Inflammasome-related gene signatures as prognostic biomarkers in osteosarcoma.

Osteosarcoma, the primary bone cancer in adolescents and young adults, is notorious for its aggressive growth and metastatic potential. Our study delved into the prognostic impact of inflammasome-related gene signatures in osteosarcoma patients, employing comprehensive genetic profiling to uncover signatures linked with patient outcomes. We identified three patient subgroups through consensus clustering, with one showing worse survival rates correlated with high FGFR3 and RARB expressions. Immune profiling revealed significant immune cell infiltration differences among these subgroups, affecting survival. Utilising advanced machine learning, including StepCox and gradient boosting machine algorithms, we developed a prognostic model with a notable c-index of 0.706, highlighting CD36 and MYD88 as key genes. Higher inflammasome risk scores from our model were associated with poorer survival, corroborated across datasets. In vitro experiments validated CD36 and MYD88's roles in promoting osteosarcoma cell proliferation, invasion and migration, emphasising their therapeutic potential. This research offers new insights into inflammasomes' role in osteosarcoma, introducing novel biomarkers for risk assessment and potential therapeutic targets. Our findings suggest a pathway towards personalised treatment strategies, potentially improving patient outcomes in osteosarcoma.

Prognostic stratification in DLBCL patients with aberrant MYC gene.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease characterized by a subset of patients who exhibit treatment resistance and poor prognoses. Genomic assays have been widely employed to identify high-risk individuals characterized by rearrangements in the MYC, BCL2 and BCL6 genes. These patients typically undergo more aggressive therapeutic treatments; however, there remains a significant variation in their treatment outcomes. This study introduces an MYC signature score (MYCSS) derived from gene expression profiles, specifically designed to evaluate MYC overactivation in DLBCL patients. MYCSS was validated across several independent cohorts to assess its ability to stratify patients based on MYC-related genetic and molecular aberrations, enhancing the accuracy of prognostic evaluations compared to conventional MYC biomarkers. Our results indicate that MYCSS significantly refines prognostic accuracy beyond that of conventional MYC biomarkers focused on genetic aberrations. More importantly, we found that nearly 50% of patients identified as high risk by traditional MYC metrics actually share similar survival prospects with those having no MYC aberrations. These patients may benefit from standard GCB-based therapies rather than more aggressive treatments. MYCSS provides a robust signature that identifies high-risk patients, aiding in the precision treatment of DLBCL, and minimizing the potential for overtreatment.

Polymer scaffolds delineate healthy from diseased states at sites distal from the pancreas in two models of type 1 diabetes.

Type 1 diabetes (T1D) prevention is currently limited by the lack of diagnostic tools able to identify disease before autoimmune destruction of the pancreatic ß cells. Autoantibody tests are used to predict risk and, in combination with glucose dysregulation indicative of ß cell loss, to determine administration of immunotherapies. Our objective was to remotely identify immune changes associated with the disease, and we have employed a subcutaneously implanted microporous poly(e-caprolactone) (PCL) scaffold to function as an immunological niche (IN) in two models of T1D. Biopsy and analysis of the IN enables disease monitoring using transcriptomic changes at a distal site from autoimmune destruction of the pancreas, thereby gaining cellular level information about disease without the need for a biopsy of the native organ. Using this approach, we identified gene signatures that stratify healthy and diseased mice in both an adoptive transfer model and a spontaneous onset model of T1D. The gene signatures identified herein demonstrate the ability of the IN to identify immune activation associated with diabetes across models.

Shared molecular signatures between systemic lupus erythematosus and osteoporosis.

This study explores the molecular interplay between systemic lupus erythematosus (SLE) and osteoporosis (OP), aiming to uncover shared gene signatures and pathways for better treatment approaches. Leveraging microarray data from the Gene Expression Omnibus (GEO) database, we employed weighted gene coexpression network analysis to identify coexpression modules in SLE and OP, with subsequent protein-protein interaction analysis clarifying the connections among shared genes. Key genes were pinpointed using CytoHubba and random forest algorithms, validated across independent GEO datasets, and further analyzed through gene set enrichment analysis (GSEA) and immune infiltration studies. We discovered two highly correlated modules in SLE and OP, isolating 30 shared genes and identifying GBP1, SOCS1, IFI16, and XAF1 as central to both conditions. Notably, XAF1 and GBP1 mRNA levels were significantly elevated in the peripheral blood of SLE patients compared with healthy and RA counterparts, underscoring their potential as biomarkers. GSEA and immune infiltration analyses indicated pronounced immune and inflammatory responses, especially in interferon signaling pathways, implicating these core-shared gene networks in the diseases' pathogenesis. The findings highlight the involvement of GBP1, SOCS1, IFI16, and XAF1 in SLE with concurrent OP and suggest that targeting immune and inflammatory responses, particularly through interferon pathways, may offer therapeutic promise for these intertwined conditions.

Transcriptomic Features of systemic lupus erythematosus Patients in Flare and Changes during Acute In-Hospital Treatment.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with varying symptoms and multi-organ damage. Relapse-remission cycles often persist for many patients for years with the current treatment. Improved understanding of molecular changes caused by SLE flare and intensive treatment may result in more targeted therapies. METHODS: RNA-sequencing was performed on peripheral blood mononuclear cells (PBMCs) from 65 SLE patients in flare, collected both before (SLE1) and after (SLE2) in-hospital treatment, along with 15 healthy controls (HC). Differentially expressed genes (DEGs) were identified among the three groups. Enriched functions and key molecular signatures of the DEGs were analyzed and scored to elucidate the transcriptomic changes during treatment. RESULTS: Few upregulated genes in SLE1 vs HC were affected by treatment (SLE2 vs SLE1), mostly functional in interferon signalling (IFN), plasmablasts, and neutrophils. IFN and plasmablast signatures were repressed, but the neutrophil signature remained unchanged or enhanced by treatment. The IFN and neutrophil scores together stratified the SLE samples. IFN scores correlated well with leukopenia, while neutrophil scores reflected relative cell compositions but not cell counts. CONCLUSIONS: In-hospital treatment significantly relieved SLE symptoms with expression changes of a small subset of genes. Notably, IFN signature changes matched SLE flare and improvement, while enhanced neutrophil signature upon treatment suggested the involvement of low-density granulocytes (LDG) in disease development.

Comprehensive Analysis of PANoptosis-Related Gene Signature of Ulcerative Colitis.

Accumulating evidence shows that the abnormal increase in the mortality of intestinal epithelial cells (IECs) caused by apoptosis, pyroptosis, and necroptosis is closely related to the function of mucous membrane immunity and barrier function in patients with ulcerative colitis (UC). As a procedural death path that integrates the above-mentioned many deaths, the role of PANoptosis in UC has not been clarified. This study aims to explore the characterization of PANoptosis patterns and determine the potential biomarkers and therapeutic targets. We constructed a PANoptosis gene set and revealed significant activation of PANoptosis in UC patients based on multiple transcriptome profiles of intestinal mucosal biopsies from the GEO database. Comprehensive bioinformatics analysis revealed five key genes (ZBP1, AIM2, CASP1/8, IRF1) of PANoptosome with good diagnostic value and were highly correlated with an increase in pro-inflammatory immune cells and factors. In addition, we established a reliable ceRNA regulatory network of PANoptosis and predicted three potential small-molecule drugs sharing calcium channel blockers that were identified, among which flunarizine exhibited the highest correlation with a high binding affinity to the targets. Finally, we used the DSS-induced colitis model to validate our findings. This study identifies key genes of PANoptosis associated with UC development and hypothesizes that IRF1 as a TF promotes PANoptosome multicomponent expression, activates PANoptosis, and then induces IECs excessive death.

Unraveling transcriptomic signatures and dysregulated pathways in systemic lupus erythematosus across disease states.

OBJECTIVES: This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission. METHODS: We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks. RESULTS: Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse. CONCLUSION: Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.

Genomic signatures in breast cancer in a real-world setting: Experience in a Brazilian Northeastern Center.

OBJECTIVE: We aim to evaluate the indication and use of genomic signatures in breast cancer patients and outcomes who in patients undergoing adjuvant chemotherapy or not. METHODS: This is a retrospective study of breast cancer patients managed in a private oncology clinic in Teresina, from November 2014 to February 2021. All patients with an indication of genomic signature were included. Clinical and pathological variables, use of genomic signatures, treatment and follow-up were obtained. The nomogram to predict Oncotype DX results (University of Tennessee Medical Center) was also calculated. Clinical risk calculation was based on MINDACT, using the modified version of Adjuvant Online. The genetic signatures performed were: the Oncotype, MammaPrint and EndoPredict. RESULTS: Fifty (50) female patients were included in the study. The mean age of the participants was 57.1 years. Among the patients receiving a genomic signature (26-52.0%), there was a change in treatment in 8 (30.7%) cases. Chemotherapy was indicated in four patients, It was contraindicated in another four patients. Treatment changed in 30.7% of the tested patients. Chemotherapy was indicated for those who would not receive it before. It was contraindicated in patients who would previously undergo chemotherapy.

Exploring the Molecular Mechanisms and Shared Gene Signatures Between Systemic Lupus Erythematosus and Bladder Urothelial Carcinoma.

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease associated with increased susceptibility to cancer, including bladder urothelial carcinoma (BLCA). This study investigates the shared molecular mechanisms and gene signatures between SLE and BLCA, shedding light on potential biomarkers and therapeutic targets. Methods: We compiled gene datasets related to SLE and BLCA from various databases and identified common genes. Differential gene expression analysis, protein-protein interaction networks, and hub gene identification were performed. We studied functional enrichment, immune infiltration, and transcription factor/miRNA regulation networks. We also explored gene-disease interactions and protein-chemical/drug networks. Hub gene expression levels and diagnostic values were validated in TCGA and GEO databases. Prognostic analysis was performed on the core gene MMP9 in the TCGA-BLCA database to study its prognostic value. Finally, the mRNA expression of MMP9 was verified in bladder cancer cell lines and BLCA patient blood. The diagnostic value of MMP9 for BLCA was verified by receiver operating characteristic(ROC) curve analysis of the expression of MMP9 in patients' blood. Results: We identified 524 common genes between SLE and BLCA, enriched in pathways related to apoptosis and cytokine regulation. Immune infiltration analysis for two diseases. Transcription factors and microRNAs were implicated in regulating these common genes. The gene-disease network linked hub genes with various diseases, emphasizing their roles in autoimmune disease and cancer. Protein-chemical/drug networks highlighted potential treatment options. Finally, our study found that MMP9 is a potential therapeutic target with diagnostic and prognostic value and Immune-related biomarkers in patients with BLCA and SLE. Conclusion: Our study reveals shared molecular mechanisms, genetic signatures, and immune infiltrates between SLE and BLCA. MMP9 emerges as a potential diagnostic and prognostic biomarker in BLCA, warranting further investigation. These findings provide insights into the pathogenesis of SLE-associated BLCA and may guide future research and therapeutic strategies.

A novel prognostic signature based on cancer stemness and metabolism-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma.

BACKGROUND: CESC is the second most commonly diagnosed gynecological malignancy. Given the pivotal involvement of metabolism-related genes (MRGs) in the etiology of multiple tumors, our investigation aims to devise a prognostic risk signature rooted in cancer stemness and metabolism. METHODS: The stemness index based on mRNA expression (mRNAsi) of samples from the TCGA dataset was computed using the One-class logistic regression (OCLR) algorithm. Furthermore, potential metabolism-related genes related to mRNAsi were identified through weighted gene co-expression network analysis (WGCNA). We construct a stemness-related metabolic gene signature through shrinkage estimation and univariate analysis, thereby calculating the corresponding risk scores. Moreover, we selected corresponding DEGs between groups with high- and low-risk score and conducted routine bioinformatic analyses. Furthermore, we validated the expression of four hub genes at the protein level through immunohistochemistry (IHC) in samples obtained from our patient cohort. RESULTS: According to the findings, it was found that six genes-AKR1B10, GNA15, ALDH1B1, PLOD2, LPCAT1, and GPX8- were differentially expressed in both TCGA-CSEC and GEO datasets among 23 differentially expressed metabolism-related genes (DEMRGs). mRNAsi exhibited a notable association with the extent of key oncogene mutation. The results showed that the AUC values for forecasting survival at 1, 3, and 5 years are 0.715, 0.689, and 0.748, individually. We observed a notable association between the risk score and different immune cell populations, along with enrichment in crucial signaling pathways in CESC. Four genes differentially expressed between different risk score groups were validated by IHC to be highly expressed in the CESC samples at the protein level. CONCLUSION: The current investigation indicated that a 3-gene signature based on stemness-related metabolic and 4 hub genes with differential expression between high and low-risk score subgroups may serve as valuable prognostic markers and potential therapeutic targets in CESC.

Investigating the Role of SNAI1 and ZEB1 Expression in Prostate Cancer Progression and Immune Modulation of the Tumor Microenvironment.

Prostate cancer (PCa) is an immunologically cold tumor and the molecular processes that underlie this behavior are poorly understood. In this study, we investigated a primary cohort of intermediate-risk PCa (n = 51) using two NanoString profiling panels designed to study cancer progression and immune response. We identified differentially expressed genes (DEGs) and pathways associated with biochemical recurrence (BCR) and clinical risk. Confirmatory analysis was performed using the TCGA-PRAD cohort. Noteworthy DEGs included collagens such as COL1A1, COL1A2, and COL3A1. Changes in the distribution of collagens may influence the immune activity in the tumor microenvironment (TME). In addition, immune-related DEGs such as THY1, IRF5, and HLA-DRA were also identified. Enrichment analysis highlighted pathways such as those associated with angiogenesis, TGF-beta, UV response, and EMT. Among the 39 significant DEGs, 11 (28%) were identified as EMT target genes for ZEB1 using the Harmonizome database. Elevated ZEB1 expression correlated with reduced BCR risk. Immune landscape analysis revealed that ZEB1 was associated with increased immunosuppressive cell types in the TME, such as naïve B cells and M2 macrophages. Increased expression of both ZEB1 and SNAI1 was associated with elevated immune checkpoint expression. In the future, modulation of EMT could be beneficial for overcoming immunotherapy resistance in a cold tumor, such as PCa.

Mast Cell-related Prognosis Signature Characterizes Immune Landscape and Predicts Prognosis of Ovarian Cancer.

BACKGROUND/AIM: Ovarian cancer (OVC) is a common, aggressive, and heterogeneous malignancy, with a widely variable prognosis. With the advances of modern immunology, mast cells (MCs) have been shown to play a significant role in the prognosis of some malignant tumors. However, the role of mast cells in the prognosis of OVC is unknown. MATERIALS AND METHODS: In this study, MC-associated prognostic genes (MRGs) were used to classify OVC from The Cancer Genome Atlas (TCGA)-OVC cohort. Genes were evaluated using univariate cox regression analysis. Twenty-nine prognostic gene signatures were identified using LASSO-COX analysis. COX regression models and principal component analysis (PCA) algorithms were used to construct MRG scores and individual MRGs patterns. External validation was performed in the TCGA-breast cancer (BRCA) and IMvigor210 cohorts. Immunity analysis based on MRGs was performed using CIBERSORT, and GSVA methods, and immunotherapy response was evaluated using the TIDE website. RESULTS: Using TCGA-OVC data, we established a model for constructing MRG scores based on the twenty-nine identified prognostic gene signatures using the PCA algorithm. MRG scores were found to be strongly correlated with immune cell infiltration and were excellent predictors of prognosis in patients with OVC. Low MRG scores were associated with better prognosis and better response to immunotherapy and chemotherapy. CONCLUSION: MC-related prognosis signature characterizes the immune landscape and predicts the prognosis of OVC. Understanding the correlation between MC-related gene signatures and immunotherapy and chemotherapy may improve the development of personalized clinical treatment strategies.

Single-cell sequencing analysis within biologically relevant dimensions.

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

Identification and validation of novel prognostic fatty acid metabolic gene signatures in colon adenocarcinoma through systematic approaches.

Background: Colorectal cancer (CRC) belongs to the class of significantly malignant tumors found in humans. Recently, dysregulated fatty acid metabolism (FAM) has been a topic of attention due to its modulation in cancer, specifically CRC. However, the regulatory FAM pathways in CRC require comprehensive elucidation. Methods: The clinical and gene expression data of 175 fatty acid metabolic genes (FAMGs) linked with colon adenocarcinoma (COAD) and normal cornerstone genes were gathered through The Cancer Genome Atlas (TCGA)-COAD corroborating with the Molecular Signature Database v7.2 (MSigDB). Initially, crucial prognostic genes were selected by uni- and multi-variate Cox proportional regression analyses; then, depending upon these identified signature genes and clinical variables, a nomogram was generated. Lastly, to assess tumor immune characteristics, concomitant evaluation of tumor immune evasion/risk scoring were elucidated. Results: A 8-gene signature, including ACBD4, ACOX1, CD36, CPT2, ELOVL3, ELOVL6, ENO3, and SUCLG2, was generated, and depending upon this, CRC patients were categorized within high-risk (H-R) and low-risk (L-R) cohorts. Furthermore, risk and age-based nomograms indicated moderate discrimination and good calibration. The data confirmed that the 8-gene model efficiently predicted CRC patients' prognosis. Moreover, according to the conjoint analysis of tumor immune evasion and the risk scorings, the H-R cohort had an immunosuppressive tumor microenvironment, which caused a substandard prognosis. Conclusion: This investigation established a FAMGs-based prognostic model with substantially high predictive value, providing the possibility for improved individualized treatment for CRC individuals.