ArticleIranian journal of biotechnology2025
MiRNA-Based Exosome-Targeted Multi-Target, A Multi-Pathway Intervention for Personalized Lung Cancer Therapy: Prognostic Prediction and Survival Risk Assessment.
Article in Iranian journal of biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 aimed to develop a prognostic prediction model for lung cancer therapy based on miRNA profiling in exosomes. By performing bioinformatics analyses, we identified miRNAs and target genes associated with lung cancer treatment and their potential relationship with patient survival outcomes. Materials and 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.