Gene Signature Analysis Research Articles

IL-4 induces CD22 expression to restrain the effector program of virtual memory T cells.

Parasitic helminths induce the production of interleukin-4 (IL-4), which causes the expansion of virtual memory CD8+ T cells (TVM cells), a cell subset that contributes to the control of coinfection with intracellular pathogens. However, the mechanisms regulating IL-4-dependent TVM cell activation and expansion remain ill defined. Here, we used single-cell RNA sequencing of CD8+ T cells to identify pathways that control IL-4-dependent TVM cell responses. Gene signature analysis of CD8+ T cells identified a cell cluster marked by CD22, a canonical regulator of B cell activation, as a selective surface marker of IL-4-induced TVM cells. CD22+ TVM cells were enriched for interferon-γ and granzyme A and retained a diverse TCR repertoire while enriched in self-reactive CDR3 sequences. CD22 intrinsically regulated the IL-4-induced CD8+ T cell effector program, resulting in reduced responsiveness of CD22+ TVM cells and regulatory functions to infection and inflammation. Thus, helminth-induced IL-4 drives the expansion and activation of TVM cells that is counterinhibited by CD22.

A Narrative Review of Prognostic Gene Signatures in Oral Squamous Cell Carcinoma Using LASSO Cox Regression.

Oral squamous cell carcinoma (OSCC) is one of the most common malignancies of the head and neck squamous cell carcinoma (HNSCC). HNSCC is recognized as the eighth most commonly occurring cancer globally in men. It is essential to distinguish between cancers arising in the head and neck regions due to significant differences in their etiologies, treatment approaches, and prognoses. As the Cancer Genome Atlas (TCGA) dataset is available in HNSCC, the survival analysis prognosis of OSCC patients based on the TCGA dataset for discovering gene expression-based prognostic biomarkers is limited. To address this paucity, we aimed to provide comprehensive evidence by recruiting studies that have reported new biomarkers/signatures to establish a prognostic model to predict the survival of OSCC patients. Using PubMed search, we have identified 34 studies that have been using the least absolute shrinkage and selection operator (LASSO)-based Cox regression analyses to establish signature prognosis that related to different pathways in OSCC from the past 4 years. Our review was focused on summarizing these signatures and implications for targeted therapy using FDA-approved drugs. Furthermore, we conducted an analysis of the LASSO Cox regression gene signatures. Our findings revealed 13 studies that correlated a greater number of regulatory T cells (Tregs) cells in protective gene signatures with increased recurrence-free and overall survival rates. Conversely, two studies displayed an opposing trend in cases of OSCC. We will also explore how the dysregulation of these signatures impacts immune status, promoting tumor immune evasion or, conversely, enhancing immune surveillance. Overall, this review will provide new insight for future anti-cancer therapies based on the potential gene that is associated with poor prognosis in OSCC.

Construction of a novel cuproptosis-related gene signature and integrative analyses in cholangiocarcinoma.

Cuproptosis is a unique form of cell death that is dependent on copper, which is fundamentally different from other recognized forms of cell death. However, the molecular and immune characteristics in cuproptosis-defined subgroups of cholangiocarcinoma (CCA) remain to be further illustrated. We conducted a comprehensive investigation into the genetic and transcriptional variation, prognostic significance, and expression profiles of 16 cuproptosis-related genes (CRGs). To construct a prognostic signature based on differentially expressed genes between molecular subtypes, we employed LASSO and multivariate Cox regression analyses. We then assessed the values of this signature in prognostic prediction, immune infiltration, and therapeutic responses in CCA. The robustness of the signature was further validated in the GEO and IMvigor210 cohorts. Additionally, qRT-PCR and Western blotting (WB) were utilized to confirm the expression of the signature genes across different cell lines. A total of 16 CRGs were analyzed revealed differentially expressed, and two CRG-related clusters were identified, which displayed contrasting survival times.Then, a robust cuproptosis-related risk model was established and was an independent prognostic factor for CCA, which was further validated across two external cohorts. Low-risk patients had a better overall survival than high-risk patients. The comprehensive results showed that a low-risk score was correlated with metabolism-related pathways, high infiltration of CD8 T cells, B cells, and M1 macrophages, active immunity and less aggressive phenotypes, high chemotherapeutic sensitivity, and more benefit from immunotherapy. In contrast, a high-risk score was associated with cancer and metastasis-related pathways, high infiltration of M2 macrophages, high expression of immune checkpoint genes, suppressive immunity and more aggressive phenotypes, and less benefit from chemotherapeutic and immunotherapy. In addition, the critical CRGs were further validated through qRT-PCR and WB. We developed a novel risk model for CCA patients, which serves as a promising biomarker for distinguishing prognosis as well as molecular and immune characteristics.

CTIM-20. IDENTIFYING CANDIDATE ANTIGEN TARGETS FOR PEDIATRIC BRAIN TUMOR IMMUNOTHERAPIES THROUGH COMPREHENSIVE IMMUNOGENOMIC ANALYSIS

Abstract BACKGROUND Identifying tumor rejection antigens continues to be a significant challenge in the development of effective immunotherapies and their application to pediatric brain tumors. METHODS To identify candidate antigen targets for Medulloblastoma (primary MB, n=170; recurrent MB, n=21), Diffuse Intrinsic Pontine Glioma (DIPG, n=10), and Pediatric High-Grade Astrocytoma (HGA, n=12) for adoptive cellular therapy, we conducted a comprehensive immunogenomic analysis of the transcription profiles of the most aggressive pediatric brain tumors and performed in silico antigen prediction across a wide range of antigen classes. IMPORTANCE OF THE STUDY Pediatric brain tumors are heterogeneous diseases, and current standard treatments do not adequately address this variability. Newer and safer patient-specific treatment approaches are needed for high-risk patients who do not respond to standard therapies. Cellular immunotherapy could be crucial for improving survival and reducing morbidity. For an effective immune response against these brain tumors, appropriate tumor antigens must be targeted. While subgroup-specific genetic mutations in medulloblastoma patients have been reported, the immunogenicity of these genetic alterations remains unknown. Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with significant implications for the development of cellular therapies for MB, DIPG, and HGA. RESULTS Antigen prediction analysis revealed that most patients express few candidate neoantigen targets that pass all filtering criteria. Overall, the proportion of predicted tumor-associated antigens (TAAs) was higher than that of neoantigens and gene fusions for all molecular subtypes, except for SHH, which exhibited a higher neoantigen burden. Notably, cancer-testis antigens and previously unrecognized neurodevelopmental antigens were found to be expressed in most patients across all medulloblastoma subgroups. Although the absolute immune cell content is predicted to be low, immune gene-signature analysis revealed subgroup-specific differences.

A high-throughput gene expression analysis software tool for developmental time series and gene signature analysis of human cardiomyocyte differentiation

Publicly available high-throughput gene expression data enable the investigation of biological processes by the scientific community. Although several bioinformatics tools offer methodologies for basic differential gene expression analysis, difficulties arise in the analysis of multiple sample groups comprising a developmental time series, especially when the identification and classification of unique gene expression patterns is the primary goal of the study. Data analysis using these tools requires programming experience, which limits the accessibility of these tools to the broader community. To streamline developmental time-series investigations, we created the Developmental Gene Expression Analysis (devGEA) tool. This environment can be implemented locally or via web browsers to expedite differential gene expression analysis. This tool provides gene signature determination methods that can classify differentially expressed genes based on their correlation with gene expression patterns. devGEA was used to characterize cardiac development gene expression signatures from high-throughput RNA-seq datasets profiling small-molecule directed cardiomyocyte differentiation of human pluripotent stem cell lines (hiPSCs). After pre-processing, discrete gene expression criteria-based expected changes were used to classify the genes into developmental signatures. Several cardiomyocyte differentiation markers and candidate cardiac genes representing different developmental signatures were experimentally validated using the GIBCOTM hiPSC line. This method was then compared to a gene signature correlation approach that classified expressed genes based on their degree of similarity with key cardiac developmental signatures representing the stages of cardiomyocyte differentiation. Therefore, devGEA provides a robust workflow for investigator-driven analysis of developmental time series, allowing for the identification of differentially expressed genes and gene signatures for extensive experimental investigation. We also introduced a method for classifying genes by their correlation with genes or developmental patterns of interest. Our correlation-based method takes advantage of a priori knowledge of an experiment and is conceptually simpler than unsupervised clustering approaches.

Immune- and metabolism-related gene signature analysis uncovers the prognostic and immune microenvironments of hepatocellular carcinoma

BackgroundMetabolic reprogramming is an emerging hallmark that influences the tumour microenvironment (TME) by regulating the behavior of cancer cells and immune cells. The relationship between metabolism and immunity remains elusive. The purpose of this study was to explore the predictive value of immune- and metabolism-related genes in hepatocellular carcinoma (HCC) and their intricate interplay with TME.MethodsWe established the immune- and metabolism-related signature (IMRPS) based on the LIHC cohort from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC) curve analysis and Cox regression analysis confirmed the prognostic value of IMRPS. We investigated differences in immune cell infiltration, clinical features, and therapeutic response between risk groups. The quantitative real-time PCR (qPCR) was used to confirm the expression of signature genes. Immunohistochemical staining was performed to evaluate immune infiltration features in HCC tissue samples. We conducted cell experiments including gene knockout, cell counting kit-8 (CCK-8), and flow cytometry to explore the role of the IMRPS key gene UCK2 in HCC. RNA-seq was used to further investigate the potential underlying mechanism involved.ResultsThe IMRPS, composed of four genes, SMS, UCK2, PFKFB4 and MAPT, exhibited significant correlations with survival, immune cell infiltration, clinical features, immune checkpoints and therapeutic response. The IMRPS was shown to be an excellent predictor of HCC prognosis. It could stratify patients appropriately and characterize the TME accurately. The high-risk HCC group exhibited an immunosuppressive microenvironment with abundant M2-like macrophage infiltration, which was confirmed by the immunohistochemistry results. The results of qPCR revealed that the expression of signature genes in 20 HCC tissues was significantly greater than that in adjacent normal tissues. After the key gene UCK2 was knocked out, the proliferation of the Huh7 cell line was significantly inhibited, and monocyte-derived macrophages polarized towards an M1-like phenotype in the coculture system. RNA-seq and GSEA suggested that the phenotypes were closely related to the negative regulation of growth and regulation of macrophage chemotaxis.ConclusionsThis study established a new IMRS for the accurate prediction of patient prognosis and the TME, which is also helpful for identifying new targets for the treatment of HCC.

Entire expressed peripheral blood transcriptome in pediatric severe malarial anemia

This study on severe malarial anemia (SMA: Hb < 6.0 g/dL), a leading global cause of childhood morbidity and mortality, compares the entire expressed whole blood host transcriptome between Kenyan children (3-48 mos.) with non-SMA (Hb ≥ 6.0 g/dL, n = 39) and SMA (n = 18). Differential expression analyses reveal 1403 up-regulated and 279 down-regulated transcripts in SMA, signifying impairments in host inflammasome activation, cell death, and innate immune and cellular stress responses. Immune cell profiling shows decreased memory responses, antigen presentation, and immediate pathogen clearance, suggesting an immature/improperly regulated immune response in SMA. Module repertoire analysis of blood-specific gene signatures identifies up-regulation of erythroid genes, enhanced neutrophil activation, and impaired inflammatory responses in SMA. Enrichment analyses converge on disruptions in cellular homeostasis and regulatory pathways for the ubiquitin-proteasome system, autophagy, and heme metabolism. Pathway analyses highlight activation in response to hypoxic conditions [Hypoxia Inducible Factor (HIF)−1 target and Reactive Oxygen Species (ROS) signaling] as a central theme in SMA. These signaling pathways are also top-ranking in protein abundance measures and a Ugandan SMA cohort with available transcriptomic data. Targeted RNA-Seq validation shows strong concordance with our entire expressed transcriptome data. These findings identify key molecular themes in SMA pathogenesis, offering potential targets for new malaria therapies.

The immunomodulatory impacts of endocrine therapy on host immunity in early-stage hormone receptor positive, HER2 negative breast cancer.

572 Background: Endocrine therapy (ET) reduces mortality in hormonally driven breast cancer (BC). The impacts of ET on the estrogen receptor are well known; however, there is limited data on the impact of ET on the host's immune system. This project aims to elucidate the potential immunomodulatory effects of ET-induced estrogen deprivation in HR+/HER2 negative early-stage breast cancer patients (pts) as well as baseline differences between BC pts and healthy controls (HC). Methods: Pts with Stage I-III HR+/HER2- BC initiating ET after completion of local regional therapy and/or chemotherapy were enrolled (NCT03719495). Whole blood, E1 (estrone) and E2 (estradiol) levels were collected at baseline (T1), 4 weeks (T2), 12 weeks (T3) and 24 weeks (T4) after ET initiation. 5 cohorts were enrolled: premenopausal and postmenopausal HCs, premenopausal pts on tamoxifen (TAM) or aromatase inhibitors (AIs) plus ovarian suppression, postmenopausal pts on AIs. Bulk RNA sequencing of whole blood was performed using NuGEN mRNA-seq on the Illumina Nova-seq platform. Differential expression analysis was performed using DESeq2 between HC and ET groups at T1, and between T1 and T4 within BC pts. The Benjamini-Hochberg method was used to adjust the P values for multiple comparisons. A curated set of 192 immune related gene signatures was used in the gene signature analysis. For each signature/module, a score was computed by taking the median of the expression values from all genes in each gene signature. CIBERSORT analysis was performed using the LM22 signature gene file and 100 permutations. Paired t-test was performed to compare T1 and T4. Results: Between 2019 and 2023, 64 pts were enrolled. Pts with missing samples or prior chemotherapy were excluded in this analysis. T1 samples of 25 pts with BC versus 14 HCs revealed marked changes in circulating immune cell (IC) composition with 537 DEGs (p value &lt; 0.05). Circulating ICs in BC pts had marked upregulation of genes associated with oxidative phosphorylation, adipogenesis, interferon alpha, reactive oxygen species compared to HCs. Immune cell composition was inferred by CIBERSORT analysis which suggests that BC pts had fewer circulating naïve B cells (p=0.048) whereas regulatory T cells (p=0.025) and activated NK cells (p=0.0039) were higher in BC pts than HCs at T1. BC pts with reductions in E1/E2 levels 6 months after ET had 77 DEGs in PBMCs between T1 and T4. Gene signature analysis shows that B cells and NK cells (p=0.0077) signatures are elevated post ET whereas a reduction of neutrophil signature (p=0.0034) was noted. Conclusions: There are significant differences in the baseline IC repertoire/activity in BC pts compared to HCs. The reduction in B cell activity/number and an increase in regulatory T cell number suggests a possible compromised immune system in BC pts. After surgery and 6 months of ET, these suppressed immune phenotypes reversed. Clinical trial information: NCT03719495 .

A study on construction of a prognosis model for liver cancer based on analgesic targets and screening therapeutic drugs.

Liver cancer is one of the most malignant liver diseases in the world, and the 5-year survival rate of such patients is low. Analgesics are often used to cure pain prevalent in liver cancer. The expression changes and clinical significance of the analgesic targets (ATs) in liver cancer have not been deeply understood. The purpose of this study is to clarify the expression pattern of ATs gene in liver cancer and its clinical significance. Through the comprehensive analysis of transcriptome data and clinical parameters, the prognosis model related to ATs gene is established, and the drug information sensitive to ATs is mined. The study primarily utilized transcriptomic data and clinical information from liver cancer patients sourced from The Cancer Genome Atlas (TCGA) database. These data were employed to analyze the expression of ATs, conduct survival analysis, gene set variation analysis (GSVA), immune cell infiltration analysis, establish a prognostic model, and perform other bioinformatic analyses. Additionally, data from liver cancer patients in the International Cancer Genome Consortium (ICGC) were utilized to validate the accuracy of the model. Furthermore, the impact of analgesics on key genes in the prognostic model was assessed using data from the Comparative Toxicogenomics Database (CTD). The study investigated the differential expression of 58 ATs genes in liver cancer compared to normal tissues. Patients were stratified based on ATs expression, revealing varied survival outcomes. Functional enrichment analysis highlighted distinctions in spindle organization, centrosome, and spindle microtubule functions. Prognostic modeling identified low TP53 expression as protective, while elevated CCNA2, NEU1, and HTR2C levels posed risks. Commonly used analgesics, including acetaminophen and others, were found to influence the expression of these genes. These findings provide insights into potential therapeutic strategies for liver cancer and shed light on the molecular mechanisms underlying its progression. The collective analysis of gene signatures associated with ATs suggests their potential as prognostic predictors in hepatocellular carcinoma patients. These findings not only offer insights into cancer therapy but also provide novel avenues for the development of indications for analgesics.

Comprehensive analysis of gene signatures associated with aging in human aortic dissection

BackgroundAortic dissection (AD) is a lethal aortic disease with limited effective therapeutic strategies. Aging increases the risk of AD, yet the underlying mechanisms remain unclear. This study aims to analyze the association of aging-related genes (Args) and AD using bioinformatic analysis. This helps provide novel insights into AD pathogenesis and contributes to developing novel therapeutic strategies. MethodsmRNA (GSE52093, GSE153434), miRNA (GSE98770) and single-cell RNA-sequencing (scRNA-seq, GSE213740) datasets of AD were downloaded from GEO database. Args were downloaded from Aging Atlas database. Differentially-expressed Args were determined by intersecting Args and differentially-expressed mRNAs of two mRNA datasets. Cytoscape was used to identify hub genes and construct hub gene regulatory networks related to miRNAs. Seurat and clusterProfiler R package were used for investigating expression patterns of hub genes at single-cell level, and functional analysis, respectively. To validate the cellular expression pattern of hub genes, the same analysis was applied to our own scRNA-seq data. Drugs targeting hub Args were determined using the DGIdb database. ResultsHGF, CXCL8, SERPINE1, HIF1A, TIMP1, ESR1 and PLAUR were identified as aging-related hub genes in AD. miR-221-3p was predicted to interact with ESR1. A decreased ESR1 expression in smooth muscle cell subpopulation 4 (SMC4) was observed in AD versus normal aortic tissues, which was validated by sequencing 197,605 aortic cells from 13 AD patients. Additionally, upregulated genes of SMC4 in AD tissues were enriched in the “cellular senescence” pathway. These data indicated that decreased ESR1 might promote SMC4 aging during AD formation. Eleven existing drugs targeting hub genes were identified, including ruxolitinib and filgrastim, which are associated with AD. ConclusionsBy sequencing transcriptomic data, this study revealed aging-related hub genes and regulatory network involved in AD formation. Additionally, this study proposed a noteworthy hypothesis that downregulated ESR1 may exacerbate AD by promoting SMC aging, which requires further investigation.

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer.

Comprehensive understanding prognostic relevance of distinct tumor microenvironment (TME) remained elusive in colon cancer. In this study, we performed in silico analysis of the stromal components of primary colon cancer, with a focus on the markers of cancer-associated fibroblasts (CAF) and tumor-associated endothelia (TAE), as well as immunological infiltrates like tumor-associated myeloid cells (TAMC) and cytotoxic T lymphocytes (CTL). The relevant CAF-associated genes (CAFG)(representing R index = 0.9 or beyond with SPARC) were selected based on stroma specificity (cancer stroma/epithelia, cS/E = 10 or beyond) and expression amounts, which were largely exhibited negative prognostic impacts. CAFG were partially shared with TAE-associated genes (TAEG)(PLAT, ANXA1, and PTRF) and TAMC-associated genes (TAMCG)(NNMT), but not with CTL-associated genes (CTLG). Intriguingly, CAFG were prognostically subclassified in order of fibrosis (representing COL5A2, COL5A1, and COL12A1) followed by exclusive TAEG and TAMCG. Prognosis was independently stratified by CD8A, a CTL marker, in the context of low expression of the strongest negative prognostic CAFG, COL8A1. CTLG were comprehensively identified as IFNG, B2M, and TLR4, in the group of low S/E, representing good prognosis. Our current in silico analysis of the micro-dissected stromal gene signatures with prognostic relevance clarified comprehensive understanding of clinical features of the TME and provides deep insights of the landscape.

IBDTransDB: a manually curated transcriptomic database for inflammatory bowel disease.

Inflammatory Bowel Disease (IBD) therapies are ineffective in at least 40% patients, and transcriptomic datasets have been widely used to reveal the pathogenesis and to identify the novel drug targets for these patients. Although public IBD transcriptomic datasets are available from many web-based tools/databases, due to the unstructured metadata and data description of these public datasets, most of these tools/databases do not allow querying datasets based on multiple keywords (e.g. colon and infliximab). Furthermore, few tools/databases can compare and integrate the datasets from the query results. To fill these gaps, we have developed IBDTransDB (https://abbviegrc.shinyapps.io/ibdtransdb/), a manually curated transcriptomic database for IBD. IBDTransDB includes a manually curated database with 34 transcriptomic datasets (2932 samples, 122 differential comparisons) and a query system supporting 35 keywords from 5 attributes (e.g. tissue and treatment). IBDTransDB also provides three modules for data analyses and integration. IBDExplore allows interactive visualization of differential gene list, pathway enrichment, gene signature and cell deconvolution analyses from a single dataset. IBDCompare supports comparisons of selected genes or pathways from multiple datasets across different conditions. IBDIntegrate performs meta-analysis to prioritize a list of genes/pathways based on user-selected datasets and conditions. Using two case studies related to infliximab treatment, we demonstrated that IBDTransDB provides a unique platform for biologists and clinicians to reveal IBD pathogenesis and identify the novel targets by integrating with other omics data. Database URL: https://abbviegrc.shinyapps.io/ibdtransdb/.

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

Novel insights into the pathogenesis of thyroid eye disease through ferroptosis-related gene signature and immune infiltration analysis.

Thyroid eye disease (TED) has brought great physical and mental trauma to patients worldwide. Although a few potential signaling pathways have been reported, knowledge of TED remains limited. Our objective is to explore the fundamental mechanism of TED and identify potential therapeutic targets using diverse approaches. To perform a range of bioinformatic analyses, such as identifying differentially expressed genes (DEGs), conducting enrichment analysis, establishing nomograms, analyzing weighted gene correlation network analysis (WGCNA), and studying immune infiltration, the datasets GSE58331, GSE105149, and GSE9340 were integrated. Further validation was conducted using qPCR, western blot, and immunohistochemistry techniques. Eleven ferroptosis-related DEGs derived from the lacrimal gland were originally screened. Their high diagnostic value was proven, and diagnostic prediction nomogram models with high accuracy and robustness were established by using machine learning. A total of 15 hub gene-related DEGs were identified by WGCNA. Through CIBERSORTx, we uncovered five immune cells highly correlated with TED and found several special associations between these immune cells and the above DEGs. Furthermore, EGR2 from the thyroid sample was revealed to be closely negatively correlated with most DEGs from the lacrimal gland. High expression of APOD, COPB2, MYH11, and MYCN, as well as CD4/CD8 T cells and B cells, was verified in the periorbital adipose tissues of TED patients. To summarize, we discovered a new gene signature associated with ferroptosis that has a critical impact on the development of TED and provides valuable insights into immune infiltration. These findings might highlight the new direction and therapeutic strategies of TED.

Abstract 7416: GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data

Abstract Identification of genotype-phenotype relationships is of utmost importance and a core effort in biology. Recent developments in efficient and precise gene targeting approaches coupled to omics methods have significantly improved deciphering of molecular interactions and relationships. However, many single gene perturbations can affect the expression of hundreds of other genes and analysis of the resulting omics-derived gene lists currently remains a significant challenge. Here we present Perturb-Seq based Gene Set Analyzer (GeneSetR), a user-friendly web-server that can analyze user-defined gene lists based on the data from a recently published genome-wide Perturb-Seq study, which targeted 9,866 genes with 11,258 sgRNAs in the K562 cell line. Through this tool, users can cluster gene lists following dimensionality reduction by various algorithms, undertake network analysis from RNA sequencing data, identify key nodes among the submitted genes, perform gene signature analyses, and generate heatmaps based on perturbation or gene expression data. GeneSetR enables researchers to readily identify gene clusters associated with specific phenotypes or biological processes, providing insights into the potential functional roles of these clusters and the role of single genes in them. With robust analysis capabilities, GeneSetR is a powerful resource to facilitate the exploration of genotype-phenotype relationships. Citation Format: Omer Faruk Kuzu, Fahri Saatcioglu. GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data [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 7416.

The molecular signature of BCR::ABLP210 and BCR::ABLT315I in a Drosophila melanogaster chronic myeloid leukemia model

Chronic myeloid leukemia (CML) is a clonal hematopoietic stem cell disorder resulting from a balanced translocation leading to BCR::ABL1 oncogene with increased tyrosine kinase activity. Despite the advancements in the development of tyrosine kinase inhibitors (TKIs), the T315I gatekeeper point mutation in the BCR::ABL1 gene remains a challenge. We have previously reported in a Drosophila CML model an increased hemocyte count and disruption in sessile hemocyte patterns upon expression of BCR::ABL1p210 and BCR::ABL1T315I in the hemolymph. In this study, we performed RNA sequencing to determine if there is a distinct gene expression that distinguishes BCR::ABL1p210 and BCR::ABL1T315I. We identified six genes that were consistently upregulated in the fly CML model and validated in adult and pediatric CML patients and in a mouse cell line expressing BCR::ABL1T315I. This study provides a comprehensive analysis of gene signatures in BCR::ABL1p210 and BCR::ABL1T315I, laying the groundwork for targeted investigations into the role of these genes in CML pathogenesis.

Predicting drug response of small cell lung cancer cell lines based on enrichment analysis of complex gene signatures

Advances in the field of genomics and transcriptomics have enabled researchers to identify gene signatures related to development and treatment of Small Cell Lung Cancer. In most cases, complex gene expression patterns are identified, comprising of genes with differential behavior. Most tools use single-genes as predictors of drug response, with only limited options for multi-gene use. Here we examine the potential of predicting drug response using these complex gene expression signatures by employing clustering and signal enrichment in Small Cell Lung Cancer. Our results demonstrate clustering genes from complex expression patterns helps identify differential activity of gene groups with alternate function which can then be used to predict drug response.

Analysis of tumor biomarkers in patients (pts) with advanced hepatocellular carcinoma (HCC) from a phase 1b study of E7386, a CREB-binding protein/β-catenin interaction inhibitor, in combination with lenvatinib.

535 Background: E7386, a novel oral anticancer agent, inhibits β-catenin binding to its transcriptional co-activator CREB-binding protein, thus modulating Wnt/β-catenin signaling. Lenvatinib is a multikinase inhibitor that targets VEGFR 1–3, FGFR 1–4, PDGFRα, RET, and KIT. We previously reported the safety/tolerability, pharmacokinetics, biomarkers, and preliminary efficacy of E7386 plus lenvatinib in 25 pts with HCC in the dose escalation part of a phase 1b study1 (NCT04008797). Here, we present the results of exploratory tumor biomarker analyses from this study. Methods: Pts with HCC were treated with E7386 10–80 mg QD and 60–120 mg BID plus lenvatinib (bodyweight &lt; 60 kg: 8 mg QD; ≥ 60 kg: 12 mg QD). Archival or fresh tumor samples were collected at baseline and post-treatment; tumor tissue was assessed for genetic alteration, immunohistochemistry (IHC), and gene expression. Gene signature scores of interest were calculated using obtained gene expression data. The preliminary association between biomarker status and efficacy was investigated. Results: Of the 25 pts enrolled, 22 had baseline tumor samples available for the gene mutation analysis. TERT (6 pts: 27%), CTNNB1 (5 pts: 23%), and TP53 (4 pts: 18%) mutations were detected. CTNNB1 mutation was confirmed in 3 of 7 responders and 2 of 13 non-responders. Nuclear β-catenin expression was confirmed by IHC analysis in 5 of 16 pts; it was expressed in 3 of 5 pts with CTNNB1 MUT and 2 of 11 pts with CTNNB1 WT. Pts with expression of nuclear β-catenin had 2 partial responses (PRs) among 5 pts; those without β-catenin expression had 2 PRs among 11 pts. Additionally, IHC analysis of tumor microvessel density (MVD; number of CD31+ vessels) at baseline suggested that greater maximum tumor shrinkage (MTS) tended to be observed in the MVD-high population compared to the MVD-low population. 7 Pts had evaluable baseline and post-baseline tumor samples and were included in the gene signature analysis. Despite the small sample size, preliminary results indicated that MTS may be associated with a decrease in Wnt signaling and angiogenesis gene signature scores (spearman r [r]=0.61 for both) and attenuation of hypoxic induction and glycolysis gene signature scores (r=0.79 and 0.68, respectively). Conclusions: These exploratory tumor biomarker analyses suggest that E7386 in combination with lenvatinib may affect the tumor microenvironment in HCC pts through effects on Wnt signaling, angiogenesis, hypoxic response, and glycolysis pathways. This concept is the basis for treatment with this combination. However, given the small sample size, further investigation is needed to confirm these effects on the tumor microenvironment. Enrollment of pts with HCC to the expansion part of Study 102 is ongoing. 1. Ikeda et al. ASCO 2023. Clinical trial information: NCT04008797 .

Comparative Transcriptomic Analysis of Cerebellar Astrocytes across Developmental Stages and Brain Regions.

Astrocytes are the most abundant glial cell type in the central nervous system, and they play a crucial role in normal brain function. While gliogenesis and glial differentiation occur during perinatal cerebellar development, the processes that occur during early postnatal development remain obscure. In this study, we conducted transcriptomic profiling of postnatal cerebellar astrocytes at postnatal days 1, 7, 14, and 28 (P1, P7, P14, and P28), identifying temporal-specific gene signatures at each specific time point. Comparing these profiles with region-specific astrocyte differentially expressed genes (DEGs) published for the cortex, hippocampus, and olfactory bulb revealed cerebellar-specific gene signature across these developmental timepoints. Moreover, we conducted a comparative analysis of cerebellar astrocyte gene signatures with gene lists from pediatric brain tumors of cerebellar origin, including ependymoma and medulloblastoma. Notably, genes downregulated at P14, such as Kif11 and HMGB2, exhibited significant enrichment across all pediatric brain tumor groups, suggesting the importance of astrocytic gene repression during cerebellar development to these tumor subtypes. Collectively, our studies describe gene expression patterns during cerebellar astrocyte development, with potential implications for pediatric tumors originating in the cerebellum.