Multi-Omics Analysis in Initiation and Progression of Meningiomas: From Pathogenesis to Diagnosis

Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.

Unveiling a Biomarker Signature of Meningioma: The Need for a Panel of Genomic, Epigenetic, Proteomic, and RNA Biomarkers to Advance Diagnosis and Prognosis

Meningiomas are the most prevalent primary intracranial tumors. The majority are benign but can undergo dedifferentiation into advanced grades classified by World Health Organization (WHO) into Grades 1 to 3. Meningiomas' tremendous variability in tumor behavior and slow growth rates complicate their diagnosis and treatment. A deeper comprehension of the molecular pathways and cellular microenvironment factors implicated in meningioma survival and pathology is needed. This review summarizes the known genetic and epigenetic aberrations involved in meningiomas, with a focus on neurofibromatosis type 2 (NF2) and non-NF2 mutations. Novel potential biomarkers for meningioma diagnosis and prognosis are also discussed, including epigenetic-, RNA-, metabolomics-, and protein-based markers. Finally, the landscape of available meningioma-specific animal models is overviewed. Use of these animal models can enable planning of adjuvant treatment, potentially assisting in pre-operative and post-operative decision making. Discovery of novel biomarkers will allow, in combination with WHO grading, more precise meningioma grading, including meningioma identification, subtype determination, and prediction of metastasis, recurrence, and response to therapy. Moreover, these biomarkers may be exploited in the development of personalized targeted therapies that can distinguish between the 15 diverse meningioma subtypes.

DNA methylation meningioma biomarkers: attributes and limitations

Meningioma, one of the most common primary central nervous system tumors, are classified into three grades by the World Health Organization (WHO) based on histopathology. The gold-standard treatment, surgical resection, is hampered by issues such as incomplete resection in some cases and a high recurrence rate. Alongside genetic alterations, DNA methylation, plays a crucial role in progression of meningiomas in the occurrence and development of meningiomas. The epigenetic landscape of meningioma is instrumental in refining tumor classification, identifying robust molecular markers, determining prognosis, guiding treatment selection, and innovating new therapeutic strategies. Existing classifications lack comprehensive accuracy, and effective therapies are limited. Methylated DNA markers, exhibiting differential characteristics across varying meningioma grades, serve as invaluable diagnostic tools. Particularly, combinatorial methylated markers offer insights into meningioma pathogenesis, tissue origin, subtype classification, and clinical outcomes. This review integrates current research to highlight some of the most promising DNA and promoter methylation markers employed in meningioma diagnostics. Despite their promise, the development and application of DNA methylation biomarkers for meningioma diagnosis and treatment are still in their infancy, with only a handful of DNA methylation inhibitors currently clinically employed for meningioma treatment. Future studies are essential to validate these markers and ascertain their clinical utility. Combinatorial methylated DNA markers for meningiomas have broad implications for understanding tumor development and progression, signaling a paradigm shift in therapeutic strategies for meningiomas.

A proteome-level view of brain tumors for a better understanding of novel diagnosis, prognosis, and therapy

INTRODUCTION

Brain tumors are complex and heterogeneous malignancies with significant challenges in diagnosis, prognosis, and therapy. Proteomics, the large-scale study of proteins and their functions, has emerged as a powerful tool to comprehensively investigate the molecular mechanisms underlying brain tumor regulation.

AREAS COVERED

This review explores brain tumors from a proteomic standpoint, highlighting recent progress and insights gained through proteomic methods. It delves into the proteomic techniques employed and underscores potential biomarkers for early detection, prognosis, and treatment planning. Recent PubMed Central proteomic studies (2017-present) are discussed, summarizing findings on altered protein expression, post-translational changes, and protein interactions. This sheds light on brain tumor signaling pathways and their significance in innovative therapeutic approaches.

EXPERT OPINION

Proteomics offers immense potential for revolutionizing brain tumor diagnosis and therapy. To unlock its full benefits, further translational research is crucial. Combining proteomics with other omics data enhances our grasp of brain tumors. Validating and translating proteomic biomarkers are vital for better patient results. Challenges include tumor complexity, lack of curated proteomic databases, and the need for collaboration between researchers and clinicians. Overcoming these challenges requires investment in technology, data sharing, and translational research.

ITF2357 induces cell cycle arrest and apoptosis of meningioma cells via the PI3K-Akt pathway

As a type of central nervous system tumor, meningioma usually compresses the nerve center due to its local expansion, further causing neurological deficits. However, there are limited therapeutic approaches for meningiomas. ITF2357, a potent class I and II histone deacetylase inhibitor (HDACi), has been shown to inhibit cell proliferation, promote apoptosis, and block the cell cycle in various sarcoma cells, including glioblastoma and peripheral T-cell lymphoma. Here, we investigated the potential role of ITF2357 on meningioma cancer cells (IOMM-Lee cells). First, we demonstrated that the half-maximal inhibitory concentration (IC50) of ITF2357 was 1.842 μM by MTT assay. In addition, ITF2357 effectively inhibited the proliferation and colonization ability of IOMM-Lee cells. Flow cytometry analysis showed that ITF2357 induced G0/G1 and G2/M phase cell cycle arrest and cell apoptosis. Mechanically, the RNA sequencing data revealed that ITF2357 could affect the PI3K-Akt signaling pathway and the cell cycle progression. Furthermore, the expression levels of Akt, PI3K, p-Akt, and p-PI3K were determined by western blotting. Collectively, our data revealed that ITF2357 induces G0 G1 and G2/M phase arrest and apoptosis by inhibiting hyperactivation of the PI3K-Akt pathway, ultimately inhibiting cell viability and proliferation of meningioma cells, which developed a new approach to the treatment of meningioma.

The Role of Artificial Intelligence in Early Cancer Diagnosis

Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.

The integrated multiomic diagnosis of sporadic meningiomas: a review of its clinical implications

Introduction

Meningiomas are generally considered “benign,” however, these tumors can demonstrate variability in behavior and a surprising aggressiveness with elevated rates of recurrence. The advancement of next-generation molecular technologies have led to the understanding of the genomic and epigenomic landscape of meningiomas and more recent correlations with clinical characteristics and behavior.

Methods

Based on a thorough review of recent peer-reviewed publications (PubMed) and edited texts, we provide a molecular overview of meningiomas with a focus on relevant clinical implications.

Results

The identification of specific somatic driver mutations has led to the classification of several major genomic subgroups, which account for more than 80% of sporadic meningiomas, and can be distinguished using noninvasive clinical variables to help guide management decisions. Other somatic genomic modifications, including non-coding alterations and copy number variations, have also been correlated with tumor characteristics. Furthermore, epigenomic modifications in meningiomas have recently been described, with DNA methylation being the most widely studied and potentially most clinically relevant. Based on these molecular insights, several clinical trials are currently underway in an effort to establish effective medical therapeutic options for meningioma.

Conclusion

As we enhance our multiomic understanding of meningiomas, our ability to care for patients with these tumors will continue to improve. Further biological insights will lead to additional progress in precision medicine for meningiomas.