Complex Gene Expression Research Articles

Circular RNA circASH1L(4,5) protects microRNA-129-5p from target-directed microRNA degradation in human skin wound healing.

Skin wound healing involves a complex gene expression program that remains largely undiscovered in humans. Circular RNAs (circRNAs) and microRNAs (miRNAs) are key players in this process. To understand the functions and potential interactions of circRNAs and miRNAs in human skin wound healing. CircRNA, linear RNA, and miRNA expression in human acute and chronic wounds were analyzed using RNA sequencing and qRT-PCR. The roles of circASH1L(4,5) and miR-129-5p were studied in human primary keratinocytes (proliferation and migration assays, microarray analysis) and ex vivo wound models (histological analysis). The interaction between circASH1L(4,5) and miR-129-5p was examined using luciferase reporter and RNA pull-down assays. We identified circASH1L(4,5) and its interaction with miR-129-5p, both of which increased during human skin wound healing. Unlike typical miRNA sponging, circASH1L enhanced miR-129 stability and silencing activity by protecting it from target-directed degradation triggered by NR6A1 mRNA. TGF-β signaling, crucial in wound healing, promoted circASH1L expression while suppressing NR6A1, thereby increasing miR-129 abundance at the post-transcriptional level. CircASH1L and miR-129 enhanced keratinocyte migration and proliferation, crucial for re-epithelialization of human wounds. Our study uncovers a novel role for circRNAs as protectors of miRNAs and highlights the importance of regulated miRNA degradation in skin wound healing.

Recording morphogen signals reveals mechanisms underlying gastruloid symmetry breaking.

Aggregates of stem cells can break symmetry and self-organize into embryo-like structures with complex morphologies and gene expression patterns. Mechanisms including reaction-diffusion Turing patterns and cell sorting have been proposed to explain symmetry breaking but distinguishing between these candidate mechanisms of self-organization requires identifying which early asymmetries evolve into subsequent tissue patterns and cell fates. Here we use synthetic 'signal-recording' gene circuits to trace the evolution of signalling patterns in gastruloids, three-dimensional stem cell aggregates that form an anterior-posterior axis and structures resembling the mammalian primitive streak and tailbud. We find that cell sorting rearranges patchy domains of Wnt activity into a single pole that defines the gastruloid anterior-posterior axis. We also trace the emergence of Wnt domains to earlier heterogeneity in Nodal activity even before Wnt activity is detectable. Our study defines a mechanism through which aggregates of stem cells can form a patterning axis even in the absence of external spatial cues.

Associations between the multitrajectory neuroplasticity of neuronavigated rTMS-mediated angular gyrus networks and brain gene expression in AD spectrum patients with sleep disorders.

The multifactorial influence of repetitive transcranial magnetic stimulation (rTMS) on neuroplasticity in neural networks is associated with improvements in cognitive dysfunction and sleep disorders. The mechanisms of rTMS and the transcriptional-neuronal correlation in Alzheimer's disease (AD) patients with sleep disorders have not been fully elucidated. Forty-six elderly participants with cognitive impairment (23 patients with low sleep quality and 23 patients with high sleep quality) underwent 4-week periods of neuronavigated rTMS of the angular gyrus and neuroimaging tests, and gene expression data for six post mortem brains were collected from another database. Transcription-neuroimaging association analysis was used to evaluate the effects on cognitive dysfunction and the underlying biological mechanisms involved. Distinct variable neuroplasticity in the anterior and posterior angular gyrus networks was detected in the low sleep quality group. These interactions were associated with multiple gene pathways, and the comprehensive effects were associated with improvements in episodic memory. Multitrajectory neuroplasticity is associated with complex biological mechanisms in AD-spectrum patients with sleep disorders. This was the first transcription-neuroimaging study to demonstrate that multitrajectory neuroplasticity in neural circuits was induced via neuronavigated rTMS, which was associated with complex gene expression in AD-spectrum patients with sleep disorders. The interactions between sleep quality and neuronavigated rTMS were coupled with multiple gene pathways and improvements in episodic memory. The present strategy for integrating neuroimaging, rTMS intervention, and genetic data provide a new approach to comprehending the biological mechanisms involved in AD.

ScEMB: Learning context representation of genes based on large-scale single-cell transcriptomics.

The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challenges. We present scEMB, a transformer-based deep learning model developed to capture context-aware gene embeddings from large-scale single-cell transcriptomics data. Trained on over 30 million single-cell transcriptomes, scEMB utilizes an innovative binning strategy that integrates data across multiple platforms, effectively preserving both gene expression hierarchies and cell-type specificity. In downstream tasks such as batch integration, clustering, and cell type annotation, scEMB demonstrates superior performance compared to existing models like scGPT and Geneformer. Notably, scEMB excels in silico correlation analysis, accurately predicting gene perturbation effects in CRISPR-edited datasets and microglia state transition, identifying a few known Alzheimer's disease (AD) risks genes in top gene list. Additionally, scEMB offers robust fine-tuning capabilities for domain-specific applications, making it a versatile tool for tackling diverse biological problems such as therapeutic target discovery and disease modeling. scEMB represents a powerful tool for extracting biologically meaningful insights from complex gene expression data. Its ability to model in silico perturbation effects and conduct correlation analyses in the embedding space highlights its potential to accelerate discoveries in precision medicine and therapeutic development.

Deciphering the cellular and molecular landscapes of Wnt/β-catenin signaling in mouse embryonic kidney development

BackgroundThe Wnt/β-catenin signaling pathway is critical in kidney development, yet its specific effects on gene expression in different embryonic kidney cell types are not fully understood. MethodsWnt/β-catenin signaling was activated in mouse E12.5 kidneys in vitro using CHIR99021, with RNA sequencing performed afterward, and the results were compared to DMSO controls (dataset GSE131240). Differential gene expression in ureteric buds and cap mesenchyme following pathway activation (datasets GSE20325 and GSE39583) was analyzed. Single-cell RNA-seq data from the Mouse Cell Atlas was used to link differentially expressed genes (DEGs) with kidney cell types. β-catenin ChIP-seq data (GSE39837) identified direct transcriptional targets. ResultsActivation of Wnt/β-catenin signaling led to 917 significant DEGs, including the upregulation of Notum and Apcdd1 and the downregulation of Crym and Six2. These DEGs were involved in kidney development and immune response. Single-cell analysis identified 787 DEGs across nineteen cell subtypes, with Macrophage_Apoe high cells showing the most pronounced enrichment of Wnt/β-catenin-activated genes. Gene expression profiles in ureteric buds and cap mesenchyme differed significantly upon β-catenin manipulation, with cap mesenchyme showing a unique set of DEGs. Analysis of β-catenin ChIP-seq data revealed 221 potential direct targets, including Dpp6 and Fgf12. ConclusionThis study maps the complex gene expression driven by Wnt/β-catenin signaling in embryonic kidney cell types. The identified DEGs and β-catenin targets elucidate the molecular details of kidney development and the pathway's role in immune processes, providing a foundation for further research into Wnt/β-catenin signaling in kidney development and disease.

A comprehensive learning based swarm optimization approach for feature selection in gene expression data

Gene expression data analysis is challenging due to the high dimensionality and complexity of the data. Feature selection, which identifies relevant genes, is a common preprocessing step. We propose a Comprehensive Learning-Based Swarm Optimization (CLBSO) approach for feature selection in gene expression data. CLBSO leverages the strengths of ants and grasshoppers to efficiently explore the high-dimensional search space. Ants perform local search and leave pheromone trails to guide the swarm, while grasshoppers use their ability to jump long distances to explore new regions and avoid local optima. The proposed approach was evaluated on several publicly available gene expression datasets and compared with state-of-the-art feature selection methods. CLBSO achieved an average accuracy improvement of 15% over the original high-dimensional data and outperformed other feature selection methods by up to 10%. For instance, in the Pancreatic cancer dataset, CLBSO achieved 97.2% accuracy, significantly higher than XGBoost-MOGA's 84.0%. Convergence analysis showed CLBSO required fewer iterations to reach optimal solutions. Statistical analysis confirmed significant performance improvements, and stability analysis demonstrated consistent gene subset selection across different runs. These findings highlight the robustness and efficacy of CLBSO in handling complex gene expression datasets, making it a valuable tool for enhancing classification tasks in bioinformatics.

The Role of Sirtuin-1 Isoforms in Regulating Mitochondrial Function.

The sirtuin-1 (SIRT1) gene contains multiple exons that usually undergo alternative splicing. The exclusion of one or more exons causes domain loss in the alternatively spliced isoforms and may change their functions. However, it is not completely established to what extent the loss of a non-catalytic domain could affect its regulatory function. Using muscle cells and SIRT1-knockout cells, we examined the function of the constitutively spliced isoform (SIRT1-v1) versus the alternatively spliced isoforms SIRT1-v2 and SIRT1-v3 that had lost part of the N-terminal region. Our data indicate that partial loss of the N-terminal domains in SIRT1-v2 and SIRT1-v3 attenuated their function. The full-length SIRT1-v1 significantly increased the oxidative phosphorylation and ATP production rate. Furthermore, SIRT1-v1 specifically upregulated the mitochondrial respiratory complex I without affecting the activity of complexes II, III, and IV. Additionally, domain loss affected the regulation of site-specific lysine acetylation in the histone H4 protein, the gene expression of respiratory complex I subunits, and the metabolic balance of oxidative phosphorylation versus glycolysis. Since alternatively spliced isoforms tend to increase with advancing age, the impact of SIRT1 isoforms on mitochondrial respiratory complexes warrants further investigation.

ScIDPMs: Single-Cell RNA-Seq Imputation Using Diffusion Probabilistic Models.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling the sequencing of mRNA in individual cells, thereby providing valuable insights into cellular gene expression and functions. However, scRNA-seq data often contain false zero values known as dropout events, which can obscure true gene expression levels and compromise downstream analysis accuracy. To address this issue, several computational approaches have been proposed for imputing missing gene expression values. Nevertheless, these methods struggle to capture dropout value distributions due to the sparsity of scRNA-seq data and complex gene expression patterns. In this study, we present a novel method called scIDPMs that utilizes conditional diffusion probabilistic models to impute scRNA-seq data. Firstly, scIDPMs identifies dropout sites based on gene expression characteristics and subsequently infers the missing values by considering available gene expression information. To effectively capture global gene expression features, scIDPMs employs a deep neural network with an attention mechanism to optimize the imputation process. We evaluated the performance of scIDPMs using simulated and real scRNA-seq datasets and compared it with ten other imputation methods. The results indicate that scIDPMs outperform other methods in restoring biologically meaningful gene expression values and improving downstream analysis.

Comparative network-based analysis of toll-like receptor agonist, L-pampo signaling pathways in immune and cancer cells

Toll-like receptors (TLRs) are critical components to stimulate immune responses against various infections. Recently, TLR agonists have emerged as a promising way to activate anti-tumor immunity. L-pampo, a TLR1/2 and TLR3 agonist, induces humoral and cellular immune responses and also causes cancer cell death. In this study, we investigated the L-pampo-induced signals and delineated their interactions with molecular signaling pathways using RNA-seq in immune cells and colon and prostate cancer cells. We first constructed a template network with differentially expressed genes and influential genes from network propagation using the weighted gene co-expression network analysis. Next, we obtained perturbed modules using the above method and extracted core submodules from them by conducting Walktrap. Finally, we reconstructed the subnetworks of major molecular signals utilizing a shortest path-finding algorithm, TOPAS. Our analysis suggests that TLR signaling activated by L-pampo is transmitted to oxidative phosphorylation (OXPHOS) with reactive oxygen species (ROS) through PI3K-AKT and JAK-STAT only in immune and prostate cancer cells that highly express TLRs. This signal flow may further sensitize prostate cancer to L-pampo due to its high basal expression level of OXPHOS and ROS. Our computational approaches can be applied for inferring underlying molecular mechanisms from complex gene expression profiles.

Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma

Impaired telomere length (TL) maintenance in ovarian tissue may play a pivotal role in the onset of epithelial ovarian cancer (OvC). TL in either target or surrogate tissue (blood) is currently being investigated for use as a predictor in anti-OvC therapy or as a biomarker of the disease progression, respectively. There is currently an urgent need for an appropriate approach to chemotherapy response prediction.We performed a monochrome multiplex qPCR measurement of TL in peripheral blood leukocytes (PBL) and tumor tissues of 209 OvC patients. The methylation status and gene expression of the shelterin complex and telomerase catalytic subunit (hTERT) were determined within tumor tissues by High-Throughput DNA methylation profiling and RNA sequencing (RNA-Seq) analysis, respectively. The patients sensitive to cancer treatment (n = 46) had shorter telomeres in PBL compared to treatment-resistant patients (n = 93; P = 0.037). In the patients with a different therapy response, transcriptomic analysis showed alterations in the peroxisome proliferator-activated receptor (PPAR) signaling pathway (q = 0.001). Moreover, tumor TL shorter than the median corresponded to better overall survival (OS) (P = 0.006). TPP1 gene expression was positively associated with TL in tumor tissue (P = 0.026).TL measured in PBL could serve as a marker of platinum therapy response in OvC patients. Additionally, TL determined in tumor tissue provides information on OvC patients' OS.

Sodium-glucose cotransporter 2 inhibitors induce anti-inflammatory and anti-ferroptotic shift in epicardial adipose tissue of subjects with severe heart failure

BackgroundSodium-glucose cotransporter 2 inhibitors (SGLT-2i) are glucose-lowering agents used for the treatment of type 2 diabetes mellitus, which also improve heart failure and decrease the risk of cardiovascular complications. Epicardial adipose tissue (EAT) dysfunction was suggested to contribute to the development of heart failure. We aimed to elucidate a possible role of changes in EAT metabolic and inflammatory profile in the beneficial cardioprotective effects of SGLT-2i in subjects with severe heart failure.Methods26 subjects with severe heart failure, with reduced ejection fraction, treated with SGLT-2i versus 26 subjects without treatment, matched for age (54.0 ± 2.1 vs. 55.3 ± 2.1 years, n.s.), body mass index (27.8 ± 0.9 vs. 28.8 ± 1.0 kg/m2, n.s.) and left ventricular ejection fraction (20.7 ± 0.5 vs. 23.2 ± 1.7%, n.s.), who were scheduled for heart transplantation or mechanical support implantation, were included in the study. A complex metabolomic and gene expression analysis of EAT obtained during surgery was performed.ResultsSGLT-2i ameliorated inflammation, as evidenced by the improved gene expression profile of pro-inflammatory genes in adipose tissue and decreased infiltration of immune cells into EAT. Enrichment of ether lipids with oleic acid noted on metabolomic analysis suggests a reduced disposition to ferroptosis, potentially further contributing to decreased oxidative stress in EAT of SGLT-2i treated subjects.ConclusionsOur results show decreased inflammation in EAT of patients with severe heart failure treated by SGLT-2i, as compared to patients with heart failure without this therapy. Modulation of EAT inflammatory and metabolic status could represent a novel mechanism behind SGLT-2i-associated cardioprotective effects in patients with heart failure.

UNLOCKING THE THERAPEUTIC POTENTIAL: EXPLORING NF-κB AS A VIABLE TARGET FOR DIVERSE PHARMACOLOGICAL APPROACHES

NF-κB is a vital transcription factor that responds to diverse stimuli like cytokines, infections, and stress. It forms different dimers, binds to specific DNA sequences, and regulates gene expression. It operates through two pathways: canonical (for inflammation and immunity) and non-canonical (for specific processes). These pathways tightly control activity of NF-κB and impacting gene expression. Aberrant NF-κB activation is linked to cancer and other diseases, making it a potential therapeutic target. This review explores the role of NF-κB in disease and its therapeutic potential in various conditions. Intricate signal transduction processes lead to NF-κB activation by phosphorylating IκB proteins, allowing NF-κB dimers to enter the nucleus and influence gene expression. This dynamic regulation involves co-activators and interactions with other transcription factors, shaping complex gene expression programs. Understanding the multifaceted functions off NF-κB is crucial as its deregulation is associated with a range of diseases, including cancer, autoimmune disorders, and inflammatory conditions. Exploring recent studies offers insights into potential therapeutic strategies aimed at modulating NF-κB activity to restore health and combat various pathological conditions. This Comprehensive review is based on the role of NF-κB in disease pathogenesis and therapeutic implications.

Diminished γδ T Cells during Murine Allergic Skin Inflammation Is Mediated by IL-4 Signaling in Keratinocytes.

Atopic dermatitis results in diminished barrier function and altered production of antimicrobial peptides. Dendritic epidermal T cells (DETCs) play an important role in the wound repair and inflammation process. Our previous work identified an IL-4-dependent loss of DETCs in Stat6VT mice and in the MC903-induced skin inflammation mouse model. However, the mechanisms through which IL-4 mediates the loss of DETCs are unclear. In this study, we show that IL-4Rα germline knockout mice (Il4ra-/-) have increased DETCs, faster wound healing, and increased epidermal differentiation complex gene and fibronectin expression. The absence of IL-4Rα minimized the MC903-induced loss of DETCs, and reciprocal bone marrow chimera experiments in Il4ra-/- and wild-type mice demonstrated structural nonhematopoietic IL-4-responsive cell-mediated DETC homeostasis. Skin keratinocyte-derived IL-15 decreased dramatically in the MC903 model, while injection of IL-15 rescued DETC loss by promoting DETC proliferation and limiting apoptosis. Conditional deletion of IL-4Rα from keratinocytes using Il4rafl/fl K14-Cre mice showed an increase of DETCs, increased IL-15 production, and diminished skin inflammation following wounding. These results suggest that IL-4-dependent effects on DETCs in allergic skin inflammation are mediated by the IL-4Rα receptor of keratinocytes.

What can we learn when fitting a simple telegraph model to a complex gene expression model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

Lysophosphatidylcholine Acetyltransferase 2 (LPCAT2) Influences the Gene Expression of the Lipopolysaccharide Receptor Complex in Infected RAW264.7 Macrophages, Depending on the E. coli Lipopolysaccharide Serotype.

Escherichia coli (E. coli) is a frequent gram-negative bacterium that causes nosocomial infections, affecting more than 100 million patients annually worldwide. Bacterial lipopolysaccharide (LPS) from E. coli binds to toll-like receptor 4 (TLR4) and its co-receptor's cluster of differentiation protein 14 (CD14) and myeloid differentiation factor 2 (MD2), collectively known as the LPS receptor complex. LPCAT2 participates in lipid-raft assembly by phospholipid remodelling. Previous research has proven that LPCAT2 co-localises in lipid rafts with TLR4 and regulates macrophage inflammatory response. However, no published evidence exists of the influence of LPCAT2 on the gene expression of the LPS receptor complex induced by smooth or rough bacterial serotypes. We used RAW264.7-a commonly used experimental murine macrophage model-to study the effects of LPCAT2 on the LPS receptor complex by transiently silencing the LPCAT2 gene, infecting the macrophages with either smooth or rough LPS, and quantifying gene expression. LPCAT2 only significantly affected the gene expression of the LPS receptor complex in macrophages infected with smooth LPS. This study provides novel evidence that the influence of LPCAT2 on macrophage inflammatory response to bacterial infection depends on the LPS serotype, and it supports previous evidence that LPCAT2 regulates inflammatory response by modulating protein translocation to lipid rafts.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions.

This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome. Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Validation of biomarkers and immunotherapy in head and neck squamous cell carcinoma using bioinformatics and Mendelian randomization

Background To promote carcinogenesis through diverse molecular pathways involving dysregulation of gene expression and abnormalities. Methods We employed Mendelian randomization (MR) to uncover causal relationships between genetic factors and HNSCC. We used the Inverse Variance Weighted (IVW) method as the primary MR analysis, and validated the results through complementary approaches like MR-Egger regression, weighted median, and mode analyses. Results Our analysis identified 2210 genes that are differentially expressed in head and neck cancer (HNSCC) compared to normal tissues. Within the protein interaction network, the genes IL1B, CXCL8, CXCL1, and CCL2 stood out as central hubs. Further investigation revealed that these key genes are involved in important biological processes like skin development, wound healing, and fat metabolism. Notably, our Mendelian randomization analysis provided evidence for a causal relationship between the expression of the IL1B gene and the development of HNSCC. Conclusions Our analysis identified 5 key genes - IL1B, CXCL8, CXCL1, CCL2, and IL1B - that show significant changes in expression in head and neck cancer. These genes could serve as important new biomarkers to help diagnose this disease and track how it progresses over time. Importantly, these genes are involved in regulating the immune system, suggesting that the body’s immune response plays a critical role in head and neck cancer. This provides new avenues for future research to better understand the complex gene expression patterns underlying this type of cancer. Further investigation of these key genes and their regulatory networks could lead to important insights and potential new treatment approaches.

Abstract LB096: Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma

Abstract Impaired telomere length (TL) maintenance in ovarian tissue may play a pivotal role in epithelial ovarian carcinoma (OvC) onset. TL in either target or surrogate tissue (blood) is currently being investigated as a predictor in anti-OvC therapy or as a biomarker of the disease progression, respectively. There is presently an urgent need for an appropriate approach to chemotherapy response prediction. We performed a monochrome multiplex qPCR measurement of TL in peripheral blood and tumor tissues of 209 OvC patients. Methylation status and gene expression of shelterin complex and telomerase catalytic subunit (hTERT) were determined within tumor tissues by High-Throughput DNA methylation profiling and RNA sequencing (RNA-Seq) analysis, respectively. The patients sensitive to cancer treatment (n=46) had shorter telomeres in peripheral blood cells compared to those treatment-resistant (n=93; P=0.037). Cancer tissue TL lower than the median predisposed to better overall survival (P=0.002). Gene expression of TPP1, which recruits telomerase to telomeres, was positively associated with TL in tumor tissue (P=0.026). TL measured in peripheral blood cells could serve as a marker of platinum therapy response in OvC patients. Additionally, TL determined in tumor tissue provides information on OvC patients' overall survival. The study was financed by the project NPO (LX22NPO05102), the Czech Health Research Council (NU21-03-00145), and the Czech Science Foundation (21-27902S). Citation Format: Kristyna Tomasova, Karolina Seborova, Michal Kroupa, Josef Horak, Ludmila Vodickova, Lukas Rob, Martin Hruda, Marcela Mrhalova, Alena Bartakova, Jiri Bouda, Thomas Fleischer, Vessela Kristensen, Pavel Vodicka, Radka Vaclavikova. Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB096.

Atopic Dermatitis Complicated by Recurrent Eczema Herpeticum Is Characterized by Multiple, Concurrent Epidermal Inflammatory Endotypes

A subgroup of patients with atopic dermatitis (AD) suffers from recurrent, disseminated herpes simplex virus skin infection, termed eczema herpeticum. To determine the transcriptional mechanisms of the skin and immune system pathobiology that underlie development of AD with eczema herpeticum (ADEH), we performed RNA-sequencing analysis of nonlesional skin (epidermis, dermis) from AD patients with and without a history of ADEH (ADEH+, n= 15; ADEH-, n= 13) along with healthy controls (n= 15). We also performed RNA sequencing on participants' plasmacytoid dendritic cells infected invitro with herpes simplex virus 1. ADEH+ patients exhibited dysregulated gene expression, limited in the dermis (14 differentially expressed genes) and more widespread in the epidermis (129 differentially expressed genes). ADEH+-upregulated epidermal differentially expressed genes were enriched in type 2 cytokine (IL4R , CCL22, CRLF2, IL7R), interferon (CXCL10, ICAM1, IFI44, IRF7), and IL-36γ (IL36G) inflammatory gene pathways. All ADEH+ participants exhibited type 2 cytokine and inteferon endotypes, and 87% were IL36G-high. In contrast, these endotypes were more variably expressed among ADEH- participants. ADEH+ skin also had dysregulated epidermal differentiation complex gene expression of the late-cornified envelope, S100A, and small proline-rich gene families, which are involved in skin barrier function and antimicrobial activities. Plasmacytoid dendritic cell transcriptional responses to herpes simplex virus 1 infection were unaltered by ADEH status. The study concluded that the pathobiology underlying ADEH+ risk is associated with a unique, multifaceted epidermal inflammation that accompanies dysregulation of epidermal differentiation complex genes. These findings will help direct future studies that define how these inflammatory patterns may drive risk of eczema herpeticum in AD.

Abstract 878: Enhancing single-cell RNA sequencing analysis in cancer research: A machine learning framework based on LightGBM for automated cell type annotation

Abstract Single-cell RNA sequencing (scRNA-seq) has been widely used in cancer research to understand the complex gene expression diversity and cancer heterogeneity. However, manual annotation of cell types in the scRNA-seq pipeline is time-consuming and depends on the expertise of analyzers, which can significantly influence the results of downstream analyses. To address this problem, we proposed a novel machine learning framework utilizing the LightGBM model for automated and efficient cell-type annotation of scRNA-seq. Two independent scRNA-seq datasets of non-small cell lung cancer (NSCLC) downloaded from the Gene Expression Omnibus (GEO) were used to train and test our model. A standard procedure is applied to both scRNA-seq datasets for quality control and preprocessing, in which poor-quality cells with low gene expressions or high scores for cellular stress/death were excluded. In addition, Harmony is applied to mitigate batch effects in scRNA-seq that could cause variability due to non-biological factors in experiments. Nine different cell types, including endothelial, epithelial, fibroblast, macrophages, mast, plasma, pulmonary alveolar, B, and T cells, were manually labeled in the two datasets by the providers, which were also examined using gene markers corresponding to different cell types from PanglaoDB and DAVID. These manually labeled cell types were used as the ground truth for training and testing our model. In the training stage, the training dataset (containing 85,000 cells from 44 NSCLC samples) of scRNA-seq was used to train the LightGBM model with its high-variable genes. Then, the model would be evaluated using an independent test dataset (containing 8,000 cells from 18 NSCLC samples) by comparing the automatically predicted and manually labeled cell types. The training result showed that our model could successfully specify the nine different cell types, achieving an overall average accuracy, F1 score, and precision of 0.86 each respectively. In the independent dataset test, the model demonstrated good generalization, showing high predictive performance across all cell types, with an average accuracy, F1 score, and precision of 0.8, 0.78, and 0.8, respectively. Specific to the predictions in the test dataset, we found that some epithelial cells were mistakenly identified as other cell types. This might be because of the complex gene expression patterns exhibited by tumor epithelial cells, making accurate predictions challenging. The proposed machine learning framework facilitates cell labeling and unravels the intricate heterogeneity within lung cancer datasets. The combination of LightGBM and standardized preprocessing establishes a benchmark for high-throughput, accurate single-cell analysis, paving the way for discoveries that are more targeted and have significant clinical impact. Citation Format: Tsung Hsien Chuang, Liang-Chuan Lai, Tzu-Pin Lu, Mong-Hsun Tsai, Hsiang-Han Chen, Eric Y. Chuang. Enhancing single-cell RNA sequencing analysis in cancer research: A machine learning framework based on LightGBM for automated cell type annotation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 878.