Nomograms for predicting long-term overall survival and disease-specific survival of patients with clear cell renal cell carcinoma

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.

Clear cell renal cell carcinoma: immunological significance of alternative splicing signatures

Background

Renal cell carcinoma (RCC) accounts for 90% of renal cancers, of which clear cell carcinoma (ccRCC) is the most usual histological type. The process of alternative splicing (AS) contributes to protein diversity, and the dysregulation of protein diversity may have a great influence on tumorigenesis. We developed a prognostic signature and comprehensively analyzed the role of tumor immune microenvironment (TIME) and immune checkpoint blocking (ICB) treatment in ccRCC.

Methods

To identify prognosis-related AS events, univariate Cox regression was used and functional annotation was performed using gene set enrichment analysis (GSEA). In this study, prognostic signatures were developed based on multivariate Cox, univariate Cox, and LASSO regression models. Moreover, to assess the prognostic value, the proportional hazards model, Kruskal-Wallis analysis, and ROC curves were used. To obtain a better understanding of TIME in ccRCC, the ESTIMATE R package, single sample gene set enrichment analysis (ssGSEA) algorithm, CIBERSORT method, and the tumor immune estimation resource (TIMER) were applied. The database was searched to verify the expression of C4OF19 in tumor and normal samples. Regulatory networks for AS-splicing factors (SFs) were visualized using Cytoscape 3.9.1.

Results

There were 9,347 AS cases associated with the survival of ccRCC patients screened. A total of eight AS prognostic signatures were developed with stable prognostic predictive accuracy based on splicing subtypes. In addition, a qualitative prognostic nomogram was developed, and the prognostic prediction showed high effectiveness. In addition, we found that the combined signature was significantly associated with the diversity of TIME and ICB treatment-related genes. C4ORF19 might become an important prognostic factor for ccRCC. Finally, the AS-SF regulatory network was established to clearly reveal the potential function of SFs.

Conclusion

We found novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.) for the prognostic prediction of ccRCC. A new and reliable prognostic nomogram was established to quantitatively predict the clinical outcome. The AS-SF networks could provide a new way for the study of potential regulatory mechanisms, and the important roles of AS events in the context of TIME and immunotherapy efficiency were exhibited. C4ORF19 was found to be a vital gene in TIME and ICB treatment.

p32 regulates glycometabolism and TCA cycle to inhibit ccRCC progression via copper-induced DLAT lipoylation oligomerization

A key player in mitochondrial respiration, p32, often referred to as C1QBP, is mostly found in the mitochondrial matrix. Previously, we showed that p32 interacts with DLAT in the mitochondria. Here, we found that p32 expression was reduced in ccRCC and suppressed progression and metastasis in ccRCC animal models. We observed that increasing p32 expression led to an increase in oxidative phosphorylation by interacting with DLAT, thus, regulating the activation of the pyruvate dehydrogenase complex (PDHc). Mechanistically, reduced p32 expression, in concert with DLAT, suppresses PDHc activity and the TCA cycle. Furthermore, our research discovered that p32 has a direct binding affinity for copper, facilitating the copper-induced oligomerization of lipo-DLAT specifically in ccRCC cells. This finding reveals an innovative function of the p32/DLAT/copper complex in regulating glycometabolism and the TCA cycle in ccRCC. Importantly, our research provides important new understandings of the underlying molecular processes causing the abnormal mitochondrial metabolism linked to this cancer.

Development and validation of prognostic nomogram for elderly patients with clear cell renal cell carcinoma based on the SEER database

This study sought to establish nomogram models of overall survival (OS) in patients with elderly clear cell renal cell carcinoma (ECCRCC). The Surveillance, Epidemiology, and End Results database provided data of the ECCRCC-afflicted patients diagnosed during the period from 2010 to 2015. This data was subsequently segregated into the training and validation sets randomly in a 7:3 ratio. The calibration curves, the receiver operating characteristic curves, the decision curve analysis and the Concordance index (C-index) were applied for the model evaluation. 9201 eligible cases from 2010 to 2015 were extracted; 6441 were included in the training cohort and 2760 in the validation cohort. The C-index for the training and validation sets were 0.710 and 0.709, respectively. The receiver operating characteristic and decision curve analysis curves demonstrated that nomograms outperformed the AJCC stage in predictive performance. Moreover, the nomogram was found to match closely with the actual observation, as indicated by the calibration plots. To make predictions with regard to the survival of the ECCRCC-afflicted individuals, and as a guide for treatment, the new nomogram could be used.

Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort

Background

On average, 5-10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) in these patients.

Methods

The nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance, Epidemiology, and End Results (SEER) database. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), and calibration curve. Decision curve analysis (DCA) was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China.

Results

Multivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis, pathological type, histological grade, T stage, N stage, molecular subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.688 (95% confidence interval [CI], 0.682-0.694) in the training cohort and 0.875 (95% CI, 0.816-0.934) in the validation cohort. The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts, respectively. Calibration curves showed favorable consistency between the predicted and actual survival probabilities. Additionally, the DCA curve produced higher net gains than by the AJCC-TNM staging system. Finally, risk stratification can accurately identify groups of patients with de novo MBC at different risk levels.

Conclusions

The nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC. Other populations from different countries or prospective studies are needed to further validate the nomogram.

Computer tomography-based shape of tumor contour and texture of tumor heterogeneity are independent prognostic indicators for clinical T1b-T2 renal cell carcinoma

PURPOSE

To evaluate association between computer tomography (CT)-based features of renal cell carcinoma (RCC) and survival outcomes.

METHODS

Data of 958 patients with clinical T1b-T2 RCC who underwent partial/radical nephrectomy from June 2003 to March 2022 were retrospectively evaluated. CT images of patients were reviewed by two radiologists for texture analysis of tumor heterogeneity and shape analysis of tumor contour. Patients were divided into three groups according to patterns of CT-based features: (1) favorable feature group (n = 117); (2) intermediate feature group (n = 606); and (3) unfavorable feature group (n = 235). Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed to evaluate overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS).

RESULTS

RCCs with unfavorable CT-based feature showed larger size on CT, higher nuclear grade, higher rate of histologic necrosis, and higher rate of capsular invasion than those in the other two groups (all p < 0.001). Unfavorable feature was associated with poorer OS (p = 0.001), CSS (p < 0.001), and RFS (p < 0.001) on Kaplan-Meier analysis. In multivariate analysis, intermediate and unfavorable features were independent predictors for recurrence (hazard ratio [HR] 2.51, 95% confidence interval [CI] 1.09-5.79, p = 0.031 and HR 3.71, 95% CI 1.58-8.73, p = 0.003, respectively), but not for overall death or RCC-specific death.

CONCLUSIONS

A combination of irregular tumor contour feature with heterogeneous tumor texture feature on CT is associated with poor RFS in clinical T1b-T2 RCC preoperatively.

A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis

PURPOSE

To study the combined model of radiomic features and clinical features based on enhanced CT images for noninvasive evaluation of microsatellite instability (MSI) status in colorectal liver metastasis (CRLM) before surgery.

METHODS

The study included 104 patients retrospectively and collected CT images of patients. We adjusted the region of interest to increase the number of MSI-H images. Radiomic features were extracted from these CT images. The logistic models of simple clinical features, simple radiomic features, and radiomic features with clinical features were constructed from the original image data and the expanded data, respectively. The six models were evaluated in the validation set. A nomogram was made to conveniently show the probability of the patient having a high MSI (MSI-H).

RESULTS

The model including radiomic features and clinical features in the expanded data worked best in the validation group.

CONCLUSION

A logistic regression prediction model based on enhanced CT images combining clinical features and radiomic features after increasing the number of MSI-H images can effectively identify patients with CRLM with MSI-H and low-frequency microsatellite instability (MSI-L), and provide effective guidance for clinical immunotherapy of CRLM patients with unknown MSI status.

FCGR2A as one novel potential target for poor survival prognosis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma. Immunoglobulin FcγRIIa receptor (FCGR2A) has been implicated in various cancers, however, its role on ccRCC is not well studied. A total of 151 patients with ccRCC were recruited for the study. Cox proportional hazards regression analysis was performed to calculate the hazard radios of FCGR2A expression and tumor characteristics. Pathological changes associated with ccRCC in tumor tissue sections were analyzed by hematoxylin-eosin staining. Immunohistochemical and immunofluorescence staining were used to detect the protein expression of FCGR2A in the tissue sections. Correlation between the expression of FCGR2A and the overall survival (OS) of ccRCC patients was analyzed by biological process neural network and support vector machine. The expression of FCGR2A was significantly correlated with the TNM of tumor, family history of ccRCC and Fuhrman stage of ccRCC. Patients with high FCGR2A expression in the tumor tissue, had poorer OS than the patients with low and moderate FCGR2A expression. The Receiver operating characteristic curve showed that FCGR2A can be used as a sensitive and specific biomarker for the diagnosis of ccRCC. Western blotting revealed that the FCGR2A was expressed at higher levels in the ccRCC tissues. Biological process neural network and support vector machine fitting showed that the R2 between FCGR2A and survival time of ccRCC patients was 0.8429 and 0.7669, respectively. FCGR2A is highly expressed in ccRCC, higher expression of FCGR2A is associated with poorer OS of ccRCC.

A novel nomogram and prognostic factor for metastatic renal cell carcinoma survival in the era of immune checkpoint inhibitors (ICIs)

Patients with metastatic renal cell cancer (mRCC) for whom surgery is ineffective may experience a poor prognosis. The different sites where cancer has spread, and the different ways to treat it in the immune checkpoint inhibitors era could help clinical decision-making. In this study, individuals with mRCC were selected from the SEER database between 2015 and 2016 based on the Food and Drug Administration (FDA) approval of ICIs. A total of 4011 mRCC patients were studied (2239 with lung metastasis vs. 797 with liver metastasis in the immune checkpoint inhibitors period). The age ≤ 64 years and male were the majority in all cases of mRCC. When the two groups (lung metastasis and liver metastasis) were compared, the liver metastasis group had more bone metastasis than the lung metastasis group (41.8% vs. 34.1%, p < 0.001), but the lung metastasis group had more brain metastasis (8.9% vs. 11.5%) (p = 0.023). In a study of overall survival (OS) in the ICI era for mRCC, we found that lung metastasis was significantly associated with improved survival compared to liver metastasis (p < 0.001: 7 months vs. 4 months). This survival advantage restricted in lung metastasis group of mRCC after adjusting age, sex, race, marital status, histological type, metastasis to bone, and brain, origin, radiotherapy record chemotherapy record, surgery on multivariable using Cox proportional hazard model (HR = 1.407; 95% CI = 1. 269-1.560; p < 0.001). The overall survival difference between the variables of the lung metastasis and liver metastasis was noted among most of the variables, with survival benefits restricted to patients in lung metastasis in the ICI era. Patients who had undergone chemotherapy and surgery were strongly positive predictors for better OS (HR = 0.427; 95% CI = 0.379-0.481; p < 0.001) (HR = 0.371; 95% CI = 0.311-0.444; p=< 0.001), and (HR = 0.313; 95% CI = 0.264-0.372; p < 0.001), (HR = 0.427; 95% CI = 0.320-0.568; p < 0.001) in lung metastasis group and liver metastasis group. The c-index of the prognostic nomogram for OS prediction was 0.74 and 0.73. This study found that patients with lung metastasis who received ICI had better survival than those with liver metastasis. Chemotherapy and surgery enhanced survival in kidney cancer patients, whereas radiation had little impact. We developed a complete and realistic nomogram for mRCC patients based on distant metastases to the lung and liver.

Machine learning for the prediction of cognitive impairment in older adults

Objective

The purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm.

Methods

The complete data of 2,226 participants aged 60-80 years were extracted from the 2011-2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application.

Results

The study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application.

Conclusions

ML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.

Prediction model of all-cause death based on balance ability among middle-aged and older Chinese adults of overweight and obesity

Background

Advances in studies using body indicators to predict death risk. Estimating the balance ability of death risk in middle-aged and older Chinese adults with overweight and obesity is still challenging.

Methods

A retrospective analysis of the data from the China Health and Retirement Study from January 2011 to December 2018. A total of 8,632 participants were randomly divided into 7:3 a training group and a verification group, respectively. Univariable Cox analysis was used to prescreen 17 potential predictors for incorporation in the subsequent multivariable Cox analysis. Nine variables were included in the nomogram finally and validated with concordance index (C-index), calibration plots, Hosmer-Lemeshow test, and internal validation population.

Results

287 participants were death in the training group. One hundred and thirteen participants were death in the verification group. A total of nine indicators were included in the modeling group, including gender, age, marriage, hypertension, diabetes, stroke, ADL, IADL, and balance ability to establish a prediction model. The nomogram predicted death with a validated concordance index of (C-index = 0.77, 95% CI: 0.74-0.80). The inclusion of balance ability variables in the nomogram maintained predictive accuracy (C-index = 0.77, 95% CI: 0.73-0.82). The calibration curve graph and Hosmer-Lemeshow test (P > 0.05 for both the modeling group and the verification group) showed the model has a good model consistency.

Conclusion

In the present study, we provide a basis for developing a prediction model for middle-aged and older people with overweight and obesity. In most cases, balance ability is more reversible than other predictors.

Prognostic Significance of 18F-FDG PET/CT Imaging in Survival Outcomes in Patients with Renal Cell Carcinoma

Objectives:

Renal cell carcinoma (RCC) comprises 85%-90% of primary renal malignant tumors originating from the renal tubular epithelium and has different genetic characteristics. This study aimed to investigate the potential predictive role of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and metabolic parameters in overall survival (OS) analysis in patients with RCC.

Methods:

¹⁸F-FDG PET/CT images of 100 patients performed for initial staging before surgical or oncological treatments were analyzed retrospectively. Maximum standard uptake value (SUV_max-T) of the primary tumor was calculated and its relationship to patient survival was analyzed. The median follow-up time was 5.61 years (0.01-8.7 years).

Results:

SUV_max-T levels in the patients ranged from 2.1 to 48.9 (median 5.9, mean 9.0±7.9). SUV_max-T was significantly higher in RCC-related death more positive than in the negative cases (p<0.001). However, there was not any statistical significance for gender and pathological subtypes on the survival outcomes of patients (p=0.264 and p=0.784). The patients’ 1-year, 3-year, and 5-year OS rates were 71%, 61%, and 57%, respectively. The highest action of SUV_max-T for estimating OS was a cut-off level of 5.4, which maintained sensitivity and specificity of 81% and 75%, respectively. However, cancer staging remained independent significance for OS (p<0.001).

Conclusion:

SUV_max of primary tumor and cancer stage were demonstrated as significant prognostic factors for OS in patients with RCC. Evaluation of 18F-FDG accumulation with PET/CT may help plan treatment strategies and predict survival outcomes of these patients at diagnosis.

The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model

Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.

Characterization of Platelet Function-Related Gene Predicting Survival and Immunotherapy Efficacy in Gastric Cancer

Immunotherapy is widely used to treat various cancers, but patients with gastric cancer (GC), which has a high mortality rate, benefit relatively less from this therapy. Platelets are closely related to GC progression and metastasis. This study aimed to find novel potential biomarkers related to platelet function to predict GC and immunotherapy efficacy. First, based on platelet activation, signaling, and aggregation (abbreviation: function)-related genes (PFRGs), we used the least absolute shrinkage and selection operator (Lasso) regression method to construct a platelet-function-related genes prognostic score (PFRGPS). PRFGPS was verified in three independent external datasets (GSE26901, GSE15459, and GSE84437) for its robustness and strong prediction performance. Our results demonstrate that PRFGPS is an independent prognostic indicator for predicting overall survival in patients with GC. In addition, prognosis, potential pathogenesis mechanisms, and the response to immunotherapy were defined via gene set enrichment analysis, tumor mutational burden, tumor microenvironment, tumor immune dysfunction and exclusion (TIDE), microsatellite instability, and immune checkpoint inhibitors. We found that the high-PRFGPS subgroup had a cancer-friendly immune microenvironment, a high TIDE score, a low tumor mutational burden, and relatively low microsatellite instability. In the immunophenoscore model, the therapeutic effect on anti-PD-1 and anti-CTLA-4 in the high-PRFGPS subgroup was relatively low. In conclusion, PRFGPS could be used as a reference index for GC prognosis to develop more successful immunotherapy strategies.

PI3K/AKT/mTOR Pathway-Associated Genes Reveal a Putative Prognostic Signature Correlated with Immune Infiltration in Hepatocellular Carcinoma

Background

The dysregulated PI3K/AKT/mTOR pathway acts as the main regulator of tumorigenesis in hepatocellular carcinoma (HCC).

Aim

Here, we identify the prognostic significance of PI3K/AKT/mTOR pathway-associated genes (PAGs) as well as their putative signature based on PAGs in an HCC patient's cohort.

Methods

The transcriptomic data and clinical feature sets were queried to extract the putative prognostic signature.

Results

We identified nine PAGs with different expressions. GO and KEGG indicated that these differentially expressed genes were associated with various carcinogenic pathways. Based on the signature-computed median risk score, we categorized the patients into groups of low risk and high risk. The survival time for the low-risk group is longer than that of the high-risk group in Kaplan-Meier (KM) curves. The prognostic value of risk score (ROC = 0.736) of receiver operating characteristic (ROC) curves performed better in comparison to that of other clinicopathological features. In both the GEO database and ICGC database, these outcomes were verified. The predictions of the overall survival rates in HCC patients of 1 year, 3 years, and 5 years can be obtained separately from the nomogram. The risk score was associated with the immune infiltrations of CD8 T cells, activated CD4 memory T cells, and follicular helper T cells, and the expression of immune checkpoints (PD-1, TIGIT, TIM-3, BTLA, LAG-3, and CTLA4) was positively relevant to the risk score. The sensitivity to several chemotherapeutic drugs can also be revealed by the signature. CDK1, PITX2, PRKAA2, and SFN were all upregulated in the tumor tissue of clinical samples.

Conclusion

A putative and differential dataset-validated prognostic signature on the basis of integrated bioinformatic analysis was established in our study, providing the immunotherapeutic targets as well as the personalized treatment in HCC with neoteric insight.

Development and Validation of a Nomogram to Predict Cancer-Specific Survival in Elderly Patients With Papillary Renal Cell Carcinoma

Objective

Papillary renal cell carcinoma (pRCC) is the second most common type of renal cell carcinoma and an important disease affecting older patients. We aimed to establish a nomogram to predict cancer-specific survival (CSS) in elderly patients with pRCC.

Methods

Patient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) project, and we included all elderly patients with pRCC from 2004 to 2018. All patients were randomly divided into a training cohort and a validation cohort. Univariate and multivariate Cox proportional risk regression models were used to identify patient independent risk factors. We constructed a nomogram based on a multivariate Cox regression model to predict CSS for 1-, 3-, and 5- years in elderly patients with pRCC. A series of validation methods were used to validate the accuracy and reliability of the model, including consistency index (C-index), calibration curve, and area under the Subject operating curve (AUC).

Results

A total of 13,105 elderly patients with pRCC were enrolled. Univariate and multivariate Cox regression analysis suggested that age, tumor size, histological grade, TNM stage, surgery, radiotherapy and chemotherapy were independent risk factors for survival. We constructed a nomogram to predict patients' CSS. The training and validation cohort's C-index were 0.853 (95%CI: 0.859–0.847) and 0.855 (95%CI: 0.865–0.845), respectively, suggesting that the model had good discrimination ability. The AUC showed the same results. The calibration curve also indicates that the model has good accuracy.

Conclusions

In this study, we constructed a nomogram to predict the CSS of elderly pRCC patients, which has good accuracy and reliability and can help doctors and patients make clinical decisions.

Nomogram for predicting the overall survival of patients with early-onset prostate cancer: A population-based retrospective study

Background

The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients.

Methods

A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results

Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS.

Conclusions

We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes.

Analysis and Prediction of the Survival Trends of Patients with Clear-Cell Renal Cell Carcinoma: A Model-Based Period Analysis, 2001-2015

Background

Clear-cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors worldwide whose poor prognosis results in a serious disease burden on patients. The changing trend of the long-term relative survival rates (RSRs) of patients with ccRCC was analyzed in this study to evaluate their treatment results over a 15-year period.

Methods

This study is a retrospective study, which assessed and predicted the 1-, 3-, and 5-year survival rates of patients with ccRCC during 2001-2005, 2006-2010, 2011-2015, and 2016-2020 using data extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Period analysis was used in this study to analyze the data from the SEER database and to assess survival differences according to age, sex, race, and socioeconomic status (SES) during the 15-year study period by comparing Kaplan-Meier curves.

Results

During 2001-2015, the 5-year RSR of patients with ccRCC increased from 78.4% to 83.0%, and the generalized linear model predicted that the 5-year RSR increased to 85.7% during 2016-2020. The RSR of patients with ccRCC differed significantly with SES, race, sex, and age. Compared with male patients, the survival advantage of female patients decreased as their age increased. The RSR of all patients with ccRCC was also lower in patients with a lower SES and of black race.

Conclusion

This study found an improvement in the RSR of patients with ccRCC during 2001-2020. Understanding the change trend of the survival rate of patients with ccRCC is helpful to improve the design of clinical trials. It also provides basic data and a scientific basis for evaluating the harm of ccRCC on the health of affected patients and the effect of cancer prevention, and developing cancer prevention plans.

Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Background

Bone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.

Methods

Patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.

Results

A total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.

Conclusions

The comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.

Basic Characteristics and Survival Outcomes of Asian-American Patients with Clear Cell Renal Cell Carcinoma and Comparisons with White Patients: A Population-Based Analysis

Background

To explore the baseline characteristics, pathological and survival outcomes of Asian-American patients with clear cell renal cell carcinoma (ccRCC), and make comparisons with White patients.

Materials and Methods

In this study, patients diagnosed with ccRCC between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Basic characteristics of Asian-American patients were analysed and compared with White patients. Then, proportional mortality ratio (PMR) analyses were performed in Asian population to investigate the proportions of different cause of deaths (CODs), and make comparisons with White patients. Moreover, Kaplan-Meier (KM) analyses were developed to investigate the survival disparities of ccRCC patients between Asian-Americans and White patients. Finally, a competing risk regression model was constructed to identify potential prognostic factors for ccRCC patients in the whole population.

Results

A total of 1586 Asian-American patients were eventually identified, and the median age at diagnosis was 61 years old. In Asian patients, those from South Asian had the youngest age at diagnosis (P<0.001) and the earliest stage of diseases (localized: 76.83%, T1: 70.73%, all P<0.05) when compared with other ethnicities. No significant differences were detected in tumor characteristics between Asian-Americans and White patients. Older age (P<0.001), earlier stage (P<0.001) and the administration of surgery (P=0.050) were tightly associated with a lower risk of dying of RCC in Asian-American patients. Additionally, Asian-American patients had comparable survival outcomes when compared with White patients. Lastly, competing risk regression model revealed that age at diagnosis (P<0.001), tumor grade (P<0.001), histological stage (P<0.001), median household income (P<0.001) and the administration of surgery (P<0.001) were prognostic factors for cancer-specific survival (CSS) in ccRCC patients, while died of other causes was regarded as a competing event.

Conclusion

Asian-American patients had similar tumor characteristics and survival outcomes with White patients. In Asian patients, those from South Asian had the youngest age at diagnosis and the earliest stage of diseases. Age, grade, histological stage, household income and surgery were identified to be closely related to CSS in ccRCC patients. In the future, prospective and well-designed studies are needed to verify our findings.

Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients' overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.

Development and Validation of Prognostic Nomogram for Young Patients with Kidney Cancer

Background

The aim of this study was to establish a nomogram model to evaluate the prognosis of early-onset kidney cancer (EOKC) in terms of overall survival (OS) and cancer-specific survival (CSS).

Methods

Patients with EOKC diagnosed between 2004 and 2015 were collected from Surveillance, Epidemiology and End Results (SEER) and randomly assigned to the training and validation set at a ratio of 2 to 1. Important variables for constructing nomograms were screened by univariate and multivariate Cox analysis. The nomogram model was evaluated using concordance index (C-index), decision curve analysis (DCA) curves, and receiver operating characteristic (ROC) curves.

Results

A total of 12,526 EOKC patients were included in the study. OS nomogram was constructed based on gender, age, race, grade, AJCC stage, TNM stage, histology, chemotherapy and radiotherapy. CSS nomogram was constructed based on listed above except gender. In the external validation, the C-index for the OS nomogram was 0.855 (95% CI 0.834–0.976), and the C-index for the CSS nomogram was 0.938 (0.925–0.951). High-quality calibration curves were noted in both OS and CSS nomogram models. ROC and DCA curves showed that nomograms had better predictive performance than TNM stage and SEER stage.

Conclusion

The nomogram model provides an applicable tool for evaluating the OS and CSS prognosis of EOKC.

The prognosis and clinicopathological features of different distant metastases patterns in renal cell carcinoma: analysis based on the SEER database

Existing data on the prognosis and clinicopathological features of patients with metastatic renal cell carcinoma (mRCC) are limited. This study aims to investigate the prognostic value and clinicopathological features of different metastatic sites in patients with mRCC. A dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database consisting of 18 registries (1973–2015) was selected for a retrospective mRCC cohort study. Information was included on the metastatic sites in lung, bone, liver, and brain. Kaplan–Meier analysis was applied to compare the survival distribution. Univariate and multivariate Cox regression models were used to analyze survival outcomes. From the SEER database, a total of 10,410 patients with primary mRCC from 2010 to 2015 were enrolled in this cohort study. Analysis indicated that 54.9%, 37.7%, 19.5%, and 10.4% of patients were found to have lung, bone, liver, and brain metastasis, respectively. There was a significantly higher risk for sarcomatoid RCC patients to develop liver metastasis as compared to patients with clear cell RCC. The median survival for patients with lung, bone, liver, or brain metastasis was 7 months, 7 months, 4 months, and 5 months, respectively. Various clinicopathological features and prognostic values are associated with different metastatic sites. Understanding these differences may enable targeted pre-treatment assessment of primary mRCC and personalized curative intervention for patients.

Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

A Retrospective, Multicenter, Long-Term Follow-Up Analysis of the Prognostic Characteristics of Recurring Non-Metastatic Renal Cell Carcinoma After Partial or Radical Nephrectomy

This study aimed to compare the cancer-specific survival (CSS) and overall survival (OS) of nephrectomized patients with non-metastatic renal cell carcinoma (nmRCC) and local recurrence without distant metastasis (LR group), those with metastasis without local recurrence (MET group), and those with both local recurrence and metastasis (BOTH group). This retrospective multicenter study included 464 curatively nephrectomized patients with nmRCC and disease recurrence between 2000 and 2012; the follow-up period was until 2017. After adjusting for significant clinicopathological factors using Cox proportional hazard models, CSS and OS were compared between the MET (n = 50, 10.7%), BOTH (n = 95, 20.5%), and LR (n = 319, 68.8%) groups. The CSS and OS rates were 34.7 and 6.5% after a median follow-up of 43.9 months, respectively. After adjusting for significant prognostic factors of OS and CSS, the MET group had hazard ratios (HRs) of 0.51 and 0.57 for OS and CSS (p = 0.039 and 0.103), respectively, whereas the BOTH group had HRs of 0.51 and 0.60 for OS and CSS (p < 0.05), respectively; LR was taken as a reference. The 2-year OS and CSS rates from the date of nephrectomy and disease recurrence were 86.9% and 88.9% and 63.5% and 67.8%, respectively, for the LR group; 89.5% and 89.5% and 48.06% and 52.43%, respectively, for the MET group; and 96.8% and 96.8% and 86.6% and 82.6%, respectively, for the BOTH group. Only the LR and BOTH groups had significant differences in the 2-year OS and CSS rates (p < 0.05). In conclusion, our study showed that the LR group had worse survival prognoses than any other group in nephrectomized patients with nmRCC.

A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases

BACKGROUND

The optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial.

METHODS

We selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model.

RESULTS

A total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively.

CONCLUSIONS

Our nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases.

Identification of a Risk Stratification Model to Predict Overall Survival and Surgical Benefit in Clear Cell Renal Cell Carcinoma With Distant Metastasis

Background: Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma and has different prognoses, especially in patients with metastasis. Here, we aimed to establish a novel model to predict overall survival (OS) and surgical benefit of ccRCC patients with distant metastasis. Methods: Using data from the Surveillance, Epidemiology, and End Results (SEER) databases, we identified 2185 ccRCC patients with distant metastasis diagnosed from 2010 to 2015. Univariate and multivariate Cox analysis were used to identify significant prognostic clinicopathological variables. By integrating these variables, a prognostic nomogram was constructed and evaluated using C-indexes and calibration curves. The discriminative ability of the nomogram was measured by analyses of receiver operating characteristic (ROC) curve. A risk stratification model was built according to each patient's total scores. Kaplan-Meier curves were performed in the low-, intermediate- and high-risk groups to evaluate the survival benefit of surgery. Results: Eight clinicopathological variables were included as independent prognostic factors in the nomogram: grade, marital status, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. The nomogram had a better discriminative ability for predicting OS than Tumor-Node-Metastasis (TNM) stage. The C-index was 0.71 (95% CI 0.68-0.74) in the training cohort. The calibration plots demonstrated that the nomogram-based predictive outcomes had good consistency with the actual prognosis results. Total nephrectomy improved prognosis in both the low-risk and intermediate-risk groups, but partial nephrectomy could only benefit the low-risk group. Conclusions: We constructed a predictive nomogram and risk stratification model to evaluate prognosis in ccRCC patients with distant metastasis, which was valuable for prognostic stratification and making therapeutic decisions.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Adjuvant targeted therapy combined with surgery for advanced and metastatic renal cell carcinoma: A protocol for systematic review and meta analysis

BACKGROUND

The aim of this systematic review and meta-analysis is to evaluate the efficacy and safety of adjuvant targeted therapy by sunitinib combined with surgery in the treatment of advanced or metastatic renal cell carcinoma.

METHODS

PubMed/Medline, Web of Science, Cochrane Library, ClinicalTrials.gov (http://www.ClinicalTrials.gov), China National Knowledge Infrastructure (CNKI) will be searched for clinical research articles related to the efficacy and safety of adjuvant therapy combined with surgery in the treatment of advanced and metastatic RCC. The identification, inclusion and exclusion flow charts will be conducted according to the PRISMA guidelines. The quality assessment will be done by Quadas-2 evaluation tool. Key parameters including OS in 10, 20, 30, and 40 months, PFS in 10, 20, and 30 months, objective response rate (ORR), stable disease (SD) rate, progressive disease (PD) rate, median OS and PFS, types of AEs and their occurrence rates, etc will be extracted. The evaluation of the efficacy and safety will be pooled by CMA.

RESULTS

This systematic review will provide evidence on the efficacy and safety of adjuvant therapy by sunitinib combined with surgery in treating advanced and metastatic RCC.

CONCLUSION

The study aims to generalize data concerning the response rate, OS, PFS and rates of adverse effects of the perioperative use of sunitinib in advanced and metastatic RCC patients. The evidence provided by this systematic review and meta-analysis will help guide the clinical decision making and enlighten the future management of advanced or metastatic RCC.

REGISTRATION

This protocol has been registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY registration number: INPLASY2020110093; INPLASY DOI number: 10.37766/inplasy2020.11.0093 Available at: https://inplasy.com).

Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92-1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

Nomograms-based prediction of overall and cancer-specific survivals for patients with chromophobe renal cell carcinoma

This study built and tested two effective nomograms for the purpose of predicting cancer-specific survival and overall survival of chromophobe renal cell carcinoma (chRCC) patients. Multivariate Cox regression analysis was employed to filter independent prognostic factors predictive of cancer-specific survival and overall survival, and the nomograms were built based on a training set incorporating 2901 chRCC patients in a retrospective study (from 2004 to 2015) downloaded from the surveillance, epidemiology, and end results (SEER) database. The nomograms were verified on a validation cohort of 1934 patients, subsequently the performances of the nomograms were examined according to the receiver operating characteristic curve, calibration curves, the concordance (C-index), and decision curve analysis. The results showed that tumor grade, AJCC and N stages, race, marital status, age, histories of chemotherapy, radiotherapy and surgery were the individual prognostic factors for overall survival, and that AJCC, N and SEER stages, histories of surgery, radiotherapy and chemotherapy, age, tumor grade were individual prognostic factors for cancer-specific survival. According to C-indexes, receiver operating characteristic curves, and decision curve analysis outcomes, the nomograms showed a higher accuracy in predicting overall survival and OSS when compared with TNM stage and SEER stage. All the calibration curves were significantly consistent between predictive and validation sets. In this study, the nomograms, which were validated to be highly accurate and applicable, were built to facilitate individualized predictions of the cancer-specific survival and overall survival to patients diagnosed with chRCC between 2004 and 2015.

Clinical value of CTLA4 combined with clinicopathological factors in evaluating the prognosis of breast cancer

Background

Clinical prediction of breast cancer prognosis relies on both clinical-pathological features and biological markers. Many studies have revealed that tumor cytotoxic T lymphocyte antigen 4 (CTLA4) expression may present prognostic predicting value in cancers. We intended to explore the prognostic value of significant clinicopathological parameters and CTLA4 for predicting survival of patients with breast cancer.

Methods

A total of 229 breast cancer patients who had radical surgery treatment between Sep 2009 and April 2011 were enrolled in this study. Immunohistochemical staining was performed to evaluate CTLA4 grade and Ki-67 index in breast cancer tissue. Univariate and multivariate logistic analysis, Kaplan-Meier survival analysis and ROC curve were used to explore the association between CTLA4 or clinicopathological parameters and disease-free survival (DFS). A nomogram was constructed based on the regression model to predict DFS of patients with breast cancer.

Results

CTLA4 grade (OR 1.730, 95% CI: 1.213-2.468, P=0.002), Ki-67 (OR 1.449, 95% CI: 1.069-1.964, P=0.017) and N stage (lymph node metastasis) (OR 2.268, 95% CI: 1.588-3.303, P=0.000) showed significantly association with DFS of breast cancer patients. All these factors were independent predictors for poor survival, as patients with stage N2-3 tumors, high CTLA4 grade and Ki-67 index showed low survival probability (P<0.01). The conjunction of these factors exhibited good discrimination value (AUC 0.815, 95% CI: 0.749-0.882, P=0.000). Nomogram performed based on CTLA4 grade, Ki-67 index and N stage provided an efficient method to predict DFS of patients with breast cancer.

Conclusions

The high expression of CTLA4 and Ki-67 together with lymph node metastasis in breast cancer are independent risk factors that affect the prognosis of breast cancer patients. They have the potentiality to be utilized conjunctively as predictor in clinical practice.

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.

The role of income disparities on survival in metastatic clear cell renal cell carcinoma in the targeted therapy era

PURPOSE

The influence of socioeconomic status on metastatic clear cell renal cell carcinoma (RCC) in the target therapy era is still unknown. This study aimed to assess the role of income disparities on prognosis of mRCC in the targeted therapy era.

PATIENTS AND METHODS

Data of patients with mRCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Median household income (MHI) was used to represent patients' socioeconomic status, and its role on overall survival (OS) and cancer-specific survival (CSS) was evaluated.

RESULTS

A total of 3791 patients with clear cell mRCC diagnosed between 2010 and 2015 were enrolled in cohort one. There was an obvious imbalance of race and insurance status in patients with difference MHI. Compared with patients in the poorest quartile 1 (Q1), those in the wealthiest Q4 had a 4-month prolonged OS (P < 0.01) and a 5-month prolonged CSS (P < 0.01), and those in Q3 and Q4 had significantly lower death risk. High income decreased cumulative cancer-specific mortality rates, and potentially favored survival in most subgroups. 6619 patients diagnosed between 2004 and 2015 were included in cohort two. We found that only those with Q4 income achieved a prolonged survival with statistical significance by comparing between patients diagnosed in 2004-2009 and 2010-2015.

CONCLUSION

In the targeted therapy era, there were survival gaps of mRCC between patients with low- and high-income. Measures should be taken to develop a comprehensive and financially sustainable plan of cancer treatment for greater equity.

A nomogram for determining the disease-specific survival in invasive lobular carcinoma of the breast: A population study

We aimed to establish and validate a nomogram for predicting the disease-specific survival of invasive lobular carcinoma (ILC) patients.The Surveillance, Epidemiology, and End Results program database was used to identify ILC from 2010 to 2015, in which the data was extracted from 18 registries in the US. Multivariate Cox regression analysis was performed to identify independent prognostic factors and a nomogram was constructed to predict the 3-year and 5-year survival rates of ILC patients based on Cox regression. Predictive values were compared between the new model and the American Joint Committee on Cancer staging system using the concordance index, calibration plots, integrated discrimination improvement, net reclassification improvement, and decision-curve analyses.In total, 4155 patients were identified. After multivariate Cox regression analysis, nomogram was established based on a new model containing the predictive variables of age, the primary tumor site, histology grade, American Joint Committee on Cancer TNM (tumor node metastasis) stages II, III, and IV, breast cancer subtype, therapy modality (surgery and chemotherapy). The concordance index for the training and validation cohorts were higher for the new model (0.781 and 0.832, respectively) than for the old model (0.733 and 0.779). The new model had good performance in the calibration plots. Net reclassification improvement and integrated discrimination improvement were also improved. Finally, decision-curve analyses demonstrated that the nomogram was clinically useful.We have developed a reliable nomogram for determining the prognosis and treatment outcomes of ILC. The new model facilitates the choosing of superior medical examinations and the optimizing of therapeutic regimens with cooperation among oncologists.

An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) ranks the fourth in terms of cancer-related mortality globally. Herein, in this research, we attempted to develop a novel immune-related gene signature that could predict survival and efficacy of immunotherapy for HCC patients.

METHODS

The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquiring immune-related genes from the ImmPort database. Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Kaplan-Meier survival curves as well as time-dependent receiver operating characteristic (ROC) curve were performed to evaluate its predictive capability. Besides, both univariate and multivariate analyses on overall survival for the IRGPI and multiple clinicopathologic factors were carried out, followed by the construction of a nomogram. Finally, we explored the possible correlation of IRGPI with immune cell infiltration or immunotherapy efficacy.

RESULTS

Analysis of 365 HCC samples identified 11 differentially expressed immune-related genes, which were selected to establish the IRGPI. Notably, it can predict the survival of HCC patients more accurately than published biomarkers. Furthermore, IRGPI can predict the infiltration of immune cells in the tumor microenvironment of HCC, as well as the response of immunotherapy.

CONCLUSION

Collectively, the currently established IRGPI can accurately predict survival, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients.

Biological Evaluation of Oxindole Derivative as a Novel Anticancer Agent against Human Kidney Carcinoma Cells

Renal cell carcinoma has emerged as one of the leading causes of cancer-related deaths in the USA. Here, we examined the anticancer profile of oxindole derivatives (SH-859) in human renal cancer cells. Targeting 786-O cells by SH-859 inhibited cell growth and affected the protein kinase B/mechanistic target of rapamycin 1 pathway, which in turn downregulated the expression of glycolytic enzymes, including lactate dehydrogenase A and glucose transporter-1, as well as other signaling proteins. Treatment with SH-859 altered glycolysis, mitochondrial function, and levels of adenosine triphosphate and cellular metabolites. Flow cytometry revealed the induction of apoptosis and G0/G1 cell cycle arrest in renal cancer cells following SH-859 treatment. Induction of autophagy was also confirmed after SH-859 treatment by acridine orange and monodansylcadaverine staining, immunocytochemistry, and Western blot analyses. Finally, SH-859 also inhibited the tumor development in a xenograft model. Thus, SH-859 can serve as a potential molecule for the treatment of human renal carcinoma.

An immune scores-based nomogram for predicting overall survival in patients with clear cell renal cell carcinoma

The role of immune cell infiltration in the prognosis of clear cell renal cell carcinoma (ccRCC) has received increasing attention. However, immune scores have not yet been introduced into routine clinical practice of ccRCC patients. The principal objective of our research was to study the correlation between immune scores and overall survival (OS) of ccRCC.In this study, Cox regression analyses were used to identify risk factors associated with OS of ccRCC based on the Cancer Genome Atlas datasets. Furthermore, an integrated nomogram combining immune scores and clinicopathologic factors was built for predicting 3- and 5-year OS of ccRCC patients. The receiver operating characteristic curve, concordance index, and calibration curves were used for the evaluation of our nomogram. Also, Kaplan-Meier (KM) survival analysis of immune scores, stromal scores, and different clinicopathological factors was performed.A total of 514 patients were divided into the low- or high-immune scores group. KM and multivariate Cox regression analyses demonstrated that ccRCC patients with high-immune scores had significantly poor OS compared with those with low-immune scores. Calibration curves showed good consistency between the predicted OS and the actual OS probability. Areas under the receiver operating characteristic curves for 3- and 5-year OS were 0.816 and 0.769, and the concordance index was 0.775, indicating that our nomogram had good accuracy for predicting OS of ccRCC patients. Additionally, KM analysis showed that older age, later T stage, distant metastasis, advanced tumor lymph node metastasis stage, higher tumor grade, left site, and low stromal scores were associated with worse OS in ccRCC patients.High-immune scores show a significant correlation with unsatisfactory prognosis in ccRCC patients. Furthermore, the immune scores-based nomogram may be helpful in predicting ccRCC prognosis.

Development and validation of prognostic nomograms for pseudomyxoma peritonei patients after surgery: A population-based study

BACKGROUND

The aim of study was to develop and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) of patients with pseudomyxoma peritonei (PMP) and compare the predictive accuracy with the American Joint Committee on Cancer (AJCC) staging system.

METHODS

Data of 4959 PMP patients who underwent surgical resection were collected between 2004 and 2015 from the Surveillance Epidemiology and End Results (SEER) database. All included patients were divided into training (n = 3307) and validation (n = 1652) cohorts. The Kaplan-Meier method and Cox proportional hazard model were applied. Nomograms were validated by discrimination and calibration. Finally, concordance index (C-index) was used to compare the predictive performance of nomograms with that of the AJCC staging system.

RESULTS

According to the univariate and multivariate analyses of training sets, both nomograms for predicting OS and CSS combining age, grade, location, N stage, M stage, and chemotherapy were identified. Nomograms predicting OS also incorporated T stage and the number of lymph nodes removed (LNR). The calibration curves showed good consistency between predicted and actual observed survival. Moreover, C-index values demonstrated that the nomograms predicting both OS and CSS were superior to the AJCC staging system in both cohorts.

CONCLUSION

We successfully developed and validated prognostic nomograms for predicting OS and CSS in PMP patients. Two nomograms were more accurate and applicable than the AJCC staging system for predicting patient survival, which may help clinicians stratify patients into different risk groups, tailor individualized treatment, and accurately predict patient survival in PMP.

Predictive Nomogram for Midterm to Long-Term Prognosis in Patients with Papillary Renal Cell Carcinoma Based on Data from the Surveillance, Epidemiology, and End Results (SEER) Program

BACKGROUND This study aimed to develop a predictive nomogram for midterm to long-term prognosis in patients with papillary renal cell carcinoma (RCC) based on data from the US Surveillance, Epidemiology, and End Results (SEER) program. MATERIAL AND METHODS Clinical pathology data and follow-up information were obtained from the SEER database for patients with papillary RCC between 1997-2014. Univariate and multivariate Cox regression models evaluated the independent prognostic factors, and the nomogram was constructed to predict the 3-year, 5-year, and 10-year survival rates. Multiple parameters were estimated to evaluate the predictive values, including the concordance indices (C-indices), calibration plots, area under the receiver operator characteristics (ROC) curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS The study included 13,926 patients with papillary RCC. Univariate and multivariate Cox regression analysis developed the nomogram that relied on the predictive variables of age, Fuhrman grade, TNM stage, surgery of the primary site, lymphadenectomy, and marital status. The C-indices of the novel model in the validation cohort were more satisfactory than those of the TNM classification. Accurate discrimination and calibration by the nomogram were identified in both cohorts. The NRI and IDI supported prediction improvements, and the DCA supported the nomogram's clinical significance. CONCLUSIONS A nomogram was developed to evaluate the prognosis of papillary RCC and to identify the patients who required specialized treatment. However, external validation of the predictive nomogram is required that also includes patients from other countries.

Identification of a Glucose Metabolism-related Signature for prediction of Clinical Prognosis in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent and invasive histological subtypes among all renal cell carcinomas (RCC). Cancer cell metabolism, particularly glucose metabolism, has been reported as a hallmark of cancer. However, the characteristics of glucose metabolism-related gene sets in ccRCC have not been systematically profiled.

Methods: In this study, we downloaded a gene expression profile and glucose metabolism-related gene set from TCGA (The Cancer Genome Altas) and MSigDB, respectively, to analyze the characteristics of glucose metabolism-related gene sets in ccRCC. We used a multivariable Cox regression analysis to develop a risk signature, which divided patients into low- and high- risk groups. In addition, a nomogram that combined the risk signature and clinical characteristics was created for predicting the 3- and 5-year overall survival (OS) of ccRCC. The accuracy of the nomogram prediction was evaluated using the area under the receiver operating characteristic curve (AUC) and a calibration plot.

Results: A total of 231 glucose metabolism-related genes were found, and 68 differentially expressed genes (DEGs) were identified. After screening by univariate regression analysis, LASSO regression analysis and multivariable Cox regression analysis, six glucose metabolism-related DEGs (FBP1, GYG2, KAT2A, LGALS1, PFKP, and RGN) were selected to develop a risk signature. There were significant differences in the clinical features (Fuhrman nuclear grade and TNM stage) between the high- and low-risk groups. The multivariable Cox regression indicated that the risk score was independent of the prognostic factors (training set: HR=3.393, 95% CI [2.025, 5.685], p<0.001; validation set: HR=1.933, 95% CI [1.130, 3.308], p=0.016). The AUCs of the nomograms for the 3-year OS in the training and validation sets were 0.808 and 0.819, respectively, and 0.777 and 0.796, respectively, for the 5- year OS.

Conclusion: We demonstrated a novel glucose metabolism-related risk signature for predicting the prognosis of ccRCC. However, additional in vitro and in vivo research is required to validate our findings.

Construction and Validation of an Autophagy-Related Prognostic Risk Signature for Survival Predicting in Clear Cell Renal Cell Carcinoma Patients

Background: Clear cell renal cell carcinoma (ccRCC) is a common type of malignant tumors in urinary system. Evaluating the prognostic outcome at the time of initial diagnosis is essential for patients. Autophagy is known to play a significant role in tumors. Here, we attempted to construct an autophagy-related prognostic risk signature based on the expression profile of autophagy-related genes (ARGs) for predicting the long-term outcome and effect of precise treatments for ccRCC patients.

Methods: We obtained the expression profile of ccRCC from the cancer genome atlas (TCGA) database and extract the portion of ARGs. We conducted differentially expressed analysis on ARGs and then performed enrichment analyses to confirm the anomalous autophagy-related biological functions. Then, we performed univariate Cox regression to screen out overall survival (OS)-related ARGs. With these genes, we established an autophagy-related risk signature by least absolute shrinkage and selection operator (LASSO) Cox regression. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, survival analysis, clinic correlation analysis, and Cox regression. Then we analyzed the function of each gene in the signature by single-gene gene set enrichment analysis (GSEA). Finally, we analyzed the correlation between our risk score and expression level of several targets of immunotherapy and targeted therapy.

Results: We established a seven-gene prognostic risk signature, according to which we could divide patients into high or low risk groups and predict their outcomes. ROC analysis and survival analysis validated the reliability of the signature. Clinic correlation analysis found that the risk group is significantly correlated with severity of ccRCC. Multivariate Cox regression revealed that the risk score could act as an independent predictor for the prognosis of ccRCC patients. Correlation analysis between risk score and targets of precise treatments showed that our risk signature could predict the effects of precise treatment powerfully.

Conclusion: Our study provided a brand new autophagy-related seven-gene prognostic risk signature, which could perform as a prognostic indicator for ccRCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and suggest therapeutic strategies in the category of precise treatment in ccRCC.

Nomograms for predicting specific distant metastatic sites and overall survival of colorectal cancer patients: A large population-based real-world study

BACKGROUND

This study aims to develop functional nomograms to predict specific distant metastatic sites and overall survival (OS) of colorectal cancer (CRC) patients.

METHODS

CRC case data were retrospectively recruited from a large population-based public dataset. Nomograms were developed to predict the probabilities of specific distant metastatic sites and OS of CRC patients. The performance of nomogram was evaluated with the concordance index (C-index), calibration curves, area under the curve (AUC), and decision curve analysis (DCA).

RESULTS

A total of 142 343 cases were included in the current study. On the basis of univariate and multivariate analyses, clinicopathological features were correlated with specific distant metastatic sites and survival outcomes and were used to establish nomograms. The nomograms showed excellent accuracy in predicting specific distant metastatic sites. The C-indexes for the prediction of liver, lung, bone, and brain metastases were 0.82 (95% confidence interval (CI), 0.81-0.83), 0.80 (95% CI, 0.78-0.81), 0.83 (95% CI, 0.79-0.86), and 0.73 (95% CI, 0.72-0.84), respectively. Then, a prognostic nomogram integrating clinicopathological features and specific distant metastatic sites was established to predict 1-, 3-, and 5-year OS of CRC, with AUCs of 0.764 (95% CI, 0.741-0.783), 0.762 (95% CI, 0.745-0.781), and 0.745 (95% CI, 0.730-0.761), respectively. DCA showed that the prognostic nomogram had a better clinical application value than current TNM staging system.

CONCLUSIONS

Based on clinicopathological features, original nomograms were constructed for clinicians to predict specific distant metastatic sites and OS of CRC patients. These models could help to support the postoperative personalized assessment.

Prognostic nomograms for predicting cause-specific survival and overall survival of stage I-III colon cancer patients: a large population-based study

Background

The purpose of this study was to build functional nomograms based on significant clinicopathological features to predict cause-specific survival (CSS) and overall survival (OS) in patients with stage I–III colon cancer.

Methods

Data on patients diagnosed with stage I–III colon cancer between 2010 and 2015 were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors, which were used to construct nomograms to predict the probabilities of CSS and OS. The performance of the nomogram was assessed by C-indexes, receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was used to compare clinical usage between the nomogram and the tumor–node–metastasis (TNM) staging system.

Results

Based on the univariate and multivariate analyses, features that correlated with survival outcomes were used to establish nomograms for CSS and OS prediction. The nomograms showed favorable sensitivity at predicting 1-, 3-, and 5-year CSS and OS, with a C-index of 0.78 (95% confidence interval (CI) 0.77–0.80) for CSS and 0.74 (95% CI 0.73–0.75) for OS. Calibration curves and ROC curves revealed excellent predictive accuracy. The clinically and statistically significant prognostic performance of the nomogram generated with the entire group of patients and risk scores was validated by a stratified analysis. DCA showed that the nomograms were more clinically useful than TNM stage.

Conclusion

Novel nomograms based on significant clinicopathological characteristics were developed and can be used as a tool for clinicians to predict CSS and OS in stage I–III colon cancer patients. These models could help facilitate a personalized postoperative evaluation.

Knockdown of Pyruvate Kinase M2 Inhibits Cell Proliferation, Metabolism, and Migration in Renal Cell Carcinoma

Emerging evidence indicates that the activity of pyruvate kinase M2 (PKM2) isoform is crucial for the survival of tumor cells. However, the molecular mechanism underlying the function of PKM2 in renal cancer is undetermined. Here, we reveal the overexpression of PKM2 in the proximal tubule of renal tumor tissues from 70 cases of patients with renal carcinoma. The functional role of PKM2 in human renal cancer cells following small-interfering RNA-mediated PKM2 knockdown, which retarded 786-O cell growth was examined. Targeting PKM2 affected the protein kinase B (AKT)/mechanistic target of the rapamycin 1 (mTOR) pathway, and downregulated the expression of glycolytic enzymes, including lactate dehydrogenase A and glucose transporter-1, and other downstream signaling key proteins. PKM2 knockdown changed glycolytic metabolism, mitochondrial function, adenosine triphosphate (ATP) level, and intracellular metabolite formation and significantly reduced 786-O cell migration and invasion. Acridine orange and monodansylcadaverine staining, immunocytochemistry, and immunoblotting analyses revealed the induction of autophagy in renal cancer cells following PKM2 knockdown. This is the first study to indicate PKM2/AKT/mTOR as an important regulatory axis mediating the changes in the metabolism of renal cancer cells.

Magnusson N. Drug-Induced Hypertension Caused by Multikinase Inhibitors (Sorafenib, Sunitinib, Lenvatinib and Axitinib) in Renal Cell Carcinoma Treatment

This paper reviews current treatments for renal cell carcinoma/cancer (RCC) with the multikinase inhibitors (MKIs) sorafenib, sunitinib, lenvatinib and axitinib. Furthermore, it compares these drugs regarding progression-free survival, overall survival and adverse effects (AE), with a focus on hypertension. Sorafenib and sunitinib, which are included in international clinical guidelines as first- and second-line therapy in metastatic RCC, are now being challenged by new-generation drugs like lenvatinib and axitinib. These drugs have shown significant clinical benefits for patients with RCC, but all four induce a variety of AEs. Hypertension is one of the most common AEs related to MKI treatment. Comparing sorafenib, sunitinib and lenvatinib revealed that sorafenib and sunitinib had the same efficacy, but sorafenib was safer to use. Lenvatinib showed better efficacy than sorafenib but worse safety. No trials have yet been completed that compare lenvatinib with sunitinib. Although axitinib promotes slightly higher hypertension rates compared to sunitinib, the overall discontinuation rate and cardiovascular complications are favourable. Although the mean rate of patients who develop hypertension is similar for each drug, some trials have shown large differences, which could indicate that lifestyle and/or genetic factors play an additional role.

A nomogram for predicting survival in patients with nodular melanoma: A population-based study

The use of traditional American Joint Committee on Cancer (AJCC) staging alone has limitations in predicting patient survival with nodular melanoma (NM). We aimed to establish a comprehensive prognostic nomogram and compare its prognostic value with the AJCC staging system.A nomogram was constructed to predict the 3-year and 5-year survival rates of NM patients by Cox regression. Several common model-validation parameters were used to evaluate the performance of our survival model.The multivariate analyses demonstrated that the age at diagnosis; being divorced, separated, or widowed; AJCC stages II, III, and IV; a regional SEER stage and the lymph-node density (LND) were risk factors for survival. The concordance index, the area under the time-dependent receiver operating characteristic curve, and calibration plots indicated that the nomogram performed well, while the net reclassification improvement and the integrated discrimination improvement showed that the nomogram performed better than the AJCC staging system. Finally, the decision curve analyses curves of the nomogram yielded net benefits that were higher than when using AJCC staging system with either the training or the validation cohort.The prognostic value of the nomogram is better than that of the AJCC staging system alone. In addition, we found that LND is an important risk factor for the survival of NM patients. The nomogram developed in this study may be a valuable tool for clinical practice when advising patients about their survival risk over the next 3 to 5 years.

Nomograms for predicting long-term overall survival and cancer-specific survival in lip squamous cell carcinoma: A population-based study

Background

The goal of this study was to establish and validate two nomograms for predicting the long‐term overall survival (OS) and cancer‐specific survival (CSS) in lip squamous cell carcinoma (LSCC).

Methods

This study selected 4175 patients who were diagnosed with LSCC between 2004 and 2015 in the SEER (Surveillance, Epidemiology, and End Results) database. The patients were allocated randomly to a training cohort and validation cohort. Variables were selected using a backward stepwise method in a Cox regression model. Based on the predictive model with the identified prognostic factors, nomograms were established to predict the 3‐, 5‐, and 8‐year survival OS and CSS rates of LSCC patients. The accuracy of the nomograms was evaluated based on the consistency index (C‐index), while their prediction accuracy was evaluated using calibration plots. Decision curve analyses (DCAs) were used to evaluate the performance of our survival model.

Results

The multivariate analyses demonstrated that age at diagnosis, marital status, sex, race, American Joint Committee on Cancer stage, surgery status, and radiotherapy status were risk factors for both OS and CSS. The C‐index, area under the time‐dependent receiver operating characteristic curve, and calibration plots demonstrated the good performance of the nomograms. DCAs of both nomograms further showed that they exhibited good 3‐, 5‐, and 8‐year net benefits.

Conclusions

We have developed and validated LSCC prognosis nomograms for OS and CSS for the first time. These nomograms can be valuable tools for clinical practice when clinicians are helping patients to understand their survival risk for the next 3, 5, and 8 years.