003eCI\u003c/em\u003e: 1.74\u0026ndash;9.69. Moreover, regardless of TMB levels, high FGF19 expression was associated with worse RFS. In patients with high TMB, high FGF19 expression indicated significantly poorer prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021, \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.47, 95% \u003cem\u003eCI\u003c/em\u003e: 1.12\u0026ndash;10.70), while a similar trend was observed in patients with low TMB (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048, \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.57, 95% \u003cem\u003eCI\u003c/em\u003e: 0.97\u0026ndash;6.76).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 FGF19 expression and its correlates in surgically resected gastric cancer\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Comparison of FGF19 expression between tumor and adjacent tissues in surgically treated patients\u003c/h2\u003e \u003cp\u003eImmunohistochemical (IHC) analysis showed strong cytoplasmic expression of FGF19 in tumor cells, presenting as brownish-yellow granular staining. Tumor tissues exhibited more extensive and intense staining than adjacent normal tissues from the same patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B). Using an IHC score of 8 as the cutoff, specimens were classified into high- and low-expression groups. Paired chi-square analysis demonstrated that high FGF19 expression was significantly more frequent in tumor tissues than in adjacent tissues (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 10.721; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of FGF19 expression between tumor and adjacent tissues in gastric cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTumor tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow expression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjacent tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Correlation between FGF19 expression and clinicopathological characteristics\u003c/h2\u003e \u003cp\u003eMultiple linear regression analysis was performed to evaluate the association between FGF19 expression and clinicopathological factors, including age, sex, T stage, N stage, TNM stage, perineural invasion, vascular invasion, tumor size, tumor location, and FGF19 expression in adjacent tissues (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Age was positively correlated with FGF19 expression (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028). No significant associations were observed for the remaining variables, including sex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.841), T stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.579), N stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.625), TNM stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.341), perineural invasion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.902), vascular invasion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.554), or adjacent-tissue expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071).\u003c/p\u003e \u003cp\u003eTo further exclude potential multicollinearity among variables, chi-square tests were performed to assess the association between FGF19 immunohistochemical scores and the above clinicopathological parameters. FGF19 expression was significantly correlated with age (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 4.081, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and perineural invasion (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 6.656, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), whereas no statistically significant associations were observed with the remaining clinicopathological features (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate linear regression analysis of the association between FGF19 expression and clinicopathological characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e (Coefficient)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower 95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper 95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineural invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjacent non-tumor FGF19 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Prognostic analysis of surgically treated gastric patients\u003c/h2\u003e \u003cp\u003eA total of 115 patients with gastric cancer who underwent curative resection were included in the analysis. The last follow-up date was May 30, 2025, with a median follow-up of 7.1 months. Six patients were lost to follow-up (5.2%). The cohort\u0026rsquo;s median RFS was 6.3 months (range, 0.5\u0026ndash;19.3 months).\u003c/p\u003e \u003cp\u003eUsing recurrence or death as the event variable, survival analysis showed that patients with high FGF19 expression had significantly shorter RFS than those with low expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). This result was consistent with the findings from bioinformatic analyses.\u003c/p\u003e \u003cp\u003eFurther log-rank analyses incorporating clinicopathological factors\u0026mdash;including tumor FGF19 expression, T stage, N stage, TNM stage, perineural invasion, vascular invasion, tumor size, tumor location, and FGF19 expression in adjacent tissues\u0026mdash;revealed that several variables were significantly associated with shorter RFS(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These included high tumor FGF19 expression (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), advanced T stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), lymph node metastasis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), higher TNM stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), vascular invasion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), tumor size\u0026thinsp;\u0026ge;\u0026thinsp;5 cm (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and tumor location in the upper stomach (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of factors associated with recurrence-free survival in patients with GC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eχ\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF19 expression in tumor tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineural invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Patients were divided into high and low FGF19 expression groups using a cutoff score of 8. T, N, and TNM stages were classified according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. Statistical significance was determined by the log-rank test, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association between tumor FGF19 expression and radiotherapy sensitivity in GC patients\u003c/h2\u003e \u003cp\u003eA total of 48 patients with gastric cancer who received radiotherapy were included in the analysis. Based on treatment response, 16 patients who achieved a partial response (PR) were categorized as the radiotherapy-sensitive group, whereas 32 patients with stable disease (SD, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25) or progressive disease (PD, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7) were classified as the radiotherapy-resistant group.\u003c/p\u003e \u003cp\u003eComparisons of clinicopathological characteristics between the two groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. No significant differences were observed in sex, age, or TNM stage (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, FGF19 expression differed significantly between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). High FGF19 expression was detected in only 12.5% (2/16) of the radiotherapy-sensitive patients, compared with 50.0% (16/32) in the radiotherapy-resistant group.\u003c/p\u003e \u003cp\u003eTo further assess the independent association between FGF19 expression and radiotherapy response, binary logistic regression was performed with sex, age, TNM stage, and FGF19 expression included as covariates. Variable assignments were as follows: radiotherapy response (sensitive\u0026thinsp;=\u0026thinsp;0, resistant\u0026thinsp;=\u0026thinsp;1); sex (female\u0026thinsp;=\u0026thinsp;0, male\u0026thinsp;=\u0026thinsp;1); TNM stage (I\u0026ndash;II\u0026thinsp;=\u0026thinsp;0, III\u0026ndash;IV\u0026thinsp;=\u0026thinsp;1); FGF19 expression (IHC score\u0026thinsp;\u0026lt;\u0026thinsp;8\u0026thinsp;=\u0026thinsp;0, \u0026ge; 8\u0026thinsp;=\u0026thinsp;1); and age was treated as a continuous variable.\u003c/p\u003e \u003cp\u003eAfter adjustment for sex, age, and TNM stage, high FGF19 expression remained an independent predictor of radiotherapy resistance (OR\u0026thinsp;=\u0026thinsp;7.014; 95% CI, 1.322\u0026ndash;37.197; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). In contrast, age (OR\u0026thinsp;=\u0026thinsp;0.990; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.772), sex (OR\u0026thinsp;=\u0026thinsp;1.601; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.571), and TNM stage (OR\u0026thinsp;=\u0026thinsp;3.014; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.299) were not significantly associated with radiotherapy sensitivity (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Clinicopathological Characteristics Between Radiotherapy-Sensitive and -Resistant Groups in Patients with GC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiotherapy-sensitive(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRadiotherapy-resistant(n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;67years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (69.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;67 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (64.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ + Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ + Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF19 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The median age of the patients in the cohort is 67 years.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary Logistic Regression Analysis of Factors Associated with Radiotherapy Sensitivity in Patients with GC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF19 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.322\u0026ndash;37.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.925\u0026ndash;1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.314\u0026ndash;8.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.375\u0026ndash;24.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Binary logistic regression analysis was performed with radiotherapy sensitivity as the dependent variable (0\u0026thinsp;=\u0026thinsp;sensitive, 1\u0026thinsp;=\u0026thinsp;resistant). High FGF19 expression (\u0026ge;\u0026thinsp;8 IHC score) was significantly associated with an increased risk of radiotherapy resistance after adjustment for age, sex, and TNM stage\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we comprehensively evaluated the expression pattern and clinical relevance of FGF19 in gastric cancer by integrating TCGA-based bioinformatic analyses with immunohistochemical data from an independent institutional cohort. Our results demonstrate that FGF19 is significantly overexpressed in gastric cancer tissues and is associated with shorter recurrence-free survival. Importantly, elevated FGF19 expression was correlated with reduced responsiveness to radiotherapy, suggesting a potential role in treatment resistance.\u003c/p\u003e \u003cp\u003eFGF19 is an endocrine fibroblast growth factor that signals primarily through FGFR4 and the co-receptor β-Klotho, activating downstream MAPK and PI3K/AKT pathways that promote tumorigenesis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The oncogenic role of the FGF19\u0026ndash;FGFR4 axis has been well documented in hepatocellular, colorectal, and breast cancers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consistent with these findings, our data indicate that high FGF19 expression predicts unfavorable recurrence-free survival in gastric cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, few prior studies have explored the relationship between FGF19 and radiotherapy outcomes, and our findings therefore extend the clinical relevance of this pathway by implicating it in radioresistance.\u003c/p\u003e \u003cp\u003eMechanistically, radioresistance is thought to arise from enhanced DNA damage repair, attenuation of oxidative stress, and sustained activation of pro-survival signaling pathways [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Activation of PI3K/AKT signaling downstream of FGFR4 can stimulate DNA-PKcs activity, thereby promoting non-homologous end-joining repair of radiation-induced DNA double-strand breaks [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In parallel, PI3K/AKT signaling may reduce intracellular reactive oxygen species and suppress radiation-induced apoptosis through antioxidant and survival pathways [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Together, these mechanisms provide a biologically plausible explanation for the reduced radiotherapy sensitivity observed in tumors with high FGF19 expression.\u003c/p\u003e \u003cp\u003eBeyond tumor-intrinsic effects, emerging evidence suggests that the FGF19\u0026ndash;FGFR4 axis may also influence the tumor immune microenvironment by promoting immunosuppressive cell infiltration and impairing cytotoxic T-cell activity, potentially limiting the efficacy of immune checkpoint inhibitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this context, preclinical studies reporting synergistic antitumor effects of combined FGFR4 inhibition and immunotherapy are of particular interest [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These observations raise the possibility that therapeutic strategies integrating radiotherapy, FGFR4-targeted agents, and immunomodulatory approaches may be especially relevant for patients with high FGF19 expression.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, FGF19 expression was associated with recurrence-free survival but not overall survival, suggesting that it may be more informative as a marker of early recurrence risk and treatment responsiveness rather than long-term prognosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Incorporating FGF19 into risk stratification frameworks could therefore assist in identifying patients who may benefit from treatment intensification, alternative radiotherapy strategies, or combination regimens. Although FGFR4-targeted therapies, including selective inhibitors and FGF19-neutralizing antibodies, have shown encouraging activity in hepatocellular carcinoma and preclinical models [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], their efficacy in gastric cancer may be influenced by tumor heterogeneity, compensatory signaling pathways, and variable β-Klotho expression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Consequently, careful patient selection and mechanistically informed clinical trial design will be critical.\u003c/p\u003e \u003cp\u003eTaken together, our findings indicate that FGF19 overexpression is linked to increased recurrence risk and reduced radiotherapy sensitivity in gastric cancer, supporting a role for the FGF19-FGFR4 axis as both a prognostic and predictive biomarker. Further mechanistic studies and prospective clinical investigations are warranted to validate these observations and to determine whether targeting this pathway can improve therapeutic outcomes in selected patient populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical statement\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No.: YX2024-279). The studies were conducted in accordance with the local legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy conception and design were contributed by Zhong Fan and Min Liu. Material preparation, data collection, and analysis were performed by Haowei Wu, Hong Zhu, Yang Ding, Junjie Liu, Rui Luo, and Xiaonan Pang. Project administration was coordinated by Bailong Liu. The first draft of the manuscript was written by Zhong Fan and Mengyu Liu, and all authors commented on and revised previous versions of the manuscript. Figures and tables were prepared by Zhong Fan. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Second Affiliated Hospital of Anhui Medical University. Written informed consent to participate in the study was obtained from all patients prior to the collection of gastric cancer tissue samples. No participants under the age of 16 were included in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to publish\u003c/p\u003e\n\u003cp\u003eInformed consent for publication was obtained from all participants included in this study.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Scientific Research Projects of Anhui Provincial Health Commission \u0026lt;AHWJ2024BAd20015\u0026gt;, the Natural Science Foundation of Anhui Province \u0026lt;2408085QH258, 2508085MH214\u0026gt;, the Anhui Medical University Research Foundation \u0026lt;2023xkj162\u0026gt;.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHan B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. 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Clin Cancer Res. 2012;18(7):1855\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-11-0699\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-11-0699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gastric cancer, FGF19, radiotherapy sensitivity, prognosis, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-8383155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8383155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective.\u003c/h2\u003e \u003cp\u003eTo evaluate fibroblast growth factor 19 (FGF19) expression in gastric cancer and its association with clinicopathological features, prognosis, and radiosensitivity.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eFGF19 expression patterns and prognostic significance were analyzed using the TCGA-STAD dataset. Immunohistochemistry was performed on tumor tissues and matched adjacent tissues obtained from 115 surgical patients and 48 patients undergoing radiotherapy at the Second Affiliated Hospital of Anhui Medical University between 2021 and 2025. Paired chi-square tests, regression analyses, Kaplan\u0026ndash;Meier survival estimates, and logistic regression models were used to assess clinical correlations and radiosensitivity.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eFGF19 expression was significantly higher in gastric cancer tissues than in adjacent tissues in both the TCGA cohort and the clinical cohort (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High FGF19 expression was associated with shorter recurrence-free survival but not overall survival. Subgroup analyses revealed poorer recurrence-free survival among female and Caucasian patients with high FGF19 levels. Immunohistochemical staining confirmed marked overexpression in tumor tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Elevated FGF19 expression was also associated with reduced radiosensitivity (p\u0026thinsp;=\u0026thinsp;0.013; OR\u0026thinsp;=\u0026thinsp;7.014).\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eFGF19 is upregulated in gastric cancer and is associated with increased recurrence risk and diminished radiosensitivity. These findings suggest that FGF19 may serve as a useful biomarker for predicting recurrence and radiotherapy response.\u003c/p\u003e","manuscriptTitle":"Aberrant FGF19 Expression Is Associated with Prognosis and Radiosensitivity in Gastric Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 09:13:53","doi":"10.21203/rs.3.rs-8383155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee187ffe-0873-4734-99c6-a76cee29526b","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T09:29:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 09:13:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8383155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8383155","identity":"rs-8383155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}