Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15
Published: Dec. 17, 2024
Esophageal squamous cell carcinoma (ESCC) remains a significant challenge in oncology due to its aggressive nature and heterogeneity. As one of the deadliest malignancies, ESCC research lags behind other cancer types. The balance between ubiquitination deubiquitination processes plays crucial role cellular functions, with disruption linked various diseases, including cancer.
Language: Английский
Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15
Published: May 16, 2024
Background Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in development and advancement cancer. Although blocking angiogenesis has shown success treating different types solid tumors, its relevance prostate adenocarcinoma (PRAD) not been thoroughly investigated. Method This study utilized WGCNA method to identify angiogenesis-related genes assessed their diagnostic prognostic value patients with PRAD through cluster analysis. A model was constructed using multiple machine learning techniques, while developed employing LASSO algorithm, underscoring PRAD. Further analysis identified MAP7D3 as most significant gene among multivariate Cox regression various algorithms. The also investigated correlation between immune infiltration well drug sensitivity Molecular docking conducted assess binding affinity angiogenic drugs. Immunohistochemistry 60 tissue samples confirmed expression MAP7D3. Result Overall, 10 key demonstrated potential immune-related implications patients. is found be closely associated prognosis response immunotherapy. Through molecular studies, it revealed that exhibits high Furthermore, experimental data upregulation PRAD, correlating poorer prognosis. Conclusion Our important target
Language: Английский
Citations
24
Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15
Published: Aug. 19, 2024
Background Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as main cause of cancer-related deaths in developing countries. The early symptoms CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there a close relationship between mast cells (MCs) tumor development. However, research on role MCs played still very limited time. Thus, study conducted single-cell multi-omics analysis human cells, aiming explore mechanisms by interact microenvironment CC. goal was provide scientific basis for prevention, diagnosis, treatment CC, hope improving patients’ prognoses quality life. Method present acquired RNA sequencing data from ten samples ArrayExpress database. Slingshot AUCcell were utilized infer assess differentiation trajectory cell plasticity subpopulations. Differential expression subpopulations performed, employing Gene Ontology, gene set enrichment analysis, variation analysis. CellChat software package applied predict communication cells. Cellular functional experiments validated functionality TNFRSF12A HeLa Caski lines. Additionally, risk scoring model constructed evaluate differences features, prognosis, immune infiltration, checkpoint, across various scores. Copy number levels computed using inference copy variations. Result obtained 93,524 high-quality classified into types, including T_NK endothelial fibroblasts, smooth muscle epithelial B plasma MCs, neutrophils, myeloid Furthermore, total 1,392 subdivided seven subpopulations: C0 CTSG+ C1 CALR+ C2 ALOX5+ C3 ANXA2+ C4 MGP+ C5 IL32+ C6 ADGRL4+ MCs. Notably, subpopulation showed associations tumor-related results indicating resided intermediate-to-late stage differentiation, potentially representing crucial transition point benign-to-malignant transformation CNVscore bulk further confirmed transforming state subpopulation. revealed key receptor involved actions Moreover, vitro indicated downregulating may partially inhibit development prognosis infiltration based marker genes provided valuable guidance patient intervention strategies. Conclusions We first identified transformative tumor-associated within critical impacted progression inhibitory effect knocking down prognostic ALOX5+MCs subset demonstrated excellent predictive value. These findings offer fresh perspective decision-making
Language: Английский
Citations
23
Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(2)
Published: Jan. 1, 2025
ABSTRACT Lung adenocarcinoma (LUAD) involves complex dysregulated cellular processes, including programmed cell death (PCD), influenced by N6‐methyladenosine (m6A) RNA modification. This study integrates bulk and single‐cell sequencing data to identify 43 prognostically valuable m6A‐related PCD genes, forming the basis of a 13‐gene risk model (m6A‐related signature [mPCDS]) developed using machine‐learning algorithms, CoxBoost SuperPC. The mPCDS demonstrated significant predictive performance across multiple validation datasets. In addition its prognostic accuracy, revealed distinct genomic profiles, pathway activations, associations with tumour microenvironment potential for predicting drug sensitivity. Experimental identified RCN1 as oncogene driving LUAD progression promising therapeutic target. offers new approach stratification personalised treatment strategies.
Language: Английский
Citations
0
Translational Oncology, Journal Year: 2025, Volume and Issue: 53, P. 102306 - 102306
Published: Feb. 4, 2025
Language: Английский
Citations
0
International Journal of Genomics, Journal Year: 2025, Volume and Issue: 2025(1)
Published: Jan. 1, 2025
Background: PANoptosis, a recently characterized inflammatory programmed cell death modality orchestrated by the PANoptosome complex, integrates molecular mechanisms of pyroptosis, apoptosis, and necroptosis. Although this pathway potentially mediates tumor progression, its role in lung adenocarcinoma (LUAD) remains largely unexplored. Methods: Through comprehensive single-cell transcriptomic profiling, we systematically identified critical PANoptosis-associated gene signatures. Prognostic determinants were subsequently delineated via univariate Cox proportional hazards regression analysis. We constructed PANoptosis-related optimal model (PROM) through integration 10 machine learning algorithms. The was initially developed using Cancer Genome Atlas (TCGA)-LUAD cohort validated across six independent LUAD cohorts. Model performance evaluated mean concordance index. Furthermore, conducted extensive multiomics analyses to delineate differential activation patterns immune infiltration profiles between PROM-stratified risk subgroups. Results: Cellular populations exhibiting elevated PANoptosis signatures demonstrated enhanced intercellular signaling networks. PROM superior prognostic capability multiple validation Receiver operating characteristic curve revealed area under values exceeding 0.7 all seven cohorts, with several achieving above 0.8, indicating robust discriminative performance. score exhibited significant correlation immunological parameters. Notably, high scores associated attenuated responses, suggesting an immunosuppressive microenvironment. Multiomics investigations alterations oncogenic pathways landscape Conclusion: This investigation establishes as clinically applicable tool for stratification. Beyond predictive utility, elucidates biological underlying progression. These findings provide novel mechanistic insights into pathogenesis may inform development targeted therapeutic interventions personalized treatment strategies optimize patient outcomes.
Language: Английский
Citations
0
Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(6)
Published: March 1, 2025
ABSTRACT Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycolipid metabolism‐related genes. Through integrated analysis of TCGA GEO datasets, established robust 15‐gene signature that effectively stratified patients into distinct risk groups. This demonstrated superior value revealed significant associations with immune infiltration patterns. High‐risk exhibited reduced infiltration, particularly in B cells NK cells, alongside increased tumour purity. Single‐cell RNA sequencing uncovered unique cellular composition patterns enhanced interaction intensities the high‐risk group, especially within epithelial smooth muscle cells. Functional validation confirmed MECP2 as promising therapeutic target, its knockdown significantly inhibiting progression both vitro vivo. Drug sensitivity identified specific agents showing potential efficacy patients. Our study provides practical tool insights relationship between metabolism immunity ESCC, offering strategies personalised treatment.
Language: Английский
Citations
0
Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(7)
Published: March 30, 2025
ABSTRACT Immune checkpoint inhibitors (ICIs) represent a groundbreaking advancement in cancer therapy, substantially improving patient survival rates. Our comprehensive research reveals significant positive correlation between coagulation scores and immune‐related gene expression across 30 diverse types. Notably, tumours exhibiting high demonstrated enhanced infiltration of cytotoxic immune cells, including CD8 + T natural killer (NK) macrophages. Leveraging the TCGA pan‐cancer database, we developed Coagulation.Sig model, sophisticated predictive framework utilising coagulation‐related genes (CRGs) to forecast immunotherapy outcomes. Through rigorous analysis ten ICI‐treated cohorts, identified validated seven critical CRGs: BIRC2, HMGB1, STAT2, IFNAR1, BID, SPATA2, IL33 IFNG, which form foundation our model. Functional analyses revealed that low‐risk characterised by higher cell populations, particularly superior ICI responses. These also exhibited increased mutation rates, elevated neoantigen loads, greater TCR/BCR diversity. Conversely, high‐risk displayed pronounced intratumor heterogeneity (ITH) NRF2 pathway activity, mechanisms strongly associated with evasion. Experimental validation highlighted BIRC2 as promising therapeutic target. Targeted knockdown, when combined anti‐PD‐1 significantly suppressed tumour growth, infiltration, amplified IFN‐γ TNF‐α secretion models. findings position model novel, approach personalised treatment, emerging both biomarker potential intervention point.
Language: Английский
Citations
0
Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)
Published: April 10, 2025
Ovarian carcinoma represents an aggressive malignancy with poor prognosis and limited therapeutic efficacy. While deubiquitinating (DUB) genes are known to regulate crucial cellular processes cancer progression, their specific roles in ovarian remain poorly understood. We conducted integrated analysis of single-cell RNA sequencing bulk transcriptome data from public databases. DUB were identified through Genecard database. Using the Seurat package, we performed cell clustering differential expression analysis. Cell-cell communications analyzed using CellChat. A DUB-related risk signature (DRS) was developed machine learning approaches integration GEO TCGA datasets. The prognostic value immune characteristics systematically evaluated. Our revealed eight distinct subtypes tumor microenvironment, including epithelial, fibroblast, myeloid, Treg cells. DUB-high cells predominantly found myeloid populations, exhibiting elevated tumor-related pathways enhanced cell-cell communication networks, particularly between fibroblasts Conversely, DUB-low enriched epithelial populations reduced activity. DRS model demonstrated robust across multiple independent cohorts. High-risk patients, as classified by DRS, showed significantly poorer survival outcomes infiltration patterns compared low-risk patients. This study provides comprehensive insights into gene different carcinoma. established offers a promising tool for stratification may guide personalized strategies. findings highlight potential role modulating microenvironment patient
Language: Английский
Citations
0
Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15
Published: Sept. 25, 2024
Lung cancer is one of the most common malignant tumours worldwide and its high mortality rate makes it a leading cause cancer-related deaths. To address this daunting challenge, we need comprehensive understanding pathogenesis progression lung in order to adopt more effective therapeutic strategies. In regard, integrating multi-omics data provides highly promising avenue. Multi-omics approaches such as genomics, transcriptomics, proteomics, metabolomics have become key tools study cancer. The application these methods not only helps resolve immunotherapeutic mechanisms cancer, but also theoretical basis for development personalised treatment plans. By multi-omics, gained process progression, discovered potential immunotherapy targets. This review summarises studies on immunology explores early diagnosis, selection prognostic assessment with aim providing options patients.
Language: Английский
Citations
2
Medicine, Journal Year: 2024, Volume and Issue: 103(21), P. e38260 - e38260
Published: May 24, 2024
Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE unclear. It helpful to improve diagnosis treatment of using new biomarkers related in blood. Three PE-related microarray datasets were obtained from Gene Expression Synthesis database. The limma software package was used identify differentially expressed genes (DEGs) control groups. Least absolute shrinkage selection operator regression, support vector machine, random forest, multivariate logistic regression analyses determine key diagnostic biomarkers, which verified clinical samples. Subsequently, functional enrichment analysis performed. In addition, combined for immune cell infiltration their relationships with core biomarkers. performance evaluated receiver operating characteristic (ROC) curve, C-index, GiViTi calibration band. Genes potential applications decision curve (DCA). Seventeen DEGs identified, 6 (FN1, MYADM, CA6, PADI4, SLC4A10, PPP4R1L) 3 types machine learning methods regression. High found through evaluation ROC, GiViti band, DCA. 2 cells (M0 macrophages activated mast cells) significantly different patients controls. All these except SLC4A10 showed significant differences expression levels groups quantitative reverse transcription-polymerase chain reaction. This model predict occurrence PE. findings may stimulate ideas prevention
Language: Английский
Citations
1