Single-cell sequencing-based study of ferroptosis mechanisms in heat stroke: identification of key biomarkers and dynamic analysis of the immune microenvironment
Bibliographic record
Abstract
BACKGROUND: Heat stroke, caused by excessive heat production or impaired dissipation, often results from prolonged heat exposure or strenuous activity. Ferroptosis, a novel form of programmed cell death, has been implicated in its pathogenesis, though its mechanisms remain unclear. OBJECTIVE: This study investigates the molecular mechanisms linking heat stroke and ferroptosis using single-cell and transcriptomic analyses to identify diagnostic and therapeutic targets. METHODS: Peripheral blood samples from 29 heat stroke patients, recruited at an early stage of the condition, underwent transcriptome sequencing, and single-cell RNA sequencing was conducted for two representative cases. Ferroptosis-related genes were identified by integrating the FerrDB database, followed by weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis to pinpoint ferroptosis-related genes most characteristic of heat stroke. Functional enrichment analyses, including GO and KEGG pathways, were performed. Single-cell RNA sequencing revealed immune microenvironment alterations and marker genes linked to heat stroke pathogenesis. Receiver operating characteristic (ROC) analysis evaluated the diagnostic potential of these genes. Additionally, pseudotime analysis elucidated cell differentiation trajectories and the roles of key genes. RESULTS: In Dataset 1, 630 differentially expressed genes (546 up-regulated, 84 down-regulated) and 1,979 heat stroke-related genes were identified. Among them, 14 intersected with 1,001 ferroptosis-related genes and were enriched in pathways like fatty acid metabolism, inflammation, and immune regulation. Single-cell sequencing showed increased monocytes and macrophages in heat stroke patients. Five core genes (ACSL1, MAPK14, ALOX5AP, PROK2, and DUSP1) were validated in Dataset 2 with high AUC values (1.0, 1.0, 0.952, 0.976, and 0.881, respectively). These genes were highly expressed in neutrophils, dendritic cells, and monocytes. Pseudotime analysis confirmed their roles in cell differentiation and disease progression. CONCLUSION: ACSL1, MAPK14, ALOX5AP, PROK2, and DUSP1 were identified as novel biomarkers for diagnosing heat stroke. ROC validation confirmed their strong association with the disease, highlighting their potential as diagnostic targets. Pseudotime analysis revealed their consistency in cellular differentiation trajectories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".