Comprehensive analysis of autophagy‐related prognostic genes in breast cancer

Accumulating evidence revealed that autophagy played vital roles in breast cancer (BC) progression. Thus, the aim of this study was to investigate the prognostic value of autophagy‐related genes (ARGs) and develop a ARG‐based model to evaluate 5‐year overall survival (OS) in BC patients. We acquired ARG expression profiling in a large BC cohort (N = 1007) from The Cancer Genome Atlas (TCGA) database. The correlation between ARGs and OS was confirmed by the LASSO and Cox regression analyses. A predictive model was established based on independent prognostic variables. Thus, time‐dependent receiver operating curve (ROC), calibration plot, decision curve and subgroup analysis were conducted to determine the predictive performance of ARG‐based model. Four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were identified using the LASSO and multivariate Cox regression analyses. A ARG‐based model was constructed based on the four ARGs and two clinicopathological risk factors (age and TNM stage), dividing patients into high‐risk and low‐risk groups. The 5‐year OS of patients in the low‐risk group was higher than that in the high‐risk group (P < 0.0001). Time‐dependent ROC at 5 years indicated that the four ARG–based tool had better prognostic accuracy than TNM stage in the training cohort (AUC: 0.731 vs 0.640, P < 0.01) and validation cohort (AUC: 0.804 vs 0.671, P < 0.01). The mutation frequencies of the four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were 0.9%, 2.8%, 8% and 1.3%, respectively. We built and verified a novel four ARG–based nomogram, a credible approach to predict 5‐year OS in BC, which can assist oncologists in determining effective therapeutic strategies.     

A 3‐miRNA signature predicts survival of patients with hypopharyngeal squamous cell carcinoma after post‐operative radiotherapy

Since the prognosis of hypopharyngeal squamous cell carcinoma (HSCC) remains poor, identification of miRNA as a potential prognostic biomarker for HSCC may help improve personalized therapy. In the 2 cohorts with a total of 511 patients with HSCC (discovery: N = 372 and validation: N = 139) after post‐operative radiotherapy, we used miRNA microarray and qRT‐PCR to screen out the significant miRNAs which might predict survival. Associations of miRNAs and the signature score of these miRNAs with survival were performed by Kaplan‐Meier survival analysis and multivariate Cox hazard model. Among 9 candidate, miRNAs, miR‐200a‐3p, miR‐30b‐5p, miR‐3161, miR‐3605‐5p, miR‐378b and miR‐4451 were up‐regulated, while miR‐200c‐3p, miR‐429 and miR‐4701 were down‐regulated after validation. Moreover, the patients with high expression of miR‐200a‐3p, miR‐30b‐5p and miR‐4451 had significantly worse overall survival (OS) and disease‐specific survival (DSS) than did those with low expression (log‐rank P < .05). Patients with a high‐risk score had significant worse OS and DSS than those with low‐risk score. Finally, after adjusting for other important prognostic confounders, patients with high expression of miR‐200a‐3p, miR‐30b‐5p and miR‐4451 had significantly high risk of overall death and death owing to HSCC and patients with a high‐risk score has approximately 2‐fold increased risk in overall death and death owing to HSCC compared with those with a low‐risk score. These findings indicated that the 3‐miRNA‐based signature may be a novel independent prognostic biomarker for patients given surgery and post‐operative radiotherapy, supporting that these miRNAs may jointly predict survival of HSCC.     

A five‐microRNA signature for individualized prognosis evaluation and radiotherapy guidance in patients with diffuse lower‐grade glioma

Despite the prognostic value of IDH and other gene mutations found in diffuse glioma, markers that judge individual prognosis of patients with diffuse lower‐grade glioma (LGG) are still lacking. This study aims to develop an expression‐based microRNA signature to provide survival and radiotherapeutic response prediction for LGG patients. MicroRNA expression profiles and relevant clinical information of LGG patients were downloaded from The Cancer Genome Atlas (TCGA; the training group) and the Chinese Glioma Genome Atlas (CGGA; the test group). Cox regression analysis, random survival forests‐variable hunting (RSFVH) screening and receiver operating characteristic (ROC) were used to identify the prognostic microRNA signature. ROC and TimeROC curves were plotted to compare the predictive ability of IDH mutation and the signature. Stratification analysis was conducted in patients with radiotherapy information. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore the biological function of the signature. We identified a five‐microRNA signature that can classify patients into low‐risk or high‐risk group with significantly different survival in the training and test datasets (P < 0.001). The five‐microRNA signature was proved to be superior to IDH mutation in survival prediction (AUCtraining = 0.688 vs 0.607). Stratification analysis found the signature could further divide patients after radiotherapy into two risk groups. GO and KEGG analyses revealed that microRNAs from the prognostic signature were mainly enriched in cancer‐associated pathways. The newly discovered five‐microRNA signature could predict survival and radiotherapeutic response of LGG patients based on individual microRNA expression.     

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PurposeTo establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.MethodsThis study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.ResultsA total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1–3 years AUC:0.774–0.837; validation cohort 1–3 years AUC:0.754–0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.ConclusionsThe CR model could predict the OS of the HCC patients after hepatectomy.     

Inference of patient‐specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient‐specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient‐specific subpathway activity profiles using a greedy search algorithm. A four‐subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high‐risk and low‐risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e‐13) and test set (median survival of 75 vs 101 months, P = 4.17e‐5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four‐subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four‐subpathway signature may be a useful biomarker for breast cancer prognosis.     

Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.     

Epigenetic dysregulation of ID4 predicts disease progression and treatment outcome in myeloid malignancies

Promoter hypermethylation‐mediated inactivation of ID4 plays a crucial role in the development of solid tumours. This study aimed to investigate ID4 methylation and its clinical relevance in myeloid malignancies. ID4 hypermethylation was associated with higher IPSS scores, but was not an independent prognostic biomarker affecting overall survival (OS) in myelodysplastic syndrome (MDS). However, ID4 hypermethylation correlated with shorter OS and leukaemia‐free survival (LFS) time and acted as an independent risk factor affecting OS in acute myeloid leukaemia (AML). Moreover, ID4 methylation was significantly decreased in the follow‐up paired AML patients who achieved complete remission (CR) after induction therapy. Importantly, ID4 methylation was increased during MDS progression to AML and chronic phase (CP) progression to blast crisis (BC) in chronic myeloid leukaemia (CML). Epigenetic studies showed that ID4 methylation might be one of the mechanisms silencing ID4 expression in myeloid leukaemia. Functional studies in vitro showed that restoration of ID4 expression could inhibit cell proliferation and promote apoptosis in both K562 and HL60 cells. These findings indicate that ID4 acts as a tumour suppressor in myeloid malignancies, and ID4 methylation is a potential biomarker in predicting disease progression and treatment outcome.     

Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome

Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814-7.630) and 2.047 (1.013-4.138) in the training cohort and validation cohort, respectively. The AUC of 3-year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high-risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.     

Prognostic role of Wnt and Fzd gene families in acute myeloid leukaemia

Wnt-Fzd signalling pathway plays a critical role in acute myeloid leukaemia (AML) progression and oncogenicity. There is no study to investigate the prognostic value of Wnt and Fzd gene families in AML. Our study screened 84 AML patients receiving chemotherapy only and 71 also undergoing allogeneic haematopoietic stem cell transplantation (allo-HSCT) from the Cancer Genome Atlas (TCGA) database. We found that some Wnt and Fzd genes had significant positive correlations. The expression levels of Fzd gene family were independent of survival in AML patients. In the chemotherapy group, AML patients with high Wnt2B or Wnt11 expression had significantly shorter event-free survival (EFS) and overall survival (OS); high Wnt10A expressers had significantly longer OS than the low expressers (all P < .05), whereas, in the allo-HSCT group, the expression levels of Wnt gene family were independent of survival. We further found that high expression of Wnt10A and Wnt11 had independent prognostic value, and the patients with high Wnt10A and low Wnt11 expression had the longest EFS and OS in the chemotherapy group. Pathway enrichment analysis showed that genes related to Wnt10A, Wnt11 and Wnt 2B were mainly enriched in ‘cell morphogenesis involved in differentiation’, ‘haematopoietic cell lineage’, ‘platelet activation, signalling and aggregation’ and ‘mitochondrial RNA metabolic process’ signalling pathways. Our results indicate that high Wnt2B and Wnt11 expression predict poor prognosis, and high Wnt10A expression predicts favourable prognosis in AML, but their prognostic effects could be neutralized by allo-HSCT. Combined Wnt10A and Wnt11 may be a novel prognostic marker in AML.     

An estrogen receptor (ER)‐related signature in predicting prognosis of ER‐positive breast cancer following endocrine treatment

Quite a few estrogen receptor (ER)‐positive breast cancer patients receiving endocrine therapy are at risk of disease recurrence and death. ER‐related genes are involved in the progression and chemoresistance of breast cancer. In this study, we identified an ER‐related gene signature that can predict the prognosis of ER‐positive breast cancer patient receiving endocrine therapy. We collected RNA expression profiling from Gene Expression Omnibus database. An ER‐related signature was developed to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. ROC analysis indicated that this signature exhibited good diagnostic efficiency for the 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that the ER‐related signature was an independent risk factor when adjusting for several clinical signatures. The prognostic value of this signature was validated in the validation sets. In addition, a nomogram was built and the calibration plots analysis indicated the good performance of this nomogram. In conclusion, combining with ER status, our results demonstrated that the ER‐related prognostic signature is a promising method for predicting the prognosis of ER‐positive breast cancer patients receiving endocrine therapy.     

Prognostic Value of E-cadherin-, CD44-, and MSH2-associated Nomograms in Patients With Stage II and III Colorectal Cancer

BACKGROUND: To evaluate the prognostic value of E-cadherin, CD44, and MSH2 expression for colorectal cancer (CRC) and construct nomograms that can predict prognosis. METHODS: We retrospectively analyzed the expression of E-cadherin, CD44, and MSH2 in 223 paraffin-embedded stage II and III CRC specimens using immunohistochemistry in the training cohort. Their prognostic values were assessed using Kaplan–Meier curves and univariate and multivariate COX regression models. Moreover, a number of risk factors were used to form nomograms to evaluate survival, and Harrell's concordance index (C-index) was used to evaluate the predictive accuracy. Further validation of the nomograms was performed in an independent cohort of 115 cases. RESULTS: Low E-cadherin expression and low CD44 expression were significantly associated with diminished overall survival (OS) and disease-free survival (DFS) in stage II and III CRC patients and patients with negative MSH2 expression had better clinical outcomes. Moreover, the multivariate COX analysis identified E-cadherin, CD44 and MSH2 expression as independent prognostic factors for DFS and OS. Using these three markers and three clinicopathological risk variables, two nomograms were constructed and externally validated for predicting OS and DFS (C-index: training cohort, 0.779 (95% CI 0.722–0.835) and 0.771 (0.720–0.822), respectively; validation cohort, 0.773 (0.709–0.837) and 0.670 (0.594–0.747), respectively). CONCLUSION: The expression levels of E-cadherin, CD44 and MSH2 were independent prognostic factors for stage II and III CRC patients. By incorporating clinicopathological features and these biomarkers, we have established two nomograms that could be used to make individualized predictions for OS and DFS.     

Prognostic significance of gamma‐glutamyl transpeptidase to albumin ratio in patients with intrahepatic cholangiocarcinoma after hepatectomy

Inflammation has been reported to play an important role in tumour progression and prognosis. In this study, we evaluated the prognostic significance of γ‐glutamyl transpeptidase (GGT) to albumin ratio (GAR) in patients with intrahepatic cholangiocarcinoma (ICC) after hepatectomy. We retrospectively analysed 650 ICC patients underwent hepatectomy at three Chinese medical centres between January 2009 and September 2017. Patients were classified into derivation cohort (n = 509) and validation cohort (n = 141). Receiver operating characteristic (ROC) curve was used to determine the optimal cut‐off value for GAR. Survival curve and cox regression analysis were applied to assess the prognostic power of GAR. The prognostic accuracy of GAR was compared with other variables by ROC curve. The optimal cut‐off value for GAR was 1.3655. Preoperative high GAR was closely related to tumour number, lymph node invasion and GGT. The survival curve of derivation and validation cohorts showed that patients in the high GAR group had significantly shorter overall survival (OS) and disease‐free survival (DFS) than patients in the low GAR group. Multivariate analysis in the derivation cohort confirmed that GAR was an independent prognostic factor for survival outcomes. Moreover, the ROC curve revealed that GAR had better predictive accuracy than other variables. High GAR predicted poor OS and DFS in ICC patients after hepatectomy. GAR may be a novel, simple and effective prognostic marker for ICC patients.     

DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management

Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13‐DNA methylation signature was identified to be crucially relevant to the relapse‐free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan‐Meier analysis indicated that the 13‐DNA methylation signature could significantly distinguish the high‐ and low‐risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13‐DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation‐associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.     

急性髓系白血病铁死亡相关基因筛选及预后模型的建立

基于急性髓系白血病(Acute Myeloid Leukemia,AML)临床大数据及多组学数据库探讨铁死亡相关基因在AML中的作用,并建立铁死亡基因表达相关预后模型。整合TCGA数据库中151例AML患者和GTEx数据库中337例正常人外周血的临床和转录组数据。将Wilcoxon检验和单因素Cox分析结果取交集,筛选出预后相关差异表达基因(Differential Expression Genes, DEGs),使用Lasso回归建立基因标志物预后模型,利用受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)评价预测价值,Kaplan-Meier法进行生存分析,对AML患者临床数据进行单因素和多因素Cox回归分析,使用差异基因表达分析等方法比较高、低风险患者间的组学差异,最后,利用BeatAML数据库对基因标志物进行验证。将差异基因表达分析和单因素分析结果取交集,得到13个预后相关DEGs。构建了8个基因标志物的预后评分模型,并将患者分为高、低风险两组;ROC曲线分析证实了模型良好的预测性能;生存分析提示高、低风险组患者的生存率具有显著差异;单因素分析显示年龄和风险评分与患者整体生存显著相关,多因素分析显示,年龄和风险评分是独立预后指标。在2个风险组之间筛选出384个DEGs,GO富集分析结果显示,富集的基因大多与中性粒细胞和白细胞的趋化与迁移等免疫相关分子和通路显著相关,KEGG富集通路主要与TNF信号通路、细胞因子与细胞因子受体相互作用相关。BeatAML数据库验证结果显示,5个基因与预后显著相关。铁死亡相关基因在AML中显著表达,且高风险患者预后较差,该研究对AML铁死亡相关潜在生物标志物的发现和应用奠定了一定的基础。     

Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis-related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta-analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis-related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC-CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan-cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well-predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.     

Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and the 5‐year survival rate was only 7.7%. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. Long non‐coding RNA (lncRNA) expression profiles as potential cancer prognostic biomarkers play critical roles in development of tumorigenesis and metastasis of cancer. However, lncRNA signatures in predicting the survival of a patient with PDAC remain unknown. In the current study, we try to identify potential lncRNA biomarkers and their prognostic values in PDAC. LncRNAs expression profiles and corresponding clinical information for 182 cases with PDAC were acquired from The Cancer Genome Atlas (TCGA). A total of 14 470 lncRNA were identified in the cohort, and 175 PDAC patients had clinical variables. We obtained 108 differential expressed lncRNA via R packages. Univariate and multivariate Cox proportional hazards regression, lasso regression was performed to screen the potential prognostic lncRNA. Five lncRNAs have been recognized to significantly correlate with OS. We established a linear prognostic model of five lncRNA (C9orf139, MIR600HG, RP5‐965G21.4, RP11‐436K8.1, and CTC‐327F10.4) and divided patients into high‐ and low‐risk group according to the prognostic index. The five lncRNAs played independent prognostic biomarkers of OS of PDAC patients and the AUC of the ROC curve for the five lncRNAs signatures prediction 5‐year survival was 0.742. In addition, targeted genes of MIR600HG, C9orf139, and CTC‐327F10.4 were explored and functional enrichment was also conducted. These results suggested that this five‐lncRNAs signature could act as potential prognostic biomarkers in the prediction of PDAC patient's survival.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.