Upregulation of LINC00511 expression by DNA hypomethylation promotes the progression of breast cancer

Liu, Chunxiao; Xu, Yuting; Liu, Xu; Fu, Yingqiang; Zhu, Kaiyuan; Niu, Zhenbo; Liu, Jiaxin; Qian, Cheng

doi:10.21037/gs-21-84

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…MS4A15 has also been suggested as a possible biomarker for colon and gastric cancer 121 . WASIR1 is a lncRNA that appears to interact with LINC00511, which is overexpressed in BC and promotes its progression 122 . PAMR1 is predicted to be involved in proteolysis 32 .…”

Section: Resultsmentioning

confidence: 99%

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Mares-Quiñones,

Galán-Vásquez,

Pérez-Rueda

et al. 2024

Sci Rep

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Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

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Section: Resultsmentioning

confidence: 99%

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Mares-Quiñones,

Galán-Vásquez,

Pérez-Rueda

et al. 2024

Sci Rep

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“…Nevertheless, the precise biological role and governing LINC00511 mechanism in SARC remain largely uninvestigated. Liu et al 74 indicate that DNA hypomethylation enhances the expression of LINC00511 in breast cancer. Thus, elevated expression of LINC00511 is closely linked with clinical pathological features and poor prognosis in breast cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Xu,

Guo,

Sheng

et al. 2024

Sci Rep

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Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

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“…In addition to this, the silencing of LINC00511 expression suppressed cell viability, proliferation, migration, and invasion, and accelerated the apoptosis of glioma cells via a suggested miR-15a-5p/AEBP1 axis [ 36 ]. Other studies have associated LINC00511 with breast cancer [ 37 ], hepatocellular carcinoma [ 38 ], and bladder carcinoma [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer

Shaath

Elango

Alajez

2021

Cancers

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Breast cancer remains the world’s most prevalent cancer, responsible for around 685,000 deaths globally despite international research efforts and advances in clinical management. While estrogen receptor positive (ER+), progesterone receptor positive (PR+), and human epidermal growth factor receptor positive (HER2+) subtypes are easily classified and can be targeted, there remains no direct diagnostic test for triple-negative breast cancer (TNBC), except for the lack of receptors expression. The identification of long non-coding RNAs (lncRNAs) and the roles they play in cancer progression has recently proven to be beneficial. In the current study, we utilize RNA sequencing data to identify lncRNA-based biomarkers associated with TNBC, ER+ subtypes, and normal breast tissue. The Marker Finder algorithm identified the lncRNA transcript panel most associated with each molecular subtype and the receiver operating characteristic (ROC) analysis was used to validate the diagnostic potential (area under the curve (AUC) of ≥8.0 and p value < 0.0001). Focusing on TNBC, findings from the discovery cohort were validated in an additional two cohorts, identifying 13 common lncRNA transcripts enriched in TNBC. Binary regression analysis identified a four lncRNA transcript signature (ENST00000425820.1, ENST00000448208.5, ENST00000521666.1, and ENST00000650510.1) with the highest diagnostic power for TNBC. The ENST00000671612.1 lncRNA transcript correlated with worse refractory free survival (RFS). Our data provides a step towards finding a novel diagnostic lncRNA-based panel for TNBC with potential therapeutic implications.

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Upregulation of LINC00511 expression by DNA hypomethylation promotes the progression of breast cancer

Cited by 9 publications

References 37 publications

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer

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