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

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

Life ◽

10.3390/life11070619 ◽

2021 ◽

Vol 11 (7) ◽

pp. 619

Author(s):

Xiuhong Li ◽

Zian Feng ◽

Rui Wang ◽

Jie Hu ◽

Xiaodong He ◽

...

Keyword(s):

Regression Analysis ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Roc Curve ◽

Cox Regression ◽

Cox Regression Analysis ◽

Rna Methylation ◽

Kaplan Meier ◽

M6a Rna Methylation ◽

Prediction Efficiency

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. This paper mainly discusses the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) to identify novel prognostic biomarkers. The gene expression data of 19 m6A methylation regulators in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. We selected three significantly differentially expressed m6A regulators in LUAD to construct the risk signature, and evaluated its prognostic prediction efficiency using the receiver operating characteristic (ROC) curve. Kaplan–Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of the risk signature. The ROC curve indicated that the area under the curve (AUC) was 0.659, which means that the risk signature had a good prediction efficiency. The results of the Kaplan–Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD. In addition, we explored the differential signaling pathways and cellular processes related to m6A methylation regulators in LUAD.

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Associations between TAB2 gene polymorphisms and dilated cardiomyopathy in a Chinese population

Biomarkers in Medicine ◽

10.2217/bmm-2019-0384 ◽

2020 ◽

Author(s):

Can Shen ◽

Yue Zhong ◽

Xingming Huang ◽

Yanyun Wang ◽

Ying Peng ◽

...

Keyword(s):

Regression Analysis ◽

Survival Analysis ◽

Dilated Cardiomyopathy ◽

Gene Polymorphisms ◽

Chinese Population ◽

Cox Regression ◽

Bonferroni Correction ◽

Cox Regression Analysis ◽

Kaplan Meier

Aim: The present study aimed to investigate the role of TAB2 gene polymorphisms in dilated cardiomyopathy (DCM) susceptibility and prognosis in a Chinese population. Materials & methods: A total of 343 DCM patients and 451 controls were enrolled and had their blood genotyped. Survival analysis was evaluated with Kaplan-Meier curves and Cox regression analysis. Results: G carriers (AG/GG) and AG genotype of rs237028 had a higher DCM susceptibility as well as a worse DCM prognosis. Additionally, C carriers (CT/CC) of rs652921 and G carriers (TG/GG) of rs521845 had a higher DCM risk and CC homozygote of rs652921 had a worse DCM prognosis. These associations were still significant after adjustment for the Bonferroni correction. Conclusion: TAB2 gene polymorphisms were associated with DCM susceptibility and prognosis in the Chinese population.

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Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N6-Methyladenosine RNA Methylation Regulators

Journal of Immunology Research ◽

10.1155/2021/6617841 ◽

2021 ◽

Vol 2021 ◽

pp. 1-23

Author(s):

Yan Zhang ◽

Yao Yao ◽

Xiaochen Qi ◽

Jianyi Li ◽

Meihong Liu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Regression Analysis ◽

Roc Curve ◽

Cox Regression ◽

Clear Cell ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

As the most prevalent internal eukaryotic modification, N6-methyladenosine (m6A) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of m6A in clear cell renal cell carcinoma (ccRCC). In this study, we researched the RNA-seq transcriptome data of ccRCC in the TCGA dataset and used bioinformatics analyses to detect the relationship between m6A RNA methylation regulators and ccRCC. First, we compared the expression of 18 m6A RNA methylation regulators in ccRCC patients and normal tissues. Then, data from ccRCC patients were divided into two clusters by consensus clustering. LASSO Cox regression analysis was used to build a risk signature to predict the prognosis of patients with ccRCC. An ROC curve, univariate Cox regression analysis, and multivariate Cox regression analysis were used to verify this risk signature’s predictive ability. Then, we internally validated this signature by random sampling. Finally, we explored the role of the genes in the signature in some common pathways. Gene distribution between the two subgroups was different; cluster 2 was gender-related and had a worse prognosis. IGF2BP3, IGF2BP2, HNRNPA2B1, and METTL14 were chosen to build the risk signature. The overall survival of the high- and low-risk groups was significantly different ( p = 7.47 e − 12 ). The ROC curve also indicated that the risk signature had a decent predictive significance ( AUC = 0.72 ). These results imply that the risk signature has a potential value for ccRCC treatment.

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A novel signature based on m6A related genes as the potential prognostic factor in hepatocellular carcinoma.

10.21203/rs.2.21963/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shaoju Luo ◽

Hao Hu ◽

Ling Yu ◽

Zhirui Cao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Immune Therapy ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Potential Biomarker ◽

Significant Difference

Abstract Background Many different signatures and models have been established for patients with hepatocellular carcinoma (HCC), but no signature based on m6A related genes was developed. The objective of this research was to establish the signature with m6A related genes in HCC. Methods Data from 377 HCC patients from The Cancer Genome Atlas (TCGA) database was downloaded. The included m6A related genes were selected by Cox regression analysis and the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, the nomogram was constructed and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the four m6A related genes (YTHDF2, YTHDF1, METTL3 and KIAA1429). Under the grouping from signature, patients in high risk group of showed the poor prognosis than those in low risk group. And significant difference was found in two kinds of immune cells (T cell gamma delta and NK cells activated) between two groups. The univariate and multivariate Cox regression analysis indicated that m6A related signature can be the potential independent prognosis factor in HCC. Finally, we developed a clinical risk model predicting the HCC prognosis and successfully verified it in C-index, calibration and ROC curve. Conclusion Our study identified the m6A related signature for predicting prognosis of HCC and provided the potential biomarker between m6A and immune therapy.

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Role of the TGFβ/PDCD4/AP-1 Signaling Pathway in Nasopharyngeal Carcinoma and Its Relationship to Prognosis

Cellular Physiology and Biochemistry ◽

10.1159/000481871 ◽

2017 ◽

Vol 43 (4) ◽

pp. 1392-1401 ◽

Cited By ~ 6

Author(s):

Jie Ma ◽

Shu-Hong Xuan ◽

Yan Li ◽

Zhi-Ping Zhang ◽

Xin-Hua Li

Keyword(s):

Regression Analysis ◽

Prognostic Factors ◽

Nasopharyngeal Carcinoma ◽

Cox Regression ◽

Advanced Tumor Stage ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Kaplan Meier ◽

Low Expression

Background: The objective of the present study was to evaluate the role of the TGFβ/PDCD4/AP-1 pathway in nasopharyngeal carcinoma (NPC) and its relationship to NPC prognosis. Methods: NPC tissues collected from 66 NPC patients were compared to 17 nasopharyngeal mucosa biopsy specimens collected as normal tissues. Immunohistochemical staining was performed to assess expression of transforming growth factor-β receptor I (TGFβRI), programmed cell death 4 (PDCD4) and activator protein-1 (AP-1). The Kaplan-Meier method was applied to evaluate NPC patient overall survival (OS) and progression-free-survival (PFS). Cox regression analysis was used to estimate independent prognostic factors for NPC. The human NPC cell line CNE2 was selected and treated with SB431542, an inhibitor of TGFβRI; expression of TGFβRI and PDCD4 in CNE2 cells was determined by western blotting. NPC tissues showed higher expression of TGFβRI and AP-1 but lower expression of PDCD4 than normal tissues (all P < 0.05). Results: The results of Kaplan-Meier analysis showed that TGFβRI-positive patients and AP-1-positive patients had shorter OS and PFS than TGFβRI-negative patients and AP-1-negative patients; additionally, PDCD4-positive patients had higher OS and PFS than PDCD4-negative patients. Cox regression analysis revealed that advanced tumor stage, overexpression of TGFβRI and AP-1, and low expression of PDCD4 were unfavorable factors influencing OS and PFS in NPC patients. Compared with the control group, expression of TGFβRI decreased and that of PDCD4 increased significantly in CNE2 cells treated with the inhibitor (all P < 0.05). These findings indicate that the TGFβ/PDCD4/AP-1 pathway may be associated with NPC development and progression. Conclusion: High expression of TGFβRI and AP-1 and low expression of PDCD4 may be unfavorable prognostic factors for NPC.

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The Identification and Validation of a Robust Immune-Associated Gene Signature in Cutaneous Melanoma

Journal of Immunology Research ◽

10.1155/2021/6686284 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Le-Bin Song ◽

Jiao-Chen Luan ◽

Qi-Jie Zhang ◽

Lin Chen ◽

Hao-Yang Wang ◽

...

Keyword(s):

Regression Analysis ◽

Survival Analysis ◽

Cutaneous Melanoma ◽

Cox Regression ◽

Risk Group ◽

Cox Model ◽

Critical Role ◽

Gene Signature ◽

Low Risk ◽

Cox Regression Analysis

Background. Cutaneous melanoma is defined as one of the most aggressive skin tumors in the world. An increasing body of evidence suggested an indispensable association between immune-associated gene (IAG) signature and melanoma. This article is aimed at formulating an IAG signature to estimate prognosis of melanoma. Methods. 434 melanoma patients were extracted from The Cancer Genome Atlas (TCGA) database, and 1811 IAGs were downloaded from the ImmPort database in our retrospective study. The Cox regression analysis and LASSO regression analysis were utilized to establish a prognostic IAG signature. The Kaplan-Meier (KM) survival analysis was performed, and the time-dependent receiver operating characteristic curve (ROC) analysis was further applied to assess the predictive value. Besides, the propensity score algorithm was utilized to balance the confounding clinical factors between the high- and low-risk groups. Results. A total of six prognostic IAGs comprising of INHA, NDRG1, IFITM1, LHB, GBP2, and CCL8 were eventually filtered out. According to the KM survival analysis, the results displayed a shorter overall survival (OS) in the high-risk group compared to the low-risk group. In the multivariate Cox model, the gene signature was testified as a remarkable prognostic factor ( HR = 45.423 , P < 0.001 ). Additionally, the ROC curve analyses were performed which demonstrated our IAG signature was superior to four known biomarkers mentioned in the study. Moreover, the IAG signature was significantly related to immunotherapy-related biomarkers. Conclusion. Our study demonstrated that the six IAG signature played a critical role in the prognosis and immunotherapy of melanoma, which might help clinicians predict patients’ survival and provide individualized treatment.

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Idenfication of a novel signature based on mTORC1 pathway for hepatocellular carcinoma.

10.21203/rs.2.23745/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shaoju Luo ◽

Zhirui Cao ◽

Hao Hu ◽

Ling Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Prognostic Factor ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Signal Pathway ◽

Cancer Genome ◽

Cox Regression Analysis

Abstract Background mTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature. Methods HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, a nomogram was established and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the six genes ( ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP ). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Conclusion The mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.

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

10.21203/rs.3.rs-230598/v1 ◽

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.

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Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

Cancer Cell International ◽

10.1186/s12935-021-02385-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhengxin Wu ◽

Jinshui Tan ◽

Yifan Zhuang ◽

Mengya Zhong ◽

Yubo Xiong ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Survival Analysis ◽

Risk Model ◽

Cox Regression ◽

Clinical Samples ◽

Pyrimidine Metabolism ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cox Analysis

Abstract Background Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear. Methods Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR. Results Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan–Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model. Conclusion In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

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Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer

BMC Cancer ◽

10.1186/s12885-021-09006-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaotao Li ◽

Shi Fu ◽

Yinglong Huang ◽

Ting Luan ◽

Haifeng Wang ◽

...

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Survival Analysis ◽

Risk Score ◽

Survival Probability ◽

Cox Regression ◽

Risk Group ◽

Differentially Expressed ◽

Related Gene ◽

Cox Regression Analysis

Abstract Background Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients. Methods First, differentially expressed MRGs between BC and normal samples were identified and used to construct a protein-protein interaction (PPI) network and perform mutation analysis. Next, univariate Cox regression analysis was utilized to select prognostic genes, and multivariate Cox regression analysis was applied to establish an MRG signature for predicting the survival probability of BC patients. Moreover, Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive capability of the MRG signature. Finally, a nomogram based on the MRG signature was established to better predict the survival of BC. Results In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. Next, an MRG signature, including MAOB, FASN and LRP1, was established by using univariate and multivariate Cox regression analysis. Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group. Finally, Cox regression analysis showed that the risk score was an independent prognostic factor, and a nomogram integrating age, pathological tumor stage and risk score was established and presented good predictive ability. Conclusion We successfully constructed a novel MRG signature to predict the prognosis of BC patients, which might contribute to the clinical treatment of BC.

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