Gene Signature Research Articles

Dental pulp mesenchymal stem cell (DPSCs)-derived soluble factors, produced under hypoxic conditions, support angiogenesis via endothelial cell activation and generation of M2-like macrophages

BackgroundCell therapy has emerged as a revolutionary tool to repair damaged tissues by restoration of an adequate vasculature. Dental Pulp stem cells (DPSC), due to their easy biological access, ex vivo properties, and ability to support angiogenesis have been largely explored in regenerative medicine.MethodsHere, we tested the capability of Dental Pulp Stem Cell-Conditioned medium (DPSC-CM), produced in normoxic (DPSC-CM Normox) or hypoxic (DPSC-CM Hypox) conditions, to support angiogenesis via their soluble factors. CMs were characterized by a secretome protein array, then used for in vivo and in vitro experiments. In in vivo experiments, DPSC-CMs were associated to an Ultimatrix sponge and injected in nude mice. After excision, Ultimatrix were assayed by immunohistochemistry, electron microscopy and flow cytometry, to evaluate the presence of endothelial, stromal, and immune cells.For in vitro procedures, DPSC-CMs were used on human umbilical-vein endothelial cells (HUVECs), to test their effects on cell adhesion, migration, tube formation, and on their capability to recruit human CD14+ monocytes.ResultsWe found that DPSC-CM Hypox exert stronger pro-angiogenic activities, compared with DPSC-CM Normox, by increasing the frequency of CD31+ endothelial cells, the number of vessels and hemoglobin content in the Ultimatrix sponges. We observed that Utimatrix sponges associated with DPSC-CM Hypox or DPSC-CM Normox shared similar capability to recruit CD45− stromal cells, CD45+ leukocytes, F4/80+ macrophages, CD80+ M1-macrophages and CD206+ M2-macropages. We also observed that DPSC-CM Hypox and DPSC-CM Normox have similar capabilities to support HUVEC adhesion, migration, induction of a pro-angiogenic gene signature and the generation of capillary-like structures, together with the ability to recruit human CD14+ monocytes.ConclusionsOur results provide evidence that DPSCs-CM, produced under hypoxic conditions, can be proposed as a tool able to support angiogenesis via macrophage polarization, suggesting its use to overcome the issues and restrictions associated with the use of staminal cells.

Common molecular basis for MASH and hepatitis C revealed via systems biology approach

BackgroundMetabolic dysfunction-associated steatohepatitis (MASH) is characterized by liver inflammation and damage caused by a buildup of fat in the liver. Hepatitis C, caused by hepatitis C virus (HCV), is a disease that can lead to liver cirrhosis, liver cancer, and liver failure. MASH and hepatitis C are the common causes of liver cirrhosis and hepatocellular carcinoma. Several studies have shown that hepatic steatosis is also a common histological feature of liver in HCV infected patients. However, the common molecular basis for MASH and hepatitis C remains poorly understood.MethodsFirstly, differentially expressed genes (DEGs) for MASH and hepatitis C were extracted from the GSE89632, GSE164760 and GSE14323 datasets. Subsequently, the common DEGs shared among these datasets were determined using the Venn diagram. Next, a protein-protein interaction (PPI) network was constructed based on the common DEGs and the hub genes were extracted. Then, gene ontology (GO) and pathway analysis of the common DEGs were performed. Furthermore, transcription factors (TFs) and miRNAs regulatory networks were constructed, and drug candidates were identified. After the MASH and hepatitis C cell model was treated with predicted drug, the expression levels of the signature genes were measured by qRT-PCR and ELISA.Results866 common DEGs were identified in MASH and hepatitis C. The GO analysis showed that the most significantly enriched biological process of the DEGs was the positive regulation of cytokine production. 10 hub genes, including STAT1, CCL2, ITGAM, PTPRC, CXCL9, IL15, SELL, VCAM1, TLR4 and CCL5, were selected from the PPI network. By constructing the TF-gene and miRNA-gene network, most prominent TFs and miRNAs were screened out. Potential drugs screening shows that Budesonide and Dinoprostone may benefit patients, and cellular experiments showed that Budesonide effectively inhibited the expression of genes related to glycolipid metabolism, fibrosis, and inflammatory factors.ConclusionWe extracted 10 hub genes between MASH and hepatitis C, and performed a series of analyses on the genes. Molecular docking and in vitro studies have revealed that Budesonide can effectively suppress the progression of MASH and hepatitis C. This study can provide novel insights into the potential drug targets and biomarkers for MASH and hepatitis C.

Identification of genetic fingerprint of type I interferon therapy in visceral metastases of melanoma

Malignant melanoma is a difficult-to-treat skin cancer with increasing incidence worldwide. Although type-I interferon (IFN) is no longer part of guidelines, several melanoma patients are treated with type-I interferon (IFN) at some point of the disease, potentially affecting its genetic progression. We run genome-wide copy number variation (CNV) analysis on previously type-I IFN-treated (n = 17) and control (n = 11) visceral metastases of melanoma patients. Results were completed with data from the TCGA and MM500 databases. We identified metastasis- and brain metastasis-specific gene signatures mostly affected by CN gains. Some cases were genetically resistant to IFN showing characteristic gene alterations (e.g. ABCA4 or ZEB2 gain and alterations of DNA repair genes). Analysis of a previously identified type-I IFN resistance gene set indicates that only a proportion of these genes was exclusive for the IFN-treated metastases reflecting a possible selective genomic pressure of endogenous IFNs during progression. Our data suggest that previous type-I IFN treatment and/or endogenous IFN production by immune response affect genomic progression of melanoma which may have clinical relevance, potentially influence immune checkpoint regulation in the tumor microenvironment.

Refining Diagnostic Subtypes of Peripheral T-cell Lymphoma Using a Multiparameter Approach

Peripheral T-cell lymphoma (PTCL) is a heterogeneous category, and many cases are unclassifiable and designated as PTCL-not otherwise specified (PTCL-NOS). Gene expression profiling (GEP) has delineated two prognostic subtypes within PTCL-NOS, PTCL-TBX21, and PTCL-GATA3, characterized by distinctive transcriptomes and a different prognosis. To further evaluate the pathologic features of these subgroups, 101 PTCL cases that did not meet specific criteria for well-defined T-cell lymphoma entities underwent detailed pathologic, immunophenotypic (including TFH biomarkers) and GEP analyses, separating them into PTCL-NOS (n=63) and PTCL-TFH (a.k.a. nodal PTCL-TFH, NOS, and TFH lymphoma, NOS) (n=38). PTCL-NOS cases were further categorized into PTCL-GATA3 (n=22; 34%) and PTCL-TBX21 (n=41; 66%), and a significant association (p < 0.02) with overall survival (OS) was reaffirmed. Histopathological assessment showed PTCL-GATA3 cases were characterized by monotonous medium-sized or large transformed cells with a minimal tumor microenvironment (TME) compared to PTCL-TBX21 cases, which consisted of pleomorphic cells in a polymorphous TME (p < 0.05). GEP analysis validated these TME distinctions. Immunophenotypic analysis showed that PTCL-GATA3 cases were predominantly CD4+CD8- and associated with significantly higher LEF1, MYC, and CD30 expression (p < 0.05). PTCL-TBX21 displayed a more diverse biomarker profile with two subgroups: one expressing cytotoxic antigens and enriched in CD8+CD4- or CD8-CD4- phenotype, and another lacking cytotoxic markers but showing a CD4+CD8- phenotype with increased ICOS expression, but devoid of other TFH markers. The PTCL-TFH cases correlated with an angioimmunoblastic T-cell lymphoma (AITL) gene signature, had more EBER-positive cells than the PTCL-GATA3 and PTCL-TBX21 cases, and a subset had some morphologic features of AITL (p < 0.01). This study highlights the unique morphologic and phenotypic variations within the newly-identified PTCL subtypes and should enable more precise diagnosis and tailored therapeutic strategies in the future.

Spatially resolved gene expression profiles of fibrosing interstitial lung diseases

Fibrosing interstitial lung diseases (ILDs) encompass a diverse range of scarring disorders that lead to progressive lung failure. Previous gene expression profiling studies focused on idiopathic pulmonary fibrosis (IPF) and bulk tissue samples. We employed digital spatial profiling to gain new insights into the spatial resolution of gene expression across distinct lung microenvironments (LMEs) in IPF, chronic hypersensitivity pneumonitis (CHP) and non-specific interstitial pneumonia (NSIP). We identified differentially expressed genes between LMEs within each condition, and across histologically similar regions between conditions. Uninvolved regions in IPF and CHP were distinct from normal controls, and displayed potential therapeutic targets. Hallmark LMEs of each condition retained distinct gene signatures, but these could not be reproduced in matched lung tissue samples. Based on these profiles and unsupervised clustering, we grouped previously unclassified ILD cases into NSIP or CHP. Overall, our work uniquely dissects gene expression profiles between LMEs within and across different types of fibrosing ILDs.

A Novel Gene Signature Based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients.

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes. For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions. Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells. We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

Identification of mitophagy-related gene signatures for predicting delayed graft function and renal allograft loss post-kidney transplantation.

Ischemia-reperfusion injury (IRI) is an unavoidable consequence post-kidney transplantation, which inevitably leads to kidney damage. Numerous studies have demonstrated that mitophagy is implicated in human cancers. However, the function of mitophagy in kidney transplantation remains poorly understood. This study aims to develop mitophagy-related gene (MRGs) signatures to predict delayed graft function (DGF) and renal allograft loss post-kidney transplantation. Differentially expressed genes (DEGs) were identified and intersected with the MRGs to obtain mitophagy-related DEGs (MRDEGs). Functional enrichment analyses were conducted. Subsequently, random forest and SVM-RFE machine learning were employed to identify hub genes. The DGF diagnostic prediction signature was constructed using LASSO regression analysis. The renal allograft prognostic prediction signature was developed through univariate Cox and LASSO regression analysis. In addition, ROC curves, immunological characterization, correlation analysis, and survival analysis were performed. Nineteen MRDEGs were obtained by intersecting 61 DEGs with 4897 MRGs. Seven hub genes were then identified through machine learning. Subsequently, a five-gene DGF diagnostic prediction signature was established, with ROC curves indicating its high diagnostic value for DGF. Immune infiltration analysis revealed that many immune cells were more abundant in the DGF group compared to the Immediate Graft Function (IGF) group. A two-gene prognostic signature was developed, which accurately predicted renal allografts prognosis. The mitophagy-related gene signatures demonstrated high predictive accuracy for DGF and renal allograft loss. Our study may provide new perspectives on prognosis and treatment strategies post-kidney transplantation.

Integrated single-cell transcriptomic analyses identify a novel lineage plasticity-related cancer cell type involved in prostate cancer progression

Cancer cell plasticity is the ability of neoplastic cells to alter their identity and acquire new biological properties under microenvironmental pressures. In prostate cancer (PCa), lineage plasticity often results in therapy resistance and trans-differentiation to neuroendocrine (NE) lineage. However, identifying the cancer cells harboring lineage plasticity-related status remains challenging. Based on 13 multi-center human PCa bulk transcriptomic cohorts (samples=3314) and 9 bulk transcriptomic datasets derived from PCa experimental models, we established an integrated lineage plasticity-related gene signature, termed LPSig. Leveraging this gene signature, AUCell enrichment analysis was applied to identify the cell population with high lineage plasticity from a comprehensive single-cell RNA-sequencing (scRNA-seq) meta-atlas assembled by us, which consisted of 10 public human PCa scRNA-seq datasets (samples=93, cells=222,529). Moreover, additional scRNA-seq dataset of human PCa, multiplex immunohistochemistry staining for human PCa tissues, invitro and invivo functional experiments, as well as qPCR and Western blot analyses were employed to validate our findings. We found that LPSig could finely capture the dynamics of tumor lineage plasticity throughout the progression of PCa, accurately estimating the status of lineage plasticity. Based on LPSig, we identified a previously undefined minority population of lineage plasticity-related PCa cells (LPCs) from the human PCa scRNA-seq meta-atlas assembled by this study. Furthermore, in-depth dissection revealed pivotal roles of LPCs in trans-differentiation, tumor recurrence, and poor patient survival during PCa progression. Furthermore, we identified HMMR as a representative cell surface marker for LPCs, which was validated using additional scRNA-seq datasets and multiplexed immunohistochemistry. Moreover, HMMR was transcriptionally inhibited by androgen receptor (AR), and was required for the aggressive adenocarcinoma features and NE phenotype. Our study uncovers a novel population of lineage plasticity-related cells with low AR activity, stemness-like traits, and elevated HMMR expression, that may facilitate poor prognosis in PCa. This work was supported by National Key R&D Program of China (2022YFA0807000), National Natural Science Foundation of China (82160584), Advanced Prostate Cancer Diagnosis and Treatment Technology Innovation Team of Kunming Medical University (CXTD202216), and Reserve Talents of Young and Middle-aged Academic Leaders in Yunnan Province (202105AC160013).

Discovery of five diagnostic biomarkers associated with immune cell infiltration in cases of acute myocardial infarction.

Acute myocardial infarction (AMI) remains one of the leading causes of mortality and morbidity worldwide. GSE61144 and GSE66360 were the sources of microarray gene expression profiles for acute myocardial infarction patients and were acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). After merging the datasets, genes that were differentially expressed were chosen. A total of 234 genes were found to have different expression levels. Of these, 206 genes were upregulated, and 28 genes were downregulated. Five coexpression modules were identified by WGCNA, with the yellow module showing a high correlation with AMI (r=0.65, P=2.0e-15). Ninety-two hub genes were selected in the yellow module by setting a threshold of module membership (MM) greater than 0.8 and gene significance (GS) higher than 0.4. By overlapping these genes with the differentially expressed genes, 81 hub genes were obtained. Five key genes (C5AR1, CXCL1, CXCL2, FPR1, and P2RY13) were identified through PPI analysis. AMI patients exhibited elevated levels of immune cell infiltration, and immune scores in AMI samples were significantly positively correlated with all five key genes. Moreover, the expression levels of these five genes were higher in AMI patients. These five genes possessed area under the curve (AUC) values exceeding 0.8 for diagnosing AMI, thereby demonstrating their efficacy as diagnostic markers. C5AR1, CXCL1, CXCL2, FPR1, and P2RY13 have the potential to be useful biomarkers in diagnosing AMI and are linked to immune cell infiltration in AMI, opening up new avenues for future research into the pathogenesis of AMI.

Identification and Experimental Validation of Prognostic miRNA Signature and Ferroptosis-Related Key Genes in Cervical Squamous Cell Carcinoma.

This study aimed to investigate the prognostic value of miRNAs and ferroptosis-related genes in cervical squamous cell carcinoma. We mined data from public databases for differentially expressed miRNAs, ferroptosis-related genes, and clinical parameters and constructed a prognostic risk model. The predictive performance of the model was evaluated using survival and receiver operating characteristic curve analyses. We combined the clinicopathological features to construct a nomogram and evaluated its efficacy using calibration and clinical decision curves. The correlation between miRNA characteristics, risk score, and the tumor microenvironment was also studied. Next, consensus and key genes were screened, and their biological functions were analyzed using KEGG, GO, GSEA, and drug sensitivity analysis. Finally, the expression of miRNAs and key genes was detected using qRT-PCR and western blotting to verify the prediction results. Seven miRNA signatures (miR-100-3p, miR-301a-5p, miR-331-3p, miR-425-5p, miR-502-3p, miR-505-5p, and miR-629-3p) were generated, and prognostic risk and nomogram models were successfully constructed. These models exhibited good accuracy. miRNA signatures correlated with the tumor microenvironment. Twelve consensus genes and three key genes (SLC2A1, ANO6, and TXNIP) were screened and their biofunctional diversity was identified using various analytical methods. qRT-PCR and western blotting were used to verify the expression of miR-301a-5p, miR-505-5p, SLC2A1, and TXNIP in cervical squamous carcinoma. The results were consistent with those of bioinformatics analyses. Seven miRNAs may serve as prognostic biomarkers of cervical squamous cell carcinoma. SLC2A1, ANO6, and TXNIP are associated with cervical squamous cell carcinoma and may serve as ferroptosis-related markers of the disease.

Transcriptome-based characterization of 3’2’-cGAMP signaling mediated immune responses

Cyclic dinucleotides (CDNs) are critical adjuvants in antiviral vaccines and cancer immunotherapy, primarily through the activation of the cGAS-STING signaling pathway. Evaluating the immune responses triggered by CDNs is essential for the development of effective adjuvants. In this study, we performed a comparative transcriptome analysis to characterize the immune responses elicited by the recently identified nuclease-resistant Drosophila and bacterial CDN, 3’2’-cGAMP, in mammalian immune cells. We detected a robust induction of innate immune gene signature following 3’2’-cGAMP stimulation in digitonin-permeabilized mouse primary macrophages, comparable to the response observed with the canonical mammalian CDN, 2’3’-cGAMP. STING deficiency remarkably reduced 3’2’-cGAMP-induced phosphorylation of TBK1 and IRF3 and the induction of IFN-β, indicating that 3’2’-cGAMP signaling-mediated immune responses were mainly STING dependent. In comparison to 2’3’-cGAMP signaling, 3’2’-cGAMP signaling preferentially elicited many STING-dependent genes involved in transcription and nucleosome positioning and assembly in the nucleus, which are likely associated with several enriched pathways, including cellular senescence, HDACs deacetylate histones, and epigenetic regulation of gene expression. The integrative analysis further revealed that 3’2’-cGAMP signaling preferentially induced genes were associated with autoimmune disease-related processes, suggesting a potential side effect that requires monitoring when used as an adjuvant. In conclusion, this study provides the first transcriptional landscape of 3’2’-cGAMP signaling in mammals and reveals the immune response characteristics and potential side effects mediated by 3’2’-cGAMP signaling. These findings may aid in the development of 3’2’-cGAMP-based adjuvants for antiviral vaccines and cancer immunotherapy.

Establishing a Novel Pyroptosis-Related Gene Signature and Predicting Chemical Drugs for Papillary Thyroid Cancer.

The present study aimed to construct a novel pyroptosis-related gene signature to predict the prognosis of papillary thyroid cancer (PTC). The gene expression level and survival and prognosis information of PTC were obtained from TCGA. The differentially expressed pyroptosis-related genes (DEPs) between cancer and control groups were selected, followed by subtype analysis. A prognostic model was built using LASSO regression analysis. The samples were then divided into high- and low-risk groups, and the differences in immune cell distribution in different risk groups were compared. The chemical drugs associated with genes in the prognostic model were extracted from the Comparative Toxicogenomics Database. A total of 31 DEPs were selected, and 3 different subtypes were obtained. A prognostic model based on 6 pyroptosis-related genes was constructed. The risk grouping was significantly correlated with the actual prognosis, and the model was found to be an independent prognostic factor. Six immune cells with significant differences in distribution in different risk groups were screened. CGP52608 could target four genes in the prognostic model, including GSDMB, NLRC4, IL1A, and IL6. The present study constructed a pyroptosis-related gene signature that could predict the prognosis of PTC. Additionally, this signature was correlated with tumor immunity.

Transcriptomic imputation identifies tissue-specific genes associated with cervical myelopathy

Degenerative cervical myelopathy (DCM) is a progressive spinal condition that can lead to severe neurological dysfunction. Despite its degenerative pathophysiology, family history has shown to be a largely important factor in incidence and progression, suggesting that inherent genetic predisposition may play a role in pathophysiology. To determine the tissue-specific, functional genetic basis of hereditary predisposition to cervical myelopathy. Retrospective case-control study using patient genetics and matched EHR from the Mount Sinai BioMe Biobank. In a large, diverse, urban biobank of 32,031 individuals, with 558 individuals with cervical myopathy, we applied transcriptomic imputation to identify genetically regulated gene expression signatures associated with DCM. We performed drug-repurposing analysis using the CMAP database to identify candidate therapeutic interventions to reverse the cervical myelopathy-associated gene signature. We identified 16 genes significantly associated with DCM across five different tissues, suggesting tissue-specific manifestations of inherited genetic risk (upregulated: HES6, PI16, TMEM183A, BDH2, LINC00937, CLEC4D, USP43, SPATA1; downregulated: TTC12, CDK5, PAFAH1B2, RCSD1, KLHL29, PTPRG, RP11-620J15.3, C1RL). Drug repurposing identified 22 compounds with the potential to reverse the DCM-associated signature, suggesting points of therapeutic intervention. The inherited genetic risk for cervical myelopathy is functionally associated with genes involved in tissue-specific nociceptive and proliferative processes. These signatures may be reversed by candidate therapeutics with nociceptive, calcium channel modulating, and antiproliferative effects. Understanding the genetic basis of DCM provides critical insights into the hereditary factors contributing to the disease, allowing for more personalized and targeted therapeutic approaches. The identification of candidate drugs through transcriptomic imputation and drug repurposing analysis offers potential new treatments that could significantly improve patient outcomes and quality of life by addressing the underlying genetic mechanisms of DCM.

Liver transcriptome analysis reveals PSC-attributed gene set associated with fibrosis progression

BackgroundPrimary Sclerosing Cholangitis (PSC) is a chronic heterogenous cholangiopathy with unknown etiology where chronic inflammation of the bile ducts leads to multifocal biliary strictures and biliary fibrosis with consecutive cirrhosis development. We here aimed to identify a PSC-specific gene signature associated with biliary fibrosis development. MethodsWe performed RNA-sequencing of 47 liver biopsies from people with PSC (n = 16), primary biliary cholangitis (PBC, n = 15), and metabolic dysfunction-associated steatotic liver disease (MASLD, n = 16) with different fibrosis stages to identify a PSC-specific gene signature associated with biliary fibrosis progression. For validation, we compared an external transcriptome data set of liver biopsies from people with PSC (n = 73) with different fibrosis stages (DOI: 10.1002/hep.31488; baseline samples from NCT01672853). ResultsDifferential gene expression analysis of the liver transcriptome from PSC patients with advanced versus early fibrosis revealed 431 genes associated with fibrosis development. Of those, 367 were identified as PSC-associated when compared to PBC or MASLD. Validation against an external data set of 73 liver biopsies from PSC patients with different fibrosis stages led to a condensed set of 150 (out of 367) DEGs. Cell type specificity assignment of those genes by using published single cell RNA-seq data revealed genetic disease drivers expressed by cholangiocytes (e.g. CXCL1, SPP1), fibroblasts, innate and adaptive immune cells while deconvolution along fibrosis progression of the PSC, PBC, MASLD samples highlighted an early involvement of macrophage- and neutrophil-associated genes in PSC fibrosis. ConclusionWe reveal a PSC-attributed gene signature associated with biliary fibrosis development that may enable the identification of potential new biomarkers and therapeutic targets in PSC-related fibrogenesis. IMPACT AND IMPLICATIONSPrimary sclerosing cholangitis (PSC) is an inflammatory liver disease that is characterized by multifocal inflammation of bile ducts and subsequent biliary fibrosis. Herein, we identify a PSC-specific gene set of biliary fibrosis progression attributing to a uniquely complex milieu of different cell types, including innate and adaptive immune cells while neutrophils and macrophages showed an earlier involvement in fibrosis initiation in PSC in contrast to PBC and MASLD. Thus, our unbiased approach lays an important groundwork for further mechanistic studies for research into PSC-specific fibrosis.

A targeted gene signature stratifying mediastinal gray zone lymphoma into classical Hodgkin lymphoma-like or primary mediastinal B-cell lymphoma-like subtypes.

Not available.

Deciphering Immune-related Gene Signatures in Diabetic Retinopathy: Insights from In silico Analysis and In vitro Experiment.

Diabetes retinopathy (DR) is one of the most common microvascular consequences of diabetes, and the economic burden is increasing. Our aim is to decipher the relevant mechanisms of immune-related gene features in DR and explore biomarkers targeting DR. Provide a basis for the treatment and prevention of DR. The immune infiltration enrichment score of DR patients was evaluated from the single- cell RNA sequencing dataset, and the samples were divided into low immune subgroups and high immune subgroups based on this result. Through weighted gene correlation network analysis, differentially expressed genes (DEGs) between two subgroups were identified and crossed with genes with the strongest immune association, resulting in significant key genes. Then divide the DR individuals into two immune related differentially expressed gene (IDEG) clusters, A and B. Submit cross DEGs between two clusters through Gene Set Enrichment Analysis (GSEA) to further explore their functions. A protein-protein interaction (PPI) network of IDEG was established to further identify central genes associated with DR. Use the discovered central genes to predict the regulatory network involved in the pathogenesis of DR. Then, the role of the identified hub gene in the pathogenesis of DR was further studied through in vitro experiments. We found that the immune scores of DR and control groups were different, and 27 IDEGs were found in the DR subgroup. Compared with cluster A, the proportion of cytotoxic lymphocytes, B lineage, monocyte lineage, and fibroblasts in DR patients in cluster B is significantly enriched. GSEA indicates that these genes are associated with T cell activation, regulation of immune response processes, lymphocyte-mediated immunity, TNF signaling pathway, and other signaling pathways. The PPI network subsequently identified 10 hub genes in DR, including SIGLEC10, RGS10, PENK, FGD2, LILRA6, CIITA, EGR2, SIGLEC7, LILRB1, and CD300LB. The upstream regulatory network and lncRNA miRNA mRNA ceRNA network of these hub genes were ultimately constructed. The discovery and identification of these genes will provide biomarkers for targeted prediction and treatment of DR. By integrating bioinformatics analysis and in vitro experiments, we have identified a set of central genes, indicating that these genes can serve as potential biomarkers for DR, which may be promising targets for future DR immunotherapy interventions.

Characterization of metastasis-specific macrophages in colorectal cancer for prognosis prediction and immunometabolic remodeling

This study develops a prognostic model to predict metastasis and prognosis in colorectal cancer liver metastases by identifying distinct macrophage subsets. Using scRNA-seq data from primary colorectal cancer and liver metastases, we dissected the cellular landscape to find unique macrophage subpopulations, particularly EEF1G + macrophages, which were prevalent in liver metastases. The study leveraged data from GSE231559, TCGA, and GEO databases to construct an 8-gene risk model named EMGS, based on the EEF1G + macrophage gene signature. Patients were divided into high-risk and low-risk groups using the median EMGS score, with the high-risk group showing significantly worse survival. This group also demonstrated upregulated pathways associated with tumor progression, such as epithelial-mesenchymal transition and angiogenesis, and downregulated metabolic pathways. Moreover, the high-risk group presented an immunosuppressive microenvironment, with a higher TIDE score indicating lower effectiveness of immunotherapy. The study identifies potential drugs targeting the high-risk group, suggesting therapeutic avenues to improve survival. Conclusively, the EMGS score identifies colorectal cancer patients at high risk of liver metastases, highlighting the role of specific macrophage subsets in tumor progression and providing a basis for personalized treatment strategies.

STAG2 loss in Ewing sarcoma alters enhancer-promoter contacts dependent and independent of EWS::FLI1.

Cohesin complexes carrying STAG1 or STAG2 organize the genome into chromatin loops. STAG2 loss-of-function mutations promote metastasis in Ewing sarcoma, a pediatric cancer driven by the fusion transcription factor EWS::FLI1. We integrated transcriptomic data from patients and cellular models to identify a STAG2-dependent gene signature associated with worse prognosis. Subsequent genomic profiling and high-resolution chromatin interaction data from Capture Hi-C indicated that cohesin-STAG2 facilitates communication between EWS::FLI1-bound long GGAA repeats, presumably acting as neoenhancers, and their target promoters. Changes in CTCF-dependent chromatin contacts involving signature genes, unrelated to EWS::FLI1 binding, were also identified. STAG1 is unable to compensate for STAG2 loss and chromatin-bound cohesin is severely decreased, while levels of the processivity factor NIPBL remain unchanged, likely affecting DNA looping dynamics. These results illuminate how STAG2 loss modifies the chromatin interactome of Ewing sarcoma cells and provide a list of potential biomarkers and therapeutic targets.

Differences in the pathological, transcriptomic, and prognostic implications of lymphoid structures between primary and metastatic cutaneous melanomas

BackgroundWhile the prognostic role of tertiary lymphoid structures (TLS) has been well studied in solid cancers, the prevalence and impact of immature precursor lymphoid structures known as lymphoid aggregates (LA)...

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine-learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C-index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single-cell RNA-seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high-risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC.