scholarly journals Malignant tumor purity reveals the correlation between CD3E and low grade glioma microenvironment

2020 ◽  
Author(s):  
WangRui Liu ◽  
Chuanyu Li ◽  
Wenhao Xu ◽  
Hao Lian ◽  
Yuanyuan Qu ◽  
...  
Keyword(s):  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Low Grade Glioma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Tumor Purity ◽  
Immune Related Genes

Abstract Background: Tumor microenvironment (TME) contributes to the initiation and progression of low grade glioma (LGG); however, we are still unclear about the specifics of LGG's TME. Methods: In this article, we selected 161 LGG patients from the Cancer Genome Atlas (TCGA) as data, and calculated the percentage of tumor infiltrating immune cells (TICs) in LGG and the tumor purity of LGG through ESTIMATE and CIBERSORT calculation methods. Immune-related genes were screened out through Cox regression and protein-protein interaction (PPI) network. The data in Gene Expression Omnibus (GEO) was selected to screen out clinically relevant genes. After combining the two, CD3E is selected as the predictor. Finally, we conducted verification at the Affiliated Hospital of YouJiang Medical University for Nationalities (AHYMUN) center. Results: We found that the higher the expression of CD3E, the lower the purity of LGG tumors and the worse the prognosis of patients. Gene Set Enrichment Analysis (GSEA) showed that genes in the high-expressing CD3E group are mainly involved in immune-related activities. This suggests that CD3E may be responsible for regulating LGG's TME and tumor purity.Conclusion: In short, the tumor purity of LGG has a considerable impact on clinical, genomic and biological status. The expression level of CD3E may help doctors evaluate the prognosis of LGG patients and develop personalized immunotherapy plans for patients. Evaluating the ratio of different tumor purity and the new role of CD3E may provide additional insights into the complex role of the LGG microenvironment and clinical treatment.

scholarly journals A Novel Ferroptosis-Related lncRNA Signature and an Immune Perspective of Cervical Cancer

2021 ◽  
Author(s):  
Pengxiang Li ◽  
Dongchun Qin ◽  
Xuefeng Lv ◽  
Lu Liu ◽  
Mengle Peng
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Immune Checkpoints ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Cervical cancer (CC) is the most common reproductive neoplasm in women, especially in developing countries. Ferroptosis, a novel type of cell death, and lncRNAs play critical roles in the prognosis of CC patients and antitumor immunity. Methods: A ferroptosis-related lncRNA signature (FRLS) was constructed by LASSO Cox regression analysis. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, multivariate analysis, and nomogram were used to evaluate and predict the FRLS. Based on the FRLS, immune-related genes, the tumor microenvironment (TME), immune checkpoints, and immunotherapy were investigated. Results: The FRLS was composed of ten lncRNAs and was markedly associated with the overall survival (OS) of CC patients. Gene set enrichment analysis (GSEA) demonstrated that the FRLS was largely associated with immune-related pathways. Weighted gene co-expression network analysis (WGCNA) was performed to analyze immune-related genes and to identify the optimal modules and genes. TLR4 was eventually identified, and its expression was verified in the Gene Expression Omnibus (GEO) database. Then, quantitative real-time PCR (qRT-PCR) was used to validate the results in CC and paracancerous tissues. Besides, our results showed that CD8+ T cells were significantly correlated between the low- and high-risk groups, and it could modulate ferroptosis during tumor immunotherapy. The expression of immune checkpoints was substantially different between the two groups. Additionally, tumor immune dysfunction and exclusion (TIDE) was applied to predict the sensitivity of immune checkpoint inhibitor (ICI) treatment. Conclusion: The FRLS established was significantly associated with prognosis; moreover, the FRLS is a prospective therapeutic target, and combined with immunotherapy, can be used in the treatment of CC patients.

scholarly journals Identification and Validation of a Novel Immune-Related Four-lncRNA Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jixin Wang ◽  
Xiangjun Yin ◽  
Yin-Qiang Zhang ◽  
Xuming Ji
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Kaplan Meier

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, the prognosis of patients with which is associated with both lncRNAs and cancer immunity. In this study, we collected gene expression data of 585 LUAD patients from The Cancer Genome Atlas (TCGA) database and 605 subjects from the Gene Expression Omnibus (GEO) database. LUAD patients were divided into high and low immune-cell-infiltrated groups according to the single sample gene set enrichment analysis (ssGSEA) algorithm to identify differentially expressed genes (DEGs). Based on the 49 immune-related DE lncRNAs, a four-lncRNA prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) regression, univariate Cox regression, and stepwise multivariate Cox regression in sequence. Kaplan–Meier curve, ROC analysis, and the testing GEO datasets verified the effectiveness of the signature in predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression suggested that the signature was an independent prognostic factor. The correlation analysis revealed that the infiltration immune cell subtypes were related to these lncRNAs.

scholarly journals Identification of a Novel Immune Landscape Signature for Predicting Prognosis and Response of Endometrial Carcinoma to Immunotherapy and Chemotherapy

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinhui Liu ◽  
Yichun Wang ◽  
Jie Mei ◽  
Sipei Nie ◽  
Yan Zhang
Keyword(s):  
Endometrial Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Selection Operator ◽  
Immune Related Genes

Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecological cancer. Here, we have investigated the significance of immune-related genes in predicting the prognosis and response of UCEC patients to immunotherapy and chemotherapy. Based on the Cancer Genome Atlas (TCGA) database, the single-sample gene-set enrichment analysis (ssGSEA) scores was utilized to obtain enrichment of 29 immune signatures. Univariate, multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to generate an immune-related prognostic signature (IRPS). The biological functions of IRPS-associated genes were evaluated using GSEA, Tumor Immune Estimation Resource (TIMER) Database analysis, Mutation analysis, Immunophenoscore (IPS) analysis, Gene Expression Profiling Interactive Analysis (GEPIA), Genomics of Drug Sensitivity in Cancer (GDSC) and Immune Cell Abundance Identifier (ImmuCellAI). Potential small molecule drugs for UCEC were predicted using the connectivity map (Cmap). The mRNA and protein expression levels of IRPS-associated genes were tested via quantitative real-time PCR (qPCR) and immunohistology. Two immune-related genes (CCL13 and KLRC1) were identified to construct the IRPS. Both genes were related to the prognosis of UCEC patients (P < 0.05). The IRPS could distinguish patients with different prognosis and was closely associated with the infiltration of several types of immune cells. Our findings showed that patients with low IRPS benefited more from immunotherapy and developed stronger response to several chemotherapies, which was also confirmed by the results of ImmuCellAI. Finally, we identified three small molecular drugs that might improve the prognosis of patients with high IRPS. IRPS can be utilized to predict the prognosis of UCEC patients and provide valuable information about their therapeutic response to immunotherapy and chemotherapy.

scholarly journals Development of Autophagy Signature-Based Prognostic Nomogram for Refined Glioma Survival Prognostication

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Yuxiang Fan ◽  
Xinyu Peng ◽  
Baoqin Li ◽  
Gang Zhao
Keyword(s):  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
World Health ◽  
Cox Regression Analysis ◽  
High Signature ◽  
Health Organization ◽  
Glioma Classification

The current glioma classification could be optimized to cover such a separate and individualized prognosis ranging from a few months to over ten years. Considering its highly conserved role and potential in therapies, autophagy might be a promising element to be incorporated as a refinement for improved survival prognostication. The expression and RNA-seq data of 881 glioma patients from the Gene Expression Omnibus and The Cancer Genome Atlas were included, mapped with autophagy-related genes. Weighted gene coexpression network analysis and Cox regression analysis were used for the autophagy signature establishment, which composed of MUL1, NPC1, and TRIM13. Validations were represented by Kaplan-Meier plots and receiver operating curves (ROC). Cluster analysis suggested the IDH1 mutant involved in the favorable prognosis of the signature clusters. The signature was also immune-related shown by the Gene Ontology analysis and the Gene Set Enrichment Analysis. The high signature risk group held a higher ESTIMATE score (p=2.6e−11) and stromal score (p=1.8e−10). CD276 significantly correlated with the signature (r=0.51, p<0.05). The final nomogram integrated with the autophagy signature, IDH1 mutation, and pathological grade was built with accuracy and discrimination (1-year survival AUC=0.812, 5-year survival AUC=0.822, and 10-year survival AUC=0.834). Its prognostic value and clinical utility were well-defined by the superiority in the comparisons with the current World Health Organization glioma classification in ROC (p<0.05) and decision curve analysis. The autophagy signature-based IDH1 mutation and grade nomogram refined glioma classification for a more individualized and clinically applicable survival estimation and inspired potential autophagy-related therapies.

scholarly journals Ferroptosis-related Gene Signature Correlates with the Tumor Immune features and Predicts the Prognosis of Glioma Patients

2021 ◽  
Author(s):  
Yan Hu ◽  
Zewei Tu ◽  
Kunjian Lei ◽  
Kai Huang ◽  
Xingen Zhu
Keyword(s):  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Who Grade ◽  
Glioma Microenvironment ◽  
Genome Atlas

Background: Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear.Method: Univariate and least absolute shrinkage and the selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene-set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application.Results: We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established a FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune check-point expression was also significantly positively correlated with the FRSig score. Ultimately, a FRSig-based nomogram model was constructed using the independent prognostic factors age, WHO grade, and FRSig score.Conclusion: We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.

scholarly journals Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

2021 ◽  
Vol 27 ◽  
Author(s):  
Aoshuang Qi ◽  
Mingyi Ju ◽  
Yinfeng Liu ◽  
Jia Bi ◽  
Qian Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Antigen Processing ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Gene Set ◽  
Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) &lt; 1), and HLA-F was risky (HR &gt; 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis

2021 ◽  
Vol 10 ◽  
Author(s):  
Ji’an Yang ◽  
Qian Yang
Keyword(s):  
Glioblastoma Multiforme ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Hypergeometric Test ◽  
Ppi Network ◽  
Neuropsychiatric Diseases ◽  
Core Genes

Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.

Mind Bomb 1 Promotes Pancreatic Cancer Proliferation by Activating β-Catenin Signaling

2020 ◽  
Vol 20 (12) ◽  
pp. 7276-7282
Author(s):  
Xiao Fu ◽  
Neng Tang ◽  
Weiqi Xie ◽  
Liang Mao ◽  
Yudong Qiu
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Metastasis ◽  
Mrna Level ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Pancreatic Tumors ◽  
Mrna Levels ◽  
Cancer Proliferation

Mind bomb 1 (MIB1), an E3 ligase, plays a vital role in chemo-resistance and cancer metastasis. According to The Cancer Genome Atlas (TCGA), MIB1 gene is preferentially amplified in pancreatic cancer. Copy number alterations in MIB1 gene are associated with worse survival. Gene Expression Omnibus (GEO) also showed that pancreatic cancer with high mRNA level of MIB1 tend to be more resistant to gemcitabine and higher mRNA levels of MIB1 are found in pancreatic tumors compared with adjacent normal tissues. MIB1 knockdown (KD) in Panc-1 and HPAF2 cell lines significantly inhibit proliferation and colony formation of pancreatic cancer. The gene set enrichment analysis (GSEA) has also showed that β-catenin is the downstream of MIB1. Western blot analysis showed that total and active β-catenin levels are decreased in MIB1 KD cells. β-catenin inhibitor also inhibits proliferation of Panc-1 and HPAF2 cells. We in this study implanted HPAF2 scramble and MIB1 KD cells orthotopically in athymic nude mice. Gemcitabine was used to treat the mice. Results revealed that after MIB1 KD HPAF2 cells were more sensitive to gemcitabine. In conclusion, we demonstrated that MIB1 promotes pancreatic cancer proliferation through activating β-catenin signaling. MIB1 may thus be a therapeutic target in pancreatic cancer therapy.

scholarly journals Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Honglan Guo ◽  
Qinqiao Fan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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