ArticleAmerican journal of translational research2021
Identification and validation of significant gene mutations to predict clinical benefit of immune checkpoint inhibitors in lung adenocarcinoma.
Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Identification of metabolism-associated molecular classification for effect and prognosis in lung adenocarcinoma based on multidatabases including the cancer genome atlas and gene expression omnibus.SAGE open medicine · 2025Article
- Comparing the cost-effectiveness of sintilimab + pemetrexed plus platinum and pemetrexed plus platinum alone as a first-line therapy for Chinese patients with nonsquamous non-small cell lung cancer.Translational cancer research · 2023Article
- Redox-associated messenger RNAs identify novel prognostic values and influence the tumor immune microenvironment of lung adenocarcinoma.Frontiers in genetics · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Characteristics of circadian rhythm-related genes and establishment of a prognostic scoring system for lung adenocarcinoma with experimental verification: a bioinformatics analysis.Journal of thoracic disease · 2022Article
- Autophagy characteristics and establishment of autophagy prognostic models in lung adenocarcinoma and lung squamous cell carcinoma.PloS one · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveImmune checkpoint inhibitors (ICI) has achieved remarkable clinical benefit in advanced lung adenocarcinoma (LUAD). However, effective clinical use of ICI agents is encumbered by the high rate of innate resistance. The aim of our research is to identify significant gene mutations which can predict clinical benefit of immune checkpoint inhibitors in LUAD.
methodsThe "mafComapre" function of "MafTools" package was used to screen the differentially mutated genes between durable clinical benefit (DCB) group and no durable clinical benefit (NDB) group based on the somatic mutation data from NSCLc_PD1_mSK_2018. Machine learning was performed to select significantly mutated genes to accurately classify patients into DCB group and NDB group. A nomogram model was constructed based on the significantly mutated genes to predict the susceptibility of patients to ICI. Finally, we explored the correlation between two classifications of immune cell infiltration, PD-1 and PD-L1 expression, tumor mutational burden (TMB) and prognosis.
resultsThrough utilize machine learning, 6 significantly mutated genes were obtained from 8 differentially mutated genes and used to accurately classify patients into DCB group and NDB group. The DCA curve and clinical impact curve revealed that the patients can benefit from the decisions made based on the nomogram model. Patients highly sensitive to ICI have elevated immune activity, higher expression of PD-1 and PD-L1, increased TMB, and well prognosis if they accept ICI treatment.
conclusionsOur research selected 6 significantly mutated genes that can predict clinical benefit of ICI in LUAD patients.
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33841639PMC8014424W3156857057What OpenQuestion holds
Registered trials
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