Decoding the molecular landscape: A novel prognostic signature for uveal melanoma unveiled through programmed cell death-associated genes.

Uveal melanoma (UM) is a rare but aggressive malignant ocular tumor with a high metastatic potential and limited therapeutic options, currently lacking accurate prognostic predictors and effective individualized treatment strategies. Public databases were utilized to analyze the prognostic relevance of programmed cell death-related genes (PCDRGs) in UM transcriptomes and survival data. Consensus clustering and Lasso Cox regression analysis were performed for molecular subtyping and risk feature construction. The PCDRG-derived index (PCDI) was evaluated for its association with clinicopathological features, gene expression, drug sensitivity, and immune infiltration. A total of 369 prognostic PCDRGs were identified, which could cluster UM into 2 molecular subtypes with significant differences in prognosis and clinicopathological characteristics. Furthermore, a risk feature PCDI composed of 11 PCDRGs was constructed, capable of indicating prognosis in UM patients. Additionally, PCDI exhibited correlations with the sensitivity to 25 drugs and the infiltration of various immune cells. Enrichment analysis revealed that PCDI was associated with immune regulation-related biological processes and pathways. Finally, a nomogram for prognostic assessment of UM patients was developed based on PCDI and gender, demonstrating excellent performance. This study elucidated the potential value of PCDRGs in prognostic assessment for UM and developed a corresponding risk feature. However, further basic and clinical studies are warranted to validate the functions and mechanisms of PCDRGs in UM.

A human neural crest model reveals the developmental impact of neuroblastoma-associated chromosomal aberrations.

Early childhood tumours arise from transformed embryonic cells, which often carry large copy number alterations (CNA). However, it remains unclear how CNAs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ female human embryonic stem cell (hESC) differentiation and single-cell transcriptome and epigenome analysis to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNAs impair the specification of trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. This effect is exacerbated upon overexpression of MYCN, whose amplification co-occurs with CNAs in NB. Moreover, CNAs potentiate the pro-tumourigenic effects of MYCN and mutant NC cells resemble NB cells in tumours. These changes correlate with a stepwise aberration of developmental transcription factor networks. Together, our results sketch a mechanistic framework for the CNA-driven initiation of embryonal tumours.

Kernel-based testing for single-cell differential analysis.

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.

We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

Identification of key eRNAs for intervertebral disc degeneration by integrated multinomial bioinformatics analysis.

BACKGROUND: Intervertebral disc degeneration (IVDD) is a common degenerative condition leading to abnormal stress distribution under load, causing intervertebral stenosis, facet joint degeneration, and foraminal stenosis. Very little is known about the molecular mechanism of eRNAs in IVDD. METHODS: Gene expression profiles of 38 annulus disc samples composed of 27 less degenerated discs (LDs) and 11 more degenerated discs (MDs) were retrieved from the GEO database. Then, differentially expressed enhancer RNAs (DEeRNAs), differentially expressed target genes (DETGs), and differentially expressed transcription factors (DETFs), hallmark of cancer signalling pathways according to GSVA; the types and quantity of immune cells according to CIBERSORT; and immune gene sets according to ssGSEA were analysed to construct an IVDD-related eRNA network. Then, multidimensional validation was performed to explore the interactions among DEeRNAs, DETFs and DEGs in space. RESULTS: A total of 53 components, 14 DETGs, 15 DEeRNAs, 3 DETFs, 5 immune cells, 9 hallmarks, and 7 immune gene sets, were selected to construct the regulatory network. After validation by online multidimensional databases, 21 interactive DEeRNA-DEG-DETF axes related to IVDD exacerbation were identified, among which the C1S-CTNNB1-CHD4 axis was the most significant. CONCLUSION: Based upon the results of our study, we theorize that the C1S-CTNNB1-CHD4 axis plays a vital role in IVDD exacerbation. Specifically, C1S recruits CTNNB1 and upregulates the expression of CHD4 in IVDD, and subsequently, CHD4 suppresses glycolysis and activates oxidative phosphorylation, thus generating insoluble collagen fibre deposits and leading to the progression of IVDD. Overall, these DEeRNAs could comprise promising therapeutic targets for IVDD due to their high tissue specificity.

Transcriptomic and metabolomic dissection of skeletal muscle of crossbred Chongming white goats with different meat production performance.

BACKGROUND: The transcriptome and metabolome dissection of the skeletal muscle of high- and low- growing individuals from a crossbred population of the indigenous Chongming white goat and the Boer goat were performed to discover the potential functional differentially expressed genes (DEGs) and differential expression metabolites (DEMs). RESULTS: A total of 2812 DEGs were detected in 6 groups at three time stages (3,6,12 Month) in skeletal muscle using the RNA-seq method. A DEGs set containing seven muscle function related genes (TNNT1, TNNC1, TNNI1, MYBPC2, MYL2, MHY7, and CSRP3) was discovered, and their expression tended to increase as goat muscle development progressed. Seven DEGs (TNNT1, FABP3, TPM3, DES, PPP1R27, RCAN1, LMOD2) in the skeletal muscle of goats in the fast-growing and slow-growing groups was verified their expression difference by reverse transcription-quantitative polymerase chain reaction. Further, through the Liquid chromatography-mass spectrometry (LC-MS) approach, a total of 183 DEMs in various groups of the muscle samples and these DEMs such as Queuine and Keto-PGF1α, which demonstrated different abundance between the goat fast-growing group and slow-growing group. Through weighted correlation network analysis (WGCNA), the study correlated the DEGs with the DEMs and identified 4 DEGs modules associated with 18 metabolites. CONCLUSION: This study benefits to dissection candidate genes and regulatory networks related to goat meat production performance, and the joint analysis of transcriptomic and metabolomic data provided insights into the study of goat muscle development.

Transcriptional profiles associated with coronary artery disease in type 2 diabetes mellitus.

Background: Coronary artery disease (CAD) is a common complication of Type 2 diabetes mellitus (T2DM). Understanding the pathogenesis of this complication is essential in both diagnosis and management. Thus, this study aimed to characterize the presence of CAD in T2DM using molecular markers and pathway analyses. Methods: The study is a sex- and age-frequency matched case-control design comparing 23 unrelated adult Filipinos with T2DM-CAD to 23 controls (DM with CAD). Healthy controls served as a reference. Total RNA from peripheral blood mononuclear cells (PBMCs) underwent whole transcriptomic profiling using the Illumina HumanHT-12 v4.0 expression beadchip. Differential gene expression with gene ontogeny analyses was performed, with supporting correlational analyses using weighted correlation network analysis (WGCNA). Results: The study observed that 458 genes were differentially expressed between T2DM with and without CAD (FDR<0.05). The 5 top genes the transcription factor 3 (TCF3), allograft inflammatory factor 1 (AIF1), nuclear factor, interleukin 3 regulated (NFIL3), paired immunoglobulin-like type 2 receptor alpha (PILRA), and cytoskeleton-associated protein 4 (CKAP4) with AUCs >89%. Pathway analyses show differences in innate immunity activity, which centers on the myelocytic (neutrophilic/monocytic) theme. SNP-module analyses point to a possible causal dysfunction in innate immunity that triggers the CAD injury in T2DM. Conclusion: The study findings indicate the involvement of innate immunity in the development of T2DM-CAD, and potential immunity markers can reflect the occurrence of this injury. Further studies can verify the mechanistic hypothesis and use of the markers.

Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data.

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

Transcriptome analysis reveals important regulatory factors for condensed tannins synthesis in acorn.

Condensed tannins are widely present in the fruits and seeds of plants and effectively prevent them from being eaten by animals before maturity due to their astringent taste. In addition, condensed tannins are a natural compound with strong antioxidant properties and significant antibacterial effects. Four samples of mature and near-mature Quercus fabri acorns, with the highest and lowest condensed tannin content, were used for genome-based transcriptome sequencing. The KEGG enrichment analysis revealed that the differentially expressed genes (DEGs) were highly enriched in phenylpropanoid biosynthesis and starch and sucrose metabolism. Given that the phenylpropanoid biosynthesis pathway is a crucial step in the synthesis of condensed tannins, we screened for significantly differentially expressed transcription factors and structural genes from the transcriptome data of this pathway and found that the expression levels of four MADS-box, PAL, and 4CL genes were significantly increased in acorns with high condensed tannin content. The quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) experiment further validated this result. In addition, yeast one-hybrid assay confirmed that three MADS-box transcription factors could bind the promoter of the 4CL gene, thereby regulating gene expression levels. This study utilized transcriptome sequencing to discover new important regulatory factors that can regulate the synthesis of acorn condensed tannins, providing new evidence for MADS-box transcription factors to regulate the synthesis of secondary metabolites in fruits.

Transcriptome-wide identification of ARF gene family in medicinal plant Polygonatum kingianum and expression analysis of PkARF members in different tissues.

BACKGROUND: Polygonatum kingianum holds significant importance in Traditional Chinese Medicine due to its medicinal properties, characterized by its diverse chemical constituents including polysaccharides, terpenoids, flavonoids, phenols, and phenylpropanoids. The Auxin Response Factor (ARF) is a pivotal transcription factor known for its regulatory role in both primary and secondary metabolite synthesis. However, our understanding of the ARF gene family in P. kingianum remains limited. METHODS AND RESULTS: We employed RNA-Seq to sequence three distinct tissues (leaf, root, and stem) of P. kingianum. The analysis revealed a total of 31,558 differentially expressed genes (DEGs), with 43 species of transcription factors annotated among them. Analyses via gene ontology and the Kyoto Encyclopedia of Genes and Genomes demonstrated that these DEGs were predominantly enriched in metabolic pathways and secondary metabolite biosynthesis. The proposed temporal expression analysis categorized the DEGs into nine clusters, suggesting the same expression trends that may be coordinated in multiple biological processes across the three tissues. Additionally, we conducted screening and expression pattern analysis of the ARF gene family, identifying 12 significantly expressed PkARF genes in P. kingianum roots. This discovery lays the groundwork for investigations into the role of PkARF genes in root growth, development, and secondary metabolism regulation. CONCLUSION: The obtained data and insights serve as a focal point for further research studies, centred on genetic manipulation of growth and secondary metabolism in P. kingianum. Furthermore, these findings contribute to the understanding of functional genomics in P. kingianum, offering valuable genetic resources.

Transcriptome analysis and functional validation reveal the novel role of LhCYCL in axillary bud development in hybrid Liriodendron.

Shoot branching significantly influences yield and timber quality in woody plants, with hybrid Liriodendron being particularly valuable due to its rapid growth. However, understanding of the mechanisms governing shoot branching in hybrid Liriodendron remains limited. In this study, we systematically examined axillary bud development using morphological and anatomical approaches and selected four distinct developmental stages for an extensive transcriptome analysis. A total of 9,449 differentially expressed genes have been identified, many of which are involved in plant hormone signal transduction pathways. Additionally, we identified several transcription factors downregulated during early axillary bud development, including a noteworthy gene annotated as CYC-like from the TCP TF family, which emerged as a strong candidate for modulating axillary bud development. Quantitative real-time polymerase chain reaction results confirmed the highest expression levels of LhCYCL in hybrid Liriodendron axillary buds, while histochemical ß-glucuronidase staining suggested its potential role in Arabidopsis thaliana leaf axil development. Ectopic expression of LhCYCL in A. thaliana led to an increase of branches and a decrease of plant height, accompanied by altered expression of genes involved in the plant hormone signaling pathways. This indicates the involvement of LhCYCL in regulating shoot branching through plant hormone signaling pathways. In summary, our results emphasize the pivotal role played by LhCYCL in shoot branching, offering insights into the function of the CYC-like gene and establishing a robust foundation for further investigations into the molecular mechanisms governing axillary bud development in hybrid Liriodendron.

Transcriptome analysis of long non-coding RNAs in Mycobacterium avium complex-infected macrophages.

Mycobacterium avium complex (MAC) is a non-tuberculous mycobacterium widely distributed in the environment. Even though MAC infection is increasing in older women and immunocompromised patients, to our knowledge there has been no comprehensive analysis of the MAC-infected host-cell transcriptome-and particularly of long non-coding RNAs (lncRNAs). By using in vitro-cultured primary mouse bone-marrow-derived macrophages (BMDMs) and Cap analysis of gene expression, we analyzed the transcriptional and kinetic landscape of macrophage genes, with a focus on lncRNAs, during MAC infection. MAC infection of macrophages induced the expression of immune/inflammatory response genes and other genes similar to those involved in M1 macrophage activation, consistent with previous reports, although Nos2 (M1 activation) and Arg1 (M2 activation) had distinct expression profiles. We identified 31 upregulated and 30 downregulated lncRNA promoters corresponding respectively to 18 and 26 lncRNAs. Upregulated lncRNAs were clustered into two groups-early and late upregulated-predicted to be associated with immune activation and the immune response to infection, respectively. Furthermore, an Ingenuity Pathway Analysis revealed canonical pathways and upstream transcription regulators associated with differentially expressed lncRNAs. Several differentially expressed lncRNAs reported elsewhere underwent expressional changes upon M1 or M2 preactivation and subsequent MAC infection. Finally, we showed that expressional change of lncRNAs in MAC-infected BMDMs was mediated by toll-like receptor 2, although there may be other mechanisms that sense MAC infection. We identified differentially expressed lncRNAs in MAC-infected BMDMs, revealing diverse features that imply the distinct roles of these lncRNAs in MAC infection and macrophage polarization.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Background: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. Methods: We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Results: Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. Conclusion: RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

Circadian transcriptome of pancreatic adenocarcinoma unravels chronotherapeutic targets.

Pancreatic ductal adenocarcinoma (PDA) is a lethal cancer characterized by a poor outcome and an increasing incidence. A significant majority (>80%) of newly diagnosed cases are deemed unresectable, leaving chemotherapy as the sole viable option, though with only moderate success. This necessitates the identification of improved therapeutic options for PDA. We hypothesized that there are temporal variations in cancer-relevant processes within PDA tumors, offering insights into the optimal timing of drug administration - a concept termed chronotherapy. In this study, we explored the presence of the circadian transcriptome in PDA using patient-derived organoids and validated these findings by comparing PDA data from The Cancer Genome Atlas with noncancerous healthy pancreas data from GTEx. Several PDA-associated pathways (cell cycle, stress response, Rho GTPase signaling) and cancer driver hub genes (EGFR and JUN) exhibited a cancer-specific rhythmic pattern intricately linked to the circadian clock. Through the integration of multiple functional measurements for rhythmic cancer driver genes, we identified top chronotherapy targets and validated key findings in molecularly divergent pancreatic cancer cell lines. Testing the chemotherapeutic efficacy of clinically relevant drugs further revealed temporal variations that correlated with drug-target cycling. Collectively, our study unravels the PDA circadian transcriptome and highlights a potential approach for optimizing chrono-chemotherapeutic efficacy.

Deciphering the role of CX3CL1-CX3CR1 in aortic aneurysm pathogenesis: insights from Mendelian randomization and transcriptomic analyses.

Background: The crucial role of inflammation in aortic aneurysm (AA) is gaining prominence, while there is still a lack of key cytokines or targets for effective clinical translation. Methods: Mendelian randomization (MR) analysis was performed to identify the causal relationship between 91 circulating inflammatory proteins and AA and between 731 immune traits and AA. Bulk RNA sequencing data was utilized to demonstrate the expression profile of the paired ligand-receptor. Gene enrichment analysis, Immune infiltration, and correlation analysis were employed to deduce the potential role of CX3CR1. We used single-cell RNA sequencing data to pinpoint the localization of CX3CL1 and CX3CR1, which was further validated by multiplex immunofluorescence staining. Cellchat analysis was utilized to infer the CX3C signaling pathway. Trajectory analysis and the Cytosig database were exploited to determine the downstream effect of CX3CL1-CX3CR1. Results: We identified 4 candidates (FGF5, CX3CL1, IL20RA, and SCF) in multiple two-sample MR analyses. Subsequent analysis of the expression profile of the paired receptor revealed the significant upregulation of CX3CR1 in AA and its positive correlation with pro-inflammatory macrophages. Two sample MR between immune cell traits and AA demonstrated the potential causality between intermediate monocytes and AA. We finally deciphered in single-cell sequencing data that CX3CL1 sent by endothelial cells (ECs) acted on CX3CR1 of intermediated monocytes, leading to its recruitment and pro-inflammatory responses. Conclusion: Our study presented a genetic insight into the pathogenetic role of CX3CL1-CX3CR1 in AA, and further deciphered the CX3C signaling pathway between ECs and intermediate monocytes.

Whole blood transcriptomics reveals the enrichment of neutrophil activation pathways during erythema nodosum leprosum reaction.

Introduction: Patients with the multibacillary form of leprosy can develop reactional episodes of acute inflammation, known as erythema nodosum leprosum (ENL), which are characterized by the appearance of painful cutaneous nodules and systemic symptoms. Neutrophils have been recognized to play a role in the pathogenesis of ENL, and recent global transcriptomic analysis revealed neutrophil-related processes as a signature of ENL skin lesions. Methods: In this study, we expanded this analysis to the blood compartment, comparing whole blood transcriptomics of patients with non-reactional lepromatous leprosy at diagnosis (LL, n=7) and patients with ENL before administration of anti-reactional treatment (ENL, n=15). Furthermore, a follow-up study was performed with patients experiencing an ENL episode at the time of diagnosis and after 7 days of thalidomide treatment (THAL, n=10). Validation in an independent cohort (ENL=8; LL=7) was performed by RT-qPCR. Results: An enrichment of neutrophil activation and degranulation-related genes was observed in the ENL group, with the gene for the neutrophil activation marker CD177 being the most enriched gene of ENL episode when compared to its expression in the LL group. A more pro-inflammatory transcriptome was also observed, with increased expression of genes related to innate immunity. Validation in an independent cohort indicated that S100A8 expression could discriminate ENL from LL. Supernatants of blood cells stimulated in vitro with Mycobacterium leprae sonicate showed higher levels of CD177 compared to the level of untreated cells, indicating that the leprosy bacillus can activate neutrophils expressing CD177. Of note, suggestive higher CD177 protein levels were found in the sera of patients with severe/moderate ENL episodes when compared with patients with mild episodes and LL patients, highlighting CD177 as a potential systemic marker of ENL severity that deserves future confirmation. Furthermore, a follow-up study was performed with patients at the time of ENL diagnosis and after 7 days of thalidomide treatment (THAL, n=10). Enrichment of neutrophil pathways was sustained in the transcriptomic profile of patients undergoing treatment; however, important immune targets that might be relevant to the effect of thalidomide at a systemic level, particularly NLRP6 and IL5RA, were revealed. Discussion: In conclusion, our study reinforces the key role played by neutrophils in ENL pathogenesis and shed lights on potential diagnostic candidates and novel therapeutic targets that could benefit patients with leprosy.

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosis and therapeutic guidance of bladder cancer. This study focuses on evaluating the potential of high-definition computed tomography (CT) imagery coupled with RNA-sequencing analysis to accurately predict bladder tumor stages, utilizing deep residual networks. Data for this study, including CT images and RNA-Seq datasets for 82 high-grade bladder cancer patients, were sourced from the TCIA and TCGA databases. We employed Cox and lasso regression analyses to determine radiomics and gene signatures, leading to the identification of a three-factor radiomics signature and a four-gene signature in our bladder cancer cohort. ROC curve analyses underscored the strong predictive capacities of both these signatures. Furthermore, we formulated a nomogram integrating clinical features, radiomics, and gene signatures. This nomogram's AUC scores stood at 0.870, 0.873, and 0.971 for 1-year, 3-year, and 5-year predictions, respectively. Our model, leveraging radiomics and gene signatures, presents significant promise for enhancing diagnostic precision in bladder cancer prognosis, advocating for its clinical adoption.

First transcriptomic insight into the working muscles of racing pigeons during a competition flight.

BACKGROUND: The currently known homing pigeon is a result of a sharp one-sided selection for flight characteristics focused on speed, endurance, and spatial orientation. This has led to extremely well-adapted athletic phenotypes in racing birds. METHODS: Here, we identify genes and pathways contributing to exercise adaptation in sport pigeons by applying next-generation transcriptome sequencing of m.pectoralis muscle samples, collected before and after a 300 km competition flight. RESULTS: The analysis of differentially expressed genes pictured the central role of pathways involved in fuel selection and muscle maintenance during flight, with a set of genes, in which variations may therefore be exploited for genetic improvement of the racing pigeon population towards specific categories of competition flights. CONCLUSIONS: The presented results are a background to understanding the genetic processes in the muscles of birds during flight and also are the starting point of further selection of genetic markers associated with racing performance in carrier pigeons.

Comparative analysis of infected cassava root transcriptomics reveals candidate genes for root rot disease resistance.

Cassava root-rot incited by soil-borne pathogens is one of the major diseases that reduces root yield. Although the use of resistant cultivars is the most effective method of management, the genetic basis for root-rot resistance remains poorly understood. Therefore, our work analyzed the transcriptome of two contrasting genotypes (BRS Kiriris/resistant and BGM-1345/susceptible) using RNA-Seq to understand the molecular response and identify candidate genes for resistance. Cassava seedlings (resistant and susceptible to root-rot) were both planted in infested and sterilized soil and samples from Initial-time and Final-time periods, pooled. Two controls were used: (i) seedlings collected before planting in infested soil (absolute control) and, (ii) plants grown in sterilized soil (mock treatments). For the differentially expressed genes (DEGs) analysis 23.912 were expressed in the resistant genotype, where 10.307 were differentially expressed in the control treatment, 15 DEGs in the Initial Time-period and 366 DEGs in the Final Time-period. Eighteen candidate genes from the resistant genotype were related to plant defense, such as the MLP-like protein 31 and the peroxidase A2-like gene. This is the first model of resistance at the transcriptional level proposed for the cassava × root-rot pathosystem. Gene validation will contribute to screening for resistance of germplasm, segregating populations and/or use in gene editing in the pursuit to develop most promising cassava clones with resistance to root-rot.

Periodic heat waves-induced neuronal etiology in the elderly is mediated by gut-liver-brain axis: a transcriptome profiling approach.

Heat stress exposure in intermittent heat waves and subsequent exposure during war theaters pose a clinical challenge that can lead to multi-organ dysfunction and long-term complications in the elderly. Using an aged mouse model and high-throughput sequencing, this study investigated the molecular dynamics of the liver-brain connection during heat stress exposure. Distinctive gene expression patterns induced by periodic heat stress emerged in both brain and liver tissues. An altered transcriptome profile showed heat stress-induced altered acute phase response pathways, causing neural, hepatic, and systemic inflammation and impaired synaptic plasticity. Results also demonstrated that proinflammatory molecules such as S100B, IL-17, IL-33, and neurological disease signaling pathways were upregulated, while protective pathways like aryl hydrocarbon receptor signaling were downregulated. In parallel, Rantes, IRF7, NOD1/2, TREM1, and hepatic injury signaling pathways were upregulated. Furthermore, current research identified Orosomucoid 2 (ORM2) in the liver as one of the mediators of the liver-brain axis due to heat exposure. In conclusion, the transcriptome profiling in elderly heat-stressed mice revealed a coordinated network of liver-brain axis pathways with increased hepatic ORM2 secretion, possibly due to gut inflammation and dysbiosis. The above secretion of ORM2 may impact the brain through a leaky blood-brain barrier, thus emphasizing intricate multi-organ crosstalk.