TERT promoter mutation predicts the prognosis value of patients with HBV- related HCC who underwent hepatectomy

doi:10.21203/rs.3.rs-2237837/v1

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Research Article

TERT promoter mutation predicts the prognosis value of patients with HBV- related HCC who underwent hepatectomy

https://doi.org/10.21203/rs.3.rs-2237837/v1

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Background and Aims Recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC), especially in those infected with HBV. Telomerase reverse transcriptase (TERT) promoter is the most prevalent site in HBV-related HCC. We attempted to evaluate the prognostic value of TERT mutation status in HBV-related HCC patients.

Methods We enrolled the 66 HBV-related HCC patients who underwent hepatectomy between January 2013 and May 2016. DNA extracted from the fresh tumor tissues of these patients was subjected to Sanger sequencing and optimized droplet polymerase chain reaction (dPCR) for detecting TERT promoter mutation. We optimized the dPCR assay by adding 7-deaza-dGTP, CviQ1, and EDTA for improving the detection of TERT promoter mutation. Furthermore, nomogram survival prediction models were developed to assess the prognostic value of HBV-related HCC after hepatectomy.

Results The frequency of TERT C228T mutation was detected 24/66 (36.36%) by Sanger sequencing and 30/66 (45.45%) by dPCR. The two methods had an initial overall agreement(concordance = 90.91%, kappa = 0.814; dPCR sensitivity, 100%; dPCR specificity, 85.71%). HBV-related HCC patients with TERT C228T mutation had reduced survival and increased risk of recurrence. TERT C228T was an independent prognostic factor for OS and DFS in multivariate analyses. The nomograms successfully differentiated TERT C228T mutant and non-mutant HBV-related HCC patients associated with OS and DFS (C-index: 0.7468 and 0.6899, respectively).

Conclusions TERT C228T mutation could be a potential molecular biomarker for evaluating the prognosis of HBV-related HCC, especially when it is detected by dPCR rather than traditional Sanger sequencing. The nomogram survival prediction model we constructed may provide a valuable reference for clinicians to develop strategies for precision treatment.

hepatocellular carcinoma (HCC)

HBV

TERT promoter mutation

droplet polymerase chain reaction (dPCR)

hepatectomy

prognostic

The second-most common cause of cancer-related death worldwide is primary liver cancer [1]. The most common primary tumor of the liver is hepatocellular carcinoma (HCC), which accounts for 80–90% of all primary malignancies [2] with approximately 906,000 new cases and 830,000 deaths in 2020 [1]. To date, radical hepatectomy is the most effective treatment, which is widely recommended and regarded as a curative treatment [3]. However, the prognosis of HCC patients is still poor due to nearly 70% of HCC patients experiencing recurrence or metastasis within five years after surgical resection [2, 4]. Therefore, in patients who have received curative treatment, tumor recurrence will be a survival hazard.

The prognosis of patients is significantly affected by liver cancer recurrence and metastasis, an urgent requirement exists to find prognostic biomarkers to select patients who are at the highest risk of recurrence and who may benefit from adjuvant therapy [5, 6]. Therefore, a better exploration of reliable biomarkers with recurrence involved in the surgical specimen of HCC is particularly valuable for developing novel predictive and therapeutic strategies for the precision medicine of HCC patients.

One of the most prevalent applications of precision medicine is the detection of somatic mutation [7]. Genomic studies have found that telomerase reverse transcriptase (TERT) is the most prevalent mutated gene in HCC [8]. Telomere shortening and telomerase reactivation are two common hallmarks of carcinogenesis and have been described in a variety of human cancers including liver cancer [7, 9]. HCC is associated with genetic factors such as telomere shortening and telomerase reactivation through mutations in the telomerase reverse transcriptase (TERT) promoter [7, 10]. An early event in HCC development is thought to be a mutation in the promoter region of the TERT gene, which is considered a key driver of HCC tumorigenesis [11]. Particularly, the C228T site in the TERT promoter mutation represents approximately 95% of TERT variants in HCC [12].

Hepatitis B virus (HBV) infection is one of the leading risk factors for HCC, especially in Asia and Africa [13]. To our knowledge, the patients with HBV-related HCC mainly had TERT promoter mutation [14]. However, it remains unclear the prognostic value of this mutation in patients with HBV-related HCC after hepatectomy. Thus, we speculate that TERT promoter mutation status might relate to it.

In TERT promoter, mutation detection has been difficult due to their high GC content (nearly 80%) [15, 16]. As a result, it is technically challenging to amplify this target region specifically and efficiently. The gold standard for detecting somatic mutations has traditionally been Sanger sequencing. However, this assay also has PCR steps and sensitivity limitations that cannot show a positive true genetic mutation [17]. Consequently, to overcome the limits above and improve the sensitivity of detection, we observed that in the previous study digital PCR (dPCR) was successfully utilized to detect and measure abnormal genomic mutations from FFPE tissues in HCC and other cancers [18–20].

Therefore, in this study, we would optimize the details of the dPCR assay and establish the threshold of detection performance. We will attempt to prove the use of this highly sensitive and specific dPCR assay to predict much more HBV-related HCC patients who have a higher risk of recurrence. Furthermore, we would develop nomogram survival prediction models to further verify the prediction value of TERT C228T mutation status which could be a potential predictor for HBV-related HCC patients after hepatectomy. We hope our findings may provide a valuable reference for clinicians to develop the precision treatment of HCC strategies.

Patient and tissue samples

Our study involved 66 HBV-related HCC patients who underwent hepatectomy at the Cancer Hospital of Peking Union Medical College and Chinese Academy of Medical Sciences between January 2013 and May 2016. We also collected fresh tumor tissue samples from the surgical specimens of these 66 patients. All patients had a clear postoperative pathological diagnosis by two expert pathologists and were confirmed as hepatocellular carcinoma. The ethical of this present study was passed by the Cancer Hospital of Peking Union Medical College and Chinese Academy of Medical Sciences Ethics Committee. All tumor biopsies had a clear postoperative pathological diagnosis by two expert pathologists confirmed as hepatocellular carcinoma and microvascular invasion (MVI).

We collected patient information and biomarker test results, including cirrhosis and alcohol status, AFP, ALT, AST, GGT, LDH, and APTT retrospectively. And we obtained clinical data on the number of tumors, tumor diameter, liver capsule invasion (LCI), blood vessel invasion (BVI), China Liver cancer staging (CNLC), and microvascular invasion (MVI) from medical records. All cases were divided into a well-differentiated group (High, n = 10), moderately differentiated group (Medium, n = 42), and poorly differentiated group (Low, n = 14) according to histological grade the criteria of Edmondson and Steiner [21]. Follow-up evaluations occurred in 2018 (first follow-up), 2019 (second follow-up), June 2021(third follow-up), and August 2022 (fourth follow-up). The maximum follow-up time was 9.3 years.

DNA extraction

An incubation solution containing 500 µL of DNA lysis solution was used to dissolve the frozen tissue samples (proteinase K 1 mg/ml, Tris-HCl (pH8.0) 10mmol/L, EDTA (pH8.0) 0.1 mol/L, SDS 0.5% (w/v)) at 55°C, overnight. After digestion, DNA was extracted by phenol and chloroform, and stored at -20°C until use. Nanodrop spectrophotometer ND-1000 (Thermo Scientific, Wilmington, USA) was used to verify DNA concentration and quality, each section recovered 5–34 µg of DNA. All DNA samples had an absorbance ratio equal to or above 1.80 at 260 nm and 280 nm.

Sanger sequencing for detecting TERT C228T mutation

We performed PCR reactions with genomic DNA in a 50 µL reaction mixture, 2.5 µL of each primer, 0.25 µL HotStartTaq Plus DNA Polymerase (Qiagen, Germany), 10 µL Q-solution 5× (Qiagen, Germany), 5 µL Coralload PCR buffer 10× (Qiagen, Germany), 1 µL dNTP 10mM (TaKaRa). In a PTC-200 Peltier Thermal Cycler (Applied Biosystems), primers for the TERT promoter region were amplified as follows: (forward) 5′-ACGAACGTGGCCAGCGGCAG-3′, and (reverse) 5′-CTGGCGTCCCTGCACCCTGG-3′. Cycling conditions: denaturing 30 seconds at 98°C, after 35 cycles of denaturing for 15 seconds at 98°C, annealing for 15 seconds at 62°C, and elongation for 30 seconds at 72°C, the final elongation at 72°C was performed for two minutes. As a result, the 474 bp amplicon was obtained [22]. Products were visualized by 1% agarose gel electrophoresis, gel-purified, and sequenced. The product was then sequenced with (forward) 5′-CAGCGCTGCCTGAAACTC-3′and (reverse) 5′-GTCCTGCCCCTTCACCTT-3′primers. Somatic mutations have been observed in the − 124 bp hotspot of TERT promoter region.

dPCR for detecting TERT C228T mutation

Our experimental conditions were verified on positive samples with known TERT promoter mutation. We performed the droplet generation process according to the manufacturer’s protocol using the Naica Crystal PCR system (Stilla Technologies). Here are the reaction mixtures used in this assay: 12.5 µL ddPCR Supermix for Probes (No dUTP) (Bio-Rad), 1.25 µL 20×primer/probe mixture (TERT C228T_113 (dHsaEXD72405942), Bio-Rad), restriction enzyme 0.2 µL (New England BioLabs; CviQI; R0639S), 0.2 µL 7-Deaza-dGTP (New England BioLabs; N0445S), 1 µL of 25mM EDTA (Thermo Fisher Scientific), 2.5 µL of 10uM fluorescein sodium salt (APEXBIO), DNA template [DNA concentrations differed depending on the experiment (0.5 to approximately 60 ng/µL)], and finally added ultrapure DNAse/RNAse free H₂O (Thermo Fisher Scientific/Gibco) to a final volume of 25ul. The cycling protocol was as follows: 1 cycle of 10 min at 37°C, 1 cycle of 10 min at 95°C, followed by 50 cycles of 95°C for the 30s and 62°C for 1min, finally hold at 4°C (2°C /s). The dPCR data was analyzed using CrystalMiner for the Nacia™ System v2.3.0 software (Stilla Technologies), and positive and negative controls were used to determine the thresholds for each mutation assay.

We used mutation probes in the channel to determine a false positive measure and therefore detected a threshold for the smallest mutation droplet, twenty wild-type DNA samples for TERT mutation were tested with their respective dPCR. To determine the limit of blank (LoB), negative controls were analyzed, and background signals were obtained. At least five Low-Level (LL) samples should be prepared independently (LL1, LL2, LL3, LL4, and LL5), and at least four replicates should be performed per sample. We calculated the limit of detection (LoD) by figuring out the concentration of mutant alleles at which LL samples should comprise representative positive samples or representative matrix samples with a maximum target concentration of one to five times the calculated LoB. The LoB of TERT C228T was detected in pure wild-type samples at 0.35 copies/µL wells. The LoD of TERT C228T in LL samples was 0.55 copies/µL wells.

Independence of the TERT mutation from clinical features

Our 66 HBV-related HCC cohort have completed clinical and survival information, including, age, gender, ALT, AST, LDH, GGT, APTT, cirrhosis, alcohol, tumor number, tumor diameter, MVI, differe, LCI, BVI, CNLC. The analysis of Cox regression with univariate and multivariate variables was conducted for confirming that predictions of the prognostic model were independent of clinical characteristics in HBV-related HCC.

Construction and evaluation of the nomogram

Multivariable Cox models were used to calculate survival probability estimates for overall survival (OS) and disease-free survival (DFS) nomograms. We calculated the OS and DFS nomograms. Up to 5 years after surgery, the accuracy of the nomogram models was evaluated based on Harrell's C-index discrimination, calibration, and overall accuracy. For evaluating model performance, calibration and discrimination are commonly used methods, and they were calculated using a bootstrapping approach with 1000 resamples. Furthermore, C-index and time-dependent receiver operating characteristic (ROC) analyses were also performed to compare predictive accuracies between the nomogram and prognostic variates separately. Additionally, to verify the clinical validity of the established nomogram, calibration curve analysis and decision curve analysis (DCA) was conducted.

Statistical

The demographic and clinical characteristics of the patients were summarized using descriptive statistics. We performed Fisher's exact testing for categorical variables and Wilcoxon rank sum tests for continuous variables to examine the causal relationship between clinicopathologic factors and TERT mutation status (no vs. yes).

Based on Cohen's kappa index, we evaluated the degree of agreement between dPCR and Sanger. Cohen proposed a categorical interpretation of the kappa index. Weak agreement of < 0.00, weak agreement of 0 ~ 0.2, moderate agreement of 0.21 ~ 0.40, average agreement of 0.41 ~ 0.60, and basic agreement of 0.61 ~ 0.80, while 0.81 ~ 1.00 is almost perfect. Considered too lenient for health-related research [23]. Calculations of the sensitivity, specificity, and accuracy of the TERT promoter mutations were performed using the Clopper-Pearson method.

We performed Kaplan-Meier plots and Cox proportional hazards models using the R packages (v 3.6.3) ‘survminer’ and ‘survival’. The OS was defined as the period from surgery until death due to any cause, and the DFS was the period after surgery until any recurrence or death. The nomograms for predicting the OS and DFS were generated using the R “rms” package.

Other statistical tests were conducted on two sides, and P < 0.05 was considered statistically significant. Statistical analyses were performed using packages of “survminer”, “rms”, “rmda”, and “timeROC” in R version 3.6.3.

Assay optimization

It has been challenging to amplify the TERT promoter because of its high guanine-cytosine GC content (more than 80%) [24] and the way it forms secondary structures, such as hairpin structures [25]. As a result, it is difficult to develop efficient and specific PCR assays for detecting and quantifying mutations in this region. However, this problem is a major threat to dPCR analysis, showing clear clusters of droplets.

Therefore, for overcoming the challenges presented by this issue, we planned to find suitable additives to accurately detect variants of the TERT promoter region using digital PCR. In previous studies, 7-deaza-dGTP has been shown to improve cluster resolution in dPCR in agreement with Colebatch et. al [26]. CviQ1 was added to a dye-binding detection assay to increase cluster tightness [27]. An EDTA titration was performed to determine the applicable Mg²⁺ concentration in the PCR mixture. We conducted with or without these additives in the PCR reactions and found a suitable concentration of these chemical additives. As a result, droplet clusters were separated. It was also necessary to vary amplification parameters and additives so that cluster separation and tightness could be maximized. The detailed amount of each reagent is shown in Fig. 2.

dPCR Specificity, Sensitivity, and Limits of Detection

An orthogonal method, such as Sanger sequencing, was used to validate the specificity of the optimized assays on mutant templates. We measured the sensitivity of the assay by serially diluting wild-type genomic DNA into TERT promoter mutant genomic DNA. We established the LoB from 20 replicate wells of tumor samples from the patient who didn't have TERT mutation in his biopsy tissue. The LoB for the C228T was 0.35 copies/µL. LoD, the lowest frequency of TERT C228T that can be accurately distinguished from five independent LL samples (each level performing four replicates) was found to be 0.55 copies/µL well, as shown in Table S1. Thirty out of 66 cases (46.67%) FAM concentrations higher than 0.55copies/µL (LoD), which treated positively for TERT C228T promoter mutations in HCC cohort samples detected by dPCR (Fig. 3).

Describe the clinical characteristic

In our study, 66 HBV-related HCC patients who underwent hepatectomy were enrolled, including 54 males and 12 females. The median diagnostic age of enrolled HBV-related HCC patients was 52 years (ranging from 46.25 to 59). All patients had hepatitis B virus infection. Cirrhosis existed in 55 patients (83.3%). Ten patients (15.2%) had multiple malignant tumor foci. Micro-vascular invasion was detected in positive 1 (n = 15, 24.2%), and positive 2 (n = 7, 11.3%). The patients were staged according to the China Liver Cancer Staging (CNLC) classification. Most patients had early HCC (CNLC I, n = 46, 73%), intermediate HCC (CNLC II, n = 10, 15.9%), or advanced (CNLC III, n = 7, 11.1%). Most patients had HCC related to chronic alcohol abuse (n = 34, 51.5%). This included 38 cases with LCI and 17 with the BVI. Regarding tumor differentiation, the majority of tumors were moderately differentiated (n = 42, 63.6%), 21.2% were poorly differentiated (n = 14), and the remaining 15.2% (n = 10) were well differentiated. Elevated AFP (> 20 ng/ml) was seen in 29 patients (43.9%). The patient characteristics are summarized in Table 1.

Table 1

Demographic data of patients who underwent hepatectomy in survival and recurrence prognostic status.
	Overall	Survival			Recurrence
Characteristic	66	Live = 44	Death = 22	P	No = 24	Yes = 42	P
Age, n (%)				0.432			0.080
≤ 55	36 (54.5%)	26 (59.1%)	10 (45.5%)		17 (70.8%)	19 (45.2%)
> 55	30 (45.5%)	18 (40.9%)	12 (54.5%)		7 (29.2%)	23 (54.8%)
Gender, n (%)				0.193			0.512
Male	54 (81.8%)	38 (86.4%)	16 (72.7%)		21 (87.5%)	33 (78.6%)
Female	12 (18.2%)	6 (13.6%)	6 (27.3%)		3 (12.5%)	9 (21.4%)
ALT, n (%)				0.501			0.817
≤ 40 U/L	47 (71.2%)	33 (75%)	14 (63.6%)		18 (75%)	29 (69%)
> 40U/L	19 (28.8%)	11 (25%)	8 (36.4%)		6 (25%)	13 (31%)
AST, n (%)				0.117			0.560
≤ 40 U/L	51 (77.3%)	37 (84.1%)	14 (63.6%)		20 (83.3%)	31 (73.8%)
> 40U/L	15 (22.7%)	7 (15.9%)	8 (36.4%)		4 (16.7%)	11 (26.2%)
LDH, n (%)				0.281			0.736
≤ 214 U/L	56 (84.8%)	39 (88.6%)	17 (77.3%)		21 (87.5%)	35 (83.3%)
> 214 U/L	10 (15.2%)	5 (11.4%)	5 (22.7%)		3 (12.5%)	7 (16.7%)
GGT, n (%)				0.025^*			0.058
≤ 55 U/L	41 (62.1%)	32 (72.7%)	9 (40.9%)		19 (79.2%)	22 (52.4%)
> 55U/L	25 (37.9%)	12 (27.3%)	13 (59.1%)		5 (20.8%)	20 (47.6%)
APTT, n (%)				1.000			0.511
≤ 34.6s	55 (83.3%)	37 (84.1%)	18 (81.8%)		19 (79.2%)	36 (85.7%)
> 34.6s	11 (16.7%)	7 (15.9%)	4 (18.2%)		5 (20.8%)	6 (14.3%)
AFP, n (%)				0.136			1.000
≤ 20ng/ml	37 (56.1%)	28 (63.6%)	9 (40.9%)		13 (54.2%)	24 (57.1%)
> 20ng/ml	29 (43.9%)	16 (36.4%)	13 (59.1%)		11 (45.8%)	18 (42.9%)
Cirrhosis, n (%)				0.739			0.733
No	11 (16.7%)	8 (18.2%)	3 (13.6%)		3 (12.5%)	8 (19%)
Yes	55 (83.3%)	36 (81.8%)	19 (86.4%)		21 (87.5%)	34 (81%)
Alcohol, n (%)				0.258			0.561
No	32 (48.5%)	24 (54.5%)	8 (36.4%)		10 (41.7%)	22 (52.4%)
Yes	34 (51.5%)	20 (45.5%)	14 (63.6%)		14 (58.3%)	20 (47.6%)
Tumor number, n (%)				0.720			0.306
1	56 (84.8%)	38 (86.4%)	18 (81.8%)		22 (91.7%)	34 (81%)
2	10 (15.2%)	6 (13.6%)	4 (18.2%)		2 (8.3%)	8 (19%)
Tumor diameter, n (%)				1.000			1.000
≤ 2cm	4 (6.1%)	3 (6.8%)	1 (4.5%)		1 (4.2%)	3 (7.1%)
> 2cm	62 (93.9%)	41 (93.2%)	21 (95.5%)		23 (95.8%)	39 (92.9%)
^aMVI, n (%)				0.052			0.722
Negative	40 (64.5%)	30 (75%)	10 (45.5%)		15 (68.2%)	25 (62.5%)
1	15 (24.2%)	7 (17.5%)	8 (36.4%)		4 (18.2%)	11 (27.5%)
2	7 (11.3%)	3 (7.5%)	4 (18.2%)		3 (13.6%)	4 (10%)
Differe, n (%)				0.132			0.322
High	10 (15.2%)	9 (20.5%)	1 (4.5%)		5 (20.8%)	5 (11.9%)
Medium	42 (63.6%)	28 (63.6%)	14 (63.6%)		16 (66.7%)	26 (61.9%)
Low	14 (21.2%)	7 (15.9%)	7 (31.8%)		3 (12.5%)	11 (26.2%)
LCI, n (%)				1.000			1.000
No	28 (42.4%)	19 (43.2%)	9 (40.9%)		10 (41.7%)	18 (42.9%)
Yes	38 (57.6%)	25 (56.8%)	13 (59.1%)		14 (58.3%)	24 (57.1%)
BVI, n (%)				0.022^*			0.325
No	49 (74.2%)	37 (84.1%)	12 (54.5%)		20 (83.3%)	29 (69%)
Yes	17 (25.8%)	7 (15.9%)	10 (45.5%)		4 (16.7%)	13 (31%)
^bCNLC, n (%)				0.368			0.156
I	46 (73%)	33 (76.7%)	13 (65%)		21 (87.5%)	25 (64.1%)
II	10 (15.9%)	7 (16.3%)	3 (15%)		2 (8.3%)	8 (20.5%)
III	7 (11.1%)	3 (7%)	4 (20%)		1 (4.2%)	6 (15.4%)
dPCR TERT mutation, n (%)				0.066			0.005^*
No	36 (54.5%)	28 (63.6%)	8 (36.4%)		19 (79.2%)	17 (40.5%)
Yes	30 (45.5%)	16 (36.4%)	14 (63.6%)		5 (20.8%)	25 (59.5%)
Sanger TERT mutation, n (%)				0.786			0.086
No	42 (63.6%)	29 (65.9%)	13 (59.1%)		19 (79.2%)	23 (54.8%)
Yes	24 (36.4%)	15 (34.1%)	9 (40.9%)		5 (20.8%)	19 (45.2%)
Categorical variables analysis were accomplished using the chi-square or Fisher exact test.
ALT alanine aminotransferase, AST aspartate aminotransferase, LDH lactate dehydrogenase, GGT gamma-glutamyl transferase, APTT activated partial thromboplastin time, AFP alpha fetoprotein, MVI Microvascular invasion, Differe tumor differentiation, LCI liver capsular invasion, BVI blood vessel invasion, CNLC China liver cancer staging.
^*P < 0.05 was significant
^aFour patients had missing data
^bThree patients had missing data

TERT promoter mutations and tumor characteristics

Correlations between TERT promoter mutation status and clinicopathological variables in Sanger sequencing and dPCR are shown in Fig. 3. Twenty-four cases were examined for TERT C228T mutation by both Sanger sequencing and dPCR. However, six patients were positive for C228T by dPCR assay but negative by Sanger sequencing. We elaborate on the sequencing result of these six samples (all dPCR positive, but Sanger negative) associated with FAM concentration and prognostic status (Fig. 4).

Sanger sequencing found that 36.36% (24/66) were positive, whereas dPCR found 45.45% (30/66). The consistency of the two methods for detecting TERT C228T was good (90.91%). The Cohen’s kappa coefficient was 0.814 (95% CI 0.675 ~ 0.953) suggesting a perfect agreement. While Sanger sequencing is regard as the gold standard for detecting sequence variation, dPCR offers improved sensitivity over Sanger sequencing. By dPCR, TERT C228T mutation was detected with 100% sensitivity and 85.71% specificity, respectively (Fig. 3).

TERT mutation detected by dPCR assay is independent of conventional clinical characteristics associated with tumor recurrence

Tumor recurrence rates were 59.5% vs. 45.2% in TERT C228T positive detected by dPCR vs. Sanger sequencing (Table 1). Among those with available data, there was a significant correlation between the tumor recurrence in the presence of the TERT C228T mutation detected by dPCR (p < 0.01). This correlation was not found when the TERT promoter detecting by Sanger sequencing. Therefore, in the next analysis we decided to choose the TERT mutation status detected by dPCR. There was no significant correlation within any other between the tumor recurrence status and the presence of age, cirrhosis, AFP level, alcohol, or other clinical variables (Table 1). Kaplan–Meier analysis of differences in OS and DFS in the study without and with the TERT promoter mutations (Fig. 5).

We performed univariate and multivariate Cox regression analyses to examine whether TERT mutations in our HCC cohort were independent of other clinical variates. Univariate Cox regression analysis was conducted, and significant variables (P ≤ 0.05) were selected for multivariate Cox regression analysis. The univariate analysis determined five parameters that were tending or significantly associated with OS: TERT mutation status [HR (95% CI) 2.125 (0.806–5.598), P = 0.127], AST [HR (95% CI) 1.454 (0.551–3.837), P = 0.449], GGT [HR (95% CI) 2.274 (0.876-5.900), P = 0.091], BVI [HR (95% CI) 2.337 (0.600-9.102), P = 0.221], MVI 1 [HR (95% CI) 1.325 (0.346–5.077), P = 0.681]and MVI 2 [HR (95% CI) 1.964 (0.423–9.122), P = 0.389] (Table 2). Multivariate Cox regression analysis including these factors (TERT mutation status [HR (95% CI) 2.430 (1.276–4.630), P = 0.007], GGT [HR (95% CI) 2.141 (1.132–4.047), P = 0.019] and BVI [HR (95% CI) 2.272 (1.148–4.496), P = 0.018]), DFS is significantly and independently determined by these factors (Table 2). In addition, the C-index was also compared between the nomograms for OS and DFS, TERT mutation and the 18 clinical characteristics. The nomograms successfully differentiated TERT C228T mutant and non-mutant patients with HBV-related HCC associated with OS and DFS (C-index: 0.7468 and 0.6899, respectively) (Fig. 6a, b). Overall, these results showed that TERT mutations consistently outperformed conventional clinical characteristics in survival prediction.

Table 2. Univariate and multivariate Cox regression analyses of OS and DFS for HCC patients after hepatectomy.

	OS				DFS
	Univariate analysis		Multivariate analysis		Univariate analysis		Multivariate analysis
Characteristics	HR(95% CI)	P	HR(95% CI)	P	HR(95% CI)	P	HR(95% CI)	P
Age (> 55/≤55)	0.647 (0.279–1.501)	0.311			0.638 (0.347–1.175)	0.149
Gender (female/male)	1.814 (0.709–4.643)	0.214			1.432 (0.684–2.998)	0.341
ALT (≤ 40 /༞40 U/L)	1.695 (0.709–4.050)	0.235			1.454 (0.755-2.800)	0.264
AST (≤ 40/༞40ng/ml)	2.838 (1.167–6.903)	0.021	1.454 (0.551–3.837)	0.449	1.698 (0.852–3.386)	0.132
LDH (≤ 214/>214 U/L)	1.706 (0.628–4.633)	0.295			1.215 (0.539–2.740)	0.639
GGT (≤ 55/༞55 U/L)	3.401 (1.428–8.102)	0.006	2.274 (0.876-5.900)	0.091	2.530 (1.368–4.680)	0.003	2.141 (1.132–4.047)	0.019
APTT (༞34.6s/≤34.6s)	0.989 (0.333–2.934)	0.984			1.453 (0.610–3.460)	0.398
AFP (> 20/≤20ng/ml)	0.472 (0.202–1.106)	0.084			0.907 (0.492–1.675)	0.756
Cirrhosis (Yes/No)	0.846 (0.250–2.864)	0.788			1.413 (0.653–3.061)	0.380
Alcohol (No/Yes)	2.130 (0.889–5.108)	0.090			0.927 (0.505–1.699)	0.806
Tumor number (1/2)	1.460 (0.492–4.328)	0.495			1.614 (0.746–3.490)	0.224
Tumor diameter (> 2cm/≤2cm)	0.623 (0.084–4.638)	0.644			1.439 (0.443–4.678)	0.545
MVI		0.038				0.634
Negative	Reference				Reference
2	3.583 (1.114–11.526)	0.032	1.964 (0.423–9.122)	0.389	1.246 (0.433–3.586)	0.683
1	2.680 (1.054–6.816)	0.038	1.325 (0.346–5.077)	0.681	1.401 (0.688–2.856)	0.353
Differe		0.086				0.086
Medium	Reference				Reference
High	0.273 (0.036–2.077)	0.210			0.743 (0.285–1.937)	0.544
Low	2.142 (0.854–5.374)	0.105			2.021 (0.995–4.105)	0.052
LCI (Yes/No)	0.972 (0.412–2.294)	0.948			1.084 (0.586–2.006)	0.797
BVI (No/Yes)	4.361 (1.824–10.428)	< 0.001	2.337 (0.600-9.102)	0.221	2.185 (1.129–4.231)	0.020	2.272 (1.148–4.496)	0.018
CNLC		0.230				0.096
I	Reference				Reference
III	2.676 (0.863–8.292)	0.088			2.496 (1.014–6.143)	0.047
II	1.136 (0.323–3.998)	0.842			1.674 (0.754–3.715)	0.206
TERT mutation (No/Yes)	2.700 (1.115–6.541)	0.028	2.125 (0.806–5.598)	0.127	2.693 (1.445–5.021)	0.002	2.430 (1.276–4.630)	0.007

A nomogram for TERT mutations was constructed and validated

Based on the final multivariable model, the nomogram was developed to predict survival at 1-year, 2-year, and 5-year and recurrence based on risk factors in HBV-related HCC patients who underwent hepatectomy. The total score was 0 ~ 100 and 0 ~ 300 respectively, and each variable was calculated and merged. A high score indicates a high risk of death and recurrence of OS and DFS respectively (Fig. 6c, e). For example, an HBV-related HCC patient with TERT mutation (100 points) GGT > 55U/L (86 points), and without BVI (0 points) had a total score of 186 points. The 1-year recurrence probability was 71%. Compared with other clinical information, TERT mutations contributed the most to the risk score (0 to 100), based on Cox multiple regression results. In our multivariate Cox regression analysis, we found that the TERT mutation contributed the most to the risk score (0 to 100) compared to the other clinical information.

OS AUCs for 1-year, 2-year, and 5-year follow-ups were 0.737, 0.828, and 0.804, respectively (Fig. 6d). DFS AUCs for 1-year, 2-year, and 5-year follow-ups were 0.849, 0.688 and 0.772, respectively. The models’ performance is commonly evaluated by calibration and discrimination (Fig. 6f). Nomogram predictions are represented on the x-axis, and actual survival and recurrence probabilities are displayed on the y-axis. An ideal curve is a 45°line, indicating good agreement between prediction and observation. Calibration curves for OS and DFS were plotted at 1-year, 2-year, and 5-year intervals, revealing consistent results between predicted and observed data. (OS: Fig. 7a, b, c; DFS: Fig. 7d, e, f). Based on the results of our study, we found that especially in the 5-year prediction, the performance of nomograms plotted as solid lines were satisfactory. (Fig. 7c, f). A decision curve analysis was used to determine whether the models could guide treatment in our HBV-related HCC cohort. Additionally, high predictive efficiency of the nomogram was observed in OS and DFS for HBV-related HCC patients after hepatectomy, based on DCA curves at 1-year, 2-year, and 5-year. (OS: Fig. 7g, h, i; DFS: Fig. j, k, l). Overall, the nomogram predicted whether HBV-related HCC patients would relapse after hepatectomy more accurately than individual prognostic factors.

In the progression of hepatocarcinogenesis, the TERT promoter mutation is considered to be an early event [11]. Under this situation, the somatic mutation status may be the predictor of poor prognosis, and previous findings also support the assumption that TERT mutation status could predict liver cancer prognosis [18, 28, 29]. In addition, TERT promoter mutation has resistance to amplification according to its GC-rich region, thereby affecting the performance of assay. For overcoming this problem, we have used digital PCR to detect the variants of TERT promoter region more accurately.

With the advantages of exceptional sensitivity and high precision, absolute quantification without the need for a standard curve, and also can directly distinguish results from positive and negative, dPCR is used in many clinical applications to detect and quantify such as pathogens, and somatic mutations [16, 30–32]. At the beginning of our study, we observed that dPCR did not seem to be as successful as Sanger sequencing in detecting TERT promoter mutation. After we optimized the details of dPCR assay, consistent with the works of literature and our expectation, the result of our experiment that detecting the DNA extracted from fresh tumor tissues of HBV-related HCC patients by dPCR has an acceptable concordance, high sensitivity, and specificity compared with Sanger sequencing. Furthermore, a remarkable part of these results is that all patients who detected TERT C228T positive in dPCR but negative in Sanger sequencing had recurrence after surgery and died during follow-up. In consequence, this outcome further confirms that improving the sensitivity of this challenging mutation point, which is technologically difficult to detect by conventional methods because of its high GC content [16], could give a better indication of prognosis. However, the previous studies have already compared these two methods and TERT promoter were validated to determine if they could predict the prognosis of HBV-HCC patients after hepatectomy, as result the outcome was unsatisfactory [18]. In these studies, it seems possible that due to the lack of follow-up data after surgery, TERT mutations were slightly associated with overall or progression-free survival.

Furthermore, Jang, J. et al have reported a significant correlation between TERT promoter mutation detected in tissue from HCC patients after surgical resection and survival time, but not found that this mutation was related to HCC whether recurs [28]. According to another study, overall survival and recurrence-free survival of HCC were not related to TERT promoter mutations independently, but the combination of rs2853669 and TERT promoter mutations was correlated to decreased survival [29]. However, in our HCC cohort, we observed that the TERT C228T mutation was not only strongly associated with overall survival but also disease-free survival time. Since this consequence has not been found elsewhere it is probably due to all of the patients in our cohort is HBV-related HCC. It was determined that the TERT promoter is the most common HBV integration site in HCC [33, 34]. While HBV-TERT genomic integrations and mutations in the TERT promoter conflicted [34], HBV integration or TERT mutations were related to elevated TERT transcriptional activation [33, 35, 36]. In Jeong-Won J.et al study, 65.4% (17/26) of HBV-related HCC had TERT promoter mutation or HBV was inserted into TERT or MLL4, suggesting that TERT or MLL4 gene alterations may play a key role in the development of HBV-related HCC [14]. This also gives support that the association between TERT promoter mutation and tumor recurrence or other prognostic factors, but further verification and validation of the prognostic relationship between TERT promoter mutation status and HBV-related HCC is still necessary.

Therefore, to further validate the relationship between TERT promoter mutation status and prognosis, our prognostic nomogram has been constructed to predict the outcome of HBV-related HCC patients after hepatectomy. Our present results suggest that this mutation site detected by dPCR could improve the prediction of HBV-related HCC recurrence after surgery. Moreover, TERT-C228T mutation is recommended to consider along with other clinical features (AST, GGT, MVI, BVI, and TERT-C228T mutation) in OS comprehensive evaluation, and other clinical features (GGT, BVI, and TERT-C228T mutation) in DFS comprehensive evaluation. After 1-year, 2-year, and 5-year of OS and DFS, the observed and predicted values showed reasonable agreement on the calibration curve. A major advantage of this model is the acquisition of complementary views about individual tumors and the development of patient-specific scoring systems. Furthermore, Andrew Zhu. et al have reported that the type of TERT mutation patients with unresectable HCC may benefit from the treatment of atezolizumab in combination with bevacizumab [37]. In the future, our nomogram may consider a valuable tool for clinicians, of course, this still needs a lot of clinical research.

Serval limitations of this study should be highlighted. First, our retrospective findings were limited by a small sample number size and the lack of validation in a large-scale cohort, which could lead to inherent biases in patient selection. Second, the lack of a randomized control group is another main limitation. The consideration of this imperfection design was based on the low incidence of non-HBV-related HCC in our hospital. Third, the results may be limited by the epidemiological characteristics of patients with HBV-related HCC that vary by race, ethnicity, and region, and by differences in surgical approaches and indications for surgery among centers.

To sum up, in this research we optimized the details of dPCR assay and established the threshold of detection performance. The highly sensitive and specific dPCR assay can predict more HBV-related HCC patients with a higher risk of recurrence than the gold standard Sanger sequencing. Our findings were not only the first study to establish nomogram models to validate the prognostic value of TERT mutation status in HBV-related HCC but also reported the clinical application of dPCR on the detection of somatic mutation. However, a larger multicenter cohort and prospective studies are needed to confirm the reliability of this nomogram model. In this study, our results may provide a valuable reference for clinicians to develop the precision treatment of HBV-related HCC strategies and bring effective insights into precision medicine. In addition, further research could enrich the types of samples, not only the fresh tissues from surgical resection but also the liquid biopsy of blood such as extracting the ctDNA from it to better monitor HCC patients' prognoses after hepatectomy. Furthermore, if this highly sensitive and specific experiment method extends to liquid biopsy of other cancers, such as urothelial carcinoma, could shed light on precision medicine.

AFP alpha fetoprotein

ALT alanine aminotransferase

APTT activated partial thromboplastin time

AST aspartate aminotransferase

AUC area under curves

BVI blood vessel invasion

CNLC China Liver cancer staging

DCA decision curve analysis

DFS disease-free survival

Differe tumor differentiation

dPCR droplet polymerase chain reaction

GGT gamma-glutamyl transferase

HBV hepatitis B virus

HCC hepatocellular carcinoma

HR hazard ratio

LCI liver capsule invasion

LDH lactate dehydrogenase

LL Low-Level

LoB limit of blank

LoD limit of detection

MVI microvascular invasion

OS overall survival

ROC receiver operating characteristic curves

TERT telomerase reverse transcriptase

TSS transcriptional start site

Supplementary Information Supplementary material associated with this article can be found, in the online version.

Author contributionsHuan Zhao, Ting Xiao, Weiqi Rong, and Zulihumaer Aizimuaji contributed to the study’s conception and design. Zulihumaer Aizimuaji and Xijun Wang performed the experiments. Zulihumaer Aizimuaji, Weiqi Rong, Sheng Ma, Xijun Wang and Yaru Wang collected the samples and clinical information. Zulihumaer Aizimuaji performed the data analysis and accessed the raw data. The first draft of the manuscript was written by Zulihumaer Aizimuaji, and all authors commented on previous versions of the manuscript. Ting Xiao and Weiqi Rong revised the manuscript and supervised the study. All authors read and approved the final manuscript.

Funding This work was supported by CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant no. 2021-I2M-050) and Beijing Hope Run Special Fund of the Cancer Foundation of China (grant no. LC2020A12).

Competing Interests The authors have no relevant financial or non-financial interests to disclose.

Data availability The data associated with this study are present in the paper or the Supplementary Materials. Data available on request from the corresponding author. The raw data is also available from the corresponding authors upon reasonable request. Enquiries for data, analytic methods and materials can be directed to the corresponding author.

Conflict of interest Zulihumaer Aizimuaji, Huan Zhao, Xijun Wang, Sheng Ma, Yaru Wang, Weiqi Rong, and Ting Xiao declare that they have no conflict of interest.

Ethical approvalThe study was approved by Ethics Committee of Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (20/455-2651).

Informed consent Cancer Hospital of Peking Union Medical College and Chinese Academy of Medical Sciences Ethics Committee deemed this study exempt.

Sung H, Ferlay J, Siegel RL, 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(3):209–49.
Llovet JM, Zucman-Rossi J, Pikarsky E, Sangro B, Schwartz M, Sherman M, Gores G. Hepatocellular carcinoma. Nat Rev Dis Primers. 2016;2:16018.
Hasegawa K, Kokudo N, Makuuchi M, Izumi N, Ichida T, Kudo M, Ku Y, Sakamoto M, Nakashima O, Matsui O, et al. Comparison of resection and ablation for hepatocellular carcinoma: a cohort study based on a Japanese nationwide survey. J Hepatol. 2013;58(4):724–9.
Vibert E, Schwartz M, Olthoff KM. Advances in resection and transplantation for hepatocellular carcinoma. J Hepatol. 2020;72(2):262–76.
Marquardt JU, Galle PR, Teufel A. Molecular diagnosis and therapy of hepatocellular carcinoma (HCC): an emerging field for advanced technologies. J Hepatol. 2012;56(1):267–75.
Shi YH, Ding WX, Zhou J, He JY, Xu Y, Gambotto AA, Rabinowich H, Fan J, Yin XM. Expression of X-linked inhibitor-of-apoptosis protein in hepatocellular carcinoma promotes metastasis and tumor recurrence. Hepatology. 2008;48(2):497–507.
Nault JC, Mallet M, Pilati C, Calderaro J, Bioulac-Sage P, Laurent C, Laurent A, Cherqui D, Balabaud C, Zucman-Rossi J. High frequency of telomerase reverse-transcriptase promoter somatic mutations in hepatocellular carcinoma and preneoplastic lesions. Nat Commun. 2013;4:2218.
Shibata T. Genomic landscape of hepatocarcinogenesis. J Hum Genet. 2021;66(9):845–51.
Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov. 2022;12(1):31–46.
Zucman-Rossi J, Villanueva A, Nault JC, Llovet JM. Genetic Landscape and Biomarkers of Hepatocellular Carcinoma. Gastroenterology. 2015;149(5):1226–39. e1224.
Nault JC, Calderaro J, Di Tommaso L, Balabaud C, Zafrani ES, Bioulac-Sage P, Roncalli M, Zucman-Rossi J. Telomerase reverse transcriptase promoter mutation is an early somatic genetic alteration in the transformation of premalignant nodules in hepatocellular carcinoma on cirrhosis. Hepatology. 2014;60(6):1983–92.
Chen YL, Jeng YM, Chang CN, Lee HJ, Hsu HC, Lai PL, Yuan RH. TERT promoter mutation in resectable hepatocellular carcinomas: a strong association with hepatitis C infection and absence of hepatitis B infection. Int J Surg. 2014;12(7):659–65.
El-Serag HB, Marrero JA, Rudolph L, Reddy KR. Diagnosis and treatment of hepatocellular carcinoma. Gastroenterology. 2008;134(6):1752–63.
Jang JW, Kim HS, Kim JS, Lee SK, Han JW, Sung PS, Bae SH, Choi JY, Yoon SK, Han DJ, et al. Distinct Patterns of HBV Integration and TERT Alterations between in Tumor and Non-Tumor Tissue in Patients with Hepatocellular Carcinoma. Int J Mol Sci. 2021;22:(13).
Kang S, Ohshima K, Shimizu M, Amirhaeri S, Wells RD. Pausing of DNA synthesis in vitro at specific loci in CTG and CGG triplet repeats from human hereditary disease genes. J Biol Chem. 1995;270(45):27014–21.
Motz M, Kilger PS. C. Improved cycle sequencing of GC-rich templates by a combination of nucleotide analogs. BioTechniques. 2000;29(2):268–70.
Tsiatis AC, Norris-Kirby A, Rich RG, Hafez MJ, Gocke CD, Eshleman JR, Murphy KM. Comparison of Sanger sequencing, pyrosequencing, and melting curve analysis for the detection of KRAS mutations: diagnostic and clinical implications. J Mol Diagn. 2010;12(4):425–32.
Pezzuto F, Izzo F, De Luca P, Biffali E, Buonaguro L, Tatangelo F, Buonaguro FM, Tornesello ML. Clinical Significance of Telomerase Reverse-Transcriptase Promoter Mutations in Hepatocellular Carcinoma. Cancers (Basel). 2021;13:(15).
Lamy PJ, Castan F, Lozano N, Montelion C, Audran P, Bibeau F, Roques S, Montels F, Laberenne AC. Next-Generation Genotyping by Digital PCR to Detect and Quantify the BRAF V600E Mutation in Melanoma Biopsies. J Mol Diagn. 2015;17(4):366–73.
Beaver JA, Jelovac D, Balukrishna S, Cochran R, Croessmann S, Zabransky DJ, Wong HY, Toro PV, Cidado J, Blair BG, et al. Detection of cancer DNA in plasma of patients with early-stage breast cancer. Clin Cancer Res. 2014;20(10):2643–50.
Edmondson HA, Steiner PE. Primary carcinoma of the liver.A study of 100 cases among 48,900 necropsies. Cancer. 1954;7(3):462–503.
Kwa WT, Effendi K, Yamazaki K, Kubota N, Hatano M, Ueno A, Masugi Y, Sakamoto M. Telomerase reverse transcriptase (TERT) promoter mutation correlated with intratumoral heterogeneity in hepatocellular carcinoma. Pathol Int. 2020;70(9):624–32.
McHugh ML. Interrater reliability: the kappa statistic. Biochemia Med. 2012;22(3):276–82.
Chiba K, Johnson JZ, Vogan JM, Wagner T, Boyle JM, Hockemeyer D. Cancer-associated TERT promoter mutations abrogate telomerase silencing. Elife. 2015; 4.
Mitas M, Dill YA, Kamp J, Chambers TJ, Haworth EJ. IS.. Hairpin properties of single-stranded DNA containing a GC-rich triplet repeat : (CTG)15.pdf. Nucleic acids research. 1995;23(6):1050–9.
Colebatch AJ, Waring WT, McArthur PM, Wong GA, Dobrovic SQ. A. Optimizing Amplification of the GC-Rich TERT Promoter Region Using 7-Deaza-dGTP for Droplet Digital PCR Quantification of TERT Promoter Mutations. Clin Chem. 2018;64(4):745–7.
Corless BC, Chang GA, Cooper S, Syeda MM, Shao Y, Osman I, Karlin-Neumann G, Polsky D. Development of Novel Mutation-Specific Droplet Digital PCR Assays Detecting TERT Promoter Mutations in Tumor and Plasma Samples. J Mol Diagn. 2019;21(2):274–85.
Jang JW, Kim JS, Kim HS, Tak KY, Lee SK, Nam HC, Sung PS, Kim CM, Park JY, Bae SH, et al. Significance of TERT Genetic Alterations and Telomere Length in Hepatocellular Carcinoma. Cancers (Basel). 2021; 13 (9).
Eunkyong Ko H-WS, Jung ES. Baek-hui Kim and Guhung Jung. The TERT promoter SNP rs2853669 decreases E2F1 transcription factor binding and increases mortality and recurrence risks in liver cancer. Oncotarget. 2016;7(1):684–99.
Li H, Bai R, Zhao Z, Tao L, Ma M, Ji Z, Jian M, Ding Z, Dai X, Bao F, et al. Application of droplet digital PCR to detect the pathogens of infectious diseases. Biosci Rep. 2018; 38 (6).
Pinheiro LB, Coleman VA, Hindson CM, Herrmann J, Hindson BJ, Bhat S, Emslie KR. Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Anal Chem. 2012;84(2):1003–11.
Goss JA, Huang AY, Smith E, Konczyk DJ, Smits PJ, Sudduth CL, Stapleton C, Patel A, Alexandrescu S, Warman ML, et al. Somatic mutations in intracranial arteriovenous malformations. PLoS One. 2019;14(12):e0226852.
Sung WK, Zheng H, Li S, Chen R, Liu X, Li Y, Lee NP, Lee WH, Ariyaratne PN, Tennakoon C, et al. Genome-wide survey of recurrent HBV integration in hepatocellular carcinoma. Nat Genet. 2012;44(7):765–9.
Kawai-Kitahata F, Asahina Y, Tanaka S, Kakinuma S, Murakawa M, Nitta S, Watanabe T, Otani S, Taniguchi M, Goto F, et al. Comprehensive analyses of mutations and hepatitis B virus integration in hepatocellular carcinoma with clinicopathological features. J Gastroenterol. 2016;51(5):473–86.
Paterlini-Brechot P, Saigo K, Murakami Y, Chami M, Gozuacik D, Mugnier C, Lagorce D, Brechot C. Hepatitis B virus-related insertional mutagenesis occurs frequently in human liver cancers and recurrently targets human telomerase gene. Oncogene. 2003;22(25):3911–6.
Ferber MJ, Montoya DP, Yu C, Aderca I, McGee A, Thorland EC, Nagorney DM, Gostout BS, Burgart LJ, Boix L, et al. Integrations of the hepatitis B virus (HBV) and human papillomavirus (HPV) into the human telomerase reverse transcriptase (hTERT) gene in liver and cervical cancers. Oncogene. 2003;22(24):3813–20.
Zhu AX, Abbas AR, de Galarreta MR, Guan Y, Lu S, Koeppen H, Zhang W, Hsu CH, He AR, Ryoo BY, et al. Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma. Nat Med. 2022;28(8):1599–611.

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TERT promoter mutation predicts the prognosis value of patients with HBV- related HCC who underwent hepatectomy

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Abstract

Figures

Introduction

Materials And Methods

Patient and tissue samples

DNA extraction

Sanger sequencing for detecting TERT C228T mutation

dPCR for detecting TERT C228T mutation

Independence of the TERT mutation from clinical features

Construction and evaluation of the nomogram

Statistical

Result

Assay optimization

dPCR Specificity, Sensitivity, and Limits of Detection

Describe the clinical characteristic

TERT promoter mutations and tumor characteristics

A nomogram for TERT mutations was constructed and validated

Discussion

Abbreviations

Declarations

References

Supplementary Files

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