CHSY3 As A Potential Therapeutic Target For Gastric Cancer

Background: Chondroitin synthase 3(CHSY3) has been reported to be closely related to the occurrence, development, recurrence, and metastasis of a variety of cancers. But its role in gastric cancer (GC) has not been studied. Methods: Tumor Immune Estimate Resource (TIMER), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) were used to detect the expression of CHSY3 in gastric cancer. Analyze the correlation between CHSY3 expression and clinicopathological parameters. Draw a Kaplan-Meier survival curve to detect the clinical prognostic signicance of CHSY3 in gastric cancer. Meta-analysis of the above results to determine the role of CHSY3 in GC.COX regression analysis was used to study whether CHSY3 is a predictor of GC patients. Analyze the methylation data from the TCGA database to determine the sites with a signicant correlation. Through GO analysis and KEGG analysis to study the regulation mechanism of CHSY3 related genes. Screen 3 genes (APOD, GJA1, SERPINE1) from the related genes of CHSY3 as a predictive signature.Constructed a nomogram to predict the 1-year and 3-year survival probability of GC patients. The TIMER database was used to study the correlation between CHSY3 and tumor inltrating immune cells.Study the drug sensitivity of CHSY3 related genes by using Gene Set Cancer Analysis (GSCA). Results: CHSY3 was highly expressed in GC and signicantly correlated with the T stage.The higher the expression level of CHSY3 in GC, the higher the methylation level of cg06610705 and cg11572844,which suggests that patients with gastric cancer have a poorer prognosis.Enrichment analysis identied multiple cancer-related pathways, for example, PI3K-Akt signaling pathway,MAPK signaling pathway, and TGF-beta signaling pathway.The risk score has a very high predictive value.CHSY3 is closely associated with CD4 + T cells, macrophages, neutrophils, and dendritic.Besides,different mutational forms of CHSY3 were associated with the immune inltration of 6 leukocytes.In addition,CHSY3 may contribute to drug resistance in GC. Conclusion: CHSY3 may be a potential prognostic biomarker for gastric cancer, and it can be used as a new therapeutic target for the treatment of gastric cancer. the relationship and tumor-inltrating immune cells was also studied. This reveals the potential role help us further possible of and Rectum adenocarcinoma(READ). CHSY3 is signicantly lower in Bladder Urothelial Carcinoma(BLCA), Liver hepatocellular carcinoma(LIHC), Lung adenocarcinoma(LUAD), Lung squamous cell carcinoma(LUSC), Thyroid carcinoma(THCA), and Uterine Corpus Endometrial Carcinoma(UCEC) than in the respective normal tissues. These results indicate that CHSY3 is abnormally expressed in a variety of cancers. We used TCGA data to draw a Scatter difference plot(p<0.001, Figure.1B) and paired difference plot(p<0.001, Figure.1C) to further analyze the expression of CHSY3 in GC.and found that the expression of CHSY3 in GC was signicantly higher than that in normal gastric tissues. We used TCGA data to draw ROC curves to evaluate the diagnostic value of CHSY3 in GC.Area under ROC curve (AUC): 0.618,95%-CI:0.867,0.644). Therefore, CHSY3 has considerable diagnostic value for GC. We obtained the immunohistochemical smear of CHSY3 in gastric cancer tissue through The drew scatter differential Meta-analysis of the data of TCGA and GEO nally that CHSY3 gastric cancer tissues than normal tissues(SWD=1.09,95% CI: 0.83-1.35; 2 judge accuracy CHSY3 predicting the GC value, curve analysis SROC curve analysis on the data of TCGA and GEO. The results show that CHSY3 a high diagnostic value.At the same time, our analysis also found that in gastric cancer, CHSY3 signicant with the T stage.Subsequently, results survival high CHSY3 expression level gastric


Introduction
According to research, cancer plays an important role in affecting human morbidity and mortality [1]. At present, the awareness of cancer needs to be improved, and the mortality rate of cancer will increase in the future [2]. Gastric cancer is the fth most common cancer, a heterogeneous malignant tumor [3], accounting for 8.2% of cancer deaths and the third leading cause of cancer deaths worldwide [4]. Almost half of GC cases are diagnosed in East Asia [5]. Although various treatment methods such as surgery, chemotherapy, radiotherapy, and immunotherapy have been developed, the 5-year survival rates of stage II and stage IIIA gastric cancer patients are approximately 34% and 20%, respectively [6]. In recent years, studying the occurrence and development of gastric cancer from the perspective of bioinformatics has gradually become mainstream. Including microsatellite instability and HER2 mutation [7]. Many studies on molecular targeted therapy and related molecular pathways have clari ed the pathogenesis of GC and have further improved the prognosis of gastric cancer patients. For example, Trastuzumab has a better effect on HER2-positive GC patients [8]. Therefore, it is very necessary to identify key new biomarkers and potential mechanisms related to GC.
Chondroitin synthase (CHSY) is one of the six enzymes responsible for CS biosynthesis in mammalian cells [9]. CHSY3 (Chondroitin Sulfate Synthase 3) is a Protein Coding gene, An important paralog of this gene is CHSY1. The methylation level of CHSY1 is related to the differentiation of T cells [10] and is also essential for bone development [11], and the lack of CHSY1 can cause temtamy preaxial brachydactyly syndrome [12]. Studies have shown that CHSY1 plays an important role in the occurrence and development of a variety of cancers. For example, CHSY1 is involved in the interaction between myeloma cells and osteoclasts [13]. The differential expression of CHSY1 was found in malignant soft tissue sarcomas [14]. Knockout of CHSY1 increased the expression of JAG2, a key molecule in glioblastoma cells [15]. In addition, the overexpression of CHSY1 enhanced the migration, invasion, and EMT of hepatocellular carcinoma [16]. Following CHSY1 knockdown, the proliferation of colorectal cancer cells will be signi cantly reduced [17]. However, there is no research on the role of CHSY3 in GC.
In this study, the expression of CHSY3, the correlation between CHSY3 expression and clinicopathological characteristics, degree of methylation, and prognosis were analyzed using the sequencing data set. The GO analysis and KEGG analysis reveal the potential regulatory mechanism of CHSY3. A prognostic signature was established through genes related to CHSY3, and then the risk score of each patient was calculated, and the relationship between the risk score and OS was explored. In addition, the relationship between CHSY3 and tumor-in ltrating immune cells was also studied. This study reveals the potential role of CHSY3 in GC, which will help us further understand the possible mechanism of gastric cancer.

Data Source and Preprocessing
Download gene expression data and clinical information of gastric cancer patients from the The Cancer Genome Atlas(TCGA)(https://portal.gdc.cancer.gov/repository) and Gene Expression Omnibus(GEO) (https://www.ncbi.nlm.nih.gov/geo/). We use Perl programming language to integrate data, match gene expression information with clinical information, and delete unknown or incomplete clinical information.
Since the sequencing data was obtained using public databases, there are no ethical issues.

CHSY3 gene expression analysis
The level of CHSY3 gene expression in a variety of cancers including GC was analyzed using Tumor Immune Estimation Resource (TIMER) database with The Cancer Genome Atlas (TCGA) data. For the data obtained from TCGA, survival package, Beeswarm package, and Limma package were used to analyze the difference in CHSY3 expression between GC and normal tissues, and P value was measured by Wilcox test. The results were visualized using scatter difference and paired difference diagrams.In addition, we drew a box scatter plot to perform CHSY3 difference analysis on ve datasets(GSE13911,GSE19826,GSE54129,GSE66229,GSE79973) from the GEO database.Finally, the above results were meta-analyzed. We then analyzed the data from TCGA and GEO using the survival package,survminer package, and timeROC package. Draw receiver operating characteristic (ROC) curve and SROC curve to assess the accuracy of CHSY3 in predicting GC patient survival. R software (V.4.0.4) Analyze the above.

Clinical correlation analysis
We selected GC tissue samples with clinicopathological data, including age, grade, stage, depth of invasion, lymph node metastasis, and distant metastasis, for correlation analysis with CHSY3 expression.
Age and distant metastasis were analyzed using Wilcox-Test, the rest were analyzed using Kruskal-test.

Survival Analysis
According to the median expression of CHSY3, patients were divided into the high expression group and low expression group. Survival software was used for survival analysis, and Kaplan-Meier survival curve visualization results were drawn. Finally, meta-analysis was performed on the analysis results of TCGA and GEO data to determine the relationship between the expression of CHSY3 and the survival prognosis of GC patients.

Univariate and Multivariate Cox Regression Analyses.
We used the Cox proportional hazard regression model to perform Univariate and multivariate analyses on the data from TCGA and GEO datasets(GSE62254, GSE84437). Univariate COX analysis was used to evaluate whether CHSY3 is a predictor of survival and prognosis in GC patients. Multivariate Cox analysis was used to evaluate whether CHSY3 can be used as an independent prognostic factor for GC. The Survival and Survminer software packages were used to analyze the data, and the forest plots were used to visualize the results.

Methylation analysis of CHSY3
Methylation data obtained from the TCGA database were analyzed to obtain the methylation data of CHSY3, then the methylation degree of CHSY3 at each site was obtained. In addition, ggpubr package and ggplot2 package were used to analyze the correlation between the expression level of CHSY3 and the methylation level of each site. The relationship between methylation at two sites and overall survival in GC patients was analyzed.
The enrichment analysis of CHSY3 First, we identi ed the co-expression genes of CHSY3 using the Linkedomics platform, and then performed GO enrichment analysis and KEGG enrichment analysis using the R package "CLUS terPro ler".P<0.05 was considered statistically signi cant.

Construction and Evaluation of the Prognostic Signatures of CHSY3
First, we obtained the 209 differential genes by constructing a co-expression network of CHSY3, (fdrFilter=0.00000001, logFC lter=10), and then performed Univariate COX analysis on these DEGs, and determined DEGs that are signi cantly different from OS, (pFilter=0.05 ), and subsequently, using LASSO regression analysis to screen the DEGs that are signi cantly related to OS. By running 1000 times of cross-validation likelihood method, The optimal value for penalization coe cient lambda (λ) is screened out. This method can avoid over tting the signature. In the case where λ is the smallest, select the most suitable gene to construct the signature.
Finally, we performed multivariate Cox regression analysis on DEGs screened by LASSO.3 genes (APOD, GJA1, SERPINE1) were selected as a predictive signature. We used the risk score calculation formula to calculate the risk score of each patient: risk score = coef gene 1×gene 1 expression+ coef gene 2×gene 2 expression +. + coef gene Ñ×gene Ñ expression. The risk score is obtained by weighting the expression level of the gene and the regression coe cient (coef). The risk ratio (HR) of the multivariate Cox regression analysis was log-transformed to calculate the coef value, and the expression of each gene involved in the prognostic characteristics was de ned as the expression of Ñ gene. GC patients in TCGA are divided into high-risk groups and low-risk groups according to the median risk score.
First, the difference of OS between the high-risk group and the low-risk group was analyzed by the K-M method, and the survival curve was obtained. We then plotted Receiver Operating Characteristic (ROC) curves using the "survivalROC" package to assess the ability of risk score and other clinical features to predict GC and to assess sensitivity and speci city by the area below the curve (AUC values). To determine whether the risk score can be used as an independent predictor of the prognosis of GC patients, we incorporated age, gender, grade, stage, T, M, N, and risk score into univariate and multivariate Cox regression analysis.

Construction and Validation of Nomogram Based on Risk Score
The nomogram can personally calculate the survival rate of GC patients, which is of great value in clinical practice. We screened the factors that affect the prognosis of GC patients and constructed a nomogram using the "survival" and "rms" packages to predict the 1-year and 3-year survival probability of GC patients. Then we drew calibration curves to evaluate the accuracy of the nomogram prediction. Finally, the time-varying ROC curves of the nomogram were drawn, and the AUC values were calculated.

Analysis of association between CHSY3 expression level and Immune In ltrates
Tumor Immune Estimation Resource(TIMER) is a public site covering 32 cancer types, including 10,897 samples from the TCGA database, designed to assess immune invasion abundance (http://cistrome.org/TIMER/). The association between CHSY3 expression and six types of in ltrating immune cells (CD8+ T cells, CD4+ T cells, B cells, dendritic cells macrophages, and neutrophils) was evaluated through the Correlation Module of the TIMER database. Meanwhile, the relationship between CHSY3 gene expression and tumor purity was analyzed. In addition, the SCNA module was used to explore the correlation between somatic copy number alteration (SCNA) of CHSY3 and the immune abundance of 6 leukocytes.

Drug sensitivity analysis of CHSY3-related drugs
We investigated drug sensitivity of CHSY3-related genes by using Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/web/GSCALite/), a web-based platform for Gene Set Cancer Analysis GSCALite.
Statistical Analysis.
R software (version 4.0.4) was used for statistical analysis. Wilcox-Test and kruskal-Test were used to analyze clinicopathological parameters and the expression level of CHSY3. Kaplan-Meier analysis was used to investigate the relationship between survival rate and CHSY3 expression level. Univariate and multivariate survival analyses were performed using Cox proportional hazard regression model. P<0.05 was considered to indicate a statistically signi cant difference.

Results
The differential expression of CHSY3 between GC tumor tissue and normal tissue.
First, we used the TIMER database to analyze the TCGARNA-seq data to evaluate the transcription level of CHSY3 in different human tumors( Figure.1A). Compared with normal gastric tissue, the expression of CHSY3 in GC tissue is signi cantly higher. In addition, CHSY3 was also found to be highly expressed in the tissues of Breast invasive carcinoma(BRCA), Cholangiocarcinoma(CHOL), Colon adenocarcinoma(COAD), Esophageal carcinoma(ESCA), Head and Neck squamous cell carcinoma(HNSC), Kidney Chromophobe(KICH), Kidney renal clear cell carcinoma(KIRC), Prostate adenocarcinoma(PRAD) and Rectum adenocarcinoma(READ). CHSY3 is signi cantly lower in Bladder Urothelial Carcinoma(BLCA), Liver hepatocellular carcinoma(LIHC), Lung adenocarcinoma(LUAD), Lung squamous cell carcinoma(LUSC), Thyroid carcinoma(THCA), and Uterine Corpus Endometrial Carcinoma(UCEC) than in the respective normal tissues. These results indicate that CHSY3 is abnormally expressed in a variety of cancers. We used TCGA data to draw a Scatter difference plot(p<0.001, Figure.1B) and paired difference plot(p<0.001, Figure.1C) to further analyze the expression of CHSY3 in GC.and found that the expression of CHSY3 in GC was signi cantly higher than that in normal gastric tissues. We used TCGA data to draw ROC curves to evaluate the diagnostic value of CHSY3 in GC.Area under ROC curve (AUC): 0.618,95%-CI:0.867,0.644). Therefore, CHSY3 has considerable diagnostic value for GC. We obtained the immunohistochemical smear of CHSY3 in gastric cancer tissue through The Human Protein Atlas (HPA)(https://www.proteinatlas.org/) to verify The differential expression of CHSY3 in GC( Figure.1E).
At the same time, we use ve datasets from GEO to draw box diagrams( Figure.  Relationship between expression of CHSY3 and clinicopathological features.
We downloaded the clinical data of gastric cancer patients from TCGA, and after deleting unknown and incomplete information, we performed the correlation analysis between CHSY3 expression and clinicopathological characteristics.The results showed that the expression of CHSY3 was signi cantly

Methylation analysis of CHSY3
Based on the methylation data downloaded from TCGA, the study found that the expression level of CHSY3 was positively correlated with the methylation degree on cg06610705( Figure.  In the BP category,we explored regulation of cell-substrate adhesion,regulation of cellular response to growth factor stimulus,transmembrane receptor protein serine/threonine and kinase signaling pathway.In the CC category,we explored contractile ber,cell-substrate junction,focal adhesion,banded collagen bril, brillar collagen trimer,complex of collagen trimers,collagen trimer,endoplasmic reticulum lumen,basement membrane,collagen−containing and extracellular matrix.In the MF category,we explored platelet-derived growth factor binding,extracellular matrix binding,sulfur compound binding,integrin binding,heparin binding,extracellular matrix structural constituent conferring tensile strength,growth factor binding,glycosaminoglycan binding,collagen binding and extracellular matrix structural constituent.

Construction of the CHSY3 Prognostic Signature for GC Patients
Using the survival information of GC patients to perform univariate Cox regression analysis on the 209 DEGs, it was found that there are 95 DEGs with signi cant prognostic differences.Seven optimal candidate genes were obtained by LASSO analysis, including APOD,GJA1,RGS5,CTHRC1,SERPINE1,DUSP1, and CXCR4, which could reduce signature over t (Figure.9A,B). Perform multivariate Cox analysis on 7 genes with prognostic signi cance, and construct a prognostic signature composed of 3 genes, including APOD, GJA1 and SERPINE1.Based on the prognosis signature, the risk score calculation formula was obtained: risk score = (0.132417559 ×GJA1 expression) + (0.177130658 × SERPINE1 expression) + (0.092985412 × APOD expression).The risk score was calculated for each GC patient based on the expression levels of 3 genes, and patients were divided into a high-risk group (n = 184) and a low-risk group (n = 184) based on the median.We constructed a heat map to show the expression of 3 genes in the high-risk group and the low-risk group, and the expression of 3 genes in the high-risk patients was higher than that in the low-risk patients( Figure.9C). Figure.9D shows the distribution of risk scores for GC patients. Patients are divided into two groups, with risk scores increasing from left to right. Figure.9E shows the distribution of survival status and survival time for patients with different risk scores.K-M curve was used to compare the difference in OS time between the high-risk group and the low-risk group (Figure.10A).Results showed that GC patients with a high-risk score had signi cantly lower OS than GC patients with a low-risk score(P<0.001).We plotted a time-dependent ROC curve to predict survival in GC patients, showing that the risk score had high sensitivity and speci city.AUC of risk score(AUC=0.657) was higher than that of age(AUC=0.589),gender(AUC=0.461),grade(AUC=0.569),stage(AUC=0.605),T stage(AUC=0.561),N stage(AUC=0.579) and M stage(AUC=0.532) (Figure.10B).The results show that the prediction result of the risk score is reliable and accurate. Figure.10C re ects the univariate Cox analysis of the relationship between the clinical features, risk score and OS of GC patients.Age(P =0.006),stage(P < 0.001),T(P =0.032),N(P =0.006),M(P =0.025) and risk score(P < 0.001) signi cantly affect the prognosis of GC patients. Figure.10D re ects multivariate Cox analyzed the relationship between the clinical features, risk score, and OS of GC patients. Age (P < 0.001) and risk score (P < 0.001) are independent prognostic risk factors for GC.

Construction and Validation of the Nomogram
We used factors such as age, grade, stage, T, M, N, and risk score to construct a nomogram to predict the survival rate of GC patients more conveniently.According to the nomogram, the scores of GC patients are calculated and then added to obtain the total score, thereby predicting the survival probability of 1 year and 3 years, which is bene cial to guide clinical decision-making.So the closer the calibration curve is to the diagonal, the more accurate the prediction result will be.The calibration curves of the nomogram show that the nomogram has good accuracy in predicting survival rates at 1 and 3 years( Figure.11B,C).The 1-year(AUC = 0.657) and 3-year(AUC = 0.608) ROC curves also show that the forecasting ability of the nomogram is very accurate. (Figure.11D)

CHSY3 Expression Is Correlated With Immune In ltration Level in GC
Tumor in ltrating lymphocyte (TIL), a component of the tumor microenvironment, has been found to be associated with cancer prognosis and treatment response [18].Therefore, we used the TIMER database to study the relationship between the expression of CHSY3

Discussion
Research has shown that cancer plays an important role in affecting human morbidity and mortality [19].
Gastric cancer is the most common malignant tumor of the digestive system. At present, the diagnosis of gastric cancer mainly relies on endoscopy [20]. Many patients are diagnosed with advanced tumors as soon as they are checked, and have lost the opportunity for surgery. Obviously, this diagnostic method has certain limitations [21]. Early diagnosis of cancer is the key to improving the overall survival rate of patients, reducing disease-free progression, and reducing the risk of recurrence. Therefore, there is an urgent need to determine reliable predictive biomarkers and new therapeutic targets to truly improve the prognosis of patients.Studies have shown that abnormal gene expression is closely related to the occurrence and development of cancer [22,23].Nan Zhou's research shows that HTRA3 is abnormally highly expressed in gastric cancer tissues, and the high expression of HTRA3 indicates a poor prognosis [24].Liya Hu's research found that PDPN is a prognostic biomarker of gastric cancer and is related to immune in ltration [25].Li Rong's research con rmed that COL1A2 is a prognostic biomarker of gastric cancer through bioinformatics and Meta-analysis, which can improve the clinical predictive ability of gastric cancer [26].Therefore, it is very necessary to carry out bioinformatics research on gastric cancer.

CHSY3 (Chondroitin Sulfate Synthase 3) is a Protein
Coding gene,An important paralog of this gene is CHSY1.At present, the research on CHSY3 bioinformatics is relatively scarce.In our research, we mainly focus on the role of CHSY3 in gastric cancer. First, we studied the differential expression of CHSY3 in a variety of cancers in the TIMER database, and determined that CHSY3 is highly expressed in the tissues These results indicate that CHSY3 is abnormally expressed in a variety of cancers.Subsequently, we downloaded the transcriptome data and clinical information of gastric cancer patients from the TCGA database and GEO database. We drew a scatter plot, a paired plot, and studied ve datasets(GSE13911,GSE19826,GSE54129,GSE66229,GSE79973) in the GEO database, and drew box scatter plots to explore the differential expression of CHSY3 in gastric cancer tissues. Meta-analysis of the data of TCGA and GEO has nally con rmed that CHSY3 is signi cantly higher in gastric cancer tissues than normal tissues(SWD=1.09,95% CI: 0.83-1.35; I 2 =44%, P=0.134).In order to judge the accuracy of CHSY3 in predicting the GC value, we performed ROC curve analysis and SROC curve analysis on the data of TCGA and GEO. The results show that CHSY3 does have a high diagnostic value.At the same time, our analysis also found that in gastric cancer, the expression of CHSY3 has a signi cant correlation with the T stage.Subsequently, the results of survival analysis showed that a high CHSY3 expression level in gastric cancer was related to poor OS.
We performed univariate and multivariate Cox proportional hazards regression analysis on the data of TCGA and GEO, and performed a meta-analysis on the results of the analysis, thereby con rming that the high expression of CHSY3 is an independent predictor of the prognosis of gastric cancer.Moreover,methylation studies showed that the higher the expression level of CHSY3 in GC, the higher the methylation level of CG06610705 and CG11572844, suggesting a poor prognosis of gastric cancer patients.In order to further investigate the potential regulatory mechanism of CHSY3 in gastric cancer.We identi ed the co-expressed genes of CHSY3 through the Linkedomics platform, and performed KEGG analysis and GO analysis on the top 50 positively related genes and negatively related genes. The results identi ed many pathways related to cancer. Including PI3K-Akt signaling pathway [27],Human papillomavirus infection [28],ECM-receptor interaction [29],Proteoglycans in cancer [30],MAPK signaling pathway [31],TGF-beta signaling pathway [32],cGMP-PKG signaling pathway [33],Small cell lung cancer,transmembrane receptor protein serine/threonine [34],kinase signaling pathway [35],focal adhesion [36],endoplasmic reticulum lumen [37],basement membrane [38],extracellular matrix [39],integrinbinding [40],heparin-binding [41],growth factor binding [42] and glycosaminoglycan binding [43].A large number of studies have con rmed that it is related to the occurrence and development of tumors and tumor immune in ltration.
We can calculate the risk score of each patient based on the prognostic characteristics. The study found that risk score is an independent risk factor affecting prognosis and can be used to predict OS in patients with gastric cancer. High-risk patients have a poor prognosis for lower-risk patients. Therefore, we speculate that the three DEGs that constitute prognostic signals are involved in the progression of GC. Through the ROC curve analysis of OS of GC patients, we found that this prognostic signature has a good predictive value for gastric cancer (AUC = 0.657) and can be used to predict the prognosis of GC patients. In order to facilitate clinical application and better predict the prognosis of patients with gastric cancer, we constructed a nomogram to predict the survival rate of patients at 1 and 3 years. Judging from the calibration curve and ROC curve, the nomogram has a better predictive effect.
It is well known that tumor in ltration lymphol (TIL) plays an important role in the occurrence and development of tumors [44,45]. The study by Yi-Ru Yu et al. showed that the metabolic challenges in tumors weaken the metabolic tness and anti-tumor activity of tumor-in ltrating T lymphocytes (TILs), thereby promoting tumor progression [46].Our results show that the high CHSY3 expression level had positive correlations with in ltrating levels of CD4 + T cells(r=0.302,P=3.82e-09), macrophages(r=0.533, P=1.40e-28), neutrophils(r=0.224,P=1.28e-05) and dendritic (r=0.347, P=6.12e-12).In addition,different mutational forms of CHSY3 were associated with the immune in ltration of 6 leukocytes.These results indicate that the role of CHSY3 in gastric cancer may be related to immune in ltration.
Our research has certain limitations. These studies are based on bioinformatics research, lack of experimental data and experiments are still needed to verify the role of CHSY3 in gastric cancer. In addition,It is necessary to conduct more in-depth prospective clinical research on the signature and nomogram.
In summary, CHSY3 is highly expressed in gastric cancer tissues, and the up-regulation of CHSY3 expression is closely related to T staging, indicating a poor prognosis, and the high expression of CHSY3 can be used as an independent predictor of the prognosis of gastric cancer.At the same time, we constructed a risk signature, including APOD, GJA1 and SERPINE1, which con rmed that risk score was an independent predictor of gastric cancer prognosis.For better clinical application, we have constructed a nomogram with a higher predictive value. In addition, the high expression of CHSY3 is related to the increase of CD4 + T cells, macrophages, neutrophils, and dendritic, and it is also related to chemotherapy resistance. Therefore, CHSY3 may be a reliable prognostic biomarker in GC and may play an important role in tumor regression and immune cell in ltration.May serve as a new target for gastric cancer treatment.

Declarations
Ethics approval and consent to participate Not applicable Consent for publication Not applicable

Availability of data and materials
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article.    Forest plots exhibit diagnostic performance of CHSY3 in GC.
Page 21/28        Analysis of drug sensitivity associated with CHSY3. Red shows a positive correlation, with higher gene expression associated with greater drug sensitivity, while blue shows the opposite.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download. Abbreviations.xlsx