Gene Signature Research Articles

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.

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis.

Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren's disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease-associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

Development and validation of a prognostic model for colon cancer based on mitotic gene signatures and immune microenvironment analysis

BackgroundMitotic processes play a pivotal role in tumor progression and immune responses. However, the correlation between mitosis-related genes, clinical outcomes, and the tumor microenvironment (TME) in colon cancer remains unclear. This study aims to develop a prognostic and therapeutic significance model for colon cancer based on mitosis-related genes.MethodsRNA expression profiles and clinical data of 453 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Mitosis-related genes were selected from the MsigDb database. The gene model was constructed using differential analysis, univariate and multivariate Cox regression, and Lasso regression analyses. The predictive model was validated using data from the GSE17536, GSE17537, and GSE39582 datasets. Predictive accuracy was evaluated via Receiver Operating Characteristic (ROC) curves, while nomograms were developed by integrating clinical and pathological features. Gene set enrichment analysis explored biological processes and pathways linked to the model. TME was assessed using ESTIMATE, and the proportion and function of immune cells were analyzed through CIBERSORT. Drug sensitivity analysis was conducted using the CTRP database.ResultsA predictive model based on 17 mitosis-related genes (KIFC1, CCNF, EME1, CDC25C, ORC1, CCNJL, ANKRD53, MEIS2, FZD3, TPD52L1, MAPK3, CDKN2A, EDN3, NPM2, PSRC1, INHBA, BIRC5) was created. The model exhibited robust predictive performance across both training and validation cohorts. Nomograms for predicting 3-, 5-, and 7-year survival rates in colon cancer (COAD) patients were generated. The model's correlation with immune cell infiltration and function was highlighted.ConclusionThe mitosis-related gene model serves as a valuable indicator for predicting survival outcomes in colon cancer patients.

Unveiling the Power of Mitochondrial Fission and Fusion: A Five-Gene Signature for Personalized Prognosis in Gastric Cancer.

Mitochondrial fission and fusion play important roles in tumorigenesis, progression and therapy. Dysregulation of these processes may lead to tumor progression, and regulation of these processes may provide novel strategies for cancer therapy. The involvement of genes related to mitochondrial fission and fusion (MD) in gastric cancer (GC) remains poorly understood. The aim of this study was to establish an MD gene signature for GC patients and to investigate its association with prognosis, tumor microenvironment and treatment response in GC. We use the TCGA-GC database as the cohort, focusing specifically on genes associated with MD. We conducted identification and consistency clustering analysis of differentially expressed genes in MD, conducted MD gene mutation and copy number variation analysis, as well as correlation and functional enrichment analysis between MD gene cluster classification and immune infiltration. TCGA-GC and GSE15459 were used to construct training and validation cohorts for the model. We used various statistical methods, including Cox and Lasso regression, to develop the model. We validated the model using bulk transcriptome and single- cell transcriptome datasets (GSE13861, GSE26901, GSE66229, and GSE13450). We used GSEA enrichment, CIBERSORT algorithm, ESTIMATE, and TIDE to gain insight into the annotation of MD signature and the characterization of the tumor microenvironment. OncoPredict was used to analyze the relationship between the PRG signature and the drug sensitivity. We validated the expression of several key genes in MD signature on GC cell lines using quantitative real-time PCR (qRT-PCR). These MDs-related subtypes exhibited different prognosis and immune filtration patterns. A five-gene signature, comprising AGT, HCFC1, KIFC3, NOX4, and RIN1, was developed. There was a clear distinction in overall survival between low- and high-risk patients. The analyses showed further confirmation of the independent prognostic value of the gene signature. There was a notable correlation between the MD signature, immune infiltration and drug susceptibility. The expression levels of AGT, HCFC1, KIFC3, NOX4 and RIN1 mRNA were all increased in these GC cells. The MD signature has the capacity to significantly contribute to the prediction of personalized outcomes and the advancement of novel therapeutic strategies tailored for GC patients.

Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients.

Melanoma, a malignant tumor of the skin, presents challenges in its treatment process involving modalities such as surgery, chemotherapy, and targeted therapy. However, there is a need for an ideal model to assess prognosis and drug sensitivity. Programmed cell death (PCD) modes play a crucial role in tumor progression and has the potential to serve as prognostic and drug sensitivity indicators for melanoma. We analyzed 13 PCD modes including apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, disulfidptosis, and alkaliptosis. These modes were used to construct a model that incorporated genes related to these 13 PCD modes to establish a cell death index (CDI) to conduct prognosis analysis. Transcriptomic, genomic, and clinical data were collected from cohorts including TCGA-SKCM, GSE19234, and GSE65904 to validate this model. A CDI consisting of ten gene signatures was established using machine learning algorithms and divided into two groups based on CDI values. The high CDI group exhibited relatively lower numbers of immune-infiltrating cells and showed resistance to commonly used drugs such as docetaxel and axitinib. Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01). The CDI model, incorporating multiple PCD modes, accurately predicts the clinical prognosis and drug sensitivity of melanoma patients.

Identification of an immune cell infiltration-related gene signature for prognosis prediction in triple-negative breast cancer.

Triple-negative breast cancer TNBC with higher immunogenicity and tumor-infiltrating lymphocyte (TIL) enrichment can benefit from immunotherapy relative to other breast cancer subtypes. Our work was designed to identify the TIL-related hub genes in TNBC and construct a prognostic signature for TNBC. TNBC gene expression files were obtained from the TCGA database. CIBERSORT algorithm and random forest risk model were used for immune infiltration group division. The TIL-related differentially expressed genes (DEGs) were then selected and subject to GO, KEGG analyses and GSEA. Next, Lasso cox regression analyses were adopted for constructing a prognostic risk model, followed by evaluation using time-dependent ROC curves. The copy number variation between the two risk groups was also analyzed, and major genomic mutation types were identified. Additionally, the nomogram was constructed with calibration curve for clinical prognosis analysis. Our results showed that totally 113 TNBC samples were allocated into the high or low-immune risk groups. We identified 243 DEGs between groups, namely TIL-related DEGs, with 128 upregulated and 115 downregulated genes. Among the TIL-related DEGs, 6 hub genes (SLITRK3, PCDHGB3, NELL2, SRRM4, ASIC2 and B4GALNT2) were screened out and constructed a prognostic risk signature, which had good performance for long-term prognosis prediction. Analysis of genomic mutation showed that the TP53, PIK3CA, TTH, etc. showed high mutation frequency in the two prognostic risk groups. Moreover, the higher risk score of the prognostic risk model predicted poor overall survival in TNBC patients, and nomogram and calibration curve confirmed the potent prediction ability of this model. To sum up, six TIL-related biomarkers (SLITRK3, PCDHGB3, NELL2, SRRM4, ASIC2 and B4GALNT2) were identified and used for the construction of the prognostic risk model, which might provide novel insight for the clinical decisions.

Integrative bioinformatics analysis for identifying the mitochondrial-related gene signature associated with immune infiltration in premature ovarian insufficiency.

Premature ovarian insufficiency (POI) is a reproductive disorder characterized by the cessation of ovarian function before the age of 40. Although mitochondrial dysfunction and immune disorders are believed to contribute to ovarian damage in POI, the interplay between these factors remains understudied. In this research, transcriptomic data related to POI were obtained from the NCBI GEO database. Hub biomarkers were identified through the construction of a protein‒protein interaction (PPI) network and further validated using RT‒qPCR and Western blot. Moreover, their expression across various cell types was elucidated via single-cell RNA sequencing analysis. A comprehensive investigation of the mitochondrial and immune profiles of POI was carried out through correlation analysis. Furthermore, potential therapeutic agents were predicted utilizing the cMap database. A total of 119 mitochondria-related differentially expressed genes (MitoDEGs) were identified and shown to be significantly enriched in metabolic pathways. Among these genes, Hadhb, Cpt1a, Mrpl12, and Mrps7 were confirmed both in a POI model and in human granulosa cells (GCs), where they were found to accumulate in GCs and theca cells. Immune analysis revealed variations in macrophages, monocytes, and 15 other immune cell types between the POI and control groups. Notably, strong correlations were observed between seven hub-MitoDEGs (Hadhb, Cpt1a, Cpt2, Mrpl12, Mrps7, Mrpl51, and Eci1) and various functions, such as mitochondrial respiratory complexes, dynamics, mitophagy, mitochondrial metabolism, immune-related genes, and immunocytes. Additionally, nine potential drugs (calyculin, amodiaquine, eudesmic acid, cefotaxime, BX-912, prostratin, SCH-79797, HU-211, and pizotifen) targeting key genes were identified. Our results highlight the crosstalk between mitochondrial function and the immune response in the development of POI. The identification of MitoDEGs could lead to reliable biomarkers for the early diagnosis, monitoring, and personalized treatment of POI.

Identifying patient subgroups in MASLD and MASH-associated fibrosis: molecular profiles and implications for drug development

The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

Identification of an immune-related genes signature in lung adenocarcinoma to predict survival and response to immune checkpoint inhibitors

BackgroundAlthough advances in immune checkpoint inhibitor (ICI) research have provided a new treatment approach for lung adenocarcinoma (LUAD) patients, their survival is still unsatisfactory, and there are issues in the era of response prediction to immunotherapy.MethodsUsing bioinformatics methods, a prognostic signature was constructed, and its predictive ability was validated both in the internal and external datasets (GSE68465). We also explored the tumor-infiltrating immune cells, mutation profiles, and immunophenoscore (IPS) in the low-and high-risk groups.ResultsAs far as we are aware, this is the first study which introduces a novel prognostic signature model using BIRC5, CBLC, S100P, SHC3, ANOS1, VIPR1, LGR4, PGC, and IGKV4.1. According to multivariate analysis, the 9-immune-related genes (IRGs) signature provided an independent prognostic factor for the overall survival (OS). The low-risk group had better OS, and the tumor mutation burden (TMB) was significantly lower in this group. Moreover, the risk scores were negatively associated with the tumor-infiltrating immune cells, like CD8+ T cells, macrophages, dendritic cells, and NK cells. In addition, the IPS were significantly higher in the low-risk group as they had higher gene expression of immune checkpoints, suggesting that ICIs could be a promising treatment option for low-risk LUAD patients.ConclusionThe combination of these 9-IRGs not only could efficiently predict overall survival of LUAD patients but also show a powerful association with the expression of immune checkpoints and response to ICIs based on IPS; hoping this model paves the way for better stratification and management of patients in clinical practice.

A Novel Sulfatase for Acesulfame Degradation in Wastewater Treatment Plants as Evidenced from Shinella Strains.

The artificial sweetener acesulfame is a persistent pollutant in wastewater worldwide. So far, only a few bacterial isolates were recently found to degrade acesulfame efficiently. In Bosea and Chelatococcus strains, a Mn2+-dependent metallo-β-lactamase-type sulfatase and an amidase signature family enzyme catalyze acesulfame hydrolysis via acetoacetamide-N-sulfonate to acetoacetate. Here, we describe a new acesulfame sulfatase in Shinella strains isolated from wastewater treatment plants in Germany. Their genomes do not encode the Mn2+-dependent sulfatase. Instead, a formylglycine-dependent sulfatase gene was found, together with the acetoacetamide-N-sulfonate amidase gene on a plasmid shared by all known acesulfame-degrading Shinella strains. Heterologous expression, proteomics, and size exclusion chromatography corroborated the physiological function of the Shinella sulfatase in acesulfame hydrolysis. Since both acesulfame sulfatase types are absent in other bacterial genomes or metagenome-assembled genomes, we surveyed 73 tera base pairs of wastewater-associated metagenome raw data sets. Bosea/Chelatococcus sulfatase gene signatures were regularly found from 2013, particularly in North America, Europe, and East Asia, whereas Shinella sulfatase gene signatures were first detected in 2020. Moreover, signatures for the Shinella sulfatase and amidase genes co-occur only in six data sets from China, Finland, and Mexico, suggesting that the Shinella genes were enriched or introduced quite recently in wastewater treatment facilities.

Loss of ARID3A perturbs intestinal epithelial proliferation-differentiation ratio and regeneration.

Intestinal stem cells at the crypt divide and give rise to progenitor cells that proliferate and differentiate into various mature cell types in the transit-amplifying (TA) zone. Here, we showed that the transcription factor ARID3A regulates intestinal epithelial cell proliferation and differentiation at the TA progenitors. ARID3A forms an expression gradient from the villus tip to the upper crypt mediated by TGF-β and WNT. Intestinal-specific deletion of Arid3a reduces crypt proliferation, predominantly in TA cells. Bulk and single-cell transcriptomic analysis shows increased enterocyte and reduced secretory differentiation in the Arid3a cKO intestine, accompanied by enriched upper-villus gene signatures of both cell lineages. We find that the enhanced epithelial differentiation in the Arid3a-deficient intestine is caused by increased binding and transcription of HNF1 and HNF4. Finally, we show that loss of Arid3a impairs irradiation-induced regeneration with sustained cell death and reprogramming. Our findings imply that Arid3a functions to fine-tune the proliferation-differentiation dynamics at the TA progenitors, which are essential for injury-induced regeneration.

Development of a Mitochondrial Membrane Permeability Transition Prognosis System in Colon Adenocarcinoma: Risk Stratification and Therapeutic Target Identification.

Explore the role of mitochondrial membrane permeability transition (MPT) in colon adenocarcinoma (COAD). Further exploration of risk stratification for COAD prognostic assessment has important clinical value. MPT-related pathways play a key role in the pathogenesis of many human diseases, including tumorigenesis. Its impact on COAD is still unknown. Bioinformatics analysis was conducted by analyzing the GEO database and TCGA database, and the bioinformatics results were verified by in vitro experiments. Through the analysis of the transcriptome data of 1008 COAD samples in the GEO database and TCGA database, the differential expressions of MPT-related genes in COAD were explored, followed by molecular subtype analysis based on MPT characteristics by univariate Cox algorithm analysis and the consensus clustering algorithm. The gene signature associated with MPT molecular subtypes was further identified and the MPT scoring system was established by the LASSO-univariate Cox analysis algorithm. After evaluating the prognostic value of the MPT scoring system in COAD patients via nomogram establishment, the clinical value of the MPT scoring system was comprehensively analyzed through somatic mutation characteristics analysis, immunotherapy response analysis, immunoinfiltration analysis, and drug sensitivity analysis. CCK-8, WB, PCR, colony formation method, and Transwell method were used to verify the effect of the screened target on the proliferation and invasion of COAD cells. We successfully established a scoring system related to MPT and validated the prognostic value of COAD patients. The potential clinical value of the MPT scoring system was also analyzed. VSIG4 was selected for further in vitro experiments to verify the effect of the screened targets on the proliferation and invasion ability of COAD cells. We established an MPT scoring system for effective risk stratification of COAD patients, demonstrating the impact of MPT on the development of COAD and its potential value as an intervention factor.

Development and Validation of a Comprehensive Prognostic and Depression Risk Index for Gastric Adenocarcinoma

Depressive disorder contributes to the initiation and prognosis of patients with cancer, but the interaction between cancer and depressive disorder remains unclear. We generated a gastric adenocarcinoma patient-derived xenograft mice model, treated with chronic unpredictable mild stimulation. Based on the RNA-sequence from the mouse model, patient data from TCGA, and MDD-related (major depressive disorder) genes from the GEO database, 56 hub genes were identified by the intersection of differential expression genes from the three datasets. Molecular subtypes and a prognostic signature were generated based on the 56 genes. A depressive mouse model was constructed to test the key changes in the signatures. The signature was constructed based on the NDUFA4L2, ANKRD45, and AQP3 genes. Patients with high risk-score had a worse overall survival than the patients with low scores, consistent with the results from the two GEO cohorts. The comprehensive results showed that a higher risk-score was correlated with higher levels of tumor immune exclusion, higher infiltration of M0 macrophages, M2 macrophages, and neutrophils, higher angiogenetic activities, and more enriched epithelial–mesenchymal transition signaling pathways. A higher risk score was correlated to a higher MDD score, elevated MDD-related cytokines, and the dysfunction of neurogenesis-related genes, and parts of these changes showed similar trends in the animal model. With the Genomics of Drug Sensitivity in Cancer database, we found that the gastric adenocarcinoma patients with high risk-score may be sensitive to Pazopanib, XMD8.85, Midostaurin, HG.6.64.1, Elesclomol, Linifanib, AP.24534, Roscovitine, Cytarabine, and Axitinib. The gene signature consisting of the NDUFA4L2, ANKRD45, and AQP3 genes is a promising biomarker to distinguish the prognosis, the molecular and immune characteristics, the depressive risk, and the therapy candidates for gastric adenocarcinoma patients.

Integrative Analysis of ATAC-Seq and RNA-Seq through Machine Learning Identifies 10 Signature Genes for Breast Cancer Intrinsic Subtypes.

Breast cancer is a heterogeneous disease composed of various biologically distinct subtypes, each characterized by unique molecular features. Its formation and progression involve a complex, multistep process that includes the accumulation of numerous genetic and epigenetic alterations. Although integrating RNA-seq transcriptome data with ATAC-seq epigenetic information provides a more comprehensive understanding of gene regulation and its impact across different conditions, no classification model has yet been developed for breast cancer intrinsic subtypes based on such integrative analyses. In this study, we employed machine learning algorithms to predict intrinsic subtypes through the integrative analysis of ATAC-seq and RNA-seq data. We identified 10 signature genes (CDH3, ERBB2, TYMS, GREB1, OSR1, MYBL2, FAM83D, ESR1, FOXC1, and NAT1) using recursive feature elimination with cross-validation (RFECV) and a support vector machine (SVM) based on SHAP (SHapley Additive exPlanations) feature importance. Furthermore, we found that these genes were primarily associated with immune responses, hormone signaling, cancer progression, and cellular proliferation.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.

Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma. We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents. Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients. The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.