Causal Relationships between Gut Microbiotas, Blood Metabolites, and Neuroendocrine Tumors: A Mediated Mendelian Randomization Study.

Introduction: Neuroendocrine tumors (NETs) are a heterogeneous group of epithelial tumors originating from different anatomical sites, and identifying the gut microbiota and metabolic mechanisms involved in the onset of NETs may help to develop appropriate disease prevention and monitoring strategies.

Methods: We employed a mediated two-sample Mendelian randomization (MR) approach, analyzing gut microbiota from German studies and NET datasets from the 10th round of the FinnGen project. Mediation analyses were conducted using the metabolites dataset from the Canadian Longitudinal Study of Aging (CLSA) and the TwinsUK study.

Associations between serum metabolites and female cancers: A bidirectional two-sample mendelian randomization study.

Female cancers, especially breast, ovarian, cervical, and endometrial cancers, constitute a major threat to women's health worldwide. In view of the complex genetic background of cancers cannot be fully explained with current genetic information, we used a bidirectional two-sample mendelian randomization approach to explore the causal associations between serum metabolites and four major female cancers-breast, ovarian, cervical, and endometrial cancers. We analyzed the metabolites dataset from the Canadian Longitudinal Study of Aging and cancer datasets from the 10th round of the Finngen project.

Associations between immune cell traits and autoimmune thyroid diseases: a bidirectional two-sample mendelian randomization study.

Autoimmune thyroid diseases (AITDs), mainly including Graves' disease (GD) and Hashimoto's thyroiditis (HT), are common autoimmune disorders characterized by abnormal immune responses targeting the thyroid gland. We conducted a bidirectional two-sample MR analysis using the largest dataset of peripheral immune cell phenotypes from Sardinia, and the AITD dataset from the 10th round of the FinnGen and the UK Biobank project. Instrumental variables (IVs) were rigorously selected based on the three assumptions of MR and analyzed using the Wald ratio, inverse-variance weighted (IVW), MR-Egger, and weighted median methods.

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.

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.

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.

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.

Drugs used in regional block analgesia for thyroidectomy: A network meta-analysis of randomized controlled trials.

Background: Postoperative pain has become one of the major obstacles to postoperative recovery and can lead to increasing demand for opioids. So far, a wide variety of drugs is used for regional block anesthesia, but which one is the best remains unclear. Thus, this study aims to obtain the best drug for thyroid surgery regional block based on their efficacy and side effects through Bayesian network meta-analysis.

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

: To systematically document alternative splicing profiles of prostate cancer in relatively large populations in order to construct a prognostic predictors model for prostate cancer. : 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.