Search Results (294)

Emerging evidence highlights the critical role of phagocytosis-related genes in CRC progression, underscoring the need for novel phagocytosis-based prognostic models to predict clinical outcomes. In this study, a four-gene (SPHK1, VSIG4, FCGR2B and FPR2) signature associated with CRC prognosis was developed using single-sample gene set enrichment analysis (ssGSEA), least absolute shrinkage and selection operator (LASSO) regression, and univariate Cox analysis. Pathway enrichment analysis was conducted on the prognostic genes, along with evaluations of the tumor microenvironment and sensitivity to immunotherapy and chemotherapy across the high- and low-risk groups. Prognostic gene validation was performed via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) using CRC cDNA and tissue microarrays. High-risk patients showed enhanced responsiveness to immunotherapy, while chemotherapy sensitivity varied across risk subgroups. qRT-PCR results revealed upregulation of SPHK1 and FPR2 in cancer tissues, whereas FCGR2B and VSIG4 were downregulated. IHC assays confirmed increased SPHK1 and FPR2 expression in cancer samples. Single-cell RNA sequencing analysis demonstrated a decrease in SPHK1 and FCGR2B, while VSIG4 and FPR2 progressively increased during macrophage differentiation. These findings provide a potential framework for targeted therapy. Full article

Background: Cancer stem cells (CSCs) are key drivers of tumorigenesis and metastasis. However, the precise roles of CSC-associated genes in these processes remain unclear. Methods: This study integrates cancer stem cell biomarkers and clinical data from The Cancer Genome Atlas (TCGA) specific to bladder cancer (BLCA). By combining differentially expressed genes (DEGs) from TCGA-BLCA samples with CSC-related biomarkers, we conducted comprehensive functional analyses and developed an 8-gene prognostic signature through Cox regression, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression. This model was validated with GEO datasets (GSE13507 and GSE32894), and the single-cell RNA seq dataset GSE222315 was subsequently analyzed to characterize the signature genes and elucidate their interactions. And a nomogram was created to stratify TCGA-BLCA patients into risk categories. The ‘oncoPredict’ algorithm based on the GDSC2 dataset assessed drug sensitivity in BLCA. Result: From the TCGA cohort, 665 CSC-related genes were identified, with 120 showing significant differential expression. The 8-gene signature (ALDH1A1, CBX7, CSPG4, DCN, FASN, INHBB, MYC, NCAM1) demonstrated strong predictive power for overall survival in both TCGA and GEO cohorts, as confirmed by Kaplan–Meier and ROC analyses. The nomogram, integrating age, tumor stage and risk scores, demonstrated high predictive accuracy. Additionally, the oncoPredict algorithm indicated varying drug sensitivities across patient groups. Based on retrospective data, we identified a novel CSC-related prognostic signature for BLCA. This finding suggests that targeting these genes could offer promising therapeutic strategies. Full article

Background: Centrosome amplification, a hallmark of cell cycle dysregulation, drives carcinogenesis through aneuploidy induction and invasive phenotype acquisition. In pancreatic adenocarcinoma—a malignancy characterized by profound genomic instability—the molecular circuitry of centrosome amplification remains enigmatic. Critical gaps persist in understanding its spatiotemporal dynamics in tumor microenvironment remodeling and therapy resistance. Methods: This study integrated centrosome amplification-related genes from TCGA and Genecards, established a prognostic risk model through univariate Cox regression–LASSO penalized Cox regression–multivariate Cox regression analyses, and validated it using GEO datasets. Single-cell sequencing analyses dissected transcriptional heterogeneity and intercellular communication networks, while spatially resolved transcriptomics unveiled spatiotemporal expression patterns and molecular regulatory mechanisms of core genes. With further experimental validation via PCR analysis of patient-derived tissue samples confirming key gene expression patterns. Results: This study identified 23 centrosome amplification-related prognostic genes in pancreatic adenocarcinoma, establishing IFI27, KIF20A, KLK10, SPINK7, and TOP2A as highly specific diagnostic and prognostic biomarkers. The constructed signature was established as an independent prognostic indicator correlating with aggressive clinicopathological characteristics and chemoresistance. Mechanistically linked to enhanced DNA repair capacity and accelerated cell cycle progression, also synergizes with KRAS mutational profiles. Tumor microenvironment analysis revealed significant associations with immunosuppressive. Single-cell resolution demonstrated cellular specificity of IFI27/KLK10 in ductal epithelial cells and fibroblasts, with intercellular communication networks exhibiting multidimensional regulatory features. Spatially resolved transcriptomics delineated tumor-region-specific expression patterns of core genes. While PCR validation on matched tumor/normal tissues confirmed significant differential expression of IFI27, KIF20A, KLK10, and TOP2A. Conclusions: This study deciphers the multidimensional clinic–molecular network orchestrated by centrosome amplification in PDAC, revealing its dual-pathogenic mechanism in fueling tumor aggressiveness through coordinated induction of genomic instability and immunosuppressive microenvironment reprogramming. These findings establish a translational framework for developing centrosome dynamics-based prognostic stratification and molecularly targeted therapeutic strategies. Full article

Fibrinolysis plays a crucial role in maintaining coagulation homeostasis, but its functions in hepatocellular carcinoma (HCC) remain poorly understood. This study aimed to develop a fibrinolysis-based molecular classification and prognostic signature for HCC and to identify a key regulatory gene. Using non-negative matrix factorization (NMF), we identified distinct fibrinolysis-related HCC subtypes with specific clinical outcomes and tumor microenvironment characteristics. A six-gene prognostic signature comprising ACAT1, GRHPR, HPX, PCK2, IYD, and PON1 was established through weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression, which effectively stratified patients into different risk groups across multiple cohorts. Hemopexin (HPX) was identified as the top candidate and functionally validated: HPX overexpression suppressed clonogenicity and migration, promoted apoptosis, and inhibited xenograft tumor growth. RNA sequencing analysis suggested associations between HPX and apoptosis as well as TNF-α signaling pathways, which were confirmed through flow cytometry apoptosis assays, mitochondrial membrane potential measurements, and TUNEL staining. Western blot and immunohistochemical analyses further demonstrated that HPX upregulates the Bax/Bcl-2 ratio via the TNF-α signaling pathway. This study defines novel molecular subtypes of HCC and reveals that HPX exerts anti-tumor effects through TNF-α-mediated mitochondrial apoptosis, characterized by an increased Bax/Bcl-2 ratio. Full article

Background/Objectives: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are low-grade B-cell lymphomas (LGBCLs) with indolent clinical courses but a lifelong risk of histologic transformation (HT) to aggressive lymphomas, particularly diffuse large B-cell lymphoma. Predicting HT can be challenging due to class imbalances and the inherent complexity of time-dependent events. While there are current prognostic indices for survival, they do not specifically address HT risk. This study aimed to develop and validate survival-based and traditional classification machine-learning models to predict HT in cohorts. Methods: Using a multicenter retrospective dataset (n = 1068), survival models (Cox proportional hazards, Lasso-Cox, Random Survival Forest, Gradient-boosted Cox [GBM-Cox], eXtreme Gradient Boosting [XGBoost]-Cox), and classification models (Logistic regression, Lasso logistic, Random Forest, Gradient Boosting, XGBoost) were compared. The best-performing survival models—XGBoost-Cox, Lasso-Cox, and GBM-Cox—were assessed on an independent test set (n = 92). Model sensitivity was maximized using optimal binary risk cutoff points based on Youden’s index. Results: Survival models showed superior predictive performance than classical classifiers, with XGBoost-Cox exhibiting the highest mean accuracy (85.3%), time-dependent area under the curve (0.795), sensitivity (98%), specificity (83.9%), and concordance index (0.836). Incorporating next-generation sequencing (NGS) data improved model accuracy and specificity, indicating that genetic factors improve HT prediction. Principal component analysis revealed distinct gene mutation patterns associated with HT risk, highlighting DNA-repair genes such as TP53, BLM, and RAD50. Conclusions: This study highlights the clinical value of survival-based machine-learning methods integrated with NGS data to personalize HT risk stratification for patients with FL and MZL. Full article

Background/Objectives: The prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is significantly affected by inflammatory state and immune dysregulation. The systemic inflammatory response index (SIRI), which reflects neutrophil, monocyte, and lymphocyte dynamics, has emerged as a potential marker of immune-inflammatory status. However, its role in predicting HBV-ACLF outcomes remains unclear. This research aims to elucidate the prognostic value of SIRI and its dynamic changes combined with disease severity scores in predicting the outcomes of HBV-ACLF. Methods: The study included HBV-ACLF patients enrolled in a multicenter clinical study between July 2019 and April 2024. Based on 90-day outcomes, the participants were categorized into survival and death groups. Clinical data and SIRI values were collected on days 0 (baseline), 3, 7, and 14. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. The predictive value of dynamic SIRI changes combined with disease severity scores was evaluated using receiver operating characteristic (ROC) curves. Results: A total of 153 patients with HBV-ACLF were analyzed, including 104 in the survival group and 49 in the death group. SIRI values were significantly lower in the survival group than in the death group across all time points. Multivariate Cox regression analysis identified that an increased ΔSIRI at day 3 (ΔSIRI3), a higher MELD score, and a lower albumin level were independently associated with increased 90-day mortality. The combination of SIRI on day three (SIRI3) and MELD-Na score on day three (MELD-Na3) demonstrated the highest predictive performance, with an AUC of 0.817 (95% CI: 0.750–0.883). Conclusions: The combination of the SIRI and MELD-Na score on day three provides a strong predictive value for the short-term prognosis of HBV-ACLF, highlighting its potential utility in early prognostic evaluation. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis. T cell exhaustion (TEX) is a key factor in tumor immune evasion and therapeutic resistance. In this study, we integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to characterize TEX-related transcriptional features in HCC. Methods: We first computed TEX scores for each sample using a curated 65-gene signature and classified them into high-TEX and low-TEX groups by the median score. Differentially expressed genes were identified separately in scRNA-seq and bulk RNA-seq data, then intersected to retain shared candidates. A 26-gene prognostic signature was derived from these candidates via univariate Cox and LASSO regression analysis. Results: The high-TEX group exhibited increased expression of immune checkpoint molecules and antigen presentation molecules, suggesting a tumor microenvironment that is more immunosuppressive but potentially more responsive to immunotherapy. Functional enrichment analysis and protein–protein interaction (PPI) network construction further validated the roles of these genes in immune regulation and tumor progression. Conclusions: This study provides a comprehensive characterization of the TEX landscape in HCC and identifies a robust gene signature associated with prognosis and immune infiltration. These findings highlight the potential of targeting TEX-related genes for personalized immunotherapeutic strategies in HCC. Full article

Objective: Sarcopenic obesity (SO) is a combination of depleted skeletal muscle mass and obesity, with a high prevalence, undetected onset, challenging diagnosis, and poor prognosis. However, studies on SO in cancer settings are limited. We aimed to explore the association between SO and mortality and to investigate potential predictors involved in the development of SO, with a further objective of constructing a model to detect its occurrence in cancer patients. Methods: The data of 1432 cancer patients from the National Health and Nutrition Examination Survey (NHANES) from the years 1999 to 2006 and 2011 to 2016 were included. For survival analysis, univariable and multivariable Cox proportional hazard models were used to examine the associations of SO with overall survival, adjusting for potential confounders. For machine learning, six algorithms, including logistic regression, stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were utilized to build models to predict the presence of SO. The predictive performances of each model were evaluated. Results: From six machine learning algorithms, cancer patients with SO were significantly associated with a higher risk of all-cause mortality (adjusted HR 1.368, 95%CI 1.107–1.690) compared with individuals without SO. Among the six machine learning algorithms, the optimal LASSO model achieved the highest area under the curve (AUC) of 0.891 on the training set and 0.873 on the test set, outperforming the other five machine learning algorithms. Conclusions: SO is a significant risk factor for the prognosis of cancer patients. Our constructed LASSO model to predict the presence of SO is an effective tool for clinical practice. This study is the first to utilize machine learning to explore the predictors of SO among cancer populations, providing valuable insights for future research. Full article

Objective This study aimed to show the clinicopathological characteristics of large retroperitoneal liposarcoma (RLS) and to develop a customized nomogram model for patients with large RLS. Methods A total of 1735 patients diagnosed with RLS were selected from the public SEER database. Among them, 1113 patients with a maximum tumor diameter greater than 150 mm were included for further analysis. Nomogram models were developed based on Lasso and multivariate Cox regression analyses. A total of 166 patients that presented in the same period at our institution were used for external validations. Results A larger tumor size in RLS was associated with worse survival outcomes. Lasso and Cox regression analyses consistently identified age, TNM stage, occurrence pattern, histology, and surgery as important prognostic factors for OS. The constructed model demonstrated robust predictive performance, with better time-ROC (time-dependent receiver operating characteristic) for 1-year (83.1%), 3-year (83.8%), and 5-year (81.4%) survival in the training cohort. The concordance index (C-index) was approximately 0.80 in both the training and validation cohorts, reflecting excellent discriminatory ability of the model. Survival risk stratification analysis revealed significant differences in survival outcomes of large RLS (HR = 4.12 [3.31–5.12], p < 0.001, in the training cohort). Decision curve analysis (DCA) confirmed that the nomogram provided greater net benefits across a range of threshold probabilities. Conclusion This study identified important prognostic factors for survival in patients with large RLS and developed a reliable nomogram for predicting OS. The model’s strong predictive performance supports its use in personalized treatment strategies, improving prognosis assessment and clinical decision making for these patients. Full article

The overall survival of hepatocellular carcinoma (HCC) remains poor, highlighting the need for better prognostic tools. Nucleotide metabolism fuels tumor progression, while the immune microenvironment dictates therapy response, but integrated models combining both features are lacking. Using TCGA-LIHC transcriptomic/clinical data, we identified nucleotide metabolism and immune-related differentially expressed genes (NMIRGs), which stratified HCC patients into two subtypes via non-negative matrix factorization. A nine-gene prognostic risk signature was constructed through LASSO/Cox regression and validated using independent GEO datasets, and the NMIRG signature was further validated experimentally via RT-qPCR in HCC cell lines and independently using the HPA database for protein-level evidence. As evaluated by our risk signature, high-risk patients exhibited altered immune profiles (T cells increasing, neutrophils decreasing), elevated tumor mutation burden and microsatellite instability, and worse predicted immunotherapy response. Gene set enrichment analysis linked high-risk genes to immune pathways and low-risk genes to metabolic processes. Our risk signature predicted HCC prognosis independent of demographic features and outperformed existing signatures with superior C-index accuracy, effectively predicting immune microenvironment status and therapy benefits. Together, this integrated NMIRG signature offers enhanced prognostication and identifies promising biomarkers for personalized HCC management. Full article

Background: Acute myeloid leukemia (AML) is a common and aggressive adults hematological malignancies. This study explored megakaryocyte–erythroid progenitors (MEPs) signature genes and constructed a prognostic model. Methods: Uniform manifold approximation and projection (UMAP) identified distinct cell types, with differential analysis between AML-MEP and normal MEP groups. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression selected biomarkers to build a risk model and nomogram for 1-, 3-, and 5-year survival prediction. Results: Ten differentially expressed genes (DEGs) related to overall survival (OS), six (AHSP, MYB, VCL, PIM1, CDK6, as well as SNHG3) were retained post-LASSO. The model exhibited excellent efficiency (the area under the curve values: 0.788, 0.77, and 0.847). Pseudotime analysis of UMAP-defined subpopulations revealed that MYB and CDK6 exert stage-specific regulatory effects during MEP differentiation, with MYB involved in early commitment and CDK6 in terminal maturation. Finally, although VCL, PIM1, CDK6, and SNHG3 showed significant associations with AML survival and prognosis, they failed to exhibit pathological differential expression in quantitative real-time polymerase chain reaction (qRT-PCR) experimental validations. In contrast, the downregulation of AHSP and upregulation of MYB in AML samples were consistently validated by both qRT-PCR and Western blotting, showing the consistency between the transcriptional level changes and protein expression of these two genes (p < 0.05). Conclusions: In summary, the integration of single-cell/transcriptome analysis with targeted expression validation using clinical samples reveals that the combined AHSP-MYB signature effectively identifies high-risk MEP-AML patients, who may benefit from early intensive therapy or targeted interventions. Full article

Background: Programmed cell death-related genes (PCDRGs) have been reported to play an important role in diagnosis, treatment and immunity regarding cancer, but their prognostic value and therapeutic potential in acute myeloid leukemia (AML) patients still need to be fully explored. Methods: Cox regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to identify PCDRGs significantly associated with the prognosis of AML patients. Furthermore, a prognostic risk model for AML patients was constructed based on the selected PCDRGs, and their immune microenvironment and biological pathways were analyzed. Cell experiments ultimately confirmed the potential role of PCDRGs in AML. Results: The results yielded four PCDRGs that were used to develop a prognostic risk model, and the prognostic significance of this model was confirmed using an independent external AML patient cohort. This prognostic risk model provides an independent prognostic risk factor for AML patients. This prognostic feature is related to immune cell infiltration in AML patients. The inhibition of solute carrier family 39 member 14 (SLC39A14) expression enhanced apoptosis and inhibited cell cycle progression in AML cells. Conclusions: This study integrates bioinformatics analysis and cellular experiments to reveal potential gene therapy targets and prognostic gene markers in AML. Full article

Background: Glioblastoma (GBM) is one of the most challenging malignancies in all of neoplasms. These malignancies are associated with unfavorable clinical outcomes and significantly compromised patient wellbeing. The immunological landscape within the tumor microenvironment (TME) plays a critical role in determining GBM prognosis. By mining data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and correlating them with immune responses in the TME, genes associated with the immune microenvironment with potential prognostic value were obtained. Method: We selected GSE16011 as the training set. Gene expression profiles were substrates scored by both ESTIMATE and xCell, and immune cell subpopulations in GBM were analyzed by CIBERSORT. Gene expression profiles associated with low immune scores were performed by lasso regression, Cox analysis and random forest (RF) to identify a prognostic model for the multiple genes associated with immune infiltration in GBM. Then we constructed a nomogram to optimize the prognostic model using GSE7696 and TCGA-GBM as validation sets and evaluated these data for gene mutation and gene enrichment analysis. Result: The prognostic correlation between the six genes (MEOX2, PHYHIP, RBBP8, ST18, TCF12, and THRB) and GBM was finally found by lasso regression, Cox regression, and RF, and the online database obtained that all six genes were differentially expressed in GBM. Therefore, a prognostic correlation model was constructed based on the six genes. Kaplan–Meier (KM) survival analysis showed that this prognostic model had excellent prognostic ability. Conclusions: Prognostic models based on tumor microenvironment and immune score stratification and the construction of related genes have potential applications for prognostic analysis of GBM patients. Full article

Regulatory T cells (Tregs) have multiple roles in the tumor microenvironment (TME), which maintain a balance between autoimmunity and immunosuppression. This research aimed to investigate the interaction between cancer stemness and Regulatory T cells (Tregs) in the breast cancer tumor immune microenvironment. Breast cancer stemness was calculated using one-class logistic regression. Twelve main cell clusters were identified, and the subsequent three subsets of Regulatory T cells with different differentiation states were identified as being closely related to immune regulation and metabolic pathways. A prognostic risk model including MEA1, MTFP1, PASK, PSENEN, PSME2, RCC2, and SH2D2A was generated through the intersection between Regulatory T cell differentiation-related genes and stemness-related genes using LASSO and univariate Cox regression. The patient’s total survival times were predicted and validated with AUC of 0.96 and 0.831 in both training and validation sets, respectively; the immunotherapeutic predication efficacy of prognostic signature was confirmed in four ICI RNA-Seq cohorts. Seven drugs, including Ethinyl Estradiol, Epigallocatechin gallate, Cyclosporine, Gentamicin, Doxorubicin, Ivermectin, and Dronabinol for prognostic signature, were screened through molecular docking and found a synergistic effect among drugs with deep learning. Our prognostic signature potentially paves the way for overcoming immune resistance, and blocking the interaction between cancer stemness and Tregs may be a new approach in the treatment of breast cancer. Full article

Background: Epigenomic instability accelerates mutations in tumor suppressor genes and oncogenes, contributing to malignant transformation. Histone modifications, particularly methylation and acetylation, significantly influence tumor biology, with chromo-, bromo-, and Tudor domain-containing proteins mediating these changes. This study investigates how genes encoding these domain-containing proteins affect colorectal cancer (CRC) prognosis. Methods: Using CRC data from the GSE39582 and TCGA datasets, we identified domain-related genes via GeneCards and developed a prognostic signature using LASSO-COX regression. Patients were classified into high- and low-risk groups, and comparisons were made across survival, clinical features, immune cell infiltration, immunotherapy responses, and drug sensitivity predictions. Single-cell analysis assessed gene expression in different cell subsets. Results: Four domain-related genes (AKAP1, ORC1, CHAF1A, and UHRF2) were identified as a prognostic signature. Validation confirmed their prognostic value, with significant differences in survival, clinical features, immune patterns, and immunotherapy responses between the high- and low-risk groups. Drug sensitivity analysis revealed top candidates for CRC treatment. Single-cell analysis showed varied expression of these genes across cell subsets. Conclusions: This study presents a novel prognostic signature based on domain-related genes that can predict CRC severity and offer insights into immune dynamics, providing a promising tool for personalized risk assessment in CRC. Full article