Prognostic Factors and Nomograms to Predict Overall and Cancer-Specific Survival for Children with Wilms' Tumor

Survival characteristics of Wilms Tumor, a reference developed from a longitudinal cohort study

BACKGROUND

Wilms tumor (WT) survival has been affected by the evolution in clinical and biological prognostic factors. Significant differences in survival rates indicate the need for further efforts to reduce these disparities. This study aims to evaluate the clinicopathological data impact on survival among patients after Wilm's diagnosis.

METHODS

The study utilized the SEERStat Database to identify Wilms tumor patients, applying SEERStat software version 8.3.9.2 for data extraction. Selection criteria involved specific codes based on the International Classification of Diseases for Oncology (ICDO-3), excluding cases with unknown SEER stage, incomplete survival data, unknown size, or lymph node status. Statistical analyses, including Kaplan-Meier estimates and Cox regression models, were conducted using R software version 3.5. Standardized mortality ratios (SMR) were computed with SEER*Stat software, and relative and conditional survival analyses were performed to evaluate long-term survival outcomes.

RESULTS

Of 2273 patients diagnosed with Wilms tumor, (1219 patients, 53.6% were females with an average age group of 3-8 years (50.2%). The overall mean survival after five years of diagnosis was 93.6% (2.6-94.7), and the overall mean survival rate was 92.5% (91.3-93.8) after ten years of diagnosis. Renal cancers were identified as the leading cause of death (77.3%), followed by nonrenal cancers (11%) and noncancer causes (11%). Additionally, robust relative survival rates of 98.10%, 92.80%, and 91.3% at one, five, and ten years, respectively, were observed, with corresponding five-year conditional survival rates indicating an increasing likelihood of survival with each additional year post-diagnosis. Univariate Cox regression identified significant prognostic factors: superior CSS for patients below 3 years (cHR 0.48) and poorer CSS for those older than 15 years (cHR 2.72), distant spread (cHR 10.24), regional spread (cHR 3.09), and unknown stage (cHR 4.97). In the multivariate model, age was not a significant predictor, but distant spread (aHR 9.22), regional spread (aHR 2.84), and unknown stage (aHR 4.98) were associated with worse CSS compared to localized tumors.

CONCLUSION

This study delving into WT survival dynamics reveals a multifaceted landscape influenced by clinicopathological variables. This comprehensive understanding emphasizes the imperative for ongoing research and personalized interventions to refine survival rates and address nuanced challenges across age, stage, and tumor spread in WT patients.

Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review

Wilms tumor (WT), or nephroblastoma, is the predominant renal malignancy in the pediatric population. This narrative review explores the evolution of personalized care strategies for WT, synthesizing critical developments in molecular diagnostics and treatment approaches to enhance patient-specific outcomes. We surveyed recent literature from the last five years, focusing on high-impact research across major databases such as PubMed, Scopus, and Web of Science. Diagnostic advancements, including liquid biopsies and diffusion-weighted MRI, have improved early detection precision. The prognostic significance of genetic markers, particularly WT1 mutations and miRNA profiles, is discussed. Novel predictive tools integrating genetic and clinical data to anticipate disease trajectory and therapy response are explored. Progressive treatment strategies, particularly immunotherapy and targeted agents such as HIF-2α inhibitors and GD2-targeted immunotherapy, are highlighted for their role in personalized treatment protocols, especially for refractory or recurrent WT. This review underscores the necessity for personalized management supported by genetic insights, with improved survival rates for localized disease exceeding 90%. However, knowledge gaps persist in therapies for high-risk patients and strategies to reduce long-term treatment-related morbidity. In conclusion, this narrative review highlights the need for ongoing research, particularly on the long-term outcomes of emerging therapies and integrating multi-omic data to inform clinical decision-making, paving the way for more individualized treatment pathways.

Development and validation of a prognostic model based on a single-cell RNA-seq in Wilms tumor in children

To analyze the heterogeneity between different cell types in pediatric Wilms tumor (WT) tissue, and identify the differentially expressed genes (DEGs) of malignant tumor cells, thereby establishing a prognostic model. The single-cell sequencing data of pediatric WT tissues were downloaded from the public database. Data filtration and normalization, principal component analysis, and T-distributed stochastic neighbor embedding cluster analysis were performed using the Seurat package of R language. Cells were divided into different clusters, malignant tumor cells were extracted, and DEGs were obtained. Then, the pseudo-time trajectory analysis was performed. Prognostic biomarkers were determined by univariate and multivariate COX regression analyses and LASSO regression analysis. Kaplan-Meier survival analysis and receiver operator characteristic curve analysis were performed. Combined with the prognostic biomarkers and clinical characteristics, a nomogram was generated to predict WT prognosis. The prognostic power was validated in the external datasets. Cells in the WT tissue were divided into 10 clusters. Three prognostic biomarkers that affected the survival time of patients were screened from 215 DEGs in malignant tumor cells, and a nomogram was constructed using the three genes and clinical characteristics. The area under the curve (AUC) values of 3- and 5-year disease-free survival were 0.756 and 0.734, respectively. In the external validation dataset, the AUC value of this nomogram model was 0.826. Based on the single-cell RNA-seq, we recognized cell clusters in the WT tissue of children, identified prognostic biomarkers in malignant tumor cells, and established a comprehensive prognostic model. Our findings might provide new ideas and methods for the diagnosis and treatment of WT.

Nomogram to predict risk and prognosis of synchronous lung metastasis in renal cell carcinoma: A large cohort analysis

We aimed to construct and validate nomogram models that predict the incidence of lung metastasis (LM) in patients with renal cell carcinoma (RCC) and evaluate overall survival (OS) and cancer-specific survival (CSS) among RCC patients with LM. The Surveillance, Epidemiology, and End Results database was analyzed for RCC patients diagnosed between 2010 and 2015. The X-tile program was used to determine the best cutoff values for age at initial diagnosis and tumor size. Logistic regression analysis was performed to explore independent risk factors for LM, and COX regression analysis was used to identify prognostic indicators for OS and CSS in lung metastatic RCC patients. Subsequently, 3 nomograms were established, and receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were utilized to validate their accuracy. We randomly assigned 10,929 patients with RCC to 2 groups with 1:1 allocation. Multivariate logistic analyses revealed that pathology, tumor (T) stage, nodes (N) stage, race, grade, surgery, metastatic sites, and tumor size were independent risk factors for LM. Multivariate Cox analyses showed that pathology, T stage, N stage, age, surgery, metastatic sites, and residence were independent prognostic factors for OS and CSS in patients with LM. Then, nomograms were developed based on the multivariate logistic and Cox regression analyses results. The ROC and DCA curves confirmed that these nomograms achieved satisfactory discriminative power. Three effective nomograms were constructed and validated that can be used to assist clinicians in predicting the incidence of LM and evaluating the prognosis of lung metastatic RCC.

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

Objective

We aimed to establish and validate nomograms to evaluate overall survival (OS) and cancer-specific survival (CSS) in patients with metastatic renal cell carcinoma (MRCC).

Methods

Between 2010 and 2015, the clinical information of patients with MRCC was selected using the Surveillance, Epidemiology, and End Results database. Two nomograms were constructed based on Cox regression analysis, and their prediction accuracy was evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

After propensity score matching, there were 568 patients with MRCC in the training group and 568 in the validation group. Multivariate analyses revealed that age, residence, pathology, T stage, N stage, surgery, and metastatic sites were independent prognostic factors for the OS and CSS of MRCC. The C-index and ROC curves indicated that the two nomograms of OS and CSS showed satisfactory discriminative power. Furthermore, DCA displayed that the nomograms achieved more clinical net benefit than the American Joint Committee on Cancer staging system.

Conclusion

We constructed and validated two effective prognostic nomograms for patients with MRCC that accurately predicted the probabilities of 1-, 2-, and 3-year OS and CSS.

Macrophages and monocytes mediated activation of oxidative phosphorylation implicated the prognosis and clinical therapeutic strategy of Wilms tumour

Wilms tumour is the fourth leading cause of paediatric malignancy, but the detailed relationship between the tumour microenvironment and prognosis remains largely unclear. In this research, gene expression profile and clinical information from TARGET and the First Affiliated Hospital of Anhui Medical University were collected. After comparing the prognostic value of the associated immune cells, we established a nomogram to predict the prognosis of Wilms tumour based on monocyte infiltration, macrophage infiltration, stage, and sex. Further results showed that the most significant relationship between matrix metallopeptidase 9 and prognosis or macrophage infiltration. Meanwhile, by gene set enrichment or variation analyses and immunohistochemistry staining, we demonstrated that the most highly enriched hub genes were closely related to the activated oxidative phosphorylation pathway. Finally, through tumour immune dysfunction and an exclusion algorithm, the satisfactory discriminative performance of our nomogram was revealed for predicting the response to clinical therapy. Anti-PD1 therapy is more suitable for Wilms tumour patients with high nomogram points, and chemotherapies are more effective for patients with low nomogram score.

A Nomogram for Predicting Cancer-Specific Survival in Children With Wilms Tumor: A Study Based on SEER Database and External Validation in China

Background

Wilms tumor (WT) is the most common tumor in children. We aim to construct a nomogram to predict the cancer-specific survival (CSS) of WT in children and externally validate in China.

Methods

We downloaded the clinicopathological data of children with WT from 2004 to 2018 in the SEER database. At the same time, we used the clinicopathological data collected previously for all children with WT between 2013 and 2018 at Children's Hospital of Chongqing Medical University (Chongqing, China). We analyzed the difference in survival between the patients in the SEER database and our hospital. Cox regression analysis was used to screen for significant risk factors. Based on these factors, a nomogram was constructed to predict the CSS of children with WT. Calibration curve, concordance index (C-index), the area under the receiver operating curve (AUC) and decision curve analysis (DCA) was used to evaluate the accuracy and reliability of the model.

Results

We included 1,045 children with WT in the SEER database. At the same time, we collected 112 children with WT in our hospital. The Kaplan-Meier curve suggested that children in China with WT had a higher mortality rate than those in the United States. Cox regression analysis revealed that age, lymph node density (LND), and tumor stage were significant prognostic factors for the patients in the SEER database. However, the patients in our hospital only confirmed that the tumor stage and the number of positive regional lymph nodes were significant factors. The prediction model established by the SEER database had been validated internally and externally to prove that it had good accuracy and reliability.

Conclusion

We have constructed a survival prognosis prediction model for children with WT, which has been validated internally and externally to prove accuracy and reliability.

The Natural Flavonoid Naringenin Inhibits the Cell Growth of Wilms Tumor in Children by Suppressing TLR4/NF-κB Signaling

BACKGROUND

Nuclear Factor-kappa B (NF-κB) is usually activated in Wilms Tumor (WT) cells and plays a critical role in WT development.

OBJECTIVE

The study's purpose was to screen for a NF-κB inhibitor from the natural product library and explore its effects on WT development.

METHODS

Luciferase assay was employed to assess the effects of natural chemicals on NF-κB activity. CCK-8 assay was conducted to assess cell growth in response to naringenin. WT xenograft model was established to analyze the effect of naringenin in vivo. Quantitative real-time PCR and Western blot were performed to examine the mRNA and protein levels of relative genes, respectively.

RESULTS

Naringenin displayed a significant inhibitory effect on NF-κB activation in SK-NEP-1 cells. In SKNEP- 1 and G-401 cells, naringenin inhibited p65 phosphorylation. Moreover, naringenin suppressed TNF-α- induced p65 phosphorylation in WT cells. Naringenin inhibited TLR4 expression at both mRNA and protein levels in WT cells. CCK-8 staining showed that naringenin inhibited cell growth of the two above WT cells in doseand time-dependent manner, whereas Toll-Like Receptor 4 (TLR4) overexpression partially reversed the above phenomena. Besides, naringenin suppressed WT tumor growth in a dose- and time-dependent manner in vivo. Western blot found that naringenin inhibited TLR4 expression and p65 phosphorylation in WT xenograft tumors.

CONCLUSION

Naringenin inhibits WT development via suppressing TLR4/NF-κB signaling.

A long non-coding RNAs expression signature to improve prognostic prediction of Wilms tumor in children

BACKGROUND

Wilms tumor (WT) is the most frequent malignancy of the kidney in children, and a subset of patients remains with a poor prognosis. This study aimed to identify key long non-coding RNAs (lncRNAs) related to prognosis and establish a genomic-clinicopathologic nomogram to predict survival in children with WT.

METHODS

Clinical data of 124 WT patients and the relevant RNA sequencing data including lncRNAs expression signature of primary WT samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) Data Matrix. Then, lncRNAs associated with overall survival (OS) were identified through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. The risk scores of 124 participants were calculated, and survival analyses were performed between low- and high-risk groups. A genomic-clinicopathologic nomogram was then developed and evaluated by time-dependent receiver operating characteristic (ROC) curves, including the area under the curve (AUC), calibration curve, and decision curve analysis. Subsequently, bioinformatics analyses were performed to explore the potential molecular mechanisms that affect the prognosis of WT. The package "DESeq2" was used to identify differentially expressed protein-coding genes (DEPCGs) between groups. Gene Set Enrichment Analysis (GSEA) was applied to explore the differences in pathways enrichment. The analytical tools CIBERSORTx and ESTIMATE were used to investigate the discrepancies of the immune microenvironment.

RESULTS

A total of 10 lncRNAs were selected as independent predictors associated with OS (P<0.05). Participants in the high-risk group had a significantly worse OS and event-free survival (EFS) than those in the low-risk group (P<2E-16 and P=2.03E-04, respectively). The risk score and 3 clinicopathological features (gender, cooperative group protocol, and stage) were identified to construct the nomogram (combined model) (P=5.11E-17). The combined model (1-year AUC: 0.9272, 3-year AUC: 0.9428, 5-year AUC: 0.9259) and risk score model (1-year AUC: 0.9285, 3-year AUC: 0.9399, 5-year AUC: 0.9266) displayed higher predictive accuracy than that of the other models. Subsequently, 105 DEPCGs were identified. The GSEA revealed 4 significant pathways. Analysis with CIBERSORTx demonstrated that monocytes, macrophages M1, activated dendritic cells, and resting mast cells had significant infiltration differences between groups.

CONCLUSIONS

This study constructed a genomic-clinicopathologic nomogram, which might present a novel and efficient method for treating patients with WT.

Radiotherapy Versus Surgery-Which Is Better for Patients With T1-2N0M0 Glottic Laryngeal Squamous Cell Carcinoma? Individualized Survival Prediction Based on Web-Based Nomograms

Background

Both radiotherapy and surgery are now recommended for early stage glottic laryngeal squamous cell carcinoma (LSCC), and both have their own advantages in patients with different characteristics. For each patient, it is hard to determine whether radiotherapy or surgery is more appropriate.

Methods

Patients with T1-2N0M0 glottic LSCC who received radiotherapy or surgery in the 2004–2016 SEER database were reviewed, then randomly divided into training and validation cohorts. Propensity score matching was used to eliminate the baseline variations, and competing risk analyses helped to exclude the effects of other causes of death. Based on univariate and multivariate analyses, we built two nomograms to visually predict the survival of each patient with different characteristics who received radiotherapy or surgery, then validated the accuracy in both training and validation cohorts. Using nomogramEx, we quantified the algorithms of the nomograms and put the nomograms on the websites.

Results

A total of 6538 patients in the SEER database were included. We found that therapy (p = 0.004), T stage (p < 0.001), age (p < 0.001), race (p < 0.044), grade (p = 0.001), and marital status (p < 0.001) were independent prognostic factors. Two nomograms were built to calculate the survival for each patient who received radiotherapy (C-index = 0.668 ± 0.050 in the training cohort and 0.578 ± 0.028 in the validation cohort) or underwent surgery (C-index = 0.772 ± 0.045 in the training cohort and 0.658 ± 0.090 in the validation cohort). Calibration plots showed the accuracy of the nomograms. Using the nomograms, we found that 3872 patients (59.22%) had no difference between the two therapies, 706 patients (10.80%) who received radiotherapy had better survival outcomes, and 1960 patients (29.98%) who underwent surgery had better survival outcome.

Conclusion

Nomograms were used to comprehensively calculate independent factors to determine which treatment (radiotherapy or surgery) is better for each patient. A website was used to offer guidance regarding surgery or radiation for patients and physicians.