Evidence map›Paper›PMID 39006258›Full record

ArticleAmerican journal of translational research2024

Analysis of the potential biological significance of glycosylation in triple-negative breast cancer on patient prognosis.

Han Zhou, Zhiwei Wang, Jun Guo, Zihui Zhu, Gang Sun

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Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

  1. Upregulated PXDNL promotes invasive breast carcinoma progression.American journal of translational research · 2025
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Han ZhouDepartment of Breast and Thyroid Surgery, The Affiliated Cancer Hospital of Xinjiang Medical University Urumqi 830011, Xinjiang, China.
Zhiwei WangDepartment of Breast and Thyroid Surgery, The Affiliated Cancer Hospital of Xinjiang Medical University Urumqi 830011, Xinjiang, China.
Jun GuoKey Laboratory of Oncology of Xinjiang Uyghur Autonomous Region Urumqi 830011, Xinjiang, China.
Zihui ZhuDepartment of Breast Surgery, The Affiliated Cancer Hospital of Xinjiang Medical University Urumqi 830011, Xinjiang, China.
Gang SunDepartment of Breast and Thyroid Surgery, The Affiliated Cancer Hospital of Xinjiang Medical University Urumqi 830011, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancer is the most common malignancy in women, with its prognosis varying greatly according to its subtype. Triple-negative breast cancer (TNBC) has the worst prognosis among all subtypes. Glycosylation is a critical factor influencing the prognosis of patients with TNBC. Our aim is to develop a tumor prognosis model by analyzing genes related to glycosylation to predict patient outcomes.

methodsThe dataset used in this study was downloaded from the Cancer Genome Atlas Program (TCGA) database, and predictive genes were identified through Cox one-way regression analysis. The model genes with the highest risk scores among the 18 samples were obtained by lasso regression analysis to establish the model. We analyzed the pathways affecting the progression of TNBC and discovered key genes for subsequent research.

resultsOur model was constructed using data from TCGA database and validated through Kaplan-Meier curve analysis and Receiver Operating Characteristic (ROC) curve assessment. Our analysis revealed that a high expression of tumor-related chemokines in the high-risk group may be associated with poor tumor prognosis. Furthermore, we conducted a random survival forest analysis and identified two significant genes, namely DPM2 and PINK1, which have been selected for further investigation.

conclusionThe prognostic analysis model, developed based on the glycosylation genes in TNBC, exhibits excellent validation efficacy. This model is valuable for the prognostic analysis of patients with TNBC.

Indexed as

DPM2glycosylationimmune infiltrationPINK1prognostic modelTNBC

Identifiers

PMID39006258
PMCPMC11236660

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