Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Aug. 29, 2024

Background Cancer stem cells (CSCs) are a subset of within tumors that possess the unique ability to self-renew and give rise diverse tumor cells. These crucial in driving metastasis, recurrence, resistance treatment. The objective this study was pinpoint essential regulatory genes associated with CSCs prostate adenocarcinoma (PRAD) assess their potential significance diagnosis, prognosis, immunotherapy patients PRAD. Method utilized single-cell analysis techniques identify cell-related evaluate relation patient prognosis PRAD through cluster analysis. By utilizing datasets employing various machine learning methods for clustering, diagnostic models were developed validated. random forest algorithm pinpointed HSPE1 as most prognostic gene among genes. Furthermore, delved into association between immune infiltration, employed molecular docking investigate relationship its compounds. Immunofluorescence staining 60 tissue samples confirmed expression correlation Result This identified 15 analysis, highlighting importance diagnosing, prognosticating, potentially treating patients. specifically linked response immunotherapy, experimental data supporting upregulation poorer prognosis. Conclusion Overall, our findings underscore significant role unveil novel target related cell.

Language: Английский

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Immune-related diagnostic markers for benign prostatic hyperplasia and their potential as drug targets

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 5, 2024

Benign prostatic hyperplasia (BPH) is a common issue among older men. Diagnosis of BPH currently relies on imaging tests and assessment urinary flow rate due to the absence definitive diagnostic markers. Developing more accurate markers crucial improve diagnosis.

Language: Английский

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Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 7, 2025

Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known play role in tumor development, metastasis, resistance treatment. A thorough understanding genes associated with is crucial for improving diagnostic precision LIHC advancement effective immunotherapy approaches. This research combines single-cell RNA sequencing machine learning techniques identify vital cell-associated that could act as prognostic biomarkers therapeutic targets LIHC. We analyzed various datasets, applying negative matrix factorization alongside algorithms reveal gene expression patterns construct models. The XGBoost algorithm was specifically utilized key regulatory related LIHC, levels significance these were validated experimentally. Our analysis identified 16 differential liver cancer cells. Cluster models constructed using confirmed Notably, S100A10 cell-related most relevant prognosis patients. Experimental validation further supports potential marker this type. Additionally, shows positive correlation cell POU5F1. results study highlight an essential predictor treatment response, particularly regarding immunotherapy. offers valuable insights into molecular mechanisms underlying suggests promising target enhancing outcomes

Language: Английский

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Targeting autophagy to enhance chemotherapy and immunotherapy in oral cancer

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 7, 2025

Oral cancer is a highly malignant disease characterized by recurrence, metastasis, and poor prognosis. Autophagy, catabolic process induced under stress conditions, has been shown to play dual role in oral development therapy. Recent studies have identified that autophagy activation epithelial cells suppresses cell survival inhibiting key pathways such as the mammalian target of rapamycin (mTOR) mitogen-activated protein kinase (MAPK), while activating adenosine monophosphate-activated (AMPK) pathway. Inducing promotes degradation eukaryotic initiation factor 4E, thus reducing metastasis enhancing efficacy chemotherapy, radiotherapy, immunotherapy. Furthermore, induction can modulate tumor immune microenvironment enhance antitumor immunity. This review comprehensively summarizes relationship between cancer, focusing on its mechanisms therapeutic potential when combined with conventional treatments. While promising, precise clinical applications inducers therapy remain be elucidated, offering new directions for future research improve treatment outcomes reduce recurrence.

Language: Английский

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Navigating the immune landscape with plasma cells: A pan‐cancer signature for precision immunotherapy

BioFactors, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

Abstract Immunotherapy has revolutionized cancer treatment; however, predicting patient response remains a significant challenge. Our study identified novel plasma cell signature, Plasma cell.Sig, through pan‐cancer single‐cell RNA sequencing analysis, which predicts outcomes to immunotherapy with remarkable accuracy. The signature was developed using rigorous machine learning algorithms and validated across multiple cohorts, demonstrating superior predictive power an area under the curve (AUC) exceeding 0.7. Notably, low‐risk group, as classified by exhibited enriched immune infiltration heightened tumor immunogenicity, indicating enhanced responsiveness immunotherapy. Conversely, high‐risk group showed reduced activity potential mechanisms of evasion. These findings not only enhance understanding intrinsic extrinsic landscapes within microenvironment but also pave way for more precise, biomarker‐guided approaches in oncology.

Language: Английский

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The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis

Biology Direct, Journal Year: 2025, Volume and Issue: 20(1)

Published: Jan. 8, 2025

Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in immunotherapy. Targeting endothelial related signaling pathways can improve effectiveness immunotherapy by normalizing blood vessels promoting immune cell infiltration. However, to date, there have been no comprehensive studies analyzing diagnosis treatment prostate adenocarcinoma (PRAD). By integrating clinical transcriptomic data from TCGA-PRAD, we initially identified key cell-related genes PRAD samples through single-cell analysis. Subsequently, cluster analysis was employed classify based on expression these genes, allowing us explore their correlation with patient prognosis outcomes. A diagnostic model then constructed validated using combination 108 machine learning algorithms. The XGBoost Random Forest algorithms highlighted significant COL1A1, further analyzed AR, EGFR multiplex immunofluorescence staining. In vitro experimental impact COL1A1 progression PRAD. Single-cell 12 differential prognostic associated cells. Cluster confirmed strong between both cancer responses. Diagnostic models developed various techniques demonstrated predictive capability cancer. Furthermore, patients' information, multiple analyses critical COL1A1. Immunofluorescence results that is highly expressed positively correlated AR EGFR. experiments confirm reducing levels inhibit progression. This study provides diagnosis, prognosis, findings, supported results, highlight as target for

Language: Английский

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Genetic variation reveals the therapeutic potential of BRSK2 in idiopathic pulmonary fibrosis

BMC Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: Jan. 21, 2025

Current research underscores the need to better understand pathogenic mechanisms and treatment strategies for idiopathic pulmonary fibrosis (IPF). This study aimed identify key targets involved in progression of IPF. We employed Mendelian randomization (MR) with three genome-wide association studies four quantitative trait loci datasets driver genes Prioritized were evaluated respiratory insufficiency transplant-free survival. The therapeutic efficacy core gene was validated cellular animal models. Additionally, we conducted a comprehensive evaluation value, mechanisms, safety through phenome-wide (PheWAS), mediation analysis, transcriptomic analyses, shared causal variant exploration, DNA methylation MR, protein interactions. Multiple MR results revealed that BRSK2 has significant impact on IPF at both transcriptional translational levels, lung tissue-specific (OR = 1.596; CI, 1.300–1.961; Pval 8.290 × 10 − 6). associated driven by high-risk factors, effects ranging from 34.452 69.665%. Elevated expression peripheral blood mononuclear cells correlated reduced function, while increased circulating levels suggested failure shorter survival patients. silencing attenuated Transcriptomic integration identified PSMB1, CTSD, CTSH as downstream effectors BRSK2, PSMB1 showing robust support (PPH4 0.800). Colocalization analysis phenotype scan deepened IPF, highlighted critical role epigenetic regulation BRSK2-driven pathogenesis. PheWAS no drug-related toxicities its potential further underscored interaction analyses. is factor strong target. Future should focus implications development targeted therapies improve patient outcomes.

Language: Английский

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Development and functional validation of a disulfidoptosis-related gene prognostic model for lung adenocarcinoma based on bioinformatics and experimental validation

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 10, 2025

Background Disulfidoptosis is increasingly linked to cancer progression, yet its immunological impacts and prognostic value in lung adenocarcinoma (LUAD) remain poorly understood. This study aims delineate the predictive significance of disulfidoptosis-related genes (DRGs) LUAD, their potential as therapeutic targets, interaction with tumor microenvironment. Methods We analyzed expression profiles 23 DRGs survival data, performing consensus clustering identify molecular subtypes. Survival analysis gene set variation (GSVA) were used explore cluster differences. Key selected for Cox LASSO regression develop a model. Tensin4 (TNS4), key model, was further evaluated through immunohistochemistry (IHC) LUAD normal tissues knockdown experiments vitro . Results Two clusters identified, 225 differentially expressed genes. A six-gene signature developed, which classified patients into high- low-risk groups, showing significant The risk score independently predicted prognosis correlated immunotherapy responses. IHC showed elevated TNS4 levels tissues, while reduced both cell proliferation migration. Conclusion highlights role validated offering new avenues targeted therapies, potentially improving treatment outcomes.

Language: Английский

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Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 25, 2025

Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays critical role development Consequently, identification new markers essential for precise diagnosis this condition. The datasets related atherosclerosis utilized research were obtained from GEO database (GSE2470, GSE24495, GSE100927 GSE43292). ssGSEA technique was first assess scores samples affected by atherosclerosis, thereby aiding discovery important regulatory genes linked via WGCNA. Following this, differential expression analysis functional evaluations carried out, after which various machine learning approaches employed determine A model then developed validated through several algorithms. Furthermore, molecular docking studies conducted analyze binding affinity these key with therapeutic agents also used measure immune cell atherosclerotic samples, exploration connection between cells. Finally, variations identified pivotal confirmed experimental validation. WGCNA 302 metabolism-related revealed that are associated multiple pathways. Through further screening using algorithms, APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, TNFRSF25 as we constructed predict occurrence high accuracy, indicated six have potential drug targets. Additionally, algorithm association levels experimentally confirmed. Our study introduces novel emphasizes their immune-related This provides valuable approach predictive targeted therapy

Language: Английский

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Hexokinase2-engineered T cells display increased anti-tumor function

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: March 20, 2025

Background T cells face significant metabolic challenges in the tumor microenvironment (TME), where cancer monopolize critical nutrients like glucose and amino acids. This competition supports growth while impairing T-cell anti-tumor responses, partly by reducing glycolytic function. Hexokinase 2 (HK2), a key enzyme glycolysis, plays pivotal role maintaining functionality. Methods To enhance function, primary human were genetically engineered to overexpress HK2 alongside tumor-specific receptor. These tested vitro vivo evaluate their therapeutic efficacy. Results HK2-engineered exhibited increased capacity, leading enhanced cytokine secretion, activation marker expression, activity compared controls. In studies using xenograft model demonstrated superior efficacy of cells, including delayed improved survival. Conclusion overexpression improves fitness functionality hostile TMEs, offering promising foundation for development next-generation immunotherapies targeting metabolism.

Language: Английский

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