Understanding the Significance of Hypoxia-Inducible Factors (HIFs) in Glioblastoma: A Systematic Review

Simple Summary This study explores hypoxia-inducible factors (HIFs) in glioblastoma development, progression, and treatment. Reviewing 104 relevant studies, it highlights diverse global contributions, with China leading at 23.1%. The most productive year was 2019, contributing 11.5% of the studies. Key factors studied included HIF1α, HIF2α, osteopontin, and cavolin-1, involving pathways such as GLUT1, GLUT3, VEGF, PI3K-Akt-mTOR, and ROS. HIF expression correlates with glioblastoma progression, survival, neovascularization, glucose metabolism, migration, and invasion. Overcoming treatment resistance and the lack of biomarkers is crucial for integrating HIF-related therapies into glioblastoma treatment to improve patient outcomes. Abstract Background: The study aims to investigate the role of hypoxia-inducible factors (HIFs) in the development, progression, and therapeutic potential of glioblastomas. Methodology: The study, following PRISMA guidelines, systematically examined hypoxia and HIFs in glioblastoma using MEDLINE (PubMed), Web of Science, and Scopus. A total of 104 relevant studies underwent data extraction. Results: Among the 104 studies, global contributions were diverse, with China leading at 23.1%. The most productive year was 2019, accounting for 11.5%. Hypoxia-inducible factor 1 alpha (HIF1α) was frequently studied, followed by hypoxia-inducible factor 2 alpha (HIF2α), osteopontin, and cavolin-1. Commonly associated factors and pathways include glucose transporter 1 (GLUT1) and glucose transporter 3 (GLUT3) receptors, vascular endothelial growth factor (VEGF), phosphoinositide 3-kinase (PI3K)-Akt-mechanistic target of rapamycin (mTOR) pathway, and reactive oxygen species (ROS). HIF expression correlates with various glioblastoma hallmarks, including progression, survival, neovascularization, glucose metabolism, migration, and invasion. Conclusion: Overcoming challenges such as treatment resistance and the absence of biomarkers is critical for the effective integration of HIF-related therapies into the treatment of glioblastoma with the aim of optimizing patient outcomes.


Introduction
Glioblastoma is a highly aggressive grade 4 glioma with an annual incidence of approximately six cases per 100,000 persons in older adults and a 15-20% proportion of all brain tumors in pediatric patients.In children, glioblastoma is highly invasive and leads to an 80% recurrence rate within two years of treatment.Survival rates are dismal, with less than 2% of the adults surviving more than three years after diagnosis [1][2][3][4].
Recent advances favor molecular analysis for the prognosis of glioblastoma, especially in younger patients where molecular factors are more important than histological grading.Biomarkers such as isocitrate dehydrogenase (IDH) mutations and O6-methylguanine DNA methyltransferase (MGMT) methylation status support prognosis [5].However, the final diagnosis of glioblastoma depends on the surgical biopsy, which is crucial for the detection of hypoxic tumor niches manifested by vascular proliferation and tissue necrosis.Hypoxia, which is prevalent in solid tumors such as glioblastoma, is due to reduced oxygen levels, which are particularly dangerous in the oxygen-dependent brain [6].Tumor progression exacerbates hypoxia and leads to uncontrolled neovascularization that perpetuates the cycle of inadequate oxygen supply.Hypoxia-induced angiogenesis is typical of the progression that occurs in escalation-grade astrocytomas and is characterized by central necrosis and pseudo-palisades on magnetic resonance imaging (MRI) scans [7].
Hypoxia-inducible factor (HIF) emerges as a key molecule in promoting neovascularization in hypoxic niches, which is critical for tumor progression [8].To date, the involvement of the hypoxic microenvironment in carcinogenesis has been extensively validated across various tumor types [9], particularly in pancreatic cancer, wherein hypoxic conditions have been shown to facilitate metastasis and drug resistance [10].The HIF1α and HIF-1β subunits form an active heterodimer that initiates the transcription of over 40 hypoxia-responsive genes, including erythropoietin (EPO), insulin-like growth factor 2 (IGF2), vascular endothelial growth factor (VEGF), and angiopoietin (Ang)-1 and -2 [11].HIF also upregulates platelet-derived growth factor (PDGF) proteins and activates oncogenic signaling pathways such as MAPK/RAS and PI3K/AKT [9].HIF1α responds acutely to hypoxia, while HIF2α regulates tumor cell response to chronic hypoxia, making it a potential therapeutic target.BEV targeting VEGF-A shows promise in inhibiting HIF1α, especially in patients with chemoresistance [12,13].However, the histologic and molecular heterogeneity of glioblastoma poses a challenge and requires research into novel multimodal therapies targeting hypoxia and HIF signaling pathways, including immunotherapy and nanoscale drug delivery [12,13].Therefore, the aim of this systematic review is to investigate the role of hypoxia and HIFs in the development, progression, and therapeutic potential of glioblastoma.

Study Design and Registration
A systematic review of the literature was conducted to investigate the role of hypoxia and HIFs in glioblastoma.The methodology followed the established PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [14].This systematic review has been registered in the Open Science Framework (OSF) Register with the unique identifier OSF-REGISTRATIONS-8GD9K-V1 [15].

Search Strategy
On 15 January 2024, a search was conducted using the PICOS method to define the main search terms (Table 1).Three databases were searched: MEDLINE (PubMed), Web of Science (Clarivate Analytics, Philadelphia, PA, USA), and Scopus.The keywords "glioblastoma" and "hypoxia-inducible factors" were searched.A detailed search strategy can be found in Appendix A. Following the PRISMA guidelines, a checklist can be found in Appendix B. Strict inclusion and exclusion criteria were applied when conducting this systematic review to ensure the selection of relevant articles that contribute significantly to the understanding of the role of HIFs in glioblastoma.The inclusion criteria focused on articles that were written in English, directly related to the interaction between HIFs and glioblastoma, and contained data relevant to the objectives of the study.Conversely, the exclusion criteria were carefully defined to refine the selection process and exclude articles that may not align with the aims of the study.Excluded articles included book chapters, conference papers, reviews, non-English language literature, and articles that did not contain relevant data.

Included Studies
A total of 1318 entries were identified from PubMed (n = 558), Web of Science (n = 89), and Scopus (n = 671).Prior to the screening, 814 duplicate entries were removed using the EndNote software (21.3) for referencing.After automatic deduplication, all the remaining duplicate manuscripts were manually excluded.After this first step, 504 records were screened, and 36 records were excluded because they could not be found.Subsequently, 468 records were screened for eligibility, resulting in the exclusion of 84 book or book chapters, 57 conference papers, 49 reviews, 29 articles from non-English literature, and 145 articles without relevant data.Finally, 104 studies were deemed suitable and included in the systematic re-examination for analysis (Figure 1).

Data Extraction
Data extraction from the studies was performed for didactic purposes, whereby the studies were divided into laboratory and clinical studies based on the tracking of different variables.The laboratory studies were further divided into genetic studies and drug-related studies as well as combined studies.For genetic studies, variables such as authors,

Data Extraction
Data extraction from the studies was performed for didactic purposes, whereby the studies were divided into laboratory and clinical studies based on the tracking of different variables.The laboratory studies were further divided into genetic studies and drugrelated studies as well as combined studies.For genetic studies, variables such as authors, country (year), study design, species, cell line(s), targeted HIF, related factors, role of HIF and related factors, gene modification, and the effect of gene modification were tracked.For drug studies, variables such as reference, country (year), study design, species, cell line(s), targeted HIF, related factors, role of HIF and related factors, target/system therapy, and pharmacologic effects were monitored.Combined laboratory studies tracked similar variables along with targeted therapy and pharmacologic effects.Clinical studies were reviewed for variables such as authors, country (year), study design, sample size (N), age, gender distribution, targeted HIF(s), and outcomes.The studies were first extracted into a single file using the EndNote software and then deduplicated.Data extraction was performed by eight researchers under the supervision of three senior researchers.Ambiguities in data extraction were resolved through online meetings and a final consensus.

Statistical Analysis
Descriptive statistics were used to present frequencies and absolute numbers, providing a quantitative summary of various key factors associated with the use of HIFs in glioblastoma.To improve the clarity and interpretation of results, graphical visualization was performed using Microsoft Excel (version 2021, Microsoft Corporation, Washington, DC, USA).BioRender (https://www.biorender.com/,accessed on 10 April 2024) license number RY26P9F0AG was used to design the scientific illustrations in the manuscript.

Included Studies' Characteristics
Among the 104 studies, contributions came from various countries.Studies from Canada, Egypt, Morocco, South Korea, Israel, the Netherlands, and Turkey together made up 1% of the total.Brazil, France, and Korea each contributed three studies (2.9%).The UK contributed four studies (3.8%), while Germany, India, and Japan each contributed five studies (4.8%).Italy contributed six studies (5.8%), and Taiwan contributed eight studies (7.7%).Significant contributions came from China with 24 studies (23.1%) and the United States with 28 studies (26.9%) (Figure 2).The years 2000, 2004, 2007, and 2009 each contributed 1%.In contrast, 2019 had 12 studies (11.5%), followed by 2018 with 11 studies (10.6%).From 2012 to 2022, annual contributions ranged from 8.7% to 9.6%.In 2021, the contribution was 7.7%, as shown in Figure 3.Most of the studies (90.4%) were laboratory-based, with 58.7% combining in vitro and in vivo methods.Pure in vitro studies made up 31.7%.Clinical research was less common, comprising 9.6% of the total, with prospective studies at 6.7% and retrospective studies at 2.9%, as shown in Figure 4. Most of the studies (90.4%) were laboratory-based, with 58.7% combining in vitro and in vivo methods.Pure in vitro studies made up 31.7%.Clinical research was less common, comprising 9.6% of the total, with prospective studies at 6.7% and retrospective studies at 2.9%, as shown in Figure 4.

Role of HIF-Related Gene Modification in the Treatment of Glioblastoma
Table 2 shows 48 of the 94 laboratory studies (51%) that investigated the role of HIFs using different genetic methods in animal species and glioblastoma models.All the studies investigated HIF1α, while four studies also investigated the role of HIF2α in addition to HIF1α [14][15][16][17].Among the included studies, deletion, overexpression, and transduction each accounted for 2.08% of the total (N = 1).Combined techniques and knockout methods each accounted for 6.25% (N = 3), while transfection was used in 22.92% of the studies (N = 11).Knockdown techniques in particular accounted for the majority of the research studies (58.33%,N = 28).

Role of HIF-Related Gene Modification in the Treatment of Glioblastoma
Table 2 shows 48 of the 94 laboratory studies (51%) that investigated the role of HIFs using different genetic methods in animal species and glioblastoma models.All the studies investigated HIF1α, while four studies also investigated the role of HIF2α in addition to HIF1α [14][15][16][17].Among the included studies, deletion, overexpression, and transduction each accounted for 2.08% of the total (N = 1).Combined techniques and knockout methods each accounted for 6.25% (N = 3), while transfection was used in 22.92% of the studies (N = 11).Knockdown techniques in particular accounted for the majority of the research studies (58.33%,N = 28).

KD
The KD of HIF1α in human and murine glioma cells impairs their migration in vitro and their invasion in vivo.

HIF's Mechanisms Explored in Genetic Studies
The included studies have shed light on the complicated mechanisms in which HIF1α is involved.For example, Hashimoto et al. [16] have shown that AMPK boosts ATM expression via the transcription factor Sp1 under severe hypoxia, contributing to radioresistance.Conversely, Ho et al. [17] elucidated the role of MIR210HG in hypoxia-mediated glioma invasion and stemness formation, which is regulated by OCT1 and affects the expression of IGFBP2 and FGFR1.In addition, Ishikawa et al. [18] showed that HIF1α activates Ror1 transcription in glioblastoma and affects cancer progression by regulating cell proliferation and migration.Other related factors studied include miR-210-3p by Agrawal et al. [19]; CXCR7, CXCR4, and IDH1 by Bianco et al. [20]; C-Met and SF/HGF by Eckerich et al. [21]; S100A4/NMIIA axis by Inukai et al. [22]; NO and VEGF by Kimura et al. [23]; BAG3 by Li et al. [24]; and tryptophan 2,3-dioxygenase (TDO2) by Mohapatra et al. [27].
Studies have shown that HIF1α and HIF2α orchestrate several cellular processes that are crucial for the pathogenesis of glioblastoma.For example, HIF1α is involved in promoting radioresistance by modulating AMPK-mediated ATM expression [16], promoting glioma invasion and stem cell formation via regulating MIR210HG and OCT1 [16], and activating Ror1 transcription to influence cancer progression [18].In addition, HIF1α is involved in the regulation of miR-210-3p, CXCR7, CXCR4, IDH1, C-Met, SF/HGF, the S100A4/NMIIA axis, NO, VEGF, BAG3, and TDO2, and influences various aspects of the glioblastoma biology such as angiogenesis, invasion, metabolism, and therapy resistance.Similarly, HIF2α contributes to glioblastoma progression by regulating genes such as GPx1, vasorin, beclin-1, and galectin-3, thereby influencing the response to oxidative stress, angiogenesis, autophagy, and cell survival.

Role of HIF-Related Targeted and Systematic Therapy of Glioblastoma
Table 3 shows a total of 26 laboratory studies addressing targeted and systemic therapies for glioblastoma in animal species with inoculated tumors or glioblastoma models.HIF1α was investigated in 25 of the 26 studies, while HIF2α was investigated in two studies.

PDT and acriflavine
Acriflavine combined with PDT attenuated the expression of HIF1α, GLUT-1, GLUT-3, and HK2 and improved tumor suppression.

Digoxin and acriflavine
The prevention of HIF1α protein synthesis and dimerization.suggesting its potential as a multi-pathway anti-glioblastoma agent.
A plethora of studies clarify the different roles of HIF1α in the progression of glioblastoma and response to treatment.Nardinocchi et al. [64] demonstrated the zinc-induced degradation of HIF1α, which inhibits VEGF-mediated signaling pathways and improves cancer therapies.Maugeri et al. [65] emphasized the role of HIF1α in angiogenesis via the upregulation of VEGF, while Ma et al. [66] linked the overexpression of HIF1α to glucose metabolism, suggesting its involvement in metabolic adaptations.D'Amico et al. [67] showed that ADNP modulates the HIF signaling pathway and reduces VEGF secretion and migration.In addition, D'Alessio et al. [68] pointed to antiangiogenic therapy targeting HIF1α and related factors to inhibit neoangiogenic events in glioblastoma.
Several related factors influence HIF1α-mediated signaling pathways in glioblastoma.These include VEGF, which is influenced by zinc, as shown by Nardinocchi et al. [64], and PA-CAP, as shown by Maugeri et al. [65], suggesting its role in regulating angiogenesis.Ma et al. [64] highlighted the association of HIF1α with glucose metabolism through the upregulation of GLUT-1, GLUT-3, and HK2.D'Amico et al. [67] revealed the modulation of the HIF signaling pathway by ADNP, reducing VEGF secretion and migration.Other factors such as M2 receptors, CXCR4, POL5551, LonP1, CT-L, PPARα, and SUMO are involved in regulating various aspects of glioblastoma progression and response to therapy, as noted by Cristofaro et al. [69], Gagner et al. [89], Douglas et al. [72], Hofstetter et al. [76], and Bernstock et al. [83].In addition, Lin et al. [71] highlighted the far-reaching influence of HIF1α on tumor cell behavior, while Lin et al. [88] investigated the regulation of pHregulatory proteins in glioblastoma by hypoxia-induced HIF1α.In the field of glioblastoma therapy, various targeted and systematic approaches have emerged to target the complex signaling pathways mediated by HIF1α.As noted by Nardinocchi et al. [64], zinc induces the proteasomal degradation of HIF1α and could thus prevent tumor progression by suppressing VEGF, MDR1, and Bcl2 signaling pathways.PACAP, identified by Maugeri et al. [65], is promising as it inhibits the release of VEGF and thus prevents the formation of new vessels in the hypoxic microenvironment of glioblastoma.Ma et al. [64] showed that acriflavine in combination with PDT effectively suppresses HIF1α expression and increases the efficacy of PDT against glioblastoma.D'Amico et al. [67] showed that ADNP can modulate the HIF signaling pathway to decrease VEGF secretion and migration, which is a targeted therapy approach.Gagner et al. [89] demonstrated the potential of the combination of B20-4.1.1 and POL5551 in reducing glioma invasion and tumor spread.As noted by Arienti et al. [73], HBO shows promise in inhibiting proliferation, downregulating HIF1α expression, and reprogramming glucose metabolism, offering the potential for the systemic therapy of glioblastoma.

Role of Combined Gene and Targeted or Systematic Therapy of Glioblastoma
A total of 23 studies used a combined gene-modifying design and targeted or systematic therapy in the context of HIF in laboratory glioblastoma models (Table 4).All studies investigated HIF1α, while five studies also investigated the role of HIF2α in addition to HIF1α.Gene modification techniques included transduction (N = 1; 4.3%), combined techniques (N = 4; 17.4%), transfection (N = 8; 34.8%), and knockdown (N = 10; 43.5%).HIF1α and HIF2α DRD2 The activation of DRD2 triggers the expression of HIF proteins and enhances the capacity for sphere formation, which serves as an indicator of the GIC state and tumorigenicity.

KD
The SH-RNA-mediated knockdown of DRD2 showed a significant reduction in sphere-forming capacity.

Chlorpromazine
The inhibition of glioblastoma growth by blocking the dopamine signaling pathway.In hypoxic glioblastoma cells, the β-catenin/TCF1 complex recruits HIF1α to promote the transcription of genes associated with neuronal differentiation.Several studies have elucidated the multiple roles of HIFs in the pathogenesis and therapy of glioblastoma.Huang et al. [90] showed that the PI3K/Akt/mTOR/HIF1α signaling pathway enhances glioblastoma cell migration and invasion under hypoxia, with mTOR pathway siRNA suppressing these effects.Chhipa et al. [91] showed that the activation of the AMPK/CREB1 axis supports glioblastoma cell bioenergetics by increasing HIF1α transcription.Pang et al. [92] highlighted the role of HIF1α-regulated lysosomal protease LGMN in TAMs and showed that its blockade prolongs survival in glioblastoma models.Hu et al. [113] identified HIF1α and AMPK as the regulators of hypoxia-induced LC3 changes, BNIP3 expression, and p62 degradation, which affect autophagy and responsiveness to bevacizumab.Barliya et al. [95] linked Hsp90 to angiogenesis, migration, and invasion, and highlighted its mediation of the HIF1α-driven signaling pathways.Hsieh et al. [103] demonstrated Nox4-mediated ROS production under cyclic hypoxia, which affects HIF1α activity and tumor growth.Kannappan et al. [97] showed that NF-kB/HIF1α/HIF2α promotes EMT and metastasis.Joseph et al. [98] elucidated the HIF1α-ZEB1 axis in mesenchymal transition and invasion.These findings emphasize the complex involvement of HIFs in glioblastoma progression and point to potential therapeutic targets.

Role of HIFs in Clinal Studies of Glioblastoma
Nine studies have investigated the expression and clinical significance of HIFs in glioblastoma (Table 5).Chen et al. [114] found that HIF1α expression correlated with high caveolin-1 (CAV1) expression, larger glioblastoma size, and shorter survival time.Bache et al. [115] observed higher expression of HIF2α, carbonic anhydrase 9 (CA9), vascular endothelial growth factor (VEGF), and other markers in glioblastoma compared to tumor-free brain tissue, with mRNA levels correlating with shorter survival.Erpolat et al. [116] reported that high levels of cytoplasmic and nuclear HIF1α and CA9 were associated with shorter survival, especially in patients with high hypoxia scores.Clara et al. [115] found that HIF1α expression correlated with increased vascular density, VEGF, and platelet-derived growth factor-C (PDGF-C) and survival.Other studies, such as those by Kaynar et al. [117] and Nobuyuki et al. [118], also emphasized the role of HIF1α in angiogenesis and radioresistance in glioblastoma.In addition, Ji et al. [119] showed that high HIF1α expression correlates with poorer outcomes and shorter survival, suggesting its potential as a prognostic marker.Sfifou et al. [120] found that negative HIF1α expression in conjunction with the positive expression of isocitrate dehydrogenase 1 (IDH1) was associated with a better prognosis.Potharaju et al. [121] observed the strong nuclear staining of HIF1α in a significant proportion of samples, which independently correlated with poor prognosis, especially in combination with the high expression of telomerase reverse transcriptase (TERT).The strong nuclear staining of HIF1α was observed in 48% of the samples, correlating with poor prognosis independently.Patients with strong HIF1α and TERT expression had the worst prognosis, indicating HIF1α as a potential prognostic marker in glioblastoma.

Common HIF-Related Pathways in Glioblastoma
Glioblastoma involves a complex interplay of molecular signaling pathways, among which the PI3K/Akt/mTOR pathway stands out.This signaling pathway exerts a profound influence on the progression of glioblastoma and modulates important cellular processes such as migration, invasion, and the expression of HIF1α.The importance of this pathway is further emphasized by the fact that it can be modulated by PTEN-PI3K interactions, offering potential therapeutic opportunities (Figure 5).The intricate relationship between HIF1α and metabolic pathways adds another layer of complexity.HIF1α not only affects glucose metabolism by upregulating the glucose transporters GLUT-1 and GLUT-3 but also enhances glycolysis through the overexpression of hexokinase 2 (HK2).This metabolic switch contributes to the robustness of glioblastoma cells and allows them to thrive in the hypoxic tumor microenvironment.In addition, the therapeutic landscape in glioblastoma is evolving with the emergence of new strategies targeting HIF1α-related axes.The synergistic blockade of the HIF1α-LGMN axis, aided by AMPK inhibition and anti-PD1 antibody therapy [92], represents a promising approach to interrupting glioblastoma progression.Furthermore, interventions targeting VEGF [64,67,68,84,86], such as digoxin, offer potential opportunities to inhibit angiogenesis and overcome multidrug resistance (MDR) mediated by pathways involving Bcl2 [64].Understanding and interfering with these pathways are key to developing more effective treatments for glioblastoma, a disease with poor prognosis and limited therapeutic options.

Common HIF-Related Pathways in Glioblastoma
Glioblastoma involves a complex interplay of molecular signaling pathways, among which the PI3K/Akt/mTOR pathway stands out.This signaling pathway exerts a profound influence on the progression of glioblastoma and modulates important cellular processes such as migration, invasion, and the expression of HIF1α.The importance of this pathway is further emphasized by the fact that it can be modulated by PTEN-PI3K interactions, offering potential therapeutic opportunities (Figure 5).The intricate relationship between HIF1α and metabolic pathways adds another layer of complexity.HIF1α not only affects glucose metabolism by upregulating the glucose transporters GLUT-1 and GLUT-3 but also enhances glycolysis through the overexpression of hexokinase 2 (HK2).This metabolic switch contributes to the robustness of glioblastoma cells and allows them to thrive in the hypoxic tumor microenvironment.In addition, the therapeutic landscape in glioblastoma is evolving with the emergence of new strategies targeting HIF1α-related axes.The synergistic blockade of the HIF1α-LGMN axis, aided by AMPK inhibition and anti-PD1 antibody therapy [92], represents a promising approach to interrupting glioblastoma progression.Furthermore, interventions targeting VEGF [64,67,68,84,86], such as digoxin, offer potential opportunities to inhibit angiogenesis and overcome multidrug resistance (MDR) mediated by pathways involving Bcl2 [64].Understanding and interfering with these pathways are key to developing more effective treatments for glioblastoma, a disease with poor prognosis and limited therapeutic options.Reactive oxygen species (ROS) and Calcium ion (Ca 2+ ) signaling activate survival pathways, involving LKB1 (Liver Kinase B1), AMPK (AMP-activated Protein Kinase), and CaMK2 (Calcium/Calmodulin-Dependent Protein Kinase II).Glucose transporters Glut1/3 facilitate glucose uptake.This network highlights potential therapeutic targets, such as mTOR, PI3K, and HIF-1α, to disrupt glioblastoma cell survival and adaptation mechanisms.The figure was created using the BioRender online commercial platform.

Research Trends
The high morbidity and mortality rate of glioblastoma has led to conventional treatments such as surgery, radiotherapy, and chemotherapy being re-evaluated due to their limited effectiveness.Researchers around the world, particularly in the United States and China, are exploring new treatments and incorporating molecular genetic features into diagnostics to better understand the pathogenesis of glioblastoma [2,124].Despite an increase in in vitro and in vivo studies focusing on hypoxia-regulated genes, clinical trials remain limited, accounting for only 9.6% of the total.Advances in diagnostic methods, particularly next-generation sequencing, have led to significant growth in research [125,126].However, the translation of promising laboratory results into clinical practice is challenging due to small sample sizes and geographic variation, making it difficult to develop standardized global diagnostic and treatment algorithms [66,116,121,123].

The Impact of HIF-Related Gene Modification on Glioblastoma Therapeutics
The importance of the knockdowns and knockouts of hypoxia-inducible factors lies in their ability to reveal the precise roles and functions of these factors in cellular processes and disease progression [127].By elucidating the effects of manipulating hypoxia-inducible factors on glioblastoma progression, these techniques provide insights into potential therapeutic targets.Key findings include the functional importance of the interaction of N-cadherin and β-catenin on the radioresistance of glioblastoma stem cells.Elevated glucose-6-phosphatase (G6PC) levels contribute to resistance to glycolytic inhibition in glioblastoma cells [128].AMPKα1 knockout affects glycolysis and tumorigenesis in a lymphoma mouse model.The overexpression of HHIF2α in AMPK knockdown GSCs possibly compensates for the loss of HIF1α.AMPKα knockdown decreases the expression of Sp1 and ATM under severe hypoxia and reduces radioresistance [91].Moreover, the overexpression of MIR210HG enhances IGFBP2 and FGFR1 promoter activities under normoxia, which is inhibited by the suppression of OCT1, and decreases under hypoxia with MIR210HG or OCT1 knockdown.These findings emphasize the multifaceted role of hypoxia-inducible factors in glioblastoma, which includes radioresistance, migration, the regulation of gene expression, and metabolic processes [17].
Laboratory-based studies, such as those listed in Table 3, involve experimental manipulations and investigations performed on cells or animal models.This controlled environment allows researchers to isolate specific mechanisms, control variables, and collect preliminary data on the effects of hypoxia-inducible factors on glioblastoma.However, human clinical trials are challenging due to ethical considerations, difficulties in obtaining tumor samples, the heterogeneity of the patient population, and the complexity of studying hypoxia-inducible factors in the clinical setting [129].Although laboratory-based studies provide valuable insights, they cannot fully reflect the complexity of human glioblastoma.Therefore, further research with human clinical trials is essential to validate the laboratory results and determine the clinical significance of hypoxia-inducible factors in glioblastoma.

Exploring HIF-Related Targeted and Systemic Therapies for Glioblastoma in Experimental Settings
Given the central role of HIF-1 in the pathophysiology of glioblastoma, the identification of a specific HIF-1 inhibitor holds promise for overcoming resistance to cytotoxic therapy and improving overall survival.Zinc is a potential candidate, as shown by Nardinocchi et al. [64], who observed its ability to induce the proteasomal degradation of HIF1α.While zinc showed similar effects in prostate cancer under hypoxic conditions, its efficacy was not present in the human RCC4 VHL-null cell line.Meanwhile, Maugeri et al. [65] found that PACAP inhibited the release of VEGF.D'Amico et al. [67] showed that this inhibition occurs through the activation of ADNP, a protein that is central to normal brain development and plays a dual role as an oncogene or tumor suppressor, depending on the tumor type.Although the involvement of PACAP in neurodegenerative diseases is well established, further investigation of the PACAP-ADNP axis in glioblastoma is warranted.
Another strategy for inhibiting VEGF is the use of BEV, an anti-VEGF monoclonal antibody that is frequently used in the treatment of glioblastomas.Preclinical and clinical studies have consistently shown that BEV is able to prolong progression-free and overall survival.However, a major challenge is to identify the patients who would benefit from this therapy, as many of them quickly develop resistance.This challenge is exacerbated by the lack of reliable biomarkers, as D'Alessio et al. [68] point out.
Despite BEV treatment, a significant proportion of glioblastoma cases (40-60%) continue to progress, as shown in the clinical studies by Hu et al. [113].Ongoing randomized clinical trials are investigating the potential of combining chloroquine with the standard treatment of glioblastoma, but a significant benefit has not yet been demonstrated.
In a 2017 study, Gagner et al. [70] used glioma models with mice and administered the anti-VEGF antibody B20-4.1.1 and showed reduced tumor invasiveness in combination with POL5551, a CXCR4 antagonist previously shown to improve survival in immunodeficient mice when combined with other therapeutic modalities.Clinical trials with various CXCR4 antagonists are ongoing.For example, the study (NCT01339039) combines BEV with AMD3100 in patients with recurrent high-grade glioma, while another study (NCT01837095) is investigating POL6326 in combination with the chemotherapeutic agent eribulin in patients with metastatic breast cancer.Kioi et al. [111] investigated the SDF-1/CXCR4 inhibitor AMD3100 and reported its superior efficacy over VEGF blockade in reducing tumor tissue perfusion after radiotherapy.
Photodynamic therapy (PDT) has impressive complete remission rates of up to 90% for skin, head, and neck tumors as well as for early-stage lung and bladder cancer.However, the efficacy of PDT in the treatment of glioblastoma has been limited in the past.However, recent advances, such as the use of acriflavine (ACF) to inhibit HIF1α, as shown by Ma et al. [66], are promising.ACF, which is known for its safety profile, has extended median survival in patients with glioblastoma to 21 months after diagnosis.Since PDT usually upregulates HIF1α expression in most tumors, the integration of HIF inhibitors is crucial.Li et al. [108] have shown that PDT enhances the effect of TMZ by suppressing glycolytic metabolism.The role of immune cells and glycolysis-related enzymes should be further explored.
Hyperbaric oxygen therapy (HBO), which is used in the treatment of ischemic diseases, is also used in carcinoma therapy alongside radiotherapy [130].Arienti et al. [73] demonstrated that HBO can inhibit the proliferation of glioma cells by increasing reactive oxygen species, which leads to DNA damage.However, preclinical studies often provide contradictory results.For example, Chen et al. [131] report the antitumor effects of HBOT, while there is evidence of tumor-promoting effects [132].Although clinical studies support the use of HBOT as an adjunct to radiotherapy, a scientific rationale for this phenomenon remains elusive.
Cardiac glycosides that are effective in the treatment of malignancies have been identified as HIF1α inhibitors.The studies by Bar et al. [106], Joseph et al. [98], and Papale et al. [106] highlight the efficacy of digoxin, while Lee et al. [40] focused on digitoxin due to its liposolubility, suggesting the possible permeability of the blood-brain barrier.
Fenofibrate, known for the treatment of hyperlipidemia, has an anticancer effect that has been demonstrated in melanoma, medulloblastoma, and GBM.Trejo-Solis et al. [133] demonstrated its inhibition of glycolysis in GBM, while Lin et al. [71] elucidated the HIF1α inhibition of fenofibrate via multiple metabolic pathways.2-Methoxyestradiol (2ME2) inhibits HIF1α, inhibits tumor growth, and is being tested in phase I and II in various cancers, including GBM, with promising efficacy and low toxicity.However, the development of resistance to 2ME2 remains enigmatic.Muh et al. [85] suggest PTEN analysis to predict patient response.Combination therapy with a PI3K inhibitor, such as LY294002, is suggested for improved efficacy.
In their effort to target glioma cell proliferation and improve the efficacy of TMZ, Douglas et al. [72] directed their research towards identifying a compound with the dual inhibition of LonP1 and CT-L.BT317 emerged as a promising candidate due to its ability to penetrate the blood-brain barrier, its low toxicity in animals, and its improved survival rates.However, in vivo tests with ritonavir led to the rapid development of resistance.In contrast, marizomib showed significant CNS toxicity in phase II studies and no improvement in survival was demonstrated in phase III trials.Hofstetter et al. [76] found that the inhibition of PP2A with LB1.2 enhanced the effect of TMZ on GBM and neuroblastoma in mouse studies, with no side effects observed during short-term monitoring.
Borneol, a terpene from traditional Chinese medicine, sensitizes cells to TMZ by promoting HIF1α degradation, as demonstrated by Lin et al. [88].Previous studies have also shown that borneol enhances the efficacy of doxorubicin [134], curcumin [135], cisplatin [136], and radiotherapy [137].Liu et al. [79] demonstrated in preclinical studies the usefulness of mannose as an adjunct to TMZ and to enhance radiotherapy, and achieved long-term survival in mice.
By combining methoxyamine and resveratrol with iododeoxyuridine, Khoei et al. [78] increased the sensitivity of GBM to radiotherapy.Ahmed et al. [104] noted that the sensitivity of GBM to cisplatin under hypoxic conditions may be independent of HIF and may be induced by the activation of CD133.Barliya et al. [95] investigated the effects of hypericin on the degradation of hsp90 and HIF1α in GBM and renal cell carcinoma cells, with modest results from phase I and phase II trials.
Hsieh et al. [103] reported the inhibition of HIF-1 activation and tumor growth by tempol, while Chou et al. [94] investigated the ability of YC-1 to enhance the efficacy of chemotherapy BCNU.Although not specific to HIF1, Chen et al. [114] demonstrated the synergistic effect of YC-1 with Bay 11-7082 by inhibiting Bcl-xL induction under hypoxiainduced TMZ resistance.
TAT-Lp15, a livin peptide inhibitor, sensitized GBM cells to radiotherapy and TMZ without affecting healthy tissues, as shown by Hsieh et al. [103].In particular, the ability of TAT-Lp15 to cross the blood-brain barrier underscores its therapeutic potential and warrants further clinical validation.
Disulfiram, known for its ability to improve the efficacy of standard chemotherapies in various carcinomas while exhibiting low toxicity to healthy cells, is hampered by its short half-life in the bloodstream.To address this problem, Kannappan et al. [97] investigated DS-PLGA, an intravenously administered formulation that prolongs the residence time of disulfiram in the bloodstream and facilitates its penetration into GBM tissues without adverse effects on vital organs.Sulfinosine (SF), known for its multiple anticancer effects via different metabolic pathways, has the potential to prevent cancer cells from developing resistance [138].Dačević et al. [80] investigated the effect of SF in small-cell lung cancer and GBM and emphasized its ability to penetrate the CNS and its compatibility with other chemotherapeutic agents.Topotecan, which is approved for cervical, ovarian, and small-cell lung cancers, acts as both a DNA topoisomerase I inhibitor and a HIF1α inhibitor [139].However, its efficacy in GBM remains limited, as noted by Bernstock et al. [83].Nelfinavir and amprenavir, which have been shown to be effective in HIV therapy, inhibit both HIF1α and VEGF and could sensitize tumor cells to radiotherapy with minimal toxicity, as shown by Mait et al. [86].Dominguez et al. [102] have identified DGKα as a promising therapeutic target for GBM and other carcinomas, with selective toxicity observed in malignant GBM cells when treated with the DGKα inhibitors R59022 and R59949.SGC707, a PRMT3 inhibitor, showed anticancer activity in GBM by inhibiting HIF1α and glycolysis while sparing normal brain cells, as found by Liao et al. [110].
Arecaidine propargyl ester (Ape) activates M2 muscarinic receptors, leading to cell cycle arrest in GBM stem cells, as reported by Cristofaro et al. [67].WIN 55,212-2, a cannabinoid receptor agonist, induces GBM cell death, suggesting cannabinoids as potential anticancer agents according to Sugimoto et al. [87].Paris saponin H, which is used in the treatment of lung cancer and malignant lymphoma, induces the apoptosis of gliomas, as shown by Bi et al. [77].Although the insulin signaling pathway plays a crucial role in the progression of GBM, drugs targeting IGF1 await the successful completion of phase III trials as the molecular mechanisms involved are not yet fully understood, as noted by Lin et al. [71].Echinomycin, a notable HIF1α inhibitor, induces apoptosis and inhibits GBM growth by targeting the HIF1α-PDGFD-PDGFRα axis, as found by Peng et al. [100].

Insights into HIF-Associated Discoveries from Clinical Investigations in GBM
Clinical studies consistently report the elevated expression of HIF1α in glioblastoma (GBM) tissues, suggesting its pivotal role in tumor progression.Chen et al. (2019) [114] observed significant HIF1α expression in both the nucleus and cytoplasm of GBM cells, correlating with tumor vasculature, indicating its involvement in angiogenesis.Similarly, the findings by Carlos Alfonsoe et al. [122] and Xiangjun et al. [119] linked HIF1α expression in GBM with increased vascular proliferation and poorer patient prognosis.Moreover, the research by Bache et al. [115] and El-Benhawy [123] described a diverse range of hypoxia-related factors, including HIF2α and OPN, contributing to the intricate tumor microenvironment, highlighting the multifaceted role of HIFs in tumor growth and survival under hypoxia.
Notably, the studies by Erpolat et al. [116] and Nobuyuki et al. [118] established a correlation between elevated HIF1α levels and reduced patient survival, indicating its potential as a prognostic marker.Conversely, the observations by Sfifou et al. [120] indicated longer survival in patients with negative HIF1α expression, reinforcing its prognostic value.High HIF expression levels correlate with aggressive GBM behavior, including rapid growth, enhanced invasiveness, and resistance to standard treatments, as demonstrated by Kaynar et al. [117] and Potharaju et al. [121], contributing to poorer patient outcomes.
These clinical findings underscore the importance of investigating hypoxia-induced tumor progression mechanisms in GBM.Developing targeted therapies to inhibit HIF activity, possibly in combination with existing treatments, holds promise for improving patient prognosis.Additionally, identifying novel biomarkers based on hypoxia-related factors could enhance early detection and treatment monitoring in GBM, ultimately improving patient outcomes.Future research efforts should focus on unraveling the complexities of the hypoxic tumor microenvironment to devise more effective interventions for managing GBM.

Advantages, Disadvantages, and Future Directions
Therapies targeting HIFs offer a promising avenue for combating GBM, a malignancy notorious for its resistance to conventional treatments.By specifically inhibiting HIF activity, these therapies hold potential for improving patient outcomes, particularly in cases where GBM displays elevated HIF expression levels [140].Combining HIF-related therapies with established treatments like surgery, radiation, and chemotherapy may enhance their effectiveness, offering a more comprehensive approach to GBM management [141,142].Research into HIFs in GBM provides crucial insights into tumor progression mechanisms, offering hope for the development of more potent therapeutic strategies.Moreover, explor-ing HIFs could lead to the identification of novel biomarkers for early diagnosis, prognosis assessment, and treatment response monitoring in patients with GBM [143].
However, challenges abound in the clinical application of HIF-related therapies.The lack of standardization in research methodologies impedes quantitative meta-analysis, while genetic mutations in GBM and therapy effects outside target sites present additional hurdles [13,144,145].The complex and dynamic nature of the hypoxic tumor microenvironment may limit the efficacy of single-target HIF therapies, potentially leading to therapy resistance.Developing combination therapies or innovative treatment strategies may be necessary to address this issue.Despite encouraging preclinical results, limited clinical data exist on the efficacy of HIF-related therapies in patients with GBM, necessitating further extensive clinical trials for validation [137,146].Safety concerns, including potential side effects and toxicity, especially when combined with other treatments, require thorough evaluation [147][148][149].
Moreover, the challenge lies in targeting HIFs without disrupting normal cellular responses to hypoxia, underscoring the need for precision in therapy development [150].The absence of reliable biomarkers to identify patients who would benefit most from HIF-related therapies complicates treatment decisions and personalized care plans.Exploring combination therapies targeting multiple GBM progression pathways, conducting advanced clinical trials with diverse populations, and investigating the mechanisms of therapy resistance are crucial steps forward [151].Additionally, advancing research to identify and validate biomarkers for early detection and treatment response monitoring is essential for the effective clinical implementation of HIF-related therapies in GBM management.

Conclusions
In conclusion, the evolving landscape of GBM research reflects a concerted effort to address the pressing challenges of poor patient outcomes associated with conventional treatments.While molecular genetic features have improved diagnostic capabilities, preclinical studies have highlighted the importance of HIFs as a therapeutic target, although clinical translation is limited.Overcoming challenges such as therapy resistance, safety concerns, and the absence of reliable biomarkers is crucial for the successful integration of HIF-related therapies into the treatment of GBM.By combining targeted approaches with conventional treatments, conducting large clinical trials, and testing combination therapies, researchers aim to optimize patient outcomes and pave the way for personalized treatment strategies in GBM.Ultimately, these multidisciplinary efforts promise to improve our understanding and treatment of GBM and provide hope for better patient care in the future.Specify study characteristics (e.g., PICOS and length of follow-up) and report characteristics (e.g., years considered, language and publication status) used as criteria for eligibility, giving rationale. 3

Information sources 7
Describe all information sources (e.g., databases with dates of coverage and contact with study authors to identify additional studies) in the search and date last searched.
3 Search 8 Present a full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
Appendix A

Study selection 9
State the process for selecting studies (i.e., screening, eligibility, included in the systematic review, and, if applicable, included in the meta-analysis). 3

Data collection process 10
Describe the method of data extraction from the reports (e.g., piloted forms, independently and in duplicate) and any processes for obtaining and confirming data from the investigators. 3

Data items 11
List and define all variables for which data were sought (e.g., PICOS and funding sources) and any assumptions and simplifications made. 3

Risk of bias in individual studies 12
Describe the methods used for assessing the risk of bias in individual studies (including the specification of whether this was performed at the study or outcome level), and how this information is to be used in any data synthesis.

Summary measures 13
State the principal summary measures (e.g., risk ratio and the difference in means).

Synthesis of results 14
Describe the methods of handling data and combining the results of studies, if performed, including the measures of consistency (e.g., I 2 ) for each meta-analysis.

N/A
Risk of bias across studies 15 Specify any assessment of the risk of bias that may affect the cumulative evidence (e.g., publication bias and selective reporting within studies).

N/A Additional analyses 16
Describe the methods of additional analyses (e.g., sensitivity or subgroup analyses and meta-regression), if performed, indicating which were pre-specified.

Study selection 17
Give the number of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

Study characteristics 18
For each study, present characteristics for which data were extracted (e.g., study size, PICOS, and follow-up period) and provide the citations.For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group and (b) effect estimates and confidence intervals, ideally with a forest plot.
Tables 1-3 Synthesis of results 21 Present the results of each meta-analysis performed, including confidence intervals and the measures of consistency.

N/A
Risk of bias across studies 22 Present the results of any assessment of the risk of bias across studies (see item 15).

N/A Additional analysis 23
Give the results of additional analyses, if performed (e.g., sensitivity or subgroup analyses and meta-regression [see item 16]).

Summary of evidence 24
Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

4-8
Limitations 25 Discuss limitations at the study and outcome level (e.g., risk of bias), and at the review level (e.g., the incomplete retrieval of identified research and reporting bias).

Figure 2 .
Figure 2. Geographical distribution of included studies.

Figure 3 .
Figure 3. Temporal distribution of included studies.

Figure 3 .
Figure 3. Temporal distribution of included studies.

Figure 4 .
Figure 4. Study design of included studies.

Figure 4 .
Figure 4. Study design of included studies.

11 N
of funding for the systematic review and other support (e.g., supply of data); and the role of funders for the systematic review./A-Not Available

Table 2 .
Laboratory studies with gene modification of HIFs in glioblastoma models and inoculated animals.

Table 3 .
Experimental investigations on hypoxia-inducible factors (HIFs) in glioblastoma models and animal subjects with induced tumors.

Table 4 .
Experimental investigations on hypoxia-inducible factors (HIFs) in glioblastoma models and animal subjects with induced tumors with combined genetic and targeted/systematic therapy.