EZH2-, CHD4-, and IDH-linked epigenetic perturbation and its association with survival in glioma patients

A Review of the Application of Spatial Transcriptomics in Neuroscience

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Manifestation of the malignant progression of glioma following initial intracerebral hemorrhage: A case report

BACKGROUND

Intracranial hemorrhage is extremely rare during the initial stages of glioma. Here, we report a case of glioma with unclassified pathology and intracranial bleeding.

CASE SUMMARY

After the second surgery for intracerebral hemorrhage, the patient experienced weakness in the left arm and leg, but could walk unassisted. One month after discharge, the weakness in the left limbs had exacerbated and the patient also suffered from headaches and dizziness. A third surgery was ineffective against the rapidly growing tumor. Intracerebral hemorrhage may be the initial symptom of glioma in some rare cases, and atypical perihematomal edema can be used for diagnosis during an emergency. Certain histological and molecular features seen in our case were similar to that of glioblastoma with a primitive neuronal component, which is termed diffuse glioneuronal tumor with features similar to oligodendroglioma and nuclear clusters (DGONC). The patient underwent three surgeries to remove the tumor. The first tumor resection had been performed when the patient was 14-years-old. Resection of the hemorrhage and bone disc decompression were performed when the patient was 39-years-old. One month after the last discharge, the patient underwent neuronavigation-assisted resection of the right frontotemporal parietal lesion plus extended flap decompression. On the 50^th d after the third operation, computed tomography imaging showed rapid tumor growth accompanied by brain hernia. The patient was discharged and died 3 d later.

CONCLUSION

Glioma can present as bleeding in the initial stage and should be considered in such a setting. We have reported a case of DGONC, which is a rare molecular subtype of glioma with a unique methylation profile.

Spectrum of qualitative and quantitative imaging of pilomyxoid, intermediate pilomyxoid and pilocytic astrocytomas in relation to their genetic alterations

PURPOSE

Pilomyxoid astrocytomas (PMA) are pediatric brain tumors predominantly located in the suprasellar region, third ventricle and posterior fossa, which are considered to be more clinically aggressive than pilocytic astrocytomas (PA). Another entity, intermediate pilomyxoid tumors (IPT), exists within the spectrum of pilocytic/pilomyxoid astrocytomas. The 2021 WHO CNS classification refrained from assigning grade 1 or 2 status to PMA, thereby reflecting the need to further elucidate their clinical and imaging characteristics.

METHODS

We included a total of 15 patients with PMA, IPT and suprasellar PA. We retrospectively evaluated immunohistochemistry, imaging findings and diffusion characteristics within these tumors as well as whole exome sequencing for three of the cases.

RESULTS

87% of the tumors were supratentorial with 11 cases suprasellar in location, 1 case located in the frontal white matter and 1 in the hippocampus. 6 cases demonstrated intraventricular extension. ADC values were higher in PMA and IPT than PA. 3 cases demonstrated KIAA1549-BRAF-fusion, 2 had BRAF[Formula: see text]-mutation and 6 were BRAF-wildtype. All cases had recurrence/progression on follow-up.

CONCLUSION

PMA and IPT do not demonstrate aggressive imaging characteristics in respect to their diffusion imaging with ADC values being higher than PA. Lack of BRAF-alteration in PMA corresponded to atypical location of tumors with atypical driver mutations and mechanisms.

The function of histone methylation and acetylation regulators in GBM pathophysiology

Glioblastoma (GBM) is the most common and lethal primary brain malignancy and is characterized by a high degree of intra and intertumor cellular heterogeneity, a starkly immunosuppressive tumor microenvironment, and nearly universal recurrence. The application of various genomic approaches has allowed us to understand the core molecular signatures, transcriptional states, and DNA methylation patterns that define GBM. Histone posttranslational modifications (PTMs) have been shown to influence oncogenesis in a variety of malignancies, including other forms of glioma, yet comparatively less effort has been placed on understanding the transcriptional impact and regulation of histone PTMs in the context of GBM. In this review we discuss work that investigates the role of histone acetylating and methylating enzymes in GBM pathogenesis, as well as the effects of targeted inhibition of these enzymes. We then synthesize broader genomic and epigenomic approaches to understand the influence of histone PTMs on chromatin architecture and transcription within GBM and finally, explore the limitations of current research in this field before proposing future directions for this area of research.

High EZH2 Protein Expression Is a Poor Prognostic Predictor in IDH1 R132H-Negative Gliomas

Accumulating data indicates that enhancer of zeste homology 2 (EZH2) and isocitrate dehydrogenase 1 (IDH1) are implicated in promoting tumourigenesis in a myriad of malignancies including gliomas. We aimed to determine the immunoexpression of EZH2 in gliomas and its correlation with clinicopathological variables. The prognostic value of the combined immunoexpression of EZH2 and IDH1 was further explored in a retrospective analysis involving 56 patients with histologically confirmed gliomas in Universiti Kebangsaan Malaysia Medical Centre from 2010 to 2016. The patients were then followed up for a period of five years. EZH2 and IDH1 R132H immunoexpressions were performed and analysed on respective tissue blocks. Five-year progression-free survival (PFS) and overall survival (OS) were estimated by Kaplan−Meier analysis. Univariate and multivariate Cox proportional hazard regression models were performed to evaluate the value of EZH2 as an independent factor for the prediction of PFS and OS. High EZH2 immunoexpression was demonstrated in 27 (48.2%) gliomas. High EZH2 expression was significantly correlated with older age (p = 0.003), higher tumour grade (p < 0.001), negative IDH1 R132H immunoexpression (p = 0.039), a poor 5-year PFS (mean = 9.7 months, p < 0.001) and 5-year OS (mean = 28.2 months, p = 0.007). In IDH1 R132H-negative gliomas, there was a trend toward shorter 5-year PFS (mean = 8.0 months, p = 0.001) and 5-year OS (mean = 28.7 months, p = 0.06) in gliomas demonstrating high EZH2 expression compared with those with low EZH2 expression. High EZH2 immunoexpression is an unfavourable independent prognostic predictor of poor survival in gliomas. EZH2 analysis might therefore be of clinical value for risk stratification, especially in patients with IDH1 R132H-negative gliomas.

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.

Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

Mitochondria-targeting nano therapy altering IDH2-mediated EZH2/EZH1 interaction as precise epigenetic regulation in glioblastoma

Glioblastoma (GBM) is a complex brain cancer with frequent relapses and high mortality and still awaits effective treatment. Mitochondria dysfunction is a pathogenic condition in GBM and could be a prime therapeutic target for ceasing GBM progression. Strategies to overcome brain solid tumor barriers and selectively target mitochondria within specific cell types may improve GBM treatment. Here, we present hypericin-conjugated gold nanoparticles (PEG-AuNPs@Hyp) where hypericin is a mitochondrion-targeting agent exhibiting multimodal therapy by critically impacting the IDH2 gene (Isocitrate dehydrogenase) and its interaction with polycomb methyltransferase EZH1/2 for GBM therapy. It significantly localizes in mitochondria by enhanced cellular uptake in the human GBM cell lines/three-dimensional (3D) culture model under red-light exposure. It triggers oxidative stress and changes the mitochondrial potential, with increased Bax/Bcl2 ratio enhancing GBM cell death. The suppressed expression of mutated IDH2 and polycomb group of proteins upon PEG-AuNPs@Hyp/light exposure regulates mitochondria-targeting-mediated GBM metabolism with epigenetic repression of complex machinery function. Polyubiquitination and proteasomal degradation of EZH1 indicate the implication of these polycomb proteins in GBM progression. Chromatin immunoprecipitation reveals the IDH2 and EZH1/EZH2 direct interaction, confirming the role played by IDH2 in modulating the expression of EZH1 and EZH2. In vivo studies further displayed better tumor ablation in a GBM tumor-bearing nude mouse model. The present multimodal nanoformulation compromised the functional dependency of polycomb on mitochondrial IDH2 and established the mechanism of GBM inhibition.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Exome sequencing of glioblastoma-derived cancer stem cells reveals rare clinically relevant frameshift deletion in MLLT1 gene

Background

Glioblastoma multiforme (GBM) is a heterogeneous CNS neoplasm which causes significant morbidity and mortality. One reason for the poor prognostic outcome of GBM is attributed to the presence of cancer stem cells (CSC) which confer resistance against standard chemo- and radiotherapeutics modalities. Two types of GBM-associated CSC were isolated from the same patient: tumor core- (c-CSC) and peritumor tissue-derived cancer stem cells (p-CSC). Our experiments are focused on glioblastoma–IDH-wild type, and no disease-defining alterations were present in histone, BRAF or other genes.

Methods

In the present study, potential differences in genetic variants between c-CSC versus p-CSC derived from four GBM patients were investigated with the aims of (1) comparing the exome sequences between all the c-CSC or p-CSC to identify the common variants; (2) identifying the variants affecting the function of genes known to be involved in cancer origin and development.

Results

By comparative analyses, we identified common gene single nucleotide variants (SNV) in all GBM c-CSC and p-CSC, a potentially deleterious variant was a frameshift deletion at Gln461fs in the MLLT1 gene, that was encountered only in p-CSC samples with different allelic frequency.

Conclusions

We discovered a potentially harmful frameshift deletion at Gln461fs in the MLLT1 gene. Further investigation is required to confirm the presence of the identified mutations in patient tissue samples, as well as the significance of the frameshift mutation in the MLLT1 gene on GBM biology and response to therapy based on genomic functional experiments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02419-4.

EZH2 as a new therapeutic target in brain tumors: Molecular landscape, therapeutic targeting and future prospects

Brain tumors are responsible for high mortality and morbidity worldwide. The brain tumor treatment depends on identification of molecular pathways involved in progression and malignancy. Enhancer of zeste homolog 2 (EZH2) has obtained much attention in recent years in field of cancer therapy due to its aberrant expression and capacity in modulating expression of genes by binding to their promoter and affecting methylation status. The present review focuses on EZH2 signaling in brain tumors including glioma, glioblastoma, astrocytoma, ependymomas, medulloblastoma and brain rhabdoid tumors. EZH2 signaling mainly participates in increasing proliferation and invasion of cancer cells. However, in medulloblastoma, EZH2 demonstrates tumor-suppressor activity. Furthermore, EZH2 can regulate response of brain tumors to chemotherapy and radiotherapy. Various molecular pathways can function as upstream mediators of EZH2 in brain tumors including lncRNAs and miRNAs. Owing to its enzymatic activity, EZH2 can bind to promoter of target genes to induce methylation and affects their expression. EZH2 can be considered as an independent prognostic factor in brain tumors that its upregulation provides undesirable prognosis. Both anti-tumor agents and gene therapies such as siRNA have been developed for targeting EZH2 in cancer therapy.

TP53 Mutation Is a Prognostic Factor in Lower Grade Glioma and May Influence Chemotherapy Efficacy

BACKGROUND

Identification of prognostic biomarkers in cancers is a crucial step to improve overall survival (OS). Although mutations in tumour protein 53 (TP53) is prevalent in astrocytoma, the prognostic effects of TP53 mutation are unclear.

METHODS

In this retrospective study, we sequenced TP53 exons 1 to 10 in a cohort of 102 lower-grade glioma (LGG) subtypes and determined the prognostic effects of TP53 mutation in astrocytoma and oligodendroglioma. Publicly available datasets were analysed to confirm the findings.

RESULTS

In astrocytoma, mutations in TP53 codon 273 were associated with a significantly increased OS compared to the TP53 wild-type (HR (95% CI): 0.169 (0.036-0.766), p = 0.021). Public datasets confirmed these findings. TP53 codon 273 mutant astrocytomas were significantly more chemosensitive than TP53 wild-type astrocytomas (HR (95% CI): 0.344 (0.13-0.88), p = 0.0148). Post-chemotherapy, a significant correlation between TP53 and YAP1 mRNA was found (p = 0.01). In O (6)-methylguanine methyltransferase (MGMT) unmethylated chemotherapy-treated astrocytoma, both TP53 codon 273 and YAP1 mRNA were significant prognostic markers. In oligodendroglioma, TP53 mutations were associated with significantly decreased OS.

CONCLUSIONS

Based on these findings, we propose that certain TP53 mutant astrocytomas are chemosensitive through the involvement of YAP1, and we outline a potential mechanism. Thus, TP53 mutations may be key drivers of astrocytoma therapeutic efficacy and influence survival outcomes.

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/.

Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model

MOTIVATION

The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway.

RESULTS

We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway.

AVAILABILITY AND IMPLEMENTATION

We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Patient-Derived Cancer Organoids for Precision Oncology Treatment

The emergence of three-dimensional human organoids has opened the door for the development of patient-derived cancer organoid (PDO) models, which closely recapitulate parental tumor tissue. The mainstays of preclinical cancer modeling include in vitro cell lines and patient-derived xenografts, but these models lack the cellular heterogeneity seen in human tumors. Moreover, xenograft establishment is resource and time intensive, rendering these models difficult to use to inform clinical trials and decisions. PDOs, however, can be created efficiently and retain tumor-specific properties such as cellular heterogeneity, cell-cell and cell-stroma interactions, the tumor microenvironment, and therapeutic responsiveness. PDO models and drug-screening protocols have been described for several solid tumors and, more recently, for gliomas. Since PDOs can be developed in clinically relevant time frames and share many characteristics of parent tumors, they may enhance the ability to provide precision oncologic care for patients. This review explores the current literature on cancer organoids, highlighting the history of PDO development, organoid models of glioma, and potential clinical applications of PDOs.

Role of Chromodomain-Helicase-DNA-Binding Protein 4 (CHD4) in Breast Cancer

Chromodomain-helicase-DNA-binding protein 4 (CHD4) is an epigenetic regulator identified as an oncogenic element that may provide a novel therapeutic target for the treatment of breast cancer (BC). CHD4—the core component of the nucleosome remodeling and deacetylase (NuRD) complex—may be mutated in patients with this disease. However, information on CHD4 mutants that might allow their use as biomarkers of therapeutic success and prognosis is lacking. The present work examines mutations in CHD4 reported in patients with breast cancer and included in public databases and attempts to identify their roles in its development. The databases revealed 81 point mutations across different types of breast cancer (19 of which also appeared in endometrial, intestinal, nervous system, kidney, and lymphoid organ cancers). 71.6% of the detected mutations were missense mutations, 13.6% were silent, and 6.2% nonsense. Over 50% affected conserved residues of the ATPase motor (ATPase and helicase domains), and domains of unknown function in the C-terminal region. Thirty one mutations were classified in the databases as either ‘deleterious’, ‘probably/possibly damaging’ or as ‘high/medium pathogenic’; another five nonsense and one splice-site variant were predicted to produce potentially harmful truncated proteins. Eight of the 81 mutations were categorized as putative driver mutations and have been found in other cancer types. Some mutations seem to influence ATPase and DNA translocation activities (R1162W), while others may alter protein stability (R877Q/H, R975H) or disrupt DNA binding and protein activity (R572*, X34_splice) suggesting CHD4 function may be affected. In vivo tumorigenecity studies in endometrial cancer have revealed R975H and R1162W as mutations that lead to CHD4 loss-of-function. Our study provides insight into the molecular mechanism whereby CHD4, and some of its mutants could play a role in breast cancer and suggest important implications for the biological comprehension and prognosis of breast cancer, identifying CHD4 as a novel therapeutic target for BC patients.

Linc00475 promotes the progression of glioma by regulating the miR-141-3p/YAP1 axis

Glioma is the most prevalent and lethal primary brain tumour. Abundant long non‐coding RNAs ( lncRNAs) are aberrant and play crucial roles in the oncogenesis of glioma. The exact functions of linc00475 in glioma remain blurred. Here, we analysed the expression levels of linc00475 by qRT‐PCR and discovered that linc00475 was up‐regulated in glioma and predicted a poor prognosis in patients with glioma. Besides, inhibiting linc00475 restrained the progression of glioma in vitro and in vivo. Further experiments confirmed that linc00475 regulated the progression of glioma by acting as a sponge for miR‐141‐3p. Moreover, we detected the binding sites of linc00475 and miR‐141‐3p, the YAP1‐ 3′UTR and miR‐141‐3p by luciferase reporters. The rescue assays confirmed that inhibiting linc00475 restrained the progression of glioma through the miR‐141‐3p/YAP1 pathway. Collectively, our research demonstrates the key roles of linc00475 in glioma, which could be a promising therapeutic target.

Clinical and mutational profiles of adult medulloblastoma groups

Adult medulloblastomas are clinically and molecularly understudied due to their rarity. We performed molecular grouping, targeted sequencing, and TERT promoter Sanger sequencing on a cohort of 99 adult medulloblastomas. SHH made up 50% of the cohort, whereas Group 3 (13%) was present in comparable proportion to WNT (19%) and Group 4 (18%). In contrast to paediatric medulloblastomas, molecular groups had no prognostic impact in our adult cohort (p = 0.877). Most frequently mutated genes were TERT (including promoter mutations, mutated in 36% cases), chromatin modifiers KMT2D (31%) and KMT2C (30%), TCF4 (31%), PTCH1 (27%) and DDX3X (24%). Adult WNT patients showed enrichment of TP53 mutations (6/15 WNT cases), and 3/6 TP53-mutant WNT tumours were of large cell/anaplastic histology. Adult SHH medulloblastomas had frequent upstream pathway alterations (PTCH1 and SMO mutations) and few downstream alterations (SUFU mutations, MYCN amplifications). TERT promoter mutations were found in 72% of adult SHH patients, and were restricted to this group. Adult Group 3 tumours lacked hallmark MYC amplifications, but had recurrent mutations in KBTBD4 and NOTCH1. Adult Group 4 tumours harboured recurrent mutations in TCF4 and chromatin modifier genes. Overall, amplifications of MYC and MYCN were rare (3%). Since molecular groups were not prognostic, alternative prognostic markers are needed for adult medulloblastoma. KMT2C mutations were frequently found across molecular groups and were associated with poor survival (p = 0.002). Multivariate analysis identified histological type (p = 0.026), metastasis (p = 0.031) and KMT2C mutational status (p = 0.046) as independent prognosticators in our cohort. In summary, we identified distinct clinical and mutational characteristics of adult medulloblastomas that will inform their risk stratification and treatment.

CGIDLA:Developing the Web Server for CpG Island Related Density and LAUPs (Lineage-Associated Underrepresented Permutations) Study

It is well known that CpG island plays an important role in gene methylation. Since CpG island is closely related to human genetic characteristics such as TATA-box, tissue expression specificity, and LAUPs (Lineage-associated Underrepresented Permutations), it is important to investigate the sequence specificity of CpG island as well as the potential genetic characteristics related to CpG island to further understand the methylation related regulation mechanism. Therefore, this study develops such an online service website for CpG island related density and LAUPs analysis (CGIDLA, www.combio-lezhang.online/cgidla/index.html), that not only can investigate the relationship among the CpG island density, TATA-box feature, and expression breadth of human genes, but also deposit LAUPs of 32 representative species to help molecular biologists investigate the relationship between CpG island and LUAPs. Moreover, CGIDLA provides the source code download service and the related LAUPs counting functions.

Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis.

RESULTS

Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma.

CONCLUSIONS

We summarize the whole process of the experiment and discuss how to expand our experiment in the future.

The prognostic significance of CDKN2A homozygous deletion in IDH-mutant lower-grade glioma and glioblastoma: a systematic review of the contemporary literature

BACKGROUND

The most recent cIMPACT-NOW update highlighted the homozygous deletion of the Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) gene as a clinically important molecular alteration in IDH-mutant glioma. Correspondingly, we systematically reviewed the contemporary literature to affirm the contemporary stance of the literature on the prognostic significance of this alteration in this setting based on the current World Health Organization (WHO) Grade classification.

METHODS

A systematic search of seven electronic databases from inception to February 2020 was conducted following PRISMA guidelines. Articles were screened against pre-specified criteria to include lower-grade glioma (LGG, WHO Grade II/III) and glioblastoma (GBM, WHO Grade IV) separately. Progression free survival (PFS) and overall survival (OS) from Kaplan-Meier and multivariable analyses were outcomes of interest.

RESULTS

Nine institutional studies describing 2193 IDH-mutant gliomas satisfied criteria for evaluation, with 1756 (80%) LGG and 437 (20%) GBM. When reported, the proportion of CDKN2A homozygous deleted gliomas ranged from 9 to 43%, with a median incidence of 22%. For LGG, Kaplan-Meier analyses demonstrated shorter PFS in the presence of CDKN2A homozygous deletion in three studies (median values, 31 versus 91 months), and shorter OS in five studies (median values, 61 versus 154 months). For GBM, Kaplan-Meier analyses demonstrated shorter PFS in the presence of CDKN2A homozygous deletion in two studies (median values, 16 versus 30 months), and shorter OS in four studies (median values, 38 versus 86 months). By multivariable analyses, CDKN2A homozygous deletion was a predictor of significantly shorter PFS and OS in both LGG and GBM across all included studies.

CONCLUSIONS

The CDKN2A homozygous deletion is an important prognostic factor for survival outcomes of IDH-mutant glioma patients across multiple histologic WHO grades with specific molecular features likely dependent on IDH-mutant status. Greater understanding of how identifying this deletion can assist in the stratification of management for these tumors to optimize clinical course is required.

Exploring the dynamics and interplay of human papillomavirus and cervical tumorigenesis by integrating biological data into a mathematical model

Background

Cervical cancer is the fourth most common tumor in women worldwide, mostly resulting from high-risk human papillomavirus (HR-HPV) with persistent infection.

Results

The present discoveries are comprised of the following: (i) A total of 16.64% of the individuals were positive for HR-HPV infection, with 13.04% having a single HR-HPV type and 3.60% having multiple HR-HPV types. (ii) Cluster analysis showed that the infection rate trends of HPV31 and HPV33 in all infections as well as HPV33 and HPV35 in single infections in precancerous stages were very similar. (iii) The single/multiple infection proportions of HR-HPV demonstrated a trend that the multiple infections rates of HR-HPV increased as the disease developed.

Conclusions

The HR-HPV prevalence in outpatients was 16.64%, and the predominant HR-HPV types in the study were HPV52, HPV58 and HPV16. HR-HPV subtypes with common biological properties had similar infection rate trends in precancerous stages. Especially, as the disease development of precancer evolved, defense against HPV infection broke, meanwhile, the potential of more HPV infection increased, which resulted in increase of multiple infections of HPV.

Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.

Discrete functional and mechanistic roles of chromodomain Y-like 2 (CDYL2) transcript variants in breast cancer growth and metastasis

Rationale: Chromodomain Y-like 2 (CDYL2) is a member of the CDY gene family involved in spermatogenesis, but its role in human cancer has not been reported. Analyses of publicly available databases demonstrate that CDYL2 is abundantly expressed in breast tumors. However, whether CDYL2 is involved in breast cancer progression remains unknown.

Methods: Quantitative real-time PCR and immunoblotting assays were used to determine the expression levels of CDYL2 transcript variants in breast cancer cell lines and primary breast tumors. The effect of CDYL2 transcript variants on the malignant phenotypes of breast cancer cells was examined through in vitro and in vivo assays. Immunofluorescent staining, RNA-seq, ATAC-seq, and ChIP-qPCR were used to investigate the underlying mechanisms behind the aforementioned observations.

Results: Here we show that CDYL2 generated four transcript variants, named CDYL2a-CDYL2d. CDYL2a and CDYL2b were the predominant variants expressed in breast cancer cell lines and breast tumors and exerted strikingly discrete functions in breast cancer growth and metastasis. CDYL2a was upregulated in the majority of the breast cancer cell lines and tumors, and promoted breast cancer cell proliferation, colony formation in vitro, and tumorigenesis in xenografts. In contrast, CDYL2b was mainly expressed in luminal- and HER2-positive types of breast cancer cell lines and tumors, and suppressed the migratory, invasive, and metastatic potential of breast cancer cells in vitro and in vivo. Mechanistically, CDYL2a partially localized to SC35-positive nuclear speckles and promoted alternative splicing of a subset of target genes, including FIP1L1, NKTR, and ADD3 by exon skipping. Elimination of full-length FIP1L1, NKTR, and ADD3 rescued the impaired cell proliferation through CDYL2a depletion. In contrast, CDYL2b localized to heterochromatin and transcriptionally repressed several metastasis-promoting genes, including HPSE, HLA-F, and SELL. Restoration of HPSE, HLA-F, or SELL expression in CDYL2b-overexpressing cells attenuated the ability of CDYL2b to suppress breast cancer cell migration and invasion.

Conclusions: Collectively, these findings establish an isoform-specific function of CDYL2 in breast cancer development and progression and highlight that pharmacological inhibition of the CDYL2a, but not the CDYL2b, isoform may be an effective strategy for breast cancer therapy.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

CpG-island-based annotation and analysis of human housekeeping genes

By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CpG islands (CGIs), their methylation states, CpG spacing and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be explained at individual gene and gene-family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI) and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA box on HK genes, remains to be elucidated. First, this study introduces such a problem-solving protocol for human-genome annotation, which is based on a combination of GTEx, JBLA and Gene Ontology (GO) analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; the HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic based on GO enrichment analysis. Finally, we demonstrate that Hadoop MapReduce-based MR-JBLA algorithm is more efficient than the original JBLA in k-mer counting and CGI-associated gene analysis.

Comprehensively benchmarking applications for detecting copy number variation

Motivation: Recently, copy number variation (CNV) has gained considerable interest as a type of genomic variation that plays an important role in complex phenotypes and disease susceptibility. Since a number of CNV detection methods have recently been developed, it is necessary to help investigators choose suitable methods for CNV detection depending on their objectives. For this reason, this study compared ten commonly used CNV detection applications, including CNVnator, ReadDepth, RDXplorer, LUMPY and Control-FREEC, benchmarking the applications by sensitivity, specificity and computational demands. Taking the DGV gold standard variants as a standard dataset, we evaluated the ten applications with real sequencing data at sequencing depths from 5X to 50X. Among the ten methods benchmarked, LUMPY performs the best for both high sensitivity and specificity at each sequencing depth. For the purpose of high specificity, Canvas is also a good choice. If high sensitivity is preferred, CNVnator and RDXplorer are better choices. Additionally, CNVnator and GROM-RD perform well for low-depth sequencing data. Our results provide a comprehensive performance evaluation for these selected CNV detection methods and facilitate future development and improvement in CNV prediction methods.

As an important type of genomic structural variation, CNVs are associated with complex phenotypes because they change the number of copies of genes in cells, affecting coding sequences and playing an important role in the susceptibility or resistance to human diseases. To identify CNVs, several experimental methods have been developed, but their resolution is very low, and the detection of short CNVs presents a bottleneck. In recent years, the advancement of high-throughput sequencing techniques has made it possible to precisely detect CNVs, especially short ones. Many CNV detection applications were developed based on the availability of high-throughput sequencing data. Due to different CNV detection algorithms, the CNVs identified by different applications vary greatly. Therefore, it is necessary to help investigators choose suitable applications for CNV detection depending upon their objectives. For this reason, we not only compared ten commonly used CNV detection applications but also benchmarked the applications by sensitivity, specificity and computational demands. Our results show that the sequencing depth can strongly affect CNV detection. Among the ten applications benchmarked, LUMPY performs best for both high sensitivity and specificity for each sequencing depth. We also give recommended applications for specific purposes, for example, CNVnator and RDXplorer for high sensitivity and CNVnator and GROM-RD for low-depth sequencing data.

Computed tomography angiography-based analysis of high-risk intracerebral haemorrhage patients by employing a mathematical model

Background

Haemorrhagic stroke accounts for approximately 31.52% of all stroke cases, and the most common origin is hypertension. However, little is known about the method to identify high-risk populations of hypertensive intracerebral haemorrhage.

Results

The results showed that the angle between the middle cerebral artery and the internal carotid artery (AMIC), the distance between the beginning of the median artery and superior trunk (DMS), and the density (CT value) of the lenticulostriate artery (CTL) were statistically significant enough to cause intracerebral haemorrhage. In addition, we chose these three potential features for the ensemble learning classification model. Our developed ensemble-learning method outperforms not only previous work but also three other classic classification methods based on accuracy measurements.

Conclusions

The developed mathematical model in the present study is efficient in predicting the probability of intracerebral haemorrhage.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2741-5) contains supplementary material, which is available to authorized users.

BRCA1 mRNA expression modifies the effect of T cell activation score on patient survival in breast cancer

Background

Effector CD8⁺ T cell activation and its cytotoxic function to eradicate tumor cells depend on the T cell recognition of tumor neoantigens, and are positively associated with improved survival in breast cancer. Tumor suppressor BRCA1 and cell cycle regulator CCND1 play a critical role in maintaining genome integrity and tumorigenesis, respectively. However, it is still unclear how BRCA1 and CCND1 expression levels affect the effect of T cell activation on breast cancer patient survival.

Methods

The interactions between T cell activation status and either BRCA1 or CCND1 expression were evaluated using Kaplan-Meier survival curves and multivariate Cox regression models in a public dataset with 1088 breast cancer patients.

Results

Among the patients with low BRCA1 or CCND1 expression, the Activation group showed better overall survival than the Exhaustion group. Adjusted hazards ratios were 0.43 (95% CI: 0.20–0.93) in patients with a low BRCA1 level, and 0.39 (95% CI: 0.19–0.81) in patients with a low CCND1 level, respectively. There was a significant trend in both subgroups (p-trend = 0.011 in the low BRCA1 group, and p-trend = 0.009 in the low CCND1 group). In contrast, there is no significant association in patients with either high BRCA1 or high CCND1 levels. There is a significant interaction between T cell activation status and BRCA1 level (p = 0.009), but not between T cell activation status and CCND1 level (p = 0.135).

Conclusions

BRCA1 expression modified the effect of T cell activation status on patient survival in breast cancer, suggesting that the existence of neoantigens and the enhancement of neoantigen presentation in combination with immune checkpoint blockade may have synergistic effects on patient outcome.

An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking

Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.

Seasonality and Trend Forecasting of Tuberculosis Incidence in Chongqing, China

Tuberculosis (TB) is a global infectious disease and one of the ten leading causes of death worldwide. As TB incidence is seasonal, a reliable forecasting model that incorporates both seasonal and trend effects would be useful to improve the prevention and control of TB. In this study, the X12 autoregressive integrated moving average (X12-ARIMA) model was constructed by dividing the sequence into season term and trend term to forecast the two terms, respectively. Data regarding the TB report rate from January 2004 to December 2015 were included in the model, and the TB report data from January 2016 to December 2016 were used to validate the results. The X12-ARIMA model was compared with the seasonal autoregressive integrated moving average (SARIMA) model. A total of 383,797 cases were reported from January 2004 to December 2016 in Chongqing, China. The report rate of TB was highest in 2005 (151.06 per 100,000 population) and lowest in 2016 (72.58 per 100,000 population). The final X12-ARIMA model included the ARIMA (3,1,3) model for the trend term and the ARIMA (2,1,3) model for the season term. The SARIMA (1,0,2) * (1,1,1)₁₂ model was selected for the SARIMA model. The mean absolute error (MAE) and mean absolute percentage error (MAPE) of fitting and predicting performance based on the X12-ARIMA model were less than the SARIMA model. In conclusion, the occurrence of TB in Chongqing is controlled, which may be attributed to socioeconomic developments and improved TB prevention and control services. Applying the X12-ARIMA model is an effective method to forecast and analyze the trend and seasonality of TB.

The master regulator FUBP1: its emerging role in normal cell function and malignant development

The human Far Upstream Element (FUSE) Binding Protein 1 (FUBP1) is a multifunctional DNA- and RNA-binding protein involved in diverse cellular processes. FUBP1 is a master regulator of transcription, translation, and RNA splicing. FUBP1 has been identified as a potent pro-proliferative and anti-apoptotic factor by modulation of complex networks. FUBP1 is also described either as an oncoprotein or a tumor suppressor. Especially, FUBP1 overexpression is observed in a growing number of cancer and leads to a deregulation of targets that includes the fine-tuned MYC oncogene. Moreover, recent loss-of-function analyses of FUBP1 establish its essential functions in hematopoietic stem cell maintenance and survival. Therefore, FUBP1 appears as an emerging suspect in hematologic disorders in addition to solid tumors. The scope of the present review is to describe the advances in our understanding of the molecular basis of FUBP1 functions in normal cells and carcinogenesis. We also delineate the recent progresses in the understanding of the master role of FUBP1 in normal and pathological hematopoiesis. We conclude that FUBP1 is not only worth studying biologically but is also of clinical relevance through its pivotal role in regulating multiple cellular processes and its involvement in oncogenesis.