Plasma proteomic profiling and molecular clustering reveal immune‐defined prognostic subtypes in lung adenocarcinoma
Bibliographic record
Abstract
Lung adenocarcinoma (LUAD) is a biologically and clinically heterogeneous disease that poses a major challenge for prognosis and treatment. In this study, we performed proteomic profiling in a cohort of 88 LUAD patients to identify molecular subgroups and investigate their clinical relevance. Unsupervised clustering of the proteomic data allowed us to identify two distinct patient groups with different demographic, clinical, and molecular characteristics. Cluster 1 consisted predominantly of older patients and showed increased expression of immune and inflammatory pathways, including significant enrichment of Tumor Necrosis Factor (TNF) and Toll-like receptor signaling. This suggests a stronger innate immune response that may be associated with better disease control. In contrast, Cluster 2 was characterized by younger demographics, a higher proportion of female patients, and a greater frequency of smoking. This cluster showed reduced activation of immune-related pathways and a significantly shorter time to disease recurrence, suggesting a more aggressive clinical course and poorer prognosis. The differential expression of immune pathways between clusters underscores the role of the tumor microenvironment in disease progression and response to treatment. Our results demonstrate the value of integrating proteomic and clinical data to identify biologically distinct LUAD subtypes. This molecular stratification can improve the understanding of tumor behavior and inform personalized treatment strategies. Thus, proteomic profiling is a promising tool to guide biomarker-directed treatment of LUAD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".