Machine learning algorithm to construct cuproptosis-and immunerelated prognosis prediction model for colon cancer

BACKGROUND Over the past few years,research into the pathogenesis of colon cancer has progressed rapidly,and cuproptosis is an emerging mode of cellular apoptosis.Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease.AIM To look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients.The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers.METHOD Data obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation.The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis-and immune-related combination model,and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients.A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer.RESULTS Once prognostic characteristics were obtained,the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer:The first was a risk factor,whereas the second was a protective factor.The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant.Within the component expressions,the expressions of HSPA1A,CDKN2A,and UCN3 differed markedly.Transcription analysis primarily reflects the differential activation of related immune cells and pathways.Furthermore,genes linked to immune checkpoint inhibitors were expressed differently between the subgroups,which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy.CONCLUSION The prognosis of the high-risk group evaluated in the combined model was poorer,and cuproptosis was highly correlated with the prognosis of colon cancer.It is possible that we may be able to improve patients’prognosis by regulating the gene expression to intervene the risk score.

Novel defined N7-methylguanosine modification-related lncRNAs for predicting the prognosis of laryngeal squamous cell carcinoma

Objective:Through integrated bioinformatics analysis,the goal of this work was to find new,characterised N7-methylguanosine modification-related long non-coding RNAs(m7G-lncRNAs)that might be used to predict the prognosis of laryngeal squamous cell carcinoma(LSCC).Methods:The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database&sanitised.Then,using co-expression analysis of m7G-associated mRNAs&lncRNAs&differential expression analysis(DEA)among LSCC&normal sample categories,we discovered lncRNAs that were connected to m7G.The prognosis prediction model was built for the training category using univariate&multivariate COX regression&LASSO regression analyses,&the model’s efficacy was checked against the test category data.In addition,we conducted DEA of prognostic m7G-lncRNAs among LSCC&normal sample categories&compiled a list of co-expression networks&the structure of prognosis m7G-lncRNAs.To compare the prognoses for individuals with LSCC in the high-&low-risk categories in the prognosis prediction model,survival and risk assessments were also carried out.Finally,we created a nomogram to accurately forecast the outcomes of LSCC patients&created receiver operating characteristic(ROC)curves to assess the prognosis prediction model’s predictive capability.Results:Using co-expression network analysis&differential expression analysis,we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs,respectively.We then constructed a prognosis prediction model for six m7G-lncRNAs(FLG−AS1,RHOA−IT1,AC020913.3,AC027307.2,AC010973.2 and AC010789.1),identified 32 DEPm7G-lncRNAs,analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs,and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients.By displaying ROC curves and a nomogram,we finally checked the prognosis prediction model's accuracy.Conclusion:By creating novel predictive lncRNA signatures for clinical diagnosis&therapy,our findings will contribute to understanding the pathogenetic process of LSCC.

Clinical and pathological characteristics and expression of related molecules in patients with airway disseminated lung adenocarcinoma

Objective:Lung adenocarcinoma exhibits diverse genetic and morphological backgrounds,in addition to considerable differences in clinical pathology and molecular biological characteristics.Among these,the phenomenon of spread through air space(STAS),a distinct mode of lung cancer infiltration,has rarely been reported.Therefore,this study aimed to explore the relationship between STAS tumor cells and the clinical and molecular characteristics of patients with lung adenocarcinoma,as well as their impact on prognosis.Methods:This study included 147 patients who were diagnosed with lung adenocarcinoma at the Inner Mongolia Autonomous Region Cancer Institute between January 2014 and December 2017.Surgical resection specimens were retrospectively analyzed.Using univariate and multivariate Cox analyses,we assessed the association between STAS and the clinicopathological features and molecular characteristics of patients with lung adenocarcinoma.Furthermore,we investigated the effects on patient prognosis.In addition,we developed a column–line plot prediction model and performed internal validation.Results:Patients with positive STAS had a significantly higher proportion of tumors with a diameter≥2 cm,with infiltration around the pleura,blood vessels,and nerves,and a pathological stage>IIB than in STAS-negative patients(P<0.05).Cox multivariate survival analysis revealed that clinical stage,STAS status,tumor size,and visceral pleural invasion were independent prognostic factors influencing the 5-year progression-free survival in patients with lung adenocarcinoma.The predictive values and P values from the Hosmer-Lemeshow test were 0.8 and 0.2,respectively,indicating no statistical difference.Receiver operating characteristic curve analysis demonstrated areas under the curve of 0.884 and 0.872 for the training and validation groups,respectively.The nomogram model exhibited the best fit with a value of 192.09.Conclusions:Clinical stage,pleural invasion,vascular invasion,peripheral nerve invasion,tumor size,and necrosis are independent prognostic factors for patients with STAS-positive lung adenocarcinoma.The nomogrambased on the clinical stage,pleural invasion,vascular invasion,peripheral nerve invasion,tumor size,and necrosis showed good accuracy,differentiation,and clinical practicality.

Short-term prognostic factors for hepatitis B virus-related acute-onchronic liver failure

BACKGROUND Acute-on-chronic liver failure(ACLF)is the abrupt exacerbation of declined hepatic function in patients with chronic liver disease.AIM To explore the independent predictors of short-term prognosis in patients with hepatitis B virus(HBV)-related ACLF and to establish a predictive short-term prognosis model for HBV-related ACLF.METHODS From January 2016 to December 2019,207 patients with HBV-related ACLF attending the 910^(th) Hospital of Chinese People's Liberation Army were continuously included in this retrospective study.Patients were stratified based on their survival status 3 mo after diagnosis.Information was collected regarding gender and age;coagulation function in terms of prothrombin time and international normalized ratio(INR);hematological profile in terms of neutrophil-tolymphocyte ratio(NLR)and platelet count(PLT);blood biochemistry in terms of alanine aminotransferase,aspartate aminotransferase,total bilirubin(Tbil),albumin,cholinesterase,blood urea nitrogen(BUN),creatinine,blood glucose,and sodium(Na);tumor markers including alpha-fetoprotein(AFP)and Golgi protein 73(GP73);virological indicators including HBV-DNA,HBsAg,HBeAg,Anti-HBe,and Anti-HBc;and complications including hepatic encephalopathy,hepatorenal syndrome,spontaneous peritonitis,gastrointestinal bleeding,and pulmonary infection.RESULTS There were 157 and 50 patients in the survival and death categories,respectively.Univariate analysis revealed significant differences in age,PLT,Tbil,BUN,NLR,HBsAg,AFP,GP73,INR,stage of liver failure,classification of liver failure,and incidence of complications(pulmonary infection,hepatic encephalopathy,spontaneous bacterial peritonitis,and upper gastrointestinal bleeding)between the two groups(P<0.05).GP73[hazard ratio(HR):1.009,95%confidence interval(CI):1.005-1.013,P=0.000],middle stage of liver failure(HR:5.056,95%CI:1.792-14.269,P=0.002),late stage of liver failure(HR:22.335,95%CI:8.544-58.388,P=0.000),pulmonary infection(HR:2.056,95%CI:1.145-3.690,P=0.016),hepatorenal syndrome(HR:6.847,95%CI:1.930-24.291,P=0.003),and HBsAg(HR:0.690,95%CI:0.524-0.908,P=0.008)were independent risk factors for short-term prognosis in patients with HBV-related ACLF.Following binary logistics regression analysis,we arrived at the following formula for predicting short-term prognosis:Logit(P)=Ln(P/1-P)=0.013×(GP73 ng/mL)+1.907×(middle stage of liver failure)+4.146×(late stage of liver failure)+0.734×(pulmonary infection)+22.320×(hepatorenal syndrome)-0.529×(HBsAg)-5.224.The predictive efficacy of the GP73-ACLF score was significantly better than that of the Model for End-Stage Liver Disease(MELD)and MELD-Na score models(P<0.05).CONCLUSION The stage of liver failure,presence of GP73,pulmonary infection,hepatorenal syndrome,and HBsAg are independent predictors of short-term prognosis in patients with HBV-related ACLF,and the GP73-ACLF model has good predictive value among these patients.

Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019(COVID-19)continues to threaten human health security,exerting considerable pressure on healthcare systems worldwide.While prognostic models for COVID-19 hospitalized or intensive care patients are currently available,prognostic models developed for large cohorts of thousands of individuals are still lacking.Methods Between February 4 and April 16,2020,we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital,Hubei Province,China.(1)Screening of key prognostic factors:A univariate Cox regression analysis was performed on 2,649 patients in the training set,and factors affecting prognosis were initially screened.Subsequently,a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis.Finally,multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis.(2)Establishment of a scoring system:The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors,calculated the C index,drew calibration curves and drew training set patient survival curves.(3)Verification of the scoring system:The scoring system assessed 1,325 patients in the test set,splitting them into high-and low-risk groups,calculated the C-index,and drew calibration and survival curves.Results The cross-sectional study found that age,clinical classification,sex,pulmonary insufficiency,hypoproteinemia,and four other factors(underlying diseases:blood diseases,malignant tumor;complications:digestive tract bleeding,heart dysfunction)have important significance for the prognosis of the enrolled patients with COVID-19.Herein,we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19.Meanwhile,the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high-and low-risk groups(using a scoring threshold of 117.77,a score below which is considered low risk).The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines(C indexes,0.95 vs.0.89).Conclusions Age,clinical typing,sex,pulmonary insufficiency,hypoproteinemia,and four other factors were important for COVID-19 survival.Compared with general statistical methods,this method can quickly and accurately screen out the relevant factors affecting prognosis,provide an order of importance,and establish a scoring system based on the nomogram model,which is of great clinical significance.

Multivariate analysis by Cox Proportional Hazards Model on prognoses of patients with bile duct carcinoma after resection

To evaluate the influence of various clinicopathologic factors on the survival of patients with bile duct carcinoma after curative resection Methods A retrospective analysis was performed on 86 cases of bile duct carcinoma treated from January 1981 to September 1995 Fifteen clinicopathologic factors that could possibly influence survival were selected A multivariate analysis of these individuals was performed using the Cox Proportional Hazards Model Results The overall cumulative survival rate was 73% for 1 year, 32% for 3 years and 19% for 5 years The results of univariate analysis showed that the major significant prognostic factors for influencing survival of these patients were type of histological lesion, lymph node metastasis, pancreatic invasion, duodenal invasion, perineural invasion, macroscopic vessel involvement, resected surgical margin and depth of cancer invasion ( P 【0 05) Pancreatic invasion, perineural invasion and lymph node metastases were the three most important prognostic factors determined by multivariate analysis using the Cox Proportional Hazards Model Conclusion Pancreatic invasion, perineural invasion and lymph node metastases are the most important prognostic factors for bile duct carcinoma after curative resection