scholarly journals Characterization of Modification Patterns, Biological Function, Clinical Implication, and Immune Microenvironment Association of m6A Regulators in Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Kun Fang ◽  
Hairong Qu ◽  
Jiapei Wang ◽  
Desheng Tang ◽  
Changsheng Yan ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune Cell ◽  
Principal Component ◽  
Genetic Mutation ◽  
Biological Functions ◽  
Immune Microenvironment ◽  
Cancer Pathogenesis ◽  
Frequent Mutations ◽  
Tcga Dataset

Objective: N6-methyladenosine (m6A) modification may modulate various biological processes. Nonetheless, clinical implications of m6A modification in pancreatic cancer are undefined. Herein, this study comprehensively characterized the m6A modification patterns in pancreatic cancer based on m6A regulators.Methods: Genetic mutation and expression pattern of 21 m6A regulators and their correlations were assessed in pancreatic cancer from TCGA dataset. m6A modification patterns were clustered using unsupervised clustering analysis in TCGA and ICGC datasets. Differences in survival, biological functions and immune cell infiltrations were assessed between modification patterns. A m6A scoring system was developed by principal component analysis. Genetic mutations and TIDE scores were compared between high and low m6A score groups.Results: ZC3H13 (11%), RBM15B (9%), YTHDF1 (8%), and YTHDC1 (6%) frequently occurred mutations among m6A regulators. Also, most of regulators were distinctly dysregulated in pancreatic cancer. There were tight crosslinks between regulators. Two m6A modification patterns were constructed, with distinct prognoses, immune cell infiltration and biological functions. Furthermore, we quantified m6A score in each sample. High m6A scores indicated undesirable clinical outcomes. There were more frequent mutations in high m6A score samples. Lower TIDE score was found in high m6A score group, with AUC = 0.61, indicating that m6A scores might be used for predicting the response to immunotherapy.Conclusion: Collectively, these data demonstrated that m6A modification participates pancreatic cancer progress and ornaments immune microenvironment, providing an insight into pancreatic cancer pathogenesis and facilitating precision medicine development.

scholarly journals Single Cell Atlas of Human Dura Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response

2021 ◽  
Author(s):  
Anthony Z Wang ◽  
Jay Bowman-Kirigin ◽  
Rupen Desai ◽  
Pujan Patel ◽  
Bhuvic Patel ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Copy Number Variant ◽  
Cell Types ◽  
Cell Receptor ◽  
Immune Microenvironment ◽  
Clinical Models ◽  
Insight Into

Recent investigation of the meninges, specifically the dura layer, has highlighted its importance in CNS immune surveillance beyond a purely structural role. However, most of our understanding of the meninges stems from the use of pre-clinical models rather than human samples. In this study, we use single cell RNA-sequencing to perform the first characterization of both non-tumor-associated human dura and meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, through T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. We also identify a functionally heterogeneous population of non-immune cell types and report copy-number variant heterogeneity within our meningioma samples. Our comprehensive investigation of both the immune and non-immune cell landscapes of human dura and meningioma at a single cell resolution provide new insight into previously uncharacterized roles of human dura.

Characterization of medicinal Senna genetic resources

2009 ◽  
Vol 7 (03) ◽  
pp. 257-259 ◽  
Author(s):  
J. B. Morris
Keyword(s):  
Pancreatic Cancer ◽  
Principal Component Analysis ◽  
Medicinal Plants ◽  
Genetic Resources ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Pharmaceutical Products ◽  
Seed Reproduction

At 50% maturity, regeneratingSennaspecies were characterized for morphological traits, seed reproduction, and evaluated for regeneration. Quality plants regenerated from all accessions produced 1018 to more than 21,215 total seeds. Principal component analysis revealed which traits contributed the greatest to variability among coffee senna accessions.Sennaspecies have potential to produce pharmaceutical products and can be grown as medicinal plants. The flavonoids quercetin and kaempferol found inSennaspecies have been clinically shown to have anti-pancreatic cancer properties.

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

scholarly journals Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Jungang Zhao ◽  
Wenming Bao ◽  
Weiyang Cai
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Clustering Analysis ◽  
Immune Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment

Intrinsic cancer cells and the tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment define the malignant phenotype of lung squamous cell carcinoma (LUSC). Understanding more about the immune microenvironment of LUSC enables the selection of high-risk patients who would derive benefit from immunotherapy. Based on large public LUSC cohorts obtained from TCGA and GEO datasets, 22 types of infiltrating immune cell subgroups were evaluated by CIBERSORT. Meta-analysis, principal component analysis (PCA), single-sample gene set enrichment analysis (ssGSEA), and hierarchical clustering analysis were used to evaluate specific immune responses of LUSC. The distribution of TIICs of LUSC was entirely different from normal. TIIC subpopulations were also found to be closely associated with clinical features and molecular subtypes. Unsupervised clustering analysis revealed that three distinct TIIC subgroups existed with different survival patterns. TIICs are extensively implicated in the pathogenesis and development of LUSC. Characterizing the composition of TIICs influences the metabolism, pathological stage, and survival of tumor patients. It is hoped that this immune landscape could provide a more accurate understanding of the development and immunotherapy of LUSC.

scholarly journals Molecular and Clinical Characterization of a Novel Prognostic and Immunologic Biomarker GPSM3 in Low-Grade Gliomas

2021 ◽  
Vol 11 (11) ◽  
pp. 1529
Author(s):  
Ming Wang ◽  
Jiaoying Jia ◽  
Yan Cui ◽  
Yong Peng ◽  
Yugang Jiang
Keyword(s):  
Immune Checkpoint ◽  
Immune Cell ◽  
Cox Regression ◽  
Functional Characterization ◽  
Low Grade ◽  
Immunotherapy Target ◽  
Tcga Dataset ◽  
Cox Analysis ◽  
Immune Related Genes

Background: as the most common malignancy of the central nervous system, low-grade glioma (LGG) patients suffered a poor prognosis. Tumor microenvironment, especially immune components, plays an important role in the progression of tumors. Thus, it is critical to explore the key immune-related genes, a comprehensive understanding of the TME in LGG helps us find novel cancer biomarkers and therapeutic targets. Methods: the GPSM3 expression level and the correlations between clinical characteristics and GPSM3 levels were analyzed with the data from CGGA and TCGA dataset. Univariate and multivariate cox regression model were built to predict the prognosis of LGG patients with multiple factors. Then the correlation between GPSM3 with immune cell infiltration was explored by ESTIMATE, CIBERSORT and TIMER2.0. At last, the correlation analyzed between GPSM3 expression and immune checkpoint related genes were also analyzed. Results: GPSM3 expression was overexpressed in LGG and negatively correlated to the GPSM3 DNA methylation. Univariate and multivariate Cox analysis demonstrated that GPSM3 expression was an independent prognostic factor in LGG patients. Functional characterization of GPSM3 revealed that it was associated with many immune processes to tumor cells. GPSM3 expression was positive related to the immune score, Stromal scores and ESTIMATE scores, but negative related to the Tumor purity. Immune features in the TME of GPSM3-high LGG group is characterized by a higher infiltrating of regulatory T cells, neutrophils, macrophages M2, and a lower proportion of monocytes than to the GPSM3-low group. Furthermore, GPSM3 expression exhibited significant correlations with the immune checkpoint-related genes, especially PD-1, PD-L1, PD-L2, CTLA4 and TIM3. Conclusions: these findings proved that GPSM3 could serve as a prognostic biomarker and potential immunotherapy target for LGG.

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 Expression of N6-Methyladenosine (m6A) Regulators Correlates With Immune Microenvironment Infiltration and Predicts Prognosis in Diffuse Large Cell Lymphoma (DLBCL)

2021 ◽  
Author(s):  
Zucheng Xie ◽  
Meiwei Li ◽  
Haoyuan Hong ◽  
Qingyuan Xu ◽  
Zhendong He ◽  
...  
Keyword(s):  
High Risk ◽  
Immune Cell ◽  
Cell Lymphoma ◽  
Principal Component ◽  
Large Cell ◽  
Consensus Clustering ◽  
Diffuse Large Cell Lymphoma ◽  
Immune Microenvironment ◽  
Large Cell Lymphoma ◽  
Checkpoint Genes

Abstract Background: N6-methyladenosine (m6A) and immune microenvironment infiltration have been widely reported to play important roles in various cancers. However, in diffuse large cell lymphoma (DLBCL), the clinical significance of m6A regulators and their relationship with immune microenvironment infiltration have not been illuminated.Methods: The expression of m6A regulators in DLBCL was investigated using The Cancer Genome Atlas and Genotype-Tissue Expression databases. The capacity of m6A regulators in dividing molecular clusters of DLBCL was determined using Consensus Clustering algorithm and validated via principal component analysis. The clinical traits and prognosis difference in m6A-sorted clusters were revealed. The m6A prognostic signature was established and validated based on Gene Expression Omnibus dataset using univariate Cox and LASSO regression analysis. The immune cell infiltration and the expression of immune checkpoint genes in m6A low/high-risk DLBCL were studied. Gene set enrichment analysis (GSEA) was adopted to unveil the underlying molecular mechanism in m6A low/high-risk DLBCL.Results: Differentially expressed m6A regulators were able to molecularly discriminate DLBCL as two clusters based on consensus clustering and principal component analysis. A six m6A regulators-based risk prediction signature was established and validated as an independent predictor, which separated patients into m6A low- and high-risk groups. High-risk m6A indicates worse survival, for which the predictive AUC at 1-, 2-, and 5-year achieved 0.605, 0.640, and 0.652, respectively. Immune cell infiltration analysis revealed the B cells naïve and T cells gamma delta were the top increased and decreased immune cells in high-risk m6A patients. Up-regulated (PDCD1 and KIR3DL1) and down-regulated (TIGIT, DO1, and BTLA) immune checkpoint genes in high-risk m6A patients were identified. GSEA analysis unveiled high-risk m6A related to tumor proliferation associated process, while low-risk m6A related to defense response associated process. Conclusions: This study provided a comprehensive analysis for the clinical significance of m6A regulators and their association with immune microenvironment infiltration. An m6A regulators-based risk signature may be applied for the risk stratification of DLBCL patients, thus may facilitate the clinical management of DLBCL. Immune microenvironment was found to be closely related to m6A risk, which may be part of the mechanism of m6A regulators in DLBCL.

scholarly journals Comprehensive Analysis of m6A RNA Methylation Regulators and the Immune Microenvironment to Aid Immunotherapy in Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yongdong Guo ◽  
Ronglin Wang ◽  
Junqiang Li ◽  
Yang Song ◽  
Jie Min ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Survival Data ◽  
Immune Cell ◽  
Small Sample Size ◽  
Small Sample ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Gene Set

Pancreatic cancer (PAAD) is one of the most malignant cancers and immune microenvironment has been proved to be involved in pathogenesis of PAAD. m6A modification, related to the expression of m6A regulators, participates in the development of multiple cancers. However, the correlation between m6A regulators and immune microenvironment was largely unknown in PAAD. And because of the small sample size of pancreatic cancer in the TCGA database, it is not enough to draw a convincing conclusion. In the present study, we downloaded seven pancreatic cancer datasets with survival data and removed batch effects among these datasets to be used as the PAAD cohort to analyze the immune landscape of PAAD and the expression pattern of m6A regulators and divided the integrated dataset into cluster 1 and cluster 2 by consensus clustering for m6A regulators. Lower m6A regulators were found to be related to higher immune cell infiltration and a better survival. Moreover, we identified six m6A regulators and constructed the prognostic signature of m6A regulators. Patients with low-risk score had a higher response to immune checkpoint inhibitor and a longer overall survival. To figure out the underlying mechanism, we analyzed the cancer immunity cycle, most altered genes, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) in risk subtypes. In summary, the present study proved m6A regulators modulated the PAAD immune microenvironment. And risk scores served as predictive indicator for immunotherapy and played a prognostic role for PAAD patients. Our study provided novel therapeutic targets to improve immunotherapy efficacy.

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