Enhanced Expression of <i>IL32</i> mRNA in Skeletal Muscles in the Context of Head and Neck Carcinomas
Bibliographic record
Abstract
BACKGROUND: Cancer-related sarcopenia (CRS) is a significant complication of head and neck carcinoma (HNC), characterised by muscle degeneration and poor clinical outcomes. Although various dietary and therapeutic interventions have been explored, most of them remain empirical, and the molecular mechanisms underlying CRS are not yet fully understood. METHODS: Transcription profiles of muscle fragments from 29 HNC patients and 8 control donors were analysed by bulk RNA sequencing (6/29 and 3/8) and/or RT-qPCR (29/29 and 5/8). In parallel, differentiating human myoblasts (AB1190) were subjected to indirect co-culture with two types of effector cells: HNC cells (FaDu) or control epithelial cells (NHEK). The contactless effects of effector cells on target myoblasts were investigated using cell imaging to assess muscular differentiation, RT-qPCR and Western blot to assess gene expression. RESULTS: Bulk RNA sequencing identified 789 differentially expressed transcripts between HNC and control samples. Subsequent RT-qPCR analysis focused on IL32 and BIRC3 mRNAs (up-regulated in HNC samples) and ACE1 mRNA (down-regulated). Among male HNC patients, the IL32/ACE1 mRNA ratio was significantly elevated in CRS cases (p = 0.0001, effect size r = 0.57) and correlated with the severity of muscle atrophy (negative correlation with the Skeletal Muscle Index at a threshold of 10%: p = 0.093, r = -0.41). In contrast, no such trend was observed for the BIRC3/ACE1 ratio. Exposure of human myoblasts to HNC cells induced inhibition of myogenesis and strong up-regulation of IL32 mRNA and protein. In contrast, these effects were absent or much smaller under exposure to NHEK controls. CONCLUSIONS: IL32 is a potential biomarker for CRS in HNC patients. In addition, the HNC-myoblast co-cultivation model provides a promising in vitro system to study CRS mechanisms, potentially reducing the reliance on animal models.
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 | 0.000 |
| 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".