Application and subgroup analysis of competing risks model based on different lymph node staging systems in differentiated thyroid cancer.

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a rising incidence worldwide. Accurate prognostic models are essential for effective patient management. This study evaluates the prognostic value of various lymph node staging systems in DTC using a competing risks model. We used SEER database records (1998-2016) of 16,527 DTC patients, analyzing N stage, positive lymph node numbers (PLNNs), metastatic lymph node ratio (MLNR), log odds of positive lymph nodes (LODDS), and log odds of the negative lymph node (NLN)/T stage ratio (LONT). Univariate and multivariate analyses in a competing risks model were performed, along with subgroup analyses based on demographic and clinical characteristics. In this study of 16,527 patients with DTC, different lymph node staging systems showed different prognostic correlations in univariate and multivariate analyses. In particular, PLNNs showed significant prognostic correlations in several subgroups. Additionally, PLNNs were more suitable as a lymph node staging system for DTC than LODDS and MLNR in N1 stage subgroups, with an optimal cut-off of 13. Receiver operating characteristic curves, calibration curves and nomograms improved the clinical utility of the prognostic model based on PLNNs. Using competing risks model and subgroup analyses, we found that PLNNs had the best prognostic discriminatory efficacy for patients with DTC, especially those with N1 stage disease, and had an optimal cut-off value of 13.

Global thyroid cancer incidence trend and age-period-cohort model analysis based on Global Burden of Disease Study from 1990 to 2019.

Background: In view of the rapid increase in the incidence of thyroid cancer (TC) and the spread of overdiagnosis around the world, the quantitative evaluation of the effect of age, period and birth cohort on the incidence of TC, and the analysis of the role of different factors in the incidence trend can provide scientific basis and data support for the national health departments to formulate reasonable prevention and treatment policies. Methods: The study collated the global burden disease study data of TC incidence from 1990 to 2019, and used APC model to analyze the contribution of age, period and birth cohort to the incidence trend of TC. Results: There was an obvious unfavorable upward trend in terms of age and cohort effect all over the world. Since 2007, the growth rate of risk slowed down and the risk in female even decreased since 2012, which mainly contributed to the developed countries. In all SDI countries, 2002 is the dividing point of risk between male and female. In 2019, The global age-standardized incidence rate (ASIR) of TC in the 5 SDI countries all showed a significant upward trend, with the largest upward trend in the middle SDI countries. Conclusion: The trend of rapid increase in the incidence of TC has begun to slow down, but the global incidence of TC has obvious gender and regional/national heterogeneity. Policy makers should tailor specific local strategies to the risk factors of each country to further reduce the burden of TC.

Receptor-ligand pair typing and prognostic risk model for papillary thyroid carcinoma based on single-cell sequencing.

The papillary thyroid carcinoma (PTC) microenvironment consists of various cancer and surrounding cells, and the communication between them is mainly performed through ligand-receptor (LR) interactions. Single-cell RNA sequencing (scRNA-seq) has been performed to investigate the role of intercellular communication networks in tumor progression. In addition, scRNA-seq can accurately identify the characteristics of immune cell subsets, which is of great significance for predicting the efficacy of immunotherapy. In this study, the cell-cell communication network was analyzed through LR pairs, and a new PTC molecular phenotype was developed based on LR pairs. Furthermore, a risk model was established to predict patient response to PD-1 blockade immunotherapy. The scRNA-seq dataset was obtained from GSE184362, and the bulk tumor RNA-seq dataset was obtained from The Cancer Genome Atlas. CellPhoneDB was used for cellular communication analysis. LR pair correlations were calculated and used to identify molecular subtypes, and the least absolute shrinkage and selection operator (Lasso) Cox regression was used to develop a risk model based on LR pairs. The IMvigor210 and GSE78220 cohorts were used as external validations for the LR.score to predict responses to PD-L1 blockade therapy. A total of 149 LR pairs with significant expression and prognostic correlation were included, and three PTC molecular subtypes were obtained from those with significant prognostic differences. Then, five LR pairs were selected to construct the risk scoring model, a reliable and independent prognostic factor in the training set, test set, and whole dataset. Furthermore, two external validation sets confirmed the predictive efficacy of the LR.score for response to PD-1 blockade therapy.

Prognostic value of LODDS in medullary thyroid carcinoma based on competing risk model and propensity score matching analysis.

Log odds of positive lymph nodes (LODDS) is an independent prognostic factor for patients with medullary thyroid carcinoma (MTC). However, the optimal cutoff value for LODDS needs to be further confirmed, and previous studies have ignored the prevalent competing events of non-cancer deaths among patients with MTC, thus possibly overestimating the risk of death from cancer. The information of patients with MTC who underwent total thyroidectomy was collected from SEER database. Restricted cubic splines (RCS) were used to determine the optimal cutoff for LODDS. Moreover, patients' overall survival (OS) and disease-specific survival (DSS) were determined using Kaplan-Meier and Cox proportional-hazards model. The competing risk models (CRM) were used to reduce the effect of competing events, and propensity score matching was performed to balance the confounding factors between groups. The cutoff value of LODDS determined by RCS was - 1.004, and a total of 2314 patients with MTC were recruited. In the CRM after PSM, factors such as age over 55 years at diagnosis, being male, treatment with chemotherapy or radiotherapy, unknown tumor size, and LODDS > - 1.004 were significantly associated with poor prognosis of patients both in univariate and multivariate analyses, while the presence of multifocal tumor indicated better prognosis. Patients with MTC who were over 55 years old at diagnosis, were male, received chemotherapy or radiation, had an unclear initial tumor size, and had LODDS > - 1.004 had a worse prognosis than patients with multifocal tumor.

Comprehensive investigation of alternative splicing and development of a prognostic risk score for prostate cancer based on six-gene signatures.

Purpose: To systematically document alternative splicing profiles of prostate cancer in relatively large populations in order to construct a prognostic predictors model for prostate cancer. Methods: Splicing data and clinical information of 495 prostate cancer patients were obtained from The Cancer Genome Atlas (TCGA). The SpliceSeq database was used to extract information regarding splicing events. Multiple bioinformatic tools were used for functional and pathway enrichment analysis as well as for construction of gene interaction networks. Candidate gene expression profiles were verified with clinical samples using QRT-PCR. Results: We detected a total of 44070 alternative splicing events of 10381 genes in prostate cancer. 7 and 14 KEGG pathways were enriched and were associated with overall and recurrence-free survival, respectively. The expression of 396 genes among the 1526 overall survival genes associated alternative splicing events were associated with overall survival. The expression of 483 genes among the 1916 recurrence-free survival genes associated alternative splicing events were associated with recurrence-free survival. Lastly, we constructed the prognosis risk score system based on the expression profiles of six-gene signatures which in combination had an AUC of 0.941 for overall survival associated alternative splicing events, followed by overall survival associated gene expressions with an AUC of 0.794, a recurrence-free survival associated gene expression with an AUC of 0.752 and recurrence-free survival associated alternative splicing events with an AUC of 0.735, indicating its strong ability to predict patient outcome. The expression profile of the six genes was also confirmed in different prostate cell lines and clinic samples. Conclusion: Our comprehensive investigation of alternative splicing not only provided insight into the biological pathways of alternative splicing involved in the development of prostate cancer but also revealed new potential biomarkers for prognosticating as well as novel therapeutic targets for development of prostate cancer treatment.