Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes DOI Creative Commons

Qiu Sang,

Linlin Yang, He Zhao

и другие.

BMC Women s Health, Год журнала: 2024, Номер 24(1)

Опубликована: Июль 27, 2024

Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and long-term outcomes patients, our objective was to develop a prognostic risk prediction model using improve accuracy UCEC prognosis prediction. The Limma, ESTIMATE, CIBERSORT methods were used for cluster analysis, immune score calculation, estimation cell proportions. Univariate multivariate analyses utilized UCEC. Risk scores nomograms evaluate models. String constructs protein-protein interaction (PPI) network genes. qRT-PCR, immunofluorescence, immunohistochemistry (IHC) all confirmed Cluster analysis divided into four subtypes. 33 independently predict construct score. survival nomogram indicated that has excellent predictive ability strong reliability predicting patients with key indicates play pivotal interactions: GZMK, IL7, GIMAP, UBD. quantitative real-time polymerase chain reaction (qRT-PCR), expression aforementioned their correlation levels. This further revealed UBD could potentially serve as biomarkers associated levels cancer. study identified related response UCEC, including UBD, which may new therapeutic targets evaluating future.

Язык: Английский

Novel Computational and Artificial Intelligence Models in Cancer Research DOI Open Access
Li Liu, Fuhai Li, Xiaoming Liu

и другие.

Cancers, Год журнала: 2025, Номер 17(1), С. 116 - 116

Опубликована: Янв. 2, 2025

The ICIBM 2023 marked the 11th annual conference of its kind, with recently becoming official International Association for Intelligent Biology and Medicine (IAIBM), showcasing cutting-edge advancements at intersection computation biomedical research [...]

Язык: Английский

Процитировано

0
Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach DOI Creative Commons
Tianshu Chen, Yuhan Yang, Zhizhong Huang

и другие.

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

Опубликована: Март 8, 2025

Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop robust predictive model integrating programmed cell death-related genes advanced machine learning techniques. Utilizing transcriptomic data from TCGA-UCEC GSE119041 datasets, we employed comprehensive approach involving 117 algorithms. Key methodologies included differential gene expression analysis, weighted co-expression network functional enrichment studies, immune landscape evaluation, multi-dimensional risk stratification. We identified 10 critical (PTGIS, TIMP3, SRPX, SNCA, HIC1, BAK1, STXBP2, TRIB3, RTKN2, E2F1) constructed superior performance. The StepCox[forward] + plsRcox algorithm combination demonstrated excellent accuracy (AUC > 0.8). Kaplan–Meier analysis revealed survival differences between high- low-risk groups in both training (HR = 3.37, p < 0.001) validation cohorts 2.05, 0.021). showed strong correlations clinical characteristics, infiltration patterns, potential therapeutic responses. presents novel, endometrial prognosis, molecular insights provide more precise stratification tool translation.

Язык: Английский

Процитировано

0
Immune microenvironment and molecular mechanisms in endometrial cancer: implications for resistance and innovative treatments DOI Creative Commons

Yijia Chen,

Lai Jiang, Lanyue Zhang

и другие.

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

Опубликована: Апрель 16, 2025

Язык: Английский

Процитировано

0
Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes DOI Creative Commons

Qiu Sang,

Linlin Yang, He Zhao

и другие.

BMC Women s Health, Год журнала: 2024, Номер 24(1)

Опубликована: Июль 27, 2024

Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and long-term outcomes patients, our objective was to develop a prognostic risk prediction model using improve accuracy UCEC prognosis prediction. The Limma, ESTIMATE, CIBERSORT methods were used for cluster analysis, immune score calculation, estimation cell proportions. Univariate multivariate analyses utilized UCEC. Risk scores nomograms evaluate models. String constructs protein-protein interaction (PPI) network genes. qRT-PCR, immunofluorescence, immunohistochemistry (IHC) all confirmed Cluster analysis divided into four subtypes. 33 independently predict construct score. survival nomogram indicated that has excellent predictive ability strong reliability predicting patients with key indicates play pivotal interactions: GZMK, IL7, GIMAP, UBD. quantitative real-time polymerase chain reaction (qRT-PCR), expression aforementioned their correlation levels. This further revealed UBD could potentially serve as biomarkers associated levels cancer. study identified related response UCEC, including UBD, which may new therapeutic targets evaluating future.

Язык: Английский

Процитировано

1