Expression of Notch1 and Numb in small cell lung cancer

Notch signaling in tumorigenesis functions as an oncogene or tumor suppressor according to the type of malignancy. Numb represses intracellular Notch signaling. Previous studies have demonstrated that Notch signaling suppresses the proliferation of small cell lung cancer (SCLC) cell lines. However, in SCLC, the association between Notch1 and Numb expression and clinicopathological factors or prognosis has remained unclear. In this study, we evaluated the expression of Notch1 and Numb in SCLC. We immunohistochemically assessed 125 SCLCs that were surgically resected at 16 institutions participating in either the Hokkaido Lung Cancer Clinical Study Group Trial (HOT) or the Fukushima Investigative Group for Healing Thoracic Malignancy (FIGHT) between 2003 and 2013. Correlations between Notch1 or Numb expression and various clinicopathological features were evaluated. Notch1 expression was associated with ECOG performance status. Numb expression was associated with age, sex, and pathological histology (SCLC or Combined SCLC). Analysis of cellular biological expression did not demonstrate a significant correlation between the expression of Notch1 and of Numb. Multivariate Cox regression analysis showed that high Notch1 expression was an independent favorable prognostic factor for SCLC(hazard ratio = 0.503, P = 0.023). High Notch1 expression, but not Numb expression, is associated with favorable prognosis in SCLC.


INTRODUCTION
Bladder cancer is the ninth most common malignancy worldwide [1]. It has been estimated about 80000 newly diagnosed bladder cancer cases in the United States in 2015 [2]. Bladder Urothelial Carcinoma (BLCA) is the most common histological subtype of bladder cancer. Overall, about 70% of bladder tumors are non-muscleinvasive bladder cancer, and the others are muscle-invasive bladder cancer [3]. Despite recent advances in the surgical technique, the overall survival of BLCA patients has not been dramatically improved, and the five-year survival rate remains at only 50-60% [4][5][6]. Therefore, it is necessary to identify novel independent biomarkers for diagnostic and prognosis and to develop new targeted therapies for BLCA patients.
In this work, we performed a comprehensive analysis for lncRNA expression profiles and corresponding clinical information of BLCA patients in the TCGA training dataset. We identified four lncRNAs significantly associated with patients' survival and constructed a four-lncRNA signature that can effectively predict patients' survival. The testing dataset and entire dataset further validated the prognostic power of the four-lncRNA signature. Our results demonstrated the four-lncRNA signature can function as novel independent biomarkers for BLCA prognosis and provide novel insights into understanding of the underlying molecular mechanism of BLCA.

Identification of prognostic lncRNAs associated with patients' survival from the training dataset
The 234 patients with BLCA were randomly divided into a training dataset (n = 117) and a testing dataset (n = 117). The training dataset was analyzed to identify prognostic lncRNAs. At first, we performed a univariate Cox regression analysis to evaluate the association between the expression profiles of each lncRNA and patients' survival in the training dataset. The result showed that four lncRNAs were identified as prognostic lncRNAs (p-value < 0.001). The detailed information of these four lncRNAs was showed in Table 1. Among these prognostic lncRNAs, the lncRNAs (AC005682.5 and CTD-2231H16.1) with higher expression profiles were associated with shorter survival (coefficient > 0), while the remaining two lncRNAs (CTB-92J24.2 and RP11-727F15.13) with higher expression profiles were associated with longer survival (coefficient < 0).

Construction and validation of a four-lncRNA signature for predicting patients' survival in the training dataset
To construct a prognostic signature, these four lncRNAs were analyzed using a multivariate Cox regression analysis in the training dataset with survival as the dependent variable and other clinical information as covariables. Then we constructed a prognostic signature by integrating the expression profiles of the four lncRNAs and corresponding estimated regression coefficient derived from above multivariate Cox regression analysis as follows: Risk score = (0.371 × expression value of AC005682.5) + (0.175 × expression value of CTD-2231H16.1) + (-0.251 × expression value of CTB-92J24.2) + (-0.232 × expression value of RP11-727F15. 13). With the four-lncRNA signature, we calculated a risk score for each patient in the training dataset and ranked them according to increased risk score. Thus, 117 patients of the training dataset were classified into a high-risk group (n = 59) and a low-risk group (n = 58) using the median risk score (-1.12) as the cutoff point. The Kaplan-Meier analysis showed a significant difference in patients' survival between the high-risk group and the low-risk group (log-rank test p-value = 8.94E-09; Figure 1A). Patients in the high-risk group had significantly shorter survival (median 5.60 months) than those in the low-risk group (median 7.52 months). To evaluate how well the four-lncRNA signature for predicting the 5-year survival, the time-dependent ROC curve analysis was carried out. The AUC for the four-lncRNA signature was 0.807 at the survival of five years ( Figure 1B), demonstrating the competitive performance of the four-lncRNA signature for survival prediction in the training dataset. In the univariate Cox regression analysis of the training dataset, the four-lncRNA risk score were significantly associated with patients' survival (p-value = 3.33E-05, HR = 23.141, 95% CI = 5.248-102.043; Table 2).
The distribution of the risk score, overall survival and prognostic lncRNA expression profiles in 117 patients of the training dataset were showed in Figure 1C, ranked according to increased risk score. Patients with high-risk scores had higher mortality than patients with low-risk scores. For patients with high risk scores, the expression profiles of lncRNAs (AC005682.5 and CTD-2231H16.1) are significantly up-regulated, while the remaining two lncRNAs (CTB-92J24.2 and RP11-727F15.13) were down-regulated.

Validation of the four-lncRNA signature for survival prediction in the testing dataset and entire dataset
To validate the prognostic power of the four-lncRNA signature for survival prediction, 117 patients of the testing dataset were divided into a high-risk group (n = 62) and a low-risk group (n = 55) with the same lncRNA signature and cutoff point derived from the training dataset. In consistent with the findings in the training dataset, the result showed that a significantly different survival between the high-risk group and the low-risk group (log-rank test p-value = 3.49E-02, median 6.62 months vs. 6.97 months; Figure 2A). The AUC for the four-lncRNA signature was 0.656 at the survival of five years in the testing dataset. In the univariate Cox regression analysis of the testing dataset, the four-lncRNA risk score were significantly associated with patients' survival (p-value = 3.97E-02, HR = 2.365, 95% CI = 1.042-5.372; Table 2). www.impactjournals.com/oncotarget  Derived from the univariate and multivariate Cox regression analyses in 117 patients of the training dataset. When the four-lncRNA signature was further applied to the entire TCGA dataset, similar results were observed. As in the training and testing dataset, the four-lncRNA signature could also classify 234 patients of the entire dataset into a high-risk group (n = 121) and a lowrisk group (n = 113) with significantly different survival (log-rank test p-value = 3.89E-08, median 6.43 months vs. 7.33 months; Figure 2B). In the entire dataset, the AUC for the four-lncRNA signature was 0.758 at the survival of five years. The univariate Cox regression analysis also demonstrated that the four-lncRNA risk score was significantly associated with patients' survival in the entire dataset (p-value = 6.18E-07, HR = 5.58, 95% CI = 2.84-10.97; Table 2). Taken together, the above results demonstrated good reliability and reproducibility of the four-lncRNA signature for predicting BLCA patients' survival.

Independence of the four-lncRNA signature for survival prediction from other clinical variables
To evaluate whether the prognostic power of the four-lncRNA signature was independent of other clinical variables including age, gender, subtype and tumor stage, the multivariate Cox regression analyses were first carried out in each dataset. The results from the three datasets demonstrated that the four-lncRNA risk score was significantly associated with patients' survival. Specifically, the four-lncRNA signature still maintained an independent association with survival after adjustment for other clinical variables in the training dataset (p-value = 9.44E-05, HR = 21.761, 95% CI = 4.637-102.129), testing dataset (p-value = 3.46E-02, HR = 2.459, 95% CI = 1.067-5.666) and entire dataset (p-value = 3.45E-06, HR = 4.975, 95% CI = 2.527-9.795; Table 2). Next, stratified analyses were then performed according to age, tumor stage and subtype, respectively. First, all 234 BLCA patients were stratified by the age (65 years old) into a younger dataset (n = 86) and an elder dataset (n = 148). The four-lncRNA signature could classify the younger dataset into a high-risk group (n = 45) and a lowrisk group (n = 41) with significantly different survival (log-rank test p-value = 1.64E-04, median 6.00 months vs. 6.77 months; Figure 3A). Similarly, the four-lncRNA signature was also able to classify the elder dataset into a high-risk group (n = 76) and a low-risk group (n = 72) with significantly different survival (log-rank test p-value = 1.02E-04, median 6.45 months vs. 7.60 months; Figure  3B). Then all patients were further stratified by the tumor stage into an early dataset (stage II and stage III, n = 156) and a late dataset (stage IV, n = 78). Similar prognostic power of the four-lncRNA signature was significant in both the early dataset and late dataset. Patients in the early dataset were classified into a high-risk group (n = 80) with shorter survival and a low-risk group (n = 76) with longer survival (log-rank test p-value = 2.25E-04, median 6.48 months vs. 7.05 months; Figure 3C). Similar results were observed in the late dataset (log-rank test p = 1.55E-05, median 5.93 months vs. 7.73 months; Figure  3D). Finally, all patients were stratified by the subtype into a non-papillary dataset (n = 165) and a papillary dataset (n = 69). Significant differences in patients' survival between the high-risk groups and the low-risk groups were also observed in the two datasets (log-rank test p-value = 5.52E-05, median 6.50 months vs. 7.13 months, Figure  3E; log-rank test p-value = 4.33E-04, median 3.73 months vs. 7.47 months, Figure 3F). The results of multivariate Cox regression analyses, together with the stratified analyses, demonstrated that the prognostic power of the four-lncRNA signature was independent of other clinical variables for survival prediction of patients with BLCA.

Functional characteristics of the four prognostic lncRNAs
To investigate potential functional roles of the four prognostic lncRNAs in BLCA tumorigenesis, we carried out functional enrichment analyses to predict their functions [44]. We first calculate Spearman correlation coefficients between lncRNAs and protein-coding genes by examining the paired lncRNA and the protein-coding gene expression profiles of 234 patients with BLCA. A total of 1405 protein-coding genes were significantly correlated with at least one of four prognostic lncRNAs (Spearman correlation coefficient > 0.40). Functional enrichment analyses of GO and KEGG pathways revealed that 1405 protein-coding genes were significantly enriched in 50 GO terms (a p-value of < 0.05 and an enrichment score of > 1.0) and 7 KEGG pathways (a p-value of < 0.05 and a fold enrichment of > 2.0). These functionally related GO terms were mainly organized into seven functional clusters including assembly and disassembly of protein and macromolecules, transcription, signal transduction and response to stimulus, cell apoptosis and death, metabolic and catabolic process, cell development, carbohydrate metabolic ( Figure 4A). Seven significantly enriched KEGG pathways were observed including tight junction, aldosterone-regulated sodium reabsorption, pathogenic escherichia coli infection, adherens junction, valine, leucine and isoleucine degradation, p53 signaling pathway, glycosphingolipid biosynthesis ( Figure 4B). These results of the functional enrichment analyses suggested that the four prognostic lncRNAs may participate in tumorigenesis through regulating or interacting protein-coding genes to affect known BLCA-related biological processes and pathways.

DISCUSSION
Considerable efforts have been made during the past years to identify expression-based prognostic biomarkers for bladder cancer at protein-coding genes and miRNAs levels [45][46][47]. More recently, accumulated evidence indicates that dysregulated lncRNAs are implicated in various tumorigenesis processes including proliferation, invasion and apoptosis by acting as tumor oncogenes or suppressor, which has developed a new area for biomarkers. Additionally, plenty of aberrant lncRNA expression in multiple cancers was discovered by the transcriptional profiling analyses [48,49], highlighting their potential roles as novel independent biomarkers for cancer prognosis. By now, several expression-based lncRNA signatures have been identified in glioblastoma, oesophageal squamous cell carcinoma, breast cancer, colorectal cancer, non-small cell lung cancer, multiple myeloma and ovarian cancer. Compared with proteincoding genes, lncRNAs exhibit greater tissue-, diseaseand developmental stage-specific expression, and their expression is more closely related to the tumor status and biological functions [50][51][52][53][54]. Indeed, several lncRNAs, such as HOTAIR, SPRY4-IT1, SUMO1P3 and PANDAR have been found to be associated with bladder prognosis [40,43]. However, to date, the prognostic power of expression-based lncRNA signature for predicting survival in patients with BLCA has not yet been investigated.
Here, we performed a comprehensive analysis for lncRNA expression profiles and corresponding clinical information of BLCA patients in the training dataset, and identified four lncRNAs significantly associated with patients' survival and constructed a four-lncRNA signature that can effectively predict survival of BLCA patients. Further ROC curve analysis demonstrated the competitive performance of the four-lncRNA signature for predicting 5-year survival in the training dataset. Then the prognostic power of the four-lncRNA signature was validated in an independent non-overlapping dataset and an entire dataset, demonstrated good reliability and reproducibility of the four-lncRNA signature for predicting BLCA patients' survival. Next, we performed multivariate Cox regression analyses in each dataset  to evaluate whether the prognostic power of the four-lncRNA signature was independently of other clinical variables, including age, gender, subtype and tumor stage. The four-lncRNA signature was demonstrated to still maintain an independent association with patients' survival after adjustment for other clinical variables. In the stratified analyses, the four-lncRNA signature showed prognostic power for the younger dataset (age ≤ 65) and the elder dataset (age > 65), in which patients belonging to the two datasets could be classified into high-risk and low-risk groups with significant differences in patients' survival. Similar prognostic power of the four-lncRNA signature was also significant in the early dataset (stage II and stage III) and the late dataset (stage IV), in which patients in the two datasets were able to classified into high-risk and low-risk groups with significantly different survival. Moreover, similar results were observed in the non-papillary dataset and the papillary dataset. Taken together, these results demonstrated that the prognostic power of the four-lncRNA signature was independent of other clinical variables for survival prediction of patients with BLCA.
Up to date, although more than tens of thousands of lncRNAs have been discovered in humans over the past few decades [55], only a handful of lncRNAs were functionally well-characterized and the functional study of lncRNAs remains in its infancy. Previous studies have suggested that lncRNAs participated in biological processes and pathways by regulating or interacting with proteincoding genes involved in the same processes, making it possible to infer lncRNA biological functions from their co-expressed protein-coding genes [44,56,57]. To detect the biological implication of the four prognostic lncRNAs in BLCA, we performed GO and KEGG functional enrichment analyses for co-expressed protein-coding genes. The results indicated the important functional roles of the four prognostic lncRNAs in tumorigenesis.
In summary, by performing a comprehensive analysis for lncRNA expression profiles and corresponding clinical information, our study identified four prognostic lncRNAs were significantly associated with BLCA patients' survival and constructed a four-lncRNA signature that can effectively predict patients' survival. The prognostic power of the four-lncRNA signature was independent of other clinical variables, and showed superior performance compared to known traditional clinical variables in a way. Our results demonstrated the four-lncRNA signature can function as novel independent biomarkers for BLCA prognosis and provided novel insights into understanding the underlying molecular mechanism of BLCA.

BLCA datasets and patient information
The lncRNA expression profiles and corresponding clinical information of BLCA patients were obtained from The Cancer Genome Atlas (TCGA) data portal (up to May 27, 2016; https://gdc-portal.nci.nih.gov/). A total of 234 patients were enrolled in this study after removal of patients without available clinical information. Clinical information of BLCA patients used in this study, including age, gender, subtype and stage. More detailed clinical characteristics of all 234 BLCA patients in this study were listed in Table 3.

Acquisition of lncRNA expression profiles
The lncRNAs derived from TCGA and lncRNAs from GENCODE project [58] were cross-reference by Ensemble ID to reduce redundant. Then the lncRNA expression profiles were defined as those with an average RPKM ≥ 0.3 across all 234 BLCA patients. Finally, we obtained expression profiles of 12730 lncRNAs in 234 BLCA patients.

Construction of a prognostic lncRNA signature
The lncRNAs expression profiles were normalized by log2 transformed. The association between the expression profiles of each lncRNA and patients' survival was evaluated in the training dataset using a univariate Cox regression analysis with survival as the dependent variable. LncRNAs whose expression profiles were significantly associated with patients' survival were identified (p-value < 0.001) as prognostic lncRNAs, and then subjected to a multivariate Cox regression analysis in the training dataset with survival as the dependent variable and other clinical information as covariables. Subsequently, a prognostic lncRNA signature was constructed based on a linear combination of the expression profiles of prognostic lncRNAs with weighted by the estimated regression coefficient as follows: where N is the number of prognostic lncRNAs, Exp i is the expression profiles of lncRNA i , and Coe i is the estimated regression coefficient of lncRNA i derived from the multivariate Cox regression analysis. The lncRNA signature could calculate a risk score for each patient.
With the lncRNA signature, BLCA patients in each dataset were classified into high-risk and low-risk groups using the median risk score derived from the training dataset as a cutoff point.

Statistical analysis
Differences in patients' survival between the high-risk group and the low-risk group in each dataset were accessed by the Kaplan-Meier survival analyses, and compared by the two-sided log-rank test using the R package "survival" [59]. Furthermore, in order to evaluate whether the prognostic power of the four-lncRNA signature was independent of other clinical variables including age, gender, subtype and tumor stage, multivariate Cox regression and stratified analyses were carried out in each dataset with survival as the dependent variable, lncRNA risk score and other clinical variables as explanatory variables. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated. The time-dependent receiver operating characteristic (ROC) curve analyses within five years as the defining points were performed using the R package "survivalROCR" [60], which has been widely used to evaluate the prognostic performance for survival prediction [61]. Area under the ROC curve (AUC) values were calculated from the ROC curves. All analyses were performed using R software and Bioconductor (version 3.3.0).

Functional enrichment analyses
Spearman correlation coefficients were computed to evaluate co-expression relationships between prognostic lncRNAs and protein-coding genes. Functional enrichment analyses for the co-expressed protein-coding genes were carried out using the DAVID Bioinformatics Tool (version 6.7) [62,63] limited to Gene ontology (GO) terms in the "Biological Process" (GOTERM-BP-FAT) and Kyoto encyclopedia of genes and genomes (KEGG) pathway categories with the human whole genome as the background. GO terms with a p-value of < 0.05 and an enrichment score of > 1.0, KEGG pathway with a p-value of < 0.05 and a fold enrichment of > 2.0 were considered as significantly enriched function annotations. Significant enrichment results were visualized and clustered based on the similar function using the Enrichment Map plugin [64] in Cytoscape (version 3.4.0) [65] and R package "goProfiles" [66].