Gene Signature Research Articles

KRAS Loss of Heterozygosity Promotes MAPK-Dependent Pancreatic Ductal Adenocarcinoma Initiation and Induces Therapeutic Sensitivity to MEK Inhibition.

Pancreatic cancer is characterized by the prevalence of oncogenic mutations in KRAS. Previous studies have reported that altered KRAS gene dosage drives progression and metastasis in pancreatic cancer. While the role of oncogenic KRAS mutations is well characterized, the relevance of the partnering wild-type KRAS allele in pancreatic cancer is less well understood and controversial. Using in vivo mouse modelling of pancreatic cancer, we demonstrated that wild-type KRAS restrains the oncogenic impact of mutant KRAS and dramatically impacts both KRAS-mediated tumorigenesis and therapeutic response. Mechanistically, deletion of wild-type Kras increased oncogenic KRAS signaling through the downstream MAPK effector pathway, driving pancreatic intraepithelial neoplasia (PanIN) initiation. In addition, in the KPC mouse model, a more aggressive model of pancreatic cancer, lack of wild-type KRAS led to accelerated initiation but delayed tumor progression. These tumors had altered stroma and an enrichment of immunogenic gene signatures. Importantly, loss of wild-type Kras sensitized Kras mutant tumors to MEK1/2 inhibition though tumors eventually became resistant and then rapidly progressed. This study demonstrates the repressive role of wild-type KRAS during pancreatic tumorigenesis and highlights the critical impact of the presence of wild-type KRAS in both tumor progression and therapeutic response in pancreatic cancer.

Identification of Immune-Related Gene Signature Model for Predicting Lung Cancer Survival and Response to Immunotherapy

Introduction: Studies have shown that immune-related genes play a crucial role in tumor development and treatment. However, the specific roles and potential value of these genes in lung cancer patients are still not fully understood. Therefore, this study aims to establish a novel risk model based on immune-related genes for evaluating the prognostic risk and response to immune therapy in lung cancer patients. Methods: Gene expression and clinical data of lung cancer patients were retrieved from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, while immune-related genes were obtained from the ImmPort database. A risk signature model was developed using univariate Cox analysis and LASSO regression analysis. The prognostic value of the model and its response to immunotherapy were analyzed by survival analysis, immune infiltration analysis, and immunotherapy response analysis. Results: We have developed a risk signature model based on eight key immune-related genes, which can classify patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly lower than that of the low-risk group and was validated in multiple GEO datasets. The mutation frequency was lower in the low-risk group compared to the high-risk group (TP53: 55% vs. 65%; TTN: 52% vs. 60%; CSMD3: 34% vs. 45%). Futhermore, CD274 expression was lower in the low-risk patients, and the high-risk patients in the IMvigor210 cohort had lower survival. Immune infiltration analyses showed that the high-risk group was negatively correlated with the infiltration level of B cells, CD4+ T cells, and NK cells. Importantly, patients in the low-risk group exhibit significantly lower TIDE scores, suggesting that they are more responsive to immunotherapy. Conclusion: Our study has established a novel and robust immune-related gene risk model that can assist in evaluating the prognostic risk and immune therapy response of lung cancer patients.

Comprehensive analysis of a necroptosis-associated diagnostic signature for myelodysplastic syndromes based on single-cell RNA-seq and bulk RNA-seq

BackgroundMyelodysplastic syndromes (MDS) are heterogeneous and clonal hematological disorders. The role and mechanism of necroptosis in MDS remain poorly understood.MethodsmRNA expression profiles and single-cell RNA-sequencing (scRNA-seq) data were sourced from the GEO database. ScRNA-seq data were processed using the “Seurat” package. After cell annotation, necroptosis-related scores (NRscores) for each cell were calculated using the “UCell” package. Differentially expressed genes (DEGs) and their associated biological functions in NRscore-related cell populations were identified. Additionally, DEGs and necroptosis-related genes (DE-NRGs) between MDS patients and healthy controls were identified. Consensus clustering was employed to classify MDS patients into distinct subclusters based on DE-NRGs. The biological functions and immune characteristics of these classifications were analyzed. Prognostic gene signatures were determined using LASSO and SVM-RFE analyses, and a nomogram was constructed based on the prognostic gene signature.ResultsA total of 12 cell types were identified in MDS and healthy controls. NRscore was found to be elevated in monocytes and common lymphoid precursors (CLPs). Enrichment analysis revealed that monocytes and CLPs with high NRscore were associated with mitochondria-related and immune-related pathways. Eleven DEGs in monocytes and CLPs between MDS patients and healthy controls were identified. Additionally, 13 DE-NRGs were identified from 951 DEGs between MDS and healthy controls. MDS patients were classified into two distinct subclusters based on these 13 DE-NRGs, revealing several immune-related processes and signaling pathways. Differences in immune subpopulations between the two subclusters were observed. A necroptosis-related diagnostic gene signature (IRF9, PLA2G4A, MLKL, BAX, JAK2, and STAT3) was identified as predictive of MDS prevalence.ConclusionNecroptosis plays a role in MDS progression by inducing inflammation. A novel necroptotic gene signature has been developed to distinguish and diagnose MDS at early stages of the disease.

PROTAC’ING CENTROSOME AMPLIfiCATION PROTEINS: TARGETED PROTEIN DEGRADATION AS A NOVEL THERAPEUTIC STRATEGY IN GLIOBLASTOMA

Abstract AIMS Glioblastomas (GBMs) are the most common and aggressive brain tumours, notoriously difficult to treat with universal treatment resistance. A key feature of cancer cells is centrosome amplification (CA), leading to chromosomal instability and worse patient outcomes, including metastasis development and treatment resistance. In this work, I identified CA proteins overexpressed in GBM and aimed to investigate how the targeted degradation of proteins associated with centrosome amplification can influence the growth and survival of GBM cells. METHOD Based on well-established literature data concerning CA-related genes, I evaluated the expression level of these targets in different types of samples from the Tumour Cancer Genome Atlas dataset of GBM patient samples. Following the identification of CA targets differentially overexpressed, to test the hypothesis that CA proteins could be involved in the survival of GBM cells and be potential therapeutic targets, I used a novel drug modality, named Proteolysis Targeting Chimeras (PROTACs) to degrade two CA proteins. PROTACs can quickly destroy specific proteins, potentially overcoming resistance to treatment. Target degradation was confirmed via immunoblotting and cell viability and cell cycle analysis were performed to evaluate the impact on GBM cells. RESULTS Both primary and recurrent GBM samples display high levels of CA gene signature when compared to noncancerous samples, and some CA genes are particularly upregulated in recurrent samples. The PROTACs were effective in degrading both CA targets in GBM cells, leading to a significant decrease in cell viability and G2 cell cycle arrest. CONCLUSION This study presents initial evidence that CA protein degradation could be a promising avenue for GBM research and therapy. Further research is needed to fully assess the translational potential of PROTACs in GBM treatment.

THE EPIGENETIC REGULATORY PROTEIN CBX2 AS A NOVEL THERAPEUTIC TARGET FOR GLIOBLASTOMA

Abstract AIMS Glioblastoma (GBM) is the deadliest and most common form of brain tumour. The average length of survival for GBM patients remains low, in part, due to a lack of targeted therapeutics. Identification and validation of novel therapeutic targets is therefore crucial. Chomobox 2 (CBX2; a component of polycomb repressive complex 1 (PRC1)), represses gene expression and has a pro-oncogenic role in several aggressive cancer subtypes. CBX2 expression is elevated in GBM, however, very little is known about its role in this deadly disease. We aim to assess the phenotypic and gene expression effects of preventing CBX2-chromatin interaction in patient-derived GBM cell models, and to determine the functional mechanisms by which CBX2 promotes GBM progression; thereby determining its potential as a novel therapeutic target. METHOD Crucial to impactful target validation is the use of translationally relevant models, this study utilises a 3D spheroid model, using patient-derived cells, which more closely recapitulates the in vivo tumour microenvironment. GBM cell monolayers and spheroids were treated with SW2_152F, a selective CBX2 inhibitor. The phenotypic effects of CBX2 inhibition were assessed by cell count, CellTiter-Glo viability assays, fluorescence microscopy, and flow cytometry. Genes and biological pathways differentially regulated following CBX2 inhibition were assessed by RNA-sequencing and Gene Set Enrichment Analysis (GSEA). RESULTS Inhibition of CBX2 induced cell death and reduced cell viability in both 2D and 3D GBM cultures. RNA-seq and GSEA of SW2_152F-treated cells identified dysregulation of gene signatures involved in pro-oncogenic MYC and E2F signalling pathways, and dysregulation of genes associated with G2/M cell cycle progression. CONCLUSION Preventing CBX2-chromatin interaction induces GBM cell death. Further research will identify CBX2- chromatin binding sites to characterise the activity and direct regulatory role of CBX2 in GBM. Furthermore, we will assess the phenotypic and gene expression effects of CBX2 inhibition and depletion on ex vivo maintained GBM tissue biopsies.

Establishment of the Prognostic Signature with Genes Related to G2/M Checkpoint in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality in the world. Prognostic indicators such as clinicopathological characteristics and single molecular signature are far from satisfactory in clinical practice. More and more researches have suggested that polygenic prognostic features could predict the prognosis of cancer more precisely than single genes nowadays. In this study, we performed gene set enrichment analysis (GSEA) to identify the sets of TCGA hallmark genes. Univariate Cox regression analysis was used to select preliminary genes, and then multivariate Cox regression analysis was used to identify genes associated with overall survival (OS). We also used Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis to validate the prognostic gene signature. Lastly, qRT-PCR was used to evaluate the expression of these genes in clinical samples, and immunohistochemical staining was performed to confirm the signature. A 12-gene signature was finally built and the risk score was significantly associated with the survival of the patients. Subsequent validation by qRT-PCR and immunohistochemical staining in clinical specimens confirmed the value of the risk score in predicting the prognosis. We developed a 12-gene signature that could predict the prognosis of HCC patients. This signature is of high precision and would help identifying subgroups of HCC patients with high or low risk of unfavorable survival.

Development and Validation of a Cholesterol-related Gene Signature for Prognostic Assessment in Head and Neck Squamous Cell Carcinoma.

This study seeks to develop a prognostic risk signature for head and neck squamous cell carcinoma (HNSCC) based on cholesterol-related genes (CholRG), aiming to enhance prognostic accuracy in clinical practice. HNSCC poses significant challenges due to its aggressive behavior and limited response to standard treatments, resulting in elevated morbidity and mortality rates.In order to improve prognostic prediction in HNSCC, our study is inspired by the realization that cholesterol metabolism plays a critical role in accelerating the progression of cancer. To this end, we are developing a unique risk signature using CholRG. The aim of this study was to create a CholRG-based risk signature to predict HNSCC prognosis, aiding in clinical decision-making accurately. The TCGA HNSCC dataset, along with GSE41613 and GSE65858, was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. A CholRG-based risk signature was then developed and validated across various independent HNSCC cohorts. Moreover, a nomogram model incorporating CholRG-based risk signature was established. Additionally, functional enrichment analysis was conducted, and the immune landscapes of the high- and low-risk groups were compared. Finally, in vitro experiments were performed using lipid-based transfection to deliver siRNAs targeting ACAT1 to SCC1 and SCC23 cell lines, further examining the effects of ACAT1 knockdown on these cells. Utilizing RNA-seq, microarray, and clinical data from public databases, we constructed and validated a CholRG-based risk signature that includes key genes such as ACAT1, CYP19A1, CYP27A1, FAXDC2, INSIG2, PRKAA2, and SEC14L2, which can effectively predict the clinical outcome of HNSCC. Additionally, our findings were reinforced by a nomogram model that integrates the risk score with clinical variables for more clinically practical prognostic assessment. In addition, patients at high risk show hypoxia and increased oncogenic pathways such as mTORC1 signaling, as well as a suppressed immune microenvironment marked by a reduction in the infiltration of important immune cells. Notably, in vitro experiments showed that ACAT1 depletion significantly suppressed the proliferation, colony formation, and invasion capabilities of HNSCC cells, confirming ACAT1's role in promoting malignancy. Collectively, our study not only underscores the importance of cholesterol metabolism in HNSCC pathogenesis but also highlights the CholRG-based risk signature as a promising tool for enhancing prognostic accuracy and personalizing therapeutic strategies.

Functional genomic analysis of the 68-1 RhCMV-Mycobacteria tuberculosis vaccine reveals an IL-15 response signature that is conserved with vector attenuation.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) is a deadly infectious disease having a major impact on global health. Using the CMV vector for development of novel vaccines is a promising new strategy that elicits strong and durable, high frequency memory T cell responses against heterologous immunogens. We conducted functional transcriptomic analysis of whole blood samples collected from cohorts of rhesus (Rh) macaques that were administered RhCMV/TB vector using a prime-boost strategy. Two modified CMV vectors were used in this study, including 68-1 RhCMV/TB-6Ag (encoding 6 Mtb protein immunogens, including Ag85A, ESAT-6, Rv3407, Rv2626, Rpf A, and Rpf D) and its attenuated variant, 68-1 RhCMV/Δpp71-TB-6Ag (a cell-to-cell spread-deficient vaccine vector lacking the Rh110 gene encoding the pp71 tegument protein). Bulk mRNA sequencing, differential gene expression, and functional enrichment analyses showed that these RhCMV/TB vaccines induce the innate and adaptive immune responses with specific transcriptomic signatures, including the IL-15-induced protective gene signature previously defined to be linked with protection against simian immunodeficiency virus (SIV) by the 68-1 RhCMV/SIV vaccine. While both vectors exhibited a transcriptomic response of the IL-15 protective signature in whole blood, we show that lack of pp71 does not maintain induction of the protective signature for the full duration of the study compared to the parental non-attenuated vector. Our observations indicate that RhCMV vector vaccines induce a transcriptomic response in whole blood that include a conserved IL-15 signature of which vector-encoded pp71 is an important component of response durability that upon future Mtb challenge may define specific vaccine protection outcomes against Mtb infection.

Integration of ubiquitination-related genes in predictive signatures for prognosis and immunotherapy response in sarcoma.

Ubiquitination is one of the most prevalent and complex post-translational modifications of proteins in eukaryotes, playing a critical role in regulating various physiological and pathological processes. Targeting ubiquitination pathways, either through inhibition or activation, holds promise as a novel therapeutic approach for cancer treatment. However, the expression patterns, prognostic significance, and underlying mechanisms of ubiquitination-related genes (URGs) in sarcoma (SARC) remain unclear. We analyzed URG expression patterns and prognostic implications in TCGA-SARC using public databases, identifying DEGs related to ubiquitination among SARC molecular subtypes. Functional enrichment analysis elucidated their biological significance. Prognostic signatures were developed using LASSO-Cox regression, and a predictive nomogram was constructed. External validation was performed using GEO datasets and clinical tissue samples. The association between URG risk scores and various clinical parameters, immune response, drug sensitivity, and RNA modification regulators was investigated. Integration of data from multiple sources and RT-qPCR confirmed upregulated expression of prognostic URGs in SARC. Single-cell RNA sequencing data analyzed URG distribution across immune cell types. Prediction analysis identified potential target genes of microRNAs and long non-coding RNAs. We identified five valuable genes (CALR, CASP3, BCL10, PSMD7, PSMD10) and constructed a prognostic model, simultaneously identifying two URG-related subtypes in SARC. The UEGs between subtypes in SARC are mainly enriched in pathways such as Cell cycle, focal adhesion, and ECM-receptor interaction. Analysis of URG risk scores reveals that patients with a low-risk score have better prognoses compared to those with high-risk scores. There is a significant correlation between DRG riskscore and clinical features, immune therapy response, drug sensitivity, and genes related to pan-RNA epigenetic modifications. High-risk SARC patients were identified as potential beneficiaries of immune checkpoint inhibitor therapy. We established regulatory axes in SARC, including CALR/hsa-miR-29c-3p/LINC00943, CASP3/hsa-miR-143-3p/LINC00944, and MIR503HG. RT-qPCR data further confirmed the upregulation of prognostic URGs in SARC. Finally, we validated the prognostic model's excellent predictive performance in predicting outcomes for SARC patients. We discovered a significant correlation between aberrant expression of URGs and prognosis in SARC patients, identifying a prognostic model related to ubiquitination. This model provides a basis for individualized treatment and immunotherapy decisions for SARC patients.

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Clostridium sporogenes-derived metabolites protect mice against colonic inflammation

ABSTRACT Gut microbiota-derived metabolites play a pivotal role in the maintenance of intestinal immune homeostasis. Here, we demonstrate that the human commensal Clostridium sporogenes possesses a specific metabolic fingerprint, consisting predominantly of the tryptophan catabolite indole-3-propionic acid (IPA), the branched-chain acids (BCFAs) isobutyrate and isovalerate and the short-chain fatty acids (SCFAs) acetate and propionate. Mono-colonization of germ-free mice with C. sporogenes (CS mice) affected colonic mucosal immune cell phenotypes, including up-regulation of Il22 gene expression, and increased abundance of transcriptionally active colonic tuft cells and Foxp3+ regulatory T cells (Tregs). In DSS-induced colitis, conventional mice suffered severe inflammation accompanied by loss of colonic crypts. These symptoms were absent in CS mice. In conventional, but not CS mice, bulk RNAseq analysis of the colon revealed an increase in inflammatory and Th17-related gene signatures. C. sporogenes-derived IPA reduced IL-17A protein expression by suppressing mTOR activity and by altering ribosome-related pathways in Th17 cells. Additionally, BCFAs and SCFAs generated by C. sporogenes enhanced the activity of Tregs and increased the production of IL-22, which led to protection from colitis. Collectively, we identified C. sporogenes as a therapeutically relevant probiotic bacterium that might be employed in patients with inflammatory bowel disease (IBD).

BNIP3+ fibroblasts associated with hypoxia and inflammation predict prognosis and immunotherapy response in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors that lacks effective treatment options. Cancer-associated fibroblasts (CAFs), an important component of the tumor microenvironment, associated with tumor progression, prognosis, and treatment response. This work aimed to explore the novel CAFs-associated target to improve treatment strategies in PDAC.MethodsThe PDAC single-cell sequencing data (CRA001160, n = 35) were downloaded and integrated based on GSA databases to classify fibroblasts into fine subtypes. Functional enrichment analysis and coexpression regulatory network analysis were used to identify the functional phenotypes and biological properties of the different fibroblast subtypes. Fibroblast differentiation trajectories were constructed using pseudochronological analysis to identify initial and terminally differentiated subtypes of fibroblasts. The changes in the proportions of different fibroblast subtypes before and after PDAC immunotherapy were compared in responsive and nonresponding patients, and the relationships between fibroblast subtypes and PDAC immunotherapy responsiveness were determined based on GSA and GEO database. Using molecular biology methods to confirm the effects of BNIP3 on hypoxia and inflammation in CAFs. CAFs were co cultured with pancreatic cancer cells to detect their effects on migration and invasion of pancreatic cancer.ResultsSingle-cell data analysis divided fibroblasts into six subtypes. The differentiation trajectory suggested that BNIP3+ Fibro subtype exhibited terminal differentiation, and the expression of genes related to hypoxia and the inflammatory response increased gradually with differentiation time. The specific overexpressed genes in the BNIP3+ Fibro subtype were significantly associated with overall and disease progression-free survival in the patients with PDAC. Interestingly, the greater the proportion of the BNIP3+ Fibro subtype was, the worse the response of PDAC patients to immunotherapy, and the CRTL treatment regimen effectively reduced the proportion of the BNIP3+ Fibro subtype. After knocking out BNIP3, the hypoxia markers and inflammatory factors of CAFs were inhibited. Co-culture of CAFs with pancreatic cancer cells can increase the migration and invasion of pancreatic cancer, but this could be reversed by knocking out BNIP3.ConclusionsThis study revealed the BNIP3+ Fibro subtype associated with hypoxia and inflammatory responses, which was closely related to the poor prognosis of patients with PDAC, and identified signature genes that predict the immunotherapy response in PDAC.

Obesity-Associated Changes in Immune Cell Dynamics During Alphavirus Infection Revealed by Single Cell Transcriptomic Analysis.

Obesity worsens disease outcomes following arthritogenic alphavirus infection.Arthritogenic alphavirus infection causes significant shifts in immune cell populations in the blood and footpad.Blood monocytes from lean mice had higher expression of interferon response genes at the later stage of infection.Footpads in lean mice have an expanded population of F4/80lo macrophages with an intense interferon response gene signature before and after alphavirus infection that is not found in obese mice.Macrophages in obese mice express lower levels of interferon response genes, have a unique necroptosis signature, and higher F4/80 expression.

Computational Microbiome Pharmacology Analysis Elucidates the Anti-Cancer Potential of Vaginal Microbes and Metabolites.

The vaginal microbiome's role in risk, progression, and treatment of female cancers has been widely explored. Yet, there remains a need to develop methods to understand the interaction of microbiome factors with host cells and to characterize their potential therapeutic functions. To address this challenge, we developed a systems biology framework we term the Pharmacobiome for microbiome pharmacology analysis. The Pharmacobiome framework evaluates similarities between microbes and microbial byproducts and known drugs based on their impact on host transcriptomic cellular signatures. Here, we apply our framework to characterization of the Anti-Gynecologic Cancer Vaginal Pharmacobiome. Using published vaginal microbiome multi-omics data from the Partners PrEP clinical trial, we constructed vaginal epithelial gene signatures associated with each profiled vaginal microbe and metabolite. We compared these microbiome-associated host gene signatures to post-drug perturbation host gene signatures associated with 35 FDA-approved anti-cancer drugs from the Library of Integrated Network-based Cellular Signatures database to identify vaginal microbes and metabolites with high statistical and functional similarity to these drugs. We found that Lactobacilli and their metabolites can regulate host gene expression in ways similar to many anti-cancer drugs. Additionally, we experimentally tested our model prediction that taurine, a metabolite produced by L. crispatus, kills cancerous breast and endometrial cancer cells. Our study shows that the Pharmacobiome is a powerful framework for characterizing the anti-cancer therapeutic potential of vaginal microbiome factors with generalizability to other cancers, microbiomes, and diseases.

Construction and validation of a histone-related gene signature for the diagnosis of endometriosis.

Endometriosis is a common chronic disease in childbearing women and a major cause of infertility. Our study aimed to identify and validate a novel gene signature for diagnosing endometriosis based on histone-related genes (HRGs), and to investigate their biological functions in endometriosis. RNA sequence data were downloaded from the Gene Expression Omnibus database, and HRGs were retrieved from the GeneCards database. We identified differentially expressed genes using the limma package, and constructed a diagnostic model using the rms package. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed for visualization, annotation, and integrated discovery. Subsequently, we validated the model using the recall and decision curve analysis (DCA). Additionally, we analyzed the immune microenvironment features using CIBERSORT. A total of 18 differentially expressed HRGs were identified in patients with endometriosis compared with controls. GO and KEGG enrichment was mainly in spindle organization, positive regulation of the cell cycle process, progesterone-mediated oocyte maturation, and cellular senescence and cell cycle. We obtained a signature of four HRGs (JUNB, FRY, LMNB1, and SPAG1). DCA revealed that the diagnostic model benefits patients with endometriosis, regardless of the incidence. CIBERSORT analysis showed that the number of plasma cells increased significantly in endometriosis samples from all four datasets. Our findings provide novel insights into the function of HRGs in the development of endometriosis and identify a new signature of four HRGs that may serve as valuable diagnostic markers and therapeutic targets for this disease.

Identification of targetable epigenetic vulnerabilities in uveal melanoma.

Uveal melanoma (UM) is the most prevalent primary intraocular malignancy in adults, which preferentially metastasizes to the liver in approximately half of all cases. Metastatic UM is notoriously resistant to therapy and is almost uniformly fatal. UM metastasis is most strongly associated with mutational inactivation of the BAP1 tumor suppressor gene. Given the role of BAP1 in epigenetic regulation as the ubiquitin hydrolase subunit of the polycomb repressive deubiquitinase (PR-DUB) complex, we conducted high-throughput drug screening using a well-characterized epigenetic compound library to identify new therapeutic vulnerabilities. We identified several promising new lead compounds, in particular the extra-terminal domain protein (BET) inhibitor mivebresib (ABBV-075). Mivebresib significantly improved survival rates in a metastatic uveal melanoma xenograft mouse model and entirely prevented detectable metastases to the bones, spinal cord, and brain. RNA sequencing revealed a notable overlap between the genes and pathways affected by HDAC and BET inhibition, including the reversal of gene signatures linked to high metastatic risk and upregulation of genes associated with a neuronal phenotype. Together, we found that UM cells are particularly vulnerable to class I HDAC and BET inhibition, and highlight the BET inhibitor mivebresib as a promising candidate for further clinical evaluation.

Identification of prognostic biomarkers related to retinoic acid metabolism in gliomas and analysis of their impact on the immune microenvironment.

Glioma is a primary tumor of the central nervous system. Numerous investigations have demonstrated that retinoic acid (RA) signaling plays an important role in glioblastoma. This research aimed to develop a RA metabolism-related gene signature associated with glioma. The RA metabolism-related differentially expressed genes were obtained through differential analysis of RA metabolism-related genes in GSE4290. The univariate Cox and least absolute shrinkage and selection operator regression analysis were adopted to build a RA metabolism-related glioma prognostic signature. We further conducted immune feature estimation and functional enrichment analysis between 2 risk subgroups. Finally, the potential drug-targeting prognostic genes were predicted through the DrugBank database. A sum of 10 RA metabolism-related differentially expressed genes between normal and tumor groups were identified. Then, a RA metabolism-related prognostic signature was built based on the 7 prognostic genes (ADH4, DHRS3, DHRS9, LRAT, RDH10, RDH12, and RDH5). Glioma patients were separated into 2 risk subgroups (low-risk vs high-risk) based on the median value of the risk score. We found that monocytes were negatively correlated with DHRS9, while activated naive CD4+T cell was positively correlated with RDH10. These prognostic genes participated in some immune-related processes, such as "B cell-mediated immunity." Finally, 4 drugs targeting DHRS3, LRAT, and RDH12 were predicted, including vitamin A, nicotinamide adenine dinucleotide, ethanol, and cyclohexylformamide. The prognostic signature comprised of ADH4, DHRS3, DHRS9, LRAT, RDH10, RDH12, and RDH5 based on RA metabolism was established, which provided a theoretical basis and reference value for the research of glioma.

Enhanced Type 1 Interferon Signature in Axial Spondyloarthritis Patients Unresponsive to Secukinumab Treatment.

Axial spondyloarthritis (axSpA) is an inflammatory disease in which overactive interleukin (IL)-17A-producing cells are implicated in a central role. Therapeutically, biologics that target IL-17A, such as secukinumab, have demonstrated improved clinical outcomes. Despite this translational success, there is a gap in understanding why some patients with axSpA do not respond to IL-17A-blocking therapy. Our study aims to discriminate immune profiles between secukinumab responders (SEC-R) and nonresponders (SEC-NR). Peripheral blood mononuclear cells were collected from 30 patients with axSpA before and 24 weeks after secukinumab treatment. Frequency of CD4+ subsets were compared between SEC-R and SEC-NR using flow cytometry. Mature CD45RO+CD45RA-CD4+ T cells were fluorescent-activated cell sorting sorted, and RNA was measured using NanoString analysis. SEC-NR had an increased frequency of IL-17A-producing RORγt+CD4+ T cells compared to healthy controls before secukinumab treatment (P < 0.01). SEC-NR had a significant increase of CXCR3+ CD4+ T cells before secukinumab treatment compared to SEC-R (P < 0.01). Differentially expressed gene analysis revealed up-regulation of type 1 interferon (IFN)-regulated genes in SEC-NR patients compared to SEC-R patients after receiving the biologic. SEC-R patients had an up-regulated cytotoxic CD4+ T cell gene signature before receiving secukinumab treatment compared to SEC-NR patients. The increased frequency of IL-17A-producing cells in SEC-NR patients suggests a larger inflammatory burden than SEC-R patients. With treatment, SEC-NR patients have a more pronounced type 1 IFN signature than SEC-R patients, suggesting a mechanism contributing to this larger inflammatory burden. The results point toward more immune heterogeneity in axSpA than has been recognized and highlights the need for precision therapeutics in this disease.

Identification and Verification of Potential Markers Related to Myocardial Fibrosis by Bioinformatics Analysis.

Mounting evidence indicates that myocardial fibrosis (MF) is frequently intertwined with immune and metabolic disorders. This comprehensive review aims to delve deeply into the crucial role of immune-related signature genes in the pathogenesis and progression of MF. This exploration holds significant importance as understanding the underlying mechanisms of MF is essential for developing effective diagnostic and therapeutic strategies. The dataset GSE9735 about myocardial fibrosis and non-fibrosis was downloaded from GEO database. Differentially expressed genes (DEGs) were identified by 'limma' package in R software. Then, the biological function of DEG was determined by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. XCell was used to estimate the composition pattern of matrix and immune cells. Protein-protein interaction (PPI) network was constructed based on STRING analysis software, and Hub genes were screened and functional modules were analyzed. The correlation between hub genes and immune cell subtypes was analyzed. Hub genes with |correlation coefficient|> 0.45 and p-value < 0.05 were used as characteristic biomarkers. Finally, the logistic regression model is used to verify the three markers in the training set and verification set (GSE97358 and GSE225336). A total of 635 DEGs were identified. Functional enrichment analysis shows that inflammation and immune response, extracellular matrix and structural remodeling play an important role in the pathological mechanism of MF. Immune cell infiltration analysis showed that immune cells (Plasma cells, Eosinophils, Chondrocytes and Th2 cells) significantly changed in MF pathological conditions. In PPI network analysis, IL1β, TTN, PTPRC, IGF1, ALDH1A1, CYP26A1, ALDH1A3, MYH11, CSF1R and CD80 were identified as hub genes, among which IL1β, CYP26A1 and GNG2 were regarded as immune-related characteristic markers. The AUC scores of the three biomarkers are all above 0.65, which proves that they have a good discrimination effect in MF. In this study, three immune-related genes were identified as diagnostic biomarkers of MF, which provided a new perspective for exploring the molecular mechanism of MF. This study takes a comprehensive approach to understanding the intricate relationship between myocardial fibrosis and immune metabolism. By identifying key immune-related biomarkers, this study not only reveals the molecular basis of myocardial fibrosis but also paves the way for the development of novel diagnostic tools and therapeutic strategies. These findings are critical for improving patient prognosis and may have broader implications for studying and treating other cardiovascular diseases associated with immune dysregulation.

Cancer-Associated Fibroblast Subtypes Reveal Distinct Gene Signatures in the Tumor Immune Microenvironment of Vestibular Schwannoma.

Cancer-associated fibroblast (CAF) composition within the same organ varies across different cancer subtypes. Distinct CAF subtypes exhibit unique features due to interactions with immune cells and the tumor microenvironment. However, data on CAF subtypes in individuals with vestibular schwannoma (VS) are lacking. Therefore, we aimed to distinguish CAF subtypes at the single-cell level, investigate how stem-like CAF characteristics influence the tumor immune microenvironment, and identify CAF subtype-specific metabolic reprogramming pathways that contribute to tumor development. Data were analyzed from three patients with VS, encompassing 33,081 single cells, one bulk transcriptome cohort, and The Cancer Genome Atlas Pan-Cancer database (RNA sequencing and clinical data). Our findings revealed that antigen-presenting CAFs are linked to substantially heightened immune activity, supported by metabolic reprogramming, which differs from tumorigenesis. High expression of the stem-like CAF gene signature correlated with poor prognosis in low-grade gliomas within the pan-cancer database. This is the first study to classify CAF subtypes in VS patients and identify a therapeutic vulnerability biomarker by developing a stem-like CAF gene signature. Personalized treatments tailored to individual patients show promise in advancing precision medicine.