scholarly journals Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Qianhui Xu ◽  
Shaohuai Chen ◽  
Yuanbo Hu ◽  
Wen Huang
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Principal Component ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Signal Pathways ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Immune Microenvironment

BackgroundIncreasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.MethodsMultiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the “maftools” R package.ResultsThree different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.ConclusionThis work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.

scholarly journals Prognostic value of tumor immune cell infiltration patterns in colon adenocarcinoma based on systematic bioinformatics analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hao Xu ◽  
Qianhui Xu ◽  
Lu Yin
Keyword(s):  
Principal Component Analysis ◽  
Immune Cells ◽  
Prognostic Value ◽  
Immune Cell ◽  
Colon Adenocarcinoma ◽  
Principal Component ◽  
Component Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Consensus Clustering

Abstract Background Although immunotherapy for colon cancer has made promising progress, only a few patients currently benefit from it. A recent study revealed that infiltrating immune cells are highly relevant to tumor prognosis and influence the expression of immune-related genes. However, the characterization of immune cell infiltration (ICI) has not yet been comprehensively analyzed and quantified in colon adenocarcinoma (COAD). Methods The multiomic data of COAD samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method and CIBERSORT analysis were conducted to estimate the subpopulations of infiltrating immune cells. COAD subtypes based on ICI pattern were identified by consensus clustering then principal-component analysis was performed to obtain ICI scores to quantify the ICI patterns in individual tumors. Kaplan–Meier analysis was employed to validate prognostic value. Gene set enrichment analysis (GSEA) was applied for functional annotation. Finally, the mutation data was analyzed by employing “maftools” package. Results Three bioinformatics algorithms were used to evaluate the ICI patterns from 538 patients with COAD. Two ICI subtypes were determined using consensus clustering, and the ICI score was constructed by performing principal component analysis. Our findings showed that a higher ICI score often indicated a more advanced tumor and worse prognosis. The high-ICI score subgroup had a higher stromal score and more M0 macrophages but fewer plasma cells and decreased CD8 T cell infiltration. In addition, patients with high ICI scores had significantly higher expression levels of HAVCR2 and PCDC1LG2. Real-time polymerase chain reaction (PCR) was conducted to determine the prognostic significances of ICI-related genes. Conclusions In conclusion, ICI score may be considered as an original and useful indicator for independent prognostic prediction and individual immune-related therapy.

scholarly journals Construction of Prognostic Risk Model of 5-Methylcytosine-Related Long Non-Coding RNAs and Evaluation of the Characteristics of Tumor-Infiltrating Immune Cells in Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhidong Huang ◽  
Junjing Li ◽  
Jialin Chen ◽  
Debo Chen
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Prognostic Value ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Non Coding Rnas

Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC.Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples.Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm.Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.

scholarly journals Correlation Between 18F-FDG Uptake and Immune Cell Infiltration in Metastatic Brain Lesions

2021 ◽  
Vol 11 ◽  
Author(s):  
Young-Sil An ◽  
Se-Hyuk Kim ◽  
Tae Hoon Roh ◽  
So Hyun Park ◽  
Tae-Gyu Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Standardized Uptake Value ◽  
Brain Lesions ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Ki 67 ◽  
Glucose Transporter 1 ◽  
Fdg Uptake

BackgroundThe purpose of this study was to investigate the correlation between 18F-fluorodeoxyglucose (FDG) uptake and infiltrating immune cells in metastatic brain lesions.MethodsThis retrospective study included 34 patients with metastatic brain lesions who underwent brain 18F-FDG positron emission tomography (PET)/computed tomography (CT) followed by surgery. 18F-FDG uptake ratio was calculated by dividing the standardized uptake value (SUV) of the metastatic brain lesion by the contralateral normal white matter uptake value. We investigated the clinicopathological characteristics of the patients and analyzed the correlation between 18F-FDG uptake and infiltration of various immune cells. In addition, we evaluated immune-expression levels of glucose transporter 1 (GLUT1), hexokinase 2 (HK2), and Ki-67 in metastatic brain lesions.ResultsThe degree of 18F-FDG uptake of metastatic brain lesions was not significantly correlated with clinical parameters. There was no significant relationship between the 18F-FDG uptake and degree of immune cell infiltration in brain metastasis. Furthermore, other markers, such as GLUT1, HK2, and Ki-67, were not correlated with degree of 18F-FDG uptake. In metastatic brain lesions that originated from breast cancer, a higher degree of 18F-FDG uptake was observed in those with high expression of CD68.ConclusionsIn metastatic brain lesions, the degree of 18F-FDG uptake was not significantly associated with infiltration of immune cells. The 18F-FDG uptake of metastatic brain lesions from breast cancer, however, might be associated with macrophage activity.

scholarly journals SERPINE1 associated with remodeling of the tumor microenvironment in colon cancer progression: a novel therapeutic target

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shaokun Wang ◽  
Li Pang ◽  
Zuolong Liu ◽  
Xiangwei Meng
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
The Relationship

Abstract Background The change of immune cell infiltration essentially influences the process of colorectal cancer development. The infiltration of immune cells can be regulated by a variety of genes. Thus, modeling the immune microenvironment of colorectal cancer by analyzing the genes involved can be more conducive to the in-depth understanding of carcinogenesis and the progression thereof. Methods In this study, the number of stromal and immune cells in malignant tumor tissues were first estimated by using expression data (ESTIMATE) and cell-type identification with relative subsets of known RNA transcripts (CIBERSORT) to calculate the proportion of infiltrating immune cell and stromal components of colon cancer samples from the Cancer Genome Atlas database. Then the relationship between the TMN Classification and prognosis of malignant tumors was evaluated. Results By investigating differentially expressed genes using COX regression and protein-protein interaction network (PPI), the candidate hub gene serine protease inhibitor family E member 1 (SERPINE1) was found to be associated with immune cell infiltration. Gene Set Enrichment Analysis (GSEA) further projected the potential pathways with elevated SERPINE1 expression to carcinogenesis and immunity. CIBERSORT was subsequently utilized to investigate the relationship between the expression differences of SERPINE1 and immune cell infiltration and to identify eight immune cells associated with SERPINE1 expression. Conclusion We found that SERPINE1 plays a role in the remodeling of the colon cancer microenvironment and the infiltration of immune cells.

scholarly journals Dissecting the Role of N6-Methylandenosine-Related Long Non-coding RNAs Signature in Prognosis and Immune Microenvironment of Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinguo Zhang ◽  
Benjie Shan ◽  
Lin Lin ◽  
Jie Dong ◽  
Qingqing Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Value ◽  
Immune Cell ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Prognostic Signature ◽  
Non Coding Rnas

Breast cancer (BC) represents a molecularly and clinically heterogeneous disease. Recent progress in immunotherapy has provided a glimmer of hope for several BC subtypes. The relationship between N6-methyladenosine (m6A) modification and long non-coding RNAs (LncRNAs) is still largely unexplored in BC. Here, with the intention to dissect the landscape of m6A-related lncRNAs and explore the immunotherapeutic value of the m6A-related lncRNA signature, we identified m6A-related lncRNAs by co-expression analysis from The Cancer Genome Atlas (TCGA) and stratified BC patients into different subgroups. Furthermore, we generated an m6A-related lncRNA prognostic signature. Four molecular subtypes were identified by consensus clustering. Cluster 3 preferentially had favorable prognosis, upregulated immune checkpoint expression, and high level of immune cell infiltration. Twenty-one m6A-related lncRNAs were applied to construct the m6A-related lncRNA model (m6A-LncRM). Survival analysis and receiver operating characteristic (ROC) curves further confirmed the prognostic value and prediction performance of m6A-LncRM. Finally, high- and low-risk BC subgroups displayed significantly different clinical features and immune cell infiltration status. Overall, our study systematically explored the prognostic value of the m6A-related LncRNAs and identified a high immunogenicity BC subtype. The proposed m6A-related LncRNA model might serve as a robust prognostic signature and attractive immunotherapeutic targets for BC treatment.

scholarly journals Immune Cell Infiltration as Signatures for the Diagnosis and Prognosis of Malignant Gynecological Tumors

2021 ◽  
Vol 9 ◽  
Author(s):  
Qi-Fang Liu ◽  
Zi-Yi Feng ◽  
Li-Li Jiang ◽  
Tong-Tong Xu ◽  
Si-Man Li ◽  
...  
Keyword(s):  
Nk Cells ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Diagnostic Model ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Strong Basis ◽  
Gynecological Tumors ◽  
Diagnosis And Prognosis

Background Malignant gynecological tumors are the main cause of cancer-related deaths in women worldwide and include uterine carcinosarcomas, endometrial cancer, cervical cancer, ovarian cancer, and breast cancer. This study aims to determine the association between immune cell infiltration and malignant gynecological tumors and construct signatures for diagnosis and prognosis.Methods We acquired malignant gynecological tumor RNA-seq transcriptome data from the TCGA database. Next, the “CIBERSORT” algorithm calculated the infiltration of 22 immune cells in malignant gynecological tumors. To construct diagnosis and prognosis signatures, step-wise regression and LASSO analyses were applied, and nomogram and immune subtypes were further identified.Results Notably, Immune cell infiltration plays a significant role in tumorigenesis and development. There are obvious differences in the distribution of immune cells in normal, and tumor tissues. Resting NK cells, M0 Macrophages, and M1 Macrophages participated in the construction of the diagnostic model, with an AUC value of 0.898. LASSO analyses identified a risk signature including T cells CD8, activated NK cells, Monocytes, M2 Macrophages, resting Mast cells, and Neutrophils, proving the prognostic value for the risk signature. We identified two subtypes according to consensus clustering, where immune subtype 3 presented the highest risk.Conclusion We identified diagnostic and prognostic signatures based on immune cell infiltration. Thus, this study provided a strong basis for the early diagnosis and effective treatment of malignant gynecological tumors.

scholarly journals An immune cell infiltration-related gene signature predicts prognosis for bladder cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hualin Chen ◽  
Yang Pan ◽  
Xiaoxiang Jin ◽  
Gang Chen
Keyword(s):  
Bladder Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
External Validation ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Prognostic Indicators ◽  
Immune Cell Infiltration ◽  
Related Gene

AbstractTo explore novel therapeutic targets, develop a gene signature and construct a prognostic nomogram of bladder cancer (BCa). Transcriptome data and clinical traits of BCa were downloaded from UCSC Xena database and Gene Expression Omnibus (GEO) database. We then used the method of Single sample Gene Set Enrichment analysis (ssGSEA) to calculate the infiltration abundances of 24 immune cells in eligible BCa samples. By weighted correlation network analysis (WGCNA), we identified turquoise module with strong and significant association with the infiltration abundance of immune cells which were associated with overall survival of BCa patients. Subsequently, we developed an immune cell infiltration-related gene signature based on the module genes (MGs) and immune-related genes (IRGs) from the Immunology Database and Analysis Portal (ImmPort). Then, we tested the prognostic power and performance of the signature in both discovery and external validation datasets. A nomogram integrated with signature and clinical features were ultimately constructed and tested. Five prognostic immune cell infiltration-related module genes (PIRMGs), namely FPR1, CIITA, KLRC1, TNFRSF6B, and WFIKKN1, were identified and used for gene signature development. And the signature showed independent and stable prognosis predictive power. Ultimately, a nomogram consisting of signature, age and tumor stage was constructed, and it showed good and stable predictive ability on prognosis. Our prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for BCa. Further researches are needed to verify the clinical effectiveness of this nomogram and these biomarkers.

scholarly journals Significance of Tumor Infiltrating Immune Cells (TIICs) in Endometrial carcinoma: A Bioinformatics Analysis

2021 ◽  
Author(s):  
Qing Hu ◽  
Jun Wang ◽  
Lina Ge ◽  
Ying Wu
Keyword(s):  
Principal Component Analysis ◽  
Immune Cells ◽  
Immune Activation ◽  
Immune Checkpoint ◽  
Clustering Algorithm ◽  
Immune Cell ◽  
Principal Component ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Immune Related Genes

Abstract Background: Increasing evidence has revealed that tumor-infiltrating immune cells (TIICs) are involved in the development of endometrial carcinoma (EC). In this study, we aimed to reveal the significance of 28 TIIC types in the classification and prognosis of EC. Methods: Single-sample gene set enrichment analysis (ssGSEA) was used to calculate the abundance of the 28 TIIC types in EC patients from TCGA database. A consensus clustering algorithm was used to group the EC patients. Principal component analysis (PCA) algorithms were performed to calculate an ICI score for each sample to predict the sensitivity to ICIs therapy based on the 28 TIIC types.Results: The EC patients from The Cancer Genome Atlas (TCGA) database were classified into two groups (cluster.A and cluster.B) based on the 28 types of TIIC using a consensus clustering algorithm. The patients in the cluster.B group had increased immune cell infiltration, and higher expression of human leucocyte antigen (HLA) genes, immune checkpoint molecules, and immune activation-related genes, suggesting that they may be more sensitive to immune checkpoint inhibitors (ICIs) therapy. Differential analysis between cluster.A and cluster.B identified 591 immune-related genes. We validated the immune regulation mechanism based on the 591 immune-related genes using another consensus clustering algorithm. The EC patients were divided into two groups (gene.cluster.A and gene.cluster.B) based on the 591 immune-related genes. The patients in the gene.cluster.B group had increased immune cell infiltration, and higher expression of HLA genes, immune checkpoint molecules, and immune activation-related genes. In addition, we also calculated an ICI score for each patient with EC to predict the sensitivity to ICIs therapy based on the 28 TIIC types using principal component analysis algorithms. Conclusions: In summary, the 28 types of TIIC play non-negligible roles in the development of EC. Our investigation of the 28 types of TIIC may help to guide ICIs treatment strategies for patients with EC.

Gene Signatures And Cancer-Immune Phenotypes Based On m6A Regulators In Breast Cancer

2021 ◽  
Author(s):  
Guanghui Zhao ◽  
Junhua An ◽  
Qian Pu ◽  
Wenwen Geng ◽  
Haiyun Song ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Clinical Samples ◽  
Immune Cell Infiltration ◽  
Related Gene ◽  
Immune Microenvironment ◽  
Gene Signatures ◽  
Immune Phenotypes ◽  
Processing Stability

Abstract Background: The N6-methyladenosine (m6A) has been considered as a new layer of epitranscriptomic regulation on mRNA processing, stability and translation. However, potential roles of m6A RNA methylation modification in tumor immune microenvironment (TIME) of breast cancer are yet fully understood.Methods: We comprehensively evaluated the genetic variations and transcript expressions of 15 m6A regulators, and clinicopathological features in 1,079 breast cancer samples from The Cancer Genome Atlas (TCGA) database. The mRNA and protein levels of several m6A regulators were validated by RT-qPCR, western blot and immunohistochemistry staining in clinical samples from 39 patients with breast cancer. The prognostic values of m6A regulators were systematically evaluated in different database. We correlated the m6A modification patterns of breast cancer with the immune microenvironment and cancer-immune phenotypes. The m6A regulators-related gene signatures were also analyzed to predict the survival of patients.Results: Some m6A regulators’ CNV events might be potential biomarkers for patient’s stage and prognosis in breast cancer. Major regulators had significantly differential mRNA and protein expression in tumor tissue compared to normal samples among different molecular subtypes of breast cancer, and especially high expression of m6A readers YTHDF1 and YTHDF3 predicted poor survival. Two clusters of breast cancer patients identified by the 15 m6A regulators’ pattern showed distinct overall survival, immune activation status and immune cell infiltration. The profiles of these two clusters accorded with that of two classical cancer-immune phenotypes, immune-excluded and immune-inflamed phenotypes. Moreover, the m6A phenotype-related gene signatures could also be survival predictor in breast cancer.Conclusions: The m6A regulators-based patterns might serve as crucial mediators of TIME in breast cancer. Comprehensive evaluation of tumor m6A modification pattern will contribute to enhance our understanding of the characterization of immune cell infiltration in the tumor microenvironment and promote the responsiveness of breast cancer to immunotherapy.

scholarly journals Immune Landscape of Gastric Carcinoma Tumor Microenvironment Identifies a Peritoneal Relapse Relevant Immune Signature

2021 ◽  
Vol 12 ◽  
Author(s):  
Chuang Zhang ◽  
Danni Li ◽  
Ruoxi Yu ◽  
Ce Li ◽  
Yujia Song ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Peritoneal Recurrence ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Gene Set Enrichment ◽  
Gene Set

BackgroundGastric cancer (GC) still represents the third leading cause of cancer-related death worldwide. Peritoneal relapse (PR) is the most frequent metastasis occurring among patients with advanced gastric cancer. Increasingly more evidence have clarified the tumor immune microenvironment (TIME) may predict survival and have clinical significance in GC. However, tumor-transcriptomics based immune signatures derived from immune profiling have not been established for predicting the peritoneal recurrence of the advanced GC.MethodsIn this study, we depict the immune landscape of GC by using transcriptome profiling and clinical characteristics retrieved from GSE62254 of Gene Expression Omnibus (GEO). Immune cell infiltration score was evaluated via single-sample gene set enrichment (ssGSEA) analysis algorithm. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to select the valuable immune cells and construct the final model for the prediction of PR. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to check the accuracy of PRIs. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore the molecular pathways associated with PRIs.ResultsA peritoneal recurrence related immune score (PRIs) with 10 immune cells was constructed. Compared to the low-PRIs group, the high-PRIs group had a greater risk. The upregulation of the focal adhesion signaling was observed in the high-PRIs subtype by GSEA and KEGG. Multivariate analysis found that both in the internal training cohort and the internal validation cohort, PRIs was a stable and independent predictor for PR. A nomogram that integrated clinicopathological features and PRIs to predict peritoneal relapse was constructed. Subgroup analysis indicated that the PRIs could obviously distinguish peritoneal recurrence in different molecular subtypes, pathological stages and Lauren subtypes, in which PRIs of Epithelial-Mesenchymal Transitions (EMT) subtype, III-IV stage and diffuse subtype are higher respectively.ConclusionOverall, we performed a comprehensive evaluation of the immune landscape of GC and constructed a predictive PR model based on the immune cell infiltration. The PRIs represents novel promising feature of predicting peritoneal recurrence of GC and sheds light on the improvement of the personalized management of GC patients after surgery.

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