scholarly journals Identification of ubiquitination-related genes in human glioma as indicators of patient prognosis

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250239
Author(s):  
Lei Wang ◽  
Yuelin Liu ◽  
Chengmin Xuan ◽  
Yong Liu ◽  
Hengliang Shi ◽  
...  
Keyword(s):  
Risk Model ◽  
Tissue Expression ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Multiple Tumor ◽  
Metabolism Regulation ◽  
Ubiquitin Proteasome ◽  
Cancer Genome Atlas ◽  
Patient Prognosis ◽  
Cox Analysis

Ubiquitination is a dynamic and reversible process of a specific modification of target proteins catalyzed by a series of ubiquitination enzymes. Because of the extensive range of substrates, ubiquitination plays a crucial role in the localization, metabolism, regulation, and degradation of proteins. Although the treatment of glioma has been improved, the survival rate of patients is still not satisfactory. Therefore, we explore the role of ubiquitin proteasome in glioma. Survival-related ubiquitination related genes (URGs) were obtained through analysis of the Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA). Cox analysis was performed to construct risk model. The accuracy of risk model is verified by survival, Receiver operating characteristic (ROC) and Cox analysis. We obtained 36 differentially expressed URGs and found that 25 URGs were related to patient prognosis. We used the 25 URGs to construct a model containing 8 URGs to predict glioma patient risk by Cox analysis. ROC showed that the accuracy rate of this model is 85.3%. Cox analysis found that this model can be used as an independent prognostic factor. We also found that this model is related to molecular typing markers. Patients in the high-risk group were enriched in multiple tumor-related signaling pathways. In addition, we predicted TFs that may regulate the risk model URGs and found that the risk model is related to B cells, CD4 T cells, and neutrophils.

scholarly journals Development and validation of an oxidative stress—associated prognostic risk model for melanoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11258
Author(s):  
Yu Yang ◽  
Xuan Long ◽  
Kun Li ◽  
Guiyun Li ◽  
Xiaohong Yu ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Cancer Progression ◽  
Risk Model ◽  
Risk Group ◽  
Roc Curves ◽  
Tissue Expression ◽  
Diagnostic Value ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Hub Genes

Background Oxidative stress (OS) is key to various diseases and is implicated in cancer progression and oncogenesis. However, the potential diagnostic value of OS-related genes in skin cutaneous melanoma (SKCM) remains unclear. Methods We used data of RNA sequencing from 471 tumor tissues and one healthy tissue acquired from The Cancer Genome Atlas (TCGA)-SKCM cohort. The Genome Tissue Expression database was used to acquire transcriptome data from 812 healthy samples. OS-related genes that were differentially expressed between SKCM and healthy samples were investigated and 16 prognosis-associated OS genes were identified. The prognostic risk model was built using univariate and Cox multivariate regressions. The prognostic value of the hub genes was validated in the GSE65904 cohort, which included 214 SKCM patients. Results The overall survival rate of SKCM patients in the high-risk group was decreased compared to the low-risk group. In both TCGA and GSE65904 cohorts, the ROC curves suggested that our prognostic risk model was more accurate than other clinicopathological characteristics to diagnose SKCM. Moreover, risk score and nomograms associated with the expression of hub genes were developed. These presented reiterated our prognostic risk model. Altogether, this study provides novel insights with regards to the pathogenesis of SKCM. The 16 hub genes identified may help in SKCM prognosis and individualized clinical treatment.

scholarly journals Tumor Immune Microenvironment Landscape in Glioma Identifies a Prognostic and Immunotherapeutic Signature

2021 ◽  
Vol 9 ◽  
Author(s):  
Chunyu Zhang ◽  
Lirui Guo ◽  
Zhongzhou Su ◽  
Na Luo ◽  
Yinqiu Tan ◽  
...  
Keyword(s):  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Mutation Burden ◽  
Tumor Immune Microenvironment ◽  
Cancer Genome Atlas ◽  
Patient Prognosis ◽  
Selection Operator ◽  
Cox Analysis ◽  
Genome Atlas

The tumor immune microenvironment (TIME) has been recognized to be associated with sensitivity to immunotherapy and patient prognosis. Recent research demonstrates that assessing the TIME patterns on large-scale samples will expand insights into TIME and will provide guidance to formulate immunotherapy strategies for tumors. However, until now, thorough research has not yet been reported on the immune infiltration landscape of glioma. Herein, the CIBERSORT algorithm was used to unveil the TIME landscape of 1,975 glioma observations. Three TIME subtypes were established, and the TIMEscore was calculated by least absolute shrinkage and selection operator (LASSO)–Cox analysis. The high TIMEscore was distinguished by an elevated tumor mutation burden (TMB) and activation of immune-related biological process, such as IL6-JAK-STAT3 signaling and interferon gamma (IFN-γ) response, which may demonstrate that the patients with high TIMEscore were more sensitive to immunotherapy. Multivariate analysis revealed that the TIMEscore could strongly and independently predict the prognosis of gliomas [Chinese Glioma Genome Atlas (CGGA) cohort: hazard ratio (HR): 2.134, p < 0.001; Gravendeel cohort: HR: 1.872, p < 0.001; Kamoun cohort: HR: 1.705, p < 0.001; The Cancer Genome Atlas (TCGA) cohort: HR: 2.033, p < 0.001; the combined cohort: HR: 1.626, p < 0.001], and survival advantage was evident among those who received chemotherapy. Finally, we validated the performance of the signature in human tissues from Wuhan University (WHU) dataset (HR: 15.090, p = 0.008). Our research suggested that the TIMEscore could be applied as an effective predictor for adjuvant therapy and prognosis assessment.

scholarly journals Comprehensive Pan-Cancer Analyses of Pyroptosis-Related Genes to Predict Survival and Immunotherapeutic Outcome

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 237
Author(s):  
Qilin Wang ◽  
Qian Liu ◽  
Sihan Qi ◽  
Junyou Zhang ◽  
Xian Liu ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Risk Model ◽  
Tissue Expression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Number Variation ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Novel Strategy

Pyroptosis is a newly characterized type of programmed cell death. However, its function in cancer progression and its response to treatments remain controversial. Here, we extensively and systematically compiled genes associated with pyroptosis, integrated multiomics data and clinical data across 31 cancer types from The Cancer Genome Atlas, and delineated the global alterations in PRGs at the transcriptional level. The underlying transcriptional regulations by copy number variation, miRNAs, and enhancers were elucidated by integrating data from the Genotype-Tissue Expression and International Cancer Genome Consortium. A prognostic risk model, based on the expression of PRGs across 31 cancer types, was constructed. To investigate the role of pyroptosis in immunotherapy, we found five PRGs associated with effectiveness by exploring the RNA-Seq data of patients with immunotherapy, and further identified two small-molecule compounds that are potentially beneficial for immunotherapy. For the first time, from a pyroptosis standpoint, this study establishes a novel strategy to predict cancer patient survival and immunotherapeutic outcomes.

scholarly journals Identification of novel autophagy-related lncRNAs associated with a poor prognosis of colon adenocarcinoma through bioinformatics analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dejun Wu ◽  
Zhenhua Yin ◽  
Yisheng Ji ◽  
Lin Li ◽  
Yunxin Li ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Notch Pathway ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Future Research ◽  
Risk Patients ◽  
Cancer Genome Atlas ◽  
Immune Score

AbstractLncRNAs play a pivotal role in tumorigenesis and development. However, the potential involvement of lncRNAs in colon adenocarcinoma (COAD) needs to be further explored. All the data used in this study were obtained from The Cancer Genome Atlas database, and all analyses were conducted using R software. Basing on the seven prognosis-related lncRNAs finally selected, we developed a prognosis-predicting model with powerful effectiveness (training cohort, 1 year: AUC = 0.70, 95% Cl = 0.57–0.78; 3 years: AUC = 0.71, 95% Cl = 0.6–0.8; 5 years: AUC = 0.76, 95% Cl = 0.66–0.87; validation cohort, 1 year: AUC = 0.70, 95% Cl = 0.58–0.8; 3 years: AUC = 0.73, 95% Cl = 0.63–0.82; 5 years: AUC = 0.68, 95% Cl = 0.5–0.85). The VEGF and Notch pathway were analyzed through GSEA analysis, and low immune and stromal scores were found in high-risk patients (immune score, cor =  − 0.15, P < 0.001; stromal score, cor =  − 0.18, P < 0.001) , which may partially explain the poor prognosis of patients in the high-risk group. We screened lncRNAs that are significantly associated with the survival of patients with COAD and possibly participate in autophagy regulation. This study may provide direction for future research.

scholarly journals A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wanting Song ◽  
Yi Bai ◽  
Jialin Zhu ◽  
Fanxin Zeng ◽  
Chunmeng Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Critical Function ◽  
Mesenchymal Transition ◽  
Patient Prognosis

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

scholarly journals Deciphering N6-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jie Zhu ◽  
Min Wang ◽  
Daixing Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Gene Expressions ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Cox Analysis

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N6-methyladenosine (m6A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m6A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m6A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m6A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m6A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m6A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p<0.05), M stages (p<0.05), T stages (p < 0.05), gender (p=0.04), and survival outcome (p=0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m6A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

scholarly journals ExonSkipDB: functional annotation of exon skipping event in human

2019 ◽  
Author(s):  
Pora Kim ◽  
Mengyuan Yang ◽  
Ke Yiya ◽  
Weiling Zhao ◽  
Xiaobo Zhou
Keyword(s):  
Functional Annotation ◽  
Exon Skipping ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Annotation Database ◽  
Reading Frame ◽  
Normal Tissues ◽  
Cancer Genome Atlas ◽  
Functional Features

AbstractExon skipping (ES) is reported to be the most common alternative splicing event due to loss of functional domains/sites or shifting of the open reading frame (ORF), leading to a variety of human diseases and considered therapeutic targets. To date, systematic and intensive annotations of ES events based on the skipped exon units in cancer and normal tissues are not available. Here, we built ExonSkipDB, the ES annotation database available at https://ccsm.uth.edu/ExonSkipDB/, aiming to provide a resource and reference for functional annotation of ES events in multiple cancer and tissues to identify therapeutically targetable genes in individual exon units. We collected 14 272 genes that have 90 616 and 89 845 ES events across 33 cancer types and 31 normal tissues from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). For the ES events, we performed multiple functional annotations. These include ORF assignment of exon skipped transcript, studies of lost protein functional features due to ES events, and studies of exon skipping events associated with mutations and methylations based on multi-omics evidence. ExonSkipDB will be a unique resource for cancer and drug research communities to identify therapeutically targetable exon skipping events.

FBXW7γ is a tumor-suppressive and prognosis-related FBXW7 transcript isoform in ovarian serous cystadenocarcinoma

2020 ◽  
Vol 16 (25) ◽  
pp. 1921-1930
Author(s):  
Zhou Xu ◽  
Lin Zhuang ◽  
Xiaoyin Wang ◽  
Qianrong Li ◽  
Yan Sang ◽  
...  
Keyword(s):  
Progression Free Survival ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
In Vivo Studies ◽  
Protein Coding ◽  
Serous Cystadenocarcinoma ◽  
Skov3 Cells ◽  
Cancer Genome Atlas

Aim: To explore FBXW7 protein-coding transcript isoform (α, β and γ) expression, their functions and prognostic value in ovarian serous cystadenocarcinoma (OSC). Materials & methods: FBXW7 transcript data were collected from The Cancer Genome Atlas and the Genotype-Tissue Expression project. IOSE, A2780 and SKOV3 cells were used for in vitro and in vivo studies. Results: FBXW7α and FBXW7γ are dominant protein-coding transcripts that were downregulated in OSC. FBXW7γ overexpression reduced the protein expression of c-Myc, Notch1 and Yap1 and suppressed OSC cell growth in vitro and in vivo. FBXW7γ expression was an independent indicator of longer disease-specific survival (HR: 0.588; 95% CI: 0.449–0.770) and progression-free survival (HR: 0.708; 95% CI: 0.562–0.892). Conclusion: FBXW7γ is a tumor-suppressive and might be the only prognosis-related FBXW7 transcript in OSC.

scholarly journals Identification of prognostic hypoxia-related genes signature on the tumor microenvironment in esophageal cancer

2021 ◽  
Vol 18 (6) ◽  
pp. 7743-7758
Author(s):  
Linlin Tan ◽  
◽  
Dingzhuo Cheng ◽  
Jianbo Wen ◽  
Kefeng Huang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Group ◽  
Roc Curves ◽  
Risk Groups ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas

<abstract> <sec><title>Background</title><p>Hypoxia is a crucial factor in the development of esophageal cancer. The relationship between hypoxia and immune status in the esophageal cancer microenvironment is becoming increasingly important in clinical practice. This study aims to clarify and investigate the possible connection between immunotherapy and hypoxia in esophageal cancer.</p> </sec> <sec><title>Methods</title><p>The Cancer Genome Atlas databases are used to find two types of esophageal cancer cases. Cox regressions analyses are used to screen genes for hypoxia-related traits. After that, the genetic signature is validated by survival analysis and the construction of ROC curves. GSEA is used to compare differences in enrichment in the two groups and is followed by the CIBERSORT tool to investigate a potentially relevant correlation between immune cells and gene signatures.</p> </sec> <sec><title>Results</title><p>We found that the esophageal adenocarcinoma hypoxia model contains 3 genes (PGK1, PGM1, SLC2A3), and the esophageal squamous cell carcinoma hypoxia model contains 2 genes (EGFR, ATF3). The findings demonstrated that the survival rate of patients in the high-risk group is lower than in the lower-risk group. Furthermore, we find that three kinds of immune cells (memory activated CD4+ T cells, activated mast cells, and M2 macrophages) have a marked infiltration in the tissues of patients in the high-risk group. Moreover, we find that PD-L1 and CD244 are highly expressed in high-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our data demonstrate that oxygen deprivation is correlated with prognosis and the incidence of immune cell infiltration in patients with both types of esophageal cancer, which provides an immunological perspective for the development of personalized therapy.</p> </sec> </abstract>

scholarly journals Mast Cells Resting-Related Prognostic Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hao Zhang ◽  
Lin Sun ◽  
Xiao Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mast Cells ◽  
Liver Cancer ◽  
Differentially Expressed Genes ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Immune Checkpoints ◽  
New Ideas ◽  
Cancer Genome Atlas

The immune microenvironment of liver cancer is of great significance for the treatment of liver cancer. After evaluating the content of mast cells resting in the transcriptome data of The Cancer Genome Atlas database by CIBERSORT analysis, this study aimed to group the samples according to the content of mast cells resting in different samples to find the differentially expressed genes in the two groups. Significant prognostic differences were found between high and low mast cells resting infiltration groups. The prognostic model was constructed according to the differentially expressed genes. The model was validated using external independent datasets. The results revealed that the constructed model was reliable. It could well distinguish the prognostic differences of patients in different characteristic groups. The high-risk group was mainly concentrated in metabolic pathways. The risk score of this model was closely related to some immune cells, immune function, and immune checkpoints. Therefore, this model may provide new ideas for immunotherapy of hepatocellular carcinoma.

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