Integrative Analysis Of Multi-omics Data Research Articles

Evidence Based on an Integrative Analysis of Multi-Omics Data on METTL7A as a Molecular Marker in Pan-Cancer.

Methyltransferase-like protein 7A (METTL7A), an RNA N6-methyladenosine (m6A) methyltransferase, has attracted much attention as it has been found to be closely associated with various types of tumorigenesis and progression. This study provides a comprehensive assessment of METTL7A from a pan-cancer perspective using multi-omics data. The gene ontology enrichment analysis of METTL7A-binding proteins revealed a close association with methylation and lipid metabolism. We then explored the expression of METTL7A in normal tissues, cell lines, different subtypes and cancers, and found that METTL7A was differentially expressed in various cancer species, tumor molecular subtypes and immune subtypes. Evaluation of the diagnostic and prognostic value of METTL7A in pan-cancer revealed that METTL7A had high accuracy in tumor prediction. Moreover, the low expression of METTL7A significantly correlated with the poor prognosis, including kidney renal clear cell carcinoma (KIRC), mesothelioma and sarcoma, indicating that METTL7A could be a potential biomarker for tumor diagnosis and prognosis. We focused on KIRC after pre-screening and analyzed its expression and prognostic value in various clinical subgroups. We found that METTL7A was significantly related to tumor stage, metastasis stage, pathologic stage, primary therapy outcome, histologic grade and gender, and that low METTL7A expression was associated with poorer outcomes. Finally, we analyzed the immune infiltration and co-expressed genes of METTL7A as well as the differentially expressed genes in the high and low expression groups. In conclusion, METTL7A is a better molecular marker for pan-cancer diagnosis and prognosis and has high potential as a diagnostic and prognostic biomarker for KIRC.

Integrative analysis of multi-omics data reveals inhibition of RB1 signaling promotes apatinib resistance of hepatocellular carcinoma.

Although apatinib is a promising drug for the treatment of liver cancer, the underlying drug resistance mechanism is still unclear. Here, we constructed apatinib-resistant HepG2 cells. We then characterized the epigenomic, transcriptomic, and proteomic landscapes both in apatinib-resistant and non-resistant HepG2 cells. Differential expression, ATAC-seq, and proteomic data analyses were performed. We found that the cell cycle related protein RB1 may play an essential role in the process of apatinib resistant to hepatocarcinoma. Moreover, there were extensive variations at the transcriptome, epigenetic, and proteomic level. Finally, quantitative PCR (qPCR) and western blot analysis showed that expression level of RB1 in apatinib-resistant cell as well as the samples of patients in progressive disease were significantly lower than that in controls. Those results also showed that the RB1 pathway inhibitors CDK2-IN-73 and Palbociclib could relieve the resistance of apatinib resistant cells. Our results further enhance our understanding of the anti-tumorigenic and anti-angiogenic efficacy of apatinib in liver cancer and provide a novel perspective regarding apatinib resistance. Furthermore, we proved that CDKN2B inhibition of RB1 signaling promoted apatinib resistance in hepatocellular carcinoma. Those findings have greatly important biological significance for the resistance of apatinib and the treatment of liver cancer.

Nasal Transcriptome and Epigenome Analysis Identifies the Pathogenic Features of Aspirin-Exacerbated Respiratory Disease.

Dysregulation of the arachidonic acid metabolic pathway is the most widely known pathomechanism of aspirin-exacerbated respiratory disease (AERD). This study aimed to perform integrative analysis of transcriptomic and epigenomic profiling with network analysis to determine the novel pathogenic features of AERD. Ten patients with asthma including 5 patients with AERD and another 5 patients with aspirin tolerant asthma (ATA) were enrolled. Nasal scraping was performed and nasal mucosa was used in omics profiling. Peripheral eosinophil counts, sputum eosinophil counts, fractional exhaled nitric oxide levels, and pulmonary function test results were evaluated. Differentially expressed genes (DEGs), differentially methylated probes (DMPs) and differentially correlated genes (DCGs) between patients with AERD and those with ATA were analyzed. Network analysis using ingenuity pathway analysis (IPA) was performed to determine the gene connection network and signaling pathways. In total, 1,736 DEGs, 1,401 DMPs, and 19 pairs for DCGs were identified. Among DCGs, genes related to vesicle transport (e.g., RAB3B and STX2) and sphingolipid dysregulation (e.g., SMPD3) were found to be hypo-methylated and up-regulated in AERD. Using the canonical pathway analysis of IPA with 78 asthma-related DEGs, signaling pathways of T helper cell differentiation/activation and Fcε receptor I were generated. Up-regulation of RORγt and FcER1A were noted in AERD. Gene expression levels of RAB3B, SYNE1, STX2, SMPD3 and RORγt were significantly associated with sputum eosinophil counts. Quantitative real-time polymerase chain reaction was performed and mRNA expression levels of STX2, SMPD3, RORγt, and FcER1A were significantly higher in AERD compared to ATA. Distinct pathogenic features were identified by using integrative multi-omics data analysis in patients with AERD.

Integrative analysis of multi-omics data for liquid biopsy.

The innovation of liquid biopsy holds great potential to revolutionise cancer management through early diagnosis and timely treatment of cancer. Integrative analysis of different tumour-derived omics data (such as genomics, epigenetics, fragmentomics, and proteomics) from body fluids for cancer detection and monitoring could outperform the analysis of single modality data alone. In this review, we focussed on the discussion of early cancer detection and molecular residual disease surveillance based on multi-omics data of blood. We summarised diverse types of tumour-derived components, current popular platforms for profiling cancer-associated signals, machine learning approaches for joint analysis of liquid biopsy data, as well as multi-omics-based early detection of cancers, molecular residual disease monitoring, and treatment response surveillance. We also discussed the challenges and future directions of multi-omics-based liquid biopsy. With the development of both experimental protocols and computational methods dedicated to liquid biopsy, the implementation of multi-omics strategies into the clinical workflow will likely benefit the clinical management of cancers including decision-making guidance and patient outcome improvement.

Integrative analysis of multiomics data identifies selenium-related gene ALAD associating with keshan disease.

Keshan disease is an endemic fatal dilated cardiomyopathy that can cause heart enlargement, heart failure, and cardiogenic death. Selenium deficiency is considered to be the main cause of Keshan disease. However, the molecular mechanism underlying Keshan disease remains unclear. Our whole-exome sequencing from 68 patients with Keshan disease and 100 controls found 199 candidate genes by gene-level burden tests. Interestingly, using multiomics data, the selenium-related gene ALAD (δ-aminolevulinic acid dehydratase) was the only candidate causative gene identified by three different analysis approaches. Based on single-cell transcriptome data, ALAD was highly expressed in cardiomyocytes and double mutations of human ALAD dramatically reduced its enzyme activity in vitro compared to negative control. Functional analysis of ALAD inhibition in mice resulted in a Keshan phenotype with left ventricular enlargement and cardiac dysfunction, whereas administration of sodium selenite markedly reversed the changes caused by ALAD inhibition. In addition, sodium selenite reversed Keshan phenotypes by affecting energy metabolism and mitochondrial function in mice as shown by the transcriptomic and proteomic data and the ultrastructure of cardiac myocytes. Our findings are the first to demonstrate that the selenium-related gene ALAD is essential for cardiac function by maintaining normal mitochondrial activity, providing strong molecular evidence supporting the hypothesis of selenium deficiency in Keshan disease. These results identified ALAD as a novel target for therapeutic intervention in Keshan disease and Keshan disease-related dilated cardiomyopathy.

Editorial: Statistical Data Science - Theory and Applications in Analyzing Omics Data

field forward.The next era's targets in data science method development and application to omics data should be aligned 49 with the following two interconnected aims: (1) integrated analysis of multi-omics data. We request novel 50 analysis pipelines, which could employ the collected measurements from all levels of the human body, Since the data often requires cloud storage, the platform should be designed to easily access data in 55 the cloud server and perform online analysis by pushing buttons. In summary, we need more advanced 56 integrated methods and a more convenient application platform for omics data to move the fields forwards.

Exercise-derived peptide protects against pathological cardiac remodeling.

SummaryBackgroundExercise training protects the heart against pathological cardiac remodeling and confers cardioprotection from heart failure. However, the underlying mechanism is still elusive.MethodsAn integrative analysis of multi-omics data of the skeletal muscle in response to exercise is performed to search for potential exerkine. Then, CCDC80tide is examined in humans after acute exercise. The role of CCDC80tide is assessed in a mouse model of hypertensive cardiac remodeling and in hypertension-mediated cell injury models. The transcriptomic analysis and immunoprecipitation assay are conducted to explore the mechanism.FindingsThe coiled-coil domain-containing protein 80 (CCDC80) is found strongly positively associated with exercise. Interestingly, exercise stimuli induce the secretion of C-terminal CCDC80 (referred as CCDC80tide hereafter) via EVs-encapsulated CCDC80tide into the circulation. Importantly, cardiac-specific expression of CCDC80tide protects against angiotensin II (Ang II)-induced cardiac hypertrophy and fibrosis in mice. In in vitro studies, the expression of CCDC80tide reduces Ang II-induced cardiomyocyte hypertrophy, cardiac microvascular endothelial cell (CMEC) inflammation, and mitigated vascular smooth muscle cell (VSMC) proliferation and collagen formation. To understand the cardioprotective effect of CCDC80tide, a transcriptomic analysis reveals a dramatic inhibition of the STAT3 (Signal transducer and activator of transcription 3) signaling pathway in CCDC80tide overexpressing cells. Mechanistically, CCDC80tide selectively interacts with the kinase-active form of JAK2 (Janus kinase 2) and consequently inhibits its kinase activity to phosphorylate and activate STAT3.InterpretationThe results provide new insights into exercise-afforded cardioprotection in pathological cardiac remodeling and highlight the therapeutic potential of CCDC80tide in heart failure treatment.FundingThis work was supported by the National Natural Science Foundation of China [Grant/Award Numbers: 81770428, 81830010, 82130012, 81900438, 82100447); Shanghai Science and Technology Committee [Grant/Award Numbers: 21S11903000, 19JC1415702]; Emerging and Advanced Technology Programs of Hospital Development Center of Shanghai [Grant/Award Number: SHDC12018129]; China Postdoctoral Science Foundation [2021M692108]; and China National Postdoctoral Program for Innovative Talents [BX20200211].

Flexible copula model for integrating correlated multi-omics data from single-cell experiments.

With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA sequencing, chromatin accessibility, and DNA methylation at different germ layers during mousegastrulation.

A Novel DNA Repair Gene Signature for Immune Checkpoint Inhibitor-Based Therapy in Gastric Cancer.

Gastric cancer is a heterogeneous group of diseases with only a fraction of patients responding to immunotherapy. The relationships between tumor DNA damage response, patient immune system and immunotherapy have recently attracted attention. Accumulating evidence suggests that DNA repair landscape is a significant factor in driving response to immune checkpoint blockade (ICB) therapy. In this study, to explore new prognostic and predictive biomarkers for gastric cancer patients who are sensitive and responsive to immunotherapies, we developed a novel 15-DNA repair gene signature (DRGS) and its related scoring system and evaluated the efficiency of the DRGS in discriminating different molecular and immune characteristics and therapeutic outcomes of patients with gastric adenocarcinoma, using publicly available datasets. The results demonstrated that DRGS high score patients showed significantly better therapeutic outcomes for ICB compared to DRGS low score patients (p < 0.001). Integrated analysis of multi-omics data demonstrated that the patients with high DRGS score were characteristic of high levels of anti-tumor lymphocyte infiltration, tumor mutation burden (TMB) and PD-L1 expression, and these patients exhibited a longer overall survival, as compared to the low-score patients. Results obtained from HPA and IHC supported significant dysregulation of the genes in DRGS in gastric cancer tissues, and a positive correlation in protein expression between DRGS and PD-L1. Therefore, the DRGS scoring system may have implications in tailoring immunotherapy in gastric cancers. A preprint has previously been published (Yuan et al., 2021).

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

BackgroundObesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels.Subjects and methodsTherefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans.ResultsBy applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases.ConclusionsThe integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Integrated analysis of multi-omics data to establish a hypoxia-related prognostic model in osteosarcoma

Integrated analysis of multi-omics data to establish a hypoxia-related prognostic model in osteosarcoma

Identification of two immune-related risk score signatures through integrated analysis of multi-omics data in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Immunotherapy has become a major treatment for advanced HCC, but the therapeutic effects remain unsatisfactory. In this study, we constructed an immune cell risk score (ICS) and an immune cell-related gene risk score (ICRGS) for the prognosis prediction of HCC through integrated analysis of bulk and single-cell RNA (scRNA) sequencing data. These two risk score signatures both showed good predictive values in the training and validation cohorts. The potential interactions among these prognostic immune cell types were elucidated by cell–cell communication analysis. The results of enrichment analysis and gene set enrichment analysis (GSEA) of the prognostic genes showed that metabolic-related processes were involved in the immune response of HCC. Furthermore, the results of correlation analyses further confirmed the hub genes that were strongly correlated with immune cells. Finally, potential therapeutic drugs targeting these hub genes were screened by CellMiner based on NCI-60 cell line set. Taken together, two useful models for the prognosis prediction of HCC patients were constructed in this study. The functional differences between the two groups of HCC patients separated by ICS or ICRGS provide fundamental knowledge for finding synergistic therapeutic targets for HCC immunotherapy.

GAIT-GM integrative cross-omics analyses reveal cholinergic defects in a C. elegans model of Parkinson\u2019s disease

Parkinson’s disease (PD) is a disabling neurodegenerative disorder in which multiple cell types, including dopaminergic and cholinergic neurons, are affected. The mechanisms of neurodegeneration in PD are not fully understood, limiting the development of therapies directed at disease-relevant molecular targets. C. elegans is a genetically tractable model system that can be used to disentangle disease mechanisms in complex diseases such as PD. Such mechanisms can be studied combining high-throughput molecular profiling technologies such as transcriptomics and metabolomics. However, the integrative analysis of multi-omics data in order to unravel disease mechanisms is a challenging task without advanced bioinformatics training. Galaxy, a widely-used resource for enabling bioinformatics analysis by the broad scientific community, has poor representation of multi-omics integration pipelines. We present the integrative analysis of gene expression and metabolite levels of a C. elegans PD model using GAIT-GM, a new Galaxy tool for multi-omics data analysis. Using GAIT-GM, we discovered an association between branched-chain amino acid metabolism and cholinergic neurons in the C. elegans PD model. An independent follow-up experiment uncovered cholinergic neurodegeneration in the C. elegans model that is consistent with cholinergic cell loss observed in PD. GAIT-GM is an easy to use Galaxy-based tool for generating novel testable hypotheses of disease mechanisms involving gene-metabolite relationships.

DLRAPom: a hybrid pipeline of Optimized XGBoost-guided integrative multiomics analysis for identifying targetable disease-related lncRNA-miRNA-mRNA regulatory axes.

The lack of a reliable and easy-to-operate screening pipeline for disease-related noncoding RNA regulatory axis is a problem that needs to be solved urgently. To address this, we designed a hybrid pipeline, disease-related lncRNA–miRNA–mRNA regulatory axis prediction from multiomics (DLRAPom), to identify risk biomarkers and disease-related lncRNA–miRNA–mRNA regulatory axes by adding a novel machine learning model on the basis of conventional analysis and combining experimental validation. The pipeline consists of four parts, including selecting hub biomarkers by conventional bioinformatics analysis, discovering the most essential protein-coding biomarkers by a novel machine learning model, extracting the key lncRNA–miRNA–mRNA axis and validating experimentally. Our study is the first one to propose a new pipeline predicting the interactions between lncRNA and miRNA and mRNA by combining WGCNA and XGBoost. Compared with the methods reported previously, we developed an Optimized XGBoost model to reduce the degree of overfitting in multiomics data, thereby improving the generalization ability of the overall model for the integrated analysis of multiomics data. With applications to gestational diabetes mellitus (GDM), we predicted nine risk protein-coding biomarkers and some potential lncRNA–miRNA–mRNA regulatory axes, which all correlated with GDM. In those regulatory axes, the MALAT1/hsa-miR-144-3p/IRS1 axis was predicted to be the key axis and was identified as being associated with GDM for the first time. In short, as a flexible pipeline, DLRAPom can contribute to molecular pathogenesis research of diseases, effectively predicting potential disease-related noncoding RNA regulatory networks and providing promising candidates for functional research on disease pathogenesis.

Abstract PD1-05: Targeting the FRA1-dependent transcriptional nexus in high FOXA1-driven endocrine-resistant and metastatic breast cancer

Abstract Background: Aberrant activation of the pioneer transcription factor (TF) FOXA1 contributes to endocrine resistance and metastasis in ER+ breast cancer (BC) by promoting genome-wide enhancer and transcriptional reprogramming that engages the AP-1 TF complex. Identification of central transcriptional nexuses in this deregulated network is key for developing new therapeutic interventions focusing on transcriptional programs. We previously identified FRA1, among the AP-1 components, as the top super-enhancer target forming a FOXA1/FRA1-centered transcriptional axis to activate genes enriched in luminal B-subtype BC and ER+ metastases. To further dissect the FOXA1/FRA1-centered transcriptional axis and its potential therapeutic role, we employed integrative multi-omics data analysis and functional studies targeting FRA1 using our MCF7-parental (P) and FOXA1-amplifed tamoxifen-resistant (TamR) preclinical models. Methods: RNA-seq data were obtained in MCF7-P and TamR cells with FOXA1 and/or AP-1 (FRA1 and c-Jun) perturbation via overexpression and/or si/shRNA knockdown (KD). Differential gene expression was analyzed using DESeq2 or the limma-voom R package. ChIP-seq-based genome-wide FOXA1 binding sites were further refined by intersection with promoter-tethered regions (PTRs) denoting Hi-C-mapped chromatin looping. Genomic binding of FOXA1/c-Jun and H3K27ac modification at target gene loci were aligned and visualized by IGV. Significance of gene set enrichment was determined using a chi-square test adjusted for multiple comparisons. Clinical relevance was examined using an RNA-seq dataset of ER+ metastatic BC (SABCS19-GS2-02). Clonogenicity, soft agar, and wound-healing assays were performed using MCF7-TamR cell derivatives engineered for doxycycline (Dox)-inducible KD of FRA1 or NS shRNA. Significance of the KD effects in functional assays was determined using a linear mixed-effects model. Results: We found that FRA1 and the two embryonic TFs SOX9 and KLF4 that harbor FOXA1-bound PTRs were commonly down-regulated in TamR cells upon KD of FOXA1, FRA1, or c-Jun. We observed increased c-Jun binding at the FOXA1-bound super-enhancer and the PTR looping to the FRA1 gene locus in TamR vs. P cells, suggesting a feed-forward mechanism by which FRA1 transcription is strengthened by the AP-1-engaged super-enhancer in TamR cells. Among the FOXA1-activated genes in TamR cells, a subset of secretory protein-encoding genes was more enriched in the genes commonly dependent on both FRA1 and c-Jun, vs. the genes depending on either FRA1 or c-Jun alone. This FOXA1/FRA1/c-Jun-activated secretome is enriched for multiple biological processes engaged in tumor metastasis and associated microenvironmental niches, and was upregulated in clinical ER+ metastases vs. primary tumors. A larger proportion of this secretome, including CXCL8 and S100P, also relies on ER preferentially in TamR vs. P cells (40% vs. 16%, respectively). FRA1 KD using two different shRNA sequences in TamR cells reduced SOX9 and KLF4 expression levels, and significantly diminished clonogenicity, soft agar colony formation, and wound healing, compared to the control NS KD. Conclusions: Our integrative bioinformatics analyses reveal a feed-forward mechanism on FRA1 activation in amplifying high-FOXA1-induced transcriptional reprogramming in endocrine resistance. A prometastatic secretome activated by FRA1/c-Jun may represent a main transcriptional output of the FOXA1/FRA1-dependent nexus in promoting ER+ disease progression. These identified key transcriptional nexuses may present network hotspots susceptible to therapeutic interventions in which FRA1 inhibition could be used as a new transcriptional program-oriented therapy to treat advanced ER+ BC. Citation Format: Chia Chia Liu, Lanfang Qin, Carmine De Angelis, Sarmistha Nanda, Resel Pereira, Martin J. Shea, Agostina Nardone, Rinath Jeselsohn, Ofir Cohen, Nikhil Wagle, Zhijie Liu, Mothaffar F. Rimawi, C. Kent Osborne, Rachel Schiff, Xiaoyong Fu. Targeting the FRA1-dependent transcriptional nexus in high FOXA1-driven endocrine-resistant and metastatic breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD1-05.

The CTCF/LncRNA-PACERR complex recruits E1A binding protein p300 to induce pro-tumour macrophages in pancreatic ductal adenocarcinoma via directly regulating PTGS2 expression.

BackgroundTumour‐associated macrophages (TAMs) play an important role in promoting the progression of pancreatic ductal adenocarcinoma (PDAC). Here, we aimed to study the epigenetic mechanisms in regulating pro‐tumour M2‐polarised TAMs in the PDAC tumour microenvironment.MethodsThis study was conducted based on ex vivo TAMs isolated from PDAC tissues and in vitro THP1‐derived TAM model. RNA‐sequencing (RNA‐seq), assay for transposase‐accessible chromatin with sequencing and chromatin immunoprecipitation sequencing were performed to investigate gene expression, chromatin accessibility, transcription factor binding sites and histone modifications. Gene knockdown in THP1‐derived TAMs was performed with lentivirus, and the impact of THP1‐derived TAMs on invasion and metastasis ability of PDAC cells were investigated with in vitro and in vivo functional assays. RNA‐chromatin interaction was analysed by chromatin isolation through RNA purification with sequencing. RNA‐protein interaction was studied by RNA immunoprecipitation and RNA pull‐down.ResultsOur data showed that the transcription factor CTCF (CCCTC‐binding factor) was highly expressed in TAMs and predicted to be significantly enriched in hyper‐accessible chromatin regions when compared to monocytes. High infiltration of CTCF+ TAMs was significantly associated with poor prognosis in PDAC patients. Knockdown of CTCF in THP1‐derived TAMs led to the down‐regulation of specific markers for M2‐polarised TAMs, including CD206 and CD163. When THP1‐derived TAMs with CTCF knockdown, they showed a decreased ability of invasion and metastasis. Further integrative analysis of multi‐omics data revealed that prostaglandin‐endoperoxide synthase 2 (PTGS2) and PTGS2 antisense NF‐κB1 complex‐mediated expression regulator RNA (PACERR) were critical downstream targets of CTCF and positively correlated with each other, which are closely situated on a chromosome. Knockdown of PACERR exhibited a similar phenotype as observed in CTCF knockdown THP1‐derived TAMs. Moreover, PACERR could directly bind to CTCF and recruit histone acetyltransferase E1A binding protein p300 to the promoter regions of PACERR and PTGS2, thereby enhancing histone acetylation and gene transcription, promoting the M2 polarization of TAMs in PDAC.ConclusionsOur study demonstrated a novel epigenetic regulation mechanism of promoting pro‐tumour M2‐polarised TAMs in the PDAC tumour microenvironment.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches.

Simple SummaryProstate cancer (PCa) is a hormone-dependent tumor characterized by a highly heterogeneous clinical outcome. This neoplastic process has become a leading cause of cancer worldwide, with over 1.4 million new cases and a total of 375,000 deaths in 2020. Despite the efforts to improve the diagnosis, risk stratification, and treatment of PCa patients, a number of challenges still need to be addressed. In this context, integration of different multi-omics datasets may represent a powerful approach for the development of novel metabolic signatures that could contribute to the clinical management of PCa patients. This review aims to provide the most relevant findings of recently published multi-omics studies with a particular focus on describing the metabolic alterations associated with PCa.Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

New insights into aging-associated characteristics of female subcutaneous adipose tissue through integrative analysis of multi-omics data

ABSTRACT Aging could be critical in limiting the application of subcutaneous adipose tissue (SAT) in tissue repair and reconstruction. However, no systematic study on the characteristics of SAT aging has been conducted. In this study, a scanning electronic microscope was used to detect the structural and compositional changes of SAT collected from nine females in three age groups. Multi-omics data of SAT from 37 females were obtained from Gene Expression Omnibus database, and 1860 genes, 56 miRNAs, and 332 methylated genes were identified as being differentially expressed during aging among non-obese females. Using Weighted Correlation Network Analysis (WGCNA), 1754 DEGs were defined as aging-associated genes for non-obese females, distributed among ten co-expression modules. Through Gene Ontology enrichment analysis and Gene Set enrichment analysis on those aging-associated DEGs, SAT aging was observed to be characterized by variations in immune and inflammatory states, mitochondria, lipid and carbohydrate metabolism, and regulation of vascular development. SUPV3L1, OGT, and ARPC1B were identified as conserved and core SAT-aging-related genes, as verified by RT-qPCR among 18 samples in different age groups. Multi-omics regulatory networks of core aging-associated biological processes of SAT were also constructed. Based on WGCNA, we performed differential co-expression analysis to unveil the differences in aging-related co-expression patterns between obese and non-obese females and determined that obesity could be an important accelerating factor in aging processes. Our work provides a landscape of SAT aging, which could be helpful for further research in fields such as repair and reconstruction as well as aging.

PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples.

Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer’s disease.

Integrative analysis of multiomics data identified acetylation as key variable of excessive energy metabolism in hyperthyroidism-induced osteoporosis rats

BackgroundResults from the previous experiment have demonstrated bone loss and excess metabolism in Hyperthyroidism-induced rats. Thus, an underlying relationship between metabolism and bone loss was speculated. In addition, previous studies have shown the influence of acetylation on metabolism in tissues and diseases. The hypothesis from this case study suggests that excessive metabolism is induced by acetylation of vital metabolism enzymes. ResultsIn the case study, a HYP-induced osteoporosis rat model was used and the glucose metabolite was tested through the acetylation of proteins by the mass spectrometer. The results showed that pivotal enzymes of Glycolysis-Tricarboxylic acid cycle-Oxidative phosphorylation were acetylated along with upregulated metabolites. With all acetyly-lysine sites of related enzymes listed, the results in this study showed that bone loss in HYP rats was accompanied by the upregulation of CREB-binding protein (Crebbp, CBP). Furthermore, it is also indicated that CBP has a close relationship with the enhancement of LDHA which promotes glucose metabolism. ConclusionsAcetylation is highly correlated with excessive energy metabolism in HYP-induced osteoporotic rats, where a representation relationship between CBP and LDHA is demonstrated. SignificanceHyperthyroidism may lead to osteoporosis. Our study found an interesting phenomenon of hyperthyroidism induced-osteoporosis is that osteoporosis is accompanied by excessive glucose metabolism. In this process, some molecular mechanisms are still unclear. This study indicates a high degree of acetylation of metabolic enzymes, which may be closely related to excessive glucose metabolism. The relationship between CBP and LDHA was also investigated in this study, which showed that CBP and LDHA had some extent interaction. Glucose metabolism and acetylation maybe all associated with hyperthyroidism induced-osteoporosis. This data provides new insights into the molecular mechanisms of hyperthyroidism induced-osteoporosis.