LncRNA HOTAIR Promotes Tumorigenicity in Glioblastoma

doi:10.21203/rs.3.rs-1340987/v1

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LncRNA HOTAIR Promotes Tumorigenicity in Glioblastoma

https://doi.org/10.21203/rs.3.rs-1340987/v1

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Background

Recently, the 2021 WHO Classification of Tumors of the Central Nervous System (fifth edition) was published. WHO CNS5 incorporates numerous molecular changes to the accurate classification of CNS neoplasms. GBM, as a special type of glioma, is confined to IDH-wildtype in WHO CNS5, which has a short medical history and poor prognosis. LncRNA HOTAIR has been found to be responsible for the poor prognosis of several human cancers in previous studies. The clinical significance, the prognostic and treatment value of HOTAIR for GBM in the new WHO CNS5 classification remain unclear.

Methods

We created a Random Forest-based prediction model to assess the IDH1 mutational status of patients in REMBRANDT cohorts. Then we analyzed the role and the potential mechanism of HOTAIR in TCGA, CGGA, and REMBRANDT cohorts. GSVA and GSEA analysis were used to detect the potential biological functions and signaling pathways. The infiltration of immune cells was quantified by Cibersort. The effectiveness of targeted therapy in the clinical application was sought through drug response analysis. Furthermore, we verified that HOTAIR affected the proliferative activity of that glioma cells.

Results

We found that HOTAIR is highly expressed in all cohorts. The patients’ samples with high HOTAIR expression have poorer overall survival. The results of the functional analysis indicated that the cell cycle and proliferation-related processes were enriched in the high HOTAIR expression group. The infiltration of immunocytes is different in the two groups. In multiple immune checkpoints, the risk score showed a strong correlation. The analysis of drug treatment response present that the high HOTAIR expression group has a better treatment response and better curative effect on the treatment of temozolomide, sorafenib, lenalidomide, bexarotene, and axitinib.

Conclusion

In conclusion, We found that HOTAIR could lead to poor prognosis of GBM mainly through regulation of cell cycle and apoptosis. And HOTAIR could be used as a marker to guide chemotherapy in GBM patients rather than immunocheckpoint inhibitor therapy.

HOTIAR

GBM

WHO CNS5

prognosis

therapy

Gliomas are the most common primary malignant tumor in the brain that arise from glial cells. Glioblastoma (GBM), which is characterized by highly malignant and rapidly progressive, has a median overall survival (OS) of only 15months after being treated with radiation and adjuvant temozolomide chemotherapy (Stupp protocol)[1, 2]. The fifth edition of the World Health Organization (WHO) Classification of Tumors of the Central Nervous System (WHO CNS5) was recently released[3]. The distinction between adult and pediatric diffuse gliomas is being made for the first time. Adult-type diffuse gliomas are classified as three types: astrocytoma, IDH-mutant; oligodendroglioma, IDH-mutant, and 1p/19q-codeleted; glioblastoma, IDH-wildtype[3]. These provide adequate conditions for selecting more homogeneous subjects for future clinical studies via reclassifying the pathologic types with the same molecular characteristics and similar prognosis into one type. With further research on the development of GBM, the role of new molecular markers in pathological diagnosis targeted therapy and immunotherapy and prognostic evaluation needed to be further explored. In this study, GBMs included were under the WHO CNS5 classification standard.

HOX transcript antisense RNA (HOTAIR) is a well-studied carcinogenic lncRNA located on human chromosome 12q13. It was the first lncRNA to be discovered to influence gene expression in trans fashion, and it is now suspected of playing a role in the carcinogenesis of several human cancers[4]. It has been reported that HOTAIR regulated the invasion and metastasis of breast carcinomas[5] and colorectal cancer[6] in a PCR2-dependent manner. Moreover, HOTAIR could block the expression of microRNA to promote bladder cancer[7], esophageal cancer[8], and gall bladder cancer[9] proliferation and invasion as a competing endogenous RNA. HOTAIR expression levels were found to be significantly positively correlated with tumor grade in a study of 295 glioma samples from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE4290 datasets. Glioma patients with high HOTAIR levels had shorter overall survival[10]. Concordantly, another study based on The Cancer Genome Atlas (TCGA), four datasets from the Oncomine database, and two independent Portuguese and French glioma series, confirmed that HOTAIR expression is far more frequent in grades III and IV than in grade II gliomas and normal brain[11]. They all demonstrated that HOTAIR could be a clinically-relevant biomarker of prognosis in GBM (2016 WHO CNS)[10, 11]. Additionally, HOTAIR may be responsible for malignant progression and poor prognosis of glioma patients by regulating cell cycle[10].

However, the prognostic value of HOTAIR for GBM in the new WHO CNS5 classification and its molecular mechanisms underlying remain elusive. In this context, we aimed to clarify the clinical significance and function of HOTAIR in GBM samples by analyzing clinical and molecular pathology features.

Publicly available sample collection

All datasets used throughout this study are available to the public. Transcriptome data and clinical information of 205 patients of the CGGA cohort[12, 13] were obtained from the CGGA website (http://www.cgga.org.cn). The clinical phenotype of 564 patients and expression data of normalized combined the TCGA brain lower-grade glioma (LGG) and glioblastoma multiforme (GBM) were obtained from UCSC Xena website[14] (http://xena.ucsc.edu). The CGGA and TCGA cohorts were the training cohorts. 179 patients from the REMBRANDT cohort[15] and associated clinical information were obtained from the GlioVis website (http://gliovis.bioinfo.cnio.es) as a validation cohort. The transcriptome data from GSE104722 was taken to construct the IDH1 mutant prediction model obtained from the GEO website (http://www.ncbi.nlm.nih.gov/geo). The details of these datasets are listed in Table 1.

IDH1 mutation status prediction

We created a Random Forest (RF)-based prediction model by R package “randomForest”[16] to assess the IDH1 mutational status of patients in REMBRANDT cohorts. The RNA-seq data of GSE104722 have been made to develop this prediction model, and samples with available IDH1 mutational status in TCGA was used as external validation for evaluating the model performance (Since the normalization method of transcriptome data of CGGA is different from that of GSE104722 and TCGA, CGGA is not taken as a validation set.). Differential expression analysis between IDH1 mutant and IDH1 wild-type samples was performed to select informative genes which served as input into the RF model (abs(Log2FC) > 1 & adjust P < 0.05). Specifically, using the out-of-bag (OOB) error as a minimization criterion, at each iteration, the least important 20% of genes were removed until the OOB error rate reached its minimum. The final RF prediction model was established with the genome with the lowest OOB error rate. Subject operating characteristic (ROC) curves were used to evaluate the predictability of the model in training and validation tests.

Pathway enrichment analysis

Based on the expression profile of each sample between high and low HOTAIR expression groups, Gene Set Variation Analysis (GSVA) and Gene set enrichment analysis (GSEA) were conducted to estimate the score of gene-set in each group. GSVA was applied to enrich hallmark gene sets using the “GSVA”[17] R package, while the kegg gene sets were as the reference in GSEA using the “clusterProfiler”[18] R package. The enrichment score of each gene-set more than 1.2 and adjusted P-value less than 0.05 was regarded as significantly enriched.

The exploration of the immune microenvironment

To further explore the immune microenvironment in GBM, the relative abundance of 22 immune cells in each patient was computed by “Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)”[19]. Moreover, single-sample GSEA (ssGSEA)[20] in the “gsva” R package was performed to calculate the relative abundance of 28 immune cells. Sets of characteristic genes for each immune cell type were obtained from the previous studies[21]. We calculated the correlation between HOTAIR expression level and the abundance of 28 immune cells.

The response to immune checkpoint blockade prediction

Based on the tumor that before immunocheckpoint inhibitor including anti-PD1 and anti-CTLA4 treatment expression profiles, the TIDE module can estimate patient response to immune checkpoint blockade[22] (http://tide.dfci.harvard.edu.).

Drug Sensitivity Analysis

Based on the GDSC2 dataset in Genomics of Drug Sensitivity in Cancer (GDSC) [23], the largest open pharmacogenomics database, a ridge regression model was constructed to predict the drug treatment response of each sample based on the transcriptome data using “pRRophetic” package. In the present study, we estimated the half-inhibitory concentrations (IC50) of temozolomide, sorafenib, lenalidomide, bexarotene, and axitinib of each individual.

Cell culture and cell transfection

The human glioma cell line U251 was purchased from the College of Life Science of Wuhan University (Wuhan, China). All cells were cultured in high-glucose Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) containing 10% fetal bovine serum (FBS, HyClone, USA) with a standard humidified incubator under 5% CO2 at 37°C.

The full-length complementary cDNAs of human HOTAIR were synthesized and cloned into the pcDNA3.1 vector (Sangon Biotech). Lipofectamine 3000 (Invitrogen, Thermo Fisher Scientific) was used to transfect the plasmids into U251 cells according to the manufacturer’s protocol. The cells transfected for 48h were used for subsequent experiments.

Real-time PCR

RNA was extracted from U251 cells by using Trizol reagent (Invitrogen, Carlsbad, CA, USA). Isolated RNA was reverse transcribed to cDNA using the cDNA synthesis kit (Thermo Fisher, TOYOBO, Japan). The resulting cDNAs were subjected to quantitative real-time PCR with a Bio-Rad CFX Connect real-time PCR detection system (Bio-Rad, Hercules, CA, USA). The primer sequences are shown in Table 2. The relative quantitative value for each gene was calculated using the 2−ΔΔCt method.

CCK-8 and colony formation assays

Transfected U251 cells were seeded in 96-well plates and cell viability was assessed using the Cell Counting kit-8 (CCK-8; Vazyme, Nanjing, China) according to the manufacturer's protocol. The absorbance at 450nm was measured using a spectrophotometer.

In the colony formation assay, cells were seeded in six-well plates and cultured for 14 days after transfection. Then, we used PBS to wash the resulting colonies twice, and used 4% formaldehyde to fix them for 15 min, finally used 0.1% crystal violet to stain for 15 min.

Statistical analysis

All statistical tests were conducted in R statistical software (Version 4.0.2) and GraphPad Prism 8.4.0 (GraphPad Software, San Diego, CA, USA) software. We used Student’s t-test, Wilcoxon rank-sum test, and Permutation test in this study. The relationship between HOTAIR and other continuous variables was measured by the Spearman method. Survival analysis was carried out using Kaplan–Meier curve, and the log-rank test was used to determine the statistical significance of differences. Multivariate cox proportional hazard regression was used to explore the related independent predictors of the prognosis. All reported P-values were two-sided, and the statistical significance was set at 0.05.

Clinical characteristics of rearrangement of included populations

According to the latest classification criteria for gliomas from WHO[3], 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)[24]. 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[25]. 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[26]. 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).

Glioblastomas were divided in the 2016 CNS WHO into GBM, IDH-wildtype, and GBM, IDH-mutant[24]. Compared to IDH-wildtype GBM, IDH-mutant GBM showed some molecular phenotypic changes, such as fewer EGFR amplification (rare vs 35%), PTEN mutations (rare vs 24%), and TERT promoter mutations (26% vs 72%), but more ATRX mutations (71% vs rare) and P53 mutations (81% vs 27%)[24]. These differences suggested that IDH-wildtype and IDH-mutant GBMs carried different molecular contexts. Given that they had different driving genes, molecular characteristics, and clinical prognosis, all IDH-mutant diffuse astrocytic tumors are considered a single type in WHO CNS5, and the IDH-mutant GBM are classified as astrocytoma, IDH-mutant, CNS WHO grade 4, instead of GBM[3]. Gliomas exhibited pronounced heterogeneous are prone to confusion in clinical diagnosis and treatment[29]. The changes of classifying gliomas into more pathological types based on the combination of biological and molecular markers are more in line with the natural course of the disease. The new classification will enable clinicians to judge the prognosis of patients, choose the more suitable treatment and promote the exploration of novel treatments and evaluate the therapeutic effect of a new therapy. We aimed to explore the role and function of HOTAIR in GBM in WHO CNS5.

In previous studies, HOTAIR positively regulated an 18 genes cell cycle-related mRNA network in human glioma samples obtained from the CGGA and verified in GBM cells[30]. The results of our study showed that HOTAIR played a pro-oncogenic role also mainly by accelerating cell cycle conversion and inhibiting apoptosis in GBM. It manifested that there was no difference in the functional mechanism of HOTAIR with the changes of classification in glioma, and further highlighting the critical role of HOTAIR in regulating the cell cycle during gliomagenesis.

Furthermore, we also found that the immune-related pathways were enriched in different HOTAIR subgroups in different cohorts, and HOTAIR was significantly correlated with CD4+ T cells, monocyte cells, and dendritic cells, and some immune checkpoint genes (LAG3, PD-L2), suggesting that HOTAIR might be involved in the immune regulation of GBM patients. It was reported that HOTAIR was required for the secretion of various cytokines and inflammatory factors including IL-6, iNOS, TNFα, and MIP-1B induced by LPS-treatment macrophage, and played a central role in NF-κB activation upon stimulation with LPS[31]. Additionally, HOTAIR regulated glucose metabolism in macrophages potentially to meet the energy needs during the immune response[32]. Combined with our findings, HOTAIR may be involved in immune signaling and inflammatory responses in GBM, which may be one of the reasons for the poor prognosis of patients. However, the predicted therapeutic response results in TIDE showed no difference between the two groups, manifesting that HOTIAR might not responsible for immune checkpoint inhibitors in GBM. But possibly due to racial differences, the response rate of CGGA patients to immunotherapy was significantly higher than that of TCGA patients.

Recently, HOTAIR was found to mediate the GBM chemoresistance through mediating the expression of hexokinase 2 by targeting miR‐125[33]. Exosome-mediated transfer of lncRNA HOTAIR regulated TMZ resistance by sponging miR-519a-3p in a study including 51 GBM patients receiving temozolomide treatment[34]. We also found significant differences in the sensitivity of temozolomide and sorafenib in different HOTAIR subgroups. In addition, HOTAIR was reported to be detected in GBM serum exosomes and observed a reduction of serum HOTAIR levels after surgery and a further reduction at the 2 weeks post-surgery follow-up in one recurrent GBM patient[35]. The si-HOTAIR has been shown to be successfully delivered via superparamagnetic iron oxide nanoparticles to promote the expression of PDCD4 at the transcriptional level, thereby reducing the proliferation, invasion, and tumorigenicity of human glioma stem cells[36]. These findings manifested that HOTAIR could be a novel target for personalized treatment and monitoring the therapeutic effect of GBM.

The current study does, however, have certain limitations. To begin, we employed the machine learning method to predict IDH1 mutation status in some individuals due to inadequate molecular information. However, in these patients, there may be a little disparity between the estimated and actual IDH1 mutation status. Second, these patients were not included in GBM with deficient information on molecular signatures required for WHO grade II-III IDH-wildtype astrocytomas to be diagnosed as GBM. Third, the detection of the IC50 of drugs in the CCLE database was based on cell lines, and compared with cell lines, the transcriptome data of clinical tumor samples are contaminated by normal tissue and stroma components, which might reduce the accuracy of drug response prediction. Finally, although we verified that HOTAIR affected the proliferative activity of that glioma cells in vitro, further experimental and clinical validations are needed to support our hypothesis about the function of HOTAIR.

In conclusion, we analyzed the role of HOTAIR in GBM in WHO CNS5, and we found that HOTAIR might regulate the cell cycle process and inhibit apoptosis in GBM patients (no matter the fourth edition or the fifth edition). In addition, HOTAIR may not be involved in the immune regulation of GBM. Overall, this study suggests that HOTAIR could be a novel insight on prognosis prediction and a novel approach for precision therapy but not immunotherapy.

GBM: Glioblastoma

WHO CNS5: The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System

HOTAIR: HOX transcript antisense RNA

GSVA: Gene Set Variation Analysis

GSEA: Gene set enrichment analysis

CIBERSORT: Cell type Identification by Estimating Relative Subsets of RNA Transcripts

IC50: half-inhibitory concentrations

Acknowledgment

Not applicable.

Availability of data and materials

The data that support the findings of this study are available from UCSC-XENA (http://xena.ucsc.edu), CGGA (http://www.cgga.org.cn), GlioVis (http://gliovis.bioin fo.cnio.es/), and GEO (http://www.ncbi.nlm.nih.gov/geo).

Funding

This work was supported by the Translational Medicine Fund of the Zhongnan Hospital of Wuhan University (No. ZLYNXM202011, ZNLH201901).

Authors’ contributions

LZQ, LT, and SXY: designed the study, reviewed relevant literature, completed cell experiments, and drafted the manuscript. WD and YDH conducted all statistical analyses. LZQ, LT, and WGH: revise the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.

Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, Ludwin SK, Allgeier A, Fisher B, Belanger Ket al: Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol2009, 10(5):459-466.
Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn Uet al: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med2005, 352(10):987-996.
Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger Get al: The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol2021, 23(8):1231-1251.
Rinn JL, Kertesz M, Wang JK, Squazzo SL, Xu X, Brugmann SA, Goodnough LH, Helms JA, Farnham PJ, Segal Eet al: Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell2007, 129(7):1311-1323.
Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, Tsai MC, Hung T, Argani P, Rinn JLet al: Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature2010, 464(7291):1071-1076.
Kogo R, Shimamura T, Mimori K, Kawahara K, Imoto S, Sudo T, Tanaka F, Shibata K, Suzuki A, Komune Set al: Long noncoding RNA HOTAIR regulates polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers. Cancer Res2011, 71(20):6320-6326.
Sun X, Du P, Yuan W, Du Z, Yu M, Yu X, Hu T: Long non-coding RNA HOTAIR regulates cyclin J via inhibition of microRNA-205 expression in bladder cancer. Cell Death Dis2015, 6(10):e1907.
Ren K, Li Y, Lu H, Li Z, Li Z, Wu K, Li Z, Han X: Long Noncoding RNA HOTAIR Controls Cell Cycle by Functioning as a Competing Endogenous RNA in Esophageal Squamous Cell Carcinoma. Transl Oncol2016, 9(6):489-497.
Ma MZ, Li CX, Zhang Y, Weng MZ, Zhang MD, Qin YY, Gong W, Quan ZW: Long non-coding RNA HOTAIR, a c-Myc activated driver of malignancy, negatively regulates miRNA-130a in gallbladder cancer. Mol Cancer2014, 13:156.
Zhang JX, Han L, Bao ZS, Wang YY, Chen LY, Yan W, Yu SZ, Pu PY, Liu N, You YPet al: HOTAIR, a cell cycle-associated long noncoding RNA and a strong predictor of survival, is preferentially expressed in classical and mesenchymal glioma. Neuro Oncol2013, 15(12):1595-1603.
Xavier-Magalhães A, Gonçalves CS, Fogli A, Lourenço T, Pojo M, Pereira B, Rocha M, Lopes MC, Crespo I, Rebelo Oet al: The long non-coding RNA HOTAIR is transcriptionally activated by HOXA9 and is an independent prognostic marker in patients with malignant glioma. Oncotarget2018, 9(21):15740-15756.
Fang S, Liang J, Qian T, Wang Y, Liu X, Fan X, Li S, Wang Y, Jiang T: Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area. AJNR Am J Neuroradiol2017, 38(10):1990-1997.
Zhao Z, Meng F, Wang W, Wang Z, Zhang C, Jiang T: Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas. Scientific Data2017, 4(1):170024.
Tomczak K, Czerwińska P, Wiznerowicz M: The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn)2015, 19(1a):A68-77.
Gusev Y, Bhuvaneshwar K, Song L, Zenklusen JC, Fine H, Madhavan S: The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci Data2018, 5:180158.
Breiman L: Random Forests. Machine Learning2001, 45(1):5-32.
Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics2013, 14:7.
Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. Omics2012, 16(5):284-287.
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA: Robust enumeration of cell subsets from tissue expression profiles. Nat Methods2015, 12(5):453-457.
Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, Schinzel AC, Sandy P, Meylan E, Scholl Cet al: Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature2009, 462(7269):108-112.
Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z: Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep2017, 18(1):248-262.
Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li Bet al: Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med2018, 24(10):1550-1558.
Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IRet al: Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res2013, 41(Database issue):D955-961.
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW: The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol2016, 131(6):803-820.
Brat DJ, Aldape K, Colman H, Holland EC, Louis DN, Jenkins RB, Kleinschmidt-DeMasters BK, Perry A, Reifenberger G, Stupp Ret al: cIMPACT-NOW update 3: recommended diagnostic criteria for "Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV". Acta Neuropathol2018, 136(5):805-810.
Ke J, Yao YL, Zheng J, Wang P, Liu YH, Ma J, Li Z, Liu XB, Li ZQ, Wang ZHet al: Knockdown of long non-coding RNA HOTAIR inhibits malignant biological behaviors of human glioma cells via modulation of miR-326. Oncotarget2015, 6(26):21934-21949.
Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, Chmielowski B, Spasic M, Henry G, Ciobanu Vet al: PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature2014, 515(7528):568-571.
Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MBet al: Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med2012, 366(26):2443-2454.
Masui K, Mischel PS, Reifenberger G: Molecular classification of gliomas. Handb Clin Neurol2016, 134:97-120.
Huang K, Sun J, Yang C, Wang Y, Zhou B, Kang C, Han L, Wang Q: HOTAIR upregulates an 18-gene cell cycle-related mRNA network in glioma. Int J Oncol2017, 50(4):1271-1278.
Obaid M, Udden SMN, Deb P, Shihabeddin N, Zaki MH, Mandal SS: LncRNA HOTAIR regulates lipopolysaccharide-induced cytokine expression and inflammatory response in macrophages. Sci Rep2018, 8(1):15670.
Obaid M, Udden SMN, Alluri P, Mandal SS: LncRNA HOTAIR regulates glucose transporter Glut1 expression and glucose uptake in macrophages during inflammation. Sci Rep2021, 11(1):232.
Zhang J, Chen G, Gao Y, Liang H: HOTAIR/miR-125 axis-mediated Hexokinase 2 expression promotes chemoresistance in human glioblastoma. J Cell Mol Med2020, 24(10):5707-5717.
Yuan Z, Yang Z, Li W, Wu A, Su Z, Jiang B: Exosome-Mediated Transfer of Long Noncoding RNA HOTAIR Regulates Temozolomide Resistance by miR-519a-3p/RRM1 Axis in Glioblastoma. Cancer Biother Radiopharm2020.
Tan SK, Pastori C, Penas C, Komotar RJ, Ivan ME, Wahlestedt C, Ayad NG: Serum long noncoding RNA HOTAIR as a novel diagnostic and prognostic biomarker in glioblastoma multiforme. Mol Cancer2018, 17(1):74.
Fang K, Liu P, Dong S, Guo Y, Cui X, Zhu X, Li X, Jiang L, Liu T, Wu Y: Magnetofection based on superparamagnetic iron oxide nanoparticle-mediated low lncRNA HOTAIR expression decreases the proliferation and invasion of glioma stem cells. Int J Oncol2016, 49(2):509-518.

Table 1

The clinical and molecular information of individuals in the TCGA, CGGA, and REMBRANDT cohorts.

	TCGA	CGGA	REMBRANDT
Number of patients	564	205	179
Age
< 60y	411	183	113
≥ 60y	127	22	46
Gender
Female	227	83	50
Male	311	122	80
Grade
II	211	76	56
III	178	28	33
IV	175	101	85
TCGA subtype
Classical	40	20	48
Mesenchymal	48	80	19
Neural	25	47	10
Proneural	26	58	8
IDH1 Status
Mutant	175	101
Wildtype	389	104
1p19q Codeletion status
Codeletion	160	16	8
Non-codeletion	400	49	32
MGMT promoter status
Methylated	423	67
Unmethylated	109	132
TERT promoter status
Mutant	138
Wildtype	142
+7/-10
Yes	130
No	430
+7/-10: Combined whole chromosome 7 gain and whole chromosome 10 loss

Table 2

The gene primer sequences used in study.

Genes	Forward (5’-3’)	Reverse (5’-3’)
HOTAIR	GGTAGAAAAAGCAACCACGAAGC	ACATAAACCTCTGTCTGTGAGTGCC
GAPDH	GCTCTCTGCTCCTCCTGTTC	CGACCAAATCCGTTGACTCC

No competing interests reported.

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LncRNA HOTAIR Promotes Tumorigenicity in Glioblastoma

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Abstract

Figures

Introduction

Methods

Publicly available sample collection

IDH1 mutation status prediction

Pathway enrichment analysis

The exploration of the immune microenvironment

The response to immune checkpoint blockade prediction

Drug Sensitivity Analysis

Cell culture and cell transfection

Real-time PCR

CCK-8 and colony formation assays

Statistical analysis

Results

Clinical characteristics of rearrangement of included populations

A robust model predicts IDH1 mutations based on transcriptome data

The expression of HOTAIR in GBM is higher and the higher the expression level, the worse the prognosis

Identification of HOTAIR-related biological processes

The relationship between HOTAIR and tumor immune microenvironment.

HOTAIR promotes the proliferation of glioma cells

Discussion

Conclusion

Abbreviations

Declarations

Acknowledgment

Availability of data and materials

Funding

Authors’ contributions

Ethics approval and consent to participate

Consent for publication

Competing interests

References

Tables

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