scholarly journals Low expression or hypermethylation of PLK2 might predict favorable prognosis for patients with glioblastoma multiforme

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7974 ◽  
Author(s):  
Xiangping Xia ◽  
Fang Cao ◽  
Xiaolu Yuan ◽  
Qiang Zhang ◽  
Wei Chen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Overall Survival ◽  
Regression Analysis ◽  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Favorable Prognosis ◽  
Low Expression

Background As the most aggressive brain tumor, patients with glioblastoma multiforme (GBM) have a poor prognosis. Our purpose was to explore prognostic value of Polo-like kinase 2 (PLK2) in GBM, a member of the PLKs family. Methods The expression profile of PLK2 in GBM was obtained from The Cancer Genome Atlas database. The PLK2 expression in GBM was tested. Kaplan–Meier curves were generated to assess the association between PLK2 expression and overall survival (OS) in patients with GBM. Furthermore, to assess its prognostic significance in patients with primary GBM, we constructed univariate and multivariate Cox regression models. The association between PLK2 expression and its methylation was then performed. Differentially expressed genes correlated with PLK2 were identified by Pearson test and functional enrichment analysis was performed. Results Overall survival results showed that low PLK2 expression had a favorable prognosis of patients with GBM (P-value = 0.0022). Furthermore, PLK2 (HR = 0.449, 95% CI [0.243–0.830], P-value = 0.011) was positively associated with OS by multivariate Cox regression analysis. In cluster 5, DNA methylated PLK2 had the lowest expression, which implied that PLK2 expression might be affected by its DNA methylation status in GBM. PLK2 in CpG island methylation phenotype (G-CIMP) had lower expression than non G-CIMP group (P = 0.0077). Regression analysis showed that PLK2 expression was negatively correlated with its DNA methylation (P = 0.0062, Pearson r = −0.3855). Among all differentially expressed genes of GBM, CYGB (r = 0.5551; P < 0.0001), ISLR2 (r = 0.5126; P < 0.0001), RPP25 (r = 0.5333; P < 0.0001) and SOX2 (r = −0.4838; P < 0.0001) were strongly correlated with PLK2. Functional enrichment analysis results showed that these genes were enriched several biological processes or pathways that were associated with GBM. Conclusion Polo-like kinase 2 expression is regulated by DNA methylation in GBM, and its low expression or hypermethylation could be considered to predict a favorable prognosis for patients with GBM.

scholarly journals Predictive Proteomic Signature for the Prognosis of Cervical Cancer

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

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

scholarly journals Construction of a prognostic model with histone modification-related genes and identification of potential drugs in pancreatic cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuan Chen ◽  
Ruiyuan Xu ◽  
Rexiati Ruze ◽  
Jinshou Yang ◽  
Huanyu Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Histone Modification ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Training Set ◽  
Test Set ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

Abstract Background Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. An effective prediction model is urgently needed for the accurate assessment of patients’ prognosis to assist clinical decision-making. Methods Gene expression data and clinicopathological data of the samples were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differential expressed genes (DEGs) analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, random forest screening and multivariate Cox regression analysis were applied to construct the risk signature. The effectiveness and independence of the model were validated by time-dependent receiver operating characteristic (ROC) curve, Kaplan–Meier (KM) survival analysis and survival point graph in training set, test set, TCGA entire set and GSE57495 set. The validity of the core gene was verified by immunohistochemistry and our own independent cohort. Meanwhile, functional enrichment analysis of DEGs between the high and low risk groups revealed the potential biological pathways. Finally, CMap database and drug sensitivity assay were utilized to identify potential small molecular drugs as the risk model-related treatments for PC patients. Results Four histone modification-related genes were identified to establish the risk signature, including CBX8, CENPT, DPY30 and PADI1. The predictive performance of risk signature was validated in training set, test set, TCGA entire set and GSE57495 set, with the areas under ROC curve (AUCs) for 3-year survival were 0.773, 0.729, 0.775 and 0.770 respectively. Furthermore, KM survival analysis, univariate and multivariate Cox regression analysis proved it as an independent prognostic factor. Mechanically, functional enrichment analysis showed that the poor prognosis of high-risk population was related to the metabolic disorders caused by inadequate insulin secretion, which was fueled by neuroendocrine aberration. Lastly, a cluster of small molecule drugs were identified with significant potentiality in treating PC patients. Conclusions Based on a histone modification-related gene signature, our model can serve as a reliable prognosis assessment tool and help to optimize the treatment for PC patients. Meanwhile, a cluster of small molecule drugs were also identified with significant potentiality in treating PC patients.

scholarly journals Increased Expression of Integrin Subunit α3 is Correlated with Worse Recurrence-Free Survival in Thyroid Cancer

2021 ◽  
Author(s):  
Jun-jie Ma ◽  
Cheng Xiang ◽  
Heng-qing Zhu ◽  
Bing-long Bai ◽  
Ping Wang
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Genetic Alterations ◽  
Functional Enrichment ◽  
Integrin Subunit ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Cox Regression Analysis

Abstract Objective: To investigate the potential effect of integrin subunit α3 (ITGA3) on thyroid cancer (THCA). Materials and Methods: Based on bioinformatics databases, the expression levels of ITGA3 were firstly analyzed, followed by evaluating the prognostic significance of ITGA3 in THCA patients. Cox regression analysis predicted the independent prognostic factors for THCA. Then, cBioportal and GSCA databases were applied to evaluate genetic alterations of ITGA3. Functional enrichment analysis was conducted using the R package. Finally, the upstream microRNAs of ITGA3 were determined. Results: ITGA3 gene and protein levels were higher in the THCA group than normal group (all P <0.05). And ITGA3 mRNA expression was significantly related to cancer stage and histological subtype (all P <0.01). Moreover, high expression of ITGA3 was associated with poor recurrence-free survival (RFS) of THCA patients in all subgroups (all P <0.01). Cox regression analysis presented that ITGA3 overexpression was an independent prognostic factor for worse RFS in THCA patients. Besides, significant relations were observed between ITGA3 and genetic alterations (FDR <0.01). Functional enrichment analysis indicated ECM-receptor interaction, and cell adhesion molecules were the shared regulatory pathways. We also found that ITGA3 might be the target gene of hsa-miR-3129, hsa-miR-181d, hsa-miR-181b, hsa-miR-199a, and hsa-miR-199b, which were all correlated with THCA patient prognosis. Conclusions: ITGA3 could be a reliable biomarker and promising therapeutic target for improving the diagnosis and clinical outcomes of THCA patients.

scholarly journals Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuntao Shi ◽  
Yingying Zhuang ◽  
Jialing Zhang ◽  
Mengxue Chen ◽  
Shangnong Wu
Keyword(s):  
Target Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Risk Groups ◽  
Normal Mucosa ◽  
Microrna Expression ◽  
Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

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

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.

scholarly journals LnCeVar: a comprehensive database of genomic variations that disturb ceRNA network regulation

2019 ◽  
Author(s):  
Peng Wang ◽  
Xin Li ◽  
Yue Gao ◽  
Qiuyan Guo ◽  
Shangwei Ning ◽  
...  
Keyword(s):  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Data Sets ◽  
Genomic Variations ◽  
Cancer Hallmarks ◽  
Cerna Network ◽  
Comprehensive Database ◽  
User Friendly

Abstract LnCeVar (http://www.bio-bigdata.net/LnCeVar/) is a comprehensive database that aims to provide genomic variations that disturb lncRNA-associated competing endogenous RNA (ceRNA) network regulation curated from the published literature and high-throughput data sets. LnCeVar curated 119 501 variation–ceRNA events from thousands of samples and cell lines, including: (i) more than 2000 experimentally supported circulating, drug-resistant and prognosis-related lncRNA biomarkers; (ii) 11 418 somatic mutation–ceRNA events from TCGA and COSMIC; (iii) 112 674 CNV–ceRNA events from TCGA; (iv) 67 066 SNP–ceRNA events from the 1000 Genomes Project. LnCeVar provides a user-friendly searching and browsing interface. In addition, as an important supplement of the database, several flexible tools have been developed to aid retrieval and analysis of the data. The LnCeVar–BLAST interface is a convenient way for users to search ceRNAs by interesting sequences. LnCeVar–Function is a tool for performing functional enrichment analysis. LnCeVar–Hallmark identifies dysregulated cancer hallmarks of variation–ceRNA events. LnCeVar–Survival performs COX regression analyses and produces survival curves for variation–ceRNA events. LnCeVar–Network identifies and creates a visualization of dysregulated variation–ceRNA networks. Collectively, LnCeVar will serve as an important resource for investigating the functions and mechanisms of personalized genomic variations that disturb ceRNA network regulation in human diseases.

scholarly journals Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yun Zhong ◽  
Zhe Liu ◽  
Dangchi Li ◽  
Qinyuan Liao ◽  
Jingao Li
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Test Set ◽  
Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

scholarly journals Differential Gene Expression and Functional Enrichment Analysis of Smoothed PDAC Spatial Transcriptomics Slides

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ben Duggan ◽  
Yusong Liu ◽  
Tongxin Wang ◽  
Jie Zhang ◽  
Travis S. Johnson ◽  
...  
Keyword(s):  
Tumor Heterogeneity ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Cellular Level ◽  
Functional Enrichment ◽  
Cancer Heterogeneity ◽  
Smoothing Methods ◽  
Single Cell Rna Sequencing ◽  
Differential Gene ◽  
Low Expression

Background: Cancer heterogeneity can impact diagnosis and therapeutics. This has been studied at the cellular level using single cell RNA sequencing (scRNA-seq) but not spatially. Spatial transcriptomics (ST) has recently enabled measuring transcriptomes at hundreds of locations in a tissue, revolutionizing our understanding of tumor heterogeneity. Low RNA quantities in scRNA-seq and ST introduce missed reads and noise. Multiple methods have been developed to impute and smooth scRNA-seq data including MAGIC and SAVER. To date, only our newly developed spatial and pattern combined smoothing (SPCS) method has been developed specifically for ST. We compared the biological interpretability of these smoothing methods to determine which method best informs tumor heterogeneity. Methods: Ten ST slides from six patients with PDAC were computationally smoothed using SAVER, MAGIC, and SPCS. ST spots from unsmoothed and smoothed slide were split into TM4SF1 high or low expression groups and differentially expressed genes (DEG) found. Significant up- and down-regulated DEGs were used for functional enrichment analysis (EA) to compare the biological insights each method provides. Results: DEGs were found in eight samples, with SPCS finding the most DEGs in six of the eight samples. The number of EA terms generally correlated with the numbers DEGs. SPCS had the most up-regulated enriched terms for every ST slide. Top ten up- and down-regulated EA terms were presented from one ST slide to demonstrate that SPCS gives more biologically interpretable results. Lastly, we present SPCS terms previously been reported in PDAC or involved in the TM4SF1 cancer pathway. Conclusion: Smoothing ST data using SPCS provided more DEGs and more useful EA terms. The EA terms found using SPCS are more consistent with our current understanding of PDAC. Not smoothing, MAGIC, and SAVER missed many important terms which SPCS found. Compared to other methods, SPCS smoothed data is more interpretable.

scholarly journals Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Lixian Chen ◽  
Zhonglu Ren ◽  
Yongming Cai
Keyword(s):  
Prognostic Factors ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

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