Pituitary adenomas are often treated with radiotherapy for the management of tumor progression or recurrence. Despite the improvement in cure rates, patients treated by radiotherapy are at risk of development of secondary malignancies. We conducted a comprehensive literature review of the secondary intracranial tumors that occurred following radiotherapy to pituitary adenomas to obtain clinicopathological characteristics. The analysis included 48 neuroepithelial tumors, 37 meningiomas, and 52 sarcomas which were published between 1959–2017, although data is missing regarding overall survival and type of irradiation in a significant proportion of the reports. The average onset age for the pituitary adenoma was 37.2 ± 14.4 years and the average latency period before the diagnosis of the secondary tumor was 15.2 ± 8.7 years. Radiotherapy was administered in pituitary adenomas at an average dose of 52.0 ± 19.5 Gy. The distribution of pituitary adenomas according to their function was prolactinoma in 10 (7.2%) cases, acromegaly in 37 (27.0%) cases, Cushing disease in 4 (2.9%) cases, PRL+GH in 1 (0.7%) case, non-functioning adenoma in 57 (41.6%) cases. Irradiation technique delivered was lateral opposing field in 23 (16.7%) cases, 3 or 4 field technique in 27 (19.6%) cases, rotation technique in 10 (7.2%) cases, radio surgery in 6 (4.3%) cases. Most of the glioma or sarcoma had been generated after lateral opposing field or 3/4 field technique. Fibrosarcomas were predominant before 1979 (p < 0.0001). The median overall survival time for all neuroepithelial tumors was 11 months (95% confidence intervals (CI), 3–14). Patients with gliomas treated with radiotherapy exhibited a non-significant positive trend with longer overall survival. The median overall survival time for sarcoma cases was 6 months (95% CI, 1.5–9). The median survival time in patients with radiation and/or chemotherapy for sarcomas exhibited a non-significant positive trend with longer overall survival. In patients treated with radiotherapy for pituitary adenomas, the risk of secondary tumor incidence warrants a longer follow up period. Moreover, radiation and/or chemotherapy should be considered in cases of secondary glioma or sarcoma following radiotherapy to the pituitary adenomas.

Multivariable analyses of global expression profiling are valid indicators of the prognosis of various diseases including brain cancers. To identify the candidates for markers of prognosis of glioblastoma, we performed multivariable analyses on the status of epithelial (EPI)–mesenchymal (MES) transition (EMT), glioma (GLI) stem cells (GSCs), molecular target therapy (MTT), and potential glioma biomarkers (PGBs) using the expression data and clinical information from patients. Random forest survival and Cox proportional hazards regression analyses indicated significant variable values for DSG3, CLDN1, CDH11, FN1, HDAC3/7, PTEN, L1CAM, OLIG2, TIMP4, IGFBP2, and GFAP. The analyses also comprised prognosis prediction formulae that could distinguish between the survival curves of the glioblastoma patients. In addition to the genes mentioned above, HDAC1, FLT1, EGFR, MGMT, PGF, STAT3, SIRT1, and GADD45A constituted complex genetic interaction networks. The calculated status scores obtained by principal component analysis indicated that GLI genes covered the status of EPI, GSC, and MTT-related genes. Moreover, survival tree analyses indicated that MES^high, MES^highGLI^low, GSC^highGLI^low, MES^highMTT^low, and PGB^high showed poor prognoses and MES^middle, GSC^low, and PGB^low showed good prognoses, suggesting that enhanced EMT and GSC are associated with poor survival and that lower expression of EPI markers and the pre-stages of EMT are relatively less malignant in glioblastoma. These results demonstrate that the assessment of EMT and GSC enables the prediction of the prognosis of glioblastoma that would help develop novel therapeutics and de novo marker candidates for the prognoses of glioblastoma.

Glioma is the most common type of primary brain tumor, accounting for 40% of malignant brain tumors. Although a single gene may not be a marker, an expression profiling and multivariate analyses for cancer immunotherapy must estimate survival of patients. In this study, we conducted expression profiling of immunotherapy-related genes, including those in Th1/2 helper T and regulatory T cells, and stimulatory and inhibitory checkpoint molecules associated with survival prediction in 571 patients with malignant and aggressive form of gliomas, glioblastoma multiforme (GBM). Expression profiling and Random forests analysis of 21 immunosuppressive genes and Kaplan-Meier analysis in 158 patients in the training data set suggested that CD276, also known as B7-H3, could be a single gene marker candidate. Furthermore, prognosis prediction formulas, composed of Th2 cell-related GATA transcription factor 3 (GATA3) and immunosuppressive galactose-specific lectin 3 (LGALS3), based on 67 immunotherapy-related genes showed poor survival with high scores in training data set, which was also validated in another 413 patients in the test data set. The CD276 expression helped distinguish survival curves in the test data set. In addition, inhibitory checkpoint genes, including T cell immunoreceptor with Ig and ITIM domains, V-set domain containing T cell activation inhibitor 1, T-cell immunoglobulin and mucin-domain containing 3, and tumor necrosis factor receptor superfamily 14, showed potential as secondary marker candidates. These results suggest that CD276 expression and the gene signature composed of GATA3 and LGALS3 are effective for prognosis in GBM and will help us understanding target pathways for immunotherapy in GBM.

MicroRNAs (miRNAs) are small RNA molecules that inhibit gene function by suppressing translation of target genes. However, in primary central nervous system lymphoma (PCNSL), the biological significance of miRNAs is largely unknown, although some miRNAs are known to be prognosis markers. Here, we analyzed 847 miRNAs expressed in 27 PCNSL specimens using microarray profiling and surveyed miRNA signature for prognostic prediction. Of these, 16 miRNAs were expressed in 27 PCNSL specimens at a frequency of 48%. Their variable importance measured by Random forest model revealed miR-192, miR-486, miR-28, miR-52, miR-181b, miR-194, miR-197, miR-93, miR-708, and let-7g as having positive effects; miR-29b-2*, miR-126, and miR-182 as having negative effects; and miR-18a*, miR-425, and miR-30d as neutral. After principal component analysis, the prediction formula for prognosis, consisting of the expression values of the above-mentioned miRNAs, clearly divided Kaplan-Meier survival curves by the calculated Z-score (HR = 6.4566, P = 0.0067). The 16 miRNAs were enriched by gene ontology terms including angiogenesis, cell migration and proliferation, and apoptosis, in addition to signaling pathways including TGF-β/SMAD, Notch, TNF, and MAPKinase. Their target genes included BCL2-related genes, HMGA2 oncogene, and LIN28B cancer stem cell marker. Furthermore, three miRNAs including miR-181b, miR-30d, and miR-93, selected from the 16 miRNAs, also showed comparable results for survival (HR = 8.9342, P = 0.0007), suggestive of a miRNA signature for prognostic prediction in PCNSL. These results indicate that this miRNA signature is useful for prognostic prediction in PCNSL and would help us understand target pathways for therapies in PCNSL.

MicroRNAs (miRNAs) inhibit protein function by silencing the translation of target mRNAs. However, in primary central nervous system lymphoma (PCNSL), the expression and functions of miRNAs are inadequately known. Here, we examined the expression of 847 miRNAs in 40 PCNSL patients with a microarray and investigated for the miRNA predictors associated with cancer immunity-related genes such as T helper cell type 1/2 (Th-1/Th-2) and regulatory T cell (T-reg) status, and stimulatory and inhibitory checkpoint genes, for prognosis prediction in PCNSL. The aim of this study is to find promising prognosis markers based on the miRNA expression in PCNSL. We detected 334 miRNAs related to 66 cancer immunity-related genes in the microarray profiling. Variable importance measured by the random survival forest analysis and Cox proportional hazards regression model elucidated that 11 miRNAs successfully constitute the survival formulae dividing the Kaplan-Meier curve of the respective PCNSL subgroups. On the other hand, univariate analysis shortlisted 23 miRNAs for overall survival times, with four miRNAs clearly dividing the survival curves—miR-101/548b/554/1202. These miRNAs regulated Th-1/Th-2 status, T-reg cell status, and immune checkpoints. The miRNAs were also associated with gene ontology terms as Ras/MAP-kinase, ubiquitin ligase, PRC2 and acetylation, CDK, and phosphorylation, and several diseases including acquired immunodeficiency syndrome, glioma, and those related to blood and hippocampus with statistical significance. In conclusion, the results demonstrated that the four miRNAs comprising miR-101/548b/554/1202 associated with cancer immunity can be a useful prognostic marker in PCNSL and would help us understand target pathways for PCNSL treatments.

Dysregulation of cell morphology and cell-cell interaction results in cancer cell growth, migration, invasion, and metastasis. Besides, a balance between the extracellular matrix (ECM) and matrix metalloprotease (MMP) is required for cancer cell morphology and angiogenesis. Here, we determined gene signatures associated with the morphology and microenvironment of primary central nervous system lymphoma (PCNSL) to enable prognosis prediction. Next-generation sequencing (NGS) on 31 PCNSL samples revealed gene signatures as follows: ACTA2, ACTR10, CAPG, CORO1C, KRT17, and PALLD in cytoskeleton, CDH5, CLSTN1, ITGA10, ITGAX, ITGB7, ITGA8, FAT4, ITGAE, CDH10, ITGAM, ITGB6, and CDH18 in adhesion, COL8A2, FBN1, LAMB3, and LAMA2 in ECM, ADAM22, ADAM28, MMP11, and MMP24 in MMP. Prognosis prediction formulas with the gene expression values and the Cox regression model clearly divided survival curves of the subgroups in each status. Furthermore, collagen genes contributed to gene network formation in glasso, suggesting that the ECM balance controls the PCNSL microenvironment. Finally, the comprehensive balance of morphology and microenvironment enabled prognosis prediction by a combinatorial expression of 8 representative genes, including KRT17, CDH10, CDH18, COL8A2, ADAM22, ADAM28, MMP11, and MMP24. Besides, these genes could also diagnose PCNSL cell types with MTX resistances in vitro. These results would not only facilitate the understanding of biology of PCNSL but also consider targeting pathways for anti-cancer treatment in personalized precision medicine in PCNSL.