Identifying Genes that Could Determine Prognostication in Sinonasal Squamous Cell Carcinoma
Bibliographic record
Abstract
Background: Sinonasal squamous cell carcinoma (SNSCC) is a multifaceted pathology, with several different genetic components and known etiologies. These differences create variable prognoses and pose a unique challenge to skull base surgeons. SNSCC has been studied in several instances, but most literature analyzes specific genes in isolation, which may leave key targets for therapy unidentified. RNA and DNA sequencing can help with identifying prognostic factors as well as in the development of molecular targets in the management of sinonasal squamous cell carcinoma. While individual genes and their role in prognosis have been studied, the effect that a group of genes have on prognosis is unclear. Objective: This article aims to assess the relationship between genetic mutations and overall survival in SNSCC. Methods: Nineteen SNSCC samples were analyzed using the Tempus xT panel, a third-party DNA and RNA sequencing service, with accompanying chart review for demographic and survival data. This panel detects single nucleotide variants, indels, and copy number variants in 648 genes and chromosomal rearrangements in a subset of 21 genes. A log-rank test was performed for each gene type to compare overall survival. Logistic regression was also performed to analyze the association between mutation type and demographic characteristics. Results: Seventy-nine percent of research subjects were male with a mean age of 67 years (range 48–92). At the time of diagnosis, 84% of participants were stage T4, and 3 tumors were associated with inverted papillomas. Among patients with documented mortality, mean survival was 23 months, while those without documented mortality had a mean follow-up of 42 months. The most frequent mutations were P53 (74%), KMT2D (42%), CDKN2A (26%), CDKN2B (21%), EGFR (21%), FAT1 (21%), and MTAP (21%). Gain of function mutations in CUL1 and EZH2 were found to be associated with higher risk of mortality ( p = 0.0247). Increased mortality rates were associated with deletion of several genes including ING5, BRAF pseudogene, PRDM11, MAP2K, PSMD2, ELMOD1, LGMN, RMND5B, PNLIPRP1, RAI1, RP11, and TMEM66 (p = 0.0359). Deletions of APOA1, CYP2D6, EMC3, FCRL3, HLA-DBQ1, HOXA11, RBPMS, SEC24A, and TMC4 were also found to be associated with higher mortality ( p < 0.001). No specific gene mutations were associated with mortality within 12 months of diagnosis, T4 status at diagnosis, or poorly differentiated tumors. Similarly, overall tumor mutation burden did not correlate with these outcomes. Conclusion: This study provides a comprehensive genomic analysis of SNSCC and identifies new targets associated with increased mortality risks. Larger studies are required to confirm these findings and help with possible molecular treatment strategies. Publication History Article published online: 07 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".