Gliomas are the most common malignant category of brain cancer. Diffuse lower-grade glioma (LGG, grade 2 or 3), and glioblastoma (GBM, grade 4) are categorized by the World Health Organization (WHO) based on combination of histologic and molecular features
[1]. Despite the multimodal approach of surgery, chemotherapy, and radiotherapy for gliomas, patient outcomes remain unsatisfactory, particularly high-grade ones like GBM, with a survival averaging less than 7 years
[2-3].Therefore, this study intends to uncover and characterize a new prognostic biomarker of glioma progression and therapeutic targets for gliomas.
The actin filament-associated protein family contains 3 adaptor proteins, namely actin filament-associated protein (AFAP) 1, actin filament-associated protein 1-like (AFAP1L) 1, and AFAP1L2/X-box binding protein 130 (XB130). The AFAP family has a highly conserved pleckstrin homology (PH) domain, a substrate domain (SD) rich in serine and threonine residues, and an Src homology domain (SH) 2/SH3 binding motif that probably interacts with with functional molecules and triggers different downstream effects
[4-6]. Although they have some structures in common, AFAP1L1 also displayed unique properties. Unlike AFAP1, AFAP1L1 is expressed in brain tissues, including the dentate nucleus, and may be involved in neurogenesis
[6]. In previous studies
[4, 7], AFAP1L1 has been shown to mediate the formation of invadopodia and promote cancer cell migration and invasion by interacting with Src family tyrosine kinases (SFKs). In addition, under the stimulation of hypoxia, the activation of hypoxia-inducible factor (HIF)-1α promotes the expression of AFAP1L1 in HUVEC, which in turn promotes angiogenesis through the downstream Yes-associated protein (YAP) pathway, that is likely to be an important factor for
AFAP1L1 to become a pro-carcinogenic gene
[8]. Despite AFAP1L1 having been identified as a cancer-promoting gene in various cancer types, including non-small cell lung, gastric cancer, and colorectal cancer, the involvement of AFAP1L1 in the formation and development of gliomas, a type of malignant brain tumor, remains unclear
[9-11]. Further research is required to elucidate the potential role of AFAP1L1 in glioma pathogenesis and to determine its clinical significance as a potential therapeutic target or prognostic marker in this disease.
To investigate and validate the potential involvement of AFAP1L1 in facilitating the progression of gliomas, we gathered glioma samples with AFAP1L1 data from The Cancer Gene Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases and then conducted evaluations on the predictive value of AFAP1L1 messenger RNA (mRNA) expression, somatic mutations, copy number variation, the immune microenvironment, and functional annotation analysis.
1 Materials and methods
1.1 Data collection and preprocessing
The flowchart of this study is shown in
Figure 1. The transcriptomic data of a total of 672 glioma samples associated with
AFAP1L1 was gathered from TCGA (
https://xenabrowser.net/). Additionally, 2 cohorts from CGGA (
http://www.cgga.org.cn/), namely messenger RNA sequencing (mRNAseq)_693 and mRNAseq_325, were obtained and incorporated into this study. After the removal of batch effects using the R package sva, CGGA325 and CGGA693 were amalgamated to form the CGGA dataset
[12]. Furthermore, TCGA therapeutically applicable research to generate effective treatments (TARGET) Genotype-Tissue Expression (GTEx) cohort was retrieved from the University of California, Santa Cruz (UCSC) Xena database (
https://ucsc.xena.edu) to obtain gene expression data from 1 141 normal samples and 689 tumor samples. Single cell data were obtained from GSE117891 of the GEO database, and data were processed using the Seurat standard process
[13]. Twenty-eight tissue samples were collected from glioma surgeries performed at Hospital, from 2022 to 2023. The study was conducted following the guidelines of the Institutional Review Board at Hospital, and written informed consent was obtained from all patients (Supplemental Table 1,
https://doi.org/10.57760/sciencedb. 37016).
1.2 Western blotting
For the purpose of performing Western blotting analysis of AFAP1L1 protein expression, we utilized glioma tumor tissues and adjacent normal tissues collected from patients at hospital. As described previously, proteins were extracted from glioma and adjacent normal tissues, separated on 10% sodium dodecyl sulfate-polyacrylamide gels, and transferred onto polyvinylidene fluoride membranes
[14].The membranes were blocked with 5% skimmed milk for 1.5 hours and then incubated overnight at 4 ℃ with rabbit anti-human AFAP1L1 antibody (Proteintech, 1꞉1 000). Afterwards, the membranes were incubated with horseradish peroxidase-conjugated goat anti-rabbit secondary antibody (Affinity, 1꞉3 000) at room temperature for 1 hour.
1.3 Real-time polymerase chain reaction
Total RNA was extracted from brain tissues using RNAiso (Takara Bio Inc., Kusatsu, Japan)
[15].Reverse transcription was carried out using the Prime Script RT Kit (Takara Bio Inc.). Targeted gene expression was measured by quantitative real-time polymerase chain reaction (qPCR) analysis using the SYBR® Premix Ex Taq
TM II system (Takara Clontech) on a Bio-Rad real-time PCR system (CFX96 Touch
TM; Bio-Rad, USA). All tissue samples were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The qPCR procedure involved initial denaturation at 95 ℃ for 30 seconds followed by 40 cycles of 95 ℃ for 15 seconds and 60 ℃ for 30 seconds. Expression fold changes were calculated using the 2
-ΔΔCt method
[16].Primer sequences used were listed as follows: GAPDH-F, 5'-GGAGCGA-GATCCCTCCAAAAT-3', GAPDH-R, 5'-GGCTGTTG-TCATACTTCTCATGG-3'; AFAP1L1-F, 5'-CGAGTA-CCTCAGCGATACCAC-3', AFAP1L1-R, 5'-CTTCAA-AGAGGGATTCCACGAA-3'. The primers were purchased from Tsingke Biotech (Changsha, China).
1.4 Functional enrichment analysis of DEGs
We utilized the DESeq2 R package to identify differentially expressed genes (DEGs) in glioma samples, specifically comparing those with low and high expression levels of
AFAP1L1[17]. The criteria for DEGs were established as |log
2 (fold change)|>1 and adjusted
P<0.05. Subsequently, we performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the identified DEGs using the clusterProfiler package
[18].
1.5 Gene set enrichment analysis and gene set variation analysis
In order to elucidate the potential functional roles of
AFAP1L1, gene set enrichment analysis (GSEA) was implemented on glioma samples derived from both the TCGA and CGGA cohorts. Spearman correlation analysis first measured the relationship between
AFAP1L1 and other genes. A gene list for GSEA was constructed and ranked from strongly positive to strongly negative. GSEA was then performed using this list and the “c2.cp.kegg.v2023.1.Hs.symbols.gmt” reference set from the Molecular Signatures Database
[19].After constructing the single cell expression matrix and the set of pathways to be analyzed, we used gene set variation analysis (GSVA) R package for GSVA.
1.6 Identification of differences in genomic alterations related to AFAP1L1
We retrieved the somatic mutations and copy number variations (CNVs) from TCGA datasets. The Genomic Identification of Significant Targets in Cancer (GISTIC) analysis was then used to investigate the genomic landscape
[20]. Using GISTIC 2.0 analysis, we characterized the copy number gains or losses at the amplified or deleted peaks based on
AFAP1L1 levels. Additionally, we investigated the mutational frequency of
AFAP1L1 in the TCGA database using cBioPortal
[21].
1.7 Statistical analysis
The normality of the data was assessed using the Shapiro-Wilk test. The Wilcoxon test was used for non-parametric data, while the t-test was used for parametric data, to compare statistical differences between the 2 groups. The median was used to split patients into 2 subgroups based on AFAP1L1 expression. The “survival ROC” and “rms” packages in R were used to generate receiver operating characteristic (ROC) and nomogram plots. The “survival” and “survminer” packages in R were used to draw AFAP1L1 Kaplan-Meier (KM) survival curves, and the log-rank test was used to compare the differences in survival data. All statistical analyses were conducted utilizing R software (v4.2.2). The significance level for all tests was set at P<0.05, with two-sided analysis conducted.
2 Results
2.1 Elevated AFAP1L1 correlates with more malignant properties in glioma
Based on the TCGA TARGET GTEx data from UCSC and Gene Expression Profiling Interactive Analysis (GEPIA) analysis, we observed a significant upregulation of
AFAP1L1 mRNA expression in Pan-glioma, GBM, and LGG samples compared to normal samples (Figure
2A-
2C, Supplemental
Figure 1A,
https://doi.org/10.57760/sciencedb.37016; all
P<0.05). The area under the ROC curve was 0.859, implying that
AFAP1L1 expression could effectively predict whether a sample came from a normal or glioma patient (
Figure 2D). We noticed that
AFAP1L1 expression levels escalated with the increasing malignancy of gliomas (
Figure 2E and Supplemental
Figure 1B,
https://doi.org/10. 57760/sciencedb.37016;
P<0.001), and were notably highest in GBM (
Figure 2F,
P<0.001). The expression of
AFAP1L1 in cancer stem cell-like cell line (CL) and mesenchymal glioblastoma (ME) subtypes exhibited a statistically significant increase compared to that in neurogenic glioblastoma (NE) and prenecentric glioblastoma (PN) subtypes (
Figure 2G, both
P<0.001). Interestingly, we also noted a notable distinction in
AFAP1L1 expression levels between CL and ME subtypes, with an ROC curve value of 0.803, supporting the predictive value of
AFAP1L1 in distinguishing between these subtypes (
Figure 2H). Moreover, we found higher
AFAP1L1 expression in isocitrate dehydrogenase (IDH) wildtype (WT) gliomas compared to those with IDH mutations (
Figure 2I and Supplemental
Figure 1C,
https://doi. org/10.57760/sciencedb.37016;
P<0.001). The ROC curve validated the predictive efficacy of
AFAP1L1 expression in identifying glioma patients with either IDH mutation or IDH WT (
Figure 2J). In glioma patients aged over 41 years or with non-co-deletion of 1p/19q, the
AFAP1L1 expression levels were notably higher (Figure
2K and
2L, Supplemental
Figure 1D,
https://doi. org/10.57760/sciencedb. 37016; all
P<0.001). We also found decreased AFAP1L1 expression in samples with methylated O
6-methylguanine-DNA methyltransferase (MGMT; Supplemental
Figure 1E,
https://doi. org/10.57760/sciencedb. 37016). The ROC curve validated the predictive efficacy of
AFAP1L1 expression in identifying glioma patients with either unMGMT or MGMT (Supplemental
Figure 1F,
https://doi. org/10.57760/sciencedb.37016). The expression levels of AFAP1L1 protein and mRNA were found to be significantly elevated in glioma tissues compared to adjacent normal brain tissues, as determined by Western blotting and real-time PCR (Figure
2M-
2O, both
P<0.05).
2.2 High AFAP1L1 expression predicts poor prognosis in glioma
AFAP1L1 expression was examined for its prognostic significance in glioma using the CGGA database. Elevated levels of
AFAP1L1 correlated with decreased survival rates in glioma patients stratified by various factors, including WHO grade, IDH status, age, MGMT promoter status, 1p/19q co-deletion status, and gender (all
P<0.05, Figure
3A-
3F).
AFAP1L1 expression demonstrated robust and accurate prediction of overall survival (OS) in glioma patients at 1 year (0.660), 3 years (0.725), and 5 years (0.736) intervals, as revealed by the area under the curve (AUC) values (Supplemental Figure
2A and
2B,
https://doi.org/10.57760/sciencedb. 37016). We devised a nomogram that incorporates
AFAP1L1 expression data along with clinical prognostic variables like WHO grade, age, and IDH status (Supplemental
Figure 2C,
https://doi.org/10.57760/sciencedb. 37016). The nomogram’s performance and ROC curves were assessed through calibration with a C-index for OS of 0.748 (Supplement
Figure 2B,
https://doi.org/10. 57760/sciencedb.37016). Both univariate Cox regression (
HR=1.6, 95%
CI 1.50 to 1.70,
P<0.001,
Figure 3G) and multivariate Cox regression (
HR=1.3, 95%
CI 1.18 to 1.40,
P<0.001,
Figure 3H) demonstrated
AFAP1L1 as an independent risk factor. These findings revealed a significant association between high
AFAP1L1 expression and poor prognosis in glioma patients.
2.3 AFAP1L1 is associated with the genomic alterations
Analyses of the TCGA dataset revealed that
AFAP1L1 expression correlated with distinct genomic alterations in gliomas, including specific somatic mutations and copy number alterations. A comprehensive CNA profile was developed by contrasting glioma samples with high
AFAP1L1 expression (
n=339) against those with low
AFAP1L1 expression (
n=338; Supplemental Figure
3A and
3B,
https://doi.org/10.57760/sciencedb.37016). Glioma samples with high
AFAP1L1 expression showed frequent chromosomal 7 amplification and chromosomal 10 loss (Supplemental
Figure 3A,
https://doi.org/10.57760/sciencedb. 37016), whereas glioma samples with low
AFAP1L1 expression frequently had 1p and 19q deletions (Supplemental
Figure 3B,
https://doi.org/10.57760/sciencedb. 37016). Analysis of glioma samples exhibiting elevated
AFAP1L1 expression revealed recurrent amplification of putative oncogenes, including epidermal growth factor receptor (
EGFR; 7p11.2) and ubiquitin specific peptidase 13 (
USP13; 3q26.33), whereas putative tumor suppressor genes such as integral membrane protein 2B (
ITM2B; 13q14.2) and cyclin-dependent kinase inhibitor 2A (
CDKN2A; 9p21.3) underwent frequent deletion (Supplemental
Figure 3B,
https://doi.org/10.57760/sciencedb. 37016). Furthermore, analysis of the relationship between
AFAP1L1 expression and the mutation frequency of specific genes revealed that glioma samples with high
AFAP1L1 expression exhibited higher mutation frequencies in
IDH1 (39%), tumor protein p53 (
TP53, 32%), titin (
TTN, 19%), and
EGFR (19%; Supplemental
Figure 3C,
https://doi.org/10.57760/sciencedb. 37016). In contrast, samples with low
AFAP1L1 expression showed a greater incidence of mutations in
IDH1 (82%),
TP53 (56%), and alpha thalassemia/mental retardation syndrome X-linked (
ATRX, 45%; Supplemental
Figure 3C,
https://doi.org/10.57760/sciencedb.37016). Based on cBioPortal, we explored
AFAP1L1 mutation frequencies across various cancer types and found kidney renal clear cell carcinoma displayed the highest
AFAP1L1 alteration frequency (approximately 6%; Supplemental Figure
3D and
3E,
https://doi.org/10. 57760/sciencedb.37016).
2.4 AFAP1L1 mediates tumor immune microenvironment
The algorithm Cell Identity By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) was employed as the primary tool to assess the relative levels of 22 immune cell types (
Figure 4A).
AFAP1L1 expression is positively associated with macrophages M0, M1, M2, neutrophils and Tregs, but negatively associated with monocytes, activated mast cells, activated natural killer (NK) cells, eosinophils, and others (
Figure 4B and Supplemental
Figure 4A,
https://doi.org/10.57760/sciencedb. 37016). Based on TCGA glioma patients stratified by
AFAP1L1 expression, we explored the composition of immune cells (Figure
4C and
4D; Supplemental
Figure 4B,
https://doi.org/10.57760/sciencedb.37016). In comparison with the
AFAP1L1 low expression group, it noted that monocytes, activated mast cells, activated NK cells, eosinophils, and memory B cells decreased in the high
AFAP1L1 expression group (
Figure 4C). Fluorescence staining analysis of glioma clinical samples revealed high
AFAP1L1 expression and concomitant infiltration of the neutrophil marker lymphocyte antigen 6G (LY6G;
Figure 4D). In both TCGA and CGGA databases, higher expression levels of
AFAP1L1 were related to higher stromal score, immune score, and Estimation of STromal and Immune cells in Malignant Tumours using Expression (ESTIMATE) score calculated by the ESTIMATE package (Supplemental
Figure 5,
https://doi.org/10.57760/sciencedb.37016), indicating overall increased immune/stromal infiltration and reduced tumor purity. Furthermore, we investigated the associations between
AFAP1L1 and immune checkpoints. The results showed that
AFAP1L1 was positively correlated with butyrophilin subfamily 2 member A2 (
BTN2A2), indoleamine 2,3-dioxygenase 1 (
IDO1), tryptophan 2,3-dioxygenase 2 (
TDO2), cluster of differentiation (
CD)
276,
CD274, programmed cell death 1 ligand 2 (
PDCD1LG2), programmed cell death 1 (
PDCD1),
CD80, cytotoxic T-lymphocyte associated protein 4 (
CTLA4), TNF receptor superfamily member 14 (
TNFRSF14), hepatitis A virus cellular receptor 2 (
HAVCR2), galectin 9 (
LGALS9), poliovirus receptor (
PVR), butyrophilin subfamily 2 member A1 (
BTN2A1)
, etc. in TCGA (
Figure 5A) and CGGA (
Figure 5B). Furthermore, we independently used the Tumor Immune Dysfunction and Exclusion (TIDE) website to predict response to immune checkpoint blockade (ICB) therapy. Our analysis showed that patients with higher
AFAP1L1 levels had higher TIDE scores (Figure
5C and
5D). Based on these predictive results from the pan-cancer TIDE model, our findings suggest the possibility that glioma patients with high
AFAP1L1 expression may derive less benefit from ICB therapy.
2.5 Enrichment analysis reveals correlation of AFAP1L1 with integrin pathway, actin cytoskeleton and focal adhesion
Using the DESeq2 r package, a sum of 2 843 DEGs (730 downregulated genes and 2 113 upregulated genes) were identified in the AFAP1L1 low expression group in comparison with the high group (Supplemental
Figure 6A,
https://doi.org/10.57760/sciencedb.37016). Collagen type III alpha 1 chain (
COL3A1), periostin (
POSTN) and matrix metallopeptidase (
MMP)
9 among the top 40 DEGs showed in the heatmap was significantly associated with integrin binding (Supplemental
Figure 6B,
https://doi.org/10.57760/sciencedb.37016). Biological process (BP), cellular component (CC), and molecular function (MF) analyses indicated the DEGs were mainly enriched in integrin-associated pathways such as “extracellular matrix organization” “collagen fibril organization” “collagen trimer” “extracellular structure organization” “ion channel complex” “extracellular matrix structural constituent” and “channel activity” (Supplemental Figure
6C-
6E,
https://doi.org/10.57760/sciencedb.37016). Additionally, based on TCGA and CGGA databases, KEGG of GSEA or DEGs were also mainly enriched in extracellular matrix (ECM)-receptor interaction, focal adhesion and actin cytoskeleton (
Figure 6 and Supplemental
Figure 6F,
https://doi.org/10.57760/sciencedb.37016).
2.6 Single cell analysis of AFAP1L1 and its functional pathways in glioma cells
We utilized the GSE117891 single cell dataset to examine the expression of
AFAP1L1 across different subpopulations of glioma. Initially, tumor cells were identified using marker genes such as SRY-box transcription factor 2 (
SOX2), nestin (
NES), glial fibrillary acidic protein (
GFAP),
EGFR, and platelet derived growth factor receptor beta (
PDGFRB;
Figure 7A and 7C)
[22]. Our analysis revealed that
AFAP1L1 expression was predominantly found in glioma tumor cells (
Figure 7A and 7B). These glioma cells were categorized based on the levels of
AFAP1L1 expression. Enrichment analysis was then performed. GO and KEGG pathway analyses indicated that the primary functions associated with
AFAP1L1 included responses to hypoxia, enhancement of cell adhesion, protein tyrosine kinase activity, resistance to EGFR tyrosine kinase inhibitors, and involvement in the programmed cell death-ligand 1 (PD-L1) expression and programmed death-1 (PD-1) checkpoint pathway in cancer, along with focal adhesion and the mitogen-activated protein kinase (MAPK) signaling pathway in gliomas (
Figure 7D and 7E). Moreover, GSVA results demonstrated that the group with high
AFAP1L1 expression exhibited significantly elevated enrichment scores in pathways related to small GTPases, cell adhesion, hypoxia, and MAPK/ERK signaling (Supplemental
Figure 7,
https://doi. org/10.57760/sciencedb.37016). We also detected the expression levels of
AFAP1L1 and HIF-1α, which ranked high in enrichment score, in glioma tissues and adjacent normal brain tissues by Western blotting. The results showed that the protein expression levels of both
AFAP1L1 and HIF-1α were significantly higher in glioma tissues compared to adjacent normal brain tissues (
Figure 8).
3 Discussion
Glioma, the most prevalent form of primary central nervous system neoplasm, refers to a tumor that arises from glial cells in the brain
[23]. GBM accounts for approximately 49% of all malignant brain tumors, while diffusely infiltrating LGG make up approximately 30% of cases
[24].Gliomas are complex tumors with poorly defined pathogenic mechanisms. Recently,
AFAP1L1 has been identified as a new potential tumor-associated protein. Elucidating the specific expression pattern and functional characterization of
AFAP1L1 in glioma may provide novel insights into the molecular basis of gliomagenesis. Through an extensive analysis using bioinformatics, we have thoroughly examined the clinical characteristics of
AFAP1L1 within the immune microenvironment. Our study has demonstrated
AFAP1L1 is significantly upregulated with increasing malignancy of gliomas. Our real-time RCR and Western blotting experiments with small samples also verified this result. Furthermore, we have discovered a strong correlation between
AFAP1L1 expression and IDH WT in GBM, making it a valuable marker for determining IDH status. Additionally, our findings indicated that
AFAP1L1 was predominantly higher in the CL subtype of gliomas, which was associated with poorer survival outcomes.
The KM survival analysis revealed glioma patients who exhibited low AFAP1L1 expression had a more favorable prognosis. Additionally, AFAP1L1 demonstrated promising survival efficacy across various subgroups. Our study identified a significant association between AFAP1L1 expression and several key factors in glioma, including age, grade, MGMT status, etc. This strong correlation with several prognostic risk factors implies that AFAP1L1 may exert a pivotal role in glioma progression. Moreover, the results obtained from univariate and multivariate Cox regression analyses, as well as the utilization of nomograms, calibration plots, and ROC curves, collectively support the notion that AFAP1L1 holds potential as a prognostic indicator for adverse outcomes in glioma patients.
Moreover, genomic alteration analysis indicated a positive association between AFAP1L1 expression and somatic mutations as well as CNVs. In samples exhibiting high AFAP1L1 expression, amplification of oncogenic drivers was observed, whereas tumor suppressor genes showed a deletion peak. The notably elevated mutation rate of the TP53 within the subgroup characterized by high AFAP1L1 expression warranted further investigation and exploration. Collectively, it was evident that AFAP1L1 may govern the advancement and proliferation of glioma cells, with heightened AFAP1L1 expression holding promise as a prognostic indicator for glioma patient survival.
The tumor immune microenvironment (TIME) encompasses a significant infiltration of immune cells, which plays a crucial role in tumor progression
[25-26]. The degree of immune cell infiltration varies across different classifications and stages of glioma
[27]. Within the TIME, immune cells exhibit abnormal components and functions that contribute to immune evasion, drug resistance, and metastasis, thereby posing challenges in the treatment of tumors
[28].
AFAP1L1 exhibited a positive association with the three scores calculated by the ESTIMATE R package, all of which were inversely linked to the patient prognosis
[29].Additionally,
AFAP1L1 displayed positive correlations with macrophages M0, M1, and M2, suggesting that
AFAP1L1 may influence macrophage activity through the modulation of macrophage cytoskeletal rearrangement. Upregulated expression of immune checkpoints within tumors facilitates immune evasion by tumor cells
[30]. Furthermore, our analysis of immune checkpoints revealed that
AFAP1L1 closely interacts with
BTN2A2,
IDO1,
TDO2,
CD276,
CD274,
PDCD1LG2,
PDCD1,
CD80,
CTLA4,
TNFRSF14,
HAVCR2,
LGALS9,
PVR,
BTN2A1 in pan-gliomas, highlighting its vital role in immune regulation. Additionally, based on TIDE scoring, patients with high
AFAP1L1 expression displayed higher scores, indicating that immune checkpoint blockade therapy may not be suitable for glioma patients with elevated
AFAP1L1 gene expression.
Tumors exist in a hypoxic microenvironment. A recent study showed that knocking down HIF-1α in HUVEC significantly reduced the expression of the
AFAP1L1 protein. This led to increased phosphorylation and degradation of the downstream molecule YAP, reversing the excessive blood vessel growth induced by low oxygen levels
[8]. Our single cell enrichment analysis revealed a significant association between higher
AFAP1L1 expression and both the HIF-1 pathway and angiogenesis pathways in glioma tumors, suggesting a possible mechanism of
AFAP1L1 as a proto-oncogene in glioma. In addition to HIF-1, in colorectal, sarcoma and gastric cancers, PI3K/AKT, PKC and tyrosine kinases such as Lyn/SFK also phosphorylate the Y136 and Y566 sites of the
AFAP1L1 protein, which then binds to and phosphorylates vav guanine nucleotide exchange factor 2 (VAV2)/NCK adaptor protein 2 (NCK2) through the SH2 domain, thereby activating small GTPases such as CDC42 and RAC1, which leads to tumor growth and migration
[7, 11]. In addition,
AFAP1L1 binds vinculin and cortactin in the invadopodia of RKO colorectal cancer cells and A7r5 smooth muscle cells, thereby inhibiting the formation of focal adhesion and affecting the degradation of extracellular matrix, resulting in altered cell morphology and motility
[6, 10].By integrating the findings from GSEA, GO, KEGG, and GSVA enrichment analysis, it has been revealed that aberrant expression of
AFAP1L1 may participate in the development of glioma by perturbing the small GTPases, HIF-1, focal adhesion, integrin and MAPK signaling.
Collectively, our bioinformatics analysis of multiple datasets provided insights into the characteristics of AFAP1L1 in gliomas. We presented a novel finding indicating aberrantly elevated expression of AFAP1L1 protein levels in glioma samples. Furthermore, AFAP1L1 expression was predominantly localized to glioma cells rather than other cell types. AFAP1L1 demonstrated the ability to predict malignant gliomas and served as an indicator of prognosis. Additionally, AFAP1L1 played a role in facilitating the development of an immune-suppressive and advantageous microenvironment within gliomas. The key signaling pathways influenced by AFAP1L1 may involve small GTPases, HIF-1, focal adhesion, integrin and MAPK signaling, providing potential avenues for targeted therapies against AFAP1L1 in glioma patients.
the National Natural Science Foundation(82073850)
the Hunan Provincial Youth Science Foundation Project(2019JJ50964)
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