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.

Characterization of driver mutations identifies gene signatures predictive of prognosis and treatment sensitivity in multiple myeloma.

In multiple myeloma (MM), while frequent mutations in driver genes are crucial for disease progression, they traditionally offer limited insights into patient prognosis. This study aims to enhance prognostic understanding in MM by analyzing pathway dysregulations in key cancer driver genes, thereby identifying actionable gene signatures. We conducted a detailed quantification of mutations and pathway dysregulations in 10 frequently mutated cancer driver genes in MM to characterize their comprehensive mutational impacts on the whole transcriptome. This was followed by a systematic survival analysis to identify significant gene signatures with enhanced prognostic value. Our systematic analysis highlighted 2 significant signatures, TP53 and LRP1B, which notably outperformed mere mutation status in prognostic predictions. These gene signatures remained prognostically valuable even when accounting for clinical factors, including cytogenetic abnormalities, the International Staging System (ISS), and its revised version (R-ISS). The LRP1B signature effectively distinguished high-risk patients within low/intermediate-risk categories and correlated with significant changes in the tumor immune microenvironment. Additionally, the LRP1B signature showed a strong association with proteasome inhibitor pathways, notably predicting patient responses to bortezomib and the progression from monoclonal gammopathy of unknown significance to MM. Through a rigorous analysis, this study underscores the potential of specific gene signatures in revolutionizing the prognostic landscape of MM, providing novel clinical insights that could influence future translational oncology research.

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 analysed 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.

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.

Systemic inflammation induced from remote extremity trauma is a critical driver of secondary brain injury.

Blast exposure, commonly experienced by military personnel, can cause devastating life-threatening polysystem trauma. Despite considerable research efforts, the impact of the systemic inflammatory response after major trauma on secondary brain injury-inflammation is largely unknown. The aim of this study was to identify markers underlying the susceptibility and early onset of neuroinflammation in three rat trauma models: (1) blast overpressure exposure (BOP), (2) complex extremity trauma (CET) involving femur fracture, crush injury, tourniquet-induced ischemia, and transfemoral amputation through the fracture site, and (3) BOP+CET. Six hours post-injury, intact brains were harvested and dissected to obtain biopsies from the prefrontal cortex, striatum, neocortex, hippocampus, amygdala, thalamus, hypothalamus, and cerebellum. Custom low-density microarray datasets were used to identify, interpret and visualize genes significant (p < 0.05 for differential expression [DEGs]; 86 neuroinflammation-associated) using a custom python-based computer program, principal component analysis, heatmaps and volcano plots. Gene set and pathway enrichment analyses of the DEGs was performed using R and STRING for protein-protein interaction (PPI) to identify and explore key genes and signaling networks. Transcript profiles were similar across all regions in naïve brains with similar expression levels involving neurotransmission and transcription functions and undetectable to low-levels of inflammation-related mediators. Trauma-induced neuroinflammation across all anatomical brain regions correlated with injury severity (BOP+CET > CET > BOP). The most pronounced differences in neuroinflammatory-neurodegenerative gene regulation were between blast-associated trauma (BOP, BOP+CET) and CET. Following BOP, there were few DEGs detected amongst all 8 brain regions, most were related to cytokines/chemokines and chemokine receptors, where PPI analysis revealed Il1b as a potential central hub gene. In contrast, CET led to a more excessive and diverse pro-neuroinflammatory reaction in which Il6 was identified as the central hub gene. Analysis of the of the BOP+CET dataset, revealed a more global heightened response (Cxcr2, Il1b, and Il6) as well as the expression of additional functional regulatory networks/hub genes (Ccl2, Ccl3, and Ccl4) which are known to play a critical role in the rapid recruitment and activation of immune cells via chemokine/cytokine signaling. These findings provide a foundation for discerning pathophysiological consequences of acute extremity injury and systemic inflammation following various forms of trauma in the brain.

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.

Systematic evaluation of the predictive gene expression signatures of immune checkpoint inhibitors in metastatic melanoma.

Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published predictive biomarkers of ICIs based on gene expression signatures in eight previously published studies with available RNA-sequencing data in public repositories. We found that signatures related to IFN-γ-responsive genes, T and B cell markers, and chemokines in the tumor immune microenvironment are generally predictive of response to ICIs in these patients. In addition, we identified that these predictive biomarkers have higher predictive values in on-treatment samples as compared to pretreatment samples in metastatic melanoma. The most frequently overlapping genes among the top 18 predictive signatures were CXCL10, CXCL9, PRF1, RANTES, IFNG, HLA-DRA, GZMB, and CD8A. From gene set enrichment analysis and cell type deconvolution, we estimated that the tumors of responders were enriched with infiltrating cytotoxic T-cells and other immune cells and the upregulation of genes related to interferon-γ signaling. Conversely, the tumors of non-responders were enriched with stromal-related cell types such as fibroblasts and myofibroblasts, as well as enrichment with T helper 17 cell types across all cohorts. In summary, our approach of validating and integrating multi-omics data can help guide future biomarker development in the field of ICIs and serve the quest for a more personalized therapeutic approach for melanoma patients.

Integrative biological analyses of responses to food deprivation reveal resilience mechanisms in sea urchin larvae.

A fundamental question in ecology is how organisms survive food deprivation. In the ocean, climate change is impacting the phenology of food availability for early life-history stages of animals. In this study, we undertook an integrative analysis of larvae of the sea urchin Strongylocentrotus purpuratus-an important keystone species in marine ecology and a molecular biological model organism in developmental biology. Specifically, to identify the mechanisms of resilience that maintain physiological state and the ability of organisms to recover from food deprivation, a suite of molecular biological, biochemical, physiological and whole organism measurements was completed. Previous studies focused on the importance of energy reserves to sustain larvae during periods of food deprivation. We show, however, that utilization of endogenous energy reserves only supplied 15% of the metabolic requirements of long-term survival (up to 22 days) in the absence of particulate food. This large energy gap was not supplied by larvae feeding on bacteria. Estimates of larval ability to transport dissolved organic matter directly from seawater showed that such substrates could fully supply metabolic needs. Integrative approaches allowed for filtering of gene expression signatures, linked with gene network analyses and measured biochemical and physiological traits, to identify biomarkers of resilience. We identified 14 biomarkers related to nutrition-responsive gene expression, of which a specific putative amino acid transporter gene was quantified in a single larva experiencing continuous nutritional stress. Advances in applications of gene expression technologies offer novel approaches to determine the physiological state of marine larval forms in ecological settings undergoing environmental change.

When type II diabetes mellitus meets COVID-19-Identification of the shared gene signatures and biological mechanism between the two diseases.

BACKGROUND: According to current studies, more than 20% of all patients diagnosed with COVID-19 globally have diabetes. Further, the mortality rate of these patients is 7.3%. Compared with non-diabetic COVID-19 patients, diabetic COVID-19 patients have higher rates of mortality and severe infection, suggesting that diabetes is associated with the severity of COVID-19 infection. This study aimed to analyse the relationship and susceptibility factors between COVID-19 and T2DM. METHODS: Using bioinformatics methods, potential targets for COVID-19 and T2DM were screened from GeneCards database. Potential targets of COVID-19 and T2DM were mapped to each other to identify overlapping targets, and a PPI network was constructed to extract the core target. The clusterProfiler package in R was used to analyse the function and pathway that core target involved. GO enrichment and KEGG pathway analysis were used to elucidate the correlation between COVID-19 and T2DM. RESULTS: A total of 277 potential pathogenic targets of COVID-19 were found, 282 potential targets were found for T2DM. Mapping of the potential COVID-19 and T2DM targets revealed 53 overlapping targets, with TNF as the core target. IL-17 signalling pathway was the most significant KEGG pathway involving TNF. CONCLUSIONS: The inflammatory cytokine, TNF, was identified as a core target between COVID-19 and T2DM, which induces inflammatory response mainly through the IL-17 signalling pathway, leading to aggravation of infection and increased difficulty in blood glucose control. This study provides a reference for further exploring the potential correlation and endogenous mechanisms between two seemingly independent and unrelated diseases-T2DM and COVID-19.

Construction of a hypoxia-immune-related prognostic model and targeted therapeutic strategies for cervical cancer.

Emerging evidence indicates that hypoxia and immunity play important roles in tumorigenesis and development. However, the hypoxia-immune-related prognostic risk model has not been established in cervical cancer (CC). We aimed to construct a hypoxia-immune-related prognostic risk model, which has potential application in predicting the prognosis of CC patients and the response to targeted therapy. The RNA-seq data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. The hypoxia status and immune status of CC patients were evaluated using the Consensus Clustering method and single-sample gene set enrichment analysis (ssGSEA), respectively. The univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to establish the prognostic risk model of CC. The chemotherapy response for six chemotherapeutic agents of each CC patient was calculated according to the Genomics of Drug Sensitivity in Cancer (GDSC). And the Connectivity Map (CMap) database was performed to screen candidate small-molecule drugs. In this study, we identified seven gene signatures (P4HA2, MSMO1, EGLN1, ZNF316, IKZF3, ISCU and MYO1B) with prognostic values. And the survival time of patients with low risk was significantly longer than those with high risk. Meanwhile, CC patients in the high-risk group yielded higher sensitivity to five chemotherapeutic agents. And we listed 10 candidate small-molecule drugs that exhibited a high correlation with the prognosis of CC. Thus, the prognostic model can accurately predict the prognosis of patients with CC and may be helpful for the development of new hypoxia-immune prognostic markers and therapeutic strategies for CC.

APE1 controls DICER1 expression in NSCLC through miR-33a and miR-130b.

Increasing evidence suggests different, not completely understood roles of microRNA biogenesis in the development and progression of lung cancer. The overexpression of the DNA repair protein apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) is an important cause of poor chemotherapeutic response in lung cancer and its involvement in onco-miRNAs biogenesis has been recently described. Whether APE1 regulates miRNAs acting as prognostic biomarkers of lung cancer has not been investigated, yet. In this study, we analyzed miRNAs differential expression upon APE1 depletion in the A549 lung cancer cell line using high-throughput methods. We defined a signature of 13 miRNAs that strongly correlate with APE1 expression in human lung cancer: miR-1246, miR-4488, miR-24, miR-183, miR-660, miR-130b, miR-543, miR-200c, miR-376c, miR-218, miR-146a, miR-92b and miR-33a. Functional enrichment analysis of this signature revealed its biological relevance in cancer cell proliferation and survival. We validated DICER1 as a direct functional target of the APE1-regulated miRNA-33a-5p and miR-130b-3p. Importantly, IHC analyses of different human tumors confirmed a negative correlation existing between APE1 and Dicer1 protein levels. DICER1 downregulation represents a prognostic marker of cancer development but the mechanisms at the basis of this phenomenon are still completely unknown. Our findings, suggesting that APE1 modulates DICER1 expression via miR-33a and miR-130b, reveal new mechanistic insights on DICER1 regulation, which are of relevance in lung cancer chemoresistance and cancer invasiveness.

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.

Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology.

BACKGROUND & AIMS: Patients with hepatocellular carcinoma (HCC) displaying overexpression of immune gene signatures are likely to be more sensitive to immunotherapy, however, the use of such signatures in clinical settings remains challenging. We thus aimed, using artificial intelligence (AI) on whole-slide digital histological images, to develop models able to predict the activation of 6 immune gene signatures. METHODS: AI models were trained and validated in 2 different series of patients with HCC treated by surgical resection. Gene expression was investigated using RNA sequencing or NanoString technology. Three deep learning approaches were investigated: patch-based, classic MIL and CLAM. Pathological reviewing of the most predictive tissue areas was performed for all gene signatures. RESULTS: The CLAM model showed the best overall performance in the discovery series. Its best-fold areas under the receiver operating characteristic curves (AUCs) for the prediction of tumors with upregulation of the immune gene signatures ranged from 0.78 to 0.91. The different models generalized well in the validation dataset with AUCs ranging from 0.81 to 0.92. Pathological analysis of highly predictive tissue areas showed enrichment in lymphocytes, plasma cells, and neutrophils. CONCLUSION: We have developed and validated AI-based pathology models able to predict the activation of several immune and inflammatory gene signatures. Our approach also provides insights into the morphological features that impact the model predictions. This proof-of-concept study shows that AI-based pathology could represent a novel type of biomarker that will ease the translation of our biological knowledge of HCC into clinical practice. LAY SUMMARY: Immune and inflammatory gene signatures may be associated with increased sensitivity to immunotherapy in patients with advanced hepatocellular carcinoma. In the present study, the use of artificial intelligence-based pathology enabled us to predict the activation of these signatures directly from histology.

Screening gene signatures for clinical response subtypes of lung transplantation.

Lung is the most important organ in the human respiratory system, whose normal functions are quite essential for human beings. Under certain pathological conditions, the normal lung functions could no longer be maintained in patients, and lung transplantation is generally applied to ease patients' breathing and prolong their lives. However, several risk factors exist during and after lung transplantation, including bleeding, infection, and transplant rejections. In particular, transplant rejections are difficult to predict or prevent, leading to the most dangerous complications and severe status in patients undergoing lung transplantation. Given that most common monitoring and validation methods for lung transplantation rejections may take quite a long time and have low reproducibility, new technologies and methods are required to improve the efficacy and accuracy of rejection monitoring after lung transplantation. Recently, one previous study set up the gene expression profiles of patients who underwent lung transplantation. However, it did not provide a tool to predict lung transplantation responses. Here, a further deep investigation was conducted on such profiling data. A computational framework, incorporating several machine learning algorithms, such as feature selection methods and classification algorithms, was built to establish an effective prediction model distinguishing patient into different clinical subgroups, corresponding to different rejection responses after lung transplantation. Furthermore, the framework also screened essential genes with functional enrichments and create quantitative rules for the distinction of patients with different rejection responses to lung transplantation. The outcome of this contribution could provide guidelines for clinical treatment of each rejection subtype and contribute to the revealing of complicated rejection mechanisms of lung transplantation.

Salmonella enterica Serovar Typhimurium Isolates from Wild Birds in the United States Represent Distinct Lineages Defined by Bird Type.

Salmonella enterica serovar Typhimurium is typically considered a host generalist; however, certain isolates are associated with specific hosts and show genetic features of host adaptation. Here, we sequenced 131 S. Typhimurium isolates from wild birds collected in 30 U.S. states during 1978-2019. We found that isolates from broad taxonomic host groups including passerine birds, water birds (Aequornithes), and larids (gulls and terns) represented three distinct lineages and certain S. Typhimurium CRISPR types presented in individual lineages. We also showed that lineages formed by wild bird isolates differed from most isolates originating from domestic animal sources, and that genomes from these lineages substantially improved source attribution of Typhimurium genomes to wild birds by a machine learning classifier. Furthermore, virulence gene signatures that differentiated S. Typhimurium from passerines, water birds, and larids were detected. Passerine isolates tended to lack S. Typhimurium-specific virulence plasmids. Isolates from the passerine, water bird, and larid lineages had close genetic relatedness with human clinical isolates, including those from a 2021 U.S. outbreak linked to passerine birds. These observations indicate that S. Typhimurium from wild birds in the United States are likely host-adapted, and the representative genomic data set examined in this study can improve source prediction and facilitate outbreak investigation. IMPORTANCE Within-host evolution of S. Typhimurium may lead to pathovars adapted to specific hosts. Here, we report the emergence of disparate avian S. Typhimurium lineages with distinct virulence gene signatures. The findings highlight the importance of wild birds as a reservoir for S. Typhimurium and contribute to our understanding of the genetic diversity of S. Typhimurium from wild birds. Our study indicates that S. Typhimurium may have undergone adaptive evolution within wild birds in the United States. The representative S. Typhimurium genomes from wild birds, together with the virulence gene signatures identified in these bird isolates, are valuable for S. Typhimurium source attribution and epidemiological surveillance.

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study.

OBJECTIVES: Clinical phenotyping and predicting treatment responses in SLE patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice. METHODS: Real-time PCR of multiple genes from the IFN M1.2, IFN M5.12, neutrophil (NPh) and plasma cell (PLC) modules, followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood-onset SLE cohorts (n = 101 and n = 34, respectively), and associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (SELENA)-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed (1) all-signatures-low, (2) only IFN high (M1.2 and/or M5.12) and (3) high NPh and/or PLC. RESULTS: All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly, in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort. CONCLUSIONS: The identified gene signatures were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices.

Machine Learning Using Gene-Sets to Infer miRNA Function.

miRNA are regulators of cell phenotype, and there is clear evidence that these small posttranscriptional modifiers of gene expression are involved in defining a cellular response across states of development and disease. Classical methods for elucidating the repressive effect of a miRNA on its targets involve controlling for the many factors influencing miRNA action, and this can be achieved in cell lines, but misses tissue and organism level context which are key to a miRNA function. Also, current technology to carry out this validation is limited in both generalizability and throughput. Methodologies with greater scalability and rapidity are required to better understand the function of these important species of RNA. To this end, there is an increasing store of RNA expression level data incorporating both miRNA and mRNA, and in this chapter, we describe how to use machine learning and gene-sets to translate the knowledge of phenotype defined by mRNA to putative roles for miRNA. We outline our approach to this process and highlight how it was done for our miRNA annotation of the hallmarks of cancer using the Cancer Genome Atlas (TCGA) dataset. The concepts we present are applicable across datasets and phenotypes, and we highlight potential pitfalls and challenges that may be faced as they are used.

Integrating Radiosensitivity Gene Signature Improves Glioma Outcome and Radiotherapy Response Prediction.

Response to radiotherapy (RT) in gliomas varies widely between patients. It is necessary to identify glioma-associated radiosensitivity gene signatures for clinically stratifying patients who will benefit from adjuvant radiotherapy after glioma surgery. Methods: Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) glioma patient datasets were used to validate the predictive potential of two published biomarkers, the radiosensitivity index (RSI) and 31-gene signature (31-GS). To adjust these markers for the characteristics of glioma, we integrated four new glioma-associated radiosensitivity predictive indexes based on RSI and 31-GS by the Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. A receiver operating characteristic (ROC) curve, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the radiosensitivity predictive ability of these six gene signatures. Subgroup analysis was used to evaluate the discriminative capacity of those gene signatures in identifying radiosensitive patients, and a nomogram was built to improve the histological grading system. Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were used to explore related biological processes. Results: We validated and compared the predictive potential of two published predictive indexes. The AUC area of 31-GS was higher than that of RSI. Based on the RSI and 31-GS, we integrated four new glioma-associated radiosensitivity predictive indexes-PI10, PI12, PI31 and PI41. Among them, a 12-gene radiosensitivity predictive index (PI12) showed the most promising predictive performance and discriminative capacity. Examination of a nomogram created from clinical features and PI12 revealed that its predictive capacity was superior to the traditional WHO classification system. (C-index: 0.842 vs. 0.787, p ≤ 2.2 × 10-16) The GO analysis and GSEA showed that tumors with a high PI12 score correlated with various aspects of the malignancy of glioma. Conclusions: The glioma-associated radiosensitivity gene signature PI12 is a promising radiosensitivity predictive biomarker for guiding effective personalized radiotherapy for gliomas.

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.