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The phenomenon of protein phase separation (PPS) underlies a wide range of cellular functions. Correspondingly, the dysregulation of the PPS process has been associated with numerous human diseases. To enable therapeutic interventions based on the regulation of this association, possible targets should be identified. For this purpose, we present an approach that combines the multiomic PandaOmics platform with the FuzDrop method to identify PPS-prone disease-associated proteins. Using this approach, we prioritize candidates with high PandaOmics and FuzDrop scores using a profiling method that accounts for a wide range of parameters relevant for disease mechanism and pharmacological intervention. We validate the differential phase separation behaviors of three predicted Alzheimer's disease targets (MARCKS, CAMKK2, and p62) in two cell models of this disease. Overall, the approach that we present generates a list of possible therapeutic targets for human diseases associated with the dysregulation of the PPS process.
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Doença de Alzheimer , Multiômica , Humanos , Proteínas , Doença de Alzheimer/tratamento farmacológico , Doença de Alzheimer/genética , Quinase da Proteína Quinase Dependente de Cálcio-CalmodulinaRESUMO
INTRODUCTION: Allergic disorders are common diseases marked by the abnormal immune response toward foreign antigens that are not pathogens. Often patients with food allergy also suffer from asthma and eczema. Given the similarities of these diseases and a shortage of effective treatments, developing novel therapeutics against common targets of multiple allergies would offer an efficient and cost-effective treatment for patients. METHODS: We employed the artificial intelligence-driven target discovery platform, PandaOmics, to identify common targets for treating asthma, eczema, and food allergy. Thirty-two case-control comparisons were generated from 15, 11, and 6 transcriptomics datasets related to asthma (558 cases, 315 controls), eczema (441 cases, 371 controls), and food allergy (208 cases, 106 controls), respectively, and allocated into three meta-analyses for target identification. Top-100 high-confidence targets and Top-100 novel targets were prioritized by PandaOmics for each allergic disease. RESULTS: Six common high-confidence targets (i.e., IL4R, IL5, JAK1, JAK2, JAK3, and NR3C1) across all three allergic diseases have approved drugs for treating asthma and eczema. Based on the targets' dysregulated expression profiles and their mechanism of action in allergic diseases, three potential therapeutic targets were proposed. IL5 was selected as a high-confidence target due to its strong involvement in allergies. PTAFR was identified for drug repurposing, while RNF19B was selected as a novel target for therapeutic innovation. Analysis of the dysregulated pathways commonly identified across asthma, eczema, and food allergy revealed the well-characterized disease signature and novel biological processes that may underlie the pathophysiology of allergies. CONCLUSION: Altogether, our study dissects the shared pathophysiology of allergic disorders and reveals the power of artificial intelligence in the exploration of novel therapeutic targets.
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Asma , Eczema , Hipersensibilidade Alimentar , Humanos , Inteligência Artificial , Interleucina-5 , Eczema/tratamento farmacológico , Hipersensibilidade Alimentar/tratamento farmacológico , Asma/tratamento farmacológicoRESUMO
PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinicoâa data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.
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Inteligência Artificial , Biomarcadores , Descoberta de Drogas , Descoberta de Drogas/métodos , Biomarcadores/metabolismo , Humanos , Software , Biologia Computacional/métodosRESUMO
Traf2- and Nck-interacting kinase (TNIK) has emerged as a key regulator of pathological metabolic signaling in several diseases and is a promising drug target. Originally studied for its role in cell migration and proliferation, TNIK possesses several newly identified functions that drive the pathogenesis of multiple diseases. Specifically, we evaluate TNIK's newfound roles in cancer, metabolic disorders, and neuronal function. We emphasize the implications of TNIK signaling in metabolic signaling and evaluate the translational potential of these discoveries. We also highlight how TNIK's role in many biological processes converges upon several hallmarks of aging. We conclude by discussing the therapeutic landscape of TNIK-targeting drugs and the recent success of clinical trials targeting TNIK.
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Envelhecimento , Neoplasias , Proteínas Serina-Treonina Quinases , Humanos , Neoplasias/metabolismo , Neoplasias/tratamento farmacológico , Envelhecimento/metabolismo , Animais , Proteínas Serina-Treonina Quinases/metabolismo , Doenças Metabólicas/metabolismo , Doenças Metabólicas/tratamento farmacológico , Transdução de SinaisRESUMO
Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.
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Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.
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Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.
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Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.
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Idioma , Estudos Prospectivos , Bases de Dados FactuaisRESUMO
Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.
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Perfilação da Expressão Gênica , Aprendizado de MáquinaRESUMO
Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.
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Neoplasias Encefálicas , Glioblastoma , Humanos , Glioblastoma/tratamento farmacológico , Glioblastoma/genética , Glioblastoma/metabolismo , Neoplasias Encefálicas/tratamento farmacológico , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/metabolismo , Envelhecimento/genética , Inteligência ArtificialRESUMO
As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual-purpose anti-aging and anti-cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue-specific age-associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large-scale pan-cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI-driven target discovery platform-PandaOmics. Age-associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age-associated cancer targets with anti-aging experimental evidence, 22 were proposed as dual-purpose targets for anti-aging and anti-cancer treatment with the same therapeutic direction. Among age-associated cancer targets without known lifespan-regulating activity, 23 genes were selected based on predicted dual-purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti-cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual-purpose target. This is the first study utilizing an innovative AI-driven approach to identify dual-purpose target candidates for anti-aging and anti-cancer treatment, supporting the value of AI-assisted target identification for drug discovery.
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Proteínas de Caenorhabditis elegans , Neoplasias , Animais , Humanos , Envelhecimento/genética , Longevidade/genética , Caenorhabditis elegans/metabolismo , Proteínas de Caenorhabditis elegans/metabolismo , Neoplasias/tratamento farmacológico , Neoplasias/genética , Inteligência Artificial , Histona Desmetilases/metabolismoRESUMO
The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 µM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.
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Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.
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Inteligência Artificial , Sarcoma , Humanos , Centríolos/genética , Carcinogênese/metabolismo , Sarcoma/metabolismo , Reparo do DNA/genéticaRESUMO
Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.
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Inteligência Artificial , Descoberta de Drogas , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos/métodos , ProteínasRESUMO
Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.
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BACKGROUND: To complement next-generation sequencing technologies, there is a pressing need for efficient pre-sequencing capture methods with reduced costs and DNA requirement. The Alu family of short interspersed nucleotide elements is the most abundant type of transposable elements in the human genome and a recognized source of genome instability. With over one million Alu elements distributed throughout the genome, they are well positioned to facilitate genome-wide sequence amplification and capture of regions likely to harbor genetic variation hotspots of biological relevance. RESULTS: Here we report on the use of inter-Alu PCR with an enhanced range of amplicons in conjunction with next-generation sequencing to generate an Alu-anchored scan, or 'AluScan', of DNA sequences between Alu transposons, where Alu consensus sequence-based 'H-type' PCR primers that elongate outward from the head of an Alu element are combined with 'T-type' primers elongating from the poly-A containing tail to achieve huge amplicon range. To illustrate the method, glioma DNA was compared with white blood cell control DNA of the same patient by means of AluScan. The over 10 Mb sequences obtained, derived from more than 8,000 genes spread over all the chromosomes, revealed a highly reproducible capture of genomic sequences enriched in genic sequences and cancer candidate gene regions. Requiring only sub-micrograms of sample DNA, the power of AluScan as a discovery tool for genetic variations was demonstrated by the identification of 357 instances of loss of heterozygosity, 341 somatic indels, 274 somatic SNVs, and seven potential somatic SNV hotspots between control and glioma DNA. CONCLUSIONS: AluScan, implemented with just a small number of H-type and T-type inter-Alu PCR primers, provides an effective capture of a diversity of genome-wide sequences for analysis. The method, by enabling an examination of gene-enriched regions containing exons, introns, and intergenic sequences with modest capture and sequencing costs, computation workload and DNA sample requirement is particularly well suited for accelerating the discovery of somatic mutations, as well as analysis of disease-predisposing germline polymorphisms, by making possible the comparative genome-wide scanning of DNA sequences from large human cohorts.
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Elementos Alu , Variação Genética , Genoma Humano , Genômica/métodos , Análise de Sequência de DNA/métodos , Humanos , MasculinoRESUMO
The SCZ (schizophrenia)-associated GABA(A) receptor (gamma-aminobutyric acid type A receptor) beta(2) subunit gene GABRB2 was recently associated with BPD (bipolar disorder). Although weaker than its association with SCZ, significant association of GABRB2 with BPD was found in both German and Chinese, especially for the haplotypes rs1816071-rs187269 and rs1816072-rs187269 for which the M-M variants showed higher frequency in disease than the control. Significant genotype-dependent reduction in GABRB2 expression was shown for BPD, but to a lesser extent than that for SCZ. Temporal effects on GABRB2 expression were observed. Moreover, for the homozygous major genotypes of rs1816071, rs1816072 and rs187269, expression increased with time in CON but decreased in SCZ and BPD. The genotypes of these three SNPs (single nucleotide polymorphisms) were further correlated with antipsychotics dosage in SCZ cohorts. The findings highlight the importance of GABRB2 in neuropsychiatric disease aetiology, with respect to haplotype association, as well as reduction of and temporal effects on gene expression in both SCZ and BPD, but to a lesser extent in the latter, supporting the suggestion that functional psychosis can be conceptualized as a continuous spectrum of clinical phenotypes rather than as distinct categories.
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Transtorno Bipolar/genética , Expressão Gênica , Receptores de GABA-A/genética , Esquizofrenia/genética , Povo Asiático/genética , Predisposição Genética para Doença , Genótipo , Haplótipos , Humanos , Polimorfismo de Nucleotídeo Único , Transtornos Psicóticos/genética , Receptores de GABA-A/metabolismo , População Branca/genéticaRESUMO
BACKGROUND: Single nucleotide polymorphisms (SNPs) and haplotypes in intron 8 of type A gamma-aminobutyric acid (GABA(A)) receptor beta2 subunit gene (GABRB2) were initially found to be associated with schizophrenia in Chinese. This finding was subjected to cross-validation in this study with Japanese (JP) and German Caucasian (GE) subjects. METHODS: Single nucleotide polymorphisms discovery and genotyping were carried out through resequencing of a 1839 base pair (bp) region in GABRB2. Tagging SNPs (tSNPs) were selected based on linkage disequilibrium (LD), combinations of which were analyzed with Bonferroni correction and permutation for disease association. Random resampling was applied to generate size- and gender-balanced cases and control subjects. RESULTS: Out of the 17 SNPs (9.2/kilobase [kb]) revealed, 6 were population-specific. Population variations in LD were observable, and at least two low LD points were identified in both populations. Although disease association at single SNP level was only shown in GE, strong association was demonstrated in both JP (p = .0002 - .0191) and GE (p = .0033 - .0410) subjects, centering on haplotypes containing rs1816072 and rs1816071. Among different clinical subtypes, the most significant association was exhibited by systematic schizophrenia. CONCLUSIONS: Cross-population validation of GABRB2 association with schizophrenia has been obtained with JP and GE subjects, with the genotype-disease correlations being strongest in systematic schizophrenia, the most severe subtype of the disease.
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Etnicidade , Predisposição Genética para Doença , Polimorfismo de Nucleotídeo Único , Receptores de GABA-A/genética , Esquizofrenia/classificação , Esquizofrenia/genética , Adulto , Idoso , Estudos de Casos e Controles , Feminino , Frequência do Gene , Genótipo , Alemanha/etnologia , Humanos , Íntrons , Japão/etnologia , Desequilíbrio de Ligação , Masculino , Pessoa de Meia-IdadeRESUMO
ZFPM2, encoding a zinc finger protein and abundantly expressed in the brain, uterus and smooth muscles, plays important roles in cardiac and gonadal development. Abnormal expression of ZFPM2 in ovarian tumors and neuroblastoma has been reported but hitherto its genetic association with cancer and effects on gliomas have not been studied. In the present study, the hexamer insertion-deletion polymorphism rs71305152, located within a large haplotype block spanning intron 1 to intron 3 of ZFPM2, was genotyped in Chinese cohorts of glioma (n = 350), non-glioma cancer (n = 354) and healthy control (n = 463) by direct sequencing and length polymorphism in gel electrophoresis, and ZFPM2 expression in glioma tissues (n = 69) of different grades was quantified by real-time RT-PCR. Moreover, potential natural selection pressure acting on the gene was investigated. Disease-association analysis showed that the overall genotype of rs71305152 was significantly associated with gliomas (P = 0.016), and the heterozygous genotype compared to the combined homozygous genotypes was less frequent in gliomas than in controls (P = 0.005) or non-glioma cancers (P = 0.020). ZFPM2 mRNA expression was negatively correlated with the grades of gliomas (P = 0.002), with higher expression levels in the low-grade gliomas. In the astrocytoma subtype, higher ZFPM2 expression was also correlated with the rs71305152 heterozygous genotype (P = 0.028). In addition, summary statistics tests gave highly positive values, demonstrating that the gene is under the influence of balancing selection. These findings suggest that ZFPM2 is a glioma susceptibility gene, its genotype and expression showing associations with incidence and severity, respectively. Moreover, the balancing selection acting on ZFPM2 may be related to the important roles it has to play in multiple organ development or associated disease etiology.