scholarly journals Two Gene Set Variation Index as Biomarker of Bacterial and Fungal Sepsis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaowen Zheng ◽  
Yifeng Luo ◽  
Qian Li ◽  
Jihua Feng ◽  
Chunling Zhao ◽  
...  
Keyword(s):  
Diagnostic Value ◽  
Curve Analysis ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Independent Data ◽  
Bacterial Sepsis ◽  
Roc Curve Analysis ◽  
Data Set ◽  
Gene Set ◽  
Fungal Sepsis

Background. The incidence of sepsis has been increasing in recent years. The molecular mechanism of different pathogenic sepsis remains elusive, and biomarkers of sepsis against different pathogens are still lacking. Methods. The microarray data of bacterial sepsis, fungal sepsis, and mock-treated samples were applied to perform differentially expressed gene (DEG) analysis to identify a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set. Functional enrichment analysis was used to explore the body’s response to bacterial sepsis and fungal sepsis. Gene set variation analysis (GSVA) was used to score individual samples against the two pathogen-specific gene sets, and each sample gets a GSVA index. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of sepsis. An independent data set was used to validate the bacterial sepsis-specific GSVA index. Results. The genes differentially expressed only in bacterial sepsis and the genes differentially expressed only in fungal sepsis were significantly involved in different biological processes (BPs) and pathways. This indicated that the body’s responses to fungal sepsis and bacterial sepsis are varied. Twenty-two genes were identified as bacterial sepsis-specific genes and upregulated in bacterial sepsis, and 23 genes were identified as fungal sepsis-specific genes and upregulated in fungal sepsis. ROC curve analysis showed that both of the two pathogen sepsis-specific GSVA indexes may be a reliable biomarker for corresponding pathogen-induced sepsis (AUC=1.000), while the mRNA of CALCA (also known as PCT) have a poor diagnostic value with AUC=0.512 in bacterial sepsis and AUC=0.705 in fungi sepsis. In addition, the AUC of the bacterial sepsis-specific GSVA index in the independent data set was 0.762. Conclusion. We proposed a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set; the bacterial sepsis GSVA index may be a reliable biomarker for bacterial sepsis.

Identification and validation of a ten-gene set variation score as a diagnostic and prognostic stratification tools in hepatocellular carcinoma

2020 ◽  
Author(s):  
Jinmin Zhao ◽  
Jiazhou Ye ◽  
Yan Lin ◽  
Kunpeng Bu ◽  
Rongyun Mai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Curve Analysis ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Roc Curve Analysis ◽  
Data Set ◽  
Aberrant Expression ◽  
Gene Set

Abstract We aimed to identify the progress of hepatocellular carcinoma (HCC)-specific gene set. Using the HCC data set from The Cancer Genome Atlas, we found that 10 genes were gradually up-graduated with the progress of HCC and associated with survival and classed as HCC-unfavorable gene set, while 29 genes were gradually down-graduated and associated with survival and classed as HCC-favorable gene set. Gene Set Variation Analysis (GSVA) was used to score individual samples against the two gene sets. ROC curve analysis showed both of HCC-unfavorable GSVA score and HCC-favorable GSVA score were reliable biomarkers for diagnosing HCC, tROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated that HCC-unfavorable GSVA score was an independently prognostic biomarkers. Moreover, the results were validated in an external independent data set. In addition, according to mutation and methylation analysis, we proposed that the aberrant expression of HCC-unfavorable gene may be driven by hypomethylation, not mutation.

scholarly journals Development and verification of immune-related long non-coding RNA prognostic signature in bladder cancer

2020 ◽  
Author(s):  
Cong Lai ◽  
Zhenyu Wu ◽  
Zhuohang Li ◽  
Hao Yu ◽  
Kuiqing Li ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Immune Cell ◽  
Prognostic Significance ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Training Data ◽  
Differentially Expressed ◽  
Roc Curve Analysis ◽  
Data Set

Abstract Background: Bladder cancer is the second most common malignant tumor in urogenital system. The research aimed to investigate the prognostic role of immune-related long non-coding RNA (lncRNA) in bladder cancer. Methods: We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single-sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis and survival analysis. Multivariate Cox analysis was used to establish the risk score model. The nomogram was established and evaluated by receiver operating characteristic (ROC) curve analysis, concordance index (C-index) analysis, calibration chart, and decision curve analysis (DCA). Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis.Results: Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune-related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high- and low-risk cohorts. Patients in the low-risk cohort had better survival outcomes than those in the high-risk cohort. The nomogram was established based on the indicators including age, gender, TNM stage, and risk score. The model’s predictive performance was confirmed by ROC curve analysis, C-index analysis, calibration chart, and DCA. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9-lncRNA signature was involved in modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway.Conclusions: The immune-related lncRNAs have the potential to predict the prognosis of bladder cancer and may play a key role in bladder cancer biology.Trial registration: It was a retrospective study and the gene expression data were obtained from the TCGA database. Trial registration was not needed.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals Cell Ratio Differences in Peripheral Blood between Early- and Late-Onset Parkinson’s Disease: A Case-Control Study

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Sen Jiang ◽  
Yuling Wang ◽  
Hua Gao ◽  
Qin Luo ◽  
Dan Wang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Roc Curve ◽  
Late Onset ◽  
Diagnostic Value ◽  
Curve Analysis ◽  
Onset Age ◽  
Immune Disorders ◽  
Roc Curve Analysis ◽  
Cell Ratio

Objectives. To explore the differences of immune disorders in peripheral blood between patients with early-onset Parkinson’s disease (EOPD) and late-onset Parkinson’s disease (LOPD). Methods. We retrospectively reviewed medical records of Parkinson’s disease (PD) patients and healthy controls between June 2002 and July 2017. At last, we included 117 PD patients who were divided into EOPD and LOPD according to whether onset age of PD was after 50 and 99 controls divided into E-Control (match for EOPD) and L-Control (match for LOPD) according to whether their age was after 53 which was onset age plus median of disease duration. We compared the ratios of cells between multiple groups and performed the multinominal logistic regression analysis to explore the relationship between ratios and subtypes of PD. We also carried out the receiver operating characteristic (ROC) curve analysis to estimate the diagnostic value of the variable. Results. Lymphocyte-red blood cell ratio (LRR) was lower in LOPD compared with that in EOPD or L-Control. LRR was also negatively associated with LOPD (OR: 0.623; 95% CI: 0.397–0.980; P=0.040). The ROC curve analysis showed the optimal cutoff value of 4.53 (×10−4) of LRR for discrimination of LOPD versus L-Control (sensitivity: 0.596, specificity: 0.764). The area under curve (AUC) was 0.721. As for LOPD versus EOPD, the optimal threshold of LRR was 4.10 (×10−4) (sensitivity: 0.516, specificity: 0.745). AUC was 0.641. Conclusions. Peripheral immune disorders might play an important part in the pathological progression of LOPD. Also, LRR has potential diagnostic value.

scholarly journals Identification and Validation of Two Lung Adenocarcinoma-Development Characteristic Gene Sets for Diagnosing Lung Adenocarcinoma and Predicting Prognosis

2020 ◽  
Vol 11 ◽  
Author(s):  
Cheng Liu ◽  
Xiang Li ◽  
Hua Shao ◽  
Dan Li
Keyword(s):  
Lung Adenocarcinoma ◽  
Roc Curve ◽  
Curve Analysis ◽  
Functional Enrichment ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Gene Sets ◽  
Development Characteristic ◽  
Score Systems

Background: Lung adenocarcinoma (LUAD) is one of the main types of lung cancer. Because of its low early diagnosis rate, poor late prognosis, and high mortality, it is of great significance to find biomarkers for diagnosis and prognosis.Methods: Five hundred and twelve LUADs from The Cancer Genome Atlas were used for differential expression analysis and short time-series expression miner (STEM) analysis to identify the LUAD-development characteristic genes. Survival analysis was used to identify the LUAD-unfavorable genes and LUAD-favorable genes. Gene set variation analysis (GSVA) was used to score individual samples against the two gene sets. Receiver operating characteristic (ROC) curve analysis and univariate and multivariate Cox regression analysis were used to explore the diagnostic and prognostic ability of the two GSVA score systems. Two independent data sets from Gene Expression Omnibus (GEO) were used for verifying the results. Functional enrichment analysis was used to explore the potential biological functions of LUAD-unfavorable genes.Results: With the development of LUAD, 185 differentially expressed genes (DEGs) were gradually upregulated, of which 84 genes were associated with LUAD survival and named as LUAD-unfavorable gene set. While 237 DEGs were gradually downregulated, of which 39 genes were associated with LUAD survival and named as LUAD-favorable gene set. ROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated both of LUAD-unfavorable GSVA score and LUAD-favorable GSVA score were a biomarker of LUAD. Moreover, both of these two GSVA score systems were an independent factor for LUAD prognosis. The LUAD-unfavorable genes were significantly involved in p53 signaling pathway, Oocyte meiosis, and Cell cycle.Conclusion: We identified and validated two LUAD-development characteristic gene sets that not only have diagnostic value but also prognostic value. It may provide new insight for further research on LUAD.

scholarly journals Use of Nuclear Morphometry Characteristics to Distinguish between Normal and Abnormal Cervical Glandular Histologies

2003 ◽  
Vol 25 (4) ◽  
pp. 193-200 ◽  
Author(s):  
Richard Swartz ◽  
Loyd West ◽  
Iouri Boiko ◽  
Anais Malpica ◽  
Calum MacAulay ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Tissue Level ◽  
Curve Analysis ◽  
Methodological Study ◽  
Roc Curve Analysis ◽  
Statistical Tool ◽  
Data Set ◽  
Tissue Samples ◽  
Specific Goal

This is a methodological study exploring the use of quantitative histopathology applied to the cervix to discriminate between normal and cancerous (consisting of adenocarcinoma and adenocarcinomain situ) tissue samples. The goal is classifying tissue samples, which are populations of cells, from measurements on the cells. Our method uses one particular feature, the IODs‐Index, to create a tissue level feature. The specific goal of this study is to find a threshold for the IODs‐Index that is used to create the tissue level feature. The main statistical tool is Receiver Operating Characteristic (ROC) curve analysis. When applied to the data, our method achieved promising results with good estimated sensitivity and specificity for our data set. The optimal threshold for the IODs‐Index was found to be 2.12.

scholarly journals Diagnostic Value of Radiomics Analysis in Contrast-Enhanced Spectral Mammography for Identifying Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yongxia Zhang ◽  
Fengjie Liu ◽  
Han Zhang ◽  
Heng Ma ◽  
Jian Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Diagnostic Value ◽  
Curve Analysis ◽  
P Value ◽  
Breast Cancer Patients ◽  
Roc Curve Analysis ◽  
Contrast Enhanced ◽  
Lemeshow Test

PurposeTo evaluate the value of radiomics analysis in contrast-enhanced spectral mammography (CESM) for the identification of triple-negative breast cancer (TNBC).MethodCESM images of 367 pathologically confirmed breast cancer patients (training set: 218, testing set: 149) were retrospectively analyzed. Cranial caudal (CC), mediolateral oblique (MLO), and combined models were built on the basis of the features extracted from subtracted images on CC, MLO, and the combination of CC and MLO, respectively, in the tumour region. The performance of the models was evaluated through receiver operating characteristic (ROC) curve analysis, the Hosmer-Lemeshow test, and decision curve analysis (DCA). The areas under ROC curves (AUCs) were compared through the DeLong test.ResultsThe combined CC and MLO model had the best AUC and sensitivity of 0.90 (95% confidence interval: 0.85–0.96) and 0.97, respectively. The Hosmer–Lemeshow test yielded a non-significant statistic with p-value of 0.59. The clinical usefulness of the combined CC and MLO model was confirmed if the threshold was between 0.02 and 0.81 in the DCA.ConclusionsMachine learning models based on subtracted images in CESM images were valuable for distinguishing TNBC and NTNBC. The model with the combined CC and MLO features had the best performance compared with models that used CC or MLO features alone.

scholarly journals Identification of lncRNA and mRNA Biomarkers in Osteoarthritic Degenerative Meniscus by Weighted Gene Coexpression Network and Competing Endogenous RNA Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jun Zhao ◽  
Yu Su ◽  
Jianfei Jiao ◽  
Zhengchun Wang ◽  
Xiangchun Fang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Roc Curve ◽  
Diagnostic Value ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Competing Endogenous Rna ◽  
Roc Curve Analysis ◽  
Competing Endogenous Rna Network ◽  
Meniscus Degeneration

Background. Long noncoding RNAs (lncRNAs) play a crucial role in varieties of biological processes. This study is aimed at investigating meniscal degeneration-specific lncRNAs and mRNAs and their related networks in knee osteoarthritis (KOA). Methods. The dataset GSE98918, which included 24 meniscus samples and related clinical data, was downloaded from the Gene Expression Omnibus database. The differentially expressed lncRNAs and mRNAs in the meniscus between KOA and control groups were identified. Based on the enriched differentially expressed lncRNAs and mRNAs, we constructed the coexpression network using WGCNA (weighted correlation network analysis) and identified the critical module related to KOA. For mRNAs in the key module, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using the DAVID database. A competing endogenous RNA network (ceRNA) based on the screened mRNAs, lncRNAs, and related miRNAs was constructed to reveal presumptive biomarkers further. Finally, the hub lncRNAs and mRNAs were screened, and the diagnostic value was evaluated using a receiver operating characteristic (ROC) curve. Hub mRNAs were validated using the dataset GSE113825. Results. We screened 208 significantly differentially expressed lncRNAs and mRNAs in menisci between the KOA and non-KOA samples, which were enriched in sixteen modules using WGCNA, especially the green module. Coexpression network based on the enriched differentially expressed lncRNAs and mRNAs in the green module uncovered 5 lncRNAs and 56 mRNAs. The lncRNA-miRNA-mRNA ceRNA network revealed that lnc-HLA-DQA1-5, lnc-RP11-127H5.1.1-1, lnc-RTN2-1, IGFBP4 (insulin-like growth factor binding protein 4), and KLF2 (Kruppel-like factor 2) were significantly correlated with the meniscus degeneration of KOA. ROC curve analysis revealed that these hub lncRNAs and mRNAs showed excellent diagnostic value for KOA. Conclusions. These hub lncRNAs and mRNAs were potential prognostic biomarkers for the meniscus degeneration of KOA. Further studies are required to validate these new biomarkers and better understand the pathological process of the meniscus degeneration of KOA.

scholarly journals Prediction of nerve block success using ultrasonographic parameters: A preliminary animal study

2021 ◽  
Author(s):  
Emiko Chiba ◽  
Kohei Hamamoto ◽  
Eiichi Kanai ◽  
Noriko Oyama-Manabe ◽  
Kiyoka Omoto
Keyword(s):  
Sciatic Nerve ◽  
Diagnostic Accuracy ◽  
Nerve Block ◽  
Roc Curve ◽  
Diagnostic Value ◽  
Curve Analysis ◽  
Block Group ◽  
Roc Curve Analysis ◽  
Biceps Femoris Muscle ◽  
Block Success

Abstract This study aimed to evaluate the diagnostic value of ultrasonographic parameters as an indicator for predicting regional nerve block success. Ultrasound-guided sciatic nerve block was performed in seven dogs using either 2% mepivacaine (nerve-block group) or saline (sham-block group). The cross-sectional area (CSA), nerve blood flow (NBF), and shear wave velocity (SWV) of the sciatic nerve (SWVN), SWV of the biceps femoris muscle (SWVM), and their ratio (SWVNMR) were measured at 0, 30, 60, and 90 min after the nerve block as well as the change rate of each parameter from the baseline. A receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic value of each parameter in the prediction of nerve block success. No significant changes were observed in the CSA or NBF in association with the nerve block. The SWVN and SWVNMR in the nerve-block group were significantly higher than those in the sham-block group at 90 min and at 30, 60, and 90 min, respectively (p < 0.05). The change rates of SWVN and SWVNMR in the nerve-block group were significantly higher than those in the sham-block group at all time points (p < 0.05). The ROC curve analysis showed that SWVN had a moderate diagnostic accuracy (area under the curve [AUC], 0.779), whereas SWVNMR and change rates of SWVN and SWVNMR had a high diagnostic accuracy (AUC, 0.947, 0.998, and 1.000, respectively). Ultrasonographic evaluation of the SWVN and SWVNMR could be used as indicators for predicting nerve block success.

scholarly journals Genome-Wide Analysis of Circular Rnas and Validation of hsa_circ_0086354 as a Promising Biomarker for Early Diagnosis of Cerebral Palsy

2021 ◽  
Author(s):  
Yuanyuan Hu ◽  
Xuzhao Bian ◽  
Chao Wu ◽  
Yan Wang ◽  
Yang Wu ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Early Diagnosis ◽  
Blood Plasma ◽  
Roc Curve ◽  
Curve Analysis ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Healthy Controls ◽  
Roc Curve Analysis ◽  
Literature Evidence

Abstract Background: Cerebral palsy (CP) is a spectrum of non-progressive motor disorders caused by brain injury during fetal or postnatal periods. Current diagnosis of CP mainly relies on neuroimaging and motor assessment. Here, we aimed to explore novel biomarkers for early diagnosis of CP. Methods: Blood plasma from five CP children and their healthy twin brothers/sisters was analyzed by gene microarray to screen out differentially expressed RNAs. Selected differentially expressed circular RNAs (circRNAs) were further validated using quantitative real-time PCR. Receiver operating characteristic (ROC) curve analysis was used to evaluate the value of using hsa_circ_0086354 as a biomarker of CP.Results: 43 up-regulated circRNAs and 2 down-regulated circRNAs were obtained by difference analysis (fold change>2, p<0.05), among which five circRNAs related to neuron differentiation and neurogenesis were chosen for further validation. Additional 30 pairs of CP children and healthy controls were recruited and five selected circRNAs were further detected, showing that hsa_circ_0086354 was significantly down-regulated in CP plasma compared with control, which was highly in accord with microarray analysis. ROC curve analysis showed that the area under curve (AUC) to discriminate CP children and healthy controls using hsa_circ_0086354 was 0.967, the sensitivity was 0.833 and the specificity was 0.966. Moreover, hsa_circ_0086354 was predicted as a competitive endogenous RNA for miR-181a, miR-4741 and miR-4656, and much literature evidence suggested that miR-181a may be a key target of hsa_circ_0086354 to regulate neuronal survival and neuronal differentiation. Conclusion: Hsa_circ_0086354 was significantly down-regulated in blood plasma of CP children, which may be a novel competent biomarker for early diagnosis of CP.

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