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Primary exploration of cell-free DNA in the plasma of patients with parathyroid neoplasms using next-generation sequencing

Cancer Cell International volume 25, Article number: 86 (2025) Cite this article

Abstract

Background and aims

Plasma cell-free DNA (cfDNA) has been used to monitor gene mutations and diagnose tumors. Discriminating parathyroid carcinoma (PC) from parathyroid adenoma (PA) before surgery is difficult because of the overlap in clinical features between parathyroid neoplasms. We aimed to detect cfDNA mutations in plasma samples from PC and PA patients before surgery to predict the CDC73 status in tumor tissue and help in the differential diagnosis of parathyroid neoplasms.

Materials and methods

Eighteen PC patients and 13 PA patients were enrolled. Plasma cfDNA was detected using next-generation sequencing, with DNA from matched peripheral white blood cells used as a control. CDC73 gene mutations were detected via whole-exome sequencing or parafibromin staining via immunohistochemistry of tumor tissues. Logistic regression was used to evaluate the ability of cfDNA mutations to predict the CDC73 status in tumor tissue and for differential diagnosis. CDC73 gene mutation or parafibromin staining loss were defined as CDC73 abnormalities.

Results

One PC patient was not tested for CDC73 abnormalities due to the absence of tumor specimen. CDC73 abnormalities were not detected in all 13 PA patients, whereas 10 PC patients harboured CDC73 abnormalities in tumor specimens (P = 0.001). Among the 10 patients, CDC73 mutations were identified in the cfDNA of 8 patients. In another 20 patients without CDC73 abnormalities in tumors, CDC73 mutation was detected in the cfDNA of 4 patients. Using the CDC73 status in cfDNA, the area under the receiver operating characteristic curve (AUC) for predicting CDC73 abnormalities in tumor tissue was 0.80 (95% CI: 0.622–0.978), and the AUC for predicting malignancy was 0.795 (95% CI 0.632–0.958).

Conclusion

This study is the first attempt to evaluate the gene mutation status of parathyroid neoplasms through the deep sequencing of plasma cfDNA, which could also help to identify PC prior to surgery.

Introduction

Primary hyperparathyroidism (PHPT) is a common endocrine disorder in which parathyroid tissue produces excessive parathyroid hormone (PTH) caused by a parathyroid adenoma (PA), hyperplasia, or carcinoma. PA accounts for approximately 85% of PHPT cases [1]. Parathyroid carcinoma (PC) is a rare malignancy with poor outcomes that accounts for approximately 1–5% of PHPT cases [2]. Although the 10-year overall survival rate is 49–91%, approximately 60% of patients with PC experience recurrence and metastasis [3, 4]. Surgery remains the mainstay for the management of PHPT. En bloc resection, including parathyroid lesions and the ipsilateral thyroid lobe, is suggested for PC to reduce the risk of recurrence, whereas local resection is suitable for benign PA, and a precise preoperative diagnosis is needed to determine the proper extent of surgical resection. However, distinguishing malignant from PA before surgery is difficult because of their overlap in clinical features. Patients with a palpable cervical mass (more than 3 cm in diameter), high levels of serum PTH (10 times above the normal value) and hypercalcemia (more than 3.5 mmol/L) are more likely to have PC [5]. Nonetheless, differential diagnosis based merely on clinical and biochemical analysis is unsatisfactory. PC is most often diagnosed as PA before surgery [6]. This diagnosis must be confirmed postoperatively by histological means such as capsular invasion, vascular infiltration, nerve involvement, and tumor infiltration of adjacent tissues [7, 8]. In addition, many patients with PA may also present with serious illness after a long-term delay in diagnosis, especially in developing countries. Percutaneous fine-needle aspiration biopsy has become the standard method for the preoperative diagnosis of many tumors, including thyroid cancer. However, fine-needle aspiration biopsy is not recommended for parathyroid neoplasms because of the risk of tumor rupture, haemorrhage, and tumor cell dissemination, although related studies with conclusive evidence are still lacking [9]. Therefore, a reliable preoperative differential diagnosis method is needed.

Several studies have attempted to identify serum biomarkers, including exosomal miRNAs, to differentiate malignant from benign parathyroid neoplasms [10]. However, the application of these methods in the clinic is complicated by difficulty in measurement standardization. The effect of PC on the serum levels of PTH is more significant than that of PA. Most centres use second- or third-generation measurements for PTH analysis. The ratio of second- and third-generation PTH measurements is also reported to differ between PC and PA [11, 12]. However, the diagnostic performance of these methods is still not satisfactory, and no follow-up studies have confirmed the result.

The CDC73 gene (formerly HRPT2) is responsible for hyperparathyroidism-jaw tumor syndrome (HPT-JT syndrome), which is an autosomal dominant disease [13]. Parafibromin, encoded by the CDC73 gene, is a part of the polymerase-associated factor 1 complex, which is key in regulating gene transcription and histone modifications [14]. An inactivating mutation in CDC73 results in negative parafibromin immunohistochemistry (IHC) staining in the nucleus of PC cells. Approximately 60% of PCs carry CDC73 gene mutations, whereas few mutations are observed in PAs [15]. The loss of parafibromin staining caused by the inactivating mutation in CDC73 was found to be promising for distinguishing PC from PA [16, 17]. Furthermore, parafibromin staining loss is closely related to the recurrence of PC [18, 19]. Therefore, CDC73 is a key gene for PC. Researchers recommend sequencing to assess germline mutations in the CDC73 gene if the tumor shows negative parafibromin staining [20]. Currently, clinicians and researchers are aware that next-generation sequencing (NGS) is a critical technology for detecting gene mutations.

Plasma cell-free DNA (cfDNA) has been found to be a promising biomarker for the diagnosis or treatment of various malignancies, including lung cancer, colorectal tumors, and pancreatic cancer [21,22,23]. cfDNA is a DNA material released into the circulation from cells throughout the human body, including tumor cells and normal cells. cfDNA is fragmented by nucleases into short double-stranded DNA fragments of approximately 160 nt, which are found in the bloodstream and have a half-life of 5–150 min [24]. The DNA molecules released from tumor cells that circulate in the blood are captured, enriched and sequenced to identify the gene mutation status in various tissues of the human body, including tumors. Given the advantages of cfDNA in tumor diagnosis, we conducted a primary exploration of parathyroid neoplasms for the first time.

The aim of this study was to carry out a feasibility test for detecting cfDNA in blood for parathyroid neoplasms. To provide a more comprehensive molecular characterization of parathyroid neoplasms, we used serum samples from patients detected by NGS retrospectively to identify possible gene mutations. The CDC73 mutation status in plasma cfDNA can be monitored before surgery, which may provide some clues for assessing CDC73 mutations in tumor tissues. According to the gene mutation status of plasma cfDNA, clinical characteristics and biochemical analysis results, a prediction model was constructed to infer the mutation status of tumor tissues and help identify parathyroid malignancy.

Materials and methods

Patients and specimens

A total of 31 patients were enrolled in this retrospective study, including 18 patients with PC and 13 patients with PA. All of the patients underwent surgery at Peking Union Medical College Hospital, a tertiary teaching hospital, from 2016 to 2022. The histopathological diagnosis of parathyroid neoplasm was based on the WHO classification criteria [8]. Atypical parathyroid adenomas were excluded. All the diagnoses were confirmed by two endocrine pathologists. PA cases with a tumor diameter > 1.0 cm were randomly selected from our parathyroid neoplasm tissue bank as controls. Peripheral blood samples (10 ml) treated with EDTA anticoagulant were obtained from the participants before the operation. White blood cell (WBC) and plasma samples were obtained by centrifugation and stored at − 80 °C until DNA extraction. Tumor tissue samples were collected from tumors during surgery and stored in RNAlater (Ambion Inc., Austin, Texas) solution at − 80 °C. Among 31 patients,.a specimen was not retained from one patient because of the limited amount of tumor tissue.In addition, 25 of the patients in our present cohort were included in our previous study, and parafibromin IHC staining or CDC73 mutation status data were available [3, 25,26,27]. None of the patients enrolled in our study had received chemotherapy, radiotherapy, or molecular targeted therapy. This study was approved by the Ethics Committee of Peking Union Medical College Hospital (JS-2054, K2494). Written informed consent was obtained from all patients for the use of samples and clinical information.

cfDNA sequencing

A panel covering 40 cancer-associated genes was used (Genecast Biotech., Wuxi, China). Plasma cfDNA was extracted from 4 ml of plasma and purified using a MAGMAX CF DNA Kit (Thermo Fisher, A29329) and the KingFisher Flex system according to the manufacturer’s instructions. DNA samples were quantified with a Qubit fluorometer, and KAPA Hyper KIT PCR Free Kits and KAPA Library Amplification Kits were used to prepare the library. Agencourt AMPure XP (BECKMAN) was used to purify the library. Then, Panel 15_pro/P9NM_plus probes (Genecast Biotech, Wuxi, China) were used for the hybridization capture of the target fragment. Sequencing was conducted using the NovaSeq 6000 platform. All data with sufficient sequencing depth and quality were included in the final analysis. Valid NGS data were mapped to the human reference genome (GRCh37) with Burrows‒Wheeler Aligner (BWA) software [28]. Picard (http://broadinstitute.github.io/picard/) and GATK were used to generate the final file in BAM format [29]. VarDict and FreeBayes were applied to call single-nucleotide variants (SNVs) and small insertions and deletions (InDels), and ANNOVAR was used for annotation [30,31,32]. For a given variant in plasma ctDNA, VAF = sequencing read count of alternate alleles/(sequencing read count of reference alleles + sequencing read count of alternate alleles) ×100%. To maximize the prognostic value of ctDNA detection, all mutations in plasma samples with clonal mutations of VAF greater than 0.4% and at least 4 high-quality reads (cumulative quality score of mutation point > 55, mean quality score of the mutation point > 13 and mutation point supported by paired-end reads) were defined as ctDNA positive [33, 34]. The exclusion criteria were as follows: (a) mutation not located in the exon region; (b) synonymous mutation; and (c) mutation with a population frequency > 0.0001 in the 1000 Genomes Project or the ExAC_EAS datasets of the Exome Aggregation Consortium (ExAC) [35, 36]. All candidate mutations were reconfirmed manually with the Integrative Genomics Viewer (IGV) [37].

DNA variation data of WBC and tumor samples

Whole-exon sequencing (WES)/whole-genome sequencing (WGS)

Among the patients, WBC and tumor whole-exon sequencing (WES)/whole-genome sequencing (WGS) data were available for 25 patients from our previous work [25,26,27]. Genomic DNA was extracted from tumor and WBC samples with a DNeasy Blood & Tissue Kit (QIAGEN, Germany). For each sample, 0.6 µg of DNA was used to prepare DNA libraries, which were sequenced using the Illumina HiSeq platform, and 150-bp paired-end reads were produced. Low-quality reads and reads containing adapter contaminants were filtered out. With Burrows‒Wheeler Aligner (BWA) software, valid sequencing reads were mapped to the reference human genome (GRCh37). SAMtools, ANNOVAR, 1000 Genomes and other related databases were used to identify and annotate SNPs and InDels. Somatic SNVs and InDels were uniformly detected by MuTect2 with Genome Analysis Toolkit (GATK) Best Practices. All filtered variants were manually verified using Integrative Genomics Viewer software.

Immunohistochemical staining

For 25 patients, IHC staining of formalin-fixed, paraffin-embedded (FFPE) tissue was performed as described in our previous study [27]. The 5-µm FFPE sections were deparaffinised, dehydrated and then incubated in 3% H2O2 solution for 10 min. After washing in distilled water three times (5 min each), the antigen retrieval slides were microwaved for 35 min in ethylenediaminetetraacetic acid solution. Bovine serum albumin (3%) was used to block nonspecific binding sites after washing. Primary antibodies (CDC73, ab223840, Abcam, Cambridge, UK) were incubated with the slides at 4 °C overnight. Diaminobenzidine was used to visualize the HRP-linked secondary antibody. We supplemented 5 patients with IHC staining in this study. Parafibromin loss in all the nuclei of the tumor cells was defined as negative IHC staining. In addition, positive internal controls were positively stained for vascular endothelial and stromal cells [38].

Statistical analysis

In this study, data analysis was conducted using SPSS 22.0 software (IBM Inc., Chicago, IL, USA). Continuous variables are presented as the means ± standard deviations (SDs). Comparisons between groups were performed using t tests for continuous variables and chi-square tests for categorical variables. Binary logistic regression models were used to predict CDC73 gene mutations in tumor tissues and for the differential diagnosis of PC. The diagnostic value was evaluated using receiver operating characteristic (ROC) analysis. P < 0.05 was considered statistically significant.

Results

Clinical characteristics of the patients in the cohort

A total of 31 patients were included in our cohort, including 4 males and 27 females. The average age at sample collection was 48.6 ± 12.3 years. No significant differences in sex, age, BMI or blood levels of PTH, calcium, alkaline phosphatase (ALP), phosphorus or creatine were observed between the two groups (Table 1, Supplementary 1). Recurrence or metastasis occurred in 15 patients with PC during follow-up.

Table 1 Comparison of clinical features between patients with parathyroid carcinoma and those with parathyroid adenoma in the present cohort
Full size table

Gene mutations and concentration of cfDNA

The average concentration of DNA in the plasma samples in the PC group was greater than that in the PA group, although the difference was not statistically significant (1.56 ± 1.92 µg/ml in the PC group vs. 0.81 ± 0.53 µg/ml in the PA group, P = 0.128). The average deduplicated sequencing depth for cfDNA was 1774 × (903×-2790×) in this study. No differences in total sequencing reads or the effective sequencing depth of cfDNA were observed between the groups (P = 0.064, P = 0.850). For each plasma sample, 28.7 gene variations for 9.5 genes were identified in the cfDNA (Fig. 1). For the whole cohort, no significant differences in mutated gene variation (P = 0.95) or gene mutation number (P = 0.12) were found.

Fig. 1
figure 1

Types of nonsynonymous mutations in cfDNA among parathyroid carcinoma (PC) and parathyroid adenoma (PA) patients. Different coloured boxes represent different types of mutations: magenta for missense mutations, blue for nonsense mutations, green for insertion mutations, purple for deletion mutations, and orange for compound heterozygous mutations

Full size image

CDC73 mutation status in parathyroid tumor tissue

The CDC73 gene mutation status in tumor tissue was available for 30 patients. We defined CDC73 gene mutation or parafibromin staining loss as CDC73 abnormalities. The mutation was not detected in 1 PC patient without available tumor specimen. CDC73 abnormalities were detected in the tumor tissue of 10 of the 17 PC patients and none of the 13 PA patients (P = 0.001). Among those 10 PC patients, all parafibromin staining loss were identified (Fig. 2). Six patients had CDC73 gene mutations, including 2 patients with germline mutations and 4 patients with somatic mutations.

Fig. 2
figure 2

Representative images of parafibromin staining in parathyroid tumor tissue. Parafibromin staining loss in all nuclei of tumor cells was defined as negative IHC staining, patterns and predictors of cervical lymph node metastasis in parathyroid carcinoma. Images under a ×200 visual field. PC, parathyroid carcinoma; PA, parathyroid adenoma

Full size image

Predicting CDC73 mutation status in parathyroid tumor tissue with cfDNA

CDC73 mutations were detected in the cfDNA of 1 PA patient and 12 (66.7%) PC patients. Among 10 patients with CDC73 abnormalities in tumor tissues, CDC73 mutations were detected in the cfDNA of 8 patients. Notably, the genotype of CDC73 harboured in the tumor tissues of 5 patients with only somatic mutations did not coincide with that in the cfDNA (Supplementary 1). One patient of PC whose tumor sample was unavailable for sequencing was observed CDC73 mutation in the cfDNA. In another 20 patients without CDC73 abnormalities in tumor tissues, the CDC73 mutation was detected in the cfDNA of 4 patients. With deep sequencing of the CDC73 gene in cfDNA, the sensitivity and specificity were 80.0% and 80.0%, respectively, for predicting CDC73 abnormalities in parathyroid tumor tissues. No mutations of other genes in the cfDNA were related to CDC73 mutations in the tumor tissue. On the basis of logistic regression analysis, the CDC73 mutation status in cfDNA can be used to predict the CDC73 status in parathyroid neoplasm tissue. The area under the receiver operating characteristic curve (AUC) was 0.800 (95% CI 0.622–0.978). When clinical parameters such as patient age and serum PTH levels were included in the logistic regression model, the AUC was 0.935 (95% CI 0.841–1.000; Fig. 3A).

Fig. 3
figure 3

CDC73 mutation status in cfDNA predicts CDC73 status in parathyroid neoplasm tissue and benign and malignant diagnoses of parathyroid tumors. (A) CDC73 status in parathyroid neoplasm tissue: blue line, the ROC curve of the CDC73 mutation status in cfDNA; red line, the ROC curve of CDC73 mutation status in cfDNA with clinical parameters. (B) Benign and malignant diagnoses of parathyroid tumors: blue line, the ROC curve of CDC73 mutation status in cfDNA; red line, the ROC curve of clinical parameters; green line, the ROC curve of CDC73 mutation status in cfDNA with clinical parameters

Full size image

Predicting parathyroid malignancy with cfDNA

According to logistic regression analysis, the CDC73 mutation status in cfDNA was the only mutation among 40 genes shown to be related to the CDC73 status in patient tissue samples (Supplementary Table 1). We utilized it to assess malignant potential in parathyroid neoplasms, with an AUC of 0.795 (95% CI 0.632–0.958; Fig. 3B). When clinical parameters were included in the prediction model, age was also shown to distinguish benign and malignant parathyroid tumors. With these prediction models, the AUC was 0.889 (95% CI 0.764–1.000; Fig. 3B).

Discussion

Although the incidence of PC is low, the number of patients has grown rapidly in China in recent years because of the large population base and widespread application of PTH detection in serum. A method of differential diagnosis before surgery is urgently needed in clinical practice to determine therapy decisions regarding surgical extent for patients.

Previously, we compared the difference in mitochondrial DNA between PA and PC because high copy numbers of mitochondria may carry a greater possibility of being identified in plasma or serum than genomic DNA. However, no special mutation features in mitochondrial DNA were identified between PC and PA that could be applied in preoperative differential diagnosis [39]. We subsequently focused on genomic DNA, and a group of genes and related variations were identified in PC, with CDC73 being the most promising candidate [15, 18].

Give that patients with PC or giant PA may lead to a similarly high level of serum PTH, liquid biopsy seems to be a viable tool for monitoring PC [40, 41]. To our knowledge, this study is the first attempt to explore the status of cfDNA for predicting gene mutations in the tumor tissues of parathyroid neoplasms. In the present study, the mutation status of 40 genes in plasma cfDNA from 18 PC patients and 13 PA patients was evaluated. CDC73 mutation has been reported to be associated with an increased risk of PC [42].

Through deep sequencing, a series of mutations in 17 genes were identified in plasma cfDNA. The most important mutation involved CDC73, which may elucidate genetic information from parathyroid tumors. This result also proved that somatic and inactivating alterations in CDC73 are the most common variants in PAs [43]. According to a literature review, somatic CDC73 mutations have been reported to occur in 40–100% of sporadic PC patients [44]. However, approximately 40% of CDC73 mutations identified in patients are germline mutations [45]. We also detected germline mutations in 2 patients. This may remind clinicians to pay attention to the management of those relatives [46]. In our limited cohort, CDC73 mutations in cfDNA were identified in 8 of 10 patients with abnormal CDC73 status in tumors, indicating a sensitivity of 80.0%. The mutation status of cfDNA in plasma may help to predict CDC73 abnormalities in tumors (AUC = 0.8). This finding indicated the potential suitability of cfDNA in the differential diagnosis of parathyroid tumors with/without CDC73 mutation. Notably, both patients with germline mutations in the CDC73 gene presented corresponding mutations in cfDNA. Multiple studies have shown that certain gene mutations in plasma cfDNA are highly consistent with those in tumor tissue. As an example, this advantage makes cfDNA testing an effective method for assessing the EGFR mutation status and selecting EGFR tyrosine kinase inhibitor treatment in patients with non-small cell lung cancer [47]. However, no patient with a somatic CDC73 mutation had the same CDC73 genotype in their cfDNA or tumor tissue in our study. In certain cases, such as those with a low tumor burden, cfDNA may be less sensitive than tissue. The relationship between gene mutations in cfDNA and gene mutation status in tumor tissues was unclear. We hope that further research will elucidate the specific mechanisms involved.

Diagnosing tumors on the basis of cfDNA in plasma is difficult because of its low concentration and short length and because the vast majority of cfDNA is from normal cells in the human body. In our present study, the CDC73 mutation could not be identified in the plasma cfDNA of 2 patients (PC11 and PC16), whereas parafibromin (encoded by the CDC73 gene) staining was negative in these 2 patients. Furthermore, the genotype of CDC73 harboured in the tumors was not consistent with that in the cfDNA of 5 PC patients with only somatic mutation. Many biological factors influence the detection rate of cfDNA. For example, the cfDNA concentration can be influenced by patient characteristics, tumor mass and infection, and DNA quality is impacted by the tumor state and access to the circulatory system [48, 49]. In addition, the current protocol of the cfDNA test is designed for the detection of mutations, including point substitutions (SNVs) and small InDels ≤ 50 nt. Other variations in the genome, such as gene fusion, large fragment insertions and deletions and copy number variations (CNVs), are difficult to detect by NGS with short read lengths < 300 bp. The difference in genotype between tumors and cfDNA may also originate from incomplete genomes carried by ctDNA. In our study, GNAS, MEN1, RNF43 and EZH2 were mostly mutated in parathyroid neoplasms. Among the 31 patient samples, all patients presented with mutations in GNAS and MEN1. Mutations in RNF43 and EZH2 were not detected in only 1 and 2 patients, respectively. This could be attributed to background mutations. MEN1 is indeed a gene commonly associated with PA, as noted in hereditary MEN1 syndrome [50]. We speculate that the presence of MEN1 mutations in all patients may partially reflect residual cfDNA fragments from clonal haematopoiesis that do not drive disease [51]. Therefore, the mutation status of these genes cannot be effectively used to differentiate between benign and malignant tumors. Variants may exist in normal cells and benign tissues, stemming from environmental exposure, natural cellular repair processes, or other factors. These variants may not drive mutations in cancer [52, 53]. Further studies are needed to delineate whether these mutations represent functional drivers in this context or are merely incidental findings without clinical significance. One study revealed that 97% of tumors carry a KRAS mutation in pancreatic cancer, and 18% of circulating tumor cells were found to carry only the KRAS wild-type allele, even those from metastatic tumors [54]. Another possibility is that these lesions are not cancerous but rather represent nonmalignant tissue abnormalities. Our results suggest that using cfDNA to predict whether CDC73 is mutated in tumors is possible. We speculated that CDC73, which was recognized as a diagnostic marker for PC, was the only key gene mutation in cfDNA. However, whether cfDNA can be directly used to predict CDC73 genotypes in tumors needs to be validated by expanding the sample size.

In addition, 4 patients, including 3 PC patients and 1 PA patient, had CDC73 mutations in their cfDNA, but no CDC73 abnormalities could be identified in their matching tumors through NGS or parafibromin staining. One key biological challenge in interpreting cfDNA screening results is that low levels of cancer-related DNA mutations are identified in plasma cfDNA from a large fraction of healthy individuals. The reason for these false-positive results may arise from somatic mosaicism, clonal haematopoiesis and subclonal mutations [55, 56]. For WES sequencing of tumor tissue, only a portion of the tumor cells is used for sequencing, and the mutation status of the undetected portion of the tumor may be ignored. The cfDNA mixture in plasma includes DNA released by all cells of the body, and its clinical significance needs to be interpreted from a holistic perspective. Notably, a cfDNA mutation with CDC73:NM_024529:exon7:c.679_680del: p.R227fs was identified repetitively in 4 false-positive cases. This InDel has been previously identified in PC, atypical adenoma or PA tissues by many authors [44, 57, 58]. This InDel was also found to be a germline mutation in one patient with PC in our present cohort. Therefore, we speculate that this mutation locus may be a hot spot that does not cause complete loss of protein function. Determining the clinical significance of this genotype before the accumulation of large-scale cfDNA sequencing data is difficult.

cfDNA can be used as a marker for monitoring patients with tumors [40]. Studies have reported that cfDNA levels are higher in patients with pancreatic cancer than in healthy people or patients with pancreatic cysts [59, 60]. In our cohort, no difference in the plasma cfDNA concentration was found between PA and PC. However, in terms of constructing a diagnostic model using gene mutations in plasma cfDNA from patients with parathyroid neoplasms, CDC73 was able to differentiate between PA and PC (AUC = 0.795). Currently, higher serum PTH and Ca levels and larger tumor sizes help to determine benignity and malignancy [8, 61]. With the combined inclusion of clinical data, the AUC reached 0.889. Therefore, the combination of non-invasive biomarkers is expected to be a promising adjunct for the preoperative diagnosis of PC. To further evaluate the diagnostic role of cfDNA for parathyroid neoplasms, establishing a cfDNA spectrum with a sufficient sample size and analysing data on the basis of the genetic background of the entire human body is necessary.

Several limitations of this study should be mentioned. First, owing to the low incidence of PC, even though our present study may be one of the largest cohorts in the serum analysis, the sample size is still be limited, which may underestimate or overestimate the significance of some biomarkers in the serum. Second, no samples from male PA patients passed DNA quality control after randomly selecting samples in this study. This may introduce a degree of bias to the statistical analysis. Third, some of the serum samples were collected from patients with recurrence, as most of the patients with PC were diagnosed after recurrence. This may carry the risk of misdiagnosis for patients without recurrence before the first operation. Bias was inevitable in this retrospective study. A prospective study is needed to assess these biomarkers for extensive clinical application.

Conclusions

In conclusion, it is possible to evaluate the gene mutation status of parathyroid neoplasms through deep sequencing of plasma cfDNA. The CDC73 abnormalities in plasma cfDNA could help in the differential diagnosis of PC before surgery.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

PC:

Parathyroid carcinoma

PA:

Parathyroid adenoma

cfDNA:

Cell-free DNA

AUC:

Area under the receiver operating characteristic curve

PHPT:

Primary hyperparathyroidism

PTH:

Parathyroid hormone

HPT-JT syndrome:

Hyperparathyroidism-jaw tumor syndrome

IHC:

Immunohistochemistry

NGS:

Next-generation sequencing

WBC:

White blood cell

BWA:

Burrows‒Wheeler Aligner

SNVs:

Single-nucleotide variants

InDels:

Insertions and deletions

ExAC:

Exome Aggregation Consortium

IGV:

Integrative Genomics Viewer

WES:

Whole-exon sequencing

WGS:

Whole-genome sequencing

FFPE:

Formalin-fixed, paraffin-embedded

TPR:

True positive rate

FPR:

False positive rate

SD:

Standard deviation

ALP:

Alkaline phosphatase

ROC:

Receiver operating characteristic

CNVs:

Copy number variations

GATK:

Genome Analysis Toolkit

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Acknowledgements

The authors would like to thank Genecast Biotechnology Co., Ltd for their assistance with sequecing and data analysis.

Funding

This work was supported by the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2021⁃I2M⁃1⁃002) and the National High Level Hospital Clinical Research Funding (2022-PUMCH-B-003).

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Author notes

  1. Qingyuan Zheng and Ming Cui contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Disease, Chinese Academy of Medical Sciences & Peking Union Medical College, Shuaifuyuan 1, Dongcheng District, Beijing, 100730, China

    Qingyuan Zheng, Ming Cui, Jinheng Xiao, Mengyi Wang, Surong Hua, Ya Hu & Quan Liao

  2. Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Ou Wang

  3. Key Laboratory of Endocrinology of the Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Science, Beijing, 100032, China

    Xiaoyan Chang

  4. Biomedical Engineering Facility, Institute of Clinical Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Tianqi Chen

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  1. Qingyuan Zheng
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Contributions

Conceptualization, Q. Z., Y.H. and Q.L.; Methodology, Q.Z. and M.C.; Validation, J.X. and T.C.; Investigation, M.C., S.H. and M.W.; Resources, X.C. and O.W.; Writing - original draft preparation, Q.Z. and M.C.; Writing - review and editing, Y. H, Q.L., X.C., O.W., S.H. and M.W. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Ya Hu or Quan Liao.

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The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Peking Union Medical College Hospital (JS-2054, K2494).

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12935_2025_3699_MOESM1_ESM.docx

Supplementary Table 1: Logistic regression analysis of gene mutation status in cfDNA to predict CDC73 mutation status in patient tissue samples

Supplementary Material 2

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Zheng, Q., Cui, M., Wang, O. et al. Primary exploration of cell-free DNA in the plasma of patients with parathyroid neoplasms using next-generation sequencing. Cancer Cell Int 25, 86 (2025). https://doi.org/10.1186/s12935-025-03699-w

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Cancer Cell International

ISSN: 1475-2867

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