Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas

Identification of NINJ1 as a novel prognostic predictor for retroperitoneal liposarcoma

BACKGROUND

Retroperitoneal liposarcoma (RPLS) is known for its propensity for local recurrence and short survival time. We aimed to identify a credible and specific prognostic biomarker for RPLS.

METHODS

Cases from The Cancer Genome Atlas (TCGA) sarcoma dataset were included as the training group. Co-expression modules were constructed using weighted gene co-expression network analysis (WGCNA) to explore associations between modules and survival. Survival analysis of hub genes was performed using the Kaplan-Meier method. In addition, independent external validation was performed on a cohort of 135 Chinese RPLS patients from the REtroperitoneal SArcoma Registry (RESAR) study (NCT03838718).

RESULTS

A total of 19 co-expression modules were constructed based on the expression levels of 26,497 RNAs in the TCGA cohort. Among these modules, the green module exhibited a positive correlation with overall survival (OS, p = 0.10) and disease-free survival (DFS, p = 0.06). Gene set enrichment analysis showed that the green module was associated with endocytosis and soft-tissue sarcomas. Survival analysis demonstrated that NINJ1, a hub gene within the green module, was positively associated with OS (p = 0.019) in the TCGA cohort. Moreover, in the validation cohort, patients with higher NINJ1 expression levels displayed a higher probability of survival for both OS (p = 0.023) and DFS (p = 0.012). Multivariable Cox analysis further confirmed the independent prognostic significance of NINJ1.

CONCLUSIONS

We here provide a foundation for the establishment of a consensus prognostic biomarker for RPLS, which should not only facilitate medical treatment but also guide the development of novel targeted drugs.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

The Future of Targeted Therapy for Leiomyosarcoma

Leiomyosarcoma (LMS) is an aggressive subtype of soft tissue sarcoma that arises from smooth muscle cells, most commonly in the uterus and retroperitoneum. LMS is a heterogeneous disease with diverse clinical and molecular characteristics that have yet to be fully understood. Molecular profiling has uncovered possible targets amenable to treatment, though this has yet to translate into approved targeted therapies in LMS. This review will explore historic and recent findings from molecular profiling, highlight promising avenues of current investigation, and suggest possible future strategies to move toward the goal of molecularly matched treatment of LMS. We focus on targeting the DNA damage response, the macrophage-rich micro-environment, the PI3K/mTOR pathway, epigenetic regulators, and telomere biology.

Genetic, Epigenetic and Transcriptome Alterations in Liposarcoma for Target Therapy Selection

Liposarcoma (LPS) is one of the most common adult soft-tissue sarcomas (STS), characterized by a high diversity of histopathological features as well as to a lesser extent by a spectrum of molecular abnormalities. Current targeted therapies for STS do not include a wide range of drugs and surgical resection is the mainstay of treatment for localized disease in all subtypes, while many LPS patients initially present with or ultimately progress to advanced disease that is either unresectable, metastatic or both. The understanding of the molecular characteristics of liposarcoma subtypes is becoming an important option for the detection of new potential targets and development novel, biology-driven therapies for this disease. Innovative therapies have been introduced and they are currently part of preclinical and clinical studies. In this review, we provide an analysis of the molecular genetics of liposarcoma followed by a discussion of the specific epigenetic changes in these malignancies. Then, we summarize the peculiarities of the key signaling cascades involved in the pathogenesis of the disease and possible novel therapeutic approaches based on a better understanding of subtype-specific disease biology. Although heterogeneity in liposarcoma genetics and phenotype as well as the associated development of resistance to therapy make difficult the introduction of novel therapeutic targets into the clinic, recently a number of targeted therapy drugs were proposed for LPS treatment. The most promising results were shown for CDK4/6 and MDM2 inhibitors as well as for the multi-kinase inhibitors anlotinib and sunitinib.

Comprehensive DNA Methylation Profiling of Medullary Thyroid Carcinoma: Molecular Classification, Potential Therapeutic Target, and Classifier System

PURPOSE

Medullary thyroid carcinoma (MTC) presents a distinct biological context from other thyroid cancers due to its specific cellular origin. This heterogeneous and rare tumor has a high prevalence of advanced diseases, making it crucial to address the limited therapeutic options and enhance complex clinical management. Given the high clinical accessibility of methylation information, we construct the largest MTC methylation cohort to date.

EXPERIMENTAL DESIGN

Seventy-eight fresh-frozen MTC samples constituted our methylation cohort. The comprehensive study process incorporated machine learning, statistical analysis, and in vitro experiments.

RESULTS

Our study pioneered the identification of a three-class clustering system for risk stratification, exhibiting pronounced epigenomic heterogeneity. The elevated overall methylation status in MTC-B, combined with the "mutual exclusivity" of hypomethylated sites displayed by MTC-A and MTC-C, distinctively characterized the MTC-specific methylation pattern. Integrating with the transcriptome, we further depicted the features of these three clusters to scrutinize biological properties. Several MTC-specific aberrant DNA methylation events were emphasized in our study. NNAT expression was found to be notably reduced in poor-prognostic MTC-C, with its promoter region overlapping with an upregulated differentially methylated region. In vitro experiments further affirmed NNAT's therapeutic potential. Moreover, we built an elastic-net logistic regression model with a relatively high AUC encompassing 68 probes, intended for future validation and systematic clinical application.

CONCLUSIONS

Conducting research on diseases with low incidence poses significant challenges, and we provide a robust resource and comprehensive research framework to assist in ongoing MTC case inclusion and facilitate in-depth dissection of its molecular biological features.

PDE3A Is a Highly Expressed Therapy Target in Myxoid Liposarcoma

Liposarcomas (LPSs) are a heterogeneous group of malignancies that arise from adipose tissue. Although LPSs are among the most common soft-tissue sarcoma subtypes, precision medicine treatments are not currently available. To discover LPS-subtype-specific therapy targets, we investigated RNA sequenced transcriptomes of 131 clinical LPS tissue samples and compared the data with a transcriptome database that contained 20,218 samples from 95 healthy tissues and 106 cancerous tissue types. The identified genes were referred to the NCATS BioPlanet library with Enrichr to analyze upregulated signaling pathways. PDE3A protein expression was investigated with immunohistochemistry in 181 LPS samples, and PDE3A and SLFN12 mRNA expression with RT-qPCR were investigated in 63 LPS samples. Immunoblotting and cell viability assays were used to study LPS cell lines and their sensitivity to PDE3A modulators. We identified 97, 247, and 37 subtype-specific, highly expressed genes in dedifferentiated, myxoid, and pleomorphic LPS subtypes, respectively. Signaling pathway analysis revealed a highly activated hedgehog signaling pathway in dedifferentiated LPS, phospholipase c mediated cascade and insulin signaling in myxoid LPS, and pathways associated with cell proliferation in pleomorphic LPS. We discovered a strong association between high PDE3A expression and myxoid LPS, particularly in high-grade tumors. Moreover, myxoid LPS samples showed elevated expression levels of SLFN12 mRNA. In addition, PDE3A- and SLFN12-coexpressing LPS cell lines SA4 and GOT3 were sensitive to PDE3A modulators. Our results indicate that PDE3A modulators are promising drugs to treat myxoid LPS. Further studies are required to develop these drugs for clinical use.

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Introduction: Association rule mining (ARM) is a powerful tool for exploring the informative relationships among multiple items (genes) in any dataset. The main problem of ARM is that it generates many rules containing different rule-informative values, which becomes a challenge for the user to choose the effective rules. In addition, few works have been performed on the integration of multiple biological datasets and variable cutoff values in ARM. Methods: To solve all these problems, in this article, we developed a novel framework MOOVARM (multi-objective optimized variable cutoff-based association rule mining) for multi-omics profiles. Results: In this regard, we identified the positive ideal solution (PIS), which maximized the profit and minimized the loss, and negative ideal solution (NIS), which minimized the profit and maximized the loss for all gene sets (item sets), belonging to each extracted rule. Thereafter, we computed the distance (d +) from PIS and distance (d -) from NIS for each gene set or product. These two distances played an important role in determining the optimized associations among various pairs of genes in the multi-omics dataset. We then globally estimated the relative closeness to PIS for ranking the gene sets. When the relative closeness score of the rule is greater than or equal to the pre-defined threshold value, the rule can be considered a final resultant rule. Moreover, MOOVARM evaluated the relative score of the rule based on the status of all genes instead of individual genes. Conclusions: MOOVARM produced the final rank of the extracted (multi-objective optimized) rules of correlated genes which had better disease classification than the state-of-the-art algorithms on gene signature identification.

ABERRANT EXPRESSION OF COL14A1, CELRS3, and CTHRC1 IN BREAST CANCER СELLS

BACKGROUND

Collagens, which are the major components of the extracellular matrix involved in the regulation of tumor microenvironment, could be differentially expressed in breast cancer (BC) with different transcriptome profiling.

AIM

To analyze the transcript level expression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, COL14A1, CTHRC1, and CELRS3 genes and the clinical relevance of their differential expression in BC.

MATERIALS AND METHODS

The transcript level expression of the genes was analyzed using the quantitative real-time PCR (qPCR) in tumor tissue of 60 BC patients.

RESULTS

Overexpression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, CTHRC, and CELRS3 anddown-regulated expression of COL14A1 were observed. COL14A1 down-regulation was associated with aggressive, basal, and Her-2/neu BC subtypes (p = 0.031). Overexpression of CELSR3 was found to be associated with the older age of the patients (> 55 years, p = 0.049). Further analysis with the TCGA BC data set has shown a concordance in the differential expression of the above genes. Furthermore, overexpression of CTHRC1 was associated with poor overall survival (OS), particularly with poor prognosis (p = 0.00042) for the luminal BC subtype. On the other hand, CELSR3 overexpression was associated with mucinous tumors and poor prognosis in post-menopausal women. In silicotarget prediction identified several BC-associated miRNAs and members of miR-154, -515, and -10 families to perform a likely regulatory role in the above ECM genes.

CONCLUSION

The present study shows that the expression of COL14A1 and CTHRC1 may serve as potential biological markers for the detection of basal BC and the prognosis of survival for patients with the luminal subtype of BC.

Dissecting molecular, pathological, and clinical features associated with tumor neural/neuroendocrine heterogeneity

Lineage plasticity, especially transdifferentiation between neural/neuroendocrine (NE) and non-NE lineage, has been observed in multiple cancer types and linked to increased tumor aggressiveness. However, existing NE/non-NE subtype classifications in various cancer types were established through ad hoc approaches in different studies, making it difficult to align findings across cancer types and extend investigations to new datasets. To address this issue, we developed a generalized strategy to generate quantitative NE scores and a web application to facilitate its implementation. We applied this method to nine datasets covering seven cancer types, including two neural cancers, two neuroendocrine cancers, and three non-NE cancers. Our analysis revealed significant NE inter-tumoral heterogeneity and identified strong associations between NE scores and molecular, histological, and clinical features, including prognosis in different cancer types. These results support the translational utility of NE scores. Overall, our work demonstrated a broadly applicable strategy for determining the NE properties of tumors.

Pediatric BCOR-Altered Tumors From Soft Tissue/Kidney Display Specific DNA Methylation Profiles

In the pediatric population, BCL6-correpresor gene (BCOR)-upregulated tumors include primitive myxoid mesenchymal tumors/undifferentiated sarcomas (PMMTI/UND), clear cell sarcomas of the kidney (CCSK), and high-grade neuroepithelial tumors (HG-NET). We investigated DNA methylation (DNAm) and copy number variation (CNV) profiling in these tumors (N = 34) using an Illumina EPIC BeadChip to better define the potential use of these tools to confirm diagnosis and predict outcomes. Twenty-seven tumors from 25 patients (age range, 0-10 years), showed molecular confirmation of genetic abnormalities as follows: BCOR internal tandem duplication in 14 PMMTI/UND, 8 CCSK, and 3 HG-NET and YWHAE fusions in 2 PMMTI/UND. The remaining 7 cases lacking informative molecular data were analyzed by immunophenotyping and were included in the study as a training cohort, clearly separated from the main study group. These were 4 PMMTI, 1 HG-NET, and 1 CCSK in which poor RNA preservation precluded the confirmation of BCOR rearrangements and 1 CCSK in which no rearrangements were found. DNAm data were compared with those of brain tumor and/or sarcoma classifier. Differentially methylated regions (DMRs) were analyzed in the 3 groups. Twenty-two cases of the 24 molecularly confirmed PMMTI/UND and CCSK and 3 of 6 of those with only immunophenotyping were classified within the methylation class "BCOR-altered sarcoma family" with optimal calibrated scores. PMMTI/UND and CCSK showed similar methylation profiles, whereas thousands of DMRs and significantly enriched pathways were evident between soft tissue/kidney tumors and HG-NET. The CNV analysis showed an overall flat profile in 19 of the 31 evaluable tumors (8/10 CCSK; 9/18 PMMTI/UND; 2/4 HG-NET). The most frequent CNVs were 1q gain and 9p and 10q loss. Follow-up time data were available for 20 patients: ≥2 CNV significantly correlated with a worse overall survival rate. In conclusion, soft tissue and kidney BCOR sarcomas matched with BCOR-altered sarcoma methylation class, whereas those from the brain matched with the central nervous system tumor classifier HG-NET BCOR, supporting the notion that DNAm profiling is an informative diagnostic tool. CNV alterations were associated with a more aggressive clinical behavior.

DNA methylation-mediated silencing of Neuronatin promotes hepatocellular carcinoma proliferation through the PI3K-Akt signaling pathway

AIMS

To explore the methylation status, function, and underlying mechanism of the imprinted gene Neuronatin (NNAT) in hepatocellular carcinoma (HCC) progression.

MAIN METHODS

Immunohistochemistry (IHC) was performed to evaluate the expression of NNAT in HCC samples. Bisulfite genomic sequencing PCR (BSP) was applied to examine the methylation status of the NNAT promoter. In addition, colony formation, 5-Ethynyl-20-deoxyuridine (EdU) assays and subcutaneous xenograft nude models were used to explore the roles of NNAT in HCC cell proliferation. Furthermore, RNA-seq and phospho-specific protein microarray assays were conducted to illustrate the underlying mechanism by which NNAT regulates HCC progression.

KEY FINDINGS

NNAT was obviously downregulated in HCC tissues, and its expression level was closely associated with tumor growth and patient prognosis. The downregulation of NNAT in HCC was induced by hypermethylation of CpG islands in the promoter region, and hypermethylation was correlated with overall survival of HCC. Moreover, the enforced expression of NNAT significantly inhibited HCC cell proliferation in vitro and in vivo. Transcriptome analysis showed that the alteration of NNAT expression was mainly related to dysregulation of the PI3K-Akt signaling pathway. Finally, phospho-specific antibody microarray detection further revealed that overexpressed NNAT can increase the phosphorylation levels of LKB1, Met, and elF4E and decrease the phosphorylation levels of PTEN, which are all involved in the PI3K-Akt signaling pathway.

SIGNIFICANCE

Our research provides new insights into the epigenetic regulation of imprinted genes in tumorigenesis and implies that the imprinted gene NNAT may act as a prognostic biomarker and tumor suppressor in HCC.

Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods

Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heterogeneity at the cellular, molecular, and epigenetic levels, where DNA methylation has been proposed to play a role in the diagnosis of sarcoma subtypes. Thus, this study is aimed at finding potential biomarkers at the DNA methylation level to distinguish different sarcoma subtypes. A machine learning process was designed to analyse sarcoma samples, each of which was represented by lots of methylation sites. Irrelevant sites were removed using the Boruta method, and remaining sites related to the target variables were kept for further analyses. Afterward, three feature ranking methods (LASSO, LightGBM, and MCFS) were adopted to rank these features, and six classification models were constructed by combining incremental feature selection and two classification algorithms (decision tree and random forest). Among these models, the performance of RF model was higher than that of DT model under all three ranking conditions. The specific expression of genes obtained from the annotation of highly correlated methylation site features, such as PRKAR1B, INPP5A, and GLI3, was proven to be associated with sarcoma by publications. Moreover, the quantitative rules obtained by decision tree algorithm helped us to understand the essential differences between various sarcoma types and classify sarcoma subtypes, providing a new means of clinical identification and determining new therapeutic targets.

The Intricate Interplay between the ZNF217 Oncogene and Epigenetic Processes Shapes Tumor Progression

The oncogenic transcription factor ZNF217 orchestrates several molecular signaling networks to reprogram integrated circuits governing hallmark capabilities within cancer cells. High levels of ZNF217 expression provide advantages to a specific subset of cancer cells to reprogram tumor progression, drug resistance and cancer cell plasticity. ZNF217 expression level, thus, provides a powerful biomarker of poor prognosis and a predictive biomarker for anticancer therapies. Cancer epigenetic mechanisms are well known to support the acquisition of hallmark characteristics during oncogenesis. However, the complex interactions between ZNF217 and epigenetic processes have been poorly appreciated. Deregulated DNA methylation status at ZNF217 locus or an intricate cross-talk between ZNF217 and noncoding RNA networks could explain aberrant ZNF217 expression levels in a cancer cell context. On the other hand, the ZNF217 protein controls gene expression signatures and molecular signaling for tumor progression by tuning DNA methylation status at key promoters by interfering with noncoding RNAs or by refining the epitranscriptome. Altogether, this review focuses on the recent advances in the understanding of ZNF217 collaboration with epigenetics processes to orchestrate oncogenesis. We also discuss the exciting burgeoning translational medicine and candidate therapeutic strategies emerging from those recent findings connecting ZNF217 to epigenetic deregulation in cancer.

Comparative oncology reveals DNMT3B as a molecular vulnerability in undifferentiated pleomorphic sarcoma

PURPOSE

Undifferentiated pleomorphic sarcoma (UPS), an aggressive subtype of soft-tissue sarcoma (STS), is exceedingly rare in humans and lacks effective, well-tolerated therapies. In contrast, STS are relatively common in canine companion animals. Thus, incorporation of veterinary patients into studies of UPS offers an exciting opportunity to develop novel therapeutic strategies for this rare human disease. Genome-wide studies have demonstrated that UPS is characterized by aberrant patterns of DNA methylation. However, the mechanisms and impact of this epigenetic modification on UPS biology and clinical behavior are poorly understood.

METHODS

DNA methylation in mammalian cells is catalyzed by the canonical DNA methyltransferases DNMT1, DNMT3A and DNMT3B. Therefore, we leveraged cell lines and tissue specimens from human and canine patients, together with an orthotopic murine model, to probe the functional and clinical significance of DNMTs in UPS.

RESULTS

We found that the DNA methyltransferase DNMT3B is overexpressed in UPS relative to normal mesenchymal tissues and is associated with a poor prognosis. Consistent with these findings, genetic DNMT3B depletion strongly inhibited UPS cell proliferation and tumor progression. However, existing hypomethylating agents, including the clinically approved drug 5-aza-2'-deoxycytidine (DAC) and the DNMT3B-inhibiting tool compound nanaomycin A, were ineffective in UPS due to cellular uptake and toxicity issues.

CONCLUSIONS

DNMT3B represents a promising molecular susceptibility in UPS, but further development of DNMT3B-targeting strategies for these patients is required.

The prognostic value of RASGEF1A RNA expression and DNA methylation in cytogenetically normal acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a significantly heterogeneous malignancy of the blood. Cytogenetic abnormalities are crucial for the prognosis of AML. However, since more than half of patients with AML are cytogenetically normal AML (CN-AML), predictive prognostic indicators need to be further refined. In recent years, gene abnormalities are considered to be strong prognostic factors of CN-AML, already having clinical significance for treatment. In addition, the relationship of methylation in some genes and AML prognosis predicting has been discovered. RASGEF1A is a guanine nucleotide exchange factors of Ras and widely expressed in brain tissue, bone marrow and 17 other tissues. RASGEF1A has been reported to be associated with a variety of malignant tumors, examples include Hirschsprung disease, renal cell carcinoma, breast cancer, diffuse large B cell lymphoma, intrahepatic cholangiocarcinoma and so on [1, 2]. However, the relationship between the RASGEF1A gene and CN-AML has not been reported.

METHODS

By integrating the Cancer Genome Atlas (TCGA) database 75 patients with CN-AML and 240 Gene Expression Omnibus (GEO) database CN-AML samples, we examined the association between RASGEF1A's RNA expression level and DNA methylation of and AML patients' prognosis. Then, we investigated the RASGEF1A RNA expression and DNA methylation's prognostic value in 77 patients with AML after allogeneic hematopoietic stem cell transplantation (Allo-HSCT) as well as 101 AML patients after chemotherapy respectively. We investigated the association between sensitivity to Crenolanib and expression level of RASGED1A in patients by integrating 191 CN-AML patients from BeatAML dadataset. We integrated the expression and methylation of RASGEF1A to predict the CN-AML patients' prognosis and investigated the relationship between prognostic of AML patients with different risk classification and expression levels or methylation levels of RASGEF1A.

RESULTS

We found that RASGEF1A gene high expression group predicted poorer event-free survival (EFS) (P< 0.0001) as well as overall survival (OS) (P< 0.0001) in CN-AML samples, and the identical results were found in AML patients receiving chemotherapy (P< 0.0001) and Allo-HSCT (P< 0.0001). RASGEF1A RNA expression level is an CN-AML patients' independent prognostic factor (EFS: HR = 5.5534, 95% CI: 1.2982-23.756, P= 0.0208; OS: HR = 5.3615, 95% CI: 1.1014-26.099, P= 0.0376). The IC50 (half maximal inhibitory concentration) of Crenolanib of CN-AML samples with RASGEF1A high expression level is lower. In addition, patients with high RASGEF1A methylation level had significant favorable prognosis (EPS: P< 0.0001, OS: P< 0.0001). Furthermore, the integrative analysis of expression and methylation of RASGEF1A could classify CN-AML patients into subgroups with different prognosis (EFS: P= 0.034, OS: P= 0.0024). Expression levels or methylation levels of RASGEF1A help to improve risk classification of 2010 European Leukemia Net.

CONCLUSION

Higher RASGEF1A RNA expression and lower DNA methylation predicts CN-AML patients' poorer prognosis. The RASGEF1A high expression level from patients with CN-AML have better sensitivity to Crenolanib. The integrative analysis of RASGEF1A RNA expression and DNA methylation can provide a more accurate classification for prognosis. Lower RASGEF1A expression is a favorable prognostic factor for AML patients receiving chemotherapy or Allo-HSCT. 2010 European Leukemia Net's risk classification can be improved by RASGEF1A expression levels or methylation levels.

In Silico Identification of Key Genes and Immune Infiltration Characteristics in Epicardial Adipose Tissue from Patients with Coronary Artery Disease

Background

The present study is aimed at identifying the differentially expressed genes (DEGs) and relevant biological processes and pathways associated with epicardial adipose tissue (EAT) from patients with coronary artery disease (CAD). We also explored potential biomarkers using two machine-learning algorithms and calculated the immune cell infiltration in EAT.

Materials and Methods

Three datasets (GSE120774, GSE64554, and GSE24425) were obtained from the Gene Expression Omnibus (GEO) database. The GSE120774 dataset was used to evaluate DEGs between EAT of CAD patients and the control group. Functional enrichment analyses were conducted to study associated biological functions and mechanisms using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). After this, the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were performed to identify the feature genes related to CAD. The expression level of the feature genes was validated in GSE64554 and GSE24425. Finally, we calculated the immune cell infiltration and evaluated the correlation between the feature genes and immune cells using CIBERSORT.

Results

We identified a total of 130 upregulated and 107 downregulated genes in GSE120774. Functional enrichment analysis revealed that DEGs are associated with several pathways, including the calcium signaling pathway, complement and coagulation cascades, ferroptosis, fluid shear stress and atherosclerosis, lipid and atherosclerosis, and regulation of lipolysis in adipocytes. TCF21, CDH19, XG, and NNAT were identified as feature genes and validated in the GSE64554 and GSE24425 datasets. Immune cell infiltration analysis showed plasma cells are significantly more numerous in EAT than in the control group (p = 0.001), whereas macrophage M0 (p = 0.024) and resting mast cells (p = 0.036) were significantly less numerous. TCF21, CDH19, XG, and NNAT were correlated with immune cells, including plasma cells, M0 macrophages, and resting mast cells.

Conclusion

TCF21, CDH19, XG, and NNAT might serve as feature genes for CAD, providing new insights for future research on the pathogenesis of cardiovascular diseases.

Comprehensive genomic profiling of EWSR1/FUS::CREB translocation-associated tumors uncovers prognostically significant recurrent genetic alterations and methylation-transcriptional correlates

To elucidate the mechanisms underlying the divergent clinicopathologic spectrum of EWSR1/FUS::CREB translocation-associated tumors, we performed a comprehensive genomic analysis of fusion transcript variants, recurrent genetic alterations (mutations, copy number alterations), gene expression, and methylation profiles across a large cohort of tumor types. The distribution of the EWSR1/FUS fusion partners-ATF1, CREB1, and CREM-and exon involvement was significantly different across different tumor types. Our targeted sequencing showed that secondary genetic events are associated with tumor type rather than fusion type. Of the 39 cases that underwent targeted NGS testing, 18 (46%) had secondary OncoKB mutations or copy number alterations (29 secondary genetic events in total), of which 15 (52%) were recurrent. Secondary recurrent, but mutually exclusive, TERT promoter and CDKN2A mutations were identified only in clear cell sarcoma (CCS) and associated with worse overall survival. CDKN2A/B homozygous deletions were recurrent in angiomatoid fibrous histiocytoma (AFH) and restricted to metastatic cases. mRNA upregulation of MITF, CDH19, PARVB, and PFKP was found in CCS, compared to AFH, and correlated with a hypomethylated profile. In contrast, S100A4 and XAF1 were differentially upregulated and hypomethylated in AFH but not CCS. Unsupervised clustering of methylation profiles revealed that CREB family translocation-associated tumors form neighboring but tight, distinct clusters. A sarcoma methylation classifier was able to accurately match 100% of CCS cases to the correct methylation class; however, it was suboptimal when applied to other histologies. In conclusion, our comprehensive genomic profiling of EWSR1/FUS::CREB translocation-associated tumors uncovered mostly histotype, rather than fusion-type associated correlations in transcript variants, prognostically significant secondary genetic alterations, and gene expression and methylation patterns.

Novel feature selection methods for construction of accurate epigenetic clocks

Epigenetic clocks allow us to accurately predict the age and future health of individuals based on the methylation status of specific CpG sites in the genome and are a powerful tool to measure the effectiveness of longevity interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to optimise the identification of predictive CpG sites. Here, we apply novel feature selection methods and combinatorial approaches including newly adapted neural networks, genetic algorithms, and 'chained' combinations. Human whole blood methylation data of ~470,000 CpGs was used to develop clocks that predict age with R2 correlation scores of greater than 0.73, the most predictive of which uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks were modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy. When compared with three published epigenetic clocks (Hannum, Horvath, Weidner) also applied to these validation datasets, our clocks outperformed all three models. We identified gene regulatory regions associated with selected CpGs as possible targets for future aging studies. Thus, our feature selection algorithms build accurate, generalizable clocks with a low number of CpG sites, providing important tools for the field.

Spontaneous and ART-induced large offspring syndrome: similarities and differences in DNA methylome

Large/abnormal offspring syndrome (LOS/AOS) is a congenital overgrowth syndrome reported in ruminants produced by assisted reproduction (ART-LOS) which exhibit global disruption of the epigenome and transcriptome. LOS/AOS shares phenotypes and epigenotypes with the human congenital overgrowth condition Beckwith-Wiedemann syndrome. We have reported that LOS occurs spontaneously (SLOS); however, to date, no study has been conducted to determine if SLOS has the same methylome epimutations as ART-LOS. In this study, we performed whole-genome bisulphite sequencing to examine global DNA methylation in bovine SLOS and ART-LOS tissues. We observed unique patterns of global distribution of differentially methylated regions (DMRs) over different genomic contexts, such as promoters, CpG Islands, shores and shelves, as well as at repetitive sequences. In addition, we included data from two previous LOS studies to identify shared vulnerable genomic loci in LOS. Overall, we identified 320 genomic loci in LOS that have alterations in DNA methylation when compared to controls. Specifically, there are 25 highly vulnerable loci that could potentially serve as molecular markers for the diagnosis of LOS, including at the promoters of DMRT2 and TBX18, at the imprinted gene bodies of IGF2R, PRDM8, and BLCAP/NNAT, and at multiple CpG Islands. We also observed tissue-specific DNA methylation patterns between muscle and blood, and conservation of ART-induced DNA methylation changes between muscle and blood. We conclude that as ART-LOS, SLOS is an epigenetic condition. In addition, SLOS and ART-LOS share similarities in methylome epimutations.

Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature

BACKGROUND

Soft-tissue sarcomas (STSs) are heterogeneous and aggressive tumors, with high metastatic risk. The immunologic constant of rejection (ICR) 20-gene signature is a signature of cytotoxic immune response. We hypothesized that ICR might improve the prognostic assessment of early-stage STS.

METHODS

We retrospectively applied ICR to 1455 non-metastatic STS and searched for correlations between ICR classes and clinicopathological and biological variables, including metastasis-free survival (MFS).

RESULTS

Thirty-four per cent of tumors were classified as ICR1, 27% ICR2, 24% ICR3, and 15% ICR4. These classes were associated with patients' age, pathological type, and tumor depth, and an enrichment from ICR1 to ICR4 of quantitative/qualitative scores of immune response. ICR1 class was associated with a 59% increased risk of metastatic relapse when compared with ICR2-4 class. In multivariate analysis, ICR classification remained associated with MFS, as well as pathological type and Complexity Index in Sarcomas (CINSARC) classification, suggesting independent prognostic value. A prognostic clinicogenomic model, including the three variables, was built in a learning set (n=339) and validated in an independent set (n=339), showing greater prognostic precision than each variable alone or in doublet. Finally, connectivity mapping analysis identified drug classes potentially able to reverse the expression profile of poor-prognosis tumors, such as chemotherapy and targeted therapies.

CONCLUSION

ICR signature is independently associated with postoperative MFS in early-stage STS, independently from other prognostic features, including CINSARC. We built a robust prognostic clinicogenomic model integrating ICR, CINSARC, and pathological type, and suggested differential vulnerability of each prognostic group to different systemic therapies.

DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis

Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Epigenetic signatures differentiate uterine and soft tissue leiomyosarcoma

Leiomyosarcomas (LMS) are diverse, rare, and aggressive mesenchymal soft tissue sarcomas. Epigenetic alterations influence multiple aspects of cancer, however epigenetic profiling of LMS has been limited. The goal of this study was to delineate the molecular landscape of LMS for subtype-specific differences (uterine LMS (ULMS) vs soft tissue LMS (STLMS)) based on integrated analysis of DNA methylation and gene expression to identify potential targets for therapeutic intervention and diagnosis. We identified differentially methylated and differentially expressed genes associated with ULMS and STLMS using DNA methylation and RNA-seq data from primary tumors. Two main clusters were identified through unsupervised hierarchical clustering: ULMS-enriched cluster and STLMS-enriched cluster. The integrated analysis demonstrated 34 genes associated with hypermethylation of the promoter CpG islands and downregulation of gene expression in ULMS or STLMS. In summary, these results indicate that differential DNA methylation and gene expression patterns are associated with ULMS and STLMS. Further studies are needed to delineate the contribution of epigenetic regulation to LMS subtype-specific gene expression and determine the roles of the differentially methylated and differentially expressed genes as potential therapeutic targets or biomarkers.

Exploring the Significance of the Exon 4-Skipping Isoform of the ZNF217 Oncogene in Breast Cancer

Oncogene alternative splicing events can create distinct functional transcripts that offer new candidate prognostic biomarkers for breast cancer. ZNF217 is a well-established oncogene but its exon 4-skipping isoform (ZNF217-ΔE4) has never been investigated in terms of clinical or biological relevance. Using in silico RNA-seq and RT-qPCR analyses, we demonstrated for the first time the existence of ZNF217-ΔE4 transcripts in primary breast tumors, and a positive correlation between ZNF217-ΔE4 mRNA levels and those of the wild-type oncogene (ZNF217-WT). A pilot retrospective analysis revealed that, in the Luminal subclass, the combination of the two ZNF217 variants (the ZNF217-ΔE4-WT gene-expression signature) provided more information than the mRNA expression levels of each isoform alone. Ectopic overexpression of ZNF217-ΔE4 in breast cancer cells promoted an aggressive phenotype and an increase in ZNF217-WT expression levels that was inversely correlated with DNA methylation of the ZNF217 gene. This study provides new insights into the possible role of the ZNF217-ΔE4 splice variant in breast cancer and suggests a close interplay between the ZNF217-WT and ZNF217-ΔE4 isoforms. Our data suggest that a dual signature combining the expression levels of these two isoforms may serve as a novel prognostic biomarker allowing better stratification of breast cancers with good prognosis and aiding clinicians in therapeutic decisions.

The predictive potential of Neuronatin for neoadjuvant chemotherapy of breast cancer

BACKGROUND

Neuronatin (NNAT) determined by immunohistochemistry is a negative prognostic biomarker for breast cancer, independent of the major clinicopathological markers.

OBJECTIVE

Here, we investigated whether NNAT is also a predictive biomarker for pathological remission after neoadjuvant chemotherapy.

METHODS

One hundred and four breast cancer patients, treated with systemic neoadjuvant chemotherapy were included in this retrospective study. NNAT was detected in formaldehyde fixed, paraffin embedded primary cancer tissue by immunohistochemistry and an immuno-reactive score (IRS) determined. Pathological remission was scored according to Sinn and by evaluation of cytopathic effects. NNAT-IRS was correlated with clinicopathological parameters as well as relapse free and overall survival and for pathological remission after neoadjuvant therapy.

RESULTS

NNAT IRS was an independent prognostic marker for relapse free and overall survival and the time from diagnosis to the "tumor-free" state. NNAT IRS was associated with Luminal-A tumors and correlated slightly negative with age and lymph-node metastasis. There was no significant correlation of NNAT-IRS with Sinn's remission score, but with cytopathic effects of chemotherapy.

CONCLUSIONS

We confirmed the prognostic impact of NNAT-IRS in an independent cohort of neoadjuvantly treated patients. Additionally, a correlation with a score for pathological remission under systemic neoadjuvant chemotherapy for breast cancer was found.

Integrated Analysis of Nine Prognostic RNA-Binding Proteins in Soft Tissue Sarcoma

RNA-binding proteins (RBPs) have been shown to be dysregulated in cancer transcription and translation, but few studies have investigated their mechanism of action in soft tissue sarcoma (STS). Here, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to identify differentially expressed RBPs in STS and normal tissues. Through a series of biological information analyses, 329 differentially expressed RBPs were identified. Functional enrichment analysis showed that differentially expressed RBPs were mainly involved in RNA transport, RNA splicing, mRNA monitoring pathways, ribosome biogenesis and translation regulation. Through Cox regression analyses, 9 RBPs (BYSL, IGF2BP3, DNMT3B, TERT, CD3EAP, SRSF12, TLR7, TRIM21 and MEX3A) were all up-regulated in STS as prognosis-related genes, and a prognostic model was established. The model calculated a risk score based on the expression of 9 hub RBPs. The risk score could be used for risk stratification of patients and had a high prognostic value based on the receiver operating characteristic (ROC) curve. We also established a nomogram containing risk scores and 9 key RBPs to predict the 1-year, 3-year, and 5-year survival rates of patients in STS. Afterwards, methylation analysis showed significant changes in the methylation degree of BYSL, CD3EAP and MEX2A. Furthermore, the expression of 9 hub RBPs was closely related to immune infiltration rather than tumor purity. Based on the above studies, these findings may provide new insights into the pathogenesis of STS and will provide candidate biomarkers for the prognosis of STS.

Comprehensive analysis based on DNA methylation and RNA-seq reveals hypermethylation of the up-regulated WT1 gene with potential mechanisms in PAM50 subtypes of breast cancer

Background

Breast cancer (BC), one of the most widespread cancers worldwide, caused the deaths of more than 600,000 women in 2018, accounting for about 15% of all cancer-associated deaths in women that year. In this study, we aimed to discover potential prognostic biomarkers and explore their molecular mechanisms in different BC subtypes using DNA methylation and RNA-seq.

Methods

We downloaded the DNA methylation datasets and the RNA expression profiles of primary tissues of the four BC molecular subtypes (luminal A, luminal B, basal-like, and HER2-enriched), as well as the survival information from The Cancer Genome Atlas (TCGA). The highly expressed and hypermethylated genes across all the four subtypes were screened. We examined the methylation sites and the downstream co-expressed genes of the selected genes and validated their prognostic value using a different dataset (GSE20685). For selected transcription factors, the downstream genes were predicted based on the Gene Transcription Regulation Database (GTRD). The tumor microenvironment was also evaluated based on the TCGA dataset.

Results

We found that Wilms tumor gene 1 (WT1), a transcription factor, was highly expressed and hypermethylated in all the four BC subtypes. All the WT1 methylation sites exhibited hypermethylation. The methylation levels of the TSS200 and 1stExon regions were negatively correlated with WT1 expression in two BC subtypes, while that of the gene body region was positively associated with WT1 expression in three BC subtypes. Patients with low WT1 expression had better overall survival (OS). Five genes including COL11A1, GFAP, FGF5, CD300LG, and IGFL2 were predicted as the downstream genes of WT1. Those five genes were dysregulated in the four BC subtypes. Patients with a favorable 6-gene signature (low expression of WT1 and its five predicted downstream genes) exhibited better OS than that with an unfavorable 6-gene signature. We also found a correlation between WT1 and tamoxifen using STITCH. Higher infiltration rates of CD8 T cells, plasma cells, and monocytes were found in the lower quartile WT1 group and the favorable 6-gene signature group. In conclusion, we demonstrated that WT1 is hypermethylated and up-regulated in the four BC molecular subtypes and a 6-gene signature may predict BC prognosis.

Tumor DNA methylation profiles correlate with response to anti-PD-1 immune checkpoint inhibitor monotherapy in sarcoma patients

BACKGROUND

Some sarcomas respond to immune checkpoint inhibition, but predictive biomarkers are unknown. We analyzed tumor DNA methylation profiles in relation to immunological parameters and response to anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI) therapy in patients with sarcoma.

PATIENTS AND METHODS

We retrospectively identified adult patients who had received anti-PD-1 ICI therapy for recurrent sarcoma in two independent centers. We performed (1) blinded radiological response evaluation according to immune response evaluation criteria in solid tumors (iRECIST) ; (2) tumor DNA methylation profiling of >850,000 probes using Infinium MethylationEPIC microarrays; (3) analysis of tumor-infiltrating immune cell subsets (CD3, CD8, CD45RO, FOXP3) and intratumoral expression of immune checkpoint molecules (PD-L1, PD-1, LAG-3) using immunohistochemistry; and (4) evaluation of blood-based systemic inflammation scores (neutrophil-to-lymphocyte ratio, leucocyte-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio). Response to anti-PD-1 ICI therapy was bioinformatically and statistically correlated with DNA methylation profiles and immunological data.

RESULTS

35 patients (median age of 50 (23-81) years; 18 females, 17 males; 27 soft tissue sarcomas; 8 osteosarcomas) were included in this study. The objective response rate to anti-PD-1 ICI therapy was 22.9% with complete responses in 3 out of 35 and partial responses in 5 out of 35 patients. Adjustment of DNA methylation data for tumor-infiltrating immune cells resulted in identification of methylation differences between responders and non-responders to anti-PD-1 ICI. 2453 differentially methylated CpG sites (DMPs; 2043 with decreased and 410 with increased methylation) were identified. Clustering of sarcoma samples based on these DMPs revealed two main clusters: methylation cluster 1 (MC1) consisted of 73% responders and methylation cluster 2 (MC2) contained only non-responders to anti-PD-1 ICI. Median progression-free survival from anti-PD-1 therapy start of MC1 and MC2 patients was 16.5 and 1.9 months, respectively (p=0.001). Median overall survival of these patients was 34.4 and 8.0 months, respectively (p=0.029). The most prominent DNA methylation differences were found in pathways implicated in Rap1 signaling, focal adhesion, adherens junction Phosphoinositide 3-kinase (PI3K)-Akt signaling and extracellular matrix (ECM)-receptor interaction.

CONCLUSIONS

Our data demonstrate that tumor DNA methylation profiles may serve as a predictive marker for response to anti-PD-1 ICI therapy in sarcoma.

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

Lidocaine prevents breast cancer growth by targeting neuronatin to inhibit nerve fibers formation

Lidocaine has been shown to inhibit the invasion and metastasis of breast cancer, but the mechanism still remains unclear. This study explored the relationship between lidocaine and circulating seeding of breast cancer cells from the perspective of nerve fiber formation. The cell lines MDA-MB-231 and 4T1 were subcutaneously inoculated in mice to simulate the tumor self-seeding by circulating cancer cells. Lidocaine was used to treat these mice and tumor growth was observed. Silver staining was performed to observe the distribution of nerve fibers in tumor-bearing tissues, and immunohistochemical analysis was performed to observe the expression levels of nerve-related proteins. The results showed that lidocaine treatment effectively inhibited tumor growth and nerve fiber formation, and down-regulated the expression levels of protein gene product 9.5, neurofilament, nerve growth factor (NGF), and neuronatin (Nnat). Overexpression NGF and Nnat both could reverse the therapeutic effects of lidocaine. These results suggest that the effect of lidocaine on inhibiting breast cancer invasion and metastasis may be achieved by targeting Nnat, regulating the production of NGFs in cancer cells, and subsequently inhibiting the formation of nerve fibers.

A DNA methylation-based test for esophageal cancer detection

Background

Esophageal cancer (ECa) is the 7th most incident cancer and the 6th leading cause of cancer-related death. Most patients are diagnosed with locally advanced or metastatic disease, enduring poor survival. Biomarkers enabling early cancer detection may improve patient management, treatment effectiveness, and survival, are urgently needed. In this context, epigenetic-based biomarkers such as DNA methylation are potential candidates.

Methods

Herein, we sought to identify and validate DNA methylation-based biomarkers for early detection and prediction of response to therapy in ECa patients. Promoter methylation levels were assessed in a series of treatment-naïve ECa, post-neoadjuvant treatment ECa, and normal esophagus tissues, using quantitative methylation-specific PCR for COL14A1, GPX3, and ZNF569.

Results

ZNF569 methylation (ZNF569me) levels significantly differed between ECa and normal samples (p < 0.001). Moreover, COL14A1 methylation (COL14A1me) and GPX3 methylation (GPX3me) levels discriminated adenocarcinomas and squamous cell carcinomas, respectively, from normal samples (p = 0.002 and p = 0.009, respectively). COL14A1me & ZNF569me accurately identified adenocarcinomas (82.29%) whereas GPX3me & ZNF569me identified squamous cell carcinomas with 81.73% accuracy. Furthermore, ZNF569me and GPX3me levels significantly differed between normal and pre-treated ECa.

Conclusion

The biomarker potential of a specific panel of methylated genes for ECa was confirmed. These might prove useful for early detection and might allow for the identification of minimal residual disease after adjuvant therapy.

Molecular tumor classification using DNA methylome analysis

Tumor classifiers based on molecular patterns promise to define and reliably classify tumor entities. The high tissue- and cell type-specificity of DNA methylation, as well as its high stability, makes DNA methylation an ideal choice for the development of tumor classifiers. Herein, we review existing tumor classifiers using DNA methylome analysis and will provide an overview on their emerging impact on cancer classification, the detection of novel cancer subentities and patient stratification with a focus on brain tumors, sarcomas and hematopoietic malignancies. Furthermore, we provide an outlook on the enormous potential of DNA methylome analysis to complement classical histopathological and genetic diagnostics, including the emerging field of epigenomic analysis in liquid biopsies.

Aberrant epigenetic silencing of neuronatin is a frequent event in human osteosarcoma

The paternally imprinted neuronatin (NNAT) gene has been identified as a target of aberrant epigenetic silencing in diverse cancers, but no association with pediatric bone cancers has been reported to date. In screening childhood cancers, we identified aberrant CpG island hypermethylation in a majority of osteosarcoma (OS) samples and in 5 of 6 human OS cell lines studied but not in normal bone-derived tissue samples. CpG island hypermethylation was associated with transcriptional silencing in human OS cells, and silencing was reversible upon treatment with 5-aza-2'-deoxycytidine. Expression of NNAT was detectable in osteoblasts and chondrocytes of human bone, supporting a potential role in bone homeostasis. Enforced expression of NNAT in human OS cells lacking endogenous expression resulted in significant reduction in colony formation and in vitro migration compared to nonexpressor control cells. We next analyzed the effect of NNAT expression on intracellular calcium homeostasis and found that was associated with an attenuated decay of calcium levels to baseline following ATP-induced release of calcium from endoplasmic reticulum (ER) stores. Furthermore, NNAT expression was associated with increased cytotoxicity in OS cells from thapsigargin, an inhibitor of calcium reuptake into ER and an inducer of the ER stress response. These results suggest a possible tumor suppressor role for NNAT in human osteosarcoma. Additional study is needed ascertain sensitization to ER stress-associated apoptosis as a mechanism of NNAT-dependent cytotoxicity. In that case, epigenetic modification therapy to effect NNAT transcriptional derepression may represent a therapeutic strategy potentially of benefit to a majority of osteosarcoma patients.

Multi-omics Analysis of Primary Cell Culture Models Reveals Genetic and Epigenetic Basis of Intratumoral Phenotypic Diversity

Uncovering the functionally essential variations related to tumorigenesis and tumor progression from cancer genomics data is still challenging due to the genetic diversity among patients, and extensive inter- and intra-tumoral heterogeneity at different levels of gene expression regulation, including but not limited to the genomic, epigenomic, and transcriptional levels. To minimize the impact of germline genetic heterogeneities, in this study, we establish multiple primary cultures from the primary and recurrent tumors of a single patient with hepatocellular carcinoma (HCC). Multi-omics sequencing was performed for these cultures that encompass the diversity of tumor cells from the same patient. Variations in the genome sequence, epigenetic modification, and gene expression are used to infer the phylogenetic relationships of these cell cultures. We find the discrepancy among the relationships revealed by single nucleotide variations (SNVs) and transcriptional/epigenomic profiles from the cell cultures. We fail to find overlap between sample-specific mutated genes and differentially expressed genes (DEGs), suggesting that most of the heterogeneous SNVs among tumor stages or lineages of the patient are functionally insignificant. Moreover, copy number alterations (CNAs) and DNA methylation variation within gene bodies, rather than promoters, are significantly correlated with gene expression variability among these cell cultures. Pathway analysis of CNA/DNA methylation-related genes indicates that a single cell clone from the recurrent tumor exhibits distinct cellular characteristics and tumorigenicity, and such an observation is further confirmed by cellular experiments both in vitro and in vivo. Our systematic analysis reveals that CNAs and epigenomic changes, rather than SNVs, are more likely to contribute to the phenotypic diversity among subpopulations in the tumor. These findings suggest that new therapeutic strategies targeting gene dosage and epigenetic modification should be considered in personalized cancer medicine. This culture model may be applied to the further identification of plausible determinants of cancer metastasis and relapse.

DNA Methylation Profiling for Diagnosing Undifferentiated Sarcoma with Capicua Transcriptional Receptor (CIC) Alterations

Undifferentiated soft tissue sarcomas are a group of diagnostically challenging tumors in the pediatric population. Molecular techniques are instrumental for the categorization and differential diagnosis of these tumors. A subgroup of recently identified soft tissue sarcomas with undifferentiated round cell morphology was characterized by Capicua transcriptional receptor (CIC) rearrangements. Recently, an array-based DNA methylation analysis of undifferentiated tumors with small blue round cell histology was shown to provide a highly robust and reproducible approach for precisely classifying this diagnostically challenging group of tumors. We describe the case of an undifferentiated sarcoma of the abdominal wall in a 12-year-old girl. The patient presented with a voluminous mass of the abdominal wall, and multiple micro-nodules in the right lung. The tumor was unclassifiable with current immunohistochemical and molecular approaches. However, DNA methylation profiling allowed us to classify this neoplasia as small blue round cell tumor with CIC alterations. The patient was treated with neoadjuvant chemotherapy followed by complete surgical resection and adjuvant chemotherapy. After 22 months, the patient is disease-free and in good clinical condition. To put our experience in context, we conducted a literature review, analyzing current knowledge and state-of-the-art diagnosis, prognosis, and clinical management of CIC rearranged sarcomas. Our findings further support the use of DNA methylation profiling as an important tool to improve diagnosis of non-Ewing small round cell tumors.

WeCoMXP: Weighted Connectivity Measure Integrating Co-Methylation, Co-Expression and Protein-Protein Interactions for Gene-Module Detection

The identification of modules (groups of several tightly interconnected genes) in gene interaction network is an essential task for better understanding of the architecture of the whole network. In this article, we develop a novel weighted connectivity measure integrating co-methylation, co-expression, and protein-protein interactions (called WeCoMXP) to detect gene-modules for multi-omics dataset. The proposed measure goes beyond the fundamental degree centrality measure through considering some formulation of higher-order connections. Thereafter, we apply the average linkage clustering method using the corresponding dissimilarity (distance) values of WeCoMXP scores, and utilize a dynamic tree cut method for identifying some gene-modules. We validate the modules through literature search, KEGG pathway, and gene-ontology analyses on the genes representing the modules. Furthermore, the top 10 TFs/miRNAs that are connected with the maximum number of gene-modules and that regulate/target the maximum number of genes from these connected gene-modules, are identified. Moreover, our proposed method provides a better performance than the existing methods in terms of several cluster-validity indices in maximum times.

Genome-wide Differential-based Analysis of the Relationship between DNA Methylation and Gene Expression in Cancer

Background::

DNA methylation is an epigenetic modification that plays an important role in regulating gene expression. There is evidence that the hypermethylation of promoter regions always causes gene silencing. However, how the methylation patterns of other regions in the genome, such as gene body and 3’UTR, affect gene expression is unknown.

Objective::

The study aimed to fully explore the relationship between DNA methylation and expression throughout the genome-wide analysis which is important in understanding the function of DNA methylation essentially.

Method::

In this paper, we develop a heuristic framework to analyze the relationship between the methylated change in different regions and that of the corresponding gene expression based on differential analysis.

Results::

To understande the methylated function of different genomic regions, a gene is divided into seven functional regions. By applying the method in five cancer datasets from the Synapse database, it was found that methylated regions with a significant difference between cases and controls were almost uniformly distributed in the seven regions of the genome. Also, the effect of DNA methylation in different regions on gene expression was different. For example, there was a higher percentage of positive relationships in 1stExon, gene body and 3’UTR than in TSS1500 and TSS200. The functional analysis of genes with a significant positive and negative correlation between DNA methylation and gene expression demonstrated the epigenetic mechanism of cancerassociated genes.

Conclusion::

Differential based analysis helps us to recognize the change in DNA methylation and how this change affects the change in gene expression. It provides a basis for further integrating gene expression and DNA methylation data to identify disease-associated biomarkers.

Genetics of human malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumors (MPNSTs) are heterogeneous, highly aggressive tumors with no widely effective treatment other than surgery. Genomic architecture of MPNST is similar to other soft tissue sarcomas, with a relatively modest burden of single nucleotide variants and an elevated frequency of copy-number alterations. Recent advances in genomic studies identified previously unrecognized critical involvement of polycomb repressor complex 2 (PRC2) core components SUZ12 and EED in transition to malignancy. Notably, somatic changes in NF1, CDKN2A/B, and PRC2 are found in most MPNST regardless of their etiology (e.g. neurofibromatosis type 1-associated vs. sporadic vs. radiation-induced), indicating that similar molecular mechanisms impact pathogenesis in these neoplasms. The timing and specific order of genetic or epigenetic changes may, however, explain the typically poorer prognosis of NF1-associated MPNSTs. Studies that reveal genes and regulatory pathways uniquely altered in malignancies are essential to development of targeted tumor therapies. Characterization of MPNST molecular profiles may also contribute to tools for earlier detection, and prediction of prognosis or drug response. Here we review the genetic discoveries and their implications in understanding MPNST biology.

Molecular characteristics and therapeutic vulnerabilities across paediatric solid tumours

The spectrum of tumours arising in childhood is fundamentally different from that seen in adults, and they are known to be divergent from adult malignancies in terms of cellular origins, epidemiology, genetic complexity, driver mutations and underlying mutational processes. Despite the immense knowledge generated through sequencing efforts and functional characterization of identified (epi-)genetic alterations over the past decade, the clinical implications of this knowledge have so far been limited. Novel preclinical platforms such as the European Innovative Therapies for Children with Cancer-Paediatric Preclinical Proof-of-Concept Platform and the US-based Pediatric Preclinical Testing Consortium are being developed to try to change this by aiming to recapitulate the extensive heterogeneity of paediatric tumours and thereby, hopefully, improve the ability to predict clinical benefit. Numerous studies have also been established worldwide to provide patients with access to real-time molecular profiling and the possibility of more precise mechanism-of-action-based treatments. In addition to tumour-intrinsic findings and mechanisms, ongoing studies are investigating features such as the immune microenvironment of paediatric tumours in comparison with adult cancers - currently of very timely clinical relevance. However, there is an ongoing need for rigorous preclinical biomarker and target validation to feed into the next generation of molecularly stratified clinical trials. This Review aims to provide a comprehensive state-of-the-art overview of the molecular landscape of paediatric solid tumours, including their underlying genomic alterations and interactions with the microenvironment, complemented with our current understanding of potential therapeutic vulnerabilities and how these can be preclinically tested using more accurate predictive methods. Finally, we provide an outlook on the challenges and opportunities associated with translating this overwhelming scientific progress into real clinical benefit.

PARP1 expression in soft tissue sarcomas is a poor-prognosis factor and a new potential therapeutic target

Soft tissue sarcomas (STSs) are aggressive tumors with few efficient systemic therapies. Poly(ADP‐ribose) polymerase‐1 (PARP1) inhibitors represent an emerging therapeutic option in tumors with genomic instability. The genomics of STSs is complex in more than half of cases, suggesting a high level of inherent DNA damage and genomic instability. Thus, STSs could be efficiently targeted with PARP inhibitors. Promising preclinical results have been reported, but few data are available regarding PARP1 expression in clinical samples. We examined PARP1 mRNA expression in 1464 clinical samples of STS, including 1432 primary tumors and 32 relapses, and searched for correlations with clinicopathological features, including metastasis‐free survival (MFS). Expression was heterogeneous across the samples, not different between primary and secondary tumors, and was correlated to PARP1 DNA copy number. In the 1432 primary tumors, the ‘PARP1‐high’ samples were associated with younger patients, more frequent locations at the extremities, superficial trunk and head and neck, more leiomyosarcomas and other STSs and less liposarcomas and myxofibrosarcomas, more grade 3, more high‐risk CINSARC tumors, and more ‘chromosomically instable’ tumors. They were associated with shorter MFS, independently of other significant prognostic features, including the CINSARC signature. We found a strong involvement of genes overexpressed in the ‘PARP1‐high’ samples in cell cycle, DNA replication, and DNA repair. PARP1 expression refines the prediction of MFS in STSs, and similar expression exists in secondary and primary tumors, supporting the development of PARP1 inhibitors.

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

An Integrative Framework for Protein Interaction Network and Methylation Data to Discover Epigenetic Modules

DNA methylation is a critical epigenetic modification that plays an important role in cancers. The available algorithms fail to fully characterize epigenetic modules. To address this issue, we first characterize the epigenetic module as a group of well-connected genes in the protein interaction network and are also co-methylated based on gene methylation profiles. Then, the epigenetic module discovery problem is transformed into an optimization problem. Then, a regularized nonnegative matrix factorization algorithm for methylation modules (RNMF-MM) is presented, where the co-methylation constraint is treated as a regularizer. Using the artificial networks with known module structure, we demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of accuracy. On the basis of breast cancer methylation data and protein interaction network, the RNMF-MM algorithm discovers methylation modules that are significantly more enriched by the known pathways than those obtained by other algorithms. These modules serve as biomarkers for predicting cancer stages and estimating survival time of patients. The proposed model and algorithm provide an effective way for the integrative analysis of protein interaction network and methylation data.

Imbalance between matrix metalloproteinases and their tissue inhibitors in preeclampsia and gestational trophoblastic diseases

The invasion cascade exhibited by placental trophoblasts and cancerous cells bears many similarities, and it is attributed to extracellular matrix degradation mediated by matrix metalloproteinases (MMPs). Although proper and controlled invasion by trophoblasts into the maternal uterus is an essential requirement for maintenance of normal pregnancy, any abnormality in this phenomenon results in the development of invasion-related disorders such as gestational trophoblastic diseases (GTDs) and preeclampsia. We studied the epigenetic basis of differential expression of two placental MMPs (MMP2 and MMP9) and tissue inhibitors of metalloproteinases (TIMP2 and TIMP1) during normal gestation and invasion-related disorders, i.e., preeclampsia and GTDs. Our study suggests the association of H3K9/27me3 with differential expression of these MMPs and their inhibitors, which regulate the placental invasion during normal pregnancy, whereas no role of CpG methylation was observed in the differential expression of MMPs/TIMPs. Further, development of GTDs was associated with abnormally higher expression of these MMPs and lower levels of their inhibitors, whereas the reverse trends were observed for MMPs and their TIMPs in case of preeclampsia, in association with abnormal changes in H3K9/27me3. These results suggest the involvement of higher levels of MMPs in an aggressive invasive behavior depicted by GTDs, whereas lower levels of these MMPs in shallow and poor invasive phenotype associated with preeclampsia. Thus, our study shows the significance of a proper balance regulated by histone trimethylation between differential expression of MMPs and their TIMPs for maintaining normal pregnancy and its deregulation as a contributing factor for pathogenesis of invasive disorders during pregnancy.

DNA Methylation-Based Classifier for Accurate Molecular Diagnosis of Bone Sarcomas

Purpose

Pediatric sarcomas provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. We developed and validated a genome-wide DNA methylation based classifier to differentiate between osteosarcoma, Ewing's sarcoma, and synovial sarcoma.

Materials and Methods

DNA methylation status of 482,421 CpG sites in 10 Ewing's sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina Infinium HumanMethylation450 array. We developed a random forest classifier trained from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples. This classifier was validated on data drawn from The Cancer Genome Atlas (TCGA) synovial sarcoma, TARGET Osteosarcoma, and a recently published series of Ewing's sarcoma.

Results

Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were accurately classified as synovial sarcoma (10/10, sensitivity and specificity 100%), all but one sample from TARGET-OS were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%) and 14/15 Ewing's sarcoma samples classified correctly (sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample demonstrated high EWSR1 and ETV1 expression on RNA-seq although no fusion was found on manual curation of the transcript sequence. Two additional clinical samples, that were difficult to classify by morphology and molecular methods, were classified as osteosarcoma when previously suspected to be a synovial sarcoma and Ewing's sarcoma on initial diagnosis, respectively.

Conclusion

Osteosarcoma, synovial sarcoma, and Ewing's sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide diagnostic assistance when histological and standard techniques are inconclusive.

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.

Aflatoxin B1-induced epigenetic alterations: An overview

Aflatoxin B1 (AFB1) is widely distributed in nature, especially in a variety of food commodities. It is confirmed to be the most toxic of all the aflatoxins. The toxicity of AFB1 has been well investigated, and it may result in severe health problems including carcinogenesis, mutagenesis, growth retardation, and immune suppression. Epigenetic modifications including DNA methylation, histone modifications and regulation of non-coding RNA play an important role in AFB1-induced disease and carcinogenesis. To better understand the evidence for AFB1-induced epigenetic alterations and the potential mechanisms of the toxicity of AFB1, we conducted a review of published studies of AFB1-induced epigenetic alterations.

Oncopig Soft-Tissue Sarcomas Recapitulate Key Transcriptional Features of Human Sarcomas

Human soft-tissue sarcomas (STS) are rare mesenchymal tumors with a 5-year survival rate of 50%, highlighting the need for further STS research. Research has been hampered by limited human sarcoma cell line availability and the large number of STS subtypes, making development of STS cell lines and animal models representative of the diverse human STS subtypes critical. Pigs represent ideal human disease models due to their similar size, anatomy, metabolism, and genetics compared to humans. The Oncopig encodes inducible KRAS^G12D and TP53^R167H transgenes, allowing for STS modeling in a spatial and temporal manner. This study utilized Oncopig STS cell line (fibroblast) and tumor (leiomyosarcoma) RNA-seq data to compare Oncopig and human STS expression profiles. Altered expression of 3,360 and 7,652 genes was identified in Oncopig STS cell lines and leiomyosarcomas, respectively. Transcriptional hallmarks of human STS were observed in Oncopig STS, including altered TP53 signaling, Wnt signaling activation, and evidence of epigenetic reprogramming. Furthermore, master regulators of Oncopig STS expression were identified, including FOSL1, which was previously identified as a potential human STS therapeutic target. These results demonstrate the Oncopig STS model’s ability to mimic human STS transcriptional profiles, providing a valuable resource for sarcoma research and cell line development.

Anticancer Natural Compounds as Epigenetic Modulators of Gene Expression

Accumulating evidence shows that hallmarks of cancer include: "genetic and epigenetic alterations leading to inactivation of cancer suppressors, overexpression of oncogenes, deregulation of intracellular signaling cascades, alterations of cancer cell metabolism, failure to undergo cancer cell death, induction of epithelial to mesenchymal transition, invasiveness, metastasis, deregulation of immune response and changes in cancer microenvironment, which underpin cancer development". Natural compounds as bioactive ingredients isolated from natural sources (plants, fungi, marine life forms) have revolutionized the field of anticancer therapeutics and rapid developments in preclinical studies are encouraging. Natural compounds could affect the epigenetic molecular mechanisms that modulate gene expression, as well as DNA damage and repair mechanisms. The current review will describe the latest achievements in using naturally produced compounds targeting epigenetic regulators and modulators of gene transcription in vitro and in vivo to generate novel anticancer therapeutics.