T cell exhaustion methylation signature drives differential immune responses in glioblastoma.

BACKGROUND: Methylation-related signatures play crucial roles in tumorigenesis and progression. However, their roles in the immune response in primary glioblastoma (GBM) remains unclear. METHODS: We analyzed the differential expression of specific members of T cell exhaustion-related pathways in GBM from the perspective of T cell exhaustion. We further screened for significantly negatively correlated methylation sites as candidate methylation markers for T cell exhaustion. Using consensus clustering, we divided the samples into two categories with significant differences in overall survival (OS). We then performed univariate and multivariate Cox regression analyses to construct the T Cell Exhaustion Methylation (TEXM) signature. Finally, we confirmed that this signature served as an independent prognostic factor, and further characterized it in terms of drug resistance and immunotherapy. RESULTS: We identified 95 significantly differentially expressed T cell exhaustion-related genes and 51 methylation markers associated with T cell exhaustion. The cancer samples were classified according to methylation site markers, thus indicating two subtypes with significant differences in OS: subtype A and subtype B. Tumor scores, stromal scores, tumor purity, and ESTIMATE scores all showed significant differences between subtypes (P < 0.05). Univariate Cox regression analysis identified five methylation sites significantly associated with OS, and multivariate Cox regression analysis was used to construct the TEXM signature model by using these five methylation sites. Significant differences in OS were found between the groups with high and low TEXM signature scores, on the basis of calculation of the TEXM signature scores of tumor samples and using the median score to divide them into high and low score groups. Survival analysis revealed that the high score group had poorer OS and DFS than the low score group in the validation set. Notably, we observed a significant difference in drug sensitivity between the high and low TEXM signature score groups, with the high score group showing higher drug resistance and poorer prognosis. The tumor immune state, as predicted with Tracking Tumor Immunophenotype (TIP), revealed significant differences in antitumor immune scores between the high and low TEXM signature score groups. Finally, we identified 43 significantly differentially regulated metabolism-associated biological processes. CONCLUSION: The epigenetic methylation-related TEXM signature plays a key role in driving differential immune responses in GBM.

Distinct phenotyping of critical patients with demand-capacity imbalance initiating acute renal replacement therapy by consensus clustering.

BACKGROUND: Certain patient subpopulations requiring dialysis initiation show varied survival rates and chances of ending renal replacement therapy (RRT). Consensus clustering can help identify these subgroups and their dialysis outcomes. METHODS: The study included patients who were over 18 years old with urine output above 400 ml per day and an estimated glomerular filtration rate over 15 ml/min/1.73 m2. They underwent acute RRT because of systemic demand-capacity imbalance. Using consensus clustering with 33 clinical variables and urea:creatinine ratio (UCR) to the variables to investigate the catabolic demand. Endpoints were all-cause mortality and being dialysis-free at 180-day follow-up after RRT initiation. RESULTS: Of 946 patients (mean 63 ± 17 years and 649 men, 68.6 %) three distinct phenotypes were identified. 509 (53.8%) patients died and 364 (38.5%) patients were weaned off dialysis. Cluster 2 showed better survival (60.23% vs. 53.18% [cluster 1] vs. 45.85% [cluster 3], P < 0.01) and higher possibility to be weaned off RRT (45.24% vs. 38.44% [cluster 1] vs. 31.62% [cluster 3], P < 0.01). High UCR had increased mortality (59.16% vs. 47.75%, P < 0.01) and a lower weaning rates (33.27%; 45.72%, P < .01). UCR with the clustering phenotype improved risk stratification. CONCLUSIONS: Among critical patients undergoing RRT due to systemic demand-capacity imbalance, more than half of the patients died. We identified distinct phenotypes in demand-capacity imbalance in a heterogeneous cohort of patients initializing RRT. Additionally, we found that pre-dialysis UCR as a novel predictor for mortality and the likelihood of being dialysis-free.

ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.

Clustering is widely used for single-cell analysis, but current methods are limited in accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method that outperforms other methods at hard clustering without the need for hyperparameter tuning. It also performs soft clustering to characterize continuum-like regions and quantify clustering uncertainty, demonstrated here by mapping the connectivity and intermediate transitions between MNIST handwritten digits and between hypothalamic tanycyte subpopulations. This hyperparameter-randomized ensemble approach improves the accuracy, robustness, ease of use, and interpretability of single-cell clustering, and may prove useful in other fields as well.

CT-based radiomic consensus clustering association with tumor biological behavior in clinical stage IA adenocarcinoma: a retrospective study.

Background: Research has demonstrated that radiomics models are capable of forecasting the characteristics of lung cancer. Nevertheless, due to radiomics' poor interpretability, its applicability in clinical settings remains restricted. This investigation sought to verify the correlation between radiomics features (RFs) and the biological behavior of clinical stage IA adenocarcinomas. Methods: A retrospective analysis was conducted on patients diagnosed with clinical stage IA lung adenocarcinoma who underwent resection between May 2005 and December 2018. Detailed radiomics examination of the primary tumor was carried out utilizing preoperative computed tomography (CT) images. Subsequently, patients were grouped based on their RFs using consensus clustering, enabling comparison of tumor biological characteristics among the clusters. Survival disparities among the clusters were evaluated through Kaplan-Meier and Cox analyses. Results: A consensus cluster analysis was performed on 669 patients [median age, 58 years; interquartile range (IQR), 50-64 years, 257 males, 412 females], and three distinct clusters were identified. Cluster 2 was associated with radiological solid adenocarcinoma [119 of 324 (36.7%), P<0.001], larger tumors with median tumor size of 2.1 cm with IQR of 1.7 to 2.5 cm (P<0.001), central tumor [91 of 324 (28.1%), P=0.002], pleural invasion [87 of 324 (26.9%), P<0.001], occult lymph node metastasis (ONM) [106 of 324 (32.7%), P<0.001], and a higher frequency of metastasis or recurrence [62 of 324 (19.1%), P<0.001]. The frequency of histological grade 3 was the highest in Cluster 3 [8 of 34 (23.5%), P<0.001]. Cluster 1 was associated with pure ground glass nodules (pGGNs) [184 of 310 (59.4%), P<0.001], smaller tumors with median tumor size of 1.1 cm with IQR of 0.8 to 1.4 cm (P<0.001), no pleural invasion [276 of 310 (89.0%), P<0.001], histological grade 1 [114 of 248 (46.0%), P<0.001], ONM negative [292 of 310 (94.2%), P<0.001], and a lower rate of metastasis or recurrence [298 of 310 (96.1%), P<0.001]. Conclusions: Differences in tumor biological behavior were detected among consensus clusters based on the RFs of clinical stage IA adenocarcinoma.

Identification of new subtypes of breast cancer based on vasculogenic mimicry related genes and a new model for predicting the prognosis of breast cancer.

Breast cancer is a malignant tumor that poses a serious threat to women's health, and vasculogenic mimicry (VM) is strongly associated with bad prognosis in breast cancer. However, the relationship between VM and immune infiltration in breast cancer and the underlying mechanisms have not been fully studied. On the basis of the Cancer Genome Atlas (TCGA), Fudan University Shanghai Cancer Center (FUSCC) database, GSCALite database, and gene set enrichment analysis (GSEA) datasets, we investigated the potential involvement of VM-related genes in the development and progression of breast cancer. We analyzed the differential expression, mutation status, methylation status, drug sensitivity, tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoints, tumor microenvironment (TME), and immune cell infiltration levels associated with VM-related genes in breast cancer. We created two VM subclusters out of breast cancer patients using consensus clustering, and discovered that patients in Cluster 1 had better survival outcomes compared to those in Cluster 2. The infiltration levels of T cells CD4 memory resting and T cells CD8 were higher in Cluster 1, indicating an immune-active state in this cluster. Additionally, we selected three prognostic genes (LAMC2, PIK3CA, and TFPI2) using Lasso, univariate, and multivariate Cox regression and constructed a risk model, which was validated in an external dataset. The prognosis of patients is strongly correlated with aberrant expression of VM-related genes, which advances our knowledge of the tumor immune milieu and enables us to identify previously unidentified breast cancer subtypes. This could direct more potent immunotherapy approaches.

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis.

In event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects' neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects' ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval.

A m6A regulators-related classifier for prognosis and tumor microenvironment characterization in hepatocellular carcinoma.

Background: Increasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification. Method: RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC). Results: Two distinct m6A modification patterns were identified based on 23 m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit. Conclusion: Our findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients.

PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.

The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients' privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient's privacy. Impact Statement-This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy.

Investigation of prognostic values of immune infiltration and LGMN expression in the microenvironment of osteosarcoma.

BACKGROUND: Osteosarcoma (OS), the most common primary malignant bone tumor, predominantly affects children and young adults and is characterized by high invasiveness and poor prognosis. Despite therapeutic advancements, the survival rate remains suboptimal, indicating an urgent need for novel biomarkers and therapeutic targets. This study aimed to investigate the prognostic significance of LGMN expression and immune cell infiltration in the tumor microenvironment of OS. METHODS: We performed an integrative bioinformatics analysis utilizing the GEO and TARGET-OS databases to identify differentially expressed genes (DEGs) associated with LGMN in OS. We conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to explore the biological pathways and functions. Additionally, we constructed protein-protein interaction (PPI) networks, a competing endogenous RNA (ceRNA) network, and applied the CIBERSORT algorithm to quantify immune cell infiltration. The diagnostic and prognostic values of LGMN were evaluated using the area under the receiver operating characteristic (ROC) curve and Cox regression analysis. Furthermore, we employed Consensus Clustering Analysis to explore the heterogeneity within OS samples based on LGMN expression. RESULTS: The analysis revealed significant upregulation of LGMN in OS tissues. DEGs were enriched in immune response and antigen processing pathways, suggesting LGMN's role in immune modulation within the TME. The PPI and ceRNA network analyses provided insights into the regulatory mechanisms involving LGMN. Immune cell infiltration analysis indicated a correlation between high LGMN expression and increased abundance of M2 macrophages, implicating an immunosuppressive role. The diagnostic AUC for LGMN was 0.799, demonstrating its potential as a diagnostic biomarker. High LGMN expression correlated with reduced overall survival (OS) and progression-free survival (PFS). Importantly, Consensus Clustering Analysis identified two distinct subtypes of OS, highlighting the heterogeneity and potential for personalized medicine approaches. CONCLUSIONS: Our study underscores the prognostic value of LGMN in osteosarcoma and its potential as a therapeutic target. The identification of LGMN-associated immune cell subsets and the discovery of distinct OS subtypes through Consensus Clustering Analysis provide new avenues for understanding the immunosuppressive TME of OS and may aid in the development of personalized treatment strategies. Further validation in larger cohorts is warranted to confirm these findings.

Endotypes of severe neutrophilic and eosinophilic asthma from multi-omics integration of U-BIOPRED sputum samples.

BACKGROUND: Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi-omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development. OBJECTIVES: To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma. METHODS: We analyzed six -omics data blocks-microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing-from induced sputum samples of 57 severe asthma patients, 15 mild-moderate asthma patients, and 13 healthy volunteers in the U-BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi-omics datasets of the 72 asthmatics. RESULTS: Five stable omics-associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL-22 cytokine activation, with the prediction of therapeutic response to anti-IL22 antibody therapy. CONCLUSION: Multi-omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high-dimensional interactions will contribute to a more comprehensive understanding of complex endotypes. KEY POINTS: Unsupervised clustering on sputum multi-omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL-22.

Signature identification based on immunogenic cell death-related lncRNAs to predict the prognosis and immune activity of patients with endometrial carcinoma.

Background: Endometrial carcinoma (EC) is one of the most prevalent gynecologic malignancies and requires further classification for treatment and prognosis. Long non-coding RNAs (lncRNAs) and immunogenic cell death (ICD) play a critical role in tumor progression. Nevertheless, the role of lncRNAs in ICD in EC remains unclear. This study aimed to explore the role of ICD related-lncRNAs in EC via bioinformatics and establish a prognostic risk model based on the ICD-related lncRNAs. We also explored immune infiltration and immune cell function across prognostic groups and made treatment recommendations. Methods: A total of 552 EC samples and clinical data of 548 EC patients were extracted from The Cancer Genome Atlas (TCGA) database and University of California Santa Cruz (UCSC) Xena, respectively. A prognostic-related feature and risk model was developed using the least absolute shrinkage and selection operator (LASSO). Subtypes were classified with consensus cluster analysis and validated with t-Distributed Stochastic Neighbor Embedding (tSNE). Kaplan-Meier analysis was conducted to assess differences in survival. Infiltration by immune cells was estimated by single sample gene set enrichment analysis (ssGSEA), Tumor IMmune Estimation Resource (TIMER) algorithm. Quantitative polymerase chain reaction (qPCR) was used to detect lncRNAs expression in clinical samples and cell lines. A series of studies was conducted in vitro and in vivo to examine the effects of knockdown or overexpression of lncRNAs on ICD. Results: In total, 16 ICD-related lncRNAs with prognostic values were identified. Using SCARNA9, FAM198B-AS1, FKBP14-AS1, FBXO30-DT, LINC01943, and AL161431.1 as risk model, their predictive accuracy and discrimination were assessed. We divided EC patients into high-risk and low-risk groups. The analysis showed that the risk model was an independent prognostic factor. The prognosis of the high- and low-risk groups was different, and the overall survival (OS) of the high-risk group was lower. The low-risk group had higher immune cell infiltration and immune scores. Consensus clustering analysis divided the samples into four subtypes, of which cluster 4 had higher immune cell infiltration and immune scores. Conclusions: A prognostic signature composed of six ICD related-lncRNAs in EC was established, and a risk model based on this signature can be used to predict the prognosis of patients with EC.

Precision probiotics supplement strategy in aging population based on gut microbiome composition.

With the increasing prevalence of age-related chronic diseases burdening healthcare systems, there is a pressing need for innovative management strategies. Our study focuses on the gut microbiota, essential for metabolic, nutritional, and immune functions, which undergoes significant changes with aging. These changes can impair intestinal function, leading to altered microbial diversity and composition that potentially influence health outcomes and disease progression. Using advanced metagenomic sequencing, we explore the potential of personalized probiotic supplements in 297 older adults by analyzing their gut microbiota. We identified distinctive Lactobacillus and Bifidobacterium signatures in the gut microbiota of older adults, revealing probiotic patterns associated with various population characteristics, microbial compositions, cognitive functions, and neuroimaging results. These insights suggest that tailored probiotic supplements, designed to match individual probiotic profile, could offer an innovative method for addressing age-related diseases and functional declines. Our findings enhance the existing evidence base for probiotic use among older adults, highlighting the opportunity to create more targeted and effective probiotic strategies. However, additional research is required to validate our results and further assess the impact of precision probiotics on aging populations. Future studies should employ longitudinal designs and larger cohorts to conclusively demonstrate the benefits of tailored probiotic treatments.

Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering.

BACKGROUND: The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes. METHODS: For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an in vitro assay using primary human muscle cells to interrogate serum-induced complement formation. FINDINGS: This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay. INTERPRETATION: Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from complement inhibition and provides a stratification strategy for clinical practice. FUNDING: CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, "Profilbildung 2020" [grant no. PROFILNRW-2020-107-A]).

Revealing the impact of autophagy-related genes in rheumatoid arthritis: Insights from bioinformatics.

Background: Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis. Methods: In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results. Results: The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated. Conclusion: This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis. AIMS: This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients. METHODS: The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures.The "limma" package was used to identify the differentially expressed genes (DEGs). "Clusterprofiler" package was used for function enrichment analysis, and the "ConsensusClusterPlus" package was performed for consensus clustering. Finally, the "GSVA" package and the Cibersort algorithm were used for the immune infiltration analysis. RESULTS: We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression. CONCLUSION: We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

Exploring the intersection of obesity and gender in COVID-19 outcomes in hospitalized Mexican patients: a comparative analysis of risk profiles using unsupervised machine learning.

Introduction: Obesity and gender play a critical role in shaping the outcomes of COVID-19 disease. These two factors have a dynamic relationship with each other, as well as other risk factors, which hinders interpretation of how they influence severity and disease progression. This work aimed to study differences in COVID-19 disease outcomes through analysis of risk profiles stratified by gender and obesity status. Methods: This study employed an unsupervised clustering analysis, using Mexico's national COVID-19 hospitalization dataset, which contains demographic information and health outcomes of patients hospitalized due to COVID-19. Patients were segmented into four groups by obesity and gender, with participants' attributes and clinical outcome data described for each. Then, Consensus and PAM clustering methods were used to identify distinct risk profiles based on underlying patient characteristics. Risk profile discovery was completed on 70% of records, with the remaining 30% available for validation. Results: Data from 88,536 hospitalized patients were analyzed. Obesity, regardless of gender, was linked with higher odds of hypertension, diabetes, cardiovascular diseases, pneumonia, and Intensive Care Unit (ICU) admissions. Men tended to have higher frequencies of ICU admissions and pneumonia and higher mortality rates than women. Within each of the four analysis groups (divided based on gender and obesity status), clustering analyses identified four to five distinct risk profiles. For example, among women with obesity, there were four profiles; those with a hypertensive profile were more likely to have pneumonia, and those with a diabetic profile were most likely to be admitted to the ICU. Conclusion: Our analysis emphasizes the complex interplay between obesity, gender, and health outcomes in COVID-19 hospitalizations. The identified risk profiles highlight the need for personalized treatment strategies for COVID-19 patients and can assist in planning for patterns of deterioration in future waves of SARS-CoV-2 virus transmission. This research underscores the importance of tackling obesity as a major public health concern, given its interplay with many other health conditions, including infectious diseases such as COVID-19.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

The Critical Role of Pyroptosis in Peri-Implantitis.

Background: Peri-implantitis (PI) is a prevalent complication of implant treatment. Pyroptosis, a distinctive inflammatory programmed cell death, is crucial to the pathophysiology of PI. Despite its importance, the pyroptosis-related genes (PRGs) influencing PI's progression remain largely unexplored. Methods: This study conducted histological staining and transcriptome analyze from three datasets. The intersection of differentially expressed genes (DEGs) and PRGs was identified as pyroptosis-related differentially expressed genes (PRDEGs). Functional enrichment analyses were conducted to shed light on potential underlying mechanisms. Weighted Gene Co-expression Network Analysis (WGCNA) and a pyroptotic macrophage model were utilized to identify and validate hub PRDEGs. Immune cell infiltration in PI and its relationship with hub PRDEGs were also examined. Furthermore, consensus clustering was performed to identify new PI subtypes. Protein-protein interaction (PPI) network, competing endogenous RNA (ceRNA) network, mRNA-mRNA binding protein regulatory (RBP) network, and mRNA-drugs regulatory network of hub PRDEGs were also analyzed. Results: Eight hub PRDEGs were identified: PGF, DPEP1, IL36B, IFIH1, TCEA3, RIPK3, NET7, and TLR3, which are instrumental in the PI's progression. Two PI subtypes were distinguished, with Cluster 1 exhibiting higher immune cell activation. The exploration of regulatory networks provided novel mechanisms and therapeutic targets in PI. Conclusion: Our research highlights the critical role of pyroptosis and identifies eight hub PRDEGs in PI's progression, offering insights into novel immunotherapy targets and laying the foundation for advanced diagnostic and treatment strategies. This contributes to our understanding of PI and underscores the potential for personalized clinical management.

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression.

Objective: Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC. Methods: Using R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package. Results: In the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups. Conclusion: In the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients.