Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach DOI Creative Commons

Jun Fan,

BoGuang Chen,

Hao Wu

и другие.

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Окт. 30, 2024

Introduction Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, crucial process in gene expression regulation, plays significant role the development and progression malignant tumors. However, chromatin regulators (CRs) lung adenocarcinoma (LUAD) remains underexplored. Methods This study developed regulator-related signature (CRRS) using 429-combination machine learning approach predict survival outcomes LUAD patients. The CRRS model was validated across multiple independent datasets. We also investigated impact on immune microenvironment, focusing cell infiltration. To identify potential therapeutic targets, TFF1, regulator, knocked down siRNA cells. assessed through apoptosis analysis, proliferation assays, vivo tumor growth studies. Additional validation performed Ki67 TUNEL assays. Results accurately predicted shown modulate infiltration microenvironment. High-risk patients demonstrated increased activity cycle regulation DNA repair pathways, along distinct mutation profiles responses compared low-risk TFF1 emerged as key target. Knockdown significantly inhibited proliferation, induced apoptosis, suppressed growth. assays confirmed regulating death. Discussion These findings highlight prognostic modeling modulation LUAD. identified promising target, suggesting that targeting could provide new treatment strategies. Further research warranted explore full applicability.

Язык: Английский

From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications DOI Creative Commons
Lei Cao, Lu Xu, Xia Wang

и другие.

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Сен. 17, 2024

Background Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play significant role in tumor recurrence and resistance to therapy. This study aimed identify characterize glioma (GSCs), analyze their interactions various cell types, develop prognostic signature. Methods Single-cell RNA sequencing data from 44 primary samples were analyzed GSC populations. Spatial transcriptomics gene regulatory network analyses performed investigate localization transcription factor activity. CellChat analysis was conducted infer cell-cell communication patterns. A signature (GSCS) developed using machine learning algorithms applied bulk multiple cohorts. In vitro vivo experiments validate the of TUBA1C, key within Results distinct population identified, characterized by high proliferative potential an enrichment E2F1, E2F2, E2F7, BRCA1 regulons. GSCs exhibited spatial proximity myeloid-derived suppressor (MDSCs). revealed active MIF signaling pathway between MDSCs. 26-gene GSCS demonstrated superior performance compared existing models. Knockdown TUBA1C significantly inhibited migration, invasion , reduced growth . Conclusion offers comprehensive characterization MDSCs, while presenting robust GSCS. The findings offer new insights into biology therapeutic targets, particularly at improving patient outcomes.

Язык: Английский

Процитировано

3
Development of a novel prognostic signature based on cytotoxic T lymphocyte-evasion genes for hepatocellular carcinoma patient management DOI Creative Commons
Qinmei Zhu,

Shiping Liao,

Ting Wei

и другие.

Discover Oncology, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 10, 2025

Cytotoxic T lymphocytes (CTLs) are major actors in innate and adaptive antitumor response. We attempted to apply cancer cell-intrinsic CTL evasion genes (CCGs) identify verify a risk stratification signature hepatocellular carcinoma (HCC) patients assess the prognosis benefits of immunotherapy, sorafenib treatment transcatheter arterial chemoembolization (TACE) treatment. developed novel prognostic including six CCGs was by LASSO Cox regression. CIBERSORT, quanTIseq, ssGSEA algorithms were used investigated correlation between CCG immune cell infiltration. also assessed performance predicting TACE with independent clinical mRNA sequencing data. The area under curve (AUC) for 1-, 3-, 5-year OS 0.77, 0.70 learning cohort, respectively. In external verification AUCs 0.71, 0.74 0.75. significantly positively related both TMB MSI. addition, responders had higher score than nonresponders when applied urothelial AUC response 0.65. further found that lower cohorts, 0.87 0.76, Finally, we identified four small molecule compounds negatively differentially expressed (DEGs) two categories HCC patients, monensin, etiocholanolone, naringenin, Prestwick-1103. has some significance may enhance patient outcomes even help develop strategies management.

Язык: Английский

Процитировано

0
Thrombospondin-2 induces M2 macrophage polarization through fatty acid metabolism to drive lung adenocarcinoma proliferation DOI

Meiling Weng,

Xiaoping Zhu

Anti-Cancer Drugs, Год журнала: 2025, Номер unknown

Опубликована: Март 7, 2025

Tumor-associated macrophages play a critical role in regulating the progression of lung adenocarcinoma (LUAD). Platelet-derived protein thrombospondin-2 (THBS2) has been identified as tumor marker and is known to be overexpressed LUAD. However, specific THBS2 M2 macrophage polarization within LUAD remains unclear. We conducted bioinformatics analyses assess clinical significance expression LUAD, which was subsequently validated using quantitative PCR. examined relationship between infiltration. A coculture system cells M0 established investigate influence on infiltration through immunofluorescence ELISA. explored impact fatty acid metabolism (FAM) oil red O staining relevant kits elucidated proliferation cell counting kit-8 (CCK-8) colony formation assays. Western blot employed changes Bax Bcl-2. highly expressed associated with poor prognosis patients. In-vitro experiments demonstrated that silencing significantly inhibited polarization. primarily activated FAM pathways, inducing promoting proliferation. enhanced by induce These findings provide theoretical basis for targeting novel therapeutic strategy

Язык: Английский

Процитировано

0
Machine Learning‐Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma DOI Creative Commons
Lin Zhu, Liang Feng, Xue Han

и другие.

Journal of Cellular and Molecular Medicine, Год журнала: 2025, Номер 29(6)

Опубликована: Март 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.

Язык: Английский

Процитировано

0
Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework DOI Creative Commons

Su-Wan Hu,

Mengting Wang

Discover Oncology, Год журнала: 2025, Номер 16(1)

Опубликована: Апрель 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

Язык: Английский

Процитировано

0
Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment DOI Creative Commons
Y Gao, Sheng Chen, Lei Li

и другие.

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Дек. 9, 2024

Introduction Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulating immune activation and therapeutic outcomes offers potential precision oncology. Methods A comprehensive pan-cancer analysis was performed using bulk RNA sequencing data to develop necroptosis-related gene signature, termed Necroptosis.Sig. Multi-omics approaches were employed identify critical pathways key regulators of necroptosis, including HMGB1. Functional validation experiments conducted A549 lung cells evaluate the effects HMGB1 knockdown on tumor proliferation malignancy. Results The Necroptosis.Sig signature effectively predicted checkpoint inhibitors (ICIs). analyses highlighted modulator with enhance efficacy. demonstrated that significantly suppressed malignancy, reinforcing targeting necroptosis. Discussion These findings underscore utility necroptosis guide personalized strategies. By advancing oncology, provides novel avenue improving treatment outcomes.

Язык: Английский

Процитировано

1
Machine learning-based prediction of gastroparesis risk following complete mesocolic excision DOI Creative Commons
Wei Wang,

Zhu Yan,

Zhanshuo Zhang

и другие.

Discover Oncology, Год журнала: 2024, Номер 15(1)

Опубликована: Сен. 27, 2024

Язык: Английский

Процитировано

0
Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach DOI Creative Commons

Jun Fan,

BoGuang Chen,

Hao Wu

и другие.

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Окт. 30, 2024

Introduction Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, crucial process in gene expression regulation, plays significant role the development and progression malignant tumors. However, chromatin regulators (CRs) lung adenocarcinoma (LUAD) remains underexplored. Methods This study developed regulator-related signature (CRRS) using 429-combination machine learning approach predict survival outcomes LUAD patients. The CRRS model was validated across multiple independent datasets. We also investigated impact on immune microenvironment, focusing cell infiltration. To identify potential therapeutic targets, TFF1, regulator, knocked down siRNA cells. assessed through apoptosis analysis, proliferation assays, vivo tumor growth studies. Additional validation performed Ki67 TUNEL assays. Results accurately predicted shown modulate infiltration microenvironment. High-risk patients demonstrated increased activity cycle regulation DNA repair pathways, along distinct mutation profiles responses compared low-risk TFF1 emerged as key target. Knockdown significantly inhibited proliferation, induced apoptosis, suppressed growth. assays confirmed regulating death. Discussion These findings highlight prognostic modeling modulation LUAD. identified promising target, suggesting that targeting could provide new treatment strategies. Further research warranted explore full applicability.

Язык: Английский

Процитировано

0