Unsupervised Clustering Analysis Research Articles

HdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis. In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors. We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes. This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

Predictors of quality of life improvement in patients undergoing catheter ablation for atrial fibrillation: a cluster-based analysis

Abstract Introduction Traditional classification of Atrial Fibrillation (AF) based on temporal patterns doesn't fully encompass the condition's complexity. Unsupervised cluster analyses have been used for classifying patients into groups with similar comorbid profiles. However, inconsistent comorbidity patterns have been found across different cohorts. Bayesian profile regression, a semi-supervised machine learning technique, may improve the precision of clustering by integrating outcome data, diminishing the variability observed with previous methods. Since quality of life (QoL) primarily drives the indication for AF ablation, we utilized the AF Effect on QualiTy-of-Life (AFEQT) questionnaire to cluster patients. Purpose Our aim is to use patient-reported outcome measures to identify clinically relevant phenotypes of AF that benefit the most from catheter ablation. Methods We implemented a single center follow-up (FUP) program for patients with AF referred for ablation since august 2020, including scheduled visits and remote monitoring through a digital health platform. AFEQT score reported by patients was analyzed using a non-linear mixed model and change in AFEQT by month 12 was used as outcome. Comorbidities, echocardiographic data, and biomarkers were collected. Profile regression mixture modelling guided by QoL improvement was performed to create clinically relevant patient groupings. Penalized multinomial logistic regression model was used for validation. Results From the 466 ablations performed, 354 patients were enrolled until July 2023. 211 patients had FUP > 12 months (age 59±10 years, 31% female, 80% paroxysmal). The overall AFEQT questionnaire completeness rate was 72%. Patients were classified into 3 clusters: 1) patients with long duration AF, left heart cavities enlargement and diastolic dysfunction (n=57); 2) patients with self-reported anxiety/depression (n=50); and 3) patients with low rates of comorbidities (n=104). For clinical application, 8 variables (dyslipidemia, AF type and duration since diagnosis, mitral E/A ratio, systolic pulmonary arterial pressure, left ventricle ejection fraction, and PROMIS T-score for anxiety and depression) were sufficient for classifying patients (85% accuracy in internal cross-validation). Patients with higher depressive and anxiety burden (cluster 2) had worse baseline AFEQT score (median: 46, IQR: 39-60) and derived less benefit (median improvement: 21, IQR: 12-32), while patients with low comorbid burden (cluster 3) displayed higher baseline (median: 58, IQR: 48-62) and final QoL (median improvement: 28, IQR: 21-36, p<0.01). Conclusion Our analysis identified 3 reproducible clinically relevant phenotypes of AF using widely available clinical data. These clusters have distinct associations with QoL, underscoring the heterogeneity of AF and importance of comorbidities and substrates. This approach may allow for the prediction of which patients are most likely to benefit from treatment.

Phenotypes and outcomes in patients with atrial fibrillation: a global unsupervised cluster analysis from Phase II/III of the GLORIA-AF registry

Abstract Background Atrial fibrillation (AF) patients are characterized by a great clinical heterogeneity and complexity across the world. Usual classifications may not adequately characterize this population. Data-driven cluster analysis reveals different possible classifications of patients. Purpose To identify different clusters of AF patients who share similar clinical phenotypes and evaluate the association between identified clusters and clinical outcomes using cluster analysis. Methods An agglomerative hierarchical cluster analysis was performed in patients from phase II/III of the GLORIA-AF registry. Associations between clusters and a composite outcome composed of all-cause death, stroke and major bleeding were evaluated using Cox regression analyses. Results The study included 26,045 AF patients (mean±SD age 70.1±10; 44.8% female) with a median [IQR] follow-up of 3 years [2.3-3.1]. Five clusters were identified: Cluster 1 was composed of young males from North America with morbid obesity and cardiovascular (CV) risk factors; Cluster 2 included older Asian patients with lower body mass index (BMI), fewer CV risk factors, lower anticoagulation rate but the highest AF ablation rate; Cluster 3 identified European males with CV comorbidities and behavioral disorders (smoking, alcohol abuse); Cluster 4 was characterized by patients with the highest rate of permanent AF, cancer, anticoagulant therapy prescription ; Cluster 5 included older women from Europe with high CHA2DS2-VASc and HAS-BLED scores, the highest heart rate, lower BMI and high rate of previous stroke and transient ischemic attack. The incidence of the composite outcome increased a stepwise fashion among clusters from 3.2% patient-years (95% confidence interval (CI): 2.4-4.1) for Cluster 1 to 6% patient-years (95% CI: 5.6-6.4) for Cluster 5 (p<0.0001): Cluster 1 versus 2 hazard ratio (HR): 0.85 (95% CI 0.65–1.11); 2 versus 3 HR: 0.84 (95% CI 0.76–0.93); 3 versus 4 HR: 0.9 (95% CI 0.82–0.99) and 4 versus 5 HR: 0.82 (95% CI 0.74–0.91). Conclusion Cluster analysis identified five statistically-driven groups of AF patients, with distinct phenotype characteristics and associated with different risks for major clinical adverse events.Baseline characteristics.Dendrogram and Kaplan Meier-curves.

#1914 Comprehensive single-cell sequencing and lipidomics identify diverse injury states and cholesterol dynamics of the PTs in AKI-to-CKD transition

Abstract Background and Aims The underlying cellular events and metabolic dysfunction of acute kidney injury (AKI) transformed to chronic kidney disease (CKD) is still largely unclear. Previous studies have suggested lipids and metabolites have a profound impact on acute and chronic inflammation. One of the main sites of renal lipid accumulation is the proximal tubule cells (PTs). Here we demonstrated the injury states and cholesterol dynamics of PTs and its potential function in AKI to CKD in unilateral ischemia-reperfusion injury (UIR) mice model. Method We employed single-cell combinatorial indexing lipidomics to analyze 15 mouse kidneys from a mouse model of failed repaired UIR at five time points. We identified cell types through unsupervised clustering analysis. Differential gene expression, enrichment analysis and main function scoring were applied to define functional heterogeneity and injury states of PTs. Next, pseudotime analysis of differentiation transitions was performed to create the diverse PT lineages. Finally, using SCENIC analysis, we identified the key transcription factors that drive the differentiation of PTs. Results We identified 13 main cell clusters and the dynamic changes in the process of AKI to CKD transition were analysed. A remarkable decrease in PTs after injury at day 1 followed with a recovery at day 3 post AKI were observed. Spatiotemporal profiling of the main cell types showed that the most injured areas in the acute stages of kidney injury are primarily concentrated in the outer stripe, specifically the region where the proximal straight tubules (PSTs) are located. In order to gain a higher resolution of the dynamics of PST subtypes during AKI to CKD, we performed sub-clustering analysis specifically on PSTs, partitioning them into 10 subclusters. Single-cell analysis identified maladaptive PSTs (PST-5) with the highest apoptosis and ferroptosis scores. Notably, pseudotime analysis revealed that PST-5 differentiated from PST-4, which has the highest lipid metabolism score. Indeed, among all the cells in the kidney, the PTs exhibited the most active metabolic reprogramming due to their differentiation and redifferentiation early after AKI. Lipid omics profiling reveals an abnormal elevation of the cholesterol metabolites 27-hydroxycholesterol (27-OH) and 25-hydroxycholesterol (25-OH) following AKI. Interestingly, the PST-4-specific expression of Cyp7b1 served as a key enzyme regulating the concentration of 27-OH. This gene has been found to be significantly absent after AKI. Moreover, the binding of 27HC, a natural ligand, to endogenous estrogen receptor alpha (ERα) leads to the upregulation of heme oxygenase 1 (Hmox1), thereby promoting ferroptosis. Finally, SCENIC analysis revealed that Cyp7b1-mediated accumulation of 27-HC is dependent on the transcription factor Atf3. Conclusion Our results indicate that Cyp7b1 deficiency confers ferroptosis, which are primarily dependent on the upregulation of cellular cholesterol homeostasis and 27-HC secretion, leading to the progression of AKI. These findings suggest that the Cyp7b1/Hmox1/Atf3/27-HC axis may serve as a potential therapeutic target for preventing the development of CKD.

#2490 Analyzing the morphology of mesothelial cells when exposed to peritoneal dialysis solutions

Abstract Background and Aims Glucose remains the predominant osmotic agent in peritoneal dialysis (PD), yet its prolonged use is associated with an inevitable decline in peritoneal filtration capacity. Persistent contact with glucose during dialysis can induce significant morphological and functional changes in the peritoneum. In this study, we explore the morphological alterations in mesothelial cells exposed to both conventional glucose-based PD solutions and an innovative biocompatible PD solution (XyloCore) containing L-carnitine and Xylitol. Method Met5A mesothelial cells were seeded and cultured on a polyester filter (with a pore size of 0.4 micrometers, Transwell, 12 well type, Millipore) utilizing a full medium. The impact of PD solutions, differentiating between glucose-based and XyloCore, was assessed on cells cultivated on transwells. These cells were exposed to PD solutions on the apical side and to culture medium on the basal side. The cells were then employed for proteomic and metabolomic assessments and were also fixed and scrutinized through scanning electron microscopy (SEM). Results Through unsupervised hierarchical clustering analysis, discernable proteomic distinctions emerged between wild-type (WT) cells (control) and cells exposed to either glucose-based PD solution or XyloCore. In SEM analysis, control cells exhibited a distinct flat epithelial appearance. Contrastingly, subjecting WT cells to treatment with a glucose-based PD solution induced significant alterations in cellular morphology. Notably, cells displayed elongated shapes, loosening of cell-cell contacts, and a consistent denuding of the culture surface. In contrast, XyloCore maintained a better cellular morphology and mitigated cell detachment. Conclusion Preserving the integrity of the mesothelial barrier is crucial for maintaining peritoneal ultrafiltration capacity. The findings suggest that glucose-based PD solutions trigger early morphological changes in cell shape and cell-cell contacts, likely stemming from alterations in cytoskeleton or adhesion molecules. The exploration of new osmotic agents in clinical practice holds paramount significance for the future of peritoneal dialysis.

Primary Squamous Cell Carcinoma of the Thyroid Has a Molecular Genetic Profile Distinct From That of Anaplastic Thyroid Carcinoma: A Whole Exome Sequencing and Gene Expression Profiling Study.

Primary squamous cell carcinoma (SCC) of the thyroid and anaplastic thyroid carcinoma (ATC) show significant clinical and histologic overlap. Their biological behaviors are so similar that the fifth WHO updates SCC as a morphologic pattern of ATC rather than a separate entity. However, molecular genomic evidence that determines them as the same histologic type is limited. We aimed to explore whether they belong to the same classification from a molecular-typing perspective. A cohort enrolled 15 SCCs and 15 ATCs was collected. Whole exome sequencing (WES) and RNA-sequencing were performed to analyze molecular genetic and gene-expression profiles. Significantly differential-mutant genes were BRAF, DPCR1, PCYOX1L, BRSK2, NRG1, PRR14L, TET1, VAMP4 suggesting differences in mutation level, as well as differences in high-frequency mutated genes, and SCC had a much lower tumor mutation burden than ATC. Mutational co-occurrence and mutual exclusion were less frequent in SCC than in ATC. 2047 differential-express genes were screened, indicating differences in gene expression were extremely strong. In principal component analysis, ATC and SCC could be notably clustered together, respectively, meanwhile they could be explicitly distinguished. Unsupervised clustering analysis validated they can indeed be clearly separated from each other which demonstrated that they may be two distinctive entities. It is controversial yet SCC is classified as a morphologic pattern of ATC. We revealed that SCC exhibited molecular genetic characteristics distinct from ATC. Although the fifth WHO categorizes them together, this study may provide strong molecular genetic evidence for the next edition of WHO classification that may allow for the separation of thyroid SCC from ATC.

Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer

BackgroundBladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standard treatments such as surgery often fail to significantly improve patient prognosis. Our research aims to predict prognosis and identify precise therapeutic targets for novel treatment interventions.MethodsWe collected and screened genes related to the TGF-β signaling pathway and performed unsupervised clustering analysis on TCGA-BLCA samples based on these genes. Our analysis revealed two novel subtypes of bladder cancer with completely different biological characteristics, including immune microenvironment, drug sensitivity, and more. Using machine learning classifiers, we identified SMAD6 as a hub gene contributing to these differences and further investigated the role of SMAD6 in bladder cancer in the single-cell transcriptome data. Additionally, we analyzed the relationship between SMAD6 and immune checkpoint genes. Finally, we performed a series of in vitro assays to verify the function of SMAD6 in bladder cancer cell lines.ResultsWe have revealed two novel subtypes of bladder cancer, among which C1 exhibits a worse prognosis, lower drug sensitivity, a more complex tumor microenvironment, and a ‘colder’ immune microenvironment compared to C2. We identified SMAD6 as a key gene responsible for the differences and further explored its impact on the molecular characteristics of bladder cancer. Through in vitro experiments, we found that SMAD6 promoted the prognosis of BCa patients by inhibiting the proliferation and migration of BCa cells.ConclusionOur study reveals two novel subtypes of BCa and identifies SMAD6 as a highly promising therapeutic target.

Single-cell RNA-seqreveals novel interaction between muscle satellite cells and fibro-adipogenic progenitors mediated with FGF7 signalling.

Muscle satellite cells (MuSCs) exert essential roles in skeletal muscle adaptation to growth, injury and ageing, and their functions are extensively modulated by microenvironmental factors. However, the current knowledge about the interaction of MuSCs with niche cells is quite limited. A 10× single-cell RNA sequencing (scRNA-seq) was performed on porcine longissimus dorsi and soleus (SOL) muscles to generate a single-cell transcriptomic dataset of myogenic cells and other cell types. Sophisticated bioinformatic analyses, including unsupervised clustering analysis, marker gene, gene set variation analysis (GSVA), AUCell, pseudotime analysis and RNA velocity analysis, were performed to explore the heterogeneity of myogenic cells. CellChat analysis was used to demonstrate cell-cell communications across myogenic cell subpopulations and niche cells, especially fibro-adipogenic progenitors (FAPs). Integrated analysis with human and mice datasets was performed to verify the expression of FGF7 across diverse species. The role of FGF7 on MuSC proliferation was evaluated through administering recombinant FGF7 to porcine MuSCs, C2C12, cardiotoxin (CTX)-injured muscle and d-galactose ( d-gal)-induced ageing model. ScRNA-seq totally figured out five cell types including myo-lineage cells and FAPs, and myo-lineage cells were further classified into six subpopulations, termed as RCN3+, S100A4+, ID3+, cycling (MKI67+), MYF6+ and MYMK+ satellite cells, respectively. There was a higher proportion of cycling and MYF6+ cells in the SOL population. CellChat analysis uncovered a particular impact of FAPs on myogenic cells mediated by FGF7, which was relatively highly expressed in SOL samples. Administration of FGF7 (10ng/mL) significantly increased the proportion of EdU+ porcine MuSCs and C2C12 by 4.03±0.81% (P<0.01) and 6.87±2.17% (P<0.05), respectively, and knockdown of FGFR2 dramatically abolished the pro-proliferating effects (P<0.05). In CTX-injured muscle, FGF7 significantly increased the ratio of EdU+/Pax7+ cells by 15.68±5.45% (P<0.05) and elevated the number of eMyHC+ regenerating myofibres by 19.7±4.25% (P<0.01). Under d-gal stimuli, FGF7 significantly reduced γH2AX+ cells by 17.19±3.05% (P<0.01) in porcine MuSCs, induced EdU+ cells by 4.34±1.54% (P<0.05) in C2C12, and restored myofibre size loss and running exhaustion in vivo (all P<0.05). Our scRNA-seq reveals a novel interaction between muscle FAPs and satellite cells mediated by FGF7-FGFR2. Exogenous FGF7 augments the proliferation of satellite cells and thus benefits muscle regeneration and counteracts age-related myopathy.

Chemokine‑ and chemokine receptor-based subtypes predict prognosis, immunotherapy and chemotherapy response in colorectal cancer patients

BackgroundThe clinical significance and comprehensive characteristics of chemokines and chemokine receptors in colorectal cancer (CRC) have not been previously reported. Our study aims to investigate the expression profiles of chemokines and chemokine receptors, as well as establish subtypes in CRC. Methods1009 CRC samples were enrolled in our study. Consensus unsupervised clustering analysis was conducted to establish subtypes, and a risk score model was developed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. 36 pairs of tissue specimens of CRC patients and two CRC cell lines were used to validate the subtypes and risk score in vitro. Quantitative real-time PCR and western blotting were employed to validate mRNA and protein expression levels, respectively. Flow cytometry was utilized for analyzing cell apoptosis, while cell viability assay and EdU assay were conducted to assess cell proliferation ability. ResultsThe Cluster B group shares similarities with the low-risk group in terms of exhibiting a higher level of immune cell infiltration and belonging to hot tumor. Patients CRC in the Cluster B group demonstrate a more favorable prognosis and exhibit better response to immunotherapy and chemotherapy. On the other hand, the Cluster A group resembles the high-risk group as it displays lower levels of immune cell infiltration, indicating a cold tumor phenotype. CRC patients in the Cluster A group have poorer prognoses and show less therapeutic efficacy towards immunotherapy and chemotherapy. Furthermore, we utilized a total of 36 pairs of tissue samples obtained from patients with CRC, along with two CRC cell lines for validation in vitro. This comprehensive approach further enhances the scientific validity and reliability of the identified subtypes and risk score in their ability to predict prognosis, response to immunotherapy, and response to chemotherapy among CRC patients. ConclusionWe first established robust prognostic subtypes based on chemokines and chemokine receptors, which could potentially serve as a novel biomarker for guiding individualized treatment in patients with CRC undergoing immunotherapy and chemotherapy.

Abstract PO3-28-08: Triple Hormone Receptor Signatures as Novel Prognostic Markers in Breast Cancer

Abstract Background Tumor phenotype across various cancers, including breast cancer, is predominantly shaped by a synergy between genomic alterations and the cell-of-origin from which the tumor emerges. These elements collectively influence crucial aspects like tumor aggressiveness, treatment response, and patient prognosis. While existing research highlights that the cell-of-origin often leaves a lasting imprint on the molecular architecture of tumors, leveraging this information for actionable clinical insights has proven challenging. Specifically, in the realm of breast cancer, utilizing cellular ancestry signatures could be a key factor in tailoring effective therapeutic strategies and improving patient outcomes. Methods In this study, we developed a unique signature anchored in the expression levels of triple hormone receptors (THR)—namely androgen (AR), estrogen (ER), and vitamin D (VDR)—in normal breast cells. Building on this, we formulated two distinct mRNA markers, THR-50 and THR-70, aimed at categorizing breast tumors based on their THR expression profiles. These markers were rigorously validated across 65 independent breast cancer studies, involving a total of 6,679 patients, utilizing Kaplan-Meier survival curves, Cox proportional hazards models, and unsupervised clustering analyses. Results Our findings indicate that both THR-50 and THR-70 are robustly associated with overall survival and recurrence-free survival in breast cancer patients across all datasets evaluated. Importantly, these THR signatures demonstrate broad applicability across various breast cancer subtypes, grades, and treatment phases—unlike conventional prognostic markers that tend to be subtype-specific. Additionally, the THR signatures have revealed four unique patient clusters with divergent survival outcomes, three of which are ER-positive and one ER-negative. When augmented with an immune-based signature (i20), THR-70 identifies a specific ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive subtypes, as well as another ER-negative subgroup characterized by exceedingly poor survival outcomes. Conclusion The THR-based cellular ancestry signatures open a new avenue in understanding the intricacies of breast cancer biology. These signatures offer a robust and stable framework for developing other prognostic markers, thus providing an enhanced stratification of existing breast cancer categories as well as creating new classifications. Citation Format: Mohamed Omar, Chuck Harrell, Rulla Tamimi, Luigi Marchionni, Tan Ince. Triple Hormone Receptor Signatures as Novel Prognostic Markers in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-28-08.

Comprehensive Gene Analysis Reveals Cuproptosis-Related Gene Signature Associated with M2 Macrophage in Staphylococcus aureus-Infected Osteomyelitis.

Osteomyelitis is a challenging disease in the field of bone infections, with its immune and molecular regulatory mechanisms still poorly understood. The aim of this study is to explore the value and potential mechanisms of cuproptosis-related genes (CRGs) in Staphylococcus aureus (S. aureus)-infected osteomyelitis from an immunological perspective. Initially, three transcriptomic datasets from public databases were integrated and analyzed, and consistent expression of CRGs in S. aureus-infected osteomyelitis was identified. Subsequently, immune infiltration analysis was performed, and M2 macrophage-related CRGs (M2R-CRGs) were further identified. Their potential molecular mechanisms were evaluated using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Finally, distinct osteomyelitis subtypes and diagnostic models based on characteristic M2R-CRGs were constructed. Through correlation analysis with immune cell infiltration, three characteristic M2R-CRGs (SLC31A1, DLD, and MTF1) were identified. Further analysis using unsupervised clustering and immune microenvironment analysis indicated that cluster 1 might activate pro-inflammatory responses, while cluster 2 was shown to exhibit anti-inflammatory effects in osteomyelitis. Compared to Cluster A, Cluster B demonstrated higher levels and a greater diversity of immune cell infiltrations in CRG-related molecular patterns, suggesting a potential anti-inflammatory role in osteomyelitis. A diagnostic model for S. aureus-infected osteomyelitis, based on the three M2R-CRGs, was constructed, exhibiting excellent diagnostic performance and validated with an independent dataset. Significant upregulation in mRNA and protein expression levels of the three M2R-CRGs was observed in rat models of S. aureus-infected osteomyelitis, aligning with bioinformatic results. The M2R-CRGs (SLC31A1, DLD, and MTF1) may be considered characteristic genes for early diagnosis and personalized immune therapy in patients with S. aureus-infected osteomyelitis.

Radially polarized light in single particle optical extinction microscopy identifies silver nanoplates

Quantifying the optical extinction cross section of a single plasmonic nanoparticle (NP) has recently emerged as a powerful method to characterize the NP morphometry, i.e., size and shape, with a precision comparable to electron microscopy while using a simple optical microscope. Here, we enhance the capabilities of extinction microscopy by introducing a high numerical aperture annular illumination coupled with a radial polarizer to generate a strong axial polarization component. This enables us to probe the NP response to axial polarized light, and, in turn, to distinguish flat-lying nanoplates from other geometries. Polarization-resolved optical extinction cross sections were acquired on 219 individual colloidal silver NPs of a nominally triangular nanoplate shape but, in practice, exhibiting heterogeneous morphometries, including decahedrons and non-plate spheroids. An unsupervised machine learning cluster analysis algorithm was developed, which allowed us to separate NPs into different groups, owing to the measured differences in cross sections. Comparison of the measurements with a computational model of the absorption and scattering cross section accounting for nanoplates of varying geometries beyond simple triangles provided insight into the NP shape of each group. The results provide a significant improvement of polarization-resolved optical extinction microscopy to reconstruct NP shapes, further boosting the utility of the method as an alternative to electron microscopy analysis.

Crosstalk of disulfidptosis-related subtypes identifying a prognostic signature to improve prognosis and immunotherapy responses of clear cell renal cell carcinoma patients

BackgroundDisulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC.MethodsThe expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort.ResultsWe identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort.ConclusionThe DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing.

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

RNA methylation patterns, immune characteristics, and autophagy-related mechanisms mediated by N6-methyladenosine (m6A) regulatory factors in venous thromboembolism

Recent studies have found a link between deep vein thrombosis and inflammatory reactions. N6-methyladenosine (m6A), a crucial element in immunological regulation, is believed to contribute to the pathophysiology of venous thromboembolism (VTE). However, how the m6A-modified immune microenvironment is involved in VTE remains unclear. In the present study, we identified a relationship between VTE and the expression of several m6A regulatory elements by analyzing peripheral blood samples from 177 patients with VTE and 88 healthy controls from public GEO databases GSE19151 and GSE48000. We used machine learning to identify essential genes and constructed a diagnostic model for VTE using multivariate logistic regression. Unsupervised cluster analysis revealed a marked difference between m6A modification patterns in terms of immune cell infiltration, inflammatory reactivity, and autophagy. We identified two m6A-related autophagy genes (i.e., CHMP2B and SIRT1) and the crucial m6A regulator YTHDF3 using bioinformatics. We also examined two potential mechanisms through which YTHDF3 may affect VTE. m6A modification, immunity, and autophagy are closely linked in VTE, offering novel mechanistic and therapeutic insights.

Multi-omics characterization of esophageal squamous cell carcinoma identifies molecular subtypes and therapeutic targets.

Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal cancer and is characterized by an unfavorable prognosis. To elucidate the distinct molecular alterations in ESCC and investigate therapeutic targets, we performed a comprehensive analysis of transcriptomic, proteomic, and phosphoproteomic data derived from 60 paired treatment-naive ESCC and adjacent non-tumor tissue samples. Additionally, we conducted a correlation analysis to describe the regulatory relationship between transcriptomic and proteomic processes, revealing alterations in key metabolic pathways. Unsupervised clustering analysis of the proteomic data stratified ESCC patients into three subtypes with different molecular characteristics and clinical outcomes. Notably, subtype III exhibited the worst prognosis and enrichment in proteins associated with malignant processes, including glycolysis and DNA repair pathways. Furthermore, translocase of inner mitochondrial membrane domain containing 1 (TIMMDC1) was validated as a potential prognostic molecule for ESCC. Moreover, integrated kinase-substrate network analysis using the phosphoproteome nominated candidate kinases as potential targets. In vitro and in vivo experiments further confirmed casein kinase II subunit alpha (CSNK2A1) as a potential kinase target for ESCC. These underlying data represent a valuable resource for researchers, which may provide better insights into the biology and treatment of ESCC.

Longitudinal study of patients with anti-SAE antibody positive dermatomyositis: a multicenter cohort study in China.

To describe the longitudinal study and long-term prognosis of multicentre large inception cohort of patients with anti-SAE positive DM. We retrospectively recruited patients with anti-SAE+DM in four tertiary referral centers from China between March 2005 and December 2022. Long-term survival analysis was performed in the enrolled patients. The Myositis Damage Index (MDI) and Cutaneous Disease Area and Severity Index (CDASI) were used to evaluate the degree of different organ damage and the extent of skin rashes. Longitudinal computed tomographic (CT) patterns were analyzed. Phenotypes were characterized using unsupervised cluster analysis. All-cause death occurred in 10.5% (4/38) of all patients, in which three patients succumbed to malignancies at 13, 18, and 36 months. Most patients had favorable long-term outcomes, 35.3% of them were in drug-free remission. Skin rashes showed significant improvement evaluated by CDASI with time. However, damage to different systems was observed in 70.6% of the surviving patients using the MDI, which mainly consisted in skin damage, accounting for 47.1%. Nine patients with anti-SAE+DM associated interstitial lung disease (ILD) underwent repeat CT showed marked radiological improvement at 6 months or being stable after 12 months. In further, different characteristics and outcomes were also showed in three clusters identified by unsupervised analysis. Anti-SAE+DM is characterized with lower mortality rate and the development of malignancies being the primary cause of death. Patients who survived showed notable cutaneous damage, while the ILD tends to stabilize. Clusters identified with unsupervised analysis could assist physicians in identifying higher risk of mortality.

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

BackgroundRheumatoid 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. MethodsIn 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. ResultsThe 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. ConclusionThis 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.

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification

ObjectivesThis study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images. MethodsThis retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase. Those features which showed high stability, non-redundancy, and indicative information were selected. An unsupervised consensus clustering analysis on these features was adopted to identify subgroups of CRLM patients. Overall survival (OS), disease-free survival (DFS), and liver-specific DFS were compared between the identified subgroups. Cox regression analysis was applied to evaluate prognostic risk factors. ResultsA total of 851 radiomics features were extracted, and 56 robust features were finally selected for unsupervised clustering analysis which identified two distinct subgroups (96 and 101 patients respectively). There were significant differences in the OS, DFS, and liver-specific DFS between the subgroups (all log-rank p < 0.05). The subgroup with worse outcome using the proposed radiomics model was consistently associated with shorter OS, DFS, and liver-specific DFS, with hazard ratios of 1.78 (95 %CI: 1.12–2.83), 1.72 (95 %CI: 1.16–2.54), and 1.59 (95 %CI: 1.10–2.31), respectively. The general performance of this radiomics model outperformed the traditional Clinical Risk Score and Tumor Burden Score in the prognosis prediction after surgery for CRLM. ConclusionRadiomics features derived from preoperative CT images can reveal the heterogeneity of CRLM and stratify the patients with CRLM into subgroups with significantly different clinical outcomes.

Development and validation of a pyroptosis-related prognostic signature associated with osteosarcoma metastasis and immune infiltration.

Pyroptosis is a programmed cell death, which has garnered increasing attention because it relates to the immune and therapy response. However, few studies focus on the application of pyroptosis-related genes (PRGs) in predicting osteosarcoma (OS) patients' prognoses. In this study, the gene expression and clinical information of OS patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Based on these PRGs and unsupervised clustering analysis, all OS samples can be classified into 2 clusters. The 8 key differential expressions for PRGs (LAG3, ITGAM, CCL2, TLR4, IL2RA, PTPRC, FCGR2B, and CD5) were established through the univariate Cox regression and utilized to calculate the risk score of all samples. According to the 8-gene signature, OS samples can be divided into high and low-risk groups and correlation analysis can be performed using immune cell infiltration and immune checkpoints. Finally, we developed a nomogram to improve the PRG-predictive model in clinical application. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low and high-risk groups. A nomogram was developed with clinical indicators and the risk scores were effective in predicting the prognosis of patients with OS. In this study, a prognostic model was constructed based on 8 PRGs were proved to be independent prognostic factors of OS and associated with tumor immune microenvironment. These 8 prognostic genes were involved in OS development and may serve as new targets for developing therapeutic drugs.