Abstract
TNFAIP8 family molecules have been recognized for their involvement in the progression of tumors across a range of cancer types. Emerging experimental data suggests a role for certain TNFAIP8 family molecules in the development of glioma. Nonetheless, the comprehensive understanding of the genomic alterations, prognostic significance, and immunological profiles of TNFAIP8 family molecules in glioma remains incomplete. In the study, using the comprehensive bioinformatics tools, we explored the unique functions of 4 TNFAIP8 members including TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 in glioma. The expressions of TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 were notably upregulated in glioma tissues compared to normal tissues. Furthermore, survival analysis indicated that elevated expression levels of TNFAIP8, TNFAIP8L1 and TNFAIP8L2 were correlated with unfavorable outcomes in terms of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) among glioma patients. Genetic modifications, such as mutations and copy number alterations, within the TNFAIP8 family exhibited a significant association with extended OS, DSS and PFS in individuals diagnosed with glioma. The findings suggest a noteworthy correlation between TNFAIP8 family members and the age and 1p/19q codeletion status of glioma patients. We also found that there were significant relationships between TNFAIP8 family expression and tumor immunity in glioma. Furthermore, functional annotation of TNFAIP8 family members and their co-expressed genes in gliomas was carried out using GO and KEGG pathway analysis. The GO analysis revealed that the primary biological processes influenced by the TNFAIP8 family co-expressed genes included cell chemotaxis, temperature homeostasis, and endocytic vesicle formation. Additionally, the KEGG analysis demonstrated that TNFAIP8 family co-expressed genes are involved in regulating various pathways such as inflammatory mediator regulation of TRP channels, pathways in cancer, prolactin signaling pathway, and Fc gamma R-mediated phagocytosis. Overall, the findings suggest that TNFAIP8 family members may play a significant role in the development of glioma and have the potential to serve as prognostic indicators and therapeutic targets for individuals with glioma.
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Introduction
Glioma, a prevalent and aggressive primary tumor of the central nervous system, primarily consists of glial cells such as astrocytoma, oligodendroglioma, ependyma, anaplastic astrocytoma, and glioblastoma1. Low-grade glioma (LGG), encompassing WHO Grade II and III tumors, exhibit a less severe clinical course compared to high-grade glioma, with patients experiencing a survival period ranging from 1 to 15 years2. Glioblastoma (GBM) represents a predominant high-grade subtype, comprising 54% of glioma in the United States3. The median survival rate for patients diagnosed with GBM at the outset is less than 15 months, despite undergoing optimal surgical resection followed by chemoradiotherapy. Overall, the prognosis of glioma patients remains challenging to ascertain, even with the comprehensive utilization of surgery, radiotherapy, and chemotherapy. Numerous studies have demonstrated interobserver variations in the pathological grading diagnosis of glioma based solely on histological discrepancies4,5. In response to this, the 2016 revision of the WHO Classification of Central Nervous System Tumors incorporated molecular parameters, which have been increasingly shown to have a stronger correlation with clinical outcomes compared to traditional histological classification6. In recent studies, a multitude of cancer-related genes have been discovered to exert a substantial influence on the onset and progression of glioma. Thus, the exploration of novel biomarkers exhibiting high specificity and sensitivity, along with the identification of new molecular targets, will aid in elucidating the molecular mechanisms underlying glioma and enhancing the prognosis of glioma patients.
The TNFAIP8 (tumor necrosis factor-α-induced protein 8; TNFAIP8) family, comprised of TNFAIP8, TNFAIP8L1 (TNFAIP8-like 1 or TIPE1), TNFAIP8L2 (TIPE2), and TNFAIP8L3 (TIPE3), are newly identified regulators of immunity and tumorigenesis7,8. These mammalian proteins exhibit high homology and play crucial roles in inflammation and oncogenesis9,10,11. Despite their sequence similarities, each member of the family is involved in distinct biological functions and displays diverse expression patterns. Moreover, TNFAIP8 family exhibits significant dysregulation in conditions such as arthritis, cancers, and various other chronic diseases. Studies have indicated that TNFAIP8 family members (including TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3) exhibit altered expression levels in various human malignant tumors, such as those found in the breast12,13, bladder14, cervical15,16, colon17, gastric18, liver19, lung20, ovarian21,22, prostate23, Head and neck cancer24 and other cancers, when compared to their respective normal tissue counterparts. Moreover, a growing body of evidence suggests that the dysfunction of TNFAIP8 family expressions can enhance the proliferation, migration, and malignant transformation of tumor cells, and expressions of TNFAIP8 family are associated with high recurrence rates and shortened survival in various cancer types. Although, several researches shown that down-regulated TNFAIP8 expression inhibited the proliferation and migration of GBM cells25, TNFAIP8L3 was upregulated in GBM and associated with poor prognosis26, and up-regulated TNFAIP8L2 inhibited proliferation of GBM cells27. The precise association and mechanism of TNFAIP8 family in glioma remains uncertain.
Recently, the advancement of bioinformatics disciplines has played a crucial role in driving progress by incorporating data repositories, open access databases, and sophisticated algorithmic analysis tools, thereby augmenting researchers' capacity to acquire profound insights into pertinent domains28. In this study, a systematic analysis was conducted on the expression, prognosis, mutations, and their correlation with cancer grades of various TNFAIP8 family members in glioma patients using bioinformatics methods. The study identified TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 molecules as having distinct implications in the progression of glioma. The TNFAIP8 family genes may potentially serve as significant factors in the future diagnosis, treatment, and prognosis evaluation of glioma patients.
Methods
Expression profile analysis, survival analysis and clinicopathological features of TNFAIP8 family
The expression levels of the TNFAIP8 family were analyzed utilizing the UCSC XENA database (https://xenabrowser.net/)29. The "ggplot2" package was employed to visually represent the expressions of the TNFAIP8 family from The Cancer Genome Atlas (TCGA). The association between mRNA expressions of the TNFAIP8 family and clinicopathological characteristics of glioma was examined and depicted using the ggplot2 package within the R statistical software. Additionally, Kaplan–Meier analysis was conducted to assess the overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in relation to the expression levels of the TNFAIP8 family. This analysis was performed using the "Survival" package within the R software30. Hazard ratios (HR) and p-values were calculated and reported. The ROC (Receiver Operating Characteristic) curve is used to evaluate the performance of binary classification models. It assesses the model's classification effectiveness by comparing sensitivity and specificity. AUC (Area Under the Curve): The closer the AUC value is to 1, the better the model's classification performance.
The "pROC" package was utilized to analyze receiver operating characteristic (ROC) curves31.
cBioPortal analysis
The cBioPortal tool was utilized to access data on alterations in TNFAIP8 family genes from the TCGA dataset32,33. All members of the TNFAIP8 family were assessed within the LGG and GBM pan-cancer atlas cohort of TCGA. The names of the TNFAIP8 family genes (TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3) were inputted into the designated field, and subsequently, genetic alteration data was retrieved from both the "OncoPrint" and "Cancer Types Summary" models available on cBioPortal.
Immune-related analysis of TNFAIP8 family genes
The ssGSEA algorithm was utilized to evaluate twenty-four types of immune cells in glioma tissue samples34. Spearman analysis was conducted to assess correlations between the expressions of TNFAIP8 family genes, immune cells, and immune checkpoint genes such as PDCD1, CTLA4, and PD-L1. Visualization of these correlations was achieved through the utilization of the "ggplot2" and "pheatmap" packages. The enrichment of immune infiltrating cells in glioma patients with high versus low expression was assessed using the Wilcoxon rank-sum test.
The analysis of biological functions
Gene expression data from glioma patients in TPM were acquired from the TCGA website. Pearson correlation coefficients (P < 0.001, |R|> 0.4) were utilized to identify co-expressed genes with TNFAIP8 family genes. Subsequently, Venn overlap analysis was employed to visualize the interactions among significantly associated TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3. To investigate the potential signal transduction pathways and biological functions regulated by TNFAIP8 family genes, the "clusterProfiler" package was utilized to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG)35 and Gene Ontology (GO) analyses of co-expressed genes36. The GO analysis included categories of molecular function (MF), cellular component (CC), and biological process (BP).
Statistical analysis
The data, which exhibited a normal distribution, were presented as means ± standard deviation. TNFAIP8 family expressions in normal and glioma tissues were analyzed using Wilcoxon rank-sum and Wilcoxon signed-rank tests in SPSS 23.0 software. The correlation between TNFAIP8 family expression and clinicopathological parameters was assessed using a chi-square test model and two-class logistics model. A significance level of P < 0.05 was considered statistically significant.
Ethical approval
The datasets utilized in this study are accessible through public repositories.
Consent to participate
The datasets employed in this study are available through public repositories.
Results
Gene expressions, survival analysis and receiver operating characteristic (ROC) analysis of TNFAIP8 family in glioma
The data obtained from the UCSC XENA database revealed the differential expressions of TNFAIP8 family members in glioma, with 1157 normal and 699 tumor samples analyzed (the sample sizes and gene expressions are provided in supplementary 1). As depicted in Fig. 1A, the expressions of TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 were notably upregulated in glioma tissues compared to normal tissues, with statistical significance (P < 0.001). The study investigated the correlation between TNFAIP8 family expression and patient survival outcomes using Kaplan–Meier curves based on data from The Cancer Genome Atlas (TCGA) (the sample sizes and survival data of TNFAIP8 family are provided in supplementary 2.1–2.12). The analysis aimed to determine the overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) profiles of glioma patients (Fig. 1B-M). Patients exhibiting high expression levels of TNFAIP8 (Fig. 1B-D), TNFAIP8L1 (Fig. 1E-G), and TNFAIP8L2 (Fig. 1H-J) demonstrated significantly shorter OS, DSS, and PFI compared to those with low expression levels of these molecules (P < 0.001). However, there was no statistically significant difference observed between high and low expression levels of TNFAIP8L3 in terms of OS, DFS, and PFI (P = 0.995, P = 0.914, and P = 0.696, respectively) as illustrated in Fig. 1K-M. In order to enhance comprehension of the prognostic predictive capabilities of TNFAIP8 family members, ROC curve analysis was conducted(Fig. 1N-Q) (the sample sizes of ROC curve analysis data are provided in supplementary3.1–3.4). The findings indicated that TNFAIP8 (AUC = 0.782, P < 0.001, Fig. 1N), TNFAIP8L2 (AUC = 0.948, P < 0.001, Fig. 1P), and TNFAIP8L3 (AUC = 0.767, P < 0.001, Fig. 1Q) exhibited superior accuracy compared to TNFAIP8L1 (AUC = 0.555, P < 0.001, Fig. 1O) in the context of glioma.
The expression, Survival analysisand The area under the curve values for receiver operator characteristic (ROC) curves of TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 in glioma tissue and normal tissue. 1A: The expression of TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 in glioma tissue and normal tissue (***p < 0.001). 1B-1 M: Survival analysis of TNFAIP8 family members in glioma patients. The overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI) survival curves comparing patients with high and low TNFAIP8 family members expression in gliomas are shown Fig. 1N-1Q: The area under the curve values for receiver operator characteristic (ROC) curves for TNFAIP8 family members across glioma (A-D).
Correlation between expressions of TNFAIP8 family and clinicopathological parameters of glioma
The study utilized a chi-square test to examine the correlation between clinicopathological factors and expressions of the TNFAIP8 family, as presented in Table 1. The results of the chi-square test indicated a significant relationship between TNFAIP8 family members (TNFAIP8, TNFAIP8L1, TNFAIP8L2, TNFAIP8L3) and the age, 1p/19q codeletion status, and histological type of glioma patients. Specifically, the expression of TNFAIP8L2 was found to be associated with the age of glioma patients, while the expressions of TNFAIP8, TNFAIP8L1, and TNFAIP8L3 did not exhibit significant differences. Notably, no significant association was observed between the expressions of the TNFAIP8 family and the race of the patients. With the exception of TNFAIP8L2, the expressions of TNFAIP8, TNFAIP8L1, and TNFAIP8L3 were all found to be significantly correlated with the primary therapy outcome.
A logistic regression analysis revealed a statistically significant correlation between all TNFAIP8 family members and the 1p/19q codeletion status (P < 0.001) and age (P < 0.05) among patients with glioma. The results suggested that “1p/19q codeletion” was considered a favorable factor; whereas “age > 60” was considered a harmful factor in the glioma related to TNFAIP8 family member. We also found that there were significant associations between TNFAIP8, TNFAIP8L1, TNFAIP8L2 and WHO grade, IDH status, and Histological type of glioma patients after fitting a logistic regression analysis. The results suggested that “WHO grade G4”, “IDH status WT”, and “Histological type Glioblastoma” were considered harmful factors in the glioma associated wiht TNFAIP8, TNFAIP8L1 and TNFAIP8l2 (Fig. 2A). And then, we further analysed the TNFAIP8 family expressions of different glioma histological types. Figure 2B showed that TNFAIP8 expression levels vary among different types of brain tumors. Glioblastoma had the highest expression level, followed by oligoastrocytoma, astrocytoma, and oligodendroglioma. Glioblastoma shows a significant difference compared to all other types, while astrocytoma has a significant difference compared to oligodendroglioma. Figure 2C showed that TNFAIP8L1 expression levels vary among different types of brain tumors. Glioblastoma had the highest expression level, followed by oligoastrocytoma, astrocytoma, and oligodendroglioma. Glioblastoma shows a significant difference compared to all other types. Figure 2D showed that TNFAIP8L2 expression levels vary among different types of brain tumors. Glioblastoma had the highest expression level, followed by oligoastrocytoma, astrocytoma, and oligodendroglioma. Glioblastoma shows a significant difference compared to all other types, astrocytoma had a significant difference compared to oligodendroglioma and oligodendroglioma, and oligodendroglioma had a significant difference compared to oligodendroglioma. Figure 2E showed that TNFAIP8L3 expression levels vary among different types of brain tumors. Glioblastoma had the highest expression level, followed by oligoastrocytoma, astrocytoma, and oligodendroglioma. Oligodendrogliom showed a significant difference compared to all other types.
A logistic regression analysis was conducted between TNFAIP8 family members and clinicopathological parameters among patients with glioma.
Mutation and correlation analysis of TNFAIP8 family in glioma
Epigenetic modifications are known to be crucial in the development of early malignancies. Utilizing the cBioPortal online tool, an analysis was conducted on alterations within the TNFAIP8 family, including mutations and copy number aberrations, in LGG and GBM. The specific genetic variations of TNFAIP8 family molecules and their respective alteration frequencies are depicted in Fig. 3A. Specifically, mutations were most commonly detected in TNFAIP8L1 (1.5%), with all identified mutations being amplifications. TNFAIP8 exhibited only missense mutations, TNFAIP8L1 showed amplifications and deep deletions, TNFAIP8L2 displayed only amplification mutations, and TNFAIP8L3 presented deep deletions and missense mutations. The genetic alterations within the TNFAIP8 family were diverse, affecting 2.53% of the 514 patients with LGG and 2.03% of the 592 patients with GBM, as illustrated in Fig. 3B. Subsequently, we conducted a Pearson correlation analysis to investigate the relationship among the TNFAIP8 family members. The results presented in Fig. 3C demonstrate significant positive correlations between TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3. Moreover, an analysis was conducted to examine the correlation between genetic modifications in the TNFAIP8 family and OS, DSS, and PFS among individuals with glioma. The results obtained from the Kaplan–Meier plot and log-rank test revealed a significant association between genetic alterations in the TNFAIP8 family and extended OS (P = 3.029e−3), DSS (P = 5.062e−3), and PFS (P = 0.0415) in glioma patients (Fig. 3D-F). These findings suggest that genetic variations in the TNFAIP8 family may play a critical role in determining the prognosis of glioma patients.
Genetic alterations in TNFAIP8 family members and their relationship with prognosis of glioma patients. Summary of alterations in different expressed TNFAIP8 families in glioma (A, B). Associations of different TNFAIP8 family members with each other (C). Genetic alterations in TNFAIP8 family were correlated to longer overall survival (OS) (D), disease-specific survival (DSS) (E), and progress-free interval (PFI) (F) of glioma patients.
Correlation between expressions of TNFAIP8 family and tumor immunity in glioma
As shown in Fig. 4, the relationships of relative abundance of 24 immune cells and expressions of TNFAIP8 family in glioma were evaluated using the ssGSEA algorithm (the data of relative abundance of 24 immune cells and expressions of TNFAIP8 family genes are provided in supplementary 4.1–4.4). The expression of TNFAIP8 and Macrophages, Neutrophils, Eosinophils, T cells, aDC, iDC, Cytotoxic cells, Th2 cells, NK CD56dim cells, Th17 cells, T helper cells, B cells, NK cells, DC and Th1 cells have a significant positive correlation; while The expression of TNFAIP8 and TReg, Tcm, TFH, NK CD56bright cells, Tgd and pDC have a significant negative correlation (Fig. 4A). As shown in Fig. 4B, 15 types of immune cells were positively correlated with TNFAIP8L1 expression, including Th1 cells, Neutrophils, Macrophages, Eosinophils, NK cells, T cells, TH1 cells, NK CD56dim cells, B cells, Mast cells, aDC, iDC, Cytotoxic cells, NK CD56bright cells and Tem; while 6 types of immune cells were negatively correlated with TNFAIP8L1 expression, including Tcm, DC, TReg, Tgd, CD8 T cells and pDC. The expression of TNFAIP8L2 and Macrophages, Neutrophils, aDC, iDC, Eosinophils, Cytotoxic cells, T cells, Th17 cells, NK CD56dim cells, B cells, T helper cells, DC, Th2 cells, Th1 cells and NK cells have a significant positive relationship; while The expression of TNFAIP8L2 and Tgd, TReg, Tcm, pDC and NK CD56bright cells have a significant negative relationship (Fig. 4C). As shown in Fig. 4D, 15 kinds of immune cells were positively correlated with TNFAIP8L3 expression, including Th1 cells, Macrophages, Neutrophils, Eosinophils, iDC, TFH, Mast cells, Th17 cells, Cytotoxic cells, aDC, T cells, B cells, Tem, NK CD56dim cells and NK cells; while 4 kinds of immune cells were negatively correlated with TNFAIP8L1 expression, including T helper cells, Th2 cells, TReg and pDC.
TNFAIP8 family expressions and tumor immunity. In the bar graph, TNFAIP8 (A), TNFAIP8L1 (B), TNFAIP8L2 (C) and TNFAIP8L3 (D) expressions were correlated with 24 immune infiltration cells. *p < 0.05, **p < 0.01, ***p < 0.001.
Wilcoxon Rank-sum test was used to detect the enrichment of immune cells in TNFAIP8 family high and low expression groups of glioma (Fig. 5). The results showed that, compared with the TNFAIP8 low expression group, the enrichment of aDC, B cells, Cytotoxic cells, Eosinphils, iDC, Macrophages, Neutrophils, NK CD56dimt cellls, NK cells, T cells, T helper cells, Th1 cells, Th17 cells and Th2 cells were higher in TNFAIP8 high expression group; while the enrichment of CD8 T cells, NK CD56bright cellls, pDC, Tcm, TFH, Tgd and TReg were lower in TNFAIP8 high expression (Fig. 5A). Compared to the TNFAIP8L1 low expression group, the enrichment of aDC, B cells, Cytotoxic cells, Eosinphils, iDC, Macrophages, Neutrophils, NK CD56bright cellls, NK CD56dimt cellls, NK cells, T cells, Th1 cells and Th2 cells were higher in TNFAIP8L1 high expression group; while the enrichment of CD8 T cells, pDC, Tcm, Tgd and TReg were lower in TNFAIP8L1 high expression (Fig. 5B). Compared with the TNFAIP8L2 low expression group, the enrichment of aDC, B cells, Cytotoxic cells, Eosinphils, iDC, Macrophages, Neutrophils, NK CD56dimt cellls, NK cells, T cells, T helper cells, Th1 cells, Th17 cells and Th2 cells were significantly up-regulated in TNFAIP8L2 high expression group; while the enrichment of NK CD56bright cellls, pDC, Tcm, Tgd and TReg were significantly down-regulated in TNFAIP8L2 high expression (Fig. 5C). Compared to the TNFAIP8L3 low expression group, the enrichment of aDC, B cells, Cytotoxic cells, Eosinphils, iDC, Macrophages, Neutrophils, NK CD56dimt cellls, T cells, TFH, Th1 cells and Th17 cells were significantly up-regulated in TNFAIP8L3 high expression group; while the enrichment of pDC, T helper cells, Th2 cells and TReg were significantly down-regulated in TNFAIP8L3 high expression (Fig. 5D).
The relationships between the enrichment of immune cells and TNFAIP8 family (A-D) high and low expression groups of glioma. *p < 0.05, **p < 0.01, ***p < 0.001.
We conducted a comprehensive correlation analysis to further explore the relationship between TNAIP8 family expressions and immune checkpoints genes in glioma. As illustrated in Fig. 6A, TNFAIP8 family expression exhibited a significantly negative correlation with ADORA2A genes in glioma; while TNFAIP8 family expressions exhibited a significantly positive correlation with the most of iimmune checkpoints genes in glioma.
Correlation analyses of the expression profiles of TNFAIP8 family with immune checkpoint molecules in glioma and genes co-expressed with TNFAIP8 family in glioma.. 6A:The horizontal and vertical ordinates represent genes, and different colors represent correlation coefficients (in the diagram, red represents positive correlation, blue represents negative correlation) Fig. 6B-E showed the top 10 co-expressed genes correlated with TNFAIP8 family positively and negatively. Moreover, the 12 intersections among TNFAIP8 family co-expressing genes (including CORO1A, OSTF1, TM6SF1, TNFRSF11A, KCNK13, PRKCD, STAT6, PIK3CD, FMNL1, FFAR4, TRPV2 and RASSF5) are shown in a Venn diagram (F). We conducted a comprehensive correlation analysis to further explore the relationship between TNAIP8 family expressions and the 12 co-expressing genes (G). *p < 0.05, **p < 0.01, ***p < 0.001.
Genes co-expressed with TNFAIP8 family in glioma
In TCGA transcriptome data, the numbers of the gene positively correlated with TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 were 3984, 1799, 1995 and 150, and the numbers of the gene negatively correlated with TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 were 1542, 116, 990 and 6 respectively (the sample sizes are provided in supplementary 5.1–5.4). Figure 6B-E showed the top 10 co-expressed genes correlated with TNFAIP8 family positively and negatively (the data of 10 genes co-expressed with TNFAIP8 family in gliomaare provided in supplementary 6.1–6.4). Moreover, the 12 intersections among TNFAIP8 family co-expressing genes (including CORO1A, OSTF1, TM6SF1, TNFRSF11A, KCNK13, PRKCD, STAT6, PIK3CD, FMNL1, FFAR4, TRPV2 and RASSF5) are shown in a Venn diagram (Fig. 6F) (the data of Venn diagram sample sizes are provided in supplementary 7). We conducted a comprehensive correlation analysis to further explore the relationship between TNAIP8 family expressions and the 12 co-expressing genes, TNFAIP8 family expressions exhibited a significantly positive correlation with the 12 co-expressing genes in glioma (Fig. 6G).
Enrichment analysis of TNFAIP8 family co-expression genes in glioma
Subsequently, to investigate the functions of TNFAIP8 family genes in glioma, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the genes co-expressed with TNFAIP8 family. GO encompasses three main components: molecular functions (MF), cellular components (CC), and biological processes (BP). The main BPs affected by the TNFAIP8 family co-expressed genes included temperature homeostasis, cell chemotaxis, positive regulation of cold-induced thermogenesis, regulation of cold-induced thermogenesis and cold-induced thermogenesis. The main CCs in associated with the TNFAIP8 family co-expressed genes included endocytic vesicle, phagocytic vesicle, secretory granule lumen, cell cortex and axonal growth cone. The main MFs influenced by the TNFAIP8 family co-expressed genes included phosphatidylinositol 3-kinase activity, phosphatidylinositol bisphosphate kinase activity, insulin receptor substrate binding, 1-phosphatidylinositol-3-kinase activity and tumor necrosis factor-activated receptor activity (Fig. 7A). The results from KEGG analysis indicated that the TNFAIP8 family co-expressed genes regulated Inflammatory mediator regulation of TRP channels, pathways in cancer, type II diabetes mellitus, non-small cell lung cancer, prolactin signaling pathway, Fc gamma R-mediated phagocytosis and etc.(Fig. 7B).
Gene Ontologies (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of TNFAIP8 family members and their co-expression genes in glioma. Bubble charts of GO (A) and KEGG (B) terms.
Discussion
Glioma is the most prevalent tumors of the central nervous system, with LGG representing a common and infiltrative subtype of primary malignant intracranial tumors, while GBM is recognized as the most frequent and aggressive variant1. Accurate prognostic assessment is crucial for guiding early treatment decisions and enhancing patient outcomes. Utilizing biomarkers in cancer therapy can enhance prognostic predictions for certain malignancies. This current study conducted a comprehensive analysis of 4 molecules within the TNFAIP8 family in glioma using self-designed indicators to assess their differential expression, prognostic value, immunity, and other relevant aspects. This research represents the inaugural investigation into the gene expressions, as well as prognostic implications, of various TNFAIP8 family members in glioma. The results of this study are anticipated to improve current understanding, inform treatment strategies, and enhance prognostic accuracy for patients diagnosed with glioma.
Studies have indicated that TNFAIP8 family exhibit altered expression levels in various human malignant tumors, such as those found in the breast12,13, bladder14, cervical15,16, colon17, gastric18, liver19, lung20, ovarian21,22, prostate23, Head and neck cancer24 and other cancers, when compared to their respective normal tissue counterparts. In our study, the expressions of TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 were significantly up-regulated in glioma compared with normal tissues. The expressions of TNFAIP8 and TNFAIP8L3 in the study were in accordcane with previous studies have shown that down-regulated TNFAIP8 expression inhibited the proliferation and migration of GBM cells25, and TNFAIP8L3 was upregulated in GBM, and associated with poor prognosis26. While, TNFAIP8L2 expression in our study was not exactly consistent with Liu report which reported up-regulated TNFAIP8L2 inhibited proliferation of GBM cells27. That is may be due to our data from patients of glioma including LGG and GBM, and our results were mainly relay on bioinformatics and public databases. We downloaded DataSet ID: mRNA sequencing (non-glioma as control, Supplementary 8) and DataSet ID: mRNAseq_325 from the CGGA database (Supplementary 9), and organized the data for TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 expression in 20 normal patients and 325 glioma patients. Statistical analysis of the TNFAIP8 gene family in the two data sets revealed that the average levels of TNFAIP8 and TNFAIP8L2 were slightly higher in gliomas, but the differences were not statistically significant. In contrast, the average levels of TNFAIP8L1 and TNFAIP8L3 were slightly higher in normal individuals compared to glioma patients, with statistically significant differences (Supplementary Fig. 1). This result differs significantly from our findings, which may be attributed to data originating from different researchers and the small number of control cases. Further experimental validation is required. Additionally, we performed online statistical analysis of the prognosis of glioma patients with respect to the TNFAIP8 gene family using the CGGA database. We found that high expression of TNFAIP8, TNFAIP8L1, and TNFAIP8L2 was significantly associated with poorer prognosis compared to low expression. However, high expression of TNFAIP8L3 showed no significant difference in prognosis compared to low expression (Supplementary Fig. 2). Until now, no research has deeply reported the role of TNFAIP8L1 in the pathogenesis and progression of glioma. We firstly reported the role of TNFAIP8L1 played in glioma. We also explored the relationship between TNFAIP8 family expression and patient survival analysis including OS, DSS and PFI. High expressions of TNFAIP8, TNFAIP8L1 and TNFAIP8L2 were significantly associated with poor OS, DSS, PFI in glioma patients. The findings indicate that TNFAIP8, TNFAIP8L1 and TNFAIP8L2 may serve as a promising biomarker for predicting the prognosis of individuals with glioma. While, there was no statistically significant variance observed in OS, DSS and PFI between high and low levels of TNFAIP8L3 expression in glioma patients. This founding was not exactly consistent with Yuan study reported TNFAIP8L3 was associated with poor prognosis in GBM patients26. This may be attributed to the utilization of data obtained from glioma patients, encompassing both LGG and GBM, with our findings predominantly reliant on bioinformatics analyses and information sourced from public databases. The findings of analysis need undergo experimental validation to ascertain the precision and dependability.
The ROC curve is commonly utilized for its efficacy in evaluating the diagnostic test's performance37. However, there is no research that has specifically investigated the diagnostic efficacy of the aforementioned 4 TNFAIP8 molecules in glioma. Hence, our analyses have been conducted in accordance with the current demands. In the assessment of glioma through diagnostic testing, all AUC values exceeded 0.8, indicating a high level of accuracy. In the evaluation of glioma diagnostics, the AUC values for TNFAIP8L2 (AUC = 0.948) exceed 0.9 better than that of TNFAIP8 (AUC = 0.782), TNFAIP8L1 (AUC = 0.555) and TNFAIP8L3 (AUC = 0.767), demonstrating exceptional performance. Our findings strongly suggest that elevated expression of TNFAIP8L2 in glioma tissues may have significant implications in the pathogenesis of glioma, indicating their potential utility as diagnostic markers and therapeutic targets for glioma patients. This holds promise for the early detection of glioma in clinical practice.
Currently, it is widely believed that the WHO grade, IDH status, and age are crucial factors influencing the prognosis of glioma patients6. The higher the WHO grade, the wild-type IDH status, and the older the age of glioma patients, the poorer their prognosis38. In our study, we found that the expressions of TNFAIP8, TNFAIP8L1 and TNFAIP8L2 were all significantly associated with WHO grade and IDH status, and TNFAIP8, TNFAIP8L1, TNFAIP8L2, TNFAIP8L3 were significantly linked to age. These indicated that high expressions of TNFAIP8, TNFAIP8L1 and TNFAIP8L2 were significantly associated with poor OS, DSS, PFI in glioma patients. The presence of 1p/19q codeletion in oligodendroglioma is generally considered a favorable prognostic indicator39. Patients with this genetic anomaly may exhibit increased sensitivity to treatment and have a better overall survival rate. Oligodendrogliomas harboring 1p/19q codeletion are often more responsive to alkylating agents and chemotherapy drugs like temozolomide40. We detected that TNFAIP8, TNFAIP8L1, TNFAIP8L2, TNFAIP8L3 were significantly linked to 1p/19q codeletion in the study. This revealed TNFAIP8 famliy genes might serve as a crucial marker in the classification and prognostic assessment of gliomas, aiding physicians in better understanding the disease characteristics and optimizing treatment strategies. We also found that TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 were significantly related to Histological type of glioma patients. The expressions of TNFAIP8 family were higher in glioblastoma than that in astrocytoma, oligoastrocytoma and oligodendroglioma. Glioblastoma represents a predominant high-grade subtype41. This founding suggested that TNFAIP8 family genes were associated with poor prognostic of glioma patients.
Epigenetic alteration played an important role in early malignancies42. In the study, we found that the genetic alterations of TNFAIP8 family were varied, 2.53% of 514 patients with LGG and 2.03% of 592 patients with GBM, respectively. This revealed there were signifiant relationship between genetic mutaions and the initiation and progression of glioma. Given the prevalence of genetic mutations in glioma, cBioPortal is utilized to assess the frequency of gene alterations and their impact on overall survival43. In our study, we found that genetic alterations in TNFAIP8 family were related to longer OS, DSS and PFS of glioma patients. We also detected that significantly positive relationships were detecteded between TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 in glioma. The result suggetsed that the genetic changes of TNFAIP8 family may crucially affect the prognosis of glioma patients.
Tumor cells have the ability to engage with the immune microenvironment, thereby impacting the initiation and progression of tumors. The immune infiltrating cells within the tumor play a crucial role in shaping the immune microenvironment44,45. Several past researches reported that TNFAIP8 family played important roles in immune regulation, inflammation, and cancer development46,47. Therefore, we conducted an analysis to estimate the immune microenvironment of the 4 TNFAIP8 family molecules. Our findings demonstrate a strong association between these molecules and various immune cells, as illustrated in Fig. 5A-D. All theTNAIP8 family molecules displayed promising results in the ssGSEA analysis of estimates. We also detected the enrichment of immune cells in TNFAIP8 family high and low expression groups of glioma. Our results suggested that the expressions of TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 were associated with immune scores of most of immune cells, suggesting a potential role in promoting the malignant progression of glioma through the regulation of immune infiltrating cells.
In recent years, immunotherapy has significantly altered the clinical oncology field, leading to notable enhancements in long-term survival rates among certain cancer patients. The immuno-therapeutic effect on glioma patients may be influenced by specific molecules that impact immune checkpoint molecules48,49. Previous research has identified limitations in central nervous system (CNS) disease immunotherapies, particularly in the context of glioma treatment50. However, recent studies have shown an increasing interest in and exploration of glioma immunotherapies, with accumulating evidence suggesting their potential efficacy. In the present investigation, our analysis of immune checkpoint correlations revealed that, aside from ADORA2A gene, which exhibits a negative correlation with TNFAIP8 family expressions, the remaining most of immune checkpoint genes demonstrated positive correlations with TNFAIP8 family genes. This finding provides additional evidence revealed that these molecules contribute to the progression of gliomas by regulating the activity of immune cells. This suggested that these molecules have the potential to play a significant role in the advancement of immune targeted therapy in the future.
Additionally, functional annotation of TNFAIP8 family members and their co-expressed genes in gliomas was conducted through GO and KEGG pathway analysis. GO analysis showed the primary biological processes influenced by the TNFAIP8 family co-expressed genes encompassed cell chemotaxis, temperature homeostasis, endocytic vesicel and so on. And, KEGG analysis showed that TNFAIP8 family co-expressed genes regulate various pathways, including inflammatory mediator regulation of TRP channels, pathways in cancer, prolactin signaling pathway, Fc gamma R-mediated phagocytosis and so on. Recently researches shown that these pathways played important roles in the process of glioma. Researchers have discovered that Astragalus membranaceus has the potential to serve as a therapeutic agent for glioma by modulating inflammatory mediators that regulate TRP channels51. A previous reaserch reported that human glioma cells promoted the proliferation via prolactin signaling pathway52. Researcheers also found that argetome analysis identified the participation of MiR-126 in the neurotrophin signaling pathway, suggesting a potential role in the prevention of glioma development53. Sereval researhes reported that Jak-STAT signaling pathway played an important role in the progression of glioma54,55,56.
Although, several limitations should be acknowledged in the current study. The complexity and heterogeneity of data in the context of gene expression, particularly within the TNFAIP8 family genes in various glioma subtypes, presents challenges in analysis. Variability in gene expression among different subtypes and individual patients complicates the identification of consistent biomarkers or therapeutic targets. Additionally, limited sample sizes in databases may constrain the statistical power and reliability of findings related to TNFAIP8 family genes in glioma samples. It is necessary to integrate various data types, including genomic, transcriptomic, proteomic, and epigenomic data. Comprehensive analysis of these data is a significant challenge. Data from different sources and technical platforms may have technical biases and batch effects, requiring complex normalization and correction. Limitations in Bioinformatics Tools: The Accuracy and Reliability of Tools and Algorithms in Analyzing TNFAIP8 Family Genes. It is noted that existing bioinformatics tools and algorithms may exhibit accuracy and reliability concerns when analyzing TNFAIP8 family genes. The utilization of different tools may lead to disparate results, thereby complicating the interpretation of findings. Furthermore, many analysis tools necessitate significant computational resources and time, particularly when handling high-throughput sequencing data. The accuracy and reliability of analysis results are directly influenced by the quality of data, which can be compromised by factors such as low-quality sequencing data, sample contamination, and data loss. Data reproducibility poses a significant challenge, especially when comparing studies conducted in various laboratories. The incomplete or unknown annotation of functions for certain TNFAIP8 family genes hinders a comprehensive understanding of their mechanisms in gliomas. Variations in gene functions among different glioma subtypes further complicate the interpretation of results. Difficulties in the Translation of Research Findings to Clinical Practice: The transition from bioinformatics analysis to practical clinical applications is often fraught with challenges. Despite the identification of potential targets and biomarkers in laboratory settings, rigorous validation processes and extensive clinical trials are necessary before these findings can be effectively implemented in clinical practice. Additionally, concerns surrounding data privacy and ethical considerations may impede the sharing and application of these research findings. Gliomas exhibit dynamic characteristics, with the gene expression and mutation profiles of tumors often undergoing significant changes throughout the course of treatment. The comprehensive capture and analysis of these dynamic alterations necessitate the utilization of more sophisticated time-series data and analytical techniques. Despite current deficiencies and constraints in the bioinformatics analysis of TNFAIP8 family genes in gliomas, ongoing advancements in technology and methodological enhancements are anticipated to progressively address these challenges. This progress is poised to enhance our comprehension of the involvement of TNFAIP8 family genes in gliomas, ultimately facilitating the advancement of novel therapeutic approaches.
Conclusion
Overall, our research has utilized databases to conduct thorough investigations into the expression patterns and prognostic implications of TNFAIP8 family genes. Additionally, we have examined their associations with clinical factors and diagnostic utility, among other findings. Concurrently, we performed a comprehensive analysis encompassing multiple omics approaches, various perspectives, and diverse functionalities to investigate the genetic alterations of these molecules, such as mutations and copy number alterations. Additionally, our immune analysis findings elucidated the distinctive immune microenvironment associated with TNFAIP8 family genes. This study represents the initial investigation into the expression profile and clinical prognostic implications of TNFAIP8 family members in glioma. These findings have implications for personalized prognostic strategies in the management of glioma patients and offer support for the development of immunotherapies targeting TNFAIP8 family members.
Data availability
Research data are shown in the supplements files.
References
Bale, T. A. & Rosenblum, M. K. The 2021 WHO classification of tumors of the central nervous system: An update on pediatric low-grade gliomas and glioneuronal tumors. Brain Pathol. 32, e13060. https://doi.org/10.1111/bpa.13060 (2022).
Lapointe, S., Perry, A. & Butowski, N. A. Primary brain tumours in adults. Lancet 392, 432–446. https://doi.org/10.1016/s0140-6736(18)30990-5 (2018).
Wirsching, H. G., Galanis, E. & Weller, M. Glioblastoma. Handb. Clin. Neurol. 134, 381–397. https://doi.org/10.1016/b978-0-12-802997-8.00023-2 (2016).
Krane, G. A. et al. Inter-pathologist agreement on diagnosis, classification and grading of canine glioma. Vet. Comp. Oncol. 20, 881–889. https://doi.org/10.1111/vco.12853 (2022).
van den Bent, M. J. Interobserver variation of the histopathological diagnosis in clinical trials on glioma: A clinician’s perspective. Acta Neuropathol. 120, 297–304. https://doi.org/10.1007/s00401-010-0725-7 (2010).
Louis, D. N. et al. The 2016 World Health Organization classification of tumors of the central nervous system: A summary. Acta Neuropathol. 131, 803–820. https://doi.org/10.1007/s00401-016-1545-1 (2016).
Goldsmith, J. R. & Chen, Y. H. Regulation of inflammation and tumorigenesis by the TIPE family of phospholipid transfer proteins. Cell. Mol. Immunol. 14, 1026. https://doi.org/10.1038/cmi.2017.127 (2017).
Tian, Z. et al. TNFAIP8 family gene expressions in the mouse tail intervertebral disc injury model. JOR Spine 3, e1093. https://doi.org/10.1002/jsp2.1093 (2020).
Padmavathi, G. et al. Novel tumor necrosis factor-α induced protein eight (TNFAIP8/TIPE) family: Functions and downstream targets involved in cancer progression. Cancer Lett. 432, 260–271. https://doi.org/10.1016/j.canlet.2018.06.017 (2018).
Lou, Y. & Liu, S. The TIPE (TNFAIP8) family in inflammation, immunity, and cancer. Mol. Immunol. 49, 4–7. https://doi.org/10.1016/j.molimm.2011.08.006 (2011).
Zhang, L., Liu, R., Luan, Y. Y. & Yao, Y. M. Tumor necrosis factor-α induced protein 8: Pathophysiology, clinical significance, and regulatory mechanism. Int. J. Biol. Sci. 14, 398–405. https://doi.org/10.7150/ijbs.23268 (2018).
Hu, W. et al. TIPE1 inhibits breast cancer proliferation by downregulating ERK phosphorylation and predicts a favorable prognosis. Front Oncol 9, 400. https://doi.org/10.3389/fonc.2019.00400 (2019).
Hu, W. et al. TIPE2 sensitizes breast cancer cells to paclitaxel by suppressing drug-induced autophagy and cancer stem cell properties. Hum. Cell 36, 1485–1500. https://doi.org/10.1007/s13577-023-00900-y (2023).
Jiang, Y. et al. A novel prognostic factor TIPE2 in bladder cancer. Pathol. Oncol. Res. 28, 1610282. https://doi.org/10.3389/pore.2022.1610282 (2022).
Jiang, J., Gao, L., Lan, Y., Wang, Y. & Zhao, P. TIPE1 promotes cervical cancer cell chemoresistance to cisplatin in a wild-type p53-dependent manner. Front Oncol 10, 593615. https://doi.org/10.3389/fonc.2020.593615 (2020).
Zhang, H., Han, W. J. & Zhang, Z. L. The importance of tumor necrosis factor-α-induced protein-8 like-2 in the pathogenesis of cervical cancer and preeclampsia via regulation of cell invasion. Tohoku J Exp Med 257, 181–191. https://doi.org/10.1620/tjem.2022.J026 (2022).
Zhong, M. et al. Expression of TIPE family members in human colorectal cancer. Oncol. Lett. 21, 118. https://doi.org/10.3892/ol.2020.12379 (2021).
Liu, W. et al. TIPE1 suppresses invasion and migration through down-regulating Wnt/β-catenin pathway in gastric cancer. J. Cell. Mol. Med. 22, 1103–1117. https://doi.org/10.1111/jcmm.13362 (2018).
Xu, D. D. et al. TIPE2 attenuates liver fibrosis by reversing the activated hepatic stellate cells. Biochem. Biophys. Res. Commun. 498, 199–206. https://doi.org/10.1016/j.bbrc.2017.11.178 (2018).
Wu, X. et al. TIPE1 function as a prognosis predictor and negative regulator of lung cancer. Oncotarget 8, 78496–78506 (2017).
Gao, H. et al. TNFAIP8 variants as potential epidemiological and predictive biomarkers in ovarian cancer. Cancer Cell Int. 20, 396. https://doi.org/10.1186/s12935-020-01490-7 (2020).
Zhang, Z. et al. TIPE1 suppresses growth and metastasis of ovarian cancer. J. Oncol. 2021, 5538911. https://doi.org/10.1155/2021/5538911 (2021).
Niture, S. et al. TNFAIP8 drives metabolic reprogramming to promote prostate cancer cell proliferation. Int. J. Biochem. Cell Biol. 130, 105885. https://doi.org/10.1016/j.biocel.2020.105885 (2021).
Lan, G. et al. Comprehensive analysis of the expression and prognosis for TNFAIPs in head and neck cancer. Sci. Rep. 11, 15696. https://doi.org/10.1038/s41598-021-95160-x (2021).
Yuan, Z. et al. Chlorotoxin fusion protein regulates miR-374a and TNFAIP8 expression and inhibits glioma cell proliferation and promotes apoptosis. Cytotechnology 72, 685–694. https://doi.org/10.1007/s10616-020-00411-w (2020).
Yuan, F. et al. TIPE3 is a regulator of cell apoptosis in glioblastoma. Cancer Lett. 446, 1–14. https://doi.org/10.1016/j.canlet.2018.12.019 (2019).
Liu, Z. J., Liu, H. L., Zhou, H. C. & Wang, G. C. TIPE2 inhibits hypoxia-induced Wnt/β-catenin pathway activation and EMT in glioma cells. Oncol. Res. 24, 255–261. https://doi.org/10.3727/096504016x14666990347356 (2016).
Zhang, X., Dapar, M. L. G., Zhang, X. & Chen, Y. A pan-cancer analysis of the oncogenic role of YKT6 in human tumors. Medicine (Baltimore) 102, e33546. https://doi.org/10.1097/md.0000000000033546 (2023).
Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678. https://doi.org/10.1038/s41587-020-0546-8 (2020).
Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400-416.e411. https://doi.org/10.1016/j.cell.2018.02.052 (2018).
Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77. https://doi.org/10.1186/1471-2105-12-77 (2011).
Cerami, E. et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404. https://doi.org/10.1158/2159-8290.Cd-12-0095 (2012).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, l1. https://doi.org/10.1126/scisignal.2004088 (2013).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7. https://doi.org/10.1186/1471-2105-14-7 (2013).
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587-d592. https://doi.org/10.1093/nar/gkac963 (2023).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 16, 284–287. https://doi.org/10.1089/omi.2011.0118 (2012).
Mandrekar, J. N. Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol. 5, 1315–1316. https://doi.org/10.1097/JTO.0b013e3181ec173d (2010).
Gritsch, S., Batchelor, T. T. & Gonzalez-Castro, L. N. Diagnostic, therapeutic, and prognostic implications of the 2021 World Health Organization classification of tumors of the central nervous system. Cancer 128, 47–58. https://doi.org/10.1002/cncr.33918 (2022).
Brandner, S. et al. Diagnostic accuracy of 1p/19q codeletion tests in oligodendroglioma: A comprehensive meta-analysis based on a Cochrane systematic review. Neuropathol. Appl. Neurobiol. 48, e12790. https://doi.org/10.1111/nan.12790 (2022).
van den Bent, M. J. et al. Interim results from the CATNON trial (EORTC study 26053–22054) of treatment with concurrent and adjuvant temozolomide for 1p/19q non-co-deleted anaplastic glioma: a phase 3, randomised, open-label intergroup study. Lancet 390, 1645–1653. https://doi.org/10.1016/s0140-6736(17)31442-3 (2017).
De Biase, G., Garcia, D. P., Bohnen, A. & Quiñones-Hinojosa, A. Perioperative management of patients with glioblastoma. Neurosurg. Clin. N. Am. 32, 1–8. https://doi.org/10.1016/j.nec.2020.09.005 (2021).
Verma, M. The role of epigenomics in the study of cancer biomarkers and in the development of diagnostic tools. Adv. Exp. Med. Biol. 867, 59–80. https://doi.org/10.1007/978-94-017-7215-0_5 (2015).
Eckel-Passow, J. E. et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N. Engl. J. Med. 372, 2499–2508. https://doi.org/10.1056/NEJMoa1407279 (2015).
Kaminska, B., Ochocka, N. & Segit, P. Single-cell omics in dissecting immune microenvironment of malignant gliomas-challenges and perspectives. Cells https://doi.org/10.3390/cells10092264 (2021).
Barthel, L. et al. Glioma: Molecular signature and crossroads with tumor microenvironment. Cancer Metastas. Rev. 41, 53–75. https://doi.org/10.1007/s10555-021-09997-9 (2022).
Hua, J., Zhuang, G. & Qi, Z. Current research status of TNFAIP8 in tumours and other inflammatory conditions (Review). Int. J. Oncol. https://doi.org/10.3892/ijo.2021.5226 (2021).
Niture, S., Moore, J. & Kumar, D. TNFAIP8: Inflammation, immunity and human diseases. J. Cell. Immunol. 1, 29–34 (2019).
Ghouzlani, A. et al. Immune checkpoint inhibitors in human glioma microenvironment. Front. Immunol. 12, 679425. https://doi.org/10.3389/fimmu.2021.679425 (2021).
Qi, Y., Liu, B., Sun, Q., Xiong, X. & Chen, Q. Immune checkpoint targeted therapy in glioma: Status and hopes. Front. Immunol. 11, 578877. https://doi.org/10.3389/fimmu.2020.578877 (2020).
Xu, S., Tang, L., Li, X., Fan, F. & Liu, Z. Immunotherapy for glioma: Current management and future application. Cancer Lett. 476, 1–12. https://doi.org/10.1016/j.canlet.2020.02.002 (2020).
Feng, Y., Zhu, P., Wu, D. & Deng, W. A network pharmacology prediction and molecular docking-based strategy to explore the potential pharmacological mechanism of Astragalus membranaceus for glioma. Int. J. Mol. Sci. https://doi.org/10.3390/ijms242216306 (2023).
Ducret, T. et al. Effects of prolactin on intracellular calcium concentration and cell proliferation in human glioma cells. Glia 38, 200–214. https://doi.org/10.1002/glia.10056 (2002).
Rouigari, M., Dehbashi, M., Ghaedi, K. & Pourhossein, M. Targetome analysis revealed involvement of MiR-126 in neurotrophin signaling pathway: A possible role in prevention of glioma development. Cell J 20, 150–156. https://doi.org/10.22074/cellj.2018.4901 (2018).
Swiatek-Machado, K. & Kaminska, B. STAT signaling in glioma cells. Adv. Exp. Med. Biol. 1202, 203–222. https://doi.org/10.1007/978-3-030-30651-9_10 (2020).
Li, T. et al. Oncogenic role of microRNA-19b-3p-mediated SOCS3 in glioma through activation of JAK-STAT pathway. Metab. Brain Dis. 38, 945–960. https://doi.org/10.1007/s11011-022-01136-9 (2023).
Fujii, T. et al. KLC1-ROS1 fusion exerts oncogenic properties of glioma cells via specific activation of JAK-STAT pathway. Cancers (Basel) https://doi.org/10.3390/cancers16010009 (2023).
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X.Z. and T.L. proposed the study idea. X.Z., X.Z., T.L. and K.S. collected and analyzed the data. X.Z. drafted the manuscript. X.Z. and K.S. critically revised the manuscript. All authors contributed to the article and approved the submitted version.
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Zhang, X., Zhang, X., Liu, T. et al. Comprehensive analysis of the prognostic and immunological signature of TNFAIP8 family genes in human glioma. Sci Rep 14, 17875 (2024). https://doi.org/10.1038/s41598-024-68784-y
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DOI: https://doi.org/10.1038/s41598-024-68784-y
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