Abstract B75: Immune cell infiltration as an indicator of the immune microenvironment of pancreatic cancer.

2013 ◽  
Author(s):  
Nobuyoshi Hiraoka
Keyword(s):  
Pancreatic Cancer ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment

scholarly journals Immune cell infiltration as an indicator of the immune microenvironment of pancreatic cancer

2013 ◽  
Vol 108 (4) ◽  
pp. 914-923 ◽  
Author(s):  
Y Ino ◽  
R Yamazaki-Itoh ◽  
K Shimada ◽  
M Iwasaki ◽  
T Kosuge ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment

Distinct genomic and immune microenvironment between thymomas and thymic carcinomas.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13529-e13529
Author(s):  
Kaicheng Wang ◽  
Suxia Lin ◽  
Xue Hou ◽  
Yongdong Liu ◽  
Meichen Li ◽  
...  
Keyword(s):  
Immune Cell ◽  
Immune Escape ◽  
Nk Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Tissue Samples ◽  
Thymic Epithelial Tumors ◽  
Immune Infiltration ◽  
Thymic Carcinomas

e13529 Background: Thymomas and thymic carcinomas which uniformly known as thymic epithelial tumors (TETs) are rare intrathoracic malignancies and a limited studies have been reported addressing the molecular biology and immune discrepancy. The main purpose of this study was to depict the genomic and transcriptomic landscape of thymomas and thymic carcinomas, as well as elucidate the differentiated immune microenvironment. Methods: Totally 15 thymomas and 7 thymic carcinomas patients were enrolled from January 2014 to July 2018. Treatment-naïve tissue samples were collected, and we also obtained matched peripheral blood mononucleocytes as negative control. DNA and RNA were co-extracted and performed with whole exon and transcriptome sequencing. The immune cell infiltration scores were estimated using ssGSEA algorithm. Results: Exome sequencing revealed that GTF2I mutation occurred in all of type A thymomas but was absent in the aggressive subtypes. The median tumor mutation burden of thymomas was 0.12/Mb, significantly lower than thymic carcinomas (median: 1.02/Mb, p = 0.001). Copy number variation was more common in thymic carcinomas than thymomas (83.3% vs 9.1%, p = 0.005). Top mutational signatures enriched in both thymomas and thymic carcinomas included age and Aristolochic acid exposure, while the APOBEC signature was more common in thymomas than thymic carcinomas (81.8% vs 16.7%, p = 0.03). As a confirmed immune escape event, loss of heterozygosity of human leukocyte antigen was identified in 9.1% of thymomas and 50% of thymic carcinomas. Via unsupervised clustering of immune infiltration, all tissue samples were classified into high- and low-infiltration subgroups. Remarkably, up to 71.4% of samples from thymic carcinomas and only 6.7% of samples from thymomas were defined as low immune cell infiltration. In consideration of specific immune cell types, macrophage ( p = 0.01) and neutrophil ( p = 0.02) were enriched in thymic carcinomas while CD56+ NK cell ( p = 0.005) was enriched in thymomas, indicating the evidential discrepancy about immune cell infiltration between two subtypes of TETs. Conclusions: This study elucidated the molecular and immune microenvironment discrepancy between two subtypes of TETs. From molecular perspective, thymomas and thymic carcinomas are entirely different diseases with different etiology and characterized by distinct immune infiltration, and thus should be managed with disparate therapeutic strategies. Findings in this study may also be useful in future targets development and exploration of immunotherapies in TETs.

scholarly journals A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingqin Ge ◽  
Jie Niu ◽  
Ping Hu ◽  
Aihua Tong ◽  
Yan Dai ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

scholarly journals YAP1 is a Prognostic Biomarker and Correlated with Immune Cell Infiltration in Pancreatic Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Kai Sun ◽  
Xue-de Zhang ◽  
Xiao-yang Liu ◽  
Pei Lu
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Prognostic Value ◽  
Immune Cell ◽  
Prognostic Biomarker ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Long Term Outcome

Yes-associated protein-1 (YAP1) is an important effector of the Hippo pathway and has crosstalk with other cancer signaling pathways. It induces an immunosuppressive tumor microenvironment by activating pathways in several cellular components. However, the mechanisms by which it drives immune infiltration in pancreatic cancer remain poorly understood. We analyzed the expression of YAP1 as well as its prognostic value and correlations with immune infiltrates in various cancers, with a focus on pancreatic cancer. In particular, using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA) database, we found that YAP1 is differentially expressed between tumor tissues and control tissues in a number of cancers and in particular, is elevated in pancreatic cancer. Using the Kaplan–Meier plotter, GEPIA, and Long-term Outcome and Gene Expression Profiling database of pan-cancers (LOGpc), we further established the prognostic value of YAP1. We found that YAP1 expression was significantly related to outcomes in multiple types of cancer based on data from The Cancer Genome Atlas, particularly in pancreatic cancer. Correlations between YAP1 and immune cell infiltration and immune cell marker expression were examined using Tumor Immune Estimation Resource and GEPIA. High expression levels of YAP1 were significantly associated with a variety of immune markers and immune cell subsets in pancreatic cancer. These results suggest that YAP1 is correlated with patient outcomes and tumor immune cell infiltration in multiple cancer types and is a valuable prognostic biomarker in pancreatic cancer.

scholarly journals Opposite Roles of Tumor Cell Proliferation and Immune Cell Infiltration in Postoperative Liver Metastasis of PDAC

2021 ◽  
Vol 9 ◽  
Author(s):  
Guangfu Wang ◽  
Shangnan Dai ◽  
Hao Gao ◽  
Yong Gao ◽  
Lingdi Yin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune Response ◽  
Cell Proliferation ◽  
T Cells ◽  
Liver Metastasis ◽  
Tumor Cell ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Tumor Cell Proliferation

BackgroundRecurrence of liver metastasis after pancreatectomy is often a predictor of poor prognosis. Comprehensive genomic analysis may contribute to a better understanding of the molecular mechanisms of postoperative liver metastasis and provide new therapeutic targets.MethodsA total of 67 patients from The Cancer Genome Atlas (TCGA) were included in this study. We analyzed differentially expressed genes (DEGs) by R package “DESeq2.” Weighted gene co-expression network analysis (WGCNA) was applied to investigate the key modules and hub genes. Immunohistochemistry was used to analyze tumor cell proliferation index and CD4+ T cells infiltration.ResultsFunctional analysis of DEGs between the liver metastatic and recurrence-free groups was mainly concentrated in the immune response. The liver metastasis group had lower immune and stroma scores and a higher TP53 mutation rate. WGCNA showed that the genes in key modules related to disease-free survival (DFS) and overall survival (OS) were mainly enriched in the cell proliferation process and tumor immune response. Immunohistochemical analysis showed that the pancreatic cancer cells of patients with early postoperative liver metastasis had higher proliferative activity, while the infiltration of CD4+ T cells in tumor specimens was less.ConclusionOur study suggested that increased immune cell infiltration (especially CD4+ T cells) and tumor cell proliferation may play an opposite role in liver metastasis recurrence after pancreatic cancer.

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 The Correlation Between Novel Survival-related Alternative Splicing Signature and Prognostic Prediction and Immune Microenvironment in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Wengang Jian ◽  
Gang Wang ◽  
Yipeng Yu ◽  
Licheng Cai ◽  
Yongchun Yu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Splicing Factors ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cell Renal Cell Carcinoma

Abstract BackgroundAlthough extensive researches related alternative splicing (AS) as the prognostic markers of patients in cancers, which remains unknown in clear cell renal cell carcinoma (ccRCC), especially in immunotherapy. Therefore, a novel survival-associated AS signature was established to predict prognosis of patients and explored its correlation with immune cell infiltration or immune checkpoint expression to explain the phenomenon of resistance to immunotherapy in ccRCC.Methodsccording to AS data, clinical information and gene expression data in ccRCC, overall survival-related AS events was identified and further the AS-related prognostic risk model established by LASSO regression and multi-Cox regression analysis was evaluated by Kaplan-Meier survival analysis, the ROC curves and a nomogram model. Then we clarified the biological processes and pathways by GSEA, and further measured the immune cell infiltration by ESTIMATE, CIBERSORT and ssGSEA. Finally we analysed the clinical features and immune features of different parental genes, and quested the splicing factors regulating riskScore-related AS events by Spearman correlation analysis.ResultsWe obtained the most significant 5 AS events, including C4orf19|69001|AT, UACA|31438|AP, FAM120C|89237|AT, TRIM16L|39629|AP and SEC31A|100881|ES, to establish the prognostic risk model, and further illustrated the stability and importance of the riskScore prognostic signatures. Then we found that in high risk group, most of the top 10 GO enrichments and the KEGG pathway were closely related to the immunity, and the higher immune cell infiltration, and higher expression of classic immune checkpoints such as PD1 and CTLA4. In addition, 6 different parental genes were obtained, including C4orf19, ARHGAP24 DNASE1L3, P4HA1, SLC39A14 and TAF1D. These 6 genes could not be the independent prognostic signatures, but the expression of these genes was closely related to immune cells infiltration and the expression of immune checkpoints. Finally, we got aberrant 52 splicing factors regulating riskScore-related AS events.ConclusionOur study discovered that overall survival-related AS events mediated by aberrant splicing factors can be constructed a prognostic risk model to predict prognosis of patiens and utilized to index the situation of immune cell infiltration and immune checkpoint expression that impact tumor immune microenvironment in ccRCC.

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