Influence of MMP12 single nucleotide polymorphism rs586701 on the prognosis of primary lung cancer patients：a multicenter prospective study

Influence of MMP12 single nucleotide polymorphism rs586701 on the prognosis of primary lung cancer patients：a multicenter prospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of MMP12 single nucleotide polymorphism rs586701 on the prognosis of primary lung cancer patients：a multicenter prospective study Chang Xu, Wei Du, Zhenyu Sun, Qiang Li, Bo Shen, Yan Shang, Junjie Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7840143/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Purpose Lung cancer is a common cancer, with a high mortality rate and poor prognosis. Predicting the prognosis of lung cancer patients and using this information to develop treatment strategies and interventions is important for prolonging patient survival. Methods Blood samples were collected from 839 patients diagnosed with lung cancer, and genomic DNA was extracted for genotyping using SNPscan technology. In order to adjust for multiple factors, the data was stratified by age, sex, smoking status, family history, TNM stage and cancer tissue type. The association between lung cancer prognosis and genotype was then analyzed using a multivariate Cox proportional risk model. Results A polymorphism in the MMP12 gene, the T > G variant at position 586701, has been associated with a worse prognosis. Patients with the TG genotype (TG vs TT, HR = 1.21, 95% CI: 1.01–1.44, P = 0.035) exhibited a worse prognosis. Stratified analyses showed that among male, younger than 60 years old and smoking patients, patients with the TG genotype had a lower survival time (HR = 1.24, 95% CI: 1.01–1.52, P = 0.04; HR = 1.58, 95% CI: 1.18–2.12, P = 0.002; HR = 1.30, 95% CI: 1.06–1.61, P = 0.013). In patients with SCC and NSCLC, survival time was shorter with the TG genotype (TG vs TT, HR = 1.48, 95% CI: 1.10–2.01, P = 0.010; HR = 1.21, 95% CI: 1.01–1.46, P = 0.038). In the designed genetic model, the dominant genotype TG + GG was associated with worse prognosis among patients aged less than 60 years (TG + GG vs TT, HR = 1.43, 95% CI: 1.07–1.90, P = 0.014). Conclusion The MMP12 polymorphism rs586701 T > G may be associated with a worse prognosis of lung cancer. MMP12 single nucleotide polymorphism rs586701 lung cancer prognosis Figures Figure 1 Figure 2 Figure 3 1 INTRODUCTION Lung cancer represents the leading cause of cancer-related mortality on a global scale [ 1 ]. Lung cancer has the highest incidence and mortality rate in China[ 2 ]. Although early diagnosis and treatment of lung cancer are improving, the clinical prognosis remains poor, with a five-year survival rate of less than 20%[ 3 ]. The establishment of accurate prognostic indicators has been demonstrated to improve treatment outcomes and prolong patient survival in lung cancer[ 4 ]. Epidemiological studies have demonstrated that age, male sex, smoking history, small cell lung cancer, and advanced lung cancer (stage III or IV) are associated with a poor prognosis for lung cancer[ 5 ]. Furthermore, an increasing number of studies have indicated that individual genetic factors may be of significant importance in the prognosis of lung cancer[ 6 ]. Single nucleotide polymorphisms (SNPs), which are common genetic variants that can affect gene expression and the prognosis of lung cancer patients, are a notable example of this[ 7 , 8 ]. The family of matrix metalloproteinases (MMPs) belongs to the family of zinc-dependent endopeptidases, a class of proteases that degrade the extracellular matrix and basement membrane barrier[ 9 , 10 ]. In addition to their involvement in multiple stages of cancer development, they also play a significant role in tumor invasion and metastasis[ 11 ]. A total of 20 or more distinct types of MMPs have been identified. MMPs can be classified according to their structural features and sensitivity to substrates. The collagenases (MMP-1, MMP-8, MMP-13 and MMP-18), gelatinases (MMP-2 and MMP-9), and matrix proteins (MMP-3, MMP-10 and so on) are examples of this classification. MMP-11, matrices (MMP-7 and MMP-26), GPI anchors (MMP-17 and MMP-25), membrane-type MMPs (MMP-14, MMP-15, MMP-16, and MMP-24), and so on[ 12 , 13 ]. MMP12 is located on chromosome 11q22.3 in the cluster of genes encoding matrix metalloproteinases and encodes a member of the M10 family of matrix metalloproteinase peptidases. Proteins in this family are involved in the breakdown of extracellular matrices during normal physiological processes, including embryonic development, reproduction, and tissue remodeling. They are also involved in disease processes, such as arthritis and metastasis. The encoded procollagen undergoes proteolytic processing to produce mature proteases that degrade soluble and insoluble elastin. Molecular epidemiological studies have demonstrated that genetic polymorphisms in matrix metalloproteinase are associated with susceptibility and prognosis in lung cancer[ 14 , 15 ], adenocarcinoma[ 16 ], colorectal cancer[ 17 ], cervical cancer[ 18 ] and other cancers[ 19 , 20 ]. The MMP9 -1562 C/T polymorphism and the MMP13 -77 G/A polymorphism have been linked to an increased risk of developing NSCLC [ 21 ]. Certain MMP3 promoter polymorphisms have been associated with an elevated susceptibility to NSCLC and an increased risk of lymph node metastasis[ 22 ]. It has been demonstrated that polymorphisms in the MMP2 , MMP9 and MMP12 genes, specifically rs243864, rs3918242 and rs652438, respectively, influence the transcriptional activity and expression level of the encoded protein[ 23 , 24 ], which in turn affects prognosis. rs586701 is situated within the gene region between MMP3 and MMP12 . However, the relationship between the MMP12 polymorphism rs586701 and lung cancer prognosis has not yet been elucidated. Therefore, we conducted a study to investigate the association between the MMP12 polymorphism rs586701 and the prognosis of lung cancer. To do this, we stratified the lung cancer population by age, sex, smoking status, family history, lung cancer stage, and tissue type. Additionally, we collected pre-treatment peripheral blood samples from lung cancer patients and performed genotyping and follow-up. 2 METHODS 2.1 Ethics Statement The entire study design protocol was approved by the Ethics Committee of the School of Life Sciences, Fudan University. Prior to the collection of blood samples, each subject was provided with a standard informed consent form, which clearly explained the purpose and procedures of the study. All data were recorded anonymously. 2.2 Subjects of study and data collection A total of 888 patients with primary lung cancer were admitted to the Naval Military Medical University (NMMU) and the Institute of Health Sciences, Fudan University Taizhou (IHSFT) between January and November 2009. Of these, 536 cases were from Changhai Hospital affiliated to the NMMU, and 352 cases were from the Institute of Health Sciences, Fudan University Taizhou. The follow-up period commenced at the time of inclusion in the study and concluded at the end of 2019. During this period, 49 patients were excluded due to incomplete clinical data. The remaining 839 patients' data were collected and subsequently analyzed. The study population consisted of Han Chinese individuals. Inclusion criteria included histological diagnosis of primary lung cancer and no history of malignant tumors in other organs. There were no restrictions on age or gender, and cancer stage was not a limiting factor. Histological diagnosis of lung cancer was based on World Health Organization criteria, and lung cancer stage was determined according to the 8th edition of TNM staging of lung cancer, with confirmation by two independent pathologists. The initial stages (I and II) were considered to be of an early nature, while the subsequent stages (III and IV) were regarded as being of a later chronology. The clinical data pertaining to the patients was obtained from their medical records, while the follow-up information was derived from telephone interviews. 2.3 SNP genotyping Venous blood samples were collected from all enrolled patients prior to the commencement of treatment. Genomic DNA was extracted using the Qiagen Blood DNA Extraction Kit (Qiagen, Hilden, Germany), and the resulting DNA samples were amplified by polymerase chain reaction (PCR), and genotyping was conducted using the 2×48-plex SNPscan TM kit (order no. G0104; Genentech, Shanghai, China) that based on double ligation and multiplex fluorescence PCR. SNPscan is a proprietary multiple SNP genotyping system that permits the simultaneous genotyping of 48, 96, 144, or 192 SNPs per sample in a single tube or sample. SNPscan employs a highly specific linkage reaction to distinguish between alleles. The genotyping quality was determined using a detailed procedure that included a successful detection rate of more than 95%, the identification of duplicate genotypes, the use of internal positive control samples, and Hardy-Weinberg equilibrium (HWE) testing. Furthermore, the laboratory personnel responsible for the genotyping analysis were unaware of the patient's clinical information. 2.4 Statistical analysis Prior to performing the association analysis, the HWE test was conducted on rs586701 within the study population using the Pearson chi-square test. Overall survival (OS) was calculated from the date of sample collection to the date of death from any cause or the date of the last follow-up visit. The median survival time (MST) was estimated by the Kaplan-Meier (K-M) method, and differences between groups were tested by the log-rank test. Univariate and multivariate Cox regression analyses were employed to estimate hazard ratios (HR) and their 95% confidence intervals (CI), adjusted for age and sex. A stratified analysis was conducted according to age, sex, smoking status, family history of malignancy, TNM stage, and histologic type of lung cancer. The relationships between SNPs and clinical outcomes in diverse populations of lung cancer patients were evaluated using four SNP genetic models: allelic, genotypic, dominant, and recessive. These models were stratified according to age, gender, smoking status, family history, lung cancer stage, and tissue type. All tests were two-sided and statistically significant at the 0.05 level. All statistical analyses were performed using R version 3.6.2. 3 RESULTS 3.1 Demographic and clinical characteristics and prognostic analysis. Table 1 presents a summary of the demographic characteristics of the 839 lung cancer patients. During the follow-up period, 668 patients (79.6%) died, 103 patients (12.3%) survived for more than five years, and 68 patients (8.1%) were lost to follow-up. The MST for all patients was 36.73 months. Of all patients, 229 (27.3%) were female and 610 (72.7%) were male. In total, 315 (37.5%) were below 60 years of age and 524 (62.5%) were above 60 years of age. A total of 237 patients (28.2%) had never smoked, while 582 patients (69.4%) had a history of smoking. Of the patients, 537 (64%) had no family history of malignancy, while 302 (36%) had a family history of malignancy. The histological diagnoses were as follows: 367 patients (43.7%) were diagnosed with adenocarcinoma (ADC), 282 patients (33.6%) with squamous cell carcinoma (SCC), 72 patients (8.6%) with small cell lung cancer (SCLC), and 118 patients (14.1%) with other types of cancer. The TNM staging system revealed that 154 cases (18.4%) exhibited stage I and II lung cancer, while 625 cases (74.5%) exhibited stage III and IV lung cancer. Table 1 Characteristic distribution in Chinese patients with lung cancer and prognosis analysis. N: number; MST: median survival time. Variables N(%) MST # P Total 839 36.73 Sex 0.01 Female 229(27.3%) 40.17 Male 610(72.7%) 34.27 Age 0.003 <60 315(37.5%) 40.87 ≥60 524(62.5%) 33.20 Smoking stage Nonsmoker 237(28.2%) 41.03 Smoker 582(69.4%) 33.90 Unknown 20(2.4%) 67 Family History 0.462 Yes 302(36%) 33.63 No 537(64%) 38.03 Subtype 0.211 ADC 367(43.7%) 38.80 SCC 282(33.6%) 33.63 SCLC 72(8.6%) 33.90 Others 118(14.1%) 36.20 TNM stage < 0.001 StageⅠ+Ⅱ 154(18.4%) 113.93 StageⅢ+Ⅳ 625(74.5%) 29.4 Unknown 60(7.1%) 66.43 The log-rank test demonstrated statistically significant differences in MST with respect to gender, age, smoking status, and clinical stage (log-rank P < 0.05). The MST was significantly longer in women, patients aged < 60 years, nonsmokers, and patients with early-stage tumors than in men (MST: 40.17 M vs. 34.27 M; P = 0.01), and in patients aged ≥ 60 years (MST: 40.87 M vs. 33.2 M). The mean survival time was 33.2 months in smokers (P < 0.001), 41.03 months in patients with advanced tumors (P < 0.001), and 113.93 months in patients who were smokers (P < 0.001). 3.2 Correlation between MMP12 polymorphisms and prognosis of lung cancer. A total of 817 genotypes were identified, with a detection rate of 97.38%. Of these, 607 were TT genotypes, 208 were TG genotypes, 24 were GG genotypes, and the frequency of the MMP12 rs586701 genotypes were consistent with the Hardy-Weinberg equilibrium (P = 0.2051), indicating that the lung cancer patients participating in the study were in a state of genetic equilibrium and that the data obtained from the lung cancer patients studied were credible. The frequencies of rs586701 alleles T and G were 84.73% (1132/1336) and 15.27% (204/1336) in deceased patients and 84.80% (290/342) and 15.20% (52/342) in surviving patients, respectively. Univariate Cox regression analysis revealed that in the overall sample, patients with the TG genotype exhibited a poorer prognosis than patients with the TT genotype (TG vs TT, HR = 1.21, 95% CI: 1.01–1.44, P = 0.034; Table 2 ). K-M survival curves demonstrated that patients with the TG genotype exhibited a shorter median survival time compared to patients with the TT genotype (MST: 38.5 M vs 29.53 M). This difference was statistically significant (Log-rank P = 0.035; Fig. 1 ). Table 2 Association between MMP12 gene polymorphism rs586701 and prognosis of Chinese lung cancer patients. Model Death/survive MST HR(95% Cl) * P HR a (95%Cl) P a Allele T(ref) 1132/290 37.13 1 1 G 204/52 32.7 1.03 (0.89–1.20) 0.698 1.03 (0.89–1.20) 0.671 Genotype T/T(ref) 479/128 38.5 1 1 T/G 174/34 29.53 1.21 (1.01–1.44) 0.034 1.21 (1.01–1.44) 0.035 G/G 15/9 67.33 0.60 (0.35–1.02) 0.059 0.61 (0.36–1.04) 0.067 Dominate T/T(ref) 479/128 38.5 1 1 T/G + G/G 189/43 31.43 1.12 (0.95–1.33) 0.181 1.12 (0.95–1.33) 0.179 Recessive T/T + T/G(ref) 653/162 36.03 1 1 G/G 15/9 67.33 0.57 (0.34–0.97) 0.038 0.58 (0.34–0.99) 0.045 HR: hazard ratio;CI: confidence interval; ref:reference; a : Adjusted by age, sex. 3.3 Stratified analysis of the association between MMP12 polymorphisms and lung cancer prognosis. A stratified analysis was conducted to investigate the association between the MMP12 polymorphism rs586701 and the prognosis of lung cancer patients. Stratified analyses showed (Table 3 ) that among male lung cancer patients, lung cancer patients younger than 60 years old, and smoking lung cancer patients, patients with the TG genotype had a shorter survival time than patients with the TT genotype (adjusted risk ratios HR = 1.24, 95% CI: 1.01–1.52, P = 0.04; HR = 1.58, 95% CI: 1.18–2.12, P = 0.002 ; HR = 1.30, 95% CI: 1.06–1.61, P = 0.013). In patients with SCC, survival time was shorter in patients with NSCLC, and in patients with the TG genotype (TG vs TT, adjusted risk ratio HR = 1.48, 95% CI: 1.10–2.01, P = 0.010; HR = 1.21, 95% CI: 1.01–1.46, P = 0.038). In the designed genetic model, the dominant genotype TG + GG was associated with a shorter survival time and worse prognosis among lung cancer patients aged less than 60 years (TG + GG vs TT, adjusted risk ratio HR = 1.43, 95% CI: 1.07–1.90, P = 0.014; Table 4 ). Table 3 Association between MMP12 polymorphism rs586701 in genotype models and prognosis of Chinese patients with lung cancer. Variables Death/survive HR(95%Cl) P HR a (95%Cl) P a TT(ref) TG Sex Male 366/85 123/17 1.23 (1.01–1.52) 0.044 1.24 (1.01–1.52) 0.042 Female 113/43 51/17 1.20 (0.86–1.67) 0.286 1.14 (0.81–1.60) 0.445 Age ≥60 317/62 109/20 1.02 (0.82–1.26) 0.885 1.05 (0.84–1.31) 0.664 <60 162/66 65/14 1.60 (1.19–2.14) 0.002 1.58 (1.18–2.12) 0.002 Smoking stage Smoker 355/76 117/14 1.30 (1.05–1.60) 0.015 1.30 (1.06–1.61) 0.013 Nonsmoker 116/46 53/18 1.12 (0.81–1.55) 0.512 1.07 (0.77–1.48) 0.706 Family History Yes 174/43 68/8 1.29 (0.97–1.70) 0.078 1.27 (0.95–1.69) 0.110 No 305/85 106/26 0.99 (0.71–1.39) 0.955 0.99 (0.71–1.39) 0.953 Subtype ADC 196/64 74/23 1.10 (0.84–1.44) 0.481 1.09 (0.83–1.42) 0.551 SCC 173/38 59/3 1.46 (1.08–1.97) 0.013 1.48 (1.10–2.01) 0.011 NSCLC 434/116 160/33 1.20 (1.00-1.44) 0.046 1.21 (1.01–1.46) 0.038 TNM stage StageⅠ+Ⅱ 58/59 18/15 1.41 (0.67–2.95) 0.367 1.48 (0.70–3.11) 0.303 Stage Ⅲ+Ⅳ 377/62 149/18 1.08 (0.77–1.51) 0.662 1.04 (0.74–1.46) 0.812 HR: hazard ratio;CI: confidence interval; ref:reference; a : Adjusted by age, sex. Table 4 Association between MMP12 polymorphism rs586701 in dominant genotype and prognosis of Chinese patients with lung cancer. Variables Death/survive HR(95%Cl) P HR a (95%Cl) P a TT(ref) TG + GG Sex Male 366/85 136/23 1.15 (0.94–1.40) 0.172 1.15 (0.94–1.40) 0.171 Female 113/43 53/20 1.11 (0.80–1.54) 0.525 1.07 (0.77–1.49) 0.692 Age ≥60 317/62 120/25 0.96 (0.78–1.19) 0.729 0.99 (0.80–1.23) 0.938 <60 162/66 65/14 1.60 (1.19–2.14) 0.002 1.58 (1.18–2.12) 0.002 Smoking stage Smoker 355/76 132/19 1.21 (0.99–1.48) 0.063 1.21 (0.99–1.49) 0.059 Nonsmoker 116/46 53/22 1.00 (0.72–1.39) 0.982 0.96 (0.69–1.34) 0.813 Family History Yes 174/43 73/12 1.18 (0.89–1.55) 0.248 1.15 (0.87–1.53) 0.314 No 305/85 116/31 1.09 (0.88–1.35) 0.429 1.10 (0.88–1.36) 0.409 Subtype ADC 196/64 80/27 1.06 (0.81–1.37) 0.678 1.05 (0.81–1.36) 0.732 SCC 173/38 66/5 1.30 (0.98–1.73) 0.074 1.31 (0.98–1.75) 0.067 NSCLC 434/116 175/42 1.12 (0.94–1.33) 0.222 1.13 (0.94–1.34) 0.189 TNM stage StageⅠ+Ⅱ 58/59 18/19 1.41 (0.67–2.95) 0.367 1.48 (0.70–3.11) 0.303 Stage Ⅲ+Ⅳ 377/62 163/23 1.04 (0.87–1.25) 0.665 1.04 (0.87–1.26) 0.647 HR: hazard ratio;CI: confidence interval; ref:reference; a : Adjusted by age, sex. The K-M survival curves demonstrated that the MMP12 polymorphism rs586701 T > G was associated with a reduction in median survival time across a broad spectrum of patients. K-M curves demonstrated that male patients with the TG genotype (MST: 27.37 M) exhibited a shorter MST compared with male patients with the TT genotype (MST: 37.17 M) (log-rank P = 0.046; Fig. 2 A). Compared with patients aged < 60 years with the TT genotype (MST: 47.57 M), patients aged < 60 years with the TG genotype (MST: 30.37 M) had a shorter MST (Log-rank P = 0.0016; Fig. 2 B), and patients aged < 60 years with the TG + GG genotype (MST: 32.7 M) had a shorter MST (Log-rank P = 0.012; Fig. 2 C). The mean survival time of patients with the TG genotype (MST: 25.6 M) was found to be significantly shorter than that of patients with the TT genotype (MST: 37.13 M) (Log-rank P = 0.016; Fig. 2 D). SCC patients with the TG genotype (MST: 25.13 M) exhibited a shorter MST compared to SCC patients with the TT genotype (MST: 38.43 M) (Log-rank P = 0.012; Fig. 2 E). Patients with non-small cell lung cancer who had the TG genotype (MST: 30.23 M) exhibited a shorter median survival time compared to patients with the TT genotype (MST: 39.37 M) (Log-rank P = 0.047; Fig. 2 F). Expression and prognostic associations were analyzed using the Cancer Genome Atlas (TCGA) database. Our findings indicate that the expression of MMP12 polymorphisms is higher in lung cancer tissues (including adenocarcinomas and squamous carcinomas) than in normal tumor walls (Fig. 3 A). Furthermore, the expression of MMP12 polymorphisms was found to be higher than that in tumor wall tissues in patients who smoked (Fig. 3 B), in patients aged less than 60 years (Fig. 3 C), and in male patients (Fig. 3 D). 4 DISCUSSION A potential correlation between the MMP12 polymorphism rs586701 and the prognosis of lung cancer was identified through genotyping and follow-up investigation of blood samples from 839 lung cancer patients. In the overall sample, we observed that the rs586701 TG genotype was associated with a worse prognosis in lung cancer. In further stratified analyses, we observed the same results in men, individuals aged < 60 years, smokers, SCC and NSCLC patients. To the best of our knowledge, our study is the first to investigate the association between the MMP12 polymorphism rs586701 and lung cancer prognosis based on genotype/allele in a Han Chinese population. Our findings indicate that the MMP12 polymorphism rs586701 T >G is associated with a worse prognosis. MMPs are a family of proteins structurally and functionally related to zinc endopeptidases that control extracellular protein hydrolysis. They are important regulators of the cellular microenvironment and have been implicated in the invasion and metastasis of a variety of tumor cells. MMP12 expression has been demonstrated to serve as a prognostic marker and therapeutic target in the development of lung cancer in humans and mice, and patients with high MMP12 expression have been shown to have a poorer prognosis[ 25 , 26 ]. One study demonstrated that elevated MMP12 expression was associated with a poor prognosis of lung cancer in mice through a mouse lung cancer model[ 27 ]. A study investigating the impact of MMP12 polymorphisms on lung function revealed that MMP12 polymorphisms were associated with reduced lung function in patients with lung cancer[ 28 ]. The present study has identified a significant association between the MMP12 polymorphism rs586701 T >G and poor prognosis in lung cancer. Furthermore, clinical stratification revealed that this association is particularly prevalent in men, individuals aged less than 60 years, smokers, and patients with SCC and NSCLC lung cancer. The results of our study indicated that the MMP12 polymorphism rs586701 T >G was associated with a poor prognosis in male lung cancer patients. The authors concluded that the prognostic differences due to gender factors are mainly closely related to sex hormones and the X chromosome. They further noted that differences in sex hormones between males and females are important biological factors affecting the prognosis of lung cancer[ 29 , 30 ]. Additionally, they observed that the expression of oncogenes encoded by the X chromosome is higher in female cancer patients[ 31 ]. A study conducted on animals and humans revealed that exposure to tobacco pollutants elevated the likelihood of lymphoid aggregation formation and heightened susceptibility to impaired lung function in female mice relative to male mice. Additionally, in human lung tissue, female smokers demonstrated an augmented number of lymphoid follicles and a less favorable prognosis for patients with lung cancer compared to their male counterparts[ 32 ]. The aforementioned findings collectively demonstrate that gender is a significant clinical factor that affects the prognosis of lung cancer patients. A stratified analysis revealed that the MMP12 polymorphism rs586701 T > G is associated with a poorer prognosis in patients with smoking-related lung cancer. Macrophages are the primary cell type responsible for the production of MMP-12, a protein that plays a crucial role in the inflammatory response. These cells are the main cell type that normally patrols the lower airways and are the primary inflammatory cell type produced during smoking. Smoking is a major risk factor for lung cancer, and the smoke produced by tobacco combustion contains dozens of lung carcinogens such as aromatic adducts, nicotine, heterocyclic aromatic amines, acrolein, alkyl adducts, tar, etc, which are known to affect gene expression through DNA adduct formation[ 33 ], epigenomic modification[ 34 ], the function of XPC (a DNA repair protein)[ 35 ], the inhibition of gluconeogenic synthase kinase 3 (GSK3) and induces the expression of involucrin (a marker of squamous differentiation)[ 36 ], and regulates the acetylcholine system to affect the proliferation and differentiation of lung cancer cells[ 37 ], among other pathways that affect the prognosis of lung cancer[ 38 ]. In humans, the expression of MMP12 in alveolar macrophages is approximately ninefold higher in smokers than in nonsmokers[ 39 ]. The overexpression of MMP12 in the lower respiratory tract results in elastin degradation, which in turn leads to the formation of elastin fragments. These fragments can initiate a positive feedback loop, further increasing macrophage production in mice and cultured human cells[ 40 ]. Furthermore, animal studies have demonstrated that mouse models of lung cancer exposed to secondhand smoke exhibit elevated MMP12 expression (9.3-fold compared to airborne polyurethane controls) and a worse prognosis for lung cancer[ 27 ]. The authors of the study concluded that the MMP12 polymorphism rs586701 T>G resulted in the upregulation of MMP12 expression, which led to a shorter median survival time and a worse prognosis for the patients.。 The specific type of cancer tissue is also a significant factor influencing the prognosis of lung cancer. In one study, 13 patients with SCC were analyzed with normal lung tissue and 13 patients with adenocarcinoma for related gene expression. It was found that MMP12 expression was significantly up-regulated in patients with SCC[ 41 ], while it was also pointed out that MMP12 was negatively correlated with prognosis [ 42 ], and that MMP12 deletion could be used as a marker for a good prognosis in SCC[ 43 ]. A study that performed a multi-omics analysis of treated NSCLC patients, including multiplex immunofluorescence, nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink, found that upregulation of MMP12 expression is associated with reduced survival in NSCLC patients[ 44 ]. A study demonstrated that atorvastatin significantly reduced the expression of MMP12 in cellular experiments, indicating that MMP12 is a potential target for the treatment of NSCLC patients[ 45 ]. Furthermore, the lack of MMP12 in lung cancer cells in vivo has been shown to reduce tumor growth and invasiveness[ 46 ]. These studies have illustrated that elevated MMP12 expression is associated with a worse prognosis. It is postulated that our findings, which indicate that the MMP12 polymorphism rs586701 T >G upregulates MMP12 expression, result in shorter survival times and a poorer prognosis for patients with SCC and NSCLC. This study is subject to both strengths and limitations. In terms of strengths, we conducted a large-sample study to detect predictive polymorphisms in all primary lung cancer patients. This was done in order to reduce data complexity and prevent important gene deletions. The aim was to investigate the relationship between rs586701 and the prognosis of lung cancer patients. However, it should be noted that the study still has some limitations. Firstly, the study samples were mainly from two hospitals, which may have introduced some selection bias. Secondly, the genetic model assessment was performed retrospectively, which is prone to recall bias. Thirdly, cellular experiments were not performed to investigate the mechanism by which the MMP12 polymorphism rs586701 T >G made lung cancer patients' prognosis worse. Additionally, the sample sizes in some of the stratified analyses were relatively small, which may have affected the statistical associations between the tested genotypes and patient characteristics. 5 CONCLUSION This is the inaugural study to investigate the correlation between the MMP12 polymorphism rs586701 and prognosis in lung cancer patients. The study revealed that the MMP12 polymorphism rs586701 was significantly associated with poor prognosis in all lung cancer patients. Furthermore, the study identified that men carrying the rs586701 TG genotype, younger than 60 years of age, smokers, and patients with SCC and NSCLC lung cancers all had a poorer prognosis. In addition, the G allele was found to be associated with a poor prognosis in lung cancer patients younger than 60 years of age. Furthermore, both the TG and dominant TG + GG genotypes were also found to have a poor prognosis in lung cancer patients younger than 60 years of age. The relationship between the MMP12 polymorphism rs586701 and the clinical characteristics of specific cancers provides a new target for "individualized anticancer" drug therapy. This can help clinicians to select appropriate individualized patients for target regulation and guide early evidence-based treatment, which is of great value for improving the prognosis of cancer patients. It provides medical personnel and their families with a more scientific basis for medical decision-making, while also helping to avoid over-medication and the waste of medical resources. In recent years, in addition to conventional surgical treatment, radiotherapy, and chemotherapy, immunotherapy has emerged as a promising therapeutic modality for lung cancer. In the context of lung cancer, cancer cells have been observed to “disguise” themselves as normal cells by expressing proteins such as PD-L1. This protein binds to PD-1 on the surface of immune cells, thereby evading the immune system's attack. Immunotherapy is an “indirect anti-cancer” therapy that utilizes immunotherapeutic drugs (e.g., PD-1/PD-L1 inhibitors) to impede the binding of PD-1/PD-L1, release the immunosuppression of cancer cells, and reactivate the T-cells to destroy cancer cells. Following the administration of immunotherapy, the immune system may develop a “memory” effect, thereby continuing to impede the growth of tumors. This approach offers several advantages, including prolonged efficacy, relatively mild and reversible side effects, and a high potential for long-term benefits. Furthermore, it is applicable to a wide range of lung cancers and can be utilized in combination with various therapeutic modalities. The prognostic efficacy of immunotherapy is contingent upon a variety of factors, including the tumor microenvironment and the patient's immune status. In the future, we will conduct more extensive clinical data collection and analysis to explore the relationship between immune checkpoint inhibitors and specific patient characteristics. We may also adopt a longitudinal study design to evaluate the efficacy and safety of ICIs in different disease stages. This would improve the prospective nature of the study and provide relevant recommendations for subsequent researchers. Declarations 6.1 Ethical Approval and Consent to participate The Ethics Committees of the School of Life Sciences of Fudan University (approval number: Discussion and Research Grant No. 244) and Shanghai Changhai Hospital (approval number: CHEC2015-100) have approved this study, and all participants gave informed consent for the study. 6.2 Human Ethics The study was conducted in compliance with the principles of the Declaration of Helsinki (1996). 6.3 Consent for publication Not applicable. 6.4 Availability of supporting data The data that support the findings of this study are not publicly available because Genomic data is private and cannot be disclosed according to Chinese law. But all data in this study are available upon reasonable request from corresponding author Yuanlin Song. 6.5 Competing interests Corresponding author declares no conflict of interest on behalf of all authors. 6.6 Funding The work is currently receiving four grants from the National Natural Science Foundation of China (Grant NO. 81372236, 82272863 and 81972822), the Shanghai Postdoctoral Research Foundation in 2021 (Class A Grant NO. 12R21411500). 6.7 Authors' contributions Chang Xu, Wei Du and Zhenyu Sun contributed equally to this work. Chang Xu, Wei Du and Zhenyu Sun: Writing-original draft. Qiang Li: Visualization. Junjie Wu and Bo Shen: Funding acquisition. Yan Shang and Yuanlin Song: Writing – review & editing. 6.8 Acknowledgments Supported in part through the computational resources and staff expertise provided by Scientific Computing at the Fudan University in Shanghai. References Sung H, Ferlay J, Siegel RL, et al. 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14:07:14","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147392,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/d65c04e69139c54fc033e7bb.html"},{"id":97261703,"identity":"0cdc4e6c-a638-4ccb-918b-f139038b10bf","added_by":"auto","created_at":"2025-12-02 14:07:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":149989,"visible":true,"origin":"","legend":"\u003cp\u003ePatients with the TG genotype exhibited a shorter median survival time (MST) compared to patients with the TT genotype (MST:38.5 M vs 29.53 M).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/d58dda5cdbd70c52f3578f1c.png"},{"id":97367696,"identity":"aa46935c-0823-41f4-b4af-edeb9cc353e3","added_by":"auto","created_at":"2025-12-03 16:20:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":586674,"visible":true,"origin":"","legend":"\u003cp\u003eImpacts of MMP12 polymorphism rs586701 on the prognosis of patients with lung cancer patients. The K-M survival curve analysis of the MMP12 polymorphism rs586701: Median survival time of TT genotype versus TG genotype in male(A), \u0026lt;60 years old(B), smoking(D), SCC(E), NSCLC(F) patients.Median survival time of TT genotype versus TG+GG dominant genotype in patients \u0026lt;60 years old(C).\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/d179828f73c84b017c6430f0.jpg"},{"id":97367783,"identity":"cccc9b2f-46b1-45b6-82a6-5870fdf5a680","added_by":"auto","created_at":"2025-12-03 16:20:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":579653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMMP12\u003c/em\u003e gene expression in lung cancer tissues(A), in smoking patients (B), patients under 60 years old(C)and male patients(D)(****, ＜0.0001）.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/cc6592699c23075a4d3ff6f2.jpg"},{"id":97372660,"identity":"91250053-ca97-44f1-a4f4-7debc2c76f87","added_by":"auto","created_at":"2025-12-03 16:32:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2361616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/f76461b0-8678-4759-8a15-44910391f7b8.pdf"},{"id":97368374,"identity":"de14bd79-16b2-4911-9d82-516f4dda7168","added_by":"auto","created_at":"2025-12-03 16:22:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18001,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/a64b1ed1a2e75bf4f3533ad0.docx"},{"id":97367849,"identity":"48ca92a6-c2ac-4711-9bb9-d5aeaf6ba2d0","added_by":"auto","created_at":"2025-12-03 16:20:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18491,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/87b587542f30a0477f5db081.docx"},{"id":97367697,"identity":"919ca05a-b610-4117-aafb-3cbe6aac164d","added_by":"auto","created_at":"2025-12-03 16:20:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19850,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/df8404e9cdf4a61fc15235ee.docx"},{"id":97261707,"identity":"33c41e93-3e0e-4fd5-ab2f-6a4a9743f44c","added_by":"auto","created_at":"2025-12-02 14:07:14","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19831,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7840143/v1/3aa9494ed562b763190f12c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of MMP12 single nucleotide polymorphism rs586701 on the prognosis of primary lung cancer patients：a multicenter prospective study","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eLung cancer represents the leading cause of cancer-related mortality on a global scale [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lung cancer has the highest incidence and mortality rate in China[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although early diagnosis and treatment of lung cancer are improving, the clinical prognosis remains poor, with a five-year survival rate of less than 20%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The establishment of accurate prognostic indicators has been demonstrated to improve treatment outcomes and prolong patient survival in lung cancer[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Epidemiological studies have demonstrated that age, male sex, smoking history, small cell lung cancer, and advanced lung cancer (stage III or IV) are associated with a poor prognosis for lung cancer[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, an increasing number of studies have indicated that individual genetic factors may be of significant importance in the prognosis of lung cancer[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Single nucleotide polymorphisms (SNPs), which are common genetic variants that can affect gene expression and the prognosis of lung cancer patients, are a notable example of this[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe family of matrix metalloproteinases (MMPs) belongs to the family of zinc-dependent endopeptidases, a class of proteases that degrade the extracellular matrix and basement membrane barrier[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition to their involvement in multiple stages of cancer development, they also play a significant role in tumor invasion and metastasis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A total of 20 or more distinct types of MMPs have been identified. MMPs can be classified according to their structural features and sensitivity to substrates. The collagenases (MMP-1, MMP-8, MMP-13 and MMP-18), gelatinases (MMP-2 and MMP-9), and matrix proteins (MMP-3, MMP-10 and so on) are examples of this classification. MMP-11, matrices (MMP-7 and MMP-26), GPI anchors (MMP-17 and MMP-25), membrane-type MMPs (MMP-14, MMP-15, MMP-16, and MMP-24), and so on[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cem\u003eMMP12\u003c/em\u003e is located on chromosome 11q22.3 in the cluster of genes encoding matrix metalloproteinases and encodes a member of the M10 family of matrix metalloproteinase peptidases. Proteins in this family are involved in the breakdown of extracellular matrices during normal physiological processes, including embryonic development, reproduction, and tissue remodeling. They are also involved in disease processes, such as arthritis and metastasis. The encoded procollagen undergoes proteolytic processing to produce mature proteases that degrade soluble and insoluble elastin.\u003c/p\u003e\u003cp\u003eMolecular epidemiological studies have demonstrated that genetic polymorphisms in matrix metalloproteinase are associated with susceptibility and prognosis in lung cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], adenocarcinoma[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], colorectal cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], cervical cancer[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and other cancers[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The \u003cem\u003eMMP9\u003c/em\u003e-1562 C/T polymorphism and the \u003cem\u003eMMP13\u003c/em\u003e-77 G/A polymorphism have been linked to an increased risk of developing NSCLC [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Certain \u003cem\u003eMMP3\u003c/em\u003e promoter polymorphisms have been associated with an elevated susceptibility to NSCLC and an increased risk of lymph node metastasis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It has been demonstrated that polymorphisms in the \u003cem\u003eMMP2\u003c/em\u003e, \u003cem\u003eMMP9\u003c/em\u003e and \u003cem\u003eMMP12\u003c/em\u003e genes, specifically rs243864, rs3918242 and rs652438, respectively, influence the transcriptional activity and expression level of the encoded protein[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which in turn affects prognosis. rs586701 is situated within the gene region between \u003cem\u003eMMP3\u003c/em\u003e and \u003cem\u003eMMP12\u003c/em\u003e. However, the relationship between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 and lung cancer prognosis has not yet been elucidated. Therefore, we conducted a study to investigate the association between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 and the prognosis of lung cancer. To do this, we stratified the lung cancer population by age, sex, smoking status, family history, lung cancer stage, and tissue type. Additionally, we collected pre-treatment peripheral blood samples from lung cancer patients and performed genotyping and follow-up.\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Ethics Statement\u003c/h2\u003e\u003cp\u003e The entire study design protocol was approved by the Ethics Committee of the School of Life Sciences, Fudan University. Prior to the collection of blood samples, each subject was provided with a standard informed consent form, which clearly explained the purpose and procedures of the study. All data were recorded anonymously.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Subjects of study and data collection\u003c/h2\u003e\u003cp\u003eA total of 888 patients with primary lung cancer were admitted to the Naval Military Medical University (NMMU) and the Institute of Health Sciences, Fudan University Taizhou (IHSFT) between January and November 2009. Of these, 536 cases were from Changhai Hospital affiliated to the NMMU, and 352 cases were from the Institute of Health Sciences, Fudan University Taizhou. The follow-up period commenced at the time of inclusion in the study and concluded at the end of 2019. During this period, 49 patients were excluded due to incomplete clinical data. The remaining 839 patients' data were collected and subsequently analyzed. The study population consisted of Han Chinese individuals. Inclusion criteria included histological diagnosis of primary lung cancer and no history of malignant tumors in other organs. There were no restrictions on age or gender, and cancer stage was not a limiting factor. Histological diagnosis of lung cancer was based on World Health Organization criteria, and lung cancer stage was determined according to the 8th edition of TNM staging of lung cancer, with confirmation by two independent pathologists. The initial stages (I and II) were considered to be of an early nature, while the subsequent stages (III and IV) were regarded as being of a later chronology. The clinical data pertaining to the patients was obtained from their medical records, while the follow-up information was derived from telephone interviews.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 SNP genotyping\u003c/h2\u003e\u003cp\u003eVenous blood samples were collected from all enrolled patients prior to the commencement of treatment. Genomic DNA was extracted using the Qiagen Blood DNA Extraction Kit (Qiagen, Hilden, Germany), and the resulting DNA samples were amplified by polymerase chain reaction (PCR), and genotyping was conducted using the 2\u0026times;48-plex SNPscan TM kit (order no. G0104; Genentech, Shanghai, China) that based on double ligation and multiplex fluorescence PCR. SNPscan is a proprietary multiple SNP genotyping system that permits the simultaneous genotyping of 48, 96, 144, or 192 SNPs per sample in a single tube or sample. SNPscan employs a highly specific linkage reaction to distinguish between alleles. The genotyping quality was determined using a detailed procedure that included a successful detection rate of more than 95%, the identification of duplicate genotypes, the use of internal positive control samples, and Hardy-Weinberg equilibrium (HWE) testing. Furthermore, the laboratory personnel responsible for the genotyping analysis were unaware of the patient's clinical information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003ePrior to performing the association analysis, the HWE test was conducted on rs586701 within the study population using the Pearson chi-square test. Overall survival (OS) was calculated from the date of sample collection to the date of death from any cause or the date of the last follow-up visit. The median survival time (MST) was estimated by the Kaplan-Meier (K-M) method, and differences between groups were tested by the log-rank test. Univariate and multivariate Cox regression analyses were employed to estimate hazard ratios (HR) and their 95% confidence intervals (CI), adjusted for age and sex. A stratified analysis was conducted according to age, sex, smoking status, family history of malignancy, TNM stage, and histologic type of lung cancer. The relationships between SNPs and clinical outcomes in diverse populations of lung cancer patients were evaluated using four SNP genetic models: allelic, genotypic, dominant, and recessive. These models were stratified according to age, gender, smoking status, family history, lung cancer stage, and tissue type. All tests were two-sided and statistically significant at the 0.05 level. All statistical analyses were performed using R version 3.6.2.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic and clinical characteristics and prognostic analysis.\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a summary of the demographic characteristics of the 839 lung cancer patients. During the follow-up period, 668 patients (79.6%) died, 103 patients (12.3%) survived for more than five years, and 68 patients (8.1%) were lost to follow-up. The MST for all patients was 36.73 months. Of all patients, 229 (27.3%) were female and 610 (72.7%) were male. In total, 315 (37.5%) were below 60 years of age and 524 (62.5%) were above 60 years of age. A total of 237 patients (28.2%) had never smoked, while 582 patients (69.4%) had a history of smoking. Of the patients, 537 (64%) had no family history of malignancy, while 302 (36%) had a family history of malignancy. The histological diagnoses were as follows: 367 patients (43.7%) were diagnosed with adenocarcinoma (ADC), 282 patients (33.6%) with squamous cell carcinoma (SCC), 72 patients (8.6%) with small cell lung cancer (SCLC), and 118 patients (14.1%) with other types of cancer. The TNM staging system revealed that 154 cases (18.4%) exhibited stage I and II lung cancer, while 625 cases (74.5%) exhibited stage III and IV lung cancer.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristic distribution in Chinese patients with lung cancer and prognosis analysis. \u003cem\u003eN: number; MST: median survival time.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMST\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e229(27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e610(72.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e315(37.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e524(62.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonsmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e237(28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e582(69.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e302(36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e537(64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e367(43.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e282(33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72(8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118(14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStageⅠ+Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154(18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStageⅢ+Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e625(74.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60(7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe log-rank test demonstrated statistically significant differences in MST with respect to gender, age, smoking status, and clinical stage (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The MST was significantly longer in women, patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, nonsmokers, and patients with early-stage tumors than in men (MST: 40.17 M vs. 34.27 M; P\u0026thinsp;=\u0026thinsp;0.01), and in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (MST: 40.87 M vs. 33.2 M). The mean survival time was 33.2 months in smokers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 41.03 months in patients with advanced tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 113.93 months in patients who were smokers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Correlation between \u003cem\u003eMMP12\u003c/em\u003e polymorphisms and prognosis of lung cancer.\u003c/h2\u003e\u003cp\u003eA total of 817 genotypes were identified, with a detection rate of 97.38%. Of these, 607 were TT genotypes, 208 were TG genotypes, 24 were GG genotypes, and the frequency of the \u003cem\u003eMMP12\u003c/em\u003e rs586701 genotypes were consistent with the Hardy-Weinberg equilibrium (P\u0026thinsp;=\u0026thinsp;0.2051), indicating that the lung cancer patients participating in the study were in a state of genetic equilibrium and that the data obtained from the lung cancer patients studied were credible. The frequencies of rs586701 alleles T and G were 84.73% (1132/1336) and 15.27% (204/1336) in deceased patients and 84.80% (290/342) and 15.20% (52/342) in surviving patients, respectively. Univariate Cox regression analysis revealed that in the overall sample, patients with the TG genotype exhibited a poorer prognosis than patients with the TT genotype (TG vs TT, HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.01\u0026ndash;1.44, P\u0026thinsp;=\u0026thinsp;0.034; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). K-M survival curves demonstrated that patients with the TG genotype exhibited a shorter median survival time compared to patients with the TT genotype (MST: 38.5 M vs 29.53 M). This difference was statistically significant (Log-rank P\u0026thinsp;=\u0026thinsp;0.035; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between \u003cem\u003eMMP12\u003c/em\u003e gene polymorphism rs586701 and prognosis of Chinese lung cancer patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeath/survive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95% Cl)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003csup\u003ea\u003c/sup\u003e(95%Cl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllele\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1132/290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e204/52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 (0.89\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03 (0.89\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/T(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e479/128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174/34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (1.01\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21 (1.01\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15/9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60 (0.35\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61 (0.36\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDominate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/T(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e479/128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/G\u0026thinsp;+\u0026thinsp;G/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189/43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12 (0.95\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12 (0.95\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/T\u0026thinsp;+\u0026thinsp;T/G(ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e653/162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15/9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.57 (0.34\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58 (0.34\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eHR: hazard ratio;CI: confidence interval; ref:reference;\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e: \u003cem\u003eAdjusted by age, sex.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Stratified analysis of the association between \u003cem\u003eMMP12\u003c/em\u003e polymorphisms and lung cancer prognosis.\u003c/h2\u003e\u003cp\u003eA stratified analysis was conducted to investigate the association between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 and the prognosis of lung cancer patients. Stratified analyses showed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) that among male lung cancer patients, lung cancer patients younger than 60 years old, and smoking lung cancer patients, patients with the TG genotype had a shorter survival time than patients with the TT genotype (adjusted risk ratios HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 1.01\u0026ndash;1.52, P\u0026thinsp;=\u0026thinsp;0.04; HR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.18\u0026ndash;2.12, P\u0026thinsp;=\u0026thinsp;0.002 ; HR\u0026thinsp;=\u0026thinsp;1.30, 95% CI: 1.06\u0026ndash;1.61, P\u0026thinsp;=\u0026thinsp;0.013). In patients with SCC, survival time was shorter in patients with NSCLC, and in patients with the TG genotype (TG vs TT, adjusted risk ratio HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.10\u0026ndash;2.01, P\u0026thinsp;=\u0026thinsp;0.010; HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.01\u0026ndash;1.46, P\u0026thinsp;=\u0026thinsp;0.038). In the designed genetic model, the dominant genotype TG\u0026thinsp;+\u0026thinsp;GG was associated with a shorter survival time and worse prognosis among lung cancer patients aged less than 60 years (TG\u0026thinsp;+\u0026thinsp;GG vs TT, adjusted risk ratio HR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.07\u0026ndash;1.90, P\u0026thinsp;=\u0026thinsp;0.014; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 in genotype models and prognosis of Chinese patients with lung cancer.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDeath/survive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%Cl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003csup\u003ea\u003c/sup\u003e(95%Cl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTT(ref)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e366/85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123/17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23 (1.01\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.24 (1.01\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113/43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51/17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.86\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.14 (0.81\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e317/62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109/20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.82\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.05 (0.84\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162/66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65/14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60 (1.19\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.58 (1.18\u0026ndash;2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e355/76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117/14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.30 (1.05\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.30 (1.06\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonsmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116/46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53/18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12 (0.81\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07 (0.77\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174/43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.29 (0.97\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.27 (0.95\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e305/85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99 (0.71\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.71\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196/64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74/23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.10 (0.84\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09 (0.83\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173/38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.46 (1.08\u0026ndash;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.48 (1.10\u0026ndash;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e434/116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160/33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.20 (1.00-1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.21 (1.01\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStageⅠ+Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58/59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18/15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41 (0.67\u0026ndash;2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.48 (0.70\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage Ⅲ+Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377/62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149/18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08 (0.77\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.04 (0.74\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eHR: hazard ratio;CI: confidence interval; ref:reference;\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e: \u003cem\u003eAdjusted by age, sex.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between MMP12 polymorphism rs586701 in dominant genotype and prognosis of Chinese patients with lung cancer.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDeath/survive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%Cl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003csup\u003ea\u003c/sup\u003e(95%Cl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTT(ref)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e366/85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136/23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15 (0.94\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.15 (0.94\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113/43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53/20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.11 (0.80\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07 (0.77\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e317/62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120/25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96 (0.78\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.80\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162/66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65/14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60 (1.19\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.58 (1.18\u0026ndash;2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e355/76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132/19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.99\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.21 (0.99\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonsmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116/46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53/22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.72\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96 (0.69\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174/43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73/12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18 (0.89\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.15 (0.87\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e305/85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116/31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09 (0.88\u0026ndash;1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.10 (0.88\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196/64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80/27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.81\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.05 (0.81\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173/38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.30 (0.98\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.31 (0.98\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSCLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e434/116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175/42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12 (0.94\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.13 (0.94\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStageⅠ+Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58/59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18/19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41 (0.67\u0026ndash;2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.48 (0.70\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage Ⅲ+Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377/62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163/23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04 (0.87\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.04 (0.87\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.647\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eHR: hazard ratio;CI: confidence interval; ref:reference;\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e: \u003cem\u003eAdjusted by age, sex.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe K-M survival curves demonstrated that the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T\u0026thinsp;\u0026gt;\u0026thinsp;G was associated with a reduction in median survival time across a broad spectrum of patients. K-M curves demonstrated that male patients with the TG genotype (MST: 27.37 M) exhibited a shorter MST compared with male patients with the TT genotype (MST: 37.17 M) (log-rank P\u0026thinsp;=\u0026thinsp;0.046; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Compared with patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years with the TT genotype (MST: 47.57 M), patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years with the TG genotype (MST: 30.37 M) had a shorter MST (Log-rank P\u0026thinsp;=\u0026thinsp;0.0016; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years with the TG\u0026thinsp;+\u0026thinsp;GG genotype (MST: 32.7 M) had a shorter MST (Log-rank P\u0026thinsp;=\u0026thinsp;0.012; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The mean survival time of patients with the TG genotype (MST: 25.6 M) was found to be significantly shorter than that of patients with the TT genotype (MST: 37.13 M) (Log-rank P\u0026thinsp;=\u0026thinsp;0.016; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). SCC patients with the TG genotype (MST: 25.13 M) exhibited a shorter MST compared to SCC patients with the TT genotype (MST: 38.43 M) (Log-rank P\u0026thinsp;=\u0026thinsp;0.012; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Patients with non-small cell lung cancer who had the TG genotype (MST: 30.23 M) exhibited a shorter median survival time compared to patients with the TT genotype (MST: 39.37 M) (Log-rank P\u0026thinsp;=\u0026thinsp;0.047; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eExpression and prognostic associations were analyzed using the Cancer Genome Atlas (TCGA) database. Our findings indicate that the expression of \u003cem\u003eMMP12\u003c/em\u003e polymorphisms is higher in lung cancer tissues (including adenocarcinomas and squamous carcinomas) than in normal tumor walls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, the expression of \u003cem\u003eMMP12\u003c/em\u003e polymorphisms was found to be higher than that in tumor wall tissues in patients who smoked (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), in patients aged less than 60 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and in male patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eA potential correlation between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 and the prognosis of lung cancer was identified through genotyping and follow-up investigation of blood samples from 839 lung cancer patients. In the overall sample, we observed that the rs586701 TG genotype was associated with a worse prognosis in lung cancer. In further stratified analyses, we observed the same results in men, individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, smokers, SCC and NSCLC patients. To the best of our knowledge, our study is the first to investigate the association between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 and lung cancer prognosis based on genotype/allele in a Han Chinese population. Our findings indicate that the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T \u0026gt;G is associated with a worse prognosis.\u003c/p\u003e\u003cp\u003eMMPs are a family of proteins structurally and functionally related to zinc endopeptidases that control extracellular protein hydrolysis. They are important regulators of the cellular microenvironment and have been implicated in the invasion and metastasis of a variety of tumor cells. \u003cem\u003eMMP12\u003c/em\u003e expression has been demonstrated to serve as a prognostic marker and therapeutic target in the development of lung cancer in humans and mice, and patients with high \u003cem\u003eMMP12\u003c/em\u003e expression have been shown to have a poorer prognosis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. One study demonstrated that elevated \u003cem\u003eMMP12\u003c/em\u003e expression was associated with a poor prognosis of lung cancer in mice through a mouse lung cancer model[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A study investigating the impact of \u003cem\u003eMMP12\u003c/em\u003e polymorphisms on lung function revealed that \u003cem\u003eMMP12\u003c/em\u003e polymorphisms were associated with reduced lung function in patients with lung cancer[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The present study has identified a significant association between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T \u0026gt;G and poor prognosis in lung cancer. Furthermore, clinical stratification revealed that this association is particularly prevalent in men, individuals aged less than 60 years, smokers, and patients with SCC and NSCLC lung cancer.\u003c/p\u003e\u003cp\u003eThe results of our study indicated that the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T \u0026gt;G was associated with a poor prognosis in male lung cancer patients. The authors concluded that the prognostic differences due to gender factors are mainly closely related to sex hormones and the X chromosome. They further noted that differences in sex hormones between males and females are important biological factors affecting the prognosis of lung cancer[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, they observed that the expression of oncogenes encoded by the X chromosome is higher in female cancer patients[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A study conducted on animals and humans revealed that exposure to tobacco pollutants elevated the likelihood of lymphoid aggregation formation and heightened susceptibility to impaired lung function in female mice relative to male mice. Additionally, in human lung tissue, female smokers demonstrated an augmented number of lymphoid follicles and a less favorable prognosis for patients with lung cancer compared to their male counterparts[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The aforementioned findings collectively demonstrate that gender is a significant clinical factor that affects the prognosis of lung cancer patients.\u003c/p\u003e\u003cp\u003eA stratified analysis revealed that the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T\u0026thinsp;\u0026gt;\u0026thinsp;G is associated with a poorer prognosis in patients with smoking-related lung cancer. Macrophages are the primary cell type responsible for the production of MMP-12, a protein that plays a crucial role in the inflammatory response. These cells are the main cell type that normally patrols the lower airways and are the primary inflammatory cell type produced during smoking. Smoking is a major risk factor for lung cancer, and the smoke produced by tobacco combustion contains dozens of lung carcinogens such as aromatic adducts, nicotine, heterocyclic aromatic amines, acrolein, alkyl adducts, tar, etc, which are known to affect gene expression through DNA adduct formation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], epigenomic modification[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], the function of XPC (a DNA repair protein)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the inhibition of gluconeogenic synthase kinase 3 (GSK3) and induces the expression of involucrin (a marker of squamous differentiation)[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and regulates the acetylcholine system to affect the proliferation and differentiation of lung cancer cells[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], among other pathways that affect the prognosis of lung cancer[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In humans, the expression of \u003cem\u003eMMP12\u003c/em\u003e in alveolar macrophages is approximately ninefold higher in smokers than in nonsmokers[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The overexpression of \u003cem\u003eMMP12\u003c/em\u003e in the lower respiratory tract results in elastin degradation, which in turn leads to the formation of elastin fragments. These fragments can initiate a positive feedback loop, further increasing macrophage production in mice and cultured human cells[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, animal studies have demonstrated that mouse models of lung cancer exposed to secondhand smoke exhibit elevated \u003cem\u003eMMP12\u003c/em\u003e expression (9.3-fold compared to airborne polyurethane controls) and a worse prognosis for lung cancer[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The authors of the study concluded that the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T\u0026gt;G resulted in the upregulation of \u003cem\u003eMMP12\u003c/em\u003e expression, which led to a shorter median survival time and a worse prognosis for the patients.。\u003c/p\u003e\u003cp\u003eThe specific type of cancer tissue is also a significant factor influencing the prognosis of lung cancer. In one study, 13 patients with SCC were analyzed with normal lung tissue and 13 patients with adenocarcinoma for related gene expression. It was found that \u003cem\u003eMMP12\u003c/em\u003e expression was significantly up-regulated in patients with SCC[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], while it was also pointed out that \u003cem\u003eMMP12\u003c/em\u003e was negatively correlated with prognosis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and that \u003cem\u003eMMP12\u003c/em\u003e deletion could be used as a marker for a good prognosis in SCC[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A study that performed a multi-omics analysis of treated NSCLC patients, including multiplex immunofluorescence, nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink, found that upregulation of \u003cem\u003eMMP12\u003c/em\u003e expression is associated with reduced survival in NSCLC patients[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A study demonstrated that atorvastatin significantly reduced the expression of \u003cem\u003eMMP12\u003c/em\u003e in cellular experiments, indicating that \u003cem\u003eMMP12\u003c/em\u003e is a potential target for the treatment of NSCLC patients[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Furthermore, the lack of \u003cem\u003eMMP12\u003c/em\u003e in lung cancer cells in vivo has been shown to reduce tumor growth and invasiveness[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. These studies have illustrated that elevated \u003cem\u003eMMP12\u003c/em\u003e expression is associated with a worse prognosis. It is postulated that our findings, which indicate that the MMP12 polymorphism rs586701 T \u0026gt;G upregulates \u003cem\u003eMMP12\u003c/em\u003e expression, result in shorter survival times and a poorer prognosis for patients with SCC and NSCLC.\u003c/p\u003e\u003cp\u003eThis study is subject to both strengths and limitations. In terms of strengths, we conducted a large-sample study to detect predictive polymorphisms in all primary lung cancer patients. This was done in order to reduce data complexity and prevent important gene deletions. The aim was to investigate the relationship between rs586701 and the prognosis of lung cancer patients. However, it should be noted that the study still has some limitations. Firstly, the study samples were mainly from two hospitals, which may have introduced some selection bias. Secondly, the genetic model assessment was performed retrospectively, which is prone to recall bias. Thirdly, cellular experiments were not performed to investigate the mechanism by which the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T \u0026gt;G made lung cancer patients' prognosis worse. Additionally, the sample sizes in some of the stratified analyses were relatively small, which may have affected the statistical associations between the tested genotypes and patient characteristics.\u003c/p\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eThis is the inaugural study to investigate the correlation between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 and prognosis in lung cancer patients. The study revealed that the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 was significantly associated with poor prognosis in all lung cancer patients. Furthermore, the study identified that men carrying the rs586701 TG genotype, younger than 60 years of age, smokers, and patients with SCC and NSCLC lung cancers all had a poorer prognosis. In addition, the G allele was found to be associated with a poor prognosis in lung cancer patients younger than 60 years of age. Furthermore, both the TG and dominant TG\u0026thinsp;+\u0026thinsp;GG genotypes were also found to have a poor prognosis in lung cancer patients younger than 60 years of age. The relationship between the \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 and the clinical characteristics of specific cancers provides a new target for \"individualized anticancer\" drug therapy. This can help clinicians to select appropriate individualized patients for target regulation and guide early evidence-based treatment, which is of great value for improving the prognosis of cancer patients. It provides medical personnel and their families with a more scientific basis for medical decision-making, while also helping to avoid over-medication and the waste of medical resources.\u003c/p\u003e\u003cp\u003eIn recent years, in addition to conventional surgical treatment, radiotherapy, and chemotherapy, immunotherapy has emerged as a promising therapeutic modality for lung cancer. In the context of lung cancer, cancer cells have been observed to \u0026ldquo;disguise\u0026rdquo; themselves as normal cells by expressing proteins such as PD-L1. This protein binds to PD-1 on the surface of immune cells, thereby evading the immune system's attack. Immunotherapy is an \u0026ldquo;indirect anti-cancer\u0026rdquo; therapy that utilizes immunotherapeutic drugs (e.g., PD-1/PD-L1 inhibitors) to impede the binding of PD-1/PD-L1, release the immunosuppression of cancer cells, and reactivate the T-cells to destroy cancer cells. Following the administration of immunotherapy, the immune system may develop a \u0026ldquo;memory\u0026rdquo; effect, thereby continuing to impede the growth of tumors. This approach offers several advantages, including prolonged efficacy, relatively mild and reversible side effects, and a high potential for long-term benefits. Furthermore, it is applicable to a wide range of lung cancers and can be utilized in combination with various therapeutic modalities. The prognostic efficacy of immunotherapy is contingent upon a variety of factors, including the tumor microenvironment and the patient's immune status. In the future, we will conduct more extensive clinical data collection and analysis to explore the relationship between immune checkpoint inhibitors and specific patient characteristics. We may also adopt a longitudinal study design to evaluate the efficacy and safety of ICIs in different disease stages. This would improve the prospective nature of the study and provide relevant recommendations for subsequent researchers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1 Ethical Approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committees of the School of Life Sciences of Fudan University (approval number: Discussion and Research Grant No. 244) and Shanghai Changhai Hospital (approval number: CHEC2015-100) have approved this study, and all participants gave informed consent for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2\u0026nbsp;Human Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in compliance with the principles of the Declaration of Helsinki (1996).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3\u0026nbsp;Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4\u0026nbsp;Availability of supporting data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available because Genomic data is private and cannot be disclosed according to Chinese law. But all data in this study are available upon reasonable request from corresponding author \u0026nbsp;Yuanlin Song.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5\u0026nbsp;Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorresponding author declares no conflict of interest on behalf of all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work is currently receiving four grants from the National Natural Science Foundation of China (Grant NO. 81372236, 82272863 and 81972822), the Shanghai Postdoctoral Research Foundation in 2021 (Class A Grant NO. 12R21411500).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.7\u0026nbsp;Authors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChang Xu, Wei Du and Zhenyu Sun contributed equally to this work. Chang Xu, Wei Du and Zhenyu Sun: Writing-original draft. Qiang Li: Visualization. Junjie Wu and Bo Shen:\u0026nbsp;Funding acquisition. Yan Shang and Yuanlin Song: Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.8\u0026nbsp;Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported in part through the computational resources and staff expertise provided by Scientific Computing at the Fudan University in Shanghai.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA: A Cancer Journal for Clinicians. 2016;66(2):115\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng H, Chen W, Zheng R, et al. 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Differential expression of matrilysin-1 (MMP‐7), 92 kD gelatinase (MMP‐9), and metalloelastase (MMP‐12) in oral verrucous and squamous cell cancer. The Journal of Pathology. 2003;202(1):14\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParra ER, Zhang J, Duose DY, et al. Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial. Clin Cancer Res. 2024;30(8):1655\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y-q, Li L-y, Yang X, et al. Prediction and validation of common targets in atherosclerosis and non-small cell lung cancer influenced by atorvastatin. BMC Complementary Medicine and Therapies. 2023;23(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElla E, Harel Y, Abraham M, et al. Matrix metalloproteinase 12 promotes tumor propagation in the lung. The Journal of Thoracic and Cardiovascular Surgery. 2018;155(5):2164-75.e1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MMP12, single nucleotide polymorphism, rs586701, lung cancer, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7840143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7840143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eLung cancer is a common cancer, with a high mortality rate and poor prognosis. Predicting the prognosis of lung cancer patients and using this information to develop treatment strategies and interventions is important for prolonging patient survival.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBlood samples were collected from 839 patients diagnosed with lung cancer, and genomic DNA was extracted for genotyping using SNPscan technology. In order to adjust for multiple factors, the data was stratified by age, sex, smoking status, family history, TNM stage and cancer tissue type. The association between lung cancer prognosis and genotype was then analyzed using a multivariate Cox proportional risk model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA polymorphism in the \u003cem\u003eMMP12\u003c/em\u003e gene, the T\u0026thinsp;\u0026gt;\u0026thinsp;G variant at position 586701, has been associated with a worse prognosis. Patients with the TG genotype (TG vs TT, HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.01\u0026ndash;1.44, P\u0026thinsp;=\u0026thinsp;0.035) exhibited a worse prognosis. Stratified analyses showed that among male, younger than 60 years old and smoking patients, patients with the TG genotype had a lower survival time (HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 1.01\u0026ndash;1.52, P\u0026thinsp;=\u0026thinsp;0.04; HR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.18\u0026ndash;2.12, P\u0026thinsp;=\u0026thinsp;0.002; HR\u0026thinsp;=\u0026thinsp;1.30, 95% CI: 1.06\u0026ndash;1.61, P\u0026thinsp;=\u0026thinsp;0.013). In patients with SCC and NSCLC, survival time was shorter with the TG genotype (TG vs TT, HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.10\u0026ndash;2.01, P\u0026thinsp;=\u0026thinsp;0.010; HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.01\u0026ndash;1.46, P\u0026thinsp;=\u0026thinsp;0.038). In the designed genetic model, the dominant genotype TG\u0026thinsp;+\u0026thinsp;GG was associated with worse prognosis among patients aged less than 60 years (TG\u0026thinsp;+\u0026thinsp;GG vs TT, HR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.07\u0026ndash;1.90, P\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe \u003cem\u003eMMP12\u003c/em\u003e polymorphism rs586701 T\u0026thinsp;\u0026gt;\u0026thinsp;G may be associated with a worse prognosis of lung cancer.\u003c/p\u003e","manuscriptTitle":"Influence of MMP12 single nucleotide polymorphism rs586701 on the prognosis of primary lung cancer patients：a multicenter prospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:07:09","doi":"10.21203/rs.3.rs-7840143/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-12T19:26:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T11:45:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187097866660801603997350310611849053227","date":"2026-01-26T07:45:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T15:43:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131064389308801096461560637680618392890","date":"2026-01-19T14:51:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T10:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117170469120611996403005622917761274851","date":"2026-01-19T09:14:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179301272715248628246131329581176235346","date":"2026-01-18T10:34:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T14:45:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169133034133641855207871901385783341445","date":"2025-12-06T15:38:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169292228255396204334968792775573527395","date":"2025-11-28T15:04:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314923837063465858684831747453780105648","date":"2025-11-27T02:31:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-26T12:41:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T17:11:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T17:09:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-10-12T10:50:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83c597c8-950b-44df-ac15-1f12cc41fc55","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T13:54:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 14:07:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7840143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7840143","identity":"rs-7840143","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}