scholarly journals Identification of Autophagy-Related Prognostic Signature and Analysis of Immune Cell Infiltration in Low-Grade Gliomas

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Qingli Quan ◽  
Xinxin Xiong ◽  
Shanyun Wu ◽  
Meixing Yu
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Roc Analysis ◽  
Immune Cell ◽  
Time Dependent ◽  
Low Grade ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Low Grade Gliomas ◽  
Key Genes

Autophagy plays an important role in cancer. Many studies have demonstrated that autophagy-related genes (ARGs) can act as a prognostic signature for some cancers, but little has been known in low-grade gliomas (LGG). In our study, we aimed to establish a prognostical model based on ARGs and find prognostic risk-related key genes in LGG. In the present study, a prognostic signature was constructed based on a total of 8 ARGs (MAPK8IP1, EEF2, GRID2, BIRC5, DLC1, NAMPT, GRID1, and TP73). It was revealed that the higher the risk score, the worse was the prognosis. Time-dependent ROC analysis showed that the risk score could precisely predict the prognosis of LGG patients. Additionally, four key genes (TGFβ2, SERPING1, SERPINE1, and TIMP1) were identified and found significantly associated with OS of LGG patients. Besides, they were also discovered to be strongly related to six types of immune cells which infiltrated in LGG tumor. Taken together, the present study demonstrated the promising potential of the ARG risk score formula as an independent factor for LGG prediction. It also provided the autophagy-related signature of prognosis and potential therapeutic targets for the treatment of LGG.

scholarly journals Prevention of lipopolysaccharide-induced CD11b+ immune cell infiltration in the kidney: role of AT2 receptors

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Sanket Patel ◽  
Isha Dhande ◽  
Elizabeth Alana Gray ◽  
Quaisar Ali ◽  
Tahir Hussain
Keyword(s):  
Immune Cells ◽  
Immune Cell ◽  
Interleukin 10 ◽  
Conditioned Media ◽  
Cell Infiltration ◽  
Low Grade ◽  
Immune Cell Infiltration ◽  
Anti Inflammatory

AbstractImmune cell infiltration plays a central role in mediating endotoxemic acute kidney injury (AKI). Recently, we have reported the anti-inflammatory and reno-protective role of angiotensin-II type-2 receptor (AT2R) activation under chronic low-grade inflammatory condition in the obese Zucker rat model. However, the role of AT2R activation in preventing lipopolysaccharide (LPS)-induced early infiltration of immune cells, inflammation and AKI is not known. Mice were treated with AT2R agonist C21 (0.3 mg/kg), with and without AT2R antagonist PD123319 (5 mg/kg) prior to or concurrently with LPS (5 mg/kg) challenge. Prior-treatment with C21, but not concurrent treatment, significantly prevented the LPS-induced renal infiltration of CD11b+ immune cells, increase in the levels of circulating and/or renal chemotactic cytokines, particularly interleukin-6 (IL-6) and monocyte chemoattractant protein-1 (MCP-1) and markers of renal dysfunction (blood urea nitrogen and albuminuria), while preserving anti-inflammatory interleukin-10 (IL-10) production. Moreover, C21 treatment in the absence of LPS increased renal and circulating IL-10 levels. To investigate the role of IL-10 in a cross-talk between epithelial cells and monocytes, we performed in vitro conditioned media (CM) studies in human kidney proximal tubular epithelial (HK-2) cells and macrophages (differentiated human monocytes, THP-1 cells). These studies revealed that the conditioned-media derived from the C21-treated HK-2 cells reduced LPS-induced THP-1 tumor necrosis factor-α (TNF-α) production via IL-10 originating from HK-2 cells. Our findings suggest that prior activation of AT2R is prophylactic in preventing LPS-induced renal immune cell infiltration and dysfunction, possibly via IL-10 pathway.

scholarly journals A four prognosis-associated lncRNAs (PALnc) based risk score system reflects immune cell infiltration and predicts patient survival in pancreatic cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hongkai Zhuang ◽  
Shanzhou Huang ◽  
Zixuan Zhou ◽  
Zuyi Ma ◽  
Zedan Zhang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Multivariate Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Roc Analysis ◽  
Immune Cell ◽  
Patient Survival ◽  
Immune Cell Infiltration ◽  
Score System ◽  
Risk Score System

Abstract Background Pancreatic cancer (PC) is one of the most common cancers and the leading cause of cancer-related death worldwide. Exploring novel predictive biomarkers for PC patients’ prognosis is in urgent need. Methods In the present study, we conducted Cox proportional hazards regression to identify critical prognosis-associated lncRNAs (PALncs) in TCGA PC dataset. Based on the results of multivariate analysis, a PALnc-based risk score system was established, and validated in GSE62452 dataset. The validity and reliability of the risk score system for prognosis of PC were evaluated through ROC analysis. And function enrichment analyses for the PALncs were also performed. Result In the multivariate analysis, four PALncs (LINC00476, C9orf163, LINC00346 and DSCR9) were screened out to develop a risk score system, which showed a high AUC at 3 and 5 years overall survival (0.785 at 3 year OS, 0.863 at 5 year OS) in TCGA datasets. And the ROC analysis of the risk score system for RFS in TCGA dataset revealed that AUC for RFS was 0.799 at 3 years and 0.909 at 5 years. Further, the AUC for OS in the validation cohort was 0.705 at 3 years and 0.959 at 5 years. Furthermore, the functional enrichment analysis revealed that these PALncs may be involved in various pathways related to cancer, including Ras family activation, autophagy in cancer, MAPK signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. And correlation analysis of these tumor infiltrating immune cells and risk score system revealed that the infiltration level of B cell naïve, plasma cells, and CD8+ T cells are negatively correlated to the risk score system, while macrophages M2 positively correlated to the risk score system. Conclusion Our study established a four PALncs based risk score system, which reflects immune cell infiltration and predicts patient survival for PC.

scholarly journals Exploration of a Robust and Prognostic Immune Related Gene Signature for Cervical Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhihua Zuo ◽  
Junjun Xiong ◽  
Chuyi Zeng ◽  
Yao Jiang ◽  
Kang Xiong ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
T Cells ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Group ◽  
External Validation ◽  
Cervical Squamous Cell Carcinoma ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Kaplan Meier

Background: Cervical squamous cell carcinoma (CESC) is one of the most frequent malignancies in women worldwide. The level of immune cell infiltration and immune-related genes (IRGs) can significantly affect the prognosis and immunotherapy of CESC patients. Thus, this study aimed to identify an immune-related prognostic signature for CESC.Methods: TCGA-CESC cohorts, obtained from TCGA database, were divided into the training group and testing group; while GSE44001 dataset from GEO database was viewed as external validation group. ESTIMATE algorithm was applied to evaluate the infiltration levels of immune cells of CESC patients. IRGs were screened out through weighted gene co-expression network analysis (WGCNA). A multi-gene prognostic signature based on IRGs was constructed using LASSO penalized Cox proportional hazards regression, which was validated through Kaplan–Meier, Cox, and receiver operating characteristic curve (ROC) analyses. The abundance of immune cells was calculated using ssGSEA algorithm in the ImmuCellAI database, and the response to immunotherapy was evaluated using immunophenoscore (IPS) analysis and the TIDE algorithm.Results: In TCGA-CESC cohorts, higher levels of immune cell infiltration were closely associated with better prognoses. Moreover, a prognostic signature was constructed using three IRGs. Based on this given signature, Kaplan–Meier analysis suggested the significant differences in overall survival (OS) and the ROC analysis demonstrated its robust predictive potential for CESC prognosis, further confirmed by internal and external validation. Additionally, multivariate Cox analysis revealed that the three IRGs signature served as an independent prognostic factor for CESC. In the three-IRGs signature low-risk group, the infiltrating immune cells (B cells, CD4/8 + T cells, cytotoxic T cells, macrophages and so on) were much more abundant than that in high-risk group. Ultimately, IPS and TIDE analyses showed that low-risk CESC patients appeared to present with a better response to immunotherapy and a better prognosis than high-risk patients.Conclusion: The present prognostic signature based on three IRGs (CD3E, CD3D, LCK) was not only reliable for survival prediction but efficient to predict the clinical response to immunotherapy for CESC patients, which might assist in guiding more precise individual treatment in the future.

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

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.

scholarly journals Phosphatase and Tensin Homolog Mutation in Immune Cell Infiltration and Clinicopathological Features of Low-Grade Gliomas

2020 ◽  
Vol 7 ◽  
Author(s):  
Peng Feng ◽  
Zhenqing Li ◽  
Yuchen Li ◽  
Yuelin Zhang
Keyword(s):  
Mrna Expression ◽  
Signaling Pathways ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Low Grade ◽  
Immune Cell Infiltration ◽  
Phosphatase And Tensin Homolog ◽  
Low Grade Gliomas ◽  
Tensin Homolog

The mutation of phosphatase and tensin homolog (PTEN) genes frequently occur in low-grade gliomas (LGGs) and are deeply associated with a poor prognosis and survival rate. In order to identify the crucial signaling pathways and genes associated with the PTEN mutation, we performed bioinformatics analysis on the RNA sequencing results, which were obtained from The Cancer Genome Atlas database. A total of 352 genes were identified as differentially expressed genes (DEGs). The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that the DEGs were significantly enriched in categories associated with cell division and multiple metabolic progressions. The histological stage was significantly associated with PTEN expression levels. In addition, the PTEN mutation was associated with an abundance of B cells, neutrophils, macrophages, dendritic cells, and CD8+ T cells during tumor infiltration. The results showed that patients with LGGs harboring the PTEN mutation had a poor prognosis and more serious immune cell infiltration occurred depending on the mRNA expression level. These results demonstrated that multiple genes and signaling pathways play a key role in LGG from low grade to high grade, and are associated with PTEN mutations. In this study, we outlined an approach to assess the influence of PTEN mutations on prognosis, overall survival, and messenger RNA (mRNA) expression. Our results provided alternative strategies for the personalized treatment of patients with LGGs harboring the PTEN mutation.

scholarly journals Bioinformatics Analysis of C3 in Brain Low Grade Gliomas as Potential Therapeutic Target and Promoting Immune Cell Infiltration

2021 ◽  
Author(s):  
Xiujuan wu ◽  
Siyi Wu ◽  
Kaiting Miao ◽  
Lijing Wang ◽  
Yuanyuan Ma
Keyword(s):  
Cell Cycle ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Low Grade ◽  
Immune Cell Infiltration ◽  
Mesenchymal Transition ◽  
Low Grade Gliomas ◽  
Regulation Of Cell Cycle ◽  
The Impact

Abstract Background Low grade gliomas is the malignant nervous tumor with distinct biological and clinical characteristics. Despite advances in diagnostic and therapeutic methods, how to significantly elongate the survival of low grade gliomas is still the challenge. Complement 3, as the critical component in the innate immune system, play an essential role in local immune response and participated into the regulation of the epithelial-mesenchymal transition and tumor microenvironment. Methods In this study, we systematically determined the expression levels of C3 in low grade gliomas using various public databases. Then, we further identified the impact of C3 expression on immune cell infiltration compared to normal tissue, indicating the effect of cellular microenvironment on overall survival of LGG patients. Results We obtained transcriptional and survival of C3 in LGG from GEPIA and cBioportal database, and the differentially expressed genes were obtained. By performing the analysis of GO and protein-protein interaction network, we have identified the top-ranked 10 hub genes, which are highly associated with regulation of cell cycle. The gene set enrichment analysis demonstrated that overexpression of C3 in LGG patient is positively correlated with regulation of cell cycle. Finally, the immune cell infiltration of C3 in LGG patients was employed and clearly showed that higher neutrophil infiltration can worsen the survival of LGG patients with higher C3 expression. These results were confirmed by the Human Protein Atlas database, in which expression level of C3 protein in gliomas patients always higher. Conclusions This investigation implied that C3 can be as the potential targets of precise therapy for patient with low grade gliomas.

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.

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

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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