Unsupervised Clustering Analysis Research Articles

Immunobiological signatures and the emerging role of SPP1 in predicting tumor heterogeneity, malignancy, and clinical outcomes in stomach adenocarcinoma.

Immunotherapy, as a form of immunobiological therapy, represents a promising approach for enhancing patients' immune responses. This work aims to present innovative ideas and insights for prognostic assessment and clinical treatment of stomach adenocarcinoma (STAD) by leveraging immunobiological signatures. We employed weighted gene co-expression network analysis (WGCNA) and unsupervised clustering analysis to identify hub genes. These hub genes were utilized to construct a prognostic risk model, and their impact on the tumor microenvironment (TME) and DNA variations was assessed using large-scale STAD patient cohorts. Additionally, we conducted transfection experiments with plasmids to investigate the influence of SPP1 on the malignancy of HGC27 and NCI-N87 cells. Unsupervised clustering of 12 immune-related genes (IRGs) revealed three distinct alteration patterns with unique molecular phenotypes, clinicopathological characteristics, prognosis, and TME features. Using LASSO and multivariate Cox regression analyses, we identified three hub genes (MMP12, SPP1, PLAU) from the IRGs to establish a risk signature. This IRG-related risk model significantly stratified the prognosis risk among STAD patients in the training (n = 522), testing (n = 521), and validation (n = 300) cohorts. Notably, there were discernible differences in therapy responses and TME characteristics, such as tumor purity and lymphocyte infiltration, between the risk model groups. Subsequently, a nomogram that incorporates the IRG signature and clinicopathological factors demonstrated superior sensitivity and specificity in predicting outcomes for STAD patients. Furthermore, down-regulation of SPP1, as observed after siRNA transfection, significantly inhibited the proliferation and migration abilities of HGC27 and NCI-N87 cells. In summary, this study highlights the critical role of immune-related signatures in STAD and offers novel insights into prognosis indicators and immunotherapeutic targets for this condition. SPP1 emerges as an independent prognostic factor for STAD and appears to regulate STAD progression by influencing the immune microenvironment.

Bladder capacity as a benchmark for patient stratification in interstitial cystitis/bladder pain syndrome

Objectives:In a previous study we reported that molecular profiling of bladder mucosal tissue from a modest number of IC/BPS patients resulted in a clear separation based on anesthetic bladder capacity (BC), with 400 cc representing the apparent breakpoint between low and non-low BC. The purpose of the current study was to revisit this earlier stratification finding, using a combination of molecular and clinical data, from a large and heterogeneous patient cohort. Materials and Methods:To provide an updated assessment of IC/BPS patient stratification based on anesthetic BC, whole genome gene expression data from 48 mucosal biopsy samples (41 IC/BPS patients; 7 controls) were analyzed with unsupervised clustering and principal component analysis (PCA) to identify primary clusters of patients. This identified three primary individual clusters: (1) IC/BPS patients with a BC between 200–500 cc (n=19), (2) IC/BPS patients with a BC of 501–1500 cc (n=22), and (3) controls. Next, complete demographic, clinical, and questionnaire data prospectively collected from an additional 450 patients from our patient registry were used to conduct a combined analysis to verify this relationship. Characteristics of all 491 IC/BPS patients were compared between those having the current low BC cutoff (≤400 cc) and the proposed new cutoff (≤500 cc) by utilizing independent samples t-test (continuous variables) and chi square tests (categorical variables; p ≤ 0.05 was considered significant). Results:A statistical comparison of the demographic and clinical characteristics of the entire 491 IC/BPS patient cohort showed that those with a bladder capacity ≤500 cc were older, were more likely to have Hunner lesions, and had higher symptom scores. This group also had a lower average number of non-urologic associated symptoms, pelvic pain syndromes, and neurologic, immune, or systemic pain syndromes. Conclusion:By combining newly acquired molecular data with clinical and demographic characteristics in a large cohort of IC/BPS patients, we conclude that anesthetic BC ≤ 500 cc provides a clinically meaningful biomarker for the bladder centric IC/BPS phenotypic subgroup.

Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data.

The aim of this study was to cluster patients with chronic complications of type 2 diabetes mellitus (T2DM) by cluster analysis in Dalian, China, and examine the variance in risk of different chronic complications and metabolic levels among the various subclusters. 2267 hospitalized patients were included in the K-means cluster analysis based on 11 variables [Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Glucose, Triglycerides (TG), Total Cholesterol (TC), Uric Acid (UA), microalbuminuria (mAlb), Insulin, Insulin Sensitivity Index (ISI) and Homa Insulin-Resistance (Homa-IR)]. The risk of various chronic complications of T2DM in different subclusters was analyzed by multivariate logistic regression, and the Kruskal-Wallis H test and the Nemenyi test examined the differences in metabolites among different subclusters. Four subclusters were identified by clustering analysis, and each subcluster had significant features and was labeled with a different level of risk. Cluster 1 contained 1112 inpatients (49.05%), labeled as "Low-Risk"; cluster 2 included 859 (37.89%) inpatients, the label characteristics as "Medium-Low-Risk"; cluster 3 included 134 (5.91%) inpatients, labeled "Medium-Risk"; cluster 4 included 162 (7.15%) inpatients, and the label feature was "High-Risk". Additionally, in different subclusters, the proportion of patients with multiple chronic complications was different, and the risk of the same chronic complication also had significant differences. Compared to the "Low-Risk" cluster, the other three clusters exhibit a higher risk of microangiopathy. After additional adjustment for 20 covariates, the odds ratios (ORs) and 95% confidence intervals (95%CI) of the "Medium-Low-Risk" cluster, the "Medium-Risk" cluster, and the"High-Risk" cluster are 1.369 (1.042, 1.799), 2.188 (1.496, 3.201), and 9.644 (5.851, 15.896) (all p<0.05). Representatively, the "High-Risk" cluster had the highest risk of DN [OR (95%CI): 11.510(7.139,18.557), (p<0.05)] and DR [OR (95%CI): 3.917(2.526,6.075), (p<0.05)] after 20 variables adjusted. Four metabolites with statistically significant distribution differences when compared with other subclusters [Threonine (Thr), Tyrosine (Tyr), Glutaryl carnitine (C5DC), and Butyryl carnitine (C4)]. Patients with chronic complications of T2DM had significant clustering characteristics, and the risk of target organ damage in different subclusters was significantly different, as were the levels of metabolites. Which may become a new idea for the prevention and treatment of chronic complications of T2DM.

Atopic dermatitis phenotypes based on cluster analysis ofthe Danish Skin Cohort.

Despite previous attempts to classify atopic dermatitis (AD) into subtypes (e.g. extrinsic vs. intrinsic), there is a need to better understand specific phenotypes in adulthood. To identify, using machine learning (ML), adult AD phenotypes. We used unsupervised cluster analysis to identify AD phenotypes by analysing different responses to predetermined variables (age of disease onset, severity, itch and skin pain intensity, flare frequency, anatomical location, presence and/or severity of current comorbidities) in adults with AD from the Danish Skin Cohort. The unsupervised cluster analysis resulted in five clusters where AD severity most clearly differed. We classified them as 'mild', 'mild-to-moderate', 'moderate', 'severe' and 'very severe'. The severity of multiple predetermined patient-reported outcomes was positively associated with AD, including an increased number of flare-ups and increased flare-up duration and disease severity. However, an increased severity of rhinitis and mental health burden was also found for the mild-to-moderate phenotype. ML confirmed the use of disease severity for the categorization of phenotypes, and our cluster analysis provided novel detailed information about how flare patterns and duration are associated with AD disease severity.

Identification of important genes associated with acute myocardial infarction using multiple cell death patterns

Acute myocardial infarction (AMI) is a global health threat, and programmed cell death (PCD) plays a crucial role in its occurrence and development. In this study, integrated bioinformatics tools were used to explore new biomarkers and therapeutic targets in AMI. Thirteen types of PCD-related genes were identified through literature review, KEGG, and GSEA pathways. Gene expression matrices and clinical data from AMI patients and healthy controls were obtained from the GEO database. Statistical analysis in R identified 377 differentially expressed genes in AMI patients. Intersection analysis between the differentially expressed genes and PCD-related genes revealed 24 genes positively correlated with immune cells such as Neutrophils and Monocytes, while negatively correlated with T cells CD4 memory resting and Plasma cells. Unsupervised clustering analysis divided patients into two groups (C1 and C2) based on the expression levels of these 24 genes. GSVA analysis showed that C2 patients were more active in pathways related to maintaining normal cell morphology and promoting phagocytosis, suggesting a lower programmed cell death rate and a higher tendency to maintain cell survival. Two hub genes, TNFAIP3 and TP53INP2, were identified through LASSO regression analysis and SVM-RFE, and were validated using an external dataset and RT-qPCR、Western blot and ELISA analysis. These hub genes showed significantly higher expression and protein secretion levels in AMI patients compared to healthy individuals. Overall, regulating and controlling PCD, particularly through the identified hub genes, TNFAIP3 and TP53INP2, may provide new therapeutic strategies for improving the prognosis of AMI patients and preventing heart failure.

Exploration of a screening model for intrahepatic cholangiocarcinoma patients prone to cuproptosis and mechanisms of the susceptibility of CD274-knockdown intrahepatic cholangiocarcinoma cells to cuproptosis.

Intrahepatic cholangiocarcinoma (ICC) is a form of liver cancer with poor long-term survival rates that requires novel therapeutic methods. Our team's previous research found that ICC patients prone to cuproptosis possessed a more satisfactory long-term prognosis and a more sensitive response to copper carrier Elesclomol. Thus, we aimed to identify new diagnostic and treatment strategies for ICC patients prone to cuproptosis and further explore the associated intracellular and extracellular mechanisms of ICC cells prone to cuproptosis. We employed FU-ICC (n = 255) as the training dataset, and validated our findings using SRRSH-ICC (from our center, n = 65), GSE26566 (n = 104), E-MTAB-6389 (n = 78), and scRNA-seq (n = 14) datasets. Single sample gene set enrichment analysis and subsequent unsupervised cluster analysis was conducted on the training dataset for the pan-programmed cell death gene set (including apoptosis, autophagy, ferroptosis, pyroptosis, necroptosis, and cuproptosis) to define and screen ICC patients prone to cuproptosis. We constructed a nomogram model using weighted gene co-expression network analysis and machine learning algorithms to predict ICC patients prone to cuproptosis, then explored its clinical value with multi-center transcriptome profiling. Furthermore, we validated the hub genes with in vitro and animal experiments to define ICC cells prone to cuproptosis. Ultimately, bulk and single-cell transcriptome profiling were utilized to explore the immune microenvironment of ICC cells prone to cuproptosis. Our nomogram model could help predict ICC patients prone to cuproptosis and possessed excellent prediction efficiency and clinical significance via internal and external verification. In vitro experiments demonstrated that ICC cells with siRNA-mediated knockdown of CD274 (PD-L1) and stimulation with elescomol-CuCl2 were prone to cuproptosis, and CD274-negative ICC cells could be defined as ICC cells prone to cuproptosis. The safety and feasibility of lenti-sh CD274+Elesclomol-CuCl2 as a therapeutic approach for ICC were verified using bioinformatics analysis and animal experiments. Bulk and single-cell transcriptome profiling indicated that the interactions between ICC cells prone to cuproptosis and monocytes/macrophages were particularly relevant. In conclusion, this study systematically and comprehensively explored cuproptosis in ICC for the first time. We constructed precise diagnostic and treatment strategies for ICC patients prone to cuproptosis and further explored the intracellular and extracellular mechanisms of ICC cells prone to cuproptosis. Further work with large prospective cohorts will help verify these conclusions.

Cytometry profiling of ex vivo recall responses to Coxiella burnetii in previously naturally exposed individuals reveals long-term changes in both adaptive and innate immune cellular compartments.

Q fever, caused by the intracellular bacterium Coxiella burnetii, is considered an occupational and biodefense hazard and can result in debilitating long-term complications. While natural infection and vaccination induce humoral and cellular immune responses, the exact nature of cellular immune responses to C. burnetii is incompletely understood. The current study seeks to investigate more deeply the nature of long-term cellular recall responses in naturally exposed individuals by both cytokine release assessment and cytometry profiling. Individuals exposed during the 2007-2010 Dutch Q fever outbreak were grouped in 2015, based on a C. burnetii-specific IFNγ release assay (IGRA), serological status, and self-reported clinical symptoms during initial infection, into asymptomatic IGRA-negative/seronegative controls, and three IGRA-positive groups (seronegative/asymptomatic; seropositive/asymptomatic and seropositive/symptomatic). Recall responses following in vitro re-stimulation with heat-inactivated C. burnetii in whole blood, were assessed in 2016/2017 by cytokine release assays (n=55) and flow cytometry (n=36), and in blood mononuclear cells by mass cytometry (n=36). Cytokine release analysis showed significantly elevated IL-2 responses in all seropositive individuals and elevated IL-1β responses in those recovered from symptomatic infection. Comparative flow cytometry analysis revealed significantly increased IFNγ, TNFα and IL-2 recall responses by CD4 T cells and higher IL-6 production by monocytes from symptomatic, IGRA-positive/seropositive individuals compared to controls. Mass cytometry profiling and unsupervised clustering analysis confirmed recall responses in seropositive individuals by two activated CD4 T cell subsets, one characterized by a strong Th1 cytokine profile (IFNγ+IL-2+TNFα+), and identified C. burnetii-specific activation of CD8 T cells in all IGRA-positive groups. Remarkably, increased C. burnetii-specific responses in IGRA-positive individuals were also observed in three innate cell subpopulations: one characterized by an IFNγ+IL-2+TNFα+ Th1 cytokine profile and lack of canonical marker expression, and two IL-1β-, IL-6- and IL-8-producing CD14+ monocyte subsets that could be the drivers of elevated secretion of innate cytokines in pre-exposed individuals. These data highlight that there are long-term increased responses to C. burnetii in both adaptive and innate cellular compartments, the latter being indicative of trained immunity. These findings warrant future studies into the protective role of these innate responses and may inform future Q fever vaccine design.

Construction of a novel disulfidptosis-related signature for improving outcomes in hepatocellular carcinoma: Observational study.

Disulfidptosis is a novel form of metabolic-related regulated cell death (RCD) that is caused by disulfide stress caused by the accumulation of excess cystine in the cell. Targeting disulfide metabolism imbalance is an emerging strategy for the treatment of cancer. However, it is undetermined how disulfidptosis-related genes (DRGs) influence hepatocellular carcinoma (HCC). Unsupervised clustering analysis was performed on the TCGA-LIHC cohort to identify various phenotypes of disulfidptosis. GSVA was used to measure the activation of characteristic gene sets, while CIBERSORT was employed to estimate the infiltration of immune cells. Disulfidptosis-related signature was generated to quantify the phenotype of disulfidptosis in HCC patients. Next, we examined the disparities among the high and low disulfidptosis score categories by considering clinical characteristics, infiltration of immune cells, functions related to the immune system, sensitivity to chemotherapeutic drugs, and effectiveness of immunotherapy. Two different disulfidptosis phenotypes with different prognoses, clinical traits, biological pathways, and immune cell infiltration were identified. Based on differently expressed genes (DEGs) among 2 disulfidptosis phenotypes, a disulfidptosis-related signature was built. The prognostic value of this signature was then evaluated in the TCGA and GEO datasets. Low disulfidptosis score indicated favorable clinical outcomes, higher levels of immune cell infiltration, lower tumor purity, and enhanced immune responses. Furthermore, we noticed a clear disparity in tumor mutation load and drug responsiveness when comparing the high and low disulfidptosis score categories. Finally, a quantitative nomogram was built with disulfidptosis score and several clinical characteristics. The disulfidptosis-related signature provides new insights into the tumor immune microenvironment and complexity in HCC. The disulfidptosis score can serve as a promising tool for personalized prognostic prediction of HCC patients and for customizing more effective immunotherapeutic strategies.

GWOKM: A novel hybrid optimization algorithm for geochemical anomaly detection based on Grey wolf optimizer and K-means clustering

Identifying the geochemical signatures of valuable mineral deposits using regional geochemical data from stream sediments is a challenging task due to the intricate characteristics of geological formations. Our team is currently investigating the potential of unsupervised clustering analysis (CA) and hybridization with the grey wolf optimizer (GWO) in developing multi-element geochemical models using stream sediment data. To cluster the geochemical data and uncover any unusual patterns, we opted to use the K-means (KM) algorithm due to its straightforward implementation, fast computation speed, and capacity to handle the large datasets. Despite its benefits, the KM method also has notable limitations, such as the random selection of cluster centroids. This can result in higher systematic uncertainty in unsupervised geochemical modeling and longer computation times. To mitigate this concern, we have introduced a new hybrid approach, grey wolf optimizer with K-means so-called the GWOKM algorithm to enhance the delineation of multi-elemental patterns in stream sediment geochemical data. This method incorporates the grey wolf optimization algorithm with KM to optimize the identification of both anomalies and backgrounds using factor analysis and sample catchment basin modeling techniques. This approach was utilized to detect anomalous multi-elemental geochemical patterns indicative of porphyry and skarn copper deposits in the Baft area, Kerman belt, Iran. Upon comparison of the geochemical models derived from KM and GWOKM clustering methods, the latter outperformed the former, as evidenced by its higher prediction rate. The outcomes affirm the efficacy of unsupervised KM clustering analysis (CA) as a means of breaking down geochemical anomaly-background populations. Moreover, the integration of clustering methods with optimization algorithms has proven to be successful for enhancing the credibility of mineralized areas, which could be advantageous in future exploration phases. Based on the results, the GWOKM approach generates more reliable and efficient geochemical anomaly targets.

OR20-01 Machine Learning-based Steroid Metabolome Analysis In Women With Polycystic Ovary Syndrome Reveals Three Distinct Androgen Excess Subtypes With Different Metabolic Risk Profiles.

Abstract Disclosure: T.P. Rocha: None. E. Melson: None. R.J. Veen: None. L. Abdi: None. T. McDonnell: None. V. Tandl: None. J. Hawley: None. L. Wittemans: None. A. Anthony: None. L. Gilligan: None. F. Shaheen: None. P. Kempegowda: None. C.D. Gillett: None. L. Cussen: None. C. Missbrenner: None. F. Lajeunesse-Trempe: None. H. Gleeson: None. A. Rees: None. L. Robinson: None. C. Jayasenna: None. H.S. Randeva: None. H.S. Randeva: None. G.K. Dimitriadis: None. L.G. Gomes: None. A. Sitch: None. E. Vradi: Employee; Self; Bayer Schering Pharma. A.E. Taylor: None. M.W. O'Reilly: None. B.M. Obermayer-Pietsch: None. M. Biehl: None. W. Arlt: None. Introduction: Polycystic ovary syndrome affects 10% of women and is associated with an increased risk of type 2 diabetes, hypertension, and fatty liver disease. Androgen excess, a defining feature of PCOS, has been implicated as a driver of metabolic risk. Adrenal-derived 11-oxygenated androgens are a component of PCOS-related androgen excess and are preferentially activated in adipose tissue. Here, we aimed to identify PCOS sub-types with distinct steroid metabolomes and compare their cardiometabolic risk parameters. Methods: We prospectively recruited 488 treatment-naïve women with PCOS fulfilling diagnostic Rotterdam criteria [median age 28 (IQR 24-32) years; BMI 27.5 (22.4-34.6) kg/m2] at eight centres in the UK &amp; Ireland (n=208), Austria (n=242), and Brazil (n=38). Participants underwent a standardised assessment, including insulin sensitivity at baseline (HOMA-IR) and across a 2-h oGTT (Matsuda ISI). We used tandem mass spectrometry to measure 11 androgenic serum steroids, including classic and 11-oxygenated androgens. Results were analysed by unsupervised k-means clustering, followed by a comparison of clinical and metabolic phenotype parameters. Results: Machine learning analysis identified three distinct androgen metabolomes: a cluster with mainly gonadal-derived androgen excess (GAE; testosterone, dihydrotestosterone; 21.5% of women), a cluster with mainly adrenal-derived androgen excess (AAE; 11-oxygenated androgens; 21.7%), and a cluster with comparably mild androgen excess (MAE; 56.8%). Age and BMI were similar between groups. Compared to GAE and MAE, the AAE cluster had the highest rates of hirsutism (76.4% vs 67.6% vs 59.9%) and female pattern hair loss (32.1% vs 14.3% vs 21.7%). The AAE cluster had significantly increased insulin resistance as indicated by higher fasting insulin,120min insulin and HOMA-IR, and lower ISI than GAE and MAE clusters (all p&amp;lt;0.01). The AAE cluster had a 2-3fold higher prevalence of impaired glucose tolerance and newly diagnosed type 2 diabetes. Conclusion: Unsupervised cluster analysis revealed three distinct androgen excess subtypes in PCOS. Women within the adrenal androgen excess cluster had a significantly higher prevalence of insulin resistance, impaired glucose tolerance and type 2 diabetes. These results implicate 11-oxygenated androgens as drivers of metabolic risk in PCOS and provide proof-of-principle for an androgen-based stratification tool to guide preventative and therapeutic strategies in PCOS. Presentation Date: Saturday, June 17, 2023

Gut Inflammation in axial Spondyloarthritis patients is characterized by a marked Type 17 skewed mucosal Innate-like T cell signature.

Patients with spondyloarthritis (SpA) often present with microscopic signs of gut inflammation, a risk factor for progressive disease. We investigated whether mucosal innate-like T cells are involved in dysregulated interleukin-23 (IL-23)/IL-17 responses in the gut-joint axis in SpA. Ileal and colonic intraepithelial lymphocytes (IELs), lamina propria lymphocytes (LPLs), and paired peripheral blood mononuclear cells (PBMCs) were isolated from treatment-naive patients with nonradiographic axial SpA with (n=11) and without (n=14) microscopic gut inflammation and healthy controls (n=15) undergoing ileocolonoscopy. The presence of gut inflammation was assessed histopathologically. Immunophenotyping of innate-like T cells and conventional T cells was performed using intracellular flow cytometry. Unsupervised clustering analysis was done by FlowSOM technology. Serum IL-17A levels were measured via Luminex. Microscopic gut inflammation in nonradiographic axial SpA was characterized by increased ileal intraepithelial γδ-hi T cells, a γδ-T cell subset with elevated γδ-T cell receptor expression. γδ-hi T cellswere also increased in PBMCs of patients with nonradiographic axial SpA versus healthy controls and were strongly associated with Ankylosing Spondylitis Disease Activity Score. The abundance of mucosal-associated invariant T cells and invariant natural killer T cells was unaltered. Innate-like T cells in the inflamed gut showed increased RORγt, IL-17A, and IL-22 levels with loss of T-bet, a signature that was less pronounced in conventional T cells. Presence of gut inflammation was associated with higher serum IL-17A levels. In patients treated with tumor necrosis factor blockade, the proportion of γδ-hi cells and RORγt expression in blood was completely restored. Intestinal innate-like T cells display marked type 17 skewing in the inflamed gut mucosa of patients with nonradiographic axial SpA. γδ-hi T cells are linked to intestinal inflammation and disease activity in SpA.

Unsupervised Cluster Analysis Reveals Distinct Subtypes of ME/CFS Patients Based on Peak Oxygen Consumption and SF-36 Scores

PurposeMyalgic encephalomyelitis, commonly referred to as chronic fatigue syndrome (ME/CFS), is a severe, disabling chronic disease and an objective assessment of prognosis is crucial to evaluate the efficacy of future drugs. Attempts are ongoing to find a biomarker to objectively assess the health status of (ME/CFS), patients. This study therefore aims to demonstrate that oxygen consumption is a biomarker of ME/CFS provides a method to classify patients diagnosed with ME/CFS based on their responses to the Short Form-36 (SF-36) questionnaire, which can predict oxygen consumption using cardiopulmonary exercise testing (CPET). MethodsTwo datasets were used in the study. The first contained SF-36 responses from 2,347 validated records of ME/CFS diagnosed participants, and an unsupervised machine learning model was developed to cluster the data. The second dataset was used as a validation set and included the cardiopulmonary exercise test (CPET) results of 239 participants diagnosed with ME/CFS. Participants from this dataset were grouped by peak oxygen consumption according to Weber's classification. The SF-36 questionnaire was correctly completed by only 92 patients, who were clustered using the machine learning model. Two categorical variables were then entered into a contingency table: the cluster with values {0,1} and Weber classification {A, B, C, D} were assigned. Finally, the Chi-square test of independence was used to assess the statistical significance of the relationship between the two parameters. FindingsThe results indicate that the Weber classification is directly linked to the score on the SF-36 questionnaire. Furthermore, the 36-response matrix in the machine learning model was shown to give more reliable results than the subscale matrix (p − value < 0.05) for classifying patients with ME/CFS. ImplicationsLow oxygen consumption on CPET can be considered a biomarker in patients with ME/CFS. Our analysis showed a close relationship between the cluster based on their SF-36 questionnaire score and the Weber classification, which was based on peak oxygen consumption during CPET. The dataset for the training model comprised raw responses from the SF-36 questionnaire, which is proven to better preserve the original information, thus improving the quality of the model.

Evaluation the role of cuproptosis-related genes in the pathogenesis, diagnosis and molecular subtypes identification of atherosclerosis

BackgroundAt present, the pathogenesis of atherosclerosis has not been fully elucidated, and the diagnosis and treatment face great challenges. Cuproptosis is a novel cell death pattern that might be involved in the development of atherosclerosis. However, no research has reported the correlation between cuproptosis and atherosclerosis. MethodsThe differential cuproptosis-related genes (CRGs) between atherosclerosis group and control group (A-CRGs) were discovered via differential expression analysis. The correlation analysis, PPI network analysis, GO, KEGG and GSEA analysis were performed to investigate the function of A-CRGs. The differences of biological function between atherosclerosis group and control group were investigated via immune infiltration analysis and GSVA. The LASSO regression, nomogram and machine learning models were constructed to predict atherosclerosis risk. The atherosclerosis molecular subtypes clusters were discovered via unsupervised cluster analysis. Subsequently, we used the above research methods to analyze the differential CRGs between clusters (M-CRGs) and evaluate the molecular subtypes identification performance of M-CRGs. Finally, we verified the diagnostic value for atherosclerosis and role in cuproptosis of these CRGs through the validation set and in vitro experiments. ResultsFive A-CRGs were identified and they were mainly related to the biological function of copper ion metabolism and immune inflammatory response. The diagnostic models and nomogram of atherosclerosis based on 5 A-CRGs indicated that these genes had well diagnostic value. A total of two molecular subtypes clusters were obtained in the atherosclerosis group. There were many differences in biological functions between these two molecular subtypes clusters, such as mitochondrial outer membrane permeabilization and primary immunodeficiency. In addition, 3 M-CRGs were identified in the 2 clusters. Machine learning models and nomogram constructed based on M-CRGs showed that these genes had well molecular subtypes identification efficacy. In the end, the results of in vitro experiment and validation set confirmed the diagnostic value for atherosclerosis and role in cuproptosis of these genes. ConclusionThe cuproptosis may be a potential pathogenesis of atherosclerosis and CRGs may be promising markers for the diagnosis and molecular subtypes identification of atherosclerosis.

Squamous Cell Carcinoma of the Bladder: Analysis of Treatments and Outcomes.

Squamous cell carcinoma (SCC) of the bladder is a rare disease composing 2-5% of all bladder cancers. Currently, no consensus exists regarding treatment recommendations for this rare malignancy, although established regimens include various permutations of chemotherapy, radiation, laser treatments, immunotherapies, and surgeries, ranging from pelvic exenteration to local excision. The present study aims to analyze outcomes of the aforementioned treatments to guide clinical decision-making for patients with the disease. Patients with bladder SCC diagnosed between 2000 and 2018 were reviewed utilizing data from the Surveillance, Epidemiology, and End Results Registry (SEER) program. Data was analyzed using SPSS and Python. Examined variables included presence of metastases at diagnosis, surgery, radiotherapy, and chemotherapy reception. Five-year overall survival (OS) curves were analyzed using Kaplan-Meier probability stratified by treatment modality. A total of 5,635 patients with bladder SCC were identified; the most abundantly reported demographics were white race (86%), patient age > 70 years (61%), and male sex (63%). Median survival was 13 months and was significantly decreased in patients treated with chemotherapy or radiation (median survival of nine months or 12 months, respectively). Patients treated with radiation and surgery saw a decreased 10-year survival rate of about 10% if no chemotherapy was administered; this difference narrowed to about a 4% decrease in patients treated with chemotherapy. All radical surgical procedures (to include partial and total cystectomy, and total and posterior exenteration) resulted in a statistically significant increase in survival, except for patients that underwent radical cystectomy including anterior exenteration (p = 0.101). Patients that underwent surgery saw an increased median survival of 15 months. Heatmap analysis demonstrated a significant correlation between tumor size and distant metastases (r = 0.898). Preliminary unsupervised cluster analysis identified two distinct patient subgroups characterized most strongly by race and treatment modality used. Bladder SCC carries a high mortality burden, with a median survival time of 13 months after diagnosis. Based on this study's analysis, radical surgery may be the most effective treatment for this disease. This study also sets a precedent for future research, as more advanced machine learning methods (including cluster and binary tree analysis, as was done in this analysis) can be used to identify the most explanatory variables related to improved patient outcomes.

Machine learning identification and immune infiltration of disulfidptosis-related Alzheimer's disease molecular subtypes.

Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis-related genes (DRGs) in AD remain unknown. Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis-related molecular clusters. Weighted gene co-expression network analysis was performed to select hub genes specific to disulfidptosis-related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets. Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD. We identified disulfidptosis-related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.

Molecular subtypes, predictive markers and prognosis in small-cell lung carcinoma

AimsA new molecular subtype classification was proposed for small-cell lung carcinoma (SCLC). We aimed to further validate the classification in various SCLC patient samples using immunohistochemistry (IHC) to highlight its...

Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation.

This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route across a variety of terrains in the vicinity of their homes. Their physical activity data were clustered to extract 'unique' groupings in a low-dimension feature space in an unsupervised learning approach, and an algorithm was created to automatically distinguish such activities. After testing three dimensionality reduction methods-namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP)-we selected tSNE due to its performance and stable outputs. Cluster formation of activities via DBSCAN only occurred after the data were reduced to two dimensions via tSNE and contained only samples for a single individual. Additionally, through analysis of the t-SNE plots, appreciable clusters in walking-based activities were only apparent with ground walking and stair ambulation. Through a combination of density-based clustering and analysis of cluster distance and density, a novel algorithm inspired by the t-SNE plots, resulting in three proposed and validated hypotheses, was able to identify cluster formations that arose from ground walking and stair ambulation. Low dimensional clustering of activities has thus been found feasible when analyzing individual sets of data and can currently recognize stair and ground walking ambulation.

Feature Selection Based on Unsupervised Clustering Mechanism on Multiple-Sequence MRIs for Predicting Neoadjuvant Chemoradiation Response in Locally Advanced Rectal Cancer

Accurate response prediction allows for personalized cancer management. We developed an unsupervised clustering mechanism to improve effectiveness and efficiency in feature selection operation for accurate patient stratification. Forty-three locally advanced rectal cancer (LARC) patients underwent neoadjuvant chemoradiation were included, pre-treatment T2 and ADC MRIs were acquired for each patient. An initial feature space consisting of 200 radiomic features extracted from manually delineated GTVs from two sequences of MR images. Additional 960 high-order radiomic features extracted from a 3D convolutional neural network (CNN). To remove redundant and irrelevant features, we developed an unsupervised clustering-based feature selection operation to determine the combination of features with potential best performance. The normal process of feature selection involves searching new feature combinations and training new classifiers for evaluating their performance via an iterative process based on selected feature set, the overall time cost is tremendous. To balance the computational cost and search efficiency, firstly, we proposed an unsupervised clustering analysis metric- Comprehensive Cluster Analysis Index (CCAI) through the K-means algorithm, where the average distances between the sample points and the cluster centroids and so on, to construct a multiple linear regression model. Secondly, we extracted sample points by varying the number of features and feature ratios between radiomic features and 3D-CNN features in the output of feature selection. Thirdly, we optimized the model using the sampling points to calculate the CCAI. Two typical feature combination search algorithms, the random forest recursive feature elimination (RF-RFE) and the differential evolution (DE), were used to perform feature selection with CCAI. The accuracy, area-under-curve (AUC) and specificity, based on combined 3D-CNN and radiomic features extracted from combined T2 and ADC images, were 0.852, 0.871, and 0.735, respectively. Our experiments illustrated higher predictive power (AUC = 0.846) based on high-order abstract features extracted from the CNN on ADC and T2 images, compared to the traditional radiomic model (AUC = 0.714). Additionally, the predictive models constructed based on radiomics and CNN features extracted from ADC images were more predictable in terms of treatment responses than the radiomic and CNN imaging features extracted from T2 images. The average computational time of DE and RF-RFE were 50.5s and 128.6s in one single computation, the average computational time were 24.2s and 91.3s with CCAI, respectively. We proposed an unsupervised clustering analysis mechanism to improve the effectiveness of feature selection while decreasing its time cost markedly, which highlight the correlation and complementarity between low- and high-level imaging features, achieving better predictive accuracy.

Analyzing the Predictive Effects of PD-L1 Expression, Early Changes of bTMB and Circulated CD8+T Cells during Treatment for Responses of RT Combined with ICI in NSCLC

The beneficial role of immunotherapy and the clinical relevance of current biomarkers remain inconclusive; thus, appropriate strategies and reliable predictors need further definition. A rational combination of biomarkers is needed. Here, we estimated potential predictive factors for responses of radiotherapy (RT) combined with immune checkpoint inhibitor (ICI) in a phase II trial to determine the efficacy and safety of combination of moderate hypofractionated RT with ICI in patients with oligometastatic NSCLC (NCT03557411). Pretreatment tumor tissue samples and longitudinal blood were collected for immune and tumor biomarker analysis. We examined pre-treatment (pre-ICI) PD-L1 expression in tumor cells. Circulating tumor cell (CTC), PD-L1+CTC, blood tumor mutation burden (bTMB), CD8+T cells, CD4+T cells, NK cells, B cells in circulation were acquired pre-ICI and 1 month after ICI starting (1-mth). In addition, early changes of CTC (CTC), PD-L1+CTC (PD-L1+CTC), bTMB (bTMB), CD8+T cells (CD8+T cells), CD4+T cells (CD4+T cells), NK cells (NK cells), B cells (B cells) were also analyzed to estimate the predictive effects for treatment. High pre-ICI bTMB and increased CD8+T cells at 1 month was associated with better PFS (p = 0.016; p = 0.006). Interaction analyses revealed that each combination of two markers in the 5 markers including PD-L1, pre-ICI bTMB, 1-mth bTMB, 1-mth CD8+T cells and CD8+T cells was significantly associated with PFS, except for CTC, PD-L1+CTC, CD4+T cells, NK cells and B cells in circulation due to low power. Unsupervised cluster analysis based on these markers revealed three sub-cohorts. Cohort-1 was overrepresented by patients with progressive disease (81%) of whom were negative for 3-4 of the 5 biomarkers. Cohort-3 was overrepresented by patients with partial response (70%) of whom were positive for 3-4 of the 5 biomarkers. Survival analyses of the 3 cohorts indicated a significant association with PFS (p = 0.017). This study suggests that a combination of PD-L1 expression, early changes of bTMB and circulated CD8+T cells as a better predictive biomarker for response to RT combined with ICI. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted.

Transcriptional profiling unveils molecular subgroups of adaptive and maladaptive right ventricular remodeling in pulmonary hypertension

Right ventricular (RV) function is critical to prognosis in all forms of pulmonary hypertension. Here we perform molecular phenotyping of RV remodeling by transcriptome analysis of RV tissue obtained from 40 individuals, and two animal models of RV dysfunction of both sexes. Our unsupervised clustering analysis identified ‘early’ and ‘late’ subgroups within compensated and decompensated states, characterized by the expression of distinct signaling pathways, while fatty acid metabolism and estrogen response appeared to underlie sex-specific differences in RV adaptation. The circulating levels of several extracellular matrix proteins deregulated in decompensated RV subgroups were assessed in two independent cohorts of individuals with pulmonary arterial hypertension, revealing that NID1, C1QTNF1 and CRTAC1 predicted the development of a maladaptive RV state, as defined by magnetic resonance imaging parameters, and were associated with worse clinical outcomes. Our study provides a resource for subphenotyping RV states, identifying state-specific biomarkers, and potential therapeutic targets for RV dysfunction.