Therapeutic and prognostic effect of disulfidptosis-related genes in lung adenocarcinoma
Bibliographic record
Abstract
Disulfidptosis, a new form of cell death, may be induced by disulfide stress associated with cystine disulfide buildup, which can promote cell toxicity, leading to cell death. Nevertheless, the role of direct prognosis and the mechanism underlying the regulation of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD) are still unknown. This study aimed to investigate the role of DRGs in LUAD prognosis and diagnosis through multiomics analysis. First, copy number variations (CNVs) and mutations in the 10 genes were assessed. Considering that five differentially expressed genes (DEGs) were associated with disulfidptosis, a novel DRG score that can be utilized to anticipate LUAD prognosis was developed. Next, the generated receiver operating characteristic (ROC) and survival curves demonstrated that the model had an excellent predictive quality in LUAD in both the training and validation cohorts. Meanwhile, substantial functional disparities between the high DRG group and the low DRG group were observed, and the second gap mitosis (G2M) checkpoint, E2 promoter-binding factor (E2F) targets, and myelocytomatosis (MYC) target activities were consistently higher in the high DRG group than in the low DRG group. Additionally, the T-cell dysfunction score and tumor inflammation signature (Merck18) were negatively correlated with DRGs, whereas myeloid-derived suppressor cells (MDSCs) were positively correlated with DRGs. Moreover, DRGs were negatively linked to most of the immunological checkpoints. Meanwhile, samples of low DRGs benefited more from immune checkpoint blockade (ICB). The correlation analysis between DRGs and clinical characteristics revealed increasing malignancy with increasing DRG scores. Drug sensitization experiment results indicated that sensitivity to cisplatin, vincristine, docetaxel, and gemcitabine was higher in the high DRG group than in the low DRG group. The function of model genes in LUAD was also verified using immunohistochemistry, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blotting, 5-ethynyl-2'-deoxyuridine (EDU), and clonogenic formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".