scholarly journals Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Zhiqiao Zhang ◽  
Tingshan He ◽  
Liwen Huang ◽  
Yanling Ouyang ◽  
Jing Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
Precision Medicine ◽  
Cancer Survival ◽  
Cox Regression ◽  
Treatment Decision ◽  
Risk Scores ◽  
Predictive Tools ◽  
Treatment Decision Making ◽  
Regulation Network ◽  
Screen System

Abstract Background The current study aimed to construct competing endogenous RNA (ceRNA) regulation network and develop two precision medicine predictive tools for colorectal cancer (CRC). Methods Differentially expressed (DE) analyses were performed between CRC tissues and normal tissues. A ceRNA regulation network was constructed based on DElncRNAs, DEmiRNAs, and DEmRNAs. Results Fifteen mRNAs (ENDOU, MFN2, FASLG, SHOC2, VEGFA, ZFPM2, HOXC6, KLK10, DDIT4, LPGAT1, BEX4, DENND5B, PHF20L1, HSP90B1, and PSPC1) were identified as prognostic biomarkers for CRC by multivariate Cox regression. Then a Fifteen-mRNA signature was developed to predict overall survival for CRC patients. Concordance indexes were 0.817, 0.838, and 0.825 for 1-, 2- and 3-year overall survival. Patients with high risk scores have worse OS compared with patients with low risk scores. Conclusion The current study provided deeper understanding of prognosis-related ceRNA regulatory network for CRC. Two precision medicine predictive tools named Smart Cancer Survival Predictive System and Gene Survival Analysis Screen System were constructed for CRC. These two precision medicine predictive tools can provide valuable precious individual mortality risk prediction before surgery and improve the individualized treatment decision-making.

scholarly journals Two predictive precision medicine tools for hepatocellular carcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhiqiao Zhang ◽  
Jing Li ◽  
Tingshan He ◽  
Yanling Ouyang ◽  
Yiyan Huang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Precision Medicine ◽  
Cox Regression ◽  
Risk Group ◽  
Operator Method ◽  
High Risk Group ◽  
Expression Data ◽  
Link Type ◽  
Screen System

Abstract Background Hepatocellular carcinoma (HCC) is a serious threat to public health due to its poor prognosis. The current study aimed to develop and validate a prognostic nomogram to predict the overall survival of HCC patients. Methods The model cohort consisted of 24,991 mRNA expression data points from 348 HCC patients. The least absolute shrinkage and selection operator method (LASSO) Cox regression model was used to evaluate the prognostic mRNA biomarkers for the overall survival of HCC patients. Results Using multivariate Cox proportional regression analyses, a prognostic nomogram (named Eight-mRNA prognostic nomogram) was constructed based on the expression data of N4BP3, -ADRA2B, E2F8, MAPT, PZP, HOXD9, COL15A1, and -NDST3. The C-index of the Eight-mRNA prognostic nomogram was 0.765 (95% CI 0.724–0.806) for the overall survival in the model cohort. The Harrell’s concordance-index of the Eight-mRNA prognostic nomogram was 0.715 (95% CI 0.658–0.772) in the validation cohort. The survival curves demonstrated that the HCC patients in the high risk group had a significantly poorer overall survival than the patients in the low risk group. Conclusion In the current study, we have developed two convenient and efficient predictive precision medicine tools for hepatocellular carcinoma. These two predictive precision medicine tools are helpful for predicting the individual mortality risk probability and improving the personalized comprehensive treatments for HCC patients. The Smart Cancer Predictive System can be used by clicking the following URL: https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_HCC_2/. The Gene Survival Analysis Screen System is available at the following URL: https://zhangzhiqiao5.shinyapps.io/Gene_Survival_Analysis_A1001/.

scholarly journals Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11412
Author(s):  
Jinsong Lin ◽  
Shubiao Lu ◽  
Zhijian Jiang ◽  
Chongjing Hu ◽  
Zhiqiao Zhang
Keyword(s):  
Overall Survival ◽  
Precision Medicine ◽  
Mortality Risk ◽  
Prognostic Model ◽  
Regulatory Mechanism ◽  
Cox Regression ◽  
Predictive Tools ◽  
On Line ◽  
The Individual ◽  
Risk Curves

Background Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients. Methods Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model. Results There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients. Conclusion The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

scholarly journals Assessing Clinical Equivalence in Oncology Biosimilar Trials With Time-to-Event Outcomes

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Hajime Uno ◽  
Deborah Schrag ◽  
Dae Hyun Kim ◽  
Dejun Tang ◽  
Lu Tian ◽  
...  
Keyword(s):  
Overall Survival ◽  
Survival Time ◽  
Primary Endpoint ◽  
Reference Product ◽  
Survival Curve ◽  
Treatment Decision ◽  
Hazard Ratio ◽  
Treatment Decision Making ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Abstract A typical biosimilar study in oncology uses the overall response evaluated at a specific time point as the primary endpoint, which is generally acceptable regulatorily, to assess clinical equivalence between a biosimilar and its reference product. The standard primary endpoint for evaluating an anticancer therapy, progression-free or overall survival would be a secondary endpoint in a biosimilar trial. With a conventional analytic procedure via, for example, hazard ratio to quantify the group difference, it is difficult and challenging to assess clinical equivalence with respect to progression-free or overall survival because the study generally has a limited number of clinical events observed in the study. In this article, we show that an alternative procedure based on the restricted mean survival time, which has been discussed extensively for design and analysis of a general equivalence study, is readily applicable to a biosimilar trial. Unlike the hazard ratio, this procedure provides a clinically interpretable estimate for assessing equivalence. Using the restricted mean survival time as a summary measure of the survival curve will enhance better treatment decision making in adopting a biosimilar product over the reference product.

scholarly journals Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jie Zhao ◽  
Rixiang Zhao ◽  
Xiaocen Wei ◽  
Xiaojing Jiang ◽  
Fan Su
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Ovarian Cancer Risk ◽  
Cox Regression Analysis ◽  
The Impact

Background. Ovarian cancer (OC) is the top of the aggressive malignancies in females with a poor survival rate. However, the roles of immune-related pseudogenes (irPseus) in the immune infiltration of OC and the impact on overall survival (OS) have not been adequately studied. Therefore, this study aims to identify a novel model constructed by irPseus to predict OS in OC and to determine its significance in immunotherapy and chemotherapy. Methods. In this study, with the use of The Cancer Genome Atlas (TCGA) combined with Genotype-Tissue Expression (GTEx), 55 differentially expressed irPseus (DEirPseus) were identified. Then, we constructed 10 irPseus pairs with the help of univariate, Lasso, and multivariate Cox regression analysis. The prognostic performance of the model was determined and measured by the Kaplan–Meier curve, a time-dependent receiver operating characteristic (ROC) curve. Results. After dividing OC subjects into high- and low-risk subgroups via the cut-off point, it was revealed that subjects in the high-risk group had a shorter OS. The multivariate Cox regression performed between the model and multiple clinicopathological variables revealed that the model could effectively and independently predict the prognosis of OC. The prognostic model characterized infiltration by various kinds of immune cells and demonstrated the immunotherapy response of subjects with cytotoxic lymphocyte antigen 4 (CTLA4), anti-programmed death-1 (PD-1), and anti-PD-ligand 1 (PD-L1) therapy. A high risk score was related to a higher inhibitory concentration (IC50) for etoposide ( P = 0.0099 ) and mitomycin C ( P = 0.0013 ). Conclusion. It was the first study to identify a novel signature developed by DEirPseus pairs and verify the role in predicting OS, immune infiltrates, immunotherapy, and chemosensitivity. The irPseus are vital factors predicting the prognosis of OC and could act as a novel potential treatment target.

scholarly journals An 8-lncRNA Signature Predicts Survival of Triple-Negative Breast Cancer Patients Without Germline BRCA1/2 Mutation

2020 ◽  
Author(s):  
Minling Liu ◽  
Wei Dai ◽  
Mengyuan Zhu ◽  
Xueying Li ◽  
Shan Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Validation Cohort ◽  
Functional Enrichment ◽  
Biological Behavior ◽  
Risk Scores ◽  
Training Cohort

Abstract Background: Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment.Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm.Results:We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P=0.00018 and P =0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion.Conclusions: We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

scholarly journals Identification Sixteen Metabolic Genes as Potential Biomarkers for Colon Adenocarcinoma 

2020 ◽  
Author(s):  
Binbin Cui ◽  
Fuqiang Zhao ◽  
Yanlong Liu ◽  
Xinyue Gu ◽  
Bomiao Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Metabolic Genes ◽  
Cox Analysis

Abstract Purpose Colon adenocarcinoma (COAD) is the most common primary malignant tumor of the digestive tract. It is still important to find important markers that affect the prognosis of COAD. This research aims to identify some key prognosis-related metabolic genes (PRMG) and establish a clinical prognosis model for COAD patients. Method We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to obtain gene expression profiles of COAD, and then identified differentially expressed prognostic-related metabolic genes through R language and Perl software, Through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to obtain target genes, established metabolic genes prognostic models and risk scores. Through COX regression analysis, independent risk factors affecting the prognosis of COAD were analyzed, and Receiver Operating Characteristic (ROC) curve analysis of independent prognostic factors was performed and a nomogram for predicting overall survival was constructed. Perform the consistency index (C-index) test and decision curve analysis (DCA) on the nomogram, and use Gene Set Enrichment Analysis (GSEA) to identify the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of model genes. Result We selected PRMG based on the expression of metabolic genes, and used LASSO Cox regression to construct 16 metabolic gene (SEPHS1, P4HA1, ENPP2, PTGDS, GPX3, CP, ASPA, POLR3A, PKM, POLR2D , XDH, EPHX2, ADH1B, HMGCL, GPD1L and MAOA) models. The risk score generated from our model can well predict the survival prognosis of COAD. A nomogram based on the clinicopathological characteristics and risk scores of COAD can personally predict the overall survival rate of COAD patients. Conclusion We comprehensively identified metabolic genes related to the prognosis of COAD. The risk score based on the expression of 16 metabolic genes can effectively predict the prognosis of patients with COAD.

scholarly journals Development of a Prognostic Signature Based on Hypoxia-related Genes for Thyroid Cancer

2021 ◽  
Author(s):  
HongYang Zhang ◽  
Sijia Li ◽  
Wei Li
Keyword(s):  
Overall Survival ◽  
Thyroid Cancer ◽  
Prognostic Value ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Prognosis Model ◽  
Cox Analysis

Abstract Background. We aimed to establish a model to predict the prognosis of patients with thyroid cancer based on differentially expressed hypoxia-related genes.Methods. By comparing the genes in TCGA database and hypoxiaDB database, we obtained differentially expressed genes (DEGs) related to hypoxia in thyroid cancer. Gene function enrichment analysis was performed, and a protein-protein interaction network was constructed using the STRING database. Univariate Cox regression were used to screen hypoxia-related genes with prognostic value. Subsequently, multivariate Cox analysis was used to determine prognostic markers based on thyroid cancer, a prognosis model based on these genes was established. The Kaplan-Meier analysis, Receiver operating characteristic (ROC) analysis and The Harrell’s concordance indexes in the training set and the validation set were used to evaluate the performance of the model. Finally, we conducted univariate analyses of the prognostic value of clinical data (including risk scores) of thyroid cancer patients.Results. 326 hypoxia-related thyroid cancer genes were found. Functional enrichment analysis demonstrated they were mainly involved in regulating biological functions. 23 genes have been proved to be associated with the prognosis of thyroid cancer with univariate Cox regression, among them, 11 marker genes were used to construct a new prognosis model by multivariate Cox analysis. Accordingly, the system of risk scores was constructed, patients with high-risk scores (P <0.005) had shorter overall survival than those with low-risk scores. The ROC curve indicated good performance of the eleven-gene signature at predicting overall survival. The Harrell’s concordance indexes in the internally validated for the 11-gene prognostic signature was 0.881. Moreover, univariate analysis showed that the risk score and age were significantly associated with patient overall survival. The model we created was significantly associated with patient overall survival.Conclusions. The model we established had excellent performance in the prognosis of thyroid cancer.

scholarly journals A Nomogram Integrating Ferroptosis- and Immune-Related Biomarkers for Prediction of Overall Survival in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengyu Chai ◽  
Xiuchun Li ◽  
Yaxin Zhang ◽  
Yemeng Tang ◽  
Pingping Shu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Immune Responses ◽  
Lung Adenocarcinoma ◽  
Tumor Immunity ◽  
Immune Cells ◽  
Cox Regression ◽  
Therapeutic Potential ◽  
Clinical Staging ◽  
Risk Scores ◽  
Immune Related Genes

Ferroptosis plays a dual role in cancer, which is known to be affected to antitumor immune responses. However, the association between ferroptosis and antitumor immune responses is uncertain in lung adenocarcinoma (LUAD). In this work, 38 ferroptosis-related genes (FRGs) and 429 immune-related genes (IRGs) were identified as being differentially expressed between tumor and normal samples. Two risk score formulas consisting of seven FRGs and four IRGs, respectively, were developed by Lasso-penalized Cox regression and verified in the GSE13213 dataset. The CIBERSORT algorithm was used to estimate the relative abundance of immune cells in tumors. The correlation between FRGs and immune cells was evaluated using the TIMER database. The results indicated that the development of ferroptosis was synergistic with that of anti-tumor immunity in LUAD. The concordance index and calibration curves showed that the performance of a nomogram that combines clinical staging and risk scores is superior to that of models using a single prognostic factor. In conclusion, ferroptosis might be synergistic with anti-tumor immunity in LUAD. The combined nomogram could reliably predict the probability of overall survival of LUAD patients. These findings may be useful for future investigation of prognostic value and therapeutic potential related to ferroptosis and tumor immunity in LUAD.

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