Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Epigenomics ◽  
2021 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aims: To investigate the prognostic significance of hypoxia- and ferroptosis-related genes for gastric cancer (GC). Materials & methods: We extracted data on 259 hypoxia- and ferroptosis-related genes from The Cancer Genome Atlas and identified the differentially expressed genes between normal (n = 32) and tumor (n = 375) tissues. A risk score was established by univariate Cox regression analysis and LASSO penalized Cox regression analysis. Results: The risk score contained eight genes showed good performance in predicting overall survival and relapse-free survival in GC patients in both the training cohort (The Cancer Genome Atlas, n = 350) and the testing cohorts (GSE84437, n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26253, n = 432). Conclusion: The eight-gene signature may help to the improve the prognostic risk classification of GC.

scholarly journals Downregulation of SPINK13 Promotes Metastasis by Regulating uPA in Ovarian Cancer Cells

2018 ◽  
Vol 45 (3) ◽  
pp. 1061-1071 ◽  
Author(s):  
Shengyun Cai ◽  
Pei Zhang ◽  
Suhe Dong ◽  
Li Li ◽  
Jianming Cai ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Cellular Proliferation ◽  
Cox Regression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background/Aims: Ovarian cancer (OC) is the fifth leading cause of cancer-related death in women, and it is difficult to diagnose at an early stage. The purpose of this study was to explore the prognostic biological markers of OC. Methods: Univariate Cox regression analysis was used to identify genes related to OC prognosis from the Cancer Genome Atlas(TCGA) database. Immunohistochemistry was used to analyse the level of SPINK13 in OC and normal tissues. Cell proliferation, apoptosis and invasion were performed using MTT assay, flow cytometric analysis and Transwell assay, respectively. Results: We identified the Kazal-type serine protease inhibitor-13 (SPINK13) gene related to OC prognosis from the Cancer Genome Atlas (TCGA) database by univariate Cox regression analysis. Overexpression of SPINK13 was associated with higher overall survival rate in OC patients. Immunohistochemistry showed that the level of SPINK13 protein was significantly lower in OC tissues than in normal tissues (P < 0.05).In vitro experiments showed that the overexpression of SPINK13 inhibited cellular proliferation and promoted apoptosis. Moreover, SPINK13 inhibited cell migration and epithelial to mesenchymal transition (EMT). SPINK13 was found to inhibit the expression of urokinase-type plasminogen activator (uPA), while recombinant uPA protein could reverse the inhibitory effect of SPINK13 on OC metastasis. Conclusion: These results indicate that SPINK13 functions as a tumour suppressor. The role of SPINK13 in cellular proliferation, apoptosis and migration is uPA dependent, and SPINK13 may be used as a potential biomarker for diagnosis and targeted therapy in OC.

scholarly journals Interferon-lambda (IFNL) germline variations and their significance for HCC and PDAC progression: an analysis of The Cancer Genome Atlas (TCGA) data

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Henriette Huschka ◽  
Sabine Mihm
Keyword(s):  
Disease Progression ◽  
Cox Regression ◽  
Genetic Variations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Disease Outcome ◽  
Ductal Adenocarcinoma ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Background Hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC) are malignancies with a leading lethality. With reference to interferons (IFNs) known to mediate antitumor activities, this study investigated the relationship between germline genetic variations in type III IFN genes and cancer disease progression from The Cancer Genome Atlas (TCGA) data. The genetic variations under study tag a gain-or-loss-of-function dinucleotide polymorphism within the IFNL4 gene, rs368234815 [TT/ΔG]. Methods The entirety of the TCGA sequencing data was used to assess genotypes of 187 patients with HCC and of 162 patients with PDAC matched for ethnicity. Stratified for IFNL genotypes, both cohorts were subjected to time-to-event analyses according to Kaplan-Meier with regard to the length of the specific progression free interval (PFI) and the overall survival (OS) time as two clinical endpoints for disease progression. Results Logrank analysis revealed a significant relationship between IFNL genotypes and disease outcome for PDAC. This relationship was not found for HCC. A multiple Cox regression analysis employing patients’ age, tumor grade and tumor stage as further covariates proved IFNL genotypes to be independent predictors for PDAC disease outcome. Conclusion This repository-based approach unveiled clinical evidence suggestive for an impact of IFNL germline variations for PDAC progression with an IFNL haplotype predisposing for IFNL4 expression being favorable.

scholarly journals Construction of a Novel Prognostic Immune-Related LncRNA Risk Model for Gastric Cancer

2021 ◽  
Author(s):  
Gen-hua Yang
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Cancer Genome Atlas ◽  
Score Model

Abstract Background and AimStudies have recently shown that immune-related lncRNAs play a vital role in the occurrence and development of human malignancies. However, the study in gastric cancer (GC) remains unclear. Here, we aimed to identify immune-related lncRNAs and construct a risk score model to predict the prognosis of GC patients.Methods:RNA expression data and clinical characteristics of GC were download from The Cancer Genome Atlas (TCGA) database. Immune genes were obtained from the Molecular Signatures Database (MSigDB). Immune-related lncRNAs were acquired by correlation coefficient between the immune genes and lncRNAs using “limma R” package and Cytoscape 3.6.1. The risk score model was constructed by univariate and multivariate Cox regression, and its prognostic value was verified in TCGA cohort. Results:A total of 146 immune-related lncRNAs were obtained compared 375 GC samples with 32 normal samples. A five immune-related lncRNA (AP001528.2, LINC02542, LINC02526, PVT1 and LINC01094) risk score model was constructed to predict prognosis of GC patients by Cox regression analysis. Moreover, GC patients with higher risk score had a poorer overall survival than that with lower risk score (P<0.001). Furthermore, ROC analysis revealed that the risk score model had the best predictive effect compared with clinicopathological features during 5 years followed-up (AUC = 0.679). Indeed, PCA analysis showed that the patients in the low- and high- group were significantly distinguished in different directions based on the risk score model. Conclusion:This study indicated that a five immune-related lncRNA risk score model possessed a satisfactory predictive prognosis, which might be potential prognostic biomarkers and immunotherapy targets for GC patients in future.

scholarly journals Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuan Zhuang ◽  
Sihan Li ◽  
Chang Liu ◽  
Guang Li
Keyword(s):  
Risk Score ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background: Lung squamous cell carcinoma (LUSC) is one of the most common histological subtypes of non-small cell lung cancer (NSCLC), and its morbidity and mortality are steadily increasing. The purpose of this study was to study the relationship between the immune-related gene (IRGs) profile and the outcome of LUSC in patients by analyzing datasets from The Cancer Genome Atlas (TCGA).Methods: We obtained publicly available LUSC RNA expression data and clinical survival data from The Cancer Genome Atlas (TCGA), and filtered IRGs based on The ImmPort database. Then, we identified risk immune-related genes (r-IRGs) for model construction using Cox regression analysis and defined the risk score in this model as the immune gene risk index (IRI). Multivariate analysis was used to verify the independent prognostic value of IRI and its association with other clinicopathological features. Pearson correlation analysis was used to explore the molecular mechanism affecting the expression of IRGs and the correlation between IRI and immune cell infiltration.Results: We screened 15 r-IRGs for constructing the risk model. The median value of IRI stratified the patients and there were significant survival differences between the two groups (p = 4.271E-06). IRI was confirmed to be an independent prognostic factor (p &lt; 0.001) and had a close correlation with the patients' age (p &lt; 0.05). Interestingly, the infiltration of neutrophils or dendritic cells was strongly upregulated in the high-IRI groups (p &lt; 0.05). Furthermore, by investigating differential transcription factors (TFs) and functional enrichment analysis, we explored potential mechanisms that may affect IRGs expression in tumor cells.Conclusion: In short, this study used 15 IRGs to build an effective risk prediction model, and demonstrated the significance of IRGs-based personalized immune scores in LUSC prognosis.

Identification of Prognostic miRNAs Targeting EZH2 in Hepatocellular Carcinoma Using The Cancer Genome Atlas Database

2021 ◽  
Author(s):  
Tianyu Lin ◽  
Xinli Guo ◽  
Qian Du ◽  
Wei Liu ◽  
Xin Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Pathway Network ◽  
Cancer Genome Atlas ◽  
And Control ◽  
Genome Atlas ◽  
Control Samples

Abstract Background: Enhancer of zeste homolog 2 (EZH2) gene have a prognostic role in hepatocellular carcinoma (HCC). This study aimed to identify the prognostic microRNAs (miRNAs) targeting EZH2 in HCC. Methods and Results: We downloaded the gene and miRNA RNA-seq data from The Cancer Genome Atlas (TCGA) database. Differences in EZH2 expression between tumor and control samples and those between tumors with different clinical variables were analyzed using the Mann-Whitney U test. Association of EZH2 expression with prognosis in HCC patients was detected using Cox regression analysis. We also identified miRNAs targeting EZH2 with negative correlations, compared the miRNA expression profiles between tumor and control tissues, and identified pathways and protein-protein interaction pairs related to EZH2. The miRNA-EZH2-pathway network was constructed accordingly. EZH2 was significantly upregulated in HCC tumors compared with control samples (p<0.0001) and in tumors with advanced T classifications (3/4 vs. 1/2, p=0.0039) and stages (III/IV vs. I/II, p=0.0028). The Cox regression analysis showed that TCGA HCC patients who had high EZH2 expression levels showed a short survival time (HR=1.677, 95% CI 1.316-2.137; p<0.0001). Among miRNAs targeting EZH2, seven miRNAs, including hsa-let-7c-5p, were negatively correlated with EZH2 expression and were significantly downregulated in HCC tumor samples compared with controls (p<0.0001). The miRNA-EZH2-pathway network included seven downregulated miRNAs and four pathways, including hsa00310: Lysine degradation. Hsa-let-7c-5p was associated with prognosis in HCC (HR=0.849 95% CI 0.739-0.975; p=0.021). Conclusions: EZH2-hsa-let-7c-5p has a significant association with HCC prognosis and the mechanism worth investigating.

scholarly journals Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiaohong - Liu ◽  
Qian - Xu ◽  
Zi-Jing - Li ◽  
Bin - Xiong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Metabolic Reprogramming ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

scholarly journals Screening and Identifying m6A Regulators as an Independent Prognostic Biomarker in Pancreatic Cancer Based on The Cancer Genome Atlas Database

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bi Lin ◽  
Yangyang Pan ◽  
Dinglai Yu ◽  
Shengjie Dai ◽  
Hongwei Sun ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Tumor Therapy ◽  
Disease Free Survival ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background. Pancreatic cancer is one of the most malignant tumors of the digestive system, and its treatment has rarely progressed for the last two decades. Studies on m6A regulators for the past few years have seemingly provided a novel approach for malignant tumor therapy. m6A-related factors may be potential biomarkers and therapeutic targets. This research is focused on the gene characteristics and clinical values of m6A regulators in predicting prognosis in pancreatic cancer. Methods. In our study, we obtained gene expression profiles with copy number variation (CNV) data and clinical characteristic data of 186 patients with pancreatic cancer from The Cancer Genome Atlas (TCGA) portal. Then, we determined the alteration of m6a regulators and their correlation with clinicopathological features using the log-rank tests, Cox regression model, and chi-square test. Additionally, we validated the prognostic value of m6A regulators in the International Cancer Genome Consortium (ICGC). Results. The results suggested that pancreatic cancer patients with ALKBH5 CNV were associated with worse overall survival and disease-free survival than those with diploid genes. Additionally, upregulation of the writer gene ALKBH5 had a positive correlation with the activation of AKT pathways in the TCGA database. Conclusion. Our study not only demonstrated genetic characteristic changes of m6A-related genes in pancreatic cancer and found a strong relationship between the changes of ALKBH5 and poor prognosis but also provided a novel therapeutic target for pancreatic cancer therapy.

scholarly journals Why MUC16 mutations lead to a better prognosis: A study based on The Cancer Genome Atlas gastric cancer cohort

2021 ◽  
Vol 9 (17) ◽  
pp. 4143-4158
Author(s):  
Yu-Jie Huang ◽  
Zhi-Fei Cao ◽  
Jie Wang ◽  
Jian Yang ◽  
Yi-Jun Wei ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

2020 ◽  
Author(s):  
Xing Chen ◽  
Junjie Zheng ◽  
Min ling Zhuo ◽  
Ailong Zhang ◽  
Zhenhui You
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

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