TFP12 as a key regulator of sentinel lymph node metastasis in breast cancer

Introduction

Breast cancer stands as one of the most prevalent malignancies, its occurrence manifesting predominantly in the advancing years, particularly between 50 and 70 years of age. Notably, a familial history of breast cancer emerges as an indispensable determinant, as a woman’s predisposition to this ailment escalates significantly should a first-degree kinship (e.g., mother or sister) bear witness to a diagnosis (1-3). Further contributing factors entail the early genesis of menstruation, delayed maternity, postponed menopause, childlessness, prolonged utilization of oral contraceptives, and primary exposure to ionizing radiation during early developmental stages. Recent advancements in elucidating the interplay between genetics and breast cancer, facilitated by the investigation of breast cancer susceptibility genes, imbue our comprehension with newfound clarity. Gene mutations, such as those impinging upon the BRCA1 and BRCA2 genes—the prime subjects of extensive study—heighten the susceptibility to breast cancer considerably. Moreover, the TP53, PTEN, and CHEK2 genes have surfaced as culprits in breast carcinogenesis (4-6). These inherited disruptions not only heighten an individual’s proneness to this affliction but also bear implications for therapeutic strategies and prognostication. Furthermore, geographic and racial disparities permeate the incidence of breast cancer. For instance, Europeans and North Americans of Caucasian heritage face an augmented risk in comparison to their Asian and African counterparts. However, several Asian regions including urban localities in Japan, South Korea, and China evince climbing breast cancer rates, a phenomenon likely explicable by shifts in lifestyle practices concomitant with the burgeoning influence of environmental agents (7,8).

The sentinel lymph nodes (SLNs), an eminent entity proximal to the primary tumor, fulfill a pivotal role as a conduit for lymphatic fluid engendering progression to other lymph nodes. For patients contending with breast cancer, the status of the SLN assumes paramount significance in staging and treatment stratagems, hinging upon the invasion by malignant neoplastic cells (9). Through an incisive biopsy, coupled with meticulous pathologic scrutiny of its constituent cells, physicians glean insights into the potential infiltration of the lymph node by the malignant entity. In the fortunate event that the SLN remains unscathed (10,11), it suggests a sanguine prognosis, not tarnishing neighboring nodes and preventing the scourge of metastasis. Conversely, should the SLN be besieged by insidious malignant cells, a recalibration of the cancer staging ensues, begetting a shift toward a higher echelon, prompting more aggressive therapeutic maneuvers such as breast excision or even lymph node dissection (6). Recognizing the SLN’s pivotal sway, the possibility emerges of harnessing concomitant genomic alterations to forecast disease progression antecedent to the advent of SLN metastasis. However, current research falters in delving into the propitious realm of marker genes unique to the SLN.

By utilizing transcriptome sequencing data derived from SLNs, we investigated the potential of integrating these molecular markers into a consolidated prognostic panel for breast cancer. Initial differential expression analysis identified genes closely associated with SLN involvement. Moreover, this study systematically evaluated the feasibility of constructing a prognostic model based on these genes to influence breast cancer prognosis significantly. The developed model’s clinical implications were rigorously analyzed, particularly its potential to reveal underlying mechanisms and establish a foundation for subsequent investigations. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2435/rc).

Methods

Data acquisition

The GSE64850 dataset contained RNA-seq data from 4 metastatic breast cancer SLNs and 3 normal lymph nodes. Differential expression analysis was conducted to identify metastasis-associated mRNA expression differences. Additional RNA-seq, genomic mutation, and clinical data from The Cancer Genome Atlas (TCGA)-breast invasive carcinoma (BRCA) were obtained via the UCSC Xena database. Gene classification and prognostic modeling were subsequently performed using TCGA-BRCA data. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Consensus clustering analysis

Drawing upon SLN gene differentials, our study endeavored to delve into distinct gene phenotypic expressions in breast cancer. To fulfill our objective, we deployed the sophisticated ConsensusClusterPlus software package, renowned for its adeptness in uncovering durable gene clusters. Within the parameters of ConsensusClusterPlus, we scrupulously computed sample similarity through the employment of the Pearson distance metric. Subsequently, we harnessed the “partition around medoids” algorithm to ascertain the correlations among samples. To ascertain the reliability and steadiness of our model, we conducted continuous analysis for K2-6 and subjected all patients to 50 rounds of resampling. Such a rigorous methodology ultimately yielded a clustering phenotype of greater precision, thus enhancing accuracy. Following phenotype characterization, marker gene identification was performed using Limma package.

Construction of SLN prognostic model

We performed a comprehensive batch Cox univariate regression analysis of differentially expressed estrogen receptor (ER) genes in cancer samples using two R packages, namely Survival (v3.2-7) and survminer (v0.4.8). Regression analysis uncovered key genes significantly linked to overall survival (OS), with significance stringently defined at P<0.05. Least absolute shrinkage and selection operator (LASSO) regression was implemented using the glmnet R package to optimize Cox-derived prognostic features. Ten-fold cross-validation determined the optimal lambda for model construction.

Functional enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were analyzed for functional annotation. Signaling pathways were investigated using clusterProfiler with P<0.05 significance threshold. Gene set variation analysis (GSVA) quantified immune cell infiltration scores based on literature-curated markers. Inter-phenotype immune cell differences were assessed via Student’s t-test. Gene Set Enrichment Analysis (GSEA) was applied to characterize gene functions, with statistical significance defined as P<0.05.

Cell transwell assay

Transwell chambers (manufacturer, city, state, country) were harvested after 72-hour incubation, followed by medium aspiration. Non-migratory cells were removed from the membrane undersurface using phosphate-buffered saline (PBS)-moistened cotton-tipped applicators. Membranes were transferred to fresh 24-well plates containing 1 mL crystal violet (0.1%) for 3–10 min staining, then triple-rinsed with PBS. Migration outcomes were documented using phase-contrast microscopy.

Wound healing assay

Post-transfection cells were mechanically wounded using a 200 µL pipette tip in standardized scratch wound healing assays. The cellular monolayer was rinsed twice with PBS (pH 7.4) followed by incubation in complete growth medium. Images of the initial wound were taken immediately after washing, followed by a 72-hour incubation period to facilitate cell migration and proliferation within the wound site.

Statistical analysis

Statistical analyses were implemented in R v4.1.2, with continuous variables expressed as mean ± standard deviation. The limma package facilitated differential expression analysis of sequencing datasets. In instances where two or multiple groups were involved, employed t-tests for data that adhered to a normal distribution. Conversely, employed non-parametric tests for data that deviated from a normal distribution to compare differences. Applied the survival R package for survival analysis. For continuous variables, initially partitioned the data into high and low expression groups based on the optimal cutoff value, subsequently employed the log-Rank test to assess prognostic outcomes. All analyses were deemed statistically significant at a significance level of P<0.05.

Results

Differential gene analysis of SLNs

Differential expression analysis between cohorts identified 218 upregulated genes in positive groups and 44 downregulated genes in negative groups (Figure 1).

Figure 1 Screening of differential genes in sentinel lymph nodes. (A) Volcano plot of differential expression analysis of sentinel lymph node-related genes; (B) heat map of differential genes in sentinel lymph nodes.

To glean further insights regarding the distinct genes, individual enrichment analyses were conducted. Within the realm of GO analysis, it emerged that low-expression genes were predominantly associated with B cell differentiation and lymphocyte differentiation (Figure 2A). Moreover, in the context of KEGG analysis, these genes were notably linked to the B cell receptor signaling pathway and Natural Killer (NK) cell-mediated cytotoxicity (Figure 2B). On the other hand, highly expressed genes were predominantly connected to GO elements such as extracellular matrix organization and myocyte differentiation (Figure 2C), while also corroborating their involvement in metabolic pathways encompassing complement and tyrosine metabolism (Figure 2D). To further elucidate the interplay between genes, a functional enrichment analysis was conducted on the differential genes. Notably, the highly expressed genes EPCAM, TIMP1, and CD19 emerged as central figures in the comprehensive interaction network (Figure 2E).

Figure 2 Functional enrichment analysis of differential genes in SLNs. (A) Scatter plot of GO analysis of genes with low expression in SLNs; (B) histogram of KEGG analysis of genes with low expression in SLNs; (C) scatter plot of GO analysis of genes with high expression in SLNs; (D) histogram of KEGG analysis of genes with high expression in SLNs; (E) differences in SLNs protein-protein interaction network diagram, in which red represents highly expressed genes and green represents lowly expressed genes. The larger the shape, the greater the number of connections. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; SLNs, sentinel lymph nodes.

Screening of key SLNs genes in breast cancer

Whilst SLNs exerts a significant impact on breast carcinoma, it is essential to acknowledge that not all genes are intricately involved in this malignancy. Consequently, we delved further into an expression analysis of the aforementioned 262 genes within the TCGA-BRCA dataset, which primarily focuses on the initiation of breast carcinoma. Subsequently, our scrutiny revealed a total of 82 genes intimately connected to the onset of breast carcinoma, encompassing 19 genes exhibiting heightened expression and 63 genes displaying diminished expression (Figure 3A). In exploring the enrichment analysis of these 82 genes, it became apparent that their primary association lies predominantly in relation to extracellular structures (Figure 3B). Moreover, a thorough investigation into the prognostic implications of these 82 genes revealed their intricate correlation to breast carcinoma prognosis. Our analysis unveiled 18 genes that exhibited a profound impact on the prognosis of breast carcinoma, among which the top four genes demonstrated the greatest impact on patients’ prognoses, ultimately resulting in their amelioration (Figure 3C). Notably, a functional enrichment analysis of the aforementioned 18 genes revealed their strong link to RNA polymerase activity (Figure 3D).

Figure 3 The role of SLNs core genes in breast cancer. (A) Volcano plot of differential expression analysis of 262 core genes in breast cancer; (B) bubble plot of functional enrichment analysis of 82 key genes; (C) survival curves of top 4 meaningful genes in prognostic analysis of 82 genes; (D) functional enrichment analysis diagram of 18 prognostically significant genes. FC, fold change; OS, overall survival; SLNs, sentinel lymph nodes.

Molecular subtyping of SLNs in breast cancer

We have successfully generated a molecular classification system for SLNs by investigating the expression patterns of the 18 genes mentioned earlier. Our analysis revealed that the most optimal classification scheme occurred when k=2 (Figure 4A). Consequently, we divided the SLNs into two distinct groups based on their molecular profiles (Figure 4B). Through prognostic analysis, we discovered that this molecular classification system significantly influenced the prognosis of breast cancer patients (Figure 4C). To gain further insights into the impact of SLNs on breast cancer, we investigated gene mutations, copy number variations, and overall methylation changes from a three-dimensional perspective. Our analysis revealed notable differences in TP53 and PIK3A gene mutations between the two groups, with the C2 group demonstrating a higher frequency (Figure 4D). Additionally, we observed that copy number variations were more pronounced in the C2 group (Figure 4E, P<0.001), and the level of methylation was higher in the C1 group (Figure 4F).

Figure 4 Construction of breast cancer molecular classification based on SLNs. (A) Cluster analysis diagram of breast cancer-specific molecular subtypes with k=2; (B) visualized heat map of molecular subtyping of breast cancer; (C) survival analysis plot of molecular subtypes of breast cancer; (D) the impact of breast cancer molecular typing on gene mutations; (E) effect of breast cancer molecular typing on copy number; (F) effect of breast cancer molecular classification on methylation. SLNs, sentinel lymph nodes; TMB, tumor mutational burden; OS, overall survival.

To explore the differential expression between the two molecularly distinct groups, we conducted a comprehensive analysis and identified 582 genes that exhibited significant differences (Figure 5A). Subsequent functional enrichment analysis indicated that these differentially expressed genes were primarily associated with Th cell differentiation, chemotaxis, and signaling pathways involving factors related to immune response (Figure 5B). This led us to speculate that the two groups may influence immune differentiation in breast cancer. Moreover, when we compared the immune scores between the different molecular types using the ESTIMATE algorithm, we observed a significantly higher immune score in the C2 group compared to the C1 group (Figure 5C). Finally, by evaluating specific immune cell scores, we discovered significant variations in multiple immune cell populations, such as B cells, T cells, and Treg cells, between the C1 and C2 groups (Figure 5D,5E).

Figure 5 Molecular typing and functional analysis of SLNs. (A) Heat map of molecular typing differential expression analysis; (B) KEGG enrichment analysis diagram of differential genes by molecular typing; (C) effect of molecular typing on immune scores; (D) heatmap visualizing immune cell scores based on different types; (E) difference analysis of immune cell scores among different types. **, P<0.01; ****, P<0.0001; ns, not significant. FC, fold change; GSVA, gene set variation analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; SLNs, sentinel lymph nodes.

Construction of SLNs breast cancer prognostic model

LASSO regression analysis of differentially expressed genes constructed a BRCA prognostic signature comprising 31 functionally relevant genes (Figure 6A). Subsequent survival analysis validated the BRCA prognostic signature’s significant association with patient outcomes (Figure 6B, P<0.001). The prognostic model was validated using Gene Expression Omnibus (GEO) datasets, confirming consistency with initial findings (Figure 6C). Ultimately, we rendered our model visually through the application of nomograms and decision curve analysis (DCA) curves (Figure 6D,6E).

Figure 6 Construction of SLNs prognostic model. (A) LASSO screens suitable mRNAs for building models; (B) survival analysis plot of prognostic model for TCGA dataset; (C) external validation plot of prognostic analysis on the GEO external validation data set; (D) nomogram of the prognostic model; (E) DCA curve for evaluation of clinical utility of the prognostic model. *, P<0.05; **, P<0.01; ***, P<0.001. DCA, decision curve analysis; GEO, Gene Expression Omnibus; LASSO, least absolute shrinkage and selection operator; OS, overall survival; SLNs, sentinel lymph nodes; TCGA, The Cancer Genome Atlas.

Functional enrichment analysis of prognostic risk subgroups revealed the model’s core mechanism involving IL-17 and TNF signaling pathway regulation (Figure 7A). Comparative immunophenotyping revealed distinct activation states of central memory T (Tcm) cells, CD4⁺ T cells, and T-helper 1 (Th1) cells across risk subgroups (Figure 7B). Based on the Immport database, we downloaded immune-related genes and further analyzed the correlation between the prognostic model and immune-related genes. After analysis, it was found that the model was mainly positively correlated with 20 genes and negatively correlated with 23 genes. At the same time, immunoassay enrichment analysis was performed on these genes. After analysis, it was found that these genes were mainly related to neutrophil disorders (Figure 7C). Furthermore, drug sensitivity analysis demonstrated significant associations between the prognostic model and therapeutic agents including Fluorouracil (Figure 7D).

Figure 7 Functional analysis of the prognostic model. (A) Prognostic model functional enrichment analysis diagram; (B) differences in multiple immune cells between the two distinct populations; (C) immune related-gene differ between the two groups; (D) the impact of prognostic models on chemotherapeutic drug resistance. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; ns, not significant. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

TFP12 affects the expression of CCL19 and NGFR, affecting the metastasis of breast cancer

In order to determine the key genes related to breast cancer, we analyzed differential genes based on Weighted Gene Co-Expression Network Analysis (WGCNA) and found that the module yellow is mainly related to tumors. The yellow gene and prognostic model gene were further analyzed, and a total of 7 key genes were obtained, among which TFP12 is the gene with the highest difference factor among the key genes (Figure 8A). To further understand the impact of TFP12 on breast cancer cell function, we generated TFP12 overexpressing cell lines and conducted transwell and scratch assays. Our results demonstrated that TFP12 expression suppresses breast cancer invasion (Figure 8B) and migration (Figure 8C). To gain insight into the underlying mechanism, we examined the co-expression of TFP12 with other metastasis-related genes in the TCGA database. Our findings revealed a significant correlation between TFP12 and CCL19 and NGFR (Figure 8D). This relationship was further confirmed in our overexpressing cell lines (Figure 8E).

Figure 8 The effect of TFP12 on breast cancer metastasis. (A) Cross-analysis was performed based on WGCNA and SLN differential genes to find key genes; (B) TFP12 affects the invasiveness of breast cancer (Transwell assay with crystal violet staining; scale bar: 100 μm); (C) overexpression of TFP12 can reduce the migration of breast cancer; (D) there is a co-expression relationship between TFP12, CCL19, and NGFR; (E) overexpression of TFP12 can lead to increased expression of CCL19 and NGFR. ***, P<0.001. OE, overexpression; SLN, sentinel lymph nodes; WGCNA, Weighted Gene Co-Expression Network Analysis.

Discussion

SLNs constitute pivotal biomarkers in breast cancer management, defined as the primary metastatic sites within tumor-draining lymphatic basins. Functioning as gatekeepers of lymphatic spread, they provide dual clinical utility for diagnostic staging accuracy and prognostic risk stratification. As the focus on SLNs increases, the genes associated with breast cancer occurrence in SLNs are increasingly recognized as early diagnostic and prognostic biomarkers. Presently, there have been some investigations into the genes related to SLNs (12,13). In the study by Song et al., RNA sequencing technology was used to construct the regulatory network of long non-coding RNAs and messenger RNAs in SLNs (14). We acknowledge that lncRNA-mediated mRNA regulation represents only one aspect of mRNA differential expression and cannot fully capture the complexity of mRNA function. Therefore, a comprehensive investigation of mRNA roles in SLNs remains essential.

There is a complex interaction relationship between the characteristics of breast cancer immune microenvironment and SLN metastasis. Studies have shown that the composition and functional status of immune cells in the tumor microenvironment directly affect the occurrence of lymph node metastasis (15). The immune microenvironment of triple-negative breast cancer is usually manifested as high tumor-infiltrating lymphocytes density and PD-L1 expression upregulated. Although this immune activation state can enhance anti-tumor effects, overactivated regulatory T cells (Tregs) and tumor-associated macrophages (TAMs) may establish an immunosuppressive microenvironment by secreting cytokines such as IL-10 and TGF-β and promote tumor cells to migrate to SLNs through lymphatic vessels (16,17). It is worth noting that CD169 macrophages were significantly reduced in metastatic lymph nodes, resulting in impaired antigen presentation function and weakening local immune surveillance capabilities. At the same time, SLN metastasis can reshape the primary focal microenvironment by secreting chemokines such as CCL5 and CXCL12, forming a “premetastasis niche” that promotes metastasis (18,19). Clinical studies have found that elevated preoperative inflammatory indicators such as neutrophil/lymphocyte ratio (NLR) are positively correlated with the risk of axillary lymph node metastasis, suggesting that systemic inflammatory response may promote lymphangiogenesis and immune escape by activating the NF-κB pathway (20). Given the key role of SLNs, in our study, we further analyzed the associations of SLN-related genes and immune cells and immune-related genes.

S100A7, encoding a calcium-binding effector of the S100 protein family, demonstrates significant oncogenic relevance in breast carcinogenesis. Elevated S100A7 expression correlates with SLN metastatic involvement, mechanistically implicating its utility as a predictive biomarker of lymphatic dissemination (21-24). TLR4 regulates immune-inflammatory responses and tumor progression. Genomic studies indicate its genetic variations are associated with elevated SLN metastasis risk in breast cancer. These findings implicate TLR4 genetic variations in breast cancer lymphatic dissemination to SLNs, thereby highlighting their pathophysiological relevance in metastatic progression (3,4,25). While some studies have been conducted, there still remains a lack of research on which genes have an impact on the prognosis of breast cancer in SLNs. The primary objective of this study is to identify the genes associated with SLNs through analysis of RNA-seq data. Furthermore, we aim to investigate the role played by these SLN-related genes in breast cancer. Additionally, a breast cancer molecular subtyping and prognostic model were constructed based on the SLN-related genes. Our study elucidates the multifaceted role of SLNs in breast cancer progression.

Differential expression analysis pinpointed pivotal SLN-associated genes with multifactorial roles in breast cancer. In the PPI protein-protein interaction network analysis, we have discovered that EPCAM and TIMP1 are the two most relevant genes to SLNs. EPCAM (Epithelial Cell Adhesion Molecule) is a cellular surface protein that is widely present on the surface of epithelial tissues and cancer cells. EPCAM plays a crucial role in maintaining cell adhesion, cellular morphology stability, and is involved in essential biological processes such as cell signaling, regulation of cell growth, and differentiation (10). The relationship between EPCAM and breast cancer is mainly manifested in tumor occurrence, development, and prognosis. Studies have found that EPCAM expression levels are significantly elevated in breast cancer and are associated with tumor malignancy and prognosis (12,13). Breast cancer patients with high expression of EPCAM often exhibit poorer survival rates and a tendency towards metastasis. TIMP1, short for Tissue Inhibitor of Metalloproteinases 1, is a member of the protein family that primarily functions by inhibiting the activity of metalloproteinases, thus regulating and limiting biological processes such as extracellular matrix remodeling, cell migration, and invasion (24). Increased expression of TIMP1 is associated with the malignancy and prognosis of breast cancer. A study found that elevated expression of TIMP1 in breast cancer tissue is positively correlated with malignant clinical characteristics such as larger tumor size, higher histological grade, lymph node metastasis, and negative ER. Furthermore, high TIMP1 expression is also associated with increased risk of recurrence and metastasis, as well as a worsened prognosis. During the process of functional enrichment analysis of differential genes, we found that these genes are mainly associated with the B-cell receptor signaling pathway (26-28). In the SLN, B cells bind with specific antigens and recognize and bind to antigens that match their antigen receptors on the cell surface. Once the antigen binds to the B cell, it begins to divide and proliferate, forming a large number of clone cells. These clone cells further differentiate into plasma cells, which are capable of producing and secreting large quantities of specific antibodies released into bodily fluids. These antibodies further participate in immune responses, aiding the body in defending against pathogen invasion.

Prognostic gene-driven clustering established an SLN-associated molecular subtyping framework. Systematic algorithm benchmarking determined optimal stratification at K=2 through cluster stability validation. Differential expression and functional enrichment analyses of molecular subtypes demonstrated significant enrichment in cytokine-related pathways. Cytokines constitute pivotal mediators of intercellular communication, orchestrating cellular crosstalk through transmembrane receptor engagement. This ligand-receptor interaction initiates multistage signaling cascades that modulate fundamental cellular processes (proliferation, differentiation, apoptosis) and immune surveillance mechanisms. As a highly heterogeneous malignancy, breast cancer progression is dynamically regulated by cytokine networks across all disease stages (29,30). Cytokines also play important roles in the treatment of breast cancer. Tumor necrosis factor-alpha (TNF-α) is a cytokine that exhibits anti-tumor properties. Several studies have shown that TNF-α exerts significant inhibitory effects on breast cancer through mechanisms such as immune cell activation and induction of cell death. Consequently, Therapeutic targeting of TNF-α in breast cancer has emerged as a major focus in translational oncology research. Cytokines also demonstrate prognostic utility in breast cancer, with prostaglandin E2 (PGE2) mediating pleiotropic oncogenic effects through inflammatory pathway activation (31,32). High expression of PGE2 is closely associated with malignant progression and prognosis of breast cancer. PGE2 orchestrates breast cancer pathogenesis through immunosuppressive microenvironment reprogramming, angiogenic niche formation, and autonomous proliferative signaling activation, thereby driving primary tumor progression and distant metastatic dissemination. Therefore, quantification of PGE2 expression levels demonstrates clinical utility as a prognostic biomarker in breast cancer.

Although in our study, we explored the potential key genes of SLNs, and carried out basic experimental verification of key genes. But there are still some problems that have not been resolved. First, although we found a model of SLNs and breast cancer prognosis through model construction, this model has not been verified in clinical samples. Secondly, among the multiple candidate genes, we only verified the function of one of the genes, and the functions of other genes still need to be further explored. Finally, in our study, we explored the correlation between prognosis models and immune cell infiltration and immune-related genes, but how these model-related genes affect the changes in the immune microenvironment of breast cancer still requires relevant single-cell sequencing and experiments to explore in the future.

Conclusions

In conclusion, we have utilized high-throughput sequencing to identify genes implicated in SLN metastasis, and concurrently, established a molecular classification system for breast cancer based on these SLNs. The molecular classification of SLNs demonstrates a remarkable capacity to prognosticate breast cancer outcomes. Lastly, we have developed a prognostic model for SLNs, thus furnishing a valuable resource for future clinical applications.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2435/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2435/prf

Funding: This study was supported by Hygienic Research Program of Gansu Province (No. GSWSKY2018-07).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2024-2435/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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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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