scholarly journals Development and Validation of a Robust Apoptosis-related Prognostic Classifier in Patients With Osteosarcoma

2021 ◽  
Author(s):  
Zhifeng Zhang ◽  
Yi Wang ◽  
Fengmei Chen ◽  
Yinquan Zhang ◽  
Zhengmao Guan
Keyword(s):  
Immune Cells ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Predictive Ability ◽  
Clinical Decision ◽  
Individual Treatment ◽  
Biological Processes ◽  
Clinicopathological Factors ◽  
Lasso Regression ◽  
Development And Validation

Abstract Background: Apoptosis plays an important role in the tumorigenesis and the development of osteosarcoma, but the reliable biomarkers for individual treatment and prognosis of osteosarcoma based on apoptosis is lacking.Methods: A total of 1476 apoptosis-related genes were extracted from pathways and biological processes associated with apoptosis downloaded from MSigDB. All of those genes were used to identified the prognosis-related genes by univariate cox regression in the TARGET dataset and the ARS was constructed using the LASSO regression. The performance of the classifier was verified in the training and validation groups. The infiltration of immune cells and the expression levels of the immune checkpoint in different groups were also analyzed. Finally, a nomogram based on ARS and other Clinicopathological factors was constructed to facilitate clinical application.Results: ARS containing 22 apoptosis-related genes were identified, and its predictive ability performed well in both the training and validation groups. Macrophages M1 were highly expressed in the low-score group, and NK cells resting was highly expressed in the high-score group. The samples with low-score had higher expression of CTLA4 and PDL1. A nomogram with excellent predictive effectiveness (AUC= 0.932, 0.984, 0.939, 0.939, 0.948) was constructed to facilitate clinical decision-making.Conclusion: A prognostic classifier based on 22 apoptosis-related genes and a nomogram were constructed to predict the overall survival of patients with osteosarcoma. The classifier also provides a reference for selecting suitable patients for immunotherapy and targeted therapy.

Identification of Immune-Related Prognostic Biomarkers in Pancreatic Cancer

2020 ◽  
Vol 12 (12) ◽  
pp. 1355-1367
Author(s):  
Xiaoyan Lin ◽  
Jiakang Ma ◽  
Kaikai Ren ◽  
Mingyu Hou ◽  
Bo Zhou ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Individual Treatment ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Immune Related Genes

Immunotherapy for pancreatic cancer (PC) faces significant challenges. It is urgent to find immunerelated genes for targeted therapy. We aimed to identify immune-related messenger ribonucleic acids (mRNAs) with multiple methods of comprehensive immunoenrichment analysis in predicting survival of PC. PC genomics and clinical data were downloaded from TCGA. We analyzed relative enrichment of 29 immune cells using ssGSEA and classified PC samples into three immuneinfiltrating subgroups. Immune cell infiltration level and pathways were evaluated by ESTIMATE data and KEGG. Independent risk factors were derived from the combined analysis of WGCNA, LASSO regression and Cox regression analyses. Immune risk score was calculated according to four mRNAs to identify its value in predicting survival. PPI analysis was used to analyze the connections and potential pathways among genes. Finally, PC samples were classified into three immuneinfiltrating subgroups. Immunity high subgroup had higher immune score, soakage of immune cells, HLA/PD-L1 expression level, immune-related pathways enrichment and better survivability. Four potential prognostic immune-related genes (ITGB7, RAC2, DNASE1L3, and TRAF1) were identified. Immune risk score could be a potential survival prediction indictor with high sensitivity and specificity (AUC values = 0.708, HR = 1.445). A PPI network with seven nodes and five potential targeted pathways were generated. In conclusion, we estimated the state of immune infiltration in the PC tumor microenvironment by calculating stromal and immune cells enrichment with ssGSEA algorithms, and identified four prognostic immune-related genes that affect the proportion and distribution of immune cells infiltration in the tumor microenvironment. They lay a theoretical foundation to be important immunity targets of individual treatment in PC.

scholarly journals A prospective evaluation of the Flacker-Kiely One Year Mortality Score and the added value of NT-proBNP

2020 ◽  
Vol 11 (2) ◽  
pp. 13
Author(s):  
Kjell Krüger ◽  
Bård R. Kittang ◽  
Sabine P. Solheim ◽  
Kristian Jansen
Keyword(s):  
Heart Failure ◽  
Failure Criterion ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Clinical Decision ◽  
Blood Chemistry ◽  
Discriminatory Power ◽  
Added Value ◽  
Youden Index ◽  
Mortality Data

Objective: Several mortality indices have been constructed to aid clinical decision making in older adults. We aimed to prospectively validate the Flacker-Kiely (FK) mortality index in a Norwegian nursing home cohort, which has not been done before, and explore whether NT-ProBNP could improve its discriminatory power.Methods: We performed a cohort/mortality study. From November 2017 to July 2018, physicians in all public long-term nursing homes in Bergen, Norway, scored residents according to the original Flacker Kiely index. Mortality data were derived from the Norwegian Cause of Death Registry and NT-ProBNP values were obtained from routinely collected blood chemistry. An alternative FK index using the NT-ProBNP-value as a marker for the presence of heart failure was constructed (FK NT-ProBNP index). The ProBNP cut-off value was selected based on a Cox regression model (“dead/alive 1 year”/” NT-ProBNP (Ng/l)”, where the value with the highest Youden index was identified. We judged index performance by using c-statistics.Results: Both the original FK index and the constructed FK NT-ProBNP index discriminated between risk strata. The FK NT-ProBNP index yielded a C-index of 0.66 compared to 0.62 for the original FK index. Optimal discriminatory power was shown with a NT-ProBNP cut-off value of 1,595 Ng/l as heart failure criterion, and FK NT-ProBNP score 6.6.Conclusions: The prospective mortality estimation ability of the FK-index was comparable to previous retrospective studies. The inclusion of NT-ProBNP as a heart failure criterion strengthen the discriminatory power and utility of the index, both in clinic and administration.

scholarly journals A importância do treinamento, reprodutibilidade e calibragem para a qualidade dos estudos

2021 ◽  
Vol 41 (1) ◽  
pp. 3-7
Author(s):  
Cristiano Susin ◽  
Cassiano Kuchenbecker Rösing
Keyword(s):  
Decision Making ◽  
Scientific Knowledge ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
International Standards ◽  
Scientific Methodology ◽  
Biological Processes ◽  
The Way

Dentistry is undergoing a deep transformation in its way of producing, using and interpreting the scientific knowledge. The need for utilizing the best possible evidence for the understanding of the physical and biological processes, as well as to clinical decision making, has risen the interest in the study of subjects that were not regarded as important before. In this scene, the capacity of evaluating the quality of different studies and of producing them under international standards has enhanced the seeking for knowledge in scientific methodology. The way by which the data are obtained and the procedures are performed in research might definitively influence its capacity to generate evidence. Thus, training, reproducibility and calibration are principles that have to be part of everything concerning the process of creating and using the knowledge.

scholarly journals Identification of IFN-β Associated Genes Signature Predicting Overall Survival for Glioblastoma

2021 ◽  
Author(s):  
Cheng Lijing ◽  
Yuan Meiling ◽  
Li Shu ◽  
Chen Junjing ◽  
Zhong Shupeng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Individual Treatment ◽  
Normal Brain ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Prognosis Prediction

Abstract Background: Brain glioblastoma (GBM) is the most common primary malignant tumor of intracranial tumors. The prognosis of this disease is extremely poor. While the introduction of IFN-β regimen in the treatment of gliomas has significantly improved the outcome of patients, the underlying mechanism remains to be elucidated. Materials and methods: mRNA expression profiles and clinicopathological data were downloaded from TCGA-GBM and GSE83300 data set from the GEO. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene IFN-β signature (including PRDX1, SEC61B, XRCC5, and BCL2L2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction (PCR) experiments. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the lncRNAs and IFN-β associated genes. A lncRNA with a correlation coefficient |R2| > 0.3 and P < 0.05 was considered to be an IFN-β associated lncRNA.Results: Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction in GBM.Conclusions: Our study established a novel IFN-β associated genes signature to predict overall survival of GBM, which may help in clinical decision making for individual treatment.

scholarly journals Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xinjie Wu ◽  
Yanlei Wang ◽  
Wei Sun ◽  
Mingsheng Tan
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Training Group ◽  
Clinical Decision ◽  
The United States ◽  
Cox Proportional Hazards ◽  
Predictive Values

Introduction. We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes ( C -indexes) and calibration plots. Results. All 813 patients were randomly divided into a training group ( n = 572 ) and a validation group ( n = 241 ). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C -indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups. Conclusions. The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.

scholarly journals A Novel Predictive Model Associated With Osteosarcoma Metastasis

2021 ◽  
Author(s):  
Han Zhang ◽  
Guanhong Chen ◽  
Xiajie Lyu ◽  
Tao Li ◽  
Rong Chun ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Cox Regression ◽  
High Reliability ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Metastasis Model ◽  
Non Coding Rnas ◽  
Novel Model

Abstract Background: Long non-coding RNAs (lncRNAs) have diverse roles in modulating gene expression on both transcriptional and translational aspects, whereas its role in the metastasis of osteosarcoma (OS) is unclear.Method: Expression and clinical data were downloaded from TARGET datasets. The OS metastasis model was established by seven lncRNAs screened by univariate cox regression, lasso regression and multivariate cox regression analysis. The area under receiver operating characteristic curve (AUC) values were used to evaluate the models.Results: The predictive ability of this model is extraordinary (1 year: AUC = 0.92, 95% Cl = 0.83–1.01; 3 years: AUC = 0.87, 95% Cl = 0.79–0.96; 5 years: AUC = 0.86, 95% Cl = 0.76–0.96). Patients in high group had poor survival compared to low group (p < 0.0001). “NOTCH_SIGNALING”, and “WNT_BETA_CATENIN_SIGNALING” were enriched via the GSEA analysis and dendritic cells resting were associated with the AL512422.1, AL357507.1 and AC006033.2 (p < 0.05).Conclusion: We constructed a novel model with high reliability and accuracy to predict the metastasis of OS patients based on seven prognosis-related lncRNAs.

scholarly journals A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

2021 ◽  
Author(s):  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

scholarly journals Five crucial prognostic-related autophagy genes stratified female breast cancer patients aged 40-60 years

2020 ◽  
Author(s):  
Xiaolong Li ◽  
Hengchao Zhang ◽  
Jingjing Liu ◽  
Ping Li ◽  
Yi Sun
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Survival Curve ◽  
Predictive Ability ◽  
Female Breast Cancer ◽  
Clinicopathological Parameters ◽  
Risk Level ◽  
Lasso Regression ◽  
Breast Cancer Patients ◽  
Auc Value

Abstract Background: Autophagy is closely related to the progression of breast cancer.The aim of this study is to establish a prognostic-related model comprised of hub autophagy-genes(AGs) to assess patitents prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40-60 years based on risk level.Methods: The hub AGs were identified through univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well.Moreover,the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results: Totally,33 prognostic-related AGs were obtained from the univariate COX regression(P<0.05).SERPINA1, HSPA8, HSPB8, MAP1LC3A, and DIRAS3 were identified to constitute the prognostic model by the LASSO regression. The survival curve of patients in high-risk and in low-risk group was statistically significant(P<0.05).The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825,respectively. Stage and riskscore were independent risk factors relevant about prognosis.RB1CC1, RPS6KB1, and BIRC6 were identified as the most predominant mutant genes. It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in ErbB signal pathway. SERPIN1, riskscore were closely related to stage(P<0.05); HSPA8, riskscore were closely related to T staging(P<0.05); HSPB8 was closely related to N staging(P<0.05). Conclusions: Our prognostic model had relatively robust predictive ability on prognosis for patients aged 40-60 years.If stage was added into the prognostic model, the predictive ability would be more powerful.

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