scholarly journals N6-Methylandenosine-Related lncRNAs are Potential Biomarkers for Predicting the Overall Survival Rate of Patients Bladder Cancer

2021 ◽  
Author(s):  
Haihang Zhang ◽  
Panpan Xie ◽  
Kaijia Shi ◽  
Danni Xu ◽  
Xingrui Cai ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Prognostic Value ◽  
Immune Cell ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Bladder Tissue ◽  
Cox Regression Analysis ◽  
Normal Bladder

Abstract Background: The prognostic value of N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) was investigated in 414 bladder cancer (BLCA) and 19 normal bladder tissue samples from The Cancer Genome Atlas (TCGA) datasets. Methods: We implemented Pearson correlation analysis to explore the lncRNAs associated with m6A, and then performed univariate Cox regression analysis to identify nine m6A-associated lncRNAs with prognostic value. The patients with BLCA were divided into two subgroups by consistency clustering. Analysis of the two groups of immune cell infiltration, immune microenvironment and clinical results were significantly different. We identified the prognostic significance of m6A-related lncRNAs by bioinformatic and statistical analysis of data from patients with BLCA.Results: We constructed an m6A-related lncRNA prognostic signature (m6A-LPS, including AL136295.2, AC104564.3, ATP1B3-AS1, EHMT2-AS1 and AC116914.2) to predict the OS of BLCA patients. It is proved that m6A LPS has independent prognostic value.

The Analysis of lncRNA-miRNA-mRNA Competitive Endogenous RNA Network to Identify 4-lncRNA Signature Related to the Prognosis of Bladder Cancer

2021 ◽  
Author(s):  
Huili Zhu ◽  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Yuping Wang ◽  
Yongli Yang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Normal Bladder ◽  
Cancer Tissues ◽  
Network Server ◽  
Competitive Endogenous Rna

Abstract BackgroundBladder cancer (BLCA) is one of the leading causes of cancer deaths in the world, and the molecular mechanism of its pathogenesis is very complicated. Long non-coding RNA (lncRNA) can interact with microRNA (miRNA) through the mechanism of competitive endogenous RNA (ceRNA), and affect the expression of Messenger RNA (mRNA), and affect the pathogenesis of bladder cancer. This study aims to construct the ceRNA-regulated bladder cancer network related to lncRNA and identify a novel lncRNA signature related to the survival prognosis of patients with bladder cancer. It was validated in GEPIA's online bioinformatics network server assists. MethodsThe RNA sequencing data of normal and adjacent bladder cancer tissues are from the Cancer Genome Atlas (TCGA). We identify differentially expressed (DE) genes by comparing gene expression between normal tissues and tumors in the TCGA dataset. Construct a ceRNA network and explore potential biological markers. Based on the ceRNA network, univariate regression analysis and multivariate regression analysis were used to screen out the lncRNA related to the overall survival (OS) of bladder cancer. It was validated in GEPIA's online bioinformatics network server assists. Receiver operating characteristic curve (ROC) analysis was used to evaluate the prognostic value of the risk score.ResultsWe screened out 666 lncRNAs, 160 microRNAs (miRNAs), and 1,820 Messenger RNAs (mRNAs) by comparing normal bladder cancer tissues and adjacent tissues (P<0.05). Then, we constructed a ceRNA regulatory network containing 44 DElncRNA, 22 DEmiRNA, and 52 DEmRNA. The survival analysis of differential genes in the ceRNA network identified 9 lncRNAs, 8 miRNAs, and 12 mRNAs that are associated with the prognosis of BLCA. Cox regression analysis of 9 LncRNAs related to the prognosis of bladder cancer showed that 4 lncRNAs (AC078778.1, ADAMTS9-AS1, ADAMTS9-AS2, and NAV2-AS2) can be independently used as prognostic markers of bladder cancer.ConclusionsBased on the construction of the bladder cancer ceRNA network, a new prognostic signature of four lncRNA-based has been discovered. It will help to better understand the mechanism of bladder cancer occurrence, development and metastasis, and provide direction for future research.

scholarly journals Biological Functions and Prognostic Value of Ferroptosis-Related Genes in Bladder Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Kezhen Yi ◽  
JingChong Liu ◽  
Yuan Rong ◽  
Cheng Wang ◽  
Xuan Tang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Protein Interactions ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Prognostic Information ◽  
Cox Regression Analysis

Background: Every year, nearly 170,000 people die from bladder cancer worldwide. A major problem after transurethral resection of bladder tumor is that 40–80% of the tumors recur. Ferroptosis is a type of regulatory necrosis mediated by iron-catalyzed, excessive oxidation of polyunsaturated fatty acids. Increasing the sensitivity of tumor cells to ferroptosis is a potential treatment option for cancer. Establishing a diagnostic and prognostic model based on ferroptosis-related genes may provide guidance for the precise treatment of bladder cancer.Methods: We downloaded mRNA data in Bladder Cancer from The Cancer Genome Atlas and analyzed differentially expressed genes based on and extract ferroptosis-related genes. We identified relevant pathways and annotate the functions of ferroptosis-related DEGs using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology functions. On the website of Search Tool for Retrieving Interacting Genes database (STRING), we downloaded the protein-protein interactions of DEGs, which were drawn by the Cytoscape software. Then the Cox regression analysis were performed so that the prognostic value of ferroptosis-related genes and survival time are combined to identify survival- and ferroptosis-related genes and establish a prognostic formula. Survival analysis and receiver operating characteristic curvevalidation were then performed. Risk curves and nomograms were generated for both groups to predict survival. Finally, RT-qPCR was applied to analyze gene expression.Results: Eight ferroptosis-related genes with prognostic value (ISCU, NFE2L2, MAFG, ZEB1, VDAC2, TXNIP, SCD, and JDP2) were identified. With clinical data, we established a prognostic model to provide promising diagnostic and prognostic information of bladder cancer based on the eight ferroptosis-related genes. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues.Conclusion: This study found that the ferroptosis-related genes is associated with bladder cancer, which may serve as new target for the treatment of bladder cancer.

Identification and Validation of a Seven m6A-related lncRNAs Signature Predicting Prognosis of Ovarian Cancer

2021 ◽  
Author(s):  
Yan Li ◽  
Xiaoying Wang ◽  
Yue Han ◽  
Xun Li
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Correlation Analysis ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Pearson Correlation ◽  
Regression Analyses ◽  
Cox Regression Analysis ◽  
Pearson Correlation Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6 - methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).Methods: The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients. Results: 275 m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.Conclusions: In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

scholarly journals Prognostic Significance of Chest Imaging by LUS and CT in COVID-19 Inpatients: The ECOVID Multicenter Study

Respiration ◽  
2021 ◽  
pp. 1-10
Author(s):  
Claudio Tana ◽  
Fabrizio Ricci ◽  
Maria Gabriella Coppola ◽  
Cesare Mantini ◽  
Fulvio Lauretani ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Prognostic Significance ◽  
Chest Ct ◽  
Lung Damage ◽  
Chest Imaging ◽  
Gold Standard Method ◽  
Cox Regression Analysis ◽  
Total Score Range

<b><i>Background:</i></b> Point-of-care lung ultrasound (LUS) score is a semiquantitative score of lung damage severity. High-resolution computed tomography (HRCT) is the gold standard method to evaluate the severity of lung involvement from the novel coronavirus disease (COVID-19). Few studies have investigated the clinical significance of LUS and HRCT scores in patients with COVID-19. Therefore, the aim of this study was to evaluate the prognostic yield of LUS and of HRCT in COVID-19 patients. <b><i>Methods:</i></b> We carried out a multicenter, retrospective study aimed at evaluating the prognostic yield of LUS and HRCT by exploring the survival curve of COVID-19 inpatients. LUS and chest CT scores were calculated retrospectively by 2 radiologists with &#x3e;10 years of experience in chest imaging, and the decisions were reached in consensus. LUS score was calculated on the basis of the presence or not of pleural line abnormalities, B-lines, and lung consolidations. The total score (range 0–36) was obtained from the sum of the highest scores obtained in each region. CT score was calculated for each of the 5 lobes considering the anatomical extension according to the percentage parenchymal involvement. The resulting overall global semiquantitative CT score was the sum of each single lobar score and ranged from 0 (no involvement) to 25 (maximum involvement). <b><i>Results:</i></b> One hundred fifty-three COVID-19 inpatients (mean age 65 ± 15 years; 65% M), including 23 (15%) in-hospital deaths for any cause over a mean follow-up of 14 days were included. Mean LUS and CT scores were 19 ± 12 and 10 ± 7, respectively. A strong positive linear correlation between LUS and CT scores (Pearson correlation <i>r</i> = 0.754; <i>R</i><sup>2</sup> = 0.568; <i>p</i> &#x3c; 0.001) was observed. By ROC curve analysis, the optimal cut-point for mortality prediction was 20 for LUS score and 4.5 for chest CT score. According to Kaplan-Meier survival analysis, in-hospital mortality significantly increased among COVID-19 patients presenting with an LUS score ≥20 (log-rank 0.003; HR 9.87, 95% CI: 2.22–43.83) or a chest CT score ≥4.5 (HR 4.34, 95% CI: 0.97–19.41). At multivariate Cox regression analysis, LUS score was the sole independent predictor of in-hospital mortality yielding an adjusted HR of 7.42 (95% CI: 1.59–34.5). <b><i>Conclusion:</i></b> LUS score is useful to stratify the risk in COVID-19 patients, predicting those that are at high risk of mortality.

PPM1D is Associated With Prognosis of Bladder Cancer in the Chinese Population

2020 ◽  
Author(s):  
Shuangqing Cao ◽  
Lei Zheng
Keyword(s):  
Bladder Cancer ◽  
Mrna Expression ◽  
Cox Regression ◽  
Histological Grade ◽  
Tnm Stage ◽  
Cox Regression Analysis ◽  
Relative Expression ◽  
Kaplan Meier ◽  
Normal Bladder ◽  
Cancer Tissues

Abstract Background: Present study was to investigate the relative expression and prognostic performance of protein phosphatase magnesium/manganese-dependent 1D (PPM1D) in bladder cancer.Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) assay was performed to examine the relative expression of PPM1D mRNA in bladder cancer tissues and adjacent normal bladder tissues. The associations of PPM1D mRNA expression with clinicopathological features and the prognostic value were statistically analyzed via Chi-square test, Kaplan-Meier method and Cox regression analysis.Results: In comparison to adjacent normal tissues, PPM1D mRNA expression was obviously increased in bladder cancer tissues (P<0.001). Abnormal PPM1D expression was remarkably related to histological grade (P=0.017), TNM stage (P=0.032) and lymph nodes metastasis (P=0.035). Kaplan-Meier method showed that a close relationship was found between PPM1D expression and overall survival time (P=0.000). Multivariate analysis indicated that PPM1D expression (P=0.000, HR=3.530, 95%CI: 2.001-6.228) was a promising independent predictor for the prognosis of bladder cancer patients, as well as TNM stage (P=0.042, HR=1.768, 95%CI: 1.021-3.062).Conclusion: Taken together, our data showed that the potential performance of PPM1D as a prognostic biomarker and therapeutic target of bladder cancer.

SATB1 and Her2 as predictive molecular and immunohistochemical markers for urothelial cell carcinoma of the bladder

2020 ◽  
pp. 1-11
Author(s):  
Samia Hussein ◽  
Anan Fathi ◽  
Nehal S. Abouhashem ◽  
Samar Amer ◽  
Mohamed Hemida ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Prognostic Significance ◽  
Immunohistochemical Analysis ◽  
Lymph Node Involvement ◽  
Genetic Alterations ◽  
Response To Therapy ◽  
Clinicopathological Parameters ◽  
Bladder Tissue ◽  
Normal Bladder ◽  
Erbb2 Mrna

Studying bladder cancer molecular biology revealed the presence of genetic alterations. So, detection of molecular biomarkers that help in monitoring the disease, evaluating the prognosis of the patients, and their response to therapy is needed. In this study, we investigated the expression and the prognostic significance of SATB-1 and ERBB2 mRNA and protein by quantitative RT-PCR and immunohistochemical analysis in urothelial bladder cancer cases and the surrounding normal bladder tissue. The correlations between the expression of both markers and the clinicopathological parameters were performed with further analysis of the correlation between the expression of SATB-1 and ERBB2. Compared to control, the expression of SATB-1 and ERBB2 mRNA and protein in cancer tissues were significantly up-regulated (p< 0.05). Also, a positive correlation between both markers was found (r= 0.53, p< 0.001). Moreover, elevated levels of both markers were significantly associated with the stage, lymph node involvement at both mRNA and protein levels (p< 0.001). In conclusion, there is a clinical significance of SATB-1 and ERBB2 as potential biomarkers for predicting bladder cancer patients of aggressive behavior and poor prognosis.

scholarly journals Tumor Infiltration by OX40+ Cells Enhances the Prognostic Significance of CD16+ Cell Infiltration in Colorectal Cancer

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482090338
Author(s):  
Fabian Haak ◽  
Isabelle Obrecht ◽  
Nadia Tosti ◽  
Benjamin Weixler ◽  
Robert Mechera ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Immune Cell ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Tumor Necrosis Factor Receptor ◽  
Tissue Microarrays ◽  
Cell Infiltration ◽  
Prognostic Impact ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Objectives: Analysis of tumor immune infiltration has been suggested to outperform tumor, node, metastasis staging in predicting clinical course of colorectal cancer (CRC). Infiltration by cells expressing OX40, a member of the tumor necrosis factor receptor family, or CD16, expressed by natural killer cells, monocytes, and dendritic cells, has been associated with favorable prognosis in patients with CRC. We hypothesized that assessment of CRC infiltration by both OX40+ and CD16+ cells might result in enhanced prognostic significance. Methods: Colorectal cancer infiltration by OX40 and CD16 expressing cells was investigated in 441 primary CRCs using tissue microarrays and specific antibodies, by immunohistochemistry. Patients’ survival was evaluated by Kaplan-Meier and log-rank tests. Multivariate Cox regression analysis, hazard ratios, and 95% confidence intervals were also used to evaluate prognostic significance of OX40+ and CD16+ cell infiltration. Results: Colorectal cancer infiltration by OX40+ and CD16+ cells was subclassified into 4 groups with high or low infiltration levels in all possible combinations. High levels of infiltration by both OX40+ and CD16+ cells were associated with lower pT stage, absence of peritumoral lymphocytic (PTL) inflammation, and a positive prognostic impact. Patients bearing tumors with high infiltration by CD16+ and OX40+ cells were also characterized by significantly longer overall survival, as compared with the other groups. These results were confirmed by analyzing an independent validation cohort. Conclusions: Combined infiltration by OX40+ and CD16+ immune cells is an independent favorable prognostic marker in CRC. The prognostic value of CD16+ immune cell infiltration is significantly improved by the combined analysis with OX40+ cell infiltration.

scholarly journals Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Huamei Tang ◽  
Lijuan Kan ◽  
Tong Ou ◽  
Dayang Chen ◽  
Xiaowen Dou ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Overall Survival ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Test Group ◽  
Training Group ◽  
The Cancer Genome Atlas ◽  
Methylation Site ◽  
Cox Regression Analysis

Abstract Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients.

scholarly journals The Prognostic Value of m6A-related LncRNAs in Patients with HNSCC

2021 ◽  
Author(s):  
Liu-qing Zhou ◽  
Jie-yu Zhou ◽  
Yao Hu
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.

scholarly journals Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

2021 ◽  
Vol 10 ◽  
Author(s):  
Liang Zhao ◽  
Jiayue Zhang ◽  
Zhiyuan Liu ◽  
Yu Wang ◽  
Shurui Xuan ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Cox Model ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Alternative Mrna Splicing

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

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