Multi-Omic Analysis Reveals Genetic Determinants and Therapeutic Targets of Chronic Kidney Disease and Kidney Function.

Chronic kidney disease (CKD) presents a significant global health challenge, characterized by complex pathophysiology. This study utilized a multi-omic approach, integrating genomic data from the CKDGen consortium alongside transcriptomic, metabolomic, and proteomic data to elucidate the genetic underpinnings and identify therapeutic targets for CKD and kidney function. We employed a range of analytical methods including cross-tissue transcriptome-wide association studies (TWASs), Mendelian randomization (MR), summary-based MR (SMR), and molecular docking. These analyses collectively identified 146 cross-tissue genetic associations with CKD and kidney function. Key Golgi apparatus-related genes (GARGs) and 41 potential drug targets were highlighted, with MAP3K11 emerging as a significant gene from the TWAS and MR data, underscoring its potential as a therapeutic target. Capsaicin displayed promising drug-target interactions in molecular docking analyses. Additionally, metabolome- and proteome-wide MR (PWMR) analyses revealed 33 unique metabolites and critical inflammatory proteins such as FGF5 that are significantly linked to and colocalized with CKD and kidney function. These insights deepen our understanding of CKD pathogenesis and highlight novel targets for treatment and prevention.

Multi-omic profiling reveals early immunological indicators for identifying COVID-19 Progressors.

We sought to better understand the immune response during the immediate post-diagnosis phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. In lymphocytes, the CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. These early stage observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19. BACKGROUND: Much of the literature on immune response post-SARS-CoV-2 infection has been in the acute and post-acute phases of infection. TRANSLATIONAL SIGNIFICANCE: We found differences at early time points of infection in approximately 160 participants. We compared multi-omic signatures in immune cells between individuals progressing to needing more significant medical intervention and non-progressors. We observed widespread evidence of a state of increased inflammation associated with progression, supported by a range of epigenomic, transcriptomic, and proteomic signatures. The signatures we identified support other findings at later time points and serve as the basis for prognostic biomarker development or to inform interventional strategies.

Rhodosporidium toruloides - A potential red yeast chassis for lipids and beyond.

The red yeast Rhodosporidium toruloides naturally produces microbial lipids and carotenoids. In the past decade or so, many studies demonstrated R. toruloides as a promising platform for lipid production owing to its diverse substrate appetites, robust stress resistance and other favorable features. Also, significant progresses have been made in genome sequencing, multi-omic analysis and genome-scale modeling, thus illuminating the molecular basis behind its physiology, metabolism and response to environmental stresses. At the same time, genetic parts and tools are continuously being developed to manipulate this distinctive organism. Engineered R. toruloides strains are emerging for enhanced production of conventional lipids, functional lipids as well as other interesting metabolites. This review updates those progresses and highlights future directions for advanced biotechnological applications.

Pan-cancer analysis revealing the multidimensional expression and prognostic and immunologic roles of TGFB1 in cancer.

OBJECTIVE: This study aimed to perform an integrated pan-cancer analysis to characterize the expression patterns, prognostic value, genetic alterations, and immunologic roles of transforming growth factor beta 1 (TGFB1) across diverse human cancer types. METHODS: Bioinformatics analyses were conducted using multiple public databases including The Cancer Genome Atlas, Genotype-Tissue Expression, Clinical Proteomic Tumor Analysis Consortium, TIMER2, GEPIA2, cBioPortal, StringDB, and others. Differential expression, survival, immune correlation, and protein interaction network analyses were performed. RESULTS: TGFB1 was overexpressed in several tumor types compared with that in normal tissues. High TGFB1 expression was associated with an advanced stage and poorer prognosis in certain cancers. TGFB1 mutations occurred in 1.3% of 10,967 cases surveyed. TGFB1 expression correlated with tumor-infiltrating immune cells and immunotherapy-related genes. CONCLUSIONS: This comprehensive multi-omics analysis revealed the complex expression and prognostic landscape of TGFB1 across cancers. TGFB1 is emerging as a potential immunotherapeutic target in certain contexts. Further research should elucidate its multifaceted tumor-promoting and tumor-suppressive mechanisms. Our pan-cancer analysis provides new insights into TGFB1 as a prognostic biomarker and immunotherapeutic target in human cancers, and our findings may guide future preclinical and clinical investigations of TGFB1-directed therapies.

Plasma protein signatures reflect systemic immunity and allograft function in kidney transplantation.

Kidney transplantation causes large perturbations of the immune system. While many studies focus on the allograft, insights into systemic effects are largely missing. Here, we analyzed the systemic immune response in 3 cohorts of kidney transplanted patients. Using serum proteomics, laboratory values, mass cytometry, histological and clinical parameters, inter-patient heterogeneity was leveraged for multi-omic co-variation analysis. We identified circulating immune modules (CIM) that describe extra-renal signatures of co-regulated plasma proteins. CIM are present in nontransplanted controls, in transplant conditions and during rejection. They are enriched in pathways linked to kidney function, extracellular matrix, signaling, and cellular activation. A complex leukocyte response in the blood during allograft quiescence and rejection is associated with CIM activity and CIM-specific cytokines. CIM activity correlates with kidney function including a 2-month prediction. Together, the data suggest a systemic and multi-layered response of transplant immunity that might be insightful for understanding allograft dysfunction and developing translational biomarkers.

Integrated analysis of genome-wide gene expression and DNA methylation profiles reveals candidate genes in ovary endometriosis.

Background: The incidence of endometriosis (EMs), a common disease in gynecology, has increased over the years. Women suffer from the symptoms caused by EMs, such as chronic pelvic pain, dysmenorrhea, and infertility. However, the etiology and pathophysiology of EMs remain unclear. This study aimed to identify candidate genes of endometriosis through integrated analysis of genome-wide gene expression and DNA methylation profiles. Results: Eutopic and ectopic endometrial tissues were collected from patients who were diagnosed as ovarian EMs. Genome-wide methylation profiling identified 17551 differentially methylated loci, with 9777 hypermethylated and 7774 hypomethylated loci. Differentially methylated loci were mainly concentrated in the gene body and intergenic regions. Genome-wide gene expression profiling identified 1837 differentially expressed genes (DEGs), with 1079 genes upregulated and 758 downregulated in ectopic groups. Integrated analysis revealed that DNA methylation was negatively correlated to gene expression in most genomic regions, such as exon, 3'UTR, 5'UTR, and promoter. We also identified promoter-related (53 downregulated and 113 upregulated) and enhancer-related DMGs (212 downregulated and 232 upregulated), which were significantly correlated to the gene expression. Further validation of the top-ranked genes belonging to differentially methylated genes (DMGs) and DEGs revealed that TMEM184A, GREM2, SFN, KIR3DX1, HPGD, ESR1, BST2, PIK3CG and RNASE1 were significant candidate genes in ovarian endometriosis. Conclusion: Our study revealed the significance of DNA methylation in the gene expression in ovary endometriosis, which provides new insights and a molecular foundation for understanding the underlying mechanisms of endometriosis.

Mechanism for Utilization of the Populus-Derived Metabolite Salicin by a Pseudomonas-Rahnella Co-Culture.

Pseudomonas fluorescens GM16 associates with Populus, a model plant in biofuel production. Populus releases abundant phenolic glycosides such as salicin, but P. fluorescens GM16 cannot utilize salicin, whereas Pseudomonas strains are known to utilize compounds similar to the aglycone moiety of salicin-salicyl alcohol. We propose that the association of Pseudomonas to Populus is mediated by another organism (such as Rahnella aquatilis OV744) that degrades the glucosyl group of salicin. In this study, we demonstrate that in the Rahnella-Pseudomonas salicin co-culture model, Rahnella grows by degrading salicin to glucose 6-phosphate and salicyl alcohol which is secreted out and is subsequently utilized by P. fluorescens GM16 for its growth. Using various quantitative approaches, we elucidate the individual pathways for salicin and salicyl alcohol metabolism present in Rahnella and Pseudomonas, respectively. Furthermore, we were able to establish that the salicyl alcohol cross-feeding interaction between the two strains on salicin medium is carried out through the combination of their respective individual pathways. The research presents one of the potential advantages of salicyl alcohol release by strains such as Rahnella, and how phenolic glycosides could be involved in attracting multiple types of bacteria into the Populus microbiome.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

Elevated temperature decreases cardiovascular toxicity of nanoplastics but adds to their lethality: A case study during zebrafish (Danio rerio) development.

To highlight the key role of global warming on the toxicity of contaminants, the cardiovascular toxicity of nanoparticles (NPs) was estimated in developing zebrafish (Danio rerio) at different exposure temperatures, and the toxicity mechanisms were explored via multi-omic analyses. Polystyrene NPs (50 nm) at 0.1 mg·L-1 entered zebrafish embryos at 24 h post-fertilization and caused cardiovascular toxicity in the developing zebrafish at 27 ℃. This was explained by the down-regulation of the branched-chain amino acid and insulin signaling pathways owing to induced oxidative stress. Elevated exposure temperatures promoted the accumulation of NPs in developing zebrafish, increased the levels of oxidative stress and enhanced the oxidative phosphorylation rate in mitochondria, thus resulting in an additive effect on the mortality of zebrafish larvae. Notably, elevated exposure temperatures reduced the cardiovascular toxicity of NPs, as the effective concentration of NPs for inhibiting embryonic heartbeat rate increased from 0.1 mg·L-1 at 27 ℃ to 1.0 mg·L-1 at 30 ℃. Experiments of transgenic zebrafish Tg(myl7:GFP) and multi-omic analyses revealed that elevated temperatures enhanced the myocardial contractility of larvae, thus reducing the cardiovascular toxicity of NPs. However, the health risks of enhanced myocardial contraction caused by NP exposure at elevated temperatures requires further consideration.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach.

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

Integrated Multi-Omic Analysis Reveals Immunosuppressive Phenotype Associated with Poor Outcomes in High-Grade Serous Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) is characterized by a complex genomic landscape, with both genetic and epigenetic diversity contributing to its pathogenesis, disease course, and response to treatment. To better understand the association between genomic features and response to treatment among 370 patients with newly diagnosed HGSOC, we utilized multi-omic data and semi-biased clustering of HGSOC specimens profiled by TCGA. A Cox regression model was deployed to select model input features based on the influence on disease recurrence. Among the features most significantly correlated with recurrence were the promotor-associated probes for the NFRKB and DPT genes and the TREML1 gene. Using 1467 transcriptomic and methylomic features as input to consensus clustering, we identified four distinct tumor clusters-three of which had noteworthy differences in treatment response and time to disease recurrence. Each cluster had unique divergence in differential analyses and distinctly enriched pathways therein. Differences in predicted stromal and immune cell-type composition were also observed, with an immune-suppressive phenotype specific to one cluster, which associated with short time to disease recurrence. Our model features were additionally used as a neural network input layer to validate the previously defined clusters with high prediction accuracy (91.3%). Overall, our approach highlights an integrated data utilization workflow from tumor-derived samples, which can be used to uncover novel drivers of clinical outcomes.

Therapeutic effects on the development of heart failure with preserved ejection fraction by the sodium-glucose cotransporter 2 inhibitor dapagliflozin in type 2 diabetes.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a common disease with high morbidity and lacks effective treatment. We investigated the protective effects of the long-term application of the sodium-glucose cotransporter 2 inhibitor (SGLT2i) dapagliflozin on diabetes-associated HFpEF in a rat model. Serum proteomics and metabolomics analysis were also conducted in type 2 diabetic patients with HFpEF treated with dapagliflozin. METHODS: Male Zucker diabetic fatty (ZDF) rats were used as a model of diabetic cardiomyopathy. From weeks 16 to 28, animals were given a vehicle or dapagliflozin (1 mg/kg) once daily. Primary blood biochemistry indices, echocardiography, histopathology, and cardiac hemodynamics were determined during the study period. The key markers of myocardial fibrosis, nitro-oxidative stress, inflammation, apoptosis, autophagy, and AMPK/mTOR signaling were examined. Additionally, healthy controls and individuals with type 2 diabetes were enrolled and 16 serum samples from 4 groups were randomly selected. Serum proteome and metabolome changes after dapagliflozin treatment were analyzed in diabetic individuals with HFpEF. RESULTS: Dapagliflozin effectively prevented the development of HFpEF in rats with diabetes by mitigating nitro-oxidative stress, pro-inflammatory cytokines, myocardial hypertrophy, and fibrosis, reducing apoptosis, and restoring autophagy through AMPK activating and mTOR pathway repressing. Proteomics and metabolomics revealed that cholesterol and high-density lipoprotein particle metabolism, nicotinate and nicotinamide metabolism, arginine biosynthesis, and cAMP and peroxisome proliferator-activated receptor (PPAR) signaling are the major disturbed pathways in HFpEF patients treated with dapagliflozin. CONCLUSION: Long-term treatment with dapagliflozin significantly prevented the development of HFpEF in diabetic rats. Dapagliflozin could be a promising therapeutic strategy in managing HFpEF individuals with type 2 diabetes.

Single-cell chromatin accessibility of developing murine pancreas identifies cell state-specific gene regulatory programs.

Numerous studies have characterized the existence of cell subtypes, along with their corresponding transcriptional profiles, within the developing mouse pancreas. The upstream mechanisms that initiate and maintain gene expression programs across cell states, however, remain largely unknown. Here, we generate single-nucleus ATAC-Sequencing data of developing murine pancreas and perform an integrated, multi-omic analysis of both chromatin accessibility and RNA expression to describe the chromatin landscape of the developing pancreas at both E14.5 and E17.5 at single-cell resolution. We identify candidate transcription factors regulating cell fate and construct gene regulatory networks of active transcription factor binding to regulatory regions of downstream target genes. This work serves as a valuable resource for the field of pancreatic biology in general and contributes to our understanding of lineage plasticity among endocrine cell types. In addition, these data identify which epigenetic states should be represented in the differentiation of stem cells to the pancreatic beta cell fate to best recapitulate in vitro the gene regulatory networks that are critical for progression along the beta cell lineage in vivo.

Distinct Metabolism of Bone Marrow Adipocytes and their Role in Bone Metastasis.

Bone marrow adipocytes (BMAs) represent 10% of the total fat mass of the human body and serve as an energy reservoir for the skeletal niche. They function as an endocrine organ by actively secreting fatty acids, cytokines, and adipokines. The volume of BMAs increases along with age, osteoporosis and/or obesity. With the rapid development of multi-omic analysis and the advance in in vivo imaging technology, further distinct characteristics and functions of BMAs have been revealed. There is accumulating evidence that BMAs are metabolically, biologically and functionally unique from white, brown, beige and pink adipocytes. Bone metastatic disease is an uncurable complication in cancer patients, where primary cancer cells spread from their original site into the bone marrow. Recent publications have highlighted those BMAs could also serve as a rich lipid source of fatty acids that can be utilized by the cancer cells during bone metastasis, particularly for breast, prostate, lung, ovarian and pancreatic cancer as well as melanoma. In this review, we summarize the novel progressions in BMAs metabolism, especially with multi-omic analysis and in vivo imaging technology. We also update the metabolic role of BMAs in bone metastasis, and their potential new avenues for diagnosis and therapies against metastatic cancers.

scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells.

Single-cell DNA methylation profiling currently suffers from excessive noise and/or limited cellular throughput. We developed scTAM-seq, a targeted bisulfite-free method for profiling up to 650 CpGs in up to 10,000 cells per experiment, with a dropout rate as low as 7%. We demonstrate that scTAM-seq can resolve DNA methylation dynamics across B-cell differentiation in blood and bone marrow, identifying intermediate differentiation states that were previously masked. scTAM-seq additionally queries surface-protein expression, thus enabling integration of single-cell DNA methylation information with cell atlas data. In summary, scTAM-seq is a high-throughput, high-confidence method for analyzing DNA methylation at single-CpG resolution across thousands of single cells.

Precision Medicine in Head and Neck Cancers: Genomic and Preclinical Approaches.

Head and neck cancers (HNCs) represent the sixth most widespread malignancy worldwide. Surgery, radiotherapy, chemotherapeutic and immunotherapeutic drugs represent the main clinical approaches for HNC patients. Moreover, HNCs are characterised by an elevated mutational load; however, specific genetic mutations or biomarkers have not yet been found. In this scenario, personalised medicine is showing its efficacy. To study the reliability and the effects of personalised treatments, preclinical research can take advantage of next-generation sequencing and innovative technologies that have been developed to obtain genomic and multi-omic profiles to drive personalised treatments. The crosstalk between malignant and healthy components, as well as interactions with extracellular matrices, are important features which are responsible for treatment failure. Preclinical research has constantly implemented in vitro and in vivo models to mimic the natural tumour microenvironment. Among them, 3D systems have been developed to reproduce the tumour mass architecture, such as biomimetic scaffolds and organoids. In addition, in vivo models have been changed over the last decades to overcome problems such as animal management complexity and time-consuming experiments. In this review, we will explore the new approaches aimed to improve preclinical tools to study and apply precision medicine as a therapeutic option for patients affected by HNCs.

Single-Cell Analysis and Tracking of Antigen-Specific T Cells: Integrating Paired Chain AIRR-Seq and Transcriptome Sequencing: A Method by the AIRR Community.

Single-cell adaptive immune receptor repertoire sequencing (scAIRR-seq) offers the possibility to access the nucleotide sequences of paired receptor chains from T-cell receptors (TCR) or B-cell receptors (BCR ). Here we describe two protocols and the downstream bioinformatic approaches that facilitate the integrated analysis of paired T-cell receptor (TR ) alpha/beta (TRA /TRB ) AIRR-seq, RNA sequencing (RNAseq), immunophenotyping, and antigen-binding information. To illustrate the methodologies with a use case, we describe how to identify, characterize, and track SARS-CoV-2-specific T cells over multiple time points following infection with the virus. The first method allows the analysis of pools of memory CD8+ cells, identifying expansions and contractions of clones of interest. The second method allows the study of rare or antigen-specific cells and allows studying their changes over time.

Disturbance of Gut Bacteria and Metabolites Are Associated with Disease Severity and Predict Outcome of NMDAR Encephalitis: A Prospective Case-Control Study.

Objective: We aimed to investigate the associations between the intestinal microbiota, metabolites, cytokines, and clinical severity in anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis and to further determine the predictive value of the intestinal microbiota or metabolites in clinical prognosis. Methods: In this prospective observational cohort study of 58 NMDAR encephalitis patients and 49 healthy controls, fecal microbiota, metabolites, and cytokines were quantified and characterized by16S rRNA gene sequencing, liquid chromatography-mass spectrometry, and the Luminex assay, respectively. Results: There were marked variations in the gut microbiota composition and metabolites in critically ill patients. We identified 8 metabolite modules (mainly characterized by fatty acid, glycerophosphoethanolamines, and glycerophosphocholines) that were distinctly classified as negatively or positively associated with bacterial co-abundance groups (CAGs). These CAGs were mainly composed of Bacteroides, Eubacterium_hallii_group, Anaerostipes, Ruminococcus, Butyricicoccus, and Faecalibacterium, which were substantially altered in patients. In addition, these fecal and serum metabolic modules were further correlated with the serum cytokines. Additionally, the combination of clinical features, microbial marker (Granulicatella), and a panel of metabolic markers could further enhance the performance of prognosis discrimination significantly, which yielded an area under the receiver operating characteristic curve of (AUC) of 0.94 (95%CI = 0.7-0.9). Patients with low bacterial diversity are more likely to develop relapse than those with higher bacterial diversity (log-rank p = 0.04, HR = 2.7, 95%CI = 1.0-7.0). Interpretation: The associations between the multi-omics data suggested that certain bacteria might affect the pathogenesis of NMDAR encephalitis by modulating the metabolic pathways of the host and affecting the production of pro-inflammatory cytokines. Furthermore, the disturbance of fecal bacteria may predict the long-term outcome and relapse in NMDAR encephalitis.

Multi-Omics Analysis of SOX4, SOX11, and SOX12 Expression and the Associated Pathways in Human Cancers.

The Sry-related HMG BOX (SOX) gene family encodes transcription factors containing highly conserved high-mobility group domains that bind to the minor groove in DNA. Although some SOX genes are known to be associated with tumorigenesis and cancer progression, their expression and prognostic value have not been systematically studied. We performed multi-omic analysis to investigate the expression of SOX genes in human cancers. Expression and phylogenetic tree analyses of the SOX gene family revealed that the expression of three closely related SOX members, SOX4, SOX11, and SOX12, was increased in multiple cancers. Expression, mutation, and alteration of the three SOX members were evaluated using the Oncomine and cBioPortal databases, and the correlation between these genes and clinical outcomes in various cancers was examined using the Kaplan-Meier, PrognoScan, and R2 database analyses. The genes commonly correlated with the three SOX members were categorized in key pathways related to the cell cycle, mitosis, immune system, and cancer progression in liver cancer and sarcoma. Additionally, functional protein partners with three SOX proteins and their probable signaling pathways were explored using the STRING database. This study suggests the prognostic value of the expression of three SOX genes and their associated pathways in various human cancers.

Signatures of Multi-Omics Reveal Distinct Tumor Immune Microenvironment Contributing to Immunotherapy in Lung Adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) contains a variety of genomic and epigenomic abnormalities; the effective tumor markers related to these abnormalities need to be further explored. Methods: Clustering analysis was performed based on DNA methylation (MET), DNA copy number variation (CNV), and mRNA expression data, and the differences in survival and tumor immune microenvironment (TIME) between subtypes were compared. Further, we evaluated the signatures in terms of both prognostic value and immunological characteristics. Results: There was a positive correlation between MET and CNV in LUAD. Integrative analysis of multi-omics data from 443 samples determined molecular subtypes, iC1 and iC2. The fractions of CD8+ T cells and activated CD4+ T cells were higher, the fraction of Tregs was lower, and the expression level of programmed death-ligand 1 (PD-L1) was higher in iC2 with a poor prognosis showing a higher TIDE score. We selected PTTG1, SLC2A1, and FAM83A as signatures of molecular subtypes to build a prognostic risk model and divided patients into high-risk group and low-risk group representing poor prognosis and good prognosis, respectively, which were validated in 180 patients with LUAD. Further, the low-risk group with lower TIDE score had more infiltrating immune cells. In 100 patients with LUAD, the high-risk group with an immunosuppressive state had a higher expression of PD-L1 and lower counts of CD8+ T cells and dendritic cells. Conclusions: These findings demonstrated that combined multi-omics data could determine molecular subtypes with significant differences of prognosis and TIME in LUAD and suggested potent utility of the signatures to guide immunotherapy.