Transcriptional landscape of pleural mesothelioma patients in relation to NF2 gene mutational status
Bibliographic record
Abstract
BACKGROUND: Pleural mesothelioma (PM) is an aggressive cancer with poor prognosis, often driven by asbestos exposure. Mutations in the NF2 gene, a key regulator of the Hippo signaling pathway, are frequently observed in PM. However, their impact on tumor biology, immune infiltration, cytokine signaling, and therapeutic response remains poorly understood. METHODS: Using data from The Cancer Genome Atlas, we analyzed 82 PM cases to assess the prevalence and consequences of NF2 mutations. Logistic regression was used to evaluate associations with clinical variables, while transcriptomic differences were examined through differential expression and functional enrichment analyses. Immune and stromal infiltration were inferred via the xCell algorithm, cytokine signaling analyzed with Cytosig, and chemotherapeutic sensitivity predicted using the pRRophetic R package. Single-cell RNA sequencing data provided further insights into transcriptional patterns in NF2-mutated tumors. RESULTS: NF2 mutations were present in 22% of cases, with no significant correlations to histological subtype, stage, or age. NF2-mutated tumors exhibited increased infiltration of basophils, naïve B cells, and pericytes, along with altered cytokine profiles, including NRG1, TGFB3, and reduced FGF2. Differentially expressed genes, such as MYL7 and HOXA11, were linked to poorer survival. Chemotherapy modeling indicated higher sensitivity to camptothecin and vinblastine in NF2-mutated tumors. CONCLUSIONS: NF2 mutations influence the tumor microenvironment, transcriptional landscape, and predicted therapeutic response in PM, underscoring their potential as prognostic biomarkers. These findings support tailored therapeutic strategies targeting NF2-related pathways, including Hippo signaling and cytokine modulation.
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 | 0.000 | 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) | 0.000 | 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".