Role for High-Affinity IgE Receptor in Prognosis of Lung Adenocarcinoma Patients
Bibliographic record
Abstract
Abstract Cancer development and biology is influenced by the host immune system. Emerging data indicate that the context of immune cell infiltrates may contribute to cancer prognosis. However, the types of infiltrating immune cells that are critical for cancer development remain controversial. In attempts to gain insights into the immune networks that regulate and/or predict tumor progression, gene expression analysis was conducted on microarray datasets of resected tumor samples from 128 early-stage non–small cell lung cancer (NSCLC) adenocarcinoma patients. By limiting analysis to immune-related genes, we identified a 9-gene signature using MAximizing R Square Algorithm that selected for the greatest separation between favorable and adverse prognostic patient subgroups. The prognostic value of this 9-gene signature was validated in 10 additional independently published microarray datasets of lung adenocarcinoma [n = 1,097; overall survival hazard ratio (HR), 2.05; 95% confidence interval, 1.64–2.56; P < 0.0001] and was found to be an independent prognostic indicator relative to tumor stage (overall survival HR, 2.09, 95% confidence interval, 1.65–2.66; P < 0.0001). Network analysis revealed that genes associated with Fcϵ complex (FCER1, MS4A2) formed the largest and most significant pathway of the signature. Using immunohistochemistry, we validated that MS4A2, the β subunit of the IgE receptor expressed on mast cells, is a favorable prognostic indicator and show that MS4A2 gene expression is an independent prognostic marker for early-stage lung cancer patient survival. Cancer Immunol Res; 5(9); 821–9. ©2017 AACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".