The hydroxymethylome of multiple myeloma identifies FAM72D as a 1q21 marker linked to proliferation

FAM72 family members serves as prognostic biomarker in liver hepatocellular carcinoma

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is a common cancer with poor prognosis. The FAM72 gene family enhances neuronal self-renewal, potentially increasing tumor formation, but its functional and predictive relevance in LIHC remains unclear. We sought to investigate the function of the FAM72 gene family in LIHC in the present study.

METHODS

We acquired TCGA-LIHC expression and phenotypic data as well as extensive clinicopathologic information from the UCSC Xena Database (https://xenabrowser.net/datapages/) database. We analyzed the association between FAM72 gene family expression in LIHC and patient prognosis and immune infiltration; Genomic and functional enrichment analysis for FAM72 genes was analyzed. Finally, Western blot method, quantitative real-time polymerase chain reaction and CCK8 detection and cell invasion experiments were used to verify the effect of FAM72A expression on LIHC.

RESULTS

The expression of FAM72 gene family is different between LIHC and normal liver tissues. The expression of FAM72 gene family increased with increasing grading of LIHC tissues. The expression of FAM72 gene family was significantly reduced in LIHC stage IV. LIHC tissues expressed significantly more FAM72 genes than did normal tissues at the T stage (p < 0.001,). which has a good value in the diagnosis of LIHC (AUC greater than 0.85), and was strongly linked with the tumor stage in LIHC. Based on Cox analysis of univariate data, the FAM72 gene family was associated with poor overall survival (OS) in patients with LIHC. Analysis of multifactorial Cox data revealed an independent relationship between FAM72 expression and OS. Increased FAM72 gene expression is associated with poor survival rates and immune cell infiltration. Methylation levels were associated with the prognosis of patients with LIHC. Ultimately, our findings revealed that FAM72A is abundantly expressed in LIHC cells, facilitating proliferation and metastasis.

CONCLUSION

These findings indicate that the FAM72 gene family is a potential molecular marker for poor prognosis in LIHC, providing additional insights into the development of therapeutic approaches and prognostic markers.

Machine learning-based identification of co-expressed genes in prostate cancer and CRPC and construction of prognostic models

The objective of this study was to employ machine learning to identify shared differentially expressed genes (DEGs) in prostate cancer (PCa) initiation and castration resistance, aiming to establish a robust prognostic model and enhance understanding of patient prognosis for personalized treatment strategies. mRNA transcriptome data associated with Castration-Resistant Prostate Cancer (CRPC) were obtained from the GEO database. Differential expression analysis was conducted using the limma R package to compare normal prostate samples with PCa samples, and PCa samples with CRPC samples. Next, we applied LASSO regression, univariate, and multivariate COX regression analyses to pinpoint genes linked to prognosis and build prognostic models. Validation was performed using the TCGA_PRAD dataset to confirm expression differences of hub genes and explore their correlation with clinical variables and prognostic significance. We successfully established a prostate cancer risk prognostic model containing seven genes (KIF4A, UBE2C, FAM72D, CCDC78, HOXD9, LIX1 and SLC5A8) and verified its accuracy on an independent data set. The results of calibration curve and decision curve show that the model has potential clinical application value. The nomogram can accurately predict the prognosis of patients. Additionally, elevated expression of KIF4A, UBE2C, and FAM72D, or reduced expression of LIX1, correlated with advanced pathological T and N stages, clinical T stage, prostate-specific antigen (PSA) level, age at diagnosis, Gleason score, and shorter progression-free interval (PFI) (P < 0.05). By integrating bioinformatics analysis and clinical data, we not only established a reliable prognostic model for prostate cancer but also identified key genes pivotal in disease progression and treatment resistance. These findings provide novel insights and methodologies for assessing prognosis and tailoring treatment strategies for prostate cancer patients.

The role of 1q abnormalities in multiple myeloma: Genomic insights, clinical implications, and therapeutic challenges

Chromosome 1q copy number variations, collectively termed +1q, are 1 of the most common cytogenetic abnormalities in multiple myeloma. 1q abnormalities are associated with overexpression of a high-risk gene signature promoting cell proliferation, apoptosis resistance, genomic instability, and treatment resistance, and acquisition or expansion of +1q subclones mediate disease development and relapse. While there remains significant controversy as to whether the presence of +1q is itself an independent driver of poor prognosis or is simply a marker of other high-risk features, +1q has recently been incorporated into multiple prognostic scoring models as a new high-risk cytogenetic abnormality. In this review, we present possible underlying genetic mechanisms of high-risk disease in +1q myeloma, implications for subclonal development, its role in modifying the tumor microenvironment, current evidence for clinical significance in newly-diagnosed and relapsed patients, and current controversies in +1q classification and prognostication.

A novel mitochondrial-related risk model for predicting prognosis and immune checkpoint blockade therapy response in uterine corpus endometrial carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC) represents a common malignant neoplasm in women, with its prognosis being intricately associated with available therapeutic interventions. In the past few decades, there has been a burgeoning interest in the role of mitochondria within the context of UCEC. Nevertheless, the development and application of prognostic models predicated on mitochondrial-related genes (MRGs) in UCEC remains in the exploratory stages. This study utilized RNA sequencing data and clinical information from the TCGA database to identify differentially expressed MRGs (DEMRGs) between UCEC and normal groups that are associated with overall survival (OS). Patients were randomly assigned to training and testing cohorts in a 1:1 ratio. In the training cohort, a risk model based on DEMRGs was developed using Lasso Cox regression analysis. Subsequently, patients in both cohorts were stratified into high-risk and low-risk groups based on their median risk scores. The prognostic performance of the model was validated through Kaplan-Meier survival analysis, ROC curves, and nomograms. Additionally, further analyses including functional enrichment, immune landscape assessment, prediction of response to ICB therapy, mutation profiling, and drug sensitivity analysis elucidated biological distinctions between the identified risk groups. We established a risk model incorporating eight MRGs. Patients classified within he high-risk group exhibited significantly poorer prognoses relative to those in the low-risk group. Functional enrichment analysis identified substantial differences in biological processes and signaling pathways between the high-risk and low-risk cohorts. Immune landscape analysis showed that patients with elevated risk scores exhibited significant immunosuppressive and immune evasion mechanisms. Conversely, low-risk patients exhibited higher expression of human leukocyte antigen (HLA) family members and immune checkpoint genes (ICGs) compared to their high-risk counterparts.Consequently, low-risk patients showed greater responsiveness to immunotherapy and potential small molecule drugs, whereas high-risk patients were more susceptible to chemotherapy. The mitochondrial-related risk model formulated in this study demonstrates efficacy in predicting both prognosis and response to immunotherapy in patients with UCEC, thereby providing a scientific basis for personalized treatment strategies. Future research endeavors should focus on further validating the clinical utility of this model and investigate the specific mechanisms of the identified MRGs in UCEC.

Expression and Function of FAM72A Gene in Multiple MyelomaFAM72A

AIMS

This study aims to comprehensively investigate the role of Family Member A with sequence similarity 72-A (FAM72A) in multiple myeloma.

BACKGROUND

Multiple myeloma poses significant challenges. This study delves into FAM72A's impact on key cellular processes, shedding light on potential therapeutic targets and enhancing our understanding of multiple myeloma progression.

OBJECTIVES

Investigate the impact of FAM72A on the proliferation, apoptosis, and bortezomib sensitivity of multiple myeloma cell line U266.

METHODS

qRT-PCR analyzed FAM72A expression levels in bone marrow samples from 30 patients with multiple myeloma and 10 healthy donors at the Second Hospital of Shanxi Medical University. Cell lines overexpressing FAM72A were constructed, and Cell Counting Kit 8 (CCK-8) and flow cytometry were used to assess U266 cell proliferation, apoptosis, and sensitivity to bortezomib. Biological predictions for FAM72A were performed to find transcription factors binding to the FAM72A promoter region, verified using a luciferase assay. U266 cells were transfected with si-POU2F2 (POU class 2 homeobox 2), and the impact on cell proliferation was validated. Western blot analysis detected the expression of downstream proteins in the p53 signaling pathway. In vivo, experiments established a xenograft mouse model further to study the role of FAM72A in multiple myeloma.

RESULTS

FAM72A was upregulated in multiple myeloma bone marrow tissues. Compared to the OE-NC group, the OE-FAM72A group showed increased Mouse Double Minute 2 homolog (MDM2) expression, decreased p53 expression, increased cell proliferation, and decreased apoptosis. POU2F2 was identified as the upstream transcription factor for FAM72A. Compared to the si-NC group, the si-POU2F2 group exhibited decreased MDM2 expression, increased p53 expression, slowed cell proliferation, and increased apoptosis. Silencing POU2F2 could reverse the pro-proliferative effect of over-expressing FAM72A in U266 cells. In vivo experiments in a xenograft mouse model further studied the role of FAM72A in multiple myeloma.

CONCLUSION

Overexpression of FAM72A promotes U266 cell proliferation, inhibits apoptosis, and reduces sensitivity to bortezomib by regulating the POU2F2/FAM72A/p53 signaling pathway.

Pan-cancer analysis and experimental validation reveal FAM72D as a potential novel biomarker and therapeutic target in lung adenocarcinoma

BACKGROUND

Cancers, particularly lung adenocarcinoma (LUAD), represent a major global health concern. However, the role of FAM72D in various cancers, including LUAD, remains poorly understood.

METHODS

We utilized databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx) and online tools to investigate the correlation between FAM72D expression and its prognostic, diagnostic, and mutational significance, as well as its impact on immune cell infiltration across multiple cancers. Additionally, we developed LUAD cell lines overexpressing FAM72D to confirm its oncogenic role.

RESULTS

FAM72D expression was elevated in cancerous tissues compared to noncancerous tissues, with diagnostic and prognostic implications in many cancers, including LUAD. Moreover, associations were identified between FAM72D expression and diverse immune subtypes, alongside factors such as microsatellite instability, neoantigens, and tumour mutational burden across pan-cancers. Additionally, FAM72D was associated with immune infiltration and various immune checkpoint-related genes in LUAD. In vitro experiments demonstrated that FAM72D promoted cell proliferation, colony formation, and migration, while inhibiting apoptosis in LUAD cells.

CONCLUSIONS

Our study establishes associations between FAM72D expression and diagnosis, prognosis, and tumour immunity across multiple cancers, as well as its oncogenic effects in LUAD. FAM72D shows promise as a biomarker and therapeutic target in LUAD.

Deciphering the molecular landscape of the FAM72 gene family: Implications for stem cell biology and cancer

Family with sequence similarity 72 (FAM72) is a protein-coding gene family located on chromosome 1 in humans, uniquely featuring four paralogs: FAM72A, FAM72B, FAM72C, and FAM72D. While FAM72's presence as a gene pair with the SLIT-ROBO Rho GTPase-activating protein 2 (SRGAP2) is intriguing, its functional roles, particularly in neural stem cells, remain incompletely understood. This review explores the distinct characteristics of FAM72, shedding light on its expression patterns, potential roles in cell cycle regulation, stem cell renewal and implications in neurogenesis and tumorigenesis.

The numerous facets of 1q21+ in multiple myeloma: Pathogenesis, clinicopathological features, prognosis and clinical progress (Review)

Multiple myeloma (MM) is a malignant neoplasm characterized by the clonal proliferation of abnormal plasma cells (PCs) in the bone marrow and recurrent cytogenetic abnormalities. The incidence of MM worldwide is on the rise. 1q21+ has been found in ~30-40% of newly diagnosed MM (NDMM) patients.1q21+ is associated with the pathophysiological mechanisms of disease progression and drug resistance in MM. In the present review, the pathogenesis and clinicopathological features of MM patients with 1q21+ were studied, the key data of 1q21+ on the prognosis of MM patients were summarized, and the clinical treatment significance of MM patients with 1q21+ was clarified, in order to provide reference for clinicians to develop treatment strategies targeting 1q21+.

FAM72 family proteins as poor prognostic markers in clear cell renal carcinoma

Purpose

This study aimed to investigate the prognostic significance of the Family with Sequence Similarity 72 member (FAM72) gene family in clear cell renal carcinoma (ccRCC) using a bioinformatic approach.

Patients and methods

To investigate the association between FAM72 and ccRCC, we utilized various databases and analysis tools, including TCGA, GEPIA, Metscape, cBioPortal, and MethSurv. We conducted an analysis of FAM72 expression levels in ccRCC tissues compared to normal kidney tissues and performed univariate and multivariate Cox analysis to determine the relationship between FAM72 expression and patient prognosis. Furthermore, we carried out Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to identify enriched biological processes associated with FAM72 expression. Additionally, we analyzed immune cell infiltration and the level of methylation in ccRCC patients. Our bioinformatic analysis revealed that FAM72 expression levels were significantly higher in ccRCC tissues than in normal kidney tissues. High expression of FAM72 was associated with poor prognosis in ccRCC patients and was found to be an independent prognostic factor for ccRCC. GO and GSEA analyses indicated that FAM72 was enriched in biological processes related to mitosis, cell cycle, and DNA metabolism. Moreover, we found a significant correlation between FAM72 and immune cell infiltration and the level of methylation in ccRCC patients.

Conclusion

Our findings suggest that FAM72 could serve as an unfavorable prognostic molecular marker for ccRCC. A comprehensive understanding of FAM72 could provide crucial insights into tumor progression and prognosis.

The pan-cancer analysis of the oncogenic role of FAM72A as a BRCA prognostic biomarker and immunotherapeutic target

In this study, we first comprehensively investigated the expression profile, mutation status, and survival analysis of FAM72A as well as the correlation between FAM72A and DNA damage repair, methylation, and cell stemness analysis using bioinformatics techniques. In addition, we also analyzed the relationship between FAM72A and immune cell infiltration and pathway enrichment. The role of FAM72A in breast cancer (BC) was so conspicuous that we analyzed the prognostic significance and clinicopathological parameter's relevance of FAM72A in BC. We also validated biological functions by applying in vitro experiments. FAM72A was highly expressed in 26 types of a total of 31 cancers, while it expressed low levels in only five cancers. FAM72A expression was relative to clinical stages in nine cancers and has a significant difference in disease-free survival among 31 kinds of cancers. In addition, FAM72A has negatively correlated with cancer-associated fibroblast and endothelial cells in BC but positively correlated with follicular helper T cells. Univariate and multivariate cox regression analyses identified T, N, M, age, and FAM72A expression as independent influences on BC prognosis, so we created a nomogram to predict patient survival benefits. In in vitro experiments, we verified that downregulation of FAM72A not only inhibited cell proliferation, colony formation, cell migration, cell invasion, and G2/M cell cycle transition but also promoted apoptosis of breast invasive carcinoma cells. Our study discovered FAM72A as a clinically meaningful biomarker for prognostic predicting and a guiding target for immune treatment in BC.

Family with Sequence Similarity 72 (FAM72) - A prospective biomarker for poor prognosis in patients with oral squamous cell carcinoma

OBJECTIVE

To study the effect of FAM72 on the prognosis of patients with oral squamous cell carcinoma (OSCC) and to explore the relationship between FAM72 and OSCC.

DESIGN

We used a vast array of databases and analytical vehicles to assess the relation between FAM72 and OSCC, including The Cancer Genome Atlas (TCGA), Metascape, and MethSurv. We made a preliminary verification of OSCC lines and tissues by real time quantitative polymerase chain reaction (RT-qPCR).

RESULTS

FAM72 was higher in OSCC than in normal tissues. Analysis of univariate COX data indicated that elevated expression of FAM72A, FAM72B, and FAM72C in OSCC was related to poor overall survival. Moreover, FAM72B and FAM72C were independent of overall survival in multiple COX regression. FAM72A-D and its coexpressed genes in Metascape were analyzed by Gene Ontology (GO), they were enriched in cellular cycle, mitotic and DNA metabolism. Gene set enrichment analysis (GSEA) demonstrated an enrichment in pathways related to cell metabolism. Additionally, high FAM72 expression related to a worse prognosis in OSCC patients. FAM72A-D linked to the infiltration of tumor immune cell in OSCC patients. We found that methylation levels are likely linked to prognosis in OSCC patients. We used RT-qPCR to ascertain the differential FAM72B and FAM72C expression levels in cancer and paracancerous tissues of OSCC, human normal oral keratinocytes (HOK), and human tongue squamous cell carcinoma (Cal-33).

CONCLUSION

Our findings indicate that FAM72B and FAM72C are potential molecular markers of poor prognosis in OSCC and may act as novel targets for OSCC treatment strategies.

Genome-wide profiling of 5-hydroxymethylcytosines in circulating cell-free DNA reveals population-specific pathways in the development of multiple myeloma

Multiple myeloma (MM) and its precursors monoclonal gammopathy of undetermined significance (MGUS) and smoldering myeloma (SMM) are 2–3 times more common in African Americans (AA) than European Americans (EA). Although epigenetic changes are well recognized in the context of myeloma cell biology, the contribution of 5-hydroxymethylcytosines (5hmC) to racial disparities in MM is unknown. Using the 5hmC-Seal and next-generation sequencing, we profiled genome-wide 5hmC in circulating cell-free DNA (cfDNA) from 342 newly diagnosed patients with MM (n = 294), SMM (n = 18), and MGUS (n = 30). We compared differential 5hmC modifications between MM and its precursors among 227 EA and 115 AA patients. The captured 5hmC modifications in cfDNA were found to be enriched in B-cell and T-cell-derived histone modifications marking enhancers. Of the top 500 gene bodies with differential 5hmC levels between MM and SMM/MGUS, the majority (94.8%) were distinct between EA and AA and enriched with population-specific pathways, including amino acid metabolism in AA and mainly cancer-related signaling pathways in EA. These findings improved our understanding of the epigenetic contribution to racial disparities in MM and suggest epigenetic pathways that could be exploited as novel preventive strategies in high-risk populations.

Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Lung Adenocarcinoma by Comprehensive Bioinformatics Analysis and In Vitro Study

Family with sequence similarity 72B (FAM72B) has been characterized in the regulation of neuronal development. Nevertheless, the prognostic value of FAM72B expression and its function in the immune microenvironment of lung adenocarcinoma (LUAD) currently remains elusive. In this study, by adopting bioinformatics methodology and experimental verification, we found that FAM72B was upregulated in lung cancer tissues and cell lines, and a higher FAM72B level predicted an unfavorable clinical outcome in LUAD patients. The knockdown of FAM72B significantly inhibited cell proliferation, cell migration, and induced cell apoptosis in LUAD. The receiver operating characteristic curve suggested that FAM72B had a high predictive accuracy for the outcomes of LUAD. Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analyses confirmed that FAM72B-related genes were involved in cell proliferation and immune-response signaling pathway. Moreover, upregulated FAM72B expression was significantly associated with immune cell infiltration in the LUAD tumor microenvironment. Meanwhile, a potential ceRNA network was constructed to identify the lncRNA-AL360270.2/TMPO-AS1/AC125807.2/has-let-7a/7b/7c/7e/7f/FAM72B regulatory axis that regulates FAM72B overexpression in LUAD and is associated with a poor prognosis. We also confirmed that AL360270.2, TMPO-AS1, and AC125807.2 were significantly upregulated in LUAD cell lines than in human bronchial epithelial cells. In conclusion, FAM72B may serve as a novel biomarker in predicting the clinical prognosis and immune status for lung adenocarcinoma.

Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models

For high dimensional gene expression data, one important goal is to identify a small number of genes that are associated with progression of the disease or survival of the patients. In this paper, we consider the problem of variable selection for multivariate survival data. We propose an estimation procedure for high dimensional accelerated failure time (AFT) models with bivariate censored data. The method extends the Buckley-James method by minimizing a penalized [Formula: see text] loss function with a penalty function induced from a bivariate spike-and-slab prior specification. In the proposed algorithm, censored observations are imputed using the Kaplan-Meier estimator, which avoids a parametric assumption on the error terms. Our empirical studies demonstrate that the proposed method provides better performance compared to the alternative procedures designed for univariate survival data regardless of whether the true events are correlated or not, and conceptualizes a formal way of handling bivariate survival data for AFT models. Findings from the analysis of a myeloma clinical trial using the proposed method are also presented.

Primary Plasma Cell Leukemia displaying t(11;14) have specific genomic, transcriptional and clinical feature

Primary plasma cell leukemia (pPCL) is an aggressive form of multiple myeloma (MM) that has not benefited from recent therapeutic advances in the field. Because very rare and heterogeneous, it remains poorly understood at the molecular level. To address this issue, we performed DNA and RNA sequencing of sorted plasma cells from a large cohort of 90 newly diagnosed pPCL, and compared to MM. We observed that pPCL presents a specific genomic landscape with a high prevalence of t(11;14) (about half) and high-risk genomic features such as del(17p), gain 1q, del(1p32). In addition, pPCL displays a specific transcriptome when compared to MM. We then aimed at specifically characterize pPCL with t(11;14). We observed that this sub-entity displayed significantly fewer adverse cytogenetic abnormalities. This translated into better overall survival when compared to pPCL without t(11;14) (39.2 months vs 17.9 months, p=0.002). Finally, pPCL with t(11;14) displayed a specific transcriptome, including differential expression of BCL2 family members. This study is the largest series of patients with pPCL reported so far.

Whole-genome Sequencing of Follicular Thyroid Carcinomas Reveal Recurrent Mutations in MicroRNA Processing Subunit DGCR8

BACKGROUND

The genomic and transcriptomic landscape of widely invasive follicular thyroid carcinomas (wiFTCs) and Hürthle cell carcinoma (HCC) are poorly characterized, and subsets of these tumors lack information on genetic driver events.

OBJECTIVE

The aim of this study was to bridge this gap.

METHODS

We performed whole-genome and RNA sequencing and subsequent bioinformatic analyses of 11 wiFTCs and 2 HCCs with a particularly poor prognosis, and matched normal tissue.

RESULTS

All wiFTCs exhibited one or several mutations in established thyroid cancer genes, including TERT (n = 4), NRAS (n = 3), HRAS, KRAS, AKT, PTEN, PIK3CA, MUTYH, TSHR, and MEN1 (n = 1 each). MutSig2CV analysis revealed recurrent somatic mutations in FAM72D (n = 3, in 2 wiFTCs and in a single HCC), TP53 (n = 3, in 2 wiFTCs and a single HCC), and EIF1AX (n = 3), with DGCR8 (n = 2) as borderline significant. The DGCR8 mutations were recurrent p.E518K missense alterations, known to cause familial multinodular goiter via disruption of microRNA (miRNA) processing. Expression analyses showed reduced DGCR8 messenger RNA expression in FTCs in general, and the 2 DGCR8 mutants displayed a distinct miRNA profile compared to DGCR8 wild-types. Copy number analyses revealed recurrent gains on chromosomes 4, 6, and 10, and fusion gene analyses revealed 27 high-quality events. Both HCCs displayed hyperploidy, which was fairly unusual in the FTC cohort. Based on the transcriptome data, tumors amassed in 2 principal clusters.

CONCLUSION

We describe the genomic and transcriptomic landscape in wiFTCs and HCCs and identify novel recurrent mutations and copy number alterations with possible driver properties and lay the foundation for future studies.

One Omics Approach Does Not Rule Them All: The Metabolome and the Epigenome Join Forces in Haematological Malignancies

Aberrant DNA methylation, dysregulation of chromatin-modifying enzymes, and microRNAs (miRNAs) play a crucial role in haematological malignancies. These epimutations, with an impact on chromatin accessibility and transcriptional output, are often associated with genomic instability and the emergence of drug resistance, disease progression, and poor survival. In order to exert their functions, epigenetic enzymes utilize cellular metabolites as co-factors and are highly dependent on their availability. By affecting the expression of metabolic enzymes, epigenetic modifiers may aid the generation of metabolite signatures that could be utilized as targets and biomarkers in cancer. This interdependency remains often neglected and poorly represented in studies, despite well-established methods to study the cellular metabolome. This review critically summarizes the current knowledge in the field to provide an integral picture of the interplay between epigenomic alterations and the cellular metabolome in haematological malignancies. Our recent findings defining a distinct metabolic signature upon response to enhancer of zeste homolog 2 (EZH2) inhibition in multiple myeloma (MM) highlight how a shift of preferred metabolic pathways may potentiate novel treatments. The suggested link between the epigenome and the metabolome in haematopoietic tumours holds promise for the use of metabolic signatures as possible biomarkers of response to treatment.

Decoding DNA methylation in epigenetics of multiple myeloma

Dysregulation of DNA methylation in B cells has been observed during their neoplastic transformation and therefore closely associated with various B-cell malignancies including multiple myeloma (MM), a malignancy of terminally differentiated plasma cells. Emerging evidence has unveiled pronounced alterations in DNA methylation in MM, including both global and gene-specific changes that can affect genome stability and gene transcription. Moreover, dysregulated expression of DNA methylation-modifying enzymes has been related with myelomagenesis, disease progression, and poor prognosis. However, the functional roles of the epigenetic abnormalities involving DNA methylation in MM remain elusive. In this article, we review current understanding of the alterations in DNA methylome and DNA methylation modifiers in MM, particularly focusing on DNA methyltransferases (DNMTs) and tet methylcytosine dioxygenases (TETs). We also discuss how these DNA methylation modifiers may be regulated and function in MM cells, therefore providing a rationale for developing novel epigenetic therapies targeting DNA methylation in MM.

Addition of Activated Carbon into a Cattle Diet to Mitigate GHG Emissions and Improve Production

Globally, the most problematic greenhouse gas (GHG) emissions of ruminant livestock is methane (CH4), with a global warming potential 25 times that of carbon dioxide. This work considers the emissions and production effects of powdered activated carbon (PAC) at 0.5% by dry matter (DM) on methanogenic rumen flora as the major source of dairy cattle enteric methane emissions. In total, 180 dairy cattle located in Brymaroo, Queensland (QLD), Australia, were studied in a three-cycle repeated measures ANOVA format with a 4 week primary interval. Emissions eructated during milking and in faecal deposits were measured, and in addition, 16S rRNA gene sequencing was performed to determine the collective populations of prokaryotic bacteria and archaea as well methanogenic communities for each treatment. Moreover, 0.5% PAC addition reduced CH4 emissions by 30–40% and CO2 emissions by 10%, while improving daily milk production by 3.43%, milk protein by 2.63% and milk fat by 6.32%, on average for the herd (p < 0.001 in all cases). rRNA gene sequencing showed populations of methanogenic flora decreased by 30% on average with a corresponding increase in the nonmethanogenic species. We strongly advocate further on-farm trials with the dietary addition of PAC in ruminant diets to mitigate emissions while maintaining or improving productivity.

The Anticancer Effects of FDI-6, a FOXM1 Inhibitor, on Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) presents an important clinical challenge, as it does not respond to endocrine therapies or other available targeting agents. FOXM1, an oncogenic transcriptional factor, has reported to be upregulated and associated with poor clinical outcomes in TNBC patients. In this study, we investigated the anti-cancer effects of FDI-6, a FOXM1 inhibitor, as well as its molecular mechanisms, in TNBC cells. Two TNBC cell lines, MDA-MB-231 and HS578T, were used in this study. The anti-cancer activities of FDI-6 were evaluated using various 2D cell culture assays, including Sulforhodamine B (SRB), wound healing, and transwell invasion assays together with 3D spheroid assays, mimicking real tumour structural properties. After treatment with FDI-6, the TNBC cells displayed a significant inhibition in cell proliferation, migration, and invasion. Increased apoptosis was also observed in the treated cells. In addition, we found that FDI-6 lead to the downregulation of FOXM1 and its key oncogenic targets, including CyclinB1, Snail, and Slug. Interestingly, we also found that the FDI-6/Doxorubicin combination significantly enhanced the cytotoxicity and apoptotic properties, suggesting that FDI-6 might improve chemotherapy treatment efficacy and reduce unwanted side effects. Altogether, FDI-6 exhibited promising anti-tumour activities and could be developed as a newly effective treatment for TNBC.

Correlations between apparent diffusion coefficient values of WB-DWI and clinical parameters in multiple myeloma

Background

Whole-body diffusion-weighted imaging (WB-DWI) is a method for evaluating bone marrow infiltration in multiple myeloma (MM). This study seeks to elucidate the correlation between the apparent diffusion coefficient (ADC) value and some selected clinical parameters.

Methods

A total of 101 Chinese patients with MM who had undergone WB-DWI from May 2017 to May 2019 were enrolled in this study. The ADC values of the MM lesions and the clinical parameters were quantified at the first (baseline) visit and after four-course induction chemotherapy. Multiple linear regression and logistic analyses were carried out to find the implicit inherent relationships within the patients’ data.

Results

The paired Wilcoxon test showed that the ADC values at the baseline visit (ADC₀) were significantly lower than the values after four-course induction chemotherapy (ADC_{4 c}) (p < 0.001), including different therapeutic responses. The Revised International Staging System (RISS) stage, type of MM, and β2-microglobulin (β2-MG) were predictors of clinically significant increases or decreases in the ADC values (p < 0.05). Multiple linear regression showed that the ADC₀ was negatively associated with β2-MG (p < 0.001) and immunoglobulin heavy chain gene rearrangement (p = 0.012), while the RISS Stage III (p = 0.001), type IgG λ (p = 0.005), and albumin were negatively associated with ADC_{4 c} (p = 0.010). The impacts of the therapeutic response were associated with ADC₀ and immunoglobulin heavy chain gene rearrangement (p < 0.001).

Conclusion

The ADC values of WB-DWI may be associated with clinical parameters of MM including the fluorescence in situ hybridization result, and may be useful in the prognosis of patients with MM.

Trial Registration: ChiCTR2000029587

FAM72 serves as a biomarker of poor prognosis in human lung adenocarcinoma

FAM72A-D promote the self-renewal of neural progenitor cells. There is accumulating evidence that FAM72 promotes tumorigenicity. However, its effects in lung adenocarcinoma (LUAD) have not been determined. Thus, we evaluated the prognostic value of FAM72A-D in LUAD using bioinformatics approaches. In particular, we evaluated the relationship between FAM72 and LUAD using a wide range of databases and analysis tools, including TCGA, GEO, GEPIA, Metascape, cBioPortal, and MethSurv. Compared with its expression in normal lung tissues, FAM72 expression was significantly increased in LUAD tissues. A univariate Cox analysis showed that high FAM72 expression levels were correlated with a poor OS in LUAD. Additionally, FAM72 expression was independently associated with OS through a multivariate Cox analysis. GO and GSEA revealed enrichment in mitotic nuclear division and cell cycle. Moreover, high FAM72 expression was associated with poor survival. An analysis of immune infiltration showed that FAM72 is correlated with immune cell infiltration. Finally, we found that the methylation level was associated with prognosis in patients with LUAD. In summary, these results indicate that FAM72 is a potential molecular marker for poor prognosis in LUAD and provide additional insight for the development of therapies and prognostic markers.

FAM72, Glioblastoma Multiforme (GBM) and Beyond

Simple Summary

Glioblastoma multiforme (GBM) is a serious and aggressive cancer disease that has not allowed scientists to rest for decades. In this review, we consider the new gene pair |-SRGAP2–FAM72-| and discuss its role in the cell cycle and the possibility of defining new therapeutic approaches for the treatment of GBM and other cancers via this gene pair |-SRGAP2–FAM72-|.

Abstract

Neural stem cells (NSCs) offer great potential for regenerative medicine due to their excellent ability to differentiate into various specialized cell types of the brain. In the central nervous system (CNS), NSC renewal and differentiation are under strict control by the regulation of the pivotal SLIT-ROBO Rho GTPase activating protein 2 (SRGAP2)—Family with sequence similarity 72 (FAM72) master gene (i.e., |-SRGAP2–FAM72-|) via a divergent gene transcription activation mechanism. If the gene transcription control unit (i.e., the intergenic region of the two sub-gene units, SRGAP2 and FAM72) gets out of control, NSCs may transform into cancer stem cells and generate brain tumor cells responsible for brain cancer such as glioblastoma multiforme (GBM). Here, we discuss the surveillance of this |-SRGAP2–FAM72-| master gene and its role in GBM, and also in light of FAM72 for diagnosing various types of cancers outside of the CNS.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

DNA hydroxymethylation is associated with disease severity and persists at enhancers of oncogenic regions in multiple myeloma

Background

Multiple myeloma (MM) is a heterogeneous plasma cell malignancy that remains challenging to cure. Global hypomethylation correlates with an aggressive phenotype of the disease, while hypermethylation is observed at particular regions of myeloma such as B cell-specific enhancers. The recently discovered active epigenetic mark 5-hydroxymethylCytosine (5hmC) may also play a role in tumor biology; however, little is known about its level and distribution in myeloma. In this study, we investigated the global level and the genomic localization of 5hmC in myeloma cells from 40 newly diagnosed patients, including paired relapses, and of control individuals.

Results

Compared to normal plasma cells, we found global 5hmC levels to be lower in myeloma (P < 0.001). Higher levels of 5hmC were found in lower grades of the International Staging System prognostic index (P < 0.05) and tend to associate with a longer overall survival (P < 0.1). From the hydroxymethylome data, we observed that the remaining 5hmC is organized in large domains overlapping with active chromatin marks and chromatin opening. We discovered that 5hmC strongly persists at key oncogenic genes such as CCND1, CCND2 and MMSET and characterized domains that are specifically hydroxymethylated in myeloma subgroups. Novel 5hmC-enriched domains were found at putative enhancers of CCND2 and MYC in newly diagnosed patients.

Conclusions

5hmC level is associated with clinical aspects of MM. Mapping 5hmC at a genome-wide level provides insights into the disease biology directly from genomic DNA, which makes it a potent mark to study epigenetics on large patient cohorts.

Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis

Aim

In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer.

Method

We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the “Rbsurv” package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032.

Results

FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 − 0.737∗TACR2 − 0.651∗PLEK2 − 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC = 0.6039) and GSE21032 GPL5188 (140 samples; AUC = 0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups.

Conclusion

A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.