Development and validation of a SEER-based prognostic nomogram for patients with bone metastatic prostate cancer

Prognostic nomogram to predict cancer-specific survival with small-cell carcinoma of the prostate: a multi-institutional study

Objective

The aim of this study is to examine the predictive factors for cancer-specific survival (CSS) in patients diagnosed with Small-Cell Carcinoma of the Prostate (SCCP) and to construct a prognostic model.

Methods

Cases were selected using the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was utilized to calculate survival rates, while Lasso and Cox regression were employed to analyze prognostic factors. An independent prognostic factor-based nomogram was created to forecast CSS at 12 and 24 months. The model's predictive efficacy was assessed using the consistency index (C-index), calibration curve, and decision curve analysis (DCA) in separate tests.

Results

Following the analysis of Cox and Lasso regression, age, race, Summary stage, and chemotherapy were determined to be significant risk factors (P < 0.05). In the group of participants who received training, the rate of 12-month CSS was 44.6%, the rate of 24-month CSS was 25.5%, and the median time for CSS was 10.5 months. The C-index for the training cohort was 0.7688 ± 0.024. As for the validation cohort, it was 0.661 ± 0.041. According to the nomogram, CSS was accurately predicted and demonstrated consistent and satisfactory predictive performance at both 12 months (87.3% compared to 71.2%) and 24 months (80.4% compared to 71.7%). As shown in the external validation calibration plot, the AUC for 12- and 24-month is 64.6% vs. 56.9% and 87.0% vs. 70.7%, respectively. Based on the calibration plot of the CSS nomogram at both the 12-month and 24-month marks, it can be observed that both the actual values and the nomogram predictions indicate a predominantly stable CSS. When compared to the AJCC staging system, DCA demonstrated a higher level of accuracy in predicting CSS through the use of a nomogram.

Conclusion

Clinical prognostic factors can be utilized with nomograms to forecast CSS in Small-Cell Carcinoma of the Prostate (SCCP).

Clinical characteristics and prognosis of patients with primary squamous cell carcinoma of the retromolar trigone: A SEER-based analysis

BACKGROUND

Primary squamous cell carcinoma of the retromolar trigone (primary SCC RMT) is an uncommon malignant tumor. There is still much to learn about its clinicopathological characteristics and prognosis. In order to better understand the clinicopathological features and predictive survival aspects of primary SCC RMT, this study examined data from the SEER database from 2000 to 2020. Furthermore, in order to forecast the overall survival (OS) and cancer-specific survival (CSS) of patients with primary SCC RMT, we created nomograms.

METHOD

The Surveillance, Epidemiology and End Results (SEER) database was used to retrieve the information on individuals with primary SCC RMT who received a diagnosis between 2000 and 2020. Both univariate and multivariate analyses were conducted using the Cox proportional risk regression model. Using R software, prognostic nomograms were created to forecast the OS and CSS likelihood. The nomograms' prediction abilities were evaluated using the consistency index (C-index), calibration curve, and receiver operating characteristic (ROC) curve.

RESULT

A total of 1717 patients with primary SCC RMT were included, they were randomly assigned to the primary and validation cohorts in a 7:3 ratio using R software. Multivariate Cox regression revealed that age, marital status, regional nodes positive, Summary stage, TNM stage, T stage, N stage, surgery were independent prognostic factors of OS, and age, marital status, regional nodes positive, tumor sizes, Summary stage, N stage, surgery were independent prognostic factors of CSS in the primary cohort. The C-index of the nomogram OS was 0.705 (95 % CI: 0.685-0.725) and the C-index of CSS was 0.734 (95 % CI:0.714-0.754) in the primary cohort. In validation cohort, the C-index of the nomogram OS and CSS were 0.730 (95 % CI: 0.710-0.750) and 0.723 (95 % CI: 0.684-0.762), respectively. The 1-, 3-, and 5-year OS and CSS rates in the primary cohort and validation cohort were approximately in line with the nomogram estimations, in accordance to the calibration curves.

CONCLUSION

We conducted an analysis using the SEER database to investigate the features, survival outcomes, and prognostic parameters of patients with primary SCC RMT. And we developed two prognostic nomograms that can be used by clinicians to forecast the 1-, 3-, and 5-year overall survival and cancer-specific survival of patients with primary SCC RMT.

Prognostic nomogram for patients with tongue squamous cell carcinoma: A SEER-based study

OBJECTIVES

In order to predict the patients' prognosis with tongue squamous cell carcinoma (SCC), this study set out to develop a clinically useful and trustworthy prognostic nomogram.

SUBJECTS AND METHODS

The Surveillance, Epidemiology, and End Results (SEER) Program was used to compile clinical information on patients with tongue SCC between 2010 and 2015. The likelihood of Cancer-Specific Survival (CSS) and Overall Survival (OS) for specific patients was predicted using a prognostic nomogram created with the help of the RStudio software. The nomogram's predictive ability was evaluated using the consistency index (C-index) and decision curve analysis, and the nomogram was calibrated for 1-, 2-, 3-, 5-, and 10-year CSS and OS.

RESULTS

Patients numbering 6453were enrolled in this study. The primary cohort (3895) and validation cohort (2558) were each randomly assigned. Sex, age, tumor-node-metastasis (TNM) stage, surgery, chemotherapy, and radiation were significant risk factors for OS, whereas age, TNM stage, surgery, chemotherapy, and radiotherapy were significant risk factors for CSS. Additionally, C-index and calibration curves indicated that the prognostic nomogram prediction and the actual observation in both cohorts would be very coherent.

CONCLUSIONS

The predictive nomogram created in this study can offer patients with tongue SCC customized treatment and survival risk assessment.

Development and validation of a nomogram for predicting suicide risk and prognostic factors in bladder cancer patients following diagnosis: A population-based retrospective study

This study sought to identify independent risk factors associated with suicide following a diagnosis of bladder cancer and to develop a predictive model with the potential to contribute to suicide rate reduction. Harnessing data from the Surveillance, Epidemiology, and End Results (SEER) database, we identified bladder cancer patients diagnosed between 2004 and 2015, randomly assigning them to training and validation cohorts. The Cox proportional hazard model was employed to identify relevant predictors, leading to the construction of prediction nomogram models. Validation of prognostic nomograms involved assessing the consistency index (C-index), receiver operating characteristic (ROC) curve, and calibration curve. A total of 109,961 eligible bladder cancer patients were enrolled, randomly divided into training and validation sets. Multivariate Cox regression analysis revealed that sex, marital status, tumor local status (T Stage), and lymph node metastatic conditions (N Stage) were independent predictors for suicide in bladder cancer patients. Evaluation of the nomogram's accuracy through the C-index and ROC curve demonstrated acceptable performance in both training and validation sets. Moreover, the calibration plot indicated moderate accuracy of the nomogram in both datasets. Overall, this study successfully identified risk factors for suicide among bladder cancer patients and developed a nomogram, offering individualized diagnosis, intervention, and risk assessment to mitigate the risk of suicide in this patient population.

Pretreatment Lymphocyte to C-Reactive Protein Ratio: An Independent Predictor of Overall Survival in Metastatic Hormone-Naïve Prostate Cancer Patients

INTRODUCTION

The purpose of this study was to investigate the clinical value of combination of systematic inflammatory factors in predicting the outcomes of primary androgen deprivation therapy (ADT) plus first-generation antiandrogen treatment in metastatic hormone-naïve prostate cancer (mHNPC) patients.

MATERIALS AND METHODS

A total of 361 consecutive mHNPC patients from the discovery (n = 165) and validation (n = 196) cohorts were analyzed. All patients received primary ADT with surgical castration or pharmacologic castration accompanied by first-generation antiandrogens. We evaluated the prognostic impact of pretreatment lymphocyte to C-reactive protein ratio (LCR) on overall survival (OS) in both cohorts.

RESULTS

The median follow-up in the discovery and validation cohorts was 43.4 and 50.9 months, respectively. In the discovery cohort, low LCR (using an optimal cutoff threshold of 14,025) was significantly correlated with poor OS compared with high LCR (P < .001). Multivariate analysis revealed that the biopsy Gleason score and LCR were independent prognostic factors for OS. In the validation cohort, low LCR was also significantly correlated with poor OS compared with high LCR (P = .001). A multivariate analysis revealed that the extent of disease on bone scan grade, lactate dehydrogenase, and LCR were all independent predictors of OS.

CONCLUSIONS

Pretreatment low LCR is an independent predictor of poor OS in mHNPC patients. This may be informative in predicting the susceptible patients' developing worse outcomes after being treated with primary ADT plus first-generation antiandrogen.

Establishment and assessment of a nomogram for predicting prognosis in bone-metastatic prostate cancer

OBJECTIVE

For the purposes of patients' consultation, condition assessments, and guidance for clinicians' choices, we developed a prognostic predictive model to evaluate the 1-, 3-, and 5-year overall survival (OS) rates of bone-metastatic prostate cancer (PCa) patients.

METHODS

We gathered data from 5522 patients with bone metastatic PCa registered in the Surveillance, Epidemiology, and End Results (SEER) database to develop a nomogram. A total of 359 bone metastatic PCas were collected from 2 hospitals to validate the nomogram and assess its discriminatory ability. In addition, we plotted the actual survival against the predicted risk to assess the calibration accuracy. Moreover, we designed a web calculator to quickly obtain accurate survival probability outcomes.

RESULTS

Univariate and multivariate Cox hazard regression analyses suggested that age, marital status, prostate-specific antigen (PSA) level, Gleason score, clinical T stage, N stage, surgery, and chemotherapy were closely associated with OS rates. The calibration charts of the training and validation groups showed a high accuracy and reliability. The decision curve analysis (DCA) suggested a favorable clinical net benefit.

CONCLUSION

Based on demography and clinical pathology, we developed a reliable nomogram to help clinicians more accurately predict the 1-, 3-, and 5-year OS rates of patients with bone metastatic PCa to guide evaluation and treatment.

Development and external validation of a novel prognostic nomogram for overall survival in prostate cancer patients with bone metastatic: a retrospective study of the SEER-based and a single Chinese center

BACKGROUND

Prostate cancer (PCa) patients with bone metastases (BM) often face a poor prognosis, a leading contributor to mortality within this group. This study aims to develop a novel prognostic nomogram to predict overall survival for them.

METHODS

We retrospectively analyzed PCa patients with BM from Surveillance, Epidemiology, and End Results (SEER) database and our hospital. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses for the creation of a nomogram. Calibration curves and receiver operating characteristic (ROC) curves, along with the concordance index (C-index) and decision curve analysis (DCA), were employed to evaluate the performance of the constructed nomogram.

RESULTS

A total of 12,344 PCa patients with BM, derived from 2010 to 2019 SEER database, were randomly allocated into a training cohort (n = 8640) and an internal validation cohort (n = 3704). Additionally, an external validation cohort (n = 126) from our hospital. The novel nomogram integrates multiple factors: age, race, histopathology, organ metastasis, chemotherapy, Gleason score, and prostate-specific antigen (PSA). C-index for the training, internal validation, and external validation cohorts were 0.770 (0.766-0.774), 0.756 (0.749-0.763), and 0.751 (0.745-0.757) respectively. Similarly, the area under the curve (AUC) for each cohort exhibited comparable results (training cohort-3-year: 0.682, 6-year: 0.775, 9-year: 0.824; internal validation cohort-3-year: 0.681, 6-year: 0.750, 9-year: 0.806; external validation cohort-2-year: 0.667, 3-year: 0.744, 4-year: 0.800), indicating that the nomogram possesses robust discriminative ability. Calibration curve and DCA curve further proved the reliability and accuracy of the prognostic nomogram.

CONCLUSION

This study determined the independent risk factors for prostate cancer (PCa) patients with bone metastasis (BM) and subsequently developed a robust prognostic nomogram to predict overall survival (OS). This tool can serve to guide precise clinical treatment strategies for these patients.

Development and validation a model for predicting overall survival of bladder cancer with lung metastasis: a population-based study

BACKGROUND

Although the number of patients with bladder cancer and lung metastasis is increasing there is no accurate model for predicting survival in these patients.

METHODS

Patients enrolled in the Surveillance, Epidemiology, and End Results database between 2010 and 2015 were selected for the study. Univariate and multivariate Cox regression were used to determine independent prognostic factors, followed by development of a nomogram based on the multivariate Cox regression models. The consistency index, receiver operating characteristic curve, and calibration curve were used to validate the prognostic nomogram.

RESULTS

506 eligible bladder cancer patients with lung metastasis were enrolled in the study and then divided randomly into training and validation sets (n = 356 vs. n = 150). Multivariate Cox regression analysis indicated that age at diagnosis, primary site, histological type, surgery of the primary site, chemotherapy, bone metastasis, and liver metastasis were prognostic factors for overall survival (OS) in patients with lung metastasis in the training set. The C-index of the nomogram OS was 0.699 and 0.747 in the training and validation sets, respectively. ROC curve estimation of the nomogram in the training and validation sets showed acceptable accuracy for classifying 1-year survival, with an area under the curve (AUC) of 0.766 and 0.717, respectively. More importantly, the calibration plot showed the nomogram had favorable predictive accuracy in both the training and validation sets.

CONCLUSIONS

The prognostic nomogram created in our study provides an individualized diagnosis, remedy, and risk evaluation for survival in patients with bladder cancer and lung metastasis. The nomogram would therefore enable clinicians to make more precise treatment decisions for patients with bladder cancer and lung metastasis.

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.

Development and validation of a prognostic nomogram for predicting cancer-specific survival in patients with metastatic clear cell renal carcinoma: A study based on SEER database

Objectives

Clear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC).

Patients and Methods

Data of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan–Meier survival analysis was applied to validate the application of the risk stratification system.

Results

Diagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks.

Conclusion

In this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

Development and validation of a prognostic nomogram for adult patients with renal sarcoma: A retrospective study based on the SEER database

Background

Renal sarcoma (RS) is rarely seen in clinical practice. The purpose of this study was to develop a prognostic nomogram model, which could predict the probability of overall survival (OS) and cancer-specific survival (CSS) in adult patients with RS.

Methods

Patients diagnosed with RS were recruited from the SEER database between 2004 and 2015, and randomized to two cohorts: the training cohort and the validation cohort. Uni- and multivariate Cox regression analyses in the training cohort were used to screen independent prognostic factors for OS and CSS. Prognostic nomograms for OS and CSS were created separately for adult RS patients based on independent risk factors. The area under the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to validate the nomograms.

Results

A total of 232 eligible patients were recruited, including 162 in the training cohort and 70 in the validation cohort. Sex, histological type, SEER stage, and surgery were independent prognostic factors for OS, while histological type, SEER stage, surgery, chemotherapy were independent prognostic factors for CSS. Based on the above independent prognostic factors, prognostic nomograms for OS and CSS were created respectively. In the training cohort, the AUCs of the nomograms for OS and CSS were 0.742 and 0.733, respectively. In the validation cohort, the AUCs of the nomograms for OS and CSS were 0.837 and 0.758, respectively. The calibration curves of the nomograms showed high consistencies between the predicted and actual survival rates. Finally, the DCA demonstrated that the nomograms in the wide high-risk threshold had a higher net benefit than the SEER stage.

Conclusion

A prognostic nomogram for renal sarcoma was created and validated for reliability and usefulness in our study, which assisted urologists in accurately assessing the prognosis of adult RS patients.

Bone marrow involvement in patients with metastatic castration sensitive prostate cancer

Introduction

The clinical significance of bone marrow (BM) metastasis in prostate cancer as well as impact on oncological prognosis is unclear. We aim to assess the prevalence and clinical outcomes of BM metastasis at initial presentation of metastatic castrate sensitive prostate cancer (CSPC).

Patients and methods

Retrospective chart review of newly diagnosed metastatic CSPC patients was performed with collection of clinicopathologic and radiologic characteristics. Descriptive univariate and multivariate analysis was performed as well as survival measures (OS and PFS), which was done using the Kaplan-Meier survival and the Log-rank test.

Results

189 patients were eligible, of which, eleven patients (6%) had biopsy proven BM involvement at diagnosis. There was a trend to poorer PFS and OS in patients with BM involvement but not statistically significant; however, factors that correlated with inferior PFS and OS in the multivariate analysis included ECOG PS, ALP, and Hb.

Conclusion

BM metastasis in prostate cancer may lead to poorer survival. Clinical features including poor performance status, anemia, and elevated ALP, could guide bone marrow biopsies in the future to diagnose bone marrow metastasis at an earlier stage.

Establishment of Prognostic Nomograms for Early-Onset Prostate Cancer Patients: A SEER Database Analysis

OBJECTIVE

Clinical prostate cancer (PCa) is rare in men aged <50 years (early-onset). A well-designed nomogram for prognosis prediction in patients with early-onset PCa has not been studied. Here, we tried to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in patients with early-onset PCa.

METHODS

The clinical variables of patients diagnosed with early-onset PCa between 2004 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups at a ratio of 7:3. Multivariate Cox regression analyses were used to select prognostic factors associated with OS or CSS, followed by the construction and validation of nomograms.

RESULTS

We enrolled 8259 patients with early-onset PCa. New nomograms were established and showed good discriminative abilities. Finally, ROC curve analysis demonstrated that these nomograms were superior to the TNM stage and Gleason score in predicting both OS and CSS for patients with early-onset PCa.

CONCLUSION

This is the first study to establish nomograms with effective and high accuracy for prognosis in patients with early-onset PCa.

Survival Analysis and a Novel Nomogram Model for Progression-Free Survival in Patients with Prostate Cancer

Background

This study sought to perform a survival analysis and construct a prognostic nomogram model based on the Gleason grade, total prostate-specific antigen (tPSA), alkaline phosphate (ALP), and TNM stage in patients with prostate cancer (PCa).

Methods

The progression-free survival (PFS) of 255 PCa patients was analyzed in this study. The prognostic value of tPSA and ALP was evaluated using the Kaplan-Meier survival curves and Cox regression analysis, and a nomogram model based on the Gleason grade, tPSA, ALP, and TNM stage was further established for PFS prediction in PCa patients.

Results

PCa patients with different Gleason grades, tPSA and ALP levels, and TNM stages presented distinct PFS. The Gleason grade, tPSA, ALP, and TNM stage were four independent prognostic indicators. The C-index of the established nomogram was 0.705 for PFS in the test cohort and 0.687 for the validation cohort, and the calibration curves indicated a good consistency between predicted and actual PFS in PCa patients.

Conclusion

The data of this study demonstrated that the Gleason grade, tPSA, ALP, and TNM stage of PCa patients are independently correlated with PFS, and a nomogram model based on these indicators may be valuable for the PFS prediction in PCa patient.

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Background

To develop a precise prognostic model of overall survival in patients with terminating cervical cancer based on surveillance, epidemiology, and end results (SEER) program.

Methods

The patients were retrieved from SEER data who are diagnosed with terminating cervical cancer from 2004 to 2016. The data were performed using univariate and multivariate analyses and constructed nomograms for predicting survival. Use C-index to validate the model accuracy.

Results

Totally 15839 patients diagnosed with cervical cancer were independently allocated into the training set (n = 11088) and validation set (n = 4751). The multivariate analysis results indicated that age, race, stage_T, stage_M, and stage_N were confirmed as independent risk predictors, and those factors are applied to construct this clinical model. The C-index of overall survival in the training set was 0.6816 (95% confidence intervene (CI), 0.694-0.763) and that in the validation set was 0.6931(95% CI, 0.613-0.779). All calibration curves of various factors were consistent with predicted and actual survival.

Conclusion

The nomogram provides a novel method for predicting the survival of patients with terminating cervical cancer, assisting in accurate therapeutic methods for patients with primary terminating cervical cancer.

Folic Acid-Modified miR-491-5p-Loaded ZIF-8 Nanoparticles Inhibit Castration-Resistant Prostate Cancer by Regulating the Expression of EPHX1

Epoxide hydrolase 1 (EPHX1) has been reported to be related to the development of several tumors. However, the regulation of castration-resistant prostate cancer (CRPC) development by EPHX1 has not been reported. We used proteomic technology and found that the EPHX1 protein was highly expressed in CRPC tissues and the CRPC cell line C4-2. We performed screening and found that EPHX1 is a direct target of miR-491-5p. High miR-491-5p expression significantly reduced the EPHX1 level in C4-2 cells and inhibited C4-2 cell proliferation and migration. Zeolite imidazolate framework-8 (ZIF-8) has good thermal stability, a simple synthesis method, tumor site stability, and specific acid responsiveness. We synthesized ZIF-8 nanodrug vectors to deliver miR-491-5p into C4-2 cells. After loading miR-491-5p into ZIF-8, we modified the ZIF-8 surface with folic acid (FA) as the target group (FA@ZIF-8). Our synthesized nanodrug carrier showed less cytotoxicity to C4-2 cells even at 200 μg/ml. Modified FA could increase the efficiency of nanomaterial entry into C4-2 cells. FA@miR-491-5p@ZIF-8 could stably release miR-491-5p for a long period in both phosphate-buffered saline (pH 7.4) and acetate buffer (pH 4.8), and miR-491-5p was released faster at the beginning of the experiment in acetate buffer (pH 4.8). FA@miR-491-5p@ZIF-8 significantly reduced C4-2 cell proliferation and migration, and FA@miR-491-5p@ZIF-8 had a better effect than miR-491-5p alone. In vivo, FA@miR-491-5p@ZIF-8 significantly inhibited CRPC growth in nude mice. Overall, we verified that miR-491-4p regulated CRPC development by targeting EPHX1. The drug nanocarrier FA@miR-491-5p@ZIF-8 not only significantly reduced C4-2 CRPC cell proliferation and migration but also significantly inhibited CRPC growth. Our research provides a theoretical basis for treatment and treatment strategies for CRPC.

Nomograms predicting prognosis for locally advanced hypopharyngeal squamous cell carcinoma

PURPOSE

This study aimed to construct nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) for patients with locally advanced hypopharyngeal squamous cell carcinoma (HSCC).

METHODS

864 patients with locally advanced HSCC during 2010-2015 from the surveillance, epidemiology and end results (SEER) database were selected. After classifying continuous data by risk, Cox regression analyses were applied to detect significant independent prognostic factors, with which nomograms were established. To evaluate the value of nomograms, concordance index (C-index), area under the receiver-operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA), Kaplan-Meier analysis was adopted. The efficacy of surgery in different risk groups was also studied to figure out people who can benefit from surgery.

RESULTS

A total of 864 locally advanced HSCC patients were randomized into the training cohort (n = 608) and the validation cohort (n = 256). Age, race, tumor size, T stage, N stage, primary site, radiotherapy, and chemotherapy were independent prognostic factors for OS and CSS (except race) and formed the nomograms. The nomograms revealed satisfied performance in C-index, AUC, DCA, and calibration curves, and prevailed over American Joint Committee on Cancer (AJCC) TNM staging system in predicting OS and CSS. After risk stratification, patients of low-risk group resulted in the best outcomes. Patients in moderate-risk may benefit from surgery.

CONCLUSIONS

Convenient and well-calibrated nomograms to predict OS and CSS for III/IVA/IVB-stage HSCC patients were set up and assessed and may do a favor to make clinical decisions.

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.

A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

Background:

The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC).

Methods:

GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed.

Results:

A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS.

Conclusion:

The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

Prognostic Models for Patients With Gleason Score 9 Prostate Cancer: A Population-Based Study

Purpose: Gleason score (GS) system is one of the most widely used histological grading methods for prostate cancer (PCa) all over the world. GS can be obtained by adding the primary Gleason pattern (GP) and secondary GP. Different proportions of GP 4 and GP 5 in prostate specimens can both lead to GS 9. In this study, we explored whether GP 5 + 4 or GP 4 + 5 was associated with different prognoses among patients with GS 9 PCa.

Materials and methods: A retrospective population-based study was conducted on 10,124 subjects diagnosed with GS 9 PCa between 2004 and 2009 from the Surveillance, Epidemiology, and End Results program. A 1:1 propensity-score matching (PSM) was performed to balance the baseline characteristics between the GP 4 + 5 and 5 + 4 groups and to compare the prognoses between the two groups. Cox regression analysis and Fine-Gray competing risk regression models were adopted to screen the covariates significantly associated with all-cause mortality (ACM) and cancer-specific mortality (CAM).

Results: GP 5 + 4 was associated with higher risks of ACM and CSM before or after PSM than GP 4 + 5. In the original cohort, there were eight independent predictors for ACM, which were age at diagnosis, race, AJCC NM stage, PSA levels, treatments, GP, and marital status, confirmed by the Cox analysis; and nine independent predictors for CSM, which were age at diagnosis, race, AJCC TNM stage, PSA levels, treatments, GP, and marital status, confirmed by the competing-risk model.

Conclusion: GP 5 + 4 was associated with a poorer overall survival and cancer-specific survival compared with GP 4 + 5.

Development and validation of a nomogram to predict synchronous lung metastases in patients with ovarian cancer: a large cohort study

Purpose: Lung metastasis is an independent risk factor affecting the prognosis of ovarian cancer patients. We developed and validated a nomogram to predict the risk of synchronous lung metastases in newly diagnosed ovarian cancer patients.

Methods: Data of ovarian cancer patients from the Surveillance, Epidemiology, and Final Results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built on the basis of logistic regression. The consistency index (C-index) was used to evaluate the discernment of the synchronous lung metastasis nomogram. Calibration plots were drawn to analyze the consistency between the observed probability and predicted probability of synchronous lung metastases. The Kaplan–Meier method was used to estimate overall survival rate, and influencing factors were included in multivariate Cox regression analysis (P<0.05) to determine the independent prognostic factors of synchronous lung metastases.

Results: Overall, 16059 eligible patients were randomly divided into training (n=11242) and validation cohorts (n=4817). AJCC T, N stage, bone metastases, brain metastases, and liver metastases were evaluated as predictors of synchronous lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was calibrated and showed good discriminative ability. Mixed histological types, chemotherapy, and primary site surgery were factors affecting the overall survival of patients with synchronous lung metastases.

Conclusion: The clinical prediction model has high accuracy and can be used to predict lung metastasis risk in newly diagnosed ovarian cancer patients, which can guide the treatment of patients with synchronous lung metastases.

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.

Novel survival nomograms for patients with lung metastatic clear cell renal cell carcinoma: A population-based study

Survival heterogeneity is observed among renal cell carcinoma (RCC) patients with metastases in different organs. Moreover, almost all previous prognostic nomograms based on data from metastatic RCC patients did not take competing events, such as death from cerebrovascular and heart diseases, into account. We aimed to construct novel prognostic nomograms for patients with lung metastatic clear cell RCC (LMCCRCC).Data of 712 non-Hispanic white LMCCRCC patients registered in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed. Nomograms for predicting overall survival (OS) and disease-specific survival (DSS) were established using the Cox approach and Fine and Gray approach, respectively, and their performances were assessed using the concordance index (C-index), calibration plots, and an independent cohort comprising 181 Hispanic patients.Sex, tumor grade, T stage, N stage, presence or absence of bone metastases, and presence or absence of brain metastases were independent predictors for both OS and DSS. Additionally, presence or absence of liver metastases was an independent predictor only for DSS. Meanwhile, age at diagnosis was independently associated with OS. The C-indexes of the nomograms were 0.702 for OS and 0.723 for DSS in internal validation. In external validation, the C-indexes were 0.700 for OS and 0.708 for DSS. Both internal and external calibration plots showed excellent consistency between the prediction and the observation.The current study developed a novel nomogram for predicting individual OS in LMCCRCC patients. Moreover, we constructed an effective competing risk nomogram for predicting their individual DSS for the first time.

Development and validation of prognostic nomogram for germ cell testicular cancer patients

The purpose of our study was to establish a reliable and practical nomogram based on significant clinical factors to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with germ cell testicular cancer (GCTC). Patients diagnosed with GCTC between 2004 and 2015 were obtained from the SEER database. Nomograms were constructed using the R software to predict the OS and CSS probabilities and the constructed nomograms were validated and calibrated. A total of 22,165 GCTC patients were enrolled in the study, including the training cohort (15,515 patients) and the validation cohort (6,650 patients). In the training cohort, multivariate Cox regression showed that age, race, AJCC stage, SEER stage and surgery were independent prognostic factors for OS, while age, race, AJCC stage, TM stage, SEER stage and radiotherapy were independent prognostic factors for CSS. Based on the above Cox regression results, we constructed prognostic nomograms of OS and CSS in GCTC patients and found that the OS nomograms had higher C-index and AUC compared to TNM stage in the training and validation cohorts. In addition, in the training and external validation cohorts, the calibration curves showed a good consistency between the predicted and actual 3-, 5- and 10-year OS and CSS rates of the nomogram. The current prognostic nomogram can provide a personalized risk assessment for the survival of GCTC patients.

Construction and validation of a novel prognostic nomogram for patients with sarcomatoid renal cell carcinoma: a SEER-based study

OBJECTIVE

The only one established prognostic nomogram for patients with sarcomatoid renal cell carcinoma (sRCC) was based on a small sample-sized study without external validation, and a nomogram can be applied to western sRCC patients has not yet been developed. Therefore, our study aimed to construct and validate an effective nomogram to predict overall survival (OS) for these patients.

METHODS

The independent predictors for OS were identified and the nomogram was constructed on the basis of a retrospective study of a training cohort consisted of 428 non-Hispanic white sRCC patients registered in the Surveillance, Epidemiology and End Results (SEER) database from January 2010 to December 2015. Then, the discriminative performance of the nomogram was assessed by the concordance index (C-index). OS calibrations of the nomogram were also performed by comparing the nomogram-predicted probability to the observed survival rate. Furthermore, our nomogram was externally validated using two independent cohorts consisted of 71 non-Hispanic black patients and 82 Hispanic patients, respectively.

RESULTS

Age at diagnosis, T stage, N stage, bone metastases, liver metastases, lung metastases and nephrectomy were identified as independent predictors for OS. In the training cohort and two validation cohorts, the C-indexes of the nomogram were 0.737, 0.801 and 0.764, respectively. Besides, excellent agreements between the nomogram prediction and the actual observation were achieved in all cohorts.

CONCLUSIONS

The current study constructed and validated an effective prognostic nomogram for patients with sRCC, which can be used to perform accurate predictions of the 0.5-, 1-, and 2-year possibilities of OS.

Role of CYP3A5 in Modulating Androgen Receptor Signaling and Its Relevance to African American Men with Prostate Cancer

Androgen receptor signaling is crucial for prostate cancer growth and is positively regulated in part by intratumoral CYP3A5. As African American (AA) men often carry the wild type CYP3A5 and express high levels of CYP3A5 protein, we blocked the wild type CYP3A5 in AA origin prostate cancer cells and tested its effect on androgen receptor signaling. q-PCR based profiler assay identified several AR regulated genes known to regulate AR nuclear translocation, cell cycle progression, and cell growth. CYP3A5 processes several commonly prescribed drugs and many of these are CYP3A5 inducers or inhibitors. In this study, we test the effect of these commonly prescribed CYP3A5 inducers/inhibitors on AR signaling. The results show that the CYP3A5 inducers promoted AR nuclear translocation, downstream signaling, and cell growth, whereas CYP3A5 inhibitors abrogated them. The observed changes in AR activity is specific to alterations in CYP3A5 activity as the effects are reduced in the CYP3A5 knockout background. Both the inducers tested demonstrated increased cell growth of prostate cancer cells, whereas the inhibitors showed reduced cell growth. Further, characterization and utilization of the observation that CYP3A5 inducers and inhibitors alter AR signaling may provide guidance to physicians prescribing CYP3A5 modulating drugs to treat comorbidities in elderly patients undergoing ADT, particularly AA.