Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer

Guo, Lu; Wang, Wei; Xie, Xiaodong; Wang, Shuihua; Zhang, Yudong

doi:10.1016/j.biopha.2023.114256

Cited by 4 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MDI indicates the degree of impurity (classification capability) for each variable according to the GINI index, which measures the probability of a particular variable being wrongly classified when it is randomly chosen. This algorithm has been selected because of its ability to assign the importance of biological features [ 11 , 23 , 24 ]. Furthermore, in a previous work in which various feature selection algorithms were compared, the MDI-based approach was one of the best performing [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model

2023

View full text Add to dashboard Cite

Chagas disease (CD) is a neglected parasitic disease caused by the protozoan Trypanosoma cruzi (T. cruzi). The disease has two clinical phases: acute and chronic. In the acute phase, the parasite circulates in the blood. The infection can be asymptomatic or can cause unspecific clinical symptoms. During the chronic phase, the infection can cause electrical conduction abnormalities and progress to cardiac failure. The use of an electrocardiogram (ECG) has been a methodology for diagnosing and monitoring CD, but it is necessary to study the ECG signals to better understand the behavior of the disease. The aim of this study is to analyze different ECG markers using machine-learning-based algorithms for the classification of the acute and chronic phases of T. cruzi infection in a murine experimental model. The presented methodology includes a statistical analysis of control vs. infected models in both phases, followed by an automatic selection of ECG descriptors and the implementation of several machine learning algorithms for the automatic classification of control vs. infected mice in acute and/or chronic phases (binomial classification), as well as a multiclass classification strategy (control vs. the acute group vs. the chronic group). Feature selection analysis showed that P wave duration, R and P wave voltages, and the QRS complex are some of the most important descriptors. The classifiers showed good results in detecting the acute phase of infection (with an accuracy of 87.5%), as well as in multiclass classification (control vs. the acute group vs. the chronic group), with an accuracy of 91.3%. These results suggest that it is possible to detect infection at different phases, which can help in experimental and clinical studies of CD.

show abstract

Section: Methodsmentioning

confidence: 99%

ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model

2023

View full text Add to dashboard Cite

show abstract

“…Additionally, the PI3K/AKT pathway has been identified as a pivotal regulator of chemosensitivity. Genetic variations in Akt1, Akt2, and PIK3CA have been identified as being associated with chemosensitivity in cervical cancer patients [ 6 ]. Furthermore, andrographolide, a potential therapeutic agent in gastric cancer, deactivates the HIF-1 signaling pathway by inhibiting the upstream PI3K-AKT pathway.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies collectively suggest that the peroxisome proliferator-activated receptor gamma (PPARG) interacts with the PTEN-PI3K/AKT signaling pathway, playing a crucial role in regulating chemosensitivity in various cancers, including HSCC [5][6][7][8]. One study discovered that PPARG expression levels were slightly downregulated in chemotherapy-sensitive HSCC patients, indicating a potential association between PPARG and chemosensitivity [9].…”

Section: Introductionmentioning

confidence: 99%

PPARG and the PTEN-PI3K/AKT Signaling Axis May Cofunction in Promoting Chemosensitivity in Hypopharyngeal Squamous Cell Carcinoma

Han,

Chen,

Chen

et al. 2024

PPAR Research

View full text Add to dashboard Cite

It has been demonstrated that PPARG may interact with the PTEN-PI3K/AKT pathway, contributing to its involvement in the chemotherapy treatment of hypopharyngeal squamous cell carcinoma (HSCC). However, the underlying mechanism remains largely unknown. In this study, gene expression profiles of 17 HSCC patients, comprising 8 chemotherapy-sensitive patients (CSP) and 9 chemotherapy-nonsensitive patients (CNSP), were collected and analyzed to investigate expression patterns, correlations, influencing factors of the PPARG-PTEN-PI3K/AKT pathway, and its role in regulating chemosensitivity. The results revealed significantly increased expression (p<0.04) of AKT1, AKT2, AKT3, PIK3CA, PPARG, and PTEN in the CSP group compared to the CNSP group. Specifically, AKT2 exhibited significant overexpression in tumor tissue (p=0.01), while AKT2, AKT3, PPARG, and PTEN displayed significant increases in normal tissue (p≤0.04). Positive correlations (R∈0.43,0.71, p<0.014) were observed between PIK3CA, AKT1, AKT2, AKT3, and PTEN, with AKT2, AKT3, and PTEN also showing significant correlations with PPARG (R∈0.35,0.47, p<0.04). Age, gender, and disease stage had no influence on PPARG, PIK3CA, and PTEN expression, but they may affect AKT expressions. Pathway analysis revealed that PPARG may interact with the PTEN-PI3K/AKT signaling pathway, playing a crucial role in regulating chemosensitivity in the normal tissue microenvironment. Our results suggest that AKT1 and PIK3CA may be associated with chemosensitivity in HSCC tumor cells, while PPARG and PTEN might exhibit a correlation with a specific segment of the PI3K/AKT pathway, potentially influencing chemosensitivity in the normal tissue microenvironment of HSCC patients.

show abstract

Machine learning for genetic prediction of chemotherapy toxicity in cervical cancer

Guo¹,

Wang²,

Xie³

et al. 2023

Biomedicine & Pharmacotherapy

View full text Add to dashboard Cite

Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer

Cited by 4 publications

References 35 publications

ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model

ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model

PPARG and the PTEN-PI3K/AKT Signaling Axis May Cofunction in Promoting Chemosensitivity in Hypopharyngeal Squamous Cell Carcinoma

Machine learning for genetic prediction of chemotherapy toxicity in cervical cancer

Contact Info

Product

Resources

About