Big data analysis and machine learning of the role of cuproptosis-related long non-coding RNAs (CuLncs) in the prognosis and immune landscape of ovarian cancer DOI Creative Commons

Mingqin Kuang,

Yue-Yang Liu,

Hongxi Chen

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 25, 2025

Ovarian cancer (OC) is a severe malignant tumor with significant threat to women's health, characterized by high mortality rate and poor prognosis despite conventional treatments such as cytoreductive surgery platinum-based chemotherapy. Cuproptosis, novel form of cell death triggered copper ion accumulation, has shown potential in therapy, particularly through the involvement CuLncs. This study aims identify risk signatures associated CuLncs OC, construct prognostic model, explore therapeutic drugs impact on OC behavior. We analyzed ovarian data (TCGA-OV) from TCGA database, including transcriptomic clinical 376 patients. Using Pearson correlation LASSO regression, we identified 8 signature model. Patients were categorized into high- low-risk groups based their scores. performed survival analysis, model validation, drug sensitivity vitro experiments assess model's performance functional key proliferation, invasion, migration. The demonstrated predictive power, an area under curve (AUC) 0.702 for 1-year, 0.640 3-year, 0.618 5-year survival, outperforming pathological features stage grade. High-risk patients exhibited higher Tumor Immune Dysfunction Exclusion (TIDE) scores, indicating stronger immune evasion ability. Drugs JQ12, PD-0325901, sorafenib showed reduced IC50 values high-risk group, suggesting benefits. In revealed that knockdown LINC01956, CuLnc signature, significantly inhibited migration cells (P<0.05). Our explored targets OC. findings highlight importance response, providing new insights future research applications.

Language: Английский

Big data analysis and machine learning of the role of cuproptosis-related long non-coding RNAs (CuLncs) in the prognosis and immune landscape of ovarian cancer DOI Creative Commons

Mingqin Kuang,

Yue-Yang Liu,

Hongxi Chen

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 25, 2025

Ovarian cancer (OC) is a severe malignant tumor with significant threat to women's health, characterized by high mortality rate and poor prognosis despite conventional treatments such as cytoreductive surgery platinum-based chemotherapy. Cuproptosis, novel form of cell death triggered copper ion accumulation, has shown potential in therapy, particularly through the involvement CuLncs. This study aims identify risk signatures associated CuLncs OC, construct prognostic model, explore therapeutic drugs impact on OC behavior. We analyzed ovarian data (TCGA-OV) from TCGA database, including transcriptomic clinical 376 patients. Using Pearson correlation LASSO regression, we identified 8 signature model. Patients were categorized into high- low-risk groups based their scores. performed survival analysis, model validation, drug sensitivity vitro experiments assess model's performance functional key proliferation, invasion, migration. The demonstrated predictive power, an area under curve (AUC) 0.702 for 1-year, 0.640 3-year, 0.618 5-year survival, outperforming pathological features stage grade. High-risk patients exhibited higher Tumor Immune Dysfunction Exclusion (TIDE) scores, indicating stronger immune evasion ability. Drugs JQ12, PD-0325901, sorafenib showed reduced IC50 values high-risk group, suggesting benefits. In revealed that knockdown LINC01956, CuLnc signature, significantly inhibited migration cells (P<0.05). Our explored targets OC. findings highlight importance response, providing new insights future research applications.

Language: Английский

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