scholarly journals The prognostic value of m6A-related LncRNAs in patients with HNSCC: bioinformatics analysis of TCGA database

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Liu-qing Zhou ◽  
Jin-xiong Shen ◽  
Jie-yu Zhou ◽  
Yao Hu ◽  
Hong-jun Xiao
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

AbstractN6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. These modifications modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood. Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a prognostic risk model, and consensus clustering analysis, we analyzed 12 m6A-related lncRNAs in HNSCC sample data from The Cancer Genome Atlas (TCGA) database. We found 12 m6A-related lncRNAs in the training cohort and validated them in all cohorts by Kaplan–Meier and Cox regression analyses, revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC survival. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs. In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC outcomes and could provide new therapeutic targets for HNSCC patients.

scholarly journals The Prognostic Value of m6A-related LncRNAs in Patients with HNSCC

2021 ◽  
Author(s):  
Liu-qing Zhou ◽  
Jie-yu Zhou ◽  
Yao Hu
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.

scholarly journals High expression of PIMREG predicts poor survival outcomes and is correlated with immune infiltrates in lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11697
Author(s):  
Feng Jiang ◽  
Min Liang ◽  
Xiaolu Huang ◽  
Wenjing Shi ◽  
Yumin Wang
Keyword(s):  
Prognostic Value ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Background PIMREG is upregulated in multiple cancer types. However, the potential role of PIMREG in lung adenocarcinoma (LUAD) remains unclear. The present study aimed to explore its clinical significance in LUAD. Methods Using the Cancer Genome Atlas (TCGA) databases, we obtained 513 samples of LUAD and 59 normal samples from the Cancer Genome Atlas (TCGA) databases to analyze the relationship between PIMREG and LUAD. We used t and Chi-square tests to evaluate the level of expression of PIMREG and its clinical implication in LUAD. The prognostic value of PIMREG in LUAD was identified through the Kaplan–Meier method, Cox regression analysis, and nomogram. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to screen biological pathways and analyze the correlation of the immune infiltrating level with the expression of PIMREG in LUAD. Results PIMREG was highly expressed in patients with LUAD. Specifically, the level of PIMREG gradually increased from pathological stage I to IV. Further, we validated the higher expression of PIMREG expressed in LUAD cell lines. Moreover, PIMREG had a high diagnostic value, with an -AUC of 0.955. Kaplan–Meier survival and Cox regression analyses revealed that the high expression of PIMREG was independently associated with poor clinical outcomes. In our prognostic nomogram, the expression of PIMREG implied a significant prognostic value. Gene set enrichment analysis (GSEA) identified that the high expression PIMREG phenotype was involved in the mitotic cell cycle, mRNA splicing, DNA repair, Rho GTPase signaling, TP53 transcriptional regulation, and translation pathways. Next, we also explored the correlation of PIMREG and tumor-immune interactions and found a negative correlation between PIMREG and the immune infiltrating level of T cells, macrophages, B cells, dendritic cells (DCs) , and CD8+ T cells in LUAD. Conclusions High levels of PIMREG correlated with poor prognosis and immune infiltrates in LUAD.

scholarly journals A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

2021 ◽  
Author(s):  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

scholarly journals Low Expression of RILPL2 Predicts Poor Prognosis and Correlates With Immune Infiltration in Endometrial Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Jinhui Liu ◽  
Mengting Xu ◽  
Zhipeng Wu ◽  
Yan Yang ◽  
Shuning Yuan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Endometrial Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Critical Role ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Promising Target

Increasing numbers of biomarkers have been identified in various cancers. However, biomarkers associated with endometrial carcinoma (EC) remain largely to be explored. In the current research, we downloaded the RNA-seq data and corresponding clinicopathological features from the Cancer Genome Atlas (TCGA) database. We conducted an expression analysis, which resulted in RILPL2 as a novel diagnostic biomarker in EC. The dysregulation of RILPL2 in EC was also validated in multiple datasets. The correlations between clinical features and RILPL2 expression were assessed by logistic regression analysis. Then, Kaplan-Meier analysis, univariate and multivariate Cox regression analysis were performed to estimate prognostic values of RILPL2 in the TCGA cohort, which revealed that increased level of RILPL2 was remarkably associated with better prognosis and could act as an independent prognostic biomarker in patients with EC. Moreover, correlation analysis of RILPL2 and tumor-infiltrating immune cells (TIICs) indicated that RILPL2 might play a critical role in regulating immune cell infiltration in EC and is related to immune response. Besides, high methylation level was a significant cause of low RILPL2 expression in EC. Subsequently, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were conducted to explore the RILPL2-involved underlying oncogenic mechanisms, and the results indicated that RILPL2 mainly regulated cell cycle. In conclusion, our findings provided evidence that downregulation of RILPL2 in EC is an indicator of adverse prognosis and RILPL2 may act as a promising target for the therapeutics of EC.

scholarly journals Comprehensive Analysis and Prognosis Prediction of N6-methyladenosine-related Lncrnas in Immune Microenvironment Infiltration of Gastric Cancer

2021 ◽  
Author(s):  
Jianfeng Huang ◽  
Wenzheng Chen ◽  
Changyu Chen ◽  
Tao Xiao ◽  
Zhigang Jie
Keyword(s):  
Gastric Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Cytotoxic T Lymphocyte ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Rna Modification ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Abstract BackgroundN6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. MethodsIn this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. ResultsThe results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, our risk model showed that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (e.g., paclitaxel and oxaliplatin). ConclusionsThis evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and my lead to more effective immunotherapy and chemotherapy for patients with GC.

scholarly journals Predictive Proteomic Signature for the Prognosis of Cervical Cancer

2021 ◽  
Author(s):  
Xiaoyu Ji ◽  
Guangdi Chu ◽  
Jinwen Jiao ◽  
Teng Lv ◽  
Yulong Chen ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Abstract Objective: Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict survival of patients with common CC. Methods and results: The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan–Meier(K-M) analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the Area Under Curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins.Conclusion: A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.

Expression patterns and a prognostic model of m6A-associated regulators in prostate adenocarcinoma

2020 ◽  
Vol 14 (18) ◽  
pp. 1717-1731
Author(s):  
Song Ou-Yang ◽  
Ji-Hong Liu ◽  
Qin-Zhang Wang
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Prostate Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Clinical Biomarkers

Aim: To study the expression patterns and prognostic value of the m6A-associated regulators in prostate adenocarcinoma (PRAD). Materials & methods: The mRNA expression and clinical data were downloaded from ‘The Cancer Genome Atlas database’. The m6A-associated variants were downloaded from m6AVar database, and combined with 14 common m6A regulators for subsequent analysis. One-way analysis of variance, univariate Cox regression analysis and least absolute shrinkage and selection operator algorithm were successively applied to obtain the ultimate regulators and prognostic model. Finally, consensus clustering, protein–protein interaction (PPI) and enrichment analysis were performed. Result: Nine regulators were obtained. PRAD patients could be classified into two risk groups and subclasses with significant survival differences by the prognostic model and consensus clustering, respectively. Conclusion: All these nine regulators were related to prognosis in PRAD, and could be used as clinical biomarkers.

Genome-wide methylation and expression profiling identify a novel epigenetic signature in gastrointestinal pan-adenocarcinomas

Epigenomics ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 907-920
Author(s):  
Wei Song ◽  
Jun Ren ◽  
Wen-Jie Wang ◽  
Chun-Tao Wang ◽  
Tao Fu
Keyword(s):  
Overall Survival ◽  
Survival Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Clinicopathologic Factors ◽  
Cancer Genome Atlas

Aim: To identify methylation-driven genes and establish a novel epigenetic signature for gastrointestinal (GI) pan-adenocarcinomas. Materials & methods: Methylation and RNA-seq data for GI adenocarcinomas were downloaded from the Cancer Genome Atlas database. A methylation-driven gene signature was established by multivariate Cox regression analysis. We developed a prognostic nomogram using a combination of methylation-driven gene risk score and clinicopathological variables. A joint survival analysis based on gene expression and methylation was conducted to further investigate the prognostic role of methylation-driven genes. Results: An epigenetic signature was established based on five methylation-driven genes. We also established a prognostic nomogram based on methylation-driven gene risk score and clinicopathologic factors, with a favorable predictive ability. Joint survival analysis revealed that 28 methylation-driven genes could be independent prognostic factors for overall survival for GI adenocarcinomas. Conclusion: An epigenetic signature was established that effectively predicts the overall survival for GI adenocarcinomas across anatomic boundaries.

scholarly journals Development and Validation of a Robust Ferroptosis-Related Prognostic Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Yen-ting Lin ◽  
Can-Xuan Li ◽  
Jie Chen
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Roc Curve ◽  
Prognostic Value ◽  
Cox Regression ◽  
Expression Patterns ◽  
The Cancer Genome Atlas ◽  
Entire Group ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background: Ferroptosis is a novel defined type of programmed cell death (PCD) with widespread functions involved in physical conditions or multiple diseases including malignancies. However, the relationship between ccRCC and ferroptosis-related regulators remains poorly known. Herein, we investigate the prognostic values and potential mechanisms of ferroptosis-related genes (FRGs) in ccRCC.Methods: Ferroptosis-related genes were obtained from FerrDb database, GeneCards database and previously published literatures. The gene expression profile of ferroptosis-related regulators and corresponding clinicopathological information were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed ferroptosis-related genes (DE-FRGs) were screened between ccRCC specimens and noncancerous specimens. Among these genes, prognostic DE-FRGs were identified using univariate COX analysis and LASSO regression analysis. Further multivariate COX regression was employed to identify prognosis-related hub DE-FRGs and establish a prognostic model. Results: We identified seven hub genes (HMGCR, MT1G, BID, EIF4A1, FOXM1, TFAP2C and CHAC1) from the DE-FRGs using univariate Cox regression analysis, LASSO and multivariate Cox regression analysis, and used them to establish a novel clinical predictive model in the TCGA train cohort (n = 374). Subsequently, we assessed the prognostic value of the model. Survival analysis showed that high-risk patients had a reduced overall survival (OS), the time-dependent receiver operating characteristic (ROC) curve analysis confirmed the signature's diagnostic performance. Additionally, multivariate Cox regression analysis suggested that the risk score was an independent prognostic factor. Additionally, we verified the prognostic performance of the risk model in the testing cohort (n=156), and the entire group (n=530) using Kaplan-Meier curve and ROC curve analyses. Functional analysis indicated that several carcinogenic pathways were enriched, and tumor-infiltrating immune cell abundances, and the expression levels of immunosuppressive molecules were different between two risk groups. Finally, external databases (ONCMINE, GEPIA, HPA, Kaplan-Meier plotter and cbioportal) were used to confirm the expression patterns, prognostic value, and genetic mutations of 7 hub FRGs in ccRCC.Conclusions: Collectively, we successfully constructed a novel ferroptosis-related risk signature that was significantly associated with the prognosis of ccRCC.

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.

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