Prognostic evaluation of the novel blueprint of DNA methylation sites by integrating bulk RNA‐sequencing and methylation modification data in endometrial cancer DOI Open Access

Huanzhen Zhou,

Yingzhi Zhang, Jing Jin

et al.

The Journal of Gene Medicine, Journal Year: 2023, Volume and Issue: 26(1)

Published: Nov. 27, 2023

Abstract Introduction Endometrial cancer (EC) is a prevalent malignancy affecting the female population, with an increasing incidence among younger age groups. DNA methylation, common epigenetic modification, well‐established to play key role in progression. We suspected whether methylation could be used as biomarkers for EC prognosis. Methods In present study, we analyzed bulk RNA‐sequencing data from 544 patients and 430 TCGA‐UCEC cohort. applied weighted correlation network analysis select gene set associated panoptosis. conducted between transcriptomic of selected genes identify valuable sites. These sites were further screened by Cox regression least absolute shrinkage selection operator analysis. Immune microenvironment differences high‐risk low‐risk groups assessed using single‐sample enrichment analysi, xCell MCPcounter algorithms. Results Our results identified five (cg03906681, cg04549977, cg06029846, cg10043253 cg15658376) significant prognostic value EC. constructed model these sites, demonstrating satisfactory predictive performance. The group showed higher immune cell infiltration. Notably, site cg03906681 was negatively related CD8 T infiltration, whereas cg04549977 exhibited positive correlations particularly macrophages, activated B cells, dendritic cells myeloid‐derived suppressor cells. PD0325901_1060 strongly correlated risk scores, indicating potential therapeutic response patients. Conclusion have developed robust methylation‐based EC, which holds promise improving prognosis prediction personalized treatment approaches. findings may contribute better management patients, identifying those at who benefit tailored interventions.

Language: Английский

The role of gut microbiota in the occurrence and progression of non-alcoholic fatty liver disease DOI Creative Commons

Huanzhuo Mai,

Xing Yang, Yulan Xie

et al.

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 14

Published: Jan. 5, 2024

Background Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic worldwide, and gut microbes are associated with development progression NAFLD. Despite numerous studies exploring changes in NAFLD, there was no consistent pattern changes. Method We retrieved on human fecal microbiota sequenced by 16S rRNA gene amplification NAFLD from NCBI database up to April 2023, re-analyzed them using bioinformatic methods. Results finally screened 12 relevant related which included a total 1,189 study subjects (NAFLD, n = 654; healthy control, 398; obesity, 137). Our results revealed significant decrease microbial diversity occurrence (SMD −0.32; 95% CI −0.42 −0.21; p < 0.001). Alpha increased abundance several crucial genera, including Desulfovibrio , Negativibacillus Prevotella can serve as an indication their predictive risk ability for (all AUC > 0.7). The significantly higher levels LPS biosynthesis, tryptophan metabolism, glutathione lipid metabolism. Conclusion This elucidated relevance identified potential risk-associated functional pathways progression.

Language: Английский

Citations

9
Unravelling infiltrating T‐cell heterogeneity in kidney renal clear cell carcinoma: Integrative single‐cell and spatial transcriptomic profiling DOI Creative Commons
Haiqing Chen,

Haoyuan Zuo,

Jinbang Huang

et al.

Journal of Cellular and Molecular Medicine, Journal Year: 2024, Volume and Issue: 28(12)

Published: June 1, 2024

Abstract Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within tumour microenvironment. Through a multifaceted approach encompassing single‐cell RNA sequencing, spatial transcriptome analysis and bulk profiling, we systematically explored contribution infiltrating to KIRC heterogeneity. Employing high‐density weighted gene co‐expression network (hdWGCNA), module scoring machine learning, identified distinct signature cell‐associated genes (ITSGs). Spatial transcriptomic data were analysed using robust type decomposition (RCTD) uncover interactions. Further analyses included enrichment assessments, infiltration evaluations drug susceptibility predictions. Experimental validation involved PCR experiments, CCK‐8 assays, plate cloning wound‐healing assays Transwell assays. Six subpopulations proliferating in KIRC, with notable dynamics observed mid‐ late‐stage disease progression. revealed significant correlations between epithelial across varying distances The ITSG‐based prognostic model demonstrated predictive capabilities, implicating these modulation metabolic pathways offering insights into sensitivity for 12 treatment agents. underscored functional relevance PPIB proliferation, invasion migration. Our study comprehensively characterizes T‐cell heterogeneity sequencing data. stable based on ITSGs unveils cells' potential, shedding light microenvironment avenues personalized immunotherapy.

Language: Английский

Citations

8
Integrative biomarker discovery and immune profiling for ulcerative colitis: a multi-methodological approach DOI Creative Commons
Lai Jiang,

Shengke Zhang,

Cheng‐Lu Jiang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 16, 2024

Background We aimed to pinpoint biomarkers, create a diagnostic model for ulcerative colitis (UC), and delve into its immune features better understand this autoimmune condition. Methods The sequencing data both the UC control groups were obtained from GEO, including bulk single-cell data. Using GSE87466 as training group, we applied differential analysis, WGCNA, PPI, LASSO, RF, SVM-RFE biomarker selection. A neural network shaped our model, corroborated by GSE92415 validation cohort with ROC assessment. Immune cell profiling was conducted using CIBERSORT. Results 53 disease-associated genes screened. Enrichment analysis highlighted roles in complement cascades adhesion. Eight biomarkers finally identified through multiple machine learning PPI: B4GALNT2, PDZK1IP1, FAM195A, REG4, MTMR11, FLJ35024, CD55, CD44. had AUCs of 0.984 (training group) 0.957 (validation group). tissues revealed heightened plasma cells, CD8 T other cells. Two unique patterns emerged, certain NK cells central modulation. Conclusion eight various methods, constructed networks, explored complexity disease, which contributes diagnosis treatment UC.

Language: Английский

Citations

4
Exploring novel biomarkers and immunotherapeutic targets for biofeedback therapies to reveal the tumor-associated immune microenvironment through a multimetric analysis of kidney renal clear cell carcinoma DOI Creative Commons
Guobing Wang,

Jinbang Huang,

Haiqing Chen

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 13, 2025

Kidney renal clear cell carcinoma (KIRC) constitutes the primary subtype of carcinoma, representing 75% to 80% cases and carrying a substantial cancer-specific mortality rate up 24%. Despite advancements in treatment options, KIRC displays notable resistance conventional therapies, emphasizing need for innovative targeted immunotherapeutic strategies. Chromatin regulators (CRs), pivotal proteins controlling gene expression critical biological processes, play crucial role initiation progression KIRC. This study employed multi-omics approach evaluate impact CR-associated genes on prognosis. The utilized TCGA-KIRC dataset LASSO Cox regression construct validate prognostic model that focuses influencing research investigated interactions among characteristics, clinical parameters, tumor microenvironment, immunotherapy, drug responsiveness. Experimental validation, encompassing various techniques such as culture, transient transfection, qPCR, Transwell assays, confirmed robust predictive capability BRD9 gene. analysis identified risk score CRs an independent factor determining Furthermore, introduced Nomogram integrates attributes assessment. Significantly, exhibited substantially elevated within cells, underscoring its driving cancer proliferation, invasion, migration. These findings suggest potential tailored immunotherapy targeting presents framework based approaches, seamlessly incorporating CRs. holds promise improving accuracy prognosis prediction patients, laying foundation development immunotherapies.

Language: Английский

Citations

0
Application of Interpretable Machine Learning Models to Predict the Risk Factors of HBV‐Related Liver Cirrhosis in CHB Patients Based on Routine Clinical Data: A Retrospective Cohort Study DOI
Wei Xia,

Yafeng Tan,

Bing Mei

et al.

Journal of Medical Virology, Journal Year: 2025, Volume and Issue: 97(3)

Published: March 1, 2025

ABSTRACT Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to virus (HBV)‐related liver cirrhosis (HBV‐LC) with poor prognoses. Early identification of HBV‐LC risk is essential for timely intervention. This study develops and compares nine machine learning (ML) models predict in CHB patients using routine clinical laboratory data. A retrospective analysis was conducted involving 777 patients, 50.45% (392/777) progressing HBV‐LC. Admission data consisted 52 variables, missing values addressed multiple imputation. Feature selection utilized Least Absolute Shrinkage Selection Operator (LASSO) regression the Boruta algorithm, identifying 24 key variables. The evaluated ML included XGBoost, logistic (LR), LightGBM, random forest (RF), AdaBoost, Gaussian naive Bayes (GNB), multilayer perceptron (MLP), support vector (SVM), k‐nearest neighbors (KNN). set partitioned into an 80% training ( n = 621) 20% independent testing 156). Cross‐validation (CV) facilitated hyperparameter tuning internal validation optimal model. Performance metrics area under receiver operating characteristic curve (AUC), Brier score, accuracy, sensitivity, specificity, F1 score. RF model demonstrated superior performance, AUCs 0.992 (training) 0.907 (validation), while reconstructed achieved 0.944 0.945 maintaining AUC 0.863 set. Calibration curves confirmed strong alignment between observed predicted probabilities. Decision indicated that provided highest net benefit across threshold SHAP algorithm identified RPR, PLT, HBV DNA, ALT, TBA as critical predictors. interpretable enhances early prediction supports decision‐making resource‐limited settings.

Language: Английский

Citations

0
Deciphering the role of tryptophan metabolism-associated genes ECHS1 and ALDH2 in gastric cancer: implications for tumor immunity and personalized therapy DOI Creative Commons
Lexin Wang,

Xue Zhou,

Haisheng Yan

et al.

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

Published: Sept. 12, 2024

Tryptophan Metabolism-associated Genes (TMGs), such as ECHS1 and ALDH2, are crucial in cancer progression through immunosuppressive mechanisms, particularly Gastric Cancer (GC). This study explores their effects on the Tumor Microenvironment (TME). Additionally, it examines potential novel immunotherapy targets.

Language: Английский

Citations

3
Reversing NK cell exhaustion: a novel strategy combining immune checkpoint blockade with drug sensitivity enhancement in the treatment of hepatocellular carcinoma DOI Creative Commons
Yuxiang Huang, Hengjian Liao,

Jiefu Luo

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: Jan. 21, 2025

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide. Natural killer cells (NK cells) play a key role in liver immunosurveillance, but tumor microenvironment, NK are readily depleted, as evidenced by down-regulation activating receptors, reduced cytokine secretion, and attenuated killing function. The up-regulation inhibitory such PD-1, TIM-3, LAG-3, further exacerbates depletion cells. Combined blockade strategies targeting these immunosuppressive mechanisms, combination PD-1 inhibitors with other pathways (eg. TIM-3 LAG-3), have shown potential to reverse cell exhaustion preclinical studies. This article explores promise innovative HCC immunotherapy, providing new therapeutic directions for optimizing function improving drug sensitivity.

Language: Английский

Citations

0
Single-cell transcriptomics reveals heterogeneity and prognostic markers of myeloid precursor cells in acute myeloid leukemia DOI Creative Commons
Guanghua He, Lai Jiang, Xuancheng Zhou

et al.

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

Published: Dec. 16, 2024

Acute myeloid leukemia (AML) is a hematologic tumor with poor prognosis and significant clinical heterogeneity. By integrating transcriptomic data, single-cell RNA sequencing data independently collected this study aims to identify key genes in AML establish prognostic assessment model improve the accuracy of prediction.

Language: Английский

Citations

2
Elucidating the multifaceted role of MGAT1 in hepatocellular carcinoma: integrative single-cell and spatial transcriptomics reveal novel therapeutic insights DOI Creative Commons
Yang Li, Yuan Chen,

Danqiong Wang

et al.

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

Published: July 16, 2024

Glycosyltransferase-associated genes play a crucial role in hepatocellular carcinoma (HCC) pathogenesis. This study investigates their impact on the tumor microenvironment and molecular mechanisms, offering insights into innovative immunotherapeutic strategies for HCC.

Language: Английский

Citations

1
Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis DOI Creative Commons
Rongzhang He, Jing Tang,

Haotian Lai

et al.

Discover Oncology, Journal Year: 2024, Volume and Issue: 15(1)

Published: Sept. 27, 2024

Language: Английский

Citations

0