Integrative Analysis Of Multi-omics Data Research Articles

Human milk miRNAs associate to maternal dietary nutrients, milk microbiota, infant gut microbiota and growth

Maternal diet influences the milk composition, yet little information is available on the impact of maternal diet on milk miRNAs expression. Further, the association of human milk miRNAs to maternal diet and milk microbiota is not explored. In addition, the role of milk miRNAs on the infant gut microbiota, infant growth and development has not been investigated. Milk samples were collected from 60 healthy lactating women at ≤15d post-partum, HTG transcriptome assay was performed to examine milk miRNA profile. Maternal clinical and dietary clusters information were available and infant anthropometric measures were followed up to one year of age. Milk and infant microbiota were analyzed by 16S rRNA gene sequencing and integrative multi-omics data analysis was performed to identify potential association between microRNA, maternal dietary nutrients and microbiota. Discriminant analysis revealed that the milk miRNAs were clustered into groups according to the maternal protein source. Interestingly, 31 miRNAs were differentially expressed (P adj<0.05) between maternal dietary clusters (Cluster 1: enriched in plant protein and fibers and Cluster 2: enriched in animal protein), with 30 miRNAs downregulated in the plant protein group relative to animal protein group. Pathway analysis revealed that the top enriched pathways (P adj<0.01) were involved in cell growth and proliferation processes. Furthermore, significant features contributing to the clustering were associated with maternal dietary nutrients and milk microbiota (r>0.70). Further, miR-378 and 320 family miRNAs involved in adipogenesis were positively correlated to the infant BMI-z-scores, weight, and weight for length-z-scores at 6 months of age. Maternal dietary source impacts the milk miRNA expression profile. Further, miRNAs were associated with maternal dietary nutrients, milk microbiota and to the infant gut microbiota and infant growth and development. The study is registered in ClinicalTrials.gov. The identification number is NCT03552939.

Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer

BackgroundProstate cancer, the second most prevalent malignancy among men, poses a significant threat to affected patients’ well-being due to its poor prognosis. Novel biomarkers are required to enhance clinical outcomes and tailor personalized treatments. Herein, we describe our research to explore the prognostic value of long non-coding RNAs (lncRNAs) deregulated by copy number variations (CNVs) in prostate cancer. MethodsThe study employed an integrative multi-omics data analysis of the prostate cancer transcriptomic, CNV and methylation datasets to identify prognosis-related subtypes. Subtype-specific expression profiles of protein-coding genes (PCGs) and lncRNAs were determined. We analysed CNV patterns of lncRNAs across the genome to identify subtype-specific lncRNAs with CNV changes. LncRNAs exhibiting significant amplification or deletion and a positive correlation were designated CNV-deregulated lncRNAs. A prognostic risk score model was subsequently developed using these CNV-driven lncRNAs. ResultsSix molecular subtypes of prostate cancer were identified, demonstrating significant differences in prognosis (P = 0.034). The CNV profiles of subtype-specific lncRNAs were examined, revealing their correlation with CNV amplification or deletion. Six lncRNAs (CCAT2, LINC01593, LINC00276, GACAT2, LINC00457, LINC01343) were selected based on significant CNV amplifications or deletions using a rigorous univariate Cox proportional risk regression model. A robust risk score model was developed, stratifying patients into high-risk and low-risk categories. Notably, our prognostic model based on these six lncRNAs exhibited exceptional predictive capabilities for recurrence-free survival (RFS) in prostate cancer patients (P = 0.024). ConclusionsOur study successfully identified a prognostic risk score model comprising six CNV-driven lncRNAs that could potentially be prognostic biomarkers for prostate cancer. These lncRNA signatures are closely associated with RFS, providing promising prospects for improved patient prognostication and personalized therapeutic strategies for novel prostate cancer treatment.

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.

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 &lt; 10 -2147 ) was the most enriched motifs in tumoral cells, and MEF2 family ( e.g. , MEF2a, p &lt; 10 -91 ) in primary cells. Co-enriched motifs (e.g., MEIS1, p &lt; 10 -10 ; NR4A2, p &lt; 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 &lt; 10 -9 ; SP2, p &lt; 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.

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.

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.

Identification of USP29 as a key regulator of nucleotide biosynthesis in neuroblastoma through integrative analysis of multi-omics data

ABSTRACT Cancer cells show enhanced nucleotide biosynthesis, which is essential for their unlimited proliferation, but the underlying mechanisms are not entirely clear. Ubiquitin specific peptidase 29 (USP29) was reported to sustain neuroblastoma progression by promoting glycolysis and glutamine catabolism; however, its potential role in regulating nucleotide biosynthesis in tumor cells remains unknown. In this study, we depleted endogenous USP29 in MYCN-amplified neuroblastoma SK-N-BE2 cells by sgRNAs and conducted metabolomic analysis in cells with or without USP29 depletion, we found that USP29 deficiency caused a disorder of intermediates involved in glycolysis and nucleotide biosynthesis. De novo nucleotide biosynthesis was analyzed using 13C6 glucose as a tracer under normoxia and hypoxia. The results indicated that USP29-depleted cells showed inhibition of nucleotide anabolic intermediates derived from glucose, and this inhibition was more significant under hypoxic conditions. Analysis of RNA sequencing data in SK-N-BE2 cells demonstrated that USP29 promoted the gene expression of metabolic enzymes involved in nucleotide anabolism, probably by regulating MYC and E2F downstream pathways. These findings indicated that USP29 is a key regulator of nucleotide biosynthesis in tumor cells.

Integrative analysis of multi-omics data for discovery of ferroptosis-related gene signature predicting immune activity in neuroblastoma.

Immunotherapy for neuroblastoma remains unsatisfactory due to heterogeneity and weak immunogenicity. Exploring powerful signatures for the evaluation of immunotherapy outcomes remain the primary purpose. We constructed a ferroptosis-related gene (FRG) signature by least absolute shrinkage and selection operator and Cox regression, identified 10 independent prognostic FRGs in a training cohort (GSE62564), and then verified them in an external validation cohort (TCGA). Associated with clinical factors, the signature accurately predicts overall survival of 3, 5, and 10years. An independent prognostic nomogram, which included FRG risk, age, stage of the International Neuroblastoma Staging System, and an MYCN status, was constructed. The area under the curves showed satisfactory prognostic predicting performance. Through bulk RNA-seq and proteomics data, we revealed the relationship between hub genes and the key onco-promoter MYCN gene and then validated the results in MYCN-amplified and MYCN-non-amplified cell lines with qRT-PCR. The FRG signature significantly divided patients into high- and low-risk groups, and the differentially expressed genes between the two groups were enriched in immune actions, autophagy, and carcinogenesis behaviors. The low-risk group embodied higher positive immune component infiltration and a higher expression of immune checkpoints with a more favorable immune cytolytic activity (CYT). We verified the predictive power of this signature with data from melanoma patients undergoing immunotherapy, and the predictive power was satisfactory. Gene mutations were closely related to the signature and prognosis. AURKA and PRKAA2 were revealed to be nodal hub FRGs in the signature, and both were shown to have significantly different expressions between the INSS stage IV and other stages after immunohistochemical validation. With single-cell RNA-seq analysis, we found that genes related to T cells were enriched in TNFA signaling and interferon-γ hallmark. In conclusion, we constructed a ferroptosis-related gene signature that can predict the outcomes and work in evaluating the effects of immunotherapy.

Multi-omic subtyping and molecular determinants of neoadjuvant therapeutic regimen for locally advanced HER2-positive and triple-negative breast cancer: Implications of novel therapy.

e12600 Background: Neoadjuvant chemotherapy is the backbone treatment modalities for BC patients for locally advanced breast cancer (BC) improves R0 resection rate. Determinants of differentially therapeutic response to anthracycline or platinum-based neoadjuvant therapy were largely unknown but were urgently needed to maximize patients’ benefit. Methods: A total of 149 local advanced (cT2-3, cN0-2, cM0-1) BC patients (HER2 positive BC, n = 54 and TNBC, n = 95) were retrospectively enrolled in the study, whole-exon sequencing (WES), mRNA sequencing and (phosphor-)proteomics were systematically conducted and comprehensively analyzed. The primary end point of the study was the pathological complete response (pCR) rate. The Cancer Genome Atlas (TCGA) BC dataset and GSE130787 set were used to validate our analysis. Cancer cell line encyclopedia (CCLE) dataset was utilized to screen putative small molecular inhibitors to BC cells with molecular features of resistance. Results: Through integrated analysis of multi-omics data, three subtypes with distinct molecular features were identified: immune-activated (IA) subtype, vesicular transport pathways activation (VTs) subtype, and kinase activation (KA) subtype. Same scenario of molecular subtypes can be repeated in TCGA dataset and prognosis of these subtypes can be differentiate ( p= 0.02). Patients with IA subtype majorly favored cisplatin-based treatment ( p= 0.005). And this phenomenon was confirmed in external dataset (GSE130787, n = 26, HER2-positive BC receiving TCbH treatment). Patients with VTs subtype, with significant enrichment of vesicular transport-related pathways, displayed anthracycline sensitivity. Anthracycline transporter, ABCB1 expression was significantly reduced in VTs subtype at both mRNA ( p &lt; 0.001) and protein ( p &lt; 0.05) levels. CCLE breast cancer cells with the characteristics of VTs subtype and lower ABCB1 expression also displayed the sensitivity of epirubicin ( p = 0.0037). Patient with KA subtype was insensitive to both platinum and anthracycline-based therapy. Multiple kinase pathways, especially, non-canonical TGF-β signaling pathway-related genes were overexpressed (p &lt; 0.05), and significantly correlated with non-pCR ( p= 0.002). Phosphor-proteomic analysis indicated TGFβ1-mediated RhoA/ROCK pathway (Cofilin, RhoA kinase, MYH14 proteins) were highly activated. Eleven drugs were found in CCLE inhibiting growth of BC cell lines with upregulated non-canonical TGF-β pathway genes ( p &lt; 0.05). Conclusions: Three multi-omic subtypes with distinct response to anthracycline or platinum-based therapy were identified. Moreover, activated non-canonical TGF-β pathway may mediate therapeutic resistance, but also indicated therapeutic vulnerabilities of several kinase inhibitors.

Comprehensive Profiling of Rapamycin Interacting Proteins with Multiple Mass Spectrometry-Based Omics Techniques.

Profiling drug-protein interactions is critical for understanding a drug's mechanism of action and predicting the possible adverse side effects. However, to comprehensively profile drug-protein interactions remains a challenge. To address this issue, we proposed a strategy that integrates multiple mass spectrometry-based omics analysis to provided global drug-protein interactions, including physical interactions and functional interactions, with rapamycin (Rap) as a model. Chemoproteomics profiling reveals 47 Rap binding proteins including the known target protein FKBP12 with high confidence. Gen Ontology enrichment analysis suggested that the Rap binding proteins are implicated in several important cellular processes, such as DNA replication, immunity, autophagy, programmed cell death, aging, transcription modulation, vesicle-mediated transport, membrane organization, and carbohydrate and nucleobase metabolic processes. The phosphoproteomics profiling revealed 255 down-regulated and 150 up-regulated phosphoproteins responding to Rap stimulation; they mainly involve the PI3K-Akt-mTORC1 signaling axis. Untargeted metabolomic profiling revealed 22 down-regulated metabolites and 75 up-regulated metabolites responding to Rap stimulation; they are mainly associated with the synthesis processes of pyrimidine and purine. The integrative multiomics data analysis provides deep insight into the drug-protein interactions and reveals Rap's complicated mechanism of action.

Establishment of a Machine Learning Model for the Risk Assessment of Perineural Invasion in Head and Neck Squamous Cell Carcinoma.

Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. A random forest classification model was established based on these differentially expressed genes and was validated by inspection of H&E-stained whole image slides. Differences in epigenetic regulation and the mutational landscape were detected by an integrative analysis of multiomics data and single-cell RNA-sequencing data were analyzed. We identified a 44-gene expression signature related to perineural invasion and enriched for genes mainly expressed in cancer cells according to single-cell RNA-sequencing data. A machine learning model was trained based on the expression pattern of the 44-gene set with the unique feature to predict occult perineural invasion. This extended classification model enabled a more accurate analysis of alterations in the mutational landscape and epigenetic regulation by DNA methylation as well as quantitative and qualitative differences in the cellular composition in the tumor microenvironment between head and neck squamous cell carcinoma with or without perineural invasion. In conclusion, the newly established model could not only complement histopathologic examination as an additional diagnostic tool but also guide the identification of new drug targets for therapeutic intervention in future clinical trials with head and neck squamous cell carcinoma patients at a higher risk for treatment failure due to perineural invasion.

Multiomics Analyses Reveal Microbiome-Gut-Brain Crosstalk Centered on Aberrant Gamma-Aminobutyric Acid and Tryptophan Metabolism in Drug-Naïve Patients with First-Episode Schizophrenia.

Schizophrenia (SCZ) is associated with complex crosstalk between the gut microbiota and host metabolism, but the underlying mechanism remains elusive. Investigating the aberrant neurotransmitter processes reflected by alterations identified using multiomics analysis is valuable to fully explain the pathogenesis of SCZ. We conducted an integrative analysis of multiomics data, including the serum metabolome, fecal metagenome, single nucleotide polymorphism data, and neuroimaging data obtained from a cohort of 127 drug-naïve, first-episode SCZ patients and 92 healthy controls to characterize the microbiome-gut-brain axis in SCZ patients. We used pathway-based polygenic risk score (PRS) analyses to determine the biological pathways contributing to genetic risk and mediation effect analyses to determine the important neuroimaging features. Additionally, a random forest model was generated for effective SCZ diagnosis. We found that the altered metabolome and dysregulated microbiome were associated with neuroactive metabolites, including gamma-aminobutyric acid (GABA), tryptophan, and short-chain fatty acids. Further structural and functional magnetic resonance imaging analyses highlighted that gray matter volume and functional connectivity disturbances mediate the relationships between Ruminococcus_torgues and Collinsella_aerofaciens and symptom severity and the relationships between species Lactobacillus_ruminis and differential metabolites l-2,4-diaminobutyric acid and N-acetylserotonin and cognitive function. Moreover, analyses of the Polygenic Risk Score (PRS) support that alterations in GABA and tryptophan neurotransmitter pathways are associated with SCZ risk, and GABA might be a more dominant contributor. This study provides new insights into systematic relationships among genes, metabolism, and the gut microbiota that affect brain functional connectivity, thereby affecting SCZ pathogenesis.

Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches.

With the rapid development of multi-omics technologies and accumulation of large-scale bio-datasets, many studies have conducted a more comprehensive understanding of human diseases and drug sensitivity from multiple biomolecules, such as DNA, RNA, proteins and metabolites. Using single omics data is difficult to systematically and comprehensively analyze the complex disease pathology and drug pharmacology. The molecularly targeted therapy-based approaches face some challenges, such as insufficient target gene labeling ability, and no clear targets for non-specific chemotherapeutic drugs. Consequently, the integrated analysis of multi-omics data has become a new direction for scientists to explore the mechanism of disease and drug. However, the available drug sensitivity prediction models based on multi-omics data still have problems such as overfitting, lack of interpretability, difficulties in integrating heterogeneous data, and the prediction accuracy needs to be improved. In this paper, we proposed a novel drug sensitivity prediction (NDSP) model based on deep learning and similarity network fusion approaches, which extracts drug targets using an improved sparse principal component analysis (SPCA) method for each omics data, and construct sample similarity networks based on the sparse feature matrices. Furthermore, the fused similarity networks are put into a deep neural network for training, which greatly reduces the data dimensionality and weakens the risk of overfitting problem. We use three omics of data, RNA sequence, copy number aberration and methylation, and select 35 drugs from Genomics of Drug Sensitivity in Cancer (GDSC) for experiments, including Food and Drug Administration (FDA)-approved targeted drugs, FDA-unapproved targeted drugs and non-specific therapies. Compared with some current deep learning methods, our proposed method can extract highly interpretable biological features to achieve highly accurate sensitivity prediction of targeted and non-specific cancer drugs, which is beneficial for the development of precision oncology beyond targeted therapy.

Abstract 3127: Comprehensive proteogenomic characterization of rare kidney tumors

Abstract Non-clear cell renal cell carcinomas (non-ccRCCs) represent ~15-20% RCCs cases comprising nearly 20 different disease subtypes and a wide spectrum of clinical behavior from benign to highly aggressive course. Clinically, metastatic non-ccRCC patients, regardless of subtypes with distinct genomic aberrations, are all treated with the same standard of care therapies, underscoring the need for precision therapeutic strategies. Diagnostic challenges also exist as benign and malignant entities often display overlapping histomorphologies that current diagnostic cytokeratin markers cannot resolve. Therefore, identification of more reliable diagnostic and prognostic non-ccRCC biomarkers remains an unmet need in this field. As part of the Clinical Tumor Analysis Consortium (CPTAC), we performed integrative analysis of multi-omics data including genomic next generation sequencing-based whole exome, whole genome, RNAseq, snRNAseq and mass spectrometry-based proteomics, post translational modifications (glycosylation and phosphorylation) and metabolomic profiles generated by CPTAC. The composition of the kidney tumor cohort (n=151) included 103 ccRCC, 15 oncocytomas, 13 papillary RCC (PRCC), 11 other rare tumors and 8 unclassified RCCs. Our multi-omic analysis revealed both unique and shared molecular features of RCC subtypes. We characterized proteogenomic, PTM and glycoproteome impact of genome instability (GI), a feature that is associated with poor prognosis in both ccRCC and non-ccRCC and affects 10-15% of cases. These analyses identified new prognostic signatures, outlier targetable kinase expression patterns, kinase-substrate relationships and differential protein glycosylation events. Glycoproteome analysis also revealed variation in cell-type specific marker expression among RCC subtypes such as FUT8 (core-fucosyltransferase) associated protein glycosylation in PRCC. Integrative analysis of snRNA-seq data predicted diverse tumor cell-of-origin and stratified RCC subtype specific proteogenomic signatures. Differential expression analysis revealed several novel diagnostic makers including MAPRE3, GPNMB, PIGR, SOSTDC1. These biomarkers were validated by IHC and their addition to existing panels results in improved diagnostic specificity. Metabolic characterization revealed RCC subtype-specific differences and increased oncometabolite SAICAR in oncocytomas that may have functional significance. The valuable proteogenomic data resource we generated contains several rare tumor types that are hard to obtain for proteogenomic characterization at the scale described here, and will certainly aid in future pan-RCC studies. Citation Format: Ginny Xiaohe Li, Yi Hsiao, Lijun Chen, Rahul Mannan, Yuping Zhang, Francesca Petralia, Hanbyul Cho, Noshad Hosseini, Anna Calinawan, Yize Li, Shankara Anand, Aniket Dagar, Yifat Geffen, Felipe V. Leprevost, Anne Le, Sean Ponce, Michael Schnaubelt, Nataly Naser Al Deen, Wagma Caravan, Andrew Houston, Chandan Kumar-Sinha, Xiaoming Wang, Seema Chugh, Gilbert S. Omenn, Daniel W. Chan, Christopher Ricketts, Rohit Mehra, Arul Chinnaiyan, Li Ding, Marcin Cieslik, Hui Zhang, Saravana M. Dhanasekaran, Alexey I. Nesvizhskii. Comprehensive proteogenomic characterization of rare kidney tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3127.

Integrative analysis of multi-omics data to identify three immune-related genes in the formation and progression of intracranial aneurysms

The prevalence of intracranial aneurysms (IAs) has increased globally. We performed bioinformatics analysis to identify key biomarkers associated with IA formation. We conducted a comprehensive analysis combined with multi-omics data and methods to identify immune-related genes (IRGs) and immunocytes involved in IAs. Functional enrichment analyses showed enhanced immune responses and suppressed organizations of extracellular matrix (ECM) during aneurysm progression. xCell analyses showed that the abundance of B cells, macrophages, mast cells, and monocytes significantly increased from levels in control to unruptured aneurysms and to ruptured aneurysms. Of 21 IRGs identified by overlapping, a three-gene (CXCR4, S100B, and OSM) model was constructed through LASSO logistic regression. The diagnostic ability of the three biomarkers in discriminating aneurysms from the control samples demonstrated a favorable diagnostic value. Among the three genes, OSM and CXCR4 were up-regulated and hypomethylated in IAs, while S100B was down-regulated and hypermethylated. The expression of the three IRGs was further validated by qRT-PCR and immunohistochemistry and mouse IA model using scRNA-seq analysis. The present study demonstrated heightened immune response and suppressed ECM organization in aneurysm formation and rupture. The three-gene immune-related signature (CCR4, S100B, and OSM) model may facilitate IA diagnosis and prevention.

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis.

Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance. Gene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction. Eighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways. Sepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening.

Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea.

The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany's application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 μM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea.

A Novel Subgroup of UCHL1-Related Cancers Is Associated with Genomic Instability and Sensitivity to DNA-Damaging Treatment

Identification of molecularly-defined cancer subgroups and targeting tumor-specific vulnerabilities have a strong potential to improve treatment response and patient outcomes but remain an unmet challenge of high clinical relevance, especially in head and neck squamous cell carcinoma (HNSC). We established a UCHL1-related gene set to identify and molecularly characterize a UCHL1-related subgroup within TCGA-HNSC by integrative analysis of multi-omics data. An extreme gradient boosting model was trained on TCGA-HNSC based on GSVA scores for gene sets of the MSigDB to robustly predict UCHL1-related cancers in other solid tumors and cancer cell lines derived thereof. Potential vulnerabilities of UCHL1-related cancer cells were elucidated by an in-silico drug screening approach. We established a 497-gene set, which stratified the TCGA-HNSC cohort into distinct subgroups with a UCHL1-related or other phenotype. UCHL1-related HNSC were characterized by higher frequencies of genomic alterations, which was also evident for UCHL1-related cancers of other solid tumors predicted by the classification model. These data indicated an impaired maintenance of genomic integrity and vulnerability for DNA-damaging treatment, which was supported by a favorable prognosis of UCHL1-related tumors after radiotherapy, and a higher sensitivity of UCHL1-related cancer cells to irradiation or DNA-damaging compounds (e.g., Oxaliplatin). Our study established UCHL1-related cancers as a novel subgroup across most solid tumor entities with a unique molecular phenotype and DNA-damaging treatment as a specific vulnerability, which requires further proof-of-concept in pre-clinical models and future clinical trials.