Low-grade gliomas (LGG) are grade I-II gliomas, mainly classified as oligodendrogliomas and astrocytomas. The prognosis of LGG is better than that of high-grade gliomas, suggesting that pathogenesis of low-grade gliomas differs from that of high-grade gliomas [1]. LGGs are inert cancers that almost always develop into high-grade aggressive tumors, such as glioblastomas, but the time course of disease-specific progression could vary widely, from as few as several months to as many as 10 years, depending on the molecular characteristics and the tumor location in the brain [2, 3]. Previous research had revealed that the immune infiltrate microenvironment of LGG is closely associated with the prognosis of patients [4]. The current treatment of LGG still favors a combination of surgical-based treatments [5, 6]. Although immunotherapy for gliomas has been developed for a long time, the results have not been satisfactory [7]. Therefore, it is important to study the immune molecular mechanism of LGG and discover new potential immune checkpoints to get therapeutic targets for the treatment of LGG.

Long non-coding RNA (lncRNA) is a type of non-coding RNA with more than 200 nucleotides in length, which plays important roles in epigenetic regulation, post-transcriptional regulation, alternative splicing and other gene regulations in gliomas. LncRNAs have a significant impact on tumor proliferation, migration, apoptosis, immunity, and autophagy [8]. For example, lncRNA maternally expressed gene 3 was found by Zhao et al. [9] to proliferation, apoptosis, and autophagy in gliomas, thus affecting the prognosis of patients. It has also been shown that the expression of lncRNA growth arrest-specific transcript 5 in LGG is associated with prognosis and its potential functions include the regulation of ribosome biogenesis and translation [10]. These studies suggest lncRNAs have a promising future as a marker of tumor prognosis in LGG.

Many studies have shown that bioinformatics-based tumors immune-related prognostic models are good predictors for tumor diagnosis, assessment, and treatment [11, 12, 13]. In our study, we integrated analysis of the lncRNA expression dataset of LGG patients in The Cancer Genome Atlas (TCGA), screened immune-related lncRNAs, constructed a clinical prognostic model of LGG, and explored the relationship between LGG immune-related irlncRNAs and indicators of immune infiltration, prognosis, and chemotherapy sensitivity.

LGG histological typing is divided into astrocytomas and oligodendrogliomas [15], both of which are relatively insensitive to chemotherapeutic agents. Compared with oligodendrogliomas, astrocytomas show up-regulated expression of inflammation-related genes [16], and consequently the overall survival of these patients is correspondingly different. Indeed, specific inflammatory chemokines, like chemokine (C-X-C motif) ligand 12 [17], have been reported to be closely related with reduced time to tumor progression in LGG. All these related studies suggest a potential role of immune infiltration in the malignant tumor transformation of LGG. It has also been shown that multiple lncRNAs are differently expressed between astrocytomas and oligodendrogliomas, and that astrocytomas can be distinguished from oligodendrogliomas by these differentially expressed lncRNAs [18]. By analyzing the data of patients in the TCGA database, authors previously suggested that six lncRNAs were closely associated with the overall survival of patients, namely KIAA0495, GAS5, PART1, MGC21881, MIAT, and PAR5 [19]. Meanwhile, many lncRNAs actually have crucial roles on the immune microenvironment of gliomas [20]. For example, lncRNA H19 can affect the level of immune infiltration in gliomas and it affects the prognosis of patients as a result [21]. All of the above studies suggest that lncRNA has an important value in the diagnosis and prognosis prediction of gliomas. Immunotherapy of LGG has become a hot topic in recent years [22]. Therefore, it is meaningful to study the immune-related factors of LGG and construct an immune-related lncRNA clinical prognostic model to select its treatment and improve the prognosis of patients.

In our research, we developed an irlncRNA-based risk prediction model using transcriptomic data and relevant clinical data of LGG patients from the TCGA database. Firstly, we collected transcriptomic data from TCGA for LGG and identified lncRNA signatures consisting of 10 irlncRNAs by co-expression analysis, univariate and multifactorial COX regression analysis. Next, we plotted 1-year receiver operating characteristic (ROC) curves, and got the area under the curve (AUC). Based on the median values, patients with LGG were classified into high-risk or low-risk groups. Finally, we evaluated the risk assessment model by correlating the risk score with clinical characteristics including survival, pathological grade, tumor-infiltrating immune cells, and drug sensitivity.

Our results showed that patients in the high-risk group screened according to this risk model had a higher degree of immune cell infiltration, most significantly in myeloid dendritic cells (p = 1.6 $\times$ 10${}^{-9}$). Gliomas are known to have many specific barriers to antitumor immunotherapy, including the blood brain barrier (BBB) and lack of classical antigen presenting cells (APC) in the central nervous system, which limit immunotherapy in gliomas. Our model suggests that antigen-presenting cells are elevated in the high-risk group, which may expand the therapeutic ideas for the high-risk group by suggesting that the immunosuppressive microenvironment of the tumor can be reversed to generate an effective antitumor immune response, such as the use of ADV vectors expressing the Fms-like tyrosine kinase 3 (Flt3) ligand to recruit immune cells to the dendritic cell (DC) and other APCs cells (DCs) and other APCs into brain tumors. Combining this approach with methods to enhance the immunogenicity of glioma antigens is a potential strategy to generate an effective anti-tumor immune response [23, 24].

In addition to chemotherapy, radiotherapy, surgery and other targeted therapies, tumor immunotherapy (also known as immuno-oncology) is now considered to be the fifth pillar of oncology treatment which is mainly due to the rapid development of immune checkpoint inhibitors [25, 26, 27]. Our results also showed a significant increase in the expression of many important immune checkpoints in the high-risk group, including familiar markers such as TNFSF14, CD274 (PD-L1), TNFRSF14, CD276 (B7-H4), CD40L (p 0.0001), etc., many of which have been well studied in LGG, such as PD-L1 and the main ligand of PD-1. The immune checkpoint inhibitors targeting PD-1/PD-L1 have been put into clinical use in other cancers (e.g., melanoma and non-small cell lung cancer) and have achieved promising results, which will not be discussed here. Moreover, significant effects have also been seen in gliomas, for example, studies have shown that PD-1 inhibitors combined with radiotherapy, dendritic cell vaccine or temozolomide chemotherapy slowed tumor growth and significantly increased survival in mice [28, 29]. The combination with Toll-like receptor 3 (TLR3) agonists for gliomas was reported to increase dendritic cell activation and T-cell proliferation [30]. Most of the anti-PD-1/PD-L1 drugs targeting glioma are currently in phase I or II clinical studies. Our risk model can screen the patient population with high expression of classical immune checkpoints, which provides a very meaningful basis for population selection and precision treatment of monoclonal antibodies against specific targets in the future.

The strength of our research lies in the advantages of the feasibility and generalizability of this novel model to clinical practice across patients. Age and tumor histological grade could be used as independent prognostic factors with a reduced IC50 for commonly used chemotherapeutic agents and a significantly worse overall prognosis than in the low-risk group, which was consistent and in line with the trend of similar related previous studies, suggesting that our prediction model is highly accurate and practical. The establishment of the immune-related lncRNA prediction model may provide new ideas for the study of molecular mechanisms for the treatment of LGG.

However, there are still some limitations in this study. For example, the construction of our risk assessment model was entirely based on the original dataset of TCGA, which was not validated by basic experiments and clinical data. In addition, this risk model based on lncRNA can only be used as a biomarker to predict LGG survival outcome, tumor microenvironment status and treatment sensitivity, without determining the specific expression and biological role of individual lncRNAs involved in the model. Therefore, the risk model constructed in this study needs more samples for in-depth validation before clinical application, although it passed the cross-validation.

In summary, this study developed a validated irlncRNA-based risk assessment model for LGG. It is not only related to the survival outcome of LGG patients, but also strongly correlated with the tumor microenvironment and chemotherapy resistance. The development of this model provides a strong theoretical basis for predicting individual differences in LGG and guiding clinical treatment protocols.

DW, XW and MZ conceived and designed the study; DW and XW performed the experiments; DW, YX and XW analyzed the data; DW, XW and CS wrote this paper. CS and MZ checked the proof of this paper.

This study did not contain any animal experiments or human tissue. All molecular and clinical data used in the study were collected from the public depository and ready for academic research use.

We thank Bainan Xu and Xiaolei Chen for their supervision.

This research received no external funding.

The authors declare no conflict of interest.