RNA-seq Gene Expression Research Articles

Corrigendum to “Differentiation of sow and mouse ovarian granulosa cells exposed to zearalenone in vitro using RNA-seq gene expression” [Toxicology and Applied Pharmacology, 350 (2018) 78–90]

Corrigendum to “Differentiation of sow and mouse ovarian granulosa cells exposed to zearalenone in vitro using RNA-seq gene expression” [Toxicology and Applied Pharmacology, 350 (2018) 78–90]

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes.

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer

Background Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. Methods In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. Results We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. Conclusion The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.

The Prognostic Value and Immunotherapeutic Characteristics of GFPT2 in Pan-cancer.

The purpose of this study is to investigate the underlying relationship of diagnosis and therapy between glutamine-fructose-6-phosphate transaminase 2 (GFPT2) and various cancers. The Cancer Genome Atlas (TCGA) database was utilized to get gene expression RNAseq and clinical data for 33 tumors. The immunotherapeutic cohorts, including GSE35640, GSE78220, GSE67501, GSE181815, and IMvigor210, were derived from the Gene Expression Omnibus database (GEO) and a previously released article. Differential expression analysis of GFPT2 was performed using several clinical factors, and prognostic analysis was performed using Cox proportional hazard regression. In addition, the Cell type Identification By Estimating Relative Subsets Of RNA transcripts (CIBERSORT) and the Estimation of STromal and Immune cells in MAlignant Tumor tissues utilizing Expression data (ESTIMATE) algorithms were used to investigate the connection between GFPT2 and the tumor microenvironment. This approach additionally incorporated dynamic immunological indicators, such as tumor mutational burden (TMB) and microsatellite instability (MSI). In addition, a correlation between GFPT2 expression and the effectiveness of anticancer drugs was plotted for discussion. GFPT2 expression significantly differed in 11 out of 33 cancers. Although the distinct correlation between GFPT2 expression and clinical parameters had no wide distribution in pan-cancer, it demonstrated the potential prognostic validity of gene expression. GFPT2 demonstrated a strong correlation with immune infiltration, immune modulators, and immunerelated biomarkers. Furthermore, a variance analysis demonstrated a significant relationship between GFPT2 and the efficacy of immunotherapy. In addition, GFPT2 was associated with increased sensitivity of drugs such as Olaparib and Lenvatinib and decreased sensitivity of drugs such as Nilotinib. Collectively, GFPT2 is potentially useful as a biomarker for prognostic prediction and immune infiltration in a variety of malignancies ,and could lead to exciting new approaches to personalized oncotherapy.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) elicited dose-dependent shifts in the murine urinary metabolome associated with hepatic AHR-mediated differential gene expression.

Epidemiological evidence suggests an association between dioxin and dioxin-like compound (DLC) exposure and human liver disease. The prototypical DLC, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), has been shown to induce the progression of reversible hepatic steatosis to steatohepatitis with periportal fibrosis and biliary hyperplasia in mice. Although the effects of TCDD toxicity are mediated by aryl hydrocarbon receptor (AHR) activation, the underlying mechanisms of TCDD-induced hepatotoxicity are unresolved. In the present study, male C57BL/6NCrl mice were gavaged every 4 days for 28 days with 0.03 - 30 μg/kg TCDD and evaluated for liver histopathology and gene expression as well as complementary 1-dimensional proton magnetic resonance (1D- 1H NMR) urinary metabolic profiling. Urinary trimethylamine (TMA), trimethylamine N-oxide (TMAO), and 1-methylnicotinamide (1MN) levels were altered by TCDD at doses ≤ 3 μg/kg; other urinary metabolites, like glycolate, urocanate, and 3-hydroxyisovalerate, were only altered at doses that induced moderate to severe steatohepatitis. Bulk liver RNA-seq data suggested altered urinary metabolites correlated with hepatic differential gene expression corresponding to specific metabolic pathways. In addition to evaluating whether altered urinary metabolites were liver-dependent, published single-nuclear RNA-seq (snRNA-seq), AHR ChIP-seq, and AHR knockout gene expression datasets provide further support for hepatic cell-type and AHR-regulated dependency, respectively. Overall, TCDD-induced liver effects were preceded by and occurred with changes in urinary metabolite levels due to AHR-mediated changes in hepatic gene expression.

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians.

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building upon this insight, we developed sex-specific 3-year survival predictive models along with a single comprehensive model. These sex-specific models outperformed the single general model despite the smaller sample sizes. We further refined our models by using the most important features extracted from these initial models. The refined sex-specific predictive models achieved improved accuracies of 92.62% for males and 91.96% for females, respectively, versus an accuracy of 87.84% from the refined comprehensive model, further highlighting the value of sex-specific analysis. Based on these findings, we created Gap-App, a web application that enables the use of individual gene expression profiles combined with sex information for personalized survival predictions. Gap-App, the first online tool aiming to bridge the gap between complex genomic data and clinical application and facilitating more precise and individualized cancer care, marks a significant advancement in personalized prognosis. The study not only underscores the importance of acknowledging sex differences in personalized prognosis, but also sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The GAP-App service is freely available at www.gap-app.org.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Beyond the Spectrum: Subtype-Specific Molecular Insights into Autism Spectrum Disorder Via Multimodal Data Integration.

Some toddlers with autism spectrum disorder (ASD) have mild social symptoms and developmental improvement in skills, but for others, symptoms and abilities are moderately or even severely affected. Those with profound autism have the most severe social, language, and cognitive symptoms and are at the greatest risk of having a poor developmental outcome. The little that is known about the underlying biology of this important profound autism subtype, points clearly to embryonic dysregulation of proliferation, differentiation and neurogenesis. Because it is essential to gain foundational knowledge of the molecular biology associated with profound, moderate, and mild autism clinical subtypes, we used well-validated, data-driven patient subtyping methods to integrate clinical and molecular data at 1 to 3 years of age in a cohort of 363 ASD and controls representative of the general pediatric population in San Diego County. Clinical data were diagnostic, language, cognitive and adaptive ability scores. Molecular measures were 50 MSigDB Hallmark gene pathway activity scores derived from RNAseq gene expression. Subtyping identified four ASD, typical and mixed diagnostic clusters. 93% of subjects in one cluster were profound autism and 93% in a different cluster were control toddlers; a third cluster was 76% moderate ability ASD; and the last cluster was a mix of mild ASD and control toddlers. Among the four clusters, the profound autism subtype had the most severe social symptoms, language, cognitive, adaptive, social attention eye tracking, social fMRI activation, and age-related decline in abilities, while mild autism toddlers mixed within typical and delayed clusters had mild social symptoms, and neurotypical language, cognitive and adaptive scores that improved with age compared with profound and moderate autism toddlers in other clusters. In profound autism, 7 subtype-specific dysregulated gene pathways were found; they control embryonic proliferation, differentiation, neurogenesis, and DNA repair. To find subtype-common dysregulated pathways, we compared all ASD vs TD and found 17 ASD subtype-common dysregulated pathways. These common pathways showed a severity gradient with the greatest dysregulation in profound and least in mild. Collectively, results raise the new hypothesis that the continuum of ASD heterogeneity is moderated by subtype-common pathways and the distinctive nature of profound autism is driven by the differentially added profound subtype-specific embryonic pathways.

Gene pointNet for tumor classification

The rising incidence of cancer underscores the imperative for innovative diagnostic and prognostic methodologies. This study delves into the potential of RNA-Seq gene expression data to enhance cancer classification accuracy. Introducing a pioneering approach, we model gene expression data as point clouds, capitalizing on the data's intrinsic properties to bolster classification performance. Utilizing PointNet, a typical technique for processing point cloud data, as our framework's cornerstone, we incorporate inductive biases pertinent to gene expression and pathways. This integration markedly elevates model efficacy, culminating in developing an end-to-end deep learning classifier with an accuracy rate surpassing 99%. Our findings not only illuminate the capabilities of AI-driven models in the realm of oncology but also highlight the criticality of acknowledging biological dataset nuances in model design. This research provides insights into application of deep learning in medical science, setting the stage for further innovation in cancer classification through sophisticated biological data analysis. The source code for our study is accessible at: https://github.com/cialab/GPNet.

Phenotypic and gene expression characteristics of Japanese eel late-stage leptocephali and glass eels after growth on different diets with a focus on nutrient-accumulation status

To evaluate the quality of glass eels that were derived from late-stage Anguilla japonica leptocephali raised on an old-type of shark egg-based diet (SE) or a new-type of fish meal-based diet (FSY), specific gravity (SG), feeding tests, and body compositional and RNA-seq gene-expression analyses were conducted. The largest content difference between the two dietary groups was in lipids, with the body-compositional index (BCI) being 29.2% lower in FSY-leptocephali and 43.1% lower in FSY-glass eels at day 3 post-metamorphosis, but the difference between groups narrowed to 21.6% at post-metamorphosis first feeding. FSY-leptocephali had heavier SG, a larger number of differentially expressed genes (DEGs) related to muscle development and/or movement, and higher creatine kinase expression, suggesting that they settled easily, and were forced to swim more against fast bottom currents in the tanks while consuming more energy. It was found that the days until first feeding in FSY-glass eels was significantly shorter than in SE glass eels, with lower variability among eels. The number of DEGs was larger in FSY-glass eels, with the largest difference of 3.6 times occurring at day 10 post-metamorphosis, especially in the metabolism category, but at first feeding, they decreased drastically in both groups narrowing the difference. In the FSY-glass eels, the number of DEGs related to lipid, amino acid, and protein metabolisms were larger at days 3–20 after metamorphosis. Total enzyme expression levels for acylglycerol degradation, ketone body biosynthesis and citric acid cycle pathways also tended to be higher, suggesting that they obtained energy aerobically using lipids and other materials. Based on the patterns of up- and down-regulation of expression levels, growth hormone, ghrelin/obestatin prepropeptide and pro-melanin-concentrating hormone-like were detected as possible orexigenic hormones, and proopiomelanocortin and nucleobindin-2 as possible anorexigenic hormones. These results indicate that diet quality affects not only SG and other characteristics of leptocephali, but even body composition, metabolism and time to first feeding in the post-metamorphic glass eels for use as seedlings for aquaculture.

Insights into gemcitabine resistance in pancreatic cancer: association with metabolic reprogramming and TP53 pathogenicity in patient derived xenografts

BackgroundWith poor prognosis and high mortality, pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Standard of care therapies for PDAC have included gemcitabine for the past three decades, although resistance often develops within weeks of chemotherapy initiation through an array of possible mechanisms.MethodsWe reanalyzed publicly available RNA-seq gene expression profiles of 28 PDAC patient-derived xenograft (PDX) models before and after a 21-day gemcitabine treatment using our validated analysis pipeline to identify molecular markers of intrinsic and acquired resistance.ResultsUsing normalized RNA-seq quantification measurements, we first identified oxidative phosphorylation and interferon alpha pathways as the two most enriched cancer hallmark gene sets in the baseline gene expression profile associated with intrinsic gemcitabine resistance and sensitivity, respectively. Furthermore, we discovered strong correlations between drug-induced expression changes in glycolysis and oxidative phosphorylation genes and response to gemcitabine, which suggests that these pathways may be associated with acquired gemcitabine resistance mechanisms. Thus, we developed prediction models using baseline gene expression profiles in those pathways and validated them in another dataset of 12 PDAC models from Novartis. We also developed prediction models based on drug-induced expression changes in genes from the Molecular Signatures Database (MSigDB)’s curated 50 cancer hallmark gene sets. Finally, pathogenic TP53 mutations correlated with treatment resistance.ConclusionOur results demonstrate that concurrent upregulation of both glycolysis and oxidative phosphorylation pathways occurs in vivo in PDAC PDXs following gemcitabine treatment and that pathogenic TP53 status had association with gemcitabine resistance in these models. Our findings may elucidate the molecular basis for gemcitabine resistance and provide insights for effective drug combination in PDAC chemotherapy.

The Sprout Inhibitor 1,4-Dimethylnaphthalene Results in Common Gene Expression Changes in Potato Cultivars with Varying Dormancy Profiles

AbstractSprout suppression is a crucial aspect of maintaining postharvest Solanum tuberosum (potato) tuber quality. 1,4-dimethylnaphthalene (DMN) has demonstrated effective sprout suppression during long-term storage of potatoes. Its mode of action, however, remains unknown, and previous studies utilizing single cultivars preclude identification of a common response to treatment. Thus, the goal of this study was to identify common transcriptomic responses of multiple potato cultivars of varying dormancy lengths to DMN exposure during two dormancy stages. RNA-seq gene expression profiling supported differing sensitivity to DMN treatment dependent upon cultivar and dormancy stage. A limited number of genes with similar expression patterns were common to all cultivars. These were primarily identified in ecodormant tubers and were associated with cell cycle progression, hormone signaling, and biotic and abiotic stress response. DMN treatment resulted in significant upregulation of members of ANAC/NAC and WRKY transcription factor families. Investigation of affected protein-protein interaction networks revealed a small number of networks responsive to DMN in all cultivars. These results suggest that response to DMN is largely cultivar and dormancy stage-dependent, and the primary response is governed by a limited number of stress and growth-related genes and protein-protein interactions.

Unveiling G-protein coupled receptors as potential targets for ovarian cancer nanomedicines: from RNA sequencing data analysis to in vitro validation

Genetic heterogeneity in ovarian cancer indicates the need for personalised treatment approaches. Currently, very few G-protein coupled receptors (GPCRs) have been investigated for active targeting with nanomedicines such as antibody-conjugated drugs and drug-loaded nanoparticles, highlighting a neglected potential to develop personalised treatment. To address the genetic heterogeneity of ovarian cancer, a future personalised approach could include the identification of unique GPCRs expressed in cancer biopsies, matched with personalised GPCR-targeted nanomedicines, for the delivery of lethal drugs to tumour tissue before, during and after surgery. Here we report on the systematic analysis of public ribonucleic acid-sequencing (RNA-seq) gene expression data, which led to prioritisation of 13 GPCRs as candidates with frequent overexpression in ovarian cancer tissues. Subsequently, primary ovarian cancer cells derived from ascites and ovarian cancer cell lines were used to confirm frequent gene expression for the selected GPCRs. However, the expression levels showed high variability within our selection of samples, therefore, supporting and emphasising the need for the future development of case-to-case personalised targeting approaches.

Inflammatory gene regulation by Cdc42 in airway epithelial cells

Cytokine release from airway epithelial cells is a key immunological process that coordinates an immune response in the lungs. We propose that the Rho GTPase, Cdc42, regulates both transcription and trafficking of cytokines, ultimately affecting the essential process of cytokine release and subsequent inflammation in the lungs. Here, we examined the pro-inflammatory transcriptional profile that occurs in bronchial epithelial cells (BEAS-2B) in response to TNF-α using RNA-Seq and differential gene expression analysis. To interrogate the role of Cdc42 in inflammatory gene expression, we used a pharmacological inhibitor of Cdc42, ML141, and determined changes in the transcriptomic profile induced by Cdc42 inhibition. Our results indicated that Cdc42 inhibition with ML141 resulted in a unique inflammatory phenotype concomitant with increased gene expression of ER stress genes, Golgi membrane and vesicle transport genes. To further interrogate the inflammatory pathways regulated by Cdc42, we made BEAS-2B knockdown strains for the signaling targets TRIB3, DUSP5, SESN2 and BMP4, which showed high differential expression in response to Cdc42 inhibition. Depletion of DUSP5 and TRIB3 reduced the pro-inflammatory response triggered by Cdc42 inhibition as shown by a reduction in cytokine transcript levels. Depletion of SESN2 and BMP4 did not affect cytokine transcript level, however, Golgi fragmentation was reduced. These results provide further evidence that in airway epithelial cells, Cdc42 is part of a signaling network that controls inflammatory gene expression and secretion by regulating Golgi integrity.Summary sentence:We define the Cdc42-regulated gene networks for inflammatory signaling in airway epithelial cells which includes regulation of ER stress response and vesicle trafficking pathways.

Screening Analysis of Predictive Markers for Cytokine Release Syndrome Risk in CAR-T Cell Therapy

Background: Chimeric Antigen Receptor (CAR)-T cell therapy has emerged as a highly effective treatment for hematological tumors. However, the associated adverse reaction, Cytokine Release Syndrome (CRS), poses a significant challenge. While numerous studies have investigated CRS biomarkers during CAR-T cell therapy, the ability to predict CRS risk prior to treatment initiation remains a crucial yet underexplored aspect. Objective: The primary purpose of this study was to address the issue of limited data, explore an alternative approach using public data to identify predictive markers for CRS risk assessment from RNA-Seq in pre-treatment patients data, and comprehend the inducible mechanisms underlying CRS. Methods: We integrated information from two public databases, the FDA Adverse Event Reporting System (FAERS) for adverse reaction reports of CAR-T cell therapy and the Cancer Genome Atlas (TCGA) for RNA-Seq data on corresponding hematological tumors. Candidate genes were screened by correlation analysis between Reported Odds Ratio (ROR) values and RNA-Seq gene expression levels, and then core factors were identified through stepwise analysis of pathway enrichment, cluster analysis, and protein interactions. Results: Our analysis highlighted the correlation between CRS risk and pre-treatment T cell activation/ proliferation, identifying key genes (IFN-γ, IL1β, IL2, IL6, and IL10) as significant CRS indicators. Conclusion: This study offers a unique perspective on predicting CRS risk before CAR-T cell therapy, circumventing the challenges of scarce clinical data by leveraging analysis of public databases. It elucidates the crucial role of T cell activation/proliferation dynamics in CRS. The analytical methods and identified markers provide a reference for the research and clinical application of CAR-T cell therapy.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNAseq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

RNA-seq gene expression profiling of the bladder in a mouse model of urogenital schistosomiasis.

Parasitic flatworms of the Schistosoma genus cause schistosomiasis, which affects over 230 million people. Schistosoma haematobium causes the urogenital form of schistosomiasis (UGS), which can lead to hematuria, fibrosis, and increased risk of secondary infections by bacteria or viruses. UGS is also linked to bladder cancer. To understand the bladder pathology during S. haematobium infection, our group previously developed a mouse model that involves the injection of S. haematobium eggs into the bladder wall. Using this model, we studied changes in epigenetics profile, as well as changes in gene and protein expression in the host bladder tissues. In the current study, we expand upon this work by examining the expression level of both host and parasite genes using RNA sequencing (RNA-seq) in the mouse bladder wall injection model of S. haematobium infection. We used a mouse model of S. haematobium infection in which parasite eggs or vehicle control were injected into the bladder walls of female BALB/c mice. RNA-seq was performed on the RNA isolated from the bladders four days after bladder wall injection. RNA-seq analysis of egg- and vehicle control-injected bladders revealed the differential expression of 1025 mouse genes in the egg-injected bladders, including genes associated with cellular infiltration, immune cell chemotaxis, cytokine signaling, and inflammation We also observed the upregulation of immune checkpoint-related genes, which suggests that while the infection causes an inflammatory response, it also dampens the response to avoid excessive inflammation-related damage to the host. Identifying these changes in host signaling and immune responses improves our understanding of the infection and how it may contribute to the development of bladder cancer. Analysis of the differential gene expression of the parasite eggs between bladder-injected versus uninjected eggs revealed 119 S. haematobium genes associated with transcription, intracellular signaling, and metabolism. The analysis of the parasite genes also revealed fewer transcript reads compared to that found in the analysis of mouse genes, highlighting the challenges of studying parasite egg biology in the mouse model of S. haematobium infection.

The Gene Expression Landscape of Disease Genes.

Fine-mapping and gene-prioritisation techniques applied to the latest Genome-Wide Association Study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate. Specifically, we combine GWAS summary statistics, gene prioritisation results and gene expression RNA-seq data from 46 tissues and 204 cell types in relation to 16 major diseases (including 8 cancers). In tissues and cell types with well-established relevance to the disease, the prioritised genes typically have higher absolute and relative (i.e. tissue/cell specific) expression compared to non-prioritised 'control' genes. Examples include brain tissues in psychiatric disorders (P-value < 1×10-7), microglia cells in Alzheimer's Disease (P-value = 9.8×10-3) and colon mucosa in colorectal cancer (P-value < 1×10-3). We also observe significantly higher expression for disease genes in multiple tissues and cell types with no established links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer's disease (P-values < 1×10-3), the cause for others is unclear and motivates further investigation that may provide novel insights into disease etiology. For example, mammary tissue in Type 2 Diabetes (P-value < 1×10-7); reproductive tissues such as breast, uterus, vagina, and prostate in Coronary Artery Disease (P-value < 1×10-4); and motor neurons in psychiatric disorders (P-value < 3×10-4). In the GTEx dataset, tissue type is the major predictor of gene expression but the contribution of each predictor (tissue, sample, subject, batch) varies widely among disease-associated genes. Finally, we highlight genes with the highest levels of gene expression in relevant tissues to guide functional follow-up studies. Our results could offer novel insights into the tissues and cells involved in disease initiation, inform drug target and delivery strategies, highlighting potential off-target effects, and exemplify the relative performance of different statistical tests for linking disease genes with tissue and cell type gene expression.

Association between clinical and disease characteristics and detectable or undetectable baseline ctDNA in patients with metastatic uveal melanoma.

9537 Background: Tebentafusp, a bispecific soluble TCR specific for a gp100 peptide, is licensed for the treatment of metastatic uveal melanoma (mUM) in HLA-A*02:01+ individuals. 82% of patients (pts) with mUM in the IMCgp100-102 (previously treated; 2L+) and 61% in IMCgp100-202 (frontline; 1L) studies, respectively, had detectable baseline ctDNA. There is a strong relationship between decrease in ctDNA by 9 weeks and overall survival (OS) [Carvajal 2022; Hassel &amp; Piperno-Neumann 2023]. Patients with undetectable baseline ctDNA had improved survival despite having macroscopic disease. In this study, we report the characteristics of baseline ctDNA undetectable vs detectable disease. Methods: Disease characteristics of 202 1L (Study 202) and 117 2L+ (Study 102) pts who had baseline undetectable or detectable ctDNA (unDNA vs detDNA) were compared in a post-hoc unplanned analysis. ctDNA detection was analysed at baseline and up to week 9 on tebentafusp using targeted mPCR-NGS assay for mutations in 15 genes including GNAQ, GNA11, SF3B1, CYSLTR2, PLCB4 and EIF1AX. Largest lesion size groups (≤ 3 cm, 3-8 cm, &gt; 8 cm) were defined by American Joint Committee on Cancer 7th Ed. Presence of oncogenic mutations in GNAQ, GNA11, SF3B1 and BAP1 were assessed by WES on metastatic tissue biopsies. Results: Baseline ctDNA levels increased in association with size of largest lesion regardless of number of prior therapies. However, there was an association between disease burden (defined by largest lesion size) and unDNA vs detDNA in both studies, with all pts with largest liver lesion &gt; 8 cm having detDNA. In Study 202, % of the population with unDNA vs detDNA was 50% vs 50% for ≤ 3cm and 27% vs 73% for 3.1-8 cm. While OS was similar for unDNA &amp; detDNA in pts with ≤ 3cm lesions, OS was longer for pts with unDNA vs detDNA who had baseline tumor size 3.1-8 cm in both studies (Table). Within this 3.1-8 cm group, pts with unDNA tended to have lower LDH, AST, and ALP. Initial analysis showed no difference in the frequency of GNAQ, GNA11, SF3B1 or BAP1 mutations between unDNA and detDNA groups. Further data on somatic mutation defined groups and RNAseq gene expression in unDNA and detDNA groups will be presented. Conclusions: Patients with unDNA were more likely to have smaller disease burden than those with detDNA; however, many pts with unDNA had a significant burden of metastatic disease. Baseline unDNA was associated with improved OS outcome vs detDNA in pts with 3.1-8 cm baseline metastatic deposits, but not in pts with ≤ 3cm disease. No association was seen between oncogenic background of GNAQ/GNA11/SF3B1/BAP1 and unDNA vs detDNA. Clinical trial information: NCT02570308 ; NCT03070392 . [Table: see text]

Upstream regulation of neurotransmitter signaling genes in metastatic colorectal cancer (mCRC) patients: Data from CALGB/SWOG 80405 trial (Alliance) and MONSTAR-SCREEN-2.

3523 Background: Evidence suggests neurotransmitter signaling may play a crucial role in tumorigenesis of gastrointestinal (GI) cancers, including migration, proliferation, and apoptosis, as well as angiogenesis and the microenvironment. We developed and applied a strategy to identify upstream regulators of neurotransmitters and their associations with patient outcomes. Methods: Prior literature yielded 85 neurotransmitter signaling genes mapped to RNA-seq gene expression in tumor tissue from 545 patients with mCRC enrolled in the phase III CALGB/SWOG 80405 trial. These samples were also sequenced by FoundationOne CDx for DNA damage. Using Cox proportional hazards regression, we identified a subset of CDx genes associated with overall survival (OS) or progression-free survival (PFS). We extended the Causal Inference Test (CIT) for time-to-event data and identified a subset of the 85 signaling genes consistent with mediation of DNA mutation effects on OS and PFS. Another CIT analysis identified potential upstream neurotransmitter regulators, gene expression mediators of mutation effects on the signaling genes. Top genes were then assessed in whole-transcriptome sequencing (WTS) expression data with Caris MI profile collected from 398 patients with advanced CRC enrolled in the MONSTAR-SCREEN-2 trial. Results: At the 0.05 alpha level, we identified 20 signaling genes with gene expression associated with PFS and/or OS, and 68 genes with evidence of DNA mutations associated with PFS and/or OS. False discovery rate (FDR) &lt; 0.05 was applied to filter the 56,674 expression features on PFS and OS associations, yielding 173 unique genes as possible mediators. The CIT yielded 53 putative mediators at FDR &lt; 0.05, suggesting upstream regulation of neurotransmitter signaling. Five genes were identified as targeting at least 8 signaling genes each, PIGU, ANO9, LY6G6D, and GPR160, and FSCN1, where the first 4 are favorable and only FSCN1 is a poorer prognostic. Following up on these 5 genes in MONSTAR-SCREEN-2 by dichotomizing at the median value of WTS expression, 3 were nominally associated with OS, PIGU ( P = 0.050; HR, 0.59; 95%CI, 0.34-1.00), ANO9( P = 0.029; HR, 0.55; 95%CI, 0.32-0.95), LY6G6D( P = 0.0024; HR, 0.44; 95%CI, 0.25-0.76), and FSCN1 was suggestive ( P = 0.079; HR, 1.62; 95%CI, 0.94-2.77), with direction of effects in agreement with 80405 results for all four. Conclusions: Our novel approach identified previously unknown upstream regulators of neurotransmitter signaling associated with treatment outcomes in mCRC. PIGU, FSCN1, and LY6G6D have been suggested in prior literature as targets for CRC and ANO9 has been suggested as a target for GI cancer. These results bolster the strategy of using mediation analysis with large-scale molecular data to identify new therapeutic targets in CRC. NCT00265850.