Characterization of RET fusions via integrated DNA and RNA sequencing in early-stage non-small cell lung cancer: a retrospective study

Introduction

Lung cancer remains one of the leading causes of cancer-related death worldwide (1,2), with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all lung cancer cases (2). Recent advances in precision oncology highlight the urgent need to identify actionable genetic alterations for targeted therapies. Among the diverse oncogenic events in NSCLC, rearranged during transfection (RET) fusions, which occur in 1–2% of cases (3-5), are particularly notable due to their association with aggressive clinical characteristics, poor prognosis, and an increased likelihood of brain metastases (6). The development of RET inhibitors has substantially improved clinical outcomes for patients harboring RET fusions, emphasizing the importance of accurate and comprehensive detection methods.

RET fusions arise from genomic rearrangements involving the RET gene and various partner genes, leading to constitutive activation of RET tyrosine kinase signaling. While KIF5B::RET and CCDC6::RET are the most prevalent canonical fusion partners, numerous noncanonical RET fusion partners have also been identified (7). The broad distribution of genomic breakpoints further complicates the identification and characterization of these fusions and their clinical implications (8). For instance, studies have shown that RET fusion partners display diverse hotspot breakpoints, potentially influencing responses to RET inhibitors (6,9). Furthermore, significant clustering of breakpoints has been observed, particularly in intron 11, highlighting the intricate patterns associated with RET fusions (10). This heterogeneity underscores the need for targeted approaches in detecting and analyzing RET rearrangements to better understand their role in various malignancies.

Traditional methods for detecting RET fusions, such as fluorescence in situ hybridization (FISH) and reverse transcription-polymerase chain reaction (RT-PCR), have established their utility in precision oncology (11). FISH provides clear visualization of gene rearrangements, while real-time PCR targets specific fusion transcripts. Nonetheless, both techniques have inherent limitations when it comes to identification of novel and noncanonical fusion genes (12). As the clinical demand for thorough fusion detection continues to grow, there is an increasing need for cost-effective and high-throughput approaches. High-throughput next-generation sequencing (NGS) technologies, including DNA sequencing (DNA-seq), targeted RNA sequencing (targeted RNA-seq), and whole-transcriptome sequencing (WTS), have expanded the capabilities of fusion detection. DNA-seq is adept at identifying specific genomic breakpoints but may overlook fusions that involve large introns, regulatory elements or those affected by post-transcriptional modifications. On the other hand, targeted RNA-seq and WTS offer enhanced sensitivity in detecting and characterizing fusion transcripts, although their effectiveness can be influenced by gene expression levels and RNA quality. Given these considerations, the development of a robust and integrative methodological framework is essential for the accurate screening and characterization of RET fusions to meet evolving clinical standards (11).

Most studies on RET fusions have primarily focused on advanced-stage diseases, resulting in a limited understanding of the incidence and characteristics of RET fusions in early-stage NSCLC. Our objective was to systematically compare the effectiveness and accuracy of various molecular profiling techniques, including DNA-seq, targeted RNA-seq, WTS, and FISH, in detecting RET fusions in early-stage NSCLC patients. By evaluating the concordance of these techniques, we sought to identify the most effective diagnostic approach for the accurate detection and characterization of RET fusions. In addition, we aimed to elucidate the clinical and biological characteristics of NSCLC patients with RET fusions (RET+), providing insights into the prognosis and therapeutic implications for this specific subpopulation. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-702/rc).

Methods

Study design

We retrospectively reviewed a total of 2,256 patients diagnosed with NSCLC at The First Affiliated Hospital with Nanjing Medical University from 2021 to 2022. Of these, 40 putative RET+ early-stage NSCLC patients detected via DNA-seq were included for further analyses. Our focus on these putative RET+ cases was aimed at evaluating the sensitivity of DNA-seq in detecting RET fusions within this specifically targeted group. To confirm the initial DNA-seq findings, WTS was conducted on all 39 (97.5%) patients. Prioritizing those with inconclusive WTS results, limited supporting reads, or adequate residual RNA, we performed targeted RNA-seq using a newly developed, clinically validated partner agnostic panel on 22 (55.0%) samples. Furthermore, all patients (100%) underwent FISH (Figure 1), ensuring a comprehensive assessment across all diagnostic modalities. Clinical and pathologic data were extracted from clinical records. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Research Ethics Board of The First Affiliated Hospital with Nanjing Medical University (No. 2021-SRFA-137). Written informed consent was also obtained from all the participants simultaneously.

Figure 1 Flow chart of the study design and procedures. A total of 40 NSCLC patients were included in the cohort and all of them underwent the DNA-seq and FISH. Among them, 39 patients underwent WTS and 22 underwent targeted RNA-seq. DNA-seq, DNA sequencing; FISH, fluorescence in situ hybridization; NSCLC, non-small cell lung cancer; RET, rearranged during transfection; RNA-seq, RNA sequencing; WTS, whole-transcriptome sequencing.

Fusion detection by DNA-seq

Targeted DNA-seq was performed using a 425-gene panel (GeneseeqPrime^â, Nanjing Geneseeq Technology Inc., China) as previously reported (13,14). Genomic DNA from formalin-fixed paraffin-embedded (FFPE) samples was extracted using the QIAamp DNA FFPE Tissue kit (Qiagen, Hilden, Germany). The quantity and quality of DNA were assessed using a Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA) and the dsDNA HS assay kit on a Qubit 3.0 fluorometer (Life Technologies, Carlsbad, CA, USA), respectively. NGS libraries were prepared using the KAPA Hyper Prep kit (KAPA Biosystems, Wilmington, MA, USA). The target-enriched library was then sequenced on HiSeq4000 NGS platforms (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. Sequence reads in FASTQ format were generated via base calling using bcl2fastq V.2.16.0.10. Quality control was performed with Trimmomatic software (15). High-quality reads were aligned to the human genome (hg19, GRCh37) using the Burrows-Wheeler Aligner (BWA) V.0.7.12 with maximal exact matches (BWA-MEM) algorithm under default parameters to create SAM files (16). SAM files were converted into compressed BAM files through Picard V.1.119 (https://broadinstitute.github.io/picard/) and then sorted according to chromosome coordinates. The Genome Analysis Toolkit (GATK) V.3.4.0 (https://software.broadinstitute.org/gatk/) was used to locally realign the BAM files at intervals with insertions/deletions (indels) mismatches and recalibrate the base quality scores of reads in the BAM files. Varscan V.2.4.0 (17) was also used to call somatic single nucleotide variants (SNVs) and indels, and somatic gene fusion variants were called by Delly (18) (https://github.com/dellytools/delly) with default parameters.

Classification of RET fusion subgroups

A comprehensive genomic analysis based on DNA-seq was conducted to define different fusion types as previously described (19). Solitary 3' RET fusions are characterized by isolated fusion events occurring exclusively at the 3' end of the RET gene. Similarly, solitary 5' RET fusions are identified by fusion events that take place solely at the 5' end of the RET gene. Reciprocal fusions are defined as both ends of the RET gene fusing to the same partner gene. Nonreciprocal fusions involve translocations in which the RET gene fuses with one partner gene at the 3' end, while the 5' end fuses with a different partner gene.

Fusion detection by RNA-seq

RNA was isolated from FFPE samples using the RNeasy FFPE kit (Qiagen, Hilden, Germany). The quality and quantity of the extracted RNA were measured using the Qubit RNA HS assay. For WTS, libraries were prepared utilizing the KAPA Stranded RNA Sequencing Library Preparation Kit (Roche, Basel, Switzerland). Targeted RNA-seq was performed using custom-designed probes to enrich for target regions associated with RET fusions (PancaRNA™, Nanjing Geneseeq Technology Inc., Nanjing, China). The prepared libraries were then sequenced on the Illumina HiSeq4000 NGS platform. FusionCatcher (V0.98.3 beta) (20) was employed to detect fusions with default parameters, and all identified rearrangements were manually verified using the Integrative Genomics Viewer (IGV).

Fusion detection by FISH

FFPE tissue samples were sectioned into 4 µm thick slices and mounted on positively charged slides, then deparaffinized in xylene and dehydrated through a graded alcohol series. The procedure employed the ZytoLight^® SPEC RET Dual Color Break Apart Probe (PL105, ZytoVision, Bremerhaven, Germany), which is designed to detect translocations involving the RET at 10q11.21. Samples were considered RET rearrangement-positive if ≥15% of the tumor cells displayed split signals or isolated 3’ signals. Conversely, isolated 5' signals were considered negative.

Statistical analysis

Categorical variables were presented as frequencies and percentages, while quantitative variables were summarized as medians with interquartile ranges (IQRs). The associations between categorical clinicopathological and molecular features across the fusion groups were assessed using the Fisher’s exact test. For continuous variables, the Wilcoxon rank-sum test was utilized for comparisons between two groups, and the Kruskal-Wallis H test was conducted for comparisons involving more than two groups, followed by post-hoc testing for significant pairwise comparisons. A two-sided P value less than 0.05 was considered statistically significant. All statistical analyses were conducted using R software (V.4.3.3).

Results

Clinical characteristics of the study cohort

The study cohort consisted of 40 patients diagnosed with putative RET+ early-stage NSCLC, identified through DNA-seq. The baseline characteristics of these patients are summarized in Table 1. Among the cohort, 27 (67.5%) patients were female, with a median age of 53 years (IQR, 45–60 years). The majority (n=39, 97.5%) of patients were diagnosed at stage I, while only 1 (2.5%) patient was diagnosed at stage II. In addition, 35 (87.5%) patients were never smokers.

Tumors were found in the right lung in 27 (67.5%) patients. The median tumor size was 1.5 cm (IQR, 1.1–2.0 cm), with a median tumor cell content of 0.5 (IQR, 0.5–0.7). Pathological analysis indicated that 32 (80.0%) patients had invasive adenocarcinoma, while eight (20.0%) patients had adenocarcinoma in situ with minimal invasion. The tumors also exhibited varied levels of the Ki67 proliferation index in 30 patients. Among them, seven (23.3%) patients had high expression (>10%), 14 (46.7%) showed intermediate expression (5–10%), and nine (30.0%) presented with low expression (<5%).

Molecular characteristics of the study cohort

The analysis of the DNA-seq results revealed four distinct categories of RET fusions within the cohort (see Methods, Figure 1, Figure 2A). Specifically, 15 patients (37.5%) exhibited solitary 3' RET fusions, while only one patient (2.5%) demonstrated a solitary 5' RET fusion. Reciprocal fusions were identified in 11 (27.5%) patients, and the nonreciprocal group comprised 13 (32.5%) patients. Beyond RET fusions, DNA-seq also revealed concurrent alterations in key oncogenes and tumor suppressor genes, including EGFR (n=2, 5.0%), ALK (n=1, 2.5%), MET (n=1, 2.5%), and TP53 (n=5, 12.5%, Table 2). Among these alterations, the EGFR exon 19 delins detected in patient P4 represents a common driver mutation frequently associated with NSCLC. Notably, patient P4 was the only individual in the solitary 5' fusion group and lacked the RET kinase domain, resulting in a non-functional RET fusion. Additionally, patient P12 harbored a MET exon 14 skipping mutation, yet RNA-seq failed to detect any RET fusions. Ultimately, DNA-seq identified a total of 39 patients with putative RET fusions.

Figure 2 Distribution of RET fusion across different methods in the study cohort. (A) Distribution of different RET fusion groups identified by DNA-seq (n=40). (B) Oncoplot illustrating clinicopathologic and molecular characteristics of the study cohort. (C) Concordance among three different methodologies for the orthogonal validation of RET fusions (n=40). (D) Comparison of the consistency across the three methodologies. (E) The results of consistency of three methods. DNA-seq, DNA sequencing; FISH, fluorescence in situ hybridization; RET, rearranged during transfection; RNA-seq, RNA sequencing; WTS, whole-transcriptome sequencing.

Our following analysis focused on the subset of 39 patients with putative RET fusions as deemed by DNA-seq. Of these, 35 (89.7%) patients exhibited canonical RET fusion partners, with KIF5B::RET and CCDC6::RET being the most prominent (Table 2 and Figure 2B). Additional 5' fusion partners included ERC1 (n=2, 5.1%), CCDC186 (n=1, 2.6%) and LOC105378269 (n=1, 2.6%, Table 2). The KIF5B::RET was the most prevalent rearrangement, present in 32 (82.1%) cases, with breakpoints primarily localized to intron 15: intron 11 in 71.8% of the patients, followed by intron 24: intron 10 in 5.1% (Figure S1A). Similar findings were corroborated by WTS- and targeted RNA-seq-based analyses. Out of the 36 RET+ patients detected by RNA-seq, KIF5B was also the most common fusion partner (n=30, 83.3%). The predominant genomic breakpoints of KIF5B::RET as assessed by RNA-seq were exon 15: exon 12, noted in 75.0% of cases (Figure S1B).

Comparisons of performance across different detection methods

Next, we conducted a comparative analysis of sensitivity and concordance across various detection techniques, specifically focusing on the subset of samples that simultaneously underwent DNA-seq, FISH and RNA-based methods. In the solitary 3' fusion subgroup (n=15), RNA-seq identified RET fusions in 14 (93.3%) patients. Among them, 11 (78.6%) patients demonstrated KIF5B::RET, while three (21.4%) had CCDC6::RET fusions (Figure 2B). Notably, patient P12 was classified as RET-negative by RNA-seq but RET+ by FISH (Table 3). At the DNA level, we detected a very rare fusion partner of LOC105378269 at the 5' end, and a MET exon 14 skipping mutation. We supposed that the breakpoint resides within intron 2, coupled with the insufficient promoter activity of LOC105378269, thereby hindering its effective transcription. Furthermore, the presence of the MET exon 14 skipping mutation may confer a selective advantage independent of the RET fusion. This intricate interplay underscores the complexity of genomic drivers in NSCLC. In parallel, FISH analysis indicated RET positivity in 14 (93.3%) patients, while only one patient (6.7%, P3) was classified as RET-negative. Notably, patient P3 exhibited a canonical KIF5B partner, as confirmed by both DNA-seq and RNA-seq, suggesting a lower sensitivity of FISH in detecting RET fusions. Consistent with the lowered sensitivity, FISH showed RET positivity in 10 out of 13 cases (76.9%) in the nonreciprocal group. On the other hand, RNA-seq confirmed RET positivity in two additional cases (P5 and P7) that FISH missed. Of them, 10 (83.3%) patients had KIF5B::RET, while the remaining 2 (16.7%) harbored ERC1::RET. The only one RET-negative patient (P1) identified by RNA-seq actually contained KIF5B::RET as evidenced by DNA-seq. We speculated that the inferior quality of the RNA sample may explain the inconsistency (21). For the reciprocal group (n=11), RNA-seq detected RET fusions in 10 (90.9%) cases, with one patient excluded from the analysis due to RNA quality control failure. Of them, 9 (90.0%) patients had KIF5B::RET, and 1 (10.0%) exhibited CCDC186::RET. The patient who failed to undergo RNA-seq (P8) was confirmed RET+ via both FISH and DNA-seq, with the latter detecting the canonical KIF5B::RET at the DNA level. By contrast, FISH results indicated positive RET status in 9 (81.8%) patients, with patients P2 and P6 assessed as RET-negative via FISH. Both patients, P2 and P6, exhibited the canonical KIF5B::RET fusion as assessed by DNA-seq and RNA-seq. In the single case of the solitary 5' fusion group (P4), it is not surprising that neither RNA-seq nor FISH revealed any RET fusions due to the non-functional fusion pattern (Figure 2B).

Overall, we identified a total of 31 RET+ patients out of 39 (79.5%) patients who demonstrated consistent RET testing results across all three methodologies (Figure 2C). Specifically, the concordance rates were 84.6% between FISH and RNA-seq (33/39), 92.3% between DNA-seq and RNA-seq (36/39), and 82.5% between DNA-seq and FISH (33/40, Figure 2D,2E). Among the 22 patients who underwent both targeted RNA-seq and WTS, the targeted approach uncovered five additional RET+ cases that were missed by WTS. Of these, three involved noncanonical fusion genes such as ERC1::RET and CCDC186::RET. This finding suggests that targeted RNA-seq may offer enhanced sensitivity and accuracy in detecting RET fusions, particularly for uncommon fusion genes that might be overlooked by WTS.

Associations between clinical and molecular features

Finally, we explored the associations between clinicopathologic features and fusion groups. A significant association was found between the nonreciprocal group and age at diagnosis, with median ages of 56.0 years for the solitary 3' fusion group, 45.0 years for the nonreciprocal group, and 55.0 years for the reciprocal group (P=0.03, Figure 3A and Table S1). Post-hoc Bonferroni analysis further confirmed that the nonreciprocal group was younger than both solitary 3' fusion group (P=0.02 after adjustment) and the reciprocal group (P=0.04 after adjustment, Figure 3A). Patients in the nonreciprocal group exhibited a lower median age compared to all other subgroups combined (P=0.01, Table S2). Moreover, this subgroup exhibited a lower Ki67 proliferation index (P=0.03, Table S2 and Figure 3B). No statistically significant associations were found for all other clinical indicators across the fusion groups.

Figure 3 Comparison of clinicopathologic factors across different fusion groups. (A) Age distribution across three different fusion groups. Post-hoc pairwise comparisons were performed using the Bonferroni adjustment. (B) Ki67 proliferation index levels comparison between nonreciprocal and other patient groups.

Discussion

Our study was specifically focused on putative RET+ tumors identified by DNA-seq to prioritize the assessment of detection sensitivity, given our constraints on sample availability and research objectives, which precluded the inclusion of RET-negative tumors for analysis. Comprehensive detection of RET fusions is essential for guiding targeted therapeutic interventions in both early- and advanced-stage NSCLC. We investigated the molecular landscape of RET fusions in early-stage NSCLC patients using a combination of different detection techniques, including DNA-seq, RNA-seq, and FISH. Our findings revealed significant diversity in RET fusions, with an overall concordance of 79.5% across the three different methodologies. This underscores the unique strengths and limitations of each technique and emphasizes the necessity of an integrated detection strategy to enhance the reliable identification of RET fusions.

Our analysis revealed a remarkable diversity of RET fusion partners, predominantly characterized by KIF5B::RET and CCDC6::RET fusions, which constituted 87.5% of cases, consistent with previous studies (6,9). These fusions are not only key drivers of tumorigenesis but also predictive biomarkers for response to RET-targeted therapies. The clinical relevance of KIF5B::RET and CCDC6::RET has been underscored by their high response rates to selective RET inhibitors, such as selpercatinib and pralsetinib (22). Subgroup analysis demonstrated that patients with CCDC6::RET fusions had a particularly favorable outcome, with a median progression-free survival of 8.4 months, highlighting the potential for personalized treatment strategies based on the specific RET fusion partners. In addition, other less frequent fusion partners, including NCOA4, TRIM33, and CLIP1 have been reported (23), though their clinical relevance has not been fully defined. We also identified rarer fusion partners, such as ERC1::RET and CCDC186::RET in our study. ERC1 was less frequently than KIF5B or CCDC6 (9), and has shown promising responses to pralsetinib in pancreatic ductal adenocarcinoma (6,24). However, we found no substantial clinical relevance for ERC1::RET in lung cancer. The rarity of CCDC186::RET is corroborated by a genomic landscape study indicating its infrequent occurrence as a fusion partner (6). Collectively, these findings underscore the importance of thorough molecular characterization in clinical practice, which not only enhances the recognition of oncogenic drivers but also informs therapeutic sensitivity to RET inhibitors.

The strengths of each sequencing methodology emerged clearly through our analysis. We observed a high concordance rate of 92.3% between RNA-seq and DNA-seq. DNA-seq proved to be indispensable, elucidating not only the presence of RET fusions but also detailed information on genomic breakpoints, rare fusion partners, and concurrent genetic alterations which may be associated with treatment resistance mechanisms. Importantly, based on the DNA-seq, we categorized the patients into four distinct groups, and found patients with nonreciprocal RET fusions tended to be younger and exhibited a lower Ki67 proliferation index. Ki67 is an established indicator of tumor cell proliferation (25). This observation suggests that this subtype may exhibit distinct biological behavior and potentially better clinical outcomes. Thus, the detection of RET fusions could not only inform targeted therapies but also support prognostic assessments in this aggressive subset of lung cancer. The implication that nonreciprocal RET fusions may represent a less aggressive phenotype in early-stage NSCLC warrants further investigation into their prognostic significance and treatment implications. Overall, DNA-seq offers a comprehensive view of tumor genomics, enabling detection of a whole spectrum of mutations, copy-number changes, and structural variants that inform clinical decision-making in the genetically complex landscape of NSCLC.

Comparatively, RNA-seq excels in confirming functional fusion transcripts, which directly correlate with therapeutic efficacy. The agnostic targeted RNA approach demonstrated markedly improved sensitivity by identifying five additional RET+ cases that were not detected by WTS. The majority of such cases harbored rare RET fusion genes, highlighting the importance of using a partner agnostic method for more comprehensive detection of RET fusions. The relatively poorer performance of WTS might be attributed to its need for high sequencing depth, which is often limited by cost and throughput, leading to reduced coverage low-abundance fusions. Additionally, RNA degradation, particularly in FFPE samples, compromises its effectiveness. In addition, it has been recommended that targeted RNA-seq assays be employed in cases that appear driver negative by DNA-seq assays, to ensure comprehensive detection of actionable gene rearrangements (26). Despite these advantages, RNA-seq’s effectiveness depends on RNA quality, as degraded samples (notably FFPE) can compromise results. In our study, one case failed due to suboptimal RNA quality, and RET fusions in patient P1 could not be detected because of inferior RNA quality, an issue not encountered with DNA-seq. This highlights the necessity for robust sample handling protocols to capitalize on RNA-seq’s sensitivity and ensure comprehensive genomic profiling. For treatment with RET inhibitors, RNA confirmation of functional transcripts is paramount since expressed RET fusions correlate directly with targetable oncoproteins and clinical outcomes. Thus, stringent quality control measures are necessary to counter false-negative results due to suboptimal RNA quality.

Case analyses of patients P4 and P12 exemplify the importance of an integrated detection strategy that combines RNA- and DNA-seq. Patient P4 had a concurrent EGFR 19delins mutation along with a non-functional RET fusion that did not retain the kinase domain. Similarly, patient P12 harbored a MET exon 14 skipping mutation and a RET fusion involving the LOC105378269 gene fused to intron 2 of RET. The functional significance of this fusion remains unclear, as the LOC105378269 fusion partner likely lacks a strong promoter, and the distance from the RET kinase domain raises doubts about its functional relevance. Thereby, patient P4 may primarily depend on the EGFR mutation as the driver, and patient P12 may present with the driver of MET 14 exon skipping mutation instead of RET fusion. These examples demonstrate that depending exclusively on either RNA- or DNA-seq may result in overlooking crucial actionable mutations that need to be targeted. RNA-seq excels in revealing functional fusions, thus confirming their clinical relevance and potential for targeted therapy, while DNA-seq provides a broader genomic context and can detect complex alterations. Together, they offer a comprehensive view that is crucial for pinpointing the most relevant targets for intervention. Thus, integrating both RNA- and DNA-seq not only enhances detection sensitivity but also ensures that treatment strategies are appropriately aligned with the underlying molecular drivers of the disease.

Despite these insights, there are limitations in this study. We were unable to assess the therapeutic implications of different RET fusion genes or fusion groups due to a lack of treatment data within our cohort. Moreover, excluding RET-negative cases limited our ability to assess diagnostic specificity. The observed association between younger onset, lower Ki67, and non-reciprocal fusions requires further investigation. Future studies should evaluate how distinct fusions respond to existing RET inhibitors and their potential for resistance development. Furthermore, incorporating larger cohorts, including RET-negative cases, will help validate the correlations between the age of onset, Ki67, and non-reciprocal fusions, and facilitate a more comprehensive assessment of specificity.

Conclusions

In conclusion, our findings highlight the critical importance of an integrative approach for accurately characterizing RET fusions in early-stage NSCLC. This comprehensive strategy not only enhances the detection of both canonical and noncanonical RET alterations but also ensures the identification of functional oncogenic drivers. By integrating the strengths of each method, we can improve our understanding of the molecular landscape of NSCLC and better inform targeted therapeutic strategies. Moving forward, adopting this multi-faceted approach will be essential in advancing precision oncology and optimizing treatment outcomes for patients with RET-altered tumors.

Acknowledgments

The authors thank all the patients and their families, the investigators who participated in this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-702/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-702/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-702/prf

Funding: This work was supported by the National Natural Science Foundation of China (Nos. 82273410, 82172991, 81773109, 81502089).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-702/coif). Y.Q.S., C.H.H., and J.C.Y. are current employees of Nanjing Geneseeq Technology Inc. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Research Ethics Board of The First Affiliated Hospital with Nanjing Medical University (No. 2021-SRFA-137). Written informed consent was also obtained from all the participants simultaneously.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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