Simple Clinical Prognostic Model for Hepatocellular Carcinoma in Developing Countries and Its Validation

2003 ◽  
Vol 21 (12) ◽  
pp. 2294-2298 ◽  
Author(s):  
Chee Kiat Tan ◽  
Ngai Moh Law ◽  
Han Seong Ng ◽  
David Machin
Keyword(s):  
Developing Countries ◽  
Simple Model ◽  
Physical Performance ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Score ◽  
Performance Status ◽  
Asia Pacific ◽  
Clinical Parameters ◽  
Validation Data

Purpose: More than 80% of hepatocellular carcinomas (HCCs) worldwide occur in developing countries, especially in Asia. It often presents at an advanced stage beyond treatment. In this circumstance, a simple prognostic model is useful. Previous prognostic models require radiologic and laboratory investigations that are not readily available in developing countries. Our aim is to formulate and then validate a simple clinical prognostic model for HCC in an Asian population using only clinical parameters and with serum alpha-fetoprotein (AFP) as the sole laboratory test. Patients and Methods: Cox regression modeling was performed on several clinical parameters and serum AFP level in 397 patients with HCC who received only supportive care in Singapore. A later group of 324 HCC patients from an Asia-Pacific–wide randomized trial was then used to validate the model. Results: Ascites, physical performance status, and serum AFP were independently predictive of survival. Cox analysis yielded a simple score based on these three variables that categorizes patients into low-, medium-, and high-risk groups with 6-month survivals of 43%, 21%, and 5%, respectively. The prospective validation data provided corresponding estimates of 33%, 15%, and 3% and give confirmation of the utility of the simple model. Conclusion: We have formulated and prospectively validated a simple prognostic score for untreated HCC that only requires a clinical evaluation for ascites and physical performance status and measurement of serum AFP. This simple model is particularly apt for developing country circumstances and can also be used to select patients for treatment trials.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

scholarly journals Integrated Analysis of Methylation-driven Genes and Pretreatment Prognostic Factors in Patients with Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Dongsheng He ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
Mengxing You ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background: The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients.Methods: The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than -0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test.Results: In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage.Conclusion: We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

scholarly journals Development of a Score Predicting Survival after Palliative Reirradiation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Carsten Nieder ◽  
Nicolaus Andratschke ◽  
Kent Angelo ◽  
Ellinor Haukland ◽  
Anca L. Grosu
Keyword(s):  
Prognostic Model ◽  
Prognostic Score ◽  
Performance Status ◽  
Palliative Radiotherapy ◽  
Survival Prediction ◽  
Survival Analyses ◽  
Multivariate Survival ◽  
Patient Groups ◽  
Favorable Group ◽  
Specific Score

Purpose. To develop a prognostic model for predicting survival after palliative reirradiation (PR).Methods and Materials. We analyzed all 87 PR courses administered at a dedicated palliative radiotherapy facility between 20.06.2007 (opening) and 31.12.2009. Uni- and multivariate survival analyses were performed, the previously published survival prediction score (SPS) was evaluated, and a PR-specific prognostic score was calculated.Results. In multivariate analysis, four parameters significantly influenced survival: performance status, use of steroids, presence of liver metastases, and pleural effusion. Based on these parameters, a 4-tiered score was developed. Median survival was 24.5 months for the favorable group, 9.7 and 2.8 months for the two intermediate groups, and 1.1 months for the unfavorable group (P=0.019for comparison between the two favorable groups andP≤0.002for all other pair-wise comparisons). All patients in the unfavorable group died within 2 months.Conclusion. The performance of PR-specific score was promising and might facilitate identification of patients who survive long enough to benefit from PR. It should be validated in independent patient groups, ideally from several institutions and countries.

scholarly journals Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11320
Author(s):  
Ying Pan ◽  
Ye Meng ◽  
Zhimin Zhai ◽  
Shudao Xiong
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Normal Control ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Clinical Parameters ◽  
Hub Genes ◽  
Key Genes

Background Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles. Methods Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters. Results GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients. Conclusion In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.

scholarly journals A Prognostic Model for Glioblastoma Patients Treated With Standard Therapy Based on a Prospective Cohort of Consecutive Non-Selected Patients From a Single Institution

2021 ◽  
Vol 11 ◽  
Author(s):  
Armita Armina Abedi ◽  
Kirsten Grunnet ◽  
Ib Jarle Christensen ◽  
Signe Regner Michaelsen ◽  
Aida Muhic ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Trials ◽  
Standard Therapy ◽  
Dna Methyltransferase ◽  
Prognostic Model ◽  
Cox Regression ◽  
Performance Status ◽  
Primary Treatment ◽  
Ecog Performance Status ◽  
Cox Regression Analysis

BackgroundGlioblastoma patients administered standard therapies, comprising maximal surgical resection, radiation therapy with concomitant and adjuvant temozolomide, have a variable prognosis with a median overall survival of 15–16 months and a 2-year overall survival of 30%. The aim of this study was to develop a prognostic nomogram for overall survival for glioblastoma patients treated with standard therapy outside clinical trials.MethodsThe study included 680 consecutive, non-selected glioblastoma patients administered standard therapy as primary treatment between the years 2005 and 2016 at Rigshospitalet, Copenhagen, Denmark. The prognostic model was generated employing multivariate Cox regression analysis modeling overall survival.ResultsThe following poor prognostic factors were included in the final prognostic model for overall survival: Age (10-year increase: HR = 1.18, 95% CI: 1.08–1.28, p < 0.001), ECOG performance status (PS) 1 vs. 0 (HR = 1.30, 95% CI: 1.07–1.57, p = 0.007), PS 2 vs. 0 (HR = 2.99, 95% CI: 1.99–4.50, p < 0.001), corticosteroid use (HR = 1.42, 95% CI: 1.18–1.70, p < 0.001), multifocal disease (HR = 1.63, 95% CI: 1.25–2.13, p < 0.001), biopsy vs. resection (HR = 1.35, 95% CI: 1.04–1.72, p = 0.02), un-methylated promoter of the MGMT (O6-methylguanine-DNA methyltransferase) gene (HR = 1.71, 95% CI: 1.42–2.04, p < 0.001). The model was validated internally and had a concordance index of 0.65.ConclusionA nomogram for overall survival was established. This model can be used for risk stratification and treatment planning, as well as improve enrollment criteria for clinical trials.

scholarly journals Tumor microenvironment related novel signature predict lung adenocarcinoma survival

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10628
Author(s):  
Juan Chen ◽  
Rui Zhou
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Prognostic Model ◽  
Cox Regression ◽  
Survival Prediction ◽  
Clinical Parameters ◽  
Ppi Network ◽  
Cox Regression Analysis

Background Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. Method The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. Result A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. Conclusion A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

scholarly journals Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fanbo Qin ◽  
Junyong Zhang ◽  
Jianping Gong ◽  
Wenfeng Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cancer Genome ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

scholarly journals Prognostic Model for De Novo and Recurrent Metastatic Breast Cancer

2021 ◽  
pp. 789-804
Author(s):  
Carlos H. Barcenas ◽  
Juhee Song ◽  
Rashmi K. Murthy ◽  
Akshara S. Raghavendra ◽  
Yisheng Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
De Novo ◽  
Predictive Accuracy ◽  
Growth Factor Receptor ◽  
Metastatic Breast ◽  
Validation Data ◽  
Epidermal Growth ◽  
Cancer Network

PURPOSE Metastatic breast cancer (MBC) has a heterogeneous clinical course. We sought to develop a prognostic model for overall survival (OS) that incorporated contemporary tumor and clinical factors for estimating individual prognosis. METHODS We identified patients with MBC from our institution diagnosed between 1998 and 2017. We developed OS prognostic models by Cox regression using demographic, tumor, and treatment variables. We assessed model predictive accuracy and estimated annual OS probabilities. We evaluated model discrimination and prediction calibration using an external validation data set from the National Comprehensive Cancer Network. RESULTS We identified 10,655 patients. A model using age at diagnosis, race or ethnicity, hormone receptor and human epidermal growth factor receptor 2 subtype, de novo versus recurrent MBC categorized by metastasis-free interval, Karnofsky performance status, organ involvement, frontline biotherapy, frontline hormone therapy, and the interaction between variables significantly improved predictive accuracy (C-index, 0.731; 95% CI, 0.724 to 0.739) compared with a model with only hormone receptor and human epidermal growth factor receptor 2 status (C-index, 0.617; 95% CI, 0.609 to 0.626). The extended Cox regression model consisting of six independent models, for < 3, 3-14, 14-20, 20-33, 33-61, and ≥ 61 months, estimated up to 5 years of annual OS probabilities. The selected multifactor model had good discriminative ability but suboptimal calibration in the group of 2,334 National Comprehensive Cancer Network patients. A recalibration model that replaced the baseline survival function with the average of those from the training and validation data improved predictions across both data sets. CONCLUSION We have generated and validated a robust prognostic OS model for MBC. This model can be used in clinical decision making and stratification in clinical trials.

scholarly journals Super-Enhancer Associated Nine-gene Prognostic Score Model for Prediction of Survival in Chronic Lymphoic Leukemia Patients

2021 ◽  
Author(s):  
Xue Liang ◽  
Ye Meng ◽  
Cong Li ◽  
Yangyang Wang ◽  
Lianfang Pu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Prognostic Markers ◽  
Cox Regression ◽  
Prognostic Score ◽  
Cox Regression Analysis ◽  
Super Enhancer ◽  
Mutational Status ◽  
Related Risk

Abstract BackgroundChronic lymphocytic leukemia (CLL) is a group of highly heterogeneous mature B cell malignancy with various disease courses and diagnoses. Super-enhancer(SE) is a novel concept drew in recent years which is a cluster of enhancers involved in cell differentiation and tumorigenesis ,and is one of the promising therapeutic targets for cancer therapy. Although there is a multitude of prognostic markers in CLL, insights into the role of super-enhancer(SE)-related risk indicators are still lacking.MethodsThe CLL-related super-enhancers in training database were processed by Lasso penalized Cox regression analysis to screen a nine-gene prognostic model. And the associations between all of the individual markers and OS of CLL were assessed by Cox regression analysis. Besides, in order to understand the connection between individual genes and selected disease characteristics, like IGHV mutation status, FISH abnormality, and ZAP70 expression level, we performed correlation analysis.ResultsA nine-gene prognostic model was screened, including TCF7, VEGFA, MNT, GMIP, SLAMF1, TNFRSF25, GRWD1, SLC6AC, and LAG3. A SE-related risk score was further constructed and the robust predictive performance with 5-year survival and time-to-treatment (TTT) area under the curve(AUC): 0.997 and 1.000 in the training database and 0.628 and 0.673 in the testing database, respectively. Besides, a high correlation was found between the risk score and known prognostic markers of CLL, including the mutational status of immunoglobulin variable region loci (IGHV), chromosomal abnormalities, and ZAP70 expression. Meantime, we noticed that the expression of TCF7, GMIP, SLAMF1, TNFRSF25, and LAG3 in CLL were different from healthy donors(P<0.01), moreover, the risk score and LAG3 level of matched pairs before and after treatment samples varied significantly, although these results were not completely consistent in different datasets. ConclusionTherefore, the SE-associated nine-gene prognostic model developed here may be used to predict the prognosis and assist in the risk stratification of CLL patients in the future.

Development and validation of stemness associated LncRNA based prognostic model for lung adenocarcinoma patients

2021 ◽  
pp. 1-11
Author(s):  
Annesha Chatterjee ◽  
Seema Khadirnaikar ◽  
Sudhanshu Shukla
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Score ◽  
Differentiation Markers ◽  
Critical Determinant ◽  
Cox Regression Analysis ◽  
Novel Biomarkers ◽  
Tcga Dataset ◽  
Development And Validation

BACKGROUND: An increasing number of studies are indicating that the stemness phenotype is a critical determinant of the Lung adenocarcinoma (LUAD) patient’s response. Thus, it is crucial to identify novel biomarkers for stemness determination. OBJECTIVE: Here, we aim to develop a robust LncRNAs based prognostic signature with a stemness association for the LUAD patients. METHODS: RNA-seq and clinical data were downloaded from the existing database. The data were analysed using Cox regression, KM-plot, GSEA, and T-test. RESULTS: Initially, we used the TCGA dataset to characterize the stemness phenotype in LUAD. The commonly expressed LncRNAs in TCGA and MCTP cohort were then used as input for the Cox-regression analysis. The top three LncRNAs were selected to build a prognostic model, which was the best prognosticator in multivariate analysis with stage and previously published prognosticators. The characterization of poor surviving patients using various analysis showed high stemness properties and low expression of differentiation markers. Furthermore, we validated the prognostic score in an independent MCTP cohort of patients. In the MCTP cohort, prognostic score significantly predicted survival independent of stage and previous prognosticators. CONCLUSION: Taken together, in this study, we have developed and validated a new prognostic score associated with the stemness phenotype.

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