Development and validation of a nomogram for elderly patients with ulcerative melanoma.

The current state of survival prediction models for elderly patients with ulcerative melanoma (uCM) is limited. We sought to develop a nomogram model that can predict overall survival of geriatric patients with uCM. The Surveillance, Epidemiology, and End Results (SEER) database served as a source for patients diagnosed with uCM between 2004 and 2015. Statistical analyses were conducted to determine the significant prognostic elements affecting overall survival using multivariate and univariate Cox proportional risk regression models. Subsequently, an independent forecasting nomogram was developed on the basis of these identified predictors. The predictive model was then assessed and validated through the utilization of receiver operating characteristic curves, calibration curves as well as decision curves. The study included a total of 5019 participants. Univariate and multivariate analyses revealed age, sex, marital status, primary site, tumor size, N stage, M stage, histological type, and surgery were independent prognostic factors. A nomogram was developed using the findings from both univariate and multivariate Cox analyses (P < 0.05). The receiver operating characteristic curves, which vary over time, and the area under the curve (AUC) for the training and validation cohorts, demonstrated the nomogram's strong discriminatory ability. Additionally, the calibration curves indicated satisfactory agreement between the predicted values from the nomogram and the practical outcomes observed in both cohorts. Furthermore, the decision curve analysis curves displayed favorable positive net gains at all times, when the critical value is most likely to occur. In this study, age, sex, marital status, primary site, tumor size, N stage, M stage, histologic type and surgery were determined as independent predictors for elderly patients with uCM. Then, a predictive model with good discriminatory ability was constructed to predict 12-, 24-, and 36-month overall survival in geriatric patients with uCM, which facilitates patients' counseling and individualized medical decision.

A practical nomogram and risk stratification system predicting the cancer-specific survival for patients aged >50 with advanced melanoma.

Objective: To investigate risk factors for advanced melanoma over 50 years of age and to develop and validate a new line chart and classification system. Methods: The SEER database was screened for patients diagnosed with advanced melanoma from 2010 to 2019 and Cox regression analysis was applied to select variables affecting patient prognosis. The area under curve (AUC), relative operating characteristic curve (ROC), Consistency index (C-index), decision curve analysis (DCA), and survival calibration curves were used to verify the accuracy and utility of the model and to compare it with traditional AJCC tumor staging. The Kaplan-Meier curve was applied to compare the risk stratification between the model and traditional AJCC tumor staging. Results: A total of 5166 patients were included in the study. Surgery, age, gender, tumor thickness, ulceration, the number of primary melanomas, M stage and N stage were the independent prognostic factors of CSS in patients with advanced melanoma (P<0.05). The predictive nomogram model was constructed and validated. The C-index values obtained from the training and validation cohorts were 0.732 (95%CI: 0.717-0.742) and 0.741 (95%CI: 0.732-0.751). Based on the observation and analysis results of the ROC curve, survival calibration curve, NRI, and IDI, the constructed prognosis model can accurately predict the prognosis of advanced melanoma and performs well in internal verification. The DCA curve verifies the practicability of the model. Compared with the traditional AJCC staging, the risk stratification in the model has a better identification ability for patients in different risk groups. Conclusion: The nomogram of advanced melanoma and the new classification system were successfully established and verified, which can provide a practical tool for individualized clinical management of patients.

The Role of NFATC4 Gene in Human Cutaneous Squamous Cell Carcinoma.

Nuclear factor of activated T-cells, cytoplasmic 4 (NFATC4) has been implicated in keratinocyte development and several types of cancer. A well-defined role for NFATC4 in cutaneous squamous cell carcinoma (CSCC) has not yet been established. In this study, NFATC4 gene function in CSCC development was examined. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to measure the mRNA expression of NFATC4 in CSCC tissues and controls. A431 and Colo16 cell proliferation, invasion, and apoptosis were measured by CCK-8 assay, transwell invasion, and flow cytometry, respectively, after an NFATC4 expression lentivirus infection. Animal models were applied to validate the function of the NFATC4 gene. (1) CSCC tissues showed a significant decrease in NFATC4 expression compared to controls. (2) Overexpression of NFATc4 suppresses A431 and Colo16 cell proliferation and invasion but promotes cell apoptosis. (3) Mouse models overexpressing NFATC4 showed reduced tumourigenesis. It was suggested that NFATC4 might be a tumour suppressor gene in CSCC.

On the Comparisons of Decorrelation Approaches for Non-Gaussian Neutral Vector Variables.

As a typical non-Gaussian vector variable, a neutral vector variable contains nonnegative elements only, and its l1 -norm equals one. In addition, its neutral properties make it significantly different from the commonly studied vector variables (e.g., the Gaussian vector variables). Due to the aforementioned properties, the conventionally applied linear transformation approaches [e.g., principal component analysis (PCA) and independent component analysis (ICA)] are not suitable for neutral vector variables, as PCA cannot transform a neutral vector variable, which is highly negatively correlated, into a set of mutually independent scalar variables and ICA cannot preserve the bounded property after transformation. In recent work, we proposed an efficient nonlinear transformation approach, i.e., the parallel nonlinear transformation (PNT), for decorrelating neutral vector variables. In this article, we extensively compare PNT with PCA and ICA through both theoretical analysis and experimental evaluations. The results of our investigations demonstrate the superiority of PNT for decorrelating the neutral vector variables.