Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications

ncRNAs-mediated overexpression of TET3 predicts unfavorable prognosis and correlates with immunotherapy efficacy in breast cancer

Breast cancer is the most frequent form of cancer in women and the primary cause of cancer-related deaths globally. DNA methylation and demethylation are important processes in human tumorigenesis. Ten-eleven translocation 3 (TET3) is a DNA demethylase. Prior research has demonstrated that TET3 is highly expressed in various human malignant tumors. However, the exact function and mechanism of TET3 in breast cancer remain unclear. In this study, we investigated TET3 expression in breast cancer and its correlation with clinicopathological characteristics of breast cancer patients. The results presented that TET3 expression was significantly increased in breast cancer and associated with the PAM50 subtype. Subsequently, we performed receiver operating characteristic, survival, and Cox hazard regression analyses. These results suggest that TET3 expression is associated with a poor prognosis and may be an indirect independent prognostic indicator in breast cancer. We also established a protein-protein interaction (PPI) network of TET3 and executed enrichment analyses of TET3 co-expressed genes, revealing their primary association with the cell cycle. Moreover, we identified noncoding RNAs (ncRNAs) contributing to TET3 overexpression using expression, correlation, and survival analyses. We identified the LINC01521/hsa-miR-29a-3p axis as the primary TET3 upstream ncRNA-related pathway in breast cancer. Furthermore, TET3 expression was positively associated with immune cell infiltration, immune cell biomarkers, and eight immune checkpoint gene expressions in breast cancer. TET3 expression also correlated with patient responses to immunotherapy. Finally, we conducted subcellular localization and immunohistochemical staining analysis of TET3 in breast cancer. We found that TET3 localized to the nucleoplasm, vesicles, and cytosol in the MCF-7 cell line, and TET3 expression was significantly upregulated in breast cancer tissues compared to para-tumor tissues. Our findings indicate that ncRNA-mediated overexpression of TET3 predicts an unfavorable prognosis and correlates with immunotherapy efficacy in breast cancer.

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package “compound.Cox”. The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.

Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Survival and reliability analyses deal with incomplete failure time data, such as censored and truncated data. Recently, the classical left-truncation scheme was generalized to analyze “field data”, defined as samples collected within a fixed period. However, existing competing risks models dealing with left-truncated field data are not flexible enough. We propose copula-based competing risks models for latent failure times, permitting a flexible parametric form. We formulate maximum likelihood estimation methods under the Weibull, lognormal, and gamma distributions for the latent failure times. We conduct simulations to check the performance of the proposed methods. We finally give a real data example. We provide the R code to reproduce the simulations and data analysis results.