Genes involved in DNA repair and replication have been recently investigated as predictive markers of response to chemotherapy in non–small cell lung cancer (NSCLC). However, few data on the expression of these genes in tumor compared with corresponding normal lung are available. The aim of this study was to evaluate differential mRNA levels of 22 DNA repair genes of five different DNA repair pathways: direct, base excision, nucleotide excision (NER), double-strand break (DSBR), and postreplicative repair. In addition, six genes involved in DNA replication (REP) and three telomere maintenance genes were investigated. Total RNAs extracted from fresh-frozen tumors and corresponding normal tissues of 50 consecutive chemo-naïve resected NSCLC patients were analyzed. Transcript levels were quantified by real-time PCR. A significant overexpression was detected in 20 of 30 (67%) genes, mostly belonging to DSBR pathways, whereas others (XPA, XPC, and UBE2N; 10%) were significantly underexpressed. For 7 of 30 (23%) genes, mostly belonging to NER pathway, no significant difference between paired tumor and normal samples was observed. Transcript overexpression of DSBR and REP genes was significantly higher in poorly differentiated carcinomas and DSBR levels were higher in men compared with women. The transcriptional overexpression of four genes (XRCC5, TOP3B, TYMS, and UNG) showed significant correlation with a shorter patients' outcome at the univariate, whereas only stage of disease appeared as an independent factor affecting prognosis, as assessed by multivariate analysis. In conclusion, genes belonging to DNA repair/replication pathways are overexpressed in NSCLC and are associated with a more aggressive phenotype. [Cancer Res 2009;69(8):3390–6]

The high incidence of genomic instability in lung cancers is well-established and in some cases it has been associated with poor prognosis (13). To guarantee genomic stability under the conditions of continuous genotoxic stress, a coordinated DNA damage surveillance system and several DNA repair pathways have been evolutionally developed. Subjects with reduced DNA repair capacity have an increased risk of developing lung cancer (4), and conversely, high DNA repair capacity has correlation with resistance to chemotherapy (5, 6). Many of the cytotoxic agents used in the systemic treatment of non–small cell lung cancer (NSCLC) are interfering with DNA activity and the possibility of individualizing DNA repair profiles is becoming a central issue in the search for improved chemotherapy results. Indeed, most of the molecular markers currently used to predict the responsiveness to chemotherapeutic treatment are genes involved in DNA damage response such as p53 (7), BRCA1 (8), ERCC1 (9), MLH1 and MSH2 (10), Rad51 (11), ERCC2, XRCC1, XPA (12), XPC, or involved in DNA replication such as RRM1 (13) and TYMS (14). However, no exhaustive data on the differential expression of these DNA repair/DNA replication genes in lung tumor versus corresponding normal tissue are currently available. The aim of the present study was to evaluate, in 50 tumor and paired normal lung tissues, the differential transcript expression of the following 22 human DNA repair genes belonging to five DNA repair pathways: MGMT in direct repair; OGG1, UNG, and XRCC1 in base excision repair (BER); XPA, XPC, ERCC1, ERCC2, ERCC4, ERCC5, ERCC6, and XAB2 in nucleotide excision repair (NER); XRCC2, XRCC3, XRCC4, XRCC5, BRCA1, BRCA2, and UBE2V2 in double-strand break repair (DSBR); and UBE2A, UBE2B, and UBE2N in postreplicative repair (PRR). In addition, nine genes involved in DNA replication (REP; TYMS, RRM2B, RRM2, RRM1, TOP3A, and TOP3B) and in telomere maintenance (TERT, TERF1, and TERF2) were also investigated (see Supplementary Data Table S1).

Patients and samples. Primary tumor samples and paired normal lung tissues of 50 consecutive NSCLC patients who received radical surgery for early NSCLC at the San Luigi Hospital, Division of Thoracic Surgery, Orbassano (Italy) between December 2003 and March 2004, were immediately snap-frozen in liquid nitrogen and stored at −80°C until RNA extraction. The age of the patients (37 males and 13 females) ranged from 41 to 82 years (median, 69 years) and none of the patients received either preoperative/postoperative radiation or chemotherapy. Based on the negative outcomes of a large phase III study performed in Europe (15), and as policy by the investigators, patients with stage II and III completely resected NSCLC did not receive any adjuvant treatments at that time. Median survival time was 56 mo and 21 out of 50 (42%) had died at the end of the study. Histologic examination was performed on adjacent formalin-fixed fragments in all cases to evaluate tumor histotype according to WHO criteria (adenocarcinomas, 29; squamous cell carcinomas, 18; and large cell carcinomas, 3), grade of differentiation (grade 1, 10; grade 2, 18; grade 3, 22), pT status (pT1, 11; pT2, 34; pT3, 5), and pN status (pN0, 35; pN1, 8; pN2, 7). Forty-four patients were smokers, two (both women with adenocarcinomas) had never smoked, and for the remaining four patients, smoking history was unknown. Follow-up until death or to the last follow-up visit were available for all cases. The study was approved by the ethical review board of our institution.

RNA extraction and cDNA synthesis. Total RNA (totRNA) was isolated from lung specimens with the RNeasy 96 Kit and Biorobot 8000 (Qiagen) according to the manufacturer's instructions. RNA was extracted from 15 to 25 mg and 60 to 80 mg of tumor and normal lung tissue specimens, respectively. Genomic DNA contaminations were removed by on-column DNaseI treatment (Qiagen). TotRNA was then quantified with an Agilent 2100 Bioanalyzer (Agilent Technologies) and stored at −80°C. Two micrograms of totRNA were finally retrotranscribed with random hexamer primers and Multiscribe Reverse transcriptase contained in the High-Capacity cDNA Archive Kit (Applied Biosystems), in accordance with the manufacturer's suggestions.

Real-time PCR. Expression levels of all target genes and two reference genes were evaluated with TaqMan Probes commercially available as “Assay on Demand” (Applied Biosystems) with optimized primer and probe concentrations (Supplementary Data Table S1). Quantitative PCR was performed on an ABI PRISM 7900HT Sequence Detection System (Applied Biosystems) in 384-well plates assembled by Biorobot 8000 and the reaction was performed in a final volume of 20 μL. All quantitative PCR mixtures contained 1 μL of cDNA template (corresponding to approximately 20 ng of retrotranscribed totRNA), 1× TaqMan Universal PCR Master Mix (2×; Applied Biosystems) and 1× Assay-on-Demand Gene Expression Assay Mix (20×). Cycle conditions were as follows: after an initial 2-min hold at 50°C to allow AmpErase-UNG activity, and 10 min at 95°C, the samples were cycled 40 times at 95°C for 15 s and 60°C for 1 min. Baseline and threshold for Ct calculation were set manually with the ABI Prism SDS 2.1 software. Automation allowed negligible intra-assay variation (≤5% coefficient of variance) and low inter-assay variation (≤10% coefficient of variance) when evaluated on raw linear expression quantities.

Quantitative PCR data analysis. For each target gene, fold change in expression levels between normal and tumor specimens were evaluated using the 2−ΔΔCt method (16). Raw data were normalized using the geometric average value (17) of two suitable reference genes (POLR2A and 18SrRNA; ref. 18). For each patient, the normalized values of each biological triplicate were averaged before the calculation of the −ΔΔCt using normal tissue as a calibrator. The calibration of tumor samples to matched normal tissues allowed the assessment of transcript deregulations strictly associated with malignant transformation, independently from patients' genetic alterations such as single nucleotide polymorphisms (19, 20). Moreover, to take into account tissue heterogeneity of fresh-frozen samples, transcript expression levels were evaluated on biological triplicates of both normal and tumor specimens.

Ki67 immunohistochemistry. Formalin-fixed paraffin-embedded tissues were cut into 4-μm sections and collected on charged slides in order to perform immunohistochemical staining. After deparaffinization and rehydration through graded alcohols and PBS (pH 7.5), the endogenous peroxidase activity was blocked by absolute methanol and 0.3% hydrogen peroxide for 15 min. To perform the antigen-retrieval procedure, the slides were heated in a pressurized heating chamber (Pascal; DakoCytomation) in EDTA at 10× buffer (pH 8.0) solution. Sections were incubated overnight with a mouse monoclonal antibody anti-Ki67 (MIB-1; DakoCytomation) diluted 1:300. Immunoreaction was revealed by a dextran-chain (biotin-free) detection system (EnVision; DakoCytomation), using 3,3′-diaminobenzidine (DakoCytomation) as a chromogen. Finally, the sections were lightly counterstained with hematoxylin. Negative control reactions were obtained by omitting the primary antibodies. Ki67 proliferation index was calculated as the percentage of positive nuclei among at least 200 nuclei counted at high magnification in areas of highest labeling.

Statistical analysis. Differences in transcript expression levels between paired tumor and normal samples were evaluated using the Wilcoxon matched-pair test on ΔCt values. Association of patient's clinicopathologic features to transcript expression levels and Ki67 was evaluated with the Mann-Whitney or Kruskal-Wallis tests. The Spearman rank correlation method was used to evaluate transcript coexpression and genes showing R > 0.7 with P < 0.01 were defined as significantly coexpressed. Univariate analyses of survival were made by the Kaplan-Meier method and validated by log-rank test with the Benjamini correction for multiple comparisons. The method by Benjamini was selected because it generated a lower number of false-negative results compared with other tests for multiple comparison, possibly as a consequence of the limited sample size of the present study. Multivariate analysis was carried out with Cox's proportional hazard model and all the factors significantly correlated with survival at the univariate analysis were included. Statistical significance was set at P = 0.05.

Variability of target gene transcript expression. All genes except TERT were detected in all normal and tumor specimens. TERT transcript was detectable in at least one specimen of the biological triplicate in 96% of tumor samples but only in 76% of normal tissues. Each target gene showed a wide range of expression within the group of either normal or tumor tissues. Expressing such variability as coefficient of variance evaluated on normalized data (i.e., ΔCt), the range of expression was broader in tumors than in normal specimens for all genes. Coefficients of variance ranged from 3.68% for TERF2 to 12.01% for UNG in the tumor tissues and from 2.72% for TERF2 to 7.28% for XRCC1 in the normal tissues. Moreover, for each target gene, the intra-sample variability (evaluated as the coefficient of variance of biological triplicates) was always found to be lower than inter-sample variability and was fairly comparable between tumor and paired normal specimens.

Differential expression of pathway genes in tumor versus normal lung. Relative expression levels (−ΔΔCt) were evaluated for all target genes, with the exclusion of TERT, for which only the transcript overexpression in 78% of tumor samples but not the extent of such deregulation was detected. TERT has therefore been excluded from subsequent analysis. Relative expression levels were extremely wide and most of the target genes were significantly differentially expressed between tumor and normal lung tissue specimens. Significant overexpression has been observed in 20 out of 30 (67%) target genes (median −ΔΔCt ranging from 0.38 for UBE2B to 3.62 for RRM2). A positive deregulation of all genes belonging to DSB repair (seven of seven), two out of three in the BER pathway, two out of eight in the NER pathway, two out of three in the PRR pathway, and seven out of eight of the genes involved in DNA replication and telomere maintenance was shown. Only three genes (10%), XPA and XPC of the NER and UBE2N of the PRR pathways, had significantly lower transcriptional expression levels in tumor specimens than normal matched samples (median −ΔΔCt −0.32, −0.46, and −0.84, respectively). For 7 out of 30 (23%) genes, mainly associated with the NER pathway, no significant differences between paired tumor and normal samples were observed. Quantitative PCR quantification allows us to qualify as “positive” only expression level variations greater than the reference gene(s) variability. Because this restriction eludes statistical assessment, we have decided to set the absolute value of “−ΔΔCt” to 1 as a threshold for evaluating the prevalence of overexpression or underexpression of our target genes in patients with NSCLC. This threshold has been selected because it contains the entire variability of the reference genes (Fig. 1). Based on the pattern of deregulation in patients with NSCLC, we have arbitrarily segregated target genes into four clusters: highly deregulated (I, deregulated in at least 50% of patients), moderately deregulated (II, deregulated in at least 25% of patients), poorly deregulated (III, deregulated in 14–18% of patients), and not deregulated (IV, deregulated in <10% of patients; see Table 1). Inconsistencies among our clustering and Wilcoxon's analysis have been observed for TERF2 and MGMT.

Figure 1.

Differential expression of pathway genes in tumor vs. normal lung tissue. Box-whisker plot represents relative expression level, reported as −ΔΔCt values, of each target gene. HKGs, housekeeping gene variability; dashed line, cutoff threshold indicating 2-fold change variation. Target genes have been grouped into highly (dark gray), moderately (light gray), poorly (dotted), and not (white) regulated based on their prevalence in our cohort of patients with NSCLC.

Figure 1.

Differential expression of pathway genes in tumor vs. normal lung tissue. Box-whisker plot represents relative expression level, reported as −ΔΔCt values, of each target gene. HKGs, housekeeping gene variability; dashed line, cutoff threshold indicating 2-fold change variation. Target genes have been grouped into highly (dark gray), moderately (light gray), poorly (dotted), and not (white) regulated based on their prevalence in our cohort of patients with NSCLC.

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Table 1.

Median “−ΔΔCt”, SD and P values of Wilcoxon-matched pair test of 30 pathway genes

Gene symbolPathwayMedian −ΔΔCtSDPOver (%)Equal (%)Under (%)Cluster
BRCA1 DSBR 1.82 1.38 0.000* 66 34  
BRCA2 DSBR 1.5 1.21 0.000* 62 36 
XRCC2 DSBR 3.51 1.43 0.000* 90 10  
XRCC3 DSBR 2.2 1.17 0.000* 76 24  
XRCC4 DSBR 0.55 0.55 0.000* 26 74  II 
XRCC5 DSBR 0.63 0.78 0.000* 38 62  II 
UBE2V2 DSBR 1.18 0.7 0.000* 64 36  
MGMT DR 0.21 0.93 0.063 16 72 12 II 
XRCC1 BER 0.45 0.98 0.001* 32 62 II 
OGG1 BER 0.01 0.64 0.233 14 84 III 
UNG BER 1.71 1.42 0.000* 70 30  
XPA NER −0.32 0.64 0.005* 84 12 III 
XPC NER −0.46 0.59 0.000*  74 26 II 
ERCC1 NER −0.04 0.6 0.633 14 82 III 
ERCC2 NER 1.12 0.81 0.000* 60 40  
ERCC4 NER 0.58 0.94 0.000* 36 64  II 
ERCC5 NER −0.12 0.66 0.111 88 IV 
ERCC6 NER 0.12 0.56 0.136 94 IV 
XAB2 NER −0.05 0.59 0.897 94  IV 
UBE2A PRR 0.69 0.94 0.000* 42 56 II 
UBE2B PRR 0.38 0.93 0.002* 32 64 II 
UBE2N PRR −0.84 0.83 0.000*  58 42 II 
RRM2B DNA-REP 0.09 0.98 0.318 20 68 12 II 
TYMS DNA-REP 3.28 1.6 0.000* 96  
RRM1 DNA-REP 1.5 1.03 0.000* 74 26  
RRM2 DNA-REP 3.62 1.37 0.000* 100   
TOP3A DNA-REP 0.62 0.69 0.000* 26 74  II 
TOP3B DNA-REP 0.41 0.52 0.000* 20 80  III 
TERF1 TM 1.18 1.24 0.000* 54 42 
TERF2 TM 0.21 0.41 0.000* 96  IV 
Gene symbolPathwayMedian −ΔΔCtSDPOver (%)Equal (%)Under (%)Cluster
BRCA1 DSBR 1.82 1.38 0.000* 66 34  
BRCA2 DSBR 1.5 1.21 0.000* 62 36 
XRCC2 DSBR 3.51 1.43 0.000* 90 10  
XRCC3 DSBR 2.2 1.17 0.000* 76 24  
XRCC4 DSBR 0.55 0.55 0.000* 26 74  II 
XRCC5 DSBR 0.63 0.78 0.000* 38 62  II 
UBE2V2 DSBR 1.18 0.7 0.000* 64 36  
MGMT DR 0.21 0.93 0.063 16 72 12 II 
XRCC1 BER 0.45 0.98 0.001* 32 62 II 
OGG1 BER 0.01 0.64 0.233 14 84 III 
UNG BER 1.71 1.42 0.000* 70 30  
XPA NER −0.32 0.64 0.005* 84 12 III 
XPC NER −0.46 0.59 0.000*  74 26 II 
ERCC1 NER −0.04 0.6 0.633 14 82 III 
ERCC2 NER 1.12 0.81 0.000* 60 40  
ERCC4 NER 0.58 0.94 0.000* 36 64  II 
ERCC5 NER −0.12 0.66 0.111 88 IV 
ERCC6 NER 0.12 0.56 0.136 94 IV 
XAB2 NER −0.05 0.59 0.897 94  IV 
UBE2A PRR 0.69 0.94 0.000* 42 56 II 
UBE2B PRR 0.38 0.93 0.002* 32 64 II 
UBE2N PRR −0.84 0.83 0.000*  58 42 II 
RRM2B DNA-REP 0.09 0.98 0.318 20 68 12 II 
TYMS DNA-REP 3.28 1.6 0.000* 96  
RRM1 DNA-REP 1.5 1.03 0.000* 74 26  
RRM2 DNA-REP 3.62 1.37 0.000* 100   
TOP3A DNA-REP 0.62 0.69 0.000* 26 74  II 
TOP3B DNA-REP 0.41 0.52 0.000* 20 80  III 
TERF1 TM 1.18 1.24 0.000* 54 42 
TERF2 TM 0.21 0.41 0.000* 96  IV 

NOTE: Target genes were further grouped into four clusters: highly (I), moderately (II), poorly (III), and not (IV) deregulated based on transcript deregulation observed in the cohort of 50 NSCLC patients.

*

Significant transcript deregulations.

Correlation of relative expression of target genes with clinical pathologic features. Generally, transcript overexpression was more frequent in squamous cell carcinoma than in adenocarcinoma, and in 12 genes (BRCA1, BRCA2, XRCC2, XRCC3, UBE2V2, XRCC1, UNG, ERCC1, XAB2, RRM1, RRM2, and TOP3A), the difference was statistically significant. For all genes belonging to BER, REP (except RRM2B), and DSB pathways (except XRCC4), and for ERCC2 associated with NER pathway and UBE2A belonging to PRR, a significant correlation with tumor grade was observed as being an overexpression rate higher in poorly differentiated carcinomas. Interestingly, most of the genes involved in DSBR pathway (BRCA1, BRCA2, XRCC2, and XRCC3) showed a significant correlation with gender, with overexpression being higher in male patients compared with female patients. Finally, only UBE2N displayed a significant correlation with pN and stage of disease, whereas association with age was observed only for TOP3B (Table 2). For 57% (17 of 30) of the pathway genes, a significant association between transcript overexpression and proliferation index, determined as Ki67 protein expression, was observed.

Table 2.

Association between relative gene expression and clinicopathologic variables

PathwayClusterGeneGender*Age*Histotype*,GradepT,§pNStage,Ki67 *,
DSB BRCA1 0.016** 0.348 0.012** 0.000** 0.065 0.332 0.072 0.000** 
DSB BRCA2 0.039** 0.984 0.033** 0.000** 0.446 0.317 0.286 0.001** 
DSB XRCC2 0.050** 0.107 0.020** 0.001** 0.407 0.778 0.834 0.012** 
DSB XRCC3 0.018** 0.899 0.001** 0.000** 0.382 0.674 0.48 0.000** 
DSB II XRCC4 0.104 0.66 0.322 0.501 0.376 0.645 0.736 0.741 
DSB II XRCC5 0.274 0.868 0.347 0.003** 0.314 0.453 0.244 0.033** 
DSB UBE2V2 0.288 0.296 0.039** 0.049** 0.646 0.33 0.178 0.010** 
Direct II MGMT 0.309 0.762 0.088 0.27 0.715 0.833 0.813 0.101 
BER II XRCC1 0.259 0.807 0.020** 0.022** 0.349 0.356 0.194 0.002** 
BER III OGG1 0.154 0.899 0.322 0.014** 0.147 0.475 0.281 0.057 
BER UNG 0.144 0.667 0.002** 0.002** 0.801 0.356 0.368 0.000** 
NER III XPA 0.877 0.822 0.895 0.938 0.893 0.37 0.224 0.565 
NER II XPC 0.965 0.74 0.214 0.58 0.346 0.298 0.34 0.264 
NER III ERCC1 0.55 0.401 0.014** 0.193 0.678 0.656 0.732 0.009** 
NER ERCC2 0.842 0.86 0.137 0.030** 0.752 0.188 0.278 0.024** 
NER II ERCC4 0.588 0.5 0.176 0.08 0.588 0.14 0.266 0.005** 
NER IV ERCC5 0.376 0.309 0.599 0.169 0.193 0.29 0.545 0.58 
NER IV ERCC6 0.493 0.732 0.233 0.851 0.255 0.83 0.701 0.482 
NER IV XAB2 0.642 0.13 0.012** 0.075 0.404 0.447 0.879 0.006** 
PRR II UBE2A 0.868 0.938 0.179 0.027** 0.637 0.267 0.243 0.007** 
PRR II UBE2B 0.452 0.429 0.824 0.714 0.455 0.078 0.118 0.125 
PRR II UBE2N 0.791 0.145 0.936 0.061 0.072 0.028** 0.004** 0.142 
REP II RRM2B 0.699 0.087 0.531 0.982 0.457 0.687 0.907 0.064 
REP TYMS 0.063 0.591 0.06 0.001** 0.555 0.905 0.814 0.001** 
REP RRM1 0.304 0.591 0.025** 0.004** 0.591 0.14 0.147 0.002** 
REP RRM2 0.106 0.476 0.020** 0.012** 0.434 0.855 0.79 0.003** 
REP II TOP3A 0.627 0.19 0.022** 0.017** 0.387 0.602 0.487 0.005** 
REP III TOP3B 0.293 0.025** 0.16 0.049** 0.831 0.623 0.781 0.163 
TM TERF1 0.748 0.363 0.054 0.066 0.197 0.124 0.073 0.07 
TM IV TERF2 0.32 0.458 0.571 0.724 0.99 0.131 0.11 0.194 
PathwayClusterGeneGender*Age*Histotype*,GradepT,§pNStage,Ki67 *,
DSB BRCA1 0.016** 0.348 0.012** 0.000** 0.065 0.332 0.072 0.000** 
DSB BRCA2 0.039** 0.984 0.033** 0.000** 0.446 0.317 0.286 0.001** 
DSB XRCC2 0.050** 0.107 0.020** 0.001** 0.407 0.778 0.834 0.012** 
DSB XRCC3 0.018** 0.899 0.001** 0.000** 0.382 0.674 0.48 0.000** 
DSB II XRCC4 0.104 0.66 0.322 0.501 0.376 0.645 0.736 0.741 
DSB II XRCC5 0.274 0.868 0.347 0.003** 0.314 0.453 0.244 0.033** 
DSB UBE2V2 0.288 0.296 0.039** 0.049** 0.646 0.33 0.178 0.010** 
Direct II MGMT 0.309 0.762 0.088 0.27 0.715 0.833 0.813 0.101 
BER II XRCC1 0.259 0.807 0.020** 0.022** 0.349 0.356 0.194 0.002** 
BER III OGG1 0.154 0.899 0.322 0.014** 0.147 0.475 0.281 0.057 
BER UNG 0.144 0.667 0.002** 0.002** 0.801 0.356 0.368 0.000** 
NER III XPA 0.877 0.822 0.895 0.938 0.893 0.37 0.224 0.565 
NER II XPC 0.965 0.74 0.214 0.58 0.346 0.298 0.34 0.264 
NER III ERCC1 0.55 0.401 0.014** 0.193 0.678 0.656 0.732 0.009** 
NER ERCC2 0.842 0.86 0.137 0.030** 0.752 0.188 0.278 0.024** 
NER II ERCC4 0.588 0.5 0.176 0.08 0.588 0.14 0.266 0.005** 
NER IV ERCC5 0.376 0.309 0.599 0.169 0.193 0.29 0.545 0.58 
NER IV ERCC6 0.493 0.732 0.233 0.851 0.255 0.83 0.701 0.482 
NER IV XAB2 0.642 0.13 0.012** 0.075 0.404 0.447 0.879 0.006** 
PRR II UBE2A 0.868 0.938 0.179 0.027** 0.637 0.267 0.243 0.007** 
PRR II UBE2B 0.452 0.429 0.824 0.714 0.455 0.078 0.118 0.125 
PRR II UBE2N 0.791 0.145 0.936 0.061 0.072 0.028** 0.004** 0.142 
REP II RRM2B 0.699 0.087 0.531 0.982 0.457 0.687 0.907 0.064 
REP TYMS 0.063 0.591 0.06 0.001** 0.555 0.905 0.814 0.001** 
REP RRM1 0.304 0.591 0.025** 0.004** 0.591 0.14 0.147 0.002** 
REP RRM2 0.106 0.476 0.020** 0.012** 0.434 0.855 0.79 0.003** 
REP II TOP3A 0.627 0.19 0.022** 0.017** 0.387 0.602 0.487 0.005** 
REP III TOP3B 0.293 0.025** 0.16 0.049** 0.831 0.623 0.781 0.163 
TM TERF1 0.748 0.363 0.054 0.066 0.197 0.124 0.073 0.07 
TM IV TERF2 0.32 0.458 0.571 0.724 0.99 0.131 0.11 0.194 
*

Mann-Whitney test.

Squamous vs. nonsquamous histotype.

Kruskal-Wallis test.

§

pT1 vs. pT2 vs. pT3.

Stage I vs. II vs. III.

Ki67 < median vs. ≥ median score.

**

Statistically significant values.

Coexpression of pathway genes. Coordinate expression was noted among the 29 target genes. Considering only Spearman R values >0.70, significant coexpression between genes belonging to the same pathway have been observed for the DBSR (BRCA1-BRCA2, R = 0.86; BRCA1-XRCC3, R = 0.83; BRCA1-XRCC2, R = 0.75; BRCA2-XRCC3, R = 0.75; BRCA2-XRCC2, R = 0.74; BRCA1-XRCC5, R = 0.70; all P < 0.01), PRR (UBE2A-UBE2B, R = 0.75; P < 0.01), and REP pathways (RRM1-TYMS, R = 0.76; RRM2-TYMS, R = 0.78; all P < 0.01).

Relative expression of target genes and association with survival. To evaluate the correlation between expression regulation of these genes and patients' survival, relative expression was categorized as 1 (overexpressed), 0 (not regulated), and −1 (underexpressed) using arbitrary ΔΔCt cutoff as thresholds. For most of the genes, a ΔΔCt cutoff = 1 was used, corresponding to a 2-fold change, but for target genes characterized by higher transcript deregulation, the median ΔΔCt values (from 1.5 to 4) were used. At univariate analysis, the deregulation of nine genes (BRCA1, XRCC3, XRCC5, UBE2V2, OGG1, UNG, TYMS, RRM1, and TOP3B) was found to be significantly associated with a poor prognosis of patients [the hazard ratio (HR) and the unadjusted P values are shown in Table 3]. After the statistical correction following the Benjamini method, four genes were found to have significant correlation with survival at the P = 0.05 level: XRCC5, TYMS, UNG, and TOP3B. Figure 2 shows the Kaplan-Meier curves of the four genes with the adjusted P values. Among the clinicopathologic variables, tumor grade [3 versus 1–2; HR, 3.75 (1.48–9.52); P = 0.005] and stage [III versus I–II; HR, 5.63 (1.57–20.17); P = 0.008] resulted as significant prognostic factors at univariate analysis. The multivariate analysis by Cox was performed including all the variables (genes and clinicopathologic factors) significantly associated with survival at the univariate and indicated that stage of disease was the only independent factor predicting patients' outcome [HR, 3.47 (1.05–11.4); P = 0.04].

Table 3.

Association between transcript deregulations and patient survival as assessed by univariate analysis

Gene−ΔΔCt cutoffHR (95% CI)Unadjusted P value
BRCA1 1.5 2.88 (1.01–8.18) 0.039* 
BRCA2 2.1 (0.789–5.6) 0.13 
XRCC2 1.56 (0.594–4.08) 0.36 
XRCC3 2.5 2.7 (1.06–6.9) 0.04* 
XRCC4 0.411 (0.12–1.41) 0.14 
XRCC5 3.4 (1.38–8.38) 0.005 
UBE2V2 3.72 (1.09–12.7) 0.024* 
MGMT 1.21 (0.352–4.16) 0.76 
XRCC1 2.01 (0.829–4.86) 0.12 
OGG1 4.02 (1.49–10.8) 0.003* 
UNG 2.5 2.06 (0.81–5.25) 0.008 
XPA 1.52 (0.352–6.56) 0.57 
XPC 0.741 (0.285–1.93) 0.54 
ERCC1 2.61 (0.947–7.22) 0.054 
ERCC2 2.17 (0.786–5.98) 0.13 
ERCC4 1.26 (0.516–3.09) 0.61 
ERCC5 — ND  
ERCC6 — ND  
XAB2 — ND  
UBE2A 1.56 (0.649–3.75) 0.32 
UBE2B 0.945 (0.363–2.46) 0.91 
UBE2N 2.34 (0.901–6.06) 0.07 
RRM2B ND  
TYMS 3.21 (1.29–7.95) 0.008 
RRM1 1.5 2.92 (1.12–7.61) 0.022* 
RRM2 3.5 1.34 (0.563–3.19) 0.51 
TOP3A 2.11 (0.826–5.41) 0.11 
TOP3B 3.57 (1.44–8.83) 0.003 
TERF1 2.44 (0.933–6.37) 0.06 
TERF2 — ND  
TERT 0.61 (0.24–1.54) 0.29 
Gene−ΔΔCt cutoffHR (95% CI)Unadjusted P value
BRCA1 1.5 2.88 (1.01–8.18) 0.039* 
BRCA2 2.1 (0.789–5.6) 0.13 
XRCC2 1.56 (0.594–4.08) 0.36 
XRCC3 2.5 2.7 (1.06–6.9) 0.04* 
XRCC4 0.411 (0.12–1.41) 0.14 
XRCC5 3.4 (1.38–8.38) 0.005 
UBE2V2 3.72 (1.09–12.7) 0.024* 
MGMT 1.21 (0.352–4.16) 0.76 
XRCC1 2.01 (0.829–4.86) 0.12 
OGG1 4.02 (1.49–10.8) 0.003* 
UNG 2.5 2.06 (0.81–5.25) 0.008 
XPA 1.52 (0.352–6.56) 0.57 
XPC 0.741 (0.285–1.93) 0.54 
ERCC1 2.61 (0.947–7.22) 0.054 
ERCC2 2.17 (0.786–5.98) 0.13 
ERCC4 1.26 (0.516–3.09) 0.61 
ERCC5 — ND  
ERCC6 — ND  
XAB2 — ND  
UBE2A 1.56 (0.649–3.75) 0.32 
UBE2B 0.945 (0.363–2.46) 0.91 
UBE2N 2.34 (0.901–6.06) 0.07 
RRM2B ND  
TYMS 3.21 (1.29–7.95) 0.008 
RRM1 1.5 2.92 (1.12–7.61) 0.022* 
RRM2 3.5 1.34 (0.563–3.19) 0.51 
TOP3A 2.11 (0.826–5.41) 0.11 
TOP3B 3.57 (1.44–8.83) 0.003 
TERF1 2.44 (0.933–6.37) 0.06 
TERF2 — ND  
TERT 0.61 (0.24–1.54) 0.29 

NOTE: P values are unadjusted log-rank tests.

*

Genes statistically significant at univariate analysis.

Genes significant at the 0.05 level after correction by the Benjamini method.

Figure 2.

Kaplan-Meier survival curves of patients with NSCLC divided by the status of regulation of the XRCC5, UNG, TYMS, and TOP3B genes, where “regulated” indicates patients with a significant transcript overexpression for each gene. P values are log-rank test–adjusted with the Benjamini correction.

Figure 2.

Kaplan-Meier survival curves of patients with NSCLC divided by the status of regulation of the XRCC5, UNG, TYMS, and TOP3B genes, where “regulated” indicates patients with a significant transcript overexpression for each gene. P values are log-rank test–adjusted with the Benjamini correction.

Close modal

The transcriptional differences of DNA repair/replication and telomere maintenance genes between tumor and normal lung tissue reported in this study highlight some of the peculiar features of neoplastic transformation. One of these hallmarks is represented by the increase of proliferation rate of neoplastic cells; indeed, all DNA replication genes analyzed, especially RRM1 and RRM2 and TYMS, showed a significant overexpression in tumors. Secondly, another feature of neoplastic cells is the unlimited replicative potential which is intimately related to the maintenance of telomeres. Despite the inability to determine the fold change variation for TERT due to its low expression in normal lung tissues, a higher TERT transcript expression in ∼78% of tumor samples has been detected. TERF1 overexpression was also significantly associated with squamous histotype and poorly differentiated tumors. Recently, the underexpression of the TERF1 transcript in lung tumor has been reported (21, 22), and this discrepancy may be related to the use of different housekeeping genes, as recently shown (18). High genomic instability resulting in frequent genomic rearrangements is another molecular feature of cancer cells. The results of the present study show a significant overexpression in 13 out of 22 (59%) DNA repair genes analyzed in a cohort of patients with NSCLC. Interestingly, among the DNA repair pathways, the DSBR, mainly in its homologous recombination mechanism, was mostly affected, with five genes in cluster I and the remaining two in cluster II. It is likely that overexpression of the homologous recombination pathway could be a reflection of the high proliferation rate of the tumors, as suggested by a significant correlation with the Ki67 score. Several studies support the hypothesis that in preneoplastic lesions, replicative stress leads to DSBs, which in turn, results in activation of DNA damage response that delays or prevents cancer progression (23, 24). At the same time, this protective barrier creates a selective pressure that eventually favors the outgrowth of malignant clones with genetic or epigenetic defects in the genome maintenance machinery (i.e., p53 or ATM; refs. 2326). It should also be considered that telomere attrition results in the activation of DNA damage response signaling (27) and that the homologous recombination machinery can play a key role in maintaining telomere length by telomerase-independent mechanisms (28).

The reduced overexpression of BRCA1, BRCA2, XRCC2, XRCC3, and XRCC4 in females compared with males is intriguing because women seem to have an increased susceptibility to tobacco carcinogens, but have a better survival in each stage of the disease compared with men (29).

Interestingly, for most genes associated with DSBR and DNA-REP pathways, transcript overexpression was significantly correlated with tumor grade, which has been previously shown to be an independent prognostic factor for survival (30). Because patients with poorly differentiated carcinomas have a higher risk of recurrence after tumor resection (30), these correlations strongly support the hypothesis that overexpression of DNA repair genes could be associated with metastasis development (31, 32).

Despite the fact that an intrinsic higher repair capacity of NSCLC cells has been reported (6), transcript overexpression is not proof of an increased enzymatic activity. Further studies should confirm this hypothesis and deepen the molecular mechanism underlying the overexpression of so many genes: the high correlation observed between transcript expression levels of some genes could, for example, suggest the presence of (a) common activator(s).

Only three DNA repair genes were found to be significantly underexpressed in lung tumor tissue: XPC, XPA, and UBE2N. XPC underexpression by at least a 2-fold change has been observed in ∼26% of patients with NSCLC, in agreement with other reports which detected hypermethylation of XPC promoter in 34% of NSCLC (22% in smokers; ref. 33). No data on UBE2N transcript deregulation are currently available. However, in addition to its role in post-replication repair, UBE2N has been recently involved in the repair of double-strand breaks by homologous recombination, and UBE2N-deficient cells are sensitive to a wide range of DNA-damaging agents (34). Data generated in this study indicate that NSCLC patients with underexpression of UBE2N have a better prognosis even if statistical significance was not reached (P = 0.07).

The large variability in interpatient transcript expression levels could generate a substantial source of variable chemosensitivity in patients with NSCLC. However, because the homologous recombination machinery plays critical roles in regulation of sensitivity to the majority of chemotherapeutic drugs currently used in cancer therapy (35), the broad overexpression of several components of this repair pathway in lung tumor tissues could partially explain the low chemosensitivity of patients with NSCLC.

With regards to the prognostic effect of the genes investigated, 9 out of 26 had significant (unadjusted) P values at univariate analysis, and 4 genes maintained their prognostic relevance after the multiple testing correction: XRCC5 (belonging to DSBR), UNG (belonging to BER), and TOP3B and TYMS (both of the DNA-REP pathway). By contrast, no gene was retained in the multivariate analysis, which indicated stage of disease as the only independent prognostic factor affecting patients' outcome. This result was not surprising and most likely due to the limited number of cases collected, which similarly, did not allow the assessment of a multigene predictive model, as recently reported (36). The prognostic indications emerging from the results of the present study are therefore preliminary and require validations in larger prospective studies of appropriate sample size.

In conclusion, this study found that lung tumors, compared with normal tissues, exhibit a significant overexpression of a wide number of DNA-repair genes, mostly associated with DSBR and PRR, DNA replication, and telomere maintenance pathways. Transcriptional overexpression of some of these genes showed strong correlation with a more aggressive clinical behavior and could partly explain the low level of responsiveness of NSCLC to combination chemotherapy.

No potential conflicts of interest were disclosed.

Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

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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Supplementary data