scholarly journals A New Risk Score Model Based on Lactate Dehydrogenase Predicting Prognosis in Esophageal Squamous Cell Carcinoma Treated With Chemoradiotherapy

2020 ◽  
Author(s):  
Jinmin Han ◽  
Tao Zhou ◽  
Chengxin Liu ◽  
Baosheng Li
Keyword(s):  
Prognostic Factors ◽  
Lactate Dehydrogenase ◽  
Risk Score ◽  
Squamous Cell ◽  
Optimal Cutoff ◽  
Tumor Length ◽  
Model Based ◽  
Cutoff Points ◽  
Score Model ◽  
Pretreatment Serum

Abstract Purpose: The current study was to assess the prognostic value of the lactate dehydrogenase (LDH) in esophageal squamous cell cancer (ESCC) patients and to generate a risk score model to predict prognosis in patients who undergone chemoradiotherapy. Patients and Methods: 614 ESCC patients who received chemoradiotherapy were performed from 2012 to 2016.The optimal cutoff points for continuous variables were calculated by the X-tile program. We analyzed the association between LDH level and clinicopathological characteristics. And a 1:3 propensity score matching analysis was used to compensate for differences in baseline characteristics. The Kaplan-Meier methods and Cox regression models were used to explore the prognostic factors for overall survival (OS) and progression-free survival (PFS). Based on the results, we developed a corresponding risk score model and assessed its predictive capacity in the subgroups. Results: The optimal cutoff points of age, CEA, Cyfra21-1, tumor length, total dose and LDH were defined as follows:69 years, 2.4 ng/ml, 6.4 ng/ml, 6.5 cm, 58.8Gy and 134 U/L, respectively. A high level of LDH was associated with advanced M stage (p=0.005) and larger tumor length (p=0.026). Patients in the high-LDH group had shorter PFS and worse OS than those in the low-LDH group. Multivariate survival analysis indicated that pretreatment serum LDH level (p=0.039)，Cyfra21-1 level (p=0.003), tumor length (p=0.013), clinical N stage (p=0.047) and clinical M stage (p=0.011) were independent predictors for OS. Furthermore, a risk score model based on these five prognostic factors was established to divide patients into three groups with obvious prognosis (χ2 = 20.53, p< 0.0001). Conclusion: Pretreatment serum LDH levels may be a reliable factor in predicting the therapeutic effect of chemoradiotherapy in ESCC. A risk score model combined LDH, Cyfra21-1 and other prognostic factors could help to guide a personalized management. Further validation is needed before widely used in clinical practice.

scholarly journals The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Guanying Feng ◽  
Feifei Xue ◽  
Yingzheng He ◽  
Tianxiao Wang ◽  
Hua Yuan
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Network Analysis ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Potential Biomarker ◽  
Score Model

ObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).ResultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.ConclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC.

scholarly journals Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ying Chen ◽  
Bo Zhao ◽  
Xiaohu Wang
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Cell Types ◽  
Difficult Problem ◽  
Model Base ◽  
Model Based ◽  
Kaplan Meier ◽  
Score Model

Abstract Background Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma. Methods CIBERSORTX algorithm was used to calculate the proportion of 22 TIIC types in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIIC types. Forward stepwise approach was used to screen a minimal set of immune cell types. Multivariate Cox PHR analysis was performed to construct an immune risk score model. Results Osteosarcoma samples with CIBERSORTX output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p = 0.047) and Monocytes (p = 0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cell types, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC = 0.724). Besides, a nomogram model base on eight immune cell types was constructed to predict the survival rate of patients with osteosarcoma. Conclusions TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.

scholarly journals Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma

2020 ◽  
Author(s):  
Bo Zhao ◽  
Xiaohu Wang
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Difficult Problem ◽  
P Value ◽  
Model Base ◽  
Model Based ◽  
Kaplan Meier ◽  
Forward Stepwise ◽  
Score Model

Abstract Background: Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that Tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma. Methods: CIBERSORT algorithm was used to calculate the proportion of 22 TIICs in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIICs. Forward stepwise approach was used to screen a minimal set of immune cells. Multivariate Cox PHR analysis was performed to construct an immune risk score model. Results: Osteosarcoma samples with CIBERSORT output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p=0.047) and Monocytes (p=0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cells, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC=0.724). Besides, a nomogram model base on eight immune cells was constructed to predict the survival rate of patients with osteosarcoma.Conclusions: TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.

A six-microRNA risk score model predicts prognosis in esophageal squamous cell carcinoma

2018 ◽  
Vol 234 (5) ◽  
pp. 6810-6819 ◽  
Author(s):  
Yu Mao ◽  
Zhanzhao Fu ◽  
Yunjie Zhang ◽  
Lixin Dong ◽  
Yanqiu Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Score Model

scholarly journals Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

2020 ◽  
Vol 8 ◽  
Author(s):  
Tingting Qi ◽  
Jian Qu ◽  
Chao Tu ◽  
Qiong Lu ◽  
Guohua Li ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Bone Lesions ◽  
Cox Regression Analysis ◽  
Super Enhancer ◽  
Score Model

Multiple myeloma (MM) is a malignant plasma cell tumor with high heterogeneity, characterized by anemia, hypercalcemia, renal failure, and lytic bone lesions. Although various powerful prognostic factors and models have been exploited, the development of more accurate prognosis and treatment for MM patients is still facing many challenges. Given the essential roles of super-enhancer (SE) associated genes in the tumorigenesis of MM, we tried to initially screen and identify the significant prognostic factors from SE associated genes in MM by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis using GSE24080 and GSE9782 datasets. Risk score model of five genes including CSGALNACT1, FAM53B, TAPBPL, REPIN1, and DDX11, was further constructed and the Kaplan-Meier (K-M) curves showed that the low-risk group seems to have better clinical outcome of survival compared to the high-risk group. Time-dependent receiver operating characteristic (ROC) curves presented the favorable performance of the model. An interactive nomogram consisting of the five-gene risk group and eleven clinical traits was established and identified by calibration curves. Therefore, the risk score model of SE associated five genes developed here could be used to predict the prognosis of MM patients, which may assist the clinical treatment of MM patients in the future.

scholarly journals Prospective validation of a preoperative risk score model based on pancreatic texture to predict postoperative pancreatic fistula after pancreaticoduodenectomy

2017 ◽  
Vol 48 ◽  
pp. 189-194 ◽  
Author(s):  
Riccardo Casadei ◽  
Claudio Ricci ◽  
Giovanni Taffurelli ◽  
Carlo Alberto Pacilio ◽  
Mariacristina Di Marco ◽  
...  
Keyword(s):  
Pancreatic Fistula ◽  
Risk Score ◽  
Postoperative Pancreatic Fistula ◽  
Preoperative Risk ◽  
Model Based ◽  
Pancreatic Texture ◽  
Score Model

scholarly journals Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254829
Author(s):  
Ying He ◽  
Rui Xu ◽  
Li Peng ◽  
Xiaoyu Hu
Keyword(s):  
T Cells ◽  
Prognostic Factors ◽  
Correlation Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Tumor Infiltrating Lymphocytes ◽  
Specificity And Sensitivity ◽  
Score Model ◽  
Infiltrating Lymphocytes

Background The important regulatory role of competitive endogenous RNAs (ceRNAs) in hepatocellular carcinoma (HCC) has been confirmed. Tumor infiltrating lymphocytes (TILs) are of great significance to tumor outcome and prognosis. This study will systematically analyze the key factors affecting the prognosis of HCC from the perspective of ceRNA and TILs. Methods The Cancer Genome Atlas (TCGA) database was used for transcriptome data acquisition of HCC. Through the analysis of the Weighted Gene Co-expression Network Analysis (WCGNA), the two modules for co-expression of the disease were determined, and a ceRNA network was constructed. We used Cox regression and LASSO regression analysis to screen prognostic factors and constructed a risk score model. The Gene Expression Omnibus (GEO) was used to validate the model. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for mRNAs functional analysis. The cell composition of TILs was analyzed by the CIBERSORT algorithm, and Pearson correlation analysis was utilized to explore the correlation between TILs and prognostic factors. Results We constructed a ceRNA regulatory network composed of 67 nodes through WGCNA, including 44 DElncRNAs, 19 DEGs, and 4 DEmiRNAs. And based on the expression of 4 DEGs in this network (RRM2, LDLR, TXNIP, and KIF23), a prognostic model of HCC with good specificity and sensitivity was developed. CIBERSORT analyzed the composition of TILs in HCC tumor tissues. Correlation analysis showed that RRM2 is significantly correlated with T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, and T cells follicular helper, and TXNIP is negatively correlated with B cells memory. Conclusions In this study, a ceRNA with prognostic value in HCC was created, and a prognostic risk model for HCC was constructed based on it. This risk score model is closely related to TILs and is expected to become a potential therapeutic target and a new predictive indicator.

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