Identification of P21 (CDKN1A) Activated Kinase 4 as a Susceptibility Gene for Familial Non-Medullary Thyroid Carcinoma

Global landscape of early-onset thyroid cancer: current burden, temporal trend and future projections on the basis of GLOBOCAN 2022

Background

With rapid social-economic development and widespread screening, the surge in incidence and overdiagnosis of thyroid cancer is more worrying among the young than the general population. This problem, however, still lacks adequate attention.

Methods

We retrieved the original data of current, past and future burden of thyroid cancer from the Global Cancer Observatory (GLOBOCAN) 2022. We calculated the age-specific mortality-to-incidence ratio (MIR) by dividing age-specific mortality rates by incidence rates to quantify potential overdiagnosis, and used Segi's world standard population to calculate the age-standardised incidence rate (ASIR) and age-standardised mortality rate (ASMR). We then assessed the correlation between the human development index (HDI) and ASIR/ASMR using the linear correlation coefficient (r). Lastly, we characterised the temporal trend with the estimated annual percentage change (EAPC) and project the early-onset thyroid cancer burdens to 2050.

Results

Globally, there were an estimated 239 362 (ASIR = 4.00 per 100 000 population) cases and 2409 (ASMR = 0.04 per 100 000 population) deaths from thyroid cancer among individuals aged <40 years in 2022. Compared to its ranking as the 7th most common cancer in the overall population, thyroid cancer rose to become the 2nd most common cancer among individuals <40 years. Nearly 99% of thyroid cancer cases in individuals <40 years of age (MIR = 0.01) may be potentially overdiagnosed, whereas 34% of cases in those >80 years (MIR = 0.66) were overdiagnosed. The ASIR of early-onset thyroid cancer varied widely (from 0.13 to 13.17 per 100 000 population), while the ASMR remains relatively similar and low across different regions. The ASIR of early-onset thyroid cancer increased with HDI (r = 0.69), while the ASMR decreased (r = -0.22). The ASIR of early-onset thyroid cancer had the fastest upward trend (EAPC in males vs. females: 9.88 vs. 9.28%) compared to early-onset cancers at other sites, while ASMR showed a downward trend (EAPC in males vs. females: -0.38% vs. -1.33%). The Republic of Korea experienced the highest EAPC for early-onset thyroid cancer ASIR (males vs. females: 29.95% vs. 23.04%). If national rates from 2022 remain stable, projected cases of early-onset thyroid cancer would decrease in high (-13.3%) and very high (-10.9%) HDI countries, but increase in low (96.5%) and medium HDI countries (11.7%).

Conclusions

The trend of early-onset thyroid cancer at the global level is more alarming than that of thyroid cancer overall. The younger age at diagnosis of thyroid cancer, the higher risk of potential overdiagnosis. Without timely interventions, the thyroid cancer burden will inevitably become a serious global public health issue, especially for the young population.

HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction

MOTIVATION

Protein-protein interactions play a fundamental role in biological systems. Accurate detection of protein-protein interaction sites (PPIs) remains a challenge. And, the methods of PPIs prediction based on biological experiments are expensive. Recently, a lot of computation-based methods have been developed and made great progress. However, current computational methods only focus on one form of protein, using only protein spatial conformation or primary sequence. And, the protein's natural hierarchical structure is ignored.

RESULTS

In this study, we propose a novel network architecture, HSSPPI, through hierarchical and spatial-sequential modeling of protein for PPIs prediction. In this network, we represent protein as a hierarchical graph, in which a node in the protein is a residue (residue-level graph) and a node in the residue is an atom (atom-level graph). Moreover, we design a spatial-sequential block for capturing complex interaction relationships from spatial and sequential forms of protein. We evaluate HSSPPI on public benchmark datasets and the predicting results outperform the comparative models. This indicates the effectiveness of hierarchical protein modeling and also illustrates that HSSPPI has a strong feature extraction ability by considering spatial and sequential information simultaneously.

AVAILABILITY AND IMPLEMENTATION

The code of HSSPPI is available at https://github.com/biolushuai/Hierarchical-Spatial-Sequential-Modeling-of-Protein.

Chromosomal localization of mutated genes in non-syndromic familial thyroid cancer

Familial non-medullary thyroid carcinoma (FNMTC) is a type of thyroid cancer characterized by genetic susceptibility, representing approximately 5% of all non-medullary thyroid carcinomas. While some cases of FNMTC are associated with familial multi-organ tumor predisposition syndromes, the majority occur independently. The genetic mechanisms underlying non-syndromic FNMTC remain unclear. Initial studies utilized SNP linkage analysis to identify susceptibility loci, including the 1q21 locus, 2q21 locus, and 4q32 locus, among others. Subsequent research employed more advanced techniques such as Genome-wide Association Study and Whole Exome Sequencing, leading to the discovery of genes such as IMMP2L, GALNTL4, WDR11-AS1, DUOX2, NOP53, MAP2K5, and others. But FNMTC exhibits strong genetic heterogeneity, with each family having its own pathogenic genes. This is the first article to provide a chromosomal landscape map of susceptibility genes associated with non-syndromic FNMTC and analyze their potential associations. It also presents a detailed summary of variant loci, characteristics, research methodologies, and validation results from different countries.