Kinase-substrate Edge Biomarkers Provide a More Accurate Prognostic Prediction in ER-negative Breast Cancer

The estrogen receptor(ER)-negative breast cancer subtype is aggressive with few treatment options available.To identify specific prognostic factors for ER-negative breast cancer,this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance,Epidemiology,and End Results(SEER)and The Cancer Genome Atlas(TCGA)databases,respectively.To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ERpositive breast cancer patients,we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network.Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge features for both subtypes of breast cancer.Two promising kinase-substrate edge features,CSNK1A1-NFATC3 and SRC-OCLN,were identified for more accurate prognostic prediction in ERnegative breast cancer patients.

Recent Advances and Future Directions of Diagnostic and Prognostic Prediction Models in Ovarian Cancer

Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,there is an urgent need to improve the early diagnosis and prognosis of ovarian cancer.Prediction models can effectively estimate the risk of disease occurrence,as well as its prognosis.Recently,many studies have established multiple ovarian cancer prediction models based on different regions and populations.These models can improve the detection rate and optimize the prognosis management to a certain extent.Herein,the construction principle of the ovarian cancer risk prediction model and its validation are summarized;furthermore,comprehensive reviews and comparisons of the different types of these models are made.Therefore,our review may be of great significance for the whole course of ovarian cancer management.

Unraveling the enigma:A comprehensive review of solid pseudopapillary tumor of the pancreas

Solid pseudopapillary tumor of the pancreas(SPTP)is a rare neoplasm predom-inantly observed in young females.Pathologically,CTNNB1 mutations,β-catenin nuclear accumulation,and subsequent Wnt-signaling pathway activation are the leading molecular features.Accurate preoperative diagnosis often relies on imaging techniques and endoscopic biopsies.Surgical resection remains the mainstay treatment.Risk models,such as the Fudan Prognostic Index,show promise as predictive tools for assessing the prognosis of SPTP.Establishing three types of metachronous liver metastasis can be beneficial in tailoring individu-alized treatment and follow-up strategies.Despite advancements,challenges persist in understanding its etiology,establishing standardized treatments for unresectable or metastatic diseases,and developing a widely recognized grading system.This comprehensive review aims to elucidate the enigma by consolidating current knowledge on the epidemiology,clinical presentation,pathology,molecular characteristics,diagnostic methods,treatment options,and prognostic factors.

A model of five genes of tumor microenvironment predicts prognosis in Cholangiocarcinoma

Background:Cholangiocarcinoma(CCA)is highly malignant and has a poor prognosis has a high malignant degree and poor prognosis.The purpose of this study is to develop a new prognostic model based on genes related to the tumor microenvironment(TME).Methods:Derived from the discerned differentially expressed genes within The Cancer Genome Atlas(TCGA)dataset,this investigation employed the methodology of weighted gene co-expression network analysis(WGCNA)to ascertain gene co-expressed modules intricately linked to the Tumor Microenvironment(TME)among Cholangiocarcinoma(CCA)patients.The genes associated with prognosis,as identified through Cox regression analysis,were employed in the formulation of a predictive model.This model underwent validation,leading to the development of a risk score formula and nomogram.Concurrently,we validated the model’s reliability using data from CCA patients in the Gene Expression Omnibus(GEO)database(accession:GSE107943).Results:6139 DEGs were divided into 10 co-expressed gene modules using WGCNA.Among these,two modules(blue module with 832 genes and brown module with 1379 genes)showed high correlation with the TME.Five prognostic genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2)were identified through Cox regression analysis,and a prognostic model and risk score formula were developed based on these genes.Risk score formula:Risk score=BNIP3×1.70520-COL4A3×2.39815+SPRED3×1.17936+CEBPB×0.40456+PLOD2×0.24785.Kaplan-Meier survival analysis revealed that the survival probabilities of the low-risk group were significantly higher than those of the high-risk group.Furthermore,the related evaluation indexes suggested that the model exhibited strong predictive ability.Conclusion:The prognostic model,based on five TME-related genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2),could accurately assess the prognosis of CCA patients to aid in guiding clinical decisions.

Prognostic Evaluation for Oral Squamous Cell Carcinoma:A Novel Method Based on m6A Methylation Regulators

Objective:This study aimed to examine a novel method for prognostic evaluation of patients with oral squamous cell carcinoma(OSCC)based on the expression of heterogeneous nuclear ribonucleoprotein C(HNRNPC),YTH domain-binding protein 2(YTHDF2),and methyltransferase 14(METTL14).Methods:We obtained the RNA sequence and clinical information of OSCC patients from The Cancer Genome Atlas database.An optical method was established by the least absolute shrinkage and selection operator Cox regression algorithm,which was used to calculate the risk score of every sample.In addition,all samples(n=239)were classified into high-risk(n=119)and low-risk(n=120)groups,and the overall survival(OS)time and clinical characteristics were compared between groups.Moreover,bioinformatics analysis was carried out.Gene set enrichment analysis was performed to investigate the signaling pathways of HNRNPC,YTHDF2,and METTL14.Results:The two groups showed significantly different OS time,tumor grades,tumor stages,and pathologic T stages(P<0.05).The receiver operating characteristic analysis identified that our method was effective and it was more accurate than use of age,gender,tumor grade,tumor stage,pathologic T stage,and pathologic N stage in OSCC prognostic prediction.Gene set enrichment analysis revealed that HNRNPC,YTHDF2,and METTL14 were mainly associated with ubiquitin-mediated proteolysis,cell cycle,RNA degradation,and spliceosome signaling pathways.Conclusion:The method based on the expression of HNRNPC,YTHDF2,and METTL14 can predict the prognosis of patients with OSCC independently,and its prognostic value is better than that of clinicopathological characteristic indicators.

WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)is a common immunogenic malignant tumor.Although the new strategies of immunotherapy and targeted therapy have made considerable progress in the treatment of HCC,the 5-year survival rate of patients is still very low.The identification of new prognostic signatures and the exploration of the immune microenvironment are crucial to the optimization and improvement of molecular therapy strategies.We studied the potential clinical benefits of the inflammation regulator miR-93-3p and mined its target genes.Weighted gene coexpression network analysis(WGCNA),univariate and multivariate COX regression and the LASSO COX algorithm are employed to identify prognostic-related genes and construct multi-gene signature-based risk model and nomogram for survival prediction.Support vector machine(SVM)based Cibersort’s deconvolution algorithm and gene set enrichment analysis(GSEA)is used to evaluate the changes in tumor immune microenvironment and pathway differences.The study found the favorable prognostic performance of miR-93-3p and identified 389 prognostic-related target genes.The risk model based on a novel 5-gene signature(cct5,cdk4,cenpa,dtnbp1 and flvcr1)was developed and has prominent prognostic significance in the training cohort(P<0.0001)and validation cohort(P=0.0016).The nomogram constructed by combining the gene signature and the AJCC stage further improves the survival prediction ability of the gene signature.The infiltration level of multiple immune cells(especially T cells,B cells and macrophages)were positively correlated with the expression of prognostic signature.In addition,we found that gene markers of T cells and B cells is monitored and regulated by prognostic signature.Meanwhile,several GSEA pathways related to the immune system are enriched in the high-risk group.In general,we integrated the WGCNA,LASSO COX and SVM algorithms to develop and verify 5-gene signatures and nomograms related to immune infiltration to improve the survival prediction of patients.

Validated tool for early prediction of intensive care unit admission in COVID-19 patients

BACKGROUND The novel coronavirus disease 2019(COVID-19)pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.AIM To develop and validate a risk stratification tool for the early prediction of intensive care unit(ICU)admission among COVID-19 patients at hospital admission.METHODS The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital.We selected 13 of 65 baseline laboratory results to assess ICU admission risk,which were used to develop a risk prediction model with the random forest(RF)algorithm.A nomogram for the logistic regression model was built based on six selected variables.The predicted models were carefully calibrated,and the predictive performance was evaluated and compared with two previously published models.RESULTS There were 681 and 296 patients in the training and validation cohorts,respectively.The patients in the training cohort were older than those in the validation cohort(median age:63.0 vs 49.0 years,P<0.001),and the percentages of male gender were similar(49.6%vs 49.3%,P=0.958).The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio,age,lactate dehydrogenase,C-reactive protein,creatinine,D-dimer,albumin,procalcitonin,glucose,platelet,total bilirubin,lactate and creatine kinase.The accuracy,sensitivity and specificity for the RF model were 91%,88%and 93%,respectively,higher than those for the logistic regression model.The area under the receiver operating characteristic curve of our model was much better than those of two other published methods(0.90 vs 0.82 and 0.75).Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%,whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata.Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A.CONCLUSION Our model can identify ICU admission risk in COVID-19 patients at admission,who can then receive prompt care,thus improving medical resource allocation.

External anal sphincter electromyography in multiple system atrophy:implications for diagnosis,clinical correlations,and novel insights into prognosis

Multiple system atrophy is a sporadic,progressive,adult-onset,neurodegenerative disorder characte rized by autonomic dysfunction symptoms,parkinsonian features,and cerebellar signs in va rious combinations.An early diagnosis of multiple system atrophy is of utmost impo rtance for the proper prevention and management of its potentially fatal complications leading to the poor prognosis of these patients.The current diagnostic criteria incorporate several clinical red flags and magnetic resonance imaging marke rs supporting diagnosis of multiple system atrophy.Nonetheless,especially in the early disease stage,it can be challenging to differentiate multiple system atrophy from mimic disorders,in particular Parkinson’s disease.Electromyography of the external anal sphincter represents a useful neurophysiological tool for diffe rential diagnosis since it can provide indirect evidence of Onuf’s nucleus degeneration,which is a pathological hallmark of multiple system atrophy.However,the diagnostic value of external anal sphincter electromyography has been a matter of debate for three decades due to controve rsial reports in the literature.In this review,after a brief ove rview of the electrophysiological methodology,we first aimed to critically analyze the available knowledge on the diagnostic role of external anal sphincter electromyography.We discussed the conflicting evidence on the clinical correlations of neurogenic abnormalities found at external anal sphincter electro myography.Finally,we repo rted recent prognostic findings of a novel classification of electromyography patterns of the external anal sphincter that could pave the way toward the implementation of this neurophysiological technique for survival prediction in patients with multiple system atrophy.

Molecular subtypes based on DNA methylation predict prognosis in patients with renal clear cell carcinoma

Background:Tumor heterogeneity is closely related to the occurrence,progression and recurrence of renal clear cell carcinoma(ccRCC),making early diagnosis and effective treatment difficult.DNA methylation is an important regulator of gene expression and can affect tumor heterogeneity.Methods:In this study,we investigated the prognostic value of subtypes based on DNA methylation status in 506 ccRCC samples with paired clinical data from the TCGA database.Differences in DNA methylation levels were associated with differences in T,N and M categories,age,stage and prognosis.Finally,the samples were divided into the training group and the testing group according to 450K and 27K.Univariate and multivariate Cox regression analysis was used to construct the prediction model in the training group,and the model was verified and evaluated in the testing group.Results:By univariate Cox regression analysis,21,122 methylation sites and 6,775 CpG sites were identified as potential DNA methylation biomarkers for overall survival of ccRCC patients(P<0.05).3,050 CpG sites independently associated with prognosis were identified with T,N,M,stage and age as covariables.Consensus cluster of 3,050 potential prognostic methylation sites was used to identify different DNA methylation subsets of ccRCC for prognostic purposes.We performed functional enrichment analysis on these 3,640 genes and identified 75 significantly enriched pathways(P<0.05).We then researched the expression of methylated genes in subgroups.Verifing with the training set,suggesting that DNA methylation levels generally reflect the expression of these genes.Conclusion:Based on TCGA database and a series of bioinformatics methods,We identified prognostic specific methylation sites and established prognostic prediction models for ccRCC patients.This model helps to identify novel biomarkers,precision drug targets and disease molecular subtypes in patients with ccRCC.Therefore,this model may be useful in predicting the prognosis,clinical diagnosis and management of patients with different epigenetic subtypes of ccRCC.

Epigenetic regulation of prostate cancer: the theories and the clinical implications

Epigenetics is the main mechanism that controls transcription of specific genes with no changes in the underlying DNA sequences. Epigenetic alterations lead to abnormal gene expression patterns that contribute to carcinogenesis and persist throughout disease progression. Because of the reversible nature, epigenetic modifications emerge as promising anticancer drug targets. Several compounds have been developed to reverse the aberrant activities of enzymes involved in epigenetic regulation, and some of them show encouraging results in both preclinical and clinical studies. In this article, we comprehensively review the up-to-date roles of epigenetics in the development and progression of prostate cancer. We especially focus on three epigenetic mechanisms: DNA methylation, histone modifications, and noncoding RNAs. We elaborate on current models/theories that explain the necessity of these epigenetic programs in driving the malignant phenotypes of prostate cancer cells. In particular, we elucidate how certain epigenetic regulators crosstalk with critical biological pathways, such as androgen receptor (AR) signaling, and how the cooperation dynamically controls cancer-oriented transcriptional profiles. Restoration of a “normal” epigenetic landscape holds promise as a cure for prostate cancer, so we concluded by highlighting particular epigenetic modifications as diagnostic and prognostic biomarkers or new therapeutic targets for treatment of the disease.