Diagnostic Assessment (Imaging) and Staging of Laryngeal Cancer

NAT10 inhibits the pyroptosis of laryngeal squamous cell carcinoma through ac4C modification of ELANE mRNA

BACKGROUND

Laryngeal squamous cell carcinoma (LSCC) is the most common type of head and neck malignancy. NAT10 is a catalytic enzyme for ac4C and is involved in the progression of a variety of cancers. This study aimed to explore the effects and potential mechanisms of NAT10 in LSCC.

METHODS

Pyroptosis was assessed by measuring the release of lactic dehydrogenase, pyroptosis rate, and pyroptosis-related proteins. The RNA and protein levels were detected by quantitative real-time PCR and western blot, respectively. Potential mechanisms were validated using flow cytometry, ac4C dot blot, methylated RNA immunoprecipitation (MeRIP), RIP, and Dual-Luciferase Reporter Assay experiments.

RESULTS

The result showed that the levels of NAT10 in LSCC tissues and cells were elevated and positively correlated with tumor grading and clinical staging. Knockdown of NAT10 promoted the pyroptosis of LSCC cells. NAT10 directly interacted with ELANE, suppressed the stability of the ELANE mRNA. NAT10 inhibited pyroptosis in LSCC by downregulating the ELANE expression in vivo and in vitro.

CONCLUSION

NAT10 inhibited the pyroptosis of LSCC cells and contributed to LSCC progression by suppressing ELANE mRNA stability in ac4C modification manner, indicating that the NAT10-ac4C-ELANE axis might be a potential target for LSCC.

Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma

The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to preoperatively distinguish between Stages I-II and III-IV of LSCC. Data from 401 histologically confirmed LSCC patients were collected from two centers (training set: 213; internal test set: 91; external test set: 97). Radiomics features were extracted from the MRI images, and seven radiomics models based on single and combined sequences were developed via random forest (RF). A DL model was constructed via ResNet 18, where DL features were extracted from its final fully connected layer. These features were fused with crucial radiomics features to create a combined model. The performance of the models was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with the radiologist performances. The predictive capability of the combined model for Progression-Free Survival (PFS) was evaluated via Kaplan-Meier survival analysis and the Harrell's Concordance Index (C-index). In the external test set, the combined model had an AUC of 0.877 (95% CI 0.807-0.946), outperforming the DL model (AUC: 0.811) and the optimal radiomics model (AUC: 0.835). The combined model significantly outperformed both the DL (p = 0.017) and the optimal radiomics models (p = 0.039), and the radiologists (both p < 0.050). Moreover, the combined model demonstrated great prognostic predictive value in patients with LSCC, achieving a C-index of 0.624 for PFS. This combined model enhances preoperative LSCC staging, aiding in making more informed clinical decisions.

Exploration of biomarkers for nursing physical examination early screening of multiple tumors

Nursing and physical examination early screening of multiple tumors is helpful to find tumors early, so as to improve the cure rate. Studying its molecular mechanisms is urgent. By logging into gene expression omnibus database, we found laryngeal cancer dataset GSE127165, bladder cancer dataset GSE65635, oral cancer dataset GSE146483, obtain differentially expressed genes, subsequently, weighted gene co-expression network analysis, protein-protein interaction networks, functional enrichment analysis, immune infiltration analysis, survival analysis, comparative toxicogenomics database analysis were conducted. Draw a heatmap of gene expression. Use targetScan to search for miRNA information about core DEG. Got 53 differentially expressed genes. In GOKEGG analysis, they were clustered in cell cycle processes, spindle poles, and protein serine/threonine/tyrosine kinase activity cell cycle, transcriptional dysregulation in cancer, RIG-I-like receptor signaling pathway, P53 signaling pathway. Protein-protein interaction analysis screened out 5 genes (NEK2, BUB1, HMMR, TTK, CCNB2). Cyclin B2 (CCNB2) and budding uninhibited by benzimidazole 1 (BUB1) were highly expressed in laryngeal cancer, bladder cancer, oral cancer. Comparative toxicogenomics database analysis found that core genes (CCNB2, BUB1) are associated with tumors, necrosis, and inflammation. Related miRNA of CCNB2 gene is hsa-miR-670-3p; related miRNAs of BUB1 gene are hsa-miR-5688, hsa-miR-495-3p. CCNB2 and BUB1 exhibit high expression in laryngeal cancer, bladder cancer, and oral cancer, suggesting their potential as molecular targets for precision therapy in these cancers.

Depth of Invasion Assessment in Laryngeal Glottic Carcinoma: A Preoperative Imaging Approach for Prognostication

OBJECTIVE

The prognostic value of depth of invasion (DOI) in oral squamous cell cancer carcinoma and cutaneous melanoma is well established, while there is a lack of reports investigating the role of DOI in laryngeal cancer. This study aims to explore the association of glottic cancer DOI with other established pathological risk factors and nodal metastasis and evaluate the feasibility of measuring DOI preoperatively using tomographic imaging.

METHODS

The medical records of glottic cancer patients treated between 2015 and 2020 in a single tertiary referral center were screened retrospectively. Pathologically measured DOI (pDOI) value was also reviewed and registered. Preoperative computer tomography (CT) was used to obtain the radiological DOI (rDOI) measured by two dedicated radiologists. Their inter-rated agreement was assessed and the correlation between pDOI and rDOI was calculated. pDOI association with the main pathology report features was assessed with univariable analysis. Cox univariable and multivariable models were used to explore the role of pDOI on survival.

RESULTS

Ninety-one patients had pDOI data available, of which 59 also had rDOI data. A strong concordance between the two radiologists was found (concordance correlation coefficient = 0.96); rDOI and pDOI were highly and significantly correlated (R = 0.85; p < 0.001). pDOI was significantly higher in patients with perineural invasion (PNI; p < 0.001), lymphovascular invasion (LVI; p < 0.001), and nodal metastasis (p < 0.001). pDOI was associated with disease-free survival at univariable analysis (p = 0.04) while it did not show a significant impact (p = 0.10) at multivariable analysis.

CONCLUSION

Glottic carcinoma DOI correlates with PNI, LVI, and nodal metastasis and it can be reliably assessed in a preoperative setting using CT imaging.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3230-3237, 2024.

Twenty questions from the surgeon to the radiologist to better plan an open partial horizontal laryngectomy

Open partial horizontal laryngectomies (OPHLs) represent a valuable therapeutic option for tumors of the intermediate T-category and, in selected cases, for locally advanced tumors with low-volume extra-laryngeal extension. The eligibility of patients treated with this type of surgery has increased with the introduction of the modular approach to OPHL planning. This strategy follows the introduction of the classification proposed by the European Laryngological Society, based on the extent of horizontal resection. Optimization of the selection is the result of a meticulous work-up process involving close cooperation between experienced surgeons and radiologists, followed by final quality control by pathologists. Computed tomography and magnetic resonance imaging are study methods whose pearls and pitfalls are well known, especially when performed at a high level of expertise. In this paper, based on the experience of two high-volume centers, a checklist of 20 questions addressed by the surgeon to the radiologist before planning an OPHL was proposed. Considerations regarding case selection are reported for each of the questioned parameters. A very simple question-and-answer process is easy to understand and mainly addressed by less experienced colleagues who wish to increase their knowledge and skills in performing this type of surgery.