Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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Prognostic signature of ovarian cancer based on 14 tumor microenvironment genes

10.21203/rs.3.rs-56329/v1 ◽

2020 ◽

Author(s):

Xiazi Nie ◽

Lina Song ◽

Xiaohua Li ◽

Yirong Wang ◽

Bo Qu

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Tumor Microenvironment ◽

Risk Score ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Mrna Levels ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Ovarian cancer is one of the lethal gynecological in women. Tumor microenvironment (TME) is emerging as a pivotal biomarker for patients’ therapeutic sensitivity and prognosis. In this study, we proposed to explore the prognostic role of TME-related genes in ovarian cancer. Methods The data of whole genome expression profiles and detailed clinicopathological information of three cohorts of ovarian cancer patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Univariate Cox regression analysis was used to screen TME-related genes with significantly prognostic value based on TCGA cohort. LASSO Cox regression analysis was adapted to the construction of prognostic model. Ovarian cancer cohorts from GEO were used as validation set for verifying the reliability of the prognostic model. Relative infiltrating proportion of 22 immune cells were estimated through CIBERSORT software. Results This study identified a total of 14 TME-related genes that finally incorporated into the prognostic model. The risk score that calculated through the prognostic model was proved as an independent prognostic signature in ovarian cancer. Nomogram that contains TNM stage and risk score could reliably predict the long-term overall survival probability. Additionally, risk score was significantly associated with the relative infiltrating proportion of several immune cells in ovarian cancer and mRNA levels of some immune checkpoint genes. Conclusions This study constructed a prognostic model for ovarian cancer, which was closely associated with the prognosis and immune status. This should provide novel clue for prognosis study in ovarian cancer.

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Identification and Validation a Costimulatory Molecule Gene Signature to Predict the Prognosis and Immunotherapy Response for Hepatocellular Carcinoma

10.21203/rs.3.rs-1064833/v1 ◽

2021 ◽

Author(s):

Yinan Hu ◽

Jingyi Liu ◽

Jiahao Yu ◽

Fangfang Yang ◽

Miao Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Immune Cells ◽

Cox Regression ◽

Costimulatory Molecule ◽

Gene Signature ◽

Consensus Clustering ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Qrt Pcr

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients.Methods: Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan–Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated in HCC using qRT-PCR method.Results: All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR indicated six CMGs with significantly differential expression in HCC tissues and normal tissues.Conclusion: In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.

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A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1871520621666210713112630 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yongmei Wang ◽

Guimin Zhang ◽

Ruixian Wang

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Differential Expression ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Differential Expression Analysis ◽

Functional Enrichment ◽

Risk Scores ◽

Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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A novel comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

10.21203/rs.3.rs-26402/v1 ◽

2020 ◽

Author(s):

Ruihua Fang ◽

Lin Chen ◽

Jing Liao ◽

Jierong Luo ◽

Chenchen Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Head And Neck ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Prognostic Signature ◽

Immune Microenvironment ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Gene

Abstract Background: Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. Growing evidence has demonstrated that the immune system plays a crucial role in the development and progression of HNSCC. The goal of our study was to develop an immune-related signature for accurately predicting the survival of HNSCC patients. Methods: Gene expression profiles were established from a total of 546 HNSCC and normal tissues to establish a training set and 83 HNSCC tissues for a validation set. Differentially expressed prognostic immune genes were identified by univariate Cox regression analysis and a corresponding network of differentially expressed transcription factors (TFs) were identified using Cytoscape. The immune-related gene signature was established and validated by univariate Cox regression analysis, least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. In addition, the prognostic value of the immune-related signature was analyzed by survival and Cox regression analysis. Finally, the correlation between the immune-related signature and the immune microenvironment was established.Results: In this study, the TF-mediated network revealed that Foxp3 plays a central role in the regulatory mechanism of most immune genes. A prognostic signature based on 10 immune-related genes, which divided patients into high and low risk groups, was developed and successfully validated using two independent databases. Our prognostic signature was significantly related to worse survival and predicted prognosis in patients with different clinicopathological factors. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature may act as an independent factor for predicting the survival of HNSCC patients. ROC analysis also revealed that our signature had superior predictive value compared with TNM stage. As for the immune microenvironment, our signature showed a positive correlation with activated mast cells and M0 macrophages, a negative correlation with Tregs, and immune checkpoint molecules PD-1 and CLTA-4. Conclusions: Our study established an immune-related gene signature, which not only provides a promising biomarker for survival prediction, but may be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

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Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/9978206 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Yang Li ◽

Rongrong Sun ◽

Rui Li ◽

Yonggang Chen ◽

He Du

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Regulatory Mechanism ◽

Cox Regression ◽

Gene Signature ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009 ). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004 ). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C -indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

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The Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma

10.21203/rs.3.rs-113464/v2 ◽

2021 ◽

Author(s):

Weiwei Jia ◽

Pengjia Li ◽

Mingxia Ma ◽

Xiaochen Niu ◽

Lina Bai ◽

...

Keyword(s):

T Cells ◽

Regression Analysis ◽

Mast Cells ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Cox Regression Analysis ◽

Cerna Network ◽

Follicular Helper ◽

Lncrna Malat1

Abstract KIRC is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. We want to explore the possible mechanisms of KIRC and effective prognostic-related biomarkers. The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of KIRC tissues and 72 cases of normal tissues is obtained from the TCGA database. Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. There are 3074 mRNAs, 359 lncRNAs and 132 miRNAs differentially expressed that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of KIRC. Construction of a prognostic model based on tumor-infiltrating immune cells, 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P < 0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R = 0.23, P < 0.001).

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Development and Validation of a Novel Prognosis Prediction Model for Patients With Stomach Adenocarcinoma

Frontiers in Medicine ◽

10.3389/fmed.2021.793401 ◽

2021 ◽

Vol 8 ◽

Author(s):

Tong Wang ◽

Weiwei Wen ◽

Hongfei Liu ◽

Jun Zhang ◽

Xiaofeng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Regression Analysis ◽

Survival Analysis ◽

Differential Expression ◽

Prognostic Model ◽

Cox Regression ◽

Risk Scores ◽

Cox Regression Analysis ◽

Stomach Adenocarcinoma ◽

Core Genes

Background: Stomach adenocarcinoma (STAD) is a significant global health problem. It is urgent to identify reliable predictors and establish a potential prognostic model.Methods: RNA-sequencing expression data of patients with STAD were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database. Gene expression profiling and survival analysis were performed to investigate differentially expressed genes (DEGs) with significant clinical prognosis value. Overall survival (OS) analysis and univariable and multivariable Cox regression analyses were performed to establish the prognostic model. Protein–protein interaction (PPI) network, functional enrichment analysis, and differential expression investigation were also performed to further explore the potential mechanism of the prognostic genes in STAD. Finally, nomogram establishment was undertaken by performing multivariate Cox regression analysis, and calibration plots were generated to validate the nomogram.Results: A total of 229 overlapping DEGs were identified. Following Kaplan–Meier survival analysis and univariate and multivariate Cox regression analysis, 11 genes significantly associated with prognosis were screened and five of these genes, including COL10A1, MFAP2, CTHRC1, P4HA3, and FAP, were used to establish the risk model. The results showed that patients with high-risk scores have a poor prognosis, compared with those with low-risk scores (p = 0.0025 for the training dataset and p = 0.045 for the validation dataset). Subsequently, a nomogram (including TNM stage, age, gender, histologic grade, and risk score) was created. In addition, differential expression and immunohistochemistry stain of the five core genes in STAD and normal tissues were verified.Conclusion: We develop a prognostic-related model based on five core genes, which may serve as an independent risk factor for survival prediction in patients with STAD.

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Prediction of Overall Survival for Early-stage Non-small Cell Lung Cancer via a Novel 7-gene Prognostic Signature and Allele Frequency Deviation (AFD)

10.21203/rs.3.rs-68377/v1 ◽

2020 ◽

Author(s):

Aisha AL-Dherasi ◽

Yuwei Liao ◽

Qi-Tian Huang ◽

Yichen Wang ◽

Rulin Hua ◽

...

Keyword(s):

Lung Cancer ◽

Allele Frequency ◽

Prognostic Model ◽

Cox Regression ◽

Gene Signature ◽

Small Cell ◽

Prognostic Signature ◽

Small Cell Lung ◽

Cox Regression Analysis ◽

Nsclc Patients

Abstract Background Due to the late and poor prognosis of non-small lung cancer(NSCLC), the mortality of patients is high, underlines the need to identify a credible prognostic marker for NSCLC patients. The aim of our study is to examine the association of allele frequency deviation (AFD) with the patient's survival, as well as identification and validation of a new prognostic signature to predict NSCLC overall survival(OS).Methods First, we developed a new algorithm to calculate AFD from whole-exome sequencing(WES) data, then we compared the predictability of the patient's survival between AFD, tumor mutation burden (TMB) and change of variants allele frequency (dVAF). Second, we overlapped the differentially expressed genes (DEGs) from our data with the genes associated with the survival of The Cancer Genome Atlas (TCGA) database to confirm all genes significantly related to the survival of lung cancer. We identified 149 genes, 31 of which are new genes and have not been reported for lung cancer, that was used to develop a new prognostic model. Lung cancer adenocarcinoma (LUAD) data from the TCGA database was used to validate the gene-signature model. The prognostic model relating to the genes was established and validated in training and LUAD validation groups.Results There was a significant association found between the high AFD value and poor survival among non-small cell lung cancer (NSCLC) patients. A novel seven genes (UCN2, RIMS2, CAVIN2, GRIA1, PKHD1L1, PGM5, CLIC6) were obtained through multivariate Cox regression analysis and significantly associated with NSCLC patients survival. Cox regression analysis confirmed that AFD and 7-gene signature are an independent prognostic marker in NSCLC patients. The AUC for 5-year survival in AFD and the AUC for 3-year survival in both training and validation groups were greater than 0.7.Conclusion As a result, AFD and 7-gene signatures were identified as new independent predictive factors used for predicting the survival among NSCLC patients.

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Comprehensive Analysis of the Prognostic Significance of Hsa-miR-100-5p and Its Related Gene Signature in Stomach Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.736274 ◽

2021 ◽

Vol 9 ◽

Author(s):

Gaoming Wang ◽

Ludi Yang ◽

Miao Hu ◽

Renhao Hu ◽

Yongkun Wang ◽

...

Keyword(s):

High Risk ◽

Cox Regression ◽

Prognostic Significance ◽

Tumor Development ◽

Risk Groups ◽

Gene Signature ◽

Risk Scores ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is one of the most common cancers in the world. However, the prognosis of STAD remains poor, and the therapeutic effect of chemotherapy and immunotherapy varies from person to person. MicroRNAs (miRNAs) play vital roles in tumor development and metastasis and can be used for cancer diagnosis and prognosis. In this study, hsa-miR-100-5p was identified as the only dysregulated miRNA in STAD samples through an analysis of three miRNA expression matrices. A weighted gene co-expression network analysis (WGCNA) was performed to select hsa-miR-100-5p-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a miR-100-5p-related prognostic signature. Kaplan–Meier analyses, nomograms, and univariate and multivariate Cox regression analyses were used to evaluate the prognostic signature, which was subsequently identified as an independent risk factor for STAD patients. We investigated the tumor immune environment between low- and high-risk groups and found that, among component types, M2 macrophages contributed the most to the difference between these groups. A drug sensitivity analysis suggested that patients with high-risk scores may be more sensitive to docetaxel and cisplatin chemotherapy and that patients in the low-risk group may be more likely to benefit from immunotherapy. Finally, external cohorts were evaluated to validate the robustness of the prognostic signature. In summary, this study may provide new ideas for developing more individualized therapeutic strategies for STAD patients.

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