Construction of Prognostic Model for Cuprotosis in Hepatocellular Carcinoma Based on TCGA Database / 肿瘤防治研究

Objective To construct a prognostic model for cuprotosis-related genes (CRGs) in patients with hepatocellular carcinoma (HCC). Methods Differential expression of CRGs in HCC was analyzed on the basis of datasets from the TCGA database. The potential mechanisms of CRGs and their related genes in HCC were explored through GO and KEGG enrichment analyses. The prognostic value of the CRGs was evaluated through Kaplan-Meier survival analysis, and the relationship between CRG expression and immune cell infiltration was investigated. CRGs significantly correlated with prognosis in patients with HCC were identified. A prognostic model was established through univariate, Lasso regression, and multivariate Cox regression analyses. The patients were divided into two groups by risk score. ROC curve was used in evaluating the prognostic model. The relationship of risk score or clinical factors with prognosis was analyzed through univariate and multivariate Cox regression analyses. Results A total of 11 differentially expressed CRGs in HCC were obtained. The main enriched GO item of CRGs and their related genes was oxidoreductase activity, acting on the aldehyde or oxo group of donors, and the main enriched KEGG pathway was carbon metabolism. The expression of CRGs was significantly correlated with pDC, T helper cells and other immune cells (P < 0.05). Three CRGs (CDKN2A, DLAT, and LIPT1) were screened and a prognostic model was constructed. There was significant difference in overall survival between the high- and low-risk groups (P < 0.001). The risk score is an independent risk factor for poor prognosis (P < 0.001). Conclusion The prognostic model for CRGs in patients with HCC is constructed using TCGA database data. This model may be used in evaluating patient prognosis.

Omics Analysis of Ferroptosis and Establishment of Prognostic Model for multiple myeloma Patients / 中国实验血液学杂志

OBJECTIVE@#To explore the role of ferroptosis-related genes in multiple myeloma(MM) through TCGA database and FerrDb, and build a prognostic model of ferroptosis-related genes for MM patients.@*METHODS@#Using the TCGA database containing clinical information and gene expression profile data of 764 patients with MM and the FerrDb database including ferroptosis-related genes, the differentially expressed ferroptosis-related genes were screened by wilcox.test function. The prognostic model of ferroptosis-related genes was established by Lasso regression, and the Kaplan-Meier survival curve was drawn. Then COX regression analysis was used to screen independent prognostic factors. Finally, the differential genes between high-risk and low-risk patients were screened, and enrichment analysis was used to explore the mechanism of the relationship between ferroptosis and prognosis in MM.@*RESULTS@#36 differential genes related to ferroptosis were screened out from bone marrow samples of 764 MM patients and 4 normal people, including 12 up-regulated genes and 24 down-regulated genes. Six prognosis-related genes (GCLM, GLS2, SLC7A11, AIFM2, ACO1, G6PD) were screened out by Lasso regression and the prognostic model with ferroptosis-related genes of MM was established. Kaplan-Meier survival curve analysis showed that the survival rate between high risk group and low risk group was significantly different(P<0.01). Univariate COX regression analysis showed that age, sex, ISS stage and risk score were significantly correlated with overall survival of MM patients(P<0.05), while multivariate COX regression analysis showed that age, ISS stage and risk score were independent prognostic indicators for MM patients (P<0.05). GO and KEGG enrichment analysis showed that the ferroptosis-related genes was mainly related to neutrophil degranulation and migration, cytokine activity and regulation, cell component, antigen processing and presentation, complement and coagulation cascades, haematopoietic cell lineage and so on, which may affect the prognosis of patients.@*CONCLUSION@#Ferroptosis-related genes change significantly during the pathogenesis of MM. The prognostic model of ferroptosis-related genes can be used to predict the survival of MM patients, but the mechanism of the potential function of ferroptosis-related genes needs to be confirmed by further clinical studies.

Construction of prognostic risk model of bladder cancer based on cuproptosis-related long non-coding RNAs / 浙江大学学报·医学版

OBJECTIVES@#To construct a prognosis risk model based on long noncoding RNAs (lncRNAs) related to cuproptosis and to evaluate its application in assessing prognosis risk of bladder cancer patients.@*METHODS@#RNA sequence data and clinical data of bladder cancer patients were downloaded from the Cancer Genome Atlas database. The correlation between lncRNAs related to cuproptosis and bladder cancer prognosis was analyzed with Pearson correlation analysis, univariate Cox regression, Lasso regression, and multivariate Cox regression. Then a cuproptosis-related lncRNA prognostic risk scoring equation was constructed. Patients were divided into high-risk and low-risk groups based on the median risk score, and the immune cell abundance between the two groups were compared. The accuracy of the risk scoring equation was evaluated using Kaplan-Meier survival curves, and the application of the risk scoring equation in predicting 1, 3 and 5-year survival rates was evaluated using receiver operating characteristic (ROC) curves. Univariate and multivariate Cox regression were used to screen for prognostic factors related to bladder cancer patients, and a prognostic risk assessment nomogram was constructed, the accuracy of which was evaluated with calibration curves.@*RESULTS@#A prognostic risk scoring equation for bladder cancer patients was constructed based on nine cuproptosis-related lncRNAs. Immune infiltration analysis showed that the abundances of M0 macrophages, M1 macrophages, M2 macrophages, resting mast cells and neutrophils in the high-risk group were significantly higher than those in the low-risk group, while the abundances of CD8+ T cells, helper T cells, regulatory T cells and plasma cells in the low-risk group were significantly higher than those in the high-risk group (all P<0.05). Kaplan-Meier survival curve analysis showed that the total survival and progression-free survival of the low-risk group were longer than those of the high-risk group (both P<0.01). Univariate and multivariate Cox analysis showed that the risk score, age and tumor stage were independent factors for patient prognosis. The ROC curve analysis showed that the area under the curve (AUC) of the risk score in predicting 1, 3 and 5-year survival was 0.716, 0.697 and 0.717, respectively. When combined with age and tumor stage, the AUC for predicting 1-year prognosis increased to 0.725. The prognostic risk assessment nomogram for bladder cancer patients constructed based on patient age, tumor stage, and risk score had a prediction value that was consistent with the actual value.@*CONCLUSIONS@#A bladder cancer patient prognosis risk assessment model based on cuproptosis-related lncRNA has been successfully constructed in this study. The model can predict the prognosis of bladder cancer patients and their immune infiltration status, which may also provide a reference for tumor immunotherapy.

Validation of the online PREDICT tool in a cohort of early breast cancer in Brazil

PREDICT is a tool designed to estimate the benefits of adjuvant therapy and the overall survival of women with early breast cancer. The model uses clinical, histological, and immunohistochemical variables. This study aimed to evaluate the model's performance in a Brazilian population. We assessed the discrimination and calibration of the PREDICT model to estimate overall survival (OS) in five and ten years of follow-up in a cohort of 873 women with early breast cancer diagnosed from January 2001 to December 2016. A total of 743 patients had estrogen receptor (ER)-positive and 130 had ER-negative tumors. The area under the receiver operating characteristic (ROC) curve (AUC) for discrimination was 0.72 (95%CI: 0.66-0.78) at five years and 0.67 (95%CI: 0.61-0.72) at ten years for women with ER-positive tumors. The AUC was 0.72 (95%CI: 0.62-0.81) at five years and 0.67 (95%CI: 0.54-0.77) at ten years for women with ER-negative tumors. The predicted survival in ER-positive tumors was 91.0% (95%CI: 90.2-91.6%) at five years and 79.3% (95%CI: 77.7-81.0%) at ten years, and the observed survival 90.7% (95%CI: 88.6-92.9%) and 77.2% (95%CI: 73.4-81.4%), respectively. The predicted survival in ER-negative tumors was 84.5% (95%CI: 82.5-86.6%) at five years and 75.0% (95%CI: 71.6-78.5%) at ten years, and the observed survival 76.3% (95%CI: 69.1-84.3%) and 67.9% (95%CI: 58.6-78.6%), respectively. In conclusion, PREDICT was accurate to estimate OS in women with ER-positive tumors and overestimated the OS in women with ER-negative tumors.

Expression of Glutathione Peroxidases and Its Effect on Clinical Prognosis in Glioma Patients / 中国医学科学院学报

Objective To investigate the relationship between the expression of glutathione peroxidase(GPX)genes and the clinical prognosis in glioma patients,and to construct and evaluate the model for predicting the prognosis of glioma. Methods The clinical information and GPX expression of 663 patients,including 153 patients of glioblastoma(GBM)and 510 patients of low-grade glioma(LGG),were obtained from The Cancer Genome Atlas(TCGA)database.The relationship between GPX expression and patient survival was analyzed.The key GPX affecting the prognosis of glioma was screened out by single- and multi-factor Cox's proportional-hazards regression models and validated by least absolute shrinkage and selection operator(Lasso)regression.Finally,we constructed the model for predicting the prognosis of glioma with the screening results and then used concordance index and calibration curve respectively to evaluate the discrimination and calibration of model. Results Compared with those in the control group,the expression levels of GPX1,GPX3,GPX4,GPX7,and GPX8 were up-regulated in glioma patients(all P<0.001).Moreover,the expression levels of other GPX except GPX3 were higher in GBM patients than in LGG patients(all P<0.001).The Kaplan-Meier curves showed that the progression-free survival of GBM with high expression of GPX1(P=0.013)and GPX4(P=0.040),as well as the overall survival,disease-specific survival,and progression-free survival of LGG with high expression of GPX1,GPX7,and GPX8,was shortened(all P<0.001).GPX7 and GPX8 were screened out as the key factors affecting the prognosis of LGG.The results were further used to construct a nomogram model,which suggested GPX7 was the most important variable.The concordance index of the model was 0.843(95%CI=0.809-0.853),and the calibration curve showed that the predicted and actual results had good consistency. Conclusion GPX7 is an independent risk factor affecting the prognosis of LGG,and the nomogram model constructed with it can be used to predict the survival rate of LGG.

Application of immune cell infiltration in the diagnosis and prognosis of non-small cell lung cancer / 生物工程学报

Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.

Application of deep learning in cancer prognosis prediction model / 生物医学工程学杂志

In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.

A prognostic nomogram for ER/PR+ and HER2- breast cancer patients based on classical immunohistochemical index / 中国肿瘤生物治疗杂志

@# Objective: To modify traditional prognostic model for patients with ER/PR+, HER2- breast cancer to meet the actual requirements in current clinical practice. Methods: 335 patients with ER/PR+, HER2- breast cancer, who were admitted in Department of Breast Surgery, Shanghai Huangpu Center Hospital from January 2009 to December 2009, were enrolled in this study. 97 variables were incorporated into the model, using SCAD variable selection method, after fully considering whether covariates existing a log-linear relationship, reasonable determination of the cut-off value of the covariates in non-logarithmic linear relationship (piecewise linear relationship) and collinear and interaction, then we set up a new Cox regression prognostic model for traditional ER/PR+, HER2-type breast cancer patients with traditional immunohistochemical indicators, and further establish its nomogram model. On this basis, a nomogram of the survival probability of 1-, 3-, and 5- years after surgery was established; The discrimination and calibration of model were compared to evaluate the predictive ability of the model. Results: The Cox regression model shows that the prognosis of patients are associated with the histologic grade, lymph node metastasis, Ki67, PR and age etc. Among them, the histologic grade and lymph node metastasis have log-linear relationship with prognosis; Ki67, PR and age have non-log-linear relationship with prognosis and the reasonable cut-off values are Ki67(60%)，PR（20%）and age（55 years old） . Area under the receiver operating characteristic (ROC) curve（AUC）of this Cox model for 1-, 3- and 5- year survival after surgery are all above 0.85, indicating high discrimination. The Grønnesby-Borgan goodness-of-fit test statistics of this model is 1.37 with P>0.05, indicating good calibration. Conclusion: The modified nomogram.could accurately, directly and effectively predict the survival probability of patients, which may exert good guidance for the clinical practice for patients with breast cancer.

Prognostic factors of colorectal cancer and establishment of colorectal cancer prognosis model / 中华医学图书情报杂志

The factors influencing the prognosis of colorectal cancer were studied after its characteristic variables were screened by stepwise logistic regression analysis, Bayesian model averaging analysis, and LASSO regression a-nalysis respectively. A model of colorectal cancer prognosis was established according to the artificial neural net-work classification algorithm for the assessment of colorectal cancer. The highest accuracy was detected in the model of colorectal cancer prognosis established by Bayesian model averaging analysis combined with artificial neural net-work classification algorithm.

The short-term prognosis evaluation model for the first ischemic cerebral stroke patients / 实用医学杂志

Objective To explore common risk factors of the first acute ischemic cerebral stroke patients′neurological deficits and build a short-term prognosis model. Methods 89 hospitalized patients with acute is-chemic cerebral stroke were chosen for study from September 2014 to December 2015 in the Fifth Affiliated Hos-pital of Zhengzhou University. Our study′s evaluation methods were using the unified questionnaires , the NIHSS score and the mRS scale. Results Traditional risk factors were no significant difference among the three groups (P > 0.05); 6 kinds of hematology indexes such as WBC count had significant difference in NIHSS score (P <0.05) and prognosis(P < 0.05); 6 kinds of hematology indexes such as D-D and the NIHSS score had a signifi-cant effect on prognosis (OR = 1.800, 0.976, 1.112, 1.327, 5.564, 6.456, 1.227); the area under ROC curve was 0.976, which proved the model had a good predictive value. Conclusion Traditional risk factors had no significant difference among the different neurological deficits groups; 6 kinds of hematology indexes such as D-D and NIHSS score on admission had a significant influence on prognosis; the model predicted the short-term prognosis of acute ischemic cerebral stroke more accurately.