LncRNAs regulates cell death in osteosarcoma
Bibliographic record
Abstract
Despite improvements, prognosis in osteosarcoma patients remains poor, making it essential to identify additional and more robust therapeutic targets. Non-apoptotic receptor-mediated cell death (RCD), which plays a crucial role in the pathogenesis of OS, is one avenue actively pursued as an alternative therapeutic target in OS. Long non-coding RNAs (lncRNAs) also play a diverse role in OS pathogenesis, and numerous studies have shown that they are attractive therapeutic targets in OS. However, whether lncRNA also plays a role in non-apoptotic RCD in OS is currently unknown. The objective of the current study was to identify if a functional lncRNA-based gene signature exists that regulates non-apoptotic RCD. We systematically screened immune-related lncRNAs associated with ferroptosis, necroptosis, and pyroptosis using the Pearson correlation algorithm (|Pearson R|> 0.4, P < 0.01) on 88 osteosarcoma patients and 122 normal controls that were selected from the TARGET and GETx databases. Univariate Cox regression analysis was employed to identify lncRNAs associated with osteosarcoma treatment. Three machine learning algorithms-Support Vector Machine, Random Forest, and Generalized Linear Model-were utilized to select feature genes. In low- and high-risk groups, immune infiltration was analyzed using CIBERSORT and gene set enrichment analysis. To verify the mechanism of signature lncRNAs, proteins related to pyroptosis, ferroptosis, and necroptosis were assessed via a combination of in vitro assays. LASSO regression analysis led to constructing a prognostic risk model consisting of four lncRNAs: AC006033.2, AC124798.1, LINC01517, and L3MBTL4-AS1. The AUC values for 1-, 3-, and 5-year survival in the testing set were 0.739, 0.809, and 0.708, respectively, while in the entire cohort, the AUC values were 0.849, 0.881, and 0.776, respectively, indicating high reliability and accuracy of the risk model. The high-risk group exhibited a worse prognosis. Five clusters were identified through non-negative matrix factorization clustering, revealing differences in immune infiltration and the tumor microenvironment. Quantitative polymerase chain reaction analysis showed that four lncRNAs were highly expressed in osteosarcoma, with LINC01517 being particularly associated with poor prognosis. Notably, silencing LINC01517 inhibited in vitro cell proliferation, activated NLRP3/caspase-1/GSDMD-mediated pyroptosis, promoted ferroptosis, and enhanced necroptosis in osteosarcoma cells. The non-apoptotic RCD-related lncRNA signature identified in this study provides valuable insights that will aid future exploration of these prognostic biomarkers as potential therapeutic targets for osteosarcoma treatment.
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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 | 0.000 |
| 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) | 0.001 | 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".