scholarly journals Genomic analysis and clinical implications of immune cell infiltration in gastric cancer

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Ming Wu ◽  
Yadong Wang ◽  
Hang Liu ◽  
Jukun Song ◽  
Jie Ding
Keyword(s):  
Gastric Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
R Package ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Cell Type ◽  
Clinical Prognosis ◽  
Immune Infiltration

Abstract The immune infiltration of patients with gastric cancer (GC) is closely associated with clinical prognosis. However, previous studies failed to explain the different subsets of immune cells involved in immune responses and diverse functions. The present study aimed to uncover the differences in immunophenotypes in a tumor microenvironment (TME) between adjacent and tumor tissues and to explore their therapeutic targets. In our study, the relative proportion of immune cells in 229 GC tumor samples and 22 paired matched tissues was evaluated with a Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithm. The correlation between immune cell infiltration and clinical information was analyzed. The proportion of 22 immune cell subsets was assessed to determine the correlation between each immune cell type and clinical features. Three molecular subtypes were identified with ‘CancerSubtypes’ R-package. Functional enrichment was analyzed in each subtype. The profiles of immune infiltration in the GC cohort from The Cancer Genome Atlas (TCGA) varied significantly between the 22 paired tissues. TNM stage was associated with M1 macrophages and eosinophils. Follicular helper T cells were activated at the late stage. Monocytes were associated with radiation therapy. Three clustering processes were obtained via the ‘CancerSubtypes’ R-package. Each cancer subtype had a specific molecular classification and subtype-specific characterization. These findings showed that the CIBERSOFT algorithm could be used to detect differences in the composition of immune-infiltrating cells in GC samples, and these differences might be an important driver of GC progression and treatment response.

scholarly journals Identification of immune-based prostate cancer subtypes using mRNA expression

2020 ◽  
Author(s):  
Jukun Song ◽  
Song He ◽  
Wei Wang ◽  
Jiaming Su ◽  
Dongbo Yuan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Immune Response ◽  
Immune Cells ◽  
Immune Cell ◽  
Molecular Classification ◽  
Cell Types ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Cell Subpopulation ◽  
Immune Cell Infiltration

Abstract Background Immune infiltration of Prostate cancer (PCa) was highly related to clinical outcomes. However, previous works failed to elucidate the diversity of different immune cell types that make up the function of the immune response system. The aim of the study was to uncover the composition of TIICs in PCa utilizing the CIBERSORT algorithm and further reveal the molecular characteristics of PCa subtypes. Method In the present work, we employed the CIBERSORT method to evaluate the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We analyzed the correlation between immune cell infiltration and clinical information. The tumor-infiltrating immune cells of the TCGA PCa cohort were analyzed for the first time. The fractions of 22 immune cell types were imputed to determine the correlation between each immune cell subpopulation and clinical feature. Three types of molecular classification were identified via R-package of “CancerSubtypes”. The functional enrichment was analyzed in each subtype. The submap and TIDE algorithm were used to predict the clinical response to immune checkpoint blockade, and GDSC was employed to screen chemotherapeutic targets for the potential treatment of PCa. Results In current work, we utilized the CIBERSORT algorithm to assess the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We investigated the correlation between immune cell infiltration and clinical data. The tumor-infiltrating immune cells in the TCGA PCa cohort were analyzed. The 22 immune cells were also calculated to determine the correlation between each immune cell subpopulation and survival and response to chemotherapy. Three types of molecular classification were identified. Each subtype has specific molecular and clinical characteristics. Meanwhile, Cluster I is defined as advanced PCa, and is more likely to respond to immunotherapy. Conclusions Our results demonstrated that differences in immune response may be important drivers of PCa progression and response to treatment. The deconvolution algorithm of gene expression microarray data by CIBERSOFT provides useful information about the immune cell composition of PCa patients. In addition, we have found a subtype of immunopositive PCa subtype and will help to explore the reasons for the poor effect of PCa on immunotherapy, and it is expected that immunotherapy will be used to guide the individualized management and treatment of PCa patients.

Prognostic and predictive value of tumor-infiltrating immune cells in Japanese patients with stage II/III gastric cancer.

2014 ◽  
Vol 32 (3_suppl) ◽  
pp. 46-46
Author(s):  
Sophie Earle ◽  
Toru Aoyama ◽  
Alexander I. Wright ◽  
Darren Treanor ◽  
Yohei Miyagi ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Adjuvant Chemotherapy ◽  
Immune Cells ◽  
Prognostic Markers ◽  
Immune Cell ◽  
Japanese Patients ◽  
Stage Ii ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Treatment Interaction

46 Background: Since the ACTS-GC trial, Japanese patients with stage II/III gastric cancer (GC) receive adjuvant S1 chemotherapy. However, selection of patients (pts) by TNM stage does not predict benefit from adjuvant S1 with certainty. Thus, there is an urgent clinical need to identify predictive biomarkers. Increasing evidence suggests tumor immune cell infiltration may be related to GC pts prognosis. We tested the hypothesis that extent and type of immune cell infiltration in GC is related to benefit from adjuvant chemotherapy. Methods: Tissue microarrays from 252 GC resections (109 pts treated by surgery alone (S), 143 pts treated by surgery and adjuvant S1 chemotherapy (SC)) from the Kanagawa Cancer Center Hospital (Yokohama, Japan) were investigated by immunohistochemistry for common leucocytes antigen (CD45), neutrophils (CD66b), macrophages (CD68 and CD163), T-cell subtypes (CD45R0, CD8, CD3), B-cells (CD20) and Treg cells (FOXP3). Staining was quantified as percentage immunoreactivity/area by automated image analysis. Relationship with overall survival was analyzed. A Cox regression model was used to identify independent prognostic markers and treatment interaction effect. Results: The hazard ratio of S1 was 0.694 in this GC cohort which is similar to the results of the ACTS-GC trial. CD45 and CD45R0 were independent prognostic markers in the S group only (CD45 p=0.032, CD45R0 p=0.003). A treatment interaction effect was seen for CD45, CD45R0, and CD68 (p value for test of interaction: CD45 p=0.062, CD45R0 p=0.082, CD68 p=0.057). Survival in the SC group was significantly poorer compared to the S group for CD45>56% or CD68>7% (p<0.05). Conclusions: This is the first study to investigate the relationship between tumor immune cell infiltration at time of surgery and benefit from adjuvant chemotherapy. Our results indicate that GC patients with high intratumoral levels of CD68, CD45, or CD45R0 positive immune cells might not benefit from adjuvant S1 chemotherapy. These findings require validation in a second independent dataset before conducting a prospective study stratifying patients with stage II/III GC based upon extent of CD45, CD45R0, or CD68 immune cell infiltration for adjuvant treatment.

scholarly journals Follistatin-like 1 (FSTL1) is a Prognostic Biomarker and Correlated with Immune Cell Infiltration in Gastric Cancer

2020 ◽  
Author(s):  
Li Li ◽  
Shanshan Huang ◽  
Yangyang Yao ◽  
Jun Chen ◽  
Junhe Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Tumor Immunity ◽  
Survival Data ◽  
Immune Cell ◽  
Bioinformatic Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Western Blot Assay ◽  
Immune Infiltration ◽  
Normal Tissues

Abstract Background: Follistatin-like 1 (FSTL1) plays a central role in the progression of tumor and tumor immunity. However, the effect of FSTL1 on the prognosis and immune infiltration of gastric cancer (GC) remains to be elucidated.Method: The expression of FSTL1 data was analyzed in Oncomine and TIMER databases. Analyses of clinical parameters and survival data were conducted by Kaplan-Meier plotter and immunohistochemistry. Western blot assay and real‐time quantitative PCR (RT-qPCR) was using to analyzed protein and mRNA expression, respectively. The correlations between FSTL1 and cancer immune infiltrates was analyzed by Tumor Immune Estimation Resource (TIME), Gene Expression Profiling Interactive Analysis (GEPIA) and LinkedOmics database.Results: The expression of FSTL1 was significantly higher in GC tissues than in normal tissues, and bioinformatic analysis and Immunohistochemistry (IHC) indicated that high FSTL1 expression significantly correlated with poor prognosis in GC. Moreover, FSTL1 was predicted as an independent prognostic factor in GC patients. Bioinformatics analysis results suggested that FSTL1 mainly involved in tumor progression and tumor immunity. And significant correlations were found between FSTL1 expression and immune cell infiltration in GC.Conclusion: The study effectively revealed useful information about FSTL1 expression, prognostic values, potential functional networks and impact of tumor immune infiltration in GC. In summary, FSTL1 can be used as a biomarker for prognosis and evaluating immune cell infiltration in GC.

scholarly journals Down-Regulated CLDN10 Predicts Favorable Prognosis and Correlates With Immune Infiltration in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
XiongHui Rao ◽  
JianLong Jiang ◽  
ZhiHao Liang ◽  
JianBao Zhang ◽  
ZheHong Zhuang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cell ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Her2 Status ◽  
Immune Cell Infiltration ◽  
Cell Markers ◽  
Immune Infiltration

Background: CLDN10, an important component of the tight junctions of epithelial cells, plays a crucial role in a variety of tumors. The effect of CLDN10 expression in gastric cancer, however, has yet to be elucidated.Methods: Differential expression of CLDN10 at the mRNA and protein levels was evaluated using Oncomine, ULCAN, HPA and TIMER2.0 databases. Real-time polymerase chain reaction (RT-PCR) was utilized to further verify the expression of CLDN10 in vitro. Correlations between CLDN10 expression and clinical outcomes of gastric cancer were explored by Kaplan-Meier Plotter. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) were performed via LinkedOmics and GeneMANIA. The correlations between CLDN10 expression and immune cell infiltration and somatic copy number alternations (SCNA) in gastric cancer were explored by TIMER2.0 and GEPIA2.0.Results: CLDN10 expression was lower in gastric cancer compared to adjacent normal tissues, and associated with better prognosis. CLDN10 also showed significant differences at different T stages, Lauren classification, treatments and HER2 status. PPI and GSEA analysis showed that CLDN10 might be involved in signal transmission, transmembrane transport and metabolism. In some major immune cells, low expression of CLDN10 was associated with increased levels of immune cell infiltration. In addition, it was found that different SCNA status in CLDN10 might affect the level of immune cell infiltration. Furthermore, the expression of CLDN10 was significantly associated with the expression of several immune cell markers, especially B cell markers, follicular helper T cell (Tfh) markers and T cell exhaustion markers.Conclusion: Down-regulated CLDN10 was associated with better overall survival (OS) in gastric cancer. And CLDN10 may serve as a potential prognostic biomarker and correlate to immune infiltration levels in gastric cancer.

scholarly journals Bioinformatics analysis of the differentially co-expressed genes and immune cell infiltration features associated with pulmonary arterial hypertension

2021 ◽  
Author(s):  
Wenshi Liu ◽  
Dongdong Zheng ◽  
Wenjing Lv ◽  
Ying Hua ◽  
Rong Huang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Integrin Subunit ◽  
Immune Cell Infiltration ◽  
Control Groups ◽  
Immune Infiltration ◽  
And Control

IntroductionThis study aimed to identify novel differentially co-expressed genes and to investigate the features of immune cell infiltration in PAH.Material and methodsThe GSE113439 and GSE117261 datasets were acquired from the Gene Expression Omnibus database. And the differentially expressed genes between PAH and control groups were identified based on the GSE117261 dataset. Weighted Gene Co-Expression Network Analysis (WGCNA) was adopted to analyze the pre-processed data. Functional enrichment analysis was then carried out to explore the biological functions of these genes modules. The differentially co-expressed key genes modules were in-depth verified by GEO2R analysis. The immune infiltration in PAH was investigated by Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT).ResultsWGCNA analysis found 15 differentially co-expressed genes modules, amongst which module blue indicated that it exhibited the strongest positive link to PAH, whereas module green presented the strongest negative association with PAH. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the genes in module blue were largely enriched in Lysosome, Complement, and coagulation cascades, and others, while the genes in module green were primarily enriched in the Chemokine signaling pathway, Platelet activation, etc. Integrin subunit alpha M (ITGAM) was identified as the differentially co-expressed key gene. Immune infiltration analysis by CIBERSORT showed that the differences between PAH and control groups or between PAH subgroups.ConclusionsITGAM was considered a promising biomarker to discriminate PAH from the control. Obvious differences were observed in immune infiltration between patients with PAH and normal groups.

scholarly journals Characteristics of the Immune Cell Infiltration Landscape in Gastric Cancer to Assistant Immunotherapy

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenlu Li ◽  
Jingjing Pan ◽  
Yinyan Jiang ◽  
Yan Yu ◽  
Zhenlin Jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Signaling Pathways ◽  
Poor Prognosis ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Therapeutic Responses

Background: Gastric cancer (GC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable indices especially immunotherapy-associated parameters that can predict the therapeutic responses to immunotherapy of GC patients.Methods: Gene expression profile of 854 GC patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets (GSE84433) with their corresponding clinical and somatic mutation data. Based on immune cell infiltration (ICI) levels, molecular clustering classification was performed to identify subtypes and ICI scores in GC patients. After functional enrichment analysis of subtypes, we further explored the correlation between ICI scores and Tumor Mutation Burden (TMB) and the significance in clinical immunotherapy response.Results: Three subtypes were identified based on ICI scores with distinct immunological and prognostic characteristics. The ICI-cluster C, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration and up-regulation of PD-L1. ICI scores were identified through using principal component analysis (PCA) and the low ICI scores were consistent with the increased TMB and the immune-activating signaling pathways. Contrarily, the high-ICI score cluster was involved in the immunosuppressive pathways, such as TGF-beta, MAPK and WNT signaling pathways, which might be responsible for poor prognosis of GC. External immunotherapy and chemotherapy cohorts validated the patients with lower ICI scores exhibited significant therapeutic responses and clinical benefits.Conclusion: This study elucidated that ICI score could sever as an effective prognostic and predictive indicator for immunotherapy in GC. These findings indicated that the systematic assessment of tumor ICI landscapes and identification of ICI scores have crucial clinical implications and facilitate tailoring optimal immunotherapeutic strategies.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we aimed to characterize infiltrating immune cells and genes associated with the immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Gens and Genomes (KEGG) analysis were applied using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, which suggest novel molecular mechanisms of sarcoma metastasis.

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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