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The tumor immunosuppressive microenvironment (IME) significantly affects tumor occurrence, progression, and prognosis, but the underlying molecular mechanisms remain to make known. We investigated the prognostic significance of PDPN and its role in IME in glioma. Weighted gene co-expression network analysis (WGCNA) found PDPN closely related to IDH wildtype status and higher immune score. Correlation analysis suggested PDPN was highly positively relevant to immune checkpoints expression and immune checkpoints block responding status. Correlation analysis together with verification in vitro suggested PDPN highly positively relevant tumor-associated neutrophils (TANs) and tumor-associated macrophages (TAMs). Least absolute shrinkage and selection operator (LASSO) regression employed to develop the prediction model with TANs and TAMs markers showed that high risk scores predicted worse prognosis. We highlight that PDPN overexpression is an independent prognostic indicator, and promotes macrophage M2 polarization and neutrophil degranulation, ultimately devotes to the formation of an immunosuppressive tumor microenvironment. Our findings contribute to re-recognizing the role of PDPN in IDH wildtype gliomas and implicate promising target therapy combined with immunotherapy for this highly malignant tumor.

BackgroundFerroptosis is a form of programmed cell death (PCD) that has been implicated in cancer progression, although the specific mechanism is not known. Here, we used the latest DepMap release CRISPR data to identify the essential ferroptosis-related genes (FRGs) in glioma and their role in patient outcomes.MethodsRNA-seq and clinical information on glioma cases were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). FRGs were obtained from the FerrDb database. CRISPR-screened essential genes (CSEGs) in glioma cell lines were downloaded from the DepMap portal. A series of bioinformatic and machine learning approaches were combined to establish FRG signatures to predict overall survival (OS) in glioma patients. In addition, pathways analysis was used to identify the functional roles of FRGs. Somatic mutation, immune cell infiltration, and immune checkpoint gene expression were analyzed within the risk subgroups. Finally, compounds for reversing high-risk gene signatures were predicted using the GDSC and L1000 datasets.ResultsSeven FRGs (ISCU, NFS1, MTOR, EIF2S1, HSPA5, AURKA, RPL8) were included in the model and the model was found to have good prognostic value (p < 0.001) in both training and validation groups. The risk score was found to be an independent prognostic factor and the model had good efficacy. Subgroup analysis using clinical parameters demonstrated the general applicability of the model. The nomogram indicated that the model could effectively predict 12-, 36-, and 60-months OS and progression-free interval (PFI). The results showed the presence of more aggressive phenotypes (lower numbers of IDH mutations, higher numbers of EGFR and PTEN mutations, greater infiltration of immune suppressive cells, and higher expression of immune checkpoint inhibitors) in the high-risk group. The signaling pathways enriched closely related to the cell cycle and DNA damage repair. Drug predictions showed that patients with higher risk scores may benefit from treatment with RTK pathway inhibitors, including compounds that inhibit RTKs directly or indirectly by targeting downstream PI3K or MAPK pathways.ConclusionIn summary, the proposed cancer essential FRG signature predicts survival and treatment response in glioma.