Next Article in Journal
Polybrominated Diphenyl Ethers (PBDEs) and Human Health: Effects on Metabolism, Diabetes and Cancer
Previous Article in Journal
Association of Hepatobiliary Phase of Gadoxetic-Acid-Enhanced MRI Imaging with Immune Microenvironment and Response to Atezolizumab Plus Bevacizumab Treatment
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 15
Issue 17
10.3390/cancers15174235
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Whole Exome-Wide Association Identifies Rare Variants in GALNT9 Associated with Middle Eastern Papillary Thyroid Carcinoma Risk

by
Rong Bu
1,†,
Abdul K. Siraj
1,†,
Saud Azam
1,
Kaleem Iqbal
1,
Zeeshan Qadri
1,
Maha Al-Rasheed
1,
Saif S. Al-Sobhi
2,
Fouad Al-Dayel
3 and
Khawla S. Al-Kuraya
1,*
1
Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Center, P.O. Box 3354, Riyadh 11211, Saudi Arabia
2
Department of Surgery, King Faisal Specialist Hospital and Research Center, P.O. Box 3354, Riyadh 11211, Saudi Arabia
3
Department of Pathology, King Faisal Specialist Hospital and Research Center, P.O. Box 3354, Riyadh 11211, Saudi Arabia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2023, 15(17), 4235; https://doi.org/10.3390/cancers15174235
Submission received: 18 July 2023 / Revised: 17 August 2023 / Accepted: 22 August 2023 / Published: 24 August 2023
Browse Figure
Review Reports Versions Notes

Abstract

:

Simple Summary

This study targeted the identification of rare variants in Middle Eastern papillary thyroid carcinoma (PTC) through an exome-wide association study. It was found that the GALNT9 gene was strongly associated with rare inactivating variants. Three genes (TRIM40, ARHGAP23, and SOX4) were associated with rare damaging variants (RDVs). Furthermore, seven genes (VARS1, ZBED9, PRRC2A, VWA7, TRIM31, TRIM40, and COL8A2) were associated with PTC risk. The study unveils some new genes to be potential candidates for PTC predisposition.

Abstract

Papillary thyroid carcinoma (PTC) is the commonest thyroid cancer. The majority of inherited causes of PTC remain elusive. However, understanding the genetic underpinnings and origins remains a challenging endeavor. An exome-wide association study was performed to identify rare germline variants in coding regions associated with PTC risk in the Middle Eastern population. By analyzing exome-sequencing data from 249 PTC patients (cases) and 1395 individuals without any known cancer (controls), GALNT9 emerged as being strongly associated with rare inactivating variants (RIVs) (4/249 cases vs. 1/1395 controls, OR = 22.75, p = 5.09 × 10−5). Furthermore, three genes, TRIM40, ARHGAP23, and SOX4, were enriched for rare damaging variants (RDVs) at the exome-wide threshold (p < 2.5 × 10−6). An additional seven genes (VARS1, ZBED9, PRRC2A, VWA7, TRIM31, TRIM40, and COL8A2) were associated with a Middle Eastern PTC risk based on the sequence kernel association test (SKAT). This study underscores the potential of GALNT9 and other implicated genes in PTC predisposition, illuminating the need for large collaborations and innovative approaches to understand the genetic heterogeneity of PTC predisposition.

1. Introduction

Thyroid cancer is the most common endocrine malignancy [1,2]. Papillary thyroid carcinoma (PTC) is a particular subtype that is responsible for a significant majority of all thyroid cancers, accounting for approximately 85% of patients [3,4]. The incidence of PTC has increased significantly in recent years [5,6]. In Saudi Arabia, PTC is of high prevalence and it is the second most common cancer affecting females [7]. Although PTC has favorable outcomes, 3–10% of patients demonstrated recurrent disease within the first decade after treatment [8,9].
Heritable genetic factors contribute to the susceptibility of PTC in around 10% of patients who have a family history of the disease in first or second-degree relatives and, in many instances, genetic effect has been shown to extend even beyond the nuclear family [10,11,12,13,14].
Despite the suggestive evidence that PTC is relatively highly heritable, understanding the genetic underpinnings and origins remains a challenging endeavor across diverse ethnic populations. Genetic studies, such as genome-wide association studies (GWAS), have offered some valuable insights, identifying several common genetic variants associated with PTC [15,16,17,18,19]. However, these identified variants account for only a modest portion of the total heritability of the disease, leaving a considerable proportion of the genetic architecture of PTC yet to be deciphered.
The advent of next-generation sequencing technologies such as whole-exome sequencing (WES) has been a powerful tool for uncovering the full spectrum of genetic variations that contribute to disease susceptibility. In contrast to GWAS, which primarily focus on common genetic variants, WES has the potential to uncover rare genetic variants that might play a significant role in various cancer susceptibilities [20,21,22]. These rare variants, which potentially are more deleterious than their common counterparts, might have been overlooked in previous studies due to their low frequencies. Therefore, WES has the potential to provide a more comprehensive picture of the genetic contributors to a complex disease like PTC, especially in a unique ethnicity where the prevalence of PTC is higher.
In this study, we harnessed the power of WES to identify rare inactivating variants (RIVs) and rare damaging variants (RDVs) associated with PTC risk within the Middle Eastern population. We hope our findings can shed light on the potential roles of several genes including GALNT9 in conferring risk for PTC in this population, thereby contributing to a better understanding of this prevalent cancer.

2. Materials and Methods

2.1. Patient Selection

Two hundred and forty-nine PTC patients diagnosed between 2006 and 2018 at King Faisal Specialist Hospital and Research Center (Riyadh, Saudi Arabia) and Prince Sultan Military Medical City (Riyadh, Saudi Arabia), with fresh non-tumor tissue and peripheral blood available, were included in the study. Baseline clinico-pathological data were collected from case records and are summarized in Table 1. The histopathological subtypes of PTC were classified based on the fifth edition of the WHO classification for thyroid tumors [23]. The staging of PTC was performed using the eighth edition of the American Joint Committee on Cancer (AJCC) staging system [24]. Cases were identified based on clinical history followed by fine needle aspiration cytology for confirmation.
The Institutional Review Board and Research Advisory Council (RAC) of the King Faisal Specialist Hospital and Research Center approved this study under project RAC# 2211 168 and RAC# 2110 031. Patients provided written consent to be part of this study.

2.2. DNA Isolation

Genomic DNA was isolated either from fresh non-tumor tissues or peripheral blood utilizing a Gentra DNA isolation kit (Gentra, Minneapolis, MN, USA), following the manufacturer’s recommendations as described previously [25].

2.3. Exome Sequencing Analysis

Whole-exome sequencing for cases (n = 249) was carried out by utilizing the SureSelectXT Target Enrichment System (Agilent, Santa Clara, CA, USA) on the Illumina sequencing platform. The Burrows–Wheeler Aligner (BWA) v0.7.15 algorithm was used to align the sequencing reads to the human reference genome hg19, followed by the use of Picard tools (v1.119, http://broadinstitute.github.io/picard/, accessed on 2 March 2023) for local realignment and PCR duplication marking. The Genome Analysis Toolkit (GATK) v4.0.12.0 was utilized to perform base-quality recalibration. FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/, accessed on 2 March 2023) and the GATK were used to obtain all quality metrics [26].
The GATK’s Haplotype Caller was used to call germline variants which were subsequently annotated using Annotate Variation (ANNOVAR) software (https://annovar.openbioinformatics.org/en/latest/, accessed on 2 March 2023) [27]. The control population included in our cohort was our in-house data from the exome sequencing of 1395 normal samples sequenced at different points in time. To provide homogeneity, all variants (cases and controls) were annotated simultaneously with ANNOVAR. Only those variants were selected that were in the exonic or splicing region of the gene. Variants were excluded if they had a minor allele frequency of >0.01 in the dbSNP, the National Heart, Lung, and Blood Institute exome sequencing project, 1000 Genomes, the Exome Aggregation Consortium (ExAC), and the Genome Aggregation Database (gnomAD). A variant was considered a true positive if the variant allele frequency (VAF) was at least 20% with a minimum altered reads of 5, and a sequencing depth in the variant location region of ≥20 with a GATK quality score of ≥100. Filtered variants were checked using the Integrated Genomics Viewer to filter out the false positives and artifacts.
Rare inactivating variants (RIVs) are deleterious variants that include frameshift insertion, frameshift deletion, stop-gain, stop-loss, and splice-site variants with allele frequencies < 1% in our control cohort and the ExAC database. On the other hand, rare damaging variants (RDVs) are the types of variants which are either deleterious/inactivating or predicted to be pathogenic/deleterious with a score greater than 0.025, utilizing the M-CAP classifier [21,28]. Furthermore, nonsynonymous variants include the variants with allele frequencies less than 0.1 in the ExAC database and our control cohort, including deleterious variants, missense single nucleotide variants, or non-frameshift deletions and insertions [21].
A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed, which is available on the Database for Annotation, Visualization and Integrated Discovery (DAVID) platform [29]. The gene list was uploaded with the default parameters for analysis. The results were considered statistically significant if p < 0.05.

2.4. Statistical Analysis

A chi square test was used to assess the departure from the Hardy–Weinberg equilibrium using a p-value < 0.001 as the cutoff. Variants were removed if they failed the Hardy–Weinberg equilibrium.
A sequence kernel association test (SKAT), which was implemented in an R package, was utilized for the association analysis. Standard parameters were used to compute the association. The p-values of non-synonymous variants were estimated using efficient resampling methods incorporated in the ‘SKATBinary_Single’ function. The exome-wide significance level was set at p < 2.5 × 10−6, whereas associations with p < 0.001 were labeled as suggestive [21].
To determine the power, simulation methods were applied which are available in the SKAT package under the ‘power logistic’ function. We repeated the simulation 100 times at different frequencies of variants in cases and odds ratios. The simulations were repeated to determine the power to detect a true association at the exome-wide (p < 2.5 × 10−6) and suggestive thresholds (p < 0.001). A power analysis was also calculated through a power simulation in R with varying samples, effect sizes, and threshold levels of significance.

3. Results

The median age of the entire cohort of cases was 39.2 years (range: 10–83 years), with a male/female ratio of 1:3.6. The majority of the tumors were of the classical variant (50.2%; 125/249), whilst 47.8% (119/249) of tumors were multifocal. Extrathyroidal extension was noted in 41.8% (104/249) of cases. Regional LN metastasis was noted in 38.2% (95/249) of cases and distant metastasis was present at diagnosis in 6.4% (16/249) (Table 1).
We detected 270,582 variants in 18,006 genes, including 91,701 RDVs (23,553 in cases and 110,797 in controls) and 13,743 RIVs (3114 in cases and 17,103 in controls). The cases and controls carried a median of 12 (interquartile range: 10–15) and 11 (interquartile range: 9–14) RIVs, respectively (Wilcoxon rank sum test p = 0.310).
The power analysis in our study was conducted utilizing the simulation-based procedure based on the variant distribution observed in our cohort. We found that our study had 80% power to identify a gene significantly enriched for RIVs with an odds ratio of 10 and a carrier rate of 7.5% at the exome-wide statistical significance level (p < 2.5 × 10−6). At the suggestive level (p < 0.001), our study had 80% power to detect an odds ratio of 10 and a carrier rate of 5% (Figure 1).
In the primary analysis focused on RIVs, GALNT9 had the strongest association (adjusted odds ratio OR = 22.75, p = 5.09 × 10−5), meeting suggestive significance (2.5 × 10−6 < p < 0.001). RIVs were carried in 4/249 (1.6%) cases compared to 1/1395 (0.1%) control (Table 2). This association was driven by three different variants including chr12: 132690466A>G, a splicing variant observed in two cases and one control (OR = 11.28, p = 0.013). The other two variants were observed in one case each and were absent in controls (OR = 16.84, p = 0.018). (Table 3). No other gene besides GALNT9 had suggestive evidence (p < 0.001) of an association with RIVs.
Furthermore, we utilized the filter-based approach to inspect the top 10 genes selected by counting the number of RIVs in the cases, which ranged from five RIVs in TEKT5 to eight in DNAH14. When comparing the cases with controls, eight genes had significant evidence of association (p < 0.05), including CYP4X1 (p = 0.004), RBM23 (p = 0.020), CRYGN (p = 0.007), TTC23L (p = 0.030), CYP2R1 (p = 0.030), AP3S1, (p = 0.002), TTC23 (p = 0.005), and TEKT5 (p = 0.005) (Table 4).
In addition, a KEGG pathway analysis was performed for signaling pathway analysis. No pathways were identified to be significantly associated with an increased risk of developing PTC.
In our secondary analysis focused on RDVs in the cases and controls, three genes passed the exome-wide significance level of association with RDVs, including TRIM40 (p = 6.31 × 10−9), ARHGAP23 (p = 1.05 × 10−8), and SOX4 (p = 1.80 × 10−7) (Table 2). In these three genes, the associations were caused by multiple RDVs, each present in only one or three individuals. The exception was Chr6:30114877C>T in TRIM40, which was present in 5/249 cases and absent in the controls (OR = 62.78, p = 1.15 × 10−7) (Table 3).
As part of the above exploratory analyses, SKAT analysis was utilized to analyze the cumulative effect of all nonsynonymous variants with an allele frequency of less than 1% in our cohort. Seven genes, valyl-tRNA synthetase 1 (VARS1), Zinc finger BED-type containing 9 (ZBED9), Proline Rich Coiled-Coil 2A (PRRC2A), Von Willebrand Factor A Domain Containing 7 (VWA7), Tripartite Motif Containing 31 (TRIM31), Tripartite Motif Containing 40 (TRIM40), and Collagen Type VIII Alpha 2 Chain (COL8A2), had exome-wide evidence of association (Table 5). All seven genes harbored at least one individual variant with p < 0.001. Five individual variants had an exome-wide significant association after Bonferroni Correction (Supplementary Table S1).

4. Discussion

Most genetic susceptibility association studies on PTC to date have focused mainly on common variants, and the majority have also focused on the European population rather than other Asian populations, especially Middle Eastern. However, the proportion of the missing heritability in PTC could be explained by low-frequency rare variants.
Our study underscores the valuable role of whole-exome sequencing in uncovering the genetic underpinning of PTC risk. We shed light on the presence of rare inactivating variants (RIVs), primarily in the GALNT9 gene, which according to our analysis had the strongest association with PTC in our research population.
The GALNT9 gene, as a part of the N-Acetylgalactosaminyl transferase (GALNT) gene family, is involved in the initial stages of O-linked protein glycosylation in the golgi apparatus [30,31]. Thus, inactivating variants in the GALNT9 gene, as detected in our study, could disrupt normal glycosylation processes and potentially contribute to oncogenic pathways. However, the precise role of GALNT9 in cancer biology, particularly in PTC, remains under-explored and needs to be the focal point of future research to elucidate the potential role of GALNT9 in cancer susceptibility.
Previous evidence indicates that mutations in GALNT12, another member of the N-Acetylgalactosaminyl transferase gene family, might explain some of the familial CRC cases of unknown etiology, and it showed that deleterious variants in GALNT12 are associated with CRC susceptibility [32,33]. In addition, several prior studies have also demonstrated the implications of other GALNT family members in different cancer types [34,35,36,37,38,39,40]. This evidence lends credence to our findings and strengthens the hypothesis that GALNT9 may have a similar role in PTC.
The enrichment of rare damaging variants (RDVs) in other genes such as TRIM40, ARHGAP23, and SOX4 further complements our understanding of the heritable genetic landscape of PTC. Each of these genes have previously been implicated in various cancers, including gastrointestinal cancers, soft tissue sarcomas, osteosarcoma and prostate cancer [41,42,43,44,45,46,47,48,49]. However, there are a lack of reports on the roles of the TRIM40, ARHGAP23 and SOX4 genes in the development of PTC.
Interestingly, in our attempt to collapse the rare variants into pathways across the genome and look for their associations with risk of PTC, we failed to identify any pathway being significantly associated with increased risk of PTC, using KEGG pathway analysis.
While our study provides an essential step toward comprehending the genetic basis of PTC, it is vital to acknowledge the challenges posed by the genetic heterogeneity of this disease. The rarity of identified variants such as the GALNT9 gene in this unique ethnic population demands larger collaborative efforts to substantiate their roles in PTC risk. The development and application of advanced statistical and bioinformatics methodologies could facilitate more robust genetic associations containing larger study cohorts, which could eventually help in the discovery of more risk-associated variants.

5. Conclusions

In conclusion, our study found novel genes and rare variants in GALNT9, TRIM40, ARHGAP23, and SOX4 to be associated with PTC in the Middle Eastern population, suggesting potential biomarkers for PTC that are unique to this ethnicity. However, larger-scale studies are warranted to validate these findings due to the relatively small number of cases tested.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers15174235/s1, Table S1: List of variants significantly related to PTC risk in seven SKAT genes.

Author Contributions

Conceptualization, K.S.A.-K., A.K.S. and R.B.; methodology, A.K.S. and R.B.; validation and formal analysis, S.A., K.I., Z.Q. and M.A.-R.; resources, S.S.A.-S. and F.A.-D.; writing—original draft preparation, K.S.A.-K.; writing—review and editing, A.K.S., R.B., S.A., K.I. and Z.Q.; supervision, K.S.A.-K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board and Research Advisory Council (RAC) of the King Faisal Specialist Hospital and Research Center under project RAC# 2211 168 and RAC# 2110 031.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available in tables and figures of this manuscript.

Acknowledgments

Authors would like to thank the department of Pathology and department of Surgery of King Faisal Specialist Hospital and Research Center for their co-operation to conduct this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
  2. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 2021, 71, 7–33. [Google Scholar] [CrossRef] [PubMed]
  3. Lim, H.; Devesa, S.S.; Sosa, J.A.; Check, D.; Kitahara, C.M. Trends in Thyroid Cancer Incidence and Mortality in the United States, 1974–2013. JAMA 2017, 317, 1338–1348. [Google Scholar] [CrossRef] [PubMed]
  4. Miranda-Filho, A.; Lortet-Tieulent, J.; Bray, F.; Cao, B.; Franceschi, S.; Vaccarella, S.; Dal Maso, L. Thyroid cancer incidence trends by histology in 25 countries: A population-based study. Lancet Diabetes Endocrinol. 2021, 9, 225–234. [Google Scholar] [CrossRef] [PubMed]
  5. Pereira, M.; Williams, V.L.; Hallanger Johnson, J.; Valderrabano, P. Thyroid cancer incidence trends in the United States: Association with changes in professional guideline recommendations. Thyroid 2020, 30, 1132–1140. [Google Scholar] [CrossRef]
  6. Kitahara, C.M.; Sosa, J.A. The changing incidence of thyroid cancer. Nat. Rev. Endocrinol. 2016, 12, 646–653. [Google Scholar] [CrossRef]
  7. Alrawaji, A.; Alshahrani, Z.; Alzahrani, W.; Alomran, F.; Almadouj, A.; Alshehri, S.; Alzahrani, A.; Bazarbashi, S.; Alhashmi, H.; Almutlaq, H. Cancer Incidence Report Saudi Arabia 2015; Saudi Cancer Registry: Riyadh, Saudi Arabia, 2018. [Google Scholar]
  8. Park, S.; Kim, W.G.; Song, E.; Oh, H.-S.; Kim, M.; Kwon, H.; Jeon, M.J.; Kim, T.Y.; Shong, Y.K.; Kim, W.B. Dynamic risk stratification for predicting recurrence in patients with differentiated thyroid cancer treated without radioactive iodine remnant ablation therapy. Thyroid 2017, 27, 524–530. [Google Scholar] [CrossRef]
  9. Leboulleux, S.; Rubino, C.; Baudin, E.; Caillou, B.; Hartl, D.M.; Bidart, J.-M.; Travagli, J.-P.; Schlumberger, M. Prognostic factors for persistent or recurrent disease of papillary thyroid carcinoma with neck lymph node metastases and/or tumor extension beyond the thyroid capsule at initial diagnosis. J. Clin. Endocrinol. Metab. 2005, 90, 5723–5729. [Google Scholar] [CrossRef]
  10. Risch, N. The genetic epidemiology of cancer: Interpreting family and twin studies and their implications for molecular genetic approaches. Cancer Epidemiol. Biomark. Prev. 2001, 10, 733–741. [Google Scholar]
  11. Frich, L.; Glattre, E.; Akslen, L.A. Familial occurrence of nonmedullary thyroid cancer: A population-based study of 5673 first-degree relatives of thyroid cancer patients from Norway. Cancer Epidemiol. Biomark. Prev. 2001, 10, 113–117. [Google Scholar]
  12. Czene, K.; Lichtenstein, P.; Hemminki, K. Environmental and heritable causes of cancer among 9.6 million individuals in the Swedish family-cancer database. Int. J. Cancer 2002, 99, 260–266. [Google Scholar] [CrossRef] [PubMed]
  13. Goldgar, D.E.; Easton, D.F.; Cannon-Albright, L.A.; Skolnick, M.H. Systematic population-based assessment of cancer risk in first-degree relatives of cancer probands. J. Natl. Cancer Inst. 1994, 86, 1600–1608. [Google Scholar] [CrossRef] [PubMed]
  14. Amundadottir, L.T.; Thorvaldsson, S.; Gudbjartsson, D.F.; Sulem, P.; Kristjansson, K.; Arnason, S.; Gulcher, J.R.; Bjornsson, J.; Kong, A.; Thorsteinsdottir, U.; et al. Cancer as a complex phenotype: Pattern of cancer distribution within and beyond the nuclear family. PLoS Med. 2004, 1, e65. [Google Scholar] [CrossRef]
  15. Gudmundsson, J.; Thorleifsson, G.; Sigurdsson, J.K.; Stefansdottir, L.; Jonasson, J.G.; Gudjonsson, S.A.; Gudbjartsson, D.F.; Masson, G.; Johannsdottir, H.; Halldorsson, G.H.; et al. A genome-wide association study yields five novel thyroid cancer risk loci. Nat. Commun. 2017, 8, 14517. [Google Scholar] [CrossRef] [PubMed]
  16. Mancikova, V.; Cruz, R.; Inglada-Perez, L.; Fernandez-Rozadilla, C.; Landa, I.; Cameselle-Teijeiro, J.; Celeiro, C.; Pastor, S.; Velazquez, A.; Marcos, R.; et al. Thyroid cancer GWAS identifies 10q26.12 and 6q14.1 as novel susceptibility loci and reveals genetic heterogeneity among populations. Int. J. Cancer 2015, 137, 1870–1878. [Google Scholar] [CrossRef]
  17. Son, H.Y.; Hwangbo, Y.; Yoo, S.K.; Im, S.W.; Yang, S.D.; Kwak, S.J.; Park, M.S.; Kwak, S.H.; Cho, S.W.; Ryu, J.S.; et al. Genome-wide association and expression quantitative trait loci studies identify multiple susceptibility loci for thyroid cancer. Nat. Commun. 2017, 8, 15966. [Google Scholar] [CrossRef]
  18. Figlioli, G.; Kohler, A.; Chen, B.; Elisei, R.; Romei, C.; Cipollini, M.; Cristaudo, A.; Bambi, F.; Paolicchi, E.; Hoffmann, P.; et al. Novel genome-wide association study-based candidate loci for differentiated thyroid cancer risk. J. Clin. Endocrinol. Metab. 2014, 99, E2084–E2092. [Google Scholar] [CrossRef]
  19. Zhou, W.; Brumpton, B.; Kabil, O.; Gudmundsson, J.; Thorleifsson, G.; Weinstock, J.; Zawistowski, M.; Nielsen, J.B.; Chaker, L.; Medici, M.; et al. GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer. Nat. Commun. 2020, 11, 3981. [Google Scholar] [CrossRef]
  20. Oh, J.J.; Shivakumar, M.; Miller, J.; Verma, S.; Lee, H.; Hong, S.K.; Lee, S.E.; Lee, Y.; Lee, S.J.; Sung, J.; et al. An exome-wide rare variant analysis of Korean men identifies three novel genes predisposing to prostate cancer. Sci. Rep. 2019, 9, 17173. [Google Scholar] [CrossRef]
  21. Grant, R.C.; Denroche, R.E.; Borgida, A.; Virtanen, C.; Cook, N.; Smith, A.L.; Connor, A.A.; Wilson, J.M.; Peterson, G.; Roberts, N.J.; et al. Exome-Wide Association Study of Pancreatic Cancer Risk. Gastroenterology 2018, 154, 719–722.e713. [Google Scholar] [CrossRef]
  22. Shivakumar, M.; Miller, J.E.; Dasari, V.R.; Gogoi, R.; Kim, D. Exome-Wide Rare Variant Analysis from the DiscovEHR Study Identifies Novel Candidate Predisposition Genes for Endometrial Cancer. Front. Oncol. 2019, 9, 574. [Google Scholar] [CrossRef] [PubMed]
  23. Baloch, Z.W.; Asa, S.L.; Barletta, J.A.; Ghossein, R.A.; Juhlin, C.C.; Jung, C.K.; LiVolsi, V.A.; Papotti, M.G.; Sobrinho-Simoes, M.; Tallini, G.; et al. Overview of the 2022 WHO Classification of Thyroid Neoplasms. Endocr. Pathol. 2022, 33, 27–63. [Google Scholar] [CrossRef] [PubMed]
  24. Tuttle, R.M.; Haugen, B.; Perrier, N.D. Updated American Joint Committee on cancer/tumor-node-metastasis staging system for differentiated and anaplastic thyroid cancer: What changed and why? J. Thyroid 2017, 27, 751–756. [Google Scholar] [CrossRef]
  25. Abubaker, J.; Jehan, Z.; Bavi, P.; Sultana, M.; Al-Harbi, S.; Ibrahim, M.; Al-Nuaim, A.; Ahmed, M.; Amin, T.; Al-Fehaily, M.; et al. Clinicopathological analysis of papillary thyroid cancer with PIK3CA alterations in a Middle Eastern population. J. Clin. Endocrinol. Metab. 2008, 93, 611–618. [Google Scholar] [CrossRef] [PubMed]
  26. Masoodi, T.; Siraj, A.K.; Siraj, S.; Azam, S.; Qadri, Z.; Parvathareddy, S.K.; Al-Sobhi, S.S.; AlDawish, M.; Alkuraya, F.S.; Al-Kuraya, K.S. Evolution and Impact of Subclonal Mutations in Papillary Thyroid Cancer. Am. J. Hum. Genet. 2019, 105, 959–973. [Google Scholar] [CrossRef] [PubMed]
  27. Wang, K.; Li, M.; Hakonarson, H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010, 38, e164. [Google Scholar] [CrossRef]
  28. Jagadeesh, K.A.; Wenger, A.M.; Berger, M.J.; Guturu, H.; Stenson, P.D.; Cooper, D.N.; Bernstein, J.A.; Bejerano, G. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat. Genet. 2016, 48, 1581–1586. [Google Scholar] [CrossRef]
  29. Laskowski, R.A. PDBsum new things. Nucleic Acids Res. 2009, 37, D355–D359. [Google Scholar] [CrossRef]
  30. Gill, D.J.; Tham, K.M.; Chia, J.; Wang, S.C.; Steentoft, C.; Clausen, H.; Bard-Chapeau, E.A.; Bard, F.A. Initiation of GalNAc-type O-glycosylation in the endoplasmic reticulum promotes cancer cell invasiveness. Proc. Natl. Acad. Sci. USA 2013, 110, E3152–E3161. [Google Scholar] [CrossRef]
  31. Schjoldager, K.T.; Vester-Christensen, M.B.; Goth, C.K.; Petersen, T.N.; Brunak, S.; Bennett, E.P.; Levery, S.B.; Clausen, H. A systematic study of site-specific GalNAc-type O-glycosylation modulating proprotein convertase processing. J. Biol. Chem. 2011, 286, 40122–40132. [Google Scholar] [CrossRef]
  32. Clarke, E.; Green, R.C.; Green, J.S.; Mahoney, K.; Parfrey, P.S.; Younghusband, H.B.; Woods, M.O. Inherited deleterious variants in GALNT12 are associated with CRC susceptibility. Hum. Mutat. 2012, 33, 1056–1058. [Google Scholar] [CrossRef]
  33. Guda, K.; Veigl, M.L.; Varadan, V.; Nosrati, A.; Ravi, L.; Lutterbaugh, J.; Beard, L.; Willson, J.K.; Sedwick, W.D.; Wang, Z.J.; et al. Novel recurrently mutated genes in African American colon cancers. Proc. Natl. Acad. Sci. USA 2015, 112, 1149–1154. [Google Scholar] [CrossRef]
  34. Hu, Q.; Tian, T.; Leng, Y.H.; Tang, Y.H.; Chen, S.; Lv, Y.Y.; Liang, J.Y.; Liu, Y.N.; Liu, T.H.; Shen, L.; et al. The O-glycosylating enzyme GALNT2 acts as an oncogenic driver in non-small cell lung cancer. Cell. Mol. Biol. Lett. 2022, 27, 71. [Google Scholar] [CrossRef]
  35. Beaman, E.M.; Carter, D.R.F.; Brooks, S.A. GALNTs: Master regulators of metastasis-associated epithelial-mesenchymal transition (EMT)? Glycobiology 2022, 32, 556–579. [Google Scholar] [CrossRef] [PubMed]
  36. Song, K.H.; Park, M.S.; Nandu, T.S.; Gadad, S.; Kim, S.C.; Kim, M.Y. GALNT14 promotes lung-specific breast cancer metastasis by modulating self-renewal and interaction with the lung microenvironment. Nat. Commun. 2016, 7, 13796. [Google Scholar] [CrossRef] [PubMed]
  37. Mao, C.; Zhuang, S.M.; Xia, Z.; Xiao, Z.W.; Huang, C.X.; Su, Q.; Chen, J.; Liao, J. Pan-cancer analysis of GALNTs expression identifies a prognostic of GALNTs feature in low grade glioma. J. Leukoc. Biol. 2022, 112, 887–899. [Google Scholar] [CrossRef] [PubMed]
  38. Kim, M.Y. Role of GALNT14 in lung metastasis of breast cancer. BMB Rep. 2017, 50, 233–234. [Google Scholar] [CrossRef]
  39. Lee, P.C.; Chen, S.T.; Kuo, T.C.; Lino, T.C.; Lin, M.C.; Huang, J.; Hung, J.S.; Hsu, C.L.; Juan, H.F.; Lee, P.H.; et al. C1GALT1 is associated with poor survival and promotes soluble Ephrin A1-mediated cell migration through activation of EPHA2 in gastric cancer. Oncogene 2020, 39, 2724–2740. [Google Scholar] [CrossRef]
  40. Liu, C.; Li, Z.; Xu, L.; Shi, Y.; Zhang, X.; Shi, S.; Hou, K.; Fan, Y.; Li, C.; Wang, X.; et al. GALNT6 promotes breast cancer metastasis by increasing mucin-type O-glycosylation of alpha2M. Aging 2020, 12, 11794–11811. [Google Scholar] [CrossRef]
  41. Lee, O.H.; Lee, J.; Lee, K.H.; Woo, Y.M.; Kang, J.H.; Yoon, H.G.; Bae, S.K.; Songyang, Z.; Oh, S.H.; Choi, Y. Role of the focal adhesion protein TRIM15 in colon cancer development. Biochim. Biophys. Acta 2015, 1853, 409–421. [Google Scholar] [CrossRef]
  42. Marzano, F.; Caratozzolo, M.F.; Pesole, G.; Sbisa, E.; Tullo, A. TRIM Proteins in Colorectal Cancer: TRIM8 as a Promising Therapeutic Target in Chemo Resistance. Biomedicines 2021, 9, 241. [Google Scholar] [CrossRef] [PubMed]
  43. Noguchi, K.; Okumura, F.; Takahashi, N.; Kataoka, A.; Kamiyama, T.; Todo, S.; Hatakeyama, S. TRIM40 promotes neddylation of IKKgamma and is downregulated in gastrointestinal cancers. Carcinogenesis 2011, 32, 995–1004. [Google Scholar] [CrossRef] [PubMed]
  44. Park, H.S.; Ryu, J.M.; Park, J.S.; Im, S.A.; Jung, S.Y.; Kim, E.K.; Park, W.C.; Min, J.W.; Lee, J.; You, J.Y.; et al. Clinicopathological Features of Patients with the BRCA1 c.5339T>C (p.Leu1780Pro) Variant. Cancer Res. Treat. 2020, 52, 680–688. [Google Scholar] [CrossRef] [PubMed]
  45. Bhatla, T.; Dandekar, S.; Lu, B.Y.; Wang, J.; Han, E.; Bitterman, D.; Jones, C.L.; Evensen, N.A.; Magid, M.; Meyer, J.A.; et al. Genomic Characterization of Poorly Differentiated Neuroendocrine Carcinoma in a Pediatric Patient. J. Pediatr. Hematol. Oncol. 2016, 38, e21–e25. [Google Scholar] [CrossRef]
  46. Scharer, C.D.; McCabe, C.D.; Ali-Seyed, M.; Berger, M.F.; Bulyk, M.L.; Moreno, C.S. Genome-wide promoter analysis of the SOX4 transcriptional network in prostate cancer cells. Cancer Res. 2009, 69, 709–717. [Google Scholar] [CrossRef] [PubMed]
  47. Zhang, J.; Liang, Q.; Lei, Y.; Yao, M.; Li, L.; Gao, X.; Feng, J.; Zhang, Y.; Gao, H.; Liu, D.-X. SOX4 Induces Epithelial–Mesenchymal Transition and Contributes to Breast Cancer Progression. Cancer Res. 2012, 72, 4597–4608. [Google Scholar] [CrossRef]
  48. Chen, J.; Ju, H.L.; Yuan, X.Y.; Wang, T.J.; Lai, B.Q. SOX4 is a potential prognostic factor in human cancers: A systematic review and meta-analysis. Clin. Transl. Oncol. 2016, 18, 65–72. [Google Scholar] [CrossRef]
  49. Moreno, C.S. SOX4: The unappreciated oncogene. Semin. Cancer Biol. 2020, 67, 57–64. [Google Scholar] [CrossRef]
Cancers 15 04235 g001
Figure 1. Power analysis for a gene to be enriched in rare inactivating variants based on an empirical procedure. (A) Power at the exome-wide significance level: p < 2.5 × 10−6. (B) Power at the suggestive significance level: p < 0.001.
Figure 1. Power analysis for a gene to be enriched in rare inactivating variants based on an empirical procedure. (A) Power at the exome-wide significance level: p < 2.5 × 10−6. (B) Power at the suggestive significance level: p < 0.001.
Cancers 15 04235 g001
Table 1. Clinico-pathological characteristics of papillary thyroid carcinoma cases.
Table 1. Clinico-pathological characteristics of papillary thyroid carcinoma cases.
Total
No.%
Total249
Age at surgery (years)
Median (range)39.2 (10.0–83.0)
<5521084.3
≥553915.7
Gender
Male5421.7
Female19578.3
Histologic subtype
Classical variant12550.2
Follicular variant5321.3
Tall cell variant3614.5
Other variants3514.0
Tumor laterality
Unilateral18072.3
Bilateral6927.7
Tumor focality
Unifocal13052.2
Multifocal11947.8
Extrathyroidal extension
Absent14558.2
Present10441.8
Lymphovascular invasion
Present9538.2
Absent15461.8
Tumor size
≤1 cm239.2
1.1–2 cm4518.1
2.1–4 cm8534.1
>4 cm9638.6
Regional LN metastasis
N011847.3
N19538.2
Nx3614.5
Distant metastasis
Present166.4
Absent23393.6
TNM Stage
I 21787.5
II208.1
III62.4
IV52.0
Table 2. List of genes significantly associated with PTC risk based on rare variant analyses.
Table 2. List of genes significantly associated with PTC risk based on rare variant analyses.
S. No.GeneNo. of Cases%No. of Controls%Odds Ratiop-ValueSignificance LevelType of Variant Analysis
1GALNT941.610.122.755.09 × 10−5suggestive RIV
2TRIM4062.400.074.506.31 × 10−9exome-wide RDV
3ARHGAP2372.810.140.321.05 × 10−8exome-wide RDV
4SOX462.410.134.401.80 × 10−7exome-wideRDV
Table 3. List of rare variants significantly associated with PTC risk.
Table 3. List of rare variants significantly associated with PTC risk.
GeneChrPositionReferenceAlternateVariant TypeNo. of Cases%No. of Controls%p-ValueOdds RatioType of Variant
GALNT912132,839,102G-Frameshift deletion10.400.00.01816.84RIV
GALNT912132,688,237-GCGGGGAGACGGCSplicing10.400.00.01816.84RIV
GALNT912132,690,466AGSplicing20.810.10.01311.28RIV
TRIM40630,114,877CTmissense52.000.01.15 × 10−762.78RDV
ARHGAP231736,667,177CTmissense31.200.04.07 × 10−539.63RDV
SOX4621,595,904CTmissense31.200.04.07 × 10−539.63RDV
ARHGAP231736,666,646CTmissense10.400.00.01816.85RDV
ARHGAP231736,614,410CTmissense10.400.00.01816.85RDV
ARHGAP231736,619,098CTmissense10.400.00.01816.85RDV
ARHGAP231736,622,633CTmissense10.400.00.01816.85RDV
SOX4621,595,481CTmissense10.400.00.01816.85RDV
SOX4621,595,276GAmissense10.400.00.01816.85RDV
SOX4621,595,618GAmissense10.400.00.01816.85RDV
TRIM40630,113,787GAmissense10.400.00.01816.85RDV
ARHGAP231736,619,383GAmissense00.010.10.6731.86RDV
SOX4621,595,955CGmissense00.010.10.6731.86RDV
Table 4. Top 10 genes by number of cases based on RIV association analysis.
Table 4. Top 10 genes by number of cases based on RIV association analysis.
S. No.GeneNo. of Cases%No. of Controls%p-ValueOdds Ratio
1DNAH1483.2%453.2%0.9911.00
2WDR6772.8%181.3%0.0712.21
3CYP4X162.4%80.6%0.0044.28
4CRYGN62.4%90.6%0.0073.80
5RBM2362.4%110.8%0.0203.11
6TTC23L62.4%120.9%0.0302.85
7CYP2R162.4%120.9%0.0302.85
8AP3S152.0%50.4%0.0025.51
9TTC2352.0%60.4%0.0054.59
10TEKT552.0%60.4%0.0054.59
Table 5. Significant genes based on non-synonymous variant associations analyzed using SKAT.
Table 5. Significant genes based on non-synonymous variant associations analyzed using SKAT.
S. No.GeneNo. of Cases%No. of Controls%p-Value (SKAT)Odds Ratio
1VARS1114.400.03.98 × 10−10134.58
2ZBED983.200.01.26 × 10−798.23
3PRRC2A72.800.08.50 × 10−786.32
4VWA772.800.08.50 × 10−786.32
5TRIM3172.800.08.50 × 10−786.32
6TRIM4072.800.08.50 × 10−786.32
7COL8A272.800.08.50 × 10−786.32
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bu, R.; Siraj, A.K.; Azam, S.; Iqbal, K.; Qadri, Z.; Al-Rasheed, M.; Al-Sobhi, S.S.; Al-Dayel, F.; Al-Kuraya, K.S. Whole Exome-Wide Association Identifies Rare Variants in GALNT9 Associated with Middle Eastern Papillary Thyroid Carcinoma Risk. Cancers 2023, 15, 4235. https://doi.org/10.3390/cancers15174235

AMA Style

Bu R, Siraj AK, Azam S, Iqbal K, Qadri Z, Al-Rasheed M, Al-Sobhi SS, Al-Dayel F, Al-Kuraya KS. Whole Exome-Wide Association Identifies Rare Variants in GALNT9 Associated with Middle Eastern Papillary Thyroid Carcinoma Risk. Cancers. 2023; 15(17):4235. https://doi.org/10.3390/cancers15174235

Chicago/Turabian Style

Bu, Rong, Abdul K. Siraj, Saud Azam, Kaleem Iqbal, Zeeshan Qadri, Maha Al-Rasheed, Saif S. Al-Sobhi, Fouad Al-Dayel, and Khawla S. Al-Kuraya. 2023. "Whole Exome-Wide Association Identifies Rare Variants in GALNT9 Associated with Middle Eastern Papillary Thyroid Carcinoma Risk" Cancers 15, no. 17: 4235. https://doi.org/10.3390/cancers15174235

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop