A seven-gene prognostic signature for rapid determination of head and neck squamous cell carcinoma survival
- Authors:
- Published online on: October 24, 2017 https://doi.org/10.3892/or.2017.6057
- Pages: 3403-3411
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Copyright: © Shen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Head and neck squamous cell carcinoma (HNSCC) constitutes approximately 4% of all new cancer diagnoses in the United States, with approximately 62,000 new cases in 2016 (1). Each year approximately 600,000 patients are affected worldwide (2). Importantly, the 5-year survival rate of HNSCC patients is only 40–50% (3). The high mortality rate is attributable to a high rate of late diagnosis, and the survival rate for cases in late stages is only 34.9% (4). These outcomes demonstrate the need for prognostic biomarkers to help predict patient outcome and outline individualized treatment plans. Age, clinical stage and smoking status are characteristics emerging as important contributors to clinical outcome that may also help us improve survival prediction (5–7). However, the traditional clinical information has limited prediction ability due to the complex molecular regulation mechanism in cancer.
Recently, the clinical importance of messenger RNA (mRNA) expression has been reported in various types of cancer including HNSCC. They play important roles in a variety of physiological and pathological processes, such as development, differentiation, cell proliferation, apoptosis and stress responses (8). Therefore, characterization of key genes in different tumors is essential not only for an urgent requirement of precision medicine (9) but also for preclinical and pharmaceutical research (10).
Prognostic models in HNSCC have been described using different biomarkers such as somatic mutations (11), microRNAs (12,13) and proteins (4) but limited studies focus on mRNA expression according to Cancer Genetics Web (14). De Cecco et al (15) reported a gene expression survival predictor using HNSCC microarray data based on a semi-supervised survival method involving principal component method (16). However, the model comprised 172 genes and was complicated for further interpretation. Now that transcriptome sequencing technologies (RNA-Seq) are being applied widely, there is a more ideal platform for cancer genetic studies (17). In addition, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories provide abundant HNSCC case resources, which may be useful to explore reliable biomarkers.
In this study, we investigated the prognostic value of seven gene expression biomarkers (AATF, APP, GNPDA1, HPRT1, LASP1, P4HA1 and ILF3) for HNSCC overall survival. The cases mainly included oropharyngeal, laryngeal and oral squamous cell carcinoma. TCGA cohort was used as the training set to generate the prognostic model by a two-stage variable selection. We also used an independent external testing set to validate the robustness and reproducibility of the model.
Materials and methods
Study population
Information for the HNSCC training set was obtained from TCGA on November 13, 2016 (18). Gene expression data were extracted from IlluminaHiSeq_RNASeqV2 platform and normalized by RSEM method (19). In addition, we performed quality control with a total of 20530 genes. Genes with more than half of values as zero were removed, 17711 genes remained with quantile normalization. Patients with complete follow-up information and gene expression values for tumor tissues were included in the study. Information for the HNSCC testing set was collected from GSE65858 (20) in GEO. Gene expression data were extracted from Illumina HumanHT-12 V4.0 expression beadchip and normalized using the robust spline normalization (RSN) method (21). Consecutive patients with primary and metachronous secondary HNSCC of oral cavity, larynx, oro- and hypopharynx were included, while tumor cell lines and those with low quality assays were excluded. All gene expression values were log2-transformed and standardized for comparability between the training and testing sets.
Weighted t-test (WTT) method as the first step for gene selection
To select differentially expressed genes combined with clinical information, WTT was used to select genes based on the method of Hu et al (22). For the ith subject with a covariate vector Zi, the Cox proportional hazards model is given by λ(t|Zi) = λ0(t)exp(βTZi) and the survival function is S(t|Zi) = exp{-λ0(t)exp(βTZi)}, where λ0(t) is the basic hazard function, β is the regression coefficient and λ0(t) is the cumulative baseline hazard function. Then we constructed a Cox regression model for each subject based on clinical information (age, sex, smoking status and clinical stage) only and defined hi = βTZi. The weights for n patients totally were calculated accordingly:
wi=hi∑i=1nhi×nwhich were assigned for the tumor cases but not the normal cases.
With the weighted tumor expression expwi = wi x expi, Students t-test was conducted for each gene to measure the difference between tumor and matched normal expression level. We also used t-test with no weight adjustment and examined the difference between the t-test statistics after and before weight adjustment, dk = tadjust - tunadjust for the kth gene. Afterwards, 1,000 total permutations were performed and dki could be got for the ith permutation. Then, we calculated the averaged order statistics, d¯k, across all 1,000 permutations. A gene was labeled as significant when |dk-d¯k| was at the top 5%.
Sure independence screening (SIS) as the second step for gene selection
After the WTT selection, there were still over 800 genes left, which were too many and not robust to build the prognostic sigature in HNSCC. The traditional univariate or multivariate Cox regression was not suitable to select the prognosis-associated genes because it easily led to overfitting and produced instable results (23). SIS was used to choose those which were truly associated with disease from the 5% genes remaining for further modeling (24). This is a two-step screening approach: it first screened all genomic features and discarded the irrelevant features whose correlation with overall survival were weak, and secondly applied LASSO penalized regression to estimate the sensitivity from the selected genomic instability data. We could significantly reduce the number of genes in the final model by the SIS method.
Statistical analysis
Continuous variables are described as mean ± SD, and categorized variables are summarized by frequency (n) and proportion (%). Chi-square test was used for rate or proportion comparison. Associations between the characteristics and the overall survival were evaluated by Cox proportional hazard models. Survival curves were drawn with the Kaplan-Meier method and were compared among subgroups using log-rank tests. To evaluate the robustness of the results, we used the bootstrap method with ‘bootcov’ function that computed a bootstrap estimate of the covariance matrix for a set of regression coefficients in rms package. The bootstrap procedure were carried out with 500 re-samplings for the multivariable Cox regression. We predicted 5-year patient survival using the nearest neighbor method for receiver operating characteristic (ROC) curves of censored survival data (25) and estimation of confidence intervals and P-values of area under the curve (AUC) was based on bootstrap resampling. In the subgroup analysis, we used the Fishers exact test to compare the proportions of different HPV status or tumor sites.
Statistical analyses were performed using R version 3.3.1 (The R Foundation). P-values are two-sided and P<0.05 indicates statistical significance.
Results
Demographic and clinical characteristics
The analysis included 512 HNSCC cases from TCGA training set and 270 cases from the GEO testing set (Table I). Cases in the training set had an average age of 60.8±11.9 years, ranging from 19 to 90 years; 149 (29.1%) individuals were followed until death. Cases in the testing set had an average age of 60.1±10.3 years, ranging from 35 to 87 years; 88 (32.6%) individuals were followed until death.
Development of biomarker signature model
To exclude a large number of genes unrelated to disease, we assessed the TCGA training set in two steps after quality control (Fig. 1A). First, of all 17711 genes with normalization, we used the WTT method to select the top 5% significant genes (n=886) from 43 pairs of tumor and matched adjacent normal tissue data. All 886 genes were significantly differentially expressed (all P≤1.15×10−5) and 199 genes were significant in univariate Cox regression analysis (P<0.05; Fig. 1B). Second, the SIS method was used for further dimension reduction. After iterative process for different genes and LASSO penalized regression with 10-fold cross-validation to select the best parameter, seven genes remained after selection. All of them were significantly overexpressed in tumor tissue (Fig. 1C). In addition, they were significantly associated with overall survival except LASP1 (P=0.056) (Fig. 1D). A Cox regression model was used to generate model coefficients. The biomarker signature model was calculated as risk score = 0.198×AATF + 0.244×APP + 0.252×GNPDA1 + 0.314×HPRT1 + 0.136×LASP1 + 0.110×P4HA1 - 0.388×ILF3. We categorized the patients into low-risk and high-risk groups and defined the cut-off value (score=0.36). This was selected by the optimum cut point according to the highest χ2 value defined by Kaplan-Meier survival analysis and log-rank test in the training test (26).
As a linear combination model of seven mRNAs, the risk score was significantly associated with the TCGA patient survival (HRunadjust = 2.79; 95% CI, 1.98–3.92; P=4.05×10−9) (Fig. 2A). In total, 16.4% in the low-risk group vs. 41.8% in the high-risk group were followed until death (χ2=38.78, P=4.76×10−10) (Fig. 2B). With the bootstrap adjustment for clinical characteristics, the results remained significant for all cases (HRadjust = 2.86; 95% CI, 1.99–4.12; P<0.0001) or cases with available HPV status (HRadjust = 3.17; 95% CI, 1.90–5.30; P<0.0001) (Table II).
Validation of the prognostic signature
In the GEO testing set, risk scores were calculated for each patient. Using the same cut-off value (score=0.36), the score showed a 2.05 times higher risk of death for the high-risk group compared to the low-risk group in univariate Cox regression (HRunadjust = 2.05; 95% CI, 1.35–3.11; P=7.98×10−4) (Fig. 2C). In total, 25.1% in the low-risk group vs. 46.3% in the high-risk group were followed until death (χ2 = 11.62, P=6.53×10−4) (Fig. 2D). Results retained statistical significance with further adjustment for covariates, including age, sex, smoking status, HPV status and clinical stage (HRadjust = 1.94; 95% CI, 1.27–2.96; P=0.002) (Table II).
Furthermore, prognostic prediction ability for 5-year survival was evaluated. The time-dependent AUCs of risk scores for HNSCC cases were 0.73 (95% CI, 0.68–0.78; P<0.001) in the training set (Fig. 3A) and 0.66 (95% CI, 0.59–0.73; P<0.001) in the testing set (Fig. 3B). Besides, we combined the scores with clinical characteristics to see whether they could improve the predictive value. In the training set, prognostic score plus clinical characteristics had a higher AUC (AUC, 0.75; 95% CI, 0.70–0.80) than the clinical characteristics (age, sex and stage) alone (AUC, 0.57; 95% CI, 0.51–0.64) (Fig. 3C). The testing set also displayed improvement in AUC from 0.63 (95% CI, 0.57–0.70) to 0.72 (95% CI, 0.65–0.78) (Fig. 3D). In brief, the risk score could better distinguish HNSCC prognosis beyond clinical information alone.
Subgroup sensitivity analysis with HPV status and tumor site
Next, we examined whether the risk score could help improve prognostication in the two datasets separately by subgroup sensitivity analysis. HPV-positive HNSCC has been widely recognized as associated with better prognosis than HPV-negative HNSCC (27). The risk score could significantly distinguish patient prognosis among 274 cases with available HPV information in the training set (HPV+, P=8.65×10−6; HPV−, P=1.24×10−7; Fig. 4A and B) and 269 cases in the testing set regardless of HPV status (HPV+, P=0.004; HPV−, P=0.014; Fig. 4C and D).
In different tumor sites, high vs. low-risk score significantly distinguished outcomes in patients with tumor at larynx (training set, P=0.001; testing set, P=0.019), oropharynx (training set, P=0.0004; testing set, P=0.003) and oral cavity in the training set (P=1.80×10−5) (Fig. 5). However, the result was not significant in oral cavity of the testing set (P=0.178), possibly due to the failure to distinguish patients with longer survival. In addition, we found that HPV-positive status improves survival in oropharyngeal HNSCC but not in non-oropharyngeal HNSCC (training set, P<0.001; testing set, P=0.010; Fig. 5E and F), which was consistent with a previous report (28).
Discussion
In the present study, we developed an HNSCC prognostic risk model that includes seven mRNAs and validated it using an independent external data set. Integrating multiple biomarkers into an aggregated model could improve prognostic value compared with single biomarker (29). Results showed that the risk score was significantly associated with patient overall survival. HNSCC patients with higher risk scores tended to have a poorer clinical outcome. In addition, this score could improve model performance combined with clinical characteristics based on 5-year overall survival.
To screen out the survival-related genes from over 20,000 total genes, we used a two-stage screening method. The WTT method was used as the first step to identify a subset of genes that were not only differentially expressed in the tumor and matched normal tissues but also had an impact on patient survival by weighting the clinical covariates. Using permutation procedures that have widely been used in biomedical data analysis (30), error rates can be controlled (22). The results show that all the selected genes were differentially expressed and a number (22.5%) were significantly associated with prognosis. The WTT method performs better than a traditional t-test, which will flag a large number of genes unrelated to disease. Afterwards, SIS was used to reduce the number of genes included in the final model as the second step. Compared with a traditional penalized regression like lasso or elastic net models, SIS is ungraded on the basis of penalized regression to reduce dimensionality from high to a moderate scale that is below the sample size (24). It has improved both speed and accuracy, and has a stronger association with disease (31). Of the seven genes in the training set, all of them are significantly differentially expressed in tumor and normal tissue. In addition, six of them are significant in a univariate Cox model and the last one shows suggestive significance (P=0.056). Our results show that the two-step gene selection method is amenable to deal with a high dimension problem.
Among the seven candidate genes, six have positive coefficients in the prognostic model and are associated with worse survival. AATF, also called Che-1, is a critical regulator of apoptosis driven by genes coding for PAR4 and p53 (32), and promotes tumor cell survival by sustaining mutant p53 transcription and inhibiting DNA damage response activation (33). It has activity in transcriptional regulation, cell cycle control, DNA damage responses, and in the execution of cell death programs (34). In addition, it interacts with NRAGE that has been found as a tumor marker in different cancers. AATF has been reported associated with multiple cancers, such as colon carcinoma (35), gastric cancer (36), hepatocellular carcinoma (37) and breast cancer (38).
APP was initially found to be associated with Alzheimers disease, but it also contributes to regulating cell growth, apoptosis, and motility of cancer cells (39). Several studies have confirmed APP as an invaluable marker for oral carcinogenesis that promotes the proliferation and carcinogenesis of oral squamous cell carcinoma (OSCC) (40–42). Notably, in addition to its effects on promoting oral carcinogenesis, APP expression could be negatively regulated by tea in OSCC, which has been demonstrated to be effective in preventing animal carcinogenesis in different experimental systems (41).
LASP1, a recognized cancer biomarker functioning in cell structure, physiological processes, and cell signaling, contributes to cancer aggressiveness by overexpression (43). Increased LASP1 levels occur in OSCC and more than ten other tumor types (44). It appears to involved in regulation of cancer cell metastatic propensity and perturb the architecture and dynamics of focal adhesion that triggers cell migration and invasion (45). In OSCC, LASP1 plays an essential role in tumor cellular growth by mediating G2/M transition.
This is not the first report for P4HA1 that was associated with HNSCC prognosis (46). It is involved in hydroxylation of collagen fibers and upregulated by HIF1 under hypoxic conditions directly that drive a series of different biological processes related with malignant progression. P4HA1 modulates target genes in cancer cell growth and tumor progression (47) and its expression increases during the invasion and metastasis of breast cancer and hepatoma as well (48,49).
Furthermore, GNPDA1 is an allosteric enzyme that catalyses the reversible conversion of D-glucosamine-6-phosphate into D-fructose-6-phosphate and ammonium (50). It has been reported upregulated in colorectal cancer cells with western blotting and immunofluorescence assay (51). The protein encoded by HPRT1 is a transferase, which plays a central role in the generation of purine nucleotides through the purine salvage pathway. It still needs further experiments to validate its prognostic value.
In contrast, ILF3 confers an onco-protective effect. Downregulation of ILF3 can delay cell cycle progression, inhibit cell proliferation and reduce tumorigenic capacity in vivo (52). ILF3 is also involved in HPV-induced oncogenesis and p53-mediated apoptosis. It is a positive regulator of HPV E6 expression and its depletion leads to the accumulation of active p53 (53). Since HPV is effective in HNSCC, targeting on this gene may be useful to control the cancer.
The present study includes some limitations. First, in the subgroup analysis, the results may not strongly be robust due to small sample size of some groups. Second, due to the different experimental methods (RNASeq versus microarray) used between the two data sets, some bias may exist. Third, the differences between the two populations may indicate a need for further validation in another independent cohort for the current prognostic signature. Finally, the prognostic value of the seven genes in HNSCC still warrants further biological functional experiments.
In conclusion, our results showed that the seven-gene prognostic score significantly distinguishes HNSCC patients prognosis and predicts 5-year overall survival in both training and testing sets. Thus, this score may be a novel biomarker based on gene expression levels and it warrants further investigation for establishing its relevance for clinical application.
Acknowledgements
We thank the patients and investigators who participated in TCGA and GEO for providing the data. The present study was supported by the National Natural Science Foundation of China (grant nos. 81530088 and 81473070 to F.C., 81302512 to J.B., 81402764 to Y.W. and 81402763 to R.Z.); and the Natural Science Foundation of Jiangsu, China (no. BK20140907 to Y.W.). It is also supported by the Nanjing Medical University international exchange and cooperation project (no. C018 to S.S.)
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