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Iranian Journal of Biotechnology، جلد ۲۳، شماره ۲، صفحات ۱-۱۵
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عنوان انگلیسی MiRNA-Based Exosome-Targeted Multi-Target, A Multi-Pathway Intervention for Personalized Lung Cancer Therapy: Prognostic Prediction and Survival Risk Assessment
چکیده انگلیسی مقاله Background: Lung cancer remains one of the most prevalent and lethal cancers globally, often diagnosed at advanced stages, which impedes effective treatment. Recent advancements have highlighted exosomes as valuable biomarkers for early detection, prognosis, and therapeutic interventions in lung cancer. Exosomes, which carry molecular information from tumor cells, reflect tumor development and metastasis, offering potential for precision medicine.

Objective: This study aims to develop a prognostic prediction model for lung cancer therapy based on miRNA profiling in exosomes. By performing bioinformatics analyses, we identify miRNAs and target genes associated with lung cancer treatment and their potential relationship with patient survival outcomes.

Methods: Using the GSE207715 dataset, we applied machine learning models and a Transformer-based deep learning approach to predict nivolumab treatment efficacy in lung cancer patients. Additionally, miRNA-target gene interactions were predicted via miRNA databases, followed by Gene Ontology and KEGG pathway enrichment analyses. A Cox proportional hazards regression model was used to assess the relationship between miRNA expression and patient survival.

Results: Significant differences were observed in the miRNA profiles of exosomes from patients with different nivolumab treatment outcomes, though the differences were relatively small. Machine learning models achieved prediction accuracies ranging from 0.6731 to 0.6923, while the deep learning model outperformed these methods with an accuracy of 0.9412. The hsa-let-7c miRNA showed statistical significance in multivariate survival risk analysis (p = 0.0152).

Conclusion: This study demonstrates the potential of miRNA profiling in exosomes for predicting treatment efficacy and survival in lung cancer patients. The deep learning model's ability to capture subtle miRNA expression differences provides a robust platform for personalized treatment strategies in non-small cell lung cancer, offering new avenues for therapeutic decision-making.
کلیدواژه‌های انگلیسی مقاله Bioinformatics,Exosome,Lung cancer,miRNA,Multi-Target Intervention,prognostic model,Survival Risk
نویسندگان مقاله Jiefeng Liu |
Department of General Surgery, The Fourth Hospital of Changsha, Hunan Normal University. Changsha 410006, China.

Yukai Tang |
Department of Oncology, Xiangya Hospital, Central South University. Changsha 410078, China

Xueying Liu |
Department of Oncology, Xiangya Hospital, Central South University. Changsha 410078, China

Yujing Gong |
Department of General Surgery, The Fourth Hospital of Changsha, Hunan Normal University. Changsha 410006, China.

Ziqi Sun |
Department of Oncology, Xiangya Hospital, Central South University. Changsha 410078, China.

Yao Yin |
Department of Oncology, Xiangya Hospital, Central South University. Changsha 410078, China

Yiping Liu |
Department of Oncology, Xiangya Hospital, Central South University. Changsha 410078, China

نشانی اینترنتی https://www.ijbiotech.com/article_221244_f9e843aa03a02fe1f5d99e4442ee4e18.pdf
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