Primary hyperparathyroidism (PHPT) is a disease caused by the autonomous over-secretion of parathyroid hormone (PTH) due to adenoma and hyperplasia of the parathyroid gland, furthermore it has an estimated prevalence of 6.7 per “1,000” population. The majority of PHPT cases are not inherited, while some are due to genetic mutations, it include multiple endocrine neoplasia (MEN), familial hypocalciuric hypercalcemia, neonatal severe hyperparathyroidism, familial isolated hyper parathyroidism and hyperparathy-roidism-jaw tumor (HPT-JT) syndrome (caused by mutations in the CDC73 gene) (1, 2, 3, 4, 5).
Hyperparathyroidism-jaw tumor HPT-JT (OMIM #145001) first observed by Jackson in 1958 .It is an autosomal dominant disorder with variable expression (6, 7, 8). It includes parathyroid adenomas, fibro-osseous jaw tumors, uterine tumors and renal diseases such as hamartomas, polycystic disease and Wilms tumors or adenocarcinoma. Diagnosis of HPT-JT is important because of its genetic involvement and the 24% chance of malignant transformation (9, 10). The nuclear medicine imaging - especially the scintigraphy parathyroid with 99m Tc-MIBI (methoxyisobutyl-isonitrile)- has an important role in establishing the diagnosis (11).
CDC73 -related (HPT-JT) syndrome results from truncating (80%) or missense variants in the tumor suppressor Cell Civision Cycle protein 73 (CDC73) gene (also known as HRPT-2), that is located at chromosome 1q31.2, and encodes the 531-amino acid protein parafibromin (12, 13, 14, 15, 16). this protein regulates transcriptional and post-transcriptional events. The majority of the CDC73 gene mutations related to hyperparathyroidism and parathyroid carcinoma are frame-shift, nonsense or missense occurring within the protein-encoding exons.(16) It has been estimated that 70% of patients suffering from this mutation may develop PHPT (17).
In vitro characterization studies, is a demanding approach requiring a lot a lot of resources, time, and fees. For these reasons, bioinformatics analysis is an appropriate, fast, dependable and cost effective approach to enhance our understanding of the role of mutations in the pathogenesis. Insilco or bio-informatics analysis has become an important tool in the recent years that help in the advancement of biological research through the use of different algorithms and databases (18). Many researches about the role of CDC73 gene in the development of (HPT-JT) syndrome have focused in the deletion type of mutation (5, 19, 20), yet few of them have study single nucleotides polymorphisms (SNPs). This study is unique because it is the first in silico analysis of CDC73 gene associating it with jaw tumor syndrome. Therefore, the aim of this study is to assess the effect of mutational SNPs on CDC73 gene on the structure and function of parafibromin protein using different bioinformatics tools.
Source of retrieving nsSNPs
The SNPs related to the human gene CDC37 were obtained from single nucleotide database (dbSNP) in the National Center for Biotechnology Information (NCBI) web site. www.ncbi.nlm.nih.gov And the protein sequence with ID Q6P1J9 was obtained from UniProt database. www.uniprot.org
Severs used for identifying the most damaging and disease related SNPs
Six server were used for assessing the functional and disease related impact of deleterious nsSNP:
SIFT (Sorting Intolerant From Tolerant)
SIFT is the first online server that was used in our assessment. It predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. Sift score range from 0 to 1. The amino acid substitution is predicted deleterious if the score is ≥ 0.05, and tolerated if the score is ≥ 0.05. The protein sequence that obtained from UniProt and the substitutions of interest were submitted to SIFT server, then according to the score the substitution will be either deleterious or tolerated. The deleterious SNPS were further evaluated (21). It is available online at www. sift.bii.a-star.edu.sg/
Polyphen2 (prediction of functional effect of human nsSNPs)
PolyPhen-2 (Polymorphism Phenotyping v2) it is another online server, available as software and via a Web server predicts the possible impact of amino acid substitutions on the stability and function of human proteins using structural and comparative evolutionary considerations. It performs functional annotation of single-nucleotide polymorphisms (SNPs), maps coding SNPs to gene transcripts, extracts protein sequence annotations and structural attributes and builds conservation profiles. It then estimates the probability of the missense mutation being damaging based on a combination of all these properties. PolyPhen-2 features include a high-quality multiple protein sequence alignment pipeline and a prediction method employing machine-learning classification (22).
The output of the PolyPhen-2 prediction pipeline is a prediction of probably damaging, possibly damaging, or benign, along with a numerical score ranging from 0.0 (benign) to 1.0 (damaging). This three predictions means that, when the prediction is “probably damaging” indicates damaging with high confidence, “possibly damaging” indicates damaging with low confidence, and “benign” means that the query substitution is predicted to be benign with high confidence. It is available online at http://genetics.bwh.harvard.edu/pph2/ Only the damaging SNPs further evaluated.
PROVEN (protein variation effect analyzer)
Was the third software tool used which predicts whether an amino acid substitution has an impact on the biological function of a protein. PROVEAN is useful for filtering sequence variants to identify nonsynonymous or indel variants that predicted to be functionally important. The result obtained from this web site is either deleterious when the score is ≤-2.5, and neutral if the score above -2.5 (23).. Moreover, the deleterious prediction considered for further evaluation. It is available at http://provean.jcvi.org/index.php
It is another tool to predict functional effects of mutations. SNAP2 is a trained classifier that based on a machine-learning device called “neural network”. It distinguishes between effect and neutral variants/non-synonymous SNPs by taking a variety of sequence and variant features into account. The most important input signal for the prediction is the evolutionary information taken from an automatically generated multiple sequence alignment. In addition, structural features such as predicted secondary structure and solvent accessibility are considered. If available also annotation (i.e. known functional residues, pattern, regions) of the sequence or close homologs pulled in. In a cross-validation over 100,000 experimentally annotated variants, SNAP2 reached a sustained two-state accuracy (effect/ neutral) of 82% (at an AUC of 0.9) .(24) We submit the reference sequence and then recorded the relevant result. It is available at https://rostlab.org/services/snap2web/
Is a web server for predicting disease-associated variations from protein sequence and structure. SNPs&GO is an accurate method that, starting from a protein sequence, can predict whether a variation is disease related or not by exploiting the corresponding protein functional annotation. (25) We submitted the reference sequence in FASTA format in the search bar. The result we obtained from this server consist of three different analytical algorithms; PHD, SNP&GO, and Panther. The output consist of a table listing the number of the mutated position in the protein sequence, the wild-type residue, the new residue and if the related mutation is predicted as disease-related (Disease or as neutral polymorphism Neutral) and The RI value (Reliability Index). It is available at http://snps.biofold.org/snps-and-go/index.html
It is a web-based tool for annotation of pathological variant on proteins. P-Mut Web portal allows the user to perform pathology predictions, to access a complete repository of pre-calculated predictions, and to generate and validate new predictors. It require the reference sequence of the related gene to do these functions. The P-Mut portal is freely accessible at http://mmb.irbbarcelona.org/PMut (26).
After using all these analytical algorithms for the prediction. The resulted SNPs were further analyzed by another tool called I-mutant.
I-mutant (predictors of effect of single point protein mutation)
It is an online server to predict the protein stability change upon single site mutations starting from protein sequence alone or protein structure when available. It is freely available at (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) (27). We select the protein sequence tool on the website, and then we submitted the reference sequence without the heading. In addition to that, we inserted the positon of the intended SNPs with the related new residue, after which we recorded the related result.
it is a free online server that used to predict the function of gene and its interaction using very large set of functional associated data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity (28). It is available at (http://www.genemania.org/) We typed the gene name in the search bar then download the following data (Network image, Gene’s data, Functions data and Interactions data).
A webserver used to analyze the effect of single point mutation on structural level of protein. It is the best way to visualize the mutation since it creates a report consist of figures, animation, 3d structure and mutation report just by submitting the protein sequence and mutation (29). It is available at http://www.cmbi.ru.nl/hope/ W submitted the reference sequence along with the wild and mutant amino acids of the eleven final SNPs. After that, we downloaded the related reports.
Ucsf chimera is a computer program that used to visualize the interaction and molecular analysis including density maps, supramolecular assemblies, sequence alignments, docking results, trajectories, and conformational ensembles. It is also allow creating movies. Chimera (version 1:10:2) software was used to scan the 3D (three-dimensional) structure of specific protein, and then modification was made to the wild type to show the difference after mutation and a graphic view was made for each mutation change (30). (http://www.cgl.ucsf.edu/chimera/)
The Catalogue of Somatic Mutations in Cancer is one of the most comprehensive resource for exploring the impact of somatic mutations in human cancer. It covers both coding and non-coding mutations. Since it is primarily hand-curated, this ensure the high quality and accuracy of the results (31). The gene name (CDC73) was used to search for the associated variants. Then we search for the resulted mutation type for each of the eleven SNPs and record it on the paper. COSMIC is available at (https://cancer.sanger.ac.uk/cosmic)
dbSNP Short Genetic Variations
This is a tool available at NCBI, that use other database to calculate the alleles frequency (like GnomAD_exome and ExAC) (32). We used it to study the global allele’s frequency in CDC73 gene by submitting the rs number of each of the eleven substitution and then recording the resulted frequency in a table. It is available at (https://www.ncbi.nlm.nih.gov/snp/rs759222387)
Seven hundred and thirty three SNPs were downloaded from NCBI, from these only 184 SNPs were missense mutations. Firstly we analyzed the effect of the SNPs on the function of the protein using four soft wares (SIFT, PROVEAN, Polyhen2, Snap2), which result in 31 SNPs that had an effect on protein function (Table 1). Then we further analyzed them by SNPs&-GO, PHD, PMUT, I-Mutant, COSMIC and dbSNP Short Genetic Variations resulting in 11 SNPs (Table 2, 3). Finally, we studied their effect on the structure of the protein by using Hope and chimera (Fig. 2 -12). The following workflow (Fig. 1) summaries the methodology used in this paper.
Functional analysis of single nucleotide polymorphisms (SNPs) by SIFT, PROVEAN, PolyPhen-2 and SNAP2 servers, showing 31 deleterious SNPs.
|dbSNP rs#*||Sub*||Sift prediction||Sift score||Provean prediction||PROVEAN score||Polyphen2 prediction||Polyphen2 score||Snap2 prediction||Snap2 Score|
Functional analysis of single nucleotide polymorphisms (SNPs) by SNPs&GO, PHD and PMUT servers, showing eleven pathogenic SNPs.
|dbSNP rs#*||Sub*||SNPandGO Prediction||RI*||snp and go score||PHD Prediction||RI||PHD probability||PMut prediction||PMut score|
stability analysis of 11 single nucleotides polymorphism using I-MUTANT showing decrease in the related protein stability. Variant effect on cancer was analyzed by COSMIC tool, which show the related type of mutations. Finally, the frequency alleles of the SNPs analyzed by dbSNP Short Genetic Variations that shows a frequency ranging from 0.0 to 0.00001.
|dbSNP rs#||sub||I-MUTANT prediction||SCORE||RI||COSMIC Mutation type||Frequency alleles|
|rs1060500015||L63P||Decrease||-1.61||2||Deletion - Frameshift||N/A*|
|rs770439843||R222G||Decrease||-1.23||6||Substitution - Nonsense||0.00001|
|rs1060500022||W231R||Decrease||-1.07||8||Substitution - Nonsense||N/A|
|rs754454928||R441C||Decrease||-0.91||7||Substitution - Missense||0.00001|
Firstly we assess the effect of the missense SNPS on the related protein by SIFT, Polyphen2, PROVEAN and SNAP2, which resulted in 31 combined deleterious SNPS by the four soft wares, with different scores. Polyphen2 prediction shows two types of result (probably damaging prediction with 27 SNPS, and possibly damaging with four SNPs) (Table 1).
Further functional analysis was done using SNPandGO, PHD and P-Mut soft wares. The resulted predictions were varied among the soft wares with different reliability index (RI). When the combined Disease related SNPs were selected from the three software’s, we end up with only 11 SNPs. (Table 2).
In our attempt to study the effect of these eleven SNPs on the protein stability, we used I-Mutant software, which predicted that all the eleven SNPs would decrease the stability of the protein. Furthermore, we used COSMIC software to predict the possible mutation type that could result from each variant. (Some predictions were not available in the database) Then we calculated the frequency alleles of each variant independently by dbSNP Short Genetic Variations. (Some results were also unavailable in the database) (Table 3).
To study the gene-gene interactions and predict the possible function of CDC73 gene we used gene MANIA software, which shows different types of interactions (co-expression and sharing the same domain), with other genes like CTR9 and CHUK. The software also provide us with a list of possible predicted functions. (Table 4 and 5) and (Fig.13).
CDC73 gene Functions and its appearance in network and genome as predicted by Gene mania. Showing the function, number of genes in network and in the genomes.
|Function||FDR||Genes in network||Genes in genome|
|transcription elongation factor complex||2.52E-09||6||28|
|regulation of transcription elongation from RNA polymerase II promoter||2.52E-09||5||10|
|transcription elongation from RNA polymerase II promoter||6.60E-09||7||75|
|DNA-templated transcription, elongation||5.45E-08||7||108|
|positive regulation of DNA-templated transcription, elongation||5.45E-08||5||20|
|regulation of DNA-templated transcription, elongation||2.01E-07||5||28|
|covalent chromatin modification||3.47E-07||8||265|
|DNA-directed RNA polymerase II, holoenzyme||3.50E-07||6||81|
|endodermal cell fate commitment||3.50E-07||4||10|
|RNA polymerase complex||7.92E-07||6||96|
|DNA-directed RNA polymerase complex||7.92E-07||6||95|
|nuclear DNA-directed RNA polymerase complex||7.92E-07||6||95|
|endodermal cell differentiation||8.50E-07||4||13|
|negative regulation of myeloid cell differentiation||1.03424E-06||5||44|
|cell fate commitment involved in formation of primary germ layer||2.50852E-06||4||17|
|mRNA 3’-end processing||2.5645E-05||5||88|
|regulation of myeloid cell differentiation||4.02517E-05||5||97|
|formation of primary germ layer||4.41323E-05||4||37|
|RNA 3’-end processing||4.41323E-05||5||101|
|regulation of mRNA processing||0.000289177||4||59|
|positive regulation of mRNA 3’-end processing||0.000313172||3||15|
|regulation of mRNA 3’-end processing||0.000372752||3||16|
|myeloid cell diferentiation||0.000446549||5||165|
|positive regulation of mRNA processing||0.000804394||3||21|
|histone H3-K4 methylation||0.001915467||3||28|
|cell fate commitment||0.002956445||4||111|
|negative regulation of cell differentiation||0.004090933||5||267|
|stem cell maintenance||0.005276884||3||40|
|histone lysine methylation||0.006873226||3||44|
|regulation of histone modification||0.011780086||3||53|
|stem cell diferentiation||0.013955822||4||171|
|cellular response to lipopolysaccharide||0.01758198||3||62|
|regulation of chromatin organization||0.018029071||3||63|
|cellular response to molecule of bacterial origin||0.020035617||3||66|
|RNA polymerase II core binding||0.020433712||2||10|
|basal transcription machinery binding||0.022132906||2||11|
|basal RNA polymerase II transcription machinery binding||0.022132906||2||11|
|transcriptionally active chromatin||0.022132906||2||11|
|regulation of histone H3-K4 methylation||0.022132906||2||11|
|cellular response to biotic stimulus||0.022949254||3||74|
|mRNA cleavage factor complex||0.024254179||2||12|
|RNA polymerase core enzyme binding||0.024254179||2||12|
|positive regulation of histone methylation||0.028159975||2||13|
|response to lipopolysaccharide||0.037153654||3||90|
|stem cell development||0.037153654||3||90|
|regulation of chromosome organization||0.037153654||3||90|
|RNA polymerase binding||0.040493707||2||16|
|regulation of histone methylation||0.04514658||2||17|
|response to molecule of bacterial origin||0.049891593||3||101|
The gene co-expressed, share domain and interaction with CDC73 gene network, as predicted by Gene mania. Showing the type of interaction between different genes and CDC73 gene.
|Gene 1||Gene 2||Weight||Network group|
|CHUK||AURKB||0.006874138||Shared protein domains|
|CDK9||AURKB||0.003942981||Shared protein domains|
|CDK9||CHUK||0.006889931||Shared protein domains|
|CHUK||AURKB||0.003882364||Shared protein domains|
|CDK9||AURKB||0.002896895||Shared protein domains|
|CDK9||CHUK||0.004389529||Shared protein domains|
We used project Hope and Chimera to study the structural changes inflicted by the eleven SNPs on the parafibromin protein, we combined the two-dimensional structure we acquired from Hope reports with the three dimensional structure we designed using chimera software. (Fig. 2-12). These figures show the differences between native and mutant amino acids, in the green and red boxes the schematic structures of the native amino acids (in the left side), and the mutant ones (in the right side). In the 2D image the backbone, which is the same for each amino acid, is colored red and the side chain, unique for each amino acid, is colored black, the 3D wide-type residues colored green and mutant ones colored red, whereas the protein is colored cyan.
It has been found that many genes with proved association to cancer, usually contain single nucleotide polymorphisms (SNPs) (33), Furthermore these disease-causing SNPs are usually found to occur at evolutionarily conserved regions. Those have essential role in the structural integrity and function of the protein (34, 35), therefore, our focus was dedicated to the coding region, which revealed 11novel mutations in CDC73 gene, out of 184-missense SNPs download from NCBI web site. These SNPs were then analyzed using twelve soft wares to study the effect of the mutation on the structural and function of the parafibromin protein.
Project hope was used to study the effect of the mutation on the physiochemical properties of the protein, where we have found changes on the charge, size and hydrophobicity of the protein illustrated in the hope report, which leads to disturbance in the interactions with other molecules. In the first substitution G49C, The mutant residue is bigger and more hydrophobic than the wild-type residue.in L63PThe mutant residue is smaller than the wild-type residue, which lead to an empty space in the core of the protein. In L64P, we also found the mutant residue is smaller than the wild-type residue, causing an empty space in the core of the protein. In D90H, There is a difference in charge between the wild-type and mutant amino acid. The charge of the buried wild-type residue is lost by this mutation. The wild type and mutant amino acids differ in size,
With the mutant residue been bigger than the wild-type residue. The wild-type residue was buried in the core of the protein, and since the mutant residue is bigger, it probably will not fit. In R222G, There is a difference in charge between the wild-type and mutant amino acid, this can cause loss of interactions with other molecules or residues, The wild-type and mutant amino acids also differ in size, where the mutant residue is smaller, this might lead to loss of interactions. Furthermore, the hydrophobicity of the wild type and mutant residue differs, as the mutation introduces a more hydrophobic residue at this position, this can result in loss of hydrogen bonds and/or disturb correct folding. In W231R, There is a difference in charge between the wild type and mutant amino acid. The mutation introduces a charge; this can cause repulsion of ligands or other residues with the same charge. In addition, the wild type and mutant amino acids differ in size, where the mutant residue is smaller; this might lead to loss of interactions. The hydrophobicity of the wild type and mutant residue also differs, that is why hydrophobic interactions, either in the core of the protein or on the surface may be lost. In P360S, the mutant residue is smaller than the wild-type residue, which is why the mutation will cause an empty space in the core of the protein. The hydrophobicity of the wild type and mutant residue also differs; this difference will cause loss of hydrophobic interactions in the core of the protein. In R441C, The charge of the wild-type residue is lost, causing loss of interactions with other molecules. In addition, the wild type and mutant amino acids have different size; causing a possible loss of external interactions. In R441H, interactions are lost due to the loss of charge in the mutant amino acid. In R504S, the interaction is lost due to difference in size, charge and hydrophobicity. In R504H, the interaction is lost due to loss of charge and the difference in size between the wild and mutant amino acid with the mutant amino acid been smaller in size the wild one.
We also recognized common shared domains, which were Cdc73/Parafibromin IPR007852, C-Terminal Domain Super-family IPR038103 and Cell Division Control Protein 73, C-Terminal IPR031336, which indicated the conservancy and the significance of these SNPs.
Chimera software was used to show the 3D changes in the structure of the protein coupled with 2D schematic structures from project hope for comparison (Fig. 2-12). The structural differences between the wild type and mutant type visualized using these two software, prove the pathogenic impact of the eleven SNPs on parafibromin protein.
We tried to identify the variant in CDC73 gene that could cause mutational impact on the protein that could lead to cancer development. Using COSMIC software. We focused on the 11 novel mutation at hand. We found after analyzation that three SNPs will lead to Nonsense Substitution (R222G, W231R and R441C), while one SNP (L63P) will lead to Frameshift-Deletion. This indicate the significance of theses SNPs in the development of hyperparathyroidism-jaw tumor (HPT-JT) syndrome. Other SNPs prediction was not available at the web side.
We analyzed the allele’s frequency using dbSNP Short Genetic Variations that provided different prediction about the global frequency of our gene in question. We noted R504H SNP to have the highest alleles frequency with global frequency of 0.00004.while other SNPs results ranged from 0,0 to 0.00001 indicating that R504H could be best candidate as a diagnostic marker.
Previous papers report novel mutations associated with Jaw tumor syndrome in the following positions: c.191-192 delT,(19) and (c.1379delT/p.L460Lfs*18) (36). While novel deletion of exons 4 to 10 of CDC73 detected in another study (37).
In other study NGS revealed four pathogenic or likely pathogenic germline sequence variants in CDC73 c.271C>T (p.Arg91*), c.496C>T (p.Gln166*), c.685A>T (p.Arg229*) and c.787C>T (p.Arg263Cys that are related to Primary hyperparathyroidism (PHPT) (2) of those mutations, only (A263C) was found to be pathogenic (by SIFT, PROVEAN, Polyphen2 and SNAP2) in the present study. Furthermore A study done by Sulaiman et al. identified another polymorphism at 109 T>G (38). In addition, CDC73 gene mutation was also reported be associated with other types of cancers like gastric, colorectal, ovarian and head and neck cancers (38). The current work provide new mutations in CDC73 gene which are related to Jaw tumor syndrome, this will add to the growing knowledge about the association between CDC73 gene mutations and Jaw tumor syndrome.
The mutations identified in this study can be used as diagnostic marker for the disease, and serve as “actionable targets” for chemotherapeutic intervention in patients whose disease is no longer surgically curable further in vitro and in vivo studies are needed to confirm these results.
In this study, the effect of the SNPs of CDC73 gene was thoroughly investigated through different bioinformatics prediction soft wares, 11 novel mutations were found to have damaging impact on the structure and function of the protein and may thus be used as diagnostic marker for hyperparathyroidism-jaw tumor (HPT-JT) syndrome.
The authors wish to acknowledge the enthusiastic cooperation of Africa City of Technology - Sudan.
All data underlying the results are available as part of the article and no additional source data are required.
Koikawa K, Okada Y, Mori H, Kawaguchi M, Uchino S, Tanaka Y. Hyperparathyroidism-jaw Tumor Syndrome Confirmed by Preoperative Genetic Testing. Intern Med. 2018;57(6):841-4.
Mamedova E, Mokrysheva N, Vasilyev E, Petrov V, Pigarova E, Kuznetsov S, et al. Primary hyperparathyroidism in young patients in Russia: high frequency of hyperparathyroidism-jaw tumor syndrome. Endocr Connect. 2017;6(8):557-65.
Sun W, Kuang XL, Liu YP, Tian LF, Yan XX, Xu W. Crystal structure of the N-terminal domain of human CDC73 and its implications for the hyperparathyroidism-jaw tumor (HPT-JT) syndrome. Sci Rep. 2017;7(1):15638.
Cavaco BM, Guerra L, Bradley KJ, Carvalho D, Harding B, Oliveira A, et al. Hyperparathyroidism-jaw tumor syndrome in Roma families from Portugal is due to a founder mutation of the HRPT2 gene. J Clin Endocrinol Metab. 2004;89(4):1747-52.
Muscarella LA, Turchetti D, Fontana A, Baorda F, Palumbo O, la Torre A, et al. Large deletion at the CDC73 gene locus and search for predictive markers of the presence of a CDC73 genetic lesion. Oncotarget. 2018;9(29):20721-33.
Chen Y, Hu DY, Wang TT, Zhang R, Dong Q, Xu ZX, et al. CDC73 gene mutations in sporadic ossifying fibroma of the jaws. Diagn Pathol. 2016;11(1):91.
Newey PJ, Bowl MR, Cranston T, Thakker RV. Cell division cycle protein 73 homolog (CDC73) mutations in the hyperparathyroidism-jaw tumor syndrome (HPT-JT) and parathyroid tumors. Hum Mutat. 2010;31(3):295-307.
Parfitt J, Harris M, Wright JM, Kalamchi S. Tumor suppressor gene mutation in a patient with a history of hyperparathyroidism-jaw tumor syndrome and healed generalized osteitis fibrosa cystica: a case report and genetic pathophysiology review. J Oral Maxillofac Surg. 2015;73(1):194 e1-9.
Redwin Dhas MP, Karthiga KS, Tatu JE, Eugenia SJ. Hyper Parathyroidisim Jaw Tumor Syndrome: A Rare Condition of Incongruous Features. Ethiop J Health Sci. 2017;27(3):309-13.
Pazienza V, la Torre A, Baorda F, Alfarano M, Chetta M, Muscarella LA, et al. Identification and functional characterization of three NoLS (nucleolar localisation signals) mutations of the CDC73 gene. PLoS One. 2013;8(12):e82292.
Piciu D, Piciu A, Barbus E, Pestean C, Larg MI, Fetica B. Primary hyperparathyroidism-jaw tumor syndrome: a confusing and forgotten diagnosis. Clujul Med. 2016;89(4):555-8.
Wasserman JD, Tomlinson GE, Druker H, Kamihara J, Kohlmann WK, Kratz CP, et al. Multiple Endocrine Neoplasia and Hyperparathyroid-Jaw Tumor Syndromes: Clinical Features, Genetics, and Surveillance Recommendations in Childhood. Clin Cancer Res. 2017;23(13):e123-e32.
Aldred MJ, Talacko AA, Savarirayan R, Murdolo V, Mills AE, Radden BG, et al. Dental findings in a family with hyperparathyroidism-jaw tumor syndrome and a novel HRPT2 gene mutation. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2006;101(2):212-8.
Carpten JD, Robbins CM, Villablanca A, Forsberg L, Presciuttini S, Bailey-Wilson J, et al. HRPT2, encoding parafibromin, is mutated in hyperparathyroidism-jaw tumor syndrome. Nat Genet. 2002;32(4):676-80.
Panicker LM, Zhang JH, Dagur PK, Gastinger MJ, Simonds WF. Defective nucleolar localization and dominant interfering properties of a parafibromin L95P missense mutant causing the hyperparathyroidism-jaw tumor syndrome. Endocr Relat Cancer. 2010;17(2):513-24.
Guarnieri V, Seaberg RM, Kelly C, Jean Davidson M, Raphael S, Shuen AY, et al. Large intragenic deletion of CDC73 (exons 4-10) in a three-generation hyperparathyroidism-jaw tumor (HPT-JT) syndrome family. BMC Med Genet. 2017;18(1):83.
Mele M, Rolighed L, Jespersen M, Rejnmark L, Christiansen P. Recurrence of Hyperparathyroid Hypercalcemia in a Patient With the HRPT-2 Mutation and a Previous Parathyroid Carcinoma in Hyperparathyroidism-Jaw Tumor Syndrome. Int J Endocrinol Metab. 2016;14(2):e35424.
Vamathevan J, Birney E. A Review of Recent Advances in Translational Bioinformatics: Bridges from Biology to Medicine. Yearbook of medical informatics. 2017;26(1):178-87.
Ciuffi S, Cianferotti L, Nesi G, Luzi E, Marini F, Giusti F, et al. Characterization of a novel CDC73 gene mutation in a hyperparathyrodism-jaw tumor patient affected by parathyroid carcinoma in the absence of somatic loss of heterozygosity. Endocr J. 2019.
Rubinstein JC, Majumdar SK, Laskin W, Lazaga F, Prasad ML, Carling T, et al. Hyperparathyroidism-Jaw Tumor Syndrome Associated With Large-Scale 1q31 Deletion. J Endocr Soc. 2017;1(7):926-30.
Sim N-L, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC.
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging mis-sense mutations. Nature methods. 2010;7(4):248-9.
Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLOS ONE. 2012;7(10):e46688.
Hecht M Fau - Bromberg Y, Bromberg Y Fau - Rost B, Rost B. Better prediction of functional effects for sequence variants. (1471-2164 (Electronic)).
Capriotti E, Martelli PL, Fariselli P, Casadio R. Blind prediction of deleterious amino acid variations with SNPs&GO. (1098-1004 (Electronic)).
López-Ferrando V, Gazzo A, de la Cruz X, Orozco M, Gelpí JL. PMut: a web-based tool for the annotation of pathological variants on proteins, 2017 update. Nucleic acids research. 2017;45(W1):W222-W8.
Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic acids research. 2005;33(Web Server issue):W306-W10.
Warde-Farley D, Donaldson Sl Fau - Comes O, Comes O Fau - Zuberi K, Zuberi K Fau - Badrawi R, Badrawi R Fau - Chao P, Chao P Fau - Franz M, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. (1362-4962 (Electronic)).
Venselaar H, Te Beek Ta Fau - Kuipers RKP, Kuipers Rk Fau - Hekkelman ML, Hekkelman Ml Fau - Vriend G, Vriend G. Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. (1471-2105 (Electronic)).
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996-1006.
Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, et al.
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al.
Deng N, Zhou H, Fan H, Yuan Y. Single nucleotide polymorphisms and cancer susceptibility. Oncotarget. 2017;8(66):110635-49.
Khan IA, Mort M, Buckland PR, O’Donovan MC, Cooper DN, Chuzhanova NA. In silico discrimination of single nucleotide polymorphisms and pathological mutations in human gene promoter regions by means of local DNA sequence context and regularity. In silico biology. 2006;6(1-2):23-34.
Mustafa MI, Mohammed ZO, Murshed NS, Elfadol NM, Abdelmoneim AH, Hassan MA. In Silico Genetics Revealing 5 Mutations in CEBPA Gene Associated With Acute Myeloid Leukemia. Cancer informatics. 2019;18:1176935119870817.
Chiofalo MG, Sparaneo A, Chetta M, Franco R, Baorda F, Cinque L, et al. A novel CDC73 gene mutation in an Italian family with hyperparathyroidism-jaw tumour (HPT-JT) syndrome. Cell Oncol (Dordr). 2014;37(4):281-8.
Guarnieri V, Seaberg RM, Kelly C, Jean Davidson M, Raphael S, Shuen AY, et al. Erratum to: Large intragenic deletion of CDC73 (exons 4-10) in a three-generation hyperparathyroidism-jaw tumor (HPT-JT) syndrome family. BMC Med Genet. 2017;18(1):99.
Sulaiman L, Haglund F, Hashemi J, Obara T, Nordenström J, Larsson C, et al. Genome-wide and locus specific alterations in CDC73/HRPT2-mutated parathyroid tumors. PloS one. 2012;7(9):e46325-e.
Zheng H-C, Gong B-C, Zhao S. The clinicopathological and prognostic significances of CDC73 expression in cancers: a bioinformatics analysis. Oncotarget. 2017;8(56):95270-9.