Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma

The characterization and validation of regulated cell death-related genes in chronic rhinosinusitis with nasal polyps

BACKGROUND

Regulated cell death (RCD), a genetically controlled process mediated by specialized molecular pathways (commonly termed programmed cell death), plays pivotal roles in diverse pathophysiological processes. However, the landscape and functional implications of RCD subtypes in chronic rhinosinusitis with nasal polyps (CRSwNP) remain poorly characterized. This study aimed to systematically investigate the involvement of RCD mechanisms in the pathogenesis and progression of CRSwNP.

METHODS

Transcriptomic datasets (GSE136825, GSE23552, GSE198950, GSE196169, GSE156285) related to CRSwNP were retrieved from the Gene Expression Omnibus (GEO) database. A comprehensive panel of 18 RCD-associated gene sets was compiled through a systematic literature review. Gene set variation analysis (GSVA) was employed to profile RCD activation patterns in CRSwNP. Integrative bioinformatics approaches including weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression were implemented to identify hub RCD-related genes and construct a cell death index (CDI). Single-cell RNA sequencing (scRNA-seq) data were analyzed to map RCD dynamics across cellular subpopulations. Clinical validation was performed using qRT-PCR quantification of key genes in nasal polyp/inferior turbinate tissues, with the concurrent assessment of symptom severity via visual analogue scale (VAS) scores.

RESULTS

GSVA revealed significant upregulation of 8 RCD subtypes in CRSwNP: apoptosis, ferroptosis, necroptosis, entotic cell death, lysosome-dependent cell death, NETosis, immunogenic cell death, and anoikis. Pathway enrichment analysis demonstrated that RCD-related differentially expressed genes were predominantly involved in epithelial-mesenchymal transition (EMT) and immune-inflammatory regulation. Furthermore, the WGCNA algorithm and LASSO analysis identified 8 key cell death genes (PTHLH, GRINA, S100A9, SCG2, HMOX1, RNF183, TYROBP, SEMA7A), which were utilized to construct the cell death-related index (CDI). In training and validation cohorts, the CDI was significantly elevated in CRSwNP compared to control and exhibited high diagnostic performance, with elevated scores correlating with enhanced immune cell infiltration. Single-cell resolution analysis uncovered cell type-specific RCD activation patterns. Clinical validation confirmed significantly higher expression of S100A9, PTHLH, and HMOX1 in eosinophilic versus non-eosinophilic polyps. Notably, expression levels of PTHLH, S100A9, HMOX1, GRINA, and TYROBP showed strong positive correlations with VAS scores.

CONCLUSIONS

Our investigation delineates an RCD activation signature in CRSwNP pathogenesis, characterized by 8 key cell death modalities and their regulatory genes. The novel CDI exhibits promising diagnostic potential, while mechanistic insights suggest RCD pathways may drive disease progression through EMT potentiation and inflammatory cascade amplification. These findings provide a framework for developing RCD-targeted therapeutic strategies in CRSwNP.

Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics

BACKGROUND

As part of the innate immune system, NK cells contribute to optimizing cancer immunotherapy strategies and are becoming a focal point in cancer research. However, limited research has been conducted to further investigate changes in NK cell subsets and their critical genes following ibrutinib treatment in CLL patients.

METHODS

Peripheral blood samples from patients clinically and pathologically diagnosed with monoclonal B-cell lymphocytosis (MBL), newly diagnosed with CLL (ND-CLL), postibrutinib-treated patients who achieved a complete response (CR) or partial response (PR), and those with Richter's syndrome (RS) were collected. Single-cell transcriptome sequencing was performed, followed by pseudotemporal analysis and functional enrichment to characterize the NK cell subsets. Mendelian randomization analysis and colocalization analysis were employed to identify key genes. Multiple algorithms were used for immune infiltration analysis, and drug sensitivity analysis was conducted to pinpoint potential therapeutic agents.

RESULTS

Three distinct NK cell subsets were identified: CD56bright_NK cells, CD56dim_NK cells, and a highly cytotoxic CLL_NK subset. The core genes of the CLL_NK subset were elucidated through Mendelian randomization and colocalization analyses. A cell subset-specific novel index (CNI) was constructed based on these core genes and was shown to be capable of predicting responses to immunotherapy. Oncopredictive algorithms and molecular docking screenings further identified semaxanib and ulixertinib as potential therapeutic candidates for CLL.

CONCLUSION

The CLL_NK subset plays a crucial role in the development and progression of CLL. The CNI, derived from its key genes, holds promise as a predictor of immune therapeutic responses, highlighting the significance of CLL_NK subset dynamics and their genetic underpinnings in CLL management.

Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, characterized by significant clinical and molecular heterogeneity, which leads to considerable variability in patient prognosis. Programmed cell death (PCD) plays a critical role in the development and progression of various cancers. A comprehensive analysis of PCD-related gene expression in DLBCL could enhance risk stratification and inform personalized treatment strategies.

METHODS

This study integrated five DLBCL datasets with 18 PCD-related gene expression profiles to identify differentially expressed genes (DEGs) associated with PCD. Patients were stratified into two subgroups (C1 and C2) using consensus clustering analysis. We further performed immune infiltration analysis, GSVA enrichment analysis, and WGCNA to uncover significant differences in the immune microenvironment and signaling pathways between the subgroups. Additionally, 12 machine learning algorithms were employed to construct predictive models for DLBCL, with performance evaluated using AUC and F-score metrics. Finally, transcriptome sequencing of the DLBCL cell line VAL and the normal human B lymphocyte cell line IM-9 was conducted to validate potential biomarkers.

RESULTS

A total of 1074 PCD-related DEGs were identified. Unsupervised clustering revealed two distinct molecular subtypes of DLBCL. The C2 subgroup exhibited upregulation of pathways involved in DNA repair, cell cycle, and energy metabolism, alongside significant downregulation of immune evasion-related pathways, indicating its classification as a high-risk group. Machine learning algorithms and transcriptome sequencing validation identified five potential biomarkers for DLBCL, including CTSB, DPYD, SCARB2, STOM, and GBP1.

CONCLUSIONS

This study identifies two distinct DLBCL subtypes based on PCD-related gene expression, with the C2 subtype characterized as high-risk due to enhanced DNA repair and cell cycle pathways. Five key biomarkers (CTSB, DPYD, SCARB2, STOM, GBP1) may improve risk stratification and understanding of DLBCL heterogeneity. These findings lay the groundwork for further exploration of DLBCL progression and potential prognostic improvements.

Machine learning and molecular subtyping reveal the impact of diverse patterns of cell death on the prognosis and treatment of hepatocellular carcinoma

Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predicting HCC prognosis and therapy response. However, the classification of HCC based on diverse PCD patterns requires further investigation. This study identified a novel molecular classification named PCD subtype (C1, C2, and C3) based on the genes associated with 19 PCD patterns, distinguished by clinical, biological functional pathways, mutations, immune characteristics, and drug sensitivity. Validated in 4 independent datasets, diverse cell death pathways were enriched in the C3 subtype, including apoptosis, pyroptosis, and autophagy, it also exhibited a highly infiltrative immunosuppressive microenvironment and demonstrated higher sensitivity to compounds such as Paclitaxel, Bortezomib, and YK-4-279, while C1 subtype was significantly enriched in cuproptosis and metabolism-related pathways, suggesting that it may be more suitable for cuproptosis-inducing agent therapy. Subsequently, utilizing the machine learning algorithms, we constructed a cell death-related index (CDRI) with 22 gene features and constructed prognostic nomograms with high predictive performance by combining CDRI with clinical features. Notably, we found that CDRI effectively predicted the response of HCC patients to therapeutic strategies, where patients with high CDRI were more suitable for sorafenib drug therapy and patients with low CDRI were more ideal for transarterial chemoembolization (TACE). In conclusion, the PCD subtype and CDRI demonstrate significant efficacy in predicting the prognosis and therapeutic outcomes for patients with HCC. These findings offer valuable insights for the development of precise, individualized treatment strategies.

Cell Function Graphics: TOGGLE delineates fate and function within individual cell types via single-cell transcriptomics

Functional RNA plays a crucial role in regulating cellular processes throughout the life cycle of a cell. Identifying functional changes at each stage, from inception to development to maturation, functional execution, and eventual death or pathological transformation, often requires systematic comparisons of functional expression across cell populations. However, because cells of the same type often exhibit similar gene expression patterns regardless of function or fate, it is challenging to distinguish the stages of cellular fate or functional states within the same cell type, which also limits our understanding of cellular memory. Cells of the same type that share structural and gene expression similarities but originate from different regions and perform slightly distinct functions often retain unique epigenetic memory signatures. Although RNA serves as a key executor of fundamental cellular functions, its high expression similarity among cells of the same type limits its ability to distinguish functional heterogeneity. To overcome this challenge, we developed TOGGLE, utilizing higher-resolution analytical methods to uncover functional diversity at the cellular level. Then we based on TOGGLE developed an innovative Graph Diffusion Functional Map, which can significantly reduce noise, thereby more clearly displaying the functional grouping of RNA and enabling the capture of more subtle functional differences in high-dimensional data. Ultimately, this method effectively removes the influence of baseline functions from classification criteria and identifies key trajectories of cell fate determination.

Identification of DYRK2 and TRIM32 as keloids programmed cell death-related biomarkers: insights from bioinformatics and machine learning in multiple cohorts

This study aims to explore the expression patterns and mechanisms of programmed cell death-related genes in keloids and identify molecular targets for early diagnosis and treatment. We first explored the expression, immune, and biological function profiles of keloids. Using various machine learning methods, two key genes, DYRK2 and TRIM32, were identified, with ROC curves demonstrating their diagnostic potential. Further analyses, including GSEA, immune cell profiling, competing endogenous RNA network, and single-cell analysis, revealed their mechanism of action and regulatory network. Finally, SB-431542 was identified as a potential therapeutic agent for keloids through CMap and molecular docking.

Unraveling the PANoptosis Landscape in Osteosarcoma: A Single-Cell Sequencing and Machine Learning Approach to Prognostic Modeling and Tumor Microenvironment Analysis

Background: Osteosarcoma (OS) is a highly aggressive bone malignancy prevalent in children and adolescents, characterized by poor prognosis and limited therapeutic options. The tumor microenvironment (TME) and cell death mechanisms such as PANoptosis-comprising pyroptosis, apoptosis, and necroptosis-play critical roles in tumor progression and immune evasion. This study is aimed at exploring the PANoptosis landscape in OS using single-cell RNA sequencing (scRNA-seq) and at developing a robust prognostic model using machine learning algorithms. Methods: Single-cell sequencing data for OS were obtained from the GEO database (GSE162454), and bulk transcriptome data were sourced from the TARGET and GEO databases. Data integration, dimensionality reduction, and cell clustering were performed using UMAP and t-SNE. PANoptosis-related genes were identified, and their expression profiles were used to score and categorize cells into PANoptosis-high and PANoptosis-low groups. A comprehensive prognostic model was constructed using 101 machine learning algorithms, including CoxBoost, to predict patient outcomes. The model's performance was validated across multiple cohorts, and its association with the mutation landscape and TME was evaluated. Results: The scRNA-seq analysis revealed 14 distinct cell clusters within OS, with significant PANoptosis activation observed in cancer-associated fibroblasts (CAFs), myeloid cells, osteoblasts, and osteoclasts. Differentially expressed genes between PANoptosis-high and PANoptosis-low groups were identified, and cell communication analysis showed enhanced interaction patterns in the PANoptosis-high group. The CoxBoost model, selected from 101 machine learning algorithms, exhibited stable prognostic performance across the TARGET and GEO cohorts, effectively stratifying patients into high-risk and low-risk groups. The high-risk group displayed worse survival outcomes, higher mutation frequencies, and distinct immune infiltration patterns, correlating with poorer prognosis and increased tumor purity. Conclusion: This study provides novel insights into the PANoptosis landscape in OS and presents a validated prognostic model for risk stratification. The integration of scRNA-seq data with machine learning approaches enhances our understanding of OS heterogeneity and its impact on patient prognosis, offering potential avenues for targeted therapeutic strategies. Further validation in clinical settings is warranted to confirm the model's utility in guiding personalized treatment for OS patients.

Identification of biomarkers associated with programmed cell death in liver ischemia-reperfusion injury: insights from machine learning frameworks and molecular docking in multiple cohorts

Introduction

Liver ischemia-reperfusion injury (LIRI) is a major reason for liver injury that occurs during surgical procedures such as hepatectomy and liver transplantation and is a major cause of graft dysfunction after transplantation. Programmed cell death (PCD) has been found to correlate with the degree of LIRI injury and plays an important role in the treatment of LIRI. We aim to comprehensively explore the expression patterns and mechanism of action of PCD-related genes in LIRI and to find novel molecular targets for early prevention and treatment of LIRI.

Methods

We first compared the expression profiles, immune profiles, and biological function profiles of LIRI and control samples. Then, the potential mechanisms of PCD-related differentially expressed genes in LIRI were explored by functional enrichment analysis. The hub genes for LIRI were further screened by applying multiple machine learning methods and Cytoscape. GSEA, GSVA, immune correlation analysis, transcription factor prediction, ceRNA network analysis, and single-cell analysis further revealed the mechanisms and regulatory network of the hub gene in LIRI. Finally, potential therapeutic agents for LIRI were explored based on the CMap database and molecular docking technology.

Results

Forty-seven differentially expressed genes associated with PCD were identified in LIRI, and functional enrichment analysis showed that they were involved in the regulation of the TNF signaling pathway as well as the regulation of hydrolase activity. By utilizing machine learning methods, 11 model genes were identified. ROC curves and confusion matrix from the six cohorts illustrate the superior diagnostic value of our model. MYC was identified as a hub PCD-related target in LIRI by Cytoscape. Finally, BMS-536924 and PF-431396 were identified as potential therapeutic agents for LIRI.

Conclusion

This study comprehensively characterizes PCD in LIRI and identifies one core molecule, providing a new strategy for early prevention and treatment of LIRI.

Machine learning-based analysis of programmed cell death types and key genes in intervertebral disc degeneration

Intervertebral disc degeneration (IVDD) is intricately associated with various forms of programmed cell death (PCD). Identifying key PCD types and associated genes is essential for understanding the molecular mechanisms underlying IVDD and discovering potential therapeutic targets. This study aimed to elucidate core PCD types, related genes, and potential drug interactions in IVDD using comprehensive bioinformatic and experimental approaches. Using datasets GSE167199, GSE176205, GSE34095, GSE56081, and GSE70362, relevant gene expression and clinical data were analyzed. Differential expression gene (DEG) analysis identified upregulated genes linked to 15 PCD types. Gene Set Variation Analysis (GSVA) was employed to pinpoint key PCD types contributing to disc degeneration. Core genes were identified through machine learning techniques, while immune infiltration and single-cell analysis helped identify apoptosis-related cell types. Molecular docking, along with in vivo and in vitro experiments using a murine IVDD model, validated potential drug interactions. The results identified apoptosis, autophagy, ferroptosis, and necroptosis as key PCD types in IVDD. A gene module associated with apoptosis showed a strong correlation with the severity of disc degeneration, revealing 34 central genes in the gene network. Drug screening identified Glibenclamide as effectively interacting with PDCD6 and UBE2K. Subsequent in vitro and in vivo experiments demonstrated that Glibenclamide reduced apoptosis and delayed disc degeneration progression. This study provides a comprehensive bioinformatics analysis of PCD in IVDD, identifying four primary PCD types contributing to the disease's progression. The findings offer novel insights into the molecular pathology of disc degeneration and suggest promising therapeutic strategies for future treatment development.

Establishment and validation of a prognostic prediction model for glioma based on key genes and clinical factors

Background

Glioma is a common brain tumour that is associated with poor prognosis. Immunotherapy has shown significant potential in the treatment of gliomas. Herein, we proposed a new prognostic risk model based on immune- and mitochondrial energy metabolism-related differentially expressed genes (IR&MEMRDEGs) to enhance the accuracy of prognostic assessment in patients with glioma.

Methods

Data from samples from 671 glioma patients and 5 normal controls with available follow-up data and prognostic outcomes were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. All data were downloaded on 13 November 2023. IR&MEMRDEGs were screened from the GeneCards website and published literature. Prognostic prediction models were constructed and analysed using Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression, Kaplan-Meier (KM) curve, and receiver operating characteristic (ROC) curve analyses. Single-sample gene set enrichment analysis (ssGSEA) was further performed to ascertain the percentage of immune cell infiltration in the glioma specimens.

Results

Bioinformatics analysis of the GEO and TCGA databases identified eleven MEMRDEGs with dysregulated expression in gliomas: EIF4EBP1, TP53, IDH1, PRKCZ, CD200, GPI, PGM2, PKLR, AK2, ATP4A, and ALDH3B1. Further analysis identified EIF4EBP1, TP53, IDH1, PRKCZ, CD200, GPI, PGM2, AK2, and ALDH3B1 as separate predictive factors for glioma, among which PGM2 and AK2 exhibited superior accuracy [area under the ROC curve (AUC) >0.9], while EIF4EBP1, TP53, IDH1, PRKCZ, GPI, and ALDH3B1 demonstrated slightly lower accuracy (0.7< AUC <0.9), and CD200 displayed poor accuracy (0.5< AUC <0.7). Among these genes, the levels of AK2, ALDH3B1, EIF4EBP1, GPI, IDH1, PGM2, and TP53 were significantly higher in the high-risk group (HRG) compared with the low-risk group (LRG) (P<0.001), indicating a negative association with patient prognosis. In contrast, CD200 and PRKCZ were significantly downregulated in the HRG compared to the LRG (P<0.05), indicating a potential correlation with patient outcomes. Subsequently, prognostic models were constructed based on IR&MEMRDEG and MEMRDEGs to anticipate the outcomes of glioma patients, while the predictive efficacy of the model was validated via KM and ROC curve analysis. The results revealed that EIF4EBP1, TP53, IDH1, PRKCZ, GPI, PGM2, ALDH3B1, and AK2 had superior accuracy in predicting glioma prognosis. The ssGSEA results showed that only IDH1 was negatively linked to the amount of immune cell infiltration in the LRG, while displaying a positive connection in the HRG (r value>0), indicating that the expression levels of IDH1 may have a distinct influence on the tumour immune microenvironment.

Conclusions

The present study confirmed the significant predictive value of IDH1 for glioma prognosis, which may guide immunotherapy for glioma treatment.

Single-cell programmed cell death regulator patterns guide intercellular communication of cancer-associated fibroblasts that contribute to colorectal cancer progression

Background

The significance of programmed cell death (PCD) in the context of cancer development and progression is widely acknowledged, yet its specific impact on cancer-associated fibroblasts (CAFs) remains a topic of ongoing investigation. Therefore, the study aims to explore the role of PCD in regulating CAFs and its potential implications for CRC progression.

Methods

CAFs from single-cell data of 23 colorectal cancer (CRC) patients were clustered by non-negative matrix factorization (NMF) and the impact of these subpopulations on the prognosis of CRC patients was predicted using public database cohorts.

Results

In total, we screened eight PCDs that are associated with significant prognostic impacts for CRC patients, and based on PCD regulators, we defined multiple subpopulations of CAFs associated with PCDs. Additionally, we found that the PCD key regulators may be closely related to the clinical and biological characteristics of CRC and the pseudotime trajectory of major CAFs subpopulations. Bulk RNA sequencing analyses revealed that subpopulations of CAFs mediated by PCD hold prognostic value for CRC patients. CellChat analysis further illustrated the extensive interactions between PCD-associated CAFs subpopulations and tumor epithelial cells. Following Cox regression and survival analyses, it was determined that the paraptosis-mediated CAFs subpopulation had the most pronounced impact on CRC patient prognosis, with DDIT3 identified as a marker protein influencing patient outcomes.

Conclusions

Our study reveals for the first time how PCD-mediated communication between CAFs regulates tumor growth in CRC patients and influences their prognosis, and has identified that DDIT3+ CAFs associated with paraptosis exhibit the most pronounced influence on the prognosis of individuals with CRC.

Astaxanthin mitigates doxorubicin-induced cardiotoxicity via inhibiting ferroptosis and autophagy: a study based on bioinformatic analysis and in vivo/vitro experiments

Background

Doxorubicin (DOX), a widely employed chemotherapeutic agent in cancer treatment, has seen restricted use in recent years owing to its associated cardiotoxicity. Current reports indicate that doxorubicin-induced cardiotoxicity (DIC) is a complex phenomenon involving various modes of cell death. Astaxanthin (ASX), a natural carotenoid pigment, has garnered significant attention for its numerous health benefits. Recent studies have shown that ASX has a broad and effective cardiovascular protective effect. Our study aims to investigate the protective effects of ASX against DIC and elucidate its underlying mechanisms. This has substantial practical significance for the clinical application of DOX.

Methods

Bioinformatic analyses were conducted using transcriptomic data from the gene expression omnibus (GEO) database to identify key mechanisms underlying DIC. Network pharmacology was employed to predict the potential pathways and targets through which ASX exerts its effects on DIC. In vitro experiments, following pretreatment with ASX, H9C2 cells were exposed to DOX. Cell viability, injury and the protein expression levels associated with ferroptosis and autophagy were assessed. In the animal experiments, rats underwent 4 weeks of gavage treatment with various doses of ASX, followed by intraperitoneal injections of DOX every 2 days during the final week. Histological, serum, and protein analyses were conducted to evaluate the effects of ASX on DIC.

Results

The bioinformatics analysis revealed that ferroptosis and autophagy are closely associated with the development of DIC. ASX may exert an anti-DIC effect by modulating ferroptosis and autophagy. The experimental results show that ASX significantly mitigates DOX-induced myocardial tissue damage, inflammatory response, oxidative stress, and damage to H9C2 cells. Mechanistically, ASX markedly ameliorates levels of ferroptosis and autophagy both in vitro and in vivo. Specifically, ASX upregulates solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4), while downregulating the expression of transferrin receptor 1 (TFRC), ferritin heavy chain (FTH1) and ferritin light chain (FTL). Additionally, ASX enhances the expression of P62 and decreases levels of Beclin1 and microtubule-associated proteins light chain 3 (LC3).

Conclusion

Our results indicate that ferroptosis and autophagy are critical factors influencing the occurrence and progression of DOX-induced cardiotoxicity. ASX can alleviate DIC by inhibiting ferroptosis and autophagy.

A Journey into the Complexity of Temporo-Insular Gliomas: Case Report and Literature Review

INTRODUCTION

Temporo-insular gliomas, rare brain tumors originating from glial cells, comprise about 30% of brain tumors and vary in aggressiveness from grade I to IV. Despite advancements in neuroimaging and surgical techniques, their management remains complex due to their location near critical cognitive areas. Techniques like awake craniotomy have improved outcomes, but tumor heterogeneity and proximity to vital structures pose challenges. Radiotherapy and chemotherapy offer benefits post-surgery, though issues like resistance and side effects persist. This article discusses a case report and literature review to deepen understanding of temporo-insular gliomas, focusing on advanced diagnostic and treatment approaches.

MATERIALS AND METHODS

A systematic review was conducted using PubMed, Embase, and Google Scholar, covering studies from 2019 to July 2024. Keywords included 'brain tumor', 'neurosurgery', and 'treatment'. Articles on glioma diagnosis, management, and outcomes were selected, excluding non-English studies, irrelevant reports, non-glioma research, and inaccessible texts.

RESULTS

From 156 studies, 11 met inclusion criteria, highlighting advanced diagnostics, surgical strategies, and adjunct therapies for temporo-insular gliomas (TIGs). Gross total resection (GTR) was achieved in 39% of cases. Awake craniotomy enhanced functional outcomes, while temozolomide and radiotherapy improved survival. Challenges included ischemic complications and treatment resistance. Two patient cases underscored the complexity of TIG management and the importance of individualized approaches, achieving satisfactory resection with minimal deficits.

CONCLUSIONS

Temporo-insular gliomas (TIGs) necessitate a multidisciplinary strategy that integrates advanced imaging, meticulous surgical methods, and cutting-edge adjuvant therapies. Despite progress with techniques like awake craniotomy and the use of temozolomide improving patient outcomes, significant challenges persist in maintaining functional integrity and addressing treatment resistance. Ongoing research into targeted therapies, immunotherapies, and innovative technologies remains critical to advancing patient care and improving long-term prognosis.

Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma

OBJECTIVE

Neuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clinical management of NECC follows neuroendocrine neoplasms and cervical cancer in general. However, the diagnosis and prognosis of NECC remain dismal. The aim of this study was to identify a specific protein signature for the diagnosis of NECC.

METHODS

Protein and gene expression data for NECC and other cervical cancers were retrieved or downloaded from self-collected samples or public resources. Eleven machine-learning algorithms were packaged into 66 combinations, of which we selected the optimal algorithm, including randomForest, SVM-RFE, and LASSO, to select key NECC specific dysregulated proteins (kNsDEPs). The diagnostic effect of kNsDEPs was validated by a set of predictive models and immunohistochemical staining method. The dysregulation patterns of kNsDEPs were further investigated in other neuroendocrine carcinomas.

RESULTS

Our results showed that NECC displays distinctive biological characteristics, such as HPV18 infection, and exhibits unique molecular features, particularly an enrichment in cytoskeleton-related functions. Furthermore, secretagogin (SCGN), adenylyl cyclase-associated protein 2 (CAP2), and calcyclin-binding protein (CACYBP) were identified as kNsDEPs. These kNsDEPs play a central role in cytoskeleton protein binding and showcase robust diagnostic ability and specificity for NECC. Moreover, the concurrent upregulation of SCGN and CACYBP, along with the downregulation of CAP2, represents a unique feature of NECC, distinguishing it from other neuroendocrine carcinomas.

CONCLUSIONS

This study uncovers the significance of kNsDEPs and elucidates their regulated networks in the context of NECC. It highlights the pivotal role of kNsDEPs in NECC diagnosis, thus offering promising prospects for the development of diagnostic biomarkers for NECC.

Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer

Background

Programmed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was to investigate the association between various programmed cell death patterns and the prognosis of breast cancer (BRCA) patients.

Methods

The levels of 19 different programmed cell deaths in breast cancer were assessed by ssGSEA analysis, and these PCD scores were summed to obtain the PCDS for each sample. The relationship of PCDS with immune as well as metabolism-related pathways was explored. PCD-associated subtypes were obtained by unsupervised consensus clustering analysis, and differentially expressed genes between subtypes were analyzed. The prognostic signature (PCDRS) were constructed by the best combination of 101 machine learning algorithm combinations, and the C-index of PCDRS was compared with 30 published signatures. In addition, we analyzed PCDRS in relation to immune as well as therapeutic responses. The distribution of genes in different cells was explored by single-cell analysis and spatial transcriptome analysis. Potential drugs targeting key genes were analyzed by Cmap. Finally, the expression levels of key genes in clinical tissues were verified by RT-PCR.

Results

PCDS showed higher levels in cancer compared to normal. Different PCDS groups showed significant differences in immune and metabolism-related pathways. PCDRS, consisting of seven key genes, showed robust predictive ability over other signatures in different datasets. The high PCDRS group had a poorer prognosis and was strongly associated with a cancer-promoting tumor microenvironment. The low PCDRS group exhibited higher levels of anti-cancer immunity and responded better to immune checkpoint inhibitors as well as chemotherapy-related drugs. Clofibrate and imatinib could serve as potential small-molecule complexes targeting SLC7A5 and BCL2A1, respectively. The mRNA expression levels of seven genes were upregulated in clinical cancer tissues.

Conclusion

PCDRS can be used as a biomarker to assess the prognosis and treatment response of BRCA patients, which offers novel insights for prognostic monitoring and treatment personalization of BRCA patients.

Comprehensive Analysis of m6A-Related Programmed Cell Death Genes Unveils a Novel Prognostic Model for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) involves complex dysregulated cellular processes, including programmed cell death (PCD), influenced by N6-methyladenosine (m6A) RNA modification. This study integrates bulk RNA and single-cell sequencing data to identify 43 prognostically valuable m6A-related PCD genes, forming the basis of a 13-gene risk model (m6A-related PCD signature [mPCDS]) developed using machine-learning algorithms, including CoxBoost and SuperPC. The mPCDS demonstrated significant predictive performance across multiple validation datasets. In addition to its prognostic accuracy, mPCDS revealed distinct genomic profiles, pathway activations, associations with the tumour microenvironment and potential for predicting drug sensitivity. Experimental validation identified RCN1 as a potential oncogene driving LUAD progression and a promising therapeutic target. The mPCDS offers a new approach for LUAD risk stratification and personalised treatment strategies.

MPCD Index for Hepatocellular Carcinoma Patients Based on Mitochondrial Function and Cell Death Patterns

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer with a poor prognosis. During the development of cancer cells, mitochondria influence various cell death patterns by regulating metabolic pathways such as oxidative phosphorylation. However, the relationship between mitochondrial function and cell death patterns in HCC remains unclear. In this study, we used a comprehensive machine learning framework to construct a mitochondrial functional activity-associated programmed cell death index (MPCDI) based on scRNA-seq and RNA-seq data from TCGA, GEO, and ICGC datasets. The index signature was used to classify HCC patients, and studied the multi-omics features, immune microenvironment, and drug sensitivity of the subtypes. Finally, we constructed the MPCDI signature consisting of four genes (S100A9, FYN, LGALS3, and HMOX1), which was one of the independent risk factors for the prognosis of HCC patients. The HCC patients were divided into high- and low-MPCDI groups, and the immune status was different between the two groups. Patients with a high MPCDI had higher TIDE scores and poorer responses to immunotherapy, suggesting that high-MPCDI patients might not be suitable for immunotherapy. By analyzing the drug sensitivity data of CTRP, GDSC, and PRISM databases, it was found that staurosporine has potential therapeutic significance for patients with a high MPCDI. In summary, based on the characteristics of mitochondria function and PCD patterns, we used single-cell and transcriptome data to identify four genes and construct the MPCDI signature, which provided new perspectives and directions for the clinical diagnosis and personalized treatment of HCC patients.

A Machine Learning Computational Framework Develops a Multiple Programmed Cell Death Index for Improving Clinical Outcomes in Bladder Cancer

Comprehensive action patterns of programmed cell death (PCD) in bladder cancer (BLCA) have not yet been thoroughly investigated. Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed a multiple programmed cell death index (MPCDI) based on a machine learning computational framework. We found that in the TCGA-BLCA training cohort and the independently validated GSE13507 cohort, the patients with high-MPCDI had a worse prognosis, whereas patients with low-MPCDI had a better prognosis. By combining clinical characteristics with the MPCDI, we constructed a nomogram. The C-index demonstrated that the nomogram was significantly more accurate compared to other variables, including MPCDI, age, gender, and clinical stage. The results of the decision curve analysis demonstrated that the nomogram had a better net clinical benefit compared to other clinical variables. Subsequently, we revealed the heterogeneity of BLCA patients, with significant differences in terms of overall immune infiltration abundance, immunotherapeutic response, and drug sensitivity in the two MPCDI groups. Encouragingly, the high-MPCDI patients showed better efficacy for commonly used chemotherapeutic drugs than the low-MPCDI patients, which suggests that MPCDI scores have a guiding role in chemotherapy for BLCA patients. In conclusion, the MPCDI developed and verified in this study is not only an emerging clinical classifier for BLCA patients, but it also serves as a reliable forecaster for both chemotherapy and immunotherapy, which can guide clinical management and clinical decision-making for BLCA patients.

Bulk and Single-Cell Transcriptome Analyses Unravel Gene Signatures of Mitochondria-Associated Programmed Cell Death in Diabetic Foot Ulcer

Mitochondrial programmed cell death (PCD) plays a critical role in the pathogenesis of diabetic foot ulcers (DFU). In this study, we performed a comprehensive transcriptome analysis to identify potential hub genes and key cell types associated with PCD and mitochondria in DFU. Using intersection analysis of PCD- and mitochondria-related genes, we identified candidate hub genes through protein-protein interaction and random forest analysis. At the single-cell level, key cell types were further validated based on the expression of hub genes. Additionally, we explored the transcription factors (TFs) regulating hub gene expression and the cellular heterogeneity of DFU. Finally, the expression of key hub genes and TFs was validated in clinical specimens. Our results identified BCL2 and LIPT1 as significantly downregulated hub genes in DFU, with Keratinocytes, as the key cell type. Immunohistochemistry confirmed downregulation of BCL2 and LIPT1 in DFU samples (p < 0.05). Additionally, TFs CEBPD and IRF1 were significantly upregulated in DFU, as confirmed by real-time polymerase chain reaction analysis (p < 0.05).

Identification of Renal Ischemia-Reperfusion Injury Subtypes and Predictive Model for Graft Loss after Kidney Transplantation Based on Programmed Cell Death-Related Genes

Introduction

Ischemia-reperfusion injury (IRI) is detrimental to kidney transplants and may contribute to poor long-term outcomes of transplantation. Programmed cell death (PCD), a regulated cell death form triggered by IRI, is often indicative of an unfavorable prognosis following transplantation. However, given the intricate pathophysiology of IRI and the considerable variability in clinical conditions during kidney transplantation, the specific patterns of cell death within renal tissues remain ambiguous. Consequently, accurately predicting the outcomes for transplanted kidneys continues to be a formidable challenge.

Methods

Eight Gene Expression Omnibus datasets of biopsied transplanted kidney samples post-IRI and 1,548 PCD-related genes derived from 18 PCD patterns were collected in our study. Consensus clustering was performed to identify distinct IRI subtypes based on PCD features (IRI PCD subtypes). Differential enrichment analysis of cell death, metabolic signatures, and immune infiltration across these subtypes was evaluated. Three machine learning algorithms were used to identify PCD patterns related to prognosis. Genes associated with graft loss were screened for each PCD type. A predictive model for graft loss was constructed using 101 combinations of 10 machine learning algorithms.

Results

Four IRI subtypes were identified: PCD-A, PCD-B, PCD-C, and PCD-D. PCD-A, characterized by high enrichment of multiple cell death patterns, significant metabolic paralysis, and immune infiltration, showed the poorest prognosis among the four subtypes. While PCD-D involved the least kind of cell death patterns with the features of extensive activation of metabolic pathways and the lowest immune infiltration, correlating with the best prognosis in the four subtypes. Using various machine learning algorithms, 10 cell death patterns and 42 PCD-related genes were identified as positively correlated with graft loss. The predictive model demonstrated high sensitivity and specificity, with area under the curve values for 0.5-, 1-, 2-, 3-, and 4-year graft survival at 0.888, 0.91, 0.926, 0.923, and 0.923, respectively.

Conclusion

Our study explored the comprehensive features of PCD patterns in transplanted kidney samples post-IRI. The prediction model shows great promise in forecasting graft loss and could aid in risk stratification in patients following kidney transplantation.

Transcriptome combined with Mendelian randomization to screen key genes associated with mitochondrial and programmed cell death causally associated with diabetic retinopathy

Background

Mitochondrial dysfunction in the retina can induce apoptosis of retinal capillary cells, leading to diabetic retinopathy (DR). This study aimed to explore key genes related to programmed cell death (PCD) and mitochondria in DR via bioinformatic analysis.

Methods

A differential analysis was performed to identify differentially expressed genes (DEGs) between DR and control samples using the GSE94019 dataset from the Gene Expression Omnibus (GEO) database. Pearson correlation analysis was then utilized to select genes linked to mitochondrial function and PCD (M-PCD). Candidate genes were identified by overlapping DR-DEGs and M-PCD genes, followed by functional annotation. Mendelian randomization (MR) analysis was employed to identify genes with causal relationships to DR. Key genes were identified through protein-protein interaction (PPI) analysis using six algorithms (DEgree, DMNC, EPC, MCC, Genes are BottleNeck, and MNC) within Cytoscape software. The expression patterns of these genes were validated using GSE94019 and GSE60436 datasets, as well as RT-qPCR. Enrichment analysis provided insights into the function and pathways of these key genes in DR. Differential immune cell profiles were determined via immune infiltration analysis, followed by exploring the relationships between immune cells, cytokines, and the identified genes. Correlations between key genes and apoptosis genes were also examined. In vivo experiments using RT-PCR, immunohistochemistry (IHC), and western blot analysis confirmed that MYC and SLC7A11 expression was significantly elevated in DR rat retinal tissues.

Results

From 658 candidate genes, 12 showed significant causal associations with DR. MYC and SLC7A11 were particularly notable, showing upregulated expression in DR samples and involvement in apoptosis and diabetes-related pathways. These genes were significantly associated with apoptotic genes and correlated positively with altered immune cell types and cytokines, suggesting a link between immune response and DR pathogenesis. In vivo findings confirmed that MYC and SLC7A11 expression was elevated in DR rat retinal tissues.

Conclusion

Key genes (MYC and SLC7A11) associated with mitochondrial function and PCD in DR were identified, offering insights into DR's pathological mechanisms and potential targets for diagnostic and therapeutic strategies.

A comprehensive investigation of associations between cell death pathways and molecular and clinical features in pan-cancer

BACKGROUND

Regulated cell death (RCD) pathways play significant roles in tumorigenesis. However, systematic investigation into correlations between RCD and various molecular and clinical features, particularly anti-tumor immunity and immunotherapy response in pan-cancer remains lacking.

METHODS

Using the single-sample gene set enrichment analysis, we quantified the activities of six RCD pathways (apoptosis, autophagy, ferroptosis, cuproptosis, necroptosis, and pyroptosis) in each cancer specimen. Then, we explored associations of these six RCD pathways with tumor immunity, genomic instability, tumor phenotypes and clinical features, and responses to immunotherapy and targeted therapies in pan-cancer by statistical analyses.

RESULTS

Our results showed that the RCD (except autophagy) activities were oncogenic signatures, as evidenced by their hyperactivation in late stage or metastatic cancer patients, positive correlations with tumor proliferation, stemness, genomic instability and intratumor heterogeneity, and correlation with worse survival outcomes in cancer. In contrast, autophagy was a tumor suppressive signature as its associations with molecular and clinical features in cancer shows an opposite pattern compared to the other RCD pathways. Furthermore, heightened RCD (except cuproptosis) activities were correlated with increased sensitivity to immune checkpoint inhibitors. Additionally, elevated activities of pyroptosis, autophagy, cuproptosis and necroptosis were associated with increased drug sensitivity in a broad spectrum of anti-tumor targeted therapies, while the elevated activity of ferroptosis was correlated with decreased sensitivity to numerous targeted therapies.

CONCLUSION

RCD (except autophagy) activities correlate with unfavorable cancer prognosis, while the autophagy activity correlate with favorable clinical outcomes. RCD (except cuproptosis) activities are positive biomarkers for anti-tumor immunity and immunotherapy response.

Single-cell transcriptomics reveals IRF7 regulation of the tumor microenvironment in isocitrate dehydrogenase wild-type glioma

Mutations in isocitrate dehydrogenase (IDH) are important markers of glioma prognosis. However, few studies have examined the gene expression regulatory network (GRN) in IDH-mutant and wild-type gliomas. In this study, single-cell RNA sequencing and spatial transcriptome sequencing were used to analyze the GRN of cell subsets in patients with IDH-mutant and wild-type gliomas. Through gene transcriptional regulation analysis, we identified the M4 module, whose transcription factor activity is highly expressed in IDH wild-type gliomas compared to IDH-mutants. Enrichment analysis revealed that these genes were predominantly expressed in microglia and macrophages, with significant enrichment in interferon-related signaling pathways. Interferon regulatory factor 7 (IRF7), a transcription factor within this pathway, showed the highest percentage of enrichment and was primarily localized in the core region of wild-type IDH tumors. A machine-learning prognostic model identified novel subgroups within the wild-type IDH population. Additionally, IRF7 was shown to promote the proliferation and migration of T98G and U251 cells in vitro, and its knockdown affected glioma cell proliferation in vivo. This study systematically established the regulatory mechanism of IDH transcriptional activity in gliomas at the single-cell level and drew a corresponding cell map. The study presents a transcriptional regulatory activity map for IDH wild-type gliomas, involving single-cell RNA sequencing and spatial transcriptomics to identify gene regulatory networks, machine learning models for IDH subtyping, and experimental validation, highlighting the role of IRF7 in glioma progression.

Pharmacological Inhibition of Phosphoglycerate Kinase 1 Reduces OxiDative Stress and Restores Impaired Autophagy in Experimental Acute Pancreatitis

Damage to pancreatic acinar cells (PAC) and intracellular metabolic disturbances play crucial roles in pancreatic necrosis during acute pancreatitis (AP). Phosphoglycerate kinase 1 (PGK1) is a crucial catalytic enzyme in glycolysis. However, the impact of PGK1-involving glycolysis in regulating metabolic necrosis in AP is unclear. Transcriptome analysis of pancreatic tissues revealed significant changes in the glycolysis pathway and PGK1 which positively correlated with the inflammatory response and oxidative stress injury in AP mice. Furthermore, we observed a substantial increase in PGK1 expression in damaged PAC, positively correlating with PAC necrosis. Treatment with NG52, a specific PGK1 inhibitor, ameliorated pancreatic necrosis, inflammatory damage, and oxidative stress. Transcriptomic data before and after NG52 treatment along with the Programmed Cell Death database confirmed that NG52 protected against PAC damage by rescuing impaired autophagy in AP. Additionally, the protective effect of NG52 was validated following pancreatic duct ligation. These findings underscore the involvement of PGK1 in AP pathogenesis, highlighting that PGK1 inhibition can mitigate AP-induced pancreatic necrosis, attenuate inflammatory and oxidative stress injury, and rescue impaired autophagy. Thus, the study findings suggest a promising interventional target for pancreatic necrosis, offering novel strategies for therapeutic approaches to clinical AP.

Leveraging mitochondrial-programmed cell death dynamics to enhance prognostic accuracy and immunotherapy efficacy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease, posing significant challenges to accurate prognosis prediction. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence programmed cell death (PCD) mechanisms, which are critical in tumorigenesis and cancer progression. However, the prognostic significance of the interplay between mitochondrial function and PCD in LUAD requires further investigation.

METHODS

We analyzed data from 1231 LUAD patients across seven global cohorts to develop a mitochondrial-related PCD signature (MPCDS) using machine learning. Validation was done using six immunotherapy cohorts (LUAD, melanoma, clear cell renal cell carcinoma; n=935) and a pan-cancer cohort of 21 tumor types. An in-house LUAD tissue cohort (n=100) confirmed the prognostic significance of nucleoside diphosphate kinase 4 (NME4). In vivo and in vitro experiments explored NME4's role in immune exclusion.

RESULTS

The MPCDS demonstrated strong predictive performance for prognosis in LUAD patients, surpassing 114 previously published LUAD signatures. Additionally, MPCDS effectively predicted outcomes in immunotherapy patients (including those with LUAD, melanoma, and clear cell renal cell carcinoma). Biologically, MPCDS was significantly associated with immune features, with the high MPCDS group exhibiting reduced immune activity and a tendency towards cold tumors. NME4, a key gene within the MPCDS (correlation=0.55, p<0.05), was associated with poorer prognosis in LUAD patients with high expression, particularly in CD8 desert phenotypes, as validated by our in-house cohort. Multiplex immunofluorescence confirmed the spatial colocalization and exclusion relationship between NME4 and immune cells such as CD3+ T cells and CD20+ B cells. Further experiments revealed that NME4 regulated the proliferation and invasion of LUAD cells both in vitro and in vivo. Importantly, inhibiting NME4 increased the abundance and activity of CD8+ T cells and enhanced the antitumor immunity of anti-programmed cell death protein-1 therapy in vivo.

CONCLUSION

The MPCDS provides personalized risk assessment and immunotherapy interventions for individual LUAD patients. NME4, a key gene within the MPCDS, has been identified as a novel oncogene associated with immune exclusion and may serve as a new target for LUAD intervention and immunotherapy.

Construction and validation of a machine learning-based immune-related prognostic model for glioma

BACKGROUND

Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine.

METHODS

Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model's performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments.

RESULTS

In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden. The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration.

CONCLUSION

This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy.

Advanced machine learning unveils CD8 + T cell genetic markers enhancing prognosis and immunotherapy efficacy in breast cancer

BACKGROUND

Breast cancer (BC) is the most common cancer in women and poses a significant health burden, especially in China. Despite advances in diagnosis and treatment, patient variability and limited early detection contribute to poor outcomes. This study examines the role of CD8 + T cells in the tumor microenvironment to identify new biomarkers that improve prognosis and guide treatment strategies.

METHODS

CD8 + T-cell marker genes were identified using single-cell RNA sequencing (scRNA-seq), and a CD8 + T cell-related gene prognostic signature (CTRGPS) was developed using 10 machine-learning algorithms. The model was validated across seven independent public datasets from the GEO database. Clinical features and previously published signatures were also analyzed for comparison. The clinical applications of CTRGPS in biological function, immune microenvironment, and drug selection were explored, and the role of hub genes in BC progression was further investigated.

RESULTS

We identified 71 CD8 + T cell-related genes and developed the CTRGPS, which demonstrated significant prognostic value, with higher risk scores linked to poorer overall survival (OS). The model's accuracy and robustness were confirmed through Kaplan-Meier and ROC curve analyses across multiple datasets. CTRGPS outperformed existing prognostic signatures and served as an independent prognostic factor. The role of the hub gene TTK in promoting malignant proliferation and migration of BC cells was validated.

CONCLUSION

The CTRGPS enhances early diagnosis and treatment precision in BC, improving clinical outcomes. TTK, a key gene in the signature, shows promise as a therapeutic target, supporting the CTRGPS's potential clinical utility.

Machine learning-based cell death marker for predicting prognosis and identifying tumor immune microenvironment in prostate cancer

Background

Prostate cancer (PCa) incidence and mortality rates are rising, necessitating precise prognostic tools to guide personalized treatment. Dysregulation of programmed cell death pathways in tumor suppression and cancer development has garnered increasing attention, providing a new research direction for identifying biomarkers and potential therapeutic targets.

Methods

Integrating multiple database resources, we constructed and optimized a prognostic signature based on the expression of programmed cell death-related genes (PCDRG) using ten machine learning algorithms. Model performance and prognostic effects were further evaluated. We analyzed the relationships between signature and clinicopathological features, somatic mutations, drug sensitivity, and the tumor immune microenvironment, and constructed a nomogram. The expression level of PCDRGs were evaluated and compared.

Results

Of 1560 PCDRGs, 149 were differentially expressed in PCa, with 34 associated with biochemical recurrence. The PCDRG-derived index (PCDI), constructed using the random forest algorithm, exhibited optimal prognostic performance, successfully stratifying PCa patients into two groups with significant prognostic differences. Patients with high PCDI scores exhibited poorer survival and lower immunotherapy benefit. PCDI was closely associated with the infiltration of specific immune cells, particularly positive correlations with macrophages and T helper cells, and negative correlations with neutrophils, suggesting that PCDI may influence the tumor immune microenvironment, thereby affecting patient prognosis and treatment response. PCDI was associated with age, pathological stage, somatic mutations, and drug sensitivity. The PCDI-based nomogram demonstrated excellent performance in predicting biochemical recurrence in PCa patients. Finally, the differential expression of these PCDRGs was verified based on cell lines and PCa patient expression profile data.

Conclusion

This study developed an effective prognostic indicator for prostate cancer, PCDI, using machine learning approaches. PCDI reflects the link between aberrant programmed cell death pathways and disease progression and treatment response.

A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration analysis: AHR is a key gene in the cuproptosis process

Background

Pulmonary arterial hypertension (PAH) is a serious condition characterized by elevated pulmonary artery pressure, leading to right heart failure and increased mortality. This study investigates the link between PAH and genes associated with hypoxia and cuproptosis.

Methods

We utilized expression profiles and single-cell RNA-seq data of PAH from the GEO database and genecad. Genes related to cuproptosis and hypoxia were identified. After normalizing the data, differential gene expression was analyzed between PAH and control groups. We performed clustering analyses on cuproptosis-related genes and constructed a weighted gene co-expression network (WGCNA) to identify key genes linked to cuproptosis subtype scores. KEGG, GO, and DO enrichment analyses were conducted for hypoxia-related genes, and a protein-protein interaction (PPI) network was created using STRING. Immune cell composition differences were examined between groups. SingleR and Seurat were used for scRNA-seq data analysis, with PCA and t-SNE for dimensionality reduction. We analyzed hub gene expression across single-cell clusters and built a diagnostic model using LASSO and random forest, optimizing parameters with 10-fold cross-validation. A total of 113 combinations of 12 machine learning algorithms were employed to evaluate model accuracy. GSEA was utilized for pathway enrichment analysis of AHR and FAS, and a Nomogram was created to assess risk impact. We also analyzed the correlation between key genes and immune cell types using Spearman correlation.

Results

We identified several diagnostic genes for PAH linked to hypoxia and cuproptosis. PPI networks illustrated relationships among these hub genes, with immune infiltration analysis highlighting associations with monocytes, macrophages, and CD8 T cells. The genes AHR, FAS, and FGF2 emerged as key markers, forming a robust diagnostic model (NaiveBayes) with an AUC of 0.9.

Conclusion

AHR, FAS, and FGF2 were identified as potential biomarkers for PAH, influencing cell proliferation and inflammatory responses, thereby offering new insights for PAH prevention and treatment.

Functional exploration and drug prediction on programmed cell death-related biomarkers in lung adenocarcinoma

Background

Our study aims to perform functional exploration and drug prediction of programmed cell death (PCD)-related biomarkers in lung adenocarcinoma (LUAD).

Methods

UCSC-Xena obtained LUAD-related genes. DESeq2 screened PCD-specific differentially expressed genes (DEGs), and these DEGs were intersected with genes identified by weighted gene co-expression network analysis (WGCNA) to pinpoint the key genes. KOBAS-i was used for enrichment analysis. String and GeneMania were used to construct protein interaction networks and gene-gene interaction networks, respectively. Using two machine learning algorithms to screen for key genes, and taking the intersection as biomarkers, validating via receiver operating characteristic (ROC) and in vitro experiments. Building a diagnostic model with a nomogram. Construct transcription factor (TF) regulatory network. CIBERSORT was used for immune infiltration analysis. Enrichr predicts targeted drugs and AutodockTools simulates molecular docking.

Results

120 hub genes related to PCD were identified, and an intersection of these genes with DEGs yielded 10 key genes, which were enriched in apoptosis-related pathways. Further machine learning screening of these genes led to the selection of 7 genes, among which 6 genes (FGR, LAPTM5, SIRPA, TLR4, ZEB2, and NLRC4) exhibited significant differences upon ROC validation, ultimately serving as biomarkers, in vitro experiments also confirmed. A nomogram demonstrated their excellent diagnostic performance. These six biomarkers are correlated with the infiltration status of most immune cells, suggesting that they affect LUAD through the immune system. TF regulation analysis identified the upstream miRNAs. Finally, drug prediction yielded three potential drugs: Lenvatinib, methadone, and trimethoprim.

Conclusion

PCD-related biomarkers in LUAD were explored, which may contribute to further understanding on PCD in LUAD.

Unraveling the regulatory cell death pathways in gastric cancer: a multi-omics study

Gastric cancer (GC) is a prevalent form of cancer worldwide and has a high death rate, with less than 40% of patients surviving for 5 years. GC demonstrates a vital characteristic of evading regulatory cell death (RCD). However, the extent to which RCD patterns are clinically significant in GC has not been well investigated. The study created a regulatory cell death index (RCDI) signature by employing 101 machine-learning algorithms. These algorithms were based on the expression files of 1292 GC patients from 6 multicenter cohorts. RCDI is a reliable and robust determinant of the likelihood of surviving in general. Furthermore, the precision of RCDI surpasses that of the 20 signatures that have been previously disclosed. The presence of RCDI signature is closely linked to immunological characteristics, such as the infiltration of immune cells, the presence of immunotherapy markers, and the activation of immune-related functions. This suggests that there is a higher level of immune activity in cases with RCDI signature. Collectively, the use of RCDI has the potential to be a strong and encouraging method for enhancing the clinical results of individual individuals with GC.

Identification and validation of an immune-derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma

OBJECTIVES

Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated.

METHODS

Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed an immune-derived multiple programmed cell death index (MPCDI) based on machine learning methods.

RESULTS

Using the median MPCDI scores, we categorized the LUAD patients into two groups: low-MPCDI and high-MPCDI. Our analysis of the TCGA-LUAD training cohort and three external GEO cohorts (GSE37745, GSE30219, and GSE68465) revealed that patients with high-MPCDI experienced a more unfavorable prognosis, whereas those with low-MPCDI had a better prognosis. Furthermore, the results of both univariate and multivariate Cox regression analyses further confirmed that MPCDI serves as a novel independent risk factor. By combining clinical characteristics with the MPCDI, we constructed a nomogram that provides an accurate and reliable quantitative tool for personalized clinical management of LUAD patients. The findings obtained from the analysis of C-index and the decision curve revealed that the nomogram outperformed various clinical variables in terms of net clinical benefit. Encouragingly, the low-MPCDI patients are more sensitive to commonly used chemotherapy drugs, which suggests that MPCDI scores have a guiding role in chemotherapy for LUAD patients.

CONCLUSION

Therefore, MPCDI can be used as a novel clinical diagnostic classifier, providing valuable insights into the clinical management and clinical decision-making for LUAD patients.

Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine

Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.

Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer

BACKGROUND

Prostate cancer is one of the most common cancers in men with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles in tumor progression and can potentially serve as prognostic and therapeutic biomarkers in PCa. This study aimed to develop a prognostic signature for BCR in PCa using PCD-related genes.

MATERIALS AND METHODS

We conducted an analysis of 19 different modes of PCD to develop a comprehensive model. Bulk transcriptomic, single-cell transcriptomic, genomic, and clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, and GSE193337. We analyzed the expression and mutations of the 19 PCD modes and constructed, evaluated, and validated the model.

RESULTS

Ten PCD modes were found to be associated with BCR in PCa, with specific PCD patterns exhibited by various cell components within the tumor microenvironment. Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature. High PCDI values were validated in five independent datasets and were found to be associated with an increased risk of BCR in PCa patients. Notably, older age and advanced T and N staging were associated with higher PCDI values. By combining PCDI with T staging, we constructed a nomogram with enhanced predictive performance. Additionally, high PCDI values were significantly correlated with decreased drug sensitivity, including drugs such as Docetaxel and Methotrexate. Patients with lower PCDI values demonstrated higher immunophenoscores (IPS), suggesting a potentially higher response rate to immune therapy. Furthermore, PCDI was associated with immune checkpoint genes and key components of the tumor microenvironment, including macrophages, T cells, and NK cells. Finally, clinical specimens validated the differential expression of PCDI-related PCDRGs at both the gene and protein levels.

CONCLUSION

In conclusion, we developed a novel PCD-based prognostic feature that successfully predicted BCR in PCa patients and provided insights into drug sensitivity and potential response to immune therapy. These findings have significant clinical implications for the treatment of PCa.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes

BACKGROUND

Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood.

METHODS

Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD.

RESULTS

Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD.

CONCLUSION

Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.

Identification and experimental validation of programmed cell death- and mitochondria-associated biomarkers in osteoporosis and immune microenvironment

Background: Prior research has demonstrated that programmed cell death (PCD) and mitochondria assume pivotal roles in controlling cellular metabolism and maintaining bone cell equilibrium. Nonetheless, the comprehensive elucidation of their mode of operation in osteoporosis (OP) warrants further investigation. Therefore, this study aimed at analyzing the role of genes associated with PCD (PCD-RGs) and mitochondria (mortality factor-related genes; MRGs) in OP. Methods: Differentially expressed genes (DEGs) were identified by subjecting the GSE56815 dataset obtained from the Gene Expression Omnibus database to differential expression analysis and comparing OP patients with healthy individuals. The genes of interest were ascertained through the intersection of DEGs, MRGs, and PCD-RGs; these genes were filtered using machine learning methodologies to discover potential biomarkers. The prospective biomarkers displaying uniform patterns and statistically meaningful variances were identified by evaluating their levels in the GSE56815 dataset and conducting quantitative real-time polymerase chain reaction-based assessments. Moreover, the functional mechanisms of these biomarkers were further delineated by constructing a nomogram, which conducted gene set enrichment analysis, explored immune infiltration, generated regulatory networks, predicted drug responses, and performed molecular docking analyses. Results: Eighteen candidate genes were documented contingent upon the intersection between 2,354 DEGs, 1,136 MRGs, and 1,548 PCD-RGs. The biomarkers DAP3, BIK, and ACAA2 were upregulated in OP and were linked to oxidative phosphorylation. Furthermore, the predictive ability of the nomogram designed based on the OP biomarkers exhibited a certain degree of accuracy. Correlation analysis revealed a strong positive correlation between CD56dim natural killer cells and ACAA2 and a significant negative correlation between central memory CD4+ T cells and DAP3. DAP3, BIK, and ACAA2 were regulated by multiple factors; specifically, SETDB1 and ZNF281 modulated ACAA2 and DAP3, whereas TP63 and TFAP2C governed DAP3 and BIK. Additionally, a stable binding force was observed between the drugs (estradiol, valproic acid, and CGP52608) and the biomarkers. Conclusion: This investigation evidenced that the biomarkers DAP3, BIK, and ACAA2 are associated with PCD and mitochondria in OP, potentially facilitate the diagnosis of OP in clinical settings.

Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation

BACKGROUND

Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.

METHODS

We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.

RESULTS

We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.

CONCLUSION

Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.

A novel signature constructed by mitochondrial function and cell death-related gene for the prediction of prognosis in bladder cancer

Bladder urothelial carcinoma (BLCA) presents a persistent challenge in clinical management. Despite recent advancements demonstrating the BLCA efficacy of immune checkpoint inhibitors (ICI) in BLCA patients, there remains a critical need to identify and expand the subset of individuals who benefit from this treatment. Mitochondria, as pivotal regulators of various cell death pathways in eukaryotic cells, exert significant influence over tumor cell fate and survival. In this study, our objective was to investigate biomarkers centered around mitochondrial function and cell death mechanisms to facilitate prognostic prediction and guide therapeutic decision-making in BLCA. Utilizing ssGSEA and LASSO regression, we developed a prognostic signature termed mitochondrial function and cell death (mtPCD). Subsequently, we evaluated the associations between mtPCD score and diverse clinical outcomes, including prognosis, functional pathway enrichment, immune cell infiltration, immunotherapy response analysis and drug sensitivity, within high- and low-risk subgroups. Additionally, we employed single-cell level functional assays, RT-qPCR, and immunohistochemistry to validate the differential expression of genes comprising the mtPCD signature. The mtPCD signature comprises a panel of 10 highly influential genes, strongly correlated with survival outcomes in BLCA patients and exhibiting robust predictive capabilities. Importantly, individuals classified as high-risk according to mtPCD score displayed a subdued overall immune response, characterized by diminished immunotherapeutic efficacy. In summary, our findings highlight the development of a novel prognostic signature, which not only holds promise as a biomarker for BLCA prognosis but also offers insights into the immune landscape of BLCA. This paradigm may pave the way for personalized treatment strategies in BLCA management.

Unveiling divergent treatment prognoses in IDHwt-GBM subtypes through multiomics clustering: a swift dual MRI-mRNA model for precise subtype prediction

BACKGROUND

IDH1-wildtype glioblastoma multiforme (IDHwt-GBM) is a highly heterogeneous and aggressive brain tumour characterised by a dismal prognosis and significant challenges in accurately predicting patient outcomes. To address these issues and personalise treatment approaches, we aimed to develop and validate robust multiomics molecular subtypes of IDHwt-GBM. Through this, we sought to uncover the distinct molecular signatures underlying these subtypes, paving the way for improved diagnosis and targeted therapy for this challenging disease.

METHODS

To identify stable molecular subtypes among 184 IDHwt-GBM patients from TCGA, we used the consensus clustering method to consolidate the results from ten advanced multiomics clustering approaches based on mRNA, lncRNA, and mutation data. We developed subtype prediction models using the PAM and machine learning algorithms based on mRNA and MRI data for enhanced clinical utility. These models were validated in five independent datasets, and an online interactive system was created. We conducted a comprehensive assessment of the clinical impact, drug treatment response, and molecular associations of the IDHwt-GBM subtypes.

RESULTS

In the TCGA cohort, two molecular subtypes, class 1 and class 2, were identified through multiomics clustering of IDHwt-GBM patients. There was a significant difference in survival between Class 1 and Class 2 patients, with a hazard ratio (HR) of 1.68 [1.15-2.47]. This difference was validated in other datasets (CGGA: HR = 1.75[1.04, 2.94]; CPTAC: HR = 1.79[1.09-2.91]; GALSS: HR = 1.66[1.09-2.54]; UCSF: HR = 1.33[1.00-1.77]; UPENN HR = 1.29[1.04-1.58]). Additionally, class 2 was more sensitive to treatment with radiotherapy combined with temozolomide, and this sensitivity was validated in the GLASS cohort. Correspondingly, class 2 and class 1 exhibited significant differences in mutation patterns, enriched pathways, programmed cell death (PCD), and the tumour immune microenvironment. Class 2 had more mutation signatures associated with defective DNA mismatch repair (P = 0.0021). Enriched pathways of differentially expressed genes in class 1 and class 2 (P-adjust < 0.05) were mainly related to ferroptosis, the PD-1 checkpoint pathway, the JAK-STAT signalling pathway, and other programmed cell death and immune-related pathways. The different cell death modes and immune microenvironments were validated across multiple datasets. Finally, our developed survival prediction model, which integrates molecular subtypes, age, and sex, demonstrated clinical benefits based on the decision curve in the test set. We deployed the molecular subtyping prediction model and survival prediction model online, allowing interactive use and facilitating user convenience.

CONCLUSIONS

Molecular subtypes were identified and verified through multiomics clustering in IDHwt-GBM patients. These subtypes are linked to specific mutation patterns, the immune microenvironment, prognoses, and treatment responses.

Prognostic implication and immunotherapy response prediction of a novel ubiquitination-related gene signature in liver cancer

HCC, also known as hepatocellular carcinoma, is a frequently occurring form of cancer with an unfavorable prognosis. This research constructed a prognostic signature related to ubiquitination and investigated its correlation with the response to immunotherapy in HCC. The Molecular Signatures Database provided a compilation of genes associated with ubiquitination. A gene signature related to ubiquitination was obtained through Cox regression using the Least Absolute Shrinkage and Selection Operator method. The genetic factors CPY26B1, MCM10, SPINK4, and TRIM54 notably impacted the outcomes of HCC. The patients were divided into two groups: one group had a high risk of poor survival while the other had a low risk but a greater chance of controlling HCC progression. Both univariate and multivariate analyses using Cox regression found the risk score to be an independent predictor of HCC prognosis. Gene set enrichment analysis (GSEA) indicated enrichment in cell cycle and cancer-related microRNAs in high-risk groups. The tumor microenvironment (TME), response to immunotherapy, and effectiveness of chemotherapy medications positively correlated with the risk score. In the high-risk group, erlotinib showed higher IC50 values compared to the low-risk group which exhibited higher IC50 values for VX-11e, AKT inhibitor VIII, AT-7519, BMS345541, Bortezomib, CP466722, FMK, and JNK-9L. The results of RT-qPCR revealed that the expression of four UEGs was higher in tumor tissue as compared to normal tissue. Based on the genes that were expressed differently and associated with ubiquitination-related tumor categorization, we have developed a pattern of four genes and a strong nomogram that can predict the prognosis of HCC, which could be useful in identifying and managing HCC.

Deciphering smooth muscle cell heterogeneity in atherosclerotic plaques and constructing model: a multi-omics approach with focus on KLF15/IGFBP4 axis

BACKGROUND

Ruptured atherosclerotic plaques often precipitate severe ischemic events, such as stroke and myocardial infarction. Unraveling the intricate molecular mechanisms governing vascular smooth muscle cell (VSMC) behavior in plaque stabilization remains a formidable challenge.

METHODS

In this study, we leveraged single-cell and transcriptomic datasets from atherosclerotic plaques retrieved from the gene expression omnibus (GEO) database. Employing a combination of single-cell population differential analysis, weighted gene co-expression network analysis (WGCNA), and transcriptome differential analysis techniques, we identified specific genes steering the transformation of VSMCs in atherosclerotic plaques. Diagnostic models were developed and validated through gene intersection, utilizing the least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods. Nomograms for plaque assessment were constructed. Tissue localization and expression validation were performed on specimens from animal models, utilizing immunofluorescence co-localization, western blot, and reverse-transcription quantitative-polymerase chain reaction (RT-qPCR). Various online databases were harnessed to predict transcription factors (TFs) and their interacting compounds, with determination of the cell-specific localization of TF expression using single-cell data.

RESULTS

Following rigorous quality control procedures, we obtained a total of 40,953 cells, with 6,261 representing VSMCs. The VSMC population was subsequently clustered into 5 distinct subpopulations. Analyzing inter-subpopulation cellular communication, we focused on the SMC2 and SMC5 subpopulations. Single-cell subpopulation and WGCNA analyses revealed significant module enrichments, notably in collagen-containing extracellular matrix and cell-substrate junctions. Insulin-like growth factor binding protein 4 (IGFBP4), apolipoprotein E (APOE), and cathepsin C (CTSC) were identified as potential diagnostic markers for early and advanced plaques. Notably, gene expression pattern analysis suggested that IGFBP4 might serve as a protective gene, a hypothesis validated through tissue localization and expression analysis. Finally, we predicted TFs capable of binding to IGFBP4, with Krüppel-like family 15 (KLF15) emerging as a prominent candidate showing relative specificity within smooth muscle cells. Predictions about compounds associated with affecting KLF15 expression were also made.

CONCLUSION

Our study established a plaque diagnostic and assessment model and analyzed the molecular interaction mechanisms of smooth muscle cells within plaques. Further analysis revealed that the transcription factor KLF15 may regulate the biological behaviors of smooth muscle cells through the KLF15/IGFBP4 axis, thereby influencing the stability of advanced plaques via modulation of the PI3K-AKT signaling pathway. This could potentially serve as a target for plaque stability assessment and therapy, thus driving advancements in the management and treatment of atherosclerotic plaques.