Construction and Evaluation of the Disulfidptosis-Related Genes(DRGs)Prognostic Risk Model for Hepatocellular Carcinoma Based on TCGA Database / 现代检验医学杂志

Objective To construct and evaluate a disulfidptosis-related genes(DRGs)prognostic risk model for hepatocellular carcinoma(HCC)based on the cancer genome atlas(TCGA)database.Methods The expression of 15 DRGs in 371 HCC samples and 50 adjacent cancer samples in the TCGA database was analyzed using bioinformatics methods,and then gene ontology(GO)functional annotation,Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis and Kaplan-Meier(KM)survival analysis were performed.Statistical significant DRGs were screened through univariate COX regression analysis,and key DRGs were selected through LASSO regression analysis and multivariate COX regression analysis to construct a prognostic risk model.HCC patients were divided into high-risk and low-risk groups based on risk scores,and the KM survival curves and time-dependent receiver operator characteristic(ROC)curves were created to validate and evaluate prognostic risk models.Results Compared with the adjacent cancer samples,FLNA,MYH9,TLN1,ACTB,MYL6,CAPZB,DSTN,ACTN4,SLC7A11,INF2,CD2AP,PDLIM1,and FLNB were all upregulated in the 15 DRGs of HCC samples,and the differences were significant(t=1 793～6 310,all P<0.001).According to GO functional annotation and KEGG enrichment analysis,DRGs were closely related to biological processes or pathways related to actin cytoskeleton and cell adhesion.The results of KM survival analysis showed that the survival rate of the high expression group of SLC7A11,INF2,CD2AP,MYL6,and ACTB were lower than that of the low expression group[HR=1.46(1.03～2.07)～1.93(1.36～2.75),all P<0.05].Univariate COX regression analysis,LASSO analysis,and multivariate COX regression analysis were used to construct a prognostic risk model,with risk score=(0.247×SLC7A11)+(0.289×INF2)+(0.076×CD2AP)+(0.06×MYL6).The risk score of the sample in this model was calculated,and the higher the risk score,the more HCC patients with poor prognosis.KM survival analysis showed that the overall survival rate of the high-risk group was lower than that of the low-risk group.The AUC values for 1,3,and 5 years were 0.709,0.661,and 0.648,respectively.Multivariate COX regression analysis showed that SLC7A11[HR=1.832(1.274～2.636),P=0.001]was an independent prognostic risk factor.Conclusion The prognostic risk model was constructed by four DRGs,which has a certain role in predicting the prognosis of HCC patients.

A nomogram model for predicting spontaneous rupture and bleeding of renal angiomyolipoma / 现代泌尿外科杂志

【Objective】 To establish a risk model for predicting spontaneous rupture bleeding of renal angiomyolipoma (RAML) in order to better assess and deal with the risk. 【Methods】 The information of 436 RAML patients diagnosed during Jan.2018 and Dec.2022 was retrospectively analyzed.According to the inclusion and exclusion criteria, 216 patients were included and divided into the rupture bleeding group (n=35) and non-rupture bleeding group (n=181).The factors influencing spontaneous rupture bleeding were identified using univariate and multivariate analysis, and a nomogram was constructed accordingly with R language.The nomogram was evaluated using Calibration curve and area under the receiver operator characteristic curve (AUC). 【Results】 It was found that clinical manifestations, tumor diameter, tumor convexity, tumor blood supply, and tuberous sclerosis complex (TSC) were significantly correlated with rupture bleeding.The Calibration curve fitted well with the nomogram.The AUC was 0.956 (95%CI: 0.856-0.943), indicating that the nomogram had good statistical performance. 【Conclusion】 The model can effectively predict the risk of spontaneous rupture bleeding of renal angiomyolipoma.

Construction of a microvascular invasion risk scoring model for hepatocellular carcinoma based on preoperative data / 中国现代医生

Objective Microvascular invasion(MVI)risk scoring model was established based on the preoperative data of hepatocellular carcinoma(HCC)patients.Methods The clinical data of 1153 HCC patients who underwent hepatectomy in Hangzhou First People's Hospital from January 2000 to December 2021 were retrospectively analyzed.Random sampling method was used to divide the samples into modeling group(n=864)and verification group(n=289)at a ratio of 3:1.The modeling group used Logistic regression analysis model to explore the independent risk factors of MVI and established a prediction model accordingly.Receiver operating characteristic(ROC)curve and correction curve were drawn to evaluate the predictive ability and performance of the risk model.Results The incidence of MVI was 24.1%(208/864)in modeling group.Multivariate Logistic regression analysis showed that alpha-fetoprotein(AFP)>160ng/ml and total tumor volume(TTV)>30cm3 were independent risk factors for MVI(P<0.05).The total score of risk scoring model was 6 points,0-1 was classified as low risk,2-3 was classified as medium risk,and 4-6 was classified as high risk.The model predicted that the area under the curve(AUC)of MVI was 0.714 in modeling group and 0.731 in verification group.The calibration diagram showed that the prediction model had good performance.Conclusion The MVI risk prediction model for HCC patients based on TTV and AFP is simple and easy to use,which is conducive to preoperative treatment decision-making and doctor-patient communication.

Effective prognostic risk model with cuproptosis-related genes in laryngeal cancer

Abstract Objective Laryngeal cancer, characterized by high recurrence rates and a lack of effective biomarkers, has been associated with cuproptosis, a regulated cell death process linked to cancer progression. In this study, we aimed to explore the roles of cuproptosis-related genes in laryngeal cancer and their potential as prognostic markers and therapeutic targets. Methods We collected comprehensive data from The Cancer Genome Atlas and Gene Expression Omnibus databases, including gene expression profiles and clinical data of laryngeal cancer patients. Using clustering and gene analysis, we identified cuproptosis-related genes with prognostic significance. A risk model was constructed based on these genes, categorizing patients into high- and low-risk groups for outcome comparison. Univariate and multivariate analyses were conducted to identify independent prognostic factors, which were then incorporated into a nomogram. Gene Set Enrichment Analysis was employed to explore pathways distinguishing high- and low-risk groups. Results Our risk model, based on four genes, including transmembrane 2, dishevelled binding antagonist of β-catenin 1, stathmin 2, and G protein-coupled receptor 173, revealed significant differences in patient outcomes between high- and low-risk groups. Independent prognostic factors were identified and integrated into a nomogram, providing a valuable tool for prognostic prediction. Gene Set Enrichment Analysis uncovered up-regulated pathways specifically associated with high-risk patient samples. Conclusion This study highlights the potential of cuproptosis-related genes as valuable prognostic markers and promising therapeutic targets in the context of laryngeal cancer. This research sheds light on new avenues for understanding and managing this challenging disease. Level of evidence: Level 4.

Construction of a competing risk model for disease-free survival of patients with non-muscle invasive bladder cancer / 现代泌尿外科杂志

【Objective】 To construct an easy-to-use individual survival prognostic tool based on competing risk analyses to predict the risk of 1-, 2- and 3- year recurrence for patients with non-muscle invasive bladder cancer (NMIBC). 【Methods】 The follow-up data of 419 NMIBC patients were obtained. The patients were randomly divided into training cohort (n=293) and validation cohort (n=126). The variables included age at diagnosis, sex, history of smoking, tumor number, tumor size, histolo-gic grade, pathological stage, and bladder perfusion drug. The cumulative incidence function (CIF) of recurrence was estimated using all variables in the training cohort and potential prognostic variables were determined with Gray’s test. The Fine-Gray subdistribution proportional hazard approach was used as a multivariate competitive risk analysis to identify independent pro-gnostic variables. A competing risk nomogram was developed to predict the recurrence. The performance of the competing risk model was evaluated with the area under the receiver operating characteristic curve (AUC), calibration curve, and Brier score. 【Results】 Five independent prognostic factors including age, number of tumors, tumor size, histologic grade and pathological stage were used to construct the competing risk model. In the validation cohort, the AUC of 1-, 2- and 3- year recurrence were 0.895 (95%CI: 0.831-0.959), 0.861(95%CI: 0.774-0.948) and 0.827(95%CI: 0.721-0.934), respectively, indicating that the model had a high predictive performance. 【Conclusion】 We successfully constructed a competing risk model to predict the risk of 1-, 2- and 3-year recurrence for NMIBC patients. It may help clinicians to improve the postoperative management of patients.

Ferroptosis-related Recurrence Risk Model Predicts Clinical Outcomes and Immune Infiltration in Glioblastoma / 肿瘤防治研究

Objective To construct a ferroptosis-related glioblastoma (GBM) recurrence risk model and evaluate the prognosis of patients. Methods Differentially expressed genes (DEGs) related to ferroptosis in recurrent GBM were screened by CGGA and FerrDb databases. Key genes were obtained by Lasso regression. Then, nomogram was constructed according to the key risk genes, and the prediction efficiency was verified using the TCGA database. GO, KEGG, and GSEA databases were used in exploring the mechanism of prognosis. ESTIMATE and TIMER were used in studying tumor immune infiltration and the expression of immune check points. Results WWTR1, PLIN2, and BID were important prognostic factors for GBM recurrence. The nomogram was constructed according to gender and age, and the observed values were in good agreement with the predicted values. The AUC values were 0.65 (1 year), 0.66 (3 years), and 0.63 (5 years) for CGGA and 0.68 (1 year), 0.76 (3 years), and 0.79 (5 years) for TCGA. Epithelial mesenchymal transition, KRAS pathway, and inflammatory response were significantly upregulated in the high-risk subtypes (P < 0.05). Immune cell infiltration was lower (P < 0.05). Risk score was positively correlated with the expression of immunosuppression check points. Conclusion Ferroptosis-related genes WWTR1, PLIN2, and BID can be used in constructing a nomogram with good predictive performance. These risk genes may affect prognosis through tumor-infiltrating immune cells and immune check points.

Establishment of a risk model of placental accreta spectrum by ultrasound combined with clinical high risk factors / 中华超声影像学杂志

Objective:To establish a risk model of placenta accreta spectrum(PAS) based on the clinical risk factors and ultrasound signs of patients with placenta accreta, and identify severe placenta accreta prenatal.Methods:A retrospective analysis was performed on 121 PAS patients admitted to Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University from January 2018 to June 2022 who were clinically classified or pathologically diagnosed during delivery. The two groups were divided into light and severe groups according to the implantation type. The clinical risk factors and ultrasound signs between the two groups were compared. A risk model of PAS was established based on the clinical risk factors and ultrasound signs to predict the perinatal complications.Results:A total of 130 cases of PAS were clinically diagnosed or pathologically diagnosed with placenta, 9 cases with incomplete clinical data or irregular ultrasound images were excluded, and the remaining 121 cases were included in the study. Among the 121 patients, 64 cases were placental accreta, 39 cases were placental increta, and 18 cases were placenta percreta. The placental accreta was defined as mild group, and the combination of placental increta and placenta percreta were referred to as severe group. There were no significant differences in placenta previa, and the number of uterine cavity operations (all P>0.05). There were significant differences in the number of cesarean section, myometrium thinning, placental lacunae, abnormal vascularization at the utero-bladder junction, bridging vessels at the utero-bladder junction, placental protuberance and cervical involvement (all P<0.05). Binary logistic regression analysis showed that placental lacunae, abnormal vasculization of the utero-bladder interface and the number of cesarean sections were independent risk factors for severe PAS. Based on this, a risk model was established and the ROC curve of each independent risk factor and risk model was plotted respectively. The AUC of the risk model was 0.826, which had better diagnostic efficacy than other independent risk factors. Conclusions:In the prenatal ultrasound classification diagnosis of high-risk patients with PAS, the placental lacunae, abnormal vascularization of utero-bladder interface and the number of cesarean section are combined to establish the risk model of PAS, which has a good diagnostic efficacy for severe placenta accreta.

Construction and validation of a Nomogram-based prediction model for the risk of gout in men / 中华内分泌代谢杂志

Objective:To investigate the risk factors of gout and establish a columnar graph model to predict the risk of gout development.Methods:A total of 1 032 Han Chinese men attending the Affiliated Hospital of Traditional Chinese Medicine of Xinjiang Medical University, People′s Hospital of Xinjiang Uygur Autonomous Region, and the First Affiliated Hospital of Xinjiang Medical University from 2018 to 2020 were selected as study subjects and divided into training set(722 cases)and validation set(310 cases)by simple random sampling method in the ratio of 7∶3. General information and biochemical indices of the subjects were collected. The collected information was used to assess the risk of gout prevalence. LASSO regression analysis of R Studio software was used to screen the best predictors, and was introduced to construct a column line graph model for predicting gout risk using receiver operating characteristic(ROC)curves, and the Hosmer-Lemeshow test was used to assess the discrimination and calibration of the column line graph model. Finally, decision curve analysis(DCA)was performed using the rmda program package to assess the clinical utility of the model in validation data.Results:Age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio were risk factors for gout( P<0.05). The column line graph prediction model based on the above five independent risk factors had good discrimination(AUC value: 0.923 for training set validation and 0.922 for validation set validation)and accuracy(Hosmer-Lemeshow test: P>0.05 for validation set validation); decision curve analysis showed that the prediction model curve had clinical practical value. Conclusion:The nomogram model established by combining age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio indicators can predict the risk of gout more accurately.

Study on predicting the risk of retinal vein occlusion based on nomogram model and systemic risk factors / 中华眼底病杂志

Objective:To establish and preliminarily validate a nomogram model for predicting the risk of retinal vein occlusion (RVO).Methods:A retrospective clinical study. A total of 162 patients with RVO (RVO group) diagnosed by ophthalmology examination in The Second Affiliated Hospital of Xi'an Jiaotong University from January 2017 to April 2022 and 162 patients with age-related cataract (nRVO group) were selected as the modeling set. A total of 45 patients with branch RVO, 45 patients with central RVO and 45 patients with age-related cataract admitted to Xi'an Fourth Hospital from January 2022 to February 2023 were used as the validation set. There was no significant difference in gender composition ratio ( χ2=2.433) and age ( Z=1.006) between RVO group and nRVO group ( P=0.120, 0.320). Age, gender, blood routine (white blood cell count, hemoglobin concentration, platelet count, neutrophil count, monocyte count, lymphocyte count, erythrocyte volume, mean platelet volume, platelet volume distribution width), and four items of thrombin (prothrombin time, activated partial thrombin time, fibrinogen, and thrombin time) were collected in detail ), uric acid, blood lipids (total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, lipoprotein a), hypertension, diabetes mellitus, coronary heart disease, and cerebral infarction. Neutrophil/lymphocyte ratio and platelet/lymphocyte ratio were calculated. The single logistic regression was used to analyze the clinical parameters of the two groups of patients in the modeling set, and the stepwise regression method was used to screen the variables, and the column graph for predicting the risk of RVO was constructed. The Bootstrap method was used to repeated sample 1 000 times for internal and external verification. The H-L goodness-of-fit test and receiver operating characteristic (ROC) curve were used to evaluate the calibration and discrimination of the nomogram model. Results:After univariate logistic regression and stepwise regression analysis, high density lipoprotein, neutrophil count and hypertension were included in the final prediction model to construct the nomogram. The χ2 values of the H-L goodness-of-fit test of the modeling set and the validation set were 0.711 and 4.230, respectively, and the P values were 0.701 and 0.121, respectively, indicating that the nomogram model had good prediction accuracy. The area under the ROC curve of the nomogram model for predicting the occurrence of post-stroke depression in the modeling set and the verification set was 0.741 [95% confidence interval ( CI) 0.688-0.795] and 0.741 (95% CI 0.646-0.836), suggesting that the nomogram model had a good discrimination. Conclusions:Low high density lipoprotein level, high neutrophil count and hypertension are independent risk factors for RVO. The nomogram model established based on the above risk factors can effectively assess and quantify the risk of post-stroke depression in patients with cerebral infarction.

Risk modeling based on HER-2 related genes for bladder cancer survival prognosis assessment / 北京大学学报(医学版)

OBJECTIVE@#To investigate the correlation between the human epidermal growth factor receptor-2-related genes (HRGs) and survival prognosis of bladder cancer and to construct a predictive model for survival prognosis of bladder cancer patients based on HRGs.@*METHODS@#HRGs in bladder cancer were found by downloading bladder tumor tissue mRNA sequencing data and clinical data from the cancer genome atlas (TCGA), downloading HER-2 related genes from the molecular signatures database (MsigDB), and crossing the two databases. Further identifying HRGs associated with bladder cancer survival (P < 0.05) by using single and multi-factor Cox regression analysis and constructing HRGs risk score model (HRSM), the bladder cancer patients were categorized into high-risk and low-risk groups accor-ding to the median risk score. Survival analysis of the patients in high- and low-risk groups was conducted using R language and correlation of HRGs with clinical characteristics. A multi-factor Cox regression analysis was used to verify the independent factors affecting the prognosis of the patients with bladder cancer. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of HRSM was calculated, and a nomogram was constructed for survival prediction of the bladder cancer patients. Analysis of HRSM and patient immune cell infiltration correlation was made using the TIMER database.@*RESULTS@#A total of 13 HRGs associated with patient survival were identified in this study. Five genes (BTC, CDC37, EGF, PTPRR and EREG) were selected for HRSM by multi-factor Cox regression analysis. The 5-year survival rate of the bladder cancer patients in the high-risk group was significantly lower than that of the patients in the low-risk group. High expression of PTPRR was found to be significantly and negatively correlated with tumor grade and stage by clinical correlation analysis, while EREG was found to be the opposite; Increased expression of EGF was associated with high grade, however, the high expression ofCDC37showed the opposite result. And no significant correlation was found between BTC expression and clinical features. Correlation analysis of HRSM with immune cells revealed a positive correlation between risk score and infiltration of dendritic cells, CD8+T cells, CD4+T cells, neutrophils and macrophages.@*CONCLUSION@#HRGs have an important role in the prognosis of bladder cancer patients and may serve as new predictive biomarkers and potential targets for treatment.

Analysis of immune microenvironment and potential sensitive drugs in esophageal squamous cell carcinoma based on GEO database and bioinformatics method / 中国胸心血管外科临床杂志

@#Objective    To construct a prognostic model of esophageal squamous cell carcinoma (ESCC) based on immune checkpoint-related genes and explore the potential relationship between these genes and the tumor microenvironment (TME). Methods     The transcriptome sequencing data and clinical information of immune checkpoint genes of samples from GSE53625 in GEO database were collected. The difference of gene expression between ESCC and normal paracancerous tissues was evaluated, and the drug sensitivity of differentially expressed genes in ESCC was analyzed. We then constructed a risk model based on survival-related genes and explored the prognostic characteristics, enriched pathway, immune checkpoints, immune score, immune cell infiltration, and potentially sensitive drugs of different risk groups. Results    A total of 358 samples from 179 patients were enrolled, including 179 ESCC samples and 179 corresponding paracancerous tissues. There were 33 males and 146 females, including 80 patients≤60 years and 99 patients>60 years. 39 immune checkpoint genes were differentially expressed in ESCC, including 14 low expression genes and 25 high expression genes. Drug sensitivity analysis of 8 highly expressed genes (TNFRSF8, CTLA4, TNFRSF4, CD276, TNFSF4, IDO1, CD80, TNFRSF18) showed that many compounds were sensitive to these immunotherapy targets. A risk model based on three prognostic genes (NRP1, ICOSLG, HHLA2) was constructed by the least absolute shrinkage and selection operator analysis. It was found that the overall survival time of the high-risk group was significantly lower than that of the low-risk group (P<0.001). Similar results were obtained in different ESCC subtypes. The risk score based on the immune checkpoint gene was identified as an independent prognostic factor for ESCC. Different risk groups had unique enriched pathways, immune cell infiltration, TME, and sensitive drugs. Conclusion     A prognostic model based on immune checkpoint gene is established, which can accurately stratify ESCC and provide potential sensitive drugs for ESCC with different risks, thus providing a possibility for personalized treatment of ESCC.

Cuproptosis-Related 4-Gene Risk Model for Predicting Immunotherapy Drug Response and Prognosis of Kidney Renal Clear Cell Carcinoma / 中国医学科学杂志(英文版)

Background Kidney renal clear cell carcinoma (KIRC) is one of the most common renal malignancies with a high mortality rate. Cuproptosis, a novel form of cell death, is strongly linked to mitochondrial metabolism and is mediated by protein lipoylation, leading to a proteotoxic stress response and cell death. To date, few studies have ellucidated the holistic role of cuproptosis-related genes (CRGs) in the pathogenesis of KIRC.Methods We comprehensively and completely analyzed the RNA sequencing data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We screened for differentially expressed CRGs and constructed a prognostic risk model using univariate and multivariate Cox proportional regression analyses. Kaplan-Meier analysis was performed and receiver operating characteristic (ROC) curves were plotted to predict the prognosis of KIRC patients. Functional enrichment analysis was utilized to explore the internal mechanisms. Immune-related functions were analyzed using single-sample gene set enrichment analysis (ssGSEA), tumour immune dysfunction and exclusion (TIDE) scores, and drug sensitivity analysis.Results We established a concise prognostic risk model consisting of four CRGs (DBT, DLAT, LIAS and PDHB) to predict the overall survival (OS) in KIRC patients. The results of the survival analysis indicated a significantly lower OS in the high-risk group as compared to the patients in the low-risk group. The area under the time-dependent ROC curve (AUC) at 1, 3, and 5 year was 0.691, 0.618, and 0.614 in KIRC. Functional enrichment analysis demonstrated that CRGs were significantly enriched in tricarboxylic acid (TCA) cycle-related processes and metabolism-related pathways. Sorafenib, doxorubicin, embelin, and vinorelbine were more sensitive in the high-risk group.Conclusions We constructed a concise CRGs risk model to evaluate the prognosis of KIRC patients and this may be a new direction for the diagnosis and treatment of KIRC.

Establishment of a risk model for severe adenovirus pneumonia and prospective study of the timing of intravenous immunoglobulin therapy in children / 中国当代儿科杂志

OBJECTIVES@#To develop a risk prediction model for severe adenovirus pneumonia (AVP) in children, and to explore the appropriate timing for intravenous immunoglobulin (IVIG) therapy for severe AVP.@*METHODS@#Medical data of 1 046 children with AVP were retrospectively analyzed, and a risk prediction model for severe AVP was established using multivariate logistic regression. The model was validated with 102 children with AVP. Then, 75 children aged ≤14 years who were considered at risk of developing severe AVP by the model were prospectively enrolled and divided into three groups (A, B and C) in order of visit, with 25 children in each group. Group A received symptomatic supportive therapy only. With the exception of symptomatic supportive therapy, group B received IVIG treatment at a dose of 1g/(kg·d) for 2 consecutive days, before progressing to severe AVP. With the exception of symptomatic supportive therapy, group C received IVIG treatment at a dose of 1 g/(kg·d) for 2 consecutive days after progressing to severe AVP. Efficacy and related laboratory indicators were compared among the three groups after treatment.@*RESULTS@#Age<18.5 months, underlying diseases, fever duration >6.5 days, hemoglobin level <84.5 g/L, alanine transaminase level >113.5 U/L, and co-infection with bacteria were the six variables that entered into the risk prediction model for severe AVP. The model had an area under the receiver operating characteristic curve of 0.862, sensitivity of 0.878, and specificity of 0.848. The Hosmer-Lemeshow test showed good consistency between the predicted values and the actual observations (P>0.05). After treatment, group B had the shortest fever duration and hospital stay, the lowest hospitalization costs, the highest effective rate of treatment, the lowest incidence of complications, the lowest white blood cell count and interleukin (IL)-1, IL-2, IL-6, IL-8, IL-10 levels, and the highest level of tumor necrosis factor alpha (P<0.05).@*CONCLUSIONS@#The risk model for severe AVP established in this study has good value in predicting the development of severe AVP. IVIG therapy before progression to severe AVP is more effective in treating AVP in children.

Combined immunophenotyping to construct risk scoring model of lung adenocarcinoma ferroptosis-related lncRNA / 中国免疫学杂志

Objective:According lung adenocarcinoma(LUAD)ferroptosis and non-coding RNA in patients with a long-chain(lncRNA)correlation,binding immunophenotyping constructing new risk rating model to assess the prognosis of LUAD patients.Methods:Based on bioinformatics technology,download the transcriptome data and clinical data of LUAD samples from the TCGA database,obtain genes related to ferroptosis from the FreeDb database,and used"caret"package to screen 504 cases of LUAD samples and randomly divided into training set and validation set according to the ratios of 50%and 50%.Pearson correlation analysis and univariate-factor Cox regression were used to screen ferroptosis-related lncRNA related to the prognosis of LUAD,and the"Conen-susClusterPlus"package of R software was used.Immune correlation analysis with CIBERSORT software,LASSO regression analysis to establish ferroptosis-related lncRNA model,receiver operating characteristic(ROC)curve and area under the curve(AUC)to test the performance of the prognostic model,and verified by validation set.Results:Univariate factor Cox and LASSO regression analysis constructed nine risk scoring models composed of lncRNA related to ferroptosis.Both univariate and multivariate Cox regression analy-sis showed that this prognostic model can be used as an independent prognostic factor(P<0.001).The model had good prediction performance in training set,internal validation set and external validation set.Conclusion:The risk score model of LUAD patients constructed in this study can be used as a new independent prognostic evaluation method,or it may have further application value.

Research progress of in-hospital mortality risk model in patients with acute myocardial infarction / 中华危重病急救医学

The incidence of in-hospital death in acute myocardial infarction (AMI) is high, which seriously threatens the life and health of patients. At present, many countries and regions have established a variety of objective assessment models for predicting the in-hospital mortality of patients with AMI, providing important decision-making support for patients with different risk levels when formulating treatment plans. With the rise of artificial intelligence, many new modeling methods also show certain advantages over the traditional models. This article systematically introduces the commonly used and newly constructed risk prediction models for in-hospital mortality of AMI, in order to provide help for medical staff to assist decision-making in clinical practice, and provide reference for the establishment of a safe and more effective risk prediction model in the future.

Establishment of a prognostic model of Wnt signaling pathway related genes in gastric cancer / 西安交通大学学报(医学版)

【Objective】 To confirm the role of Wnt signaling pathway in the occurrence and development of gastric cancer （GC）， establish a prognostic model composed of Wnt pathway related genes， and then evaluate the predictive value of the model. 【Methods】 We downloaded the gene expression data and survival data of GC in TCGA database， and used GSEA enrichment analysis to verify the enrichment of Wnt pathway in GC and para-cancer samples. In this study， univariable COX regression analysis and survival curve analysis were used to select the prognosis-related genes of GC. Then the multivariate COX proportional hazard regression model was used to obtain the prognostic model of Wnt signaling pathway related genes. Then， receiver operating characteristic （ROC） curve and forest plot were used to verify the clinical predictive value of the model. The model was then validated in GEO external database. Finally， by utilizing quantitative real-time PCR （qPCR）， we detected the expressions of Wnt signaling pathway related genes in 8 pairs of clinical GC and para-cancer samples. 【Results】 We downloaded 32 samples of normal para-cancer samples and 375 cancer samples and their corresponding clinical data. GSEA enrichment showed that compared with normal samples， Wnt pathway was significantly enriched in GC samples （P<0.05）. The results of univariate COX analysis showed that 13 Wnt pathway genes were closely related to the prognosis of GC patients. Multivariate COX determined that the model was multiplied and accumulated by ETV2， SERPINE1， CPZ， VPS35 and IGFBP1 and their corresponding coefficient β. The survival curve and ROC curve showed that the model could accurately predict the prognosis of GC patients， and the 1-year， 3-year， and 5-year areas under the curve （AUC） were 68.0%， 69.4% and 78.5%， respectively. Clinical univariate and multivariate COX analyses showed that the model could become an independent prognostic factor other than TNM system of GC. The external data set （GSE84437） validation results of GC showed that the model could better predict the prognosis of GC patients. qPCR results indicated that ETV2， SERPINE1， CPZ， VPS35 and IGFBP1 expressions were upregulated in GC samples compared with para-cancer samples. 【Conclusion】 This study further confirmed that Wnt pathway plays an important role in the progress of GC from the perspective of bioinformatics， and we have established a prognosis-related risk model， providing a new perspective for clinical genetic testing， targeted therapy and individualized therapy.

Determinants of bone health in elderly Japanese men: study design and key findings of the Fujiwara-kyo Osteoporosis Risk in Men (FORMEN) cohort study

BACKGROUND@#The Fujiwara-kyo Osteoporosis Risk in Men (FORMEN) study was launched to investigate risk factors for osteoporotic fractures, interactions of osteoporosis with other non-communicable chronic diseases, and effects of fracture on QOL and mortality.@*METHODS@#FORMEN baseline study participants (in 2007 and 2008) included 2012 community-dwelling men (aged 65-93 years) in Nara prefecture, Japan. Clinical follow-up surveys were conducted 5 and 10 years after the baseline survey, and 1539 and 906 men completed them, respectively. Supplemental mail, telephone, and visit surveys were conducted with non-participants to obtain outcome information. Survival and fracture outcomes were determined for 2006 men, with 566 deaths identified and 1233 men remaining in the cohort at 10-year follow-up.@*COMMENTS@#The baseline survey covered a wide range of bone health-related indices including bone mineral density, trabecular microarchitecture assessment, vertebral imaging for detecting vertebral fractures, and biochemical markers of bone turnover, as well as comprehensive geriatric assessment items. Follow-up surveys were conducted to obtain outcomes including osteoporotic fracture, cardiovascular diseases, initiation of long-term care, and mortality. A complete list of publications relating to the FORMEN study can be found at https://www.med.kindai.ac.jp/pubheal/FORMEN/Publications.html .

Prognostic Risk Model of Colorectal Cancer Constructed by Bioinformatics Method and Functional Study of The Gene CLCA1 & SPINK4

Background Colorectal cancer (CRC) is the most common malignant tumor of digestive system. The metastasesis the main cause of mortality in CRC patients, of whom the initial diagnosis is about 25%. In our study, weaimed to identify potential gene biomarkers based on RNA sequencing data to predict and improve CRCpatient survival.Method In this study, by screening differentially expressed genes of colon cancer related to liver metastasis, asurvival prognostic risk model was constructed by bioinformatics analysis. Here, we conducted our data mininganalysis for CRC by integrating the differentially expressed genes acquired from Gene Expression Omnibus(GEO) database by primary tumor versus liver metastasis (GSE81582，GSE41258，GSE49355，GSE68468)into The Cancer Genome Atlas (TCGA) database which includes 415 primary tumor and 132 liver metastasistissue. At the same time, we used transwell, RT-PCR and western to examine the effects of CLCA1 and SPINK4on the migration of colorectal cancer cells at the cell level.Results We identified intersections of 197 genes (117 up-regulated and 80 down-regulated) between GEO dataand TCGA data. Differentially expressed genes in TCGA-COAD by single factor cox analysis, lasso cycle trainingand multifactor cox analysis composed a survival prognosis prediction model consisted of 7 genes ORM1,CLCA1, C8B, SPINK4, ALDOB, GAMT, C8G. And results of transwell experiments showed that high expression ofCLCA1 and SPINK4 can inhibit the migration ability of colon cancer cells LOVO and SW620, meanwhile westernblotting showed that the high expression of both genes can upregulate the expression of epithelial phenotypicmarker E-cadherin, and Vimentin expression is down-regulated.Conclusion In this study, 197 differentially expressed genes were selected and a relatively robust survivalprognosis prediction model was constructed. The model consisted of seven genes: GAMT, C8G, ORM1, CLCA1,C8B, SPINK4, and ALDOB. At the same time, we found that CLCA1 and SPINK4 are closely related to survivalprognosis. The predictive model nomogram will enable patients with CRC to be more accurately managed intrials testing new drugs and in clinical practice.

Survival status and influencing factors of HIV/AIDS on highly active anti-retrovial therapy in Shandong province / 中华流行病学杂志

Objective: To understand the survival status and influencing factors for HIV/AIDS patients on highly active anti-retroviral therapy (HAART) in Shandong province. Methods: Both Kaplan-Meier (K-M) method and cumulative incidence function (CIF) were used to calculate the cumulative incidence of AIDS-related death respectively, and Fine-Gray model was used to identify the influencing factors related to survival time. Results: Through K-M method, a higher AIDS-related cumulated death rate than the CIF, was estimated. Among all the HIV/AIDS patients who initiated HAART from 2003 to 2015 in Shandong, 5 593 of them met the inclusion criteria. The cumulative incidence rate for AIDS-related death was 3.08% in 1 year, 4.21% in 3 years, 5.37% in 5 years, and 7.59% in 10 years respectively by CIF. Results from the F-G analysis showed that HIV/AIDS patients who were on HAART, the ones who had college degree or above (HR=0.40, 95%CI: 0.24-0.65) were less likely to die of AIDS-associated diseases. However, HIV/AIDS patients who were on HAART and living in the western areas of Shandong (HR=1.33, 95%CI: 1.01-1.89), diagnosed by medical institutions (HR=1.39, 95%CI: 1.06-1.80), started to receive care ≥1 year after diagnosis (HR=2.02, 95%CI: 1.30-3.15), their CD(4) cell count less than 200 cells/μl (HR=3.41, 95%CI: 2.59-4.59) at the time of diagnosis, with NVP in antiviral treatment (ART) regime (HR=1.36, 95%CI: 1.03-1.88), at Ⅲ/Ⅳ clinical stages (HR=2.61, 95%CI: 1.94-3.53) and CD(4) cell count less than 350 cells/μl (HR=5.48,95%CI: 2.32-12.72) at initiation of HAART ect., were more likely to die of AIDS-associated diseases. Conclusions: With the existence of competing risks, the cumulative incidence rate for AIDS-related death was overestimated by K-M, suggesting that competing risk models should be used in the survival analysis. Measures as early diagnoses followed by timely care and early HAART could end up with the reduction of AIDS-related death.

Comparison of ADNEX model, simple rules risk model and risk of malignancy index in diagnosis of benign and malignant ovarian tumors / 中国医学影像技术

Objective To compare the value of ADNEX model, simple rules risk model and the risk of malignancy index (RMI) in diagnosis of benign and malignant ovarian tumors. Methods The preoperative ultrasonic images of 286 patients with ovarian tumors were retrospectively analyzed. ADNEX model, simple rules risk model and RMI were used to differentiate benign and malignant ovarian tumors. Taken histopathological results after surgery as golden standards, the sensitivity and specificity were calculated and compared among 3 methods. ROC curve was used to obtain the area under the curves. Results Among 286 ovarian tumors, 142 were benign and 144 were malignant. The sensitivity of ADNEX model, simple rules risk model and RMI was 83.33% (120/144), 80.56% (116/144) and 65.97% (95/144), respectively, while the specificity was 89.44% (127/142), 92.96% (132/142) and 90.14% (128/142), respectively. There was no statistical difference of sensitivity nor specificity between ADNEX model and simple rules risk model (χ2=0.352, 1.784, P=0.554, 0.182). The sensitivity of ADNEX model and simple rules risk model was higher than that of RMI (χ2=16.691, 7.533, respectively, both P<0.001), while there was no statistical difference of specificity (χ2=0, 0.561, P=1, 0.454). The AUC of ADNEX model, simple rules risk model and RMI was 0.864, 0.868 and 0.788, respectively (all P<0.001). Conclusion ADNEX model and simple rules risk model are better than RMI in differentiating benign and malignant ovarian tumors.