Multiomic Analysis of Methylation and Transcriptome Reveals a Novel Signature in Esophageal Cancer

Background: Epigenetic alterations have been shown to lead to human carcinogenesis. The aim of this study was to perform an integrative analysis to develop an epigenetic signature to predict overall survival (OS) of esophageal cancer. Methods: DNA methylation and messenger RNA expression data of esophageal cancer samples were downloaded from The Cancer Genome Atlas database and were incorporated and analyzed using an R package MethylMix. Functional enrichment analysis of the methylation-related differentially expressed genes (DEGs) was performed. Epigenetic signature and nomogram associated with the OS of esophageal cancer were established by the multivariate Cox model. Results: A total of 71 methylation-related DEGs were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these genes were involved in the biological process related to the initiation and progression of esophageal cancer. Two-gene (FAM24B and FAM200A) risk signature for OS was developed by multivariate Cox analysis, of which had high accuracy. The signature is independent of clinicopathological variables and indicated better predictive power than other clinicopathological variables. Moreover, we developed a novel prognostic nomogram based on risk score and 3 clinicopathological factors. Conclusions: Our study indicated possible methylation-related DEGs and established an epigenetic signature, which may provide novel insights for understanding the pathogenesis of esophageal cancer.

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The Prognostic Value of Risk Score Based on Immunogenenomic Landscape Analysis in Glioma

10.21203/rs.2.20513/v1 ◽

2020 ◽

Author(s):

Haitao Luo ◽

Kai Huang ◽

Chuming Tao ◽

Mioaojing Wu ◽

Minhua Ye ◽

...

Keyword(s):

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Low Grade ◽

Clinical Prognosis ◽

Cox Analysis ◽

Genome Atlas

Abstract Background: Glioma is a lethal intracranial tumor, and inflammation plays an important role in the initiation and development of glioma. Hence, there is an urgent need to conduct a bioinformatics analysis of immune-related genes (IRGs) for glioma. The present study aims to explore the association of the risk score with clinical outcomes and predict the prognosis with glioma. Methods: In The Cancer Genome Atlas (TCGA) database, 462 low grade glioma (LGG) samples and 166 glioblastoma (GBM) samples were reviewed, and IRGs correlated with the prognosis were selected by performing a survival analysis and establishing a Cox regression model. The potential molecular mechanism of these IRGs were also explored with assistance of computational biology. The risk score based on seven survival-associated IRGs was determined with the help of the multivariable Cox analysis, the patients were divided into two subgroups according to their risk score. Results: It was found that these differentially expressed IRGs were involved with the cytokine-cytokine receptor through functional enrichment analysis. The risk score based on the seven IRGs (SSTR5、CXCL10、CCL13、SAA1、CCL21、CCL27 and HTR1A) performed well in predicting patient’s the overall survival (OS), and correlated with age, 1p/19q codeletion status, IDH status, and WHO grades, both in the training (TCGA) datasets and the validation ((Chinese Glioma Genome Atlas) CGGA) datasets. The risk score also could reflect infiltration through several types of immune cells. Conclusions: This present study screened some IRGs associated with the patient’s clinical characteristic and prognosis, connect to the immune repertoire, demonstrated the importance of the risk score as a promising biomarker for estimating the clinical prognosis of glioma.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽

10.12688/f1000research.24956.2 ◽

2020 ◽

Vol 9 ◽

pp. 709 ◽

Cited By ~ 1

Author(s):

Liis Kolberg ◽

Uku Raudvere ◽

Ivan Kuzmin ◽

Jaak Vilo ◽

Hedi Peterson

Keyword(s):

Gene List ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Automated Analysis ◽

R Package ◽

Biological Data ◽

Functional Enrichment ◽

Link Type ◽

Functional Profiling ◽

Rest Api

g:Profiler (https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

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Weighted Gene Correlation Network Analysis Identifies Specific Functional Modules and Genes in Esophageal Cancer

Journal of Oncology ◽

10.1155/2021/8223263 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Wei Xu ◽

Jian Xu ◽

Zhiqiang Wang ◽

Yuequan Jiang

Keyword(s):

Esophageal Cancer ◽

Network Analysis ◽

Cell Cycle Progression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Future Research ◽

Hub Genes ◽

Pathological Staging

Objective. Esophageal cancer (ESCA) is one of the most aggressive malignancies globally with an undesirable five-year survival rate. Here, this study was conducted for determining specific functional genes linked with ESCA initiation and progression. Methods. Gene expression profiling of ESCA was curated from TCGA (containing 160 ESCA and 11 nontumor specimens) and GSE38129 (30 paired ESCA and nontumor tissues) datasets. Differential expression analysis was conducted between ESCA and nontumor tissues with adjusted p value <0.05 and |log2fold-change|>1. Weighted gene coexpression network analysis (WGCNA) was conducted for determining the ESCA-specific coexpression modules and genes. Thereafter, ESCA-specific differentially expressed genes (DEGs) were intersected. Functional enrichment analysis was then presented with clusterProfiler package. Protein-protein interaction was conducted, and hub genes were determined. Association of hub genes with pathological staging was evaluated, and survival analysis was presented among ESCA patients. Results. This study determined 91 ESCA-specific DEGs following intersection of DEGs and ESCA-specific genes in TCGA and GSE38129 datasets. They were remarkably linked to cell cycle progression and carcinogenic pathways like the p53 signaling pathway, cellular senescence, and apoptosis. Ten ESCA-specific hub genes were determined, containing ASPM, BUB1B, CCNA2, CDC20, CDK1, DLGAP5, KIF11, KIF20 A, TOP2A, and TPX2. They were prominently associated with pathological staging. Among them, KIF11 upregulation was in relation to undesirable prognosis of ESCA patients. Conclusion. Collectively, we determined ESCA-specific coexpression modules and hub genes, which offered the foundation for future research concerning the mechanistic basis of ESCA.

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Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer

Journal of Translational Medicine ◽

10.1186/s12967-021-03179-5 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Hanxiao Zhou ◽

Yue Gao ◽

Xin Li ◽

Shipeng Shang ◽

Peng Wang ◽

...

Keyword(s):

Human Cancer ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Computational Method ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Pathophysiological Mechanism ◽

Multiple Cancer ◽

Cancer Types ◽

Genomic Clusters

Abstract Background Emerging evidence has revealed that some long intergenic non-coding RNAs (lincRNAs) are likely to form clusters on the same chromosome, and lincRNA genomic clusters might play critical roles in the pathophysiological mechanism. However, the comprehensive investigation of lincRNA clustering is rarely studied, particularly the characterization of their functional significance across different cancer types. Methods In this study, we firstly constructed a computational method basing a sliding window approach for systematically identifying lincRNA genomic clusters. We then dissected these lincRNA genomic clusters to identify common characteristics in cooperative expression, conservation among divergent species, targeted miRNAs, and CNV frequency. Next, we performed comprehensive analyses in differentially-expressed patterns and overall survival outcomes for patients from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) across multiple cancer types. Finally, we explored the underlying mechanisms of lincRNA genomic clusters by functional enrichment analysis, pathway analysis, and drug-target interaction. Results We identified lincRNA genomic clusters according to the algorithm. Clustering lincRNAs tended to be co-expressed, highly conserved, targeted by more miRNAs, and with similar deletion and duplication frequency, suggesting that lincRNA genomic clusters may exert their effects by acting in combination. We further systematically explored conserved and cancer-specific lincRNA genomic clusters, indicating they were involved in some important mechanisms of disease occurrence through diverse approaches. Furthermore, lincRNA genomic clusters can serve as biomarkers with potential clinical significance and involve in specific pathological processes in the development of cancer. Moreover, a lincRNA genomic cluster named Cluster127 in DLK1-DIO3 imprinted locus was discovered, which contained MEG3, MEG8, MEG9, MIR381HG, LINC02285, AL132709.5, and AL132709.1. Further analysis indicated that Cluster127 may have the potential for predicting prognosis in cancer and could play their roles by participating in the regulation of PI3K-AKT signaling pathway. Conclusions Clarification of the lincRNA genomic clusters specific roles in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types.

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Long Noncoding RNA RP11-357H14.17 Plays an Oncogene Role in Gastric Cancer by Activating ATF2 Signaling and Enhancing Treg Cells

BioMed Research International ◽

10.1155/2021/6635936 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Tang Xiaoli ◽

Wang Wenting ◽

Zhang Meixiang ◽

Zuo Chunlei ◽

Hu Chengxia

Keyword(s):

Gastric Cancer ◽

Noncoding Rna ◽

Prognostic Value ◽

Malignant Tumors ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Treg Cells ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Biological Behavior

Background. Gastric cancer (GC) is one of the most common malignant tumors in the world. The potential functions and mechanisms of long noncoding RNAs (lncRNAs) in GC development are still unclear. It is of great significance to explore the prognostic value of LncRNA signatures for GC. Methods. LncRNAs differently expressed in GC and their prognostic value were studied based on The Cancer Genome Atlas (TCGA) database. The functional regulatory network and immune infiltration of RP11-357H14.17 were further studied using a variety of bioinformatics tools and databases. Results. We found that the high expression of RP11-357H14.17 was closely associated with shortened overall survival (OS) and poor prognosis in gastric cancer patients. We also found that its expression was related to clinical features including tumor volume, metastasis, and differentiation. Functional enrichment analysis revealed that RP11-357H14.17 is closely related to enhanced DNA replication and metabolism; ssGSEA analysis implied the oncogenic roles of RP11-357H14.17 was related to ATF2 signaling and Treg cell differentiation. Furthermore, we verified such link by using real-time PCR and IHC staining in human GC samples. Conclusion. We demonstrate that RP11-357H14.17 may play a crucial role in the occurrence, development, and malignant biological behavior of gastric cancer as a potential prognostic marker for gastric cancer.

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Identification of long noncoding RNA RP11-89K21.1 and RP11-357H14.17 as prognostic signature of endometrial carcinoma via integrated bioinformatics analysis

10.21203/rs.3.rs-23177/v2 ◽

2020 ◽

Author(s):

Lingling Gao ◽

Xin Nie ◽

Wenchao Zhang ◽

Rui Gou ◽

Yuexin Hu ◽

...

Keyword(s):

Endometrial Carcinoma ◽

Noncoding Rna ◽

Growth Regulation ◽

Malignant Tumors ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Biological Behavior ◽

Vessel Development

Abstract Background: Endometrial carcinoma (EC) is one of the most common malignant tumors in gynecology. The potential functions and mechanisms of long noncoding RNAs (lncRNAs) in the occurrence and progression of EC remains unclear. It’s meaningful to explore lncRNAs signature for providing prognostic value of EC. Methods：The differentially expressed lncRNAs and their prognostic values in EC were investigated based on The Cancer Genome Atlas (TCGA) database; the transcriptional factors (TFs), the competing endogenous RNA (ceRNA) mechanism, functional regulatory network and immune infiltration of RP11-89K21.1 and RP11-357H14.17 were further explored by various bioinformatics tools and databases. Results: We first identified high expression of RP11-89K21.1 and RP11-357H14.17 were closely associated with shorten overall survival (OS) and poor prognosis in patients with EC. We also elucidated the networks of transcription factor and co-expression genes associated with RP11-89K21.1 and RP11-357H14.17. Furthermore, the ceRNA network mechanism was successfully constructed through 2 lncRNAs (RP11-89K21.1 and RP11-357H14.17), 11 miRNAs and 183 mRNAs. Functional enrichment analysis revealed that the targeting genes of RP11-89K21.1 and RP11-357H14.17 were strongly associated with microRNAs in cancer, vessel development, growth regulation, growth factor and cell differentiation, and involved in pathways including pathways in cancer, microRNAs in cancer and apoptotic signaling pathway. Conclusions: We demonstrated for the first time that RP11-89K21.1 and RP11-357H14.17 may play crucial roles in the occurrence, development and malignant biological behavior of EC, and can be regarded as potential prognostic biomarkers for EC.

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Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

10.21203/rs.3.rs-56767/v1 ◽

2020 ◽

Author(s):

Yang Wang ◽

Chengping Hu

Keyword(s):

Lung Adenocarcinoma ◽

Cox Regression ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Protein Coding ◽

Cox Regression Analysis ◽

Protein Coding Genes

Abstract Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

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Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.746058 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiarong Yi ◽

Wenjing Zhong ◽

Haoming Wu ◽

Jikun Feng ◽

Xiazi Zouxu ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Microenvironment ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Ppi Network ◽

Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

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Predictive Proteomic Signature for the Prognosis of Cervical Cancer

10.21203/rs.3.rs-327396/v1 ◽

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.

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