scholarly journals Bioinformatics Analysis of Autophagy-related LncRNAs in Esophageal Carcinoma

2020 ◽  
Author(s):  
junbai fan ◽  
Dan Wu ◽  
Yi Ding
Keyword(s):  
High Risk ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Risk Patients ◽  
Risk Patients

Abstract Background: Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules, and to identify gene co-expression networks associated with autophagy in ESCA. Methods: We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database, and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis. Results: The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed. Conclusion: These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

scholarly journals Bioinformatics Analysis of Autophagy-related lncRNAs in Esophageal Carcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Dan Wu ◽  
Yi Ding ◽  
JunBai Fan
Keyword(s):  
High Risk ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Risk Patients ◽  
Risk Patients

Background: Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules and to identify gene co-expression networks associated with autophagy in ESCA. Methods: We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis. Results: The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed. Conclusion: These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

scholarly journals Bioinformatics analysis of autophagy-related lncRNAs in esophageal carcinoma

2020 ◽  
Author(s):  
Dan Wu ◽  
Yi Ding ◽  
junbai fan
Keyword(s):  
High Risk ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Risk Patients ◽  
Risk Patients

Abstract Background: Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules, and to identify gene co-expression networks associated with autophagy in ESCA. Methods: We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database, and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis. Results: The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed. Conclusion: These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

scholarly journals Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

2021 ◽  
Vol 8 ◽  
Author(s):  
Li Zhang ◽  
Xianzhe Tang ◽  
Jia Wan ◽  
Xianghong Zhang ◽  
Tao Zheng ◽  
...  
Keyword(s):  
High Risk ◽  
Soft Tissue ◽  
Cox Regression ◽  
Soft Tissue Sarcomas ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Consensus Clustering ◽  
Immune Microenvironment

Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations.Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan–Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature.Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS.Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.

scholarly journals Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qingyu Liang ◽  
Gefei Guan ◽  
Xue Li ◽  
Chunmi Wei ◽  
Jianqi Wu ◽  
...  
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Treatment Strategies ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Risky Behaviors ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Transition ◽  
Genome Atlas

Abstract Background Molecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlighted that long non-coding RNAs (lncRNAs) are key mediators in cancer mesenchymal transition. However, the relationship between lncRNAs and PMT in glioma has not been systematically investigated. Methods Gene expression profiles from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, and Rembrandt with available clinical and genomic information were used for analyses. Bioinformatics methods such as weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), Cox analysis, and least absolute shrinkage and selection operator (LASSO) analysis were performed. Results According to PMT scores, we confirmed that PMT status was positively associated with risky behaviors and poor prognosis in glioma. The 149 PMT-related lncRNAs were identified by WGCNA analysis, among which 10 (LINC01057, TP73-AS1, AP000695.4, LINC01503, CRNDE, OSMR-AS1, SNHG18, AC145343.2, RP11-25K21.6, RP11-38L15.2) with significant prognostic value were further screened to construct a PMT-related lncRNA risk signature, which could divide cases into two groups with distinct prognoses. Multivariate Cox regression analyses indicated that the signature was an independent prognostic factor for high-grade glioma. High-risk cases were more likely to be classified as the mesenchymal subtype, which confers enhanced immunosuppressive status by recruiting macrophages, neutrophils, and regulatory T cells. Moreover, six lncRNAs of the signature could act as competing endogenous RNAs to promote PMT in glioblastoma. Conclusions We profiled PMT status in glioma and established a PMT-related 10-lncRNA signature for glioma that could independently predict glioma survival and trigger PMT, which enhanced immunosuppression.

scholarly journals Identification of RPS28 as a Promising Therapeutic Target for Osteosarcoma Patients with Poor Prognoses Stratified by a Seven-Gene Signature

2021 ◽  
Author(s):  
Yin Sun ◽  
Juan Zhou ◽  
Yongjie Wang ◽  
Lin Zeng ◽  
Chao Liang ◽  
...  
Keyword(s):  
High Risk ◽  
Therapeutic Target ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
High Risk Patients ◽  
Ppi Networks ◽  
Promising Therapeutic Target ◽  
Risk Patients

Osteosarcoma (OSA) is the most common primary malignant bone tumor. More than 40% of patients with OSA have poor prognoses. We aimed to discover a biomarker for patient stratification and therapeutic targets for these high-risk patients. Using Single Sample Gene Set Enrichment Analysis (ssGSEA) and univariate Cox analysis, six hallmarks were identified as significant prognostic factors for overall survival (OS). Three were selected to construct a multivariate Cox model. Then, WGCNA, univariate Cox regression, Kaplan-Meier (KM) survival analyses, and multivariate Cox analyses were combined to filter promising candidates and establish a seven-gene signature to predict OS, whose prognostic value was validated internally and externally. Subsequently, Differential Expression Analysis was conducted between high- and low-risk patients, and the Robust Rank Aggregation algorithm was used to determine the robust DEGs. Metascape was used to perform pathway and process enrichment analyses as well as construct protein-protein interaction (PPI) networks. Finally, RPS28 was identified as an independent risk factor by using univariate and multivariate Cox regression, which was preliminarily validated as a promising therapeutic target by using RNA interference. In conclusion, we might contribute to optimizing risk stratification and an excellent therapeutic target for high-risk patients with OSA.

scholarly journals Identification the ferroptosis-related gene signature in patients with esophageal adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lei Zhu ◽  
Fugui Yang ◽  
Lingwei Wang ◽  
Lin Dong ◽  
Zhiyuan Huang ◽  
...  
Keyword(s):  
Esophageal Adenocarcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Risk Group ◽  
Apoptotic Cell ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mucosal Tissues

Abstract Background Ferroptosis is a recently recognized non-apoptotic cell death that is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in esophageal adenocarcinoma (EAC) remains unclear. This study aims to explore the ferroptosis-related genes (FRG) expression profiles and their prognostic values in EAC. Methods The FRG data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regressions were used to identify the prognostic FRG, and the predictive ROC model was established using the independent risk factors. GO and KEGG enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and TIMER database. Finally, SDG were verified in clinical EAC specimens and normal esophageal mucosal tissues. Results Twenty-eight significantly different FRG were screened from 78 EAC and 9 normal tissues. Enrichment analyses showed these SDG were mainly related to the iron-related pathways and metabolisms of ferroptosis. Gene network demonstrated the TP53, G6PD, NFE2L2 and PTGS2 were the hub genes in the biology of ferroptosis. Cox regression analyses demonstrated four FRG (CARS1, GCLM, GLS2 and EMC2) had prognostic values for overall survival (OS) (all P < 0.05). ROC curve showed better predictive ability using the risk score (AUC = 0.744). Immune cell enrichment analysis demonstrated that the types of immune cells and their expression levels in the high-risk group were significant different with those in the low-risk group (all P < 0.05). The experimental results confirmed the ALOX5, NOX1 were upregulated and the MT1G was downregulated in the EAC tissues compared with the normal esophageal mucosal tissues (all P < 0.05). Conclusions We identified differently expressed ferroptosis-related genes that may involve in EAC. These genes have significant values in predicting the patients’ OS and targeting ferroptosis may be an alternative for therapy. Further studies are necessary to verify these results of our study.

scholarly journals Identification the Roles of Ferroptosis-Related Gene Signatures in Patients with Esophageal Adenocarcinoma

2020 ◽  
Author(s):  
Lei Zhu ◽  
Fugui Yang ◽  
Lingwei Wang ◽  
Lin Dong ◽  
Zhiyuan Huang ◽  
...  
Keyword(s):  
Esophageal Adenocarcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Risk Group ◽  
Apoptotic Cell ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mucosal Tissues

Abstract Background Ferroptosis is a recently recognized non-apoptotic cell death that is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in esophageal adenocarcinoma (EAC) remains unclear. The aim of this study was to explore the ferroptosis-related genes (FRG) expression profiles and their prognostic values in EAC.Methods The FRG data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regressions were used to identify the prognostic FRG, and the predictive ROC model was established using the independent risk factors. GO and KEGG enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA). Finally, significantly different genes were verified in our clinical EAC specimens and normal esophageal mucosal tissues.Results: Twenty-eight significantly different FRG were screened from 78 EAC and 9 normal tissues. GO and KEGG enrichments showed these SDG were mainly related to the iron-related pathways and metabolisms of ferroptosis. Gene network demonstrated the TP53, G6PD, NFE2L2 and PTGS2 were the hub genes in the biology of ferroptosis. Cox regression analyses demonstrated four FRG (CARS1, GCLM, GLS2 and EMC2) had prognostic values for overall survival (OS) (all P<0.001). ROC curves showed better efficacy to predict survival using the risk score (AUC=0.744). Immune cell enrichment analysis demonstrated that the types of immune cells and their expression levels in the high-risk group were significant different with those in the low-risk group (all P<0.05). The experimental results confirmed the ALOX5, NOX1 were upregulated and the MT1G was downregulated in the EAC tissues compared with the normal esophageal mucosal tissues (all P<0.05).Conclusions: We identified differently expressed ferroptosis-related genes that may involve in the process in EAC. These genes have significant values in predicting the patients’ OS and targeting ferroptosis may be an alternative for therapy. Further studies are necessary to verify these results of our study.

scholarly journals Abnormal TACC3 Expression is an Independent Prognostic Biomarker in Lung Carcinoma

2020 ◽  
Author(s):  
Dongwei He ◽  
Xiaoyan Fan ◽  
Yulong Zhang ◽  
You Li
Keyword(s):  
Risk Factor ◽  
Lung Carcinoma ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Coiled Coil ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Roc Curve Analysis

Abstract Background: Abnormal expression of transforming acidic coiled-coil protein 3 (TACC3) correlates with tumorigenesis of many human malignancies. However, the expression pattern of TACC3 and its clinical significance have not been well characterized in lung carcinoma (LUAD) so far. Objective: To investigate the association of TACC3 expression level with the clinicopathological characteristics and prognosis of LUAD patients.Methods: In the study, based on Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and The Cancer Genome Atlas (TCGA) databases, the expression of TACC3 was determined in LUAD patients. Further, the expression of TACC3 was established using qRT-PCR in LUAD patients. Results: Our results showed that TACC3 was significantly overexpressed in LUAD tumors compared with non-tumors in the above public databases (all p<0.01). A receiver operating characteristic (ROC) curve analysis suggested that TACC3 may have diagnostic value in LUAD patients (normal vs tumor: AUC = 0.940). Kaplan-Meier analysis further demonstrated that high TACC3 expression in tumors was significantly associated with worse overall survival (OS) in LUAD patients (all p<0.01). In addition, Univariate and multivariate Cox regression analyses showed that TACC3 was an independent risk factor for OS among LUAD patients (HR = 1.02, 95% CI: 1.01-1.04, p = 0.00823; HR=1.43, 95% CI: 1.17-1.70, p <0.001). Finally, using gene set enrichment analysis (GSEA 3.0), we found that a series of potential pathways related to TACC3 were highly enriched with the high TACC3 expression phenotype (p = 0.024, p = 0.003, respectively). Conclusions: The present study provides evidence that TACC3 expression is upregulated in tumors and may be an independent risk factor for prognosis in LUAD patients.

scholarly journals A Pyroptosis-Related LncRNA Signature for Predicting Prognosis and Treatment in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Yuhao Zhang ◽  
Jiaxin Zhang ◽  
Fengxian Wei ◽  
Haodong Zhang ◽  
Dongdong Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Value ◽  
Immune Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Clinical Treatment ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Infiltration

Abstract Background: Hepatocellular carcinoma (HCC), which carries a very bad prognosis, is a common malignant tumor worldwide. This study aim to identify a pyroptosis-related long non-coding RNA(pyLncRNA) prognostic signature in HCC by integrated bioinformatics analysis. Methods: All expression profiles of HCC were obtained from The Cancer Genome Atlas (TCGA) and pyroptosis-related genes were from the GSEA website. After identified differentially expressed pyLncRNAs, univariate Cox regression and Lasso analysis were used to identify a pyroptosis-related LncRNAs prognositic signature(py-LPS). Internal validation was used to validate the prognostic value of the py-LPS via the Kaplan-Meier(K-M) curve and receiver operating characteristic(ROC) curve. Additional, we established the nomogram and analyzed the correlation between the signature and immune immune infiltration as well as clinical treatment. Result: 7 pyLncRNAs were established the signature for HCC prognosis. K-M curves exhibited the low risk group presented a markedly longer OS than the high. Clinical subgroups analysis based age, gender, grade and stage yielded the similar results. The signature had an independent prognostic value for HCC(p<0.001). Nomogram estimated one-, three- and five-year survival were 0.777, 0.741 and 0.709. Then, gene set enrichment analysis(GSEA) demostrated significant pathways. Futhermore, we found immune cell infiltration and immunotherapy targets was associated with the signature,which could provided clinical recommendations for chemotherapy.Conclusion: In this study, a novel pyroptosis-related LncRNAs porgnostic signature of HCC, correlated with immune infiltration, could predict the survival of HCC patients and give suggestions for clinical treatment.

scholarly journals Two predicted models based on ceRNAs and immune cells in lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11029
Author(s):  
Miaomiao Zhang ◽  
Peiyan Zheng ◽  
Yuan Wang ◽  
Baoqing Sun
Keyword(s):  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Risk Scores ◽  
Immune Microenvironment

Background It is well accepted that both competitive endogenous RNAs (ceRNAs) and immune microenvironment exert crucial roles in the tumor prognosis. The present study aimed to find prognostic ceRNAs and immune cells in lung adenocarcinoma (LUAD). Materials and Methods More specifically, we explored the associations of crucial ceRNAs with the immune microenvironment. The Cancer Genome Atlas (TCGA) database was employed to obtain expression profiles of ceRNAs and clinical data. CIBERSORT was utilized to quantify the proportion of 22 immune cells in LUAD. Results We constructed two cox regression models based on crucial ceRNAs and immune cells to predict prognosis in LUAD. Subsequently, seven ceRNAs and seven immune cells were involved in prognostic models. We validated both predicted models via an independent cohort GSE72094. Interestingly, both predicted models proved that the longer patients were smoking, the higher risk scores would be obtained. We further investigated the relationships between seven genes and immune/stromal scores via the ESTIMATE algorithm. The results indicated that CDC14A and H1F0 expression were significantly related to stromal scores/immune scores in LUAD. Moreover, based on the result of the ceRNA model, single-sample gene set enrichment analysis (ssGSEA) suggested that differences in immune status were evident between high- and low-risk groups.

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