Non-negative Matrix Factorization Clustering Research Articles

Characterization of PANoptosis-related genes in Crohn's disease by integrated bioinformatics, machine learning and experiments.

Currently, the biological understanding of Crohn's disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to participate in numerous diseases, including CD. In our study, we aimed to uncover the roles of PANoptosis in CD. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified by overlapping PANoptosis-related genes and differentially expressed genes between CD and normal samples in a combined microarray dataset. Three machine learning algorithms were adopted to detect hub DE-PRGs. To stratify the heterogeneity within CD patients, nonnegative matrix factorization clustering was conducted. In terms of immune landscape analysis, the "ssGSEA" method was applied. qRT-PCR was performed to examine the expression levels of the hub DE-PRGs in CD patients and colitis model mice. Ten hub DE-PRGs with satisfactory diagnostic performance were identified and validated: CD44, CIDEC, NDRG1, NUMA1, PEA15, RAG1, S100A8, S100A9, TIMP1 and XBP1. These genes displayed significant associations with certain immune cell types and CD-related genes. We also constructed gene‒microRNA, gene‒transcription factor and drug‒gene interaction networks. CD samples were classified into two PANoptosis patterns according to the expression levels of the hub DE-PRGs. Our results suggest that PANoptosis plays a nonnegligible role in CD by modulating the immune system and interacting with CD-related genes.

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells

BackgroundGlycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC).MethodsData on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays.ResultsA total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model’s prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation.ConclusionThe GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.

Research on heart and lung sound separation method based on DAE–NMF–VMD

Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors. Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on deep autoencoder (DAE), nonnegative matrix factorization (NMF) and variational mode decomposition (VMD). Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, variational mode decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to contrast methods.

Non-apoptotic regulatory cell death scoring system to predict the clinical outcome and drug choices in breast cancer

BackgroundBreast cancer (BC), the most common cancer among women globally, has been shown by numerous studies to significantly involve non-apoptotic regulatory cell death (RCD) in its pathogenesis and progression. MethodsWe obtained the RNA sequences and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database for the training set, while datasets GSE96058, GSE86166, and GSE20685 from The Gene Expression Omnibus (GEO) database were utilized as validation cohorts. Initially, we performed non-negative matrix factorization (NMF) clustering analysis on the BC samples from the TCGA database to discern non-apoptotic RCD-related molecular subtypes. To identify prognostically-relevant non-apoptotic RCD genes (NRGs) and construct a prognostic model, we implemented three machine learning algorithms: lasso regression, random forest, and XGBoost analysis. The expression of selected genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR), single-cell RNA-sequencing (scRNA-seq) analysis, and The Human Protein Atlas (HPA) database. The risk signature was evaluated concerning clinical characteristics and drug sensitivity. Furthermore, we developed a nomogram to predict BC patient survival. ResultsThe NMF method successfully compartmentalized patients from the TCGA database into three distinct non-apoptotic RCD-related subtypes, with significant variations observed in immune characteristics and prognostic stratification across these subtypes. We identified 5 differentially expressed NRGs used in establishing the risk signature. Patients with different risk groups exhibited distinct clinicopathological features, drug sensitivity, and prognostic outcomes. A nomogram was subsequently developed, incorporating the NRGs-related risk signature, age, T stage, and N stage, to aid clinical decision-making. ConclusionWe identified a novel NRGs-related risk signature, which was expected to become a potential prognostic marker in BC.

Comprehensive genetic profiling reveals frequent alterations of driver genes on the X chromosome in extranodal NK/T-cell lymphoma.

Extranodal NK/T-cell lymphoma (ENKTCL) is an Epstein-Barr virus (EBV)-related neoplasm with male dominance and a poor prognosis. A better understanding of the genetic alterations and their functional roles in ENKTCL could help improve patient stratification and treatments. Here, we performed comprehensive genetic analysis of 177 ENKTCL cases to delineate the landscape of mutations, copy number alterations (CNAs), and structural variations, identifying 34 driver genes including six previously unappreciated ones, namely HLA-B, HLA-C, ROBO1, CD58, POT1, and MAP2K1. Among them, CD274 (24%) was the most frequently altered, followed by TP53 (20%), CDKN2A (19%), ARID1A (15%), HLA-A (15%), BCOR (14%), and MSN (14%). Chromosome X (chrX) losses were the most common arm-level CNAs in females (~40%), and alterations of four X-linked driver genes (MSN, BCOR, DDX3X, and KDM6A) were more frequent in males and females harboring chrX losses. Among X-linked drivers, MSN was the most recurrently altered, and its expression was lost in approximately one-third of cases using immunohistochemical analysis. Functional studies of human cell lines demonstrated that MSN disruption promoted cell proliferation and NF-κB activation. Moreover, MSN inactivation increased sensitivity to NF-κB inhibition in vitro and in vivo. In addition, recurrent deletions were observed at the origin of replication in the EBV genome (6%). Finally, by integrating the 34 drivers and 19 significant arm-level CNAs, non-negative matrix factorization and consensus clustering identified two molecular groups with different genetic features and prognosis irrespective of clinical prognostic factors. Together, these findings could help improve diagnostic and therapeutic strategies in ENKTCL.

The Molecular Characteristics and Therapeutic Implications of O-Glycan Synthesis in Pancreatic Cancer by Integrating Transcriptome and Single-Cell Data.

Glycans constitute the primary components of proteins that regulate key carcinogenic processes in cancer progression. This study investigated the significance of O-glycan synthesis in the pathogenesis, outcome, and therapy of pancreatic cancer (PC). Transcriptomic data and clinical prognostic information of PC were acquired via TCGA and GEO databases. CSA database was used to obtain single-cell data of PC. The O-glycan biosynthesis signaling pathway and its related genes were acquired via the MSigDB platform. The nonnegative matrix factorization (NMF) clustering was utilized to construct the O-glycan biosynthesis-associated molecular subtypes in PC. The LASSO and Cox regression were utilized to build the prognostic prediction model. We utilized real-time quantitative PCR (qRT-PCR) to verify the expressed levels of model genes. Single-cell analysis was utilized to investigate the levels of target genes and O-glycan biosynthesis signaling pathway in the PC tumour microenvironment. We obtained 30 genes related to O-glycan biosynthesis, among which 15 were associated with the prognosis of PC. All PC samples were grouped into two distinct molecular subtypes associated with O-glycan biosynthesis: OGRGcluster C1 and OGRGcluster C2, and compared to OGRGcluster C1. PCs in OGRGcluster C2 had a more advanced clinical stage and pathological grade, worse prognosis, and more active O-glycan biosynthesis function. Immune analysis indicated that naïve B cell, CD8+ T cell, memory-activated CD4+ T cell, and monocytes displayed remarkably higher infiltration levels in OGRGcluster C1 while resting NK cell, macrophages M0, resting dendritic cell, activated dendritic cell, and neutrophils exhibited markedly higher infiltration levels in OGRGcluster C2. OGRGcluster C1 exhibited higher sensitivities to drugs, such as cisplatin, irinotecan, KRAS(G12C) inhibitor-12, oxaliplatin, paclitaxel, and sorafenib. Besides, we built the O-glycan biosynthesis-related prognostic model (including SPRR1B, COL17A1, and ECT2) with a good prediction performance. SPRR1B, COL17A1, and ECT2 were remarkably highly expressed in PC tissues and linked to a poor outcome. Single-cell analysis revealed that Oglycan biosynthesis was observed only in PC, and consistent with this, the target genes were significantly enriched in PC. We first constructed molecular subtypes and prognostic models related to O-glycan biosynthesis in PC. It is clear that O-glycan biosynthesis is related to the development, prognosis, immune microenvironment, and treatment of PC. This provides new strategies for stratification, diagnosis, and treatment of PC patients.

The equivalences of community detection methods for bipartite networks

Abstract Investigating the community structures of bipartite networks is a frequent topic of discussion in the ecological and social fields. The most widely used methods, as proposed by numerous academics from varying perspectives, include spectral graph partitioning, modularity, nonnegative matrix factorization, and stochastic block model. In this paper, we demonstrate three equivalences among these four methods. One, both Dhillon spectral graph partitioning and Barber modularity clustering are equivalent to solving for the matrix's left and right singular vectors after relaxing the discrete constraints. Two, the nonnegative matrix factorization clustering is equivalent to the Dhillon spectral graph partitioning. Three, The bipartite stochastic block model is equivalent to the constraint-based NMF that uses K-L divergence as its cost function. These equivalences, obtained through rigorous mathematical derivations, will aid in the future development of efficient algorithms for community detection in bipartite networks.

Abstract 2758: Stemness subtype gastric cancer accelerate hematogenous metastasis

Abstract Oncogenic progression and metastasis correlate with the acquisition of stem cell-like characteristics and the loss of differentiated phenotype. Poorly differentiated primary tumors often lead to unfavorable oncologic outcomes due to a heightened likelihood of metastasis to distant organs across various cancers. However, the relationship between stemness traits and metastasis in gastric cancer (GC) remains insufficiently explored. In this investigation, we analyzed bulk RNA-sequencing from microdissected primary tumor specimens and clinical data to pinpoint molecular subtypes linked to distant organ metastasis. Transcriptomic data from patient-derived xenograft (PDX) samples and available single-cell RNA datasets validated our bulk RNAseq findings. Comprehensive analysis highlighted the molecular traits of GC tied to hematogenous metastasis. Microdissection of primary tumor FFPE samples facilitated bulk RNA sequencing, encompassing samples from 33 metastatic and 31 metastasis-free patients, with 8 paired normal samples. Most samples contained less than 10% of tumor microenvironment (TME) components like fibroblasts, endothelial cells, and immune cells. Using non-negative matrix factorization (NMF) clustering, we achieved unsupervised subtyping of the gastric cancer gene expression matrix. Permutation tests based on subsampling indicated consistent high-level cophenetic coefficients at k=2, suggesting robust bifurcation of transcriptomic data into two clusters. One showed augmented stemness traits while the other emphasized differentiated mucosal genes, labeled as stemness and differentiated subtypes, respectively. GSEA highlighted a significant boost in the Hallmark angiogenesis score in the stemness subtype, not attributed to endothelial cell infiltration as verified by CIBERSORTx. This subtype also exhibited a marked decrease in hematogenous metastasis-free survival (HMFS) (p=0.008, log-rank test). Further, PDX samples were scrutinized. Post classification of the PDX graft expression matrix via logistic regression, we examined the endothelial cell fraction, determined by CIBERSORTx. Stemness subtype PDX grafts revealed a significant elevation in the host (mouse) endothelial cell fraction (p<0.05). In addition, stemness PDX patient showed significant decreased HMFS (p = 0.03). We also categorized the pseudobulk expression matrix of malignant cells into stemness or differentiated subtypes using GSE183904 scRNA cohort. scRNA sample analysis consistently presented an elevated endothelial cell fraction in the stemness GC subtype. The outcomes suggest a propensity for angiogenesis in the TME of the stemness subtype of GC. In conclusion, our findings demonstrate that the stemness subtype of GC accelerates hematogenous metastasis, as evidenced through clinical data, bulk RNAseq, PDX, and scRNAseq data. Citation Format: Seungho Lee, Jaeun Yoo, Seungbok Lee, Hyun Myong Kim, Kyoungyun Jeong, Yie-Ri Yoo, Ji-Yeon Shin, Kyoung Un Park, Hye Seung Lee, Seong-Ho Kong, Do Joong Park, Hyuk-Joon Lee, Han-Kwang Yang. Stemness subtype gastric cancer accelerate hematogenous metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2758.

An analysis of the effect of motor experience on muscle synergy in the badminton jump smash

The jump smash is badminton's most aggressive technical manoeuvre, which is often the key to winning a match. This paper aims to explore the neuromuscular control strategies of advanced and beginner players when jumping smash in different ways. Collecting sEMG and kinematic data from 18 subjects with different motor experiences when jumping smash. Nonnegative Matrix Factorization and K-Means clustering were used to extract muscle synergies and exclude irrelevant combined synergies. Uncontrolled manifold analysis was then used to explore the association between synergies and shoulder stability. In addition, motor output at the spinal cord level was assessed by mapping sEMG to each spinal cord segment. The study found that advanced subjects could respond to different jump smash styles by adjusting the coordinated activation strategies of the upper-limb and postural muscles. Long-term training can induce a rapid decrease in the degree of co-variation of the synergies before contact with a shuttlecock to better cope with an upcoming collision. It is recommended that beginners should focus more on training the coordination of upper-limb muscles and postural muscles.

Analysis and experimental validation of necroptosis-related molecular classification, immune signature and feature genes in Alzheimer's disease.

Necroptosis, a programmed cell death pathway, has been demonstrated to be activated in Alzheimer's disease (AD). However,the precise role of necroptosis and its correlation with immune cell infiltration in AD remains unclear. In this study, we conducted non-negative matrix factorization clustering analysis to identify three subtypes of AD based on necroptosis-relevant genes. Notably, these subtypes exhibited varying necroptosis scores, clinical characteristics and immune infiltration signatures. Cluster B, characterized by high necroptosis scores, showed higher immune cell infiltration and was associated with a more severe pathology, potentially representing a high-risk subgroup. To identify potential biomarkers for AD within cluster B, we employed two machine learning algorithms: the least absolute shrinkage and selection operator regression and Random Forest. Subsequently, we identified eight feature genes (CARTPT, KLHL35, NRN1, NT5DC3, PCYOX1L, RHOQ, SLC6A12, and SLC38A2) that were utilized to develop a diagnosis model with remarkable predictive capacity for AD. Moreover, we conducted validation using bulk RNA-seq, single-nucleus RNA-seq, and in vivo experiments to confirm the expression of these feature genes. In summary, our study identified a novel necroptosis-related subtype of AD and eight diagnostic biomarkers, explored the roles of necroptosis in AD progression and shed new light for the clinical diagnosis and treatment of this disease.

Immunogenic Cell Death-related Signature Evaluates the Tumor Microenvironment and Predicts the Prognosis in Diffuse Large B-Cell Lymphoma.

Current literatures suggest a growing body of evidence highlighting the pivotal role of Immunogenic Cell Death (ICD) in multiple tumor types. Nevertheless, the potential and mechanisms of ICD in diffuse large B-cell lymphoma (DLBCL) remain inadequately studied. To address this gap, our current study aims to examine the impact of ICD on DLBCL and identify a corresponding gene signature in DLBC. Using the expression profiles of ICD-associated genes, the gene expression omnibus (GEO) samples were segregated into ICD-high and ICD-lowsubtypes utilizing non-negative matrix factorization clustering. Next, univariate and LASSO Cox regression analyses were employed to establish the ICD-related gene signature. Subsequently, the CIBERSORT tool, ssGSEA, and ESTIMATE algorithm were utilized to examine the association between the signature and tumor immune microenvironment of DLBC. Finally, the oncoPredict algorithm was implemented to evaluate the drug sensitivity prediction of DLBCL patients. These findings suggest that the immune microenvironment of the ICD-high group with a poor prognosis was significantly suppressed. An 8-gene ICD-related signature was identified and validated to prognosticate and evaluate the tumor immune microenvironment in DLBCL. Similarly, the high-risk group exhibited a worse prognosis compared to the low-risk group, and the immune function was considerably suppressed. Moreover, the results of oncoPredict algorithm indicated that patients in the high-risk group exhibited higher sensitivity to Cisplatin, Cytarabine, Epirubicin, Oxaliplatin, and Vincristine with low IC50. In conclusion, the present study provides novel insights into the role of ICD in DLBCL by identifying a new biomarker for the disease and may have implications for the development of immune-targeted therapies for the tumor.

Hypoxia-derived molecular subtype and gene signature characterize prognoses and therapeutic responses in head and neck squamous cell carcinoma.

Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. This study aims to investigate the role of hypoxia-related genes (HRG) in the immune landscape, treatment response, and prognosis of head and neck squamous cell carcinoma (HNSCC). The transcriptome and clinical data of HNSCC were downloaded from TCGA and GEO databases, and HNSCC molecular subtypes were identified using non-negative matrix factorization (NMF) clustering. Prognostic models were constructed using univariate, Lasso, and multivariate Cox regression analyses. The relationship between HRGs and immune cell infiltration, immune therapy response, and drug sensitivity was evaluated, and a nomogram was constructed. 47 HRGs were differentially expressed in HNSCC, among which 10 genes were significantly associated with HNSCC prognosis. Based on these 10 genes, 2 HNSCC molecular subtypes were identified, which showed significant heterogeneity in terms of prognosis, immune infiltration, and treatment response. A total of 3280 differentially expressed genes were identified between the subtypes. After univariate, Lasso, and multivariate Cox regression analysis, 18 genes were selected to construct a novel prognostic model, which showed a significant correlation with B cells, T cells, and macrophages. Using this model, HNSCC was classified into high-risk and low-risk groups, which exhibited significant differences in terms of prognosis, immune cell infiltration, immune therapy response, and drug sensitivity. Finally, a nomogram based on this model and radiotherapy was constructed, which showed good performance in predicting HNSCC prognosis and guiding personalized treatment strategies. The decision curve analysis demonstrated its better clinical applicability compared to other strategies. HRGs can identify 2 HNSCC molecular subtypes with significant heterogeneity, and the HRG-derived risk model has the potential for prognostic prediction and guiding personalized treatment strategies.

A novel anoikis-related gene signature identifies LYPD1 as a novel therapy target for bladder cancer

Bladder cancer (BLCA) is a malignant tumor associated with unfavorable outcomes. Studies suggest that anoikis plays a crucial role in tumor progression and cancer cell metastasis. However, its specific role in bladder cancer remains poorly understood. Our objective was to identify anoikis-related genes (ARGs) and subsequently construct a risk model to assess their potential for predicting the prognosis of bladder cancer.The transcriptome data and clinical data of BLCA patients were sourced from The Cancer Genome Atlas and GEO database. We then performed the differential expression analysis to screen differentially expressed ARGs. Subsequently, we conducted non-negative matrix factorization (NMF) clustering analysis to establish molecular subtypes based on the differentially expressed ARGs. The CIBERSORT algorithm was used to estimate the quantification of different cell infiltration in BLCA tumor microenviroment. A prognostic risk model containing 7 ARGs was established using Lasso-Cox regression analysis. The nomogram was built for predicting the survival probability of BLCA patients. To determine the drug sensitivity of each sample from the high- and low-risk groups, the R package “pRRophetic” was performed. Finally, the role of LYPD1 was explored in BLCA cell lines.We identified 90 differential expression ARGs and NMF clustering categorizated the BLCA patientss into two distinct groups (cluster A and B). Patients in cluster A had a better prognosis than those in cluster B. Then, we established a ARGs risk model including CALR, FASN, FOSL1, JUN, LYPD1, MST1R, and SATB1, which was validated in the train and test set. The results suggested overall survival rate was much higher in low risk group than high risk group. The cox regression analysis, ROC curve analysis, and nomogram collectively demonstrated that the risk model served as an independent prognostic factor. The high risk group had a higher level TME scores compared to the low risk group. Furthermore, LYPD1 was low expression in BLCA cells and overexpression of LYPD1 inhibits the prolifearation, migration and invasion.In the current study, we have identified differential expression ARGs and constructed a risk model with the promise for guiding prognostic predictions and provided a therapeutic target for patients with BLCA.

Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis.

Unsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution. In this study, we investigated large-scale data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques such as non-negative matrix factorization (NMF) and hierarchical clustering with various optimization methods. We developed software scripts incorporating knowledge of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical clustering. Hierarchical clustering was performed using cross-correlation instead of conventional Euclidean distances, resulting in rotation-corrected diffractions and shift-corrected maps of major components. An experimental analysis was conducted on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix and crystalline precipitates with an average diameter of approximately 7nm, which were challenging to detect using conventional STEM techniques. Combining 4D-STEM and optimized unsupervised machine learning enables comprehensive bimodal (i.e., spatial and reciprocal) analyses of material nanostructures.

Spatial lipidomics of coronary atherosclerotic plaque development in a familial hypercholesterolemia swine model

Coronary atherosclerosis is caused by plaque build-up, with lipids playing a pivotal role in its progression. However, lipid composition and distribution within coronary atherosclerosis remain unknown. This study aims to characterize lipids and investigate differences in lipid composition across disease stages to aid in the understanding of disease progression. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to visualize lipid distributions in coronary artery sections (n = 17) from hypercholesterolemic swine. We performed histology on consecutive sections to classify the artery segments and to investigate colocalization between lipids and histological regions of interest in advanced plaque, including necrotic core and inflammatory cells. Segments were classified as healthy (n = 6), mild (n = 6), and advanced disease (n = 5) artery segments. Multivariate data analysis was employed to find differences in lipid composition between the segment types, and the lipids' spatial distribution was investigated using non-negative matrix factorization (NMF). Through this process, MALDI-MSI detected 473 lipid-related features. NMF clustering described three components in positive ionization mode: triacylglycerides (TAG), phosphatidylcholines (PC), and cholesterol species. In negative ionization mode, two components were identified: one driven by phosphatidylinositol(PI)(38:4), and one driven by ceramide-phosphoethanolamine(36:1). Multivariate data analysis showed the association between advanced disease and specific lipid signatures like PC(O-40:5) and cholesterylester(CE)(18:2). Ether-linked phospholipids and LysoPC species were found to colocalize with necrotic core, and mostly CE, ceramide, and PI species colocalized with inflammatory cells. This study, therefore, uncovers distinct lipid signatures correlated with plaque development and their colocalization with necrotic core and inflammatory cells, enhancing our understanding of coronary atherosclerosis progression.

Single-cell disulfidptosis regulator patterns guide intercellular communication of tumor microenvironment that contribute to kidney renal clear cell carcinoma progression and immunotherapy.

Disulfidptosis, an emerging type of programmed cell death, plays a pivotal role in various cancer types, notably impacting the progression of kidney renal clear cell carcinoma (KIRC) through the tumor microenvironment (TME). However, the specific involvement of disulfidptosis within the TME remains elusive. Analyzing 41,784 single cells obtained from seven samples of KIRC through single-cell RNA sequencing (scRNA-seq), this study employed nonnegative matrix factorization (NMF) to assess 24 disulfidptosis regulators. Pseudotime analysis, intercellular communication mapping, determination of transcription factor activities (TFs), and metabolic profiling of the TME subgroup in KIRC were conducted using Monocle, CellChat, SCENIC, and scMetabolism. Additionally, public cohorts were utilized to predict prognosis and immune responses within the TME subgroup of KIRC. Through NMF clustering and differential expression marker genes, fibroblasts, macrophages, monocytes, T cells, and B cells were categorized into four to six distinct subgroups. Furthermore, this investigation revealed the correlation between disulfidptosis regulatory factors and the biological traits, as well as the pseudotime trajectories of TME subgroups. Notably, disulfidptosis-mediated TME subgroups (DSTN+CD4T-C1 and FLNA+CD4T-C2) demonstrated significant prognostic value and immune responses in patients with KIRC. Multiple immunohistochemistry (mIHC) assays identified marker expression within both cell clusters. Moreover, CellChat analysis unveiled diverse and extensive interactions between disulfidptosis-mediated TME subgroups and tumor epithelial cells, highlighting the TNFSF12-TNFRSF12A ligand-receptor pair as mediators between DSTN+CD4T-C1, FLNA+CD4T-C2, and epithelial cells. Our study sheds light on the role of disulfidptosis-mediated intercellular communication in regulating the biological characteristics of the TME. These findings offer valuable insights for patients with KIRC, potentially guiding personalized immunotherapy approaches.

Integrating single-cell and bulk RNA sequencing data unveils antigen presentation and process-related CAFS and establishes a predictive signature in prostate cancer

BackgroundCancer-associated fibroblasts (CAFs) are heterogeneous and can influence the progression of prostate cancer in multiple ways; however, their capacity to present and process antigens in PRAD has not been investigated. In this study, antigen presentation and process-related CAFs (APPCAFs) were identified using bioinformatics, and the clinical implications of APPCAF-related signatures in PRAD were investigated.MethodsSMART technology was used to sequence the transcriptome of primary CAFs isolated from patients undergoing different treatments. Differential expression gene (DEG) screening was conducted. A CD4 + T-cell early activation assay was used to assess the activation degree of CD4 + T cells. The datasets of PRAD were obtained from The Cancer Genome Atlas (TCGA) database and NCBI Gene Expression Omnibus (GEO), and the list of 431 antigen presentation and process-related genes was obtained from the InnateDB database. Subsequently, APP-related CAFs were identified by nonnegative matrix factorization (NMF) based on a single-cell seq (scRNA) matrix. GSVA functional enrichment analyses were performed to depict the biological functions. A risk signature based on APPCAF-related genes (APPCAFRS) was developed by least absolute shrinkage and selection operator (LASSO) regression analysis, and the independence of the risk score as a prognostic factor was evaluated by univariate and multivariate Cox regression analyses. Furthermore, a biochemical recurrence-free survival (BCRFS)-related nomogram was established, and immune-related characteristics were assessed using the ssGSEA function. The immune treatment response in PRAD was further analyzed by the Tumor Immune Dysfunction and Exclusion (TIDE) tool. The expression levels of hub genes in APPCAFRS were verified in cell models.ResultsThere were 134 upregulated and 147 downregulated genes, totaling 281 differentially expressed genes among the primary CAFs. The functions and pathways of 147 downregulated DEGs were significantly enriched in antigen processing and presentation processes, MHC class II protein complex and transport vesicle, MHC class II protein complex binding, and intestinal immune network for IgA production. Androgen withdrawal diminished the activation effect of CAFs on T cells. NMF clustering of CAFs was performed by APPRGs, and pseudotime analysis yielded the antigen presentation and process-related CAF subtype CTSK + MRC2 + CAF-C1. CTSK + MRC2 + CAF-C1 cells exhibited ligand‒receptor connections with epithelial cells and T cells. Additionally, we found a strong association between CTSK + MRC2 + CAF-C1 cells and inflammatory CAFs. Through differential gene expression analysis of the CTSK + MRC2 + CAF-C1 and NoneAPP-CAF-C2 subgroups, 55 significant DEGs were identified, namely, APPCAFRGs. Based on the expression profiles of APPCAFRGs, we divided the TCGA-PRAD cohort into two clusters using NMF consistent cluster analysis, with the genetic coefficient serving as the evaluation index. Four APPCAFRGs, THBS2, DPT, COL5A1, and MARCKS, were used to develop a prognostic signature capable of predicting BCR occurrence in PRAD patients. Subsequently, a nomogram with stability and accuracy in predicting BCR was constructed based on Gleason grade (p = n.s.), PSA (p < 0.001), T stage (p < 0.05), and risk score (p < 0.01). The analysis of immune infiltration showed a positive correlation between the abundance of resting memory CD4 + T cells, M1 macrophages, resting dendritic cells, and the risk score. In addition, the mRNA expression levels of THBS2, DPT, COL5A1, and MARCKS in the cell models were consistent with the results of the bioinformatics analysis.ConclusionsAPPCAFRS based on four potential APPCAFRGs was developed, and their interaction with the immune microenvironment may play a crucial role in the progression to castration resistance of PRAD. This novel approach provides valuable insights into the pathogenesis of PRAD and offers unexplored targets for future research.

Development of a prognostic model based on different disulfidptosis related genes typing for kidney renal clear cell carcinoma.

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC. Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated. Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets. Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.

Whole-transcriptome defines novel glucose metabolic subtypes in colorectal cancer.

Colorectal cancer (CRC) is the most prevalent malignancy of the digestive system. Glucose metabolism plays a crucial role in CRC development. However, the heterogeneity of glucose metabolic patterns in CRC is not well characterized. Here, we classified CRC into specific glucose metabolic subtypes and identified the key regulators. 2228 carbohydrate metabolism-related genes were screened out from the GeneCards database, 202 of them were identified as prognosis genes in the TCGA database. Based on the expression patterns of the 202 genes, three metabolic subtypes were obtained by the non-negative matrix factorization clustering method. The C1 subtype had the worst survival outcome and was characterized with higher immune cell infiltration and more activation in extracellular matrix pathways than the other two subtypes. The C2 subtype was the most prevalent in CRC and was characterized by low immune cell infiltration. The C3 subtype had the smallest number of individuals and had a better prognosis, with higher levels of NRF2 and TP53 pathway expression. Secreted frizzled-related protein 2 (SFRP2) and thrombospondin-2 (THBS2) were confirmed as biomarkers for the C1 subtype. Their expression levels were elevated in high glucose condition, while their knockdown inhibited migration and invasion of HCT 116 cells. The analysis of therapeutic potential found that the C1 subtype was more sensitive to immune and PI3K-Akt pathway inhibitors than the other subtypes. To sum up, this study revealed a novel glucose-related CRC subtype, characterized by SFRP2 and THBS2, with poor prognosis but possible therapeutic benefits from immune and targeted therapies.

Bioinformatics analyses and experimental validation of the role of phagocytosis in low-grade glioma.

Phagocytosis is of vital importance in tumor immune response. The alteration of phagocytosis in low-grade glioma (LGG) has not been investigated. The mRNA, copy number variation, single nucleotide variation, and methylation levels of phagocytosis-related genes were summarized in pan-cancer. Non-negative matrix factorization clustering was utilized to identify two LGG subtypes. LASSO regression analysis was performed to construct a phagocytosis-related prognostic signature (PRPS). Immune characteristics, immunotherapy response, and targeted-drug sensitivity were further explored. The phagocytosis activity in glioma was evaluated using scRNA-seq data. Multiplex immunohistochemical (m-IHC) technology was performed to identify the tumor-infiltrating immune cells in LGG. The phagocytosis-related genes altered obviously in pan-cancer compared with corresponding normal tissues. Two LGG subtypes were obtained and the subtype with poor prognosis was combined with lower tumor purity, more active immune-related pathways, increasing infiltration of CD4+ T cells, CD8+ T cells, and natural killer (NK) cells, decreasing infiltration of macrophages, mast cells, and neutrophils, distinct pathway activity and cell death status, greater response to immunotherapy, and higher sensitivity to cyclophosphamide, erlotinib, gefitinib, lapatinib, and sorafenib. In addition, a PRPS involving 10 genes (i.e., SLC11A1, CAMK1D, PLA2G5, STAP1, ALOX15, PLCG2, SFTPD, AZU1, RAB27A, and LAMTOR2) was constructed to estimate the risk level of each LGG sample and high risk LGG patients had poor prognosis, upregulated infiltration of neutrophil, macrophage, Treg, and myeloid dendritic cell, down regulated infiltration of monocyte and NK cell, and increasing expression of large number of immune checkpoint genes. The phagocytosis activity is notably active in monocyte/macrophage. The m-IHC results confirmed increased infiltration of macrophages and neutrophils in LGG samples with high SLC11A1 expression. The molecular characteristics of phagocytosis were revealed and the PRPS laid the foundation for personalized therapy in LGG.