A survival model for prognostic prediction based on ferroptosis-associated genes and the association with immune infiltration in lung squamous cell carcinoma
Bibliographic record
Abstract
Lung squamous cell carcinoma (LUSC) is the primary pathological type of lung cancer with a less favorable prognosis. This study attempts to construct a ferroptosis-associated signature associated with overall survival (OS) that can predict the prognosis of LUSC and explore its relationship with immune infiltration. A 5 ferroptosis-associated gene model was constructed by LASSO-penalized regression analysis to predict the prognosis of patients with LUSC in the TCGA database and validated in the GEO and TCGA databases. Patients were stratified into high-risk and low-risk groups by the median value of the risk scores, and the former prognosis was significantly worse (P<0.001). Additionally, we found a certain association between the two risk groups and immune infiltration through CIBERSORT. Meanwhile, the differentially expressed genes (DEGs) between normal and tumor tissue were used to perform functional analysis, which showed a significant association with leukocyte transendothelial migration pathways in the TCGA cohort. In addition, immune cell infiltration analysis confirmed that M2 macrophages were significantly highly expressed in the high-risk group. Overall, the model successfully established by ferroptosis-associated genes suggests that ferroptosis may be related to immune infiltration in LUSC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".