Increased expression of HBXIP (LAMTOR5) predicts poor prognosis and is correlated with immune-cell infiltration in glioma

Introduction

Glioma is the most common and most malignant primary tumor of the brain (1), and its prognosis depends on clinicopathologic factors such as age, histological type, and World Health Organization (WHO) grade (2). Among the types of glioma, glioblastoma (GBM) is associated with the worst prognosis (3,4), with a median survival of only 12 to 15 months (5) among afflicted patients, even after standard treatment. At present, the treatment of glioma mainly includes surgery, radiotherapy, and chemotherapy (6). With the recent advancements being made in the research on the mutational status of isocitrate dehydrogenase 1 and 2 (IDH1/2), the codeletion status of chromosome arms 1p and 19q (1p19q), and the promoter methylation of O(6)-methylguanine-DNA methyltransferase (MGMT) (7,8), there has been a concomitant breakthrough in understanding of the molecular characteristics of brain glioma. Unfortunately, the prognosis of those with high grade glioma remains poor, and postoperative recurrence is very common (9). Therefore, there is an urgent need to develop therapeutic strategies, which can improve patient outcomes, and to identify biomarkers, which can facilitate monitoring and evaluation of patient prognosis.

Clinical trials of immunotherapy are currently underway (10-13). Immunotherapy has become an indispensable force in the field of solid tumor therapy. Since their earliest use in melanoma, immune checkpoint inhibitors have provided benefit in a variety of tumor types (14,15). Indeed, glioma patients exhibiting elevated expression levels of immune-related genes such as PD-1 and CTLA-4 tend to have a poorer prognosis. However, anti-PD-1 or anti-CTLA-4 antibodies exert little effect on gliomas (16,17). The mechanism behind this lack of efficacy is highly complex and may be related to the unique tumor microenvironment (TME) in glioma. In healthy individuals, the blood-brain barrier effectively prevents the infiltration of immune cells. However, in cancer patients, the protective function of the blood-brain barrier is compromised, resulting in the infiltration of T cells and other peripheral leukocytes (18,19). Therefore, to better guide the immunotherapy of glioma, novel markers of immune targets need to be identified.

Late endosomal/lysosomal adaptor and MAPK and MTOR activator 5 (LAMTOR5), which encodes hepatitis B virus x-interacting protein (HBXIP), was initially discovered as being bound to the hepatitis B virus X protein. In vitro and in vivo studies have shown that HBXIP may be an oncogene (20), and its elevated expression plays an important role in promoting tumor proliferation and migration, thus leading to poor prognosis of many types of cancers (21-24). However, there is still no comprehensive report on HBXIP in glioma.

In this study, we evaluated the difference in HBXIP levels between in variety of cancers, including glioma, and the adjacent tissues using the Gene Expression Profiling Interactive Analysis (GEPIA) database (25) and further analyzed whether HBXIP expression is correlated with clinicopathological characteristics and survival in patients with glioma using the Chinese Glioma Genome Atlas (CGGA) (26) and The Cancer Genome Atlas (TCGA). Moreover, we investigated the function of HBXIP in terms of immune cell infiltration and immune checkpoint analysis and examined the prognostic value of HBXIP. Based on these results, a nomogram was established to predict the overall survival (OS) of patients with glioma. Our findings may serve as a reference for clinical work in the prognosis of patients of glioma and may provide a basis for the future development of therapeutics targeting HBXIP to improve immunotherapy responses in those with glioma. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1982/rc).

Methods

Patients and data sources

A total of 325 patients with glioma from the CGGA database were included as the training group and 702 patients with glioma from the TCGA database as the validation group. Clinicopathological information was collected, including age, sex, histological type, grade, primary relapse status, and whether or not patients were treated with chemotherapy or radiotherapy. Molecular pathological information included IDH1/2, 1p19q, and MGMT status. All patients were followed up every 3 months to obtain survival data. Tumor samples were stored in liquid nitrogen immediately after resection. Patient transcriptome sequencing data were generated using the HiSeq platform (Illumina, San Diego, CA, USA). The data resources of the training group were downloaded from the CGGA website (http://www.cgga.org.cn/), and the validation group’s data were obtained from a public database (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

GEPIA and the Human Protein Atlas (HPA)

The GEPIA database (http://gepia.cancer-pku.cn/) is an interactive web server established based on TCGA and the Genotype-Tissue Expression (GTEx) data, which can analyze the difference in gene expression between tumor tissue and normal tissue online. We used the GEPIA database to analyze the difference in HBXIP expression in a variety of cancers, including glioma, and normal tissues. The HPA database (http://www.proteinatlas.org/) is an open-access resource for human proteins, which can conveniently identify the expression of proteins in normal tissues and tumor tissues through the tissue atlas module and the pathology atlas module. We determined the difference in HBXIP protein expression between glioma tissue and normal brain tissue in the HPA database and downloaded the immunohistochemical images.

Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

Pearson correlation coefficients were used to calculate the genes most associated with HBXIP, and the genes positively correlated with HBXIP expression were screened according to the criteria of correlation coefficient >0.5 and P value <0.05. The resulting gene list was uploaded to the Database for Annotation, Visualization, and Integrated Discovery (DAVID; https://david.ncifcrf.gov/summary.jsp) platform. Finally, results from GO analysis and KEGG pathway enrichment analysis were obtained. With the official gene symbol selected as an identifier and Homo sapiens selected as the species, the top six results with the greatest statistical significance in biological process (BP), cellular component (CC), molecular function (MF) and signaling pathway were obtained.

Survival analysis and nomogram construction

After the removal of patients lacking survival information data, the survival analysis included 313 patients with glioma from the CGGA database and 603 patients from the TCGA database. Kaplan-Meier curve analysis was used to determine the survival status of patients with high HBXIP expression and low HBXIP expression, and the log-rank test was used to characterize the differences between the two groups. The hazard ratio (HR) for OS entailed the use of univariate Cox proportional hazards regressions, based on which multivariate analysis was performed for those factors identified as significant in the univariate analyses (P<0.05). A nomogram was constructed from the training group using the “rms” and “survival” package in R (The R Foundation for Statistical Computing). We then calculated the total score through the scoring system at the upper part of the model, thus predicting the 1-, 2-, 3-, 5-, and 10-year survival rate in the prediction system at the bottom of the nomogram. Calibrate curves and concordance index (C-index) values were used to validate the accuracy of the survival prediction.

Systematic analysis of immune infiltration

Tumor Immune Estimation Resource (TIMER; https://cistrome.shinyapps.io/timer/) is an online platform for analyzing the relationship between gene expression and tumor-infiltrating immune cells (27). We analyzed the correlation between HBXIP expression level and the degree of infiltration of immune cells, including B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells (DCs), in glioma using TIMER. The relationship between cumulative survival and abundance of immune infiltrates or HBXIP expression was also examined. A P value <0.05 was considered to indicate statistical significance.

Statistical analysis

Statistical analysis was performed using R software v. 4.2.1 and SPSS 26.0 (IBM Corp., Armonk, NY, USA). The t-test was used to analyze the difference in HBXIP expression between two groups, and one-way analysis of variance was used to ascertain the difference in HBXIP expression between three groups or more. Receiver operating characteristic (ROC) curves were used to assess the specificity of HBXIP expression in histological types. Pearson correlation coefficient was used to analyze the correlation between HBXIP and other gene expressions. The statistical methods for gene function analysis and prognostic analysis are described in Method. R software was used to draw heat maps, bar charts, ROC curves, survival charts, nomograms, and correlation chords via the “pheatmap”, “ggplot”, “pROC”, “Survival”, “rms”, “circlize” R packages, respectively, among others. For all statistical tests, a P value <0.05 was considered statistically significant.

Results

Expression of HBXIP in pancancer

We first clarified the expression of HBXIP in different types of cancer in the GEPIA database, as shown in Figure 1. Compared with normal tissues, the tissues in most cancers showed a higher expression of HBXIP, with this difference being statistically significant in lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), GBM, pancreatic adenocarcinoma (PAAD), skin cutaneous melanoma (SKCM), and thymoma (THYM). We further found a significantly low expression of HBXIP in kidney chromophobe (KICH) and acute myeloid leukemia (LAML). This suggests that HBXIP may play a key role in the development and progression of some cancers.

Figure 1 Expression analysis for HBXIP in various cancers. The expression of HBXIP in 33 types of human cancer based on TCGA cancer tissues and with corresponding TCGA and GTEx normal tissues. The tumor name shown in red represents a significantly high HBXIP expression in this tumor, while green represents a significantly low HBXIP expression. TPM, transcripts per million; TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, Esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; KIRP, Kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, Thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

HBXIP was highly expressed in glioma

Next, we used the GEPIA database to characterize the expression of HBXIP in glioma, and the results are shown in Figure 2A. The expression of HBXIP in GBM and brain low-grade glioma (LGG) was higher than that in normal tissues, and the high expression of HBXIP in GBM was significantly different. Figure 2B shows the results from the HPA database. HBXIP was highly expressed in glioma tissue compared to normal brain tissue.

Figure 2 HBXIP expression in glioma and normal tissues. (A) HBXIP mRNA expression levels were detected in glioma in the GEPIA database. (B) Verification of HBXIP protein levels according to immunohistochemical data from the Human Protein Atlas database (Normal: https://www.proteinatlas.org/ENSG00000134248-LAMTOR5/tissue/cerebral+cortex#img; Glioma: https://www.proteinatlas.org/ENSG00000134248-LAMTOR5/cancer/glioma#img). *, P<0.05. GBM, glioblastoma; LGG, low-grade glioma; GEPIA, Gene Expression Profiling Interactive Analysis.

Expression of HBXIP was enriched in gliomas with malignant markers

Through the analysis of CGGA database and TCGA database, we found that patients exhibited different clinicopathological features according to different HBXIP levels. With the increase in HBXIP expression, age, gender, histological type, grade, IDH, 1p19q, and MGMT were variably distributed, as shown in Figure 3A,3B. Subsequently, we analyzed the HBXIP expression in different subgroups derived from clinicopathological data. In the CGGA database, we found that HBXIP expression increased with higher histological grade. Moreover, HBXIP was highly expressed in the IDH-wild type group, 1p19q non-codeletion group, and MGMT non-methylation group (Figure 3C-3F). The results were verified in TCGA database (Figure 3G-3J), with all the results being statistically significant except for MGMT in the CGGA database. Overall, these findings suggest that HBXIP is more likely to be enriched in gliomas carrying markers predictive of malignancy.

Figure 3 Association between HBXIP and the clinicopathological characteristics of gliomas. (A) The landscape of HBXIP-related clinicopathological features of gliomas in the CGGA database. (B) The landscape of HBXIP-related clinicopathological features of gliomas in TCGA database. (C,G) HBXIP was significantly increased with the increase of histological grade in the CGGA and TCGA databases. The significance of the difference was tested via one-way analysis. (D,H) HBXIP was significantly increased in IDH wild-type gliomas in the CGGA and TCGA databases. (E,I) HBXIP was significantly increased in 1p19q non-codeletion gliomas in the CGGA and TCGA databases. (F,J) HBXIP was increased in the MGMT promoter-unmethylated gliomas. This difference was statistically significant in TCGA database but not in the CGGA database. OS, overall survival; CGGA, Chinese Glioma Genome Atlas; MGMT, O(6)-methylguanine-DNA methyltransferase; 1p19q, chromosome arms 1p and 19q; IDH, isocitrate dehydrogenase; A, astrocytoma; AO, anaplastic oligodendroglioma; AA, anaplastic astrocytoma; GBM, glioblastoma; O, oligodendroglioma; WHO, World Health Organization; AOA, anaplastic oligoastrocytoma; OA, oligoastrocytoma; TCGA, The Cancer Genome Atlas.

HBXIP as a potential biomarker for the histological type of glioma

Since histological types are associated with the prognosis of glioma, we continued to analyze the expression of HBXIP in different histological types of glioma based on CGGA and TCGA databases. The results showed that in the CGGA database, the expression of HBXIP in the GBM group was the highest, followed by astrocytoma (A) and anaplastic astrocytoma (AA), with that of anaplastic oligodendroglioma (AO) and oligodendroglioma (O) being lower (Figure 4A). Therefore, the ROC curve was used to evaluate its diagnostic ability. The area under the curve in CGGA database was 77.2% (Figure 4B). This phenomenon was also observed in TCGA database, and the expression of HBXIP was the highest in GBM group, representing a significant difference compared to oligodendrogliomas (Figure 4C). The area under the curve in TCGA database was 91.3% (Figure 4D). The optimal cutoff value derived from the CGGA database was 100.3, featuring a sensitivity of 61.0% and a specificity of 81.3%. The optimal cutoff value, sensitivity, and specificity obtained through analysis in the TCGA database were 10.4, 84.5%, and 83.6% respectively.

Figure 4 HBXIP was specifically enriched in the glioblastoma histology type. (A,C) High expression of HBXIP in glioblastoma in the CGGA and TCGA databases. The significance of the difference was tested with one-way analysis of variance. (B,D) The ROC curve showed the specificity of high HBXIP expression for glioblastoma in the CGGA and TCGA databases. CGGA, Chinese Glioma Genome Atlas; A, astrocytoma; AA, anaplastic astrocytoma; AO, anaplastic oligodendroglioma; GBM, glioblastoma; O, oligodendroglioma; AUC, area under the curve; TCGA, The Cancer Genome Atlas; ROC, receiver operating characteristic.

GO analysis and KEGG pathway analysis

To identify the biological functions associated with HBXIP, we screened the genes most associated with HBXIP in the CGGA database and the TCGA database according to the screening conditions described in Methods. GO enrichment analysis and KEGG enrichment analysis were carried out on the DAVID platform. In the CGGA database, we found that the BPs most associated with HBXIP included messenger RNA (mRNA) splicing via spliceosome, with RNA splicing, mRNA processing and translation, etc. Nucleoplasm, nucleus, and cytosol are the most relevant components of HBXIP. In addition, the MFs most relevant to HBXIP included RNA binding, protein binding, and structural constituent of ribosome. The most relevant HBXIP signaling pathways were spliceosome, proteasome, Parkinson disease, and Huntington disease. The results obtained based on TCGA database were highly coincident with those from the CGGA database, and the respective results are shown in Figure 5A-5H.

Figure 5 Biological functions associated with HBXIP. (A-C) BP, CC, and MF were mostly correlated with HBXIP in the CGGA database. (D) KEGG pathway analysis of HBXIP in the CGGA database. (E-G) BP, CC, and MF were mostly associated with HBXIP in the TCGA database. (H) KEGG pathway analysis of HBXIP in TCGA database. The size of the circle represents the gene count in GO analysis, and the horizontal number represents the –log₁₀ P value. The numbers at the upper of the KEGG analysis represent the gene count, while the –log₁₀ P values are provided at the bottom. CGGA, Chinese Glioma Genome Atlas; BP, biological process; TCGA, The Cancer Genome Atlas; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; ATP, adenosine triphosphate; NADH, nicotinamide adenine dinucleotide plus hydrogen.

HBXIP was an independent prognostic factor for OS in patients with glioma

As HBXIP is highly expressed in gliomas and enriched in gliomas with higher histologic grade. We sought to determine the prognostic value of HBXIP in those with glioma. First, survival analysis of patients with high or low HBXIP expression was conducted using Kaplan-Meier curve based on the CGGA database and TCGA database, respectively. The results showed that in the CGGA database, the survival rate (median OS time: 398 days) of patients in the high-HBXIP expression group was significantly lower than that in the low-HBXIP expression group (median OS time: 3,174 days), and the log-rank P value was less than 0.0001 (Figure 6A). The Kaplan-Meier survival curve of the TCGA database (Figure 6B) confirmed the adverse prognostic effect of a high expression of HBXIP in glioma. Next, according to the CGGA database and TCGA database, clinicopathological factors associated with the prognosis of those with glioma, including age, gender, histological grade, IDH, 1p19q, and MGMT, were included in univariate and multivariate Cox analyses. The results indicated HBXIP to be an independent prognostic factor associated with OS in patients with glioma (Tables 1,2).

Figure 6 The relationship between HBXIP expression and OS of glioma according to Kaplan-Meier survival curves. (A) Survival analysis in the CGGA. (B) Survival analysis in TCGA databases. Grouping according to HBXIP expression level; the cutoff value was the median expression of HBXIP gene. The significance of the prognostic value was tested with the log-rank test. CGGA, Chinese Glioma Genome Atlas; HR, hazard ratio; CI, confidence interval; TCGA, The Cancer Genome Atlas.

Visualization of the predictive power of the prognostic nomogram models

For the purpose of promoting the clinical application of the prognostic model, we constructed a prognostic model based on HBXIP expression, primary relapse status, histological type, chemotherapy, 1p19q, and MGMT to predict the OS of patients with glioma. Figure 7A shows the total score calculated with this nomogram, which can predict the 1-, 2-, 3-, 5-, and 10-year survival probabilities of patients with glioma. The use of the model is described in Methods. Figure 7B shows the calibration curve used to determine the overlap between the OS predicted by the model and the actual OS. The results show that the overlap between the training group and the external verification group is satisfactory, which also indicates a high level of model prediction. The C-index of this model was 0.801, higher than that predicted by each single factor. The results are shown in a bar chart in Figure 7C.

Figure 7 The individualized prediction models for OS in glioma. (A) The 1-, 2- 3-, 5-, and 10-year survival rate of patients with glioma could be precisely predicted by the nomogram. (B) Calibration curves showing the comparison between predicted and actual 1-, 2- 3-, 5-, and 10-year survival probabilities in the training and validation groups. (C) The predictive performance of the prognostic model; the association of HBXIP and the clinical prognostic factors with the OS in patients with glioma was evaluated with the C-index. OS, overall survival; PRS, primary or recurrent status; 1p19q, chromosome arms 1p and 19q; MGMT, O(6)-methylguanine-DNA methyltransferase; O, oligodendroglioma; A, astrocytoma; AO, anaplastic oligodendroglioma; AA, anaplastic astrocytoma; GBM, glioblastoma.

Activation of established cancer immune checkpoint inhibitors was positively correlated with high HBXIP expression

Based on data from CGGA and TCGA databases, we investigated the relationship between HBXIP and established inhibitory immune checkpoints including PD-1, PD-L1, CTLA-4, PD-2, PD-L2, TIM-3, B7-H3, CD200R1, CD47, CD80, HVEM, and IDO1. We found that analyses based on both CGGA and TCGA databases suggested that HBXIP expression was positively correlated with the expression of these inhibitory immune checkpoints, and the correlation chorography is shown in Figure 8A,8B. This finding may imply that HBXIP is associated with tumor immune escape and that elevated HBXIP expression inhibits the immune microenvironment of glioma.

Figure 8 The correlation between HBXIP expression and immune checkpoints molecules (PD-1, PD-L1, CTLA-4, PD-2, PD-L2, TIM-3, B7-H3, CD200R1, CD47, CD80, HVEM, and IDO1) in the CGGA and TCGA databases. (A) The chord diagram illustrating the Pearson correlation coefficients in CGGA. (B) The chord diagram illustrating the Pearson correlation coefficients in TCGA. The red parts represent a positive correlation, while the green parts represent a negative correlation. The depth of the color indicates the degree of correlation. The correlation was tested using Pearson correlation coefficient analysis. CGGA, Chinese Glioma Genome Atlas; TCGA, The Cancer Genome Atlas.

HBXIP expression was positively correlated with immune cell infiltration in glioma

After finding a positive correlation between HBXIP expression and immune checkpoint expression, we further investigated whether HBXIP expression was associated with immune cell infiltration in glioma. Therefore, we first confirmed that the immune cell infiltration level was significantly different between high and low expression of HBXIP in glioma via the TIMER database. The correlation between the levels of HBXIP expression and immune cell infiltration was also evaluated. We found that the correlation was weak in GBM, while in LGG, HBXIP was positively correlated with the infiltration of B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and DCs, with correlations of 0.466, 0.207, 0.42, 0.4, 0.488, 0.497 respectively. The results are provided in Figure 9A, while Figure 9B shows that with the increase in HBXIP expression and the abundance of immune infiltration, the cumulative survival of patients with glioma worsens. Finally, in order to clarify the relationship between HBXIP expression and tumor-infiltrating lymphocytes, the transcriptome sequencing data of CGGA and TCGA databases were used to determine the correlation between HBXIP expression and the expression of marker genes in glioma tumor-infiltrating lymphocytes. The results were shown in Figure 10, indicating that the expression level of HBXIP was positively correlated with the marker genes of various cells. Therefore, these findings indicated that the interaction between HBXIP and immune-cell populations may affect the prognosis of patients with glioma, HBXIP high expression associated with immune infiltration may be one of the reasons for its carcinogenesis effect in glioma.

Figure 9 HBXIP levels and immune cell infiltration in glioma. (A) Correlation of HBXIP levels with B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells. (B) Association between cumulative survival and abundance of immune infiltrates or HBXIP expression. TPM, transcripts per million; GBM, glioblastoma; LGG, low-grade glioma.

Figure 10 The correlation between HBXIP expression and the marker gene of various immune cells. The intensity of the heat map indicates the degree of correlation. ***, P value <0.001. CGGA, Chinese Glioma Genome Atlas; TCGA, The Cancer Genome Atlas.

Discussion

Glioma is the most common malignant tumor of the central nervous system, and those afflicted with this disease have a poor prognosis (1). The treatment methods of surgery, radiotherapy, and chemotherapy remain the standard regimen for glioma (6), and to date there have not been any novel therapies to improve overall survival (9,16). Immune checkpoint inhibitors, adoptive cell therapy and vaccines are the current therapies targeting the immune microenvironment. Immune checkpoint inhibitors such as PD-1 and CTLA-4 have not achieved significant benefits in glioma (16), which is consistent with previous researchers’ notion that gliomas are immunologically cold tumors. The compromised monitoring function of the blood-brain barrier in glioma facilitates immune cell infiltration within the TME. In addition to B cells, CD8⁺ T cells, CD4⁺ T cells, and NK cells, distinct type of astrocytes, neurons, and microglia are also present in this environment (18). The proportion of immune cells ranges from 20% to 40% (19). IDH mutant gliomas—predominantly low-grade tumors—exhibit favorable prognoses and are characterized by a tumor immune microenvironment largely composed of microglia. Conversely, most IDH wild-type brain gliomas manifest as GBMs; alongside microglia, mononuclear macrophages and lymphocytes infiltrate the immune landscape. Astrocytes prevalent in GBM may also engage in the immune response. Notably, tumor-associated macrophages derived from resident microglia are regarded as immunosuppressive elements within the glioma immune environment that can promote tumor invasion and metastasis through cellular signaling pathways (18). A recent study has indicated an increase in anti-inflammatory myeloid-derived suppressor cells (MDSCs) within the recurrent and metastatic gliomas microenvironment (19). These cells represent potential targets for immune checkpoint inhibitors. Consequently, scholars have increasingly focused on elucidating the complexities of the glioma immune microenvironment to advance therapeutic strategies. Regrettably, however, survival benefits arising from these investigations for patients with glioma remain limited. It is necessary to find novel immune targets.

Long term follow-up with detailed molecular data is critical in those with cancer. However, due to the lack of studies with adequate follow-up and detailed molecular samples, the data to support novel therapies of glioma is relatively insufficient (28-31). The use of accurate tools to identify prognosis and make more effective clinical decisions may improve patient outcomes. Hence, there is an urgent need to identify biomarkers that can effectively balance prognosis and serve as therapeutic targets. This endeavor is undeniably crucial for the advancing the treatment of glioma.

HBXIP is a conserved protein that was originally isolated in the hepatitis B virus. Research has established that HBXIP is overexpressed in a variety of tumor tissues, including lung cancer, breast cancer, stomach cancer, colorectal cancer, and liver cancer, compared to corresponding normal or paracancerous tissues (21-24). Furthermore, a high expression of HBXIP is usually associated with tumor neurovascular invasion, lymph node metastasis, antitumor therapy resistance and drug resistance, and poor prognosis (32-34). HBXIP can mediate certain pathways to accelerate the proliferation of breast cancer cells (35,36), and the destruction of the pathway can effectively inhibit tumor progression (37,38). In our study, based on the mRNA sequencing data of patients with glioma in online databases, we found that HBXIP was also highly expressed in glioma, indicating that HBXIP may be an oncogene in the development of glioma. In recent years, the discovery of certain molecules, including IDH, 1p19q, and MGMT, has advanced the diagnosis and treatment of glioma to a new stage (4,7). Currently, it is believed that IDH wild type, 1p19q non-codeletion, and MGMT non-methylated in glioma are associated with a worse prognosis (39). In our group analysis, we found that HBXIP tended to be concentrated in groups with a poor prognosis. GBM is the most malignant of all the histological types of glioma, and the expression level of HBXIP is the highest in GBM. With the increase in histological grade, HBXIP expression level increases in kind. This further suggests that HBXIP is a poor prognostic marker. Next, we used GO analysis and KEGG analysis to explore the function of HBXIP. A study has confirmed that HBXIP is a transcriptional activator that promotes the development of cancer (35). Our findings suggest that HBXIP plays an important role in nucleic acid synthesis and the processing of glioma cells. We conclude that high expression of HBXIP may promote nucleic acid synthesis and the processing of glioma cells, leading to tumor invasion and metastasis.

Since HBXIP is enriched in more malignant gliomas, survival analysis and univariate and multivariate Cox analyses were used to determine whether HBXIP could be used as a prognostic indicator. Patients were divided into high and low groups according to the median HBXIP expression level. Kaplan-Meier survival curve analysis suggested that the OS of patients with glioma in the high-HBXIP expression group was shorter. Through univariate and multivariate Cox analyses, we concluded that HBXIP was an independent prognostic factor associated with poor OS of patients with glioma. To predict the OS of patients with glioma, we established a model via a nomogram, which mainly included the currently commonly used clinicopathologic information, such as age, primary recurrence status, histological type, grade, and administration of chemotherapy. With the development of molecular level studies on glioma, 1p19q and MGMT status can also be determined via immunohistochemistry and genetic testing. The C-index of the model reached 0.801. We also used the calibration curve to verify the model internally and externally. The consistency of the curve indicated that the prediction performance was accurate. The relatively simple operation method and easy-to-obtain parameters enhance the generalizability of the model. In the CGGA database, the average expression value of HBXIP in patients with glioma was 116.55, while that in TCGA was 10.204. Naturally, the difference in the expression value of HBXIP in the two databases may be related to different kits. Therefore, we treated the expression level of HBXIP as a dichotomous variable according to the median, which effectively solved this issue. We will attempt to define a cutoff value in future experiments.

A recent study has shown that HBXIP and PD-L1 expression are positively correlated in breast cancer tissues and cells and that HBXIP promotes the development of breast cancer by activating PD-L1 transcription (40). This suggests that HBXIP may drive a series of immune responses. Therefore, we analyzed and found that HBXIP was positively correlated with the expression of common inhibitory immune checkpoints in glioma. Tumor-infiltrating immune cells will affect the body’s immune system and further modify the patient’s response to immunotherapy and thus prognosis (41). Our results indicated that glioma patients exhibiting elevated HBXIP expression demonstrate a significant increase in B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and DCs infiltration within the immune microenvironment. Furthermore, as HBXIP expression levels rise, the extent of immune infiltration also escalates, especially in LGG, ultimately leading to a poorer prognosis for glioma patients with high HBXIP expression. Finally, we found that HBXIP was positively correlated with the marker genes of immune cells. These results suggest that the carcinogenic effect of HBXIP in glioma may be related to immune escape and that HBXIP may lead to poor prognosis due to upregulating tumor immune-cell infiltration.

In our study, a strength of the data was the longer follow-up time of CGGA and TCGA databases to establish a prognostic model: the longest follow-up time of patients with glioma in the CGGA database was 4,809 days, and the longest follow-up time of patients in the TCGA database was 6,330 days. However, several limitations were also present. First, although the prognostic model was established with data from a relatively large number of patients across two diverse databases, its clinical application requires validation in additional datasets to assess additional molecular markers to predict survival. Therefore, in our future work, we will continue to collect the molecular data of patients with glioma to optimize the parameters of the prognostic model. In addition, we did not verify HBXIP expression using tissue specimens. However, in order to ensure the reliability and repeatability of the experimental results, each step of our analysis was based on two databases, CGGA and TCGA. In the future, we will evaluate the effectiveness of HBXIP inhibitors in the treatment of brain glioma through cell and animal experiments to verify our inference.

This study was the first to report HBXIP being upregulated in glioma and enriched in the patients with glioma of higher histologic grade. Higher HBXIP expression was associated with poor prognosis and was an independent prognostic factor correlated with the OS of patients with glioma. HBXIP expression was also positively correlated with the level of immune-cell infiltration in LGG and the expression of inhibitory immune checkpoints. Comprehensive analysis of HBXIP in glioma can enhance the monitoring of patients with this disease and improve their prognosis.

Conclusions

HBXIP may serve as a prognostic marker in glioma and is correlated with immune-cell infiltration. We have developed a simple and accurate tool for the prediction of OS in patients with glioma, which may provide new ideas for further research in glioma immunotherapy.

Acknowledgments

We express our heartfelt thanks to those who created and maintain public databases, such as the CGGA and TCGA databases.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1982/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1982/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1982/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
2. Curran WJ Jr, Scott CB, Horton J, et al. Recursive partitioning analysis of prognostic factors in three Radiation Therapy Oncology Group malignant glioma trials. J Natl Cancer Inst 1993;85:704-10. [Crossref] [PubMed]
3. Alexander BM, Cloughesy TF. Adult Glioblastoma. J Clin Oncol 2017;35:2402-9. [Crossref] [PubMed]
4. Lapointe S, Perry A, Butowski NA. Primary brain tumours in adults. Lancet 2018;392:432-46. [Crossref] [PubMed]
5. Zhang H, Wang R, Yu Y, et al. Glioblastoma Treatment Modalities besides Surgery. J Cancer 2019;10:4793-806. [Crossref] [PubMed]
6. Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352:987-96. [Crossref] [PubMed]
7. Cancer Genome Atlas Research Network. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med 2015;372:2481-98. [Crossref] [PubMed]
8. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 2021;23:1231-51. [Crossref] [PubMed]
9. Weller M, van den Bent M, Tonn JC, et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol 2017;18:e315-29. [Crossref] [PubMed]
10. Besse B, Pons-Tostivint E, Park K, et al. Biomarker-directed targeted therapy plus durvalumab in advanced non-small-cell lung cancer: a phase 2 umbrella trial. Nat Med 2024;30:716-29. [Crossref] [PubMed]
11. Deutsch JS, Cimino-Mathews A, Thompson E, et al. Association between pathologic response and survival after neoadjuvant therapy in lung cancer. Nat Med 2024;30:218-28. [Crossref] [PubMed]
12. Lorusso D, Xiang Y, Hasegawa K, et al. Pembrolizumab or placebo with chemoradiotherapy followed by pembrolizumab or placebo for newly diagnosed, high-risk, locally advanced cervical cancer (ENGOT-cx11/GOG-3047/KEYNOTE-A18): a randomised, double-blind, phase 3 clinical trial. Lancet 2024;403:1341-50. [Crossref] [PubMed]
13. Choueiri TK, Tomczak P, Park SH, et al. Overall Survival with Adjuvant Pembrolizumab in Renal-Cell Carcinoma. N Engl J Med 2024;390:1359-71. [Crossref] [PubMed]
14. Huang AC, Zappasodi R. A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. Nat Immunol 2022;23:660-70. [Crossref] [PubMed]
15. Topalian SL, Hodi FS, Brahmer JR, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 2012;366:2443-54. [Crossref] [PubMed]
16. Hilf N, Kuttruff-Coqui S, Frenzel K, et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 2019;565:240-5. [Crossref] [PubMed]
17. Lim M, Xia Y, Bettegowda C, et al. Current state of immunotherapy for glioblastoma. Nat Rev Clin Oncol 2018;15:422-42. [Crossref] [PubMed]
18. Klemm F, Maas RR, Bowman RL, et al. Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells. Cell 2020;181:1643-1660.e17. [Crossref] [PubMed]
19. Arrieta VA, Dmello C, McGrail DJ, et al. Immune checkpoint blockade in glioblastoma: from tumor heterogeneity to personalized treatment. J Clin Invest 2023;133:e163447. [Crossref] [PubMed]
20. Xiu M, Zeng X, Shan R, et al. The oncogenic role of HBXIP. Biomed Pharmacother 2021;133:111045. [Crossref] [PubMed]
21. Wang X, Feng Q, Yu H, et al. HBXIP: a potential prognosis biomarker of colorectal cancer which promotes invasion and migration via epithelial-mesenchymal transition. Life Sci 2020;245:117354. [Crossref] [PubMed]
22. Wu Y, Wang X, Xu F, et al. The regulation of acetylation and stability of HMGA2 via the HBXIP-activated Akt-PCAF pathway in promotion of esophageal squamous cell carcinoma growth. Nucleic Acids Res 2020;48:4858-76. [Crossref] [PubMed]
23. Wang Y, Fang R, Cui M, et al. The oncoprotein HBXIP up-regulates YAP through activation of transcription factor c-Myb to promote growth of liver cancer. Cancer Lett 2017;385:234-42. [Crossref] [PubMed]
24. Zhang J, Sun B, Ruan X, et al. Oncoprotein HBXIP promotes tumorigenesis through MAPK/ERK pathway activation in non-small cell lung cancer. Cancer Biol Med 2021;18:105-19. [Crossref] [PubMed]
25. Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017;45:W98-W102. [Crossref] [PubMed]
26. Zhao Z, Zhang KN, Wang Q, et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients. Genomics Proteomics Bioinformatics 2021;19:1-12. [Crossref] [PubMed]
27. Li T, Fan J, Wang B, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 2017;77:e108-10. [Crossref] [PubMed]
28. Wang KY, Huang RY, Tong XZ, et al. Molecular and clinical characterization of TMEM71 expression at the transcriptional level in glioma. CNS Neurosci Ther 2019;25:965-75. [Crossref] [PubMed]
29. Huang R, Li G, Zhao Z, et al. RGS16 promotes glioma progression and serves as a prognostic factor. CNS Neurosci Ther 2020;26:791-803. [Crossref] [PubMed]
30. Gittleman H, Sloan AE, Barnholtz-Sloan JS. An independently validated survival nomogram for lower-grade glioma. Neuro Oncol 2020;22:665-74. [Crossref] [PubMed]
31. George E, Kijewski MF, Dubey S, et al. Voxel-Wise Analysis of Fluoroethyltyrosine PET and MRI in the Assessment of Recurrent Glioblastoma During Antiangiogenic Therapy. AJR Am J Roentgenol 2018;211:1342-7. [Crossref] [PubMed]
32. Zhang S, Wang R, Wang X, et al. HBXIP is a novel regulator of the unfolded protein response that sustains tamoxifen resistance in ER+ breast cancer. J Biol Chem 2022;298:101644. [Crossref] [PubMed]
33. Fu XL, Guo SM, Ma JQ, et al. HBXIP induces PARP1 via WTAP-mediated m6A modification and CEBPA-activated transcription in cisplatin resistance to hepatoma. Acta Pharmacol Sin 2024;45:2405-19. [Crossref] [PubMed]
34. Zhang L, Li XM, Shi XH, et al. Sorafenib triggers ferroptosis via inhibition of HBXIP/SCD axis in hepatocellular carcinoma. Acta Pharmacol Sin 2023;44:622-34. [Crossref] [PubMed]
35. Liu BW, Wang TJ, Li LL, et al. Oncoprotein HBXIP induces PKM2 via transcription factor E2F1 to promote cell proliferation in ER-positive breast cancer. Acta Pharmacol Sin 2019;40:530-8. [Crossref] [PubMed]
36. Zhang L, Zhou X, Liu B, et al. HBXIP blocks myosin-IIA assembly by phosphorylating and interacting with NMHC-IIA in breast cancer metastasis. Acta Pharm Sin B 2023;13:1053-70. [Crossref] [PubMed]
37. Zhou XL, Zhu CY, Wu ZG, et al. The oncoprotein HBXIP competitively binds KEAP1 to activate NRF2 and enhance breast cancer cell growth and metastasis. Oncogene 2019;38:4028-46. [Crossref] [PubMed]
38. Gao X, Yang L. HBXIP knockdown inhibits FHL2 to promote cycle arrest and suppress cervical cancer cell proliferation, invasion and migration. Oncol Lett 2023;25:186. [Crossref] [PubMed]
39. Gerber NK, Goenka A, Turcan S, et al. Transcriptional diversity of long-term glioblastoma survivors. Neuro Oncol 2014;16:1186-95. [Crossref] [PubMed]
40. Xu FF, Sun HM, Fang RP, et al. The modulation of PD-L1 induced by the oncogenic HBXIP for breast cancer growth. Acta Pharmacol Sin 2022;43:429-45. [Crossref] [PubMed]
41. Fu T, Dai LJ, Wu SY, et al. Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J Hematol Oncol 2021;14:98. [Crossref] [PubMed]

(English Language Editor: J. Gray)