Exome-Wide Rare Variant Analysis From the DiscovEHR Study Identifies Novel Candidate Predisposition Genes for Endometrial Cancer

Flipping the script: Advances in understanding how and why P4-ATPases flip lipid across membranes

Type IV P-type ATPases (P4-ATPases) are a family of transmembrane enzymes that translocate lipid substrates from the outer to the inner leaflet of biological membranes and thus create an asymmetrical distribution of lipids within membranes. On the cellular level, this asymmetry is essential for maintaining the integrity and functionality of biological membranes, creating platforms for signaling events and facilitating vesicular trafficking. On the organismal level, this asymmetry has been shown to be important in maintaining blood homeostasis, liver metabolism, neural development, and the immune response. Indeed, dysregulation of P4-ATPases has been linked to several diseases; including anemia, cholestasis, neurological disease, and several cancers. This review will discuss the evolutionary transition of P4-ATPases from cation pumps to lipid flippases, the new lipid substrates that have been discovered, the significant advances that have been achieved in recent years regarding the structural mechanisms underlying the recognition and flipping of specific lipids across biological membranes, and the consequences of P4-ATPase dysfunction on cellular and physiological functions. Additionally, we emphasize the requirement for additional research to comprehensively understand the involvement of flippases in cellular physiology and disease and to explore their potential as targets for therapeutics in treating a variety of illnesses. The discussion in this review will primarily focus on the budding yeast, C. elegans, and mammalian P4-ATPases.

RBM12 regulates the progression of hepatocellular cancer via miR-497-5p/CPNE1 Axis

BACKGROUND

Hepatocellular Carcinoma (HCC), also called hepatocellular cancer, has emerged as a highly prevalent malignancy globally. By binding to specific RNA via one or more spherical RNA Domains (RBDs) or RNA Motifs (RBMs), RNA Binding Proteins (RBPs) can affect RNA modification, splicing, localization, translation, and stability.

METHODS

This paper builds on previous research by further investigating the impact of RBM12 on LC progression. In order to determine the effect of RBM12 expression on the prognosis of patients with hepatocellular cancer, we first investigated its expression in liver cancer cells (LCC) and tissues. The effect of RBM12 on the malignant biological behavior of LCC was subsequently detected using cytological experiments. To explore the upstream mechanism affecting RBM12, we predicted the miRNA targeting RBM12. According to the database, miR-497-5p was the best candidate gene. The double Luciferase reporter gene experiment was executed to validate the bounding of miR-497-5p with RBM12.

RESULTS

According to the cytological experiments, a high RBM12 expression promoted the propagation, migration, and invasion of LCC and impeded liver cancer cell apoptosis. By secreting TGF-β1, RBM12 could induce the EMT process. The miR-497-5p expression is suppressed in hepatocellular cancer. As shown by the CCK8, plate cloning, Transwell, EDU, and other experiments, miR-497-5p suppressed RBM12 expression and tumor growth. The double Luciferase reporter gene system was utilized to verify the combination of miR-497-5p and RBM12. The CPNE1 is a downstream gene regulated by RBM12. A high CPNE1 expression was exhibited in LCC and tissues. The CPNE1 is essential in the process where RBM12 promotes the incidence and progression of liver cancer.

CONCLUSIONS

By elucidating the exact molecular mechanism through which RBM12 promotes the initiation and progression of LC, thus, the current investigation provides some reference for the clinical management of LC.

Type IV P-Type ATPases: Recent Updates in Cancer Development, Progression, and Treatment

Adaptations of cancer cells for survival are remarkable. One of the most significant properties of cancer cells to prevent the immune system response and resist chemotherapy is the altered lipid metabolism and resulting irregular cell membrane composition. The phospholipid distribution in the plasma membrane of normal animal cells is distinctly asymmetric. Lipid flippases are a family of enzymes regulating membrane asymmetry, and the main class of flippases are type IV P-type ATPases (P4-ATPases). Alteration in the function of flippases results in changes to membrane organization. For some lipids, such as phosphatidylserine, the changes are so drastic that they are considered cancer biomarkers. This review will analyze and discuss recent publications highlighting the role that P4-ATPases play in the development and progression of various cancer types, as well as prospects of targeting P4-ATPases for anti-cancer treatment.

Whole Exome-Wide Association Identifies Rare Variants in GALNT9 Associated with Middle Eastern Papillary Thyroid Carcinoma Risk

Papillary thyroid carcinoma (PTC) is the commonest thyroid cancer. The majority of inherited causes of PTC remain elusive. However, understanding the genetic underpinnings and origins remains a challenging endeavor. An exome-wide association study was performed to identify rare germline variants in coding regions associated with PTC risk in the Middle Eastern population. By analyzing exome-sequencing data from 249 PTC patients (cases) and 1395 individuals without any known cancer (controls), GALNT9 emerged as being strongly associated with rare inactivating variants (RIVs) (4/249 cases vs. 1/1395 controls, OR = 22.75, p = 5.09 × 10-5). Furthermore, three genes, TRIM40, ARHGAP23, and SOX4, were enriched for rare damaging variants (RDVs) at the exome-wide threshold (p < 2.5 × 10-6). An additional seven genes (VARS1, ZBED9, PRRC2A, VWA7, TRIM31, TRIM40, and COL8A2) were associated with a Middle Eastern PTC risk based on the sequence kernel association test (SKAT). This study underscores the potential of GALNT9 and other implicated genes in PTC predisposition, illuminating the need for large collaborations and innovative approaches to understand the genetic heterogeneity of PTC predisposition.

Identification and characterization of RBM12 as a novel regulator of fetal hemoglobin expression

The fetal-to-adult hemoglobin transition is clinically relevant because reactivation of fetal hemoglobin (HbF) significantly reduces morbidity and mortality associated with sickle cell disease (SCD) and β-thalassemia. Most studies on the developmental regulation of the globin genes, including genome-wide genetics screens, have focused on DNA binding proteins, including BCL11A and ZBTB7A/LRF and their cofactors. Our understanding of RNA binding proteins (RBPs) in this process is much more limited. Two RBPs, LIN28B and IGF2BP1, are known posttranscriptional regulators of HbF production, but a global view of RBPs is still lacking. Here, we carried out a CRISPR/Cas9-based screen targeting RBPs harboring RNA methyltransferase and/or RNA recognition motif (RRM) domains and identified RNA binding motif 12 (RBM12) as a novel HbF suppressor. Depletion of RBM12 induced HbF expression and attenuated cell sickling in erythroid cells derived from patients with SCD with minimal detrimental effects on cell maturation. Transcriptome and proteome profiling revealed that RBM12 functions independently of major known HbF regulators. Enhanced cross-linking and immunoprecipitation followed by high-throughput sequencing revealed strong preferential binding of RBM12 to 5' untranslated regions of transcripts, narrowing down the mechanism of RBM12 action. Notably, we pinpointed the first of 5 RRM domains as essential, and, in conjunction with a linker domain, sufficient for RBM12-mediated HbF regulation. Our characterization of RBM12 as a negative regulator of HbF points to an additional regulatory layer of the fetal-to-adult hemoglobin switch and broadens the pool of potential therapeutic targets for SCD and β-thalassemia.

A Novel Approach: Combining Prognostic Models and Network Pharmacology to Target Breast Cancer Necroptosis-Associated Genes

Breast cancer (BC) accounts for the highest proportion of the all cancers among women, and necroptosis is recognized as a form of caspase-independent programmed cell death. We created prognostic signatures using univariate survival analysis, and lasso regression, to assess immune microenvironments between subgroups. We then used network pharmacology to bind our drugs to target differentially expressed genes (DEGs). A signature comprising a set of necroptosis-related genes was established to predict patient outcomes based on median risk scores. Those above and below the median were classified as high-risk group (HRG) and low-risk group (LRG), respectively. Patients at high risk had lower overall survival, and poorer predicted tumor, nodes, and metastases stages (TNM). The novel prognostic signature can effectively predict the prognosis of breast cancer patients docking of β,β-dimethyl acryloyl shikonin (DMAS) to possible targets to cure breast cancer. We found that all current prognostic models do not offer suitable treatment options. In additional, by docking drugs DMAS that have been initially validated in our laboratory to treat breast cancer. We hope that this novel approach could contribute to cancer research.

Prebiotic Chemistry: The Role of Trimetaphosphate in Prebiotic Chemical Evolution

Life’s origins have always been a scientific puzzle. Understanding the production of biomolecules is crucial for understanding the evolution of life on Earth. Numerous studies on trimetaphosphate have been conducted in the field of prebiotic chemistry. However, its role in prebiotic chemistry has been documented infrequently in the review literature. The goal of this thesis is to review the role of trimetaphosphate in the early Earth’s biomolecule synthesis and phosphorylation. Additionally, various trimetaphosphate-mediated reaction pathways are discussed, as well as the role of trimetaphosphate in prebiotic chemistry. Finally, in our opinion, interactions between biomolecules should be considered in prebiotic synthesis scenarios since this may result in some advances in subsequent research on this subject. The research establishes an essential and opportune foundation for an in-depth examination of the “mystery of life".

A Genome-First Approach to Estimate Prevalence of Germline Pathogenic Variants and Risk of Pancreatic Cancer in Select Cancer Susceptibility Genes

Patients with germline pathogenic variants (GPV) in cancer predisposition genes are at increased risk of pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer. The genes most frequently found to harbor GPV in unselected PDAC cases are ATM, BRCA1, BRCA2, CDKN2A, CHEK2, and PALB2. However, GPV prevalence and gene-specific associations have not been extensively studied in the general population. To further explore these associations, we analyzed genomic and phenotypic data obtained from the UK Biobank (UKB) and Geisinger MyCode Community Health Initiative (GHS) cohorts comprising 200,600 and 175,449 participants, respectively. We estimated the frequency and calculated relative risks (RRs) of heterozygotes in both cohorts and a subset of individuals with PDAC. The combined frequency of heterozygous carriers of GPV in the general population ranged from 1.22% for CHEK2 to 0.05% for CDKN2A. The frequency of GPV in PDAC cases varied from 2.38% (ATM) to 0.19% (BRCA1 and CDKN2A). The RRs of PDAC were elevated for all genes except for BRCA1 and varied widely by gene from high (ATM) to low (CHEK2, BRCA2). This work expands our understanding of the frequencies of GPV heterozygous carriers and associations between PDAC and GPV in several important PDAC susceptibility genes.

Increased RBM12 expression predicts poor prognosis in hepatocellular carcinoma based on bioinformatics

Background

Liver cancer is one of the major causes of cancer death worldwide, incurring high mortality and a significant financial burden on the healthcare system. Abnormal RNA-binding proteins (RBPs) have been found to be associated with carcinogenesis in liver cancer. Among these, RNA-binding motif protein 12 (RBM12) is located in the exon junction complex (EJC). The goal of this study was to determine what role RBM12 plays in hepatocellular carcinoma (HCC) from a biological perspective.

Methods

The Tumor IMmune Estimation Resource (TIMER) and the Human Protein Atlas database were used to examine the expression level of RBM12, with the UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) databases used to investigate the relationship between RBM12 and other noteworthy clinical features. RBM12 expression in cells and tissue samples was detected using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot analysis. The functional network of RBM12 in HCC was studied using LinkedOmics and gene set enrichment analysis (GSEA), while the effects of hypomethylation on the expression of RBM12 in HCC was investigated using methylation databases. Finally, we used TIMER and CIBERSORT to investigate the relationship between immune cell infiltration and RBM12 in HCC.

Results

RBM12 is highly elevated in HCC tissues and cells, and it can be used to predict the prognosis of patients with HCC. Analysis with LinkedOmics and GSEA revealed RBM12 to be closely linked with tumor progression. Furthermore, hypomethylation was linked to an increase in RBM12 expression in HCC, while RBM12 was associated with immune cell infiltration.

Conclusions

This study shows that an elevated level of RBM12 in HCC indicates a poor patient prognosis. Furthermore, according to LinkedOmics and GSEA analyses, RBM12 was implicated in the most important hallmark pathways. Our findings suggest that RBM12 overexpression is caused by hypomethylation and that RBM12 plays a key role in liver cancer tumor immunity.

Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank

BACKGROUND

Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer.

METHODS

We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case-control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched controls in a discovery set and 1,991 patients with cancer and 2,504 matched controls in a validation set across nine cancer types. Further, The Cancer Genome Atlas (TCGA) germline data were used to replicate the findings.

RESULTS

We identified 133 significant pathway-cancer pairs (85 replicated) and 90 significant gene-cancer pairs (12 replicated). In addition, we identified 18 genes and 3 pathways that were associated with survival outcome across cancers (Bonferroni P < 0.05).

CONCLUSIONS

In this study, we identified potential predisposition genes and pathways based on rare variants in nine cancers.

IMPACT

This work adds to the knowledge base and progress being made in precision medicine.

Construction and topological analysis of an endometriosis-related exosomal circRNA-miRNA-mRNA regulatory network

Novel biomarkers are needed to accelerate the diagnosis and treatment of endometriosis. We performed RNA sequencing to explore the expression profiles of exosomal circular RNAs (circRNAs), microRNAs (miRNAs) and mRNAs in patients with ovarian endometriomas, eutopic endometria and normal endometria. Differentially expressed genes between the different pairs of groups were analyzed and functionally annotated. Then, miRNA-target RNA pairs were identified, competing endogenous RNA (ceRNA) scores were calculated, gene expression characteristics were determined, and these parameters were used to construct an exosomal ceRNA network. We identified 36 candidate hub genes with high degrees of gene connectivity. We also topologically analyzed the ceRNA network to obtain a hub ceRNA network of circRNAs with the highest closeness and ceRNA efficiency. Twelve genes overlapped between the 36 candidate hub genes and the genes in the hub ceRNA network. These 12 genes were considered to be exosomal RNA-based biomarkers, and circ_0026129/miRNA-15a-5p/ATPase H+ transporting V1 subunit A (ATP6V1A) were at the center of the ceRNA network. By determining the exosomal RNA expression profiles of endometriosis patients and constructing a circRNA-associated ceRNA network, these findings provide insight into the molecular pathways of endometriosis and new resources for its diagnosis and treatment.

Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants

Gene-based tests of association (e.g., variance components and burden tests) are now common practice for analyses attempting to elucidate the contribution of rare genetic variants on common disease. As sequencing datasets continue to grow in size, the number of variants within each set (e.g., gene) being tested is also continuing to grow. Pathway-based methods have been used to allow for the initial aggregation of gene-based statistical evidence and then the subsequent aggregation of evidence across the pathway. This “multi-set” approach (first gene-based test, followed by pathway-based) lacks thorough exploration in regard to evaluating genotype–phenotype associations in the age of large, sequenced datasets. In particular, we wonder whether there are statistical and biological characteristics that make the multi-set approach optimal vs. simply doing all gene-based tests? In this paper, we provide an intuitive framework for evaluating these questions and use simulated data to affirm us this intuition. A real data application is provided demonstrating how our insights manifest themselves in practice. Ultimately, we find that when initial subsets are biologically informative (e.g., tending to aggregate causal genetic variants within one or more subsets, often genes), multi-set strategies can improve statistical power, with particular gains in cases where causal variants are aggregated in subsets with less variants overall (high proportion of causal variants in the subset). However, we find that there is little advantage when the sets are non-informative (similar proportion of causal variants in the subsets). Our application to real data further demonstrates this intuition. In practice, we recommend wider use of pathway-based methods and further exploration of optimal ways of aggregating variants into subsets based on emerging biological evidence of the genetic architecture of complex disease.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.

Rare variant association testing in the non-coding genome

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

Exome-Wide Analysis of the DiscovEHR Cohort Reveals Novel Candidate Pharmacogenomic Variants for Clinical Pharmacogenomics

Recent advances in next-generation sequencing technology have led to the production of an unprecedented volume of genomic data, thus further advancing our understanding of the role of genetic variation in clinical pharmacogenomics. In the present study, we used whole exome sequencing data from 50,726 participants, as derived from the DiscovEHR cohort, to identify pharmacogenomic variants of potential clinical relevance, according to their occurrence within the PharmGKB database. We further assessed the distribution of the identified rare and common pharmacogenomics variants amongst different GnomAD subpopulations. Overall, our findings show that the use of publicly available sequence data, such as the DiscovEHR dataset and GnomAD, provides an opportunity for a deeper understanding of genetic variation in pharmacogenes with direct implications in clinical pharmacogenomics.

The pan-cancer landscape of prognostic germline variants in 10,582 patients

Background

While clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions. Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed multiple cancers in an unbiased way to identify genetic loci that can improve patient outcome predictions made using clinical factors.

Methods

We analyzed sequencing data from the over 10,000 cancer patients available through The Cancer Genome Atlas to identify germline variants associated with patient outcome using multivariate Cox regression models.

Results

We identified 79 prognostic germline variants in individual cancers and 112 prognostic germline variants in groups of cancers. The germline variants identified in individual cancers provide additional predictive power about patient outcomes beyond clinical information currently in use and may therefore augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least 12 of the germline variants are likely associated with patient outcome through perturbation of protein structure and at least five through association with gene expression differences. Almost half of these germline variants are in previously reported tumor suppressors, oncogenes or cancer driver genes with the other half pointing to genomic loci that should be further investigated for their roles in cancers.

Conclusions

Germline variants are predictive of outcome in cancer patients and specific germline variants can improve patient outcome predictions beyond predictions made using clinical factors alone. The germline variants also implicate new means by which known oncogenes, tumor suppressor genes, and driver genes are perturbed in cancer and suggest roles in cancer for other genes that have not been extensively studied in oncology. Further studies in other cancer cohorts are necessary to confirm that germline variation is associated with outcome in cancer patients as this is a proof-of-principle study.

Electronic supplementary material

The online version of this article (10.1186/s13073-020-0718-7) contains supplementary material, which is available to authorized users.