PMCID string | Title string | Sentences string |
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PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Consequently, DLBCL patients were classified into two subgroups, C1 (35 patients) and C2 (60 patients). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Further comparison of PCD-related DEGs revealed that 306 genes, including CTSB, CEBPB, CTSA, SIPPA, FTH1, TLR2, PLA2G7, GLA, ACP2, and SERPINA1, were significantly upregulated in C1 (P < 0.05), while 148 genes, such as PDK1, RPL8, HAX1, OGG1, HINT1, RPS7, NPRL2, and RPS3, were significantly upregulated in C2 (P < 0.05). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Principal component analysis (PCA) further validated that the expression of PCD-related DEGs effectively distinguished the two subgroups (Fig. 1D) (Supplementary File 3, S3).Fig. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 1Consensus Clustering Analysis of PCD-Related DEGs in DLBCL Patients. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A Cumulative Distribution Function (CDF) curve showing the evaluation of different k-values in consensus clustering analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The slope of the CDF curve reaches its maximum at k = 2, indicating the optimal number of clusters. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B Tracking plot displaying the stability of cluster assignments across different values of k. The consistent colors within each row at k = 2 suggest a stable clustering solution. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | C Heatmap illustrating the clear division of DLBCL samples into two subgroups (C1 and C2) based on PCD-related DEGs. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The distinct clustering patterns further confirm the effectiveness of the consensus clustering. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | D Principal Component Analysis (PCA) plot demonstrating the separation of the two subgroups, C1 and C2, based on the expression profiles of PCD-related DEGs. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | This separation confirms the validity of the clustering approach Consensus Clustering Analysis of PCD-Related DEGs in DLBCL Patients. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A Cumulative Distribution Function (CDF) curve showing the evaluation of different k-values in consensus clustering analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The slope of the CDF curve reaches its maximum at k = 2, indicating the optimal number of clusters. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B Tracking plot displaying the stability of cluster assignments across different values of k. The consistent colors within each row at k = 2 suggest a stable clustering solution. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | C Heatmap illustrating the clear division of DLBCL samples into two subgroups (C1 and C2) based on PCD-related DEGs. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The distinct clustering patterns further confirm the effectiveness of the consensus clustering. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | D Principal Component Analysis (PCA) plot demonstrating the separation of the two subgroups, C1 and C2, based on the expression profiles of PCD-related DEGs. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | This separation confirms the validity of the clustering approach The immune infiltration analysis revealed significant differences in the proportions of CD4+ T cells, regulatory T cells, naive B cells, memory B cells, activated NK cells, and M0 macrophages between the C1 and C2 subgroups (P < 0.05, Fig. 2A). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | GSVA enrichment analysis showed that upregulated pathways in the C2 subgroup were related to DNA repair, cell cycle, and energy metabolism, suggesting that this subgroup may have higher proliferative capacity and metabolic activity. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Conversely, downregulated pathways in the C2 subgroup were associated with immune response and lysosomal function, indicating the potential presence of immune evasion mechanisms and reduced intracellular degradation capacity (Fig. 2B).Fig. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 2PCD-related immune infiltration and GSVA enrichment analysis between DLBCL subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A Immune infiltration analysis comparing the proportions of various immune cell types between the C1 and C2 subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Significant differences were observed in the proportions of CD4+ T cells, regulatory T cells, naive B cells, memory B cells, activated NK cells, and M0 macrophages between the two subgroups (P < 0.05). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B GSVA enrichment analysis illustrating the upregulated and downregulated pathways in the C2 subgroup compared to the C1 subgroup. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Upregulated pathways in the C2 subgroup include those related to DNA repair, cell cycle, and energy metabolism, indicating higher proliferative capacity and metabolic activity. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Downregulated pathways are associated with immune response and lysosomal function, suggesting potential immune evasion mechanisms and reduced intracellular degradation capacity in the C2 subgroup PCD-related immune infiltration and GSVA enrichment analysis between DLBCL subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A Immune infiltration analysis comparing the proportions of various immune cell types between the C1 and C2 subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Significant differences were observed in the proportions of CD4+ T cells, regulatory T cells, naive B cells, memory B cells, activated NK cells, and M0 macrophages between the two subgroups (P < 0.05). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B GSVA enrichment analysis illustrating the upregulated and downregulated pathways in the C2 subgroup compared to the C1 subgroup. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Upregulated pathways in the C2 subgroup include those related to DNA repair, cell cycle, and energy metabolism, indicating higher proliferative capacity and metabolic activity. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Downregulated pathways are associated with immune response and lysosomal function, suggesting potential immune evasion mechanisms and reduced intracellular degradation capacity in the C2 subgroup WGCNA analysis was performed on the transcriptome expression data between DLBCL and healthy controls, as well as between the C1 and C2 subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The modules with the smallest Padjust values were selected (Padjust = 4e−124 for the DLBCL vs. healthy controls module; Padjust = 1e−10 for the C1 vs. C2 subgroup module) (Fig. 3A and 3B). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Module genes were filtered using the criteria "geneSigFilter = 0.5" and "moduleSigFilter = 0.8," resulting in 1266 and 26 module genes, respectively. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | By intersecting these results, seven characteristic genes of DLBCL were identified (Fig. 3C).Fig. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 3WGCNA Analysis of DLBCL and Healthy Controls, and Between C1 and C2 Subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A Module-trait relationships identified through WGCNA analysis between DLBCL patients and healthy controls. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The heatmap shows the correlation between module eigengenes (ME) and traits (control vs. treatment). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The module with the smallest Padjust value (Padjust = 4e−124) was selected for further analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B Module-trait relationships identified through WGCNA analysis between the C1 and C2 subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The heatmap displays the correlation between module eigengenes (ME) and traits (C1 vs. C2). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The module with the smallest Padjust value (Padjust = 1e−10) was selected for further analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | C Venn diagram showing the intersection of module genes identified in WGCNA analyses between DLBCL and healthy controls, and between the C1 and C2 subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A total of seven characteristic genes of DLBCL were identified by intersecting the results from both analyses WGCNA Analysis of DLBCL and Healthy Controls, and Between C1 and C2 Subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A Module-trait relationships identified through WGCNA analysis between DLBCL patients and healthy controls. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The heatmap shows the correlation between module eigengenes (ME) and traits (control vs. treatment). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The module with the smallest Padjust value (Padjust = 4e−124) was selected for further analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B Module-trait relationships identified through WGCNA analysis between the C1 and C2 subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The heatmap displays the correlation between module eigengenes (ME) and traits (C1 vs. C2). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The module with the smallest Padjust value (Padjust = 1e−10) was selected for further analysis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | C Venn diagram showing the intersection of module genes identified in WGCNA analyses between DLBCL and healthy controls, and between the C1 and C2 subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A total of seven characteristic genes of DLBCL were identified by intersecting the results from both analyses We integrated 12 machine learning algorithms to predict the expression patterns of programmed cell death (PCD)-related genes in DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | To enhance model performance, we combined these algorithms into 91 different models. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | F-score evaluations showed that most combination models exhibited stable, high predictive performance on both training and test sets. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | AUC analysis revealed that most models achieved AUC values between 0.7 and 0.9, indicating strong classification ability. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Ensemble methods like XGBoost and GBM showed the highest AUC values, demonstrating superior performance in classifying DLBCL and PCD-related genes. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Feature importance analysis identified genes such as CTSB, DPYD, STOM, GBP1, SERPING1, C3AR1, and SCARB2 as critical for PCD-related expression patterns in DLBCL patients (Fig. 4).Fig. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 4Construction and performance evaluation of machine learning models for PCD-related gene expression in DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A AUC (Area Under the Curve) values for various machine learning models applied to the training set (DatasetA) and two test sets (DatasetB and DatasetC). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The color gradient indicates the AUC values, with higher values suggesting better classification performance. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The models are listed on the y-axis, and the color-coding on the x-axis shows the respective datasets used for evaluation. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B F-score evaluations for the same machine learning models across the training and test sets. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The F-score provides a balanced assessment by combining precision and recall. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The color gradient indicates F-score values, with higher values suggesting better overall performance. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The datasets used for evaluation are color-coded on the x-axis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | C Feature importance rankings across various machine learning models. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The bar plot shows the average rank of each gene, with the color gradient indicating the number of models in which the gene was considered important. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Genes such as CTSB, DPYD, STOM, GBP1, SERPING1, C3AR1, and SCARB2 consistently ranked high, indicating their crucial roles in PCD-related gene expression patterns of DLBCL patients Construction and performance evaluation of machine learning models for PCD-related gene expression in DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | A AUC (Area Under the Curve) values for various machine learning models applied to the training set (DatasetA) and two test sets (DatasetB and DatasetC). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The color gradient indicates the AUC values, with higher values suggesting better classification performance. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The models are listed on the y-axis, and the color-coding on the x-axis shows the respective datasets used for evaluation. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | B F-score evaluations for the same machine learning models across the training and test sets. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The F-score provides a balanced assessment by combining precision and recall. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The color gradient indicates F-score values, with higher values suggesting better overall performance. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The datasets used for evaluation are color-coded on the x-axis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | C Feature importance rankings across various machine learning models. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The bar plot shows the average rank of each gene, with the color gradient indicating the number of models in which the gene was considered important. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Genes such as CTSB, DPYD, STOM, GBP1, SERPING1, C3AR1, and SCARB2 consistently ranked high, indicating their crucial roles in PCD-related gene expression patterns of DLBCL patients By comparing the differential gene expression between a DLBCL cell line (VAL) and a normal B lymphocyte cell line (IM-9), we identified a set of programmed cell death (PCD)-related genes and DLBCL-specific genes that were significantly upregulated or downregulated. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Specifically, 863 PCD-related genes were found to be differentially expressed (P < 0.05, Supplementary File 4, S4). |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Notably, the DLBCL-specific genes CTSB, DPYD, and SCARB2 were significantly upregulated, while STOM and GBP1 were significantly downregulated (Fig. 5).Fig. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | 5Gene expression levels of selected PCD-related genes in DLBCL (VAL) and normal B lymphocyte (IM-9) Cell Lines Gene expression levels of selected PCD-related genes in DLBCL (VAL) and normal B lymphocyte (IM-9) Cell Lines Diffuse large B-cell lymphoma (DLBCL) is one of the most common hematological malignancies worldwide. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Despite advancements in immunotherapy, the prognosis for DLBCL patients remains poor due to the disease's high heterogeneity and complexity . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Consequently, there is an urgent need to identify novel biomarkers and explore potential DLBCL subtypes to enhance risk assessment. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Programmed cell death (PCD) is critical for normal development and the maintenance of homeostasis in organisms . |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Increasing evidence suggests that PCD is closely linked to the initiation and metastasis of malignant tumors, and dysregulation of PCD pathways may offer valuable insights into prognosis and subtype classification in DLBCL [21–23]. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | This study employed a multi-layered and multi-method analytical approach to comprehensively explore the expression patterns of PCD-related genes in DLBCL patients and their potential roles in clinical prognosis. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Using consensus clustering analysis, we stratified DLBCL patients into two distinct subgroups (C1 and C2) and further analyzed the differences between these subgroups in the immune microenvironment and key biological pathways through immune infiltration and GSVA enrichment analyses. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Immune infiltration analysis revealed that the higher proportions of M0 macrophages, regulatory T cells, and memory B cells in the C2 subgroup suggest a more complex immune evasion mechanism, while the higher infiltration of CD4+ T cells and activated NK cells in the C1 subgroup indicates a stronger anti-tumor immune response. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | These findings highlight how the C2 subgroup may regulate the immune microenvironment to facilitate tumor progression and drug resistance. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | GSVA enrichment analysis further revealed upregulation of DNA repair, cell cycle, and energy metabolism pathways in the C2 subgroup, which are typically associated with rapid tumor cell proliferation and enhanced anti-apoptotic capacity. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Additionally, the downregulation of immune-related pathways, such as Toll-like receptor signaling, in the C2 subgroup suggests possible immune evasion, reinforcing the high-risk characteristics of this subgroup. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | These results align with the malignant features of the C2 subgroup, indicating the need for more aggressive clinical intervention strategies. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | WGCNA analysis identified key gene modules within the DLBCL subgroups, playing a central role in the molecular mechanisms of DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | By screening differential gene modules between DLBCL and healthy controls, as well as between the C1 and C2 subgroups, we identified a set of characteristic genes that may be crucial in the development and progression of DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | These genes exhibited significant differences between subgroups, providing valuable insights for further clinical research. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The construction and evaluation of machine learning models further deepened our understanding of the molecular characteristics of DLBCL subgroups. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | By integrating 12 machine learning algorithms, we developed high-performance predictive models for DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Ensemble learning methods such as XGBoost and GBM demonstrated superior AUC values, underscoring their efficacy in handling complex gene expression data. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | Feature importance analysis revealed that genes such as SCARB2, GBP1, and STOM consistently ranked highly across multiple models, indicating their critical roles in the PCD-related mechanisms of DLBCL. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | To validate the expression characteristics of PCD-related genes and the seven DLBCL-specific genes in patient samples, we conducted transcriptome sequencing analysis on the DLBCL cell line VAL and the normal human B lymphocyte cell line IM-9. |
PMC12003219 | Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning | The results showed that the significant differential expression of PCD-related genes in the VAL cell line closely matched the molecular characteristics of the C2 subgroup, further supporting their potential as high-risk biomarkers. |
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