Clinical characteristics of rearrangement of included populations
According to the latest classification criteria for gliomas from WHO, WHO grade 4 astrocytomas, IDH mutant in WHO CNS5 is mainly consistent with glioblastoma, IDH-mutant in the 2016 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (2016 CNS WHO). Glioblastoma, IDH-wildtype in WHO CNS5 includes the glioblastoma, IDH-wildtype in 2016 CNS WHO. Therefore, IDH-wildtype glioblastoma patients in the previous classification were reserved for GBM in this study. IDH-mutant, WHO grade II-III gliomas were retained as IDH-mutant gliomas in the previous classification. While IDH-wildtype, WHO grade II-III gliomas combination of TERT promoter mutation or EGFR gene amplification or the combined gain of entire chromosome 7 and loss of entire chromosome 10 (+7/−10) were classified as GBM. In this study, all gliomas included were reclassified according to WHO CNS5. A total of 948 glioma patients with prognostic information were included from three clinical cohorts (TCGA, CGGA, REMBRANDT). The detailed information of all clinical cohorts in this study is summarized in Table 1, and the flow chart of this study design is shown in Figure 1.
A robust model predicts IDH1 mutations based on transcriptome data
Utilizing the RF method, an effective prediction model of IDH1 mutation was constructed on the GSE104722 cohort. As shown in Figure 2a, this prediction model can accurately divide the 20 samples of GSE104722 into 2 groups (IDH1 wild-type and IDH1 mutant). The prediction accuracy of the model was 100% in the GSE104722 cohort and 95.7% in the TCGA cohort. The AUC of the model was 1.00 in the training cohort and 0.96 in the validation cohort, respectively, indicating that the model could effectively predict IDH1 mutation in other transcriptome cohorts (Figure 2b, 2c). Next, we applied the model to GSE104722 to identify the estimated IDH1 wild-type samples. For subsequent studies, GBM samples from TCGA and CGGA cohorts were invoked as a training set, and GBM samples estimated from the REMBRANDT cohort were used as an independent test set.
The expression of HOTAIR in GBM is higher and the higher the expression level, the worse the prognosis
Firstly, lncRNAs with abnormal expression in GBM than other types of glioma were screened out from TCGA and CGGA cohorts respectively (Figure 3a). HOTAIR is highly expressed in both cohorts and verified in TEMBTANDT cohorts (Figure 3b, c). According to the median expression of HOTAIR, patients in the training and test sets were split into the high HOTAIR expression group and the low HOTAIR expression group. Kaplan-Meier survival analysis showed that GBM patients with low expression of HOTAIR had significantly higher OS than patients with high expression of HOTAIR (P < 0.05; Figure 3d). Multivariate analysis was performed on GBM patients in TCGA, CGGA, and REMBRANDT cohorts to assess whether HOTAIR was an independent prognostic factor. Variables with statistical significance (P <0.05) were used as independent prognostic factors in multivariate analysis. HOTAIR was found to be an independent prognostic factor in TCGA and CGGA cohorts (Figure 3e). Figure 3f illustrates the relationship between HOTAIR and routine clinical and molecular features in TCGA, CGGA, and REMBRANDT cohorts, respectively, there is a significant correlation between the HOTAIR subgroup and TCGA subgroups in CGGA.
Identification of HOTAIR-related biological processes
Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were carried out to investigate which biological processes were associated with poor prognosis in GBM patients with high HOTAIR expression. Firstly, the expression profiles of patients with high and low expression of HOTAIR were analyzed by GSVA. We believe that the biological process of enrichment in the high HOTAIR expression group is related to the poor prognosis of patients. The results reported that cell cycle-related processes, such as G2M checkpoint and E2F targets, showed the highest correlation with poor prognosis. Proliferation-related processes, such as Myc targets V1 and Myc targets V2, autophagy-related mTORC1 signaling, and angiogenesis were also related to poor prognosis. Meanwhile, the apoptosis and metabolism-related processes, such as cholesterol homeostasis, fatty acid metabolism, and bile acid metabolism, were enriched in the group with low expression of HOTAIR (Figure 4a). The results of GSEA also suggested that the disorder of cycle-related processes may be a possible factor resulting in poor prognosis in patients with high HOTAIR expression (Figure 4b). It has been reported that the knockdown of HOTAIR induced the cell cycle arrest in G0/G1 phase in glioma cells. Thus, we analyzed the relationship of the expression of HOTAIR and cell cycle markers (CCNB1(cyclin B1), CCND1(cyclin D1), CDC25A, and CDC25C) and apoptosis markers (CASP3, BAX, BCL2, MCL1). We found that the higher HOTAIR expression was positively correlated with these markers except BAX (Figure 4c, 4d). These results indicate that HOTAIR might promote the transformation of the cell cycle process and inhibit apoptosis, leading to a poor prognosis of GBM patients.
The relationship between HOTAIR and tumor immune microenvironment.
From the GSVA results of TCGA and CGGA cohorts, there is an interesting phenomenon that immune-related pathways, such as inflammatory response and interferon-gamma response, are enriched in different groups (Figure 4a). In CGGA, the inflammatory response pathway was enriched in high HOTAIR expression patients, while the result of TCGA was the opposite. Thus, we estimated the composition of 22 immune cells in each patient of CGGA and TCGA cohorts by CIBERSORT. Comparing the composition of the immune cells of high and low HOTAIR expression groups, the patients in the high HOTAIR expression group had a lower proportion of CD8+ T cells and monocytes in TCGA, and activated natural killer cells in CGGA, but a higher proportion of follicular helper T cells in TCGA, and activated memory CD4+ T cells, and activated dendritic cells in CGGA (adjusted P < 0.05, Figure 5a). Only M0 macrophages were statistically different between the two risk groups in both the TCGA and the CGGA cohort. Combined with the results of ssGSEA, there was no correlation between the expression level of HOTAIR and the activated immune cells (adjusted P > 0.05, Figure 5b). Since the enrichment of immune cells and immune pathways in TCGA and CGGA was different, we speculated this might lead to differences in immunotherapy. So, the relationship of HOTAIR and immune checkpoint markers was analyzed in two cohorts. Some immune checkpoint genes, such as PDCD1(PD1), CTLA4, and LAG3 have a relatively higher expression in the high HOTAIR expression group in both two cohorts, especially LAG3 (P < 0.05 in both TCGA and CGGA cohorts). However, CD274(PDL1) and PDCD1LG2(PD-L2) were negatively correlated with HOTAIR expression in TCGA, but it was the opposite in CGGA, especially PDCD1LG2(PD-L2) (P < 0.05 in both TCGA and CGGA cohorts) (Figure 5c). Due to the intensity of intratumoral CD8+ T cell infiltrates and tumor programmed cell death ligand 1 (PDL1) expression have been proposed as distinct biomarkers of response to anti-PDL1 therapies[27, 28]. In addition, in TCGA and CGGA cohorts, the density of CD8+ T cell and the expression of PDL1 were different in the HOTAIR subgroups, although it was not statistically significant. Hence, we evaluated the therapeutic response in two cohorts to immune checkpoint inhibitors via TIDE. The results showed that there was no significant correlation between the expression of HOTAIR and patients’ response to immune checkpoint inhibitors. Interestingly, we found more patients responded to immune checkpoint blockade in CGGA than in TCGA (Figure 5d). The IC50 of other commonly used antitumor drugs was predicted in different groups by using the pRRophetic algorithm. The high HOTAIR expression group had higher IC50 of the chemotherapeutic agents (Figure 5e).
HOTAIR promotes the proliferation of glioma cells
Based on our findings and previous studies, HOTAIR promotes cell proliferation by regulating the cell cycle and apoptosis. The role of HOTAIR in GBM was confirmed by the cell experimental method. HOTAIR was overexpressed in glioma cell line U251, and the transduction efficiency was confirmed by qRT-PCR (Figure 6a). Cell proliferation assays, as well as colony formation assays, were performed to investigate the influence of HOTAIR overexpression on tumor malignancy in GBM cells. The results indicated that HOTAIR overexpression promoted cell growth (Figure 6b), showed a marked increase in colony-forming ability (Figure 6c).