Sequential Proteomic and N-Glycoproteomic Analyses of Bronchoalveolar Lavage Fluids for Potential Biomarker Discovery of Lung Adenocarcinoma
Bibliographic record
Abstract
Lung adenocarcinoma (LUAD) is the most common histological subtype of nonsmall-cell lung cancer. Herein, a multiomics method, which combined proteomic and N-glycoproteomic analyses, was developed to analyze the normal and cancerous bronchoalveolar lavage fluids (BALFs) from six LUAD patients to identify potential biomarkers of LUAD. The data-independent acquisition proteomic analysis was first used to analyze BALFs, which identified 59 differentially expressed proteins (DEPs). The bioinformatic analyses of 59 DEPs have shown that a potential marker protein, beta-1,4-galactosyltransferase 1 (B4GALT1), was consistently downregulated in all cancerous lung lobes (CLLs). As the downregulation of B4GALT1 may indicate changes in protein N-glycosylation, site-specific N-glycoproteome analysis of BALFs from the normal lung lobes (NLLs) and CLLs was further performed by using a fully automated glycopeptide enrichment and separation system. Comparing the glycan structures containing free GlcNAc in BALFs between NLLs and CLLs qualitatively, the percentage of unique glycan structure for free GlcNAc existing only in NLLs was 52.8%, which was significantly higher than the 46.3% existing only in CLLs. Furthermore, the sequential proteomic and N-glycoproteomic analyses allowed us to identify a panel of functionally related potential biomarkers consisting of one protein (B4GALT1) and four glycoproteins (NFKB1, F2, LTF, and DLD).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".