Differential DNA methylation analysis of SUMF2,ADAMTS5,and PXDN provides novel insights into colorectal cancer prognosis prediction in Taiwan

BACKGROUND Patients with colorectal cancer(CRC)undergo surgery,as well as perioperative chemoradiation or adjuvant chemotherapy primarily based on the tumor–node–metastasis(TNM)cancer staging system.However,treatment responses and prognostic outcomes of patients within the same stage vary markedly.The potential use of novel biomarkers can improve prognostication and shared decision making before implementation into certain therapies.AIM To investigate whether SUMF2,ADAMTS5,and PXDN methylation status could be associated with CRC prognosis.METHODS We conducted a Taiwan region cohort study involving 208 patients with CRC recruited from TriService General Hospital and applied the candidate gene approach to identify three genes involved in oncogenesis pathways.A methylation-specific polymerase chain reaction(MS-PCR)and Epi TYPER DNA methylation analysis were employed to detect methylation status and to quantify the methylation level of candidate genes in tumor tissue and adjacent normal tissue from participants.We evaluated SUMF2,ADAMTS5,and PXDN methylation as predictors of prognosis,including recurrence-free survival(RFS),progression-free survival(PFS),and overall survival(OS),using a Cox regression model and Kaplan–Meier analysis.RESULTS We revealed various outcomes related to methylation and prognosis.Significantly shorter PFS and OS were associated with the CpG_3+CpG_7 hypermethylation of SUMF2 from tumor tissue compared with CpG_3+CpG_7 hypomethylation[hazard ratio(HR)=2.24,95%confidence interval(CI)=1.03-4.85 for PFS,HR=2.56 and 95%CI=1.08-6.04 for OS].By contrast,a significantly longer RFS was associated with CpG_2 and CpG_13 hypermethylation of ADAMTS5 from normal tissue compared with CpG_2 and CpG_13 hypomethylation[HR(95%CI)=0.15(0.03-0.71)for CpG_2 and 0.20(0.04-0.97)for CpG_13].The relationship between the methylation status of PXDN and the prognosis of CRC did not reach statistical significance.CONCLUSION Our study found that CpG_3+CpG_7 hypermethylation of SUMF2 from tumor tissue was associated with significantly shorter PFS and OS compared with CpG_3+CpG_7 hypomethylation.CpG_2 and CpG_13 hypermethylation of ADAMTS5 from normal tissue was associated with a significantly longer RFS compared with CpG_2 and CpG_13 hypomethylation.These methylationrelated biomarkers which have implications for CRC prognosis prediction may aid physicians in clinical decision-making.

Validation and Refinement of Two Interpretable Models for Coronavirus Disease 2019 Prognosis Prediction

Objective:To validate two proposed coronavirus disease 2019(COVID-19)prognosis models,analyze the characteristics of different models,consider the performance of models in predicting different outcomes,and provide new insights into the development and use of artificial intelligence(AI)predictive models in clinical decision-making for COVID-19 and other diseases.Materials and Methods:We compared two proposed prediction models for COVID-19 prognosis that use a decision tree and logistic regression modeling.We evaluated the effectiveness of different model-building strategies using laboratory tests and/or clinical record data,their sensitivity and robustness to the timings of records used and the presence of missing data,and their predictive performance and capabilities in single-site and multicenter settings.Results:The predictive accuracies of the two models after retraining were improved to 93.2% and 93.9%,compared with that of the models directly used,with accuracies of 84.3% and 87.9%,indicating that the prediction models could not be used directly and require retraining based on actual data.In addition,based on the prediction model,new features obtained by model comparison and literature evidence were transferred to integrate the new models with better performance.Conclusions:Comparing the characteristics and differences of datasets used in model training,effective model verification,and a fusion of models is necessary in improving the performance of AI models.

Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway

BACKGROUND The ubiquitin-proteasome pathway(UPP)has been proven to play important roles in cancer.AIM To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.METHODS In this study,UPP-related E1,E2,E3,deubiquitylating enzyme,and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)database,aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based RESULTS Five genes(including autophagy related 10,proteasome 20S subunit alpha 8,proteasome 20S subunit beta 2,ubiquitin specific peptidase 17 like family member 2,and ubiquitin specific peptidase 8)were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer.Among training,validation,and Gene Expression Omnibus sets,the overall survival differed significantly between the high-risk and low-risk groups.The expression of the five genes was significantly associated with immunocyte infiltration,tumor stage,and postoperative recurrence.A total of 111 differentially expressed genes(DEGs)were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways.Cell division cycle 20,Kelch repeat and BTB domain containing 11,and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.CONCLUSION We have constructed a prognosis predictive model in patients with liver cancer,which contains five genes that associate with immunocyte infiltration,tumor stage,and postoperative recurrence.

Relationship between heart rate variability and posterior circulation cerebral infarction and its value in prognosis prediction

Objective To investigate the characteristic of heart rate variability(HRV)changes in patients with posteriorcirculation cerebral infarction and its value in prognosis prediction.Methods Fifty-four cases continuously diagnosed with acute posterior circulation cerebral infarction from March 2015 to November 2015 in the Department

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.

MH-STRALP:A scoring system for prognostication in patients with upper gastrointestinal bleeding

BACKGROUND Upper gastrointestinal bleeding(UGIB)is a common medical emergency and early assessment of its outcomes is vital for treatment decisions.AIM To develop a new scoring system to predict its prognosis.METHODS In this retrospective study,692 patients with UGIB were enrolled from two cen-ters and divided into a training(n=591)and a validation cohort(n=101).The clinical data were collected to develop new prognostic prediction models.The en-dpoint was compound outcome defined as(1)demand for emergency surgery or vascular intervention,(2)being transferred to the intensive care unit,or(3)death during hos-pitalization.The models’predictive ability was compared with previously esta-blished scores by receiver operating characteristic(ROC)curves.RESULTS Totally 22.2%(131/591)patients in the training cohort and 22.8%(23/101)in the validation cohort presented poor outcomes.Based on the stepwise-forward Lo-gistic regression analysis,eight predictors were integrated to determine a new post-endoscopic prognostic scoring system(MH-STRALP);a nomogram was de-termined to present the model.Compared with the previous scores(GBS,Rock-all,ABC,AIMS65,and PNED score),MH-STRALP showed the best prognostic prediction ability with area under the ROC curves(AUROCs)of 0.899 and 0.826 in the training and validation cohorts,respectively.According to the calibration cur-ve,decision curve analysis,and internal cross-validation,the nomogram showed good calibration ability and net clinical benefit in both cohorts.After removing the endoscopic indicators,the pre-endoscopic model(pre-MH-STRALP score)was conducted.Similarly,the pre-MHSTRALP score showed better predictive value(AUROCs of 0.868 and 0.767 in the training and validation cohorts,respectively)than the other pre-endoscopic scores.CONCLUSION The MH-STRALP score and pre-MH-STRALP score are simple,convenient,and accurate tools for prognosis prediction of UGIB,and may be applied for early decision on its management strategies.

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.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Artificial intelligence in gastric cancer: Application and future perspectives

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe,with a 5-year survival rate of less than 40%.In recent years,several applications of artificial intelligence(AI)have emerged in the gastric cancer field based on its efficient computational power and learning capacities,such as imagebased diagnosis and prognosis prediction.AI-assisted diagnosis includes pathology,endoscopy,and computerized tomography,while researchers in the prognosis circle focus on recurrence,metastasis,and survival prediction.In this review,a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed,Embase,Web of Science,and the Cochrane Library.Thereby the current status of AI-applications was systematically summarized in gastric cancer.Moreover,future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

Artificial intelligence in small intestinal diseases:Application and prospects

The small intestine is located in the middle of the gastrointestinal tract,so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases.However,with the extensive application of artificial intelligence in the field of small intestinal diseases,with its efficient learning capacities and computational power,artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods,which improves the accuracy of diagnosis and prediction and reduces the workload of doctors.In this review,a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases.Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced,and the challenges and prospects in this field were also analyzed.

Comparative research on the prognostic ability of improved early warning and APACHE Ⅱ evaluation for hospitalized patients in the emergency department

Objective： To compare the feasibility and applicability of predicting the prognosis of patients using the Early Warning Score（MEWS） system and the Acute Physiology and Chronic Health Evaluation（APACHE Ⅱ） system in the Emergency Department.Methods： Using a prospective study method, the APACHE Ⅱ and MEWS data for 640 patients hospitalized in the Emergency Internal Medicine Department were collected. The prognoses, two scores to predict the corresponding prediction index of sensitivity, specificity and positive predictive value for the prognosis,the negative predictive value and the ROC curve for predicting the prognosis were analyzed for all patients.Results： In the prediction of the risk of mortality, the MEWS system had a high resolution. The MEWS area under the ROC curve was 0.93. The area under the ROC curve for the APACHE score was 0.79, and the difference was statistically significant（Z =4.348, P 〈 0.01）.Conclusions： Both the MEWS and APACHE Ⅱ systems can be used to determine the severity of emergency patients and have a certain predictive value for the patient＇s mortality risk. However, the MEWS system is simple and quick to operate, making it a useful supplement for APACHE Ⅱ score.

Proposal and validation of a modified staging system to improve the prognosis predictive performance of the 8th AJCC/ UICC pTNM staging system for gastric adenocarcinoma: a multicenter study with external validation

Background:The 8th edition of the American Joint Committee on Cancer/Union for International Cancer Control(AJCC/UICC)pathological tumor-node-metastasis(pTNM)staging system may have increased accuracy in predicting prognosis of gastric cancer due to its important modifications from previous editions.However,the homogeneity in prognosis within each subgroup classified according to the 8th edition may still exist.This study aimed to compare and analyze the prognosis prediction abilities of the 8th and 7th editions of AJCC/UICC pTNM staging system for gastric cancer and propose a modified pTNM staging system with external validation.Methods:In total,clinical data of 7911 patients from three high-capacity institutions in China and 10,208 cases from the Surveillance,Epidemiology,and End Results(SEER)Program Registry were analyzed.The homogeneity,discrimina-tory ability,and monotonicity of the gradient assessments of the 8th and 7th editions of AJCC/UICC pTNM staging system were compared using log-rank χ^(2),linear-trend χ^(2),likelihood-ratioχ2 statistics and Akaike information criterion(AIC)calculations,on which a modified pTNM classification with external validation using the SEER database was proposed.Results:Considerable stage migration,mainly for stage III,between the 8th and 7th editions was observed in both cohorts.The survival rates of subgroups of patients within stage IIIA,IIIB,or IIIC classified according to both editions were significantly different,demonstrating poor homogeneity for patient stratification.A modified pTNM staging system using data from the Chinese cohort was then formulated and demonstrated an improved homogeneity in these abovementioned subgroups.This staging system was further validated using data from the SEER cohort,and similar promising results were obtained.Compared with the 8th and 7th editions,the modified pTNM staging system displayed the highest log-rank χ^(2),linear-trend χ^(2),likelihood-ratio χ^(2),and lowest AIC values,indicating its superior discriminatory ability,monotonicity,homogeneity and prognosis prediction ability in both populations.Conclusions:The 8th edition of AJCC/UICC pTNM staging system is superior to the 7th edition,but still results in homogeneity in prognosis prediction.Our modified pTNM staging system demonstrated the optimal stratification and prognosis prediction ability in two large cohorts of different gastric cancer populations.

Improvement in the risk assessment of oral leukoplakia through morphology-related copy number analysis

Oral leukoplakia is the most common type of oral potentially malignant disorders and considered a precursor lesion to oral squamous cell carcinoma.However,a predictor of oral leukoplakia prognosis has not yet been identified.We investigated whether copy number alteration patterns may effectively predict the prognostic outcomes of oral leukoplakia using routinely processed paraffin sections.Comparison of copy number alteration patterns between oral leukoplakia with hyperplasia(HOL,n=22)and dysplasia(DOL,n=21)showed that oral leukoplakia with dysplasia had a higher copy number alteration rate(86%)than oral leukoplakia with hyperplasia(46%).Oral leukoplakia with dysplasia exhibited a wider range of genomic variations across all chromosomes compared with oral leukoplakia with hyperplasia.We also examined a retrospective cohort of 477 patients with oral leukoplakia with hyperplasia with detailed follow-up information.The malignant transformation(MT,n=19)and leukoplakia recurrence(LR,n=253)groups had higher frequencies of aneuploidy events and copy number loss rate than the free of disease(FD,n=205)group.Together,our results revealed the association between the degree of copy number alterations and the histological grade of oral leukoplakia and demonstrated that copy number alteration may be effective for prognosis prediction in oral leukoplakia patients with hyperplasia.

Association of an anaplastic lymphoma kinase pathway signature with cell de-differentiation, neoadjuvant chemotherapy response, and recurrence risk in breast cancer

Background:Aberrant activation of anaplastic lymphoma kinase(ALK)signaling has been found to be involved in the tumorigenesis of multiple types of cancer.The aim of this study was to determine the role of this pathway in the pathogenesis of breast cancer.Methods:An ALK pathway signature that we generated previously was used to compute the ALK pathway activity in 6381 breast cancer samples from 42 microarray datasets,and the associations between ALK pathway signature score and clinical variables were examined using logistic regression and survival analyses.Results:Our results indicated that high ALK pathway activity was a significant risk factor for hormone receptor-negative,high-grade breast cancer in the 42 datasets.ALK pathway activity was positively associated with pathological complete response(pCR)in 15 datasets annotated with patient’s neoadjuvant chemotherapy response information(overall odds ratio=1.67,P<0.01),and this association was more significant in HER2-negative and grade 1&2 tumors than in HER2-positive and grade 3 tumors.ALK pathway activity was also positively associated with recurrence risk in breast cancer patients from 30 datasets annotated with survival information(overall hazard ratio=1.21,P<0.01),particularly in patients with age>50 years old,with positive lymph nodes,or with residual disease after neoadjuvant chemotherapy.Conclusions:ALK may be involved in breast cancer tumorigenesis,and ALK pathway signature score may serve as a prognostic biomarker for breast cancer.