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. 2020 Oct 7;20(6):332. doi: 10.3892/ol.2020.12195

Profiling of inhibitory immune checkpoints in glioblastoma: Potential pathogenetic players

Salvo Danilo Lombardo 1, Alessia Bramanti 2, Rosella Ciurleo 2, Maria Sofia Basile 2, Manuela Pennisi 3, Rita Bella 4, Katia Mangano 3, Placido Bramanti 2, Ferdinando Nicoletti 3, Paolo Fagone 3,✉
PMCID: PMC7583708  PMID: 33123243

Abstract

Glioblastoma (GBM) represents the most frequent glial tumor, with almost 3 new cases per 100,000 people per year. Despite treatment, the prognosis for GBM patients remains extremely poor, with a median survival of 14.6 months, and a 5-year survival less than 5%. It is generally believed that GBM creates a highly immunosuppressive microenvironment, sustained by the expression of immune-regulatory factors, including inhibitory immune checkpoints, on both infiltrating cells and tumor cells. However, the trials assessing the efficacy of current immune checkpoint inhibitors in GBM are still disappointing. In the present study, the expression levels of several inhibitory immune checkpoints in GBM (CD276, VTCN1, CD47, PVR, TNFRSF14, CD200, LGALS9, NECTIN2 and CD48) were characterized in order to evaluate their potential as prognostic and eventually, therapeutic targets. Among the investigated immune checkpoints, TNFRSF14 and NECTIN2 were identified as the most promising targets in GBM. In particular, a higher TNFRSF14 expression was associated with worse overall survival and disease-free survival, and with a lower Th1 response.

Keywords: glioblastoma, immune checkpoint, inhibitory check-points, astrocytoma, CD276, VTCN1, CD47, PVR, TNFRSF14, CD200, LGALS9, NECTIN2, CD48

Introduction

According to the World Health Organization (WHO) classification of the central nervous system (CNS) tumors, glioblastoma (GBM) is defined as a grade IV astrocytoma (1). GBM represents the most malignant glioma and it is characterized by necrosis, neovascularization and histological heterogeneity (2). GBM represents the most frequent glial tumor, with almost 3 new cases per 100,000 people per year (3). The current standard of care for GBM consists of surgical resection, followed by radiotherapy and chemotherapy with temozolomide (4). Despite treatment, the prognosis for GBM patients remains extremely poor, with a median survival period of 14.6 months, and the 5-year survival is less than 5% (4).

In recent years, great progress has been made in the area of immunotherapy and accumulating preclinical and clinical data seem to suggest potential novel therapeutic avenues for GBM patients (5,6). It is generally believed that GBM creates a highly immunosuppressive/immuneregulatory microenvironment. Several checkpoint molecules capable of inhibiting the immune responses against neo-antigens, including CTLA4 and PD1/PDL-1, are expressed on both T cells and cancer cells. Immune checkpoint inhibitors, such as nivolumab, ipilimumab and pembrolizumab, have strikingly improved patient survival in solid tumors, such as non-small lung cancer and melanoma. However, the trials assessing the efficacy of immune checkpoint inhibitors in GBM are still disappointing (7). A retrospective study of the use of pembrolizumab in the treatment of recurrent CNS tumors, including GBM, demonstrated that patients treated with Pembrolizumab did not have improved survival (7). Another Phase III randomized trial comparing radiation and concomitant temozolomide with or without nivolumab showed that no progression-free survival benefits were obtained by the addition of nivolumab. However, in a Phase II trial, preoperative administration of nivolumab increased chemokine expression and T-cell receptor clonal diversity, which likely promotes immune-cell infiltration and antitumor immune response (7).

It is reasonable that targeting multiple immune checkpoints in combination with cytotoxic drugs could represent a promising strategy for GBM. The present study characterized the expression levels of several inhibitory immune checkpoints in GBM (i.e., CD276, VTCN1, CD47, PVR, TNFRSF14, CD200, LGALS9, NECTIN2 and CD48) in order to evaluate their prognostic value. Moreover, their potential effects in regulating immune-cell infiltration was investigated.

Materials and methods

Profiling of inhibitory immune checkpoints in GBM

In order to evaluate the expression levels of inhibitory immune checkpoints in GBM as compared to lower grade astrocytomas and normal brain samples, RSEM-normalized RNA Seq data were downloaded from the The Cancer Genome Atlas (TCGA) databank. Selected genes were CD276, VTCN1, CD47, PVR, TNFRSF14, CD200, LGALS9, NECTIN2 and CD48. Complete clinical data of the patients were retrieved and only data from primary tumors, with no neoadjuvant therapy prior to excision, were selected. Data were subjected to logarithmic transformation and Linear Model for Microarray Analysis (LIMMA) was used to assess statistical significance for the differences among cancer types. Overall, this study comprised 153 GBM samples, 130 anaplastic astrocytoma (grade III) samples, 63 astrocytoma (grade II) samples and 5 normal brain samples. The results shown here are based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga). TCGA Ethics & Policies were originally published by the National Cancer Institute.

Survival analysis

Samples were stratified in quartiles based on the expression of the genes of interest and samples in the upper and lower quartiles were selected for comparison. Kaplan-Meier curves were constructed for overall survival and disease-free survival and its significance analyzed by log-rank (Mantel-Cox) test.

Computational deconvolution of infiltrating immune cells

In order to evaluate the relative proportions of the infiltrating immune cell subsets in GBM samples diverging for the expression of the selected immune checkpoints and stratified in accordance to survival analysis, we performed a computational deconvolution analysis. The web-based utility, xCell, was used. It is a computational tool that is able, by using gene signatures, to infer the presence in a sample of various cell types, including immature dendritic cells (iDCs), conventional DCs (cDCs), active DCs (aDCs), plasmacytoid DCs (pDCs), B cells, CD4+ naive T cells, memory B cells, plasma cells, Th1 cells, Th2 and Treg cells and macrophages (8).

Statistical analysis

Gene expression differences were evaluated using LIMMA on log-transformed RSEM-normalized expression values. FDR <0.05 was considered for statistical significance. Gene expression was visualized as heatmap, using the group mean value. Clustering was performed for both sample groups and genes of interest, using Pearson correlation as distance metrics. Correlation analysis was performed using the Pearson's correlation test. Survival analysis was performed using Kaplan-Meier and its significance analyzed by the log-rank (Mantel-Cox) test. For the analysis, P<0.05 was considered to indicate a statistically significant difference. Statistical analysis was performed with GraphPad Prism 8 (GraphPad Software, Inc.) and SPSS 24 (IBM Corp.).

Results

Expression of inhibitory immune checkpoints in GBM

A significant upregulation in the expression levels of CD276, VTCN1, TNFRSF14, LGALS9, NECTIN2 and CD48 was observed in GBM as compared to normal brain samples (Fig. 1A, Table I). On the contrary, a significant downregulation of CD47 and CD200 was observed in GBM as compared to normal brain samples, while a trend of downregulation was observed for PVR (Fig. 1A, Table I). Along the same lines, with the exception of LGALS9 and CD200, a significant modulation in the expression levels of the investigated immune checkpoints was observed between the GBM and anaplastic astrocytoma groups of samples (Fig. 1A, Table I). Moreover, CD276, TNFRSF14, LGALS9 and CD48 resulted significantly upregulated in anaplastic astrocytoma samples as compared to grade II astrocytomas (Fig. 1A, Table I). A significant direct correlation was observed for CD276, PVR, TNFRSF14, NECTIN2 and CD48 (Fig. 1B). Among the GBM samples, a significant negative correlation was instead observed between VTCN1 and PVR, NECTIN2 and CD48 (Fig. 1B).

Figure 1.

Figure 1.

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Expression of immune checkpoints in glioblastoma. Relative expression levels of the selected inhibitory immune checkpoints in glioblastoma, lower grade astrocytomas and normal brain samples are presented as heatmap (A). Correlation of the selected inhibitory immune checkpoints (B). Pearson correlation coefficient is presented in blue-red gradient and significance in yellow gradient.

Table I.

Expression of selected immune checkpoints in gliomas.

CD276 VTCN1 CD47 PVR TNFRSF14 CD200 LGALS9 CD48 NECTIN2
Glioblastoma
  Log (mean ± SD) 11.47±0.61 2.09±1.61 11.3±0.45 9.17±0.55 9.09±0.84 8.9±0.83 10.2±0.89 6.77±1.43 10.37±0.61
Anaplastic astrocytoma
  Log (mean ± SD) 10.46±0.70 2.78±1.54 11.10±0.44 8.77±0.55 8.19±1.14 9.03±0.71 10.12±1 4.77±2.10 9.7±0.69
Astrocytoma grade II
  Log (mean ± SD) 9.99±0.63 2.98±1.36 11.074±0.52 8.62±0.52 7.81±0.78 9.17±0.75 9.69±0.94 3.81±1.97 9.53±0.49
Normal
  Log (mean ± SD) 8.79±0.36 0.15±0.83 12.17±0.13 9.69±0.51 7.75±0.46 10.91±0.46 8.59±0.49 3.21±0.93 8.98±0.33
Glioblastoma vs. anaplastic astrocytoma
  Adjusted P-value 1.38896E-30 0.000373262 3.08282E-05 6.10399E-09 2.15289E-13 0.20064689 0.41158742 1.06584E-17 8.13945E-17
Glioblastoma vs. astrocytoma grade II
  Adjusted P-value 9.64928E-39 0.000241293 0.000199704 1.78558E-10 1.31977E-16 0.031546623 0.00035424 2.96428E-23 8.10789E-17
Glioblastoma vs. normal
  Adjusted P-value 7.41774E-16 0.013900759 0.00035894 0.068411075 0.006012107 1.76232E-07 0.000529205 7.99978E-05 7.29563E-06
Anaplastic astrocytoma vs. astrocytoma grade II
  Adjusted P-value 0.000115669 0.58144605 0.81457853 0.18047059 0.042150598 0.43026862 0.018488223 0.005033033 0.19248255
Anaplastic astrocytoma vs. normal
  Adjusted P-value 6.24305E-07 0.001064924 7.15003E-06 0.001204611 0.46713257 2.27282E-06 0.001895901 0.12621635 0.032178745
Astrocytoma grade II vs. normal
  Adjusted P-value 0.000601994 0.000684009 1.00213E-05 0.000269684 0.95525837 2.65911E-05 0.039250672 0.649572 0.1347119
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Survival analysis

Samples were stratified in quartiles based on the expression of the genes of interest, and samples in the upper and lower quartiles were selected for comparison. As shown in Table II and Fig. 2, higher expression levels of TNFRSF14 in GBM were associated to a significantly lower overall survival. No significance was observed for any of the other immune checkpoints. Accordingly, higher TNFRSF14 levels were associated to a shorter disease-free time (Fig. 3 and Table III). Lower levels of CD276 and NECTIN2 were also significantly associated to better disease-free time (Fig. 3 and Table III). Unexpectedly, higher levels of VTCN1 were associated to a longer disease-free time (Fig. 3 and Table III).

Table II.

Overall survival for the selected immune checkpoints in glioblastoma.

Mean Median
95% CI 95% CI Log-rank (Mantel-Cox)
Estimate SE Lower bound Upper bound Estimate SE Lower bound Upper bound Chi-square Significance
CD276
  Low 2,106.954 437.733 1,248.998 2,964.910 1,511.000 231.470 1,057.320 1,964.680
  High 1,170.322 156.700   863.189 1,477.455 1,124.000 247.168   639.551 1,608.449
  Overall 1,750.302 291.264 1,179.424 2,321.180 1,275.000 67.205 1,143.278 1,406.722 2.771 0.096
VTCN1
  Low 1,501.133 334.027   846.440 2,155.825 1,173.000 388.775   411.000 1,935.000
  High 2,332.769 419.625 1,510.304 3,155.233 1,419.000 273.388   883.160 1,954.840
  Overall 1,915.041 270.368 1,385.119 2,444.963 1,298.000 131.107 1,041.029 1,554.971 3.254 0.071
CD47
  Low 1,550.759 265.609 1,030.166 2,071.352 1,275.000 165.243   951.123 1,598.877
  High 1,563.020 348.405   880.146 2,245.894 1,101.000 292.895   526.925 1,675.075
  Overall 1,565.487 229.512 1,115.643 2,015.332 1,183.000 126.196   935.656 1,430.344 0.018 0.893
PVR
  Low 1,812.897 344.718 1,137.251 2,488.544 1,491.000 227.945 1,044.228 1,937.772
  High 1,316.999 227.311   871.470 1,762.527 1,124.000 184.382   762.612 1,485.388
  Overall 1,579.214 214.791 1,158.224 2,000.205 1,255.000 102.776 1,053.560 1,456.440 1.711 0.191
TNFRSF14
  Low 2,376.561 534.969 1,328.021 3,425.100 1,495.000 123.626 1,252.693 1,737.307
  High 1,249.227 179.475   897.456 1,600.999 1,028.000 293.848   452.057 1,603.943
  Overall 1,696.854 245.369 1,215.931 2,177.777 1,298.000 150.294 1,003.424 1,592.576 4.148 0.042
CD200
  Low 1,538.345 268.458 1,012.168 2,064.523 1,294.000 191.464   918.730 1,669.270
  High 1,960.634 491.408   997.474 2,923.794 1,419.000 184.815 1,056.762 1,781.238
  Overall 1,647.845 242.431 1,172.680 2,123.010 1,298.000 157.111   990.063 1,605.937 0.184 0.668
LGALS9
  Low 1,678.101 252.097 1,183.991 2,172.212 1,495.000 126.246 1,247.557 1,742.443
  High 1,775.014 291.699 1,203.283 2,346.744 1,403.000 234.376   943.623 1,862.377
  Overall 1,754.181 202.451 1,357.377 2,150.985 1,403.000 115.366 1,176.882 1,629.118 0.011 0.918
NECTIN2
  Low 1,914.122 383.529 1,162.406 2,665.839 1,495.000 117.504 1,264.692 1,725.308
  High 1,467.364 265.874   946.251 1,988.477 1,124.000 146.219   837.410 1,410.590
  Overall 1,693.007 232.899 1,236.525 2,149.490 1,275.000 187.661   907.184 1,642.816 1.066 0.302
CD48
  Low 1,535.158 253.307 1,038.675 2,031.640 1,376.000 182.438 1,018.422 1,733.578
  High 1,685.861 357.415   985.327 2,386.395 1,275.000 260.223   764.962 1,785.038
  Overall 1,630.422 240.486 1,159.068 2,101.775 1,298.000 156.655   990.956 1,605.044 0.001 0.970
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SE, standard error; CI, confidence interval.

Figure 2.

Figure 2.

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Effect of immune checkpoint expression on overall survival in glioblastoma. Kaplan-Meier curve for the overall survival of glioblastoma patients stratified on the expression levels of TNFRSF14.

Figure 3.

Figure 3.

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Effect of immune checkpoint expression on disease-free survival in glioblastoma. (A) Kaplan-Meier curve for the disease-free survival of glioblastoma patients stratified on the expression levels of CD276; (B) Kaplan-Meier curve for the disease-free survival of glioblastoma patients stratified on the expression levels of VTCN1; (C) Kaplan-Meier curve for the disease-free survival of glioblastoma patients stratified on the expression levels of TNFRSF14; (D) Kaplan-Meier curve for the disease-free survival of glioblastoma patients stratified on the expression levels of NECTIN2.

Table III.

Disease-free survival for the selected immune checkpoints in glioblastoma.

Mean Median
95% CI 95% CI Log-rank (Mantel-Cox)
Estimate SE Lower bound Upper bound Estimate SE Lower bound Upper bound Chi-square Significance
CD276
  Low 19.417 4.607 10.386 28.447 11.270 1.527 8.277 14.263
  High   6.726 1.132   4.507   8.945   4.300 0.760 2.810   5.790
  Overall 13.590 2.671   8.354 18.825   7.590 1.724 4.211 10.969 10.000 0.002
VTCN1
  Low   8.510 1.615   5.346 11.675   4.730 0.966 2.836   6.624
  High 19.102 4.450 10.380 27.824   9.460 2.380 4.795 14.125
  Overall 13.618 2.366   8.981 18.255   5.980 1.070 3.883   8.077   5.944 0.015
CD47
  Low 11.583 2.051   7.562 15.603   8.510 1.797 4.987 12.033
  High 11.562 2.939   5.801 17.323   5.390 0.833 3.757   7.023
  Overall 11.544 1.811   7.994 15.094   7.030 1.056 4.960   9.100   0.182 0.670
PVR
  Low 13.275 3.278   6.851 19.699   5.910 2.202 1.593 10.227
  High   6.936 1.188   4.608   9.264   4.860 0.715 3.458   6.262
  Overall 10.343 1.906   6.608 14.078   5.190 0.506 4.199   6.181   2.563 0.109
TNFRSF14
  Low 19.741 5.110   9.725 29.756   7.620 1.766 4.158 11.082
  High   7.659 1.124   5.455   9.862   5.390 0.715 3.988   6.792
  Overall 13.405 2.628   8.253 18.557   5.910 0.974 4.000   7.820   4.168 0.041
CD200
  Low   8.966 1.925   5.194 12.738   5.160 0.873 3.448   6.872
  High 17.597 4.920   7.954 27.239   8.410 1.419 5.628 11.192
  Overall 12.064 2.308   7.539 16.588   6.670 0.761 5.179   8.161   2.805 0.094
LGALS9
  Low 14.228 2.693   8.950 19.507 10.580 2.960 4.779 16.381
  High 10.808 2.001   6.887 14.729   6.340 1.336 3.722   8.958
  Overall 12.388 1.641   9.171 15.604   7.620 1.210 5.248   9.992   1.283 0.257
NECTIN2
  Low 14.843 3.950   7.101 22.584   7.030 1.789 3.524 10.536
  High   7.493 1.462   4.627 10.359   4.860 0.698 3.491   6.229
  Overall 10.843 1.991   6.942 14.745   5.190 0.480 4.249   6.131   4.010 0.045
CD48
  Low 13.008 2.508   8.092 17.924   8.510 3.055 2.522 14.498
  High 12.505 3.101   6.426 18.584   6.410 1.560 3.352   9.468
  Overall 13.026 2.283   8.552 17.501   7.360 1.459 4.501 10.219   0.392 0.531
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SE, standard error; CI, confidence interval.

Deconvolution analysis

Deconvolution analysis of cell infiltration in GBM was performed on samples dichotomized on the expression levels of the immune checkpoints associated to a significant modulation of survival, i.e., CD276, VTCN1, TNFRSF14 and NECTIN2. As shonw in Fig. 4, higher levels of CD276, TNFRSF14 and NECTIN2 were associated with a significant lower proportion of infiltrating plasma cells. Higher VTCN1 levels were associated to higher proportions of infiltrating plasma cells, along with higher infiltration of Th1, aDCs and cDCs (Fig. 4B). Samples with high expression levels of TNFRSF14 were characterized by a significant lower infiltration of Th1 cells and cDC, and higher proportions of iDCs, aDCs, pDCs and of macrophages (both M1 and M2) (Fig. 4C). A significantly higher infiltration of iDCs, aDCs and M1 macrophages, along with reduced proportions of Th1, Th2 and CD8 T cells, were observed in GBM samples with high NECTIN2 expression levels (Fig. 4D).

Figure 4.

Figure 4.

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Deconvolution analysis of infiltrating immune cells in glioblastoma. Infiltrating immune cell populations were predicted using the web-based deconvolution analysis utility, xCell, for glioblastoma patients stratified on the expression of (A) CD276, (B) VTCN1, (C) TNFRSF14 and (D) NECTIN2. *P<0.05; **P<0.01; ***P<0.001.

Discussion

Conventional immune checkpoint inhibitors, Nivolumab/ Pembrolizumab for PD-1/PDL1 blockade or Ipilimumab for CTLA4, have proven beneficial effects on the clinical course of different cancer types, including metastatic melanoma, non-small cell lung cancer, renal cell carcinoma, and Hodgkin lymphoma (9–11). However, these treatments have often failed in gliomas (12–14). A possible explanation for this outcome seems to be due to two main glioma features: the low tumor mutational burden (TMB) and a highly immunosuppressive microenvironment. Identifying genomic markers of response to immune checkpoint may benefit cancer patients by providing predictive biomarkers for patient stratification and identifying resistance mechanisms for therapeutic targeting.

The present investigation evaluated the potential role of a series of inhibitory immune checkpoints not previously studied or only marginally characterized in GBM, i.e., CD276, VTCN1, CD47, PVR, TNFRSF14, CD200, LGALS9, NECTIN2 and CD48. To this aim, a computational analysis of RNA-seq data obtained from the TCGA (The Cancer Genome Atlas) database was performed. Whole-genome expression data was largely used (15) to identify pathogenic pathways and therapeutic targets for several disorders, including autoimmune diseases (16–23) and cancer (24–29).

We found that VTCN1 and CD200 are highly over-expressed in GBM, anaplastic astrocytoma and astrocytoma grade II compared to normal brain. Previously, Yao et al (30) showed that VTCN1 has a crucial role in the creation and maintenance of the immunosuppressive microenvironment in gliomas, correlating with prognosis and malignant grades. Furthermore, lower levels of VTCN1 are associated with a higher survival in a clinical trial of DC based vaccination (31). This is in contrast with our observations, which appears to show a protective role for VTCN1 in GBM. The reasons for this counterintuitive data is currently object of further exploration.

On the contrary, CD200 expression levels resulted in significantly reduced astrocytomas in comparison to normal brain. CD200 is a type I transmembrane glycoprotein that plays an inhibitory role in the activation of microglia. For this reason, many studies have shown that its expression is enhanced in brain tumors (32), and especially in higher grade tumors (33). However, its role is still controversial, indeed in the same study Wang et al (33) found that CD200 down-expression can lead to a particular microglia tumor microenvironment that promotes tumor progression, in agreement with our results. Recent studies in dogs also showed that targeting CD200, enhanced the capacity of antigen-presenting cells to prime T-cells to mediate an anti-glioma response (34).

PVR and CD47 were also found down-expressed in astrocytomas when compared to normal brain, while higher levels of expression were found for LGALS9, TNFRSF14, CD48, CD276 and NECTIN2. PVR has been described as regulator of cell adhesion in a rat model of GBM (35) and a recent study in mice proved that the combination of anti-PD-1 and anti-PVR leads to a better survival (36).

CD47 is a member of the immunoglobulin superfamily that activates the signal regulatory protein-α (SIRP-α) expressed on macrophages, preventing phagocytosis. In contrast with previous studies (37,38), we found decreased levels in gliomas compared to normal brain. We consider that this down-expression can represent an attempt to maintain homeostasis. Recent studies have associated CD47 with the tumor-associated macrophages (TAMs) in the GBM microenvironment. Zhang et al (39) have also proven that anti-CD47 treatment leads to enhanced tumor cell phagocytosis by both M1 and M2 macrophage subtypes with a higher phagocytosis rate by M1 macrophages. A combination of anti-CD47 treatment and temozolamide has also been reported (40).

TNFRSF14 was found to be elevated in aggressive gliomas and its expression seemed to be associated with amplification of EGFR and loss of PTEN (41). TNFRSF14 plays an important role in the recruitment and activation of immune system in the tumor microenvironment. We showed that TNFRSF14 seems to have a significant impact on both the overall survival and the disease-free time. Interestingly, in metastatic melanoma, TNFRSF14 shows a similar behavior (42), further reinforcing our observations and suggesting that similar mechanisms can be shared also in glioma and that a combinatory blocking strategy can improve patients outcome.

Finally, we performed a deconvolution analysis showing that higher levels of CD276, TNFRSF14 and NECTIN2 are associated with a significant lower proportion of infiltrating plasma cells, while higher levels of VTCN1 were associated to higher proportions of infiltrating plasma cells, Th1, aDCs and cDCs. Higher levels of TNFRSF14 were associated with a major infiltration of iDCs, aDCs, pDCs and macrophages, but lower levels of Th1 cells and cDCs. Higher expression of NECTIN2, associated with shorter survival, is associated with reduced proportions of Th1, Th2 and CD8 T cells. Together these findings suggest that the main immune cell types that help to reduce the tumor mass and improve the survival are Th1 and cDCs, and that their expression is strictly dependent on these immune checkpoints. In agreement with our hypothesis, previous studies have shown that in gliomas, there is a prevalent Th2 response and that switching from Th2 to Th1 can help to block glioma growth (43). Additionally, recent studies have proven that combinational therapy that blocks more immune checkpoints is a possibility to create a more vigorous Th1 antitumor response (44,45) and its association with better outcome (46). Future preclinical and clinical studies are necessary to ascertain whether, in addition to the prognostic value we have highlighted, the dysregulated expression of the inhibitory immune checkpoint presently studied may translate into clinical applications, as novel immunotherapeutic approaches for the treatment of gliomas and possibly other types of cancers.

Collectively, in this study, we evaluated the expression of several inhibitory immune checkpoints that can play a role in glioma progression. Among the investigated immune checkpoints, TNFRSF14 and NECTIN2 were identified as the most promising targets in GBM. In particular, TNFRSF14 expression is associated with worse overall survival and disease-free survival, correlating with a lower Th1 response and suggesting that it could become an interesting biomarker or therapeutic target.

Acknowledgements

Not applicable.

Funding

This study was supported by current research funds 2020 of IRCCS ‘Centro Neurolesi Bonino-Pulejo’, Messina, Italy.

Availability of data and materials

All the data in this study are available for download from TCGA (The Cancer Genome Atlas) databank.

Authors' contributions

Conceptualization: FN and PF; data curation: SDL, RB, KM and PF; formal analysis: MP and KM; funding acquisition: AB, PB and FN; investigation: RC; project administration: PB; supervision: FN; visualization: MSB; writing-original draft: SDL, RC, MSB, MP and RB; writing-review and editing: AB, KM, PB, FN and PF.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All the data in this study are available for download from TCGA (The Cancer Genome Atlas) databank.

Articles from Oncology Letters are provided here courtesy of Spandidos Publications

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