In recent years, many efforts have been made to fully determine the biology of specific tumors so that with accurate tumor diagnosis, patients can appropriately and beneficially be treated. Researchers try to find biomarkers with high specificity and sensitivity. Examination of biomarkers, such as microRNAs, is an effective way to detect diseases early and track their progression or response to treatment. Therefore, microRNAs have been proposed as new target molecules for the diagnosis and treatment of glioblastoma (Shea et al. 2016). TCGA analysis using Cox regression and Kaplan-Meier analyses has identified that miR-31, miR-148a, and mir-221 as risky microRNAs that accompany with the survival of patients with glioblastoma (Srinivasan et al. 2011, Wong et al. 2015). Wong et al. (2014) used two Antagomirs to inhibit miR-31 and miR-148a in orthotopic xenograft mouse models and reported an inhibited tumor growth and increased mouse lifespan. This result is inconsistent with the result obtained from cells in culture medium (Wong et al. 2015). Using StarBase V2.0, FIH1 molecule or hypoxia-inducing factor1α inhibitor was obtained as the common target of these microRNAs. The results obtained in our study showed a significant difference in the expression of the three microRNAs between the case and control groups, and is in line with the results of some previous studies and contradicts some other studies. For example, Michela Visani et al. (2013) compared the expression of 19 microRNAs in 60 paraffin-embedded glioblastoma samples with other types of grade I-III brain neoplasias. Examination by Real-Time PCR showed that the expression of miR-31 was inhibited or decreased in glioblastoma samples (Visani et al. 2014). Moreover, Zhou et al. (2015) reported that miR-31 inhibits glioblastoma tumors and acts as an apoptosis enhancer. Also, it increases the sensitivity of glioblastoma cells to temozolomide )TMZ( and can be a predictor of the tumor by inhibiting STAT3 activation (Zhou et al. 2015). Growing evidence shows that the miR-148/152 family has the potential to act as both tumor suppressor and oncogene in various cancers (Ilango et al. 2020). Using deep sequencing microarray technique, Monika Pwecka et al. (2015) compared the expression level of microRNA in tumor tissue and tumor margin tissue and observed that the expression of miR-148a was increased in tumor tissue (Piwecka et al. 2015). In the study of Daming Cui et al., it was found that miR-148a promotes human glioma cell with IDH1R132H mutation invasion and tumorigenesis by downregulating GADD45A (Cui et al. 2017). Many previous studies of miR-221 have shown increased expression of this gene (Le Sage et al. 2007, Conti et al. 2009, Silber et al. 2009). Le Sage et al. (2007) showed that the levels of miR-221 and miR-222 increase in primary glioblastoma samples, and this increase is associated with low levels of p27Kip1 (Le Sage et al. 2007). In some tumors (e.g. GBM), the oncogenic properties of miR-221 and miR-222 increase through the inactivation of suppressors, P27 and P57 (Gillies and Lorimer 2007). Chen et al. (2011) found that downregulation of miR-221/222 increases the sensitivity of glioma cells to temozolomide, and this occurs by regulating apoptosis, which is independent of the TP53 pathway (Chen et al. 2012). Chun-Hua Xu et al. (2019) reported that by silencing the microRNA-221/222 cluster, the glioblastoma angiogenesis process can be inhibited, which is performed by the suppressor of cytokine signaling-3 dependent JAK/STAT pathway (Xu et al. 2019).
Contradiction in the results obtained from gene expression studies is due to different reasons, especially sample selection or preparation, which is presented as heterogeneity and this has been covered a lot in recent years. This is one of the issues that has caused glioblastoma to be considered as a treatment-refractory brain tumor (Brennan et al. 2013, Patel et al. 2014, Meyer et al. 2015, Neftel et al. 2019).
According to the study of Anoop P. Patel et al. in 2018, the correlation of individual cells within a tumor showed a wide range (correlation coefficient ~ 0.2–0.7), which confirmed the intratumoral heterogeneity within the tumor and these heterogeneous mixtures have also been observed in different subtypes of glioblastoma. Each tumor has a dominant subtype in terms of gene expression but contains cells with alternate gene expression (Sottoriva et al. 2013, Patel et al. 2014). Comparing the expression profiles of microRNAs that are spatially located at different sites of a glioblastoma tumor has shown intratumoral heterogeneity (Soeda et al. 2015) and these expression changes occur in three different areas of core, rim, and invasive margin (Alfardus et al. 2021). The study conducted by Puchalski et al. that was published in 2018 as the Ivy GBM Atlas reported that the heterogeneity at different regions of a single tumor is greater than expected and even much higher between areas anatomically similar to different patients' tumors (Puchalski et al. 2018). Intratumoral exchange of microRNAs by extracellular vesicles (EVs) increases the heterogeneity of glioblastoma stem cells (GSCs) and alters subpopulation-specific microRNA signature (Bronisz et al. 2016). Both miR-148a and miR-31 in these vesicles increase heterogeneity in these cells (Godlewski et al. 2017). Importantly, findings from various studies of tumor heterogeneity indicate that a single biopsy is insufficient to determine tumor signature (Khalafallah et al. 2021).Unlike other genes, hypoxia gene signatures, such as HIF1α signatures, correspond to multiple biopsies from multiple areas throughout a tumor, and intratumoral heterogeneity is highly true about them. When only one biopsy of a tumor sample is available, hypoxia gene signature may give us a more reliable estimate of the total hypoxia of the tumor than other genes, but in these cases, multiple biopsies provide complete assurance of tumor classification. However, unfortunately, they are not always available (Lukovic et al. 2019). Tumor environmental stresses, such as hypoxia, are factors that increase epigenetic heterogeneity in tumor cells (Singh et al. 2012, Eriksen et al. 2016, Ramón y Cajal et al. 2020) and thus make a difference in the results of expression analysis. The experimental design and data analysis are also very important (Xu and Wong 2010, Iorio and Croce 2012). Another important issue is the use of different controls for data normalization, which explains some of the differences observed in different studies (Peltier and Latham 2008). In this study, the TBP gene was used as the HIF1α reference gene. Kreth, S., et al. investigated the expression of 19 typical reference genes in order to evaluate the expression of HIF1α, and found that among different types of glioma, glioblastoma has the highest level and diversity of reference genes and this is unlike normal brain cells. Due to the difference in the expression levels between the glioblastoma and normal brain cells, the selection of the reference gene in brain tumors has more sensitivity than other tumors and is very important in expression analysis. In their study, Normfinder analysis showed the most stable expression of IPO8 and TBP genes (Kreth et al. 2010). In the study of Susanne Grube et al. (2014), geNorm expression stability analysis, BestKeeper expression stability analysis, and NormFinder expression stability analysis showed that TBP is an appropriate reference gene for studying the expression in glioblastoma and normal brain cells (Grube et al. 2015). The same results were obtained from the study of Kang IN et al. in 2015 (Kang et al. 2015). Another important issue that complicates the situation is that in response to cellular stress conditions such as hypoxia, there is an immediate and dynamic regulation in the miRNA expression rate, and the expression can be different at the time of sample collection and examination (Marsit et al. 2006, Kulshreshtha et al. 2007). Hypoxia occurs in solid tumors (e.g. glioblastoma) by increasing the tumor diameter to about 1 mm (Vaupel et al. 1989) and stimulates the self-repair of glioblastoma cancer stem cells. This phenomenon is common in glioblastoma (Heddleston et al. 2009, Colwell et al. 2017). There is ample evidence that hypoxia induces cancer stem cells to cause tumor resistance and recurrence after conventional therapy (Tang et al. 2021, Zhang et al. 2021). Contrary to this, there is other evidence that hypoxia makes tumor cells more sensitive to chemotherapy (Emami Nejad et al. 2021). Therefore, studying this pathway as one of the major pathways in this disease, is very important. During hypoxic conditions and therapeutic stress, CSCs use special signaling pathways to regulate their stemness, and HIF signaling plays a key role in regulating these pathways (Qiang et al. 2012, Mimeault and Batra 2013, Vadde et al. 2017, Yu et al. 2018, Yang et al. 2020). Based on the results of MsigDB Hallmark and KEGG pathways enrichment, it was found that in glioblastoma, the three mentioned microRNAs have the highest importance in signaling pathways, which are involved in specific patterns of glioma cell infiltration. Pathways that make these three microRNAs important for the migration and invasion of glioblastoma are shown schematically in Fig. 6.
This movement and invation is through the myelinated nerve fibers of white matter tracts Which is different from the perivascular space around blood vessels and subarachnoid space (Lefranc et al. 2005, Cuddapah et al. 2014). Cell motility along white matter tracts, a second route of glioma cell invasion, is mediated by a group of proteins called axonal guidance molecules (Armento et al. 2017). In addition to, these molecules being involved in the development of the CNS, they play an important role in cancer-associated processes (Mancino et al. 2011). The most distinguished axonal guidance molecules are: ephrins; netrins; Slits; semaphorins; plexin; neuropilin and Robo, which show different expression in different cancers (Ronca et al. 2017) In addition, they are not narrowing to nervous system and these are prominently expressed in many developing and mature organs (Hinck 2004). Their normal function in the adult CNS and other adult tissues is essentially unknown and There is a need for more research (Chedotal et al. 2005). Altered expression of axonal guidance proteins contributes to tumor cell migration and invasion, this special infiltrative route has been observed in glioblastoma cells (Chedotal et al. 2005)
It has been determined from previous studies extracellular matrix molecules in addition to constructing a structural scaffold can significantly influence axon guidance cue function, so proteoglycans can be considered as Modulators of Axon Guidance Function (Wit and Verhaagen 2007).
In this study Proteoglycan in cancer, the next important signaling pathway, that contains molecules that provide the same extracellular matrix (ECM) needed for glioblastoma cell motility. Many compounds of this pathway are involved in glioma cell infiltration. These molecules construct structural support and work as a leader scaffold or barrier (Friedl and Alexander 2011, McGranahan and Swanton 2017). The brain parenchyma unlike the ECM of the other cells, lacks tight and stiff components such as collagens; fibrinogen and laminin (Vollmann-Zwerenz et al. 2020) and instead have proteoglycan; hyaluronic acid; tenascin-C; Aggrecan; Nidogen; agrin and ... (Dityatev et al. 2010, Friedl and Alexander 2011, McGranahan and Swanton 2017, Ferrer et al. 2018). Proteoglycans (PGs) are complex macromolecules composed of a central core protein that linked to sulfated glycosaminoglycan (GAG) chains covalently (Nikitovic et al. 2018, Schwartz and Domowicz 2018). Those which are common component of the extracellular matrix and include, aggrecan (ACAN), versican (VCAN), neurocan (NCAN) and brevican (BCAN) (Wade et al. 2013); those which attach to the cell surface via a glycosylphosphatidylinositol (GPI) anchor and include glypican (GPC) family; and those are the transmembrane family, including the syndecans, neuroglycan, appicans and NG-2 (Schwartz and Domowicz 2018). It has been shown that the ECM, cell–cell and cell–ECM connecting molecules and proteases, have an important role on glioma cell migration (Baumann et al. 2009, Onken et al. 2014). The composition of the ECM highlights specific glioblastoma stem cell niches and causes migration and invasion in specific form (Nguyen-Ngoc et al. 2012). One of the most important niches within the diseased brain is the hypoxic niche. This is A hypoxic environment stimulates the expression of the hypoxia-inducible factors (HIFs) HIF1 and HIF2 that stimulate invasiveness (Li et al. 2009). An acidic environment also stimulates HIF function (Filatova et al. 2016) and then MMPs are activates (Cong et al. 2014) of course, the results of MSigDB_Hallmark pathways enrichment also introduced hypoxia signaling pathway as one of the target signaling pathways that There is less involvement from these micoRNAs and the importance of these microRNAs is highlighted in other signaling pathways. Due to the function of the obtained signaling pathways, these three microRNAs can be considered in Glioblastoma cell infiltration through the myelinated nerve fibers of white matter tracts.