PMCID string | Title string | Sentences string |
|---|---|---|
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Our study builds on this emerging evidence and further supports PA2G4 as an oncogenic cofactor in MYC-driven cancers. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | We show that PA2G4 plays a critical role in stabilizing MYC proteins, reinforcing its function as a MYC family co-regulator. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Previous studies have shown that PA2G4 interacts with FBXW7α, sequestering it in the cytoplasm and thereby preventing it from targeting MYC for proteasomal degradation . |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | This interaction effectively prolongs the half-life of MYC proteins, enhancing their oncogenic potential. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | This interaction leads to increased MYC protein stability and sustained oncogenic signaling. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | By shielding MYC from degradation, PA2G4 amplifies MYC-driven transcriptional programs and tumorigenic potential. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Taken together with prior studies, our findings in neuroblastoma highlight PA2G4 as a conserved and functionally significant MYC regulatory factor which may have a role in many c-MYC-driven malignancies. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Targeting the PA2G4–MYC axis represents a promising therapeutic strategy for MYC-driven cancers. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | One promising therapeutic avenue is the use of PA2G4 inhibition to enhance the efficacy of conventional chemotherapies. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | In cancers where MYC plays a critical role in tumor growth and chemoresistance, combining PA2G4 inhibition with chemotherapy could provide a more effective strategy for overcoming drug resistance and improving patient outcomes. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Additionally, the potential synergy between PA2G4 inhibitors and other targeted therapies, such as those aimed at MYC-interacting proteins like FBXW7 or Aurora A, could further enhance the therapeutic benefit. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | These combinatorial approaches warrant further investigation as a strategy to exploit MYC-driven vulnerabilities and broaden treatment options for patients with high-risk malignancies. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | While our findings highlight the therapeutic potential of targeting PA2G4 in MYC-driven cancers, several important limitations remain. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Although we demonstrated that PA2G4 is essential for maintaining c-MYC stability and function in vitro, the in vivo efficacy of PA2G4 inhibition in human c-MYC-driven tumors has yet to be fully established. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Previous studies have shown that WS6 is effective in MYCN-driven neuroblastoma models; however, its activity in other MYC-driven cancers remains to be determined. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Moreover, we observed that WS6-induced cytotoxicity varied across cancer cell lines, suggesting the possibility of off-target effects or cell line-specific differences in dependency on PA2G4. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | These findings underscore the need to further characterize the broader network of PA2G4 interactions, particularly with proteins involved in MYC regulation, to better understand its context-specific roles in oncogenesis. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | A major limitation of WS6 as a therapeutic agent is its known toxicity in normal tissues, which significantly restricts its translational potential. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | To overcome this, our previous work focused on developing and characterizing WS6 analogs with improved selectivity, reduced toxicity, and enhanced drug-like properties . |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | These efforts aim to generate more clinically viable PA2G4 inhibitors for future therapeutic application. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | To further advance this therapeutic strategy, structural studies of the PA2G4–MYC complex will be crucial. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Future studies will focus on elucidating the precise mechanism by which PA2G4 regulates MYC stability, including evaluation of MYC phosphorylation status and its regulation via the proteasome pathway in PA2G4-deficient and -proficient contexts across multiple cancer types. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | High-resolution structural analyses could reveal key binding sites and conformational changes that occur upon interaction, providing a blueprint for rational drug design. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | Ultimately, understanding the structural basis of PA2G4′s oncogenic function will be essential for developing potent and selective inhibitors that disrupt its role in cancer while minimizing effects on its physiological functions. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | In summary, our study identifies PA2G4 as a novel and critical regulator of MYC protein stability, establishing it as a promising therapeutic target in MYC-driven cancers. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | By demonstrating that disruption of PA2G4 impairs MYC stability and tumor cell viability, we provide a compelling rationale for targeting the PA2G4–MYC axis as an alternative to direct MYC inhibition. |
PMC12468391 | PA2G4 Functions as a Cofactor for MYC Family Oncoproteins in MYC-Driven Malignancies | These findings lay the groundwork for future development of clinically viable PA2G4 inhibitors and open new avenues for the treatment of high-risk, MYC-driven malignancies. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 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. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Programmed cell death (PCD) plays a critical role in the development and progression of various cancers. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A comprehensive analysis of PCD-related gene expression in DLBCL could enhance risk stratification and inform personalized treatment strategies. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | This study integrated five DLBCL datasets with 18 PCD-related gene expression profiles to identify differentially expressed genes (DEGs) associated with PCD. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Patients were stratified into two subgroups (C1 and C2) using consensus clustering analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 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. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Additionally, 12 machine learning algorithms were employed to construct predictive models for DLBCL, with performance evaluated using AUC and F-score metrics. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 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. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A total of 1074 PCD-related DEGs were identified. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Unsupervised clustering revealed two distinct molecular subtypes of DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 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. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Machine learning algorithms and transcriptome sequencing validation identified five potential biomarkers for DLBCL, including CTSB, DPYD, SCARB2, STOM, and GBP1. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 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. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Five key biomarkers (CTSB, DPYD, SCARB2, STOM, GBP1) may improve risk stratification and understanding of DLBCL heterogeneity. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | These findings lay the groundwork for further exploration of DLBCL progression and potential prognostic improvements. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL), accounting for approximately 30–40% of adult NHL cases. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | In recent years, the incidence of DLBCL has shown a gradual increase. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | According to statistics, there were 544,000 new cases of DLBCL globally in 2020, representing about 2.8% of all newly diagnosed cancers, with a mortality rate reaching 259,000 deaths. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The prognosis of DLBCL patients is associated with baseline characteristics such as clinical stage, age, β2-microglobulin levels, and lactate dehydrogenase levels . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Moreover, DLBCL exhibits a high degree of phenotypic and genetic heterogeneity. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The standard treatment regimen for DLBCL is the R-CHOP protocol, consisting of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone, with a typical cure rate of 50–70%. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Compared to the R-CHOP regimen as first-line treatment for DLBCL, the pola-R-CHP regimen reduces the relative risk of disease progression, relapse, or death by 27% while maintaining a similar safety profile. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | However, approximately 30–40% of patients experience disease relapse or increased drug resistance within 2 to 5 years after initial treatment, leading to poor prognosis . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The success of CAR-T cell therapy as a second-line treatment for relapsed or refractory large B-cell lymphoma has catalyzed its exploration as a second-line therapeutic option, particularly in clinical settings with poor outcomes from standard treatments, including refractory disease or early relapse within 12 months after first-line chemoimmunotherapy. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Furthermore, conventional salvage immunochemotherapy followed by autologous stem cell transplantation is effective in only 10% of patients with refractory or relapsed disease . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Therefore, identifying highly specific biomarkers is crucial for the screening, diagnosis, and prognosis prediction of DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Programmed cell death (PCD) is characterized by distinct morphological features and energy-dependent biochemical mechanisms, and is recognized as a key regulatory factor in various cellular processes, including tissue growth, embryogenesis, cell turnover, and immune responses. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | To date, at least 18 distinct forms of PCD have been reported, including apoptosis, pyroptosis, ferroptosis, and cuproptosis, among others . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Each PCD modality operates through unique molecular mechanisms and results in specific functional outcomes, though crosstalk between different PCD pathways is also common. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Aberrant PCD plays a critical role in a variety of human diseases, including neurodegenerative disorders, ischemic injury, autoimmune diseases, and various types of cancer . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Extensive research has demonstrated a close association between PCD and the progression and metastasis of multiple malignancies. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Consequently, cancer therapies that induce cell death have become a focal point in cancer biology research. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | In recent years, machine learning has emerged as a powerful tool for identifying and prioritizing interactomic hub genes, which play central roles in protein interaction networks and biological pathways . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | By leveraging network analysis, feature selection, and predictive modeling, machine learning algorithms can effectively uncover key genes that drive disease mechanisms and progression. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | This approach has been particularly valuable in complex diseases such as cancer, where identifying critical molecular players can inform diagnostic and therapeutic strategies. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | This study aims to construct diagnostic models for DLBCL based on PCD-related features using machine learning ensemble methods, with the goal of uncovering the potential roles of PCD-related genes in the development and progression of DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | By improving the accuracy of DLBCL diagnosis and prediction, this research ultimately seeks to contribute to personalized and effective therapeutic interventions. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | We obtained five datasets from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo), including GSE132929, GSE43677, GSE156309, GSE190847, and GSE12453 [7–10]. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The GSE132929 and GSE43677 datasets served as the training set (95 DLBCL and 40 normal samples), GSE156309 and GSE190847 as validation set A (61 DLBCL and 28 normal samples), and GSE12453 as validation set B (11 DLBCL and 15 normal samples). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | RNA-seq data were corrected for batch effects and log-transformed. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The 18 forms of programmed cell death (PCD) genes investigated, totaling 1,965, were derived from the literature . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | After removing duplicates, 1,545 unique PCD-related genes were included (Supplementary File 1, S1). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The GSE132929 and GSE43677 datasets were annotated and merged using R, followed by differential expression analysis of PCD-related genes using the "limma" package. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Differentially expressed genes (DEGs) were identified with the criteria of |log2FC|> 1 and P < 0.05.Based on the differential expression levels of DEGs in DLBCL, unsupervised consensus clustering analysis was performed using the "ConsensusClusterPlus" package, employing the "PAM" algorithm and Euclidean distance as the metric . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Subsequently, the clustering classification was assessed through Principal Component Analysis (PCA), and DEG expression profiles across different DLBCL subtypes were compared using the "ggpubr" package . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Immune cell infiltration across different subgroups was quantitatively analyzed using the CIBERSORT package, and boxplots were used to visually depict the differences in immune infiltration between the subgroups . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Additionally, gene set variation analysis (GSVA) was applied to correct the gene expression data of DLBCL subgroups, followed by KEGG pathway enrichment analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Pathways were considered significant if they met the criteria of P < 0.05 and |t|> 1 . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | To better visualize the results, the top 10 upregulated and downregulated pathways were presented as bar plots using the "ggpubr" package. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | We utilized the "WGCNA" package to identify core modules and key genes between DLBCL and healthy control groups, as well as between subgroups C1 and C2 . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The top 25% of DEGs with the highest variance were selected, and outlier samples were removed. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A suitable soft-thresholding power (β) was chosen to construct the gene clustering tree. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The adjacency matrix was converted into a Topological Overlap Matrix (TOM) to assess gene relationships, and dissimilarity (1-TOM) was calculated. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The minimum module size was set to 100 genes, and the dynamic tree cut algorithm grouped genes with similar expression patterns into modules. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | We then calculated the correlation between module eigengenes and clinical traits, evaluated the significance of each gene within the modules, and generated scatter plots for the core module genes. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | By intersecting results from different analyses, we identified the characteristic genes of DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | We integrated 12 machine learning algorithms to construct predictive models for diffuse large B-cell lymphoma (DLBCL) based on programmed cell death (PCD)-related genes . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | These algorithms included Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (Lasso), Gradient Boosting Machine (GBM), Ridge Regression, K-Nearest Neighbors (KNN), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Decision Tree (DT), XGBoost, Elastic Net Regression (ENR), Stepwise Regression, and Naive Bayes . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | All models were implemented using the respective R packages for efficient computation. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | To enhance model performance, we combined these 12 algorithms into 91 possible combinations, each evaluated through cross-validation. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The F-score, balancing precision and recall, and the Area Under the Curve (AUC), which assesses overall classification ability, were used as key metrics to evaluate model stability and accuracy. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The DLBCL cell line VAL (purchased from Zhejiang Meisen Cell Technology Co., Ltd.) and the human normal B lymphocyte cell line IM-9 (purchased from Guangzhou Kofan Biotechnology Co., Ltd.) were cultured at 37 ℃ in a 5% CO2 incubator. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Cells in the logarithmic growth phase were selected for subsequent experiments. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Total RNA was extracted from VAL and IM-9 cells using the Trizol method, and library preparation was performed by Beijing Qingke Biotechnology Co., Ltd., followed by second-generation high-throughput sequencing using the Illumina NovaSeq platform. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | After data acquisition, paired sample t-tests were conducted on the core genes using Read count values, and genes with significantly different p-values were selected for further analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The bioinformatics method and statistical method used in this paper were both carried out in R language version 4.3.3. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Paired sample T-test was performed on the two groups of data conforming to normal distribution, and P < 0.05 indicated that the difference was statistically significant. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Gene annotation of the GSE132929 and GSE43677 datasets resulted in the identification of 12,447 genes, from which 1545 PCD-related genes were extracted for differential expression analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A total of 1235 PCD-related DEGs were identified, with 1074 of them meeting the significance criterion of P < 0.05. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | These included 415 apoptosis-related genes, 32 pyroptosis-related genes, 58 ferroptosis-related genes, 249 autophagy-related genes, 63 necroptosis-related genes, 15 cuproptosis-related genes, 3 PARP-1-dependent cell death (parthanatos)-related genes, 11 entotic cell death-related genes, 6 netotic cell death-related genes, 169 lysosome-dependent cell death-related genes, 6 alkaliptosis-related genes, 2 oxeiptosis-related genes, 29 immunogenic cell death-related genes, 257 anoikis-related genes, 45 paraptosis-related genes, 18 NETosis-related genes, 3 methuosis-related genes, and 17 entosis-related genes (Supplementary File 2, S2). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | We performed consensus clustering analysis based on the expression data of 1074 differentially expressed genes (DEGs) related to programmed cell death (PCD) and determined that the optimal number of clusters was k = 2. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | At k = 2, the cumulative distribution function (CDF) curve reached its maximum (Fig. 1A), and the tracking plot showed high stability with consistent colors or markers within the same row (Fig. 1B). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The heatmap also clearly divided the samples (Fig. 1C). |
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