A novel machine learning-driven immunogenic cell death signature for predicting ovarian cancer prognosis
Bibliographic record
Abstract
Background: Ovarian cancer (OC) is one of the most lethal malignancies in women, primarily due to the absence of reliable predictive biomarkers and effective therapies. The complex role of immunogenic cell death (ICD) in OC remains poorly understood, despite its critical implications for enhancing immune responses against tumors. We are committed to developing and validating a novel ICD-related gene signature and producing certain guiding value for the clinical treatment of OC. Methods: We employed single-sample gene set enrichment analysis (ssGSEA) and weighted gene coexpression network analysis (WGCNA) on The Cancer Genome Atlas (TCGA)-ovarian carcinoma dataset to identify ICD-associated genes. A combination of 10 different machine learning approaches was used to construct an ICD-related signature (ICDRS), which was then validated across multiple datasets. The model's predictive power was integrated into a clinical nomogram to predict patient outcomes. Ultimately, we assessed the reaction of various risk subgroups to screen pharmaceuticals designed to address specific risk factors in the context of personalized medicine. Results: We identified 72 prognostic genes related to ICD. An unanimous ICDRS was developed using a 101-combination machine learning computational structure, demonstrating outstanding predictive accuracy for prognosis and clinical use. Patients categorized as low ICDRS varied from those of high ICDRS in terms of biological processes, mutation profiles, and immune cell penetration in the tumor microenvironment. In addition, potential medications that target specific subgroups at risk were identified. Conclusions: The ICDRS presents a significant advancement for prognostication of patients with OC, facilitating refined predictions and the exploration of personalized treatment pathways. Prospective clinical trials are necessary to validate its clinical utility and expand the application of this model to other cancer types.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".