An angiogenesis-related lncRNA signature predicts the immune microenvironment and prognosis of breast cancer

Wang, Yawen; Liu, Can; Chen, Yan-Duo; Yang, Bin; Xu, Chi; Ma, Guangxin; Tian, Yaru; Bo, Xiangkun; Zhang, Kai

doi:10.18632/aging.204930

Cited by 2 publications

(1 citation statement)

References 43 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these lncRNAs in models are closely related to immunotherapy and drug response but not to ccRCC prognosis. Notably, the growth of advanced ccRCC is highly dependent on the blood supply, and angiogenesis significantly impacts the metastasis risk, recurrence risk and prognosis of ccRCC patients 30 , 31 . Therefore, a prognostic model constructed with AR-lncRNAs could be an optimal model with good prognostic value and predictive performance for ccRCC.…”

Section: Discussionmentioning

confidence: 99%

A Model to Predict Prognosis of Renal Cell Clear Cell Carcinoma Based on 3 Angiogenesis-related Long Non-coding RNAs

Chen,

Zhang,

et al. 2024

J. Cancer

View full text Add to dashboard Cite

Background: Tumor angiogenesis is closely related to the progression of clear cell renal cell carcinoma (ccRCC). Long non-coding RNAs (lncRNAs) regulating angiogenesis could be potential biomarkers for predicting ccRCC prognosis. With this study, we aimed to construct a prognostic model based on lncRNAs and explore its underlying mechanisms. Methods: RNA data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Angiogenesis-related genes (ARGs) were extracted from the Molecular Signatures database. Pearson correlation and LASSO and COX regression analyses were performed to identify survival-related AR-lncRNAs (sAR-lncRNAs) and construct a prognostic model. The predictive power of the prognostic model was verified according to Kaplan-Meier curve, receiver operating characteristic (ROC) curve and nomogram analyses. The correlation between the prognostic model and clinicopathological characteristics was assessed via univariate and multivariate analyses. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was subsequently performed to elucidate the mechanisms of the sAR-lncRNAs. In vitro qPCR, immunohistochemistry, migration and invasion assays were conducted to confirm the angiogenic function of sAR-lncRNAs. Results: Three sAR-lncRNAs were used to construct a prognostic model. The model was moderately accurate in predicting 1-, 3-and 5-year ccRCC prognosis, and the risk score according to this model was closely related to clinicopathological characteristics such as T grade and T stage. A nomogram was constructed to precisely estimate the overall survival of ccRCC patients. KEGG enrichment analysis indicated that the MAPK and Notch pathways were highly enriched in high-risk patients. Additionally, we found that the expression of the lncRNAs AC005324.4 and AC104964.4 in the prognostic model was lower in ccRCC cell lines and cancer tissues than in the HK-2 cell line and paracancerous tissues, while the expression of the lncRNA AC087482.1 showed the opposite trend. In a coculture model, knockdown of lncRNA AC005324.4 and lncRNA AC104964.4 significantly promoted the migration and invasion of human umbilical vein endothelial cells (HUVECs), but siR-AC087482.1 transfection alleviated these effects. Conclusions:We constructed a prognostic model based on 3 sAR-lncRNAs and validated its value in clinicopathological characteristics and prognostic prediction of ccRCC patients, providing a new perspective for ccRCC treatment decision making.

show abstract

Section: Discussionmentioning

confidence: 99%

A Model to Predict Prognosis of Renal Cell Clear Cell Carcinoma Based on 3 Angiogenesis-related Long Non-coding RNAs

Chen,

Zhang,

et al. 2024

J. Cancer

View full text Add to dashboard Cite

show abstract

Identification of Lysine lactylation (kla) -related lncRNA signatures using XGBoost to predict prognosis and immune microenvironment in breast cancer patients

Chen,

Lin,

et al. 2024

Preprint

View full text Add to dashboard Cite

Breast cancer (BC) stands as a predominant global malignancy, significantly contributing to female mortality. Recently uncovered, histone lysine lactylation (kla) has assumed a crucial role in cancer progression. However, the correlation with lncRNAs remains ambiguous. Scrutinizing lncRNAs associated with Kla not only improves clinical breast cancer management but also establishes a groundwork for antitumor drug development. We procured breast tissue samples, encompassing both normal and cancerous specimens, from The Cancer Genome Atlas (TCGA) database. Utilizing Cox regression and XGBoost methods, we developed a prognostic model using identified kla-associated lncRNAs. The model's predictive efficacy underwent validation across training, testing, and the overall cohort. Functional analysis concerning kla-related lncRNAs ensued. We identified and screened 8 kla- associated lncRNAs to formulate the risk model. Pathway analysis disclosed the connection between immune-related pathways and the risk model of kla-associated lncRNAs. Significantly, the risk scores exhibited a correlation with both immune cell infiltration and immune function, indicating a clear association. Noteworthy is the observation that patients with elevated risk scores demonstrated an increased tumor mutation burden (TMB) and decreased tumor immune dysfunction and exclusion (TIDE) scores, suggesting heightened responses to immune checkpoint blockade. Our study uncovers a potential link between Kla-related lncRNAs and BC, providing innovative therapeutic guidelines for BC management.

show abstract

An angiogenesis-related lncRNA signature predicts the immune microenvironment and prognosis of breast cancer

Cited by 2 publications

References 43 publications

A Model to Predict Prognosis of Renal Cell Clear Cell Carcinoma Based on 3 Angiogenesis-related Long Non-coding RNAs

A Model to Predict Prognosis of Renal Cell Clear Cell Carcinoma Based on 3 Angiogenesis-related Long Non-coding RNAs

Identification of Lysine lactylation (kla) -related lncRNA signatures using XGBoost to predict prognosis and immune microenvironment in breast cancer patients

Contact Info

Product

Resources

About