scholarly journals New prognostic models in glioblastoma: based ferroptosis-related genes and immune scores

2021 ◽  
Author(s):  
Xin-yu Li ◽  
Xi-tao Yang
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Roc Curves ◽  
Prognostic Models ◽  
Roc Curve Analysis ◽  
Prognostic Assessment ◽  
Expression Of Genes ◽  
Prognosis Model ◽  
Degree Of Malignancy ◽  
High Degree

Abstract Background: Glioblastoma multiforme (GBM) has a high degree of malignancy and the clinical outcomes is dismal. Ferroptosis is critical to the development and progression of many diseases, such as cancer, cardiovascular diseases and aging. This study was designed to establish a sensitive prognostic model based on ferroptosis-related genes and immune scores to predict overall survival (OS) in patients with GBM. Methods: The expression of genes and associated clinical parameters was obtained from the publicly available TCGA , CGGA and GEO database. According to the immune scores, patient samples were assigned into two groups. Their biological function analyses were performed through differently expressed genes. By means of LASSO, unadjusted and adjusted Cox regression analyses, this predictive signature was constructed and validated by external databases.Results: A total of 4 ferroptosis-related genes (HMOX1,HSPB1,STEAP3,ZEB1)were ultimately screened as associated hub genes and utilized to construct a prognosis model. Then our constructed riskScore model significantly passed the validation in the external datasets of OS (all p < 0.05). Receiver operating characteristic (ROC) curve analysis was conducted. Finding the area under the ROC curves (AUCs) were 0.82 at 1 years, 0.75 at 3 years, 0.67 at 5 years. Functional analysis revealed that immune related processes were different between two risk groups.We also explored its association with immune infiltration.Conclusion: Our study successfully constructed a prognostic model containing 4 hub ferroptosis-related genes for GBM, helping clinicians predict patients’ OS and making the prognostic assessment more standardized. Future prospective studies are required to validate our findings.

A Conundrum of Peer Evaluation of Predicting Poor Prognostic Models Affecting the Mortality Rate of Covid -19

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Yashaswini J ◽  
Niranjan K R ◽  
Beena Ullala Mata B N ◽  
Kaliprasad C S
Keyword(s):  
Prognostic Model ◽  
Statistical Information ◽  
Prognostic Models ◽  
Peer Evaluation ◽  
Research Teams ◽  
Emerging Nations ◽  
The World ◽  
Prognosis Model ◽  
Real World Datasets ◽  
Number Of Individuals

Mankind is confronting these days a histrionic pandemic scene with the Coronavirus proliferation over all continents. The Covid-19 pandemic outbreak is as yet not very much portrayed, and numerous research teams everywhere on the world are chipping away at one or the other restorative therapeutic issues or immunization issues. The outburst of COVID-19 has constituted a danger to wellbeing of world. With the expanding number of individuals tainted, medical services frameworks, particularly those in economically emerging nations, are bearing gigantic pressing factor for the devising a prognostic model. There is a dire requirement for the analysis of COVID-19 and the anticipation of inpatients. To diminish these issues, a data statistical information driven clinical aid framework is advanced in this paper. In view of two real world datasets in Wuhan, China, the proposed framework coordinates information from various sources with tools of Machine Learning (ML) to anticipate COVID-19 tainted likelihood of suspected patients in their first visit, and afterward foresee mortality of affirmed cases. As opposed to picking an interpretable calculation, this framework isolates the clarifications from ML models. It can do help to patient triaging and give some valuable guidance to specialists and doctors. A prognosis model is in the way of extraordinary premium for specialists to adjust their consideration methodology for therapeutic or diagnosis procedure.

A new prognostic strategy based on four DNA repair-associated lncRNAs for hepatocellular carcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Hanyi Zeng ◽  
Chengdong Liu ◽  
Xiaohan Zhou ◽  
Li Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Repair ◽  
Clinical Utility ◽  
Prognostic Model ◽  
Cox Regression ◽  
Reduction Process ◽  
Roc Curves ◽  
Clinical Impact ◽  
Functional Enrichment ◽  
Prognostic Signature

Background: Hepatocellular carcinoma (HCC) is a malignant tumour with poor prognosis. The effect of DNA repair on prognosis cannot be ignored; and long non-coding RNA (lncRNA) can regulate the DNA repair process. Objective: : To obtain DNA repair-associated lncRNA (DR-lncRNA) prognostic signature for improved ability to prediction of HCC prognosis. Methods: Our study used the Cancer Genome Atlas database. Gene set variation analysis was performed to differentiate high and low levels of DNA repair to identify DR-lncRNAs. By performing univariate Cox regression, LASSO regression, and multivariate Cox regression analyses, we finally obtained a DR-lncRNA prognostic signature and constructed a nomogram prognostic model. Time-dependent receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact curves were used to assess predictive ability and clinical utility. Differentially expressed genes (DEGs) functional enrichment analysis was performed to further explore the underlying mechanisms that influence HCC prognosis. Results: We obtained a DR-lncRNA prognostic signature—AP002478.1, AC116351.1, LINC02580, and LINC00861. The ROC curves and calibration plots showed good discrimination and calibration properties. Combining the DR-lncRNA prognostic signature and tumour stages, we established a nomogram prognostic model. DCA and clinical impact curves showed the clinical utility of the nomogram prognostic model. DEGs of high-risk and low-risk groups predicted by the DR-lncRNA prognostic were significantly associated with cell cycle and various metabolic pathways and biological processes such as the oxidation-reduction process and cell division. Conclusion: We identified a DR-lncRNA prognostic signature and constructed a nomogram prognostic model, which could be a beneficial prognostic strategy for HCC.

scholarly journals Nonnegative Matrix Factorization Model-Based Construction For Molecular Clustering and Prognostic Assessment of Squamous Carcinoma of Head and Neck

2021 ◽  
Author(s):  
Xin-Yu Li ◽  
Xi-Tao Yang
Keyword(s):  
Head And Neck ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Cox Regression ◽  
Nonnegative Matrix ◽  
Squamous Carcinoma ◽  
Prognostic Models ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Prognostic Assessment

Abstract Purpose: Exploring nonnegative matrix factorization (NMF) model-based clustering and prognostic modeling of head and neck squamous carcinoma (HNSCC). Methods: The transcriptome microarray data of HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Shanghai Ninth People’s Hospital, and NMF clustering was constructed using the R software package. Relevant prognostic models were developed based on clustering. Results: Based on NMF, all samples were divided into 2 subgroups. Predictive models were constructed by analysing the differential gene between the two subgroups. Results of survival analysis in the current study revealed that the high-risk group had a poor prognosis. Further, results of multi-factor Cox regression analysis revealed that the predictive model was an independent predictor of prognosis. Conclusion: It was evident that the NMF-based prognostic model is a useful guide to the prognostic assessment of HNSCC.

scholarly journals A Novel Nine Apoptosis-Related Genes Signature Predicting Overall Survival for Kidney Renal Clear Cell Carcinoma and its Associations with Immune Infiltration

2021 ◽  
Vol 8 ◽  
Author(s):  
Yi Wang ◽  
Yinhao Chen ◽  
Bingye Zhu ◽  
Limin Ma ◽  
Qianwei Xing
Keyword(s):  
Overall Survival ◽  
Cell Carcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Roc Curves ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

Background: This study was designed to establish a sensitive prognostic model based on apoptosis-related genes to predict overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).Methods: Obtaining the expression of apoptosis-related genes and associated clinical parameters from online datasets (The Cancer Genome Atlas, TCGA), their biological function analyses were performed through differently expressed genes. By means of LASSO, unadjusted and adjusted Cox regression analyses, this predictive signature was constructed and validated by internal and external databases (both TCGA and ArrayExpress).Results: A total of nine apoptosis-related genes (SLC27A2, TNFAIP2, IFI44, CSF2, IL4, MDK, DOCK8, WNT5A, APP) were ultimately screened as associated hub genes and utilized to construct a prognosis model. Then our constructed riskScore model significantly passed the validation in both the internal and external datasets of OS (all p &lt; 0.05) and verified their expressions by qRT-PCR. Moreover, we conducted the Receiver Operating Characteristic (ROC), finding the area under the ROC curves (AUCs) were all above 0.70 which indicated that riskScore was a stable independent prognostic factor (p &lt; 0.05). Furthermore, prognostic nomograms were established to figure out the relationship between 1-, 3- and 5-year OS and individual parameters for ccRCC patients. Additionally, survival analyses indicated that our riskScore worked well in predicting OS in subgroups of age, gender, grade, stage, T, M, N0, White (all p &lt; 0.05), except for African, Asian and N1 (p &gt; 0.05). We also explored its association with immune infiltration and applied cMap database to seek out highly correlated small molecule drugs.Conclusion: Our study successfully constructed a prognostic model containing nine hub apoptosis-related genes for ccRCC, helping clinicians predict patients’ OS and making the prognostic assessment more standardized. Future prospective studies are required to validate our findings.

scholarly journals Clinical Prognostic Model of Autophagy-Related LncRNA Genes in The Esophageal Adenocarcinoma (EAC) to Predicting Overall Survival (OS) of The Patients：The Evidence From Bioinformatic Analysis

2021 ◽  
Author(s):  
Liusheng Wu ◽  
Xiaoqiang Li ◽  
Jixian Liu ◽  
Da Wu ◽  
Dingwang Wu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Esophageal Adenocarcinoma ◽  
Clinical Data ◽  
Roc Curve ◽  
Prognostic Model ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Prognostic Models ◽  
Cox Regression Analysis

Abstract Objective: Autophagy-related LncRNA genes play a vital role in the development of esophageal adenocarcinoma.Our study try to construct a prognostic model of autophagy-related LncRNA esophageal adenocarcinoma, and use this model to calculate patients with esophageal adenocarcinoma. The survival risk value of esophageal adenocarcinoma can be used to evaluate its survival prognosis. At the same time, to explore the sites of potential targeted therapy genes to provide valuable guidance for the clinical diagnosis and treatment of esophageal adenocarcinoma.Methods: Our study have downloaded 261 samples of LncRNA-related transcription and clinical data of 87 patients with esophageal adenocarcinoma from the TCGA database, and 307 autophagy-related gene data from www.autuphagy.com. We applied R software (Version 4.0.2) for data analysis, merged the transcriptome LncRNA genes, autophagy-related genes and clinical data, and screened autophagy LncRNA genes related to the prognosis of esophageal adenocarcinoma. We also performed KEGG and GO enrichment analysis and GSEA enrichment analysis in these LncRNA genes to analysis the risk characteristics and bioinformatics functions of signal transduction pathways. Univariate and multivariate Cox regression analysis were used to determine the correlation between autophagy-related LncRNA and independent risk factors. The establishment of ROC curve facilitates the evaluation of the feasibility of predicting prognostic models, and further studies the correlation between autophagy-related LncRNA and the clinical characteristics of patients with esophageal adenocarcinoma. Finally, we also used survival analysis, risk analysis and independent prognostic analysis to verify the prognosis model of esophageal adenocarcinoma.Results: We screened and identified 22 autophagic LncRNA genes that are highly correlated with the overall survival (OS) of patients with esophageal adenocarcinoma. The area under the ROC curve（AUC=0.941）and the calibration curve have a good lineup, which has statistical analysis value. In addition, univariate and multivariate Cox regression analysis showed that the autophagy LncRNA feature of this esophageal adenocarcinoma is an independent predictor of esophageal adenocarcinoma.Conclusion: These LncRNA screened and identified may participate in the regulation of cellular autophagy pathways, and at the same time affect the tumor development and prognosis of patients with esophageal adenocarcinoma. These results indicate that risk signature and nomogram are important indicators related to the prognosis of patients with esophageal adenocarcinoma.

scholarly journals The Identification and Analysis of a Novel Model Based on Ferroptosis-Related Genes for Predicting the Prognosis of Diffuse Large B-Cell Lymphomas

2021 ◽  
Author(s):  
Jiayi Wang ◽  
Hongling Peng ◽  
Guangsen Zhang ◽  
Yunxiao Xu ◽  
Wenzhe Yan
Keyword(s):  
B Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Survival Curve ◽  
B Cell Lymphoma ◽  
B Cell Lymphomas ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Large B Cell

Abstract Background Diffuse large B-cell lymphomas (DLBCLs) are the most common B-cell lymphoma featured as phenotypically and genetically heterogeneous. Ferroptosis is a new found programmed cell death and have a crucial role in the chemoresistance of tumor. We aim to build a ferroptosis-related genes (FRGs) prognostic signature to predict the outcome of DLBCLs. Methods Our study retrospectively investigated the mRNA expression level and clinical data of 604 DLBCL patients from 3 GEO public datasets. A series of bioinformatic approaches including Cox regression analysis, function enrich analysis, immune infiltration analysis, ROC curve analysis, Kaplan–Meier survival curve and the least absolute shrinkage and selection operator (LASSO) method by the corresponding R packages in R statistical software were combined to explored the heterogenicity of FRG based clusters and to build prognostic model. Immunohistochemistry was used to exam the protein expression of six FRGs in different molecular type of DLBCL. Results We first identified 19 FRGs with potential prognostic values and classfied the patients into cluster 1 and cluster 2, Results indicated that cluster 1 tend to have a shorter overall survival (OS) time, while patients in the two clusters have different patterns of infiltrating immune cells among. Furthermore, the LASSO was used to generated a six-genes (GCLC, LPCAT3, NFE2L2, ABCC1, SLC1A5, and GOT1) risk signature which constructed a risk score formula and prognostic model for the OS of DLBCL patients. Kaplan–Meier survival analysis proved that poorer OS was exhibited in higher risk patients stratified by the prognostic model in both the training cohort and test cohort. In addition, we constructed nomograms to predict the OS of DLBCL patients. Both the decision curve(DCA) and the calibration plots showed that the nomogram had good agreement between predicted results and actual observation. Finally, the validation by immunohistochemistry indicated the GCLC, LPCAT3, ABCC1, SLC1A5, and GOT1 were high expressed in DLBCL with various prognostic adverse molecular factor. Conclusion In sum, we built a new FRG-based prognostic model which will help improve diagnosis and treatment for DLBCL patients.

scholarly journals Bioinformatics Analysis Reveals an Association Between Cancer Cell Stemness, Gene Mutations, and the Immune Microenvironment in Stomach Adenocarcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Zaisheng Ye ◽  
Miao Zheng ◽  
Yi Zeng ◽  
Shenghong Wei ◽  
Yi Wang ◽  
...  
Keyword(s):  
Immune Cells ◽  
Clinical Characteristics ◽  
Prognostic Model ◽  
Cox Regression ◽  
Tumor Therapy ◽  
Gene Mutations ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Immune Microenvironment ◽  
Stomach Adenocarcinoma

Cancer stem cells (CSCs), characterized by infinite proliferation and self-renewal, greatly challenge tumor therapy. Research into their plasticity, dynamic instability, and immune microenvironment interactions may help overcome this obstacle. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena Browser. Tumor purity and infiltrating immune cells in stomach adenocarcinoma (STAD) tissues were predicted using the ESTIMATE R package and CIBERSORT method, respectively. Differentially expressed genes (DEGs) between the high and low mRNAsi groups were used to construct prognostic models with weighted gene co-expression network analysis (WGCNA) and Lasso regression. The association between cancer stemness, gene mutations, and immune responses was evaluated in STAD. A total of 6,739 DEGs were identified between the high and low mRNAsi groups. DEGs in the brown (containing 19 genes) and blue (containing 209 genes) co-expression modules were used to perform survival analysis based on Cox regression. A nine-gene signature prognostic model (ARHGEF38-IT1, CCDC15, CPZ, DNASE1L2, NUDT10, PASK, PLCL1, PRR5-ARHGAP8, and SYCE2) was constructed from 178 survival-related DEGs that were significantly related to overall survival, clinical characteristics, tumor microenvironment immune cells, TMB, and cancer-related pathways in STAD. Gene correlation was significant across the prognostic model, CNVs, and drug sensitivity. Our findings provide a prognostic model and highlight potential mechanisms and associated factors (immune microenvironment and mutation status) useful for targeting CSCs.

Development and validation of prognostic models in patients with metastatic renal cell carcinoma (mRCC) treated with sunitinib: A Greek-French collaboration.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15040-e15040
Author(s):  
Vasilios Karavasilis ◽  
Kimon Tzanis ◽  
Christina Bamia ◽  
Reza-Thierry Elaidi ◽  
Efthimios Kostouros ◽  
...  
Keyword(s):  
Kinase Inhibitors ◽  
Cox Regression ◽  
External Validation ◽  
Prognostic Index ◽  
Roc Curves ◽  
Risk Groups ◽  
High Risk Group ◽  
Prognostic Models ◽  
Clinical Factors ◽  
Complicated Model

e15040 Background: The use of tyrosine kinase inhibitors (TKIs) in mRCC has improved prognosis but the individual outcome remains largely unpredictable. The MSKCC model, used to identify risk groups, was developed in cytokine-treated patients and has not been externally validated in the TKI era. It contains 3 laboratory factors (total 5), making its application to retrospective series somewhat problematic. Subsequently, a more complicated model, using 4 laboratory factors (total 6) has been described. The Hellenic Cooperative Oncology Group recently described a simpler model with only 3 clinical factors. We are describing the application and external validation of this model. Methods: 128 Greek patients with mRCC treated with 1st line sunitinib were included. All had had nephrectomy. Previous interferon was allowed. Cox regression was used to develop a predictive model for overall survival (OS). Our model was compared to that of MSKCC and Heng’s using ROC curves and Harrell’s Concordance Index. Risk groups were defined by the calculated prognostic index and by clinical factors. External validation was done using a sample of 226 French patients. The Royston and Sauerbrei D statistic was used as a measure of discrimination of the survival model. Results: Time from diagnosis of RCC to start of sunitinib (<12), PS (>1) and number of metastatic sites (>1) were independent adverse prognostic factors in the Greek dataset. The co-efficients for each factor were: 0.51, 0.97, 0.61, respectively. The 3 risk groups were defined by the 25th and 75th percentiles of the prognostic index values (Table 1). The model was of equal prognostic value to the MSKCC (p=.272) and Heng’s (p=.075). French had better survival than Greek patients especially in the high risk group (for all models). Validation of our model in the French data showed that it was applicable (R2 D: 0.14, SE: 0.09), especially for the low/medium risk groups. Conclusions: Our model is the only one externally validated in TKI-treated patients. It may be considered as a simpler alternative to those currently applied. [Table: see text]

Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer

2021 ◽  
Author(s):  
Wenjing Zhu ◽  
Tao Zhang ◽  
Shaohong Luan ◽  
Qingnuan Kong ◽  
Wenmin Hu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Prediction Model ◽  
Prognostic Model ◽  
Cox Regression ◽  
Roc Curves ◽  
Clinicopathological Parameters ◽  
Systematic Research ◽  
Cox Regression Analysis ◽  
Plot Analysis ◽  
Number Variation

Abstract Background: Increasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV).Material/methods: 379 OV patients with RNA-Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of snoRNAs. ROC curves and KM plot curves were applied to validate the prediction model. Besides, the model was validated in 5 paired clinical tissues by real-time PCR, H&E staining and immunohistochemistry. Results: A prognostic model was constructed on the basis of SnoRNAs in OV patients.Patients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumorsize, advanced stage and with tumor status. KM plot analysis confirmed that patients with high RiskScore had poorer prognosis in subgroup of age, tumor size and stage. 7 of 9 snoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 snoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, H&E staining and immunohistochemistry of Ki67, P53 and P16 were confirmed that patients with higher RiskScore are more malignant. Conclusions: In summary, we identified a nine-snoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.

A Conundrum of Peer Evaluation of Predicting Poor Prognostic Models Affecting the Mortality Rate of Covid -19

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Yashaswini J ◽  
Niranjan K R ◽  
Beena Ullala Mata B N ◽  
Kaliprasad C S
Keyword(s):  
Prognostic Model ◽  
Statistical Information ◽  
Prognostic Models ◽  
Peer Evaluation ◽  
Research Teams ◽  
Emerging Nations ◽  
The World ◽  
Prognosis Model ◽  
Real World Datasets ◽  
Number Of Individuals

Mankind is confronting these days a histrionic pandemic scene with the Coronavirus proliferation over all continents. The Covid-19 pandemic outbreak is as yet not very much portrayed, and numerous research teams everywhere on the world are chipping away at one or the other restorative therapeutic issues or immunization issues. The outburst of COVID-19 has constituted a danger to wellbeing of world. With the expanding number of individuals tainted, medical services frameworks, particularly those in economically emerging nations, are bearing gigantic pressing factor for the devising a prognostic model. There is a dire requirement for the analysis of COVID-19 and the anticipation of inpatients. To diminish these issues, a data statistical information driven clinical aid framework is advanced in this paper. In view of two real world datasets in Wuhan, China, the proposed framework coordinates information from various sources with tools of Machine Learning (ML) to anticipate COVID-19 tainted likelihood of suspected patients in their first visit, and afterward foresee mortality of affirmed cases. As opposed to picking an interpretable calculation, this framework isolates the clarifications from ML models. It can do help to patient triaging and give some valuable guidance to specialists and doctors. A prognosis model is in the way of extraordinary premium for specialists to adjust their consideration methodology for therapeutic or diagnosis procedure.

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