Purpose: Disruption of the balance of insulin-like growth factor I (IGF-I) and IGF-binding protein 3 (IGFBP3) has been implicated in the etiology and progression of lung and other cancers. Single nucleotide polymorphisms (SNP) in IGF1 and IGFBP3 have been reported to be associated with the expression of the IGF-I/IGFBP3 axis. Therefore, we hypothesized that SNPs in these two genes were associated with lung cancer survival.

Experimental Design: We selected and genotyped 21 tagging and potentially functional SNPs in IGF1 and IGFBP3 by using Illumina Goldengate Genotyping Chip in a case cohort of 568 patients diagnosed with non–small cell lung cancer (NSCLC) in a Chinese population. Log-rank test and Cox proportional hazard models were used for the survival analyses.

Results: We found that rs5742714C/G in the 3′-untranslated region of IGF1 was associated significantly with NSCLC survival after adjustment for demographic and clinicopathologic factors, showing an improved median survival time in patients carrying variant CG/GG genotypes [median survival time, 28.5 months for CG/GG and 23.0 for CC; crude hazard ratio (HR), 0.74; 95% confidence interval (95% CI), 0.57-0.95, and adjusted HR, 0.77; 95% CI, 0.60-0.99]. This protective effect was more predominant for patients receiving surgical operation (HR, 0.58; 95% CI, 0.40-0.85; P for heterogeneity test = 0.045), along with a significant multiplicative interaction between variant genotypes and operation status (P = 0.028).

Conclusions: Our findings suggest that rs5742714 in IGF1 may be a genetic modifier for NSCLC prognosis in this Chinese population, especially among patients with surgical operation. Clin Cancer Res; 16(4); 1236–44

Translational Relevance

Although many studies have been conducted to explore the prognostic biomarkers of non–small cell lung cancer (NSCLC), stable markers for clinical outcome prediction are still scarce. Epidemiologic and laboratory-based studies have identified the disruption of the balance of insulin-like growth factor I (IGF-I) and IGF-binding protein 3 (IGFBP3) as a prognostic factor of lung and other cancers. In this study, we found that variant genotypes of rs5742714C/G in the 3′-untranslated region of IGF1 were associated with significantly improved NSCLC survival, especially among patients after surgical operation. Our findings offer an opportunity to use IGF1 polymorphism as a biomarker in prediction of NSCLC outcome and in the identification of individuals with poor prognosis for more aggressive treatments. In addition, this discovery also supports the concept that IGF-I contributes to lung cancer progression and suggests that it may be a potential target for the treatment of lung cancer.

Lung cancer, predominantly non–small cell lung cancer (NSCLC), is the leading cause of cancer-related deaths worldwide (1). Despite some advances achieved in the diagnosis and treatment in the last decades, the prognosis of lung cancer remains poor (1). Many studies have revealed that application of specific prognostic biomarkers, incorporated with traditional factors such as age, sex, histologic type, and stage at diagnosis, could largely help to improve prognosis prediction and guide medical care of lung cancer (2).

The insulin-like growth factor (IGF) signaling system plays a central role in cellular growth, differentiation, and proliferation (3, 4). Studies have shown that IGF-I is a potent proliferative molecule, affecting almost every cell type via the RAS-mitogen-activated protein kinase signaling pathway, and a powerful antiapoptotic molecule through activation of the phosphatidylinositol-3 kinase–AKT pathway, which ultimately activates downstream transcription factors that regulate gene expression of proliferative, differentiation, and antiapoptotic factors (5, 6). Besides, the bioactivity of IGF-I is modulated by a group of high-affinity IGF-binding proteins (IGFBP1 to 6; refs. 7, 8), and among them, IGFBP3 is the most abundant circulating binding partner for IGFs and shows strong growth-inhibitory effects by binding and subsequently sequestrating IGF-I in the extracellular milieu (9, 10).

Clinical and epidemiologic evidence further indicate that disruption of the balance of IGF-I and IGFBP3 is implicated in the etiology and progression of multiple cancers, revealing that increased levels of IGF-I, reduced levels of IGFBP3, or an increased ratio of IGF-I to IGFBP3 in the circulation is associated with an increased risk as well as a poor prognosis of several common cancers (1117), and genetic studies have further correlated the single nucleotide polymorphisms (SNP) of IGF1 and IGFBP3 with the prognosis of breast cancer (18). With regard to lung cancer, cumulative experimental evidence in vivo and in vitro suggests that suppression of IGF-I signaling can induce apoptosis and inhibit invasion or metastasis of NSCLC cells (1922). However, clinical and epidemiologic data are still rare (23, 24), with a doubtful result on IGF-I that high plasma level is associated with a favorable prognosis (23), which seems to conflict with the laboratory studies and the result from some other cancer (16). In addition, no epidemiologic studies to date have evaluated the role of SNPs in the genes of the IGF axis with lung cancer survival.

Given the important role of IGFs in stimulating cellular growth, influencing apoptotic pathway, and inhibiting invasion and metastasis, it is conceivable that SNPs in these genes may have an effect on progression and prognosis of lung cancer. To test this hypothesis, we comprehensively selected 21 common genetic variations in IGF1 and IGFBP3 by a strategy of integrating both tagging SNPs and potentially functional SNPs, and evaluated the associations with NSCLC survival among a cohort of 568 NSCLC cases in a Chinese population.

Study population

Our study was approved by the Institutional Review Board of Nanjing Medical University, Nanjing, China. All the patients were newly diagnosed and histopathologically confirmed without prior history of other cancers or previous chemotherapy or radiotherapy, and were prospectively recruited into an ongoing study of lung cancer molecular epidemiology from July 2003 to April 2008. Overall, 828 NSCLC patients with complete follow-ups and clinical information were prospectively recruited from the Cancer Hospital of Jiangsu Province and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China (25, 26). All patients were face-to-face interviewed to collect demographic data and exposure information, including age, sex, and cigarette smoking. Those who smoked <1 cigarette per day and <1 year in their lifetime were defined as nonsmokers; otherwise, they were considered as smokers. Each patient donated 5 mL venous blood after providing a written informed consent. Patients were followed up prospectively every 3 mo from the time of enrollment by personal or family contacts until death or last time of follow-up. The maximum follow-up time was 72 mo (last follow-up in July 2009) and the median follow-up time was 18.8 mo. We selected the patients with adequate DNA sample by checking quality and quantity for Illumina genotyping assay. As a result, 568 NSCLC patients were included and genotyped in this study.

SNP selection and genotyping

Polymorphisms were selected by an approach combining both tagging SNPs and potentially functional SNPs of the IGF1 and IGFBP3 genes. All common (minor allele frequency >0.05 in Asians) polymorphisms were searched within nucleotide -2,000 upstream from the start codon of each gene and their 3′-untranslated regions (3′UTR) using the HapMap (last search date, February 2008) and the National Center for Biotechnology Information dbSNPs public databases (build 127). Potentially functional polymorphisms were identified to meet the following criteria: (a) located in the 5′-flanking regions, 5′-UTR, 3′-UTR, and coding regions with amino acid changes; (b) were shown to be of biological significance according to the literature review; and (c) were associated with gene expression and/or cancer risk/survival in previous studies. Tagging SNPs (tSNP) were chosen from genotyped SNPs of Chinese Han Beijing (CHB) in the HapMap database (minor allele frequency ≥0.05, Hardy-Weinberg equilibrium P ≥ 0.05, and call rate ≥90%) in Haploview 4.1 software on the basis of pairwise linkage disequilibrium (r2 threshold = 0.8) and with a priority of forcing the potentially functional SNPs in the evaluation. Among the 24 SNPs (13 in IGF1 and 11 in IGFBP3) selected, three SNPs (rs5031032 and rs6220 in IGF1 and rs6413441 in IGFBP3) could not be genotyped because of Illumina design quantity scores <0.5. Therefore, a total of 21 SNPs (11 in IGF1 and 10 in IGFBP3) were genotyped by using the Illumina GoldenGate platform in Taizhou, Jiangsu province, China. Information on assay conditions, primers, and probes is available upon request. Quality control was followed according to the quality criteria (i.e., one blank well and three repeated samples) in our previous studies (27). In the end, 17 SNPs were successfully genotyped with call rates >95% and consistent with those expected from the Hardy-Weinberg equilibrium (P > 0.05), whereas three SNPs (rs12579108 of IGF1, rs2854744 and rs34480712 of IGFBP3) that could not be genotyped and one (rs13241830 in IGFBP3) with a call rate of 79.5% and that deviated from Hardy-Weinberg equilibrium (P = 0.01) were removed from our further analysis.

Statistical analysis

Deviation of genotype distribution from the Hardy-Weinberg equilibrium for each SNP was tested by a goodness-of-fit χ2 test. Survival time was calculated from the date of lung cancer diagnosis to the date of death or to last follow-up. The different survival times according to demographic characteristics, clinical features, and IGF1 and IGFBP3 SNPs were estimated by using the Kaplan-Meier method and compared by the log-rank test. Mean survival time was presented when the median survival time (MST) could not be calculated. Univariate or multivariate Cox regression analysis was done to determine predictive factors of NSCLC prognosis by estimating the crude hazard ratios (HR), adjusted HRs, and their 95% confidence intervals (95% CI), with adjustment for age, sex, smoking status, histology, stage, surgical operation, and chemotherapy or radiotherapy status. In addition, we used the PHASE 2.1 Bayesian algorithm (28) to infer haplotypes based on the observed genotypes of the SNPs. All the statistical analyses were carried out by Statistical Analysis System software (version 9.1.3, SAS Institute).

Patient characteristics and clinical features

The demographic characteristics and clinical information for the 568 NSCLC patients retained in the study are summarized in Table 1. The median age was 60 years (range, 25-83 years), and there were 434 males (76.4%) and 367 smokers (64.6%). Among these patients, 353 (62.2%) were adenocarcinomas, 184 (32.4%) were squamous cell carcinomas, and the others (31 patients, 5.5%) were large cell, undifferentiated, and mixed-cell carcinomas. In a period of up to 72 months of follow-up, 311 patients died from NSCLC, and 3 died from other causes. For disease-specific survival analysis, the latter were considered as censored data in the analyses. Clinical stage and surgical operation status, but not chemotherapy or radiotherapy status, were significantly associated with survival time (log-rank P < 0.001). As the stage increased, the risk of death for NSCLC showed a significant increase in a dose-response manner by using univariate Cox regression analysis (compared with stage I, HR, 1.37; 95% CI, 0.86-2.19 for stage II; HR, 3.04; 95% CI, 2.17-4.26 for stage III; HR, 5.75; 95% CI, 4.03-8.21 for stage IV; and P < 0.001 for the trend test). Patients with surgical operation (MST, 36.8 months) had a 67% significantly decreased risk of death (HR, 0.33; 95% CI, 0.26-0.41), compared with those without surgical operation (MST, 15.3 months).

Table 1.

Patient characteristics and clinical features

VariablePatientsDeathsMST (mo)Log-rank PHR (95% CI)
n = 568 (%)n = 311
Age (y)    0.690  
    ≤60 285 (50.2) 155 24.7  1.00 
    >60 283 (49.8) 156 25.0  1.05 (0.84-1.31) 
Sex    0.805  
    Male 434 (76.4) 241 24.7  1.00 
    Female 134 (23.6) 70 27.8  0.97 (0.74-1.26) 
Smoking    0.792  
    Never 201 (35.4) 111 24.8  1.00 
    Ever 367 (64.6) 200 24.3  1.03 (0.82-1.30) 
Histology    0.519  
    Adenocarcinoma 353 (62.2) 192 26.2  1.00 
    Squamous cell 184 (32.4) 99 24.0  0.97 (0.76-1.23) 
    Others* 31 (5.5) 20 17.9  1.28 (0.81-2.03) 
Clinical stage    <0.001  
    I 144 (25.4) 46 60.4  1.00 
    II 71 (12.5) 29 47.6  1.37 (0.86-2.19) 
    III 221 (38.9) 133 19.6  3.04 (2.17-4.26) 
    IV 132 (23.2) 103 13.4  5.75 (4.03-8.21) 
Surgical operation    <0.001  
    None 200 (35.2) 155 15.3  1.00 
    Yes 368 (64.8) 156 36.8  0.33 (0.26-0.41) 
Chemotherapy or radiotherapy    0.102  
    None 106 (18.7) 47 33.0  1.00 
    Yes 462 (81.3) 264 23.5  1.30 (0.95-1.77) 
VariablePatientsDeathsMST (mo)Log-rank PHR (95% CI)
n = 568 (%)n = 311
Age (y)    0.690  
    ≤60 285 (50.2) 155 24.7  1.00 
    >60 283 (49.8) 156 25.0  1.05 (0.84-1.31) 
Sex    0.805  
    Male 434 (76.4) 241 24.7  1.00 
    Female 134 (23.6) 70 27.8  0.97 (0.74-1.26) 
Smoking    0.792  
    Never 201 (35.4) 111 24.8  1.00 
    Ever 367 (64.6) 200 24.3  1.03 (0.82-1.30) 
Histology    0.519  
    Adenocarcinoma 353 (62.2) 192 26.2  1.00 
    Squamous cell 184 (32.4) 99 24.0  0.97 (0.76-1.23) 
    Others* 31 (5.5) 20 17.9  1.28 (0.81-2.03) 
Clinical stage    <0.001  
    I 144 (25.4) 46 60.4  1.00 
    II 71 (12.5) 29 47.6  1.37 (0.86-2.19) 
    III 221 (38.9) 133 19.6  3.04 (2.17-4.26) 
    IV 132 (23.2) 103 13.4  5.75 (4.03-8.21) 
Surgical operation    <0.001  
    None 200 (35.2) 155 15.3  1.00 
    Yes 368 (64.8) 156 36.8  0.33 (0.26-0.41) 
Chemotherapy or radiotherapy    0.102  
    None 106 (18.7) 47 33.0  1.00 
    Yes 462 (81.3) 264 23.5  1.30 (0.95-1.77) 

*Other carcinomas include large cell, undifferentiated and mixed-cell carcinomas.

Effects of genetic variants of IGF1 and IGFBP3 on NSCLC survival

Cox regression analyses were used to access the associations of IGF1 and IGFBP3 genotypes with NSCLC survival in different genetic models (Table 2). In the additive model, three polymorphisms were significantly associated with the survival of NSCLC (P = 0.013, 0.006, and 0.023 for rs5742714 of IGF1, and rs2132572 and rs10255707 of IGFBP3, respectively). As shown in Table 3, however, rs2132572 and rs10255707 of IGFBP3 were associated with higher risk of death only before adjustment for demographic and clinicopathologic factors (crude HR, 1.28; 95% CI, 1.02-1.61 for rs2132572 variant genotypes in a dominant model, and crude HR, 1.74; 95% CI, 1.01-2.97 for rs10255707 variant genotype in a recessive model). Log-rank test and Cox regression analyses revealed that variant genotypes of IGF1 rs5742714 CG/GG had a significantly improved survival (MST, 28.5 months; crude HR, 0.74; 95% CI, 0.57-0.95, and adjusted HR, 0.77; 95% CI, 0.60-0.99) compared with the homozygote CC (MST, 23.0 months; Fig. 1A and B).

Table 2.

Genotyping of selected SNPs of IGF1 and IGFBP3 and their associations with NSCLC survival in genetic models

GeneSNPs*LocationNo. (%) of genotypingMAF in patientsHWECox P
Additive modelDominant modelRecessive model
IGF1 rs9919733 5′ near gene 99.7 0.265 1.00 0.242 0.256 0.519 
rs35767 5′ near gene 99.3 0.318 0.53 0.560 0.673 0.566 
rs5742612 5′ near gene 99.8 0.271 0.72 0.329 0.330 0.621 
rs2288377 5′ near gene 97.7 0.272 0.84 0.240 0.238 0.565 
rs12821878 Intron 99.8 0.050 1.00 0.212 0.282 0.011 
rs4764699 Intron 99.8 0.252 0.84 0.432 0.678 0.248 
rs1520220 Intron 99.8 0.417 0.93 0.313 0.228 0.718 
rs6218 3′UTR 99.5 0.240 0.93 0.339 0.522 0.254 
rs6214 3′UTR 99.5 0.478 0.28 0.211 0.760 0.078 
rs5742714 3′UTR 96.4 0.170 0.87 0.013 0.018 0.170 
IGFBP3 rs2132572 5′ near gene 100.0 0.198 0.79 0.006 0.037 0.004 
rs2854746 A32G 99.8 0.242 0.65 0.123 0.394 0.026 
rs9282734 P164H 99.8 0.043 1.00 0.540 0.580 0.624 
rs2471551 Intron 100.0 0.044 0.61 0.173 0.192 0.490 
rs3110697 Intron 100.0 0.260 0.48 0.206 0.473 0.076 
rs10255707 Intron 99.5 0.178 0.49 0.023 0.060 0.045 
rs2453839 Intron 99.7 0.226 0.70 0.056 0.048 0.502 
GeneSNPs*LocationNo. (%) of genotypingMAF in patientsHWECox P
Additive modelDominant modelRecessive model
IGF1 rs9919733 5′ near gene 99.7 0.265 1.00 0.242 0.256 0.519 
rs35767 5′ near gene 99.3 0.318 0.53 0.560 0.673 0.566 
rs5742612 5′ near gene 99.8 0.271 0.72 0.329 0.330 0.621 
rs2288377 5′ near gene 97.7 0.272 0.84 0.240 0.238 0.565 
rs12821878 Intron 99.8 0.050 1.00 0.212 0.282 0.011 
rs4764699 Intron 99.8 0.252 0.84 0.432 0.678 0.248 
rs1520220 Intron 99.8 0.417 0.93 0.313 0.228 0.718 
rs6218 3′UTR 99.5 0.240 0.93 0.339 0.522 0.254 
rs6214 3′UTR 99.5 0.478 0.28 0.211 0.760 0.078 
rs5742714 3′UTR 96.4 0.170 0.87 0.013 0.018 0.170 
IGFBP3 rs2132572 5′ near gene 100.0 0.198 0.79 0.006 0.037 0.004 
rs2854746 A32G 99.8 0.242 0.65 0.123 0.394 0.026 
rs9282734 P164H 99.8 0.043 1.00 0.540 0.580 0.624 
rs2471551 Intron 100.0 0.044 0.61 0.173 0.192 0.490 
rs3110697 Intron 100.0 0.260 0.48 0.206 0.473 0.076 
rs10255707 Intron 99.5 0.178 0.49 0.023 0.060 0.045 
rs2453839 Intron 99.7 0.226 0.70 0.056 0.048 0.502 

Abbreviations: MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium.

*All the SNPs served as tagging SNPs except for rs9919733 in IGF1 and rs2854746 in IGFBP3.

Hardy-Weinberg equilibrium was examined by goodness-of-fit χ2 test.

Table 3.

Polymorphisms of IGF1 and IGFBP3 genes and NSCLC patients’ survival

LocusGenotypePatientsDeathsMST (mo)Log-rank PCrude HR (95% CI)Adjusted HR (95% CI)*
IGF1 rs5742714  n = 547 n = 301     
    Codominant model CC 377 219 23.0  1.00 1.00 
CG 153 76 28.2  0.77 (0.60-1.01) 0.82 (0.63-1.08) 
GG 17 26.5 0.043 0.54 (0.24-1.21) 0.48 (0.21-1.10) 
    Dominant model CC 377 219 23.0  1.00 1.00 
CG/GG 170 82 28.5 0.017 0.74 (0.57-0.95) 0.77 (0.60-0.99) 
    Recessive model CC/CG 530 295 24.3  1.00 1.00 
GG 17 26.5 0.163 0.57 (0.25-1.27) 0.50 (0.22-1.12) 
    Additive model G allele     0.75 (0.60-0.94) 0.77 (0.61-0.96) 
  Trend P     0.010 0.023 
IGFBP3 rs2132572  n = 568 n = 311     
    Codominant model GG 366 194 25.9  1.00 1.00 
AG 178 97 24.8  1.19 (0.93-1.51) 1.04 (0.81-1.34) 
AA 24 20 16.7 0.005 2.07 (1.31-3.30) 1.43 (0.90-2.29) 
    Dominant model GG 366 194 25.9  1.00 1.00 
AG/AA 202 117 22.1 0.037 1.28 (1.02-1.61) 1.09 (0.86-1.39) 
    Recessive model GG/AG 544 291 25.9  1.00 1.00 
AA 24 20 16.7 0.003 1.96 (1.25-3.10) 1.39 (0.88-2.20) 
    Additive model A allele     1.31 (1.08-1.58) 1.12 (0.92-1.36) 
Trend P     0.006 0.247 
IGFBP3 rs10255707  n = 565 n = 309     
    Codominant model GG 383 200 25.5  1.00 1.00 
AG 161 95 24.8  1.20 (0.94-1.53) 1.03 (0.80-1.32) 
AA 21 14 15.0 0.046 1.84 (1.07-3.16) 1.47 (0.84-2.56) 
    Dominant model GG 383 200 25.5  1.00 1.00 
AG/AA 182 109 23.1 0.060 1.25 (0.99-1.58) 1.08 (0.84-1.37) 
    Recessive model AA/AG 544 295 25.1  1.00 1.00 
AA 21 14 15.0 0.042 1.74 (1.01-2.97) 1.47 (0.85-2.54) 
    Additive model A allele     1.26 (1.03-1.54) 1.11 (0.90-1.36) 
Trend P     0.023 0.338 
LocusGenotypePatientsDeathsMST (mo)Log-rank PCrude HR (95% CI)Adjusted HR (95% CI)*
IGF1 rs5742714  n = 547 n = 301     
    Codominant model CC 377 219 23.0  1.00 1.00 
CG 153 76 28.2  0.77 (0.60-1.01) 0.82 (0.63-1.08) 
GG 17 26.5 0.043 0.54 (0.24-1.21) 0.48 (0.21-1.10) 
    Dominant model CC 377 219 23.0  1.00 1.00 
CG/GG 170 82 28.5 0.017 0.74 (0.57-0.95) 0.77 (0.60-0.99) 
    Recessive model CC/CG 530 295 24.3  1.00 1.00 
GG 17 26.5 0.163 0.57 (0.25-1.27) 0.50 (0.22-1.12) 
    Additive model G allele     0.75 (0.60-0.94) 0.77 (0.61-0.96) 
  Trend P     0.010 0.023 
IGFBP3 rs2132572  n = 568 n = 311     
    Codominant model GG 366 194 25.9  1.00 1.00 
AG 178 97 24.8  1.19 (0.93-1.51) 1.04 (0.81-1.34) 
AA 24 20 16.7 0.005 2.07 (1.31-3.30) 1.43 (0.90-2.29) 
    Dominant model GG 366 194 25.9  1.00 1.00 
AG/AA 202 117 22.1 0.037 1.28 (1.02-1.61) 1.09 (0.86-1.39) 
    Recessive model GG/AG 544 291 25.9  1.00 1.00 
AA 24 20 16.7 0.003 1.96 (1.25-3.10) 1.39 (0.88-2.20) 
    Additive model A allele     1.31 (1.08-1.58) 1.12 (0.92-1.36) 
Trend P     0.006 0.247 
IGFBP3 rs10255707  n = 565 n = 309     
    Codominant model GG 383 200 25.5  1.00 1.00 
AG 161 95 24.8  1.20 (0.94-1.53) 1.03 (0.80-1.32) 
AA 21 14 15.0 0.046 1.84 (1.07-3.16) 1.47 (0.84-2.56) 
    Dominant model GG 383 200 25.5  1.00 1.00 
AG/AA 182 109 23.1 0.060 1.25 (0.99-1.58) 1.08 (0.84-1.37) 
    Recessive model AA/AG 544 295 25.1  1.00 1.00 
AA 21 14 15.0 0.042 1.74 (1.01-2.97) 1.47 (0.85-2.54) 
    Additive model A allele     1.26 (1.03-1.54) 1.11 (0.90-1.36) 
Trend P     0.023 0.338 

*Adjusted for age, gender, smoking status, histology, stage, surgical operation, and chemotherapy or radiotherapy status.

Mean survival time was provided when MST could not be calculated.

Assuming an additive effect of the variant alleles.

Fig. 1.

Kaplan-Meier plots of survival by IGF1 genotypes in NSCLC-specific survival. A, IGF1 rs5742714 genotypes and NSCLC-specific survival (log-rank P = 0.043) in a codominant model. B, IGF1 rs5742714 genotypes and NSCLC-specific survival (log-rank P = 0.017) in a dominant model. C, Kaplan-Meier plots of survival by combination of IGF1 genotypes and operation status in NSCLC-specific survival (log-rank P < 0.001). 0, patients with variant genotypes (CC or CG) and receiving operation; 1, those with common genotype (GG) and receiving operation; 2, those with variant genotypes but without operation; 3, those with common genotypes and without operation.

Fig. 1.

Kaplan-Meier plots of survival by IGF1 genotypes in NSCLC-specific survival. A, IGF1 rs5742714 genotypes and NSCLC-specific survival (log-rank P = 0.043) in a codominant model. B, IGF1 rs5742714 genotypes and NSCLC-specific survival (log-rank P = 0.017) in a dominant model. C, Kaplan-Meier plots of survival by combination of IGF1 genotypes and operation status in NSCLC-specific survival (log-rank P < 0.001). 0, patients with variant genotypes (CC or CG) and receiving operation; 1, those with common genotype (GG) and receiving operation; 2, those with variant genotypes but without operation; 3, those with common genotypes and without operation.

Close modal

We carried out haplotype inference using the PHASE 2.1 based on the known genotypes of the SNPs for IGF1 and IGFBP3, respectively. For statistical consideration, we ignored all haplotypes with a frequency <0.05. However, no statistically significant association between the haplotypes of IGF1 and IGFBP3 and lung cancer survival was evident (data not shown).

Stepwise cox regression model for NSCLC survival

We further did a multivariate stepwise Cox regression analysis for the effects of demographic characteristics, clinical features, and IGF1 and IGFBP3 genotypes on NSCLC survival. Five variables (stage, surgical operation status, chemotherapy or radiotherapy status, smoking status, and IGF1 rs5742714) were included in the regression model with a significance level of 0.05 for entering and 0.051 for removing a variable (P < 0.001 for stage and surgical operation status; P = 0.014, 0.046, and 0.036 for chemotherapy or radiotherapy status, smoking status, and IGF1 rs5742714, respectively).

Stratified analyses and interaction

The associations between IGF1 rs5742714 polymorphism and NSCLC survival were further evaluated by stratified analysis of smoking status, histology, stage, surgical operation, and radiotherapy or chemotherapy status. As shown in Table 4, there was no obvious evidence of differentiated association between the protective effects of variant genotypes of rs5742714 and NSCLC survival among different subgroups of smoking status, histology, stage, and radiotherapy or chemotherapy status. However, the decreased risk was more predominant among subjects receiving surgical operation (HR, 0.58; 95% CI, 0.40-0.85, P for heterogeneity test = 0.045). Therefore, a gene- surgical operation interaction analysis was carried out (Table 5), and there was a statistically significant multiplicative interaction between the genotypes of rs5742714 and operation status on NSCLC survival (P for multiplicative interaction = 0.028). Kaplan-Meier plots of survival by combination of IGF1 genotypes and operation status in NSCLC-specific survival are shown in Fig. 1C.

Table 4.

Stratified analysis of rs5742714 genotypes associated with NSCLC patients survival

Variablesrs5742714 (deaths/patients)Adjusted HR (95% CI)*Heterogeneity P
CCCG/GG
Smoking    0.173 
    Nonsmokers 75/138 30/55 0.98 (0.63-1.53)  
    Smokers 144/239 52/115 0.67 (0.49-0.93)  
Histology    0.729 
    Adenocarcinoma 137/237 48/100 0.77 (0.55-1.08)  
    Squamous cell 69/120 27/59 0.75 (0.48-1.18)  
    Others 13/20 7/11 1.25 (0.38-4.15)  
Stage    0.535 
    Early stage (I-II) 52/138 18/67 0.68 (0.39-1.18)  
    Late stage (III-IV) 167/239 64/103 0.83 (0.61-1.11)  
Surgical operation    0.045 
    None 107/139 45/57 0.99 (0.69-1.42)  
    Yes 112/238 37/113 0.58 (0.40-0.85)  
Chemotherapy or radiotherapy    0.356 
    None 31/72 14/31 1.01 (0.51-2.03)  
    Yes 188/305 68/139 0.71 (0.53-0.94)  
Variablesrs5742714 (deaths/patients)Adjusted HR (95% CI)*Heterogeneity P
CCCG/GG
Smoking    0.173 
    Nonsmokers 75/138 30/55 0.98 (0.63-1.53)  
    Smokers 144/239 52/115 0.67 (0.49-0.93)  
Histology    0.729 
    Adenocarcinoma 137/237 48/100 0.77 (0.55-1.08)  
    Squamous cell 69/120 27/59 0.75 (0.48-1.18)  
    Others 13/20 7/11 1.25 (0.38-4.15)  
Stage    0.535 
    Early stage (I-II) 52/138 18/67 0.68 (0.39-1.18)  
    Late stage (III-IV) 167/239 64/103 0.83 (0.61-1.11)  
Surgical operation    0.045 
    None 107/139 45/57 0.99 (0.69-1.42)  
    Yes 112/238 37/113 0.58 (0.40-0.85)  
Chemotherapy or radiotherapy    0.356 
    None 31/72 14/31 1.01 (0.51-2.03)  
    Yes 188/305 68/139 0.71 (0.53-0.94)  

*Adjusted for age, sex, smoking, histology, stage, surgical operation and chemotherapy or radiotherapy status.

Other carcinomas include large cell, undifferentiated and mixed-cell carcinomas.

Table 5.

Interaction between rs5742714 genotypes and surgical operation

VariablesPatientsDeathsMST (mo)Crude HR (95% CI)Adjusted HR (95% CI)*
CC without operation 139 107 16.4 1.00 1.00 
CG/GG without operation 57 45 13.7 1.01 (0.71-1.44) 0.99 (0.69-1.42) 
CC with operation 238 112 32.5 0.38 (0.29-0.50) 0.67 (0.48-0.94) 
CG/GG with operation 113 37 28.2 0.24 (0.16-0.35) 0.34 (0.21-0.56) 
P for multiplicative interaction    0.043 0.028 
VariablesPatientsDeathsMST (mo)Crude HR (95% CI)Adjusted HR (95% CI)*
CC without operation 139 107 16.4 1.00 1.00 
CG/GG without operation 57 45 13.7 1.01 (0.71-1.44) 0.99 (0.69-1.42) 
CC with operation 238 112 32.5 0.38 (0.29-0.50) 0.67 (0.48-0.94) 
CG/GG with operation 113 37 28.2 0.24 (0.16-0.35) 0.34 (0.21-0.56) 
P for multiplicative interaction    0.043 0.028 

*Adjusted for age, sex, smoking, histology, stage and chemotherapy or radiotherapy status.

Mean survival time was provided when MST could not be calculated.

We also evaluated the interactions between the genotypes of IGF1 rs5742714, and IGFBP3 rs2132572 and rs10255707, but no statistical significance was observed (data not shown).

In this lung cancer study with a relatively comprehensive selection of SNPs in IGF1 and IGFBP3, we investigated the effects of 17 tagging and potentially functional SNPs on NSCLC-specific survival. The results indicated that the IGF1 rs5742714 G allele was associated with a significantly improved survival of NSCLC in a dominant model (crude HR, 0.74; 95% CI, 0.57-0.95 and adjusted HR, 0.77; 95% CI, 0.60-0.99), and this protective effect was more predominant for patients receiving surgical operation (HR, 0.58; 95%CI, 0.40-0.85; P for heterogeneity test = 0.045), along with a significant interaction of gene-surgical operation (adjusted P for multiplicative interaction = 0.028). To the best of our knowledge, this is the first report to evaluate the role of genetic variations in the genes of IGF1-IGFBP3 on NSCLC survival.

According to previous studies, IGF-I and its binding proteins play key roles in the genesis of many types of tumors including lung cancer (29), and evidence of the influence on disease progression and prognosis has accumulated recently (1922, 30). Following the initial in vitro experiment showing a dose-dependent increase of breast cancer cell proliferation with increased IGF-I concentration (30), a series of laboratory studies on NSCLC cell lines revealed that inhibition of IGF-I receptor led to a decrease of cell survival and an increase of apoptosis (1921), and suppression of IGF-I signaling inhibited A549 lung cancer cell invasion in vitro as well as metastasis in a mice model in vivo (22). Besides, epidemiologic studies have shown that high circulating level of IGF-I was a risk factor for disease progression and was associated with poor prognosis of breast, ovarian, and prostate cancers (16, 29, 31), and genetic studies have reported that variation of IGF1 was associated with progression and prognosis of common cancers, including breast and prostate cancers (16, 18, 32).

In the current study, by systematically evaluating the association of SNPs in IGF1 and IGFBP3 with NSCLC survival in a Chinese population, we found a significant association between the rs5742714 in the 3′UTR of IGF1 with NSCLC survival. Given the position of 3′UTR where rs5472714 located, it is biologically plausible that such a variation at this position may have an effect on mRNA stability and lead to altered binding activity to microRNAs, which might downregulate gene expression by mRNA cleavage or translational repression (33). It should be noted, however, that the association of this polymorphism with lung cancer prognosis might also be mediated by linkage disequilibrium with other causal loci. According to the HapMap database, there was another polymorphism (rs6219) in 3′UTR of IGF1 in complete linkage disequilibrium with rs5742714, which is also a candidate for the true association. In stratified analyses, the association was more predominant among patients receiving surgical operation, and there was a significant multiplicative interaction between risk genotypes and operation status. Both experimental and clinical studies have shown that the IGF-I receptor is overexpressed in lung and other carcinomas compared with normal tissues (3437), which might eliminate the downregulation effect of the variant genotypes of rs5742714 among patients without operation.

We conducted this study in a relative large cohort, and with such a sample size the statistical power for the estimated crude and adjusted effects (HR) of rs5742714 in a dominant model was 74.19% and 62.63%, respectively, and for the interaction of gene-surgical operation, it was 97.7% and 98.9%, respectively, assuming the significant level P = 0.05. However, we failed to find significant associations between any SNPs of IGFBP3 and the prognosis of NSCLC, although some of the polymorphisms evaluated in our study were linked to prognosis of other cancers or altered circulating levels of IGFBP3 (18, 38, 39). For example, rs3110697 and rs2854746 were shown to be associated with IGFBP3 levels in a multiethnic population in America (38, 39), and rs3110697 and rs2471551 were associated with breast cancer survival in premenopausal women in a Chinese population (18). Besides the diverse genetic background, the discrepancy of these findings may be explained by different tumor sites or therapeutic issues. For instance, reduced IGFBP3 was correlated with poor survival with lung and ovarian cancers (17, 23, 24), whereas an inverse prognostic effect was revealed in prostate cancer (40), indicating the variation among different tumors. Additionally, it has been reported that rs35767 in IGF1 was associated with the circulating IGF-I level in Americans (41), but this variant revealed no significance with relation to NSCLC survival in our study, which may result from the ethnic difference.

Besides, there were still some limitations in this study. Firstly, after adjusting for multiple comparisons, none of the 17 SNPs attained significant level (0.003), so our findings need to be confirmed by additional studies with large sample size. Secondly, among the cohort of 828 NSCLC patients, only 568 patients (68.6%) with adequate DNA sample for Illumina genotyping assay were included in the current study, which could potentially influence the final estimation due to selection bias. Thirdly, in this study, 4 of 21 SNPs were removed from further analysis due to the failure in genotyping, which may result in losses of information. However, it was due to technical limitation and may not lead to selection bias.

In summary, our findings suggest that rs5742714 in IGF1 may be a genetic modifier for NSCLC prognosis in this Chinese population, especially among patients after surgical operation. Further validation in larger cohort or independent population and functional characterizations are needed.

No potential conflicts of interest were disclosed.

Grant Support: Supported in part by Key Grant of National Natural Science Foundation of China (30730080); National Outstanding Youth Science Foundation of China (30425001), National Natural Science Foundation of China (30972541, 30901233), Natural Science Foundation of Jiangsu Province (BS2006005) and Jiangsu Key Discipline of Medicine (Grants No.: XK200718).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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