Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients

Sep 21, 2026

Uploaded by Asher

Breast cancer is a major public health concern for women worldwide and is linked to palmitoylation, a post-translational protein modification. Prognostic implications of palmitoylation remain unclear. This study identifies palmitoylation-related prognostic factors in breast cancer and evaluates their ability to predict chemotherapy and immunotherapy responses. Differential expression and hub genes are derived from limma and WGCNA, followed by LASSO Cox model building, nomogram creation, and response assessment using immunophenoscore and pRophetic.

LanguageEnglish

CategoryResearch & Report

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Pages16

Reading Time40 min

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Abstract

Background

Methods

Results

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Introduction

Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients Hongxia Zhu, MS 1,2,\#, Haihong Hu, MS 1,2,\#, Bo Hao, BD2, Wendi Zhan, MS 1,2, Ting Yan, MS3, Jingdi Zhang, MS 1,2, Siyu Wang, MS4, Hongjuan Hu, MS5, and Taolan Zhang, MD2,6 Technology in Cancer Research & Treatment Volume 23: 1-16 © The Author(s) 2024 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/15330338241263434](DOI: 10.1177/15330338241263434)[ ](DOI: 10.1177/15330338241263434)[journals.sagepub.com/home/tct](journals.sagepub.com/home/tct) Abstract Background: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modiﬁcation. Despite increasing focus on palmitoylation, its speciﬁc implications for breast cancer prognosis remain unclear. The work aimed to identify prognostic factors linked to palmitoylation in breast cancer and assess its effectiveness in predicting responses to chemotherapy and immunotherapy. Methods: We utilized the “limma”package to analyze the differential expression of palmitoylation-related genes between breast cancer and normal tissues. Hub genes were identiﬁed using the “WGCNA” package. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identiﬁed a prognostic feature associated with palmitoylation and developed a prognostic nomogram with the “regplot” package. The predictive values of the model for chemotherapy and immunotherapy responses were assessed using immunophenoscore (IPS) and the “pRophetic” package. Results: We identiﬁed 211 differentially expressed genes related topalmitoylation, among which 44 demonstrated prognostic potential. Subsequently, a predictive model comprising eleven palmitoylation-related genes was developed. Patients were classiﬁed into high-risk and low-risk groups based on the median risk score. The ﬁndings revealed that individuals in the high-risk group exhibited lower survival rates, while those in the low-risk group showed increased immune cell inﬁltration and improved responses to chemotherapy and immunotherapy. Moreover, the BC-Palmitoylation Tool website was established. Conclusion: This study developed the ﬁrst machine learning-based predictive model for palmitoylation-related genes and created a corresponding website, providing clinicians with a valuable tool to improve patient outcomes. Keywords breast cancer, palmitoylation, prognostic model, nomogram, website application Received: March 7, 2024; Revised: May 10, 2024; Accepted: May 30, 2024. 1 School of Pharmacy, Hengyang Medical College, University of South China, Hengyang, China 2 Department of Pharmacy, The First Afﬁliated Hospital, Hengyang Medical School, University of South China, Hengyang, China 3 Department of Breast and Thyroid Surgery, The First Afﬁliated Hospital, Hengyang Medical School, University of South China, Hengyang, China 4 Department of Medical Oncology, The First Afﬁliated Hospital, Hengyang Medical School, University of South China, Hengyang, China 5 Department of Public Health Service, The First Afﬁliated Hospital, Hengyang Medical School, University of South China, Hengyang, China 6 Phase I Clinical Trial Center, The First Afﬁliated Hospital, Hengyang Medical School, University of South China, Hengyang, China \# These authors contributed equally to this work. Corresponding Author: Taolan Zhang, Department of Pharmacy, The First Afﬁliated Hospital, Hengyang Medical School, University of South China, 69 Chuanshan Road, Hengyang, Hunan 421000, China. [Email: TaolZhan@usc.edu.cn](Email: TaolZhan@usc.edu.cn) Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ([https://cr

FAQs

What is the main goal of the study?

To develop a machine learning-based gene model related to palmitoylation that predicts prognosis and treatment response in breast cancer patients.

Which methods are used to construct the prognostic model?

Differential gene expression is analyzed using limma, hub genes are identified using WGCNA, and a prognostic feature and nomogram are built using LASSO Cox regression and regplot.

How are patients classified and what differences are observed?

Patients are divided into high-risk and low-risk groups by median risk score; high-risk patients show lower survival, while low-risk patients show greater immune cell infiltration and better chemotherapy and immunotherapy responses.

Sep 21, 2026

Uploaded by Asher