scholarly journals Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza

2022 ◽  
Vol 11 ◽  
Author(s):  
Siran Lin ◽  
YuBing Peng ◽  
Yuzhen Xu ◽  
Wei Zhang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Risk Score ◽  
Model Building ◽  
Severe Disease ◽  
Differentially Expressed Gene ◽  
H1n1 Influenza ◽  
Functional Enrichment ◽  
Early Prediction ◽  
Lasso Regression ◽  
Mild Disease ◽  
Score Model

H1N1 is the most common subtype of influenza virus circulating worldwide and can cause severe disease in some populations. Early prediction and intervention for patients who develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of 180 PBMC samples from three published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) were performed to provide candidate DEGs for model building. Functional enrichment and CIBERSORT analyses were also performed to evaluate the differences in composition and function of PBMCs between patients with severe and mild disease. Finally, a risk score model was built using lasso regression analysis, with six genes (CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.

scholarly journals Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

2020 ◽  
Vol 2020 ◽  
pp. 1-43
Author(s):  
Beilei Wu ◽  
Lijun Tao ◽  
Daqing Yang ◽  
Wei Li ◽  
Hongbo Xu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Score ◽  
Immune Cells ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model

Objective. Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores. Methods. A cohort of CRC patients (n=433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset. Results. Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients’ prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration. Conclusion. Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

scholarly journals Identification of the Pyroptosis-Related Gene Signature for Overall Survival Prediction in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Liu ◽  
Ruonan Shao ◽  
Xiaoyun Bu ◽  
Yujie Xu ◽  
Ming Shi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Multivariable Analysis ◽  
Low Risk ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Incidence And Mortality ◽  
Risk Patients

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor worldwide, with an increasing incidence and mortality. Due to general resistance to antitumor drugs, only limited therapies are currently available for advanced HCC patients, leading to a poor prognosis with a 5-year survival rate less than 20%. Pyroptosis is a type of inflammation-related programmed cell death and may become a new potential target for cancer therapy. However, the function and prognostic value of pyroptosis-related genes (PRGs) in HCC remain unknown. Here, we identified a total of 58 PRGs reported before and conducted a six-PRG signature via the LASSO regression method in the GEO training cohort, and model efficacy was further validated in an external dataset. The HCC patients can be classified into two subgroups based on the median risk score. High-risk patients have significantly shorter overall survival (OS) than low-risk patients in both training and validation cohorts. Multivariable analysis indicated that the risk score was an independent prognostic factor for OS of HCC patients. Functional enrichment analysis and immune infiltration evaluation suggested that immune status was more activated in the low-risk group. In summary, PRGs can be a prediction factor for prognosis of HCC patients and targeting pyroptosis is a potential therapeutic alternative in HCC.

scholarly journals Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Weige Zhou ◽  
Shijing Zhang ◽  
Hui-biao Li ◽  
Zheyou Cai ◽  
Shuting Tang ◽  
...  
Keyword(s):  
Risk Score ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lasso Regression

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

scholarly journals Development and validation of a nomogram with an autophagy-related gene risk model for predicting survival in patients with melanoma

2020 ◽  
Author(s):  
Hao Zuo ◽  
Luojun Chen ◽  
Na Li ◽  
Qibin Song
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Risk Model ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
High Morbidity ◽  
Clinical Variables ◽  
Kaplan Meier ◽  
Score Model

Abstract Background: Melanoma is the third most common skin malignant tumor in the clinic, with high morbidity and mortality. Autophagy plays an important role in the development and progression of melanoma. We aimed to establish an autophagy-related genes(ARGs) expression based risk model for individualized prognosis prediction in patients with melanoma.Methods: Differentially expressed autophagy-related genes (DEARGs) in melanoma and normal skin samples were screened using TCGA and GTEx database. These DEARGs were used to perform KEGG functional enrichment analysis and GO analysis. Univariate and multivariate Cox regression analyses were performed on DEARGs to identify the optimal prognosis-related genes. These prognosis-related DEARGs were used to construct a risk score model, and the predictive effect of this risk model on the prognosis of melanoma patients was tested by the Kaplan-Meier curve, log-rank test, and ROC curve. Method of univariate and multivariate analysis were used to confirmed that the risk model of independent predictive value relative to other clinical variables, and build a nomogram based on the independent prognostic factors in the univariate analysis to predict overall survival(OS) in patients with melanoma, we used internal validation and calculation of concordance index (C-index) to test prediction effect of the nomogram. We also used the t-test to analyze the relationship between risk factors (risk genes and risk score) and clinical variables in the risk model.Results: We screened and finally obtained 6 optimal DEARGs (risk gene) through univariate and multivariate Cox analysis to construct the risk model: EIF2AK2(HR=0.403, P=0.007), IFNG(HR=0.659, P=0.003), DAPK2(HR=0.441, P=0.022), PTK6(HR=1.609, P=6.04E-05), BIRC5(HR=2.479, P=0.001), and EGFR(HR=1.474, P=0.004) were selected to establish the prognostic risk score model and validated in the entire melanoma cohort. The results of GO enrichment analysis showed that the gene function of the DEARGs was concentrated in the functions of gland morphogenesis, protein insertion into membrane, and autophagy. The results of KEGG enrichment analysis showed that the function of the DEARGs was concentrated in the autophagy–animal, p53 signaling pathway, and platinum drug resistance. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P=6.402E−11). The model was identified as an independent prognostic factor. Finally, a prognostic nomogram including the risk model, T-stage, N-stage, and radiotherapy was constructed, and the calibration plots indicated its excellent predictive performance.Conclusion: The autophagy-related six-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for melanoma. The prognostic nomogram could help individualized survival prediction and improve treatment strategies.

scholarly journals Expression of immune-related genes as prognostic biomarkers for the assessment of osteosarcoma clinical outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junjie Guo ◽  
Xiaoyang Li ◽  
Shen Shen ◽  
Xuejian Wu
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Survival Prediction ◽  
Promising Therapeutic Approach ◽  
Score Model ◽  
Immune Related Genes

AbstractCancer immunotherapy is a promising therapeutic approach, but the prognostic value of immune-related genes in osteosarcoma (OS) is unknown. Here, Target-OS RNA-seq data were analyzed to detect differentially expressed genes (DEGs) between OS subgroups, followed by functional enrichment analysis. Cox proportional risk regression was performed for each immune-related gene, and a risk score model to predict the prognosis of patients with OS was constructed. The risk scores were calculated using the risk signature to divide the training set into high-risk and low-risk groups, and validation was performed with GSE21257. We identified two immune-associated clusters, C1 and C2. C1 was closely related to immunity, and the immune score was significantly higher in C1 than in C2. Furthermore, we validated 6 immune cell hub genes related to the prognosis of OS: CD8A, KIR2DL1, CD79A, APBB1IP, GAL, and PLD3. Survival analysis revealed that the prognosis of the high-risk group was significantly worse than that of the low-risk group. We also explored whether the 6-gene prognostic risk model was effective for survival prediction. In conclusion, the constructed a risk score model based on immune-related genes and the survival of patients with OS could be a potential tool for targeted therapy.

scholarly journals Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Tan ◽  
Hecheng Zhu ◽  
Guihua Tang ◽  
Hongwei Liu ◽  
Siyi Wanggou ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
High Morbidity ◽  
Rna Seq ◽  
Mesenchymal Transition ◽  
Score Model ◽  
Genome Atlas

Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.

scholarly journals Prognostic model based on autophagy-related lncRNAs in gastric cancer

2020 ◽  
Author(s):  
Mengqiu Cheng ◽  
Wei Cao ◽  
Bo Chen
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Lasso Regression ◽  
Training Set ◽  
Kegg Analysis ◽  
Kaplan Meier ◽  
Score Model

Abstract Background: Gastric cancer (GC) is one of the most pravelent cancer in the world. Although increasing studies have indicated that autophagy-related long non-coding RNA (lncRNA) plays an essential role in the occurrence of GC, the prognosis of GC based on autophagy is still deficient.Method: Autophagy-related lncRNAs were obtained by using the correlation test with the autophagy-related gene. Data was downloaded from The Cancer Genome of Atlas stomach adenocarcinoma (TCGA-STAD) dataset. The prognostic autophagy-related lncRNAs significantly correlated with survival of TCGA-STAD dataset were obtained by using Kaplan-Meier and univariate Cox regression analysis. TCGA-STAD dataset was separated into a training set and a testing set randomly. The model was constructed based on the training set through the least absolute shrinkage and selection operator (LASSO) regression. The testing set and TCGA-STAD were used to validate the accuracy of the model. Every patient got a risk score (RS) and patients were separate into high-risk group and low-risk group due to the median RS. The prognostic network was built and the mRNAs in the system were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The signaling pathways that the differentially expressed genes (DEGs) between two types of risk group mainly participated in were distinguished through Gene Set Enrichment Analysis (GSEA). The individual’s survival rate was predicted through the nomogram.Results: 24 autophagy-related lncRNAs were found strongly associated with the survival of the TCGA-STAD dataset. Among them, 11 lncRNAs were selected to build the risk score model through LASSO regression. The multivariate Cox analysis showed that the RS could be an independent prognosis predictor. The Kaplan-Meier survival analysis and the Receiver Operating Characteristic (ROC) curve indicated the model had an excellent prediction effect. GO, and KEGG analysis revealed that the mRNAs in the prognostic network were mainly involved in the autophagy and ubiquitin-like protein ligase binding. GSEA analysis uncovered that the DEGs in high-risk group partially participated in the ECM receptor interaction and other signaling pathways.Conclusions: Our results indicated that the risk score model based on the autophagy-related lncRNAs performed well in the prediction of prognosis for patients with GC.

scholarly journals A Risk Score Model Incorporating Three m6A RNA Methylation Regulators and a Related Network of miRNAs-m6A Regulators-m6A Target Genes to Predict the Prognosis of Patients With Ovarian Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Qian Li ◽  
Chen-Chen Ren ◽  
Yan-Nan Chen ◽  
Li Yang ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Regulatory Network ◽  
Target Genes ◽  
Cox Regression ◽  
Mrna Levels ◽  
Lasso Regression ◽  
Rna Methylation ◽  
M6a Rna Methylation ◽  
Score Model

Ovarian cancer (OC) is the leading cause of cancer-related death among all gynecological tumors. N6-methyladenosine (m6A)-related regulators play essential roles in various tumors, including OC. However, the expression of m6A RNA methylation regulators and the related regulatory network in OC and their correlations with prognosis remain largely unknown. In the current study, we obtained the genome datasets of OC from GDC and GTEx database and analyzed the mRNA levels of 21 key m6A regulators in OC and normal human ovarian tissues. The expression levels of 7 m6A regulators were lower in both the OC tissues and the high-stage group. Notably, the 5-year survival rate of patients with OC presenting low VIRMA expression or high HNRNPA2B1 expression was higher than that of the controls. Next, a risk score model based on the three selected m6A regulators (VIRMA, IGF2BP1, and HNRNPA2B1) was built by performing a LASSO regression analysis, and the moderate accuracy of the risk score model to predict the prognosis of patients with OC was examined by performing ROC curve, nomogram, and univariate and multivariate Cox regression analyses. In addition, a regulatory network of miRNAs-m6A regulators-m6A target genes, including 2 miRNAs, 3 m6A regulators, and 47 mRNAs, was constructed, and one of the pathways, namely, miR-196b-5p-IGF2BP1-PTEN, was initially validated based on bioinformatic analysis and assay verification. These results demonstrated that the risk score model composed of three m6A RNA methylation regulators and the related network of miRNAs-m6A regulators-m6A target genes is valuable for predicting the prognosis of patients with OC, and these molecules may serve as potential biomarkers or therapeutic targets in the future.

scholarly journals SAT0541 THE SEVERITY OF FMF MAY BE ASSOCIATED WITH CO-MORBIDITIES

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1227.3-1228
Author(s):  
M. E. Tezcan ◽  
N. Şen ◽  
M. Yilmaz ◽  
Ö. Volkan ◽  
E. Tükel ◽  
...  
Keyword(s):  
Familial Mediterranean Fever ◽  
Scoring System ◽  
Disease Duration ◽  
Severe Disease ◽  
Smoking History ◽  
Disease Group ◽  
Severity Scoring ◽  
Mild Disease ◽  
Co Morbidity ◽  
Mediterranean Fever

Background:Familial Mediterranean fever (FMF) is an auto inflammatory disease with recurrent attacks of serositis. Frequent attacks and disease related sequels may be associated with co-morbidities in FMF patients.Objectives:One of the tools for evaluating the FMF severity is the international severity scoring system for FMF (ISSF)1. This score includes disease related sequels, acute phase measurements, attack features and exertional leg pain. Therefore, more severe disease may be link with subclinical inflammation, amyloidosis and frequent, prolonged and widespread attacks. All these components may augment the frequency of non-disease related co-morbidities.Methods:We enrolled 158 FMF patients who fulfilled modifiedTel-HashomerDiagnosisCriteria2. The patients dichotomized based upon disease severity (mild disease or severe disease). Patients with ISSF scores lower or equal to 2 were accepted to have mild disease. Then, we compared frequency of non-disease related co-morbidities between the groups. These co-morbidities arehypertension, hypothyroidism, hyperthyroidism cardiovascular diseases, coronary artery diseases, cerebrovascular diseases, chronic renal disease (non-FMF related), chronic obstructive pulmonary diseases, and diabetes mellitus. This study was approved by the Local Research Ethics Committee and carried out in compliance with the Helsinki Declaration. All the patients gave written informed consent. P-value lower than 0.05 was considered as statistically significant.Results:Demographic features, disease duration, smoking history and body mass index (BMI) were similar between the groups. Frequency of co-morbidity in severe disease group was statistically higher than mild disease group (p=0.02). Most frequent co-morbidity was hypertension in both groups.Table.Features of mild and severe FMF groupsMild (n=135)Severe (n=23)pGender (M/F)47/8811/120.23Age36.4±11.336.5±14.30.68Smoking (%)38 (28.1)5 (21.7)0.52BMI (kg/m2)24.3±9.224.0±8.90.34Disease duration (year)7.7±11.38.6±14.30.09Amyloidosis (%)2 (1.4)3 (13.0)0.02Exon 10 homozygote (%)35 (25.9)9 (39.1)0.19Colchicine dosage (mg/day)1.2±0.41.4±0.50.02ISSF scores0.7 ±0.73.4±0.5<0.001Co-morbidity (%)25 (18.5)9 (39.1)0.02Conclusion:In our FMF patient cohort, we found that severity of the disease may be associated with higher frequency of co-morbidities. Therefore, clinicians should be aware of the high possibility of co-morbidities in patients with more severe FMF and addressed these co-morbidities timely and properly.References:[1]Demirkaya E, et al. Development and initial validation of international severity scoring system for familial Mediterranean fever (ISSF). Ann Rheum Dis 2016;75:1051-6.[2]Berkun Y, et al. Diagnostic criteria of familial Mediterranean fever. Autoimmun Rev 2014;13:388-90.Acknowledgments:NoneDisclosure of Interests:None declared

A Case Series of SARS-CoV-2 RT-PCR-Positive Hospitalized Infants 60 Days of Age or Younger From 2 New York City Pediatric Emergency Departments

2021 ◽  
Vol 60 (4-5) ◽  
pp. 247-251
Author(s):  
Ameer Hassoun ◽  
Nessy Dahan ◽  
Christopher Kelly
Keyword(s):  
New York ◽  
Case Reports ◽  
Severe Disease ◽  
Case Series ◽  
Pediatric Emergency ◽  
Rt Pcr ◽  
Vulnerable Group ◽  
Mild Disease ◽  
Young Infants ◽  
Novel Coronavirus

The emergence of novel coronavirus disease-2019 poses an unprecedented challenge to pediatricians. While the majority of children experience mild disease, initial case reports on young infants are conflicting. We present a case series of 8 hospitalized infants 60 days of age or younger with coronavirus disease-2019. A quarter of these patients had coinfections (viral or bacterial). None of these infants had severe disease. Continued vigilance in testing this vulnerable group of infants is warranted.

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