tRF-29-86J8WPMN1EJ3: a tRNA-derived small RNA promoting esophageal cancer progression

Introduction

Esophageal cancer (EC) is the seventh most common cause of cancer worldwide (511,000 cases) and the sixth most common cancer death (445,000 deaths) (1). The extremely aggressive nature and poor survival rate make EC one of the deadliest cancers worldwide (2). The 5-year survival rate for EC remains disappointingly low, and the best way to improve prognosis is early diagnosis, which is often referred to as the early stages of the disease (3). Therefore, novel biomarkers for early detection and innovative therapeutic strategies for EC are urgently needed.

Recently, transfer RNA-derived small RNAs (tsRNAs), a newly recognized class of non-coding RNAs, have emerged as key regulators in the pathobiology of diverse cancers. tsRNAs, which are 18–40 nt in length, are derived from transfer RNAs (tRNAs) (4). Different enzymes cut tRNA precursors or mature tRNAs to produce tsRNAs (5). Based on the cutting site, tsRNAs are divided into two categories: tRNA derived fragments (tRFs) and tRNA halves (tiRNAs) (6). tRFs are produced by Dicer or angiogenin and are found ubiquitously in various biofluids under normal conditions (4,7). Diet, diseases, or external stimulation can affect their abundance (8). tiRNAs, which are 30–40 nt in length, are produced by angiogenin under stress conditions (9). tiRNAs play biological roles through several mechanisms, including regulation of gene expression, interactions with proteins or messenger RNA (mRNA), control of the cell cycle, as well as regulation of chromatin and epigenetic modifications (10). Studies have shown the tsRNA signature in a variety of cancers, suggesting that tsRNAs have the potential to become biomarkers for disease diagnosis and prognosis (11,12). In the context of EC, a handful of studies have begun to uncover the potential of tsRNAs. Salivary exosomal tsRNAs showed diagnostic promise (13). Beyond diagnostics, other studies have implicated specific tsRNAs in EC progression. For instance, Wang et al. reported that exosomal tsRNA-10522 derived from MMP14-positive fibroblasts could inhibit PD-1 immunotherapy efficacy (14). Other research has focused on developing diagnostic signatures based on circulating tsRNAs and miRNAs (15), or using pre-operative serum tsRNAs to predict pathological complete response after neoadjuvant chemoradiotherapy (16). While these studies highlight the clinical relevance of tsRNAs in EC, they predominantly focus on liquid biopsies (serum, saliva) or specific single tsRNAs in the tumor microenvironment. A comprehensive profiling of dysregulated tsRNAs directly in EC tissue, coupled with an investigation into their functional roles and mechanisms, remains largely unexplored. In this study, we retrieved EC-associated tsRNA profiles from MINTbase and further generated tsRNA profiles for four paired EC tumors and matched adjacent normal tissues. We identified differentially expressed tsRNAs and explored their roles in EC using bioinformatics methods and in vitro experiments. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1436/rc).

Methods

Dataset of The Cancer Genome Atlas (TCGA)

To systematically investigate the role of tsRNAs in EC, we initiated our study by obtaining tsRNA profiles for EC in TCGA (ESCA) from MINTbase (https://cm.jefferson.edu/tcga-mintmap-profiles, accessed June 2022) (17) and obtained the data of 184 EC tissues and 13 adjacent normal tissues. We merged the downloaded files labeled as “exclusive” to extract raw read counts, and then processed them with DESeq2 (R package version 1.32) (18) in R (version 4.0.1). First, we excluded tsRNAs whose median count was <10. Second, we normalized the raw read counts of the remaining tsRNAs. Finally, we screened differentially expressed tsRNAs using the following criteria: a fold change greater than two and an adjusted P value less than 0.05. We converted each MINTbase Unique ID into its corresponding sequence with our in-house R package MINTplates (R package version 1.0.1), which was also employed to annotate the type and origin of every tsRNA.

RNA sequencing (RNA-Seq) (STAR-Counts) and corresponding clinical data for the TCGA-ESCA cohort were retrieved from the GDC Data Portal (https://portal.gdc.cancer.gov/, accessed June 2022). A total of 184 primary EC samples and 13 adjacent normal samples were obtained. After merging the count matrices, genes with zero counts in ≥10% of samples were discarded, and the remaining counts were normalized using DESeq2. Differentially expressed genes were identified using the same criteria as those for tsRNAs.

Specimens

To validate our bioinformatic findings from public databases, we proceeded with clinical sample collection and in-house sequencing. A total of 40 EC patients were recruited from The Affiliated Cancer Hospital of Nanjing Medical University (ACHNMU) between 2010 and 2011. The inclusion criteria were that the patients had EC and had not received any anticancer treatment. EC tissues and adjacent normal tissues were collected, soaked in RNAfixer (Yuanpinghao Bio, Beijing, China) for 24 h, and then stored at −80 ℃ after removing the RNAfixer. The study protocol was approved by the ethics committee of The Affiliated Cancer Hospital of Nanjing Medical University (approval No. 2019Ke-013). Informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Profiling of tsRNAs

To generate an independent tsRNA expression profile for EC, we performed small-RNA-seq on a subset of our cohort. From the ACHNMU cohort, we randomly selected four EC-normal tissue pairs for small-RNA-Seq, which was performed by Aksomics (Shanghai, China). Raw reads have been deposited in Gene Expression Omnibus (GEO) database under accession GSE207635. Differentially expressed tsRNAs were identified with DESeq2 as above, except that only tsRNAs detected (non-zero counts) in all four EC or all four normal samples were retained.

Survival analysis

Survival analyses were performed as previously described (19). After excluding patients with zero-day follow-up or lost to follow up, the remaining 112 patients were dichotomized according to the median expression of the target tsRNA. Overall survival (OS) was compared by log-rank test using the survival package (R package version 3.1-7). Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed with the Cox proportional-hazards model, and forest plots were generated with the forestplot package (R package version 3.4.3).

Real-time quantitative polymerase chain reaction (RT-qPCR)

To further validate the expression of the candidate tsRNAs identified from the sequencing data in a larger cohort, we performed RT-qPCR on all 40 paired tissues. Total RNA was extracted with TRIzol, and tsRNA levels were quantified by stem-loop RT-qPCR using U6 snRNA for normalization. Reverse transcription employed tsRNA-specific stem-loop primers and the HiScript III 1st Strand cDNA Synthesis Kit (+gDNA wiper) (Nanjing Vazyme Biotech Co., Ltd., Nanjing, China). All primer sequences are listed in Table S1. The expression of tsRNA was analyzed using the SYBR qPCR Master Mix (Nanjing Vazyme Biotech Co., Ltd.) on the Roche LightCycler 480 II. Each experimental run included blank controls. The relative expression levels were calculated using the ΔΔCt method.

Prediction of target genes for tsRNAs

To investigate the potential molecular mechanisms through which the identified tsRNAs might function, we predicted their target genes. Assuming a miRNA-like mode of action (10), we used miRDB (20) to predict the mRNA targets of tRF-28-P4R8YP9LOND5, tRF-29-86J8WPMN1EJ3, and tRF-29-P4R8YP9LONHK. Spearman correlation between each tsRNA and its predicted targets retained only genes with rho value <−0.3 and P<0.05 for downstream analysis.

Enrichment analyses of the potential target genes

Because tsRNAs down-regulate their target genes, we intersected the predicted target genes of the three tsRNAs with the down-regulated genes identified in TCGA. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was performed with the DAVID web server (https://davidbioinformatics.nih.gov/). Gene Ontology (GO) analysis was carried out using clusterProfiler (R package version 4.0.1) (18). The results of the enriched pathways were visualized with the GOplot package (R package version 1.0.2).

Cell culture

Following the identification of tRF-29-86J8WPMN1EJ3 as a key candidate, we sought to experimentally validate its oncogenic functions in vitro using human esophageal squamous cell carcinoma (ESCC) cell lines. ESCC cell lines KYSE150 and KYSE30 were purchased from the Cell Culture Collection of the Chinese Academy of Sciences (Shanghai, China) and maintained in RPMI-1640 (KeyGEN BioTECH, Nanjing, China) supplemented with 10% fetal bovine serum (FBS), 80 U/mL penicillin and 100 µg/mL streptomycin at 37 ℃ in a humidified 5% CO₂ atmosphere.

Transfection assay

tRF-29-86J8WPMN1EJ3 mimics (tRF-mimics) and negative controls of mimics (mimics-NC) were synthetized by Sangon Biotech (Shanghai, China). The tRF-mimics and mimics-NC were transfected into cells using the Nepa21 pulse generator (Nepa Gene, Chiba, Japan) according to the manufacturers’ introduction.

Colony formation assay

Twenty-four hours post-transfection, cells were harvested, dissociated into single cells, and plated at 2,000 cells per well in 6-well plates. After 7 days, colonies were fixed with paraformaldehyde, rinsed with phosphate buffer saline (PBS), stained with 0.05% crystal violet, dried at room temperature, and photographed for enumeration and analysis.

Transwell assay

Matrigel-coated Transwell inserts (BD Biosciences, CA, USA) were air-dried, and 8×10⁴ transfected cells in 250 µL serum-free medium were seeded in the upper chamber; 800 µL of 20% FBS medium was placed below. After 24 h, non-invading cells on the upper surface were removed with a cotton swab. Invaded cells on the underside were fixed with 4% paraformaldehyde, stained with 0.05% crystal violet, imaged with an inverted microscope (Carl Zeiss, Oberkochen, Germany), and counted in three random fields.

Wound healing assay

A confluent monolayer of transfected cells was scratched with a sterile pipette tip, rinsed with PBS to remove debris, and incubated in serum-free medium. Images were taken immediately (0 h) and after 24 h; the percentage of wound closure was calculated to quantify cell migration.

Statistical analysis

The in vitro experiments were repeated three times. Statistical analyses were conducted using R software (version 4.1.1). The Shapiro-Wilk normality test was used to assess normal distribution. Mann-Whitney test was used to compare continuous variables without normal distribution, and t-test or one-way analysis of variance (ANOVA) was used to compare continuous variables with a normal distribution. A P value less than 0.05 was considered statistically significant.

Results

Identification of differentially expressed tsRNAs in TCGA cohort

Totally, 29,623 exclusive tsRNAs were detected in the 184 EC tissues and 13 adjacent normal tissues from MINTbase. However, most tsRNAs were present at very low abundance. After filtering out tsRNAs with a median read count <10, we identified 123 up-regulated tsRNAs and 87 down-regulated tsRNAs (Table S2). The heatmap illustrates the expression patterns of the 210 differentially expressed tsRNAs (Figure 1A). The volcano plot shows the log₂ (fold change) versus the adjusted P values (Figure 1B). The scatter plot depicts the tsRNA expression levels between EC tissues and controls (Figure 1C).

Figure 1 Analysis results of tsRNA profiles for EC patients in the TCGA cohort. (A) Heatmap of differentially expressed tsRNAs. (B) Volcano plot of log₂ (fold change) versus adjusted P values. Red dots: up-regulated tsRNAs. Black dots: non-differentially expressed tsRNAs. Green dots: down-regulated tsRNAs. (C) Scatter plot of tsRNA expression levels between EC and controls. Red dots: up-regulated tsRNAs. Black dots: non-differentially expressed tsRNAs. Green dots: down-regulated tsRNAs. (D) Survival analyses identified 15 tsRNAs associated with overall survival in EC patients. CI, confidence interval; EC, esophageal cancer; HR, hazard ratio; TCGA, The Cancer Genome Atlas; tsRNA, transfer RNA-derived small RNA.

Differentially expressed tsRNAs associated with OS of EC patients in TCGA cohort

The association between expression levels of the 210 differentially expressed tsRNAs and OS of EC patients was assessed using the TCGA cohort. We obtained 15 tsRNAs associated with OS of EC patients, including five harmful tsRNAs and 10 beneficial tsRNAs (Figure 1D).

The expression characteristics of tsRNAs in four paired tissues from ACHNMU cohort

We detected the expression levels of tsRNAs in the four paired EC and adjacent normal tissues from ACHNMU cohort, and detected 431 tsRNAs in the 8 specimens. The PCA plot shows the distribution of each EC and control specimen (Figure 2A).

Figure 2 Expression characteristics of tsRNAs in four paired EC and adjacent normal tissues from ACHNMU cohort. (A) PCA plot showing the distribution of each EC and control specimen. (B) Number of tsRNAs detected in EC tissues and normal tissues. (C) Distributions of tsRNA subtypes in EC tissues. (D) Distributions of tsRNA subtypes in adjacent normal tissues. (E) Bar plot showing the numbers of tsRNA subtypes against tRNAs in EC tissues. (F) Bar plot showing the numbers of tsRNA subtypes against tRNAs in adjacent normal tissues. ACHNMU, The Affiliated Cancer Hospital of Nanjing Medical University; EC, esophageal cancer; PCA, principal component analysis; tsRNA, transfer RNA-derived small RNA.

To compare the expression characteristics of tsRNAs, we calculated the average counts of per million mapped reads (CPM) for each group, and removed the tsRNAs with an average CPM less than 20 in each group, obtaining 290 tsRNAs (Figure 2B). Pie charts were prepared to show the distributions of tsRNA subtypes in EC tissues and adjacent normal tissues (Figure 2C,2D). Because tsRNAs are derived from tRNA, we used bar plot to show the numbers of tsRNA subtypes against tRNAs (Figure 2E,2F). The lengths of the tsRNAs in the two groups have similar distributions, i.e., most tsRNAs had a length shorter than 19 or longer than 27 (Figure S1).

Differentially expressed tsRNAs identified from ACHNMU cohort

Before analyzing differentially expressed tsRNAs, we retained only tsRNAs detected (read count >0) in all four EC tissues or in all four adjacent normal tissues. We identified 99 up-regulated tsRNAs and 60 down-regulated tsRNAs (Table S3). Figure 3A shows the expression patterns of the 159 differentially expressed tsRNAs. The volcano plot illustrates the log₂ (fold change) versus the adjusted P values (Figure 3B). The scatter plot depicts the tsRNA expression levels between EC tissues and controls (Figure 3C).

Figure 3 Identification of the three key tsRNAs. (A) Heatmap, (B) volcano plot, and (C) scatter plot of tsRNA profiles of four paired EC tissues and adjacent normal tissues from ACHNMU cohort. Red dots: up-regulated tsRNAs. Black dots: non-differentially expressed tsRNAs. Green dots: down-regulated tsRNAs. (D) The three differentially expressed tsRNAs identified in both the TCGA and ACHNMU cohort. Beeswarm plots show the data from TCGA. (E) Survival analysis of the three tsRNAs using TCGA data, showing their association with OS in EC patients. (F) Validation of the expression levels of the three tsRNAs using RT-qPCR in 40 paired EC tissues and adjacent normal tissues. ACHNMU, The Affiliated Cancer Hospital of Nanjing Medical University; EC, esophageal cancer; OS, overall survival; RT-qPCR, real-time quantitative polymerase chain reaction; tsRNA, transfer RNA-derived small RNA.

We intersected the 159 differentially expressed tsRNAs with the 210 differentially expressed tsRNAs from TCGA and obtained three tsRNAs, i.e., tRF-28-P4R8YP9LOND5, tRF-29-86J8WPMN1EJ3, and tRF-29-P4R8YP9LONHK. This cross-dataset validation strategy was employed to identify high-confidence tsRNAs with reproducible dysregulation, minimizing the impact of potential technical variations between the TCGA dataset and our in-house sequencing data. All three tsRNAs were up-regulated in EC tissues (Figure 3D) and high expression levels were associated with shorter OS (Figure 3E).

We further detected the expression levels of these three tsRNAs in 40 paired EC tissues and adjacent normal tissues. All three tsRNAs were markedly up-regulated in EC tissues relative to their matched adjacent normal counterparts (Figure 3F).

Enrichment analyses

We performed GO and KEGG enrichment analyses to uncover the roles of the three tsRNAs in EC. GO analysis revealed that the predicted targets of the three tsRNAs were significantly enriched across 11 GO terms (Figure 4A), mainly involving channel activity. KEGG enrichment analysis indicated that the target genes were enriched in 21 pathways (Table S4), with the top 10 pathways shown in Figure 4B.

Figure 4 GO and KEGG enrichment analyses of the potential target genes of tRF-28-P4R8YP9LOND5, tRF-29-86J8WPMN1EJ3, and tRF-29-P4R8YP9LONHK. The target genes were predicted by miRDB and were differentially expressed in the TCGA dataset. (A) The differentially expressed potential target genes were enriched in 11 GO terms. (B) The top 10 enriched KEGG pathways of the differentially expressed potential target genes. FC, fold change; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Identification of genes inversely correlated with the three tsRNAs

We assessed the correlations between the three tsRNAs and their potential target genes using Spearman’s test and identified five interactions with a rho value less than −0.3 and a P value less than 0.05 (Figure 5A-5E). Figure 5F presents the expression levels of the four genes involved in these five interactions, and only Synaptotagmin-like 3 (SYTL3) was found to be significantly down-regulated in EC tissues. Because tRF-28-P4R8YP9LOND5 and tRF-29-P4R8YP9LONHK differ by only a single base in their sequences, it may be difficult to distinguish their functions. Therefore, we then investigated the functional role of tRF-29-86J8WPMN1EJ3 in ESCC cell lines.

Figure 5 Identification of potential target genes inversely correlated with tRF-28-P4R8YP9LOND5, tRF-29-86J8WPMN1EJ3, and tRF-29-P4R8YP9LONHK. (A-E) Five interactions identified by Spearman’s test with a rho <−0.3 and P<0.05. (F) Expression levels of the four genes in the TCGA dataset. TCGA, The Cancer Genome Atlas.

tRF-29-86J8WPMN1EJ3 promotes proliferation, migration and invasion of EC cells

To interrogate the biological roles of tRF-29-86J8WPMN1EJ3, KYSE30, and KYSE150 cells were transfected with either tRF-mimics or mimics-NC. Colony-formation assays revealed that tRF-mimic-transfected cells formed significantly more colonies than controls, indicating enhanced proliferation (Figure 6A). In wound-healing assays, the scratch gap closed faster in tRF-mimic groups, reflecting accelerated migration (Figure 6B). Transwell assays further demonstrated that tRF-mimics markedly increased both migratory and invasive capacities (Figure 6C,6D). These findings suggest that tRF-29-86J8WPMN1EJ3 may promote proliferation, migration and invasion of KYSE30 and KYSE150.

Figure 6 tRF-29-86J8WPMN1EJ3 promoted proliferation, migration, and invasion of EC cell lines. (A) Colony formation assay. The cells were stained with crystal violet. (B) Wound healing assay. (C) Transwell migration assay. The cells were stained with crystal violet. (D) Transwell invasion assay. The cells were stained with crystal violet. *, P<0.05; **, P<0.001; ***, P<0.001. EC, esophageal cancer; mimics-NC, negative controls of mimics; tRF-mimics, tRF-29-86J8WPMN1EJ3 mimics.

Discussion

In the early 1970s, researchers have discovered tsRNAs in the urine of cancer patients, which were initially considered random degradation products and did not receive much attention (21-24). With the emergence of high-throughput sequencing technology, numerous small non-coding RNAs have been identified (21). Recently, growing evidence suggests that tsRNAs are produced from mature tRNAs or pre-tRNAs by specific cleavage under specific conditions, and they are functional RNA molecules rather than mere degradation products (24,25). Dysregulation of tsRNAs is closely related to various cancers, thus tsRNAs can be used as diagnostic and prognostic biomarkers for cancers.

Here, we interrogated tsRNA expression profiles of TCGA-ESCA patients. We also profiled tsRNAs in four paired EC tissues and adjacent normal tissues. Based on these two datasets, we identified three tsRNAs that were differentially expressed in both datasets. These three tsRNAs were up-regulated in EC tissues, and lower expression levels were associated with better OS, suggesting that these tsRNAs may act as tumor promoters in EC. To our knowledge, four studies have reported on tsRNAs in EC (13-16). However, no studies have specifically investigated tsRNAs in EC tissues. The present study is the first of its kind.

Totally, 29,623 tsRNAs were detected in the TCGA cohort. After filtering out tsRNAs with a median read count <10, only 330 tsRNAs were retained, suggesting that most tsRNAs were expressed at low levels. Samilar results were observed in the ACHNMU cohort, in which 431 tsRNAs were detected and 255 tsRNAs had a median read count greater than 10. We also found that most of the detected tsRNAs were derived from the 5'-end of tRNAs (Figure 2C,2D), which is consistent with the findings of Hua et al. (26). However, the low levels of 3' tsRNAs may be attributed to technical limitations in detecting 3' tsRNAs with terminal and internal modifications (26).

Based on tsRNA sequencing and RT-qPCR, we identified three differentially expressed tsRNAs which were also associated with OS of EC patients. The association between high expression of these three tsRNAs and shorter OS in EC patients strongly suggested that they were not merely bystanders but active drivers of tumor aggressiveness. We postulated that the shortened survival linked to these tsRNAs may be mediated through their collective impact on key cellular processes and signaling pathways. Because tsRNAs can exert their effects in a miRNA-like manner (27), we predicted their target genes. Specifically, the enrichment of their predicted targets in pathways like p53 signaling (which controls cell cycle arrest and apoptosis) and calcium/cAMP signaling (which influences cell migration, invasion, and proliferation) provided a direct link (28-30). The results of GO annotation showed that the potential target genes were mainly enriched in GO terms associated with channel activity. It has been showed that channel activity is associated with migration, invasion, proliferation, metastasis and drug-resistance in cancers (31-35). By potentially repressing genes within these critical pathways, the three tsRNAs may collectively enhance tumor cell survival, accelerate metastatic dissemination, and potentially confer resistance to therapy—all of which could be associated with poor prognosis in EC. These findings may provide valuable insights for researchers to further explore the roles of tsRNAs in EC.

We further explored the relationships between the three tsRNAs and their potential target genes, and identified five significant interactions. tRF-28-P4R8YP9LOND5 and tRF-29-P4R8YP9LONHK, which differ by a single base in their sequence, were both inversely correlated with the expression levels of SYTL3. We also found that SYTL3 was down-regulated in EC tissues. SYTL3, encoded on chromosome 6q25.3, plays a crucial role in vesicular trafficking (36). However, no study have yet explored its roles in cancer. Leicht et al. reported that TGM2 is overexpressed in esophageal adenocarcinomas compared to metaplastic Barrett’s esophagus, and its overexpression is associated with higher tumor stage, poor differentiation, and increased inflammatory and desmoplastic response (37). In contrast, we did not observe a difference in TGM2 expression levels between EC and adjacent normal tissues. This conflicted result may be due to differences in the control tissues used for comparison. Our results suggest that tRF-29-86J8WPMN1EJ3 promote proliferation, migration and invasion of EC cells. Tumor suppressor candidate-2 (TUSC2), a novel tumor suppressor gene, is a potential target gene of tRF-29-86J8WPMN1EJ3. TUSC2 has been shown to exert anti-cancer effects in EC (38). Although we did not find significant differences in TUSC2 expression levels between EC and adjacent normal tissues, EC tissues exhibited lower expression levels, which might be attributed to the small sample size of the control samples.

There are several limitations in this study. First, the expression of the three tsRNAs was validated in only 40 paired EC tissues. The small sample size limits the statistical power, which may weaken the conclusions of the present study. Second, while our study identified potential target genes and pathways associated with the three tsRNAs through bioinformatics analyses, we did not experimentally validate these targets or pathways. Experimental validation, such as through functional assays or animal models, is crucial to confirm the biological roles of these tsRNAs and their targets in EC. Lastly, the clinical relevance of our findings, such as the potential use of the identified tsRNAs as biomarkers for diagnosis or prognosis, was not fully evaluated. Further studies should assess the clinical utility of these tsRNAs through larger-scale clinical trials and validation studies.

Conclusions

In conclusion, we presented the expression characteristics of tsRNAs in EC, and identified three up-regulated tsRNAs in EC tissues, namely tRF-28-P4R8YP9LOND5, tRF-29-P4R8YP9LONHK, and tRF-29-86J8WPMN1EJ3. Lower expression levels of these tsRNAs were associated with better OS in EC patients. Additionally, tRF-29-86J8WPMN1EJ3 promotes the proliferation, migration, and invasion of EC cells.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1436/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1436/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1436/prf

Funding: This study was funded by the Natural Science Foundation of Jiangsu Province (No. BK20241993), Key Scientific Research Projects of Jiangsu Provincial Health Commission (No. K2023070), and the Young Talents Program of Jiangsu Cancer Hospital (No. QL201810).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1436/coif). The 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 protocol was approved by the ethics committee of The Affiliated Cancer Hospital of Nanjing Medical University (approval No. 2019Ke-013). Informed consent was obtained from all participants.

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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