DNA damage repair gene signature model for predicting prognosis and chemotherapy outcomes in lung squamous cell carcinoma

The LUSC tissue data were clustered into k (2 to 9) groups by the ConsensuClusterPlus package in R software based on DNA repair genes. The k value was optimized according to the unsupervised clustering method, and LUSC cancer tissues showed consistent clustering. Two subgroups were obtained and verified by PCA. The survival of patients was compared by Kaplan–Meier analysis.

The DNA damage repair genes and the clinical information of the patients from whom the LUSC samples were derived were downloaded from the TCGA database. The information can be found in additional file Table S1. In total, 504 lung squamous cell cancer tissues were included in this study. A list including 150 DNA damage repair genes was downloaded from the hallmark gene set of the GSEA database to screen the gene expression matrix.

The expression levels of DNA damage repair genes were compared by the Wilcoxon rank-sum test between the normal and tumour groups. The screening criteria were FDR (false discovery rate) < 0.05 and log₂|fold change|> 1. The results of the differential DNA repair gene analysis are presented as volcano plots, heatmaps and box.

First, univariate Cox regression with the Wald χ² test was used to establish the relationship between overall survival (OS) and DNA damage repair genes in LUSC patient tumour tissue. DNA repair genes with p values calculated by the Wald χ² test less than 0.05 were considered statistically significant. According to the median expression level of DNA damage repair genes, the patients were divided into two groups: high and low expression groups. The overall survival of the two groups was analysed by the log-rank test, and survival curves were drawn. The multivariate Cox regression model was constructed by applying all the statistically significant variables in the univariate Cox regression. It was optimized by the AIC value in a stepwise algorithm. Then, a risk score based on the significant prognosis-related DNA damage repair genes was developed for LUSC patients: (\(\mathrm{riskscore}={h}_{0}(\mathrm{t})\mathrm{exp}({\sum }_{j=1}^{n}{\mathrm{Coef}}_{j}\times {\mathrm{X}}_{j})\), where n is the quantity of sorted genes, h₀(t) is the baseline risk function, Coef_j is the coefficient of each DNA repair gene, and X_j is the relative expression level of each DNA damage repair gene. The survival of LUSC patients with different risk scores was evaluated with prognostic hazard curves. Then, significant prognosis-related DNA damage repair genes were employed to construct a prognostic model with other clinical factors by multivariate Cox regression analysis. The predictive ability of the risk score and other clinical features were evaluated by time-dependent receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses. The survival ROC package in R software was applied to draw the ROC curve. The AUC value, which indicates the sensitivity and specificity of the predictive indicators, varied from 0.5 to 1. The predictive ability of prognostic indicators increases with increasing AUC. The prognostic prediction model was ultimately developed into a nomogram, the calibration of which was measured with a calibration curve, and the discriminative ability was measured by C-index analysis.

To validate the prognostic predictive value of the risk score calculated based on the prognosis-related DNA repair genes, a gene expression level data matrix of lung cancer tissues with corresponding patient clinical data was downloaded from the GEO database (GSE31210). The risk score was calculated based on the formula constructed by the TCGA database. The prognosis-predicting ability of the risk score was estimated by time-dependent ROC curve analysis. According to the median risk score, the lung cancer patients in the GSE31210 dataset were divided into two groups: a high-risk group and a low-risk group. Kaplan–Meier curves of the two groups were drawn and compared by the log-rank test. Subsequently, the prognostic value of the risk score was estimated by univariate Cox proportional hazard regression. Furthermore, multivariate Cox proportional hazard regression revealed the risk score as an independent prognostic predictor.

Single-sample gene set enrichment analysis was performed by the "GSVA" package in R software with the method “ssGSEA” to calculate the infiltration scores of 16 types of immune cells. The infiltration scores of each tumour sample from LUSC patients in the high-risk group and low-risk group were calculated and compared by the Wilcoxon rank sum test. The immune infiltration scores of each type of immune cell and patient group were displayed as a box plot, as were the activities of 13 immune-related pathways (see additional file Table S2) [13, 14].

The IC50 values of six kinds of anticancer agents (etoposide, imatinib, methotrexate, rapamycin, vinorelbine, and vorinostat) were analysed in each lung squamous carcinoma sample. The pRRophetic package [15] in R software was applied to calculate the IC50 of each drug on the Genomics of Drug Sensitivity in Cancer website [16]. The half maximal inhibitory concentrations of drugs were compared between the high groups and low groups by the Wilcoxon rank-sum test.

Total RNA from each specimen was purified by TRIzol (Invitrogen, USA). Then, RNA was transcribed into cDNA (complementary DNA) by the PrimeScript® RT Reagent Kit with gDNA (genomic DNA) Eraser (Takara, Japan). Real-time quantitative PCR was performed using a SYBR green master mix kit (ABI technology, USA). The QuantStudio System (Q6, Applied Biosystems, USA) was used to perform RT–qPCR. All samples were normalized to endogenous GAPDH (glyceraldehyde-3-phosphate dehydrogenase) with 2^−△△Ct algorithms. GenScript company (China) provided the primers for each gene.

The LUSC tissue microarrays were incubated with antibodies (anti-RAE1, anti-POLR2H, anti-RAD51, anti-ZWINT and anti-RFC4) for immunohistochemical staining. The intensity and extent of staining were taken into consideration by the scoring system. Staining intensity was classified as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong). The IHC score result was stratified as follows: 0 to 1, negative (-); 2 to 4, weakly positive (+ +); 5 to 8, moderately positive (+ +), and 9 to 12, strongly positive (+ + +).

An analysis flowchart is shown in Fig. 1A. To investigate the characteristics of DNA damage repair genes in LUSC, we divided the LUSC samples from TCGA into subgroups based on the expression of 150 genes related to DNA damage repair, which were downloaded from the GSEA website by the R package ConsensusClusterPlus. Clustering stability was analysed from k = 2 to 9 for the TCGA datasets, and k = 2 was identified as the best value, showing expression similarity of the DNA damage repair-related genes. The subgroups were divided into Cluster 1 and Cluster 2 and the division of lung squamous carcinoma samples by DNA repair genes showed a good differentiation effect validated by PCA analysis (Fig. 1B-E). Survival analysis also showed a significant difference between these 2 subgroups (P value = 0.013) (Fig. 1F). These results suggested that two groups of lung squamous carcinoma patients stratified by concensus cluster were different in clinical characters.

The expression profile dataset, which included 504 LUSC samples, was obtained from the TCGA database. Clinical information of these 504 patients was listed in Table 1. First, univariate Cox proportional hazard regression with the Wald χ² test was used to identify 16 DNA damage repair genes (POLD4, HPRT1, MRPL40, ITPA, ERCC3, AK1, DGUOK, TK2, POLR3GL, RFC4, VPS28, POLR2H, CANT1, NCBP2, SDCBP, and CCNO). The expression level of these genes was significantly correlated with the overall survival of LUSC patients (Fig. 2A). Moreover, multivariate Cox regression models were constructed using these genes. The model with the lowest AIC value was selected for further analysis to avoid overfitting. After optimization based on the AIC value, ten DNA repair genes (POLD4, MRPL40, ITPA, ERCC3, TK2, POLR3GL, VPS28, CANT1, SDCBP, and CCNO) were preserved in the last multivariate Cox regression model and had potential to be prognostic factors (Fig. 2B, additional file Table S3).

Based on their relationship with LUSC patient survival (HR > 1), six genes (SDCBP, POLD4, VPS28, CANT1, TK2, and ITPA) were considered risk factors, but the other four genes (POLR3GL, MRPL40, ERCC3, and CCNO) played protective roles (HR < 1). Ultimately, the risk scores of the patients were calculated based on the expression of these ten significant prognosis-related DNA damage repair genes and their coefficients in the multivariate Cox regression model. The median risk score was used to classify the LUSC patients into a high-risk group and a low-risk group. Overall survival was significantly different between the two groups of patients (median time = 2.64 years vs. 6.16 years, log rank p value < 0.001, Fig. 2C).

Prognostic hazard curves for the LUSC patients showed the distribution of the risk score. The survival time and risk score results were visualized with a scatter plot to display the survival time of each patient with the corresponding risk score (Fig. 2D-E). The results revealed that patients with higher risk scores had shorter survival.

The risk score and TNM stage were used to perform univariate Cox regression analysis. Pathologic stage (stage III vs. stage I HR = 1.542, P value = 0.037) and the risk score (HR = 1.610, P value < 0.001) were correlated with the overall survival (OS) of LUSC patients (Fig. 3A). Multivariate Cox regression analysis of these clinical features showed that T stage (HR = 2.757, P value = 0.031) and the risk score (HR = 1.490, P value < 0.001) were independent risk factors for survival (Fig. 3B).

To assess the difference between risk scores and other prognosis-related clinical features, time-dependent ROC curves were constructed for 1-year, 3-year and 5-year survival. Moreover, we used the area under the curve (AUC) values to assess the ability of each prognostic predictor to discriminate between patients who survived and those who died. The AUC of the risk score was larger than that of age, stage and T stage at 1 year, 3 years and 5 years, which indicated that the risk score was a better prognostic predictor than other clinical features (risk score AUC = 0.662, 0.708, 0.741 for 1 year, 3 years and 5 years, respectively) (Fig. 3C).

We applied a t test or Kruskal–Wallis test to assess the correlation between the DNA damage repair genes and TNM stage. The expression levels of CANT1 and VPS28 were increased in advanced stage compared with stage I-II to stage III-IV disease (P value = 0.004 and P value = 0.008) (Fig. 3D). In T3-T4 stage patients, the expression level of VPS28 was higher than that in early T stage patients (P value = 0.02), implying its dangerous role in the development of LUSC (Fig. 3E). Furthermore, the expression level of CANT1 was higher in N1-N3 LUSC tissues than in N0 stage LUSC tissues, which was determined based on the distribution of CANT1 expression levels between N0 stage and N1-N3 stage tissues (Fig. 3F). Thus, we conclude that the disruption of DNA damage repair might be responsible for the poor prognosis of patients with LUSC.

The RNA-seq data and clinical data of the lung cancer tissues were downloaded from the GEO database (GSE31210). Totally 226 patients were involved into research, after the filtration of patients without survival time. The detail of clinical information was presented in Table 1. The risk score was calculated with the formula based on patient data from the TCGA database and the expression level of prognostic genes in the GSE31210 dataset. Patients in GSE31210 were divided into a high-risk group and a low-risk group according to the median risk score. The difference in overall survival between the high-risk group and the low-risk group was statistically significant (Fig. 4A) (log-rank test P value = 1.901⁻⁰³). The ability of the risk score to predict prognosis was estimated by the area under the curve (AUC) of the time-dependent ROC curve (Fig. 4B). Prognostic hazard curves were drawn to analyse the utility of the prognostic DNA repair genes (Fig. 4C-D). The survival is much higher in both high and low risk groups in the validation GEO data (GSE31210) set as compared to the discovery TCA set. It is perhaps due to only early stages (stage I and stage II) cancer patients’ data in this validation (Table 1). Actually, early LUSC detection leads to the better survival outcomes of patients. We also analysed the hazard ratio of the risk score using univariate and multivariate Cox regression (Fig. 4E-F). Similar results were derived from the GSE31210 cohort and the TCGA LUSC cohort. Therefore, the risk scores were correlated with the overall survival (OS) of LUSC patients, and univariate or multivariate Cox regression analyses verified DNA repair-related gene-based model could be served as an independent prognostic indicator of LUSC.

A nomogram was generated to utilize the constructed prognostic model for LUSC patients. We selected tumour stage, N stage, T stage, risk score, sex and age to establish the nomogram (Fig. 5A). The discriminatory ability of the nomogram was estimated based on the C-index, which varied from 0.5 to 1. The discriminatory ability increased with increasing C-index. The results showed that the C-index of the constructed nomogram was 0.669. Furthermore, the calibration curves of the nomogram at 1 year, 3 years and 5 years are displayed in Fig. 5B. The closer the calibration curve is to the diagonal line, the more precise the calibration is. Taken together, these C-index and calibration curve data suggest that the nomogram can be used to predict the prognosis of LUSC patients.

pRRophetic was applied to estimate the sensitivity of the high-risk group and the low-risk group of LUSC patients to anticancer agents, including etoposide, imatinib, methotrexate, rapamycin, vinorelbine and vorinostat. The analysis of anticancer agent sensitivity demonstrated that etoposide, methotrexate and vinorelbine had higher IC50 levels in the high-risk group, implying that low-risk group patients is more sensitive to the three drugs. In contrast, the IC50 values of imatinib, vorinostat and rapamycin were higher in the low-risk group, which indicated that high-risk group patients is more sensitive to the three drugs (Fig. 6A).

DNA damage repair defects will lead to increased genomic instability and tumour tumorigenesis, which may activate the tumour immune response. The infiltration scores of 16 kinds of immune cells and the enrichment scores of 13 corresponding immune functions were estimated by the “ssGSEA” method, which is provided in the “GSVA” R package. The analysis results revealed that 15 kinds of immune cell subpopulations (B cells, NK cells, macrophages, mast cells, Tregs, T helper cells, TILs, Th1 cells, Th2 cells, Tfh cells, CD8 + T cells, DCs, iDCs, neutrophils, and pDCs) had lower scores in the low-risk group than in the high-risk group (Fig. 6B). Furthermore, we also found that the scores of 7 immune functions were also significantly lower in the low-risk group, including T-cell costimulation, parainflammation, APC costimulation, CCR, checkpoint, HLA and type II IFN response (Fig. 6C). These results suggest that immunological functions are more active in the high-risk group than in the low-risk group, and these functions may be related to the expression level of DNA damage repair genes. Combining DDR-targeting drugs and tumourimmunotherapy to treat LUSC holds wide application prospects.

Differentially expressed DNA repair genes were retrieved from the gene expression profile dataset downloaded from the TCGA database. The dataset included 49 normal lung tissue samples and 501 lung squamous cancer tissue samples. Thirty-four differentially expressed DNA repair genes (DEGs) were ultimately retrieved. Thirty-one genes were upregulated and three genes were downregulated in the tumour group compared with the normal group. The genes are displayed in additional file Table S4. The DEGs are presented in volcano plots, box plots and heatmaps (Fig. 7A-C). Most of the DNA repair genes were upregulated in the tumour group, which indicates that cancer cells might have better DNA repair abilities that help them survive in a hostile environment.

To further verify the differentially expressed DNA repair genes in LUSC, we used real-time quantitative PCR (qRT–PCR) and immunohistochemistry (IHC) to analyse the four genes (POLR2H, RFC4, ZWINT, and RAD51) that had the most significantly different differences in expression. The qRT–PCR results showed that the four genes were upregulated in the tumour group compared with adjacent normal tissues, which was consistent with the results of the differential expression analysis of the RNA-seq data from TCGA (Fig. 8A). To confirm the RNA-seq results, the four genes were validated by immunohistochemical staining in lung squamous cancer tissue microarrays. POLR2H, RAD51, ZWINT and RFC4 were expressed at higher levels in LUSC tissues than in adjacent normal tissues (Fig. 8B). These genes should be validated in larger-scale clinical studies in the future. The molecular biological function of these genes deserves further exploration.