A novel predictive model incorporating immune-related gene signatures for overall survival in melanoma patients
Bibliographic record
Abstract
Melanoma is the most invasive type of skin cancer, in which the immune system plays a vital role. In this study, we aimed to establish a prognostic prediction nomogram for melanoma patients that incorporates immune-related genes (IRGs). Ninety-seven differentially expressed IRGs between melanoma and normal skin were screened using gene expression omnibus database (GEO). Among these IRGs, a two-gene signature consisting of CCL8 and DEFB1 was found to be closely associated with patient prognosis using the cancer genome atlas (TCGA) database. Survival analysis verified that the IRGs score based on the signature gene expressions efficiently distinguished between high- and low-risk patients, and was identified to be an independent prognostic factor. A nomogram integrating the IRGs score, age and TNM stage was established to predict individual prognosis for melanoma. The prognostic performance was validated by the TCGA/GEO-based concordance indices and calibration plots. The area under the curve demonstrated that the nomogram was superior than the conventional staging system, which was confirmed by the decision curve analysis. Overall, we developed and validated a nomogram for prognosis prediction in melanoma based on IRGs signatures and clinical parameters, which could be valuable for decision making in the clinic.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".