1.Incidence differences in different aged GBM patients.
As showed in flow chart in Figure. 1, 61,997 GBM cases (ranging from the year of 1974 to 2016) from the Surveillance, Epidemiology, and End Results (SEER) database were selected to conduct an epidemiological research. The range of age at diagnosis was 18-101 years old (63.27±13.69), with a median age of 64 years old. A significantly sharp increasing of GBM incidence was observed among patients between 35 to 40 years old, while the incidence of GBM patients aged from 18 to 35 years old only presented slight and gentle increasing trend (Figure. 2A). To systematically investigate the age-related data from SEER, gender and tumor site distribution characteristics among differently aged patients were further evaluated. As showed in Figure. 2B and Supplementary Table. 1, the general ratio of male to female incidence was 1.36:1 (ranging from 1.47 to 1.75 among the patients under 50 years old, then the proportion of female increased among patients over 50 years old). Moreover, tumor site distributions among GBM patients of different ages also revealed specific characters. Frontal lobe accounted for the largest proportion of all cerebral lobes in the GBM primary site (15941cases, 29.07% of total), followed by the temporal lobe (14562 cases), overlapping lesion of brain and parietal lobe (10618 cases and 10035 cases), occipital lobe and other parts (including brain stem, cerebellum and ventricle). The proportions of temporal lobe among patients under 40 years old were significantly lower than those among patients equal and greater than 40 years old (Figure 2C-D, Table. 1 and Supplementary Table 1). The detailed distribution of gender and primary site were provided as Table. 1.
2.Prognostic differences among GBM patients of different ages
To further elucidate the age-related distribution of GBM patients in detail, we then analyzed the prognosis of these patients that treated with (total resection and subtotal resection) or without (autopsy without radiotherapy and chemotherapy, termed as the natural progression group) resection interventions in the SEER database and radiotherapy or chemotherapy. In order to evaluate the age-related outcome differences, the GBM patients were then divided into groups by 10 years intervals of age. We noticed that the outcome of GBM patients under 40 years old were significantly better than their equal and greater than 40 years old counterparts. No statistically differences were observed between patients of 18-29 years old and 30-39 years old. In order to further validated these phenomena, patients were divided into two groups by age, namely under and equal and greater than 40 years old. We revealed that in both the natural progression group and resection group, 40 years old was the cut-off value for glioblastoma. The median survival OS were 2 months in under 40 years old group (95% CI: 0.130 - 3.870) and 1 months in equal and greater than 40 years old group (95% CI: 0.945 - 1.055) in the natural progression group; while in the resection group, the median survival OS were 27 months in under 40 years old group (95% CI 23.894 - 30.106) and 12 months in equal and greater than 40 years old group (95% CI 11.664 - 12.336), respectively (Figure. 3A-B). ROC curve of 3-, 6-, 12-month in natural progression group, as well as 12-, 24- and 36 months in resection group were produced. AUC values in the natural progression group were 0.632, 0.702 and 0.770 respectively. And in the resection group, the AUC values were 0.692,0.682 and 0.703 respectively (Figure. 3C). Thus, age was confirmed as an important risk factor on the outcome of GBM. To verify the impact of other risk factors on the prognosis of GBM, COX regression analysis was performed with the factors of sex, age at diagnosis, tumor location, tumor size, resection range, radiotherapy and chemotherapy or not in the resection group. As showed in the nomogram and COX regression result, the C-index value for the predicted OS was 0.684. (Supplementary Figure. 1A-B).
- Age-related difference of Tumor Mutation Burden (TMB) and the components in Tumor microenvironment (TME)
Tumor mutation burden (TMB) could reflect the type and number of surface antigens of tumor cells, and thus can be used as an important indicator of tumor immunogenicity. It had been reported that TMB could be alternated with age. In order to confirm whether age could affect tumor immunity by regulating TMB, further experiments were then conducted. The mutation data of GBM patients were downloaded from the TCGA database for calculating TMB. It was revealed by correlation curve in Figure. 4A that TMB was positively correlated with age (p < 0.001, r = 0.31). Furthermore, as it was showed in Figure. 4B, the median values of TMB in patients under and equal and greater than 40 years old were 0.81 and 1.19, respectively (loge (W Wilcoxon) = 8.99, p < 0.001). Stromal cells and immune cells were the two of the most important components in TME. The level of these immune components was then evaluated by different bioinformatic methods. Patients equal and greater than 40 years old had higher stromal scores and ESTIMATE scores than the patients under 40 years old confirmed by data from different datasets. In the TCGA dataset, the media stromal scores were -106.51 in the patients under 40 years old against 135.84 in the patients equal and greater than 40 years old (loge(W Wilcoxon) = 9.62, p = 0.035) and the ESTIMATE scores were 694.09 and 1121.96 (loge(W Wilcoxon) = 9.61, p=0.048) separately in patients under and equal and greater than 40 years old. Data in the CCGA datasets also showed consistent results with TCGA. No significant difference of immune scores were observed between the two groups, in TCGA and CGGA mRNA array GBM dataset and CCGA mRNA seq primary GBM dataset. But in CGGA mRNA seq recurrent GBM dataset, the immune scores were higher in the patients equal and greater than 40 years old (-98.24 vs 440.18, loge (W Wilcoxon) = 7.41, p = 0.002) (Figure 4C).
- Tumor associated fibroblasts (TAFs) was closely related to the incidence and prognosis among GBM patients of different ages.
To further clarify the underlying mechanism contributing to the difference in prognosis of different ages, we conducted further experiments. Total number of 29 differentially expressed genes (DEGs) were screened out by GSEA and differential gene expression analysis between the groups of patients over and under 40 years old (Supplement table 2). Correlation between TME constituents and DEGs were then evaluated. As showed in Figure. 5A-B, stromal cells, composed of endothelial cells and tumor associated fibroblasts (TAFs), were significantly correlated with some of the DEGs (p<0.05), while no significant correlation between the immune cells (T cells, CD8 T cells, cytotoxic lymphocytes, NK cells, B lineage, monocytic lineage, myeloid dendritic cells, and neutrophils) with DEGs (p> 0.05). Moreover, further analysis showed that the level of TAFs showed obvious difference between the two groups, which was 6.39 in the patients under 40 years old, and 6.86 in the patients equal and greater than 40 years old (loge (W Wilcoxon) = 9.71, p =0.001), however, there was no significant difference in the level expression of endothelial cells (Figure. 5C). The level of TAFs was also negatively correlated with the prognosis in patients of different groups. We found that lower level of TAFs had longer median OS time of 454 days, than that in higher level (404 days) (cut off value of TAFs = 6.32; p = 0.017) (Supplementary Figure. 2 and Figure. 5D). Moreover, some of DEGs had most obvious correlation with the level of TAFs. For example, as showed in Figure. 5E, the expression of TAGLN was the most correlated genes with the level of TAFs (correlation coefficient = 0.72, p <0.001).
- Epithelial mesenchymal transition (EMT) was the most enriched hallmark among different aged GBM patients
GSEA analysis showed that the gene expressions of the two groups (under 40 years old vs equal and greater than 40 years old) were significantly enriched in the EMT pathway (the NES was -1.60 (NOM p value = 0.027, FDR q value = 0.053)) and there were the largest number of DEGs in the EMT pathway (Figure. 6A-D). 10 genes related to prognosis were selected by LASSO-COX analysis from the 29 DEGs (Figure. 6E-F). 7 genes with MDG (Mean Decrease in the Gini index) values greater than 4 were considered to mostly contribute to the classification of the two groups (less 40 years old group and equal and greater than 40 years old) (Table. 2), and they were included in the prognosis-related genes screened by LASSO-COX analysis. These genes were then divided into high and low expression groups by the cut off points (Supplementary Figure. 3A-G). The Kaplan-Meier survival analysis suggested that the median survival time of the low expression groups were significantly longer than those of the high expression groups (p <0.001) (Table. 2). It was revealed that the expression of these genes in the patients under 40 years old were significantly lower than those in the patients equal and greater than 40 years old (Figure. 6G). Inspired by multiple researches, the EMT score was conducted by the method of ssGSEA[29] (referred as EMTs) and arithmetic mean with the EMT-related gene expressions based on the EMT gene set in the HALLMARK pathway, (performed in log2 scale, EMTs-mean). ROC curves revealed that this EMT scores could function as a feasible tool to quantify the level of EMT(Correlated pAUC was 69.5% (85-100% SP) and 75.8% (85-100% SE), respectively), with a strongly positive relation between two methods (R = 0.84, P = 2.2e -16).(Figure.7A-B) The cut off points of EMTs expression level was 0.5 (Supplementary Figure. 4). Low score of EMTs score had longer median OS (503 days vs 406 days, p <0.001) (Figure. 7C). Moreover, it was also revealed that the EMTs was strongly correlated to TAFs (r = 0.81, p<0.001) (Figure 7D).
- Differences in subtype distribution and cellular pathology between patients under and equal and greater than 40 years old.
GBM could be divided into at least three subgroups, namely proneural (PN), classical (CL) and mesenchymal (MES) subtypes. Among them, MES had the worst outcomes. The transition from the subtype of PN into MES was considered as proneural-mesenchymal transition process, namely EMT process in GBM, which was considered as the aggressiveness and progressiveness of GBM. In order to confirmed whether the EMT differences among patients under and equal and greater than 40 years old could result in the proportion of different subtypes and then result in different outcomes, further data mining was then performed. As showed in Figure. 8A, a distinct distribution of subtypes appears among patients of different ages. Patients of 18-39 years old had the largest proportion of neural and proneural subtype, while the smallest proportion of MES subgroup. On the contrary, patients equal and greater than 40 years old, MES and CL subtypes gained a significantly increasing proportion, while the proportion of PN and neural subtypes decreased dramatically compared to patients under 40 years old. Considering the prognosis of subgroups, the subtypes were then divided into mesenchymal and non-mesenchymal group. It showed that the proportion of mesenchymal accounted for 12% among patients under 40 years old, while in the patients equal and greater than 40 years old, the proportion of mesenchymal accounted for 33% (chi-square= 9.64, p=0.002). Grouped by the interval of every ten years old, the mesenchymal GBM in each age group accounted for higher ratios, when the age was more than 40 years old. And the proportion of mesenchymal GBM increased significantly after the age of 40 (Figure.8B).To verify whether the difference of TAFs was the potential mechanism of the subtype distribution differences, the level of TAFs of mesenchymal and non-mesenchymal was then compared and showed that higher level of TAFs in mesenchymal group compared to non-mesenchymal group (loge (W Wilcoxon) = 10.69, p < 0.001) (Figure.8C). These results correspond to the previous conclusions, that was, age differences could cause differences in TAFs, which affects the distribution of GBM subtypes, and in turn leads to differences in prognosis. It was reported that TAFs functioned as frame-like constituents in GBM both in vivo and in vitro, and contributed to the EMT process of many types of tumors. Thus, primary glioblastoma cells from clinical patients were used to conduct cellular experiments. It was showed in Figure. 8D-E, Transwell assay revealed that primary glioblastoma cells from patients under 40 years old had significantly greater migration capacity compared to those from patients equal and greater than 40 years old (p = 0.0019). Western blotting assay showed higher expression of mesenchymal markers such as Vimentin and CD44, but lower expression of proneural (epithelial) marker E-cadherin in equal and greater than 40 years old group (Figure. 8F-G).