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

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.

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

Bioscience Reports ◽

10.1042/bsr20193308 ◽

2020 ◽

Vol 40 (5) ◽

Cited By ~ 1

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.

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

Frontiers in Immunology ◽

10.3389/fimmu.2021.651033 ◽

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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Developing a risk scoring system based on immune-related lncRNAs for patients with gastric cancer

Bioscience Reports ◽

10.1042/bsr20202203 ◽

2020 ◽

Author(s):

Yuzhi Wang ◽

Yu Zou ◽

Yi Zhang ◽

Chengwen Li

Keyword(s):

Gastric Cancer ◽

Immune Cells ◽

Scoring System ◽

Immune Cell ◽

Differentially Expressed ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Risk Scoring ◽

Risk Scoring System

The immune system and the tumor interact closely during tumor development. Aberrantly-expressed long non-coding RNAs (lncRNAs) may be potentially applied as diagnostic and prognostic markers for gastric cancer (GC). At present, the diagnosis and treatment of GC patients remain a formidable clinical challenge. This study aimed to build a risk scoring system to improve the prognosis of GC patients. In this study, ssGSEA was used to evaluate the infiltration of immune cells in GC tumor tissue samples, and the samples were split into a high immune cell infiltration group and a low immune cell infiltration group. 1262 differentially expressed lncRNAs between the high immune cell infiltration group and the low immune cell infiltration group. 3204 differentially expressed lncRNAs between GC tumor tissues and paracancerous tissues were identified. Then, 621 immune-related lncRNAs were screened using a Venn analysis based on the above results, and 85 prognostic lncRNAs were identified using a univariate Cox analysis. We constructed a prognostic signature using LASSO analysis and evaluated the predictive performance of the signature using ROC analysis. GO and KEGG enrichment analyses were performed on the lncRNAs using the R package, “clusterProfiler.” The TIMER online database was used to analyze correlations between the risk score and the abundances of the six types of immune cells. In conclusion, our study found that specific immune-related lncRNAs were clinically significant. These lncRNAs were used to construct a reliable prognostic signature and analyzed immune infiltrates, which may assist clinicians in developing individualized treatment strategies for GC patients.

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Identification of immune-based prostate cancer subtypes using mRNA expression

10.21203/rs.2.23038/v1 ◽

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.

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Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma

International Journal of Ophthalmology ◽

10.18240/ijo.2021.08.03 ◽

2021 ◽

Vol 14 (8) ◽

pp. 1151-1159

Author(s):

Chen-Lu Liao ◽

◽

Xing-Yu Sun ◽

Qi Zhou ◽

Min Tian ◽

...

Keyword(s):

Prognostic Value ◽

Prognostic Markers ◽

Immune Cell ◽

Cox Regression ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Profile Data ◽

Small Molecule Drugs

AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package ‘edgeR’. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

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

Frontiers in Oncology ◽

10.3389/fonc.2021.618705 ◽

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.

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BATF2 and PDK4 As Diagnostic Molecular Markers of Sarcoidosis and Their Relationship With Immune Infiltration

10.21203/rs.3.rs-557951/v1 ◽

2021 ◽

Author(s):

Xiaoyan Li ◽

Jing Zhou ◽

Jie He

Keyword(s):

Immune Cells ◽

Immune Cell ◽

External Validation ◽

Pulmonary Sarcoidosis ◽

Gene Expression Omnibus ◽

Diagnostic Markers ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Disorder ◽

Qpcr Method

Abstract Background: Sarcoidosis (SA) is an immune disorder disease featured with granulomas formation. The work purposed to uncover potential markers for sarcoidosis (SA) diagnosis and explore how immune cell infiltration contributes to the pathogenesis of SA.Methods: Sarcoidosis GSE83456 samples and GSE42834 from Gene Expression Omnibus (GEO) were analyzed as the training and external validation sets, respectively. R statistical software was employed to uncover the differentially expressed genes (DEGs) of GSE83456. SVM algorithms and LASSO logistic regression were applied for screening and verification of the diagnostic markers for key module genes. The infiltration of immune cells in sarcoidosis patients’ blood samples was assessed by CIBERSORT. The expression of serum BATF2 and PDK4 was detected by RT-qPCR method, and the value of BATF2 and PDK4 mRNA expression in the diagnosis of pulmonary sarcoidosis was analyzed.Results: In total, 580 DEGs were identified from the key module. PDK4 (AUC=0.942) and BATF4 (AUC=0.980) were revealed as diagnostic markers of sarcoidosis. We found that monocytes, T cells regulatory (Tregs), mast cells, macrophages，NK cells, and dendritic cells may contribute to sarcoidosis development. In addition, PDK4 and BATF4 were closely associated with these immune cells. BATF2 and PDK4 were highly expressed in pulmonary sarcoidosis. BATF2 and PDK4 combined to predict the area under the ROC curve of pulmonary sarcoidosis was 0.922.Conclusions: PDK4 and BATF4 could be used as diagnostic markers of sarcoidosis, and immune cell infiltration severs an important role in sarcoidosis.

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The prognostic value of a tumor microenvironment-based immune cell infiltration score model in colon cancer

10.21203/rs.3.rs-496500/v1 ◽

2021 ◽

Author(s):

Xiaofen Pan ◽

Xingkui Tang ◽

Minling Liu ◽

Xijun Luo ◽

Mengyuan Zhu ◽

...

Keyword(s):

Colon Cancer ◽

Signaling Pathway ◽

Immune Cells ◽

Evaluation System ◽

Prognostic Value ◽

Immune Cell ◽

Wnt Signaling Pathway ◽

Mapk Signaling ◽

Cell Infiltration ◽

Immune Cell Infiltration

Abstract BackgroundTumor microenvironment consists of tumor cells, immune cells and other matric components. Tumor infiltration immune cells are associated with prognosis. But all the current prognosis evaluation system dose not take tumor immune cells other matrix component into consideration. In the current study, we aimed to construct a prognosis predictive model based on tumor microenvironment.MethodCIBERSORT and ESTIMATE algorithms were used to reveal the immune cell infiltration landscape of colon cancer. Patients were classified into three clusters by ConsensusClusterPlus algorithm. Immune cell infiltration (ICI) scores of each patient were determine by principal-component analysis. Patients were divided to high and low ICI score groups. Survival, gene expression and somatic mutation of the two groups were compared.ResultsPatients with no lymph node invasion, no metastasis, T1-2 disease and stage I-II had higher ICI scores. Calcium signaling pathway, leukocyte transendothelial migration pathway, MAPK signaling pathway, TGF β pathway, and WNT signaling pathway were enriched in high ICI score group. Immune-checkpoint genes and immune-activity associated genes were significantly decreased in high ICI score. Patients in high ICI score group had better survival than low ICI score group. Prognostic value of ICI score was independent of TMB.ConclusionICI score might serve as an independent prognostic biomarker in colon cancer.

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

BMC Cancer ◽

10.1186/s12885-021-08536-7 ◽

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.

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Patterns of immune infiltration in gastric cancer and their clinical significance

10.21203/rs.3.rs-41448/v1 ◽

2020 ◽

Author(s):

Yin Jin ◽

Liping Tao ◽

Shengnan Li ◽

Shuqing Jin ◽

Weiyang Cai

Keyword(s):

Gastric Cancer ◽

Immune Cells ◽

Clustering Analysis ◽

Immune Cell ◽

Molecular Subtypes ◽

Immune Cell Infiltration ◽

Cancer Microenvironment ◽

Clinical Implementation ◽

Survival Patterns ◽

First Time

Abstract Background: The malignant phenotypes of cancer are defined not only by its intrinsic tumor cells but also by the tumor- infiltrating immune cells (TIICs) activated and recruited to the cancer microenvironment. However, a comprehensive introduction of gastric cancer (GC) immune cell infiltration has not been identified so far.Reuslts: In this study, we comprehensively analyzed the TIICs abundance in GC for the first time by CIBERSORT. The fraction of TIICs subpopulations was also evaluated to determine the associations with clinical features and molecular subtypes. Unsupervised clustering analysis revealed there existed three distinct TIICs subgroups with distinct survival patterns. We also focused on analyzing the prognostic influence of TIICs in TP53, TTP and PIK3CA molecular subtypes.Conclusions: Collectively, our data explored the differences of TIICs in GC, and these variations were likely to be important clues for prognosis and management of its future clinical implementation.

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