ArticleTranslational lung cancer research2019
Does selected immunological panel possess the value of predicting the prognosis of early-stage resectable non-small cell lung cancer?
Article in Translational lung cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: toward precision medicine.Annals of nuclear medicine · 2022Review
- Screening and Validation of Significant Genes with Poor Prognosis in Pathologic Stage-I Lung Adenocarcinoma.Journal of oncology · 2022Article
- Artificial intelligence-based analysis for immunohistochemistry staining of immune checkpoints to predict resected non-small cell lung cancer survival and relapse.Translational lung cancer research · 2021Article
- The landscape of immune checkpoints expression in non-small cell lung cancer: a narrative review.Translational lung cancer research · 2021Review
- Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: Introduction.Annals of translational medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe immune status in the tumor micro-environment of lung cancer is highly informative, and expression status of some biomarkers in the tumor micro-environment may have deep connected statistical characteristics which could indicate immune status and predict related clinical outcomes. In this study, a panel of 9 immune biomarkers are selected for their possible predictive value of clinical outcomes in early-stage resectable non-small cell lung cancer (NSCLC).
methodsWe introduced a new way of interpreting the expression of Immunohistochemistry-based biomarkers in predicting the prognosis of patients. Immunohistochemistry were performed on surgical samples from 139 patients with NSCLC. Cox regression and subgroup analysis were conducted for the screening of biomarkers that were significant in survival and prognosis. Afterwards, Principle Component Analysis was conducted on screened biomarkers in order to extract characteristic root, which were utilized by an Artificial Neuron Network (ANN) for the prediction of clinical outcomes.
resultsSix out of 9 biomarkers were considered significant and were selected for the data analysis. The KMO-Bartlett's Sphericity test was valid (0.658>0.5, P=0.0001). The principle component regression results indicated that the survival was associated with the principle component Z4 (Y=0.316×Z4+2.298, R=0.189, P=0.026). Also, regression-free survival was associated with the principle component Z4 and Z2 (Y=0.314×Z4+0.255×Z2+2.061, R=0.249, P=0.013). Principle components ranking (PCrank) was calculated and after the determination of a cutoff of 0.2, the intergroup comparison of subgrouphigh (PCrank value above 0.2) and subgrouplow (PCrank value below 0.2) was significant in overall survival (OS) (P=0.025). A database search validated our results. Afterwards, the ANN model successfully predicted the clinical outcome, with accuracies of 94.1% and 96.2% respectively in models 1 and 2.
conclusionsThe selected immunological panels have promising potential for predicting the prognosis of resectable NSCLC.
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.