Molecular characteristics, potential mechanisms and prognostic gene model of younger female patients with gastric cancer

Molecular characteristics, potential mechanisms and prognostic gene model of younger female patients with 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 Molecular characteristics, potential mechanisms and prognostic gene model of younger female patients with gastric cancer Xiaoyi Luan, Lulu Zhao, Wanqing Wang, Penghui Niu, Xue Han, Zerong Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143457/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Male patients were twice as likely to develop gastric cancer (GC) compared to females, partly due to the protective effect of estrogen. However, the proportion of females increased in the young GC patients. The study was designed to explore comprehensive molecular profiles of younger female GC patients, as well as develop a prognostic gene model for female GC patients. Methods Gene expression and clinical data of GC and non-tumor patients were downloaded from the Gene Expression Omnibus (GEO) database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to find molecular characteristics and potential mechanisms of younger female GC patients. The prognostic gene model containing 6 differential expressed genes (DEGs), which were between younger and older female patients, was established using Lasso-Cox regression. Its performance was validated by external validation. Then, receiver operating characteristic (ROC) curve was applied to determine the prognostic value of the prognostic gene model. Results Six GEO cohorts with 305 female GC patients (69 younger patients and 236 older patients) and 38 female non-tumor patients were included. A total of 4557 DEGs between female GC patients and non-tumor patients were identified, including 2212 up-regulated genes and 2345 down-regulated genes. Estrogen response early (p < 0.001) and estrogen response late (p < 0.001) were enriched in female GC patients. In KEGG analysis, aldosterone (p = 0.023) and relaxin pathways (p = 0.043) were concentrated in younger group. Moreover, we further used GSE84437 cohort to construct a prognostic gene model containing 6 genes, namely NREP , GAD1 , SLCO4A1 , KRT17 , DEFB1 , and P3H2 , to predict the overall survival (OS) of female GC patients (AUC = 0.810). Younger female patients, who were related with high-risk at the genetic level, showed worse OS compared with older female patients who showed low-risk (HR = 5.7688, 95%CI: 3.0108–11.0530, P < 0.001). Conclusions In conclusion, we provided the comprehensive molecular profiles of younger female GC patients and found that there was a significant difference in enriched hormone-related pathways between younger group and older group. In addition, we found younger female patients showed worse OS compared with older female patients using the prognostic gene model we created. Gastric Cancer sex pathogenesis prognostic gene model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Gastric cancer (GC) is the fifth most common cancer and the fourth leading cause of cancer-related death worldwide[ 1 , 2 ]. Male patients were twice as likely to develop GC compared to females[ 3 ]. However, the proportion of females increased in the young GC patients, with the female/male ratio ranging from 1:1–2:1[ 4 – 11 ]. Although several studies believed the female dominance in the young GC patients was related to hormonal factors, the basis of this marked disparity in incidence was poorly understood[ 6 , 12 ]. In this context, an increasing number of researchers have conducted mechanism studies in the field of younger patients with GC[ 13 – 18 ]. Previous studies suggested that the germline mutation of the CDH1 gene might lead to hereditary diffuse GC in younger patients[ 13 – 17 ], however, the conclusions above did not focus on younger female patients only. Only one study, on the basis of cell lines, found that estrogen-induced transcription of HOX antisense intergenic RNA (HOTAIR) might contribute to the pathogenesis mechanisms in diffuse GC of younger female patients[ 18 ]. However, this finding only focused on the vitro grown cell lines supported by an artificial niche, which might not closely model the in vivo environment. Therefore, the mechanism lacked sufficiently convincing evidence from animal models or population cohorts[ 19 ]. In addition, it has been gradually acknowledged that younger female GC patients had a poorer prognosis compared to the older with an abundance of clinical evidence[ 4 , 20 – 24 ]. However, little was known concerning the genetic mechanisms of poor prognosis in younger female patients with GC. As such, our study aimed to explore the molecular characteristics and potential mechanisms of younger female GC patients, and also to construct a prognostic gene model for evaluating the prognosis of female patients with GC, which provided valuable biological insights into this disease. Methods Data collection and sample information The overall design of this study is shown in Fig. 1 . Gene expression and clinical data of GC and non-tumor patients were downloaded from the GEO database. The GC group included 305 female patients with GC from 3 eligible cohorts (GSE84437, GSE15459, GSE62254), while non-tumor group, including 38 female non-tumor patients, was provided by 3 other cohorts (GSE31802, GSE8167, GSE60427). The discovery cohort contained 137 female GC patients retrieved from the GEO database (GSE84437), which was used in the construction of the prognostic gene model. Two independent cohorts were used for external validations (GSE15459, GSE62254). In addition, 137 female GC samples in South Korea (GSE84437), 67 female GC samples in Singapore (GSE15459), and 101 GC samples in the Asian Cancer Research Group (ACRG) study (GSE62254) had complete characteristic information and survival duration, which were available at: https://www.ncbi.nlm.nih.gov/geo/ . Batch effects were removed with ComBat function of “sva” package. All datasets were normalized using “limma” package. Identification of DECs in GC All datasets were downloaded through “GEOquery” package. The “limma” package was used to screen DEGs. The criteria of DEGs between GC patients and non-tumor controls was |log2FC| > 1 and p-value 0.5 and p-value < 0.05. Functional and pathway enrichment analysis Functional annotation of the DEGs was implemented via “clusterProfiler” package, and the further analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed. The gene set enrichment analysis (GSEA) was also conducted for ascertaining the difference in pathways through “clusterProfiler” package. The gene sets of “c5.all.v7.0.entrez” and “h.all.v7.0.entrez” were acquired from the Molecular Signatures Database (MSigDB) to run GSEA. A pathway term with |Normalized Enrichment Score (NES)| > 1, adjusted p value < 0.05, and false discovery rate (FDR) < 0.25 was considered to be significantly enriched. Bar chart was plotted by https://www.bioinformatics.com.cn (last accessed on 10 July 2023), an online platform for data analysis and visualization. Construction and validation of a prognostic risk model The 266 candidate DEGs related to prognosis were selected through "survival" package. Survival-related DEGs were subjected to Lasso Cox regression analysis, and 6 genes were finally identified and involved in the construction of the prognostic gene model, including NREP , GAD1 , SLCO4A1 , KRT17 , DEFB1 and P3H2 . The risk score was calculated according to the following formula: risk score = \({\sum }_{\text{i}=1}^{\text{n}}(\text{E}\text{x}\text{p}\text{i}\ast \text{C}\text{o}\text{e}\text{f}\text{i})\) . Expi represents the gene expression levels, and Coefi represents the risk coefficients. The cutoff point of risk score was determined via “survminer” package. Based on the cutoff point of discovery cohort, patients from discovery cohort were divided into low-risk and high-risk groups. The ratio of high-risk/low-risk in the discovery cohort was used as the criteria for dividing the external validation cohort. Construction and validation of the prognostic gene model The differences in overall survival (OS) between low-risk group and high-risk group were evaluated through the Kaplan–Meier analysis generated by “survival” package. For the predicted assessment of female patients with GC in the prognostic value of the prognostic gene model, the receiver operating characteristic (ROC) curve analysis and the time-dependent ROC curve analysis were performed for obtaining the area under the curve (AUC). We performed univariate and multivariate Cox proportional hazards regression for all cohorts to determine whether the risk score was an independent predictor of prognosis. Assessment of tumor microenvironment In order to explore the changes of tumor microenvironment (TME) in female GC patients between different risk, we used “estimate” package to score the stromal score, immune score, ESTMATE score, and Tumor Purity of three cohorts through the algorithm. Drug Sensitivity Analysis In order to study the clinical manifestations of chemotherapy drugs in female patients, we calculated the semi-inhibitory concentration (IC50) values of 198 drugs through “oncoPredict” package. Wilcoxon signed-rank test was used to explore the difference in IC50 between low-risk and high-risk groups. The results were performed via “ggpubr” package. Statistical analysis The correlations between continuous variables were assessed using Spearman correlation analysis, and differences in the variables between different risk groups were evaluated with the Student t test, one-way ANOVA, Pearson’s chi-squared test, or Wilcoxon signed-rank test. Results with two-sided p-value < 0.05 were deemed significant. The statistical analysis of this study was performed using R-4.3.0 software (2023-04-21 ucrt). Results Characteristics of females GC patients The clinicopathologic characteristics of female GC patients was listed in Table 1. There was no statistically significant difference in OS between younger and older female patients (P = 0.85). In comparison to older female patients, younger female patients were more likely to be Lauren diffuse-type GC (84.6% vs. 53.5%, P = 0.002). No variations in tumor stage were observed between the two groups (P > 0.05). To investigate the molecular characteristics of female GC patients, a total of 4557 DEGs, including 2212 up-regulated genes and 2345 down-regulated genes, were identified, which were statistically significant between female GC patients and non-tumor controls (Fig. 2 A). The 5 top up-regulated were RPL27A , CDH17 , COL6A3 , GCNT3 , and RPL37A . The specific expression of the screened DEGs was demonstrated by the heatmap as shown in Fig. 2 B. In order to further explore the underlying mechanisms involved in female GC development, functional enrichment analyses of the DEGs were conducted. GO analysis indicated that in biological processes (BP), up-regulated genes were mainly enriched in wound healing, mesenchyme development, extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, and down-regulated genes were mainly enriched in proteasome-mediated ubiquitin-dependent protein catabolic process, establishment of protein localization to organelle, positive regulation of cellular catabolic process, cellular response to peptide, regulation of intracellular transport. The cell component (CC) and molecular function (MF) of GO analysis were showed in Figs. 2 C, 2 E. Moreover, the KEGG analysis revealed that up-regulated genes were enriched in IL-17 signaling pathway, base excision repair, protein digestion and absorption, cell cycle, human papillomavirus (HPV) infection, epithelial cell signaling in Hp infection, and down-regulated genes were enriched in ribosome, neurotrophin signaling pathway, salmonella infection (Figs. 2 D, 2 F). Combined with the results of GO and KEGG, we found that the DEGs between female GC patients and non-tumor controls were significantly enriched in metabolic pathways, extracellular structure, cell cycle, and Hp infection. Enrichment plots from GSEA in C5 collection were showed in Figs. 2 G, 2 F. The results of GSEA based on the Hallmarker gene sets showed that epithelial-mesenchymal transition, inflammatory response, TNFα signaling via NF-κB, KRAS signaling, estrogen response early and late, IL-6/JAK/STAT3 signaling, angiogenesis, and G2M checkpoint were significantly up-regulated in female GC patients, which suggested there was a potential relationship between GC and estrogen pathways (estrogen response early: NES = 1.838, p < 0.001, FDR < 0.001; estrogen response late: NES = 1.842, p < 0.001, FDR < 0.001, Fig. 2 I). Differences in potential mechanisms between younger and older female GC patients Female GC patients included in our study were divided into 2 groups according to age 50 (≤ 50 years of age, n = 69; > 50 years of age, n = 236). A total of 4633 DEGs were identified in younger group, including 2241 up-regulated genes and 2392 down-regulated genes. In older group, a total of 4583 DEGs were identified, including 2241 up-regulated genes and 2342 down-regulated genes. The DEGs of two groups were displayed by volcano maps as shown in Fig. 3 A and Fig. 3 C. The distribution of the DEGs were demonstrated by the heatmaps as shown in Figs. 3 B, 3 D. In order to further explore the potential mechanisms involved in GC development of younger female patients, the enrichment analysis of GO and KEGG were performed on 4633 DEGs between younger female patients and non-tumor controls. Enrichment results of GO analysis were shown in Figs. 4 A, 4 C. GO analysis indicated that in BP, up-regulated genes were mainly enriched in ameboidal-type cell migration, wound healing, mesenchyme development, cell-substrate adhesion, extracellular matrix organization, extracellular structure organization, and down-regulated genes were mainly enriched in proteasome-mediated ubiquitin-dependent protein catabolic process, establishment of protein localization to organelle, ribonucleoprotein complex biogenesis, positive regulation of cellular catabolic process, response to oxidative stress. The KEGG pathways related to up-regulated genes were mainly concentrated in the pathways of protein digestion and absorption, IL-17 signaling pathway, epithelial cell signaling in Hp infection, HPV infection, proteoglycans in cancer. Considering the down-regulated genes, the KEGG pathways were mainly concentrated in the pathways of ribosome, neurotrophin signaling pathway, ubiquitin mediated proteolysis, insulin signaling pathway, salmonella infection. As the results of GSEA based on the Hallmarker gene sets, estrogen response early and late were enriched in younger group (estrogen response early: NES = 1.774, p < 0.001, FDR < 0.001; estrogen response late: NES = 1.699, p < 0.001, FDR < 0.001, Fig. 4 G). Other results were showed in Fig. 4 . Functional and pathway enrichment analysis of the DEGs of older group was showed in Figure S1 . Combining the results of the two groups, there was no significant difference in enriched pathways between younger group and older group in GO analysis. The above GO terms were involved in cancer occurrence and development to some extent, especially the up-regulated DEGs which were majorly associated with metabolic pathways and extracellular structure (Figure S1 A). Comparing the KEGG analysis results of the two groups, we found GC in the two groups were both related to Hp infection, because the DEGs were enriched in IL-17 signaling pathway and epithelial cell signaling in Hp infection. However, there was a significant difference in enriched hormone-related pathways between two groups. The down-regulated genes of two groups were both enriched in insulin, prolactin, and glucagon signaling pathway. Aldosterone (p = 0.023) and relaxin pathways (p = 0.043) were concentrated in younger group, while growth hormone synthesis, secretion and action (p = 0.046) and thyroid hormone signaling pathway (p = 0.049) were concentrated in older group (Fig. 4 H). The results of GSEA based on the Hallmarker gene sets showed that estrogen response early and late were enriched in older group, which were consistent with the results of GSEA in younger group (estrogen response early: NES = 1.827, p < 0.001, FDR < 0.001; estrogen response late: NES = 1.895, p < 0.001, FDR < 0.001, Figure S1 G). Establishment prognostic gene model and survival outcomes in GC We further used GSE84437 cohort to construct a prognostic gene model for predicting the overall survival (OS) of female GC patients. The DEGs of GSE84437 cohort were identified between 30 younger and 107 older female GC patients, including 463 up-regulated genes and 184 down-regulated genes (Figs. 5 A, 5 B). The enrichment analysis of GO and KEGG were performed on the DEGs of GSE84437. The results of GO analysis were shown in Figs. 5 C, 5 E. GO analysis indicated that in BP, extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, regulation of angiogenesis, and regulation of vasculature development were mainly enriched in younger female patients, while nuclear division, organelle fission, chromosome segregation, nuclear chromosome segregation, and mitotic nuclear division were mainly enriched in older female patients. As a result of KEGG, younger female patients showed significant enrichment in pathways related cancer progression, such as focal adhesion, vascular smooth muscle contraction, complement and coagulation cascades, ECM-receptor interaction, and tight junction. However, older female patients showed main enrichment in cell cycle pathways, such as cell cycle, IL-17 signaling pathway, progesterone-mediated oocyte maturation, and oocyte meiosis. Enrichment results from GSEA in C5 collection showed similar results to GO and KEGG analysis (Figs. 5 G, 5 H). Top 5 pathways associated with tumor progression in younger female patients were epithelial-mesenchymal transition, apical junction, KRAS signaling, angiogenesis, and coagulation (Fig. 5 I). In older female patients, top 5 pathways associated with tumor progression were E2F targets, G2/M checkpoint, mTORC1 signaling, MYC targets V1 and V2 (Fig. 5 J). After further analysis of univariate Cox regression in the DEGs, we identified 266 genes related to survival time (p < 0.05). As a next step, survival-related DEGs were subjected to Lasso Cox regression analysis, and 6 genes were finally identified and involved in the construction of the prognostic gene model, including NREP , GAD1 , SLCO4A1 , KRT17 , DEFB1 and P3H2 (Figs. 6 A, 6 B). Based on the results of the Lasso Cox regression analysis, the prognostic gene model was constructed as follows: \(\text{R}\text{i}\text{s}\text{k} \text{s}\text{c}\text{o}\text{r}\text{e} = \text{E}\text{x}\text{p}(\) NREP ) \(\times\) (0.425262001)+ \(\text{E}\text{x}\text{p}(\) GAD1 ) \(\times\) (-0.033627453)+ \(\text{E}\text{x}\text{p}(\) SLCO4A1 ) \(\times\) (-0.066757311)+ \(\text{E}\text{x}\text{p}(\) KRT17 ) \(\times\) (0.119705227)+ \(\text{E}\text{x}\text{p}(\) DEFB1 ) \(\times\) (0.032480849)+ \(\text{E}\text{x}\text{p}(\) P3H2 ) \(\times\) (0.076266766). After further analysis of applying risk score, there was a significant difference in the risk score between younger female GC patients and older female GC patients (p = 0.026, Fig. 6 C). The optimal cutoff was identified to classify female GC patients into two groups (high-risk and low-risk groups) with the most distinct survivals by a method based on maximally selected rank statistics (Fig. 6 D). The result revealed that the perfect ratio of high-risk/low-risk seemed to be 1:2, and female GC patients in the discovery cohort were well dispersed in high-risk group (n = 46) and low-risk group (n = 91). The distribution plot of the risk score demonstrated that the survival times were reduced while the risk score increased (Figs. 6 E, 6 F). Kaplan-Meier survival curves comparing high-risk and low-risk patients were also constructed to further evaluate the prognostic potential of the prognostic gene model. The result suggested that high-risk patients showed worse OS compared with low-risk patients (p < 0.01, Fig. 6 G). ROC analyses indicated that the prognostic value (AUC) of the prognostic gene model for predicting OS were 0.81 (Fig. 6 H). Moreover, the AUC values of this prognostic gene model for the 1-year, 3-year, and 5-year OS of female patients with GC were 0.78, 0.78, and 0.75, respectively (Fig. 6 I). In conclusion, the prognostic gene model exhibited high prognostic value in the discovery cohort GSE84437. Validation of the prognostic gene model Two independent cohorts were used for external validations (GSE15459, GSE62254). The patients of two validation cohorts were assigned into different risk subgroups based on the unified ratio consistent with the discovery cohort. The risk plot of the prognostic gene model indicated that as risk score increased, OS time decreased while mortality rise (Figs. 7 A, 7 B). As illustrated in Figures 7 C, 7 D, low-risk patients had a better OS than high-risk patients whether in the GSE15459 cohort or the GSE62254 cohort (GSE15459, p = 0.015; GSE62254, p = 0.003, respectively). The AUC values of the prognostic gene model in the GSE15459 cohort and the GSE62254 cohort were 0.71 and 0.68 respectively. Furthermore, we estimated the AUC values for predicting OS at 1-year, 3-year, and 5-year in the GSE15459 cohort and the GSE62254 cohort, respectively (GSE15459, 1-year: 0.68, 3-year: 0.68, 5-year: 0.72; GSE62254, 1-year: 0.61, 3-year: 0.63, 5-year: 0.65). As showed in Figures 7 C, 7 D, the AUC values were as expected, implying this prognostic gene model was an effective instrument for the prognostic risk classification of female GC patients. To determine the independent prognostic value of prognostic gene model for female GC patients, we performed univariate Cox regression analysis and multivariate Cox regression analysis to explore prognostic independence of multiple clinical factors. Table 2 showed the result of univariate Cox regression analysis in discovery cohort, which suggested the risk score was significantly associated with the prognosis of GC in female patients (HR = 6.601, 95%CI: 3.630-12.000, P < 0.001). After further multivariate Cox regression analysis, Table 3 showed that the risk score presented as an independent prognostic factor after adjusting for other clinicopathologic characteristics (HR = 5.769, 95%CI: 3.011–11.053, P < 0.001). Based on the same analysis performed in external validations, the results were concordant with the findings available in the discovery cohort (GSE15459, HR = 2.171, 95%CI: 1.052–4.479, P = 0.036; GSE62254, HR = 3.136, 95%CI: 1.577–6.236, P = 0.001, Table S1 -4). Overall, the high-risk patients’ prognosis was poorer compared to low-risk patients. The clinicopathologic characteristics of GC patients stratified by risk were listed in Table 4. Younger female GC patients were more likely to be in high-risk group compared with the older (31.1% vs. 18.3%, P = 0.018). In addition, the stage of GC was more advanced in high-risk group than that in low-risk group (TNM I: 7.0% vs. 18.9%; TNM II: 17.5% vs. 29.7%; TNM III: 35.1% vs. 26.1%; TNM IV:40.4% vs. 25.2%, P = 0.022). However, there was no significant difference in Lauren classification between the two groups (p = 0.453). Establishment of a nomogram to predict survival Due to the high correlation between prognostic gene model and patients’ prognosis, by integrating the risk scores and well-known prognostic factors, a nomogram was constructed by using the discovery cohort for OS prediction. This nomogram was developed to predict 1-year, 3-year, and 5-year OS rates in female patients with GC (Fig. 8 A). For the discovery cohort, the AUC values were as expected, implying this nomogram had an excellent predictive ability for prognosis (Figs. 8 B, 8 C). The calibration curve showed well performance for the nomogram between actual observations and predicted values (Figs. 8 D). The clinical usefulness of the nomogram was quantified by the decision curve, and we found that this prognostic model with diverse clinical factors presented more net benefits for predicting the prognosis (Figs. 8 E). As shown in Figures 8 F-I, the similar results were showed in GSE62254 cohort. Assessment of the tumor microenvironment and drug sensitivity analysis TME played an important role in tumor prevention and therapy. The risk score of prognostic gene model was positively linked to stromal score (p < 0.001, Fig. 9 A and Figure S2A, S2E). Meanwhile, this result was consistent with the GO analysis and GSEA results of younger female GC patients in discovery cohort (Fig. 5 C, 5 G), because the stromal cells, such as cancer-associated fibroblasts, could secrete a variety of extracellular matrix components. However, the results revealed that the risk score was not associated with immune score in either discovery or two external validation cohorts (all p > 0.05, Fig. 9 B and Figure S2B, S2F). To explore suitable drugs for high-risk group, we estimated the IC50 values of 198 drugs in GSE84437, GSE15459 and GSE62254 patients. All results of drug sensitivity analysis for each cohort were exhibited in Table S5-S7. We discovered that female GC patients with high-risk might positively react to Dasatinib (targeting drug, ABL, SRC, Ephrins, PDGFR, and KIT inhibitor) and AZD1332 (targeting drug, receptor tyrosine kinase inhibitor; Dasatinib: GSE84437: p < 0.001, GSE15459: p < 0.001, and GSE62254: p = 0.001; AZD1332: GSE84437: p < 0.001, GSE15459: p = 0.023, and GSE62254: p = 0.027, Fig. 10 A and Fig. 10 C, 10 D). Female GC patients from high-risk group exhibited greater resistance to 25 drugs, including those of Navitoclax (targeting drug, Bcl-2 inhibitor), Vorinostat (targeting drug, HDAC inhibitor), MK-2206 (targeting drug, AKT inhibitor), Palbociclib (targeting drug, CDK4/6 inhibitor), Sorafenib (targeting drug, PDGFR, KIT, VEGFR, and RAF inhibitor), Oxaliplatin (Chemotherapy drug), GSK1904529A (targeting drug, IGF1R, IR inhibitor), PF-4708671 (targeting drug, S6K1 inhibitor), Tamoxifen (Chemotherapy drug), BMS-345541 (targeting drug, IKK inhibitor), LGK974 (targeting drug, PORCN inhibitor), VE-822 (targeting drug, ATR inhibitor), ML323 (targeting drug, USP1, UAF1 inhibitor), Ribociclib (targeting drug, CDK4/6 inhibitor), TAF1_5496 (targeting drug, TAF1 inhibitor), Selumetinib (targeting drug, MEK1/2 inhibitor), Fulvestrant (targeting drug, ESR inhibitor), Dihydrorotenone (mitochondrial inhibitor), ABT737 (targeting drug, Bcl-2 inhibitor), AZD6738 (targeting drug, ATR inhibitor), Ipatasertib (targeting drug, AKT inhibitor), P22077 (targeting drug, USP7/47 inhibitor), VX-11e (targeting drug, ERK2 inhibitor), Uprosertib (targeting drug, AKT inhibitor), and VE821 (targeting drug, ATR inhibitor), than those from low-risk group patients (Fig. 10 B). Overall, these results indicated that the prognostic gene model was correlated with drug sensitivity. Discussion In this study, we investigated the molecular characteristics of female GC patients on the basis of six GEO cohort, suggesting that the pathogenesis mechanism of GC in female patients was associated with estrogen. Furthermore, compared with older female patients, we found aldosterone and relaxin pathways were concentrated in younger group. The most critical finding in the present study was that the younger female GC patients showed worse OS compared with the older using the prognostic gene model we created. To the best of our knowledge, our study was the first to investigate the comprehensive molecular profiles of younger female GC patients, which manifested the mechanisms of pathogenesis and prognosis in younger females with GC. Previous prospective cohort studies and human cell lines studies suggested that female hormones might play a protective role in GC risk[ 25 – 31 ]. However, the occurrence of GC was frequently observed that females were more susceptible than males in younger group, which prompted a reconsideration of the potential mechanisms of estrogen[ 4 – 11 ]. In our study, with the exception of other cancer- and metastasis-associated pathways, female GC patients were markedly enriched in estrogen response early and late with GSEA enrichment analysis, which suggested there was a potential relationship between GC and estrogen pathways. Notably, when we used GSEA enrichment analysis to evaluate potential mechanism differences between younger and older female GC patients, estrogen response early and late were enriched in both two groups. This finding suggested that estrogen might not be the key influence in the susceptibility of younger female patients to develop GC. However, as the results of KEGG analysis, we found there was a significant difference in enriched other hormone-related pathways between younger group and older group. Aldosterone and relaxin pathways were concentrated in younger group, while growth hormone synthesis, secretion and action and thyroid hormone signaling pathway were concentrated in older group. Although there were many studies suggesting the relationship between these hormones and GC or other cancer, a lack of consensus existed regarding these hormones as a significant factor of pathogenesis mechanisms in younger female patients with GC, and further research was needed to explore their implications[ 32 – 41 ]. Most of the relevant clinical studies showed that younger female GC patients had a poorer prognosis compared to the older, however, the evidence regarding the mechanisms underlying such outcomes was scarce at the genetic level[ 4 , 20 – 24 ]. In this study, we constructed the effective prognostic gene model containing 6 genes, namely NREP , GAD1 , SLCO4A1 , KRT17 , DEFB1 , and P3H2 , and demonstrated its predictive ability. We found that the younger female GC patients had higher risk scores compared with older female GC patients in the prognostic gene model we created, suggesting that younger female patients showed worse OS compared with the older, which was consistent with previous studies. Most of the genes in the prognostic gene model have been reported to be associated with cancer development. NREP played an important role in the progression of GC through diverse mechanisms, such as epithelial-mesenchymal transition activation, cancer-associated fibroblasts activation, actin cytoskeleton remodeling, and M2 macrophage infiltration, and its expression was powerfully associated with T stage and histologic grade[ 42 – 44 ]. KRT17 could promote the proliferation, migration, and invasion of GC cells, and its expression was positively correlated with the TMN stage, lymphatic metastasis, depth of invasion and vascular invasion[ 45 – 47 ]. Moreover, they found KRT17 could regulate cell cycle and modulate cell cycle proteins, suggesting that KRT17 might be a possible molecular target for targeted therapy in GC[ 46 , 47 ]. Pignata et al reported that P3H2 was a new molecular player involved in new vessels formation, and they found that P3H2 knockdown prevented pathological angiogenesis in vivo[ 48 ]. Pathological angiogenesis in GC might be associated with its overexpression. The expression of Human β-defensin 1 (encoded by DEFB1 ) was found to be associated with Hp infection, while the mechanisms by which DEFB1 achieved its effects in GC remained unclear[ 49 , 50 ]. In contrast to our findings, most studies indicated the elevated expression of SLCO4A1 might facilitate the progression of cancer, because they found that SLCO4A1 mediated the cellular uptake of many substrates including hormones, which regulated the progression of cancer through binding the hormone receptors[ 51 – 53 ]. It was possible that SLCO4A1 mediated the cellular uptake of estrone-sulfate and dehydroepiandrosterone-sulfate in GC, which were the most important estrogen precursors in the plasma of postmenopausal females[ 53 ]. Therefore, estrogen might play a potentially protective role in the progression of GC. GAD1 contributed to the progression in various types of cancer, however, it was identified as a favorable gene for prognosis of female GC patients in our study[ 54 – 57 ]. Currently, no relevant study has been conducted to investigate the role of GAD1 in GC. Two critical genes in the prognostic gene model, NREP and KRT17 , were all related to epithelial-mesenchymal transition, which suggested that epithelial-mesenchymal transition could be the main reason for the high-risk group with poor prognosis. Epithelial-mesenchymal transition is a reversible cellular process that transiently induces epithelial cells to quasi-mesenchymal cell characteristics[ 58 ]. During carcinoma progression, epithelial-mesenchymal transition can increase metastatic powers and elevate therapeutic resistance of cancer[ 58 ]. Consistent with this, our study showed that the high-risk group had more advanced stage of GC and were resistant to more drugs than the low-risk group. The risk score calculated by the prognostic gene model correlated significantly with clinicopathologic characteristics of female GC patients. After controlling confounding parameters, the results indicated that the risk score was an independent predictor for female GC patients’ survival outcomes. To further improve the accuracy of prognostic prediction, we constructed and validated a nomogram by screening various indexes, and made it easier to use the prognostic gene model. TME, the environment in which the tumor resided, consisted of malignant cells, immune cells, stromal cells, extracellular matrix, and a variety of cytokines, and was nourished by a vascular network[ 59 , 60 ]. Stromal cells were thought to have important roles in tumour growth, disease progression and drug resistance, and the percentage of stromal cells in TME represents the stromal score[ 61 – 65 ]. Based on previous studies, GC patients with stromal score-high showed poor OS and identified as an independent prognostic factor[ 64 , 65 ]. Consistent with this finding, our study found that the risk score of prognostic gene model was positively linked to stromal score, which suggested the poor prognosis of high-risk group might be closely correlated with stromal cells and extracellular matrix in TME. Whereas surgery remains a mainstay, multimodality treatment is the standard of care for advanced resectable GC, with particular emphasis on perioperative chemotherapy[ 66 – 68 ]. However, increasing evidence has shown that stromal cells, such as cancer-associated fibroblasts, mediate chemotherapy resistance in several tumors by releasing cytokines, exosomes and metabolites[ 69 – 71 ]. Thus, we explored the sensitivity of various drugs in female GC patients between two risk subgroups. In our study, we found out that female patients of high-risk group were resistant to 25 drugs and sensitive to 2 drugs. Notably, we found out that the high-risk group was resistant to ESR inhibitor Fulvestrant, which might provide new insights into the role of estrogen in female GC. A key advantage of this study was that the significant difference in enriched hormone-related pathways between younger and older group were identified, and a prognostic gene model based on the DEGs between younger and older female patients was constructed. Another advantage of the current study was the consistent statistical results in three independent cohorts, confirming the robustness and precision of the prognostic gene model. The limitations of this study included the fact that all gene expression and clinical data from public databases were obtained retrospectively, and inherent selection bias might affect the accuracy of the analysis results. Additionally, extensive prospective studies and complementary in vivo and in vitro experimental studies were necessary to gain insight into the potential mechanisms involved in GC development of younger female patients, thus confirming our findings. Conclusion In conclusion, we provided the comprehensive molecular profiles of younger female GC patients and found that there was a significant difference in enriched hormone-related pathways between younger group and older group. In addition, we found younger female patients showed worse OS compared with older female patients using the prognostic gene model we created. Declarations Ethics approval and consent to participate All data can be found in GEO databases. Ethical approval has been obtained for this study. Consent for publication For this type of study formal consent is not required. Availability of data and materials The public datasets analyzed in this study can be found in GSE (https://www.ncbi.nlm.nih.gov/geo/). Competing interests The authors declare no competing interests. Funding This work was supported by the grant from National Key R&D Program of China (No. 2017YFC0908300) and 2023 Scientific Research Project of Chronic Diseases Control and Health Education (No. BJMB0012023024005). Authors' contributions Luan X.Y. : Writing-original draft, Data curation; Zhao L.L. : Writing-original draft, Visualization; Wang W.Q. : Formal analysis; Niu P.H. : Formal analysis; Han X. : Data curation; Wang Z.R. : Resources; Zhang X.J. : Writing-review & editing; Zhao D.B. : Writing-review & editing, Supervision; and Chen Y.T. : Conceptualization, Resources, Methodology, Writing-review & editing, Supervision. All authors discussed the findings and approved the final version of the manuscript. 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Fiori ME, Di Franco S, Villanova L, Bianca P, Stassi G, De Maria R: Cancer-associated fibroblasts as abettors of tumor progression at the crossroads of EMT and therapy resistance . Mol Cancer 2019, 18 (1):70. Dasari S, Fang Y, Mitra AK: Cancer Associated Fibroblasts: Naughty Neighbors That Drive Ovarian Cancer Progression . Cancers (Basel) 2018, 10 (11). He X, Lee B, Jiang Y: Extracellular matrix in cancer progression and therapy . Med Rev (Berl) 2022, 2 (2):125-139. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementary.zip Table1.xls Table 1. The clinicopathologic characteristics of female GC patients. Table2.xls Table 2. The result of univariate Cox regression analysis in discovery cohort. Table3.xls Table 3. The result of multivariate Cox regression analysis in discovery cohort. Table4.xls Table 4. The clinicopathologic characteristics of female GC patients stratified by risk. 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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-4143457","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286065672,"identity":"12c9cf04-8722-49a5-a0ed-908dc4b07ef0","order_by":0,"name":"Xiaoyi Luan","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Luan","suffix":""},{"id":286065673,"identity":"294023a0-c6bb-4b2e-8716-2de2b7d0bfce","order_by":1,"name":"Lulu 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female gastric cancer (GC) patients and non-tumor controls. (A) Volcano plot of the DEGs with the top 15 DEGs. (B) Heat map of the DEGs. (C, D) GO function, KEGG pathway analysis of up-regulated DEGs. (E, F) GO function, KEGG pathway analysis of down-regulated DEGs. (G) Enrichment plots from GSEA in C5 collection of up-regulated genes. (H) Enrichment plots from GSEA in C5 collection of down-regulated genes. (I) Pathways associated with tumor progression in female GC patients.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/b86b62d77909ea49c56ac823.png"},{"id":54161350,"identity":"509c7032-c111-4b9d-aa25-bb03154b7dcb","added_by":"auto","created_at":"2024-04-05 13:05:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4683473,"visible":true,"origin":"","legend":"\u003cp\u003eThe differentially expressed genes (DEGs) of the two groups. (A) Volcano plot of the DEGs with the top 15 DEGs in the younger group. (B) Heat map of the DEGs in the younger group. (C) Volcano plot of the DEGs with the top 15 DEGs in the older group. (D) Heat map of the DEGs in the older group.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/b31a1631d34a3094fc54cb23.png"},{"id":54161356,"identity":"5b0e6ecb-2cab-4bca-858d-bf97687d9421","added_by":"auto","created_at":"2024-04-05 13:05:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3106250,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional annotation of DEGs in female GC patients of the younger group. (A, B) GO function, KEGG pathway analysis of up-regulated DEGs. (C, D) GO function, KEGG pathway analysis of down-regulated DEGs. (E) Enrichment plots from GSEA in C5 collection of up-regulated genes. (F) Enrichment plots from GSEA in C5 collection of down-regulated genes. (G) Endocrine-related pathways in the enrichment plot from GSEA in HALLMARK collection. (H) Differences in endocrine-related pathways in KEGG pathway analysis between the younger and older groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/2fdbf9134c4e489f0eb7b491.png"},{"id":54161341,"identity":"669ae207-ba21-4581-afd0-abeb2fa0bbbd","added_by":"auto","created_at":"2024-04-05 13:05:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4356072,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional annotation of DEGs in female GC patients of the discovery cohort. (A) Volcano plot of the DEGs with the top 15 DEGs. (B) Heat map of the DEGs. (C, D) GO function, KEGG pathway analysis of up-regulated DEGs. (E, F) GO function, KEGG pathway analysis of down-regulated DEGs. (G) Enrichment plots from GSEA in C5 collection of up-regulated genes. (H) Enrichment plots from GSEA in C5 collection of down-regulated genes. (I) Top 5 pathways associated with tumor progression in younger female GC patients. (J) Top 5 pathways associated with tumor progression in older female GC patients.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/b046da77e6a5d4e854b55ff3.png"},{"id":54163173,"identity":"fc0ec395-a284-4796-b149-192f244d472e","added_by":"auto","created_at":"2024-04-05 13:13:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":120946,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between risk score and prognosis in female GC patients. (A) Six gene expression signatures based on DEGs were selected by the LASSO Cox models. (B) Cross-validation for tuning parameter selection in the LASSO model. (C) The distribution of risk scores in younger and older female patients. (D) The optimal cutoff based on maximally selected rank statistics. (E) The survival status of female patients (ranked by increasing risk score). (F) The distribution of risk scores in total female patients. (G) Kaplan–Meier curves of survival for different risk groups. (H) ROC curve of the risk score model. (I) Time-dependent ROC curve of the risk score model for predicting 1, 3 and 5 years.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/239b483c65df40bb4c8ecefe.png"},{"id":54161338,"identity":"362f51f8-8872-49c8-b4c0-c6d56a439723","added_by":"auto","created_at":"2024-04-05 13:05:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":103687,"visible":true,"origin":"","legend":"\u003cp\u003eThe results from external validation cohorts. (A) The distribution of survival status and risk scores in female patients of GSE15459. (B) The distribution of survival status and risk scores in female patients of GSE62254. (C) Kaplan–Meier curves and ROC curve from GSE15459. (D) Kaplan–Meier curves and ROC curve from GSE62254.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/107b5352596ac3678ed2c7c9.png"},{"id":54161347,"identity":"74c40759-765a-4d90-9ebe-a66ce161690d","added_by":"auto","created_at":"2024-04-05 13:05:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":891219,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of a nomogram. (A) Nomogram for predicting 1, 3 and 5 years OS of female GC patients in the discovery cohort. (B) ROC curves of the nomograms in the discovery cohort. (C) Time-dependent ROC curve of the nomogram in the discovery cohort for predicting 1, 3 and 5 years. (D) Calibration curves of the nomogram for predicting of 1, 3 and 5 years OS in the discovery cohort. (F-I) The validation of the GSE62254 cohorts.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/5ba8cab266c0576584e3fabd.png"},{"id":54161344,"identity":"33bf4a9a-8854-4011-ab61-876c966074d8","added_by":"auto","created_at":"2024-04-05 13:05:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":384381,"visible":true,"origin":"","legend":"\u003cp\u003eThe assessment of TME in female GC patients from the discovery cohort. (A-D) Stromal score, immune score, ESTIMATE score, and Tumor Purity in GSE84437.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/c13c88fd9184437396f98aea.png"},{"id":54161348,"identity":"87f98c26-17f2-42dd-b4cc-547d3bca6a97","added_by":"auto","created_at":"2024-04-05 13:05:36","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1023060,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis of total female GC patients. (A) Sensitive drugs of the high-risk group. (B) Resistant drugs of the high-risk group. (C) Relationships between risk score and the sensitivity of AZD1332. (D) Relationships between risk score and the sensitivity of Dasatinib.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/87eb857dd0e79c92d1343d68.png"},{"id":60743365,"identity":"43f8fc7e-cc08-4290-96ad-20a0182e358a","added_by":"auto","created_at":"2024-07-20 14:24:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29762181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/ba4dabd4-fbba-4cc0-be06-b008991340e6.pdf"},{"id":54161339,"identity":"9dc6e21a-33d2-4bcb-b1e6-95287b3fb2ef","added_by":"auto","created_at":"2024-04-05 13:05:35","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":537034,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/c21c36bf4cb650806d37498c.zip"},{"id":54161349,"identity":"14afee26-89c3-4d65-9173-f31e19678da3","added_by":"auto","created_at":"2024-04-05 13:05:36","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23552,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1. The clinicopathologic characteristics of female GC patients.\u003c/p\u003e","description":"","filename":"Table1.xls","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/775f6cfd523117c62052a52a.xls"},{"id":54161345,"identity":"f93c1145-22c5-4f05-a9b6-2eb9c79dfbc2","added_by":"auto","created_at":"2024-04-05 13:05:35","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20480,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2. The result of univariate Cox regression analysis in discovery cohort.\u003c/p\u003e","description":"","filename":"Table2.xls","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/6379d9c64787b8a4bc055f6e.xls"},{"id":54161352,"identity":"39984463-7099-4991-980f-4d891f5d0fa5","added_by":"auto","created_at":"2024-04-05 13:05:36","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20480,"visible":true,"origin":"","legend":"\u003cp\u003eTable 3. The result of multivariate Cox regression analysis in discovery cohort.\u003c/p\u003e","description":"","filename":"Table3.xls","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/6bfe950c3162c5636ffd48bf.xls"},{"id":54161353,"identity":"e4202961-6d50-4d52-bd11-d65f767106e4","added_by":"auto","created_at":"2024-04-05 13:05:36","extension":"xls","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":23552,"visible":true,"origin":"","legend":"\u003cp\u003eTable 4. The clinicopathologic characteristics of female GC patients stratified by risk.\u003c/p\u003e","description":"","filename":"Table4.xls","url":"https://assets-eu.researchsquare.com/files/rs-4143457/v1/fe4d5d665808d676ebc45eab.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular characteristics, potential mechanisms and prognostic gene model of younger female patients with gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is the fifth most common cancer and the fourth leading cause of cancer-related death worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Male patients were twice as likely to develop GC compared to females[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the proportion of females increased in the young GC patients, with the female/male ratio ranging from 1:1\u0026ndash;2:1[\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although several studies believed the female dominance in the young GC patients was related to hormonal factors, the basis of this marked disparity in incidence was poorly understood[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, an increasing number of researchers have conducted mechanism studies in the field of younger patients with GC[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous studies suggested that the germline mutation of the \u003cem\u003eCDH1 gene\u003c/em\u003e might lead to hereditary diffuse GC in younger patients[\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], however, the conclusions above did not focus on younger female patients only. Only one study, on the basis of cell lines, found that estrogen-induced transcription of HOX antisense intergenic RNA (HOTAIR) might contribute to the pathogenesis mechanisms in diffuse GC of younger female patients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, this finding only focused on the vitro grown cell lines supported by an artificial niche, which might not closely model the in vivo environment. Therefore, the mechanism lacked sufficiently convincing evidence from animal models or population cohorts[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, it has been gradually acknowledged that younger female GC patients had a poorer prognosis compared to the older with an abundance of clinical evidence[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, little was known concerning the genetic mechanisms of poor prognosis in younger female patients with GC.\u003c/p\u003e \u003cp\u003e As such, our study aimed to explore the molecular characteristics and potential mechanisms of younger female GC patients, and also to construct a prognostic gene model for evaluating the prognosis of female patients with GC, which provided valuable biological insights into this disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and sample information\u003c/h2\u003e \u003cp\u003eThe overall design of this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Gene expression and clinical data of GC and non-tumor patients were downloaded from the GEO database. The GC group included 305 female patients with GC from 3 eligible cohorts (GSE84437, GSE15459, GSE62254), while non-tumor group, including 38 female non-tumor patients, was provided by 3 other cohorts (GSE31802, GSE8167, GSE60427). The discovery cohort contained 137 female GC patients retrieved from the GEO database (GSE84437), which was used in the construction of the prognostic gene model. Two independent cohorts were used for external validations (GSE15459, GSE62254). In addition, 137 female GC samples in South Korea (GSE84437), 67 female GC samples in Singapore (GSE15459), and 101 GC samples in the Asian Cancer Research Group (ACRG) study (GSE62254) had complete characteristic information and survival duration, which were available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Batch effects were removed with ComBat function of \u0026ldquo;sva\u0026rdquo; package. All datasets were normalized using \u0026ldquo;limma\u0026rdquo; package.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DECs in GC\u003c/h2\u003e \u003cp\u003eAll datasets were downloaded through \u0026ldquo;GEOquery\u0026rdquo; package. The \u0026ldquo;limma\u0026rdquo; package was used to screen DEGs. The criteria of DEGs between GC patients and non-tumor controls was |log2FC| \u0026gt; 1 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (FC, fold change). The DEGs between younger and older female GC patients were defined as |log2FC| \u0026gt; 0.5 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eFunctional annotation of the DEGs was implemented via \u0026ldquo;clusterProfiler\u0026rdquo; package, and the further analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed. The gene set enrichment analysis (GSEA) was also conducted for ascertaining the difference in pathways through \u0026ldquo;clusterProfiler\u0026rdquo; package. The gene sets of \u0026ldquo;c5.all.v7.0.entrez\u0026rdquo; and \u0026ldquo;h.all.v7.0.entrez\u0026rdquo; were acquired from the Molecular Signatures Database (MSigDB) to run GSEA. A pathway term with |Normalized Enrichment Score (NES)| \u0026gt; 1, adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 was considered to be significantly enriched. Bar chart was plotted by \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (last accessed on 10 July 2023), an online platform for data analysis and visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of a prognostic risk model\u003c/h2\u003e \u003cp\u003eThe 266 candidate DEGs related to prognosis were selected through \"survival\" package. Survival-related DEGs were subjected to Lasso Cox regression analysis, and 6 genes were finally identified and involved in the construction of the prognostic gene model, including \u003cem\u003eNREP\u003c/em\u003e, \u003cem\u003eGAD1\u003c/em\u003e, \u003cem\u003eSLCO4A1\u003c/em\u003e, \u003cem\u003eKRT17\u003c/em\u003e, \u003cem\u003eDEFB1\u003c/em\u003e and \u003cem\u003eP3H2\u003c/em\u003e. The risk score was calculated according to the following formula: risk score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{\\text{i}=1}^{\\text{n}}(\\text{E}\\text{x}\\text{p}\\text{i}\\ast \\text{C}\\text{o}\\text{e}\\text{f}\\text{i})\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eExpi\u003c/em\u003e represents the gene expression levels, and \u003cem\u003eCoefi\u003c/em\u003e represents the risk coefficients. The cutoff point of risk score was determined via \u0026ldquo;survminer\u0026rdquo; package. Based on the cutoff point of discovery cohort, patients from discovery cohort were divided into low-risk and high-risk groups. The ratio of high-risk/low-risk in the discovery cohort was used as the criteria for dividing the external validation cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the prognostic gene model\u003c/h2\u003e \u003cp\u003eThe differences in overall survival (OS) between low-risk group and high-risk group were evaluated through the Kaplan\u0026ndash;Meier analysis generated by \u0026ldquo;survival\u0026rdquo; package. For the predicted assessment of female patients with GC in the prognostic value of the prognostic gene model, the receiver operating characteristic (ROC) curve analysis and the time-dependent ROC curve analysis were performed for obtaining the area under the curve (AUC). We performed univariate and multivariate Cox proportional hazards regression for all cohorts to determine whether the risk score was an independent predictor of prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of tumor microenvironment\u003c/h2\u003e \u003cp\u003eIn order to explore the changes of tumor microenvironment (TME) in female GC patients between different risk, we used \u0026ldquo;estimate\u0026rdquo; package to score the stromal score, immune score, ESTMATE score, and Tumor Purity of three cohorts through the algorithm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDrug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eIn order to study the clinical manifestations of chemotherapy drugs in female patients, we calculated the semi-inhibitory concentration (IC50) values of 198 drugs through \u0026ldquo;oncoPredict\u0026rdquo; package. Wilcoxon signed-rank test was used to explore the difference in IC50 between low-risk and high-risk groups. The results were performed via \u0026ldquo;ggpubr\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe correlations between continuous variables were assessed using Spearman correlation analysis, and differences in the variables between different risk groups were evaluated with the Student t test, one-way ANOVA, Pearson\u0026rsquo;s chi-squared test, or Wilcoxon signed-rank test. Results with two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed significant. The statistical analysis of this study was performed using R-4.3.0 software (2023-04-21 ucrt).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of females GC patients\u003c/h2\u003e \u003cp\u003eThe clinicopathologic characteristics of female GC patients was listed in Table\u0026nbsp;1. There was no statistically significant difference in OS between younger and older female patients (P\u0026thinsp;=\u0026thinsp;0.85). In comparison to older female patients, younger female patients were more likely to be Lauren diffuse-type GC (84.6% vs. 53.5%, P\u0026thinsp;=\u0026thinsp;0.002). No variations in tumor stage were observed between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTo investigate the molecular characteristics of female GC patients, a total of 4557 DEGs, including 2212 up-regulated genes and 2345 down-regulated genes, were identified, which were statistically significant between female GC patients and non-tumor controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The 5 top up-regulated were \u003cem\u003eRPL27A\u003c/em\u003e, \u003cem\u003eCDH17\u003c/em\u003e, \u003cem\u003eCOL6A3\u003c/em\u003e, \u003cem\u003eGCNT3\u003c/em\u003e, and \u003cem\u003eRPL37A\u003c/em\u003e. The specific expression of the screened DEGs was demonstrated by the heatmap as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to further explore the underlying mechanisms involved in female GC development, functional enrichment analyses of the DEGs were conducted. GO analysis indicated that in biological processes (BP), up-regulated genes were mainly enriched in wound healing, mesenchyme development, extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, and down-regulated genes were mainly enriched in proteasome-mediated ubiquitin-dependent protein catabolic process, establishment of protein localization to organelle, positive regulation of cellular catabolic process, cellular response to peptide, regulation of intracellular transport. The cell component (CC) and molecular function (MF) of GO analysis were showed in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE. Moreover, the KEGG analysis revealed that up-regulated genes were enriched in IL-17 signaling pathway, base excision repair, protein digestion and absorption, cell cycle, human papillomavirus (HPV) infection, epithelial cell signaling in Hp infection, and down-regulated genes were enriched in ribosome, neurotrophin signaling pathway, salmonella infection (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Combined with the results of GO and KEGG, we found that the DEGs between female GC patients and non-tumor controls were significantly enriched in metabolic pathways, extracellular structure, cell cycle, and Hp infection.\u003c/p\u003e \u003cp\u003eEnrichment plots from GSEA in C5 collection were showed in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF. The results of GSEA based on the Hallmarker gene sets showed that epithelial-mesenchymal transition, inflammatory response, TNFα signaling via NF-κB, KRAS signaling, estrogen response early and late, IL-6/JAK/STAT3 signaling, angiogenesis, and G2M checkpoint were significantly up-regulated in female GC patients, which suggested there was a potential relationship between GC and estrogen pathways (estrogen response early: NES\u0026thinsp;=\u0026thinsp;1.838, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001; estrogen response late: NES\u0026thinsp;=\u0026thinsp;1.842, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in potential mechanisms between younger and older female GC patients\u003c/h2\u003e \u003cp\u003eFemale GC patients included in our study were divided into 2 groups according to age 50 (\u0026le;\u0026thinsp;50 years of age, n\u0026thinsp;=\u0026thinsp;69; \u0026gt; 50 years of age, n\u0026thinsp;=\u0026thinsp;236). A total of 4633 DEGs were identified in younger group, including 2241 up-regulated genes and 2392 down-regulated genes. In older group, a total of 4583 DEGs were identified, including 2241 up-regulated genes and 2342 down-regulated genes. The DEGs of two groups were displayed by volcano maps as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. The distribution of the DEGs were demonstrated by the heatmaps as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to further explore the potential mechanisms involved in GC development of younger female patients, the enrichment analysis of GO and KEGG were performed on 4633 DEGs between younger female patients and non-tumor controls. Enrichment results of GO analysis were shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC. GO analysis indicated that in BP, up-regulated genes were mainly enriched in ameboidal-type cell migration, wound healing, mesenchyme development, cell-substrate adhesion, extracellular matrix organization, extracellular structure organization, and down-regulated genes were mainly enriched in proteasome-mediated ubiquitin-dependent protein catabolic process, establishment of protein localization to organelle, ribonucleoprotein complex biogenesis, positive regulation of cellular catabolic process, response to oxidative stress. The KEGG pathways related to up-regulated genes were mainly concentrated in the pathways of protein digestion and absorption, IL-17 signaling pathway, epithelial cell signaling in Hp infection, HPV infection, proteoglycans in cancer. Considering the down-regulated genes, the KEGG pathways were mainly concentrated in the pathways of ribosome, neurotrophin signaling pathway, ubiquitin mediated proteolysis, insulin signaling pathway, salmonella infection. As the results of GSEA based on the Hallmarker gene sets, estrogen response early and late were enriched in younger group (estrogen response early: NES\u0026thinsp;=\u0026thinsp;1.774, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001; estrogen response late: NES\u0026thinsp;=\u0026thinsp;1.699, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Other results were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional and pathway enrichment analysis of the DEGs of older group was showed in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Combining the results of the two groups, there was no significant difference in enriched pathways between younger group and older group in GO analysis. The above GO terms were involved in cancer occurrence and development to some extent, especially the up-regulated DEGs which were majorly associated with metabolic pathways and extracellular structure (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Comparing the KEGG analysis results of the two groups, we found GC in the two groups were both related to Hp infection, because the DEGs were enriched in IL-17 signaling pathway and epithelial cell signaling in Hp infection. However, there was a significant difference in enriched hormone-related pathways between two groups. The down-regulated genes of two groups were both enriched in insulin, prolactin, and glucagon signaling pathway. Aldosterone (p\u0026thinsp;=\u0026thinsp;0.023) and relaxin pathways (p\u0026thinsp;=\u0026thinsp;0.043) were concentrated in younger group, while growth hormone synthesis, secretion and action (p\u0026thinsp;=\u0026thinsp;0.046) and thyroid hormone signaling pathway (p\u0026thinsp;=\u0026thinsp;0.049) were concentrated in older group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). The results of GSEA based on the Hallmarker gene sets showed that estrogen response early and late were enriched in older group, which were consistent with the results of GSEA in younger group (estrogen response early: NES\u0026thinsp;=\u0026thinsp;1.827, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001; estrogen response late: NES\u0026thinsp;=\u0026thinsp;1.895, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment prognostic gene model and survival outcomes in GC\u003c/h2\u003e \u003cp\u003eWe further used GSE84437 cohort to construct a prognostic gene model for predicting the overall survival (OS) of female GC patients. The DEGs of GSE84437 cohort were identified between 30 younger and 107 older female GC patients, including 463 up-regulated genes and 184 down-regulated genes (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe enrichment analysis of GO and KEGG were performed on the DEGs of GSE84437. The results of GO analysis were shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eE. GO analysis indicated that in BP, extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, regulation of angiogenesis, and regulation of vasculature development were mainly enriched in younger female patients, while nuclear division, organelle fission, chromosome segregation, nuclear chromosome segregation, and mitotic nuclear division were mainly enriched in older female patients. As a result of KEGG, younger female patients showed significant enrichment in pathways related cancer progression, such as focal adhesion, vascular smooth muscle contraction, complement and coagulation cascades, ECM-receptor interaction, and tight junction. However, older female patients showed main enrichment in cell cycle pathways, such as cell cycle, IL-17 signaling pathway, progesterone-mediated oocyte maturation, and oocyte meiosis. Enrichment results from GSEA in C5 collection showed similar results to GO and KEGG analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Top 5 pathways associated with tumor progression in younger female patients were epithelial-mesenchymal transition, apical junction, KRAS signaling, angiogenesis, and coagulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). In older female patients, top 5 pathways associated with tumor progression were E2F targets, G2/M checkpoint, mTORC1 signaling, MYC targets V1 and V2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003eAfter further analysis of univariate Cox regression in the DEGs, we identified 266 genes related to survival time (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As a next step, survival-related DEGs were subjected to Lasso Cox regression analysis, and 6 genes were finally identified and involved in the construction of the prognostic gene model, including \u003cem\u003eNREP\u003c/em\u003e, \u003cem\u003eGAD1\u003c/em\u003e, \u003cem\u003eSLCO4A1\u003c/em\u003e, \u003cem\u003eKRT17\u003c/em\u003e, \u003cem\u003eDEFB1\u003c/em\u003e and \u003cem\u003eP3H2\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Based on the results of the Lasso Cox regression analysis, the prognostic gene model was constructed as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{R}\\text{i}\\text{s}\\text{k} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e} = \\text{E}\\text{x}\\text{p}(\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003eNREP\u003c/em\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e(0.425262001)+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{x}\\text{p}(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eGAD1\u003c/em\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e(-0.033627453)+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{x}\\text{p}(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eSLCO4A1\u003c/em\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e(-0.066757311)+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{x}\\text{p}(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eKRT17\u003c/em\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e(0.119705227)+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{x}\\text{p}(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eDEFB1\u003c/em\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e(0.032480849)+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{x}\\text{p}(\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eP3H2\u003c/em\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e(0.076266766).\u003c/p\u003e \u003cp\u003eAfter further analysis of applying risk score, there was a significant difference in the risk score between younger female GC patients and older female GC patients (p\u0026thinsp;=\u0026thinsp;0.026, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The optimal cutoff was identified to classify female GC patients into two groups (high-risk and low-risk groups) with the most distinct survivals by a method based on maximally selected rank statistics (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The result revealed that the perfect ratio of high-risk/low-risk seemed to be 1:2, and female GC patients in the discovery cohort were well dispersed in high-risk group (n\u0026thinsp;=\u0026thinsp;46) and low-risk group (n\u0026thinsp;=\u0026thinsp;91). The distribution plot of the risk score demonstrated that the survival times were reduced while the risk score increased (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Kaplan-Meier survival curves comparing high-risk and low-risk patients were also constructed to further evaluate the prognostic potential of the prognostic gene model. The result suggested that high-risk patients showed worse OS compared with low-risk patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). ROC analyses indicated that the prognostic value (AUC) of the prognostic gene model for predicting OS were 0.81 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Moreover, the AUC values of this prognostic gene model for the 1-year, 3-year, and 5-year OS of female patients with GC were 0.78, 0.78, and 0.75, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). In conclusion, the prognostic gene model exhibited high prognostic value in the discovery cohort GSE84437.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the prognostic gene model\u003c/h2\u003e \u003cp\u003eTwo independent cohorts were used for external validations (GSE15459, GSE62254). The patients of two validation cohorts were assigned into different risk subgroups based on the unified ratio consistent with the discovery cohort. The risk plot of the prognostic gene model indicated that as risk score increased, OS time decreased while mortality rise (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). As illustrated in Figures\u0026ensp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, low-risk patients had a better OS than high-risk patients whether in the GSE15459 cohort or the GSE62254 cohort (GSE15459, p\u0026thinsp;=\u0026thinsp;0.015; GSE62254, p\u0026thinsp;=\u0026thinsp;0.003, respectively). The AUC values of the prognostic gene model in the GSE15459 cohort and the GSE62254 cohort were 0.71 and 0.68 respectively. Furthermore, we estimated the AUC values for predicting OS at 1-year, 3-year, and 5-year in the GSE15459 cohort and the GSE62254 cohort, respectively (GSE15459, 1-year: 0.68, 3-year: 0.68, 5-year: 0.72; GSE62254, 1-year: 0.61, 3-year: 0.63, 5-year: 0.65). As showed in Figures\u0026ensp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, the AUC values were as expected, implying this prognostic gene model was an effective instrument for the prognostic risk classification of female GC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the independent prognostic value of prognostic gene model for female GC patients, we performed univariate Cox regression analysis and multivariate Cox regression analysis to explore prognostic independence of multiple clinical factors. Table\u0026nbsp;2 showed the result of univariate Cox regression analysis in discovery cohort, which suggested the risk score was significantly associated with the prognosis of GC in female patients (HR\u0026thinsp;=\u0026thinsp;6.601, 95%CI: 3.630-12.000, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After further multivariate Cox regression analysis, Table\u0026nbsp;3 showed that the risk score presented as an independent prognostic factor after adjusting for other clinicopathologic characteristics (HR\u0026thinsp;=\u0026thinsp;5.769, 95%CI: 3.011\u0026ndash;11.053, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Based on the same analysis performed in external validations, the results were concordant with the findings available in the discovery cohort (GSE15459, HR\u0026thinsp;=\u0026thinsp;2.171, 95%CI: 1.052\u0026ndash;4.479, P\u0026thinsp;=\u0026thinsp;0.036; GSE62254, HR\u0026thinsp;=\u0026thinsp;3.136, 95%CI: 1.577\u0026ndash;6.236, P\u0026thinsp;=\u0026thinsp;0.001, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-4). Overall, the high-risk patients\u0026rsquo; prognosis was poorer compared to low-risk patients.\u003c/p\u003e \u003cp\u003eThe clinicopathologic characteristics of GC patients stratified by risk were listed in Table\u0026nbsp;4. Younger female GC patients were more likely to be in high-risk group compared with the older (31.1% vs. 18.3%, P\u0026thinsp;=\u0026thinsp;0.018). In addition, the stage of GC was more advanced in high-risk group than that in low-risk group (TNM I: 7.0% vs. 18.9%; TNM II: 17.5% vs. 29.7%; TNM III: 35.1% vs. 26.1%; TNM IV:40.4% vs. 25.2%, P\u0026thinsp;=\u0026thinsp;0.022). However, there was no significant difference in Lauren classification between the two groups (p\u0026thinsp;=\u0026thinsp;0.453).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of a nomogram to predict survival\u003c/h2\u003e \u003cp\u003eDue to the high correlation between prognostic gene model and patients\u0026rsquo; prognosis, by integrating the risk scores and well-known prognostic factors, a nomogram was constructed by using the discovery cohort for OS prediction. This nomogram was developed to predict 1-year, 3-year, and 5-year OS rates in female patients with GC (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). For the discovery cohort, the AUC values were as expected, implying this nomogram had an excellent predictive ability for prognosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The calibration curve showed well performance for the nomogram between actual observations and predicted values (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). The clinical usefulness of the nomogram was quantified by the decision curve, and we found that this prognostic model with diverse clinical factors presented more net benefits for predicting the prognosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). As shown in Figures\u0026ensp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eF-I, the similar results were showed in GSE62254 cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the tumor microenvironment and drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eTME played an important role in tumor prevention and therapy. The risk score of prognostic gene model was positively linked to stromal score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and Figure S2A, S2E). Meanwhile, this result was consistent with the GO analysis and GSEA results of younger female GC patients in discovery cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), because the stromal cells, such as cancer-associated fibroblasts, could secrete a variety of extracellular matrix components. However, the results revealed that the risk score was not associated with immune score in either discovery or two external validation cohorts (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eB and Figure S2B, S2F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore suitable drugs for high-risk group, we estimated the IC50 values of 198 drugs in GSE84437, GSE15459 and GSE62254 patients. All results of drug sensitivity analysis for each cohort were exhibited in Table S5-S7. We discovered that female GC patients with high-risk might positively react to Dasatinib (targeting drug, ABL, SRC, Ephrins, PDGFR, and KIT inhibitor) and AZD1332 (targeting drug, receptor tyrosine kinase inhibitor; Dasatinib: GSE84437: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, GSE15459: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and GSE62254: p\u0026thinsp;=\u0026thinsp;0.001; AZD1332: GSE84437: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, GSE15459: p\u0026thinsp;=\u0026thinsp;0.023, and GSE62254: p\u0026thinsp;=\u0026thinsp;0.027, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eC, \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). Female GC patients from high-risk group exhibited greater resistance to 25 drugs, including those of Navitoclax (targeting drug, Bcl-2 inhibitor), Vorinostat (targeting drug, HDAC inhibitor), MK-2206 (targeting drug, AKT inhibitor), Palbociclib (targeting drug, CDK4/6 inhibitor), Sorafenib (targeting drug, PDGFR, KIT, VEGFR, and RAF inhibitor), Oxaliplatin (Chemotherapy drug), GSK1904529A (targeting drug, IGF1R, IR inhibitor), PF-4708671 (targeting drug, S6K1 inhibitor), Tamoxifen (Chemotherapy drug), BMS-345541 (targeting drug, IKK inhibitor), LGK974 (targeting drug, PORCN inhibitor), VE-822 (targeting drug, ATR inhibitor), ML323 (targeting drug, USP1, UAF1 inhibitor), Ribociclib (targeting drug, CDK4/6 inhibitor), TAF1_5496 (targeting drug, TAF1 inhibitor), Selumetinib (targeting drug, MEK1/2 inhibitor), Fulvestrant (targeting drug, ESR inhibitor), Dihydrorotenone (mitochondrial inhibitor), ABT737 (targeting drug, Bcl-2 inhibitor), AZD6738 (targeting drug, ATR inhibitor), Ipatasertib (targeting drug, AKT inhibitor), P22077 (targeting drug, USP7/47 inhibitor), VX-11e (targeting drug, ERK2 inhibitor), Uprosertib (targeting drug, AKT inhibitor), and VE821 (targeting drug, ATR inhibitor), than those from low-risk group patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Overall, these results indicated that the prognostic gene model was correlated with drug sensitivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the molecular characteristics of female GC patients on the basis of six GEO cohort, suggesting that the pathogenesis mechanism of GC in female patients was associated with estrogen. Furthermore, compared with older female patients, we found aldosterone and relaxin pathways were concentrated in younger group. The most critical finding in the present study was that the younger female GC patients showed worse OS compared with the older using the prognostic gene model we created. To the best of our knowledge, our study was the first to investigate the comprehensive molecular profiles of younger female GC patients, which manifested the mechanisms of pathogenesis and prognosis in younger females with GC.\u003c/p\u003e \u003cp\u003ePrevious prospective cohort studies and human cell lines studies suggested that female hormones might play a protective role in GC risk[\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29 CR30\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the occurrence of GC was frequently observed that females were more susceptible than males in younger group, which prompted a reconsideration of the potential mechanisms of estrogen[\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In our study, with the exception of other cancer- and metastasis-associated pathways, female GC patients were markedly enriched in estrogen response early and late with GSEA enrichment analysis, which suggested there was a potential relationship between GC and estrogen pathways. Notably, when we used GSEA enrichment analysis to evaluate potential mechanism differences between younger and older female GC patients, estrogen response early and late were enriched in both two groups. This finding suggested that estrogen might not be the key influence in the susceptibility of younger female patients to develop GC. However, as the results of KEGG analysis, we found there was a significant difference in enriched other hormone-related pathways between younger group and older group. Aldosterone and relaxin pathways were concentrated in younger group, while growth hormone synthesis, secretion and action and thyroid hormone signaling pathway were concentrated in older group. Although there were many studies suggesting the relationship between these hormones and GC or other cancer, a lack of consensus existed regarding these hormones as a significant factor of pathogenesis mechanisms in younger female patients with GC, and further research was needed to explore their implications[\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost of the relevant clinical studies showed that younger female GC patients had a poorer prognosis compared to the older, however, the evidence regarding the mechanisms underlying such outcomes was scarce at the genetic level[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, we constructed the effective prognostic gene model containing 6 genes, namely \u003cem\u003eNREP\u003c/em\u003e, \u003cem\u003eGAD1\u003c/em\u003e, \u003cem\u003eSLCO4A1\u003c/em\u003e, \u003cem\u003eKRT17\u003c/em\u003e, \u003cem\u003eDEFB1\u003c/em\u003e, and \u003cem\u003eP3H2\u003c/em\u003e, and demonstrated its predictive ability. We found that the younger female GC patients had higher risk scores compared with older female GC patients in the prognostic gene model we created, suggesting that younger female patients showed worse OS compared with the older, which was consistent with previous studies.\u003c/p\u003e \u003cp\u003eMost of the genes in the prognostic gene model have been reported to be associated with cancer development. \u003cem\u003eNREP\u003c/em\u003e played an important role in the progression of GC through diverse mechanisms, such as epithelial-mesenchymal transition activation, cancer-associated fibroblasts activation, actin cytoskeleton remodeling, and M2 macrophage infiltration, and its expression was powerfully associated with T stage and histologic grade[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u003cem\u003eKRT17\u003c/em\u003e could promote the proliferation, migration, and invasion of GC cells, and its expression was positively correlated with the TMN stage, lymphatic metastasis, depth of invasion and vascular invasion[\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, they found \u003cem\u003eKRT17\u003c/em\u003e could regulate cell cycle and modulate cell cycle proteins, suggesting that \u003cem\u003eKRT17\u003c/em\u003e might be a possible molecular target for targeted therapy in GC[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Pignata et al reported that \u003cem\u003eP3H2\u003c/em\u003e was a new molecular player involved in new vessels formation, and they found that \u003cem\u003eP3H2\u003c/em\u003e knockdown prevented pathological angiogenesis in vivo[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Pathological angiogenesis in GC might be associated with its overexpression. The expression of Human β-defensin 1 (encoded by \u003cem\u003eDEFB1\u003c/em\u003e) was found to be associated with Hp infection, while the mechanisms by which \u003cem\u003eDEFB1\u003c/em\u003e achieved its effects in GC remained unclear[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In contrast to our findings, most studies indicated the elevated expression of \u003cem\u003eSLCO4A1\u003c/em\u003e might facilitate the progression of cancer, because they found that \u003cem\u003eSLCO4A1\u003c/em\u003e mediated the cellular uptake of many substrates including hormones, which regulated the progression of cancer through binding the hormone receptors[\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. It was possible that \u003cem\u003eSLCO4A1\u003c/em\u003e mediated the cellular uptake of estrone-sulfate and dehydroepiandrosterone-sulfate in GC, which were the most important estrogen precursors in the plasma of postmenopausal females[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, estrogen might play a potentially protective role in the progression of GC. \u003cem\u003eGAD1\u003c/em\u003e contributed to the progression in various types of cancer, however, it was identified as a favorable gene for prognosis of female GC patients in our study[\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Currently, no relevant study has been conducted to investigate the role of \u003cem\u003eGAD1\u003c/em\u003e in GC.\u003c/p\u003e \u003cp\u003eTwo critical genes in the prognostic gene model, \u003cem\u003eNREP\u003c/em\u003e and \u003cem\u003eKRT17\u003c/em\u003e, were all related to epithelial-mesenchymal transition, which suggested that epithelial-mesenchymal transition could be the main reason for the high-risk group with poor prognosis. Epithelial-mesenchymal transition is a reversible cellular process that transiently induces epithelial cells to quasi-mesenchymal cell characteristics[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. During carcinoma progression, epithelial-mesenchymal transition can increase metastatic powers and elevate therapeutic resistance of cancer[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Consistent with this, our study showed that the high-risk group had more advanced stage of GC and were resistant to more drugs than the low-risk group.\u003c/p\u003e \u003cp\u003eThe risk score calculated by the prognostic gene model correlated significantly with clinicopathologic characteristics of female GC patients. After controlling confounding parameters, the results indicated that the risk score was an independent predictor for female GC patients\u0026rsquo; survival outcomes. To further improve the accuracy of prognostic prediction, we constructed and validated a nomogram by screening various indexes, and made it easier to use the prognostic gene model.\u003c/p\u003e \u003cp\u003eTME, the environment in which the tumor resided, consisted of malignant cells, immune cells, stromal cells, extracellular matrix, and a variety of cytokines, and was nourished by a vascular network[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Stromal cells were thought to have important roles in tumour growth, disease progression and drug resistance, and the percentage of stromal cells in TME represents the stromal score[\u003cspan additionalcitationids=\"CR62 CR63 CR64\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Based on previous studies, GC patients with stromal score-high showed poor OS and identified as an independent prognostic factor[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Consistent with this finding, our study found that the risk score of prognostic gene model was positively linked to stromal score, which suggested the poor prognosis of high-risk group might be closely correlated with stromal cells and extracellular matrix in TME.\u003c/p\u003e \u003cp\u003eWhereas surgery remains a mainstay, multimodality treatment is the standard of care for advanced resectable GC, with particular emphasis on perioperative chemotherapy[\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. However, increasing evidence has shown that stromal cells, such as cancer-associated fibroblasts, mediate chemotherapy resistance in several tumors by releasing cytokines, exosomes and metabolites[\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Thus, we explored the sensitivity of various drugs in female GC patients between two risk subgroups. In our study, we found out that female patients of high-risk group were resistant to 25 drugs and sensitive to 2 drugs. Notably, we found out that the high-risk group was resistant to ESR inhibitor Fulvestrant, which might provide new insights into the role of estrogen in female GC.\u003c/p\u003e \u003cp\u003eA key advantage of this study was that the significant difference in enriched hormone-related pathways between younger and older group were identified, and a prognostic gene model based on the DEGs between younger and older female patients was constructed. Another advantage of the current study was the consistent statistical results in three independent cohorts, confirming the robustness and precision of the prognostic gene model. The limitations of this study included the fact that all gene expression and clinical data from public databases were obtained retrospectively, and inherent selection bias might affect the accuracy of the analysis results. Additionally, extensive prospective studies and complementary in vivo and in vitro experimental studies were necessary to gain insight into the potential mechanisms involved in GC development of younger female patients, thus confirming our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we provided the comprehensive molecular profiles of younger female GC patients and found that there was a significant difference in enriched hormone-related pathways between younger group and older group. In addition, we found younger female patients showed worse OS compared with older female patients using the prognostic gene model we created.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data can be found in GEO databases. Ethical approval has been obtained for this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this type of study formal consent is not required.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe public datasets analyzed in this study can be found in GSE (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grant from National Key R\u0026amp;D Program of China (No. 2017YFC0908300) and 2023 Scientific Research Project of Chronic Diseases Control and Health Education (No. BJMB0012023024005).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuan X.Y. : Writing-original draft, Data curation; Zhao L.L. : Writing-original draft, Visualization; Wang W.Q. : Formal analysis; Niu P.H. : Formal analysis; Han X. : Data curation; Wang Z.R. : Resources; Zhang X.J. : Writing-review \u0026amp; editing; Zhao D.B. : Writing-review \u0026amp; editing, Supervision; and Chen Y.T. : Conceptualization, Resources, Methodology, Writing-review \u0026amp; editing, Supervision. All authors discussed the findings and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made substantial contributions to the intellectual content of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n\u003cli\u003eLiu Y, Liu Y, Ye S, Feng H, Ma L: \u003cstrong\u003eA new ferroptosis-related signature model including messenger RNAs and long non-coding RNAs predicts the prognosis of gastric cancer patients\u003c/strong\u003e. \u003cem\u003eJ Transl Int Med 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Jiang Y: \u003cstrong\u003eExtracellular matrix in cancer progression and therapy\u003c/strong\u003e. \u003cem\u003eMed Rev (Berl) \u003c/em\u003e2022, \u003cstrong\u003e2\u003c/strong\u003e(2):125-139.\u003c/li\u003e\n\u003c/ol\u003e "},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\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, sex, pathogenesis, prognostic gene model","lastPublishedDoi":"10.21203/rs.3.rs-4143457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4143457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMale patients were twice as likely to develop gastric cancer (GC) compared to females, partly due to the protective effect of estrogen. However, the proportion of females increased in the young GC patients. The study was designed to explore comprehensive molecular profiles of younger female GC patients, as well as develop a prognostic gene model for female GC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGene expression and clinical data of GC and non-tumor patients were downloaded from the Gene Expression Omnibus (GEO) database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to find molecular characteristics and potential mechanisms of younger female GC patients. The prognostic gene model containing 6 differential expressed genes (DEGs), which were between younger and older female patients, was established using Lasso-Cox regression. Its performance was validated by external validation. Then, receiver operating characteristic (ROC) curve was applied to determine the prognostic value of the prognostic gene model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSix GEO cohorts with 305 female GC patients (69 younger patients and 236 older patients) and 38 female non-tumor patients were included. A total of 4557 DEGs between female GC patients and non-tumor patients were identified, including 2212 up-regulated genes and 2345 down-regulated genes. Estrogen response early (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and estrogen response late (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were enriched in female GC patients. In KEGG analysis, aldosterone (p\u0026thinsp;=\u0026thinsp;0.023) and relaxin pathways (p\u0026thinsp;=\u0026thinsp;0.043) were concentrated in younger group. Moreover, we further used GSE84437 cohort to construct a prognostic gene model containing 6 genes, namely \u003cem\u003eNREP\u003c/em\u003e, \u003cem\u003eGAD1\u003c/em\u003e, \u003cem\u003eSLCO4A1\u003c/em\u003e, \u003cem\u003eKRT17\u003c/em\u003e, \u003cem\u003eDEFB1\u003c/em\u003e, and \u003cem\u003eP3H2\u003c/em\u003e, to predict the overall survival (OS) of female GC patients (AUC\u0026thinsp;=\u0026thinsp;0.810). Younger female patients, who were related with high-risk at the genetic level, showed worse OS compared with older female patients who showed low-risk (HR\u0026thinsp;=\u0026thinsp;5.7688, 95%CI: 3.0108\u0026ndash;11.0530, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn conclusion, we provided the comprehensive molecular profiles of younger female GC patients and found that there was a significant difference in enriched hormone-related pathways between younger group and older group. In addition, we found younger female patients showed worse OS compared with older female patients using the prognostic gene model we created.\u003c/p\u003e","manuscriptTitle":"Molecular characteristics, potential mechanisms and prognostic gene model of younger female patients with gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 13:05:24","doi":"10.21203/rs.3.rs-4143457/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":"8ac356c6-4f16-4fb8-8839-0d469f4f5ab2","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-20T14:15:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 13:05:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4143457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4143457","identity":"rs-4143457","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}