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Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related
Xin Li1,2, Huiqiang Liu1,2, Fanchen Wang1,2
1Research Center for Clinical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China.
Current Medicinal Chemistry
|February 15, 2024
Summary
This study introduces a new prediction model for ovarian cancer (OC) patients using 9 differentially expressed immune-related lncRNAs (DEir-lncRNAs). This model aids in predicting responses to chemotherapy and immunotherapy, improving patient prognosis.
Area of Science:
- Oncology
- Genomics
- Immunotherapy
Background:
- Ovarian cancer (OC) presents a significant challenge due to poor prognosis, drug resistance, and limited tools for predicting treatment responses.
- Effective prediction models are crucial for tailoring anti-cancer therapies and improving patient outcomes.
Purpose of the Study:
- To develop and validate a novel prediction model for therapeutic responses in ovarian cancer patients.
- To identify key biomarkers associated with paclitaxel resistance and immune response in OC.
Main Methods:
- RNA sequencing (RNA-seq) identified differentially expressed paclitaxel-resistant lncRNAs (DE-lncRNAs).
- The Cancer Genome Atlas (TCGA)-OV and ImmPort databases were utilized to identify immune-related lncRNAs (ir-lncRNAs).
- Univariate, multivariate, and LASSO Cox regression analyses were employed to construct a predictive model using 9 DEir-lncRNAs.
Main Results:
- 186 DE-lncRNAs and 225 ir-lncRNAs were identified.
- A prediction model comprising 9 DEir-lncRNAs was constructed, demonstrating potential as biomarkers for predicting therapeutic responses.
- Patients with lower prediction scores showed better prognosis, while higher scores indicated resistance to immunotherapy and chemotherapy.
Conclusions:
- The developed 9 DEir-lncRNA prediction model serves as a valuable tool for forecasting immunotherapeutic and chemotherapeutic responses in ovarian cancer.
- This model can significantly aid in predicting patient prognosis and guiding treatment decisions for ovarian cancer.
