Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer
Alterations in cellular metabolism are known to play a crucial role in the development and progression of cancer. The lipoxygenase pathway, which controls unsaturated fatty acid metabolism, has been shown to regulate tumour progression and is commonly altered in breast cancer. In this study, we firs...
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| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | RNA Biology |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15476286.2025.2544440 |
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| author | Xiaoyu Fu Weixing Wang Bradley M. Downs Juanjuan Li |
| author_facet | Xiaoyu Fu Weixing Wang Bradley M. Downs Juanjuan Li |
| author_sort | Xiaoyu Fu |
| collection | DOAJ |
| description | Alterations in cellular metabolism are known to play a crucial role in the development and progression of cancer. The lipoxygenase pathway, which controls unsaturated fatty acid metabolism, has been shown to regulate tumour progression and is commonly altered in breast cancer. In this study, we first obtained 11 lipoxygenase pathway genes from the Molecular Signatures Database (MSigDB). We then explored the lipoxygenase pathway-related long non-coding RNAs (lncRNAs) in breast cancer tissues from The Cancer Genome Atlas (TCGA) database. Information from our analysis was used to construct a risk prediction model (ProlncSig) to predict breast cancer prognosis. We found that ProlncSig could accurately identify breast cancer patients that had significantly shorter overall survival from those that had longer overall survival. Moreover, ProlncSig performs better in predicting prognosis than the clinicopathological features. Furthermore, GO and KEGG enrichment analysis showed that ProlncSig risk score had a high correlation with immune signature. We developed and validated an accurate prognostic risk prediction model (ProlncSig) based on lipoxygenase pathway-related lncRNAs, which has strong potential to provide prognostic information and may provide novel guidance for immunotherapeutic strategies for breast cancer patients. |
| format | Article |
| id | doaj-art-a0eb6eca87f346888677d62c9bf16e98 |
| institution | Kabale University |
| issn | 1547-6286 1555-8584 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | RNA Biology |
| spelling | doaj-art-a0eb6eca87f346888677d62c9bf16e982025-08-20T03:44:06ZengTaylor & Francis GroupRNA Biology1547-62861555-85842025-12-0122111910.1080/15476286.2025.2544440Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancerXiaoyu Fu0Weixing Wang1Bradley M. Downs2Juanjuan Li3Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USABreast Cancer Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, National Key Clinical Specialty Construction Discipline, Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan Clinical Research Center for Breast Cancer, Wuhan, ChinaDepartment of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, ChinaAlterations in cellular metabolism are known to play a crucial role in the development and progression of cancer. The lipoxygenase pathway, which controls unsaturated fatty acid metabolism, has been shown to regulate tumour progression and is commonly altered in breast cancer. In this study, we first obtained 11 lipoxygenase pathway genes from the Molecular Signatures Database (MSigDB). We then explored the lipoxygenase pathway-related long non-coding RNAs (lncRNAs) in breast cancer tissues from The Cancer Genome Atlas (TCGA) database. Information from our analysis was used to construct a risk prediction model (ProlncSig) to predict breast cancer prognosis. We found that ProlncSig could accurately identify breast cancer patients that had significantly shorter overall survival from those that had longer overall survival. Moreover, ProlncSig performs better in predicting prognosis than the clinicopathological features. Furthermore, GO and KEGG enrichment analysis showed that ProlncSig risk score had a high correlation with immune signature. We developed and validated an accurate prognostic risk prediction model (ProlncSig) based on lipoxygenase pathway-related lncRNAs, which has strong potential to provide prognostic information and may provide novel guidance for immunotherapeutic strategies for breast cancer patients.https://www.tandfonline.com/doi/10.1080/15476286.2025.2544440LipoxygenasesLncRNAprognostic signaturetumour immunityImmunotherapybreast cancer |
| spellingShingle | Xiaoyu Fu Weixing Wang Bradley M. Downs Juanjuan Li Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer RNA Biology Lipoxygenases LncRNA prognostic signature tumour immunity Immunotherapy breast cancer |
| title | Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer |
| title_full | Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer |
| title_fullStr | Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer |
| title_full_unstemmed | Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer |
| title_short | Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer |
| title_sort | developing the risk prediction model prolncsig from lipoxygenase pathway related lncrnas for prognosis prediction in breast cancer |
| topic | Lipoxygenases LncRNA prognostic signature tumour immunity Immunotherapy breast cancer |
| url | https://www.tandfonline.com/doi/10.1080/15476286.2025.2544440 |
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