Are head and neck versus abdominal paragangliomas driven by different single nucleotide events?

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Krzysztof Kotlarz, Katarzyna Ziemnicka, Bartłomiej Budny, Magda Mielczarek, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5237808/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Journal of Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Paragangliomas (PGLs) are a heterogeneous group of tumors of the nonepithelial neuroendocrine type with a significant percentage being genetically determined. They can develop from cells of the parasympathetic as well as the sympathetic nervous system. Tumors located in head and neck usually have a parasympathetic origin, whereas those in abdomen have a sympathetic origin. The aim of this study was to determine whether the development of PGLs at both locations is associated with specific variants of genes with proven relevance for the formation of these tumors. 31 patients with abdominal PGL and 16 with head and neck PGLs were analyzed at 12 genes whose defects are among the most common genetic determinants of PGLs. The impact of SNPs on differentiation between both tumor types was assessed by fitting a decision tree and quantifying genotype effects of SNPs by the Shapley Additive Explanation metric. The study demonstrated that SNPs rs3748576 within KIF1B gene and rs10060259 within SDHA gene increase the probability of abdominal tumour locations, while heterozygous GA genotypes of rs2435351 located within RET gene increase the probability of head and neck locations. The SNPs marked genes involved in the formation and functioning of the nervous system, but are located in introns, and thus themselves do not contribute to protein diversity. Still, intronic SNPs can indirectly affect the transcriptome by influencing alternative splicing, mRNA stability, or overlap with non-coding genes and other regulatory elements that affect transcription. Given this, it seems important to consider variants from non-coding regions in genetic analyses. Bioinformatics Decision tree Location heterogeneity Paraganglioma Single Nucleotide Polymorphism Figures Figure 1 Figure 2 Figure 3 Introduction Paragangliomas (PGL) represent a heterogeneous group of rare tumors that belong to nonepithelial neuroendocrine neoplasms (Mete et al. 2022 ). PGLs exhibit a very high heritability compared to other types of tumors ranging between 0.30 and 0.35. Until now, almost 30 genes have been linked to the genetic background of PGLs (Papathomas et al. 2021 ). The incidence of PGLs reaches approximately 0.6 cases per 100,000 persons per year (Berends et al. 2018 ). Paragangliomas originate from both the parasympathetic and sympathetic nervous system, with intra-adrenal paraganglioma called a pheochromocytoma (Mete et al. 2022 ). Parasympathetic PGLs are rarely hormonally active and are located primarily in the head and neck region, while only 5% of tumors in this region originate from the sympathetic nervous system (Papathomas et al. 2021 , Valero & Ganly 2022). Parasympathetic PGLs are usually located along the cranial and thoracic branches of the vagus and glossopharyngeal nerves (Patel et al. 2020 ), whereas sympathetic PGLs develop primarily in the abdominal region (Papathomas et al. 2021 ). So far, studies have focused on the search for new genetic variants and their association with a specific phenotype, while the aspect of possible differences in genetic predisposition to tumor development in the head and neck region and the abdominal region has not yet been studied. Whereas our study aimed to investigate whether among the genes associated with the development and course of PGLs, there are some that may also influence tumor localization of tumors in the head and neck or in the abdominal region. Materials and Methods Study population This study considered 47 patients with no previous family history of PGL, assigned to two groups based on tumour location only in the adrenal medulla and retroperitoneal space (class 0) or with head and neck paragangliomas (class 1). All patients were diagnosed in the Department of Endocrinology, Metabolism and Internal Diseases and the Department of Otolaryngology and Laryngological Oncology at the Poznan University of Medical Sciences. For patients with abdominal PGLs, tumor hormonal activity was evaluated by measuring daily urinary excretion of metanephrines and normetanephrines, while patients with head and neck PGL patients were not assessed for tumour endocrine activity before surgery. Patients with pheochromocytoma and retroperitoneal paraganglioma were routinely tested during qualification for surgery. All pheochromocytomas appeared to be hormonally active, whereas retroperitoneal PGLs were hormonally inactive. The baseline characteristics of the patients studied were summarised in Table 1 . Table 1 Baseline characteristics of patients with PPGL Parameter Abdominal PPGL (0) Head-and-Neck PPGL (1) Number of patients 17 female / 14 male 12 female / 4 male Age at diagnosis [years] 48.6 ± 2.5 [26–74] 56.5 ±- SD 9.1 [35–70] Pheochromocytoma 28 - Retroperitoneal PGL 3 - Carotid body PGL - 4 Jugular PGL - 1 Middle ear PGL - 6 Vagal PGL - 3 Parapharyngeal PGL - 2 Methods Genetic analysis DNA for testing was isolated from the peripheral blood of the patients. Genetic testing was conducted in the Molecular Endocrinology Laboratory in the Department of Endocrinology, Metabolism and Internal Medicine of the Poznan University of Medical Sciences. The Ion Torrent Personal Genome Machine (Ion PGM™ Thermo Fisher Scientific Inc.) was used to evaluate the custom-designed gene panel. The panel was constructed using an algorithm developed by Thermo Fisher Scientific (Ampliseq Designer, http://www.ampliseq.com ), allowing for optimal design of primer pairs up to 400 bp in length. The coding regions of the following genes, described in the literature (Papathomas et al. 2021 ) as one of the most common defects sites associated with PGL, were selected for sequencing: FH, KIF1B, MEN1, NF1, RET, SDHA, SDHAF2, SDHB, SDHC, SDHD, TMEM127 , and VHL . The final coverage of the coding sequences with amplicons varied between 95% and 100%. According to the manufacturer's protocol, library construction and subsequent enrichment of paired DNA samples were performed using the Ion OneTouch v2 system (Thermo Fisher Scientific Inc.). The Ion PGM Sequencing 400 Kit reagents and Ion 316 v2 sequencing chips were used for direct sequencing. The average sequencing coverage varied between 110x and 150x. Sequence quality filtering of the acquired reads was performed using the Torrent server and the Ion Reporter software. Further standard steps involved signal processing, read filtering, alignment to the GRCh37/hg19 human reference assembly, and genotype calling of 41 SNPs located on 6 chromosomes. Pathogenicity evaluation of coding variants was performed using the Ensembl VEP tool (McLaren et al. 2016). Bioinformatic analysis Decision trees were implemented through the ScikitLearn Python library (the DecisionTree module) to assess the impact of SNPs on patient classification (Pedregosa et al. 2011 ). The final tree structure was selected using the 6-fold cross-validation with the optimal tree architecture based on the accuracy metric to classify the abdominal (class code 0) or head and neck (class code 1) tumor location. The impact of SNP genotypes from the final decision tree was quantified by the Shapley Additive Explanation metric (SHAP) (Lundberg et al. 2020 ). Results For downstream analysis, only 11 SNPs with a minimum of five alternative alleles were included. The final tree architecture yielded an average classification accuracy (over the six cross-validation sets) of 72.92% (± 2.67%) for the training set and 60.42% (± 8.59%) for the validation dataset. The optimal tree architecture was visualised in Fig. 1 , and the impacts of SNP genotypes on classification, expressed by SHAP values, were shown in Fig. 2 . Note that positive SHAP values indicate an association with head and neck location (class 1), while negative values indicate an association with abdominal location (class 0). All three SNPs that contributed to the final decision tree were located in the non-coding part of the genes: (1) a SNP on chromosome 1 within the 15 intron of KIF1B (chr1:10342629, rs3748576), (2) a SNP on chromosome 5 within intron 9 of SDHA (chr5:235548, rs10060259), and (3) a SNP on chromosome 10 within intron 2 of RET (chr10:43596179, rs2435351). The genomic location of SNPs in the GRCh37/hg19 reference assembly was visualised in Fig. 3 . The functional annotation of the SNPs was summarised in Table 2 . Table 2 The basic characteristics of SNPs constituting the optimal decision tree Genomic location [GRCh37] Alleles SNP ID COSMIC ID Genomic annotation chr1:103426 G/A rs3748576 COSV55814192 15. intron of KIF1B chr5:235548 T/C rs10060259 - noncoding transcript 9. intron of SDHA chr10:43596179 G/A rs2435351 COSV60687075 2. intron of RET Discussion Paragangliomas can be caused by defects in various genes. Some of these defects are found to be common to different tumour sites (Baysal 2002 , Neumann et al. 2004). Analysis of SNPs from the studied gene panel demonstrated that there are polymorphisms whose genotypes are indicative of tumor location. In particular, SNP rs3748576 (GA and AA genotypes) located on chromosome 1 within the KIF1B gene, as well as SNP rs10060259 (TC and CC genotypes) located on chromosome 5 within the SDHA gene increase the probability of abdominal tumour locations, while heterozygous genotypes GA of SNP rs2435351 located on chromosome 10 within the RET gene increase the probability of head and neck locations. Although all three SNPs are located in introns and thus do not contribute to protein diversity. Still, intronic SNPs can indirectly affect transcriptome diversity and expressivity by influencing alternative splicing (Bush et al. 2017 ) or stability of mRNA (Gupta et al. 2013 ), as well as overlap with non-coding RNA genes and other regulatory elements, especially enhancers that affect transcription (Chorev and Carmel 2012 ). KIF1B (Kinesin Family Member 1B) is a protein belonging to a family of kinesins involved in the transport of cellular materials to specified destinations (Hirokawa & Tanaka 2015). The family of these kinesins is involved in the morphogenesis, function, and survival of neuronal cells (Zhao et al. 2001 ). Mutations in the KIF1B gene in patients with PPGL have been described as germline and somatic. However, the potential importance of this gene in the development of these tumors remains elusive (Welander et al. 2014). SDHA (succinate dehydrogenase complex flavoprotein A) is a subunit of succinate dehydrogenase (SDH), an enzyme that links two mitochondrial respiratory chain pathways: the Krebs cycle and oxidative phosphorylation. It is also one of the tumour suppressor genes (Burnichon et al. 2010 ). Mutations in this gene have been described in patients with PGL with abdominal, cardiac, carotid body tumours, and in various other locations and origins, sympathetic or parasympathetic (Burnichon et al. 2010 , Shi et al. 2023, Yoshihama et al. 2023, Nölting et al. 2022). Germline mutations in SDHA are described in approximately 10% of PPGL cases and have a low penetrance (Hanson et al. 2023 ). The polymorphism of the SDHA gene described in our study is located in intron 9 in a part of the gene that is close to the region coding the FAD binding domain that plays a role in maintaining the proper function of SDHA (Van Vranken et al. 2015). The RET (Rearranged During Transfection) protooncogene encodes a receptor tyrosine kinase, a membrane protein whose natural ligands are GDNF ( glial-derived neurotrophic factor ), neurturin, perception, and artemin. This protein controls the migration, survival, differentiation, proliferation, and maturation of vagal and sacral neural crest cells. Activation of receptor tyrosine kinase leads to the activation of numerous signalling pathways, including RAS/MAPK, PLCg, or PI3K (Bhattarai et al. 2022 ). RET germline mutations are detected primarily in patients with pheochromocytoma and much less frequently in those with paraganglioma (Currás-Freixes et al. 2015). The detected polymorphism in the RET protooncogene was located in intron 2 of the gene and therefore was within the region associated with the coding sequences of the cadherin-like domains located in the extracellular part of the receptor kinase. Genetic defects in this region may lead to impaired expression of RET on the cell surface (Takahashi et al. 2020 ). The PGLs of the head and neck and the PGLs of the upper mediastinum are mainly associated with the parasympathetic system and are not hormonally active. In rare situations (5%), catecholamine-secreting head and neck PGLs that arise from either the carotid body or the carotid sympathetic chain can be identified. PGLs that develop in the lower mediastinum, abdomen, and pelvis are associated with the sympathetic nervous system and are usually hormonally active (Lee and Duh 2008 ). In our study, we dealt with head and neck tumours, which were unknown whether they were hormonally active before surgery. However, their location indicated a high degree of probability of parasympathetic origin. Therefore, it can be assumed that these two distinguished classes, 0 and 1 represented sympathetic (abdominal PGLs) and parasympathetic (head and neck PGLs) tumours, respectively. From a bioinformatics perspective, the major limitation of the analysis was a small study population that impedes formal significance testing of individual variants. Furthermore, the group of patients with PGLs of the head and neck was almost half as numerous as those with PGL of the abdomen. Conclusions By pinpointing variants located in introns, we emphasise the importance of non-coding regions in oncology, since they contain regulatory elements, as well as non-coding genome regions. Specifically, in this analysis, we demonstrated that non-protein changing variation allows for differentiating PGL tumour location. Declarations Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request. Funding This research was funded by Poznan University of Medical Sciences. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval Approval was granted by the Bioethical Committee of Poznan University of Medical Sciences (approval no. 953/16). Consent to participate This study was performed in accordance with the principles of the Declaration of Helsinki and consent has been obtained from each patient after full explanation of the purpose and nature of all applied procedures. Authors contribution J.S., K.Z. conceptualization, data analysis and interpretation, writing of the manuscript; B.B. laboratory investigation, writing of the manuscript, K.K., J.L., T.S. data analysis, M.M. data analysis and interpretation, E.W. laboratory investigation, J.Kaz., J.Kał. collecting patients and clinical data, P.D. consultation and text editing, M.W., M.R consultation. References Baysal BE (2002) Hereditary paraganglioma targets diverse paraganglia. 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JMA J 3:175–181. https://doi.org/10.31662/jmaj.2020-0021 Zhao C, Takita J, Tanaka Y, et al (2001) Charcot-Marie-Tooth Disease Type 2A Caused by Mutation in a Microtubule Motor KIF1Bβ. Cell 105:587–597. https://doi.org/10.1016/S0092-8674(01)00363-4 Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Journal of Applied Genetics → Version 1 posted Editorial decision: Minor Revisions Needed 08 May, 2025 Reviewers agreed at journal 04 Dec, 2024 Reviewers invited by journal 11 Oct, 2024 Editor assigned by journal 11 Oct, 2024 First submitted to journal 10 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Next to each node, the probability of belonging to class 1 and the percentage of the remaining patients were given.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5237808/v1/2bf816692a35f891edd8757e.png"},{"id":67281594,"identity":"9e795152-6ea5-4019-994c-987cb7844450","added_by":"auto","created_at":"2024-10-23 09:05:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134632,"visible":true,"origin":"","legend":"\u003cp\u003eSHAP values of SNP genotypes constituting the optimal decision tree. Positive values represent an association with the head and neck tumour location class (class 1), negative values represent an association with the abdominal tumour location class (class 0)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5237808/v1/8b5ccdbfe3d35b840e9d5b63.png"},{"id":67280533,"identity":"b53c6a7d-7b5e-4520-869c-e2cc202a274b","added_by":"auto","created_at":"2024-10-23 08:57:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":310003,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of chr1:10342629 (rs3748576), chr5:235548 (rs10060259), and chr10:43596179 (rs2435351) on the GRCh37/hg19 reference assembly\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5237808/v1/4626256f7294fea33dc3e595.png"},{"id":88268466,"identity":"6952c6ed-076a-4942-8bab-8077903511cc","added_by":"auto","created_at":"2025-08-04 16:51:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1044241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5237808/v1/237dbc4c-8962-4d1f-9948-a7d2d4947743.pdf"}],"financialInterests":"","formattedTitle":"Are head and neck versus abdominal paragangliomas driven by different single nucleotide events?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParagangliomas (PGL) represent a heterogeneous group of rare tumors that belong to nonepithelial neuroendocrine neoplasms (Mete et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). PGLs exhibit a very high heritability compared to other types of tumors ranging between 0.30 and 0.35. Until now, almost 30 genes have been linked to the genetic background of PGLs (Papathomas et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The incidence of PGLs reaches approximately 0.6 cases per 100,000 persons per year (Berends et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Paragangliomas originate from both the parasympathetic and sympathetic nervous system, with intra-adrenal paraganglioma called a pheochromocytoma (Mete et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Parasympathetic PGLs are rarely hormonally active and are located primarily in the head and neck region, while only 5% of tumors in this region originate from the sympathetic nervous system (Papathomas et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Valero \u0026amp; Ganly 2022). Parasympathetic PGLs are usually located along the cranial and thoracic branches of the vagus and glossopharyngeal nerves (Patel et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), whereas sympathetic PGLs develop primarily in the abdominal region (Papathomas et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). So far, studies have focused on the search for new genetic variants and their association with a specific phenotype, while the aspect of possible differences in genetic predisposition to tumor development in the head and neck region and the abdominal region has not yet been studied. Whereas our study aimed to investigate whether among the genes associated with the development and course of PGLs, there are some that may also influence tumor localization of tumors in the head and neck or in the abdominal region.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study considered 47 patients with no previous family history of PGL, assigned to two groups based on tumour location only in the adrenal medulla and retroperitoneal space (class 0) or with head and neck paragangliomas (class 1). All patients were diagnosed in the Department of Endocrinology, Metabolism and Internal Diseases and the Department of Otolaryngology and Laryngological Oncology at the Poznan University of Medical Sciences. For patients with abdominal PGLs, tumor hormonal activity was evaluated by measuring daily urinary excretion of metanephrines and normetanephrines, while patients with head and neck PGL patients were not assessed for tumour endocrine activity before surgery. Patients with pheochromocytoma and retroperitoneal paraganglioma were routinely tested during qualification for surgery. All pheochromocytomas appeared to be hormonally active, whereas retroperitoneal PGLs were hormonally inactive. The baseline characteristics of the patients studied were summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients with PPGL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbdominal PPGL (0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHead-and-Neck PPGL (1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 female / 14 male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 female / 4 male\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis [years]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 [26\u0026ndash;74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5 \u0026plusmn;- SD 9.1 [35\u0026ndash;70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePheochromocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetroperitoneal PGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotid body PGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJugular PGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle ear PGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVagal PGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParapharyngeal PGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic analysis\u003c/h2\u003e \u003cp\u003eDNA for testing was isolated from the peripheral blood of the patients. Genetic testing was conducted in the Molecular Endocrinology Laboratory in the Department of Endocrinology, Metabolism and Internal Medicine of the Poznan University of Medical Sciences. The Ion Torrent Personal Genome Machine (Ion PGM\u0026trade; Thermo Fisher Scientific Inc.) was used to evaluate the custom-designed gene panel. The panel was constructed using an algorithm developed by Thermo Fisher Scientific (Ampliseq Designer, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ampliseq.com\u003c/span\u003e\u003cspan address=\"http://www.ampliseq.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), allowing for optimal design of primer pairs up to 400 bp in length. The coding regions of the following genes, described in the literature (Papathomas et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) as one of the most common defects sites associated with PGL, were selected for sequencing: \u003cem\u003eFH, KIF1B, MEN1, NF1, RET, SDHA, SDHAF2, SDHB, SDHC, SDHD, TMEM127\u003c/em\u003e, and \u003cem\u003eVHL\u003c/em\u003e. The final coverage of the coding sequences with amplicons varied between 95% and 100%. According to the manufacturer's protocol, library construction and subsequent enrichment of paired DNA samples were performed using the Ion OneTouch v2 system (Thermo Fisher Scientific Inc.). The Ion PGM Sequencing 400 Kit reagents and Ion 316 v2 sequencing chips were used for direct sequencing. The average sequencing coverage varied between 110x and 150x. Sequence quality filtering of the acquired reads was performed using the Torrent server and the Ion Reporter software. Further standard steps involved signal processing, read filtering, alignment to the GRCh37/hg19 human reference assembly, and genotype calling of 41 SNPs located on 6 chromosomes. Pathogenicity evaluation of coding variants was performed using the Ensembl VEP tool (McLaren et al. 2016).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioinformatic analysis\u003c/h3\u003e\n\u003cp\u003eDecision trees were implemented through the ScikitLearn Python library (the DecisionTree module) to assess the impact of SNPs on patient classification (Pedregosa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The final tree structure was selected using the 6-fold cross-validation with the optimal tree architecture based on the accuracy metric to classify the abdominal (class code 0) or head and neck (class code 1) tumor location. The impact of SNP genotypes from the final decision tree was quantified by the Shapley Additive Explanation metric (SHAP) (Lundberg et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFor downstream analysis, only 11 SNPs with a minimum of five alternative alleles were included. The final tree architecture yielded an average classification accuracy (over the six cross-validation sets) of 72.92% (\u0026plusmn;\u0026thinsp;2.67%) for the training set and 60.42% (\u0026plusmn;\u0026thinsp;8.59%) for the validation dataset. The optimal tree architecture was visualised in Fig. \u003cspan\u003e1\u003c/span\u003e, and the impacts of SNP genotypes on classification, expressed by SHAP values, were shown in Fig. \u003cspan\u003e2\u003c/span\u003e. Note that positive SHAP values indicate an association with head and neck location (class 1), while negative values indicate an association with abdominal location (class 0). All three SNPs that contributed to the final decision tree were located in the non-coding part of the genes: (1) a SNP on chromosome 1 within the 15 intron of \u003cem\u003eKIF1B\u003c/em\u003e (chr1:10342629, rs3748576), (2) a SNP on chromosome 5 within intron 9 of \u003cem\u003eSDHA\u003c/em\u003e (chr5:235548, rs10060259), and (3) a SNP on chromosome 10 within intron 2 of \u003cem\u003eRET\u003c/em\u003e (chr10:43596179, rs2435351). The genomic location of SNPs in the GRCh37/hg19 reference assembly was visualised in Fig. \u003cspan\u003e3\u003c/span\u003e. The functional annotation of the SNPs was summarised in Table \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe basic characteristics of SNPs constituting the optimal decision tree\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic location [GRCh37]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlleles\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSNP ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOSMIC ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic annotation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr1:103426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3748576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOSV55814192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15. intron of \u003cem\u003eKIF1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr5:235548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10060259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enoncoding transcript\u003c/p\u003e\n \u003cp\u003e9. intron of \u003cem\u003eSDHA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echr10:43596179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2435351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOSV60687075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. intron of \u003cem\u003eRET\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eParagangliomas can be caused by defects in various genes. Some of these defects are found to be common to different tumour sites (Baysal \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Neumann \u003cem\u003eet al.\u003c/em\u003e 2004). Analysis of SNPs from the studied gene panel demonstrated that there are polymorphisms whose genotypes are indicative of tumor location. In particular, SNP rs3748576 (GA and AA genotypes) located on chromosome 1 within the \u003cem\u003eKIF1B\u003c/em\u003e gene, as well as SNP rs10060259 (TC and CC genotypes) located on chromosome 5 within the \u003cem\u003eSDHA\u003c/em\u003e gene increase the probability of abdominal tumour locations, while heterozygous genotypes GA of SNP rs2435351 located on chromosome 10 within the \u003cem\u003eRET\u003c/em\u003e gene increase the probability of head and neck locations. Although all three SNPs are located in introns and thus do not contribute to protein diversity. Still, intronic SNPs can indirectly affect transcriptome diversity and expressivity by influencing alternative splicing (Bush et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or stability of mRNA (Gupta et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), as well as overlap with non-coding RNA genes and other regulatory elements, especially enhancers that affect transcription (Chorev and Carmel \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKIF1B (Kinesin Family Member 1B) is a protein belonging to a family of kinesins involved in the transport of cellular materials to specified destinations (Hirokawa \u0026amp; Tanaka 2015). The family of these kinesins is involved in the morphogenesis, function, and survival of neuronal cells (Zhao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Mutations in the \u003cem\u003eKIF1B\u003c/em\u003e gene in patients with PPGL have been described as germline and somatic. However, the potential importance of this gene in the development of these tumors remains elusive (Welander et al. 2014). SDHA (succinate dehydrogenase complex flavoprotein A) is a subunit of succinate dehydrogenase (SDH), an enzyme that links two mitochondrial respiratory chain pathways: the Krebs cycle and oxidative phosphorylation. It is also one of the tumour suppressor genes (Burnichon et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Mutations in this gene have been described in patients with PGL with abdominal, cardiac, carotid body tumours, and in various other locations and origins, sympathetic or parasympathetic (Burnichon et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Shi \u003cem\u003eet al.\u003c/em\u003e 2023, Yoshihama \u003cem\u003eet al.\u003c/em\u003e 2023, N\u0026ouml;lting \u003cem\u003eet al.\u003c/em\u003e 2022). Germline mutations in \u003cem\u003eSDHA\u003c/em\u003e are described in approximately 10% of PPGL cases and have a low penetrance (Hanson et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The polymorphism of the SDHA gene described in our study is located in intron 9 in a part of the gene that is close to the region coding the FAD binding domain that plays a role in maintaining the proper function of SDHA (Van Vranken et al. 2015). The \u003cem\u003eRET\u003c/em\u003e (Rearranged During Transfection) protooncogene encodes a receptor tyrosine kinase, a membrane protein whose natural ligands are GDNF (\u003cem\u003eglial-derived neurotrophic factor\u003c/em\u003e), neurturin, perception, and artemin. This protein controls the migration, survival, differentiation, proliferation, and maturation of vagal and sacral neural crest cells. Activation of receptor tyrosine kinase leads to the activation of numerous signalling pathways, including RAS/MAPK, PLCg, or PI3K (Bhattarai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eRET germline mutations are detected primarily in patients with pheochromocytoma and much less frequently in those with paraganglioma (Curr\u0026aacute;s-Freixes et al. 2015).\u003c/em\u003e The detected polymorphism in the RET protooncogene was located in intron 2 of the gene and therefore was within the region associated with the coding sequences of the cadherin-like domains located in the extracellular part of the receptor kinase. Genetic defects in this region may lead to impaired expression of RET on the cell surface (Takahashi et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PGLs of the head and neck and the PGLs of the upper mediastinum are mainly associated with the parasympathetic system and are not hormonally active. In rare situations (5%), catecholamine-secreting head and neck PGLs that arise from either the carotid body or the carotid sympathetic chain can be identified. PGLs that develop in the lower mediastinum, abdomen, and pelvis are associated with the sympathetic nervous system and are usually hormonally active (Lee and Duh \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In our study, we dealt with head and neck tumours, which were unknown whether they were hormonally active before surgery. However, their location indicated a high degree of probability of parasympathetic origin. Therefore, it can be assumed that these two distinguished classes, 0 and 1 represented sympathetic (abdominal PGLs) and parasympathetic (head and neck PGLs) tumours, respectively.\u003c/p\u003e \u003cp\u003eFrom a bioinformatics perspective, the major limitation of the analysis was a small study population that impedes formal significance testing of individual variants. Furthermore, the group of patients with PGLs of the head and neck was almost half as numerous as those with PGL of the abdomen.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy pinpointing variants located in introns, we emphasise the importance of non-coding regions in oncology, since they contain regulatory elements, as well as non-coding genome regions. Specifically, in this analysis, we demonstrated that non-protein changing variation allows for differentiating PGL tumour location.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Poznan University of Medical Sciences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was granted by the Bioethical Committee of Poznan University of Medical Sciences (approval no. 953/16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in accordance with the principles of the Declaration of Helsinki and consent has been obtained from each patient after full explanation of the purpose and nature of all applied procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.S., K.Z. conceptualization, data analysis and interpretation, writing of the manuscript; B.B. laboratory investigation, writing of the manuscript, K.K., J.L., T.S. data analysis, M.M. data analysis and interpretation, E.W. laboratory investigation, J.Kaz., J.Kał. collecting patients and clinical data, P.D. consultation and text editing, M.W., M.R consultation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaysal BE (2002) Hereditary paraganglioma targets diverse paraganglia. Journal of Medical Genetics 39:617\u0026ndash;622. https://doi.org/10.1136/jmg.39.9.617\u003c/li\u003e\n\u003cli\u003eBerends AMA, Buitenwerf E, De Krijger RR, et al (2018) Incidence of pheochromocytoma and sympathetic paraganglioma in the Netherlands: A nationwide study and systematic review. European Journal of Internal Medicine 51:68\u0026ndash;73. https://doi.org/10.1016/j.ejim.2018.01.015\u003c/li\u003e\n\u003cli\u003eBhattarai C, Poudel PP, Ghosh A, et al (2022) The \u003cem\u003eRET\u003c/em\u003e gene encodes RET protein, which triggers intracellular signaling pathways for enteric neurogenesis, and \u003cem\u003eRET\u003c/em\u003e mutation results in Hirschsprung\u0026rsquo;s disease. AIMSN 9:128\u0026ndash;149. https://doi.org/10.3934/Neuroscience.2022008\u003c/li\u003e\n\u003cli\u003eBurnichon N, Bri\u0026egrave;re J-J, Lib\u0026eacute; R, et al (2010) SDHA is a tumor suppressor gene causing paraganglioma. Human Molecular Genetics 19:3011\u0026ndash;3020. https://doi.org/10.1093/hmg/ddq206\u003c/li\u003e\n\u003cli\u003eBush SJ, Chen L, Tovar-Corona JM, Urrutia AO (2017) Alternative splicing and the evolution of phenotypic novelty. Phil Trans R Soc B 372:20150474. https://doi.org/10.1098/rstb.2015.0474\u003c/li\u003e\n\u003cli\u003eChorev M, Carmel L (2012) The Function of Introns. Front Gene 3:. https://doi.org/10.3389/fgene.2012.00055\u003c/li\u003e\n\u003cli\u003eGupta SK, Carmi S, Ben-Asher HW, et al (2013) Basal Splicing Factors Regulate the Stability of Mature mRNAs in Trypanosomes. Journal of Biological Chemistry 288:4991\u0026ndash;5006. https://doi.org/10.1074/jbc.M112.416578\u003c/li\u003e\n\u003cli\u003eHanson H, Durkie M, Lalloo F, et al (2023) UK recommendations for SDHA germline genetic testing and surveillance in clinical practice. Journal of Medical Genetics 60:107\u0026ndash;111. https://doi.org/10.1136/jmedgenet-2021-108355\u003c/li\u003e\n\u003cli\u003eLee JA, Duh Q-Y (2008) Sporadic Paraganglioma. World Journal of Surgery 32:262. https://doi.org/10.1007/s00268-007-9360-4\u003c/li\u003e\n\u003cli\u003eLundberg SM, Erion G, Chen H, et al (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2:56\u0026ndash;67. https://doi.org/10.1038/s42256-019-0138-9\u003c/li\u003e\n\u003cli\u003eMete O, Asa SL, Gill AJ, et al (2022) Overview of the 2022 WHO Classification of Paragangliomas and Pheochromocytomas. Endocr Pathol 33:90\u0026ndash;114. https://doi.org/10.1007/s12022-022-09704-6\u003c/li\u003e\n\u003cli\u003ePapathomas TG, Suurd DPD, Pacak K, et al (2021) What Have We Learned from Molecular Biology of Paragangliomas and Pheochromocytomas? Endocr Pathol 32:134\u0026ndash;153. https://doi.org/10.1007/s12022-020-09658-7\u003c/li\u003e\n\u003cli\u003ePatel D, Phay JE, Yen TWF, et al (2020) Update on Pheochromocytoma and Paraganglioma from the SSO Endocrine/Head and Neck Disease-Site Work Group. Part 1 of 2: Advances in Pathogenesis and Diagnosis of Pheochromocytoma and Paraganglioma. Ann Surg Oncol 27:1329\u0026ndash;1337. https://doi.org/10.1245/s10434-020-08220-3\u003c/li\u003e\n\u003cli\u003ePedregosa F, Varoquaux G, Gramfort A, et al (2011) Scikit-learn: Machine Learning in Python. J Mach Learn Res 12:2825\u0026ndash;2830\u003c/li\u003e\n\u003cli\u003eTakahashi M, Kawai K, Asai N (2020) Roles of the RET Proto-oncogene in Cancer and Development. JMA J 3:175\u0026ndash;181. https://doi.org/10.31662/jmaj.2020-0021\u003c/li\u003e\n\u003cli\u003eZhao C, Takita J, Tanaka Y, et al (2001) Charcot-Marie-Tooth Disease Type 2A Caused by Mutation in a Microtubule Motor KIF1B\u0026beta;. Cell 105:587\u0026ndash;597. https://doi.org/10.1016/S0092-8674(01)00363-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bioinformatics, Decision tree, Location heterogeneity, Paraganglioma, Single Nucleotide Polymorphism","lastPublishedDoi":"10.21203/rs.3.rs-5237808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5237808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParagangliomas (PGLs) are a heterogeneous group of tumors of the nonepithelial neuroendocrine type with a significant percentage being genetically determined. They can develop from cells of the parasympathetic as well as the sympathetic nervous system. Tumors located in head and neck usually have a parasympathetic origin, whereas those in abdomen have a sympathetic origin. The aim of this study was to determine whether the development of PGLs at both locations is associated with specific variants of genes with proven relevance for the formation of these tumors. 31 patients with abdominal PGL and 16 with head and neck PGLs were analyzed at 12 genes whose defects are among the most common genetic determinants of PGLs. The impact of SNPs on differentiation between both tumor types was assessed by fitting a decision tree and quantifying genotype effects of SNPs by the Shapley Additive Explanation metric. The study demonstrated that SNPs rs3748576 within \u003cem\u003eKIF1B\u003c/em\u003e gene and rs10060259 within \u003cem\u003eSDHA\u003c/em\u003e gene increase the probability of abdominal tumour locations, while heterozygous GA genotypes of rs2435351 located within \u003cem\u003eRET\u003c/em\u003e gene increase the probability of head and neck locations. The SNPs marked genes involved in the formation and functioning of the nervous system, but are located in introns, and thus themselves do not contribute to protein diversity. Still, intronic SNPs can indirectly affect the transcriptome by influencing alternative splicing, mRNA stability, or overlap with non-coding genes and other regulatory elements that affect transcription. Given this, it seems important to consider variants from non-coding regions in genetic analyses.\u003c/p\u003e","manuscriptTitle":"Are head and neck versus abdominal paragangliomas driven by different single nucleotide events?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 08:57:40","doi":"10.21203/rs.3.rs-5237808/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2025-05-08T14:03:46+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-12-04T10:48:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-11T20:04:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-11T08:39:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Genetics","date":"2024-10-10T04:23:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dda836cb-e446-445e-80ff-9e0ab9961714","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:45:29+00:00","versionOfRecord":{"articleIdentity":"rs-5237808","link":"https://doi.org/10.1007/s13353-025-00994-0","journal":{"identity":"journal-of-applied-genetics","isVorOnly":false,"title":"Journal of Applied Genetics"},"publishedOn":"2025-07-29 16:21:38","publishedOnDateReadable":"July 29th, 2025"},"versionCreatedAt":"2024-10-23 08:57:40","video":"","vorDoi":"10.1007/s13353-025-00994-0","vorDoiUrl":"https://doi.org/10.1007/s13353-025-00994-0","workflowStages":[]},"version":"v1","identity":"rs-5237808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5237808","identity":"rs-5237808","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}