Identification of key genes and pathways associated with resting mast cells in meningioma

Noncoding RNA landscape and their emerging roles as biomarkers and therapeutic targets in meningioma

Meningiomas are among the most prevalent primary CNS tumors in adults, accounting for nearly 38% of all brain neoplasms. The World Health Organization (WHO) grade assigned to meningiomas guides medical care in patients and is primarily based on tumor histology and malignancy potential. Although often considered benign, meningiomas with complicated histology, limited accessibility for surgical resection, and/or higher malignancy potential (WHO grade 2 and WHO grade 3) are harder to combat, resulting in significant morbidity. With limited treatment options and no systemic therapies, it is imperative to understand meningioma tumorigenesis at the molecular level and identify novel therapeutic targets. The last decade witnessed considerable progress in understanding the noncoding RNA landscape of meningioma, with microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) emerging as molecular entities of interest. This review aims to highlight the commonly dysregulated miRNAs and lncRNAs in meningioma and their correlation with meningioma progression, malignancy, recurrence, and radioresistance. The role of "key" miRNAs as biomarkers and their therapeutic potential has also been reviewed in detail. Furthermore, current and emerging therapeutic modalities for meningioma have been discussed, with emphasis on the need to identify and subsequently employ clinically relevant miRNAs and lncRNAs as novel therapeutic targets and biomarkers.

Identification of the Key Immune Cells and Genes for the Diagnostics and Therapeutics of Meningioma

BACKGROUND

Dysregulation of immune infiltration critically contributes to the tumorigenesis and progression of meningiomas. However, the landscape of immune microenvironment and key genes correlated with immune cell infiltration remains unclear.

METHODS

Four Gene Expression Omnibus data sets were included. CIBERSORT algorithm was utilized to analyze the immune cell infiltration in samples. Wilcoxon test, Random Forest algorithm, and Least Absolute Shrinkage and Selection Operator regression were adopted in identifying significantly different infiltrating immune cells and differentially expressed genes (DEGs). Functional enrichment analysis was performed by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology. The correlation between genes and immune cells was evaluated via Spearman's correlation analysis. Receiver Operator Characteristic curve analysis evaluated the markers' diagnostic effectiveness. The mRNA-miRNA and Drug-Gene-Immune cell interaction networks were constructed to identify potential diagnostic and therapeutic targets.

RESULTS

Plasma cells, M1 macrophages, M2 macrophages, neutrophils, eosinophils, and activated NK cells were the significantly different infiltrating immune cells in meningioma. A total of 951 DEGs, associated with synaptic function and structure, ion transport regulation, brain function, and immune-related pathways, were identified. Among 11 hub DEGs, RYR2 and TTR were correlated with plasma cells; SNCG was associated with NK cells; ADCY1 exhibited excellent diagnostic effectiveness; and ADCY1, BMX, KCNA5, SLCO4A1, and TTR could be considered as therapeutic targets.

CONCLUSIONS

ADCY1 can be identified as a diagnostic marker; ADCY1, BMX, KCNA5, SLCO4A1, and TTR are potential therapeutic targets, and their associations with macrophages, neutrophils, NK cells, and plasma cells might impact the tumorigenesis of meningiomas.

Identification and Validation of the Prognostic Panel in Clear Cell Renal Cell Carcinoma Based on Resting Mast Cells for Prediction of Distant Metastasis and Immunotherapy Response

Clear cell renal cell carcinoma (ccRCC) has a high metastatic rate, and its incidence and mortality are still rising. The aim of this study was to identify the key tumor-infiltrating immune cells (TIICs) affecting the distant metastasis and prognosis of patients with ccRCC and to construct a relevant prognostic panel to predict immunotherapy response. Based on ccRCC bulk RNA sequencing data, resting mast cells (RMCs) were screened and verified using the CIBERSORT algorithm, survival analysis, and expression analysis. Distant metastasis-associated genes were identified using single-cell RNA sequencing data. Subsequently, a three-gene (CFB, PPP1R18, and TOM1L1) panel with superior distant metastatic and prognostic performance was established and validated, which stratified patients into high- and low-risk groups. The high-risk group exhibited lower infiltration of RMCs, higher tumor mutation burden (TMB), and worse prognosis. Therapeutically, the high-risk group was more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy, whereas the low-risk group displayed a better response to anti-PD-L1 immunotherapy. Furthermore, two immune clusters revealing distinct immune, clinical, and prognosis heterogeneity were distinguished. Immunohistochemistry of ccRCC samples verified the expression patterns of the three key genes. Collectively, the prognostic panel based on RMCs is able to predict distant metastasis and immunotherapy response in patients with ccRCC, providing new insight for the treatment of advanced ccRCC.

Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3

INTRODUCTION

Prognostic biomarkers for osteosarcoma (OS) are still very few, and this study aims to examine 2 novel prognostic biomarkers for OS through combined bioinformatics and experimental approach.

MATERIALS AND METHODS

Expression profile data of OS and paraneoplastic tissues were downloaded from several online databases, and prognostic genes were screened by differential expression analysis, Univariate Cox analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis to construct prognostic models. The accuracy of the model was validated using principal component analysis, constructing calibration plots, and column line plots. We also analyzed the relationship between genes and drug sensitivity. Gene expression profiles were analyzed by immunocytotyping. Also, protein expressions of the constructed biomarkers in OS and paraneoplastic tissues were verified by immunohistochemistry.

RESULTS

Heparan sulfate 2-O-sulfotransferase 1 (HS2ST1) and Syndecan 3 (SDC3, met all our requirements after screening. The constructed prognostic model indicated that patients in the high-risk group had a much lower patient survival rate than in the low-risk group. Moreover, these genes were closely related to immune cells (P < .05). Drug sensitivity analysis showed that the 2 genes modeled were strongly correlated with multiple drugs. Immunohistochemical analysis showed significantly higher protein expression of both genes in OS than in paraneoplastic tissues.

CONCLUSIONS

HS2ST1 and SDC3 are significantly dysregulated in OS, and the prognostic models constructed based on these 2 genes have much lower survival rates in the high-risk group than in the low-risk group. HS2ST1 and SDC3 can be used as glycolytic and immune-related prognostic biomarkers in OS.

Synthesizing Molecular and Immune Characteristics to Move Beyond WHO Grade in Meningiomas: A Focused Review

No portion of this manuscript has previously been presented. Meningiomas, the most common primary intracranial tumors, are histologically categorized by the World Health Organization (WHO) grading system. While higher WHO grade is generally associated with poor clinical outcomes, a significant subset of grade I tumors recur or progress, indicating a need for more reliable models of meningioma behavior. Several groups have developed risk scores based on molecular or immunologic characteristics. These classification schemes show promise, with several models preliminarily demonstrating similar or superior accuracy to WHO grading. Improved understanding of immune system recognition and targeting of meningioma subtypes is necessary to advance the predictive power, as well as develop new therapies. Here, we characterize meningioma molecular drivers, predictive of recurrence and progression, and describe specific aspects of the immune response to meningiomas while highlighting critical questions and ongoing research. Relevant manuscripts of interest were identified using a systematic approach and synthesized into this focused review. Finally, we summarize the ongoing and completed clinical trials for immunotherapy in meningiomas and offer perspective on future directions.