Unsupervised Clustering Analysis Research Articles

Plasma cytokines pattern as a prognostic marker for esophageal squamous cell carcinoma via unsupervised clustering analyses

Plasma cytokines pattern as a prognostic marker for esophageal squamous cell carcinoma via unsupervised clustering analyses

Unsupervised Machine Learning to Identify Risk Factors of Pyeloplasty Failure in Ureteropelvic Junction Obstruction.

Introduction: In adult patients with ureteropelvic junction obstruction (UPJO), little data exist on predicting pyeloplasty outcome, and there is no unified definition of pyeloplasty success. As such, defining pyeloplasty success retrospectively is particularly vulnerable to bias, allowing researchers to choose significant outcomes with the benefit of hindsight. To mitigate these biases, we performed an unsupervised machine learning cluster analysis on a dataset of 216 pyeloplasty patients between 2015 and 2023 from a multihospital system to identify the defining risk factors of patients that experience worse outcomes. Methods: A KPrototypes model was fitted with pre- and perioperative data and blinded to postoperative outcomes. T-test and chi-square tests were performed to look at significant differences of characteristics between clusters. SHapley Additive exPlanation values were calculated from a random forest classifier to determine the most predictive features of cluster membership. A logistic regression model identified which of the most predictive variables remained significant after adjusting for confounding effects. Results: Two distinct clusters were identified. One cluster (denoted as "high-risk") contained 111 (51.4%) patients and was identified by having more comorbidities, such as old age (62.7 vs 35.7), high body mass index (BMI) (26.9 vs 23.8), hypertension (66.7% vs 17.1%), and previous abdominal surgery (72.1% vs 37.1%) and was found to have worse outcomes, such as more frequent severe postoperative complications (7.2% vs 1.0%). After adjusting for confounding effects, the most predictive features of high-risk cluster membership were old age, low preoperative estimated glomerular filtration rate (eGFR), hypertension, greater BMI, previous abdominal surgery, and left-sided UPJO. Conclusions: Adult UPJO patients with older age, lower eGFR, hypertension, greater BMI, previous abdominal surgery, and left-sided UPJO naturally cluster into to a group that more commonly suffers from perioperative complications and worse outcomes. Preoperative counseling and perioperative management for patients with these risk factors may need to be thought of or approached differently.

A Circadian Rhythm-related Signature to Predict Prognosis, Immunei Infiltration, and Drug Response in Breast Cancer.

Circadian rhythm genes (CRRGs) play essential roles in cancer occurrence and development. However, the prognostic significance of CRRGs in breast cancer (BC) has not been fully elucidated. Our study aimed to develop a prognostic gene signature based on CRRGs that can accurately and stably predict the prognosis of BC. The transcriptome data and clinical information for BC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus unsupervised clustering analysis was carried out to investigate the roles of CRRGs in BC. A CRRGs-related prognostic risk model was established by using logistic least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox regression analyses. Kaplan-Meier (KM) curves analysis, time-dependent receptor operation characteristics (ROC) curves analysis, and nomogram were plotted to evaluate the predictive efficacy of the model. The relevance of risk score to the immune cell infiltration, tumor burden mutation (TMB), and therapeutic response was assessed. A risk model comprising six CRRGs (SLC44A4, SLC16A6, TPRG1, FABP7, GLYATL2, and FDCSP) was constructed and validated, demonstrating a good predictor of BC. The low-risk group displayed a higher number of immune activities and immune checkpoint expression and a lower burden of tumor mutation. Additionally, drug sensitivity analysis demonstrated the prognostic signature may serve as a potential chemosensitivity predictor. We established 6 CRRGs-related risk signatures for the prognosis of BC, which is of great value in predicting the prognosis of patients with BC and guiding the treatment for BC.

Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic approaches. A molecular-level understanding and diagnosis of this patient cohort could significantly guide clinical interventions. In this study, we amalgamated our sequencing data (n = 46) with a publicly accessible database (n = 38) to perform an unsupervised cluster analysis of the integrated dataset. The eighty-four rosacea patients were partitioned into two distinct clusters. Neurovascular biomarkers were found to be elevated in cluster 1 compared to cluster 2. Pathways in cluster 1 were predominantly involved in neurotransmitter synthesis, transmission, and functionality, whereas cluster 2 pathways were centered on inflammation-related processes. Differential gene expression analysis and WGCNA were employed to delineate the characteristic gene sets of the two clusters. Subsequently, a diagnostic model was constructed from the identified gene sets using linear regression methodologies. The model's C index, comprising genes PNPLA3, CUX2, PLIN2, and HMGCR, achieved a remarkable value of 0.9683, with an area under the curve (AUC) for the training cohort's nomogram of 0.9376. Clinical characteristics from our dataset (n = 46) were assessed by three seasoned dermatologists, forming the NR validation cohort (NR, n = 18; non-neurogenic rosacea, n = 28). Upon application of our model to NR diagnosis, the model's AUC value reached 0.9023. Finally, potential therapeutic candidates for both patient groups were predicted via the Connectivity Map. In summation, this study unveiled two clusters with unique molecular phenotypes within rosacea, leading to the development of a precise diagnostic model instrumental in NR diagnosis.

AI-based cluster analysis enables outcomes prediction among patients with increased LVM.

The traditional classification of left ventricular hypertrophy (LVH), which relies on left ventricular geometry, fails to correlate with outcomes among patients with increased LV mass (LVM). To identify unique clinical phenotypes of increased LVM patients using unsupervised cluster analysis, and to explore their association with clinical outcomes. Among the UK Biobank participants, increased LVM was defined as LVM index ≥72 g/m2 for men, and LVM index ≥55 g/m2 for women. Baseline demographic, clinical, and laboratory data were collected from the database. Using Ward's minimum variance method, patients were clustered based on 27 variables. The primary outcome was a composite of all-cause mortality with heart failure (HF) admissions, ventricular arrhythmia, and atrial fibrillation (AF). Cox proportional hazard model and Kaplan-Meier survival analysis were applied. Increased LVM was found in 4,255 individuals, with an average age of 64 ± 7 years. Of these patients, 2,447 (58%) were women. Through cluster analysis, four distinct subgroups were identified. Over a median follow-up period of 5 years (IQR: 4-6), 100 patients (2%) died, 118 (2.8%) were admissioned due to HF, 29 (0.7%) were admissioned due to VA, and 208 (5%) were admissioned due to AF. Univariate Cox analysis demonstrated significantly elevated risks of major events for patients in the 2nd (HR = 1.6; 95% CI 1.2-2.16; p < .001), 3rd (HR = 2.04; 95% CI 1.49-2.78; p < .001), and 4th (HR = 2.64; 95% CI 1.92-3.62; p < .001) clusters compared to the 1st cluster. Further exploration of each cluster revealed unique clinical phenotypes: Cluster 2 comprised mostly overweight women with a high prevalence of chronic lung disease; Cluster 3 consisted mostly of men with a heightened burden of comorbidities; and Cluster 4, mostly men, exhibited the most abnormal cardiac measures. Unsupervised cluster analysis identified four outcomes-correlated clusters among patients with increased LVM. This phenotypic classification holds promise in offering valuable insights regarding clinical course and outcomes of patients with increased LVM.

The utility of DNA methylation profiling in the diagnosis of un-, de- and trans-differentiated melanoma: a series of 11 cases.

Melanomas are recognised for their remarkable morphological plasticity. Some tumours may lose conventional features and/or acquire non-melanocytic characteristics, referred to as undifferentiated, dedifferentiated and transdifferentiated melanoma. Despite this phenotypical variability, melanomas typically maintain their cancer driver aberrations, affecting genes such as BRAF, NRAS and NF1. Currently, little is known about whether the DNA methylation profile follows the loss or change of differentiation or is retained despite extensive morphological transformation. In this study we analysed 11 melanoma cases, comprising six males and five females, with a median age of 67 years, including five undifferentiated, four trans-differentiated and two de-differentiated melanomas. Undifferentiated and trans-differentiated tumours either arose in a patient with known melanoma and/or presented in the groin/axilla with molecular alterations consistent with melanoma. Cases with heterologous differentiation resembled chondrosarcoma, osteosarcoma, angiosarcoma and rhabdomyosarcoma both morphologically and immunohistochemically, while undifferentiated tumours resembled undifferentiated pleomorphic sarcoma. Methylome profiling was performed, and unsupervised clustering analysis revealed nine cases (five undifferentiated, three trans-differentiated and one de-differentiated) to cluster closely together with conventional melanomas from a reference set. Two cases clustered separately with a distinct group of conventional melanomas exhibiting H3K27me3 loss. Despite loss of differentiation and phenotypical plasticity, methylation patterns seem to be retained in undifferentiated, de-differentiated and trans-differentiated melanomas and represent useful diagnostic tools to enhance diagnostic precision in these diagnostically challenging cases.

P016 METAPROTEOMICS ANALYSIS IN HEALTHY INDIVIDUALS REVEALS A HIGH CARDIOVASCULAR RISK PROFILE ACROSS BLOOD PRESSURE, DIET, HUMAN AND MICROBIAL PROTEINS

Background/Objective: The gut microbiota is a crucial link between diet and cardiovascular health by producing beneficial metabolites, such as short-chain fatty acids (SCFAs), through fibre fermentation. However, most studies have focused on microbial DNA to infer microbiota function. Here, we employed metaproteomics, a novel method to capture gut human and microbiota protein expression profiles concurrently. We aimed to investigate the crosstalk between gut microbiota, diet, host gut health, and blood pressure (BP). Methods: Mass spectrometry-based metaproteomic analysis was performed on 61 faecal samples with paired ambulatory BP monitoring, food intake (food frequency questionnaire), and demographic data. Results: We identified 21,594 microbial and 1,072 human proteins. Traditional risk factors (age, BMI, sex, BP) explained only ∼10% of the proteome variance. However, unsupervised clustering analysis of both human and microbial proteome revealed two distinct clusters (low-risk and high-risk) with significantly different 24-hour systolic BP (mean±SD, low-risk 116±13mmHg vs high-risk: 126±15mmHg, P=0.016), women prevalence (72.4% vs 40.6%, P=0.025), and diet quality (assessed as Athlete Diet Index, 61.6±6.7 vs 57.8±7.6, P=0.044). In the human proteome, the low-risk group had lower expression of the VEGFA-VEGFR2 signalling pathway and tight junction proteins and higher SCFA production (particularly propionate), suggesting an anti-inflammatory gut environment. In the microbial proteome, the low-risk group had higher activity of inorganic ion transport and metabolism-related proteins, with increased expression of acetate kinase that produces both the SCFAs acetate and propionate, particularly in primary fibre-fermenting bacteria, including Phocaeicola dorei and Blautia wexlerae. Finally, via co-expression networks, we identified profilin-1 (PFN1), which contributes to hypertension and atherosclerosis, as a crucial human protein associated with several bacteria present in the high-risk group. Conclusions: Employing an unsupervised clustering based on human and microbial proteomics, we identified two groups of participants with varying traditional and emerging cardiovascular risk factors. This was explained by complex interactions among diet, gut microbiota, and BP. This underscores the need for sophisticated models to understand multifactorial risk factors for cardiovascular disease.

Cluster analysis identifies clinical phenotypes of primary hyperhidrosis.

Introduction Identifying subgroups of patients with primary hyperhidrosis (PHH) can improve the understanding of the disease pathophysiology. The study objective is to determine the naturally occurring subgroups of patients with PHH based on clinical characteristics. Methods In this retrospective cohort study, data was collected from participants included in a clinical trial. The data was collected between January 2020 and June 2021 from outpatients with PHH attending a dermatologic department in Denmark. Overall, 84 patients with PHH were screened for inclusion in the clinical trial. Of these, 41 met the eligibility criteria. Four participants were excluded because of missing data. The main outcome was the identification of subgroups of patients with PHH using an unsupervised hierarchical cluster analysis. Results Overall, 37 patients were included (28 [76.7%] females; median age at inclusion 28.0 [interquartile range (IQR) 24.0-38.3]; median body mass index 24.9 [IQR 20.9-27.4); median age of onset 13.0 [IQR 9.5-18.5]; and 26 [70.3%] had a familial disposition towards PHH). Two clusters of 18 and 17 patients were identified. The first cluster had, when compared to the second, a younger age of onset (median age 11.0 [IQR 0-13.0] vs. 17.0 [IQR 15.0-21.0], p=0.003) and higher sweat rates on gravimetry (median 175.0 [IQR 121.2-252.5] vs. 40.0 [IQR 20.0-60.0] milligrams of sweat/5 minutes, p&lt;0.001) and transepidermal water loss (median 93.7 [IQR 91.2-97.8] vs. 59.0 [IQR 44.4-73.2] grams/meter2/hour, p&lt;0.001). No differences were observed for the other variables. Conclusions This study identifies two subgroups of patients with PHH. The patients with an onset of PHH during childhood had a substantially higher sweat and evaporation rate in adulthood than those with an onset during adolescence. These findings may imply a changed understanding of the pathophysiology of PHH, by indicating that an early disease onset can lead to a worse disease course.

Exploring the transcriptomic and metabolomic profiles of adipose tissues: Insights and implications for fat grafting

BackgroundExpanding on our prior research on murine fat grafts' metabolic shift, this study delves deeper into the metabolic profiles of human adipose tissues, specifically the superficial subcutaneous adipose tissue (SSAT) and deep subcutaneous adipose tissue (DSAT). MethodsUtilizing RNA-sequencing, metabolome, and metabolic flux analyses, we examined SSAT and DSAT samples obtained during deep inferior epigastric perforator flap breast reconstructions. Transcript data underwent unsupervised hierarchical clustering and Gene set enrichment analysis. Metabolomics involved analyzing samples for cationic and anionic metabolites via Capillary Electrophoresis Time-of-Flight Mass Spectrometry (CE-TOFMS), followed by principal component analysis (PCA) and heat map generation. Additionally, primary adipocytes from SSAT and DSAT were assessed using the Seahorse® extracellular flux analyzer. ResultsPCA and heat map analyses highlighted distinct transcriptome and metabolome differences between SSAT and DSAT. SSAT predominantly upregulated genes linked to adipogenesis (false discovery rate [FDR] q < 0.0001), oxidative phosphorylation (FDR q < 0.0001), fatty acid metabolism (FDR q <0.0001), and glycolysis (FDR q = 0.001). In contrast, DSAT showed a significant upregulation in inflammatory response genes (FDR q <0.05). Metabolite analysis revealed an abundance of glycolytic metabolites in SSAT, whereas DSAT was rich in metabolites associated with fatty acid metabolism and oxidative phosphorylation. Cellular flux analysis further confirmed SSAT's elevated glycolysis and spare oxidative phosphorylation capacities. ConclusionOur results highlight the metabolic uniqueness of SSAT and DSAT in humans, with SSAT exhibiting superior metabolic flexibility. The implications of these metabolic differences, especially in fat grafting procedures, necessitate further research and exploration in future studies. Data availabilityThe ribonucleic acid sequencing data were deposited in the Gene Expression Omnibus database (GSE216803). https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE216803 (GEO token: qnsngiuqplqdpaf).

Clinical phenotypes of heart failure patients with supranormal ejection fraction.

Heart failure (HF) with supranormal ejection fraction (HFsnEF) represents a distinct clinical entity characterized by limited treatment options and an unfavourable prognosis. Revealing its phenotypic diversity is crucial for understanding disease mechanism and optimizing patient management. We aim to identify phenotypic subgroups in HFsnEF using unsupervised clustering analysis. Consecutive hospitalized patients with a diagnosis of HF and a left ventricular ejection fraction ≥65% at baseline echocardiographic evaluations were included for analysis. We conducted unsupervised hierarchical clustering analysis on principal components (HCPC) to identify HFsnEF phenogroups using mixed data variables including demographics, HF duration, vital signs, anthropometrics, smoking/drinking status, HF aetiology, comorbid diseases, laboratory tests and echocardiographic parameters. We then employed decision tree modelling to identify parameters capable of distinguishing distinct clusters. Clinical outcomes, including all-cause death, cardiovascular (CV) death and CV readmission for different clusters, were examined. Three mutually exclusive clusters were identified from the cohort of 221 HFsnEF patients. Cluster 1 (52.5%) predominantly consisted of patients with valvular heart disease, who had larger cardiac chambers and a higher prevalence of atrial fibrillation/atrial flutter. Cluster 2 (26.2%) primarily comprised older ischaemic patients with a higher prevalence of metabolic comorbidities. Cluster 3 (21.3%) were mainly hypertrophic cardiomyopathy patients. Two clinical variables were identified that could be used to group all HFsnEF patients into one of the clusters; they were HF aetiology and comorbid diabetes. During the median follow-up of 53.4months, 46 (20.8%) all-cause deaths occurred, among them 39 of CV causes. Seventy (31.7%) patients experienced CV readmissions. Three clusters showed distinct differences in mortality outcomes, with Cluster 1 exhibiting the highest risk of all-cause mortality [Cluster 1 vs. Cluster 2: adjusted hazard ratio (aHR)=3.32, P=0.022; Cluster 1 vs. Cluster 3: aHR=3.81, P=0.036; Cluster 2 vs. Cluster 3: aHR=1.15, P=0.865] and CV mortality (Cluster 1 vs. Cluster 2: aHR=3.73, P=0.022; Cluster 1 vs. Cluster 3: aHR=4.27, P=0.020; Cluster 2 vs. Cluster 3: aHR=1.15, P=0.870). CV readmission risk was comparable among the three clusters (Cluster 1 vs. Cluster 2: aHR=0.82, P=0.590; Cluster 1 vs. Cluster 3: aHR=1.04, P=0.900; Cluster 2 vs. Cluster 3: aHR=1.28, P=0.580). In a heterogeneous HFsnEF cohort, three clusters were identified by unsupervised HCPC with distinct clinical characteristics and outcomes.

Comprehensive analysis of paraspeckle-associated gene modules unveils prognostic signatures and immunological relevance in multi-cancers

BackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, characterized by high rates of angiogenesis and immune evasion. Paraspeckle genes, involved in gene regulation and RNA metabolism, have recently been linked to tumor progression. This study aims to elucidate the relationship between paraspeckle genes and HCC prognosis, focusing on SFPQ, DDX39B, and UBAP2.MethodsWe analyzed HCC (LIHC) and prostate cancer (PRAD) samples from the TCGA database to explore the correlation between paraspeckle genes and angiogenesis. We conducted unsupervised clustering, risk scoring, and survival analysis to identify distinct patient groups and their clinical outcomes. Gene expression data were used to perform differential analysis and Gene Ontology (GO) enrichment.ResultsOur analysis identified significant correlations between paraspeckle genes and angiogenesis across multiple cancer types. Elevated expression levels of SFPQ, DDX39B, and UBAP2 were associated with poor prognosis in HCC patients, and all of them has statistical significance. Unsupervised clustering of HCC samples based on paraspeckle gene expression revealed two distinct clusters, with high-risk patients exhibiting stronger immune suppression and tumor immune evasion. GO enrichment highlighted critical pathways related to angiogenesis and immune regulation. Additionally, a risk scoring model based on these genes effectively distinguished high-risk and low-risk patient groups, providing valuable prognostic insights.ConclusionThis study demonstrates that SFPQ, DDX39B, and UBAP2 are significantly associated with poor prognosis in HCC, likely due to their roles in promoting angiogenesis and immune suppression. These findings highlight the potential of paraspeckle genes as prognostic biomarkers and therapeutic targets, offering new avenues for personalized treatment strategies in HCC. Further research into their functional mechanisms and clinical applicability is crucial for advancing HCC treatment and improving patient outcomes.

Clinical stratification of 1318 Primary Sjögren's Syndrome patients

ObjectivePrimary Sjögren's Syndrome (pSS) is a complex autoimmune disorder characterized by diverse clinical manifestations yet lacking effective therapeutic strategies currently. This study aims to gain a thorough understanding of the clinical landscape of pSS and further delineate its clinical subtypes, thereby enabling the efficient management for pSS. MethodsWe conducted a cross-sectional observational study of 1318 pSS patients. The pSS patients were categorized and compared based on gender, anti-SSA antibodies, and labial salivary gland biopsies (LGSB). Unsupervised clustering analysis was employed to identify pSS subtypes using systemic involvement among patients. Furthermore, we assessed clinical and biological variances among these subtypes. ResultsThrough group comparisons, we observed more pronounced extraglandular manifestations among male patients, SSA-negative group, and those with positive LGSB results. Based on systemic involvement, pSS patients were categorized into four groups. C1 exhibited minimal systemic involvement, lacking hematologic or serologic manifestations, with the lowest ESSDAI scores. C2 presented with serologic changes in all patients, partial joint involvement, and no hematologic systemic manifestations. C3 lacked joint involvement but all members displayed hematologic systemic involvement, with higher rates of renal, cutaneous, and systemic manifestations. C4 encompassed patients with joint and hematologic involvement, displaying the highest ESSDAI scores. The positivity rates of antibodies, immunological parameters, and inflammatory markers exhibited significant differences among the groups. Furthermore, notable variances were observed in the expression of peripheral blood transcriptomic modules among these groups. ConclusionIn this cohort study, we summarized the clinical characteristics of Chinese patients with pSS and identified four distinct subgroups of pSS based on systemic involvement, revealing clinical and molecular disparities that unveil distinct pathobiological endotypes. Our findings hold significant implications for clinical management.

Machine Learning and Statistical Shape Modelling Methodologies to Assess Vascular Morphology before and after Aortic Valve Replacement.

Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify subgroups within a given population in relation to shape features. We tested the application of both methods in the clinically relevant scenario of patients undergoing aortic valve repair (AVR). Every year, around 5000 patients undergo surgical AVR in the UK. Aims: Evaluate aortic morphology and identify subgroups amongst patients who had undergone AVR, including Ozaki, Ross, and valve-sparing procedures using SSM and unsupervised hierarchical clustering analysis. This methodological framework can evaluate both pre- and post-surgical variability across subgroups undergoing different surgeries. Methods: Pre- (n = 47) and post- (n = 35) operative three-dimensional (3D) aortic models were reconstructed from computed tomography (CT) and cardiac magnetic resonance (CMR) images. Computational analyses for SSM and hierarchical clustering were run separately for the two subgroups, assessing (a) ascending aorta only and (b) the whole aorta. This allows for exploring possible variations in morphological classification related to the input shape. Results: Most patients in the Ross procedure subgroup exhibited differences in aortic morphology from other subgroups, including an elongated ascending and wide aortic arch pre-operatively, and an elongated ascending aorta with a slightly enlarged sinus post-operatively. In hierarchical clustering, the Ross aortas also appeared to cluster together compared to the other surgical procedures, both pre-operatively and post-operatively. There were significant differences between clusters in terms of clustering distance in the pre-operative analyses (p = 0.003 for ascending aortas, p = 0.016 for whole aortas). There were no significant differences between the clusters in post-operative analyses (p = 0.47 for ascending, p = 0.19 for whole aorta). Conclusions: We demonstrated the feasibility of evaluating aortic morphology before and after different aortic valve surgeries using SSM and hierarchical clustering. This framework could be used to further explore shape features associated with surgical decision-making pre-operatively and, importantly, to identify subgroups whose morphology is associated with poorer clinical outcomes post-operatively. Statistical shape modelling (SSM) and unsupervised hierarchical clustering are two statistical methods that can be used to assess morphology, show morphological variations, with the latter being able to identify subgroups within a population. These methods have been applied to the population of aortic valve replacement (AVR) patients since there are different surgical procedures (traditional AVR, Ozaki, Ross, and valve-sparing). The aim is to evaluate aortic morphology and identify subgroups within this population before and after surgery. Computed tomography and cardiac magnetic resonance images were reconstructed into 3D models of the ascending aorta and whole aorta, which were then input into SSM and hierarchical clustering. The results show that the Ross aortic morphology is quite different from the other aortas. The clustering did not classify the aortas based on the surgical procedures; however, most of the Ross group did cluster together, indicating low variability within this surgical group.

New insights on hidradenitis suppurativa phenotypes and treatment response: An exploratory automated analysis of the SUNSHINE and SUNRISE trials.

Defining hidradenitis suppurativa (HS) subtypes was previously limited by small sample sizes and poor interrater reliability; no study has investigated subtype treatment responses. The objective of this analysis was to characterize HS clusters in adult patients with moderate to severe HS and evaluate secukinumab treatment responses between clusters. Clusters were identified via an unsupervised machine learning clustering analysis using baseline data from the randomized, placebo-controlled SUNSHINE (NCT03713619) and SUNRISE (NCT03713632) phase 3 trials. To assess treatment responses, patients received secukinumab every 2 (SECQ2W) or 4 weeks (SECQ4W) or placebo, for 16 weeks, after which, placebo patients randomly switched to SECQ2W/SECQ4W, and SECQ2W/SECQ4W patients maintained their original treatment, until week 52. Baseline outcomes included patient characteristics, disease characteristics and severity, HS-associated comorbidities and previous treatment exposures. Treatment response was assessed via the HS clinical response (HiSCR), abscess and inflammatory nodule (AN) count, flares and NRS30 (skin pain). Based on baseline data, three clusters were identified from 1084 patients (Cluster 1: 54.1%, Cluster 2: 17.8%, Cluster 3: 28.1%). Cluster 1 was predominantly female (65.4%) and was characterized by milder HS. Cluster 2 had more patients from the Asia Pacific, Middle East and Africa region (58.5%) and was characterized by moderate HS. Cluster 3 had the highest rates of previous exposure to biologics (45.9%) and prior HS-related surgeries (47.5%) and was characterized by severe HS. SECQ2W and SECQ4W demonstrated efficacy versus placebo in all clusters at week 16; SECQ2W and SECQ4W efficacy was maintained to week 52. SECQ2W treatment showed a trend for greater efficacy versus SECQ4W in Cluster 3 through week 52. Three HS clusters were identified. Secukinumab demonstrated benefit over placebo in all clusters. However, patients with more severe disease may take longer to respond and more frequent secukinumab dosing may be required for these patients. SUNSHINE (NCT03713619) and SUNRISE (NCT03713632).

Clinical phenotypes and outcomes of patients with left ventricular thrombus: an unsupervised cluster analysis

ObjectiveLeft ventricular thrombus (LVT) can develop in a diverse group of patients with various underlying causes, resulting in divergent natural histories and trajectories with treatment. Our aim was to use cluster analysis to identify unique clinical profiles among patients with LVT and then compare their clinical characteristics, treatment strategies, and outcomes. MethodsWe conducted a retrospective study involving 472 patients with LVT whose data were extracted from a tertiary center's echocardiography database, from March 2011 to January 2021. We used the TwoStep cluster analysis method, examining 19 variables. ResultsOur analysis of the 472 patients with LVT revealed two distinct patient clusters. Cluster 1, comprising 247 individuals (52.3%), was characterized by younger patients with a lower incidence of traditional cardiovascular risk factors and relatively fewer comorbidities compared with Cluster 2. Most patients had LVT attributed to an underlying ischemic condition, with a larger proportion being due to post-acute myocardial infarction in Cluster 1 (68.8%), and due to ischemic cardiomyopathy in Cluster 2 (57.8%). Notably, patients in Cluster 2 exhibited a reduced likelihood of LVT resolution (hazard ratio [HR] 0.58, 95% confidence interval [CI] 0.44–0.77, p < 0.001) and a higher risk of all-cause mortality (HR 2.27, 95% CI 1.43–3.60, p = 0.001). These associations persisted even after adjusting for variables such as anticoagulation treatment, the presence of left ventricular aneurysms, and specific LVT characteristics such as mobility, protrusion, and size. ConclusionThrough TwoStep cluster analysis, we identified two distinct clinical phenotypes among patients with LVT, each distinguished by unique baseline clinical attributes and varying prognoses.

Novel PAX3::MAML3 Fusion Identified in Alveolar Rhabdomyosarcoma, Using DNA Methylation Profiling to Expand the Genetic Spectrum of “Fusion-Positive” Cases

Alveolar rhabdomyosarcoma (ARMS) with FOXO1 gene rearrangements is an aggressive pediatric rhabdomyosarcoma subtype that is prognostically distinct from embryonal rhabdomyosarcoma and fusion-negative ARMS. Here, we report 2 cases of ARMS with PAX3::MAML3 fusions. The tumors arose in an infant and an adolescent as stage IV metastatic disease (by Children’s Oncology Group staging system). Histologically, both cases were small round blue cell tumors arranged in vague nests and solid sheets that were diffusely positive for desmin and myogenin. By methylation profiling and unsupervised clustering analysis, the tumors clustered with ARMS with classic FOXO1 rearrangements and ARMS with variant PAX3::NCOA1/INO80D fusions, but not with biphenotypic sinonasal sarcoma (BSNS) with PAX3::MAML3/NCOA2/FOXO1/YAP1 fusions nor with other small round blue cell tumors, including embryonal rhabdomyosarcoma. The differentially methylated genes between ARMS and BSNS were highly enriched in genes involved in myogenesis, and 21% of these genes overlap with target genes of the PAX3::FOXO1 fusion transcription factor. On follow-up after initiation of vincristine/actinomycin/cyclophosphamide chemotherapy, the tumors showed partial and complete clinical responses, consistent with typical upfront chemotherapy responsiveness of ARMS with the classic FOXO1 rearrangement. We conclude that PAX3::MAML3 is a novel variant fusion of ARMS, which displays a methylation signature distinct from BSNS despite sharing similar PAX3 fusions. These findings highlight the utility of methylation profiling in classifying ARMS with noncanonical fusions.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer

BackgroundPancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. MethodsStudy data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. ResultsA total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. ConclusionOur study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

Comprehensive Molecular Characterization of a Large Series of Calcified Chondroid Mesenchymal Neoplasms Widening Their Morphologic Spectrum.

Recently, FN1 fusions to receptor tyrosine kinase genes have been identified in soft tissue tumors with calcified chondroid matrix named calcifying chondroid mesenchymal neoplasms (CCMNs). We collected 33 cases of CCMN from the French network for soft tissue and bone tumors. We performed whole-exome RNA sequencing, expression analysis, and genome-wide DNA methylation profiling in 33, 30, and 20 cases of CCMN compared with a control group of tumors, including noncalcified tenosynovial giant cell tumor (TGCT). Among them, 15 cases showed morphologic overlap with soft tissue chondroma, 8 cases with tophaceous pseudogout, and 10 cases with chondroid TGCT. RNA-sequencing revealed a fusion of FN1 in 76% of cases (25/33) with different 5' partners, including most frequently FGFR2 (14 cases), TEK or FGFR1. Among CCMN associated with FGFR1 fusions, 2 cases had overexpression of FGF23 without tumor-induced osteomalacia. Four CCMN had PDGFRA::USP8 fusions; 3 of which had histologic features of TGCT and were located in the hip, foot, and temporomandibular joint (TMJ). All cases with FN1::TEK fusion were located at TMJ and had histologic features of TGCT with or without chondroid matrix. They formed a distinct cluster on unsupervised clustering analyses based on whole transcriptome and genome-wide methylome data. Our study confirms the high prevalence of FN1 fusions in CCMN. In addition, through transcriptome and methylome analyses, we have identified a novel subgroup of tumors located at the TMJ, exhibiting TGCT-like features and FN1::TEK fusions.

Mechanical power ratio threshold for ventilator-induced lung injury

RationaleMechanical power (MP) is a summary variable incorporating all causes of ventilator-induced-lung-injury (VILI). We expressed MP as the ratio between observed and normal expected values (MPratio).ObjectiveTo define a threshold value of MPratio leading to the development of VILI.MethodsIn a population of 82 healthy pigs, a threshold of MPratio for VILI, as assessed by histological variables and confirmed by using unsupervised cluster analysis was 4.5. The population was divided into two groups with MPratio above or below the threshold.Measurements and main resultsWe measured physiological variables every six hours. At the end of the experiment, we measured lung weight and wet-to-dry ratio to quantify edema. Histological samples were analyzed for alveolar ruptures, inflammation, alveolar edema, atelectasis. An MPratio threshold of 4.5 was associated with worse injury, lung weight, wet-to-dry ratio and fluid balance (all p < 0.001). After 48 h, in the two MPratio clusters (above or below 4.5), respiratory system elastance, mean pulmonary artery pressure and physiological dead space differed by 32%, 36% and 22%, respectively (all p < 0.001), being worse in the high MPratio group. Also, the changes in driving pressure, lung elastance, pulmonary artery occlusion pressure, central venous pressure differed by 17%, 64%, 8%, 25%, respectively (all p < 0.001).LimitationsThe main limitation of this study is its retrospective design. In addition, the computation for the expected MP in pigs is based on arbitrary criteria. Different values of expected MP may change the absolute value of MP ratio but will not change the concept of the existence of an injury threshold.ConclusionsThe concept of MPratio is a physiological and intuitive way to quantify the risk of ventilator-induced lung injury. Our results suggest that a mechanical power ratio > 4.5 MPratio in healthy lungs subjected to 48 h of mechanical ventilation appears to be a threshold for the development of ventilator-induced lung injury, as indicated by the convergence of histological, physiological, and anatomical alterations. In humans and in lungs that are already injured, this threshold is likely to be different.

A clustering approach identifies an Autism Spectrum Disorder subtype more responsive to chronic oxytocin treatment

Over the last decade, a number of clinical trials have reported effects of chronic treatment with intranasal oxytocin on autistic symptoms but with inconsistent findings. Autism is a heterogeneous disorder and one factor which may influence treatment outcome is whether a subtype of individuals is more sensitive to oxytocin. In a recent cross-over trial on 41 young autistic children we reported that 44% showed a reliable improvement in clinical symptoms (Autism Diagnostic Observation Schedule, ADOS-2) after a placebo-controlled, 6-week intranasal oxytocin intervention where treatment was given every other day followed by a period of positive social interaction. In the current re-assessment of the data, we used an unsupervised data-driven cluster analysis approach to identify autism subtypes using 23 different demographic, social subtype, endocrine, eye-tracking and clinical symptom measures taken before treatment and this revealed an optimum of two different subtypes. We then assessed the proportion of identified responders to oxytocin and found that while 61.5% of one subtype included responders only 13.3% of the other did so. During the placebo phase there was no difference between the two subtypes for the small proportion of responders (19.2% vs 6.7%). This oxytocin-sensitive subtype also showed overall significant post-treatment clinical and eye-tracking measure changes. The oxytocin-sensitive subtype was primarily characterized at baseline by lower initial clinical severity (ADOS-2) and greater interest in the eye-region of emotional faces. These features alone were nearly as efficient in identifying the two subtypes as all 23 baseline measures and this easy-to-conduct approach may help rapidly and objectively screen for oxytocin responders. Future clinical trials using oxytocin interventions may therefore achieve greater success by focusing on children with this specific autism subtype and help develop individualized oxytocin intervention.