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A genome-wide association study of papillary thyroid carcinoma (PTC) pinpointed two independent SNPs (rs944289 and rs965513) located in regions containing no annotated genes (14q13.3 and 9q22.33, respectively). Here, we describe a unique, long, intergenic, noncoding RNA gene (lincRNA) named Papillary Thyroid Carcinoma Susceptibility Candidate 3 (PTCSC3) located 3.2 kb downstream of rs944289 at 14q.13.3 and the expression of which is strictly thyroid specific. By quantitative PCR, PTCSC3 expression was strongly down-regulated (P = 2.84 x 10–14) in thyroid tumor tissue of 46 PTC patients and the risk allele (T) was associated with the strongest suppression (genotype [TT] (n = 21) vs. [CT] (n = 19), P = 0.004). In adjacent unaffected thyroid tissue, the genotype [TT] was associated with up-regulation of PTCSC3 ([TT] (n = 21) vs. [CT] (n = 19), P = 0.034). The SNP rs944289 was located in a binding site for the CCAAT/enhancer binding proteins (C/EBP) α and β. The risk allele destroyed the binding site in silico. Both C/EBPα and C/EBPβ activated the PTCSC3 promoter in reporter assays (P = 0.0009 and P = 0.0014, respectively) and the risk allele reduced the activation compared with the nonrisk allele (C) (P = 0.026 and P = 0.048, respectively). Restoration of PTCSC3 expression in PTC cell line cells (TPC-1 and BCPAP) inhibited cell growth (P = 0.002 and P = 0.019, respectively) and affected the expression of genes involved in DNA replication, recombination and repair, cellular movement, tumor morphology, and cell death. Our data suggest that SNP rs944289 predisposes to PTC through a previously uncharacterized, long intergenic noncoding RNA gene (PTCSC3) that has the characteristics of a tumor suppressor.

Significance Papillary thyroid carcinoma (PTC) displays a strong hereditary component that is, in part, due to the additive effects of numerous low-penetrance genes or variants, but virtually no mechanistic information is available. Here, we studied a well-known low-penetrance variant (SNP rs965513) located in a region devoid of coding genes. We show that at least four variants located in the immediate vicinity of rs965513 reside in enhancer elements that bind to the promoter region shared by two adjacent thyroid-related genes, forkhead box E1 ( FOXE1 ) and PTC susceptibility candidate 2 ( PTCSC2 ), regulating their expression. The role of intergenic regulatory variants in cancer predisposition and carcinogenesis is growing. Further mechanistic understanding of how these variants work such as described here needs to be acquired. The [A] allele of SNP rs965513 in 9q22 has been consistently shown to be highly associated with increased papillary thyroid cancer (PTC) risk with an odds ratio of ∼1.8 as determined by genome-wide association studies, yet the molecular mechanisms remain poorly understood. Previously, we noted that the expression of two genes in the region, forkhead box E1 ( FOXE1 ) and PTC susceptibility candidate 2 ( PTCSC2 ), is regulated by rs965513 in unaffected thyroid tissue, but the underlying mechanisms were not elucidated. Here, we fine-mapped the 9q22 region in PTC and controls and detected an ∼33-kb linkage disequilibrium block (containing the lead SNP rs965513) that significantly associates with PTC risk. Chromatin characteristics and regulatory element signatures in this block disclosed at least three regulatory elements functioning as enhancers. These enhancers harbor at least four SNPs (rs7864322, rs12352658, rs7847449, and rs10759944) that serve as functional variants. The variant genotypes are associated with differential enhancer activities and/or transcription factor binding activities. Using the chromosome conformation capture methodology, long-range looping interactions of these elements with the promoter region shared by FOXE1 and PTCSC2 in a human papillary thyroid carcinoma cell line (KTC-1) and unaffected thyroid tissue were found. Our results suggest that multiple variants coinherited with the lead SNP and located in long-range enhancers are involved in the transcriptional regulation of FOXE1 and PTCSC2 expression. These results explain the mechanism by which the risk allele of rs965513 predisposes to thyroid cancer.

Genome-wide association studies (GWASs) have identified at least 10 single-nucleotide polymorphisms (SNPs) associated with papillary thyroid cancer (PTC) risk. Most of these SNPs are common variants with small to moderate effect sizes. Here we assessed the combined genetic effects of these variants on PTC risk by using summarized GWAS results to build polygenic risk score (PRS) models in three PTC study groups from Ohio (1,544 patients and 1,593 controls), Iceland (723 patients and 129,556 controls), and the United Kingdom (534 patients and 407,945 controls). A PRS based on the 10 established PTC SNPs showed a stronger predictive power compared with the clinical factors model, with a minimum increase of area under the receiver-operating curve of 5.4 percentage points (P ≤ 1.0 × 10−9). Adding an extended PRS based on 592,475 common variants did not significantly improve the prediction power compared with the 10-SNP model, suggesting that most of the remaining undiscovered genetic risk in thyroid cancer is due to rare, moderate- to high-penetrance variants rather than to common low-penetrance variants. Based on the 10-SNP PRS, individuals in the top decile group of PRSs have a close to sevenfold greater risk (95% CI, 5.4–8.8) compared with the bottom decile group. In conclusion, PRSs based on a small number of common germline variants emphasize the importance of heritable low-penetrance markers in PTC.

The main nonmedullary form of thyroid cancer is papillary thyroid carcinoma (PTC) that accounts for 80-90% of all thyroid malignancies. Only 3-10% of PTC patients have a positive family history of PTC yet the familiality is one of the highest of all cancers as measured by case control studies. A handful of genes have been implicated accounting for a small fraction of this genetic predisposition. It was therefore of considerable interest that a mutation in the HABP2 gene was recently implicated in familial PTC. The present work was undertaken to examine the extent of HABP2 variant involvement in PTC. The HABP2 G534E variant (rs7080536) was genotyped in blood DNA from 179 PTC families (one affected individual per family), 1160 sporadic PTC cases and 1395 controls. RNA expression of HABP2 was tested by qPCR in RNA extracted from tumor and normal thyroid tissue from individuals that are homozygous wild-type or heterozygous for the variant. The variant was found to be present in 6.1% familial cases, 8.0% sporadic cases (2 individuals were homozygous for the variant) and 8.7% controls. The variant did not segregate with PTC in one large and 6 smaller families in which it occurred. In keeping with data from the literature and databases the expression of HABP2 was highest in the liver, much lower in 3 other tested tissues (breast, kidney, brain) but not found in thyroid. Given these results showing lack of any involvement we suggest that the putative role of variant HABP2 in PTC should be carefully scrutinized.

Small nuclear RNAs (snRNAs) are essential factors in messenger RNA splicing. By means of homozygosity mapping and deep sequencing, we show that a gene encoding U4atac snRNA, a component of the minor U12-dependent spliceosome, is mutated in individuals with microcephalic osteodysplastic primordial dwarfism type I (MOPD I), a severe developmental disorder characterized by extreme intrauterine growth retardation and multiple organ abnormalities. Functional assays showed that mutations (30G>A, 51G>A, 55G>A, and 111G>A) associated with MOPD I cause defective U12-dependent splicing. Endogenous U12-dependent but not U2-dependent introns were found to be poorly spliced in MOPD I patient fibroblast cells. The introduction of wild-type U4atac snRNA into MOPD I cells enhanced U12-dependent splicing. These results illustrate the critical role of minor intron splicing in human development.

Papillary thyroid carcinoma (PTC) displays strong but so far largely uncharacterized heritability. Here we studied genetic predisposition in a family with six affected individuals. We genotyped all available family members and conducted whole exome sequencing of blood DNA from two affected individuals. Haplotype analysis and other genetic criteria narrowed our list of candidates to a germline variant in the serine/arginine repetitive matrix 2 gene ( SRRM2 ). This heterozygous variant, c.1037C > T (Ser346Phe or S346F; rs149019598) cosegregated with PTC in the family. It was not found in 138 other PTC families. It was found in 7/1,170 sporadic PTC cases and in 0/1,404 controls (p = 0.004). The encoded protein SRRM2 (also called SRm300) is part of the RNA splicing machinery. To evaluate the possibility that the S346F missense mutation affects alternative splicing, we compared RNA-Seq data in leukocytes from three mutation carriers and three controls. Significant differences in alternative splicing were identified for 1,642 exons, of which a subset of 7 exons was verified experimentally. The results confirmed a higher ratio of inclusion of exons in mutation carriers. These data suggest that the S346F mutation in SRRM2 predisposes to PTC by affecting alternative splicing of unidentified downstream target genes.

Apart from alterations in the RET/PTC-RAS-BRAF pathway, comparatively little is known about the genetics of papillary thyroid carcinoma (PTC). We show that numerous microRNAs (miRNAs) are transcriptionally up-regulated in PTC tumors compared with unaffected thyroid tissue. A set of five miRNAs, including the three most up-regulated ones (miR-221, -222, and -146), distinguished unequivocally between PTC and normal thyroid. Additionally, miR-221 was up-regulated in unaffected thyroid tissue in several PTC patients, presumably an early event in carcinogenesis. Tumors in which the up-regulation (11- to 19-fold) of miR-221, -222, and -146 was strongest showed dramatic loss of KIT transcript and Kit protein. In 5 of 10 such cases, this down expression was associated with germline single-nucleotide changes in the two recognition sequences in KIT for these miRNAs. We conclude that up-regulation of several miRs and regulation of KIT are involved in PTC pathogenesis, and that sequence changes in genes targeted by miRNAs can contribute to their regulation.

Abstract Context Previous genome-wide association studies have shown that single-nucleotide polymorphism (SNP) rs2439302 in chromosome 8p12 is significantly associated with papillary thyroid carcinoma (PTC) risk and dysregulated NRG1 expression. The underlying mechanisms remain to be discovered. Objective To evaluate the expression of NRG1 isoforms, candidate functional variants, and potential genes downstream of NRG1 in thyroid tissue. Methods Quantitative reverse transcription polymerase chain reaction was applied for gene expression analysis. SNaPshot assay, haplotype, and computer analyses were performed to evaluate candidate functional variants. Other functional assays [chromatin immunoprecipitation (ChIP) assay, luciferase assay, small interfering RNA knockdown, and RNA sequencing] were performed. Results Three NRG1 isoforms (NM_004495, NM_013958, and NM_001160008) tested were highly expressed in thyroid tissue. The expression levels of the three isoforms were significantly correlated with the genotypes of rs2439302. A DNA block of ~32 kb containing the risk G allele of rs2439302 was revealed, harboring multiple candidate functional variants. ChIP assay for active chromatin markers indicated at least nine regions in the DNA block showing strong H3Kme1 and H3K27Ac signals in thyroid tissue. Luciferase reporter assays revealed differential allelic activities associated with seven SNPs. Knocking down NRG1 in primary thyroid cells revealed downstream or interacting genes related to NRG1. Conclusions Our data suggest a role for transcriptional regulation of NRG1 in the predisposition to PTC. The thyroid cancer risk [G] allele of rs2439302 belongs to a haplotype whose SNPs regulate NRG1 expression followed by transcriptional regulation of NRG1-targeting or -interacting genes.

This paper proposes an intelligent network traffic scheduling algorithm based on deep reinforcement learning and graph neural network (GNN) to solve traffic scheduling problems in large-scale dynamic network environments. The algorithm combines the decision-making ability of deep reinforcement learning and the advantage of GNNs in processing graph structure data. Through hierarchical reinforcement learning framework, it realizes efficient decision-making process from macro-strategy formulation to micro-operation execution. Experimental results show that compared with traditional algorithms, the proposed algorithm has significant advantages in key performance indicators such as average delay time, throughput and resource utilization. The algorithm not only surpasses Dijkstra, Shortest Path First (SPF) and Weighted Round Robin (WRR) algorithms under standard test conditions, but also shows excellent robustness and generalization ability under complex scenarios such as different traffic demand intensity, link failure and network topology change. Experimental results show that the proposed traffic scheduling algorithm based on deep reinforcement learning and graph neural network has significant advantages in multiple key performance indicators. Specifically, in a large-scale network environment (including 100,000 traffic flows and 3,000 links, each with a bandwidth of 1 Gbps), compared with the Dijkstra algorithm, the shortest path first (SPF) algorithm, and the weighted round-robin (WRR) algorithm, the proposed algorithm achieves lower average latency (10.5 milliseconds vs. 16.2 milliseconds), higher throughput (9800 Mbps vs. 8900 Mbps), and better resource utilization (92% vs. 85%). In addition, the algorithm also shows good adaptability, maintaining low latency under different traffic demand intensities while improving overall network performance. In addition, through model optimization and parameter adjustment, the convergence speed and learning efficiency of the algorithm are significantly improved when dealing with large-scale networks, which provides strong technical support for automatic network traffic management.