Bladder cancer survival nomogram: Development and validation of a prediction tool, using the SEER and TCGA databases

Nomogram for predicting postoperative ileus after radical cystectomy and urinary diversion: a retrospective single-center study

OBJECTIVE

To predict the incidence of postoperative ileus in bladder cancer patients after radical cystectomy.

METHODS

We retrospectively analyzed the perioperative data of 452 bladder cancer patients who underwent radical cystectomy with urinary diversion at the Second Hospital of Tianjin Medical University between 2016 and 2021. Univariate and multivariate logistic regression were used to identify the risk factors for postoperative ileus. Finally, a nomogram model was established and verified based on the independent risk factors.

RESULTS

Our study revealed that 96 patients (21.2%) developed postoperative ileus. Using multivariate logistic regression analysis, we found that the independent risk factors for postoperative ileus after radical cystectomy included age > 65.0 years, high or low body mass index, constipation, hypoalbuminemia, and operative time. We established a nomogram prediction model based on these independent risk factors. Validation by calibration curves, concordance index, and decision curve analysis showed a strong correlation between predicted and actual probabilities of occurrence.

CONCLUSION

Our nomogram prediction model provides surgeons with a simple tool to predict the incidence of postoperative ileus in bladder cancer patients undergoing radical cystectomy.

A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings

Background

Hepatocellular carcinoma (HCC) is an invasive malignant tumor, and pyroptosis makes an important contribution to the pathology and progression of liver cancer. Many prognostic models have been proposed for HCC based on the quantitative expression level of candidate genes, which are unsuitable for clinical application due to their vulnerability against experimental batch effects. The aim of this study was to develop a novel pyroptosis-related long non-coding RNA (lncRNA)-based prognostic index (PLPI) for HCC based on relative expression orderings (REOs).

Methods

Firstly, the pyroptosis-related lncRNAs were identified through the Wilcoxon rank-sum test and gene co-expression analyses. Then, the novel prognostic model PLPI was constructed by pyroptosis-related lncRNA pairs, which were identified by multiple machine learning algorithms. Gene set enrichment, somatic mutation, and drug sensitivity analyses were conducted to measure the differences between high- and low-risk patients. Multiple immune analyses were used to explore the association between PLPI and the immunological microenvironment.

Results

In this study, a novel prognostic model PLPI based on 10 pyroptosis-related lncRNA pairs was constructed, which was proven to be an independent prognostic risk factor. The receiver operating characteristic (ROC) curves showed that the model had a good prognostic ability in the training, testing, and external set, respectively [5-year area under the curve (AUC) =0.73, 5-year AUC =0.81, 4-year AUC =0.79]. The results of survival, somatic mutation, and immune analyses showed that the patients in the low-risk group had a better prognosis, lower rates of somatic mutation, and better immune cell infiltration. Personalized chemotherapeutic drugs were also identified for the patients with HCC.

Conclusions

The novel PLPI not only greatly predicted the prognosis of patients with HCC but could also offer novel ideas and approaches for the therapeutic management of HCC.

Identification of novel lactate metabolism-related lncRNAs with prognostic value for bladder cancer

Background: Bladder cancer (BCA) has high recurrence and metastasis rates, and current treatment options show limited efficacy and significant adverse effects. It is crucial to find diagnostic markers and therapeutic targets with clinical value. This study aimed to identify lactate metabolism-related lncRNAs (LM_lncRNAs) to establish a model for evaluating bladder cancer prognosis. Method: A risk model consisting of lactate metabolism-related lncRNAs was developed to forecast bladder cancer patient prognosis using The Cancer Genome Atlas (TCGA) database. Kaplan‒Meier survival analysis, receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) were used to evaluate the reliability of risk grouping for predictive analysis of bladder cancer patients. The results were also validated in the validation set. Chemotherapeutic agents sensitive to lactate metabolism were assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: As an independent prognostic factor for patients, lactate metabolism-related lncRNAs can be used as a nomogram chart that predicts overall survival time (OS). There were significant differences in survival rates between the high-risk and low-risk groups based on the Kaplan‒Meier survival curve. decision curve analysis and receiver operating characteristic curve analysis confirmed its good predictive capacity. As a result, 22 chemotherapeutic agents were predicted to positively affect the high-risk group. Conclusion: An lactate metabolism-related lncRNA prediction model was proposed to predict the prognosis for patients with bladder cancer and chemotherapeutic drug sensitivity in high-risk groups, which provided a new idea for the prognostic evaluation of the clinical treatment of bladder cancer.

Use of machine learning to predict bladder cancer survival outcomes: a systematic literature review

INTRODUCTION

The objective of this systematic review is to summarize the use of machine learning (ML) in predicting overall survival (OS) in patients with bladder cancer.

METHODS

Search terms for bladder cancer, ML algorithms, and mortality were used to identify studies in PubMed and Web of Science as of February 2022. Notable inclusion/exclusion criteria contained the inclusion of studies that utilized patient-level datasets and exclusion of primary gene expression-related dataset studies. Study quality and bias were assessed using the International Journal of Medical Informatics (IJMEDI) checklist.

RESULTS

Of the 14 included studies, the most common algorithms were artificial neural networks (n = 8) and logistic regression (n = 4). Nine articles described missing data handling, with five articles removing patients with missing data entirely. With respect to feature selection, the most common sociodemographic variables were age (n = 9), gender (n = 9), and smoking status (n = 3), with clinical variables most commonly including tumor stage (n = 8), grade (n = 7), and lymph node involvement (n = 6). Most studies (n = 10) were of medium IJMEDI quality, with common areas of improvement being the descriptions of data preparation and deployment.

CONCLUSIONS

ML holds promise for optimizing bladder cancer care through accurate OS predictions, but challenges related to data processing, feature selection, and data source quality must be resolved to develop robust models. While this review is limited by its inability to compare models across studies, this systematic review will inform decision-making by various stakeholders to improve understanding of ML-based OS prediction in bladder cancer and foster interpretability of future models.

Development and validation of a prognostic nomogram for neuroendocrine prostate cancer, based on the SEER database

Background

The tumor biology of neuroendocrine prostate cancer (NEPC) is different from that of ordinary prostate cancer, herefore, existing clinical prognosis models for prostate cancer patients are unsuitable for NEPC. The specialized individual situation assessment and clinical decision-making tools for NEPC patients are urgently needed. This study aimed to develop a valid NEPC prognostic nomogram and risk stratification model to predict risk associated with patient outcomes.

Methods

We collected 340 de-novo NEPC patients from the SEER database, and randomly selected 240 of them as the training set and the remaining 100 as the validation set. Cox regression model was used to screen for risk factors affecting overall survival (OS) and cancer-specific survival (CSS) and construct a corresponding nomogram. The receiver operating characteristic (ROC) curves, calibration curves, C-indexes, and decision curve analysis (DCA) curves are used to verify and calibrate nomograms.

Results

NEPC prognosis nomograms were constructed by integrating independent risk factors. The C-indexes, ROC curves, calibration curves, and DCA curves revealed excellent prediction accuracy of the prognostic nomogram. Furthermore, we demonstrated that NEPC patients in the high-risk group had significantly lower OS and CSS than those in the low-risk group with risk scores calculated from nomograms.

Conclusions

The nomogram established in this research has the potential to be applied to the clinic to evaluate the prognosis of NEPC patients and support corresponding clinical decision-making.

Prognostic risk factor analysis and nomogram construction for primary liver cancer in elderly patients based on SEER database

OBJECTIVE

To evaluate the risk factors and construct a nomogram model for the prognosis of primary liver cancer in the elderly based on the data from the US SEER database.

METHODS

The latest data of patients with primary liver cancer were extracted from the SEER database using SEER*STAT software, and the required variables were included. The data were screened and then divided into a training cohort and a validation cohort. A nomogram model was constructed by screening the variables through univariate and multivariate Cox analysis. The C-Index, ROC and calibration curves were used for model evaluation.

RESULTS

A total of 10 824 eligible cases from 2004 to 2017 were extracted, among which, 7757 cases were included in the training cohort and 3247 in the validation cohort. The C-Index of the model was 0.747 (in the training cohort) and 0.773 (in the validation cohort). The 3-year area under the curve (AUCs) of the training and the validation cohorts were 0.760 and 0.750, and the 5-year AUCs of the two cohorts were 0.761 and 0.748. The calibration curves showed an ideal calibration of the constructed model.

CONCLUSIONS

The nomogram model constructed followed by Cox regression analysis showed moderate calibration and discrimination property, and can provide reference to a certain extent for furture clinical application of primary liver cancer in the elderly.

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Childhood with Clear Cell Sarcoma of the Kidney Based on the SEER Database

Objective. Clear cell sarcoma of the kidney (CCSK) is a lethal pediatric renal malignancy with poor prognosis. A prognostic nomogram needs to be established for overall survival (OS) prediction of patients with CCSK. Methods. Eligible 2588 CCSK patients (age 0–19) diagnosed between 2000 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomized into training and validation cohorts (7 : 3). Independent prognostic factors were identified by univariate and multifactorial Cox regression analyses and used to construct a nomogram. Receiver operating characteristics (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to validate the nomogram. Moreover, a risk classification system was established based on the risk scores of the nomogram. Results. Cox analyses revealed that age, combined stage, and origin were most significant prognostic factors. Based on these prognostic factors, a nomogram was established for predicting 3- and 5-year OS of patients with CCSK. The area under the ROC curve (AUC) of 3- and 5-year OS was 0.733 and 0.728 in the training cohort, corresponding to 0.69 and 0.674 in the validation cohort. The C-index of calibration curves in the training and validation cohorts was 0.724 and 0.686. DCAs indicated the clinical utility of this nomogram. A risk classification system stratified CCSK patients into three different risk cohorts. The OS time of low-, intermediate-, and high-risk patients was 76, 68, and 65 months in the training cohort, corresponding to 69.5, 66, and 72 months in the validation cohort. Conclusion. A nomogram-based risk classification system has high accuracy for the prognostic prediction of CCSK.

HYAL3 as a potential novel marker of BLCA patient prognosis

BACKGROUND

It has been previously demonstrated that hyaluronan (HA) potentially regulates the initiation and propagation of bladder cancer (BLCA). HYAL3 encodes hyaluronidase and is a potential therapeutic target for BLCA. We aimed to explore the role that HYAL3 plays in BLCA pathogenesis.

METHODS

HYAL3 expression in BLCA specimens was analyzed using The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) cohort as well as confirmed in cell lines and The Human Protein Atlas. Then, associations between HYAL3 expression and clinicopathological data were analyzed using survival curves and receiver-operating characteristic (ROC) curves. The functions of HYAL3 were further dissected using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and the protein-protein interaction network. Finally, we harnessed the Tumor IMmune Estimation Resource and Gene Expression Profiling Interactive Analysis to obtain correlations between HYAL3 expression, infiltrating immunocytes, and the corresponding immune marker sets.

RESULTS

HYAL3 expression varied greatly between many types of cancers. In addition, a higher HYAL3 expression level predicted a poor overall survival (OS) in both TCGA-BLCA and GEO gene chips (P < 0.05). HYAL3 also exhibited an acceptable diagnostic ability for the pathological stage of BLCA (area under the receiver-operating characteristic curve = 0.769). Furthermore, HYAL3 acted as an independent prognostic factor in BLCA patients and correlated with the infiltration of various types of immunocytes, including B cells, CD8⁺ T cells, cytotoxic cells, T follicular helper cells, and T helper (Th) 2 cells.

CONCLUSION

HYAL3 might serve as a potential biomarker for predicting poor OS in BLCA patients and correlated with immunocyte infiltration in BLCA.

Beta-blocker use and urothelial bladder cancer survival: a Swedish register-based cohort study

BACKGROUND

Recent observational studies linked β-adrenergic receptor blocker use with improved survival in patients with several cancer types, but there is no information on the potential effects of β-blockers in patients with bladder cancer. Literature from pre-clinical studies is also limited, but urothelial cancer can exhibit significant overexpression of β-adrenergic receptors relative to normal urothelial tissue, suggesting that urothelial cancer may benefit from β-blockade therapy. We thus aimed to explore the possible association between β-blocker use and bladder cancer-specific mortality (BCSM) among patients with urothelial bladder cancer.

MATERIAL AND METHODS

Patients diagnosed during 2006-2014 and identified from the Swedish Cancer Register (n = 16,669) were followed until 31 December 2015. Cox regression was used to evaluate the association of β-blockers dispensed within 90 days prior to cancer diagnosis with BCSM (primary outcome) and all-cause mortality, while controlling for socio-demographic factors, tumor characteristics, comorbidity, other medications and surgical procedures. Hazard ratios (HR) with 95% confidence intervals (CI) were reported.

RESULTS

Overall, β-blocker use was associated with lower BCSM [HR 0.88 (95%CI 0.81-0.96)]. Especially use of nonselective β-blockers showed a clear inverse association in comparison with both nonuse [0.66 (0.50-0.86)] and use of other antihypertensive medications [0.72 (0.54-0.95)]. The inverse association was most pronounced among patients with locally advanced/metastatic disease: [0.35 (0.18-0.68)]. A lower-magnitude inverse association was observed for selective β-blocker use [0.91 (0.83-0.99)]. Largely similar inverse associations were observed for hydrophilic [0.82 (0.70-0.95)] and lipophilic [0.91 (0.83-1.00)] β-blocker use.

CONCLUSION

β-blocker use, particularly of the nonselective type, was associated with lower BCSM, especially in patients with locally advanced/metastatic urothelial bladder cancer.

Nomogram for Predicting Overall Survival in Acral Lentiginous Melanoma: A Population-based Study

Background

The objective of this study was to establish a nomogram for predicting the overall survival (OS) of patients with acral lentiginous melanoma (ALM).

Materials and Methods

The study sample was selected from 1785 patients diagnosed with ALM from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database, and R software was used to divide patients into the training cohort and validation cohort at a ratio of 7: 3. Stepwise selection method in the Cox regression model was used in the training cohort to select predictive variables to construct the nomogram, and model validation parameters were used in the validation cohort to evaluate the performance of the nomogram.

Results

The nomogram showed that age at diagnosis had the greatest impact on OS in patients with ALM, followed by AJCC stage, surgical treatment, SEER stage, sex, race, and marital status. The index of concordance, area under the receiver operating characteristic curve, calibration plots, net reclassification improvement, integrated discrimination improvement, and decision curve analysis demonstrate the good performance of this nomogram.

Conclusion

The prognostic value of the nomogram is superior to that of the AJCC staging system alone, and it helps clinicians to better predict 3-, 5-, and 8-year OS in patients with ALM.

Comparison of analgesic modalities for patients undergoing midline laparotomy: a systematic review and network meta-analysis

BACKGROUND

Midline laparotomy is associated with severe pain. Epidural analgesia has been the established standard, but multiple alternative regional anesthesia modalities are now available. We aimed to compare continuous and single-shot regional anesthesia techniques in this systematic review and network meta-analysis.

METHODS

We included randomized controlled trials on adults who were scheduled for laparotomy with solely a midline incision under general anesthesia and received neuraxial or regional anesthesia for pain. Network meta-analysis was performed with a frequentist method, and continuous and dichotomous outcomes were presented as mean differences and odds ratios, respectively, with 95% confidence intervals. The quality of evidence was rated with the  grading of recommendations, assessment, development, and evaluation system.

RESULTS

Overall, 36 trials with 2,056 patients were included. None of the trials assessed erector spinae plane or quadratus lumborum block, and rectus sheath blocks and transversus abdominis plane blocks were combined into abdominal wall blocks (AWB). For the co-primary outcome of pain score at rest at 24 hr, with a minimal clinically important difference (MCID) of 1, epidural was clinically superior to control and single-shot AWB; epidural was statistically but not clinically superior to continuous wound infiltration (WI); and no statistical or clinical difference was found between control and single-shot AWB. For the co-primary outcome of cumulative morphine consumption at 24 hr, with a MCID of 10 mg, epidural and continuous AWB were clinically superior to control; epidural was clinically superior to continuous WI, single-shot AWB, single-shot WI, and spinal; and continuous AWB was clinically superior to single-shot AWB. The quality of evidence was low in view of serious limitations and imprecision. Other results of importance included: single-shot AWB did not provide clinically relevant analgesic benefit beyond two hr; continuous WI was clinically superior to single-shot WI by 8-12 hr; and clinical equivalence was found between epidural, continuous AWB, and continuous WI for the pain score at rest, and epidural and continuous WI for the cumulative morphine consumption at 48 hr.

CONCLUSIONS

Single-shot AWB were only clinically effective for analgesia in the early postoperative period. Continuous regional anesthesia modalities increased the duration of analgesia relative to their single-shot counterparts. Epidural analgesia remained clinically superior to alternative continuous regional anesthesia techniques for the first 24 hr, but reached equivalence, at least with respect to static pain, with continuous AWB and WI by 48 hr.

TRIAL REGISTRATION

PROSPERO (CRD42021238916); registered 25 February 2021.

Development of Nomograms for Predicting Lymph Node Metastasis and Distant Metastasis in Newly Diagnosed T1-2 Non-Small Cell Lung Cancer: A Population-Based Analysis

Background

For different lymph node metastasis (LNM) and distant metastasis (DM), the diagnosis, treatment and prognosis of T1-2 non-small cell lung cancer (NSCLC) are different. It is essential to figure out the risk factors and establish prediction models related to LNM and DM.

Methods

Based on the surveillance, epidemiology, and end results (SEER) database from 1973 to 2015, a total of 43,156 eligible T1-2 NSCLC patients were enrolled in the retrospective study. Logistic regression analysis was used to determine the risk factors of LNM and DM. Risk factors were applied to construct the nomograms of LNM and DM. The predictive nomograms were discriminated against and evaluated by Concordance index (C-index) and calibration plots, respectively. Decision curve analysis (DCAs) was accepted to measure the clinical application of the nomogram. Cumulative incidence function (CIF) was performed further to detect the prognostic role of LNM and DM in NSCLC-specific death (NCSD).

Results

Eight factors (age at diagnosis, race, sex, histology, T-stage, marital status, tumor size, and grade) were significant in predicting LNM and nine factors (race, sex, histology, T-stage, N-stage, marital status, tumor size, grade, and laterality) were important in predicting DM(all, P< 0.05). The calibration curves displayed that the prediction nomograms were effective and discriminative, of which the C-index were 0.723 and 0.808. The DCAs and clinical impact curves exhibited that the prediction nomograms were clinically effective.

Conclusions

The newly constructed nomograms can objectively and accurately predict LNM and DM in patients suffering from T1-2 NSCLC, which may help clinicians make individual clinical decisions before clinical management.

Rab6c is a new target of miR‑218 that can promote the progression of bladder cancer

Bladder cancer has high morbidity and mortality rates among the male genitourinary system tumor types. MicroRNA‑218 (miR‑218) is associated with the development of a variety of cancer types, including bladder cancer. Rab6c is a member of the Rab family and is involved in drug resistance in MCF7 cells. The aim of the present study was to clarify the relationship between Rab6c and miR‑218 in bladder cancer cell lines. In this study, the expression levels of miR‑218 and Rab6c were evaluated via reverse transcription‑quantitative PCR and western blotting, respectively. The association between Rab6c and miR‑218 was recognized via TargetScan analysis and dual luciferase reporter gene detection. Cell proliferation was analyzed using Cell Counting Kit‑8 and colony formation assays, and the invasive ability was measured via Transwell assays. Rab6c was highly expressed in bladder cancer, while miR‑218 had abnormally low expression in bladder cancer. In addition, there was a mutual regulation between Rab6c and miR‑218 in bladder cancer. It was found that overexpression of Rab6c significantly enhanced the proliferation, colony formation and invasion of T24 and EJ cells. Furthermore, miR‑218 overexpression blocked the promoting effects of Rab6c on the malignant behavior of bladder cancer cells. Thus, Rab6c promotes the proliferation and invasion of bladder cancer cells, while miR‑218 has the opposite effect, which may provide a novel insight for the treatment of bladder cancer.

Construction of a novel mRNA-signature prediction model for prognosis of bladder cancer based on a statistical analysis

Background

Bladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer.

Methods

The RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes.

Results

SORBS2, GPC2, SETBP1, FGF11, APOL1, and H1–2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer.

Conclusions

In this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08611-z.

A Novel Ferroptosis-Related Gene Model for Overall Survival Predictions of Bladder Urothelial Carcinoma Patients

Introduction

Bladder cancer is the most common urinary tract malignancy, and 90% of bladder tumors are urothelial cell carcinomas. Ferroptosis is a new form of cell death discovered in recent years, which is an iron-dependent form of cell death characterized by the lethal intracellular accumulation of lipid-based reactive oxygen species. Ferroptosis is considered to be a double-edged sword for cancer and cancer therapy.

Materials and Methods

In the current study, expression profiles of bladder cancer (BLCA) specimens were obtained from The Cancer Genome Atlas (TCGA) RNA-Seq database. Ferroptosis-related genes were downloaded from the FerrDb website. The ferroptosis-related differentially expressed genes (DEGs) which were related to overall survival (OS) were first identified. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression methods were utilized to develop a ferroptosis-related prognostic model (FRPM). In addition, a nomogram model based on FRPM and clinicopathological features was successfully constructed and validated. In addition, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) methods were utilized in this study in order to compare the DEGs between the high-risk and low-risk groups. This study also adopted RT-qPCR, CCK-8 assay, and scratch assay methods to perform experimental verification processes.

Results and Discussion

A 7-gene FRPM was constructed in this research investigation in order to stratify the patients into two groups according to their risk scores. The results of this study’s survival analysis and time-dependent receiver operating characteristic (ROC) analysis demonstrated that the model had achieved a stable performance level. This multivariate Cox regression results revealed that the FRPM was an independent prognostic predictor for the OS of BLCA patients and the results were displayed using a nomogram. In addition, the ROC analysis, concordance index (C-index), calibration plots, and decision curve analysis (DCA) curves further indicated that this study’s nomogram method enabled valuable prediction results. The functional enrichment analysis results suggested that the DEGs between the high- and low-risk groups played vital roles in the progression of the ferroptosis. Also, the ssGSEA indicated that the immune status was different between the two groups. This study found that the RT-qPCR results had confirmed the differential expressions of DEGs in the tissue samples, and the CCK-8 assay and scratch assay results confirmed the promoting effects of SCD on the proliferation and migration of tumor cells.

Conclusions

This study defined a novel prognostic model of seven ferroptosis-related genes, which proved to be independently associated with the OS of BLCA. A nomogram method was developed for the purpose of providing further insight into the accurate predictions of BLCA prognoses.

Incidence trends and survival prediction of urothelial cancer of the bladder: a population-based study

Background

The aim of this study is to determine the incidence trends of urothelial cancer of the bladder (UCB) and to develop a nomogram for predicting the cancer-specific survival (CSS) of postsurgery UCB at a population-based level based on the SEER database.

Methods

The age-adjusted incidence of UCB diagnosed from 1975 to 2016 was extracted, and its annual percentage change was calculated and joinpoint regression analysis was performed. A nomogram was constructed for predicting the CSS in individual cases based on independent predictors. The predictive performance of the nomogram was evaluated using the consistency index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), a calibration plot and the receiver operating characteristics (ROC) curve.

Results

The incidence of UCB showed a trend of first increasing and then decreasing from 1975 to 2016. However, the overall incidence increased over that time period. The age at diagnosis, ethnic group, insurance status, marital status, differentiated grade, AJCC stage, regional lymph nodes removed status, chemotherapy status, and tumor size were independent prognostic factors for postsurgery UCB. The nomogram constructed based on these independent factors performed well, with a C-index of 0.823 and a close fit to the calibration curve. Its prediction ability for CSS of postsurgery UCB is better than that of the existing AJCC system, with NRI and IDI values greater than 0 and ROC curves exhibiting good performance for 3, 5, and 8 years of follow-up.

Conclusions

The nomogram constructed in this study might be suitable for clinical use in improving the clinical predictive accuracy of the long-term survival for postsurgery UCB.

Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study

Background

The aim was to develop a personalized survival prediction deep learning model for adenosarcoma patients using the surveillance, epidemiology and end results (SEER) database.

Methods

A total of 797 uterine adenosarcoma patients were enrolled in this study. Duplicated and useless variables were excluded, and 15 variables were selected for further analyses, including age, grade, positive lymph nodes or not, marital status, race, tumor extension, stage, and surgery or not. We created our deep survival learning (DSL) model to manipulate the data, which was randomly split into a training set (n = 519, 65%), validation set (n = 143, 18%) and testing set (n = 143, 18%). The Cox proportional hazard (CPH) model was also included comparatively. Finally, personalized survival curves were plotted for randomly selected patients.

Results

The c-index for the CPH model was 0.726, and the Brier score was 0.17. For our deep survival learning model, we achieved a c-index of 0.774 and a Brier score of 0.14 in the external testing set. In addition, the limitations of the traditional staging system were revealed, and a personalized survival prediction system based on our risk scoring grouping was developed.

Conclusions

Our study developed a deep neural network model for adenosarcoma. The performance of this model was superior to that of the traditional Cox proportional hazard model. In addition, a personalized survival prediction system was developed based on our deep survival learning model, which provided more accurate prognostic information for adenosarcoma patients.

Clinical characteristics and overall survival nomogram of second primary malignancies after prostate cancer, a SEER population-based study

Prostate cancer (PCa) is the most prevalent cancer among males and the survival period of PCa has been significantly extended. However, the probability of suffering from second primary malignancies (SPMs) has also increased. Therefore, we downloaded SPM samples from the SEER database and then retrospectively analyzed the general characteristics of 34,891 PCa patients diagnosed between 2000 and 2016. After excluding cases with unknown clinical information, 2203 patients were used to construct and validate the overall survival (OS) nomogram of SPM patients after PCa. We found that approximately 3.69% of PCa patients were subsequently diagnosed with SPMs. In addition, the three most prevalent sites of SPM were respiratory and intrathoracic organs, skin, and hematopoietic system. The top three histological types of SPMs were squamous cell carcinoma, adenoma and adenocarcinoma, nevi and melanoma. Through univariate and multivariate Cox regression analysis, we found that the site of SPM, age, TNM stage, SPM surgery history, and PCa stage were associated with the OS of SPM. By virtue of these factors, we constructed a nomogram to predict the OS of SPM. The C-index in the training set and validation set were 0.824 (95CI, 0.806-0.842) and 0.862 (95CI, 0.840-0.884), respectively. Furthermore, we plotted the receiver operating characteristic curve (ROC) and the area under curve (AUC) which showed that our model performed well in assessing the 3-year (0.861 and 0.887) and 5-year (0.837 and 0.842) OS of SPMs in the training and validation set. In summary, we investigated the general characteristics of SPMs and constructed a nomogram to predict the prognosis of SPM following PCa.

Identification of immune-related LncRNA for predicting prognosis and immunotherapeutic response in bladder cancer

Long noncoding RNAs (lncRNAs) have multiple functions in the cancer immunity response and the tumor microenvironment. To investigate the immune-related lncRNA (IRlncRNA) signature for predicting prognosis and immunotherapeutic response in bladder cancer (BLCA), we extracted BLCA data from The Cancer Genome Atlas (TCGA) database. Finally, a total of 405 cases were enrolled and 8 prognostic IRlncRNAs (MIR181A2HG, AC114730.3, LINC00892, PTPRD-AS1, LINC01013, MRPL23-AS1,LINC01395, AC002454.1) were identified in the training set. Risk scores were calculated to divide patients into high-risk and low-risk groups, and the high-risk patients tended to have a poor overall survival (OS). Multivariate Cox regression analysis confirmed that the IRlncRNA signature could be an independent prognostic factor. The results were subsequently confirmed in the validating set. Additionally, this 8-IRlncRNA classifier was related to recurrence free survival (RFS) of BLCA. Functional characterization revealed this signature mediated immune-related phenotype. This signature was also associated with immune cell infiltration (i.e., macrophages M0, M2, Tregs, CD8 T cells, and neutrophils) and immune checkpoint inhibitors (ICIs) immunotherapy-related biomarkers [mismatch repair (MMR) genes, tumor mutation burden (TMB) and immune checkpoint genes]. The present study highlighted the value of the 8-IRlncRNA signature as a predictor of prognosis and immunotherapeutic response in BLCA.

Identification of the Six-RNA-Binding Protein Signature for Prognosis Prediction in Bladder Cancer

RNA-binding proteins (RBPs) are a kind of gene regulatory factor that presents a significant biological effect in the initiation and development of various tumors, including bladder cancer (BLCA). However, the RBP-based prognosis signature for BLCA has not been investigated. In this study, we attempted to develop an RBP-based classifier to predict overall survival (OS) for BLCA based on transcriptome analysis. We extracted data of BLCA patients from The Cancer Genome Atlas database (TCGA) and UCSC Xena. Finally, a total of 398 cases without missing clinical data were enrolled and six RBPs (FLNA, HSPG2, AHNAK, FASTKD3, POU5F1, and PCSK9) associated with OS of BLCA were identified through univariate and multivariate Cox regression analysis. Online analyses and immunohistochemistry validated the prognostic value and expression of six RBPs. Risk scores were calculated to divide patients into high-risk and low-risk level, and patients in the high-risk group tended to have a poor prognosis. In addition, the receiver operating characteristic (ROC) curve analysis was performed to assess the prognostic value of RBPs, and the area under the curve (AUC) values were 0.711 and 0.706, respectively, in the training set and validating set. The findings were further validated in an external validation set. Subsequently, the 6-RBP-based signature and pathological stage were used to construct the nomogram to predict the 3- and 5-years OS of BLCA patients. Also, this 6-RBP-based signature was highly related to recurrence-free survival of BLCA. Weighted co-expression network analysis (WGCNA) combined with functional enrichment analysis contributed to study the potential pathways of six RBPs, including keratinocyte differentiation, RHO GTPases activate PNKs, epithelial tube morphogenesis, establishment or maintenance of cell polarity, and so on. In summary, the 6-RBP-based signature holds the potentiality to serve as a novel prognostic predictor of OS for BLCA.

Evaluation of the urinary bladder using three-dimensional CT cinematic rendering

Three-dimensional (3D) visualizations of volumetric data from computed tomography (CT) acquisitions can be important adjuncts to interpretation of two-dimensional (2D) reconstructions. Recently, the 3D technique known as cinematic rendering (CR) was introduced, allowing photorealistic images to be created from standard CT acquisitions. CR methodology is under increasing investigation for use in the display of regions of complex anatomy and as a tool for education and preoperative planning. In this article, we will illustrate the potential utility of CR for evaluating the urinary bladder and associated pathology. The urinary bladder is susceptible to a multitude of neoplastic and inflammatory conditions and their sequelae. The intrinsic properties of CR may prove useful for the display of subtle mucosal/luminal irregularities, the simultaneous display of soft tissue detail with high-resolution maps of associated tumor neovasculature, and the improved display of spatial relationships to aid pre-procedural planning. Further refinement of presets for CR image creation and prospective evaluation of urinary bladder CR in real-world settings will be important for widespread clinical adoption.

Correlation of APE1 with VEGFA and CD163+ macrophage infiltration in bladder cancer and their prognostic significance

The present study sought to estimate the applicability of apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1), vascular endothelial growth factor A (VEGFA) expression and CD163⁺ tumor-associated macrophage (TAM) ratio as prognostic factors in bladder cancer (BCa). A total of 127 patients with bladder urothelial cancer who underwent radical cystectomy at Daping Hospital were recruited between January 2013 and January 2017, including 45 cases of non-muscle invasive BCa (NMIBC) and 82 of MIBC. Immunohistochemical detection of APE1, VEGFA and CD163, as well as multiple immunofluorescence staining for APE1, VEGFA, CD163 and CD34, were performed on tissue samples. For APE1 and VEGFA, the staining was graded based on intensity (0–3), while CD163 was graded (0–3) based on the percentage of positively stained cells. The prognostic value of APE1, VEGF and CD163 was assessed using Kaplan-Meier and Cox regression analysis. The results suggested that in BCa, high APE1 expression was associated with high VEGFA expression and more infiltration of CD163⁺ TAM. Furthermore, high expression of APE1 was associated with lymphovascular invasion of BCa, as well as reduced survival time. This indicates that APE1 may be associated with CD163⁺ TAM infiltration in BCa, with VEGFA as a possible influencing factor.