A telomere-related gene panel predicts the prognosis and Immune Status in gastric cancer

A telomere-related gene panel predicts the prognosis and Immune Status in gastric cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A telomere-related gene panel predicts the prognosis and Immune Status in gastric cancer Dai Zhang, Dingli Song, Yiche Li, Fenfen He, Qian Hao, Yujiao Deng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598908/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 Telomeres play a crucial role in the development and progression of cancers. However, the impact of telomere-related genes (TRGs) on the prognosis and tumor immune microenvironment (TIME) of gastric cancer (GC) remains unclear. Therefore, a comprehensive investigation of the association between TRGs and GC is necessary. The TRG risk panel was constructed by combining differentially expressed gene analysis, weighted gene co-expression network analyses, the Least Absolute Shrinkage and Selection Operator regression, and stepwise regression analysis in the TCGA cohort and has been validated in a GEO cohort. The major impacts of the signature on the TIME and immunotherapy response were also evaluated. The prognosis model comprised 9 TRGs (CABP2, CALML6, CFAP58, DST, ELOVL2, HIST1H3G, MYF6, PDE1B and TOP3B), stratifying patients into two risk groups. Individuals with low-risk scores exhibited superior prognoses than those with high-risk scores ( P < 0.001). The prognostic signature was found to be an independent factor with good predictive power for overall survival. The high-risk group tended to have higher TME scores and an inert immune status with a higher infiltration proportion of Treg cells, M2 macrophages, resting dendritic cells and resting NK cells. Additionally, the low-risk group had higher TMB, lower TIDE and a higher immunotherapy response rate. Additionally, we confirmed the expression of the nine genes in GC tissues using RT-qPCR. Our TRG-based panel has a significant role in the prognosis, TIME, and immunotherapy response. This may suggest that the TRG panel could be a powerful tool for guiding clinical treatment decisions. Biological sciences/Cancer Biological sciences/Genetics Biological sciences/Immunology Health sciences/Biomarkers Health sciences/Diseases Health sciences/Gastroenterology Health sciences/Medical research telomere related genes gastric cancer tumor microenvironment prognosis immune status Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Gastric cancer (GC) is one of the most common malignancies worldwide [ 1 ]. The treatment landscape for GC has evolved significantly in recent years, particularly with the development of systemic therapeutic options for GC, including chemotherapy, targeted therapy and immunotherapy [ 2 ]. However, the overall treatment outcome of GC remains unsatisfactory due to the challenge of early detection and the high incidence of distant metastases in advanced GC [ 3 ]. Therefore, there is an urgent need to screen high-risk patients and to search for more valuable effective diagnostic, prognostic and therapeutic sensitivity biomarkers. The telomeres are the nucleoprotein complexes that cap the very ends of the eukaryotic chromosomes, consisting of a tract of tandemly repeated short DNA repeats and associated protective proteins [ 4 ]. These specialized structures are essential for chromosome stability. In normal dividing cells, with each cell replication telomeres gradually shorten, until a critical level is reached, after which cells undergo senescence [ 5 ]. This gradual shortening of telomeres associated with cellular aging is believed to be a protective mechanism against uncontrolled growth [ 6 ]. The activation of a telomere maintenance mechanism (TMM), which consists of telomerase activation and Alternative-lengthening of Telomere (ALT), to prevent the shortening of telomeres is necessary for the continued sustained proliferation of cancer cells [ 7 ]. There is also evidence that telomere shortening induces chromosomal instability and cancer initiation. Initiated tumors require telomerase activation to prevent high levels of chromosomal instability that induce genetic chaos and tumor cell death [ 8 ]. So, it seems that telomere biology plays a critical and complex role in the initiation and progression of cancer. Previous studies have mainly focused on telomere length in different types of cancer[ 9 – 11 ]. However, the relationship between dysregulation of telomere-related genes (TRGs), cancer prognosis and tumor immune microenvironment (TIME) has only been investigated in pancreatic cancer, breast cancer, kidney cancer, lung cancer, hepatocellular cancer, and glioma[ 12 – 16 ]. A recent study focused on telomerase regulation-related lncRNAs in GC, which showed downregulation of AC023590.1, PVT1, BX890604.1 and UBL7AS1 was associated with a good prognosis for GC patients[ 17 ]. However, a comprehensive investigation of the relationship between TRGs and GC is still lacking. Therefore, in the present study, we comprehensive studied telomere-related genes in GC and constructed a prognostic TRG panel for GC patients, and evaluated the underlying impact of TRGs on the TIME. 2. Methods and Materials 2.1 Data Sourcing GC patients with RNA-seq and clinical information were obtained from The Cancer Genome Atlas (TCGA) data portal ( http://portal.gdc.cancer.gov/ ) as a training set, including 375 GC records and 32 normal controls. And the gene expression data from 174 normal gastric tissues were downloaded from the Genotype-Tissue Expression (GTEx) database ( https://commonfund.nih.gov/GTEx ) to supplement the data of normal samples for the training cohort. The validation cohort GSE62254, based on the GPL570 platform and including 300 GC samples, was obtained from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo/ ) [ 18 ]. Furthermore, we acquired 2093 TRGs from the TelNet database ( http://www.cancertelsys.org/telnet/ ) [ 19 ]. TCGA-STAD somatic mutation data was also downloaded from the UCSC xena ( http://xenabrowser.net ). The immune inhibitor treatment cohort IMvigor210 of metastatic urothelial carcinoma was downloaded from http://research-pub.gene.com/IMvigor210Core Biologies. 2.2 Screening hub differential telomere-related genes The study extracted expression profiles of 2038 overlapping TRGs from transcriptome RNA sequencing data of combined gastric tissue samples (375 GC tissues and 206 normal gastric tissues) to screen for differentially expressed telomere-associated genes (DE-TRGs) using the limma package. The threshold was set at a |log2 fold change| >1 and an adjusted P-value < 0.05. All the samples were also subjected to weighted gene co-expression network analysis (WGCNA) using the WGCNA R package to identify key genes[ 20 ]. Then we determined the optimal soft threshold of the data to ensure that the genes interaction conformed to the scale-free distribution to a maximum extent. The adjacency and similarity between genes were calculated, and the cluster dendrogram was established. The modules were further segmented using the dynamic tree-cutting algorithm, and similar modules were merged. We evaluated the Pearson correlation between each module and sample traits and the highly differential TRGs between gastric tumor and normal tissues in the WCGNA results were screened for further analysis. We then took the intersection of the TRGs in the hub modules after WCGNA and the DE-TRGs for subsequent analysis, and displayed them in the form of a Venn diagram [ 21 ]. 2.3 Consensus clustering The Consensus Clustering method is widely used for tumor analysis. Cluster analysis was performed by the “ConsensusClusterPlus” package based on the prognostic TRGs, which were identified through univariate Cox regression analysis in the TCGA training set (P < 0.05), and TCGA cohort patients were divided into different subtypes for further analysis according to the clustering results of K = 2 to 9. The effect of consistency clustering was evaluated through the utilization of PCA, T-distributed stochastic neighbor embedding (t-SNE), and UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction). We utilized R packages "highcharter", "ggplot2", and "ggalluvial" to create a Sankey diagram to illustrate the distribution of risk groups, clusters, and survival outcomes. 2.4 Development and reliability evaluation of prognosis-related signature The prognostic TRGs were used for least absolute shrinkage and selection operator (LASSO) Cox regression analysis, performed by the R package “glmnet” in order to prevent overfitting[ 22 ]. The TRG signature was constructed using multivariate Cox regression analysis. Subsequently, the risk score for each patient was calculated based on this prognostic signature. The formula of risk score was as follows: risk score = Σ (Ci × EXPi), where EXP is the gene expression level and C is the coefficient for the corresponding gene in the multivariate Cox model[ 23 ]. The median risk score was used as a cutoff to categorize patients in the training into high- and low-risk groups. Differences in survival between the high- and low-risk groups were assessed using the "survival" R package. We calculated the area under the curve (AUC) using the R package "timeROC" to evaluate the prognostic capability of the TRG signature. The GEO dataset was used for external validation. The same methods were performed to estimate a risk score for each case. The correlation between these two risk groups and clinicopathological features were also studied. 2.5 Tissue samples acquisition and real-time quantitative polymerase chain reaction (RT-qPCR) Ten groups of GC and corresponding normal tissues were harvested from GC patients at Xijing Hospital of Digestive Diseases. Informed consent was obtained from each patient, The study was permitted by the Ethics Committee of Xijing Hospital. All experiments were performed in accordance with relevant guidelines and regulations. Total RNA was extracted using the kit (RNAfast200, fastagen, China), followed by reverse transcription reactions performed with Prime Script RTase (Takara, China) following the protocol. Then RT-PCR was used to measure mRNA expression levels using SYBR green (Takara, China). The primer sequences used for qRT-PCR in this study are listed in Table S1 . To normalize the gene expression, the expression of B-ACTIN was utilized as a reference. 2.6 Establishment and assessment of the nomogram Univariate and multivariate Cox regression analyses were conducted to further verify the independent prognostic value of the TRG panel. To evaluate the survival probability of GC patients, a nomogram was created using the R package 'rms' in combination with clinicopathological factors to predict 1-, 3-, and 5-year survival. The efficiency of the nomogram was assessed using calibration diagrams, AUC, and decision curves (DCA) with the 'ggDCA' and 'survival' packages, respectively[ 24 ]. 2.7 The TIME, genetic variations and immunotherapy response analysis “ESTIMATE” algorithm was applied to assess the TME scores, including immune infiltration, stromal score and estimate score of each patient using “limma” and “estimate” R packages. The tumor mutation landscape of patients with GC was depicted by using “matfool” R package. Waterfall plots were generated to assess the number of somatic point mutations in each GC sample and to illustrate the relationship between tumor mutation burden (TMB) and risk groups. In addition, to explore the immune characteristics of patients with GC, expression data was imported into CIBERSORT ( http://cibersort.stanford.edu/ ) and iterated 1000 times to estimate the relative proportions of 22 immune cell types[ 25 ]. The ssGSEA R scripts were used to quantify the relative proportion of infiltrating immune functional related pathways. Moreover, we performed GSEA and GSVA on certain gene signature and compared the score between different groups to understand the immune and molecular functions. Finally, we compared the immune checkpoint activation between the two risk groups via “ggpubr” R package. We draw the heatmap to present the difference of immune cell infiltration landscape between clusters and other clinicopathological features. Additionally, TIDE (Tumor Immune Dysfunction and Exclusion) score was calculated online following the instructions ( https://tide.dfci.harvard.edu/ )[ 26 ]. In addition, we used TRGscore to classify the anti-PDL1 cohort (IMvigor210) samples. The “ggplot2” package was used to analyze the varying degrees of response to immunotherapy and the proportion of patients who responded versus those who did not. 2.8 Functional enrichment assessment To explore the potential molecular function and pathways, we performed gene set various analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) based on defined gene sets, “c5.go.symbols.gmt”, and “c2.cp.kegg.symbols.gmt”, which were downloaded from MSigDB database[ 27 ]. In addition to this, functional enrichment analysis was performed by the “clusterProfiler” package in the R software, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. P < 0.05 was considered to indicate significant differences. 2.9 Drug sensitivity prediction As part of the TCGA cohort, the “oncoPredict” R package was used to determine the half-maximum inhibitory concentration (IC50) commonly used in chemotherapeutic and targeted drugs for each GC patient. There were 198 drugs from Genomics of Drug Sensitivity in Cancer (GDSC; https://www.cancerrxgene.org/ ) that were compared for sensitivity in the different risk groups[ 28 ]. P < 0.001 was set as the threshold for significance. 2.10 Statistical analysis All analyses were performed using R version 4.3.2. To calculate prognostic values and to compare patient survival in different subgroups in each data set, Kaplan–Meier survival analysis, and the log-rank test was used. The non-parametric Wilcoxon rank sum test was used to test the relationship between the two groups for continuous variables. And the results of RT-qPCR were conducted statistical analysis using pair t test. Correlation coefficients were examined using spearman correlation analysis. In most statistical investigations, p < 0.05 was considered statistically significant. 3. Results 3.1 The baseline characteristics of GC patients in TCGA and GEO cohort The TCGA-STAD cohort (375 GC records and 32 normal controls) was considered as training cohort in this study. And the GEO cohort GSE62254 containing 300 GC sample were defined as the testing cohort. The detailed information of TCGA GC patients was showed in Table S2, while the clinical information available in GEO cohorts was not as detailed as that in training set. 2093 telomere-related genes (TRGs) were acquired from the TelNet database (Table S3). After overlapping with GC mRNAs, 2038 TRGs were obtained for our study. When using the TCGA cohort for differential analysis of TRGs expression, given the severe imbalance between normal and tumor samples, we downloaded 174 normal gastric samples from GTEx database as a complement. 3.2 Identification of Highly Correlated DE-TRGs 2038 TRGs RNA sequence profiles were selected for screening the DE-TRGs and conducting WGCNA. A total of 259 DE-TRGs between 375 tumor tissue and 206 normal tissues were identified through differentially expression analysis, including 115 highly expressed genes and 144 low expressed genes in GC patients according to the screened criteria: |log FC| > 1 and FDR < 0.05 (Fig. 1 A). The expression landscape of the top 50 up- and down-regulated DE-TRGs has been shown in the heat map (Figure S1 A). We performed WCGNA based on 2038 TRGs to identify the highly correlated gene modules in the training cohort. We set the soft thresholding power to 14, based on a scale-free R 2 (R 2 = 0.85) (Fig. 1 B and S1B), and three gene modules were identified based on the gene dendrogram: MEturquoise, MEblue and MEgrey modules (Fig. 1 C). Among these gene modules, there were strong correlations between tumor occurrence and the turquoise module (the coefficient was 0.9 and p-value was 8 × 10 − 213 ) and blue module (the coefficient was 0.83 and p-value was 1 × 10 − 150 ), composed of 240 genes and 131 genes respectively (Fig. 1 D). In addition, the gene significance and module membership were highly correlated in the turquoise module, suggesting a significant association of genes in this module with tumors (Fig. 1 E and S1C). Likewise, the blue module showed a high correlation with tumors (Fig. 1 F), whereas genes within the grey module exhibited no association with tumors (Figure S1 D). Finally, we extracted 100 hub genes through crossing DE-TRGs and the turquoise or blue gene modules (Fig. 1 G) for downstream analysis. 3.3 Analysis of consensus clustering, immune infiltration and GSVA We further implemented univariate Cox analysis to screen prognosis-related TRGs among the100 hub DE-TRG (p < 0.05). Consensus clustering analysis was conducted to cluster patients with GC into various subgroups based on 22 prognosis-related TRGs (Table S4). The consensus clustering heatmap indicated an optimal and stable classification with K = 3 (Figure S2A). Patients with GC were classified into three subgroups (Fig. 2 A-C). PCA showed good results for consistent clustering (Fig. 2 D). A substantial difference in prognosis among the three subtypes was revealed by the survival analysis, cluster A had a higher survival probability than clusters B and C (p < 0.001, Fig. 2 E). A heatmap of gene expression and clinical factors shows the differences between the three clusters (Fig. 2 F). Several immune assessment algorithms were used to evaluate the TME landscape of GC patients in three clusters. According to the ESTIMATE algorithm, patients in Cluster B had higher stromal, immune, and ESTIMATE scores than those in Cluster A. In contrast, patients in Cluster C had lower stromal, immune, and ESTIMATE scores than those in Cluster A (Fig. 2 G-I). The boxplot demonstrated the considerable variation in immune cell infiltration levels among the three groups (Figure S2B). We similarly found that the percentage of most immune cell infiltration was highest in cluster B and lowest in cluster C. To sum up it is demonstrated that TRGs are linked to the prognosis and immune infiltration landscape in GC. Subsequently, differential enrichment of KEGG pathways and GO between cluster A and B and cluster A and C was performed with GSVA software (Figure S3A-D). Molecular KEGG results show that cluster B and C with worse prognosis was mainly associated with “focal adhesion” “ECM receptor interaction” and “basal cell carcinoma”. The GO item results showed that these genes were associated with the adhesive junction pathway and some common tumor-associated pathways, including “regulation of cell junction assembly”, “negative regulation of BMP signaling pathway”, “regulation of non-canonical Wnt signaling pathway” and “negative regulation of transmembrane receptor protein serine threonine kinase signaling pathway” in the biological process (BP) class, and the Molecular Function (MF) item “transforming growth factor beta (TGF-β) binding”, “lipoprotein particle receptor binding”. Results above indicated that TRGs in cluster B and C may influence the bad prognosis of patients in different and complex ways. 3.4 Construction and validation of TRG risk model in GC patients To find the associations among 22 prognostic TRGs, we built a protein-protein interaction network. The network showed a strong interaction activity among these molecules at protein level (Fig. 3 A). To perceive the prognosis of GC patients more directly, we built a predictive prognostic model using LASSO and multivariate Cox regression. After 1000 iterations, we successfully established a nine TRGs signature in TCGA cohort (Fig. 3 B). Finally, we got 9 TRGs with independent prognostic value. The coefficients of the nine genes (CABP2, CALML6, CFAP58, DST, ELOVL2, HIST1H3G, MYF6, PDE1B and TOP3B) were presented in Table S5 and Fig. 3 C. The risk score based on the signature was calculated according to the following formula: TRGscore = (1.8852 * expression of CABP2) + (-1.2041 * expression of CALML6) + (-2.7573 * expression of CFAP58) + (0.5049 * expression of DST) + (0.4338 * expression of ELOVL2) + (-0.5110 * expression of HIST1H3G) + (3.1045 * expression of MYF6) + (0.4694 * expression of PDE1B) + (-2.3596 * expression of TOP3B). Based on the median of risk score, the patients were divided into high and low risk groups. Patients in the high-risk group in the TCGA cohort had a worse prognosis, according to KM curve (Fig. 3 D). Furthermore, the TRGscore prognostic signature demonstrated high sensitivity and specificity in predicting overall survival (OS), with AUC values of 0.708, 0.731, and 0.783 at 1-year, 3-year, and 5-year intervals, respectively (Fig. 3 E). Additionally, we examined the distribution of risk scores and survival status in the TCGA cohort, revealing a consistent increase in mortality with higher risk scores (Fig. 3 F). The good performance of the risk model has also been validated in GSE62254 dataset, which showed high AUC values (0.715, 0.729 and 0.754 at 1-year, 3-year, and 5-year) (Fig. 3 G-I). Furthermore, we explored the association between the various clinicopathological characteristics and two groups based on the expression of nine panel genes in the TCGA set (Fig. 4 A). Moreover, there were significant differences in risk scores among the three clusters, with cluster A exhibiting the lowest risk score (p < 0.001) compared to the other two clusters (Fig. 4 B). Alluvial plots depicted the association of clusters, risk, and survival status with TRGs, revealing that most patients in clusters B and C are high-risk individuals (Fig. 4 C). Subgroup analysis of clinicopathological characteristics was then conducted to investigate the stability and reliability of the TRGscore. Survival analysis results revealed that individuals in the high-risk group demonstrated a worse prognosis across diverse subgroups, including TNM stage, grade, age, and gender (Figs. 4 D-K). 3.5 Validation of the expression levels of the nine TRGs for the prognostic signature Ten GC tissues and adjacent normal tissues utilized to assess the mRNA expression of nine genes in this risk panel via RT-qPCR. As showed in the Figure S4, we observed noteworthy disparities in the expression levels of the genes between GC and peritumoral tissues. DST, TOP3B, CABP2, PDE1B, MYF6, CFAP58 and CALML6 were downregulate in the GC tissues compared to the normal tissues, whereas HIST1H3G and ELOVL2 exhibited significant upregulation in GC tissues, which were consistent with our previous results. 3.6 TIME and genetic variations landscape in different risk groups GC is a highly heterogeneous cancer type, particularly in terms of the TIME. Our analysis, based on the estimation algorithm, revealed that the high-risk group had higher immune scores, stromal scores, and ESTIMATE scores (Fig. 5 A). There was a positive correlation between the TME score and the risk score (Fig. 5 B). Additional analysis was performed to investigate the immune microenvironment status of the low- and high-risk groups using CIBERSORT. Bar graph was utilized to display the infiltration levels of 22 immune cells (Fig. 5 C). Correlation analysis between the risk score and immune cell infiltration indicated a positive correlation with Treg cells, resting mast cells, M2 macrophages, resting dendritic cells, and resting NK cells (Figure S5A). Conversely, follicular helper T cells, activated Mast cells, M0 Macrophages, activated Dendritic cells and activated NK cells exhibited a negative correlation with the risk scores (Figure S5B). Furthermore, we investigated the correlation between the abundance of immune cells and the nine-panel genes. The findings revealed a significant association between several immune cells and 9 genes (Fig. 5 D). Additionally, we conducted a K-M survival analysis to examine the correlation between the abundance ratios of immune cells and overall survival. The survival analysis conducted on 22 immune cell types identified seven immune cell types associated with OS (P < 0.05) (Figure S5C-I). Resting NK cells, M2 Macrophages, and resting Dendritic cells were associated with poorer OS, while follicular helper T cells, activated dendritic cells, M0 Macrophages, and activated mast cells were related to better OS. Based on the ssGSEA analysis, the proportions of immune component levels and functions of relevant pathways significantly increased in nearly all high-risk groups (Fig. 5 E). Survival analysis indicated that seven immune-relevant pathways were associated with OS (Figure S5J-P). Additionally, most immune checkpoints exhibited a higher degree of activation (Fig. 5 F). These findings demonstrated that the TRG risk panel is closely associated with the TIME of GC. We further characterized the distribution variations of somatic mutations between the two risk groups in the TCGA cohort using maftools. We observed minimal alterations occurring in the nine panel genes among GC patients from TCGA. TTN, TP53, and MUC16 were the most frequently mutated genes in two group. However, there were fewer TTN mutations and more mutations of other genes in the high-risk group (Fig. 5 G and H). Patients with a low-risk score exhibited higher frequencies of all these mutations, compared to those with a high-risk score. Missense variations were the most common mutation type, followed by Multi Hit. These analyses suggest that immune checkpoint inhibitors (ICIs) may be beneficial for the low-risk set. Given the statistical correlation between TRGscore and various factors such as immune cell infiltration levels, immune functions, gene mutations, and the expression of immune checkpoint genes, it is likely that patients' immunotherapy outcomes could be influenced. 3.7 Evaluation of the immunotherapy response based on TRGscore Although immunotherapy has shown significant efficacy and few serious adverse events, it is crucial to recognize that immunotherapy resistance remains prevalent. Therefore, molecular subtypes are essential for identifying populations that respond favorably to immunotherapy. Additionally, TMB is clinically relevant to the outcomes of ICIs. We compared differences in TMB between patients in high and low risk groups. The low-risk group exhibited higher TMB (Fig. 6 A), which showed a negative correlation with risk scores (R=-0.37, p = 4.3e − 13) (Fig. 6 B). The K-M analysis demonstrated that GC patients with higher TMB and lower risk scores had a better prognosis, while those with lower TMB and higher risk scores had a worse prognosis (Fig. 6 C and D). These analyses suggested that ICIs may be beneficial for the low-risk set. Under normal conditions, a higher TIDE score predicts a worse response to immunotherapy. We detected the TIDE, exclusion, and dysfunction scores in the low and high-risk groups. The results showed that the TIDE score was higher in the high-risk group (Figs. 6 E). Furthermore, significant differences in T-cell exclusion score and T-cell dysfunction were observed between the two risk groups (Figs. 6 F and G), suggesting that immunotherapy may be more beneficial for the low-risk group. We also validated this inference in the anti-PD-L1 immunotherapy cohort (IMvigor210) and found that the low-risk group had more patients with complete response (CR) and partial response (PR), whereas the high-risk group had more patients with stable disease (SD) and progressive disease (PD) (Fig. 6 H). Additionally, the non-responder group (SD/PD) exhibited a higher risk score (Fig. 6 I). In conclusion, our TRGscore may serve as an effective tool for assessing patients' sensitivity to immunotherapy. 3.8 Functional evaluation of the TRGs signature GSVA and GSEA analysis were employed to analyze the potential molecular mechanisms of the DEGs in the low- and high-risk groups. The GSVA algorithm was employed to analyze the KEGG terms of each GC sample, revealing significant downregulation of pathways associated with maintaining homeostasis in the high-risk group, which included DNA damage repair pathways such as homologous recombination, mismatch repair, nucleotide excision repair, and base excision repair, as well as cell cycle regulation and RNA degradation. Pathways related to ECM receptor interaction and cell adhesion molecules (CAMs) were significantly upregulated in the high-risk group (Figure S6A). GO term analysis showed upregulation of biological processes related to material transport in the high-risk group (Figure S6B). Additionally, GSEA analysis identified upregulation of pathways such as CAMs and focal adhesion in the high-risk group (Figure S6C). Furthermore, several biological processes including DNA-dependent DNA replication and tRNA metabolic process were found to be significantly downregulated in high-risk GC patients (Figure S6D). 3.9 Construction and Assessment of a Nomogram Combining with clinical pathological features, we identified the risk score was an independent indicator through univariate and multivariate Cox regression in TCGA cohort (Fig. 7 A and B). The hazard ratio of the risk model was 1.51(95% CI: 1.38–1.65; p < 0.0001), 1.46(95% CI:1.32–1.61; p < 0.0001) in univariate and multivariate Cox regression, respectively. To improve the clinical application of prediction model, we constructed a clinically adaptable nomogram score system with the TRGscore and other clinicopathological features to predict the 1-, 3-, and 5-year survival GC patients (Fig. 7 C). The nomogram showed a good accuracy in predicting short survival time. The calibration plot of the nomogram revealed better consistency between the prediction by the nomogram and the actual observation (Fig. 7 D). The AUCs of the nomogram at 1-, 3-, and 5-year OS were 0.758, 0.767 and 0.737, respectively, which were better than the risk models and single clinical factors (Fig. 7 E-G). Furthermore, DCA curves of the nomogram for predicting OS in patients with GC demonstrated its superior performance when compared to the risk model and various clinicopathological characteristics (Fig. 7 H-J). 3.10 Drugs susceptibility analysis We further investigated the differences in drug sensitivity between high-risk and low-risk GC patients. A total of 100 drugs showed significant differences in sensitivity between these two groups (P < 0.001) (Table S6). 91 drugs showed higher sensitivity in the high-risk group, among these drugs, 10 commonly used chemotherapeutic agents for GC patients, namely 5-fluorouracil, cyclophosphamide, docetaxel, oxaliplatin, paclitaxel, vinblastine, vincristine, and vinorelbine (Figure S7A). Conversely, the other 9 drugs, including AZD8055, AZD8186, BMS-754807, Dasatinib, Doramapimod, JQ1, NU7441, SB216763 and WZ4003 may not be ideal for the high-risk patients (Figure S7B). 4. Discussion Telomere biology plays a crucial and intricate role in the onset and advancement of cancer [ 29 ]. Dysfunctional telomeres can impede cancer development by inducing replicative senescence or apoptotic pathways. However, they can also facilitate tumor initiation and progression by inducing oncogenic chromosomal rearrangements. Recent studies have shown that telomere dysfunction increases the sensitivity of cancer is related to T cell immune dysfunction [ 30 ]. It is noteworthy that telomerase reverse transcriptase (TERT), a subunit of telomerase, plays a significant role in angiogenesis, invasion, epithelial-mesenchymal transformation, inflammation, immunosuppression, and other critical gene expression profiles. These TERT-mediated activities can significantly affect the dynamic equilibrium of the TME [ 31 , 32 ]. Recent investigations have also uncovered the involvement of TERT in the lymphatic and vascular metastasis of GC, which leads to a poor prognosis [ 33 , 34 ]. GC is a significant contributor to mortality associated with tumors. It is a highly heterogeneous, with significant variations in ethnic, regional, and pathological features. These differences result in distinct treatment options and outcomes for patients with gastric cancer in Eastern and Western countries. However, the overall prognosis of gastric cancer remains suboptimal [ 35 , 36 ]. To date, the TNM stage is the primary predictor of prognosis in GC patients. However, individuals with the same TNM stage often have different clinical outcomes. This suggests that the current TNM staging guidelines are not adequate for disease risk stratification. Several biomarkers, including PD-L1 combined positive score (CPS), microsatellite instability-high (MSI-H), and TMB, are widely regarded as promising for predicting the immune response in GC. These biomarkers have some limitations, including predictive instability, intra-tumor heterogeneity, and limited predictive benefit for the population [ 37 , 38 ]. Therefore, the advancement telomere-related new biomarkers for GC that can accurately forecast prognosis and response to immunotherapy would be highly significant. Recently, several studies have constructed similar models, including gene models related to DNA damage repair, senescence, pyroptosis, neutrophil extracellular traps, and immunity [ 39 – 45 ]. These models can predict prognosis and immunotherapy response. Our model has an above-average capacity to predict overall survival in GC patients compared to other models. And all these models, including ours, can distinguish to some extent between GC patients that are sensitive and resistant to immunotherapy. Therefore, our model is a valuable supplement to current biomarkers for forecasting GC patient prognosis and identifying potential responders to ICI therapy. Our study utilized publicly available databases to identify 100 telomere-related hub genes using differential expression analysis and WGCNA. We then constructed a 9-TRG risk panel based on uni-Cox, LASSO, and multi-Cox regression analyses to predict the prognosis of GC patients and built three molecular subtypes (cluster A, B and C) based on a consensus clustering algorithm. The cluster B and C had higher risk scores than cluster A. We also validated that the risk panel showed good performance in the GEO validation cohort. Finally, we established a nomogram incorporating the TRGscore and clinicopathological factors to predict OS rates of GC patients. A high TRG risk score was correlated with poor survival and pro-tumor TIME of GC. By constructing a TRG-related risk panel, we not only focused on its predictive significance but also better analyzed the immune infiltration and treatment sensitivity differences among patients. Our TRGscore can guide personalized chemotherapy and immunotherapy for patients with GC, thereby improving their prognosis. In our study, the TRG risk panel was composed of 9 genes (CALML6, CFAP58, CABP2, DST, ELOVL2, HIST1H3G, MYF6, PDE1B and TOP3B) that have been reported in cancer development and progression to some extent. CALML6, (Calmodulin-Like Protein 6) is an oncogene in GC and is involved in mitochondrial reprogramming. Down-regulation of its level has been associated with reduced survival in rectal cancer patients [ 46 , 47 ]. Aberrant expression of CFAP58 (Cilia- And Flagella-Associated Protein 58) is associated with poor outcome in bladder cancer, endometrial cancer, lung cancer and triple-negative breast cancer [ 48 – 51 ]. CABP2 (Calcium Binding Protein 2) expression is upregulated in the MUT-high subtype of diffuse large B-cell lymphoma and correlates with reduced T-cell immune infiltration in the TME [ 52 ]. Aberrant expression of the DST (Dystonin) has been observed in various cancers, including melanoma, lung cancer, metastatic prostate cancer and invasive breast cancer [ 53 – 55 ]. DST may affect the development and progression of breast cancer by influencing the TIME [ 56 ]. The downregulation of DST could be indicative of an invasive phenotype and metastasis of cancers [ 57 ]. ELOVL2 (Elongation of very long chain Fatty acid elongase 2) is involved in the regulation of autophagy and the activity of the mTOR signaling pathway, and it’s upregulated in kidney cancer and downregulated in breast cancer are associated with tumor growth and poor prognosis [ 58 , 59 ]. ELOVL2 is involved in lipid metabolic reprogramming and dysregulated immune status, which correlates with adverse outcomes in retroperitoneal liposarcoma [ 60 ]. HIST1H3G (Histone Cluster 1 H3 Family Member G) is expressed abnormally in several types of cancer. High expression of HIST1H3G is linked to poor prognosis in glioma and laryngeal squamous cell carcinoma, and to chemosensitivity in ovarian cancer [ 61 – 64 ]. MYF6 (Myogenic Factor 6, a telomerase transcription factor) hypermethylation has been reported to be associated with lung cancer [ 65 ]. PDE1B (Phosphodiesterase 1B) has the highest weight coefficient in our panel. Interestingly, studies indicated that PDE1B may have a regulatory function in the differentiation of various immune cell types by reducing intracellular levels of both cAMP and cGMP [ 66 ]. Moreover, previous studies found that through cytokines like GM-CSF, the upregulation of PDE1B might facilitate the differentiation of macrophages into M1 subtype macrophages, thus bolstering the anti-tumor immune response and potentially enhancing patient prognosis [ 67 , 68 ]. TOP3B (DNA Topoisomerase III Beta) is involved in DNA repair/damage response. Based on data from a randomized phase III clinical trial, SAMIT, a machine learning study has identified TOP3B as a predictive marker for paclitaxel sensitivity in GC [ 69 ]. We also found that certain genes share similar functions to some extent [ 70 ]. For instance, ELOVL2, CALML6, CABP2, and HIST1H3G are involved in material metabolism and cell signalling, while DST and PDE1B are involved in regulating the TIME. Several genes (CFAP58, CABP2, DST, ELOVL2, HIST1H3G, MYF6, and PDE1B) were found to be associated with GC for the first time. Therefore, more research is warranted to uncover the mechanism of action of these genes in GC. The TME is a critical determinant of tumor development, growth, and migration, and response to therapy in GC, with immune cell infiltration playing a pivotal role [ 71 ]. In the present study, patients with higher risk scores had a higher TME score. Our findings indicated that substantial disparities in immune landscape across distinct groups of GC. Particularly, the high-risk group exhibited an increased infiltration of Tregs, M2 macrophages, resting mast cells and resting NK cells. These findings suggest an exhausted immune phenotype of the TME and a poor prognosis for patients with GC. Consistent with our findings, previous research has confirmed that the infiltration of M2 macrophages in the TME is associated with a poor prognosis for GC [ 72 , 73 ]. Another study showed that tumor-infiltrating mast cells foster immune suppression through TNF-α-PD-L1 pathway and stimulate Treg cells through an IL-33 and IL-2 axis to promote GC progression [ 74 , 75 ]. Further functional enrichment analyses in different risk groups and clusters showed involvement of signaling pathways associated with cancer cell progression and immune suppression such as TGF-β signaling pathway, ECM receptor pathways in the groups with high riskscores. Previous studies showed that ECM is an important component of the TME and can regulate cancer behaviors. ECM remodeling is crucial in the regulation of GC progression [ 76 , 77 ]. The TGF signaling pathway can suppress tumors, including cell cycle arrest and apoptosis [ 78 ]. TGF-β in the gastric tumor microenvironment is reported to promote the differentiation and expansion of both Tregs and M2 macrophages[ 79 , 80 ]. These results suggested that our gene panel can distinguish between immune infiltration characteristics of high- and low-risk groups and that the poor prognosis of GC patients in the high-risk group might be associated with the immunodepression microenvironment. Immunotherapy is a crucial element in treating GC. In this study, we examined the TIME to elucidate the relationship between TIME and TRGs in GC. Given the influence of immune checkpoints in the immunotherapy, we evaluated the differences in immune checkpoints between low and high-risk patients [ 81 ]. We found that most of the immune checkpoint genes were down-regulated in the low-risk patients. Furthermore, research has shown that a high TMB is correlated with a positive response to immune and targeted therapies in cancer patients, and is often indicative of favorable survival rates [ 82 , 83 ]. Consistent with the previous results, our study found TMB were more significant in the low-risk group and patients with high TMB scores showed better the prognosis. Our research also showed that the low-risk group had higher mutation rates for TTN, TP53 and MUC16. Several studies have found that mutations in MUC16 and TP53 are linked to a better prognosis and higher TMB in GC, while TTN mutations are associated with a better response to ICIs therapy in solid tumors [ 84 , 85 ]. Although TP53 is one of the most mutated gene, its prognostic significance in GC is still controversial [ 18 , 86 ]. Additionally, significant differences were observed in our study between the various risk groups for several frequently applied immunotherapy biomarkers. The TIDE score is a recently developed method for predicting the effectiveness of anti-PD1 and anti-CTLA4 therapy. It is more accurate than TMB or PD-L1 expression [ 87 ]. The TIDE contains two potential tumor immunologic evasion mechanisms: T-cell exclusion and T-cell dysfunction. A higher TIDE score indicates a poorer tumor response to ICI therapy and a worse prognosis [ 88 ]. Our study showed that patients with high-risk scores had significantly higher TIDE, T-cell dysfunction, and T-cell exclusion scores than those with low-risk scores. This suggests that patients with low-risk scores may be more responsive to immunotherapies, which was confirmed in the immunotherapy cohort. This suggested that TRGscore is a valid biomarker for predicting response to immunotherapy. The low TRGscore group may have a better response to ICI therapy. Chemotherapy sensitivity analysis also proved the distinct sensitivity of each subtype to certain drugs. The present study still has some highlights, even though other comparable publications have been published employing built characteristics to predict the prognosis of GC patients. First, we examined the patient’s prognosis for GC for the first-time using telomere-related mRNAs, and our models have a high level of predictive power. Second, we successfully confirmed the signature using in the public validation cohort. Moreover, we examined clinical tissue samples to confirm the mRNA levels of the genes responsible for the composition of the panel. It is admitted that our study has certain limitations. This study relies mainly on public databases without real-world cohort for validation. To further assess this signature, future large multicenter randomized controlled investigations are required. Additionally, further in vivo and vitro research is necessary to investigate the expression, prognostic predictive relevance, and particular mechanisms of these genes in GC. In conclusion, we have developed a TRG-panel based on a recognized and effective strategy for predicting GC prognosis and immunotherapy efficacy. In addition, by identifying the complex relationship between TRGs and oncogenic pathways, we provided insight into TRGs' role in tumorigenesis and TME reshaping. In combination with immune infiltration, immune checkpoint factors, and other biomarkers, we demonstrated that TRGscore effectively distinguishes responders and non-responders, enabling ICI therapy to be more precisely stratified by benefit. Therefore, this work might facilitate the identification of prognostic biomarkers and provide guidance for developing personalized immunotherapy. Declarations Ethics approval and consent to participate The study was permitted by the Ethics Committee of Xijing Hospital. Consent for publication Not applicable. Data Availability Statement The datasets generated and analyzed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), UCSC xena (https://xenabrowser.net/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. Competing interests The authors declare no competing interests. Funding The present study was supported by the National Natural Science Foundation of China (No. 82073202, 81972710, 81902677), the Natural Science Basic Research Program of Shaanxi (2021JZ-29). Authors' contributions All authors contributed to data analysis, and drafting or revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work. Acknowledgements We would like to thank all teammates for contributing this work. References Siegel, R.L., et al., Cancer statistics, 2023. CA Cancer J Clin, 2023. 73 (1): p. 17-48. Guan, W.L., Y. He, and R.H. Xu, Gastric cancer treatment: recent progress and future perspectives. J Hematol Oncol, 2023. 16 (1): p. 57. Qiu, H., S. Cao, and R. Xu, Cancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. Cancer Commun (Lond), 2021. 41 (10): p. 1037-1048. Blackburn, E.H., E.S. Epel, and J. Lin, Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science, 2015. 350 (6265): p. 1193-8. Shay, J.W. and W.E. Wright, Hayflick, his limit, and cellular ageing. Nat Rev Mol Cell Biol, 2000. 1 (1): p. 72-6. Shay, J.W. and W.E. Wright, Telomeres and telomerase: three decades of progress. Nat Rev Genet, 2019. 20 (5): p. 299-309. Hanahan, D. and R.A. Weinberg, Hallmarks of cancer: the next generation. Cell, 2011. 144 (5): p. 646-74. Rudolph, K.L., D. Hartmann, and O.G. Opitz, Telomere dysfunction and DNA damage checkpoints in diseases and cancer of the gastrointestinal tract. Gastroenterology, 2009. 137 (3): p. 754-62. Wang, Z., et al., Telomere length and risk of developing gastric adenocarcinoma: The Singapore Chinese Health Study. Gastric Cancer, 2018. 21 (4): p. 598-605. Haycock, P.C., et al., Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study. JAMA Oncol, 2017. 3 (5): p. 636-651. Aviv, A., J.J. Anderson, and J.W. Shay, Mutations, Cancer and the Telomere Length Paradox. Trends Cancer, 2017. 3 (4): p. 253-258. Han, X., et al., The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma. Front Immunol, 2023. 14 : p. 1220100. Li, S.C., et al., Telomere-related gene risk model for prognosis and drug treatment efficiency prediction in kidney cancer. Front Immunol, 2022. 13 : p. 975057. Jung, S.J., et al., Clinical Characteristics of TZAP (ZBTB48) in Hepatocellular Carcinomas from Tissue, Cell Line, and TCGA. Medicina (Kaunas), 2022. 58 (12). Chen, S., et al., Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer. Sci Rep, 2023. 13 (1): p. 10586. Hu, Y., et al., Telomere-Associated Gene Signatures Correlate with Prognosis, Tumor Microenvironment, and Chemosensitivity in Breast Cancer. Med Sci Monit, 2023. 29 : p. e939921. Feng, J., et al., A telomerase regulation-related lncRNA signature predicts prognosis and immunotherapy response for gastric cancer. J Cancer Res Clin Oncol, 2023. 149 (1): p. 135-146. Cristescu, R., et al., Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med, 2015. 21 (5): p. 449-56. Braun, D.M., et al., TelNet - a database for human and yeast genes involved in telomere maintenance. BMC Genet, 2018. 19 (1): p. 32. Langfelder, P. and S. Horvath, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008. 9 : p. 559. Jia, A., L. Xu, and Y. Wang, Venn diagrams in bioinformatics. Brief Bioinform, 2021. 22 (5). Wang, H., et al., Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinformatics, 2019. 35 (7): p. 1181-1187. Zhang, D., et al., Prediction of Overall Survival Among Female Patients With Breast Cancer Using a Prognostic Signature Based on 8 DNA Repair-Related Genes. JAMA Netw Open, 2020. 3 (10): p. e2014622. Vickers, A.J., et al., Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak, 2008. 8 : p. 53. Newman, A.M., et al., Robust enumeration of cell subsets from tissue expression profiles. Nat Methods, 2015. 12 (5): p. 453-7. Fu, J., et al., Large-scale public data reuse to model immunotherapy response and resistance. Genome Med, 2020. 12 (1): p. 21. Hänzelmann, S., R. Castelo, and J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 2013. 14 : p. 7. Yang, W., et al., Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res, 2013. 41 (Database issue): p. D955-61. Xu, L., S. Li, and B.A. Stohr, The role of telomere biology in cancer. Annu Rev Pathol, 2013. 8 : p. 49-78. Schratz, K.E., et al., T cell immune deficiency rather than chromosome instability predisposes patients with short telomere syndromes to squamous cancers. Cancer Cell, 2023. 41 (4): p. 807-817.e6. Liu, Z., et al., Telomerase reverse transcriptase promotes epithelial-mesenchymal transition and stem cell-like traits in cancer cells. Oncogene, 2013. 32 (36): p. 4203-13. Noureen, N., et al., Integrated analysis of telomerase enzymatic activity unravels an association with cancer stemness and proliferation. Nat Commun, 2021. 12 (1): p. 139. Wu, Y., et al., Telomerase reverse transcriptase methylation predicts lymph node metastasis and prognosis in patients with gastric cancer. Onco Targets Ther, 2016. 9 : p. 279-86. Sasaki, T., et al., AKT activation and telomerase reverse transcriptase expression are concurrently associated with prognosis of gastric cancer. Pathobiology, 2014. 81 (1): p. 36-41. Smyth, E.C., et al., Gastric cancer. Lancet, 2020. 396 (10251): p. 635-648. Zhang, Z., Z. Liu, and Z. Chen, Comparison of Treatment Efficacy and Survival Outcomes Between Asian and Western Patients With Unresectable Gastric or Gastro-Esophageal Adenocarcinoma: A Systematic Review and Meta-Analysis. Front Oncol, 2022. 12 : p. 831207. Fuchs, C.S., et al., Safety and Efficacy of Pembrolizumab Monotherapy in Patients With Previously Treated Advanced Gastric and Gastroesophageal Junction Cancer: Phase 2 Clinical KEYNOTE-059 Trial. JAMA Oncol, 2018. 4 (5): p. e180013. Muro, K., et al., Pembrolizumab for patients with PD-L1-positive advanced gastric cancer (KEYNOTE-012): a multicentre, open-label, phase 1b trial. Lancet Oncol, 2016. 17 (6): p. 717-726. Yan, J., et al., DNA damage response-related immune activation signature predicts the response to immune checkpoint inhibitors: from gastrointestinal cancer analysis to pan-cancer validation. Cancer Biol Med, 2023. Zhao, Z., et al., The DNA damage repair-related lncRNAs signature predicts the prognosis and immunotherapy response in gastric cancer. Front Immunol, 2023. 14 : p. 1117255. Shen, X., et al., Senescence-related genes define prognosis, immune contexture, and pharmacological response in gastric cancer. Aging (Albany NY), 2023. 15 (8): p. 2891-2905. Shao, W., et al., The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer. Front Cell Dev Biol, 2021. 9 : p. 676485. Li, M., et al., Neutrophil extracellular traps-related signature predicts the prognosis and immune infiltration in gastric cancer. Front Med (Lausanne), 2023. 10 : p. 1174764. Xu, X., et al., A SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer. Pharmaceuticals (Basel), 2022. 15 (11). Zhang, C., et al., Development and verification of an immune-related gene prognostic index for gastric cancer. Sci Rep, 2022. 12 (1): p. 15693. Wang, Y., X.Y. Jiang, and X.Y. Yu, BRD9 controls the oxytocin signaling pathway in gastric cancer via CANA2D4, CALML6, GNAO1, and KCNJ5. Transl Cancer Res, 2020. 9 (5): p. 3354-3366. Penrose, H.M., et al., In colonic ρ(0) (rho0) cells reduced mitochondrial function mediates transcriptomic alterations associated with cancer. Oncoscience, 2017. 4 (11-12): p. 189-198. Gu, C., et al., The IFN-γ-related long non-coding RNA signature predicts prognosis and indicates immune microenvironment infiltration in uterine corpus endometrial carcinoma. Front Oncol, 2022. 12 : p. 955979. Ademuyiwa, F.O., et al., Immunogenomic profiling and pathological response results from a clinical trial of docetaxel and carboplatin in triple-negative breast cancer. Breast Cancer Res Treat, 2021. 189 (1): p. 187-202. Wu, H., et al., Prediction of bladder cancer outcome by identifying and validating a mutation-derived genomic instability-associated long noncoding RNA (lncRNA) signature. Bioengineered, 2021. 12 (1): p. 1725-1738. Liu, Y., et al., Focused Analysis of Exome Sequencing Data for Rare Germline Mutations in Familial and Sporadic Lung Cancer. J Thorac Oncol, 2016. 11 (1): p. 52-61. You, H., et al., Genomic complexity is associated with epigenetic regulator mutations and poor prognosis in diffuse large B-cell lymphoma. Oncoimmunology, 2021. 10 (1): p. 1928365. Brown, A., et al., The mouse dystonia musculorum gene is a neural isoform of bullous pemphigoid antigen 1. Nat Genet, 1995. 10 (3): p. 301-6. Shimbo, T., et al., Serum anti-BPAG1 auto-antibody is a novel marker for human melanoma. PLoS One, 2010. 5 (5): p. e10566. Kang, J., et al., Response and acquired resistance to MET inhibitors in de novo MET fusion-positive advanced non-small cell lung cancer. Lung Cancer, 2023. 178 : p. 66-74. Qiu, X., et al., Identification of m6A-Associated Gene DST as a Prognostic and Immune-Associated Biomarker in Breast Cancer Patients. Int J Gen Med, 2022. 15 : p. 523-534. Choi, J.S., et al., Identification of new genes of pleomorphic adenoma. Medicine (Baltimore), 2019. 98 (51): p. e18468. Tanaka, K., et al., ELOVL2 promotes cancer progression by inhibiting cell apoptosis in renal cell carcinoma. Oncol Rep, 2022. 47 (2). Kang, Y.P., et al., Spheroid-Induced Epithelial-Mesenchymal Transition Provokes Global Alterations of Breast Cancer Lipidome: A Multi-Layered Omics Analysis. Front Oncol, 2019. 9 : p. 145. Wang, Z., et al., Insight of a lipid metabolism prognostic model to identify immune landscape and potential target for retroperitoneal liposarcoma. Front Immunol, 2023. 14 : p. 1209396. Zhang, M. and S.C. Luo, Gene expression profiling of epithelial ovarian cancer reveals key genes and pathways associated with chemotherapy resistance. Genet Mol Res, 2016. 15 (1). Xiang, F., et al., Network Pharmacology and Bioinformatics Methods Reveal the Mechanism of Zao-Jiao-Ci in the Treatment of LSCC. J Oncol, 2021. 2021 : p. 8862821. Zhang, G.H., et al., Seven genes for the prognostic prediction in patients with glioma. Clin Transl Oncol, 2019. 21 (10): p. 1327-1335. Castañeda-Partida, L., et al., Global expression profiling of CD10 + /CD19 + pre-B lymphoblasts from Hispanic B-ALL patients correlates with comparative TARGET database analysis. Discov Oncol, 2022. 13 (1): p. 28. Zhao, Y., et al., Abnormal methylation of seven genes and their associations with clinical characteristics in early stage non-small cell lung cancer. Oncol Lett, 2013. 5 (4): p. 1211-1218. Azevedo, M.F., et al., Clinical and molecular genetics of the phosphodiesterases (PDEs). Endocr Rev, 2014. 35 (2): p. 195-233. Heymann, M.F., F. Lézot, and D. Heymann, The contribution of immune infiltrates and the local microenvironment in the pathogenesis of osteosarcoma. Cell Immunol, 2019. 343 : p. 103711. Bender, A.T. and J.A. Beavo, PDE1B2 regulates cGMP and a subset of the phenotypic characteristics acquired upon macrophage differentiation from a monocyte. Proc Natl Acad Sci U S A, 2006. 103 (2): p. 460-5. Sundar, R., et al., Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial. Gut, 2022. 71 (4): p. 676-685. Ru, B., et al., TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics, 2019. 35 (20): p. 4200-4202. de Visser, K.E. and J.A. Joyce, The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell, 2023. 41 (3): p. 374-403. Piao, H., et al., A positive feedback loop between gastric cancer cells and tumor-associated macrophage induces malignancy progression. J Exp Clin Cancer Res, 2022. 41 (1): p. 174. Chen, Y., et al., Tumor-recruited M2 macrophages promote gastric and breast cancer metastasis via M2 macrophage-secreted CHI3L1 protein. J Hematol Oncol, 2017. 10 (1): p. 36. Lv, Y., et al., Tumor-infiltrating mast cells stimulate ICOS(+) regulatory T cells through an IL-33 and IL-2 axis to promote gastric cancer progression. J Adv Res, 2023. Lv, Y., et al., Increased intratumoral mast cells foster immune suppression and gastric cancer progression through TNF-α-PD-L1 pathway. J Immunother Cancer, 2019. 7 (1): p. 54. Wang, X., et al., Succinylation Inhibits the Enzymatic Hydrolysis of the Extracellular Matrix Protein Fibrillin 1 and Promotes Gastric Cancer Progression. Adv Sci (Weinh), 2022. 9 (27): p. e2200546. Jurj, A., et al., The extracellular matrix alteration, implication in modulation of drug resistance mechanism: friends or foes? J Exp Clin Cancer Res, 2022. 41 (1): p. 276. Colak, S. and P. Ten Dijke, Targeting TGF-β Signaling in Cancer. Trends Cancer, 2017. 3 (1): p. 56-71. Ning, J., et al., Imbalance of TGF-β1/BMP-7 pathways induced by M2-polarized macrophages promotes hepatocellular carcinoma aggressiveness. Mol Ther, 2021. 29 (6): p. 2067-2087. Knochelmann, H.M., et al., When worlds collide: Th17 and Treg cells in cancer and autoimmunity. Cell Mol Immunol, 2018. 15 (5): p. 458-469. Gaikwad, S., et al., Immune checkpoint proteins: Signaling mechanisms and molecular interactions in cancer immunotherapy. Semin Cancer Biol, 2022. 86 (Pt 3): p. 137-150. Yarchoan, M., A. Hopkins, and E.M. Jaffee, Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med, 2017. 377 (25): p. 2500-2501. Marabelle, A., et al., Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol, 2020. 21 (10): p. 1353-1365. Li, X., et al., Association of MUC16 Mutation With Tumor Mutation Load and Outcomes in Patients With Gastric Cancer. JAMA Oncol, 2018. 4 (12): p. 1691-1698. Yang, Y., et al., MUC4, MUC16, and TTN genes mutation correlated with prognosis, and predicted tumor mutation burden and immunotherapy efficacy in gastric cancer and pan-cancer. Clin Transl Med, 2020. 10 (4): p. e155. Robles, A.I. and C.C. Harris, Clinical outcomes and correlates of TP53 mutations and cancer. Cold Spring Harb Perspect Biol, 2010. 2 (3): p. a001016. Becht, E., et al., Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol, 2016. 17 (1): p. 218. Jiang, P., et al., Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med, 2018. 24 (10): p. 1550-1558. Additional Declarations No competing interests reported. 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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-4598908","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":325861817,"identity":"7da5e693-c337-4be2-874c-31f17e75abb4","order_by":0,"name":"Dai Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3RMQrCMBTG8VcCr0toHFMKeoVIQBx6E5eK0ElB6FowILRX0GMUL1B44FRw7e4FKi66iI7dGjfB/PcfvI8H4HL9YIKJTj9fkqFvLElYgsoAYz/gtSVRF1B3wFSMZWJLCJanLacIw2vVQh4vhg8jIH2QpDFKszmc040ZIgF5heKKVhitZ9IzNEyAmP/gCe2KsLEkI0LQvE4ZSm5Jwj2H6dHEDPlnS2KzRYgG1M1INimpars8ttjSS1m/pke+FS6Xy/UfvQGgXThm9/bywwAAAABJRU5ErkJggg==","orcid":"","institution":"Xijing Hospital, Fourth Military Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dai","middleName":"","lastName":"Zhang","suffix":""},{"id":325861818,"identity":"67b341c3-8d02-4621-8339-451b7b9d5846","order_by":1,"name":"Dingli Song","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Dingli","middleName":"","lastName":"Song","suffix":""},{"id":325861819,"identity":"a3b32b23-a27a-435b-8071-ff8ad3bac643","order_by":2,"name":"Yiche Li","email":"","orcid":"","institution":"Shaanxi Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiche","middleName":"","lastName":"Li","suffix":""},{"id":325861820,"identity":"45f10e52-6b41-4a5a-bb66-bd7d1dfcb599","order_by":3,"name":"Fenfen He","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fenfen","middleName":"","lastName":"He","suffix":""},{"id":325861821,"identity":"3119c11f-bbe6-4e15-89cb-12ed00acbd65","order_by":4,"name":"Qian Hao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Hao","suffix":""},{"id":325861822,"identity":"a05aef22-11e4-4528-bf42-afa1e95b415f","order_by":5,"name":"Yujiao Deng","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yujiao","middleName":"","lastName":"Deng","suffix":""},{"id":325861823,"identity":"ccaa86e4-cce4-4e85-98de-19db469358cd","order_by":6,"name":"Si Yang","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Si","middleName":"","lastName":"Yang","suffix":""},{"id":325861825,"identity":"143e9978-08e6-402a-9f38-dda252df4d48","order_by":7,"name":"Hui Wang","email":"","orcid":"","institution":"Xijing Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":325861827,"identity":"0568c936-3134-41a4-9a7a-016cde1786bf","order_by":8,"name":"Jianghao Chen","email":"","orcid":"","institution":"Xijing Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianghao","middleName":"","lastName":"Chen","suffix":""},{"id":325861829,"identity":"1704168d-71d0-4dcc-ad89-177572d16688","order_by":9,"name":"Ting Wang","email":"","orcid":"","institution":"Xijing Hospital, Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-18 09:28:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4598908/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4598908/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60618350,"identity":"e7b26e95-51fc-4e44-b70c-950b5324598d","added_by":"auto","created_at":"2024-07-18 20:36:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed hub genes in tumor and normal tissues of patients with GC. \u003c/strong\u003e(A) Volcano plot of DE-TRGs identified from tumor and normal tissues of GC patients (|log2 fold change| \u0026gt;1 and an adjusted P-value \u0026lt;0.05). (B) Selection of the optimal soft threshold power in WGCNA. (C) A dendrogram of the differentially expressed genes clustered based on different metrics. Each branch in the figure represents one gene, and every color below represents one co-expression module. (D) Correlations of gene modules with clinical traits. Boolean variables denote the phenotypes of the clinical traits, where 0 represents “Normal” and 1 represents “Tumor”. The ME-turquoise module and ME-blue module show the higher significant difference. (E, F) The correlation between gene significance and module membership shown in scatter plot in turquoise and blue modules.\u003cstrong\u003e \u003c/strong\u003e(G) Venn diagram displaying the overlapping differentially expressed hub genes from the DE-TRGs list and the turquoise or blue hub gene modules.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/b15a339d7cb05151bf95fc12.png"},{"id":60618353,"identity":"30f71152-5e15-4751-9e98-a7a723041f7a","added_by":"auto","created_at":"2024-07-18 20:36:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":260075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsensus clustering analysis of GC patients based on the prognostic TRGs. \u003c/strong\u003e(A) Heatmap of sample cluster when k=3. (B) CDF curve for k=2–9. (C) Relative change in the area under the CDF curve for k=2 – 9. (D) Principal component analysis of the three clusters. (E) K-M survival analysis for overall survival of three clusters. (F) The heatmap showed the TRGs expression profile in molecular subtypes and the associations between clinicopathologic characteristics and different molecular subtypes. (G-I) Stromal score, immune score and ESTIMATE score in different clusters were evaluated by ESTIMATE analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/f04f5dbb2f5d850f28203e1a.png"},{"id":60618349,"identity":"81005ab6-438a-42da-aeef-ad1cf6785b16","added_by":"auto","created_at":"2024-07-18 20:36:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of TRG-risk panel in GC patients. \u003c/strong\u003e(A). The protein-protein interaction network of 22 prognostic TRGs. (B) LASSO regression analysis with 10-fold cross-validation obtained 15 prognostic TRGs. (C) Forest plot illustrating the multivariable Cox model results of each gene in 9-TRG risk signature. The survival curve, ROC curve, the distribution of risk scores and survival status of GC patients in the TCGA cohort (D-F) and the GEO cohort (G-I).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/c2601e233ecba79d18431267.png"},{"id":60619250,"identity":"2c12452b-2268-49ad-ab83-d6a2a42f3747","added_by":"auto","created_at":"2024-07-18 20:44:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between risk groups, clustering, and clinical features. \u003c/strong\u003e(A) The heatmap showed the TRGs expression profile in different risk groups and the associations between clinicopathologic characteristics and the groups. (B) Risk scores differed among the three clusters. (C) Sankey plot for patients in different clusters, risk groups and survival status. (D-K) Survival analysis of clinicopathologic characteristics of high- and low-risk patients.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/70ca445bd5aaf8bd1d2e2a9c.png"},{"id":60619252,"identity":"d38b1ffd-f4e2-4aa7-abe1-f279da465ac7","added_by":"auto","created_at":"2024-07-18 20:44:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":345864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor microenvironment in the high and low risk groups. \u003c/strong\u003e(A) Comparison of the stromal score, immune score, and ESTIMATE score between high-risk and low-risk groups. (B) The correlation between TME score and risk score. (C) The landscape of immune cell infiltration between high-risk and low-risk groups. (D) Correlation analyses between the abundance of tumor infiltration immune cells and 9 TRGs in the proposed model. (E) The differences of immune functions between the two groups with ssGSEA. (F) The differences of common checkpoints between high and low risk groups. (G, H) The waterfall plot of somatic mutation characteristics in high- and low- TRGscore groups. P \u0026lt; 0.05 was considered to be statistically significant. * indicated P \u0026lt; 0.05, ** indicated P \u0026lt; 0.01, *** indicated P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/78678f295f8f2fb98d38e5be.png"},{"id":60621222,"identity":"60b01e99-0d42-461b-a1b6-fb9367962d61","added_by":"auto","created_at":"2024-07-18 21:00:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":134561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessment of Immunotherapy response of high and low risk groups.\u003c/strong\u003e (A) The TMB score in different groups. (B) The correlation between risk score and TMB. (C) Kaplan–Meier survival analysis of the low- and high-TMB groups (D) Survival differences among patients with different TMB scores combined with different risk scores. (E-G) The TIDE score, exclusion score and dysfunction score in different risk groups. (H) The percentage rates of clinical response (complete response [CR]/partial response [PR] and stable disease [SD]/progressive disease [PD]) to anti–PD-L1 immunotherapy in high- or low-TRGscore groups in the IMvigor210. (I) The non-responder group (SD/PD) exhibited a higher risk score. P \u0026lt; 0.05 was considered to be statistically significant. * indicated P \u0026lt; 0.05, ** indicated P \u0026lt; 0.01, *** indicated P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/f2f7fc6b07ec4f262c083bb8.png"},{"id":60620435,"identity":"15059717-e47b-4048-a5c4-82ad281c3cdc","added_by":"auto","created_at":"2024-07-18 20:52:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":195349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndependent prognosis analysis of the TRGs prognostic signature and construction and assessment of nomogram. \u003c/strong\u003e(A, B) Univariate cox and multivariate Cox regression analyses of the TRGs signature and clinical characteristics. (C) Nomogram model construction based on the clinicopathological characteristics and TRGs prognostic signature. (D) The calibration curve shows the consistency between the predictive power and actual survival of 1, 3, and 5 years. (E-G) ROC curves for nomogram, risk scores and other clinical characteristics at 1, 3, and 5 years. (H-J) DCA curves for nomogram, risk scores and other clinical characteristics at 1, 3, and 5 years.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/e43c3d1436b7e71942981f3d.png"},{"id":68697125,"identity":"be426548-d4d0-441f-9fcf-d6cfd347dc38","added_by":"auto","created_at":"2024-11-11 06:54:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2268579,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/6d1209b7-ea0c-4a0b-9911-d1c219dd9dd9.pdf"},{"id":60619253,"identity":"1de0e181-cd10-4418-86c0-2725207a2127","added_by":"auto","created_at":"2024-07-18 20:44:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1514178,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598908/v1/13f95bb7ce6318f2488b50ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A telomere-related gene panel predicts the prognosis and Immune Status in gastric cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) is one of the most common malignancies worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The treatment landscape for GC has evolved significantly in recent years, particularly with the development of systemic therapeutic options for GC, including chemotherapy, targeted therapy and immunotherapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the overall treatment outcome of GC remains unsatisfactory due to the challenge of early detection and the high incidence of distant metastases in advanced GC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, there is an urgent need to screen high-risk patients and to search for more valuable effective diagnostic, prognostic and therapeutic sensitivity biomarkers.\u003c/p\u003e \u003cp\u003eThe telomeres are the nucleoprotein complexes that cap the very ends of the eukaryotic chromosomes, consisting of a tract of tandemly repeated short DNA repeats and associated protective proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These specialized structures are essential for chromosome stability. In normal dividing cells, with each cell replication telomeres gradually shorten, until a critical level is reached, after which cells undergo senescence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This gradual shortening of telomeres associated with cellular aging is believed to be a protective mechanism against uncontrolled growth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The activation of a telomere maintenance mechanism (TMM), which consists of telomerase activation and Alternative-lengthening of Telomere (ALT), to prevent the shortening of telomeres is necessary for the continued sustained proliferation of cancer cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There is also evidence that telomere shortening induces chromosomal instability and cancer initiation. Initiated tumors require telomerase activation to prevent high levels of chromosomal instability that induce genetic chaos and tumor cell death [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. So, it seems that telomere biology plays a critical and complex role in the initiation and progression of cancer.\u003c/p\u003e \u003cp\u003ePrevious studies have mainly focused on telomere length in different types of cancer[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the relationship between dysregulation of telomere-related genes (TRGs), cancer prognosis and tumor immune microenvironment (TIME) has only been investigated in pancreatic cancer, breast cancer, kidney cancer, lung cancer, hepatocellular cancer, and glioma[\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A recent study focused on telomerase regulation-related lncRNAs in GC, which showed downregulation of AC023590.1, PVT1, BX890604.1 and UBL7AS1 was associated with a good prognosis for GC patients[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, a comprehensive investigation of the relationship between TRGs and GC is still lacking. Therefore, in the present study, we comprehensive studied telomere-related genes in GC and constructed a prognostic TRG panel for GC patients, and evaluated the underlying impact of TRGs on the TIME.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sourcing\u003c/h2\u003e \u003cp\u003eGC patients with RNA-seq and clinical information were obtained from The Cancer Genome Atlas (TCGA) data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"http://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as a training set, including 375 GC records and 32 normal controls. And the gene expression data from 174 normal gastric tissues were downloaded from the Genotype-Tissue Expression (GTEx) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://commonfund.nih.gov/GTEx\u003c/span\u003e\u003cspan address=\"https://commonfund.nih.gov/GTEx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to supplement the data of normal samples for the training cohort. The validation cohort GSE62254, based on the GPL570 platform and including 300 GC samples, was obtained from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, we acquired 2093 TRGs from the TelNet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cancertelsys.org/telnet/\u003c/span\u003e\u003cspan address=\"http://www.cancertelsys.org/telnet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. TCGA-STAD somatic mutation data was also downloaded from the UCSC xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xenabrowser.net\u003c/span\u003e\u003cspan address=\"http://xenabrowser.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The immune inhibitor treatment cohort IMvigor210 of metastatic urothelial carcinoma was downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://research-pub.gene.com/IMvigor210Core\u003c/span\u003e\u003cspan address=\"http://research-pub.gene.com/IMvigor210Core\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Biologies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Screening hub differential telomere-related genes\u003c/h2\u003e \u003cp\u003eThe study extracted expression profiles of 2038 overlapping TRGs from transcriptome RNA sequencing data of combined gastric tissue samples (375 GC tissues and 206 normal gastric tissues) to screen for differentially expressed telomere-associated genes (DE-TRGs) using the limma package. The threshold was set at a |log2 fold change| \u0026gt;1 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All the samples were also subjected to weighted gene co-expression network analysis (WGCNA) using the WGCNA R package to identify key genes[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Then we determined the optimal soft threshold of the data to ensure that the genes interaction conformed to the scale-free distribution to a maximum extent. The adjacency and similarity between genes were calculated, and the cluster dendrogram was established. The modules were further segmented using the dynamic tree-cutting algorithm, and similar modules were merged. We evaluated the Pearson correlation between each module and sample traits and the highly differential TRGs between gastric tumor and normal tissues in the WCGNA results were screened for further analysis. We then took the intersection of the TRGs in the hub modules after WCGNA and the DE-TRGs for subsequent analysis, and displayed them in the form of a Venn diagram [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Consensus clustering\u003c/h2\u003e \u003cp\u003eThe Consensus Clustering method is widely used for tumor analysis. Cluster analysis was performed by the \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package based on the prognostic TRGs, which were identified through univariate Cox regression analysis in the TCGA training set (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and TCGA cohort patients were divided into different subtypes for further analysis according to the clustering results of K\u0026thinsp;=\u0026thinsp;2 to 9. The effect of consistency clustering was evaluated through the utilization of PCA, T-distributed stochastic neighbor embedding (t-SNE), and UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction). We utilized R packages \"highcharter\", \"ggplot2\", and \"ggalluvial\" to create a Sankey diagram to illustrate the distribution of risk groups, clusters, and survival outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Development and reliability evaluation of prognosis-related signature\u003c/h2\u003e \u003cp\u003eThe prognostic TRGs were used for least absolute shrinkage and selection operator (LASSO) Cox regression analysis, performed by the R package \u0026ldquo;glmnet\u0026rdquo; in order to prevent overfitting[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The TRG signature was constructed using multivariate Cox regression analysis. Subsequently, the risk score for each patient was calculated based on this prognostic signature. The formula of risk score was as follows: risk score\u0026thinsp;=\u0026thinsp;Σ (Ci \u0026times; EXPi), where EXP is the gene expression level and C is the coefficient for the corresponding gene in the multivariate Cox model[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The median risk score was used as a cutoff to categorize patients in the training into high- and low-risk groups. Differences in survival between the high- and low-risk groups were assessed using the \"survival\" R package. We calculated the area under the curve (AUC) using the R package \"timeROC\" to evaluate the prognostic capability of the TRG signature. The GEO dataset was used for external validation. The same methods were performed to estimate a risk score for each case. The correlation between these two risk groups and clinicopathological features were also studied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Tissue samples acquisition and real-time quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTen groups of GC and corresponding normal tissues were harvested from GC patients at Xijing Hospital of Digestive Diseases. Informed consent was obtained from each patient, The study was permitted by the Ethics Committee of Xijing Hospital. All experiments were performed in accordance with relevant guidelines and regulations. Total RNA was extracted using the kit (RNAfast200, fastagen, China), followed by reverse transcription reactions performed with Prime Script RTase (Takara, China) following the protocol. Then RT-PCR was used to measure mRNA expression levels using SYBR green (Takara, China). The primer sequences used for qRT-PCR in this study are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. To normalize the gene expression, the expression of B-ACTIN was utilized as a reference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Establishment and assessment of the nomogram\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were conducted to further verify the independent prognostic value of the TRG panel. To evaluate the survival probability of GC patients, a nomogram was created using the R package 'rms' in combination with clinicopathological factors to predict 1-, 3-, and 5-year survival. The efficiency of the nomogram was assessed using calibration diagrams, AUC, and decision curves (DCA) with the 'ggDCA' and 'survival' packages, respectively[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 The TIME, genetic variations and immunotherapy response analysis\u003c/h2\u003e \u003cp\u003e\u0026ldquo;ESTIMATE\u0026rdquo; algorithm was applied to assess the TME scores, including immune infiltration, stromal score and estimate score of each patient using \u0026ldquo;limma\u0026rdquo; and \u0026ldquo;estimate\u0026rdquo; R packages. The tumor mutation landscape of patients with GC was depicted by using \u0026ldquo;matfool\u0026rdquo; R package. Waterfall plots were generated to assess the number of somatic point mutations in each GC sample and to illustrate the relationship between tumor mutation burden (TMB) and risk groups. In addition, to explore the immune characteristics of patients with GC, expression data was imported into CIBERSORT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cibersort.stanford.edu/\u003c/span\u003e\u003cspan address=\"http://cibersort.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and iterated 1000 times to estimate the relative proportions of 22 immune cell types[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The ssGSEA R scripts were used to quantify the relative proportion of infiltrating immune functional related pathways. Moreover, we performed GSEA and GSVA on certain gene signature and compared the score between different groups to understand the immune and molecular functions. Finally, we compared the immune checkpoint activation between the two risk groups via \u0026ldquo;ggpubr\u0026rdquo; R package. We draw the heatmap to present the difference of immune cell infiltration landscape between clusters and other clinicopathological features. Additionally, TIDE (Tumor Immune Dysfunction and Exclusion) score was calculated online following the instructions (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, we used TRGscore to classify the anti-PDL1 cohort (IMvigor210) samples. The \u0026ldquo;ggplot2\u0026rdquo; package was used to analyze the varying degrees of response to immunotherapy and the proportion of patients who responded versus those who did not.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Functional enrichment assessment\u003c/h2\u003e \u003cp\u003eTo explore the potential molecular function and pathways, we performed gene set various analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) based on defined gene sets, \u0026ldquo;c5.go.symbols.gmt\u0026rdquo;, and \u0026ldquo;c2.cp.kegg.symbols.gmt\u0026rdquo;, which were downloaded from MSigDB database[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition to this, functional enrichment analysis was performed by the \u0026ldquo;clusterProfiler\u0026rdquo; package in the R software, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate significant differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Drug sensitivity prediction\u003c/h2\u003e \u003cp\u003eAs part of the TCGA cohort, the \u0026ldquo;oncoPredict\u0026rdquo; R package was used to determine the half-maximum inhibitory concentration (IC50) commonly used in chemotherapeutic and targeted drugs for each GC patient. There were 198 drugs from Genomics of Drug Sensitivity in Cancer (GDSC; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) that were compared for sensitivity in the different risk groups[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 was set as the threshold for significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using R version 4.3.2. To calculate prognostic values and to compare patient survival in different subgroups in each data set, Kaplan\u0026ndash;Meier survival analysis, and the log-rank test was used. The non-parametric Wilcoxon rank sum test was used to test the relationship between the two groups for continuous variables. And the results of RT-qPCR were conducted statistical analysis using pair t test. Correlation coefficients were examined using spearman correlation analysis. In most statistical investigations, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The baseline characteristics of GC patients in TCGA and GEO cohort\u003c/h2\u003e \u003cp\u003eThe TCGA-STAD cohort (375 GC records and 32 normal controls) was considered as training cohort in this study. And the GEO cohort GSE62254 containing 300 GC sample were defined as the testing cohort. The detailed information of TCGA GC patients was showed in Table S2, while the clinical information available in GEO cohorts was not as detailed as that in training set. 2093 telomere-related genes (TRGs) were acquired from the TelNet database (Table S3). After overlapping with GC mRNAs, 2038 TRGs were obtained for our study. When using the TCGA cohort for differential analysis of TRGs expression, given the severe imbalance between normal and tumor samples, we downloaded 174 normal gastric samples from GTEx database as a complement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of Highly Correlated DE-TRGs\u003c/h2\u003e \u003cp\u003e2038 TRGs RNA sequence profiles were selected for screening the DE-TRGs and conducting WGCNA. A total of 259 DE-TRGs between 375 tumor tissue and 206 normal tissues were identified through differentially expression analysis, including 115 highly expressed genes and 144 low expressed genes in GC patients according to the screened criteria: |log FC| \u0026gt; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The expression landscape of the top 50 up- and down-regulated DE-TRGs has been shown in the heat map (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). We performed WCGNA based on 2038 TRGs to identify the highly correlated gene modules in the training cohort. We set the soft thresholding power to 14, based on a scale-free R\u003csup\u003e2\u003c/sup\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and S1B), and three gene modules were identified based on the gene dendrogram: MEturquoise, MEblue and MEgrey modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Among these gene modules, there were strong correlations between tumor occurrence and the turquoise module (the coefficient was 0.9 and p-value was 8 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;213\u003c/sup\u003e) and blue module (the coefficient was 0.83 and p-value was 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;150\u003c/sup\u003e), composed of 240 genes and 131 genes respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In addition, the gene significance and module membership were highly correlated in the turquoise module, suggesting a significant association of genes in this module with tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and S1C). Likewise, the blue module showed a high correlation with tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), whereas genes within the grey module exhibited no association with tumors (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). Finally, we extracted 100 hub genes through crossing DE-TRGs and the turquoise or blue gene modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) for downstream analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of consensus clustering, immune infiltration and GSVA\u003c/h2\u003e \u003cp\u003eWe further implemented univariate Cox analysis to screen prognosis-related TRGs among the100 hub DE-TRG (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Consensus clustering analysis was conducted to cluster patients with GC into various subgroups based on 22 prognosis-related TRGs (Table S4). The consensus clustering heatmap indicated an optimal and stable classification with K\u0026thinsp;=\u0026thinsp;3 (Figure S2A). Patients with GC were classified into three subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). PCA showed good results for consistent clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A substantial difference in prognosis among the three subtypes was revealed by the survival analysis, cluster A had a higher survival probability than clusters B and C (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). A heatmap of gene expression and clinical factors shows the differences between the three clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Several immune assessment algorithms were used to evaluate the TME landscape of GC patients in three clusters. According to the ESTIMATE algorithm, patients in Cluster B had higher stromal, immune, and ESTIMATE scores than those in Cluster A. In contrast, patients in Cluster C had lower stromal, immune, and ESTIMATE scores than those in Cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-I). The boxplot demonstrated the considerable variation in immune cell infiltration levels among the three groups (Figure S2B). We similarly found that the percentage of most immune cell infiltration was highest in cluster B and lowest in cluster C. To sum up it is demonstrated that TRGs are linked to the prognosis and immune infiltration landscape in GC. Subsequently, differential enrichment of KEGG pathways and GO between cluster A and B and cluster A and C was performed with GSVA software (Figure S3A-D). Molecular KEGG results show that cluster B and C with worse prognosis was mainly associated with \u0026ldquo;focal adhesion\u0026rdquo; \u0026ldquo;ECM receptor interaction\u0026rdquo; and \u0026ldquo;basal cell carcinoma\u0026rdquo;. The GO item results showed that these genes were associated with the adhesive junction pathway and some common tumor-associated pathways, including \u0026ldquo;regulation of cell junction assembly\u0026rdquo;, \u0026ldquo;negative regulation of BMP signaling pathway\u0026rdquo;, \u0026ldquo;regulation of non-canonical Wnt signaling pathway\u0026rdquo; and \u0026ldquo;negative regulation of transmembrane receptor protein serine threonine kinase signaling pathway\u0026rdquo; in the biological process (BP) class, and the Molecular Function (MF) item \u0026ldquo;transforming growth factor beta (TGF-β) binding\u0026rdquo;, \u0026ldquo;lipoprotein particle receptor binding\u0026rdquo;. Results above indicated that TRGs in cluster B and C may influence the bad prognosis of patients in different and complex ways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Construction and validation of TRG risk model in GC patients\u003c/h2\u003e \u003cp\u003eTo find the associations among 22 prognostic TRGs, we built a protein-protein interaction network. The network showed a strong interaction activity among these molecules at protein level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To perceive the prognosis of GC patients more directly, we built a predictive prognostic model using LASSO and multivariate Cox regression. After 1000 iterations, we successfully established a nine TRGs signature in TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Finally, we got 9 TRGs with independent prognostic value. The coefficients of the nine genes (CABP2, CALML6, CFAP58, DST, ELOVL2, HIST1H3G, MYF6, PDE1B and TOP3B) were presented in Table S5 and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. The risk score based on the signature was calculated according to the following formula: TRGscore = (1.8852 * expression of CABP2) + (-1.2041 * expression of CALML6) + (-2.7573 * expression of CFAP58) + (0.5049 * expression of DST) + (0.4338 * expression of ELOVL2) + (-0.5110 * expression of HIST1H3G) + (3.1045 * expression of MYF6) + (0.4694 * expression of PDE1B) + (-2.3596 * expression of TOP3B). Based on the median of risk score, the patients were divided into high and low risk groups. Patients in the high-risk group in the TCGA cohort had a worse prognosis, according to KM curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Furthermore, the TRGscore prognostic signature demonstrated high sensitivity and specificity in predicting overall survival (OS), with AUC values of 0.708, 0.731, and 0.783 at 1-year, 3-year, and 5-year intervals, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Additionally, we examined the distribution of risk scores and survival status in the TCGA cohort, revealing a consistent increase in mortality with higher risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The good performance of the risk model has also been validated in GSE62254 dataset, which showed high AUC values (0.715, 0.729 and 0.754 at 1-year, 3-year, and 5-year) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-I). Furthermore, we explored the association between the various clinicopathological characteristics and two groups based on the expression of nine panel genes in the TCGA set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Moreover, there were significant differences in risk scores among the three clusters, with cluster A exhibiting the lowest risk score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the other two clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Alluvial plots depicted the association of clusters, risk, and survival status with TRGs, revealing that most patients in clusters B and C are high-risk individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Subgroup analysis of clinicopathological characteristics was then conducted to investigate the stability and reliability of the TRGscore. Survival analysis results revealed that individuals in the high-risk group demonstrated a worse prognosis across diverse subgroups, including TNM stage, grade, age, and gender (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-K).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of the expression levels of the nine TRGs for the prognostic signature\u003c/h2\u003e \u003cp\u003eTen GC tissues and adjacent normal tissues utilized to assess the mRNA expression of nine genes in this risk panel via RT-qPCR. As showed in the Figure S4, we observed noteworthy disparities in the expression levels of the genes between GC and peritumoral tissues. DST, TOP3B, CABP2, PDE1B, MYF6, CFAP58 and CALML6 were downregulate in the GC tissues compared to the normal tissues, whereas HIST1H3G and ELOVL2 exhibited significant upregulation in GC tissues, which were consistent with our previous results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 TIME and genetic variations landscape in different risk groups\u003c/h2\u003e \u003cp\u003eGC is a highly heterogeneous cancer type, particularly in terms of the TIME. Our analysis, based on the estimation algorithm, revealed that the high-risk group had higher immune scores, stromal scores, and ESTIMATE scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). There was a positive correlation between the TME score and the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additional analysis was performed to investigate the immune microenvironment status of the low- and high-risk groups using CIBERSORT. Bar graph was utilized to display the infiltration levels of 22 immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Correlation analysis between the risk score and immune cell infiltration indicated a positive correlation with Treg cells, resting mast cells, M2 macrophages, resting dendritic cells, and resting NK cells (Figure S5A). Conversely, follicular helper T cells, activated Mast cells, M0 Macrophages, activated Dendritic cells and activated NK cells exhibited a negative correlation with the risk scores (Figure S5B). Furthermore, we investigated the correlation between the abundance of immune cells and the nine-panel genes. The findings revealed a significant association between several immune cells and 9 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Additionally, we conducted a K-M survival analysis to examine the correlation between the abundance ratios of immune cells and overall survival. The survival analysis conducted on 22 immune cell types identified seven immune cell types associated with OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figure S5C-I). Resting NK cells, M2 Macrophages, and resting Dendritic cells were associated with poorer OS, while follicular helper T cells, activated dendritic cells, M0 Macrophages, and activated mast cells were related to better OS. Based on the ssGSEA analysis, the proportions of immune component levels and functions of relevant pathways significantly increased in nearly all high-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Survival analysis indicated that seven immune-relevant pathways were associated with OS (Figure S5J-P). Additionally, most immune checkpoints exhibited a higher degree of activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These findings demonstrated that the TRG risk panel is closely associated with the TIME of GC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further characterized the distribution variations of somatic mutations between the two risk groups in the TCGA cohort using maftools. We observed minimal alterations occurring in the nine panel genes among GC patients from TCGA. TTN, TP53, and MUC16 were the most frequently mutated genes in two group. However, there were fewer TTN mutations and more mutations of other genes in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG and H). Patients with a low-risk score exhibited higher frequencies of all these mutations, compared to those with a high-risk score. Missense variations were the most common mutation type, followed by Multi Hit. These analyses suggest that immune checkpoint inhibitors (ICIs) may be beneficial for the low-risk set. Given the statistical correlation between TRGscore and various factors such as immune cell infiltration levels, immune functions, gene mutations, and the expression of immune checkpoint genes, it is likely that patients' immunotherapy outcomes could be influenced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Evaluation of the immunotherapy response based on TRGscore\u003c/h2\u003e \u003cp\u003eAlthough immunotherapy has shown significant efficacy and few serious adverse events, it is crucial to recognize that immunotherapy resistance remains prevalent. Therefore, molecular subtypes are essential for identifying populations that respond favorably to immunotherapy. Additionally, TMB is clinically relevant to the outcomes of ICIs. We compared differences in TMB between patients in high and low risk groups. The low-risk group exhibited higher TMB (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), which showed a negative correlation with risk scores (R=-0.37, p\u0026thinsp;=\u0026thinsp;4.3e\u0026thinsp;\u0026minus;\u0026thinsp;13) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The K-M analysis demonstrated that GC patients with higher TMB and lower risk scores had a better prognosis, while those with lower TMB and higher risk scores had a worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and D). These analyses suggested that ICIs may be beneficial for the low-risk set.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder normal conditions, a higher TIDE score predicts a worse response to immunotherapy. We detected the TIDE, exclusion, and dysfunction scores in the low and high-risk groups. The results showed that the TIDE score was higher in the high-risk group (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Furthermore, significant differences in T-cell exclusion score and T-cell dysfunction were observed between the two risk groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF and G), suggesting that immunotherapy may be more beneficial for the low-risk group. We also validated this inference in the anti-PD-L1 immunotherapy cohort (IMvigor210) and found that the low-risk group had more patients with complete response (CR) and partial response (PR), whereas the high-risk group had more patients with stable disease (SD) and progressive disease (PD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Additionally, the non-responder group (SD/PD) exhibited a higher risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). In conclusion, our TRGscore may serve as an effective tool for assessing patients' sensitivity to immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Functional evaluation of the TRGs signature\u003c/h2\u003e \u003cp\u003eGSVA and GSEA analysis were employed to analyze the potential molecular mechanisms of the DEGs in the low- and high-risk groups. The GSVA algorithm was employed to analyze the KEGG terms of each GC sample, revealing significant downregulation of pathways associated with maintaining homeostasis in the high-risk group, which included DNA damage repair pathways such as homologous recombination, mismatch repair, nucleotide excision repair, and base excision repair, as well as cell cycle regulation and RNA degradation. Pathways related to ECM receptor interaction and cell adhesion molecules (CAMs) were significantly upregulated in the high-risk group (Figure S6A). GO term analysis showed upregulation of biological processes related to material transport in the high-risk group (Figure S6B). Additionally, GSEA analysis identified upregulation of pathways such as CAMs and focal adhesion in the high-risk group (Figure S6C). Furthermore, several biological processes including DNA-dependent DNA replication and tRNA metabolic process were found to be significantly downregulated in high-risk GC patients (Figure S6D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Construction and Assessment of a Nomogram\u003c/h2\u003e \u003cp\u003eCombining with clinical pathological features, we identified the risk score was an independent indicator through univariate and multivariate Cox regression in TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and B). The hazard ratio of the risk model was 1.51(95% CI: 1.38\u0026ndash;1.65; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), 1.46(95% CI:1.32\u0026ndash;1.61; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in univariate and multivariate Cox regression, respectively. To improve the clinical application of prediction model, we constructed a clinically adaptable nomogram score system with the TRGscore and other clinicopathological features to predict the 1-, 3-, and 5-year survival GC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The nomogram showed a good accuracy in predicting short survival time. The calibration plot of the nomogram revealed better consistency between the prediction by the nomogram and the actual observation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The AUCs of the nomogram at 1-, 3-, and 5-year OS were 0.758, 0.767 and 0.737, respectively, which were better than the risk models and single clinical factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-G). Furthermore, DCA curves of the nomogram for predicting OS in patients with GC demonstrated its superior performance when compared to the risk model and various clinicopathological characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH-J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Drugs susceptibility analysis\u003c/h2\u003e \u003cp\u003eWe further investigated the differences in drug sensitivity between high-risk and low-risk GC patients. A total of 100 drugs showed significant differences in sensitivity between these two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table S6). 91 drugs showed higher sensitivity in the high-risk group, among these drugs, 10 commonly used chemotherapeutic agents for GC patients, namely 5-fluorouracil, cyclophosphamide, docetaxel, oxaliplatin, paclitaxel, vinblastine, vincristine, and vinorelbine (Figure S7A). Conversely, the other 9 drugs, including AZD8055, AZD8186, BMS-754807, Dasatinib, Doramapimod, JQ1, NU7441, SB216763 and WZ4003 may not be ideal for the high-risk patients (Figure S7B).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTelomere biology plays a crucial and intricate role in the onset and advancement of cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Dysfunctional telomeres can impede cancer development by inducing replicative senescence or apoptotic pathways. However, they can also facilitate tumor initiation and progression by inducing oncogenic chromosomal rearrangements. Recent studies have shown that telomere dysfunction increases the sensitivity of cancer is related to T cell immune dysfunction [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It is noteworthy that telomerase reverse transcriptase (TERT), a subunit of telomerase, plays a significant role in angiogenesis, invasion, epithelial-mesenchymal transformation, inflammation, immunosuppression, and other critical gene expression profiles. These TERT-mediated activities can significantly affect the dynamic equilibrium of the TME [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Recent investigations have also uncovered the involvement of TERT in the lymphatic and vascular metastasis of GC, which leads to a poor prognosis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. GC is a significant contributor to mortality associated with tumors. It is a highly heterogeneous, with significant variations in ethnic, regional, and pathological features. These differences result in distinct treatment options and outcomes for patients with gastric cancer in Eastern and Western countries. However, the overall prognosis of gastric cancer remains suboptimal [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To date, the TNM stage is the primary predictor of prognosis in GC patients. However, individuals with the same TNM stage often have different clinical outcomes. This suggests that the current TNM staging guidelines are not adequate for disease risk stratification. Several biomarkers, including PD-L1 combined positive score (CPS), microsatellite instability-high (MSI-H), and TMB, are widely regarded as promising for predicting the immune response in GC. These biomarkers have some limitations, including predictive instability, intra-tumor heterogeneity, and limited predictive benefit for the population [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, the advancement telomere-related new biomarkers for GC that can accurately forecast prognosis and response to immunotherapy would be highly significant. Recently, several studies have constructed similar models, including gene models related to DNA damage repair, senescence, pyroptosis, neutrophil extracellular traps, and immunity [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These models can predict prognosis and immunotherapy response. Our model has an above-average capacity to predict overall survival in GC patients compared to other models. And all these models, including ours, can distinguish to some extent between GC patients that are sensitive and resistant to immunotherapy. Therefore, our model is a valuable supplement to current biomarkers for forecasting GC patient prognosis and identifying potential responders to ICI therapy.\u003c/p\u003e \u003cp\u003eOur study utilized publicly available databases to identify 100 telomere-related hub genes using differential expression analysis and WGCNA. We then constructed a 9-TRG risk panel based on uni-Cox, LASSO, and multi-Cox regression analyses to predict the prognosis of GC patients and built three molecular subtypes (cluster A, B and C) based on a consensus clustering algorithm. The cluster B and C had higher risk scores than cluster A. We also validated that the risk panel showed good performance in the GEO validation cohort. Finally, we established a nomogram incorporating the TRGscore and clinicopathological factors to predict OS rates of GC patients. A high TRG risk score was correlated with poor survival and pro-tumor TIME of GC. By constructing a TRG-related risk panel, we not only focused on its predictive significance but also better analyzed the immune infiltration and treatment sensitivity differences among patients. Our TRGscore can guide personalized chemotherapy and immunotherapy for patients with GC, thereby improving their prognosis.\u003c/p\u003e \u003cp\u003eIn our study, the TRG risk panel was composed of 9 genes (CALML6, CFAP58, CABP2, DST, ELOVL2, HIST1H3G, MYF6, PDE1B and TOP3B) that have been reported in cancer development and progression to some extent. CALML6, (Calmodulin-Like Protein 6) is an oncogene in GC and is involved in mitochondrial reprogramming. Down-regulation of its level has been associated with reduced survival in rectal cancer patients [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Aberrant expression of CFAP58 (Cilia- And Flagella-Associated Protein 58) is associated with poor outcome in bladder cancer, endometrial cancer, lung cancer and triple-negative breast cancer [\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. CABP2 (Calcium Binding Protein 2) expression is upregulated in the MUT-high subtype of diffuse large B-cell lymphoma and correlates with reduced T-cell immune infiltration in the TME [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Aberrant expression of the DST (Dystonin) has been observed in various cancers, including melanoma, lung cancer, metastatic prostate cancer and invasive breast cancer [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. DST may affect the development and progression of breast cancer by influencing the TIME [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The downregulation of DST could be indicative of an invasive phenotype and metastasis of cancers [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. ELOVL2 (Elongation of very long chain Fatty acid elongase 2) is involved in the regulation of autophagy and the activity of the mTOR signaling pathway, and it\u0026rsquo;s upregulated in kidney cancer and downregulated in breast cancer are associated with tumor growth and poor prognosis [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. ELOVL2 is involved in lipid metabolic reprogramming and dysregulated immune status, which correlates with adverse outcomes in retroperitoneal liposarcoma [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. HIST1H3G (Histone Cluster 1 H3 Family Member G) is expressed abnormally in several types of cancer. High expression of HIST1H3G is linked to poor prognosis in glioma and laryngeal squamous cell carcinoma, and to chemosensitivity in ovarian cancer [\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. MYF6 (Myogenic Factor 6, a telomerase transcription factor) hypermethylation has been reported to be associated with lung cancer [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. PDE1B (Phosphodiesterase 1B) has the highest weight coefficient in our panel. Interestingly, studies indicated that PDE1B may have a regulatory function in the differentiation of various immune cell types by reducing intracellular levels of both cAMP and cGMP [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Moreover, previous studies found that through cytokines like GM-CSF, the upregulation of PDE1B might facilitate the differentiation of macrophages into M1 subtype macrophages, thus bolstering the anti-tumor immune response and potentially enhancing patient prognosis [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. TOP3B (DNA Topoisomerase III Beta) is involved in DNA repair/damage response. Based on data from a randomized phase III clinical trial, SAMIT, a machine learning study has identified TOP3B as a predictive marker for paclitaxel sensitivity in GC [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. We also found that certain genes share similar functions to some extent [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. For instance, ELOVL2, CALML6, CABP2, and HIST1H3G are involved in material metabolism and cell signalling, while DST and PDE1B are involved in regulating the TIME. Several genes (CFAP58, CABP2, DST, ELOVL2, HIST1H3G, MYF6, and PDE1B) were found to be associated with GC for the first time. Therefore, more research is warranted to uncover the mechanism of action of these genes in GC.\u003c/p\u003e \u003cp\u003eThe TME is a critical determinant of tumor development, growth, and migration, and response to therapy in GC, with immune cell infiltration playing a pivotal role [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In the present study, patients with higher risk scores had a higher TME score. Our findings indicated that substantial disparities in immune landscape across distinct groups of GC. Particularly, the high-risk group exhibited an increased infiltration of Tregs, M2 macrophages, resting mast cells and resting NK cells. These findings suggest an exhausted immune phenotype of the TME and a poor prognosis for patients with GC. Consistent with our findings, previous research has confirmed that the infiltration of M2 macrophages in the TME is associated with a poor prognosis for GC [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Another study showed that tumor-infiltrating mast cells foster immune suppression through TNF-α-PD-L1 pathway and stimulate Treg cells through an IL-33 and IL-2 axis to promote GC progression [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Further functional enrichment analyses in different risk groups and clusters showed involvement of signaling pathways associated with cancer cell progression and immune suppression such as TGF-β signaling pathway, ECM receptor pathways in the groups with high riskscores. Previous studies showed that ECM is an important component of the TME and can regulate cancer behaviors. ECM remodeling is crucial in the regulation of GC progression [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. The TGF signaling pathway can suppress tumors, including cell cycle arrest and apoptosis [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. TGF-β in the gastric tumor microenvironment is reported to promote the differentiation and expansion of both Tregs and M2 macrophages[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. These results suggested that our gene panel can distinguish between immune infiltration characteristics of high- and low-risk groups and that the poor prognosis of GC patients in the high-risk group might be associated with the immunodepression microenvironment.\u003c/p\u003e \u003cp\u003eImmunotherapy is a crucial element in treating GC. In this study, we examined the TIME to elucidate the relationship between TIME and TRGs in GC. Given the influence of immune checkpoints in the immunotherapy, we evaluated the differences in immune checkpoints between low and high-risk patients [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. We found that most of the immune checkpoint genes were down-regulated in the low-risk patients. Furthermore, research has shown that a high TMB is correlated with a positive response to immune and targeted therapies in cancer patients, and is often indicative of favorable survival rates [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Consistent with the previous results, our study found TMB were more significant in the low-risk group and patients with high TMB scores showed better the prognosis. Our research also showed that the low-risk group had higher mutation rates for TTN, TP53 and MUC16. Several studies have found that mutations in MUC16 and TP53 are linked to a better prognosis and higher TMB in GC, while TTN mutations are associated with a better response to ICIs therapy in solid tumors [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Although TP53 is one of the most mutated gene, its prognostic significance in GC is still controversial [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Additionally, significant differences were observed in our study between the various risk groups for several frequently applied immunotherapy biomarkers. The TIDE score is a recently developed method for predicting the effectiveness of anti-PD1 and anti-CTLA4 therapy. It is more accurate than TMB or PD-L1 expression [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The TIDE contains two potential tumor immunologic evasion mechanisms: T-cell exclusion and T-cell dysfunction. A higher TIDE score indicates a poorer tumor response to ICI therapy and a worse prognosis [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Our study showed that patients with high-risk scores had significantly higher TIDE, T-cell dysfunction, and T-cell exclusion scores than those with low-risk scores. This suggests that patients with low-risk scores may be more responsive to immunotherapies, which was confirmed in the immunotherapy cohort. This suggested that TRGscore is a valid biomarker for predicting response to immunotherapy. The low TRGscore group may have a better response to ICI therapy. Chemotherapy sensitivity analysis also proved the distinct sensitivity of each subtype to certain drugs.\u003c/p\u003e \u003cp\u003eThe present study still has some highlights, even though other comparable publications have been published employing built characteristics to predict the prognosis of GC patients. First, we examined the patient\u0026rsquo;s prognosis for GC for the first-time using telomere-related mRNAs, and our models have a high level of predictive power. Second, we successfully confirmed the signature using in the public validation cohort. Moreover, we examined clinical tissue samples to confirm the mRNA levels of the genes responsible for the composition of the panel. It is admitted that our study has certain limitations. This study relies mainly on public databases without real-world cohort for validation. To further assess this signature, future large multicenter randomized controlled investigations are required. Additionally, further in vivo and vitro research is necessary to investigate the expression, prognostic predictive relevance, and particular mechanisms of these genes in GC.\u003c/p\u003e \u003cp\u003eIn conclusion, we have developed a TRG-panel based on a recognized and effective strategy for predicting GC prognosis and immunotherapy efficacy. In addition, by identifying the complex relationship between TRGs and oncogenic pathways, we provided insight into TRGs' role in tumorigenesis and TME reshaping. In combination with immune infiltration, immune checkpoint factors, and other biomarkers, we demonstrated that TRGscore effectively distinguishes responders and non-responders, enabling ICI therapy to be more precisely stratified by benefit. Therefore, this work might facilitate the identification of prognostic biomarkers and provide guidance for developing personalized immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was permitted by the Ethics Committee of Xijing Hospital.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), UCSC xena (https://xenabrowser.net/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China (No. 82073202, 81972710, 81902677), the Natural Science Basic Research Program of Shaanxi (2021JZ-29).\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to data analysis, and drafting or revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank all teammates for contributing this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R.L., et al., \u003cem\u003eCancer statistics, 2023.\u003c/em\u003e CA Cancer J Clin, 2023. \u003cstrong\u003e73\u003c/strong\u003e(1): p. 17-48.\u003c/li\u003e\n\u003cli\u003eGuan, W.L., Y. He, and R.H. Xu, \u003cem\u003eGastric cancer treatment: recent progress and future perspectives.\u003c/em\u003e J Hematol Oncol, 2023. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 57.\u003c/li\u003e\n\u003cli\u003eQiu, H., S. Cao, and R. Xu, \u003cem\u003eCancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020.\u003c/em\u003e Cancer Commun (Lond), 2021. \u003cstrong\u003e41\u003c/strong\u003e(10): p. 1037-1048.\u003c/li\u003e\n\u003cli\u003eBlackburn, E.H., E.S. Epel, and J. Lin, \u003cem\u003eHuman telomere biology: A contributory and interactive factor in aging, disease risks, and protection.\u003c/em\u003e Science, 2015. \u003cstrong\u003e350\u003c/strong\u003e(6265): p. 1193-8.\u003c/li\u003e\n\u003cli\u003eShay, J.W. and W.E. Wright, \u003cem\u003eHayflick, his limit, and cellular ageing.\u003c/em\u003e Nat Rev Mol Cell Biol, 2000. \u003cstrong\u003e1\u003c/strong\u003e(1): p. 72-6.\u003c/li\u003e\n\u003cli\u003eShay, J.W. and W.E. Wright, \u003cem\u003eTelomeres and telomerase: three decades of progress.\u003c/em\u003e Nat Rev Genet, 2019. \u003cstrong\u003e20\u003c/strong\u003e(5): p. 299-309.\u003c/li\u003e\n\u003cli\u003eHanahan, D. and R.A. Weinberg, \u003cem\u003eHallmarks of cancer: the next generation.\u003c/em\u003e Cell, 2011. \u003cstrong\u003e144\u003c/strong\u003e(5): p. 646-74.\u003c/li\u003e\n\u003cli\u003eRudolph, K.L., D. Hartmann, and O.G. Opitz, \u003cem\u003eTelomere dysfunction and DNA damage checkpoints in diseases and cancer of the gastrointestinal tract.\u003c/em\u003e Gastroenterology, 2009. \u003cstrong\u003e137\u003c/strong\u003e(3): p. 754-62.\u003c/li\u003e\n\u003cli\u003eWang, Z., et al., \u003cem\u003eTelomere length and risk of developing gastric adenocarcinoma: The Singapore Chinese Health Study.\u003c/em\u003e Gastric Cancer, 2018. \u003cstrong\u003e21\u003c/strong\u003e(4): p. 598-605.\u003c/li\u003e\n\u003cli\u003eHaycock, P.C., et al., \u003cem\u003eAssociation Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study.\u003c/em\u003e JAMA Oncol, 2017. \u003cstrong\u003e3\u003c/strong\u003e(5): p. 636-651.\u003c/li\u003e\n\u003cli\u003eAviv, A., J.J. Anderson, and J.W. Shay, \u003cem\u003eMutations, Cancer and the Telomere Length Paradox.\u003c/em\u003e Trends Cancer, 2017. \u003cstrong\u003e3\u003c/strong\u003e(4): p. 253-258.\u003c/li\u003e\n\u003cli\u003eHan, X., et al., \u003cem\u003eThe combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.\u003c/em\u003e Front Immunol, 2023. \u003cstrong\u003e14\u003c/strong\u003e: p. 1220100.\u003c/li\u003e\n\u003cli\u003eLi, S.C., et al., \u003cem\u003eTelomere-related gene risk model for prognosis and drug treatment efficiency prediction in kidney cancer.\u003c/em\u003e Front Immunol, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 975057.\u003c/li\u003e\n\u003cli\u003eJung, S.J., et al., \u003cem\u003eClinical Characteristics of TZAP (ZBTB48) in Hepatocellular Carcinomas from Tissue, Cell Line, and TCGA.\u003c/em\u003e Medicina (Kaunas), 2022. \u003cstrong\u003e58\u003c/strong\u003e(12).\u003c/li\u003e\n\u003cli\u003eChen, S., et al., \u003cem\u003eTelomere-related prognostic biomarkers for survival assessments in pancreatic cancer.\u003c/em\u003e Sci Rep, 2023. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 10586.\u003c/li\u003e\n\u003cli\u003eHu, Y., et al., \u003cem\u003eTelomere-Associated Gene Signatures Correlate with Prognosis, Tumor Microenvironment, and Chemosensitivity in Breast Cancer.\u003c/em\u003e Med Sci Monit, 2023. \u003cstrong\u003e29\u003c/strong\u003e: p. e939921.\u003c/li\u003e\n\u003cli\u003eFeng, J., et al., \u003cem\u003eA telomerase regulation-related lncRNA signature predicts prognosis and immunotherapy response for gastric cancer.\u003c/em\u003e J Cancer Res Clin Oncol, 2023. \u003cstrong\u003e149\u003c/strong\u003e(1): p. 135-146.\u003c/li\u003e\n\u003cli\u003eCristescu, R., et al., \u003cem\u003eMolecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes.\u003c/em\u003e Nat Med, 2015. \u003cstrong\u003e21\u003c/strong\u003e(5): p. 449-56.\u003c/li\u003e\n\u003cli\u003eBraun, D.M., et al., \u003cem\u003eTelNet - a database for human and yeast genes involved in telomere maintenance.\u003c/em\u003e BMC Genet, 2018. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 32.\u003c/li\u003e\n\u003cli\u003eLangfelder, P. and S. Horvath, \u003cem\u003eWGCNA: an R package for weighted correlation network analysis.\u003c/em\u003e BMC Bioinformatics, 2008. \u003cstrong\u003e9\u003c/strong\u003e: p. 559.\u003c/li\u003e\n\u003cli\u003eJia, A., L. Xu, and Y. Wang, \u003cem\u003eVenn diagrams in bioinformatics.\u003c/em\u003e Brief Bioinform, 2021. \u003cstrong\u003e22\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eWang, H., et al., \u003cem\u003ePrecision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data.\u003c/em\u003e Bioinformatics, 2019. \u003cstrong\u003e35\u003c/strong\u003e(7): p. 1181-1187.\u003c/li\u003e\n\u003cli\u003eZhang, D., et al., \u003cem\u003ePrediction of Overall Survival Among Female Patients With Breast Cancer Using a Prognostic Signature Based on 8 DNA Repair-Related Genes.\u003c/em\u003e JAMA Netw Open, 2020. \u003cstrong\u003e3\u003c/strong\u003e(10): p. e2014622.\u003c/li\u003e\n\u003cli\u003eVickers, A.J., et al., \u003cem\u003eExtensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.\u003c/em\u003e BMC Med Inform Decis Mak, 2008. \u003cstrong\u003e8\u003c/strong\u003e: p. 53.\u003c/li\u003e\n\u003cli\u003eNewman, A.M., et al., \u003cem\u003eRobust enumeration of cell subsets from tissue expression profiles.\u003c/em\u003e Nat Methods, 2015. \u003cstrong\u003e12\u003c/strong\u003e(5): p. 453-7.\u003c/li\u003e\n\u003cli\u003eFu, J., et al., \u003cem\u003eLarge-scale public data reuse to model immunotherapy response and resistance.\u003c/em\u003e Genome Med, 2020. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 21.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nzelmann, S., R. Castelo, and J. Guinney, \u003cem\u003eGSVA: gene set variation analysis for microarray and RNA-seq data.\u003c/em\u003e BMC Bioinformatics, 2013. \u003cstrong\u003e14\u003c/strong\u003e: p. 7.\u003c/li\u003e\n\u003cli\u003eYang, W., et al., \u003cem\u003eGenomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.\u003c/em\u003e Nucleic Acids Res, 2013. \u003cstrong\u003e41\u003c/strong\u003e(Database issue): p. D955-61.\u003c/li\u003e\n\u003cli\u003eXu, L., S. Li, and B.A. Stohr, \u003cem\u003eThe role of telomere biology in cancer.\u003c/em\u003e Annu Rev Pathol, 2013. \u003cstrong\u003e8\u003c/strong\u003e: p. 49-78.\u003c/li\u003e\n\u003cli\u003eSchratz, K.E., et al., \u003cem\u003eT cell immune deficiency rather than chromosome instability predisposes patients with short telomere syndromes to squamous cancers.\u003c/em\u003e Cancer Cell, 2023. \u003cstrong\u003e41\u003c/strong\u003e(4): p. 807-817.e6.\u003c/li\u003e\n\u003cli\u003eLiu, Z., et al., \u003cem\u003eTelomerase reverse transcriptase promotes epithelial-mesenchymal transition and stem cell-like traits in cancer cells.\u003c/em\u003e Oncogene, 2013. \u003cstrong\u003e32\u003c/strong\u003e(36): p. 4203-13.\u003c/li\u003e\n\u003cli\u003eNoureen, N., et al., \u003cem\u003eIntegrated analysis of telomerase enzymatic activity unravels an association with cancer stemness and proliferation.\u003c/em\u003e Nat Commun, 2021. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 139.\u003c/li\u003e\n\u003cli\u003eWu, Y., et al., \u003cem\u003eTelomerase reverse transcriptase methylation predicts lymph node metastasis and prognosis in patients with gastric cancer.\u003c/em\u003e Onco Targets Ther, 2016. \u003cstrong\u003e9\u003c/strong\u003e: p. 279-86.\u003c/li\u003e\n\u003cli\u003eSasaki, T., et al., \u003cem\u003eAKT activation and telomerase reverse transcriptase expression are concurrently associated with prognosis of gastric cancer.\u003c/em\u003e Pathobiology, 2014. \u003cstrong\u003e81\u003c/strong\u003e(1): p. 36-41.\u003c/li\u003e\n\u003cli\u003eSmyth, E.C., et al., \u003cem\u003eGastric cancer.\u003c/em\u003e Lancet, 2020. \u003cstrong\u003e396\u003c/strong\u003e(10251): p. 635-648.\u003c/li\u003e\n\u003cli\u003eZhang, Z., Z. Liu, and Z. Chen, \u003cem\u003eComparison of Treatment Efficacy and Survival Outcomes Between Asian and Western Patients With Unresectable Gastric or Gastro-Esophageal Adenocarcinoma: A Systematic Review and Meta-Analysis.\u003c/em\u003e Front Oncol, 2022. \u003cstrong\u003e12\u003c/strong\u003e: p. 831207.\u003c/li\u003e\n\u003cli\u003eFuchs, C.S., et al., \u003cem\u003eSafety and Efficacy of Pembrolizumab Monotherapy in Patients With Previously Treated Advanced Gastric and Gastroesophageal Junction Cancer: Phase 2 Clinical KEYNOTE-059 Trial.\u003c/em\u003e JAMA Oncol, 2018. \u003cstrong\u003e4\u003c/strong\u003e(5): p. e180013.\u003c/li\u003e\n\u003cli\u003eMuro, K., et al., \u003cem\u003ePembrolizumab for patients with PD-L1-positive advanced gastric cancer (KEYNOTE-012): a multicentre, open-label, phase 1b trial.\u003c/em\u003e Lancet Oncol, 2016. \u003cstrong\u003e17\u003c/strong\u003e(6): p. 717-726.\u003c/li\u003e\n\u003cli\u003eYan, J., et al., \u003cem\u003eDNA damage response-related immune activation signature predicts the response to immune checkpoint inhibitors: from gastrointestinal cancer analysis to pan-cancer validation.\u003c/em\u003e Cancer Biol Med, 2023.\u003c/li\u003e\n\u003cli\u003eZhao, Z., et al., \u003cem\u003eThe DNA damage repair-related lncRNAs signature predicts the prognosis and immunotherapy response in gastric cancer.\u003c/em\u003e Front Immunol, 2023. \u003cstrong\u003e14\u003c/strong\u003e: p. 1117255.\u003c/li\u003e\n\u003cli\u003eShen, X., et al., \u003cem\u003eSenescence-related genes define prognosis, immune contexture, and pharmacological response in gastric cancer.\u003c/em\u003e Aging (Albany NY), 2023. \u003cstrong\u003e15\u003c/strong\u003e(8): p. 2891-2905.\u003c/li\u003e\n\u003cli\u003eShao, W., et al., \u003cem\u003eThe Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer.\u003c/em\u003e Front Cell Dev Biol, 2021. \u003cstrong\u003e9\u003c/strong\u003e: p. 676485.\u003c/li\u003e\n\u003cli\u003eLi, M., et al., \u003cem\u003eNeutrophil extracellular traps-related signature predicts the prognosis and immune infiltration in gastric cancer.\u003c/em\u003e Front Med (Lausanne), 2023. \u003cstrong\u003e10\u003c/strong\u003e: p. 1174764.\u003c/li\u003e\n\u003cli\u003eXu, X., et al., \u003cem\u003eA SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer.\u003c/em\u003e Pharmaceuticals (Basel), 2022. \u003cstrong\u003e15\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eZhang, C., et al., \u003cem\u003eDevelopment and verification of an immune-related gene prognostic index for gastric cancer.\u003c/em\u003e Sci Rep, 2022. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 15693.\u003c/li\u003e\n\u003cli\u003eWang, Y., X.Y. Jiang, and X.Y. Yu, \u003cem\u003eBRD9 controls the oxytocin signaling pathway in gastric cancer via CANA2D4, CALML6, GNAO1, and KCNJ5.\u003c/em\u003e Transl Cancer Res, 2020. \u003cstrong\u003e9\u003c/strong\u003e(5): p. 3354-3366.\u003c/li\u003e\n\u003cli\u003ePenrose, H.M., et al., \u003cem\u003eIn colonic \u0026rho;(0) (rho0) cells reduced mitochondrial function mediates transcriptomic alterations associated with cancer.\u003c/em\u003e Oncoscience, 2017. \u003cstrong\u003e4\u003c/strong\u003e(11-12): p. 189-198.\u003c/li\u003e\n\u003cli\u003eGu, C., et al., \u003cem\u003eThe IFN-\u0026gamma;-related long non-coding RNA signature predicts prognosis and indicates immune microenvironment infiltration in uterine corpus endometrial carcinoma.\u003c/em\u003e Front Oncol, 2022. \u003cstrong\u003e12\u003c/strong\u003e: p. 955979.\u003c/li\u003e\n\u003cli\u003eAdemuyiwa, F.O., et al., \u003cem\u003eImmunogenomic profiling and pathological response results from a clinical trial of docetaxel and carboplatin in triple-negative breast cancer.\u003c/em\u003e Breast Cancer Res Treat, 2021. \u003cstrong\u003e189\u003c/strong\u003e(1): p. 187-202.\u003c/li\u003e\n\u003cli\u003eWu, H., et al., \u003cem\u003ePrediction of bladder cancer outcome by identifying and validating a mutation-derived genomic instability-associated long noncoding RNA (lncRNA) signature.\u003c/em\u003e Bioengineered, 2021. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 1725-1738.\u003c/li\u003e\n\u003cli\u003eLiu, Y., et al., \u003cem\u003eFocused Analysis of Exome Sequencing Data for Rare Germline Mutations in Familial and Sporadic Lung Cancer.\u003c/em\u003e J Thorac Oncol, 2016. \u003cstrong\u003e11\u003c/strong\u003e(1): p. 52-61.\u003c/li\u003e\n\u003cli\u003eYou, H., et al., \u003cem\u003eGenomic complexity is associated with epigenetic regulator mutations and poor prognosis in diffuse large B-cell lymphoma.\u003c/em\u003e Oncoimmunology, 2021. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 1928365.\u003c/li\u003e\n\u003cli\u003eBrown, A., et al., \u003cem\u003eThe mouse dystonia musculorum gene is a neural isoform of bullous pemphigoid antigen 1.\u003c/em\u003e Nat Genet, 1995. \u003cstrong\u003e10\u003c/strong\u003e(3): p. 301-6.\u003c/li\u003e\n\u003cli\u003eShimbo, T., et al., \u003cem\u003eSerum anti-BPAG1 auto-antibody is a novel marker for human melanoma.\u003c/em\u003e PLoS One, 2010. \u003cstrong\u003e5\u003c/strong\u003e(5): p. e10566.\u003c/li\u003e\n\u003cli\u003eKang, J., et al., \u003cem\u003eResponse and acquired resistance to MET inhibitors in de novo MET fusion-positive advanced non-small cell lung cancer.\u003c/em\u003e Lung Cancer, 2023. \u003cstrong\u003e178\u003c/strong\u003e: p. 66-74.\u003c/li\u003e\n\u003cli\u003eQiu, X., et al., \u003cem\u003eIdentification of m6A-Associated Gene DST as a Prognostic and Immune-Associated Biomarker in Breast Cancer Patients.\u003c/em\u003e Int J Gen Med, 2022. \u003cstrong\u003e15\u003c/strong\u003e: p. 523-534.\u003c/li\u003e\n\u003cli\u003eChoi, J.S., et al., \u003cem\u003eIdentification of new genes of pleomorphic adenoma.\u003c/em\u003e Medicine (Baltimore), 2019. \u003cstrong\u003e98\u003c/strong\u003e(51): p. e18468.\u003c/li\u003e\n\u003cli\u003eTanaka, K., et al., \u003cem\u003eELOVL2 promotes cancer progression by inhibiting cell apoptosis in renal cell carcinoma.\u003c/em\u003e Oncol Rep, 2022. \u003cstrong\u003e47\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eKang, Y.P., et al., \u003cem\u003eSpheroid-Induced Epithelial-Mesenchymal Transition Provokes Global Alterations of Breast Cancer Lipidome: A Multi-Layered Omics Analysis.\u003c/em\u003e Front Oncol, 2019. \u003cstrong\u003e9\u003c/strong\u003e: p. 145.\u003c/li\u003e\n\u003cli\u003eWang, Z., et al., \u003cem\u003eInsight of a lipid metabolism prognostic model to identify immune landscape and potential target for retroperitoneal liposarcoma.\u003c/em\u003e Front Immunol, 2023. \u003cstrong\u003e14\u003c/strong\u003e: p. 1209396.\u003c/li\u003e\n\u003cli\u003eZhang, M. and S.C. Luo, \u003cem\u003eGene expression profiling of epithelial ovarian cancer reveals key genes and pathways associated with chemotherapy resistance.\u003c/em\u003e Genet Mol Res, 2016. \u003cstrong\u003e15\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eXiang, F., et al., \u003cem\u003eNetwork Pharmacology and Bioinformatics Methods Reveal the Mechanism of Zao-Jiao-Ci in the Treatment of LSCC.\u003c/em\u003e J Oncol, 2021. \u003cstrong\u003e2021\u003c/strong\u003e: p. 8862821.\u003c/li\u003e\n\u003cli\u003eZhang, G.H., et al., \u003cem\u003eSeven genes for the prognostic prediction in patients with glioma.\u003c/em\u003e Clin Transl Oncol, 2019. \u003cstrong\u003e21\u003c/strong\u003e(10): p. 1327-1335.\u003c/li\u003e\n\u003cli\u003eCasta\u0026ntilde;eda-Partida, L., et al., \u003cem\u003eGlobal expression profiling of CD10\u003c/em\u003e\u003cem\u003e\u0026thinsp;+\u003c/em\u003e\u003cem\u003e\u0026thinsp;/CD19\u003c/em\u003e\u003cem\u003e\u0026thinsp;+\u003c/em\u003e\u003cem\u003e\u0026thinsp;pre-B lymphoblasts from Hispanic B-ALL patients correlates with comparative TARGET database analysis.\u003c/em\u003e Discov Oncol, 2022. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 28.\u003c/li\u003e\n\u003cli\u003eZhao, Y., et al., \u003cem\u003eAbnormal methylation of seven genes and their associations with clinical characteristics in early stage non-small cell lung cancer.\u003c/em\u003e Oncol Lett, 2013. \u003cstrong\u003e5\u003c/strong\u003e(4): p. 1211-1218.\u003c/li\u003e\n\u003cli\u003eAzevedo, M.F., et al., \u003cem\u003eClinical and molecular genetics of the phosphodiesterases (PDEs).\u003c/em\u003e Endocr Rev, 2014. \u003cstrong\u003e35\u003c/strong\u003e(2): p. 195-233.\u003c/li\u003e\n\u003cli\u003eHeymann, M.F., F. L\u0026eacute;zot, and D. Heymann, \u003cem\u003eThe contribution of immune infiltrates and the local microenvironment in the pathogenesis of osteosarcoma.\u003c/em\u003e Cell Immunol, 2019. \u003cstrong\u003e343\u003c/strong\u003e: p. 103711.\u003c/li\u003e\n\u003cli\u003eBender, A.T. and J.A. Beavo, \u003cem\u003ePDE1B2 regulates cGMP and a subset of the phenotypic characteristics acquired upon macrophage differentiation from a monocyte.\u003c/em\u003e Proc Natl Acad Sci U S A, 2006. \u003cstrong\u003e103\u003c/strong\u003e(2): p. 460-5.\u003c/li\u003e\n\u003cli\u003eSundar, R., et al., \u003cem\u003eMachine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial.\u003c/em\u003e Gut, 2022. \u003cstrong\u003e71\u003c/strong\u003e(4): p. 676-685.\u003c/li\u003e\n\u003cli\u003eRu, B., et al., \u003cem\u003eTISIDB: an integrated repository portal for tumor-immune system interactions.\u003c/em\u003e Bioinformatics, 2019. \u003cstrong\u003e35\u003c/strong\u003e(20): p. 4200-4202.\u003c/li\u003e\n\u003cli\u003ede Visser, K.E. and J.A. Joyce, \u003cem\u003eThe evolving tumor microenvironment: From cancer initiation to metastatic outgrowth.\u003c/em\u003e Cancer Cell, 2023. \u003cstrong\u003e41\u003c/strong\u003e(3): p. 374-403.\u003c/li\u003e\n\u003cli\u003ePiao, H., et al., \u003cem\u003eA positive feedback loop between gastric cancer cells and tumor-associated macrophage induces malignancy progression.\u003c/em\u003e J Exp Clin Cancer Res, 2022. \u003cstrong\u003e41\u003c/strong\u003e(1): p. 174.\u003c/li\u003e\n\u003cli\u003eChen, Y., et al., \u003cem\u003eTumor-recruited M2 macrophages promote gastric and breast cancer metastasis via M2 macrophage-secreted CHI3L1 protein.\u003c/em\u003e J Hematol Oncol, 2017. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 36.\u003c/li\u003e\n\u003cli\u003eLv, Y., et al., \u003cem\u003eTumor-infiltrating mast cells stimulate ICOS(+) regulatory T cells through an IL-33 and IL-2 axis to promote gastric cancer progression.\u003c/em\u003e J Adv Res, 2023.\u003c/li\u003e\n\u003cli\u003eLv, Y., et al., \u003cem\u003eIncreased intratumoral mast cells foster immune suppression and gastric cancer progression through TNF-\u0026alpha;-PD-L1 pathway.\u003c/em\u003e J Immunother Cancer, 2019. \u003cstrong\u003e7\u003c/strong\u003e(1): p. 54.\u003c/li\u003e\n\u003cli\u003eWang, X., et al., \u003cem\u003eSuccinylation Inhibits the Enzymatic Hydrolysis of the Extracellular Matrix Protein Fibrillin 1 and Promotes Gastric Cancer Progression.\u003c/em\u003e Adv Sci (Weinh), 2022. \u003cstrong\u003e9\u003c/strong\u003e(27): p. e2200546.\u003c/li\u003e\n\u003cli\u003eJurj, A., et al., \u003cem\u003eThe extracellular matrix alteration, implication in modulation of drug resistance mechanism: friends or foes?\u003c/em\u003e J Exp Clin Cancer Res, 2022. \u003cstrong\u003e41\u003c/strong\u003e(1): p. 276.\u003c/li\u003e\n\u003cli\u003eColak, S. and P. Ten Dijke, \u003cem\u003eTargeting TGF-\u0026beta; Signaling in Cancer.\u003c/em\u003e Trends Cancer, 2017. \u003cstrong\u003e3\u003c/strong\u003e(1): p. 56-71.\u003c/li\u003e\n\u003cli\u003eNing, J., et al., \u003cem\u003eImbalance of TGF-\u0026beta;1/BMP-7 pathways induced by M2-polarized macrophages promotes hepatocellular carcinoma aggressiveness.\u003c/em\u003e Mol Ther, 2021. \u003cstrong\u003e29\u003c/strong\u003e(6): p. 2067-2087.\u003c/li\u003e\n\u003cli\u003eKnochelmann, H.M., et al., \u003cem\u003eWhen worlds collide: Th17 and Treg cells in cancer and autoimmunity.\u003c/em\u003e Cell Mol Immunol, 2018. \u003cstrong\u003e15\u003c/strong\u003e(5): p. 458-469.\u003c/li\u003e\n\u003cli\u003eGaikwad, S., et al., \u003cem\u003eImmune checkpoint proteins: Signaling mechanisms and molecular interactions in cancer immunotherapy.\u003c/em\u003e Semin Cancer Biol, 2022. \u003cstrong\u003e86\u003c/strong\u003e(Pt 3): p. 137-150.\u003c/li\u003e\n\u003cli\u003eYarchoan, M., A. Hopkins, and E.M. Jaffee, \u003cem\u003eTumor Mutational Burden and Response Rate to PD-1 Inhibition.\u003c/em\u003e N Engl J Med, 2017. \u003cstrong\u003e377\u003c/strong\u003e(25): p. 2500-2501.\u003c/li\u003e\n\u003cli\u003eMarabelle, A., et al., \u003cem\u003eAssociation of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study.\u003c/em\u003e Lancet Oncol, 2020. \u003cstrong\u003e21\u003c/strong\u003e(10): p. 1353-1365.\u003c/li\u003e\n\u003cli\u003eLi, X., et al., \u003cem\u003eAssociation of MUC16 Mutation With Tumor Mutation Load and Outcomes in Patients With Gastric Cancer.\u003c/em\u003e JAMA Oncol, 2018. \u003cstrong\u003e4\u003c/strong\u003e(12): p. 1691-1698.\u003c/li\u003e\n\u003cli\u003eYang, Y., et al., \u003cem\u003eMUC4, MUC16, and TTN genes mutation correlated with prognosis, and predicted tumor mutation burden and immunotherapy efficacy in gastric cancer and pan-cancer.\u003c/em\u003e Clin Transl Med, 2020. \u003cstrong\u003e10\u003c/strong\u003e(4): p. e155.\u003c/li\u003e\n\u003cli\u003eRobles, A.I. and C.C. Harris, \u003cem\u003eClinical outcomes and correlates of TP53 mutations and cancer.\u003c/em\u003e Cold Spring Harb Perspect Biol, 2010. \u003cstrong\u003e2\u003c/strong\u003e(3): p. a001016.\u003c/li\u003e\n\u003cli\u003eBecht, E., et al., \u003cem\u003eEstimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression.\u003c/em\u003e Genome Biol, 2016. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 218.\u003c/li\u003e\n\u003cli\u003eJiang, P., et al., \u003cem\u003eSignatures of T cell dysfunction and exclusion predict cancer immunotherapy response.\u003c/em\u003e Nat Med, 2018. \u003cstrong\u003e24\u003c/strong\u003e(10): p. 1550-1558.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"telomere related genes, gastric cancer, tumor microenvironment, prognosis, immune status","lastPublishedDoi":"10.21203/rs.3.rs-4598908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4598908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTelomeres play a crucial role in the development and progression of cancers. However, the impact of telomere-related genes (TRGs) on the prognosis and tumor immune microenvironment (TIME) of gastric cancer (GC) remains unclear. Therefore, a comprehensive investigation of the association between TRGs and GC is necessary. The TRG risk panel was constructed by combining differentially expressed gene analysis, weighted gene co-expression network analyses, the Least Absolute Shrinkage and Selection Operator regression, and stepwise regression analysis in the TCGA cohort and has been validated in a GEO cohort. The major impacts of the signature on the TIME and immunotherapy response were also evaluated. The prognosis model comprised 9 TRGs (CABP2, CALML6, CFAP58, DST, ELOVL2, HIST1H3G, MYF6, PDE1B and TOP3B), stratifying patients into two risk groups. Individuals with low-risk scores exhibited superior prognoses than those with high-risk scores (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). The prognostic signature was found to be an independent factor with good predictive power for overall survival. The high-risk group tended to have higher TME scores and an inert immune status with a higher infiltration proportion of Treg cells, M2 macrophages, resting dendritic cells and resting NK cells. Additionally, the low-risk group had higher TMB, lower TIDE and a higher immunotherapy response rate. Additionally, we confirmed the expression of the nine genes in GC tissues using RT-qPCR. Our TRG-based panel has a significant role in the prognosis, TIME, and immunotherapy response. This may suggest that the TRG panel could be a powerful tool for guiding clinical treatment decisions.\u003c/p\u003e","manuscriptTitle":"A telomere-related gene panel predicts the prognosis and Immune Status in gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 20:36:05","doi":"10.21203/rs.3.rs-4598908/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":"61bdbfc3-20ea-49a9-8d73-291a045fc45f","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34477803,"name":"Biological sciences/Cancer"},{"id":34477804,"name":"Biological sciences/Genetics"},{"id":34477805,"name":"Biological sciences/Immunology"},{"id":34477806,"name":"Health sciences/Biomarkers"},{"id":34477807,"name":"Health sciences/Diseases"},{"id":34477808,"name":"Health sciences/Gastroenterology"},{"id":34477809,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-11-11T06:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 20:36:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4598908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4598908","identity":"rs-4598908","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}