Cancer is still one of the major causes of death worldwide, with cancer stem cells (CSCs) being a particularly complex component [1, 2]. In the WHO Classification of Tumors of the Central Nervous System 2021, the tumors covered include glioma, meningioma, medulloblastoma and pituitary tumors [3]. However, tumors related to the peripheral nervous system include neurofibromas and schwannomas [4]. The incidence of each tumor in the nervous system varies widely depending on the histology. Incidence rates were highest for neurofibroma (1/3000), meningioma (7.44 per 100,000), glioblastomas (3.19 per 100,000) and medulloblastoma (0.71 per 100,000) [5, 6]. As one of the major tumor types, glioma is highly malignant and has a poor prognosis even after the application of comprehensive treatment. Recent studies have shown that glioblastoma has surpassed pancreatic cancer and liver cancer as the most difficult to treat tumors [7]. Therefore, it is of great interest to investigate CSCs in the nervous system.

Stem cells are able to differentiate into any cell of an organism and have the ability of self-renewal [8]. Among them, CSCs are a small population of stem cells, and are one of the underlying causes of highly metastatic, recurrence-prone and untreatable cancers. Not only can they give rise to all cell types present in the tumor tissue from which they originate through cell differentiation, they can also drive tumorigenesis and cause recurrence [9, 10, 11]. CSC biomarkers can indicate the malignancy of tumors and respond to the development of tumors. Certain molecules are specifically expressed in CSCs or cancer stem cell-like cells [9]. These natural growth molecules can be detected in body fluids or tissues, thus acting as biomarkers to identify CSC and monitor treatment effectiveness, and used to isolate cell populations with CSC characteristics [12]. However, because CSC biomarkers are not universally expressed across tumor types, one marker cannot be used for all tumors [13]. The diagnosis, treatment and prognosis of the same tumor may also involve different biomarkers [12]. Therefore, classification studies on CSC biomarkers of different tumors are of great interest for monitoring and treating tumors.

A large number of biomarkers have been identified in the nervous system. However, there are relatively fewer CSC biomarkers. Moreover, most studies have concentrated on a single CSC biomarker rather than multiple biomarkers that may be more reliable for CSC identification. Furthermore, given the intrinsic connection between the nervous system and mental illness, there may be a potential function of drugs applied to mental illness on neurological CSCs [14]. To address the above three shortcomings, this research aims to review CSC biomarkers for their diagnostic and prognostic potential in several neurological tumors, including glioma, medulloblastoma, meningioma, and neurofibromas.

An authorship-based bibliometric map showed that neurological CSC biomarker research tends to be in “small teams” (Fig. 1A). Meanwhile, each team formed a self-completeness academic community, and the mutual communication between each community was much less than that within the community. It can often be self-sufficient and has less interaction with the outside world. However, neurological CSC marker research involves a complicated process, which is difficult for an isolated group to generate systematic data. Team collaboration to achieve an aggregation effect on CSC biomarker research will be highly appreciated.

Fig. 1.

Content of bibliometric analysis. (A) Authorship-based bibliometric map of the studies. (B) Co-occurrences of the keywords based on the full counting method. The strength of the links was normalized by the association strength method and the weight of keywords refers to the total link strength. Only the links with a minimum strength of three are displayed.

The word frequency analysis on neurological CSC (Fig. 1B) suggests that most of the literature focused on hotspot information about stem cells, such as expression, glioma, biomarker, and prognosis. Expression clustered with the words gene, proliferation, tumor, and overexpression, predicts that overexpression of biomarkers such as genes can inhibit tumor cell metastasis and proliferation. It also induces apoptosis and reduces the stemness of CSCs. The high association of CSCs with words such as recognition, invasion, and epithelial-mesenchymal transition (EMT) suggests that CSC formation is related to EMT and that identification and detection of CSCs by flow cytometry methods are also important. EMT is a key process in CSC formation. In this process, epithelial cells acquire the characteristics of mesenchymal cells, with enhanced cell invasion and migration ability, and allow the cells to acquire properties such as self-renewal. The co-occurrence of the terms glioma and glioblastoma, CD133, and growth indicates that most of the research on glioma, one of the major tumor species, has focused on glioblastoma. It also predicts a correlation between the formation of the biomarker CD133 and the growth of the tumor. A careful reading of the literature also confirms that glycosylation of CD133 maintains the self-renewal of CSCs and tumor growth and promotes the ability of glioma stem cells (GSCs) to resist chemotherapy. In addition, the words prognosis, meningioma, DNA methylation, and survival time were clustered into one category, indicating that the study of prognostic markers mainly focused on meningioma and that the expression level of prognostic markers correlated with postoperative survival time. Further studies showed that the differences in DNA methylation among meningioma patients led to different clinical prognostic characteristics. The expression levels of prognostic markers were significantly correlated with postoperative survival time. If the expression of markers was positively correlated with prognosis, increased markers indicated a good prognosis and a longer postoperative survival time for the patients. In conclusion, the primary subjects for most of the literature are humans versus animals. By constructing xenograft models derived from cell lines with biomarkers, the investigators found that they were more malignant and aggressive, exhibiting a strong stemness phenotype.

Non-CSC diagnostic biomarkers in glioma include APRP protein. Ali et al. [43] reported that glioma patients with higher tumor grades had increased levels of positive APRP fibrinogen and reduced levels of negative APRP biomarker albumin. In glioma, non-CSC therapeutic biomarkers such as PD-L1, as well as methyladenine DNA modifications, can serve as potential targets for therapy [44, 45]. Xie et al. [45] found that N6-methyladenine DNA modification was enriched in human glioblastoma. Reducing this modification, which can inhibit tumor growth, is an effective therapeutic strategy. The expression levels of VASH1, TP73 mRNA, HOXA5 and FMR1NB protein are prognostic biomarkers for glioma patients [46, 47, 48, 49]. Aili et al. [46] showed that high VASH1 expression is associated with poor prognosis and that VASH1 affects tumor cell invasion and migration by influencing microtubule formation and immune infiltration in the tumor microenvironment. Ding et al. [49] found that HOXA5 was a prognostic biomarker, and its overexpression could promote glioma progression by affecting glioma cell proliferation.

The non-CSC diagnostic biomarkers for MB are CADH1, FGFR4 and FIBB and other urinary proteins. Hao et al. [50] found that urinary proteins such as CADH1, FGFR4 and FIBB can be used to differentiate between MB patients and healthy individuals. The biomarkers of non-CSC prognosis in MB include miR-137, PBK and UBE2G1 [51, 52, 53]. Ji et al. [53] demonstrated that upregulated miR-137 inhibited cell proliferation, migration, invasion and cell cycle progression, and induced apoptosis by targeting KDM1A. Thus, it was concluded that miR-137 downregulation was significantly associated with poor prognosis of MB.

The non-CSC diagnostic biomarkers for intracranial MG are miR-497 and VIMENTIN [54, 55]. Abdelrahman et al. [54] found that serum miR-497 is a valid and easily measured biomarker for the diagnosis and classification of MG in clinical studies. Kim et al. [55] showed that all primary cell lines derived from both benign and atypical MG express high levels of the MG biomarker VIMENTIN, which is strongly associated with malignancy. The DVL1 protein is a biomarkers of non-CSC treatment in intracranial meningioma. Bukovac et al. [56] showed that higher nuclear expression of DVL1 protein and higher expression of active $\beta$-linked protein significantly correlated with higher MG grade. Therefore, DVL1 protein can be used as a biomarker for MG development and Wnt signaling pathway activation. Moreover, biomarkers associated with the prognosis of non-CSC in intracranial MG include total DNA methylation and GSKIP protein [57, 58]. Barciszewska et al. [57] concluded that since 5-methylcytosine levels in peripheral blood can reflect graded levels in tumor tissue, changes in 5-methylcytosine can be used as a biomarker of DNA damage and tumor malignancy in intracranial MG. Cheng et al. [58] found that high GSKIP protein expression was associated with poor prognosis of MG. Moreover, the GSKIP protein is bound to protein kinase A and glycogen synthase kinase 3$\beta$ to regulate different biological processes and malignant tumorigenesis through the Wnt pathway.

For diagnostic biomarkers of neurofibromas, a study identified the downregulation of miR-204 as a novel diagnostic biomarker and potential therapeutic target for patients with NF1 with MPNSTs.16 [59]. Biomarkers for non-CSC treatment in neurofibromas include PD-L1 [60]. Farschtschi et al. [60] found that PD-L1 is upregulated in MPNST patients in a study and could be a potential biomarker for malignant transformation in NF1 patients.

Overall, there is some overlap between CSC and non-CSC type biomarkers in the nervous system, but these biomarkers may play different roles in different pathways. Ki67, for instance, serves as a biomarker for non-stem cell therapy in intracranial MG and as a diagnostic biomarker for stem cells in MB [38, 61]. De Carvalho et al. [61] found that Ki67 observed in recurrent tumors of MG was higher than in primary tumors and correlated with poor prognosis. Moreover, elevated Ki67 indicates larger recurrent tumors and more severe peritumor edema. These findings indicate that Ki67 plays multiple roles in diverse tumor types and holds potential as both a diagnostic indicator and a prognostic factor. However, HOXA5 is a prognostic biomarker for both glioma cells and GSCs, both of which are associated with poor patient survival and regulate the proliferation of GSCs [36, 49].

Vimentin serves as a diagnostic biomarker for both CSCs and non-CSCs in intracranial MG [55, 62]. In non-CSCs, strong expression of Vimentin is significantly associated with malignancy [55]. However, Vimentin is a CSC biomarker that regulates several signaling pathways, such as MAPK, PI3K/Akt, and Wnt/$\beta$-catenin. This protein affects the proliferation, differentiation, and invasiveness of CSCs, contributing to the recurrence of rare and aggressive MG variants (Table 3, Ref. [43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]).

Because neurological tumors such as gliomas are resistant to conventional treatment and prone to recurrence, research to explore their precise molecular mechanisms and reliable therapeutic targets has received widespread attention [63]. Baisiwala demonstrated that overexpression of LNX1 activates the Notch1 signaling pathway and increases the population of GSCs [20]. However, knocking down LNX1 modulates Notch1 activity, leading to downregulation of the Notch1. CSCs are significantly reduced after TMZ treatment, thereby improving patient survival. These results suggest that LNX1-regulated Notch1 signaling contributes to treatment resistance and is a possible therapeutic target. Moreover, Lu et al. [64] discovered that knockdown of EH Domain Containing 1 (EHD1) reduced CSC-like traits in glioma cells. EHD1 binding to CD44 enhances CD44 stability, whereas CD44 overexpression reduces the inhibitory effect of EHD1 knockdown on GSC-like traits. Thus, EHD1 is positively correlated with the expression of CSC biomarkers in glioma tissues and has the potential to be an effective target for glioma progression. Mazor et al. [65] showed that lncRNA TP73-AS1 is associated with the aggressiveness of glioblastoma, and its high expression is associated with poor patient prognosis. Meanwhile, TP73-AS1 downregulation resensitizes GBM cancer stem cells to TMZ treatment and may serve as a promising therapeutic target.

Eckerdt et al. [37, 66] also found that signaling in the PI3K-AKT-mTOR axis was blocked by dual inhibition of PI3K$\alpha$ and mTOR. It also enhanced the inhibition of tumor growth in MB in vivo, contributing to the development of more effective therapeutic strategies. Wang et al. [67] showed that miR-222-3p is also a therapeutic target. It not only inhibits cell viability, but also suppresses MB stem cell-like cell formation via the Notch2/c-myc pathway. Filipponi et al. [68] discovered that OCT4 induces the BMP4 signaling pathway, which promotes both stem cell pluripotency and MB tumorigenesis. Conversely, inhibiting BMP4-ALK2/3 signaling reduces tumor-initiating cell (TIC) ability and CSC biomarker expression, suggesting potential implications for MB treatment.

Additionally, published research suggests that taking antipsychotic medication may reduce the risk of cancer development in patients. Many antipsychotic drugs can also treat tumors in the nervous system and inhibit tumor growth [14]. SOX2-OT, an evolutionarily conserved long noncoding RNA, contains SOX2 in its intronic region, which can regulate SOX2 expression [69]. SOX2-OT have been linked to several health conditions, including psychiatric disorders, cancer and diabetes complications. SOX2 is a proven biomarker for CSCs in many nervous system tumors. Therefore, CSCs of the nervous system are associated with the occurrence of mental illness. Penflulidol is an FDA-approved antipsychotic drug specifically for schizophrenia that has potential anticancer effects against glioblastoma. Ranjan et al. [70] discovered that penfluridol inhibits the activation of Akt by blocking its phosphorylation at Ser473, thereby reducing Akt-mediated GLI1 expression. Furthermore, it directly regulates CSC biomarkers such as OCT4, Nanog and Sox2, decreasing CSC characteristics and inhibiting tumor growth. Dong et al. [71] found that the traditional antipsychotic drug fluspirilene can be utilized as a targeted treatment for GSCs. It works by inhibiting the STAT3-related signaling pathway, thereby reducing the proliferation of GSCs and their stemness, ultimately treating GBM. In addition, Cheng et al. [72] screened the antipsychotic drug thioridazine by public gene expression data. It was shown to have potent anti-GBM and anti-GSCs properties, inhibiting GBM tumorigenesis and inducing autophagy in vivo. And pimozide, which is used clinically for the treatment of schizophrenia, inhibits USP1 in GBM cells, resulting in decreased levels of ID1, Sox2, as well as inhibiting stem cell maintenance [73].

Apart from their medical applications, antipsychotic drugs have demonstrated additional pharmacological properties. They can regulate cell signaling pathways and participate in various intracellular functions, ultimately weakening the characteristics of CSCs and inhibiting cancer cell proliferation [74].

The proportion of CSCs in tumor tissue is very low, typically only 0.01–2% of the total tumor mass. CSCs and normal stem cells also share similar biomarkers and signaling pathways [75]. Therefore, it is important to identify and detect CSCs in the nervous system. Many recent studies have focused on the pathological characteristics and detection methods of neurological CSCs.

CSCs can be identified by specific surface markers. For example, CD133 can be used to identify stem cells in normal and cancerous tissues and has been widely used as a marker to identify CSCs [76]. The assessment of the expression rate of relevant genes and protein markers in CSC reflects their density in the patient’s tumor tissue [77]. However, as with non-CSC, most identification of molecular biomarkers for CSC is based on antibody recognition of specific proteins. Cell surface markers are usually detected using immunohistochemistry, western blot (WB), and flow cytometry (FCM) methods. For immunohistochemistry staining and WB, the avidin-biotin-peroxidase complex method can be used, where the expression level of biomarkers is assessed by the percentage of positively stained cells and the intensity of staining [34, 78]. FCM involves incubating cells with a fluorescent dye The expression of biomarkers can also be determined using a cell sorter by further incubation with labeled secondary antibodies [79].

Second, Hematoxylin and eosin is the most essential pathological staining technique when examing tissues and cells under the microscope. Sometimes, abdominal tumors are embedded in paraffin and then sectioned for Hematoxylin and eosinstaining [37]. Moreover, microstructural characterization was performed with a high-resolution scanning electron microscope [80].

Detection tools for CSCs include fluorescence-activated cell sorter (FACS), magnetic-activated cell sorter (MACS), sphere formation assays, and techniques such as immunohistochemistry, FCM and WB for cell surface markers. For CSC characterization, FACS and MACS provide data for identifying individual CSCs in cell populations [81, 82]. FACS selection is a strategy for isolating CSCs based on specific markers expressed by CSCs using flow cytometry. By labeling cells with fluorescent or magnetic bead-coupled antibodies, the CSC population can be separated from tumor cells by FACS or MACS. In practice, FACS selection is purer, more accurate and superior to MACS [83].

Alternatively, the colony-forming ability of CSCs was used for isolation and identification [75]. Sphere formation assays is an in vitro method that is commonly used to identify CSCs and study their properties [82]. In vitro formation of floating clone (from one cell) colonies, called different “glioma spheres” or errant “neurospheres”, is commonly used as an indicator of potential stem cells. These colonies themselves can generate new colonies and can give rise to a variety of cell types within the tumor [84].

This review has established the credibility and repeatability of the research through strict literature screening standards in the early stage. However, most of the literature is one-sided in describing biomarkers of a single tumor and is not well-informed in terms of diagnosis, treatment and prognosis. In contrast, this study comprehensively summarizes the sets of multiple CSC biomarkers, including neurological tumors such as glioma, MB, MG and neurofibroma, as a more accurate and reliable method to monitor the effects. The commonalities and differences in their functional pathways were also analyzed, providing a broader perspective. The bibliometric analysis we completed also remedied the shortcomings of other articles by obtaining more comprehensive information about CSC biomarkers in the nervous system. In addition, since most tumor recurrence and metastasis are caused by the promotion of CSCs, research addresses CSCs from the root cause to provide guidance for clinical application of CSC biomarkers in oncology.

However, CSCs with self-renewal and pluripotency capabilities are plastic and their plasticity is affected by the microenvironment [85, 86]. Recent studies have revealed that infiltrating macrophages can regulate the PI3K/AKT/GSK3$\beta$ signaling pathway to induce stem cell-like characteristics and promote the growth of neurofibroma [42], so the tumor microenvironment plays an important role in regulating cell stemness. In addition, due to the heterogeneity of CSCs and the different specific signaling pathways they activate, a single CSC biomarker is incompetent to discriminate between CSC and non-CSC. The blockage of a single CSC-associated pathway may not effectively block the occurrence of cancers [85].

To date, the impact of CSC on mental illness in the nervous system has not been confirmed. Previous studies have reported that antipsychotic drugs might be able to treat CSC through pathways such as STAT3 and Akt, inhibiting the proliferation and spread of cancer cells [70, 71]. However, the effects of these drugs on different types of tumors and CSCs may vary. Meanwhile, current research is relatively limited and not only does not directly point out that neurological CSC accelerates the degree of tumor progression but also does not provide a specific assessment of the change in the degree of progression. Therefore, further research on this issue is also necessary to address these concerns.

This review provides a comprehensive summary of CSC biomarkers for neurological tumors such as glioma, meningioma, medulloblastoma and neurofibroma. In glioma, the CSC diagnostic biomarkers include NADH. And biomarkers of CSC treatment are CD133, ALDH1, KLF4, SOX2, NANOG, MYC, ABCG2, Nestin and Msi-1. The expression levels of CD133, IL6, TGF-$\beta$, BRAP, HOXB5 and GAS5 are prognostic biomarkers in glioma patients. Also, the CSC diagnostic biomarkers of MB include CD133, Nestin, SOX2, CD24 and CD15. Biomarkers for the treatment of CSC include CD133, CD15, OCT4, NANOG, Sox2, Klf4, Nestin and Bmi1. Biomarkers for the diagnosis of intracranial MG CSC include KLF4, MYC, CD133, SOX2, OCT4, NANOG, NESTIN and VIMENTIN. And the biomarker of prognosis for intracranial MG CSC is SOX2. In neurofibroma, the CSC diagnostic biomarkers include PROM1 and NKX2-2. The biomarkers of neurofibroma CSC treatment are CD68, OCT3/4, NANOG and SOX2. In addition, many antipsychotic drugs can also treat tumors in the nervous system and inhibit tumor growth. Therefore, this study hopes to provide guidance for future in-depth exploration and monitoring of tumors for clinical applications.

CSC, cancer stem cell; EMT, epithelial-mesenchymal transition; GSC, glioma stem cell; GBM, glioblastoma multiform; MB, medulloblastoma; MG, meningioma; NF1, neurofibromatosis type 1; NF2, neurofibromatosis type 2; ALDH1, dehydrogenase 1; PI3K, phosphatidylinositol 3 kinase; MPNSTs, nerve sheath tumors; FACS, fluorescence-activated cell sorter; MACS, magnetic-activated cell sorter; FCM, flow cytometry; WB, western blot; EHD1, EH Domain Containing 1.

HX, JF and XL conceived and designed the work. XL, JH, YK, XC, QY and LZ collected and summarized the literature. XL, YK, HX and JF interpreted the content of the review, generated tables and figures and wrote the original manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

Not applicable.

We thank our laboratory members for technical help and insightful discussions, as well as the assistance from Biorender software in creating illustrations.

This research was funded in part by the National Natural Science Foundation of China (No. 42307366), the Fundamental Research Funds for the Central Universities (2021RC207) and the Beijing Natural Science Foundation (7214242).

The authors declare no conflict of interest.