ArticleInternational journal of general medicine2021
Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Urea Transporters in Cancer: Emerging Roles and Their Clinical Implications.Biomedicines · 2025Review
- Multiomics Approach Distinguishes SPTBN4 as a Key Molecule in Diagnosis, Prognosis, and Immune Suppression of Testicular Seminomas.International journal of genomics · 2025Article
- Ailanthone Increases Cisplatin-induced Apoptosis and Autophagy in Cisplatin Resistance Non-small Cell Lung Cancer Cells through the PI3K/AKT/mTOR Pathway.Current medicinal chemistry · 2025Article
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- Expression of lncRNA LINC00943 in lung squamous cell carcinoma and its relationship with tumor progression.Journal of cardiothoracic surgery · 2024Article
- Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress.Cancer discovery · 2024Article
- Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment.Bioinformatics (Oxford, England) · 2023Article
- A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer.Computational and structural biotechnology journal · 2023Article
- The Potential Role of MUC16 (CA125) Biomarker in Lung Cancer: A Magic Biomarker but with Adversity.Diagnostics (Basel, Switzerland) · 2022Review
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7 authors.
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Abstract
purposeLung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, has the highest incidence and cancer-related mortality worldwide. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients benefit from this treatment regimen; thus, it is worth identifying lung cancer patients who are resistant or sensitive to platinum-based therapy.
methodsThe drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used available cell line data to explore the potential mechanisms.
resultsIn this work, the drug response and sequencing data of 170 lung cancer cell lines were downloaded from the GDSC database, and SVMs and beam search were used to screen a panel of genes related to lung cancer cell line resistance to cisplatin. A final panel of nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) was identified, and achieved an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the mutant-type (panel-MT) group was significantly higher than that of the wild-type (panel-WT) group, regardless of the lung cancer subtype. The differentially expressed pathways between the two groups may explain this difference.
conclusionIn this study, we found that a panel of nine genes can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.
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