scholarly journals cg04448376, cg24387542, cg08548498, and cg14621323 as a Novel Signature to Predict Prognosis in Kidney Renal Papillary Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ying-Lei Wang ◽  
Ying-Ying Zhang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cell Carcinoma ◽  
Proportional Hazards ◽  
Expression Patterns ◽  
Cox Proportional Hazards ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Hub Genes

Introduction. DNA methylation plays a vital role in prognosis prediction of cancers. In this study, we aimed to identify novel DNA methylation site biomarkers and create an efficient methylated site model for predicting survival in kidney renal papillary cell carcinoma (KIRP). Methods. DNA methylation and gene expression profile data were downloaded from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Differential methylated genes (DMGs) and differential expression genes (DEGs) were identified and then searched for the hub genes. Cox proportional hazards regression was applied to identify DNA methylated site biomarkers from the hub genes. Kaplan–Meier survival and ROC analyses were used to validate the effective prognostic ability of the methylation gene site biomarker. The biomarker sites were validated in the GEO cohorts. The GO and KEGG annotation was done to explore the biological function of DNA methylated site signature. Results. Nine DMGs with opposite expression patterns containing 47 methylated sites were identified. Finally, four methylated sites were identified using the hazard regression model (cg04448376, cg24387542, cg08548498, and cg14621323) located in UTY, LGALS9B, SLPI, and PFN3, respectively. These sites classified patients into high- and low-risk groups in the training cohort. The 5-year survival rates for patients with low-risk and high-risk scores were 97.5% and 75.9% ( P < 0.001 ). The prognostic accuracy and signature methylation sites were validated in the test (TCGA, n = 87 ) and GEO cohorts ( n = 14 ). Multivariate regression analysis showed that the signature was an independent prediction prognostic factor for KIRP. Based on this analysis, we developed methylated site signature nomogram that predicts an individual’s risk of survival. Functional analysis suggested that these signature genes are involved in the biological processes of protein binding. Conclusions. Our study demonstrated that the methylated gene site signature might be a powerful prognostic tool for evaluating survival rate and guiding tailored therapy for KIRP patients.

scholarly journals Association of variably methylated tumour DNA regions with overall survival for invasive lobular breast cancer

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Medha Suman ◽  
Pierre-Antoine Dugué ◽  
Ee Ming Wong ◽  
JiHoon Eric Joo ◽  
John L. Hopper ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Lobular Breast Cancer ◽  
Genome Wide ◽  
Invasive Lobular Breast Cancer

Abstract Background Tumour DNA methylation profiling has shown potential to refine disease subtyping and improve the diagnosis and prognosis prediction of breast cancer. However, limited data exist regarding invasive lobular breast cancer (ILBC). Here, we investigated the genome-wide variability of DNA methylation levels across ILBC tumours and assessed the association between methylation levels at the variably methylated regions and overall survival in women with ILBC. Methods Tumour-enriched DNA was prepared by macrodissecting formalin-fixed paraffin embedded (FFPE) tumour tissue from 130 ILBCs diagnosed in the participants of the Melbourne Collaborative Cohort Study (MCCS). Genome-wide tumour DNA methylation was measured using the HumanMethylation 450K (HM450K) BeadChip array. Variably methylated regions (VMRs) were identified using the DMRcate package in R. Cox proportional hazards regression models were used to assess the association between methylation levels at the ten most significant VMRs and overall survival. Gene set enrichment analyses were undertaken using the web-based tool Metaspace. Replication of the VMR and survival analysis findings was examined using data retrieved from The Cancer Genome Atlas (TCGA) for 168 ILBC cases. We also examined the correlation between methylation and gene expression for the ten VMRs of interest using TCGA data. Results We identified 2771 VMRs (P < 10−8) in ILBC tumours. The ten most variably methylated clusters were predominantly located in the promoter region of the genes: ISM1, APC, TMEM101, ASCL2, NKX6, HIST3H2A/HIST3H2BB, HCG4P3, HES5, CELF2 and EFCAB4B. Higher methylation level at several of these VMRs showed an association with reduced overall survival in the MCCS. In TCGA, all associations were in the same direction, however stronger than in the MCCS. The pooled analysis of the MCCS and TCGA data showed that methylation at four of the ten genes was associated with reduced overall survival, independently of age and tumour stage; APC: Hazard Ratio (95% Confidence interval) per one-unit M-value increase: 1.18 (1.02–1.36), TMEM101: 1.23 (1.02–1.48), HCG4P3: 1.37 (1.05–1.79) and CELF2: 1.21 (1.02–1.43). A negative correlation was observed between methylation and gene expression for CELF2 (R = − 0.25, P = 0.001), but not for TMEM101 and APC. Conclusions Our study identified regions showing greatest variability across the ILBC tumour genome and found methylation at several genes to potentially serve as a biomarker of survival for women with ILBC.

scholarly journals A Prognostic Nomogram Combining Gene Expression Profiles and TNM Stage Predicts Survival in Gastric Cancer

2021 ◽  
Author(s):  
Jian Huang ◽  
Dongcun Wang ◽  
Xiaoliang Wang ◽  
Xiaoxing Ye ◽  
Jiping Da
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Tnm Staging ◽  
Cox Proportional Hazards ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
High Morbidity

Abstract BackgroundGastric carcinoma (GC) is a highly aggressive malignancy and is associated with high morbidity and mortality rates around the world, the current tumor-node-metastasis (TNM) staging system is inadequate to predict overall survival (OS) in GC patients. therefore, potential forecasting methods for prognosis are important to investigate.MethodsDifferentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas (TCGA). We then construct a risk score signature model by univariate Cox proportional hazards regression (CPHR) analysis, the Kaplan-Meier method（KM）and multivariate CPHR analysis. Using TNM stage, we developed a signature-based nomogram. Finally, we utilize an independent Gene Expression Omnibus dataset (GSE62254) validate the prognostic value of risk score signature model and nomogram.ResultsWe identified five OS-related mRNAs among 1113 mRNAs that were differentially expressed between GC and normal samples in the TCGA dataset. We then constructed a five-mRNA signature model, which efficiently distinguished high-risk from low-risk patient in both cohort, and even viable in the TNM stage-III, gender(male, female) and age(＜65-year-old, ≥65-year-old) subgroups (P<0.05). Utilizing TNM stage, we developed a signature-based nomogram, which performed better than use the TNM stage or five-mRNA signature alone for prognostic prediction in the TCGA and GSE62254 dataset.ConclusionsThese results suggest that both risk signature and nomogram were effective prognostic indicators for patients with GCs, and could potentially be used for individualized management of such patients.

scholarly journals Identification of a Novel Immune-Related CpG Methylation Signature to Predict Prognosis in Stage II/III Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Feng Chen ◽  
Lijuan Pei ◽  
Siyao Liu ◽  
Yan Lin ◽  
Xinyin Han ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Stage Ii ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Methylation Patterns

With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC. We investigated DNA methylation patterns in patients with stage II/III CRC using datasets from The cancer genome atlas (TCGA). Overall, 182 patients were randomly divided into training (n = 127) and test groups (n = 55). In the training group, five immune-related methylated CG sites (cg11621464, cg13565656, cg18976437, cg20505223, and cg20528583) were identified, and CG site-based risk scores were calculated using univariate Cox proportional hazards regression in patients with stage II/III CRC. Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan–Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.

scholarly journals Hub Gene Identification and Prognostic Model Establishment for Patients with HBV-related Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Lianmei Wang ◽  
Jing Liu ◽  
Zhong Xian ◽  
Jingzhuo Tian ◽  
Chunying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cyclin B1 ◽  
Mitotic Checkpoint ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Hub Genes

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~ 50% of HCC cases. Establishment of a prognostic model is pivotal for clinical therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. DNA topoisomerase II alpha (TOP2A), Disks large-associated protein 5 (DLGAP5), RAD51 associated protein 1 (RAD51AP1), ZW10 interactor (ZWINT), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Cyclin B2 (CCNB2), Aurora kinase A (AURKA), and Cyclin-dependent kinase 1 (CDK1) were identified as the top-ten hub genes. These hub-genes were verified by the Liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP) project, The Cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. FOXM1 and CDK1 were found to be prognostic-related molecules for HBV-HCC patients. The expression patterns of FOXM1 and CDK1were consistently in human and mouse. Furthermore, a nomogram model based on histology grade, pathology stage, sex and, expression of FOXM1 and CDK1 was built to predict the prognosis for HBV–HCC patients. The nomogram model could be used to predict the prognosis of HBV-HCC cases.

scholarly journals Prognostic Value of a Three-DNA Methylation Biomarker in Patients with Soft Tissue Sarcoma

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuxiao Chen ◽  
Rui Zhu ◽  
Min Chen ◽  
Wenna Guo ◽  
Xin Yang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Soft Tissue ◽  
Proportional Hazards ◽  
Prognostic Biomarker ◽  
Close Association ◽  
Characteristic Curve ◽  
Soft Tissue Sarcomas ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Tumors

Soft tissue sarcomas (STS) are a highly aggressive and heterogeneous group of malignant mesenchymal tumors. The prognosis of patients with advanced or metastatic STS remains poor, and the main therapy of STS patients combines primary surgery, radiotherapy, and chemotherapy. Aberrant DNA methylation shows close association with the pathogenesis and tumor progression. Therefore, DNA methylation biomarkers might have the potential in accurately predicting the survival of STS patients. In order to identify a prognostic biomarker based on DNA methylation sites, a comprehensive analysis of the DNA methylation profile of STS patients in the Cancer Genome Atlas (TCGA) database was performed. All samples were randomly divided into training and testing datasets. Cox proportional hazards regression analysis was performed to identify a prognostic biomarker that contains three DNA methylation sites. The Kaplan–Meier analysis demonstrated that the 3-DNA methylation biomarker discriminated patients into high-risk and low-risk groups, both in the training and in the testing datasets, and the area under the receiver operating characteristic curve values (AUCs) were 0.844 (P<0.001, 95% CI: 0.740–0.948) and 0.710 (P=0.002, 95% CI: 0.595–0.823), respectively. Besides, this biomarker presented superior prognostic performance in STS patients with different age, sex, tissue of origin, therapy, and histologic subtypes. Compared with other prognostic biomarkers, this biomarker tended to be a more precise prognostic factor in STS patients. Moreover, methylation sites in this biomarker might provide a new way for clinicians to make decisions regarding the intervention and assess the effectiveness of an individual therapeutic strategy.

scholarly journals The Prognostic Significance of PDE7B in Cytogenetically Normal Acute Myeloid Leukemia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ling Cao ◽  
Weilong Zhang ◽  
Xiaoni Liu ◽  
Ping Yang ◽  
Jing Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Gene Expression Profiles ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Acute Myeloid

AbstractAcute myeloid leukemia (AML) is a malignant hematological disease in which nearly half have normal cytogenetics. We have tried to find some significant molecular markers for this part of the cytogenetic normal AML, which hopes to provide a benefit for the diagnosis, molecular typing and prognosis prediction of AML patients. In the present study, we calculated and compared the gene expression profiles of cytogenetically normal acute myeloid leukemia (CN-AML) patients in database of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and dataset Vizome (a total of 632 CN-AML samples), and we have demonstrated a correlation between PDE7B gene and CN-AML. Then we proceeded to a survival analysis and prognostic risk analysis between the expression levels of PDE7B gene and CN-AML patients. The result showed that the event-free survival (EFS) and overall survival (OS) were significantly shorter in CN-AML patients with high PDE7B levels in each dataset. And we detected a significantly higher expression level of PDE7B in the leukemia stem cell (LSC) positive group. The Cox proportional hazards regression model showed that PDE7B is an independent risk predictor for CN-AML. All results indicate that PDE7B is an unfavorable prognostic factor for CN-AML.

scholarly journals Identification of a prognostic ferroptosis-related lncRNA signature in the tumor microenvironment of lung adenocarcinoma

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yugang Guo ◽  
Zhongyu Qu ◽  
Dandan Li ◽  
Fanghui Bai ◽  
Juan Xing ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Proportional Hazards ◽  
Risk Model ◽  
Principal Component ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Poor Overall Survival

AbstractFerroptosis is closely linked to various cancers, including lung adenocarcinoma (LUAD); however, the factors involved in the regulation of ferroptosis-related genes are not well established. In this study, we identified and characterized ferroptosis-related long noncoding RNAs (lncRNAs) in LUAD. In particular, a coexpression network of ferroptosis-related mRNAs and lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were performed to establish a prognostic ferroptosis-related lncRNA signature (FerRLSig). We obtained a prognostic risk model consisting of 10 ferroptosis-related lncRNAs: AL606489.1, AC106047.1, LINC02081, AC090559.1, AC026355.1, FAM83A-AS1, AL034397.3, AC092171.5, AC010980.2, and AC123595.1. High risk scores according to the FerRLSig were significantly associated with poor overall survival (hazard ratio (HR) = 1.412, 95% CI = 1.271–1.568; P < 0.001). Receiver operating characteristic (ROC) curves and a principal component analysis further supported the accuracy of the model. Next, a prognostic nomogram combining FerRLSig with clinical features was established and showed favorable predictive efficacy for survival risk stratification. In addition, gene set enrichment analysis (GSEA) revealed that FerRLSig is involved in many malignancy-associated immunoregulatory pathways. Based on the risk model, we found that the immune status and response to immunotherapy, chemotherapy, and targeted therapy differed significantly between the high-risk and low-risk groups. These results offer novel insights into the pathogenesis of LUAD, including the contribution of ferroptosis-related lncRNAs, and reveal a prognostic indicator with the potential to inform immunological research and treatment.

scholarly journals SELE gene as a characteristic prognostic biomarker of colorectal cancer

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052110043
Author(s):  
Na Li ◽  
Honghe Xiao ◽  
Jiangli Shen ◽  
Ximin Qiao ◽  
Fenjuan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Expression Profiles ◽  
Immunohistochemical Analysis ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Cox Regression Analysis

Objective To investigate the expression and clinical value of the E-selectin gene ( SELE) in colorectal cancer (CRC). Methods Using gene expression profiles and clinicopathological data for patients with CRC from The Cancer Genome Atlas, and tumor and adjacent normal tissues from 31 patients with CRC from Xianyang Central Hospital, we studied the correlation between SELE gene expression and clinical parameters using Kaplan–Meier and Cox proportional hazards regression analyses. Results Higher expression of SELE was significantly associated with a poorer prognosis and shorter survival in patients with CRC. The median expression level of SELE was significantly higher in CRC tissues compared with healthy adjacent tissue. Cox regression analysis showed that the prognosis of CRC was significantly correlated with the expression of SELE. Immunohistochemical analysis also showed that positive expression of E-selectin increased significantly in line with increasing TNM stage. Conclusion: This study confirmed that SELE gene expression is an independent prognostic factor in patients with CRC.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Author(s):  
Yinglian Pan ◽  
LiPing Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Risk Groups ◽  
Cox Proportional Hazards ◽  
Low Risk ◽  
Patient Choice ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment

Abstract Background: Ovarian cancer (OV) is the most common type of primary female reproductive cancer. BRCA1/2 gene is an important biomarker for evaluating the risk of OV, breast cancer and other related tumors and influences patient choice of individualized treatment. A powerful signature to predict OV prognosis and improve treatment personalization is urgently needed. This study aimed to identify a novel OV-related lncRNA prognostic biomarker.Methods: A Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from The Cancer Genome Atlas (TCGA) database. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was assessed, and the sensitivity and specificity of the prediction model were determined.Results: The signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as a criterion for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The 3-year overall survival (OS) rates for the high- and low-risk groups were approximately 38% and 100%, respectively. Chemotherapy treatment survival rates indicated that high-risk groups had significantly shorter OS rates with adjuvant chemotherapy than the low-risk groups. The OS of 1-, 3- and 5- years were 100%, 40%, and 15% in the high-risk groups respectively. The survival rate of the high-risk group declined rapidly after two years of OA chemotherapy treatment. In addition, multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development.Conclusion: In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with in BRCA1/2 mutations to predict prognosis and chemotherapy efficiency.

scholarly journals Weighted Gene Co-Expression Network Analysis to Construct Competitive Endogenous RNAs Network in Chromogenic Renal Cell Carcinoma

2020 ◽  
Author(s):  
Yon-Bo Chen ◽  
Liang Gao ◽  
Liang-You Tang ◽  
Yu-Chang Tian ◽  
Guan-Qiang Tian ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Network Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Messenger Rnas ◽  
Competitive Endogenous Rnas ◽  
Sequence Profiles

Abstract Background: This study aimed to construct the competing endogenous RNA (ceRNA) network in chromophobe renal cell carcinoma (ChRCC). Methods: Clinical and RNA sequence profiles of patients with ChRCC, including messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs), were obtained from The Cancer Genome Atlas (TCGA) database. “EdgeR” and “clusterProfiler” packages were utilized to obtain the expression matrices of differential RNAs (DERNAs) and to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Weighted gene co-expression network analysis (WGCNA) was performed to screen the highly related RNAs, and miRcode, StarBase, miRTarBase, miRDB, and TargetScan datasets were used to predict the connections between them. Univariate and multivariate Cox proportional hazards regressions were performed in turn to elucidate prognosis-related mRNAs in order to construct the ceRNA regulatory network. Results: A total of 1628 DElncRNAs, 104 DEmiRNAs, and 2619 DEmRNAs were identified. WGCNA showed significant correlation in 1534 DElncRNAs, 98 DEmiRNAs, and 2543 DEmRNAs, which were related to ChRCC. Fourteen DEmiRNAs, 113 DElncRNAs, and 43 DEmRNAs were screened. Nine mRNAs (ALPL, ARHGAP29, CADM2, KIT, KLRD1, MYBL1, PSD3, SFRP1, SLC7A11) significantly contributed to the overall survival (OS) of patients with ChRCC (P < 0.05). Furthermore, two mRNAs (CADM2, SFRP1) appeared to be independent risk factors for ChRCC. Conclusion: The findings revealed the molecular mechanism of ChRCC and potential therapeutic targets for the disease.

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