scholarly journals A m6A-related lncRNA Signature as a Novel Prognostic Factor for Gastric Cancer

2020 ◽  
Author(s):  
Haixu Wang ◽  
Qingkai Meng ◽  
Bin Ma
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Complete Dataset ◽  
M6a Rna Methylation

Abstract Background: N6-methyladenosine (m6A)isa common form of mRNA modification regulated by m6A RNA methylation regulators. However, studies have not explored the role of m6A-related lncRNA in gastric cancer (GC). This study aimed atexploring biological and prognostic roles of m6A-related lncRNA in GC. Methods: We identified 800 m6A-related lncRNAs through correlation analysis of 13 main m6A RNA methylation regulators and all lncRNAs expressed in GC. We further categorized patients into train group and testing group equally. Results: A total of 11 m6A-related lncRNA signature associated with prognosis of GC were identified through univariate cox regression analysis and LASSO analysis which was validated using the testing dataset and complete dataset, respectively. More deaths and shorter survival time were reported for patients in the high-risk group compared to low-risk group. lncRNA signature is an independent prognosis predictor as shown by cox regression analysis of the complete dataset. Moreover, genes involved in base excision repair were highly expressed in patients in the high-risk group as shown by gene set enrichment analysis (GSEA) result whereas ECM receptor interaction and focal adhesion pathway were enriched in low-risk group. A nomogram on independent factors showed clinical net benefit asan overall survival predictor of GC. In addition, we identified four subgroups of GC patients with significant differences in overall survival (OS).Subgroup C1 and C2 responded well to immunotherapy, compared to subgroup C3 and C4. Conclusions: m6A-related lncRNA signature and four molecular subgroups provide information on the underlying molecular mechanism of GC and provide for a basis for development ofpersonalized therapy.

scholarly journals Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinzhi Lai ◽  
Hainan Yang ◽  
Tianwen Xu
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Regulatory Network ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

Abstract Background Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM. Methods We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan–Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs). Results A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM. Conclusion Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

scholarly journals Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Haige Zheng ◽  
Huixian Liu ◽  
Yumin Lu ◽  
Hengguo Li
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Cox Regression Analysis

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment.Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment.Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters.Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

scholarly journals Development and Validation of a Unique Gignature of Cancer Stemness-related Genes for Predicting the Overall survival of Colorectal Cancer Patients

2020 ◽  
Author(s):  
Ran Wei ◽  
Jichuan Quan ◽  
Shuofeng Li ◽  
Zhao Lu ◽  
Xu Guan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cancer Stemness ◽  
Poor Overall Survival ◽  
Tissue Samples ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Background: Cancer stem cells (CSCs), which are characterized by self-renewal and plasticity, are highly correlated with tumor metastasis and drug resistance. To fully understand the role of CSCs in colorectal cancer (CRC), we evaluated the stemness traits and prognostic value of stemness-related genes in CRC.Methods: In this study, the data from 616 CRC patients from The Cancer Genome Atlas (TCGA) were assessed and subtyped based on the mRNA expression-based stemness index (mRNAsi). The correlations of cancer stemness with the immune microenvironment, tumor mutational burden (TMB) and N6-methyladenosine (m6A) RNA methylation regulators were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify the crucial stemness-related genes and modules. Furthermore, a prognostic expression signature was constructed using Lasso-penalized Cox regression analysis. The signature was validated via multiplex immunofluorescence staining of tissue samples in an independent cohort of 48 CRC patients.Results: This study suggests that high mRNAsi scores are associated with poor overall survival in stage Ⅳ CRC patients. Moreover, the levels of TMB and m6A RNA methylation regulators were positively correlated with mRNAsi scores, and low mRNAsi scores were characterized by increased immune activity in CRC. The analysis identified 2 key modules and 34 key genes as prognosis-related candidate biomarkers. Finally, a 3-gene prognostic signature (PARPBP, KNSTRN and KIF2C) was explored together with specific clinical features to construct a nomogram, which was successfully validated in an external cohort. Conclusions: There is a unique correlation between CSCs and the prognosis of CRC patients, and the novel biomarkers related to cell stemness could accurately predict the clinical outcomes of these patients.

scholarly journals A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

2021 ◽  
Author(s):  
Shaopei Ye ◽  
Wenbin Tang ◽  
Ke Huang
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

scholarly journals N6-Methyladenosine RNA Demethylase FTO Promotes Gastric Cancer Metastasis by Down-Regulating the m6A Methylation of ITGB1

2021 ◽  
Vol 11 ◽  
Author(s):  
Duo Wang ◽  
Xiujuan Qu ◽  
Wenqing Lu ◽  
Yizhe Wang ◽  
Yue Jin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Factor ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Migration And Invasion ◽  
Integrin Β1 ◽  
Rna Methylation ◽  
Protein Levels ◽  
M6a Rna Methylation

Abnormal RNA m6A methylation is known to lead to the occurrence and progression of multiple cancers including gastric cancer (GC). However, the integrative effects of all m6A methylation regulators on GC prognosis are unclear. Our research aimed to globally analyze the prognosis values of all 33 m6A RNA methylation regulators in GC by univariate and multivariate Cox regression analyses. Among all 33 m6A RNA methylation regulators, fat mass and obesity-associated protein (FTO), an m6A demethylase, was identified as a key prognostic risk factor on overall survival (OS) of GC patients. It was found that FTO could promote GC cell migration and invasion abilities, and we predicted that ITGB1 was a demethylated target of FTO. Knockdown (KD) of FTO significantly down-regulated ITGB1 expression at both mRNA and protein levels and augmented ITGB1 mRNA m6A modification level. Moreover, overexpression (OE) of ITGB1 could partially reverse FTO-KD-inhibited migration and invasion of GC cells. Our study found that FTO was an independent risk factor for overall survival (OS) of GC patients and FTO could promote GC metastasis by upregulating the expression of Integrin β1(ITGB1) via decreasing its m6A level. These results indicated that FTO can be a potent GC biomarker for prognosis prediction as well as a potential target in GC treatment.

scholarly journals m6A RNA methylation regulators can contribute to malignant progression and impact the prognosis of bladder cancer

2019 ◽  
Vol 39 (12) ◽  
Author(s):  
Mei Chen ◽  
Zhen-yu Nie ◽  
Xiao-hong Wen ◽  
Yuan-hui Gao ◽  
Hui Cao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Malignant Progression ◽  
Low Risk ◽  
Rna Methylation ◽  
M6a Rna Methylation ◽  
Clinicopathological Variables

Abstract N6-methyladenosine (m6A) is the most common form of messenger RNA (mRNA) modification. An increasing number of studies have proven that m6A RNA methylation regulators are overexpressed in many cancers and participate in the development of cancer through the dynamic regulation of m6A RNA methylation regulators. However, the prognostic role of m6A RNA methylation regulators in bladder cancer (BC) is poorly understood. In the present study, we downloaded the mRNA expression data from The Cancer Genome Atlas (TCGA) database and the corresponding clinical and prognostic information. The relationship between m6A RNA methylation regulators and clinicopathological variables of BC patients was assessed by the Kolmogorov–Smirnov test. The expression of the m6A RNA methylation regulators was differentially associated with different clinicopathological variables of BC patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was then applied to identify three m6A RNA methylation regulators. The risk signature was constructed as follows: 0.164FTO − (0.081YTHDC1+0.032WTAP). Based on the risk signature, the risk score of each patient was calculated, and the patients were divided into a high-risk group and a low-risk group. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The risk signature was not only an independent prognostic marker for BC patients but also a predictor of clinicopathological variables. In conclusion, m6A RNA methylation regulators can participate in the malignant progression of BC, and a risk signature with three selected m6A RNA methylation regulators may be a promising prognostic biomarker to guide personalized treatment for BC patients.

scholarly journals Expression Status and Prognostic Value of m6A RNA Methylation Regulators in Lung Adenocarcinoma

2020 ◽  
Author(s):  
Rui Wang ◽  
Zian Feng ◽  
Jie Hu ◽  
Xiaodong He ◽  
Zuojun Shen
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
Prediction Efficiency

Abstract Background: N6-methyladenosine (m6A) RNA modification is the most abundant modification method in mRNA, and it plays an important role in the occurrence and development of many cancers. However, data on the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) are still lacking. This paper mainly discusses the role of m6A RNA methylation regulators in LUAD, to identify novel prognostic biomarkers.Methods: The gene expression data of 19 m6A methylation regulator in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm were performed to construct a risk signature and evaluated its prognostic prediction efficiency by using the receiver operating characteristic (ROC) curve. The risk score of each patient was calculated according to the risk signature, and LUAD patients were divided into high-risk group and low-risk group. Kaplan-Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of risk signature. Finally, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the differential signaling pathways and cellular processes between the two groups.Results: The expression of 15 m6A RNA methylation regulators in LUAD tissues was significantly different than that in normal tissues. YTHDF3, YTHDF2, KIAA1429, HNRNPA2B1, RBM15, METTL3, HNRNPC, YTHDF1, IGF2BP2, IGF2BP3, IGF2BP1 were significantly up-regulated in LUAD, and the expressions of FTO, ZC3H13, WTAP, and METL14 were significantly down-regulated. We selected IGF2BP1, HNRNPC, and HNRNPA2B1 to construct the risk signature. ROC curve indicated the area under the curve (AUC) was 0.659, which means the risk signature had a good prediction efficiency. The results of Kaplan-Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD.Conclusions: The m6A RNA methylation regulators IGF2BP1, HNRNPC, and HNRNPA2B1 have a significant correlation with the clinicopathological characteristics of LUAD, which may be a promising prognostic feature and clinical treatment target.

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