Analysis in epithelial ovarian cancer identifies KANSL1 as a biomarker and target gene for immune response and HDAC inhibition

The genetic architecture of complete blood counts in lactating Holstein dairy cows

Complete blood counts (CBCs) measure the abundance of individual immune cells, red blood cells, and related measures such as platelets in circulating blood. These measures can indicate the health status of an animal; thus, baseline circulating levels in a healthy animal may be related to the productive life, resilience, and production efficiency of cattle. The objective of this study is to determine the heritability of CBC traits and identify genomic regions that are associated with CBC measurements in lactating Holstein dairy cattle. The heritability of CBCs was estimated using a Bayes C0 model. The study population consisted of 388 cows with genotypes at roughly 75,000 markers and 16 different CBC phenotypes taken at one to three time points (n = 33, 131, and 224 for 1, 2, and 3 time points, respectively). Heritabilities ranged from 0.00 ± 0.00 (red cell distribution width) to 0.68 ± 0.06 (lymphocytes). A total of 96 different 1-Mb windows were identified that explained more than 1% of the genetic variance for at least one CBC trait, with 10 windows explaining more than 1% of the genetic variance for two or more traits. Multiple genes in the identified regions have functions related to immune response, cell differentiation, anemia, and disease. Positional candidate genes include RAD52 motif-containing protein 1 (RDM1), which is correlated with the degree of immune infiltration of immune cells, and C-X-C motif chemokine ligand 12 (CXCL12), which is critically involved in neutrophil bone marrow storage and release regulation and enhances neutrophil migration. Since animal health directly impacts feed intake, understanding the genetics of CBCs may be useful in identifying more disease-resilient and feed-efficient dairy cattle. Identification of genes responsible for variation in CBCs will also help identify the variability in how dairy cattle defend against illness and injury.

The role of the placenta-brain axis in psychoneuroimmune programming

Gestational exposures have enduring impacts on brain and neuroimmune development and function. Perturbations of pregnancy leading to placental structure/function deficits, cell stress, immune activation, and endocrine changes (metabolic, growth factors, etc.) all increase neuropsychiatric risk in offspring. The existing literature links obstetric diseases with placental involvement to offspring neuroimmune outcomes and neurodevelopmental risk. Psychoneuroimmune outcomes in offspring brain include changes to microglia, cytokine/chemokine production, cell stress, and long-term immunoreactivity. These outcomes are altered by structural, anti-angiogenic/hypoxic, inflammatory, and metabolic diseases of the placenta. This fetal programming occurs via direct placental passage or production of factors which can act directly on fetal brain substrates, or indirectly via action of circulating factors on intermediates in the placenta. Placental neuroendocrine, vascular/angiogenic, immune, and extracellular vesicular mechanisms are detailed. These mechanisms interact within various placental and pregnancy conditions. An increased understanding of the placental origins of psychoneuroimmunology will yield dividends for human health. Identifying maternal and placental biomarkers for fetal neuroimmune health may also revolutionize early diagnosis and precision psychiatry, empowering patients to make the best healthcare decisions for their families. Targeting placental mechanisms may be a valuable approach for the prevention and mitigation of intergenerational, lifelong neuropathology.

Investigation of common genetic risk factors between thyroid traits and breast cancer

Breast cancer (BC) risk is suspected to be linked to thyroid disorders, however observational studies exploring the association between BC and thyroid disorders gave conflicting results. We proposed an alternative approach by investigating the shared genetic risk factors between BC and several thyroid traits. We report a positive genetic correlation between BC and thyroxine (FT4) levels (corr = 0.13, p-value = 2.0 × 10-4) and a negative genetic correlation between BC and thyroid-stimulating hormone (TSH) levels (corr = -0.09, p-value = 0.03). These associations are more striking when restricting the analysis to estrogen receptor-positive BC. Moreover, the polygenic risk scores (PRS) for FT4 and hyperthyroidism are positively associated to BC risk (OR = 1.07, 95%CI: 1.00-1.13, p-value = 2.8 × 10-2 and OR = 1.04, 95%CI: 1.00-1.08, p-value = 3.8 × 10-2, respectively), while the PRS for TSH is inversely associated to BC risk (OR = 0.93, 95%CI: 0.89-0.97, p-value = 2.0 × 10-3). Using the PLACO method, we detected 49 loci associated to both BC and thyroid traits (p-value < 5 × 10-8), in the vicinity of 130 genes. An additional colocalization and gene-set enrichment analyses showed a convincing causal role for a known pleiotropic locus at 2q35 and revealed an additional one at 8q22.1 associated to both BC and thyroid cancer. We also found two new pleiotropic loci at 14q32.33 and 17q21.31 that were associated to both TSH levels and BC risk. Enrichment analyses and evidence of regulatory signals also highlighted brain tissues and immune system as candidates for obtaining associations between BC and TSH levels. Overall, our study sheds light on the complex interplay between BC and thyroid traits and provides evidence of shared genetic risk between those conditions.

Histone and Histone Acetylation-Related Alterations of Gene Expression in Uninvolved Psoriatic Skin and Their Effects on Cell Proliferation, Differentiation, and Immune Responses

Psoriasis is a chronic immune-mediated skin disease in which the symptom-free, uninvolved skin carries alterations in gene expression, serving as a basis for lesion formation. Histones and histone acetylation-related processes are key regulators of gene expression, controlling cell proliferation and immune responses. Dysregulation of these processes is likely to play an important role in the pathogenesis of psoriasis. To gain a complete overview of these potential alterations, we performed a meta-analysis of a psoriatic uninvolved skin dataset containing differentially expressed transcripts from nearly 300 individuals and screened for histones and histone acetylation-related molecules. We identified altered expression of the replication-dependent histones HIST2H2AA3 and HIST2H4A and the replication-independent histones H2AFY, H2AFZ, and H3F3A/B. Eight histone chaperones were also identified. Among the histone acetyltransferases, ELP3 and KAT5 and members of the ATAC, NSL, and SAGA acetyltransferase complexes are affected in uninvolved skin. Histone deacetylation-related alterations were found to affect eight HDACs and members of the NCOR/SMRT, NURD, SIN3, and SHIP HDAC complexes. In this article, we discuss how histone and histone acetylation-related expression changes may affect proliferation and differentiation, as well as innate, macrophage-mediated, and T cell-mediated pro- and anti-inflammatory responses, which are known to play a central role in the development of psoriasis.

Associations between prediagnostic aspirin use and ovarian tumor gene expression

BACKGROUND

Aspirin use has been associated with reduced ovarian cancer risk, yet the underlying biological mechanisms are not fully understood. To gain mechanistic insights, we assessed the association between prediagnosis low and regular-dose aspirin use and gene expression profiles in ovarian tumors.

METHODS

RNA sequencing was performed on high-grade serous, poorly differentiated, and high-grade endometrioid ovarian cancer tumors from the Nurses' Health Study (NHS), NHSII, and New England Case-Control Study (n = 92 cases for low, 153 cases for regular-dose aspirin). Linear regression identified differentially expressed genes associated with aspirin use, adjusted for birth decade and cohort. False discovery rates (FDR) were used to account for multiple testing and gene set enrichment analysis was used to identify biological pathways.

RESULTS

No individual genes were significantly differentially expressed in ovarian tumors in low or regular-dose aspirin users accounting for multiple comparisons. However, current versus never use of low-dose aspirin was associated with upregulation of immune pathways (e.g., allograft rejection, FDR = 5.8 × 10-10 ; interferon-gamma response, FDR = 2.0 × 10-4 ) and downregulation of estrogen response pathways (e.g., estrogen response late, FDR = 4.9 × 10-8 ). Ovarian tumors from current regular aspirin users versus never users were also associated with upregulation in interferon pathways (FDR <1.5 × 10-4 ) and downregulation of multiple extracellular matrix (ECM) architecture pathways (e.g., ECM organization, 4.7 × 10-8 ).

CONCLUSION

Our results suggest low and regular-dose aspirin may impair ovarian tumorigenesis in part via enhancing adaptive immune response and decreasing metastatic potential supporting the likely differential effects on ovarian carcinogenesis and progression by dose of aspirin.

CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

BACKGROUND

Brain tumours are the leading cause of cancer-related death in children, and there is no effective treatment. A growing body of evidence points to deregulated epigenetics as a tumour driver, particularly in paediatric cancers as they have relatively few genomic alterations, and key driver mutations have been identified in histone 3 (H3). Cancer stem cells (CSC) are implicated in tumour development, relapse and therapy resistance and thus particularly important to target. We therefore aimed to identify novel epigenetic treatment targets in CSC derived from H3-mutated high-grade glioma (HGG) through a CRISPR-Cas9 knockout screen.

RESULTS

The knockout screen identified more than 100 novel genes essential for the growth of CSC derived from paediatric HGG with H3K27M mutation. We successfully validated 12 of the 13 selected hits by individual knockout in the same two CSC lines, and for the top six hits we included two additional CSC lines derived from H3 wild-type paediatric HGG. Knockout of these genes led to a significant decrease in CSC growth, and altered stem cell and differentiation markers.

CONCLUSIONS

The screen robustly identified essential genes known in the literature, but also many novel genes essential for CSC growth in paediatric HGG. Six of the novel genes (UBE2N, CHD4, LSM11, KANSL1, KANSL3 and EED) were validated individually thus demonstrating their importance for CSC growth in H3-mutated and wild-type HGG. These genes should be further studied and evaluated as novel treatment targets in paediatric HGG.

A Novel Constitutional t(3;8)(p26;q21) and ANKRD26 and SRP72 Variants in a Child with Myelodysplastic Neoplasm: Clinical Implications

BACKGROUND

Childhood myelodysplastic neoplasm (cMDS) often raises concerns about an underlying germline predisposition, and its verification is necessary to guide therapeutic choice and allow family counseling. Here, we report a novel constitutional t(3;8)(p26;q21) in a child with MDS, inherited from the father, the ANKRD26 and SRP72 variants from the maternal origin, and the acquisition of molecular alterations during MDS evolution.

CASE PRESENTATION

A 4-year-old girl showed repeated infections and severe neutropenia. Bone marrow presented hypocellularity with dysplastic features. The patient had a t(3;8)(p26;q21)c identified by G-banding and FISH analysis. The family nucleus investigation identified the paternal origin of the chromosomal translocation. The NGS study identified ANKRD26 and SRP72 variants of maternal origin. CGH-array analysis detected alterations in PRSS3P2 and KANSL genes. Immunohistochemistry showed abnormal p53 expression during the MDS evolution.

CONCLUSION

This study shows for the first time, cytogenetic and genomic abnormalities inherited from the father and mother, respectively, and their clinical implications. It also shows the importance of investigating patients with constitutional cytogenetic alterations and/or germline variants to provide information to their family nucleus for genetic counseling and understanding of the pathogenesis of childhood MDS.

Treatment for ovarian clear cell carcinoma with combined inhibition of WEE1 and ATR

BACKGROUND

Standard platinum-based therapy for ovarian cancer is inefficient against ovarian clear cell carcinoma (OCCC). OCCC is a distinct subtype of epithelial ovarian cancer. OCCC constitutes 25% of ovarian cancers in East Asia (Japan, Korea, China, Singapore) and 6-10% in Europe and North America. The cancer is characterized by frequent inactivation of ARID1A and 10% of cases of endometriosis progression to OCCC. The aim of this study was to identify drugs that are either FDA-approved or in clinical trials for the treatment of OCCC.

RESULTS

High throughput screening of 166 compounds that are either FDA-approved, in clinical trials or are in pre-clinical studies identified several cytotoxic compounds against OCCC. ARID1A knockdown cells were more sensitive to inhibitors of either mTOR (PP242), dual mTOR/PI3K (GDC0941), ATR (AZD6738) or MDM2 (RG7388) compared to control cells. Also, compounds targeting BH3 domain (AZD4320) and SRC (AZD0530) displayed preferential cytotoxicity against ARID1A mutant cell lines. In addition, WEE1 inhibitor (AZD1775) showed broad cytotoxicity toward OCCC cell lines, irrespective of ARID1A status.

CONCLUSIONS

In a selection of 166 compounds we showed that inhibitors of ATR and WEE1 were cytotoxic against a panel of OCCC cell lines. These two drugs are already in other clinical trials, making them ideal candidates for treatment of OCCC.

Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma

Aim: To find biomarkers for immunity and immunotherapy in lung adenocarcinoma (LUAD) through multiomics analysis. Materials & methods: The multiomics data of patients with LUAD were downloaded from the TCGA and GEO databases. CIBERSORT, quanTIseq, ESTIMATEScore, k-means clustering, gene set enrichment analysis, gene set variation analysis, immunophenoscore and logistic regression were used in this study. Results: PSMB8 HypoMet-HighExp group patients have more active immune-related pathways, more antitumor immune cells, less protumor immune cells, higher immunophenoscore and longer progression-free survival of immune checkpoint inhibitor therapy than HyperMet-LowExp group. In multivariate analysis, PSMB8 showed an independent value. Conclusion: The combination of DNA methylation and mRNA expression of PSMB8 could independently distinguish types of tumor immune microenvironment and predict programmed cell death protein 1/programmed cell death-ligand 1 inhibitors' effects in patients with LUAD.

RB1 Is an Immune-Related Prognostic Biomarker for Ovarian Cancer

Background

Ovarian cancer (OC) is one of the most lethal gynecologic malignancies and a leading cause of death in the world. Thus, this necessitates identification of prognostic biomarkers which will be helpful in its treatment.

Methods

The gene expression profiles from The Cancer Genome Atlas (TCGA) and GSE31245 were selected as the training cohort and validation cohort, respectively. The Kaplan–Meier (KM) survival analysis was used to analyze the difference in overall survival (OS) between high and low RB transcriptional corepressor 1 (RB1) expression groups. To confirm whether RB1 was an independent risk factor for OC, we constructed a multivariate Cox regression model. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were conducted to identify the functions of differentially expressed genes (DEGs). The associations of RB1 with immune infiltration and immune checkpoints were studied by the Tumor Immune Estimation Resource (TIMER 2.0) and the Gene Expression Profiling Interactive Analysis (GEPIA). The immunohistochemistry (IHC) was performed to compare the expression level of RB1 in normal tissues and tumor samples, and to predict the prognosis of OC.

Results

The KM survival curve of the TCGA indicated that the OS in the high-risk group was lower than that in the low-risk group (HR = 1.61, 95% CI: 1.28-2.02, P = 3×10^-5), which was validated in GSE31245 (HR = 4.08, 95% CI: 1.21–13.74, P = 0.01) and IHC. Multivariate Cox regression analysis revealed that RB1 was an independent prognostic biomarker (HR = 1.66, 95% CI: 1.31-2.10, P = 2.02×10^-5). Enrichment analysis suggested that the DEGs were mainly involved in cell cycle, DNA replication, and mitochondrial transition. The infiltration levels of fibroblast, neutrophil, monocyte and macrophage were positively correlated with RB1. Furthermore, RB1 was associated with immune checkpoint molecules (CTLA4, LAG3, and CD274). The IHC staining revealed higher expression of RB1 in tumor tissues as compared to that in normal tissues (P = 0.019). Overexpression of RB1 was associated with poor prognosis of OC (P = 0.01).

Conclusion

These findings suggest that RB1 was a novel and immune-related prognostic biomarker for OC, which may be a promising target for OC treatment.

SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson's disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.

Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.

Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.

Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.

Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.