Multi-omics integration reveals potential stage-specific druggable targets in T-cell acute lymphoblastic leukemia.

T-cell acute lymphoblastic leukemia (T-ALL), a heterogeneous hematological malignancy, is caused by the developmental arrest of normal T-cell progenitors. The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease. In this study, we performed multi-omics integration analysis, which included mRNA expression, chromatin accessibility, and gene-dependency database analyses, to identify potential stage-specific druggable targets and repositioned drugs for this disease. This multi-omics integration helped identify 29 potential pathological genes for T-ALL. These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle, hematopoietic stem cell differentiation, and the AMPK signaling pathway. Of these, four known druggable targets (CDK6, TUBA1A, TUBB, and TYMS) showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL. The TUBA1A expression level was higher in the early T cell precursor (ETP)-ALL cells, while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage. CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naïve to maturation stages. Furthermore, mebendazole and gemcitabine, which target TUBA1A and TYMS, respectively, exerted stage-specific inhibitory effects on T-ALL cell lines, indicating their potential stage-specific antileukemic role in T-ALL. Collectively, our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.

Integration of LC-HRMS and 1H NMR metabolomics data fusion approaches for classification of Amarone wine based on withering time and yeast strain.

Two untargeted metabolomics approaches (LC-HRMS and 1H NMR) were combined to classify Amarone wines based on grape withering time and yeast strain. The study employed a multi-omics data integration approach, combining unsupervised data exploration (MCIA) and supervised statistical analysis (sPLS-DA). The results revealed that the multi-omics pseudo-eigenvalue space highlighted a limited correlation between the datasets (RV-score = 16.4%), suggesting the complementarity of the assays. Furthermore, the sPLS-DA models correctly classified wine samples according to both withering time and yeast strains, providing a much broader characterization of wine metabolome with respect to what was obtained from the individual techniques. Significant variations were notably observed in the accumulation of amino acids, monosaccharides, and polyphenolic compounds throughout the withering process, with a lower error rate in sample classification (7.52%). In conclusion, this strategy demonstrated a high capability to integrate large omics datasets and identify key metabolites able to discriminate wine samples based on their characteristics.

Decoding the spatiotemporal heterogeneity of tumor-associated macrophages.

Tumor-associated macrophages (TAMs) are pivotal in cancer progression, influencing tumor growth, angiogenesis, and immune evasion. This review explores the spatial and temporal heterogeneity of TAMs within the tumor microenvironment (TME), highlighting their diverse subtypes, origins, and functions. Advanced technologies such as single-cell sequencing and spatial multi-omics have elucidated the intricate interactions between TAMs and other TME components, revealing the mechanisms behind their recruitment, polarization, and distribution. Key findings demonstrate that TAMs support tumor vascularization, promote epithelial-mesenchymal transition (EMT), and modulate extracellular matrix (ECM) remodeling, etc., thereby enhancing tumor invasiveness and metastasis. Understanding these complex dynamics offers new therapeutic targets for disrupting TAM-mediated pathways and overcoming drug resistance. This review underscores the potential of targeting TAMs to develop innovative cancer therapies, emphasizing the need for further research into their spatial characteristics and functional roles within the TME.

Multi-Omics Classification of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.

Intrahepatic cholangiocarcinoma (ICC) is a heterogeneous disease characterized by a dismal prognosis. Various attempts have been made to classify ICC subtypes with varying prognoses, but a consensus has yet to be reached. This systematic review aims to gather relevant data on the multi-omics-based ICC classification. The PubMed, Embase, and Cochrane databases were searched for terms related to ICC and multi-omics analysis. Studies that identified multi-omics-derived ICC subtypes and investigated clinicopathological predictors of long-term outcomes were included. Nine studies, which included 910 patients, were considered eligible. Mean 3- and 5-year overall survival were 25.7% and 19.6%, respectively, for the multi-omics subtypes related to poor prognosis, while they were 70.2% and 63.3%, respectively, for the subtypes linked to a better prognosis. Several negative prognostic factors were identified, such as genes' expression profile promoting inflammation, mutations in the KRAS gene, advanced tumor stage, and elevated levels of oncological markers. The subtype with worse clinicopathological characteristics was associated with worse survival (Ref.: good prognosis subtype; pooled hazard ratio 2.06, 95%CI 1.67-2.53). Several attempts have been made to classify molecular ICC subtypes, but they have yielded heterogeneous results and need a clear clinical definition. More efforts are required to build a comprehensive classification system that includes both molecular and clinical characteristics before implementation in clinical practice to facilitate decision-making and select patients who may benefit the most from comprehensive molecular profiling in the disease's earlier stages.

Influences of Stocking Density on Antioxidant Status, Nutrients Composition, and Lipid Metabolism in the Muscles of Cyprinus carpio under Rice-Fish Co-Culture.

Cyprinus carpio is a significant freshwater species with substantial nutritional and economic value. Rice-carp co-culture represents one of its principal cultivation methods. However, in the system, the optimal farming density for carp and the impact of high stocking density on their muscle nutritional composition have yet to be explored. Thus, the objective of the current study was to investigate the influences of stocking density on the muscle nutrient profiles and metabolism of C. carpio in rice-fish co-culture systems. Common carp were cultured at three stocking densities, low density (LD), medium density (MD), and high density (HD), over a period of 60 days. Following this, comprehensive analyses incorporating physiological, biochemical, and multi-omics sequencing were conducted on the muscle tissue of C. carpio. The results demonstrated that HD treatment led to a reduction in the antioxidant capacity of C. carpio, while resulting in elevated levels of various fatty acids in muscle tissue, including saturated fatty acids (SFAs), omega-3 polyunsaturated fatty acids (n-3 PUFAs), and omega-6 polyunsaturated fatty acids (n-6 PUFAs). The metabolome analysis showed that HD treatment caused a marked reduction in 43 metabolites and a significant elevation in 30 metabolites, primarily linked to lipid and amino acid metabolism. Additionally, transcriptomic analysis revealed that the abnormalities in lipid metabolism induced by high-stocking-density treatment may be associated with significant alterations in the PPAR signaling pathway and adipokine signaling pathway. Overall, our findings indicate that in rice-fish co-culture systems, high stocking density disrupted the balance of antioxidant status and lipid metabolism in the muscles of C. carpio.

Concept of Normativity in Multi-Omics Analysis of Axon Regeneration.

Transcriptomes and proteomes can be normalized with a handful of RNAs or proteins (or their peptides), such as GAPDH, ß-actin, RPBMS, and/or GAP43. Even with hundreds of standards, normalization cannot be achieved across different molecular mass ranges for small molecules, such as lipids and metabolites, due to the non-linearity of mass by charge ratio for even the smallest part of the spectrum. We define the amount (or range of amounts) of metabolites and/or lipids per a defined amount of a protein, consistently identified in all samples of a multiple-model organism comparison, as the normative level of that metabolite or lipid. The defined protein amount (or range) is a normalized value for one cohort of complete samples for which intrasample relative protein quantification is available. For example, the amount of citrate (a metabolite) per µg of aconitate hydratase (normalized protein amount) identified in the proteome is the normative level of citrate with aconitase. We define normativity as the amount of metabolites (or amount range) detected when compared to normalized protein levels. We use axon regeneration as an example to illustrate the need for advanced approaches to the normalization of proteins. Comparison across different pharmacologically induced axon regeneration mouse models entails the comparison of axon regeneration, studied at different time points in several models designed using different agents. For the normalization of the proteins across different pharmacologically induced models, we perform peptide doping (fixed amounts of known peptides) in each sample to normalize the proteome across the samples. We develop Regen V peptides, divided into Regen III (SEB, LLO, CFP) and II (HH4B, A1315), for pre- and post-extraction comparisons, performed with the addition of defined, digested peptides (bovine serum albumin tryptic digest) for protein abundance normalization beyond commercial labeled relative quantification (for example, 18-plex tandem mass tags). We also illustrate the concept of normativity by using this normalization technique on regenerative metabolome/lipidome profiles. As normalized protein amounts are different in different biological states (control versus axon regeneration), normative metabolite or lipid amounts are expected to be different for specific biological states. These concepts and standardization approaches are important for the integration of different datasets across different models of axon regeneration.

Enhanced ε-Poly-L-Lysine Production in Streptomyces albulus through Multi-Omics-Guided Metabolic Engineering.

Safe and eco-friendly preservatives are crucial to preventing food spoilage and illnesses, as foodborne diseases caused by pathogens result in approximately 600 million cases of illness and 420,000 deaths annually. ε-Poly-L-lysine (ε-PL) is a novel food preservative widely used in many countries. However, its commercial application has been hindered by high costs and low production. In this study, ε-PL's biosynthetic capacity was enhanced in Streptomyces albulus WG608 through metabolic engineering guided by multi-omics techniques. Based on transcriptome and metabolome data, differentially expressed genes (fold change >2 or <0.5; p < 0.05) and differentially expressed metabolites (fold change >1.2 or <0.8) were separately subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The integrative analysis of transcriptome, metabolome, and overexpression revealed the essential roles of isocitrate lyase, succinate dehydrogenase, flavoprotein subunit, diaminopimelate dehydrogenase, polyphosphate kinase, and polyP:AMP phosphotransferase in ε-PL biosynthesis. Subsequently, a strain with enhanced ATP supply, L-lysine supply, and ε-PL synthetase expression was constructed to improve its production. Finally, the resulting strain, S. albulus WME10, achieved an ε-PL production rate of 77.16 g/L in a 5 L bioreactor, which is the highest reported ε-PL production to date. These results suggest that the integrative analysis of the transcriptome and metabolome can facilitate the identification of key pathways and genetic elements affecting ε-PL synthesis, guiding further metabolic engineering and thus significantly enhancing ε-PL production. The method presented in this study could be applicable to other valuable natural antibacterial agents.

Automated preparation of plasma lipids, metabolites, and proteins for LC/MS-based analysis of a high-fat diet in mice.

Blood plasma is one of the most commonly analyzed and easily accessible biological samples. Here, we describe an automated liquid-liquid extraction (LLE) platform that generates accurate, precise, and reproducible samples for metabolomic, lipidomic, and proteomic analyses from a single aliquot of plasma while minimizing hands-on time and avoiding contamination from plasticware. We applied mass spectrometry to examine the metabolome, lipidome, and proteome of 90 plasma samples to determine the effects of age, time of day, and a high-fat diet in mice. From 25 µL of mouse plasma, we identified 907 lipid species from 16 different lipid classes and subclasses, 233 polar metabolites, and 344 proteins. We found that the high-fat diet induced only mild changes in the polar metabolome, upregulated Apolipoproteins, and induced substantial shifts in the lipidome, including a significant increase in arachidonic acid (AA) and a decrease in eicosapentaenoic acid (EPA) content across all lipid classes.

Efficient and Powerful Integration of Targeted Metabolomics and Transcriptomics for Analyzing the Metabolism Behind Desirable Traits in Plants.

Through current mass spectrometry methods and multiple RNA-Seq technologies, large metabolomics and transcriptomics datasets are readily obtainable, which provide a powerful and global perspective on metabolism. Indeed, one "omics" method is often not enough to draw strong conclusions about metabolism. Combining and interpreting multiple "omics" datasets remains a challenging task that requires careful statistical considerations and pre-planning. Here we describe a protocol for obtaining high-quality metabolomics and transcriptomics datasets in developing plant embryos followed by a robust approach to integration of the two. This protocol is readily adjustable and scalable to any other metabolically active organ or tissue.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types.

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction.

By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of -4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response.

Multiomics Analysis Reveals Gut Virome-Bacteria-Metabolite Interactions and Their Associations with Symptoms in Patients with IBS-D.

The gut microbiota is involved in the pathogenesis of diarrhea-predominant irritable bowel syndrome (IBS-D), but few studies have focused on the role of the gut virome in IBS-D. We aimed to explore the characteristics of the gut virome in patients with IBS-D, its interactions with bacteria and metabolites, and the associations between gut multiomics profiles and symptoms. This study enrolled twelve patients with IBS-D and eight healthy controls (HCs). The stool samples were subjected to metavirome sequencing, 16S rRNA gene sequencing, and untargeted metabolomic analysis. The participants completed relevant scales to assess the severity of their gastrointestinal symptoms, depression, and anxiety. The results revealed unique DNA and RNA virome profiles in patients with IBS-D with significant alterations in the abundance of contigs from Siphoviridae, Podoviridae, Microviridae, Picobirnaviridae, and Tombusviridae. Single-omics co-occurrence network analyses demonstrated distinct differences in the gut virus, bacteria, and metabolite network patterns between patients with IBS-D and HCs. Multiomics networks revealed that short-chain fatty acid-producing bacteria occupied more core positions in IBS-D networks, but had fewer links to viruses. Amino acids and their derivatives exhibit unique connectivity patterns and centrality features within the IBS-D network. The gastrointestinal and psychological symptom factors of patients with IBS-D were highly clustered in the symptom-multiomics network compared with those of HCs. Machine learning models based on multiomics data can distinguish IBS-D patients from HCs and predict the scores of gastrointestinal and psychological symptoms. This study provides insights into the interactions among gut viruses, bacteria, metabolites, and clinical symptoms in patients with IBS-D, indicating further classification and personalized treatment for IBS-D.

Metabolomics and Multi-Omics Determination of Potential Plasma Biomarkers in PRV-1-Infected Atlantic Salmon.

Metabolomic analysis has been explored to search for disease biomarkers in humans for some time. The application to animal species, including fish, however, is still at the beginning. In the present study, we have used targeted and untargeted metabolomics to identify metabolites in the plasma of Atlantic salmon (Salmo salar) challenged with Piscine orthoreovirus (PRV-1), aiming to find metabolites associated with the progression of PRV-1 infection into heart and skeletal muscle inflammation (HSMI). The metabolomes of control and PRV-1-infected salmon were compared at three time points during disease development by employing different biostatistical approaches. Targeted metabolomics resulted in the determination of affected metabolites and metabolic pathways, revealing a substantial impact of PRV-1 infection on lipid homeostasis, especially on several (lyso)phosphatidylcholines, ceramides, and triglycerides. Untargeted metabolomics showed a clear separation of the treatment groups at later study time points, mainly due to effects on lipid metabolism pathways. In a subsequent multi-omics approach, we combined both metabolomics datasets with previously reported proteomics data generated from the same salmon plasma samples. Data processing with DIABLO software resulted in the identification of significant metabolites and proteins that were representative of the HSMI development in the salmon.

A Multiomics, Molecular Atlas of Breast Cancer Survivors.

Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.

Co-expression analyses reveal key Cd stress response-related metabolites and transcriptional regulators in Kentucky bluegrass.

Kentucky bluegrass (Poa pratensis) is known for its high cadmium (Cd) tolerance and accumulation, and it is therefore considered to have the potential for phytoremediation of Cd-contaminated soil. However, the mechanisms underlying the accumulation and tolerance of Cd in Kentucky bluegrass are largely unknown. In this study, we examined variances in the transcriptome and metabolome of a Cd-tolerant variety (Midnight, M) and a Cd-sensitive variety (Rugby II, R) to pinpoint crucial regulatory genes and metabolites associated with Cd response. We also validated the role of the key metabolite, l-phenylalanine, in Cd transport and alleviation of Cd stress by applying it to the Cd-tolerant variety M. Metabolites of the M and R varieties under Cd stress were subjected to co-expression analysis. The results showed that shikimate-phenylpropanoid pathway metabolites (phenolic acids, phenylpropanoids, and polyketides) were highly induced by Cd treatment and were more abundant in the Cd-tolerant variety. Gene co-expression network analysis was employed to further identify genes closely associated with key metabolites. The calcium regulatory genes, zinc finger proteins (ZAT6 and PMA), MYB transcription factors (MYB78, MYB62, and MYB33), ONAC077, receptor-like protein kinase 4, CBL-interacting protein kinase 1, and protein phosphatase 2A were highly correlated with the metabolism of phenolic acids, phenylpropanoids, and polyketides. Exogenous l-phenylalanine can significantly increase the Cd concentration in the leaves (22.27%-55.00%) and roots (7.69%-35.16%) of Kentucky bluegrass. The use of 1 mg/L of l-phenylalanine has been demonstrated to lower malondialdehyde levels and higher total phenols, flavonoids, and anthocyanins levels, while also significantly enhancing the uptake of Cd and its translocation from roots to shoots. Our results provide insights into the response mechanisms to Cd stress and offer a novel l-phenylalanine-based phytoremediation strategy for Cd-containing soil.

The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges.

Newborn screening programs have seen significant evolution since their initial implementation more than 60 years ago, with the primary goal of detecting treatable conditions within the earliest possible timeframe to ensure the optimal treatment and outcomes for the newborn. New technologies have driven the expansion of screening programs to cover additional conditions. In the current era, the breadth of screened conditions could be further expanded by integrating omic technologies such as untargeted metabolomics and genomics. Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research.

Performance comparison between multi-level gene expression data in cancer subgroup classification.

Cancer is a serious disease that can affect various parts of the body such as breast, colon, lung or stomach. Each of these cancers has their own treatment dependent historical subgroups. Hence, the correct identification of cancer subgroup has almost same importance as the timely diagnosis of cancer. This is still a challenging task and a system with highest accuracy is essential. Current researches are moving towards analyzing the gene expression data of cancer patients for various purposes including biomarker identification and studying differently expressed genes, using gene expression data measured in a single level (selected from different gene levels including genome, transcriptome or translation). However, previous studies showed that information carried by one level of gene expression is not similar to another level. This shows the importance of integrating multi-level omics data in these studies. Hence, this study uses tumor gene expression data measured from various levels of gene along with the integration of those data in the subgroup classification of nine different cancers. This is a comprehensive analysis where four different gene expression data such as transcriptome, miRNA, methylation and proteome are used in this subgrouping and the performances between models are compared to reveal the best model.

Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy.

Background: Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD. Method: Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD. Result: Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis. Conclusion: Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

Diagnostic and prognostic role of LINC01767 in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a primary contributor to cancer-related mortality on a global scale. However, the underlying molecular mechanisms are still poorly understood. Long noncoding RNAs are emerging markers for HCC diagnosis, prognosis, and therapeutic target. No study of LINC01767 in HCC was published. AIM: To conduct a multi-omics analysis to explore the roles of LINC01767 in HCC for the first time. METHODS: DESeq2 Package was used to analyze different gene expressions. Receiver operating characteristic curves assessed the diagnostic performance. Kaplan-Meier univariate and Cox multivariate analyses were used to perform survival analysis. The least absolute shrinkage and selection operator (LASSO)-Cox was used to identify the prediction model. Subsequent to the validation of LINC01767 expression in HCC fresh frozen tissues through quantitative real time polymerase chain reaction, next generation sequencing was performed following LINC01767 over expression (GSE243371), and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes/Gene Set Enrichment Analysis/ingenuity pathway analysis was carried out. In vitro experiment in Huh7 cell was carried out. RESULTS: LINC01767 was down-regulated in HCC with a log fold change = 1.575 and was positively correlated with the cancer stemness. LINC01767 was a good diagnostic marker with area under the curve (AUC) [0.801, 95% confidence interval (CI): 0.751-0.852, P = 0.0106] and an independent predictor for overall survival (OS) with hazard ratio = 1.899 (95%CI: 1.01-3.58, P = 0.048). LINC01767 nomogram model showed a satisfied performance. The top-ranked regulatory network analysis of LINC01767 showed the regulation of genes participating various pathways. LASSO regression identified the 9-genes model showing a more satisfied performance than 5-genes model to predict the OS with AUC > 0.75. LINC01767 was down-expressed obviously in tumor than para-tumor tissues in our cohort as well as in cancer cell line; the over expression of LINC01767 inhibit cell proliferation and clone formation of Huh7 in vitro. CONCLUSION: LINC01767 was an important tumor suppressor gene in HCC with good diagnostic and prognostic performance.

Multi-omics analyses and botanical perfumer hypothesis provide insights into the formation and maintenance of aromatic characteristics of Dendrobium loddigesii flowers.

Dendrobium loddigesii, a member of the Orchidaceae family, is a valuable horticultural crop known for its aromatic qualities. However, the mechanisms responsible for the development of its aromatic characteristics remain poorly understood. To elucidate these underlying mechanisms, we assembled the first chromosome-level reference genome of D. loddigesii using PacBio HiFi-reads, Illumina short-reads, and Hi-C data. The assembly comprises 19 pseudochromosomes with N50 contig and N50 scaffold sizes of 55.15 and 89.94 Mb, respectively, estimating the genome size to be 1.68 Gb, larger than that of other sequenced Dendrobium species. During the flowering stages, we conducted a comprehensive analysis combining volatilomics and transcriptomics to understand the characteristics and biosynthetic mechanisms pathways of the floral scent. Our findings emphasize the significant contribution of aromatic terpenoids, especially monoterpenoids, in defining the floral aroma. Furthermore, we identified two crucial terpene synthase (TPS) genes that play a key role in maintaining the aroma during flowering. Through the integration volatilomics data with catalytic assays of DlTPSbs proteins, we identified specific compounds responsible for the aromatic characteristics of D. loddigesii. This integrated analysis of the genome, transcriptome, and volatilome, offers valuable insights into the development and preservation of D. loddigesii's aromatic characteristics, setting the stage for further exploration of the botanical perfumer hypothesis.