Multi-omics Data Analysis Research Articles

OmicsSuite: a customized and pipelined suite for analysis and visualization of multi-omics big data.

With the advancements in high-throughput sequencing technologies such as Illumina, PacBio, and 10X Genomics platforms, and gas/liquid chromatography-mass spectrometry, large volumes of biological data in multiple formats can now be obtained through multi-omics analysis. Bioinformatics is constantly evolving and seeking breakthroughs to solve multi-omics problems; however, it is challenging for most experimental biologists to analyse data using command-line interfaces, coding, and scripting. Based on experience with multi-omics, we have developed OmicsSuite, a desktop suite that comprehensively integrates statistics and multi-omics analysis and visualization. The suite has 175 sub-applications in 12 categories, including Sequence, Statistics, Algorithm, Genomics, Transcriptomics, Enrichment, Proteomics, Metabolomics, Clinical, Microorganism, Single Cell, and Table Operation. We created the user interface with Sequence View, Table View, and intelligent components based on JavaFX and the popular Shiny framework. The multi-omics analysis functions were developed based on BioJava and 300+ packages provided by the R CRAN and Bioconductor communities, and it encompasses over 3000 adjustable parameter interfaces. OmicsSuite can directly read multi-omics raw data in FastA, FastQ, Mutation Annotation Format, mzML, Matrix, and HDF5 formats, and the programs emphasize data transfer directions and pipeline analysis functions. OmicsSuite can produce pre-publication images and tables, allowing users to focus on biological aspects. OmicsSuite offers multi-omics step-by-step workflows that can be easily applied to horticultural plant breeding and molecular mechanism studies in plants. It enables researchers to freely explore the molecular information contained in multi-omics big data (Source: https://github.com/OmicsSuite/, Website: https://omicssuite.github.io, v1.3.9).

CfOmics: a cell-free multi-Omics database for diseases.

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis.

Single-cell multiomics techniques have been widely applied to detect the key signature of cells. These methods have achieved a single-molecule resolution and can even reveal spatial localization. These emerging methods provide insights elucidating the features of genomic, epigenomic and transcriptomic heterogeneity in individual cells. However, they have given rise to new computational challenges in data processing. Here, we describe Single-cell Single-molecule multiple Omics Pipeline (ScSmOP), a universal pipeline for barcode-indexed single-cell single-molecule multiomics data analysis. Essentially, the C language is utilized in ScSmOP to set up spaced-seed hash table-based algorithms for barcode identification according to ligation-based barcoding data and synthesis-based barcoding data, followed by data mapping and deconvolution. We demonstrate high reproducibility of data processing between ScSmOP and published pipelines in comprehensive analyses of single-cell omics data (scRNA-seq, scATAC-seq, scARC-seq), single-molecule chromatin interaction data (ChIA-Drop, SPRITE, RD-SPRITE), single-cell single-molecule chromatin interaction data (scSPRITE) and spatial transcriptomic data from various cell types and species. Additionally, ScSmOP shows more rapid performance and is a versatile, efficient, easy-to-use and robust pipeline for single-cell single-molecule multiomics data analysis.

Prioritization of risk genes in colorectal cancer by integrative analysis of multi-omics data and gene networks.

Genome-wide association studies (GWASs) have identified over 140 colorectal cancer (CRC)-associated loci; however, target genes at the majority of loci and underlying molecular mechanisms are poorly understood. Here, we utilized a Bayesian approach, integrative risk gene selector (iRIGS), to prioritize risk genes at CRC GWAS loci by integrating multi-omics data. As a result, a total of 105 high-confidence risk genes (HRGs) were identified, which exhibited strong gene dependencies for CRC and enrichment in the biological processes implicated in CRC. Among the 105 HRGs, CEBPB, located at the 20q13.13 locus, acted as a transcription factor playing critical roles in cancer. Our subsequent assays indicated the tumor promoter function of CEBPB that facilitated CRC cell proliferation by regulating multiple oncogenic pathways such as MAPK, PI3K-Akt, and Ras signaling. Next, by integrating a fine-mapping analysis and three independent case-control studies in Chinese populations consisting of 8,039 cases and 12,775 controls, we elucidated that rs1810503, a putative functional variant regulating CEBPB, was associated with CRC risk (OR=0.90, 95%CI=0.86-0.93, P=1.07×10-7). The association between rs1810503 and CRC risk was further validated in three additional multi-ancestry populations consisting of 24,254 cases and 58,741 controls. Mechanistically, the rs1810503 A to T allele change weakened the enhancer activity in an allele-specific manner to decrease CEBPB expression via long-range promoter-enhancer interactions, mediated by the transcription factor, REST, and thus decreased CRC risk. In summary, our study provides a genetic resource and a generalizable strategy for CRC etiology investigation, and highlights the biological implications of CEBPB in CRC tumorigenesis, shedding new light on the etiology of CRC.

Integrative Analysis of Multi-Omics Data with Deep Learning: Challenges and Opportunities in Bioinformatics.

The advent of high-throughput technologies has ushered in an era of unprecedented data generation in the field of bioinformatics. Omics data, including genomics, transcriptomics, proteomics, and metabolomics, provide comprehensive insights into biological systems, but their integration poses significant challenges. Integrative analysis of multi-omics data holds the promise of unraveling complex biological phenomena and enabling personalized medicine. [1] Deep learning, a subset of machine learning, has gained prominence in bioinformatics due to its ability to automatically extract intricate patterns from large-scale multi-omics datasets. This paper presents an overview of the challenges and opportunities associated with the integrative analysis of multi-omics data using deep learning techniques in bioinformatics.The challenges in multi-omics integration primarily stem from data heterogeneity, dimensionality, and noise. One of the key opportunities presented by deep learning is its ability to capture complex, non-linear relationships in multi-omics data. The paper emphasizes the importance of interpretability and explainability in deep learning models applied to bioinformatics, as they play a crucial role in gaining biological insights and facilitating clinical decision-making. The integration of domain knowledge and biological context is highlighted as a critical aspect of model development. The paper showcases real-world applications of deep learning in multi-omics data integration, such as disease subtype classification, biomarker discovery, and drug response prediction. As the field continues to evolve, addressing these challenges and harnessing the potential of deep learning approaches will pave the way for transformative advancements in our understanding of complex biological systems and the development of precision medicine strategies.

Integrative analysis of multi-omics data reveals a pseudouridine-related lncRNA signature for prediction of glioma prognosis and chemoradiotherapy sensitivity

BackgroundGlioblastoma is the most common type of glioma with a high incidence and poor prognosis, and effective medical treatment remains challenging. Pseudouridine (Ψ) is the first post-transcriptional modification discovered and one of the most abundant modifications to RNA. However, the prognostic value of Ψ-related lncRNAs (ΨrLs) for glioma patients has never been systematically evaluated. This study aims to construct a risk model based on ΨrLs signature and to validate the predictive efficiency of the model. MethodTranscriptomic data, genomic data, and relevant clinical data of glioma patients were extracted from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). ΨrLs with significant correlation with Ψ-related genes were identified, and univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were used to further select biomarkers and construct a ΨrLs signature risk model. Then, the expression of lncRNAs of ΨrLs signature in multiple glioma cell lines was detected by qPCR. Further, ROC analysis, stratification analysis, correlation analysis, survival analysis, nomogram, enrichment analysis, immune infiltration analysis, chemoradiotherapy sensitivity analysis, somatic mutation, and recurrent copy number variation (CNV) analysis were used to validate the predictive efficiency of ΨrLs signature in TCGA and CGGA datasets. ResultsA four-lncRNA ΨrLs signature (DNAJC27-AS1, GDNF-AS1, ZBTB20-AS4, and DNMBP-AS1) risk model was constructed. By ROC analysis, stratified analysis, correlation analysis, survival analysis, and nomogram, the signature showed satisfactory predictive efficiency. Functional enrichment analysis revealed the differences in immune-related biological processes between high- and low-risk groups. Immune infiltration analysis showed that the high-risk group had lower tumor purity and higher stromal, immune and ESTIMATE scores. Mitoxantrone was identified as effective drug for low-risk group of glioma patients. Key genes in glioma development, including IDH1, EGFR, PTEN, etc., were differentially mutated between risk groups. The main recurrent CNVs in low-risk groups were 19q13.42 deletion and 7q34 amplification; 10q23.31 deletion and 12q14.1 in the high-risk group. ConclusionsOur study identified a four-lncRNA ΨrLs signature that effectively predicts the prognosis of glioma patients and may serve as a diagnostic tool. Risk scores of glioma patients generated by the signature is associated with immune-related biological processes and chemoradiotherapy sensitivity. These findings may inform the development of more targeted and effective therapies for glioma patients.

Multi-omics data analysis reveals the biological implications of alternative splicing events in lung adenocarcinoma.

Cancer is characterized by the dysregulation of alternative splicing (AS). However, the comprehensive regulatory mechanisms of AS in lung adenocarcinoma (LUAD) are poorly understood. Here, we displayed the AS landscape in LUAD based on the integrated analyses of LUAD's multi-omics data. We identified 13,995 AS events in 6309 genes as differentially expressed alternative splicing events (DEASEs) mainly covering protein-coding genes. These DEASEs were strongly linked to "cancer hallmarks", such as apoptosis, DNA repair, cell cycle, cell proliferation, angiogenesis, immune response, generation of precursor metabolites and energy, p53 signaling pathway and PI3K-AKT signaling pathway. We further built a regulatory network connecting splicing factors (SFs) and DEASEs. In addition, RNA-binding protein (RBP) mutations that can affect DEASEs were investigated to find some potential cancer drivers. Further association analysis demonstrated that DNA methylation levels were highly correlated with DEASEs. In summary, our results can bring new insight into understanding the mechanism of AS and provide novel biomarkers for personalized medicine of LUAD.

P13.12.A SUBTYPES AND SURVIVAL ANALYSIS ANALYSIS OF PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA WITH RADIOMICS FEATURES

Abstract BACKGROUND Primary Central Nervous System Lymphoma (PCNSL) is a rare and heterogeneous disease with dismal prognosis. Recently, four molecular clusters with clinical relevance have been identified with different potential therapeutic targets in each group. Nevertheless, multi-omics data collection and analysis are expensive and not adapted for clinical practice. Therefore, the identification of surrogate markers to identify PCNSL subtypes from routine data is required, like using hematoxylin and eosin slides from brain biopsies. MATERIAL AND METHODS We used a cohort of 108 patients and we selected the 5000 nuclei for each patient among roughly 1,5M nuclei. Once hematoxylin and eosin slides have been digitized, tessellated, normalized and the nuclei have been segmented and filtered with the computation of a solidity score, the PyRadiomics package provides us with more than 800 features for each nuclei. Firstly, we were interested in survival analysis. In a second time, we also used these features for training classification models. We used a partial least squared Cox model, which is a classic Cox model applied to latent components constructed by using linear combinations of the original variables. RESULTS Results for our first cohort are promising (C-index of 0.87, std 0.01), with a significant increase compared to the clinical features model (C-index of 0.68, std 0.03). We are now challenging these results with three other cohorts of brain and systemic lymphoma. CONCLUSION This study paves the way for a stratification of the clinical evolution based on the machine learning analysis of digital pathology in PCNSL that could be easily translated to a broad range of diseases or other brain tumors.

Spatial transformation of multi-omics data unlocks novel insights into cancer biology.

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation of the genome and often apply stringent p-value corrections that likely result in the loss of true positive associations. Here, we present GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), a framework for integrating multi-omics data using deep learning models developed for advanced image analysis. The GENIUS framework is able to transform multi-omics data into images with genes displayed as spatially connected pixels and successfully extract relevant information with respect to the desired output. We demonstrate the utility of GENIUS by applying the framework to multi-omics datasets from the Cancer Genome Atlas. Our results are focused on predicting the development of metastatic cancer from primary tumors, and demonstrate how through model inference, we are able to extract the genes which are driving the model prediction and are likely associated with metastatic disease progression. We anticipate our framework to be a starting point and strong proof of concept for multi-omics data transformation and analysis without the need for statistical correction.

Spatial transformation of multi-omics data unlocks novel insights into cancer biology

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation of the genome and often apply stringent p-value corrections that likely result in the loss of true positive associations. Here, we present GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), a framework for integrating multi-omics data using deep learning models developed for advanced image analysis. The GENIUS framework is able to transform multi-omics data into images with genes displayed as spatially connected pixels and successfully extract relevant information with respect to the desired output. We demonstrate the utility of GENIUS by applying the framework to multi-omics datasets from the Cancer Genome Atlas. Our results are focused on predicting the development of metastatic cancer from primary tumors, and demonstrate how through model inference, we are able to extract the genes which are driving the model prediction and are likely associated with metastatic disease progression. We anticipate our framework to be a starting point and strong proof of concept for multi-omics data transformation and analysis without the need for statistical correction.

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, there has been an increasing interest in introducing graph neural networks (GNNs) into multi-omics learning. However, existing methods have not fully exploited these graphical priors since none have been able to integrate knowledge from multiple sources simultaneously. To solve this problem, we propose a multi-omics data analysis framework by incorporating multiple prior knowledge into graph neural network (MPK-GNN). To the best of our knowledge, this is the first attempt to introduce multiple prior graphs into multi-omics data analysis. Specifically, the proposed method contains four parts: (1) a feature-level learning module to aggregate information from prior graphs; (2) a projection module to maximize the agreement among prior networks by optimizing a contrastive loss; (3) a sample-level module to learn a global representation from input multi-omics features; (4) a task-specific module to flexibly extend MPK-GNN for various downstream multi-omics analysis tasks. Finally, we verify the effectiveness of the proposed multi-omics learning algorithm on the cancer molecular subtype classification task. Experimental results show that MPK-GNN outperforms other state-of-the-art algorithms, including multi-view learning methods and multi-omics integrative approaches.

Multi-omic and clinical data analysis of pre-menopausal women under 45 years of age diagnosed with endometrial cancer and enrolled in the ASAP trial (2154)

Multi-omic and clinical data analysis of pre-menopausal women under 45 years of age diagnosed with endometrial cancer and enrolled in the ASAP trial (2154)

Abstract P225: The Reninness Score: Integrative Analysis Of Multi-omic Data To Define Renin Cell Identity.

Renin cell (RC) descendants regain the endocrine renin phenotype to overcome homeostatic threats, a process known as recruitment. The determinants that control the identity, fate, and recruitment of RCs are not well understood. We aim to 1) define the chromatin pattern that determines RC identity; and 2) develop a computational tool to score samples by similarity to that pattern. We compared open chromatin regions (ATAC-seq) between non-renin-expressing (n = 184) and renin-expressing samples (n = 19) from three different sources and stimulation states: 1) primary RCs in basal state (WT); 2) primary RCs chronically stimulated to produce renin (recruited); and 3) constitutively renin-expressing tumoral cells. Differential analyses between the three types of RCs and the non-RCs revealed 1,525 unique open chromatin regions shared in at least two RC groups, including regions associated with RC-specific genes: Ren1 and Akr1b7. Increased accessibility regions in RC groups are enriched in renin-related GO terms ( e.g., cAMP and Notch signaling pathway). The bZip family ( e.g. , Atf3, p < 10 -2147 ) was the most enriched motifs in tumoral cells, and MEF2 family ( e.g. , MEF2a, p < 10 -91 ) in primary cells. Co-enriched motifs (e.g., MEIS1, p < 10 -10 ; NR4A2, p < 10 -9 ) in Ren1 and Akr1b7 regions suggest a group of TFs govern their co-expression. In addition, we identified unique open chromatin regions and enriched motifs ( e.g., E2F4, p < 10 -9 ; SP2, p < 10 -7 ) in recruited RCs, suggesting associated TFs involved in the recruitment.Next, we used a machine-learning approach to calculate a "reninness" score by training a genomic region model with renin and non-renin ATAC-seq data. We identified a cluster of regions (n = 112,992) around the trained renin label, which included 89% (21,820 of 24,612) of the increased accessibility regions from the differential analyses. We calculated the reninness score based on the distance between the trained label embeddings and predicted samples using the trained model. Homogeneous WT RCs had the highest score, followed by renin-expressing tumoral and primary cells. Non-RCs had the lowest score. In conclusion, we identified the chromatin configurations defining the identity and recruitment of RCs and developed a computational score to quantify it.

Protein Sequencing and Multi-Omics Data Analysis of Human Insulin

Abstract: Advancements in molecular biology have led to a deeper understanding of complex biological processes through the analysis of multi-omics data. In this study, we focused on the protein sequencing and multi-omics data analysis of human insulin, a pivotal hormone with critical roles in regulating glucose metabolism. In this study, we delved into the intricate realm of human insulin, a critical hormone regulating glucose metabolism, using advanced molecular biology techniques. Leveraging the Protein Data Bank (PDB) and the WorldWide Protein Data Bank (wwPDB), we explored 3D protein structures through sources like X-ray Crystallography and NMR spectrometry. RasMol software aided dynamic visualization of molecular structures, allowing manipulation and annotation. The Molecular Modelling Database (MMDB) facilitated structure identification and cross-species sequence alignment. The National Centre for Biotechnology Information (NCBI) provided genetic and biomedical insights, enhancing our understanding of insulin's molecular intricacies. Computational tools, including the FASTA format, accelerated sequence analysis, while BLAST unveiled evolutionary relationships and functional conservation. COBALT enabled incremental protein sequence alignment. Python-based PyMOL enriched analyses with specialized plugins, promoting efficient complex research.

Multi-omics analysis revealing the interplay between gut microbiome and the host following opioid use

ABSTRACT Opioid crisis is an ongoing epidemic since the past several decades in the United States. Opioid use-associated microbial dysbiosis is emerging as a key regulator of intestinal homeostasis and behavioral responses to opioid. However, the mechanistic insight into the role of microbial community in modulating host response is unavailable. To uncover the role of opioid-induced dysbiosis in disrupting intestinal homeostasis we utilized whole genome sequencing, untargeted metabolomics, and mRNA sequencing to identify changes in microbiome, metabolome, and host transcriptome respectively. Morphine treatment resulted in significant expansion of Parasuterella excrementihominis, Burkholderiales bacterium 1_1_47, Enterococcus faecalis, Enterorhabdus caecimuris and depletion of Lactobacillus johnsonii. These changes correlated with alterations in lipid metabolites and flavonoids. Significant alteration in microbial metabolism (metabolism of lipids, amino acids, vitamins and cofactors) and increased expression of virulence factors and biosynthesis of lipopolysaccharides (LPS) and lipoteichoic acid (LTA) were observed in microbiome of morphine-treated animals. In concurrence with changes in microbiome and metabolome extensive changes in innate and adaptive immune response, lipid metabolism, and gut barrier dysfunction were observed in the host transcriptome. Microbiome depleted mice displayed lower levels of inflammation, immune response and tissue destruction compared to mice harboring a dysbiotic microbiome in response to morphine treatment, thus establishing dysbiotic microbiome as mediator of morphine gut pathophysiology. Integrative analysis of multi-omics data highlighted the associations between Parasutterella excrementihominis, Burkholderiales bacterium 1_1_47, Enterococcus faecalis, Enterorhabdus caecimuris and altered levels of riboflavin, flavonoids, and lipid metabolites including phosphocholines, carnitines, bile acids, and ethanolamines with host gene expression changes involved in inflammation and barrier integrity of intestine. Omic analysis also highlighted the role of probiotic bacteria Lactobacillus johnsonii, metabolites flavonoids and riboflavin that were depleted with morphine as important factors for intestinal homeostasis. This study presents for the first time ever an interactive view of morphine-induced changes in microbial metabolism, strain level gut microbiome analysis and comprehensive view of changes in gut transcriptome. We also identified areas of potential therapeutic interventions to limit microbial dysbiosis and present a unique resource to the opioid research community.

Multiomics Data Analysis Identified CpG Sites That Mediate the Impact of Smoking on Cardiometabolic Traits.

Understanding the epigenome paths through which smoking contributes to cardiometabolic traits is important for downstream applications. In this study, an SNP-based analytical pipeline was used to integrate several publicly available datasets in order to identify CpG sites that mediate the impact of smoking on cardiometabolic traits and to investigate the underlying molecular mechanisms. After applying stringent statistical criteria, 11 CpG sites were detected that showed significant association (p < 5 × 10-8) with cardiometabolic traits at both the discovery and replication stages. By integrating eQTL data, I found genes behind a number of these associations. cg05228408 was hypomethylated in smokers and contributed to higher blood pressure by lowering the expression of the CLCN6 gene. cg08639339 was hypermethylated in smokers and lowered the metabolic rate by increasing the expression of RAB29; furthermore, I noted TMEM120A mediated the impact of smoking-cg17325771 on LDL, and LTBP3 mediated the smoking-cg07029024 effect on heart rate. The pathway analysis identified processes through which the identified genes impact their traits. This study provides a list of CpG sites that mediates the impact of smoking on cardiometabolic traits and a framework to investigate the underlying molecular paths using publicly available data.

Multilayer omics reveals the molecular mechanism of early infection of Clonorchis sinensis juvenile

BackgroundClonorchiasis remains a non-negligible global zoonosis, causing serious socioeconomic burdens in endemic areas. Clonorchis sinensis infection typically elicits Th1/Th2 mixed immune responses during the course of biliary injury and periductal fibrosis. However, the molecular mechanism by which C. sinensis juvenile initially infects the host remains poorly understood.MethodsThe BALB/c mouse model was established to study early infection (within 7 days) with C. sinensis juveniles. Liver pathology staining and observation as well as determination of biochemical enzymes, blood routine and cytokines in blood were conducted. Furthermore, analysis of liver transcriptome, proteome and metabolome changes was performed using multi-omics techniques. Statistical analyses were performed using Student's t-test.ResultsHistopathological analysis revealed that liver injury, characterized by collagen deposition and inflammatory cell infiltration, occurred as early as 24 h of infection. Blood indicators including ALT, AST, WBC, CRP and IL-6 indicated that both liver injury and systemic inflammation worsened as the infection progressed. Proteomic data showed that apoptosis and junction-related pathways were enriched within 3 days of infection, indicating the occurrence of liver injury. Furthermore, proteomic and transcriptomic analysis jointly verified that the detoxification and antioxidant defense system was activated by enrichment of glutathione metabolism and cytochrome P450-related pathways in response to acute liver injury. Proteomic-based GO analysis demonstrated that biological processes such as cell deformation, proliferation, migration and wound healing occurred in the liver during the early infection. Correspondingly, transcriptomic results showed significant enrichment of cell cycle pathway on day 3 and 7. In addition, the KEGG analysis of multi-omics data demonstrated that numerous pathways related to immunity, inflammation, tumorigenesis and metabolism were enriched in the liver. Besides, metabolomic screening identified several metabolites that could promote inflammation and hepatobiliary periductal fibrosis, such as CA7S.ConclusionsThis study revealed that acute inflammatory injury was rapidly triggered by initial infection by C. sinensis juveniles in the host, accompanied by the enrichment of detoxification, inflammation, fibrosis, tumor and metabolism-related pathways in the liver, which provides a new perspective for the early intervention and therapy of clonorchiasis.Graphical

Prognostic and biological function value of OSBPL3 in colorectal cancer analyzed by multi-omic data analysis

BackgroundColorectal cancer (CRC) is one of the most common malignancies in the world. This study proposes to reveal prognostic biomarkers for the prognosis and treatment of CRC patients.MethodsDifferential analysis of OSBPL3 was performed in pan-cancer, and the correlation between clinical stage and OSBPL3 was analyzed. Multiple omics analysis was used to compare the relationship between survival of patients and copy number variation, single nucleotide variant, and methylation status. Survival differences between high and low OSBPL3 expression groups were analyzed. Differentially expressed genes (DEGs) between high and low OSBPL3 expression groups were obtained, and functional enrichment analysis was implemented. Correlations between immune cells and OSBPL3 was analyzed. Drug sensitivity between the two OSBPL3 expression groups was compared. Moreover, the expression of OSBPL3 was verified by immunohistochemistry and real-time quantitative PCR.ResultsOSBPL3 was differentially expressed in 13 tumors and had some correlations with T and N stages. OSBPL3 expression was regulated by methylation and higher OSBPL3 expression was associated with poorer prognosis in CRC. 128 DEGs were obtained and they were mainly involved in signaling receptor activator activity, aspartate and glutamate metabolism. T cell gamma delta and T cell follicular helper were significantly different in the high and low OSBPL3 expression groups. Moreover, OSBPL3 showed negative correlations with multiple drugs. OSBPL3 was significantly upregulated in CRC samples compared to normal samples.ConclusionsA comprehensive analysis demonstrated that OSBPL3 had potential prognostic value, and guiding significance for CRC chemotherapeutic.

Integrated multiomics analysis of chromosome 19 miRNA cluster in bladder cancer

With 46 microRNAs (miRNAs) embedded tandemly over a distance of ~100 kb, chromosome 19 microRNA cluster (C19MC) is the largest miRNA cluster in the human genome. The C19MC is transcribed from a long noncoding genomic region and is usually expressed simultaneously at a higher level. Hence, we performed an integrative multiomics data analysis to examine C19MC regulation, expression patterns, and their impact on bladder cancer (BCa). We found that 43 members of C19MC were highly expressed in BCa. However, its co-localization with recurrent copy number variation (CNV) gain was not statistically significant to implicate its upregulation. It has been reported that C19MC expression is regulated by a well-established CpG island situated 17.6 kb upstream of the transcription start site, but we found that CpG probes at this island were hypomethylated, which was not statistically significant in the BCa cohort. In addition, the promoter region of C19MC is strongly regulated by a group of seven transcription factors (NR2F6, SREBF1, TBP, GATA3, GABPB1, ETV4, and ZNF444) and five chromatin modifiers (SMC3, KDMA1, EZH2, RAD21, and CHD7). Interestingly, these 12 genes were found to be overexpressed in BCa patients. Further, C19MC targeted 42 tumor suppressor (TS) genes that were downregulated, of which 15 were significantly correlated with patient survival. Our findings suggest that transcription factors and chromatin modifiers at the promoter region may regulate C19MC overexpression. The upregulated C19MC members, transcription regulators, and TS genes can be further exploited as potential diagnostic and prognostic indicators as well as for therapeutic management of BCa.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.