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 Effects of 1p/19q Codeletion on Immune Phenotype in Low Grade Glioma

2021 ◽  
Vol 15 ◽  
Author(s):  
Lei Lv ◽  
Yuliu Zhang ◽  
Yujia Zhao ◽  
Qinqin Wei ◽  
Ye Zhao ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Low Grade ◽  
Immune Cell Infiltration ◽  
Biological Processes ◽  
Chromosome 1P ◽  
Tumor Immune Microenvironment ◽  
Immune Related Genes ◽  
Checkpoint Genes

Background: Chromosome 1p/19q codeletion is one of the most important genetic alterations for low grade gliomas (LGGs), and patients with 1p/19q codeletion have significantly prolonged survival compared to those without the codeletion. And the tumor immune microenvironment also plays a vital role in the tumor progression and prognosis. However, the effect of 1p/19q codeletion on the tumor immune microenvironment in LGGs is unclear.Methods: Immune cell infiltration of 281 LGGs from The Cancer Genome Atlas (TCGA) and 543 LGGs from the Chinese Glioma Genome Atlas (CGGA) were analyzed for immune cell infiltration through three bioinformatics tools: ESTIMATE algorithm, TIMER, and xCell. The infiltrating level of immune cells and expression of immune checkpoint genes were compared between different groups classified by 1p/19q codeletion and IDH (isocitrate dehydrogenase) mutation status. The differential biological processes and signaling pathways were evaluated through Gene Set Enrichment Analysis (GSEA). Correlations were analyzed using Spearman correlation.Results: 1p/19q codeletion was associated with immune-related biological processes in LGGs. The infiltrating level of multiple kinds of immune cells and expression of immune checkpoint genes were significantly lower in 1p/19q codeletion LGGs compared to 1p/19q non-codeletion cohorts. There are 127 immune-related genes on chromosome 1p or 19q, such as TGFB1, JAK1, and CSF1. The mRNA expression of these genes was positively correlated with their DNA copy number. These genes are distributed in multiple immune categories, such as chemokines/cytokines, TGF-β family members, and TNF family members, regulating immune cell infiltration and expression of the immune checkpoint genes in tumors.Conclusion: Our results indicated that 1p/19q codeletion status is closely associated with the immunosuppressive microenvironment in LGGs. LGGs with 1p/19q codeletion display less immune cell infiltration and lower expression of immune checkpoint genes than 1p/19q non-codeletion cases. Mechanistically, this may be, at least in part, due to the deletion of copy number of immune-related genes in LGGs with 1p/19q codeletion. Our findings may be relevant to investigate immune evasion in LGGs and contribute to the design of immunotherapeutic strategies for patients with LGGs.

scholarly journals An Immune Checkpoint-Related Gene Signature for Predicting Survival of Pediatric Acute Myeloid Leukemia

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Feng Jiang ◽  
Xin-Yu Wang ◽  
Ming-Yan Wang ◽  
Yan Mao ◽  
Xiao-Lin Miao ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Immune Checkpoint ◽  
Myeloid Leukemia ◽  
Immune Cell ◽  
Cox Regression ◽  
Secondary Data ◽  
Gene Signature ◽  
Pediatric Acute Myeloid Leukemia ◽  
Pediatric Aml ◽  
Acute Myeloid

Objective. The aim of this research was to create a new genetic signature of immune checkpoint-associated genes as a prognostic method for pediatric acute myeloid leukemia (AML). Methods. Transcriptome profiles and clinical follow-up details were obtained in Therapeutically Applicable Research to Generate Effective Treatments (TARGET), a database of pediatric tumors. Secondary data was collected from the Gene Expression Omnibus (GEO) to test the observations. In univariate Cox regression and multivariate Cox regression studies, the expression of immune checkpoint-related genes was studied. A three-mRNA signature was developed for predicting pediatric AML patient survival. Furthermore, the GEO cohort was used to confirm the reliability. A bioinformatics method was utilized to identify the diagnostic and prognostic value. Results. A three-gene (STAT1, BATF, EML4) signature was developed to identify patients into two danger categories depending on their OS. A multivariate regression study showed that the immune checkpoint-related signature (STAT1, BATF, EML4) was an independent indicator of pediatric AML. By immune cell subtypes analyses, the signature was correlated with multiple subtypes of immune cells. Conclusion. In summary, our three-gene signature can be a useful tool to predict the OS in AML patients.

Identification of Immune-Related Prognostic Biomarkers in Pancreatic Cancer

2020 ◽  
Vol 12 (12) ◽  
pp. 1355-1367
Author(s):  
Xiaoyan Lin ◽  
Jiakang Ma ◽  
Kaikai Ren ◽  
Mingyu Hou ◽  
Bo Zhou ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Individual Treatment ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Immune Related Genes

Immunotherapy for pancreatic cancer (PC) faces significant challenges. It is urgent to find immunerelated genes for targeted therapy. We aimed to identify immune-related messenger ribonucleic acids (mRNAs) with multiple methods of comprehensive immunoenrichment analysis in predicting survival of PC. PC genomics and clinical data were downloaded from TCGA. We analyzed relative enrichment of 29 immune cells using ssGSEA and classified PC samples into three immuneinfiltrating subgroups. Immune cell infiltration level and pathways were evaluated by ESTIMATE data and KEGG. Independent risk factors were derived from the combined analysis of WGCNA, LASSO regression and Cox regression analyses. Immune risk score was calculated according to four mRNAs to identify its value in predicting survival. PPI analysis was used to analyze the connections and potential pathways among genes. Finally, PC samples were classified into three immuneinfiltrating subgroups. Immunity high subgroup had higher immune score, soakage of immune cells, HLA/PD-L1 expression level, immune-related pathways enrichment and better survivability. Four potential prognostic immune-related genes (ITGB7, RAC2, DNASE1L3, and TRAF1) were identified. Immune risk score could be a potential survival prediction indictor with high sensitivity and specificity (AUC values = 0.708, HR = 1.445). A PPI network with seven nodes and five potential targeted pathways were generated. In conclusion, we estimated the state of immune infiltration in the PC tumor microenvironment by calculating stromal and immune cells enrichment with ssGSEA algorithms, and identified four prognostic immune-related genes that affect the proportion and distribution of immune cells infiltration in the tumor microenvironment. They lay a theoretical foundation to be important immunity targets of individual treatment in PC.

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 Signature Based on Immune-Related LncRNA Can Predict Overall Survival of Osteosarcoma Patients

2020 ◽  
Author(s):  
Longqing Li ◽  
Lianghao Zhang ◽  
Manhas Adbul Khader ◽  
Yan Zhang ◽  
Xinchang Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Immune Checkpoint ◽  
High Throughput Sequencing ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Checkpoint Gene ◽  
Cox Regression Analysis

Abstract Background: Osteosarcoma is a malignant bone tumor common in children and adolescents. Metastatic status remains the most important guideline for classifying patients and making clinical decisions. Despite many efforts, newly diagnosed patients receive the same therapy that patients have received over the last 4 decades. With the development of high-throughput sequencing technology and the rise of immunotherapy, it is necessary to deeply explore the immune molecular mechanism of osteosarcoma.Methods: We obtained RNA-seq data and clinical information of osteosarcoma patients from TCGA database and TARGET database. With the help of co-expression analysis we identified immune-related lncRNA and then by means of univariate Cox regression analysis prognostic-related lncRNA was screened out. And also by using least absolute shrinkage and selection operator regression method a model based on immune-related lncRNA was constructed. The differences in overall survival, immune infiltration, immune checkpoint gene expression, and tumor microenvironmental immunity type between the two groups were evaluated.Results: We constructed a signature consisting of 13 lncRNA. Our results show that signatures can reliably predict the overall survival of patients with osteosarcoma and can bring net clinical benefits. Further more, the signatures can be used for further risk stratification of the metastasis patients. Patients in the low-risk group had higher immune cell infiltration and immune checkpoint gene expression. The results from gene set variation analysis show that patients in low-risk group are closely related to immune-related pathways when compared with patients in high-risk group. Finally, patients in the low-risk group are more likely to be classified as TMIT I and hence more likely to benefit from immunotherapy.Conclusion: Our signature may be a reliable marker for predicting the overall survival of patients with osteosarcoma.

scholarly journals Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingyu Chen ◽  
Haotian Chen ◽  
Dong He ◽  
Yaxin Cheng ◽  
Yuxing Zhu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Immune Cell ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Checkpoint Inhibitor ◽  
Clinical Prognosis

The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has clarified its clinical significance in predicting outcomes and efficacy. However, there are no studies on the systematic analysis of TME characteristics in bladder cancer. In this study, we comprehensively evaluated the TME invasion pattern of bladder cancer in 1,889 patients, defined three different TME phenotypes, and found that different subtypes were associated with the clinical prognosis and pathological characteristics of bladder cancer. We further explored the signaling pathways, cancer-immunity cycle, copy number, and somatic mutation differences among the different subtypes and used the principal component analysis algorithm to calculate the immune cell (IC) score, a tool for comprehensive evaluation of TME. Univariate and multivariate Cox regression analyses showed that ICscore is a reliable and independent prognostic biomarker. In addition, the use of anti-programmed death-ligand (PD-L1) treatment cohort, receiver operating characteristic (ROC) curve, Tumor Immune Dysfunction and Exclusion (TIDE), Subnetwork Mappings in Alignment of Pathways (SubMAP), and other algorithms confirmed that ICscore is a reliable prognostic biomarker for immune checkpoint inhibitor response. Patients with higher ICscore showed a significant therapeutic advantage in immunotherapy. In conclusion, this study improves our understanding of the characteristics of TME infiltration in bladder cancer and provides guidance for more effective personalized immunotherapy strategies.

scholarly journals A novel tumor doubling time-related immune gene signature for prognosis prediction in hepatocellular carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Genhao Zhang ◽  
Lisa Su ◽  
Xianping Lv ◽  
Qiankun Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Doubling Time ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Gene Signature ◽  
Tumor Doubling Time ◽  
Prognosis Prediction ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) has become a global health issue of wide concern due to its high prevalence and poor therapeutic efficacy. Both tumor doubling time (TDT) and immune status are closely related to the prognosis of HCC patients. However, the association between TDT-related genes (TDTRGs) and immune-related genes (IRGs) and the value of their combination in predicting the prognosis of HCC patients remains unclear. The current study aimed to discover reliable biomarkers for anticipating the future prognosis of HCC patients based on the relationship between TDTRGs and IRGs. Methods Tumor doubling time-related genes (TDTRGs) were acquired from GSE54236 by using Pearson correlation test and immune-related genes (IRGs) were available from ImmPort. Prognostic TDTRGs and IRGs in TCGA-LIHC dataset were determined to create a prognostic model by the LASSO-Cox regression and stepwise Cox regression analysis. International Cancer Genome Consortium (ICGC) and another cohort of individual clinical samples acted as external validations. Additionally, significant impacts of the signature on HCC immune microenvironment and reaction to immune checkpoint inhibitors were observed. Results Among the 68 overlapping genes identified as TDTRG and IRG, a total of 29 genes had significant prognostic relevance and were further selected by performing a LASSO-Cox regression model based on the minimum value of λ. Subsequently, a prognostic three-gene signature including HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1), C-type lectin domain family 1 member B (CLEC1B), and Collectin sub-family member 12 (COLEC12) was finally identified by stepwise Cox proportional modeling. The signature exhibited superior accuracy in forecasting the survival outcomes of HCC patients in TCGA, ICGC and the independent clinical cohorts. Patients in high-risk subgroup had significantly increased levels of immune checkpoint molecules including PD-L1, CD276, CTLA4, CXCR4, IL1A, PD-L2, TGFB1, OX40 and CD137, and are therefore more sensitive to immune checkpoint inhibitors (ICIs) treatment. Finally, we first found that overexpression of CLEC1B inhibited the proliferation and migration ability of HuH7 cells. Conclusions In summary, the prognostic signature based on TDTRGs and IRGs could effectively help clinicians classify HCC patients for prognosis prediction and individualized immunotherapies.

scholarly journals Three Immune-Associated Subtypes of Diffuse Glioma Differ in Immune Infiltration, Immune Checkpoint Molecules, and Prognosis

2020 ◽  
Vol 10 ◽  
Author(s):  
Quanwei Zhou ◽  
Xuejun Yan ◽  
Weidong Liu ◽  
Wen Yin ◽  
Hongjuan Xu ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Checkpoints ◽  
Signal Pathways ◽  
Diffuse Glioma ◽  
Immune Infiltration ◽  
Immune Related Genes

Diffuse glioma is one of the most prevalent malignancies of the brain, with high heterogeneity of tumor-infiltrating immune cells. However, immune-associated subtypes of diffuse glioma have not been determined, nor has the effect of different immune-associated subtypes on disease prognosis and immune infiltration of diffuse glioma patients. We retrieved the expression profiles of immune-related genes from The Cancer Genome Atlas (TCGA) (n = 672) and GSE16011 (n = 268) cohorts and used them to identify subtypes of diffuse glioma via Consensus Cluster Plus analysis. We used the limma, clusterProfiler, ESTIMATE, and survival packages of R for differential analysis, functional enrichment, immune and stromal score evaluation respectively in three subtypes, and performed log-rank tests in immune subtypes of diffuse glioma. The immune-associated features of diffuse glioma in the two cohorts were characterized via bioinformatic analyses of the mRNA expression data of immune-related genes. Three subtypes (C1–3) of diffuse glioma were identified from TCGA data, and were verified using the GSE16011 cohort. We then evaluated their immune characteristics and clinical features. Our mRNA profiling analyses indicated that the different subtypes of diffuse glioma presented differential expression profile of specific genes and signal pathways in the TCGA cohort. Patients with subtype C1, who were mostly diagnosed with grade IV glioma, had poorer outcomes than patients with subtype C2 or C3. Subtype C1 was characterized by a higher degree of immune cell infiltration as estimated by GSVA, and more frequent wildtype IDH1. By contrast, subtype C3 included more grade II and IDH1-mutated glioma, and was associated with more infiltration of CD4+T cells. Most subtype C2 had the features between subtypes C1 and C3. Meanwhile, immune checkpoints and their ligand molecules, including PD1/(PD-L1/PDL2), CTLA4/(CD80/CD86), and B7H3/TLT2, were significantly upregulated in subtype C1 and downregulated in subtype C3. In addition, patients with subtype C1 exhibited more frequent gene mutations. Univariate and multivariate Cox regression analyses revealed that diffuse glioma subtype was an effective, independent, and better prognostic factor. Therefore, we established a novel immune-related classification of diffuse glioma, which provides potential immunotherapy targets for diffuse glioma.

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