Unraveling the molecular mechanisms of smoking-associated non-small cell lung cancer: a comprehensive analysis of genetic, therapeutic, and immunological

Introduction

Lung cancer represents the most prevalent cancer globally and remains the leading cause of cancer-related mortality worldwide (1,2). In 2022, a staggering 2,480,301 new cases of lung cancer were diagnosed, and 1,817,172 deaths were attributed to the disease (2). Non-small cell lung cancer (NSCLC) constitutes approximately 80–85% of all lung cancer cases, exhibiting a dismal 5-year survival rate of less than 15% for advanced-stage disease, encompassing both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) (3). Efforts to reduce the incidence and mortality of lung cancer have long been a primary focus of clinical research and practice.

Tobacco is a well-established risk factor for a spectrum of cardiovascular and respiratory diseases, as well as over 20 different types of cancer (4). It is widely recognized that smoking is strongly correlated with lung cancer, accounting for 78% of male lung cancer cases and 53% of female lung cancer cases (4,5). A comprehensive meta-analysis of 287 studies revealed that, compared to never smokers, smokers face a more than five-fold increased risk of developing lung cancer (6). The National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines recommend smoking cessation for patients diagnosed with cancer at any stage (7). Nevertheless, the precise molecular mechanisms underlying smoking-associated carcinogenesis remain incompletely elucidated.

In this study, we leveraged data from multiple public databases to systematically investigate the molecular mechanisms underlying smoking-associated NSCLC. By comparing the genetic profiles, therapeutic vulnerabilities, and immunological characteristics of NSCLC patients with varying smoking histories—never smokers, reformed smokers, and current smokers—we aimed to uncover critical differences that could inform personalized treatment approaches. Our findings reveal distinct molecular signatures and immune profiles among these groups, highlighting the potential for targeted therapies and immunomodulatory strategies tailored to individual smoking histories.

Methods

Data collection and processing

We retrieved a comprehensive cohort of 1,087 NSCLC patients with whole-exome sequencing (WES), RNA sequencing, and detailed clinical characteristics from the publicly available The Cancer Genome Atlas (TCGA) database (https://www.cbioportal.org/). Furthermore, an additional cohort of 321 NSCLC patients who received immunotherapy was included in the study. Based on their smoking status, patients were stratified into three categories: never smokers, reformed smokers and current smokers. Moreover, to ensure a holistic analysis, we incorporated data on the immune microenvironment from a previously published study, thereby facilitating a comprehensive integration of molecular and immunological features (8). To maintain data integrity, we implemented stringent quality control measures to exclude samples with incomplete or low-quality data, yielding a high-quality, refined dataset for subsequent analyses. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Analyzing mutational signatures

To elucidate the mutational patterns associated with smoking in NSCLC, mutational signature analysis was performed to resolve single nucleotide variants (SNVs) into a set of characteristic mutational signatures for each sample. Initially, SNVs were categorized into 96 base substitution types, thereby capturing the trinucleotide context of each mutation. Bayesian non-negative matrix factorization (NMF) was employed to deconvolute the mutational spectra and identify distinct mutational signatures. The identified signatures were subsequently compared with the well-curated Catalog of Somatic Mutations in Cancer (COSMIC) mutational signatures (Version 3.4, available at http://cancer.sanger.ac.uk/cosmic/signatures) to evaluate their biological relevance and potential novelty (9). Furthermore, hierarchical clustering based on cosine similarity was applied to quantify the similarity between the identified signatures and those in the COSMIC database.

Statistical analysis

All statistical analyses were performed using R (version 4.3.3). Data visualization was conducted using the ggplot2 package (version 3.4.0) in R, which enabled the creation of high-quality graphical representations of the results. For comparative analyses among the three study groups (never smokers, reformed smokers, and current smokers), we implemented the following analytical approaches: for continuous variables, normality assumptions were rigorously assessed using the Shapiro-Wilk test, complemented by visual inspection of Q-Q plots. For normally distributed variables (as confirmed by Shapiro-Wilk test with P>0.05), one-way analysis of variance (ANOVA) was employed to compare group means. Categorical variables were analyzed using the Chi-squared test or Fisher’s exact test, as appropriate. A two-sided P value of <0.05 was considered statistically significant.

Results

Patient demographic and clinical data

A total of 1,087 patients with NSCLC were enrolled, including 614 cases of LUAD and 473 cases of LUSC. Patient characteristics were summarized in Table 1. Never smokers were more likely to develop LUAD (P<0.001), suggesting that tobacco exposure may be more strongly associated with the development of LUSC. The never smokers had a higher ratio of females to males than current smokers and reformed smokers (2.16:1 vs. 0.51:1 vs. 0.65:1, P<0.001). Current smokers were diagnosed with cancer at a younger average age of 63 years. Additionally, significant differences were observed in T stage across the three groups (P<0.001).

Prognostic disparities across diverse smoking status in NSCLC patients

To elucidate the impact of smoking status on the survival of NSCLC patients, we conducted a comprehensive analysis of the correlation between smoking status and survival outcomes using data from TCGA and Memorial Sloan Kettering (MSK). Surprisingly, no significant association was observed between smoking status and overall survival (OS) (P=0.35, Figure S1A). Subsequent subgroup analyses revealed that both reformed smokers and current smokers with LUSC exhibited significantly longer progression-free survival (PFS) (P=0.001, Figure 1A) and OS (P=0.009) compared to never smokers (Figure 1B). Notably, the survival benefit was more pronounced in reformed smokers than in the other two groups. Conversely, no significant differences in PFS or OS were observed among patients with LUAD based on smoking status (Figure S1B,S1C). Additionally, within the cohort receiving immune checkpoint inhibitors (ICI), reformed smokers and current smokers demonstrated longer PFS (Figure 1C). Although the differences in treatment efficacy did not reach statistical significance, a discernible trend was observed wherein current smokers exhibited a higher response rate to immunotherapy compared to reformed smokers and never smokers (P=0.09, Figure S1D).

Figure 1 The relationship between smoking status and clinical outcomes in NSCLC. (A) Kaplan-Meier survival curves depict PFS of patients stratified by smoking status in LUSC cohort. (B) Kaplan-Meier survival curves illustrate OS of patients stratified by smoking status in LUSC cohort. (C) Kaplan-Meier survival curves show PFS of patients stratified by smoking status in NSCLC cohort receiving immunotherapy. LUSC, lung squamous cell carcinoma; NSCLC, non-small cell lung cancer; OS, overall survival; PFS, progression-free survival.

Distinct landscapes of somatic mutations in NSCLC across smoking status

In order to better understand how smoking affects genomic patterns in NSCLC, we analyzed the differences in genomic alterations between never smokers, reformed smokers and current smokers. The most frequently somatic mutations were TP53 (50.41%), TTN (50.32%), and MUC16 (35.14%, Figure 2A) in NSCLC patients. Then, we compared the genome differences of top 15 genes among the three groups (Figure 2B). The most frequent somatic mutations were EGFR (31.53%), TP53 (25.23%), and TTN (17.12%) in never-smoking patients. Regarding current smokers’ patients, the common somatic mutations were TP53 (60.52%), TTN (58.67%), and MUC16 (41.70%). Regarding reformed smokers, the common somatic mutations were TTN (52.34%), TP53 (50.50%), and CSMD3 (36.74%). Furthermore, 23 genes exhibited significant differences across diverse smoking statuses (Figure 2B). Intriguingly, there were no significant differences in high-frequency genes between smokers and never smokers. Additionally, our analysis revealed that current smokers exhibited the highest tumor mutational burden (TMB), followed by reformed smokers, while never smokers demonstrated the lowest TMB levels (Figure 2C). Furthermore, we conducted a comparative analysis of the differences in the top 15 genes among different subgroups of lung cancer. In LUAD, distinct mutational patterns were observed: never smokers predominantly exhibited mutations in EGFR (36.56%), TP53 (21.51%), and TTN (12.90%), while current smokers showed higher frequencies of TTN (50.69%), TP53 (50.69%), and MUC16 (47.22%) mutations. Reformed smokers demonstrated an intermediate profile with mutations in TTN (41.11%), MUC16 (39.26%), and TP53 (38.46%). Notably, all examined genes except PCDHA1 showed statistically significant differences in mutation frequencies among subgroups (P<0.05; Figure 2D). In LUSC, the mutational landscape varied similarly by smoking status: never smokers displayed frequent mutations in TP53 (44.44%), MUC16 (38.89%), and TTN (38.89%), whereas current smokers exhibited elevated mutation rates in TP53 (71.65%), TTN (67.71%), and LRP1B (36.22%). Reformed smokers showed mutation frequencies in TTN (65.24%), TP53 (64.33%), and CSMD3 (40.55%). Among the top 15 genes, six demonstrated significant differential mutation frequencies across subgroups: PKHD1L1 (P=0.02), DNAH5 (P=0.006), PDE4DIP (P=0.03), GRIN2A (P=0.004), PRDM9 (P<0.001), and CACNA1C (P=0.002) (Figure 2E).

Figure 2 Landscapes of frequently mutated genes in NSCLC stratified by smoking status. (A) The most frequently mutated genes in NSCLC. (B) Significant differences in variant allele frequencies for the top 15 genes among various smoking status cohorts. (C) Difference for TMB among various smoking status. (D) Significant differences in variant allele frequencies for the top 15 genes among various smoking status in LUAD cohorts. (E) Significant differences in variant allele frequencies for the top 15 genes among various smoking status in LUSC cohorts. *, P<0.05; **, P<0.01; ***, P<0.001. LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; ns, not significant; NSCLC, non-small cell lung cancer; TMB, tumor mutational burden.

Clinically actionable alterations in NSCLC across smoking status

To investigate the difference in treatment strategies based on smoking status, we performed a correlation analysis between smoking status and clinically actionable genes. Our findings revealed that 63.06% (70/111) of never smokers, 73.43% (199/271) of current smokers, and 67.66% (477/705) of reformed smokers harbored at least one clinically actionable alteration (Figure 3A). Notably, the frequency of level A alterations (biomarkers with either approved therapies or guidelines for cancers other than NSCLC) was significantly higher in current smokers compared to reformed smokers and never smokers (46.13% vs. 35.46% vs. 21.62%, P<0.001). Conversely, the frequency of level B alterations (biomarkers with strong biological evidence or clinical trials indicating actionability for NSCLC) was lower in current smokers compared to reformed smokers and never smokers (9.96% vs. 11.35% vs. 18.92%, P=0.04). Further analysis of clinically actionable genes revealed that never-smoking patients exhibited a higher frequency of EGFR mutations compared to reformed smokers and current smokers (40.54% vs. 10.21% vs. 6.64%, P<0.001). Interestingly, current smokers had a higher frequency of CDK12 mutations than reformed smokers and never smokers (5.17% vs. 1.13% vs. 3.60%, P<0.001) (Figure 3B, Table S1). Additionally, statistically significant differences were observed in the mutation frequencies of NF1 (P=0.049), BRIP1 (P=0.049), IDH2 (P=0.01), and BRCA2 (P=0.046). Although the mutation frequencies of KRAS (P=0.06), PDGFRA (P=0.07), and PTEN (P=0.07) did not reach statistical significance, they demonstrated notable trends consistent with their established roles in oncogenic signaling and tumor suppression.

Figure 3 Clinical actionability in NSCLC stratified by smoking status. (A) Differences in the levels of actionable alterations in various smoking status groups. (B) Differences in the frequency of clinically operable gene mutations in various smoking status groups. Level A (OncoKB levels 1 and 2), represents the presence of biomarkers with either an approved therapy or guidelines. Level B (OncoKB levels 3 and 4), represents biomarkers with strong biological evidence or clinical trials indicating that they are actionable. On-label indicates a treatment registered by federal authorities for bladder cancer, whereas in-label indicates a registration for other tumor types. *, P<0.05; ***, P<0.001. NSCLC, non-small cell lung cancer.

Mutational signature analysis in NSCLC across smoking status

To elucidate the molecular characteristics of NSCLC across different smoking statuses, somatic substitution and mutational signature analyses were conducted. C to T (C>T) substitutions were the dominant mutation type in never-smoking groups (41.8%). Dissimilarly, C to A (C>A) substitutions were the dominant mutation type in reformed smoking groups (33.8%) and current smoker groups (36.5%) (Figure S2). Additionally, the transitions which are interchanges of two-ring purines (A-G), or of one-ring pyrimidines (C-T), were dominant DNA substitution mutations in never-smoking groups (52.1%). Inversely, the transversions, which are interchanges of purine for pyrimidine bases involving the exchange of one- and two-ring structures, were dominant DNA substitution mutations in reformed smoking groups (60.4%) and current smoking groups (64.4%). Interestingly, the tobacco smoking signature (SBS4) and APOBEC mutational signatures (SBS2 and SBS13) were present in all groups (Figure 4A-4C). The SBS6 signature, indicative of defective DNA mismatch repair, was consistently observed in patients with a history of smoking. The signature 40a (etiology unknown) was detected in both current smokers and never-smoking groups, while signature 40b (etiology unknown) was exclusively present in never-smoking group. Notably, signature 5 (clock-like signature) and signature 7b [ultraviolet (UV) light exposure-associated] were uniquely identified in reformed smoking group. Furthermore, signature 3, a well-established smoking-related mutational signature, was exclusively found in current smoking group. Of particular interest, our data suggest that smoking-related features and APOBEC-mediated mutations constitute the dominant mutational processes in all three cohorts (Figure 4D). Although SBS4 was found in all groups, we found that the proportion of SBS4 in never smokers was significantly lower than that in former smokers and current smokers (18.6% vs. 31.8% vs. 36.1%, Figure 4D). Furthermore, we obtained similar results using the Jamal-Hanjani dataset validation, indicating that SBS4 exists in all groups (Figure S3).

Figure 4 Dominant mutational signatures by smoking status. (A) Dominant mutational signatures in the never smokers. (B) Dominant mutational signatures in the reformed smokers. (C) Dominant mutational signatures in the current smoking groups. (D) The proportion of main signature contribution of each group.

Association between smoking status and immune cell composition

Accumulating evidence suggests that smoking significantly modulates the immune microenvironment, which may influence lung cancer progression and treatment response. In this study, we explored the correlation between different smoking statuses and immune cell populations (10). Our analysis revealed substantial heterogeneity in the immune microenvironment based on smoking status. Compared to never smokers, current smokers and reformed smokers exhibited significantly higher proportions of naive B cells, lymphocytes, M0 macrophages, resting NK cells, CD8⁺ T cells, mast cells activated, plasma cells, and T follicular helper (Tfh) cells (P<0.05, Figure 5A). Conversely, never smokers demonstrated elevated levels of dendritic cells (both activated and resting subsets), M2 macrophages, resting mast cells, resting CD4⁺ memory T cells, and regulatory T cells (Tregs). Notably, the proportions of naive B cells, dendritic cells, lymphocytes, mast cells, plasma cells, and Tregs were nearly identical between current smokers and reformed smokers, suggesting a persistent immunological imprint of smoking even after cessation. Furthermore, the proportion of M0 macrophages in reformed smokers closely resembled that in never smokers, indicating a partial restoration of the immune microenvironment following smoking cessation. We also observed a significant T-helper (Th) 1/Th2/Th17 imbalance. Specifically, current smokers exhibited lower enrichment of Th1 and Th17 cells but higher enrichment of Th2 cells, highlighting a potential shift towards a pro-tumorigenic immune milieu (Figure 5B). Similar trends were observed in LUAD (Figure 5C,5D). However, distinct patterns emerged. LUAD with never smokers showed higher infiltration of activated dendritic cells and macrophages but lower M1 macrophages and activated mast cells compared to smokers (P<0.05, Figure 5C). In contrast, LUSC never smokers exhibited significantly lower M2 macrophages but higher M1 macrophages and Th1 cell enrichment (P<0.05, Figure 5E,5F).

Figure 5 CIBERSORT-derived immune cell composition across smoking status groups. (A) Differences in immune cell infiltration among NSCLC patients with different smoking statuses. (B) Variations in Th cells among NSCLC patients stratified by smoking status. (C) Immune cell profile disparities in LUAD patients based on smoking status. (D) Th cell heterogeneity in LUAD patients grouped by smoking history. (E) Smoking status-associated immune cell differences in LUSC patients. (F) Th cell distribution patterns in LUSC patients categorized by smoking exposure. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; NSCLC, non-small cell lung cancer; Th, T-helper.

Discussion

Cigarette smoking has been unequivocally established as a principal etiological factor in both the pathogenesis and progression of NSCLC (11). In this study, we systematically integrated and analyzed data from multiple publicly available genomic databases to elucidate the molecular mechanisms underlying smoking-associated NSCLC. Our comprehensive analysis revealed significant disparities in mutational landscapes, therapeutic vulnerabilities, and tumor immune microenvironments among never smokers, reformed smokers, and current smokers with NSCLC. Furthermore, our findings underscore the critical importance of smoking cessation in both the prevention and therapeutic management of lung cancer.

In this study, we investigated the intricate relationship between smoking status and clinical outcomes in NSCLC. Our comprehensive analysis revealed no significant association between smoking and OS in the general NSCLC population, a finding consistent with previous studies by Steuer et al. and Cao et al. (12,13). Intriguingly, a distinct pattern emerged when analyzing LUSC patients, demonstrating a statistically significant correlation between smoking and both reduced PFS and OS. This pivotal discovery underscores the histology-specific impact of smoking on NSCLC prognosis, while highlighting the unique vulnerability of LUSC to the deleterious effects of tobacco exposure. The biological plausibility of our findings is supported by emerging evidence suggesting that tobacco carcinogens may induce distinct molecular alterations in squamous cell carcinoma, including epigenetic modifications and genomic instability (14). Furthermore, our data demonstrated that smoking cessation was associated with superior survival outcomes compared to both never smokers and current smokers, emphasizing the crucial importance of smoking cessation in improving LUSC patient prognosis. These findings provide compelling evidence for the implementation of targeted smoking cessation interventions in clinical practice.

Our study reveals a compelling observation that reformed smokers exhibit greater benefits from ICI therapy compared to never smokers or current smokers, consistent with previous reports (15). This finding suggests that smoking cessation may enhance the immunogenicity of NSCLC tumors, potentially through the reversal of smoking-induced immune suppression and the restoration of anti-tumor immune responses (16). These results underscore the importance of smoking cessation as a modifiable factor that could significantly improve the efficacy of immunotherapy in NSCLC patients. Our analysis reveals significant heterogeneity in the immune microenvironment based on smoking status, highlighting smoking’s profound impact on pulmonary immunity. Compared to never smokers, current smokers and reformed smokers exhibited significantly elevated proportions of naive B cells, lymphocytes, M0 macrophages, resting NK cells, CD8⁺ T cells, mast cells activated, plasma cells, Tfh cells, Th2 cells and reduced levels of dendritic cells (both activated and resting subsets), M2 macrophages, mast cells resting, resting CD4⁺ memory T cells, Tregs, Th1/Th17 cells. These findings provide crucial insights into how smoking modulates immune responses and promotes lung cancer pathogenesis. Elevated naive B cells may result from persistent tobacco-induced antigenic stimulation, driving compensatory proliferation but potentially increasing autoimmune risk (17). Reduced B memory cells indicate impaired secondary immunity, likely due to smoking’s suppression of memory differentiation and survival, exacerbated by chronic inflammation (18). The increased levels of M0 macrophages and the concomitant reduction in anti-inflammatory M2 macrophages suggest a state of immune dysregulation, fostering a predominantly pro-inflammatory microenvironment (19). Elevated resting NK cells indicate immune imbalance, potentially impairing cancer cell recognition and elimination, promoting tumorigenesis (20). The elevated lymphocyte count observed in smokers may reflect a systemic immune response to chronic exposure to cigarette smoke. Increased CD8⁺ T cells, plasma cells, and Tfh cells, alongside reduced dendritic cells (DCs), highlight the complex interaction between smoking and adaptive immunity. While heightened CD8⁺ T cells may enhance anti-tumor responses, diminished DC levels could impair antigen presentation and T cell activation, compromising immune surveillance and facilitating tumor immune evasion, contributing to smoking-related lung cancer progression (21,22). Elevated plasma and Tfh cells suggest altered humoral immunity, potentially promoting autoantibodies or pro-tumor cytokines. Imbalanced Tfh, Th2, and Treg, Th1, and Th17 ratios further disrupt immune regulation, with increased Tfh and Th2 responses fostering chronic inflammation and tissue remodeling, while reduced Treg, Th1, and Th17 levels impair anti-tumor immunity and increase infection susceptibility. This intricate immune dysregulation underscores smoking’s multifaceted impact on autoimmune and oncogenic processes (23). Similar immune cell proportions in current and former smokers highlight smoking’s lasting immunological effects, indicating irreversible immune changes. However, partial M0 macrophage recovery in former smokers suggests immune microenvironment restoration post-cessation. aligning with the improved immunotherapy response observed in former smokers, potentially due to reversed immune suppression and restored anti-tumor immunity. These findings imply that smoking cessation can both reduce cancer risk and enhance immunotherapy efficacy in NSCLC patients by reversing immunosuppression and restoring immune function, thus improving treatment outcomes. This underscores smoking cessation’s critical role in cancer prevention and therapy.

Our comprehensive analysis of mutational signatures in lung cancer patients across diverse smoking statuses has yielded critical insights into the molecular mechanisms of tumorigenesis. The ubiquitous presence of the smoking-related SBS4 signature across all cohorts, including never smokers, strongly suggests potential exposure to tobacco-related carcinogens through secondhand smoke, a finding that aligns well with established epidemiological evidence (24). Additionally, the consistent detection of APOBEC mutational signature across all populations further supports the notion that smoking, whether direct or indirect, plays a pivotal role in lung cancer development across different groups (25,26). Of particular interest is the predominant observation of the SBS6 signature, indicative of defective DNA mismatch repair, in patients with a history of smoking. This finding supports the hypothesis that tobacco exposure may impair DNA repair mechanisms, leading to an accumulation of mutations and genomic instability (27,28). The absence of the defective homologous recombination DNA damage repair signature in reformed smokers suggests a potential restoration of this repair pathway following smoking cessation. This observation highlights the dynamic nature of DNA repair mechanisms in response to changes in smoking behavior and provides a promising avenue for further investigation into the reversibility of smoking-induced DNA damage. In the present study, we observed the presence of signature 40a (etiology unknown) in both current smokers and never smokers, suggesting the existence of a common but yet unidentified mutational process independent of smoking status. Particularly noteworthy is the unique identification of signature 5 (clock-like signature) and signature 7b (associated with UV radiation exposure) among reformed smokers. This intriguing finding raises important questions regarding potential biological alterations or environmental exposures associated with smoking cessation. The clock-like nature of signature 5 may reflect age-related accumulation of mutations, while the presence of signature 7b, typically associated with UV exposure, suggests either a novel environmental factor or an incompletely understood biological process in former smokers.

We must acknowledge several limitations in our study that could influence the interpretation and generalizability of our findings. First, our sample data were exclusively sourced from public databases. While these databases provide a wealth of information, they may introduce selection bias due to variations in data collection methods, patient demographics, race, and clinical characteristics across different institutions or regions. Second, our analysis did not account for the duration of smoking cessation or the intensity of smoking history (e.g., pack-years). These factors are critical in understanding the nuanced relationship between smoking and lung cancer, as they may significantly influence tumor biology, clinical behavior, and treatment outcomes. Third, while our study focused on analyzing data from public databases, we did not evaluate potential confounders such as environmental exposures, comorbidities, or genetic predispositions, which could contribute to the heterogeneity observed in lung cancer. These factors might interact with smoking status to influence disease progression, treatment response, and outcomes. Finally, while our study achieved an adequate overall sample size, certain subgroups (particularly never-smoking LUSC patients) remain underpowered, which may introduce bias in LUSC-specific analyses. In conclusion, while our study provides valuable insights, these limitations underscore the need for further research to address these gaps. By incorporating more diverse datasets, detailed smoking history, and potential confounders, future studies can refine our understanding of the mechanisms underlying smoking-associated lung cancer and inform more effective, personalized therapeutic strategies.

Conclusions

In summary, our study elucidates the intricate molecular mechanisms underlying smoking-associated NSCLC and underscores the critical importance of incorporating smoking history into therapeutic decision-making. By delineating the genetic, molecular, and immunological disparities between smoking-related and non-smoking NSCLC, we have laid the groundwork for the development of more personalized and efficacious treatment strategies. Furthermore, our findings reinforce the pivotal role of smoking cessation in both cancer prevention and management. Future research endeavors should focus on validating these findings in larger prospective cohorts and exploring the translational potential of our discoveries in clinical settings.

Acknowledgments

We thank the patients and their families, who kindly agreed to provide samples to support this study. We would like to thank American Journal Experts (AJE) for English language editing.

Footnote

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1328/prf

Funding: This study was supported by a grant from Tianjin Science and Technology Program Project (No. 20JCQNJC00270).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1328/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.

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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