Laryngeal squamous cell carcinoma (LSCC) is recognized as the second most common malignant tumor of the respiratory tract. The study aimed to identify the roles of FOXO1, hsa-miR-96-5p, and lncRNA ADAMTS9-AS2 in the molecular pathogenesis of LSCC patients based on the systems biology data. The LSCC patient tissue samples (n = 50) and the same individual's adjacent normal tissues (n = 50) were collected from the candidates (aged 57.75 ± 9.3 years) of surgery. The miR-96-5p and lncRNA ADAMTS9-AS2 were predicted using the specific servers. The Kaplan Meier analysis was employed using TCGA data. The FOXO1and ncRNA gene expression levels were measured with the RT-qPCR technique. The Western blot technique was applied to estimate FOXO1/pFOXO1 protein values. A FOXO1/miR-96-5p/ADAMTS9-AS2 gene network was constructed and enriched using the bioinformatics data. The FOXO1 (p 0.037) correlated with ADAMTS9-AS2 (p 0.04) gene expression levels and was reduced in the LSCC patient tissue samples despite the elevated miR-96-5p expression levels (p 0.047). Moreover, the FOXO1 (p < 0.01) and pFOXO1 (p < 0.0001) protein values were reduced in the LSCC. The high FOXO1 and ADAMTS9-AS2 gene expression levels significantly increased the survival probability (HR 0.61 and 0.65, respectively). The FOXO1 and ADAMTS9-AS2 genes might act as molecular suppressors in the cell growth pathways. Furthermore, miR-96-5p is suggested as an oncogenic miRNA in the LSCC.

A novel tRNA-derived fragment tRF-Val-CAC-008 as a diagnostic biomarker and pyroptosis regulator in LSCC.

Accurate assessment of thyroid cartilage invasion is crucial for treatment decision-making and prognosis evaluation in laryngeal squamous cell carcinoma (LSCC). This study aimed to compare the performance of the radiomics and deep learning (DL) models for predicting thyroid cartilage invasion in LSCC patients, and evaluate prognostic value of the optimal predictive model. A total of 418 pathologically confirmed LSCC patients from two centers were enrolled and divided into a training cohort (n = 247), an internal validation cohort (n = 110), and an external validation cohort (n = 61). Models were developed based on venous-phase CT images and compared with two radiologists. A nomogram incorporating the optimal model and clinical risk factors was also constructed. Additionally, the prognostic value of the optimal model was assessed regarding disease-free survival (DFS). The 2D DL model showed better performance in predicting thyroid cartilage invasion, and the corresponding nomogram integrating 2D DL signature and clinical risk factors achieved the highest AUCs. However, no differences in AUCs were found in the external validation cohort (p > 0.05 for all). Additionally, the 2D DL signature and clinical N stage were independent predictors of DFS. The 2D DL-based nomogram demonstrated satisfactory predictive performance for thyroid cartilage invasion and prognosis in patients with LSCC.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-23809-y.

To explore and compare the potential value of radiomics models based on contrast-enhanced computed tomography (CT) for noninvasive preoperative prediction of lymphovascular invasion (LVI) in laryngeal squamous cell carcinoma (LSCC). This multicenter diagnostic study retrospectively enrolled LSCC patients from three tertiary hospitals who underwent surgical treatment. Standardized preprocessing was performed on the CT images, followed by region-of-interest (ROI) segmentation and extraction of traditional radiomics features and deep learning features. Features were selected using least absolute shrinkage and selection operator (LASSO) regression. Traditional radiomics models and deep learning radiomics models (DLR) were established using logistic regression, random forest, and multilayer perceptron algorithms, respectively. A transformer-based hybrid model was developed by integrating radiomics and deep learning features. The predictive performance of the three types of models was evaluated and compared using the area under the curve (AUC), decision curve analysis (DCA), sample probability distribution histograms, confusion matrices, calibration curves, net reclassification index (NRI), and integrated discrimination improvement (IDI). A total of 1,024 patients were allocated to the training set (center1, n=291), internal validation set (n=126), and external test sets (center 2, n=437; center 3, n=170). Three radiomics models and three DLR models were constructed, and the optimal performance was observed in the DLR_ Random Forest model (AUC: 0.812-0.867). The transformer hybrid model demonstrated superior predictive performance, with AUC values of 0.881, 0.843, 0.833, and 0.836 in the training, internal validation, and external test sets, respectively. Decision curve analysis indicated a higher net benefit for the transformer model, along with an improved NRI and IDI. Radiomics models based on CT images exhibit potential for noninvasive prediction of LVI in LSCC, with the transformer hybrid model achieving the highest diagnostic performance. This approach may provide clinicians with a preoperative decision support tool to optimize treatment strategies for patients with LSCC.

Prognostic impact and risk factors of level IV/V lymph nodes metastasis in laryngeal squamous cell carcinoma.

ObjectiveNeddylation is a crucial posttranscriptional modification involved in tumor progression. This study aimed to explore neddylation-associated biomarkers and the underlying mechanism in laryngeal squamous cell carcinoma (LSCC).MethodsThis study evaluated the expression of neddylation-related genes (NRGs) retrieved from the Reactome and TCGA databases to conduct a series of analyses and constructed an LSCC prognostic risk model followed by functional enrichment and mechanism prediction. Moreover, the key genes involved in this signature were also confirmed in an in vitro cell model.ResultsA total of 79 NRGs were differentially expressed in LSCC (P.adj <0.05). A prognostic gene signature was constructed, and COMMD2, WSB2 and CUL9 were determined to be prognostic genes. The nomogram indicated that this gene signature performed well in forecasting the 1-, 3-, and 5-year overall survival of LSCC patients. The CUL9 and WSB2 genes were enriched in RIBOSOME, and silencing WSB2 significantly inhibited the malignant behaviors of LSCC cells. In this gene signature, patients could be markedly distinguished into high- and low-risk groups characterized by different immune infiltration and drug sensitivity between them. WSB2 and COMMD2 jointly predicted that hsa-miR-185-5p, hsa-miR-4644 and hsa-miR-4306 were the common microRNAs (miRNAs) and regulatory networks.ConclusionThis study successfully established a neddylation-associated prognostic risk model for LSCC and revealed that COMMD2, WSB2, and CUL9 could act as new therapeutic targets, which might provide valuable information for the research and treatment of LSCC.

Objective:To explore the predictive value of preoperative peripheral hematological parameters combined with clinicopathological features for cervical lymph node metastasis（CLNM） in patients with laryngeal squamous cell carcinoma（LSCC）, and to construct and validate a nomogram model for CLNM. Methods:A retrospective analysis was conducted on the clinical data of 264 LSCC patients who underwent surgical treatment and were pathologically confirmed, collected from the Second Affiliated Hospital of Shandong First Medical University and Taian 88 Hospital. Specifically, 161 patients from one hospital were allocated to the training cohort, while 103 patients from another hospital constituted the validation cohort. Based on postoperative pathological results, patients were categorized into CLNM-positive and CLNM-negative groups. The general clinical data, clinicopathological features, and hematological parameters of the two groups were analyzed and compared. A preoperative predictive model for CLNM was developed using logistic regression analysis, followed by validation and sensitivity analysis to evaluate the robustness of the model's predictive performance. Results:The results showed that there were significant differences in tumor location, tumor size, tumor differentiation, neutrophil percentage, lymphocyte count, lymphocyte percentage, c-reactive protein（CRP）, fibrinogen, neutrophil-to-lymphocyte ratio（NLR）, platelet-to-lymphocyte ratio（PLR）, systemic immune-inflammation index（SII）, systemic inflammation response index（SIRI）, and prognostic inflammatory index（PIV） between the CLNM-positive and CLNM-negative groups（P<0.05）. Lasso regression identified tumor location, clinical T stage, tumor size, tumor differentiation degree, red blood cell distribution width（RDW） -coefficient of variation（RDW-CV）, CRP, FIB, D-dimer, NLR, and lymphocyte-to-monocyte ratio（LMR） were the most predictive parameters. Multivariate logistic regression revealed that tumor location, tumor size, tumor differentiation degree, CRP, and NLR were independent risk factors for CLNM in LSCC patients（P<0.05）. A nomogram was constructed based on these five factors. The model demonstrated excellent discrimination, with a C-index of 0.837（95%CI 0.766-0.908） in the training cohort and 0.809（95%CI 0.698-0.920） in the validation cohort. Calibration curves and DCA curves in both cohorts confirmed the clinical utility of the model. Sensitivity analysis further supported the robustness of the results, showing good discrimination and calibration across different age and BMI subgroups. Conclusion:Tumor location, tumor size, tumor differentiation degree, CRP, and NLR were independent risk factors for CLNM in LSCC patients. The nomogram based on these variables exhibits strong discrimination, calibration, and clinical applicability, and may serve as a valuable tool for preoperative risk assessment and individualized treatment planning.

Hypofractionation regimens shorten the overall duration of treatment, thereby reducing the risk of accelerated tumor cell repopulation following the initiation of radiotherapy. These regimens have been shown to improve overall survival and locoregional control in patients with laryngeal cancer. The toxic effects from radiotherapy for laryngeal squamous cell carcinoma (SCC) include dysphagia, mucositis, laryngeal edema, weight loss, and pain. To evaluate early toxicity and opioid usage associated with hypofractionation treatment of the larynx compared to standard fractionated radiotherapy. We retrospectively analyzed 127 laryngeal SCC patients who underwent radiotherapy. Among these, 50% with early glottic cancer received hypofractionation (2.25 Gy per fraction, totaling 63 Gy) directed at the larynx, while 50% with advanced-stage disease underwent standard fractionation (2 Gy per fraction, totaling 70 Gy) targeting both the larynx and bilateral neck, with or without concurrent chemotherapy. Patients in the hypofractionation group required significantly higher dosages of opioids due to increased pain and swallowing discomfort (P < 0.05). Those in the hypofractionation group who received dexamethasone boluses experienced significantly less weight loss compared to hypofractionation patients who did not receive steroids, with some even experiencing weight gain (P < 0.005). Patients with advanced-stage cancer treated with chemoradiotherapy exhibited greater toxicity than those receiving radiotherapy alone. Patients undergoing hypofractionation treatment generally require significantly higher doses of opioids than those treated with standard fractionation. Treatment protocols for patients receiving hypofractionation should include effective pain management strategies and, where feasible, the use of corticosteroids.

ABSTRACTObjectiveCurrently, there are no blood biomarkers available for the early diagnosis of laryngeal squamous cell carcinoma (LSCC). The objective of this study was to search for potential plasma biomarkers for the diagnosis of LSCC.MethodsPlasma samples were taken from patients with LSCC and healthy controls for proteomic analysis. An enzyme‐linked immunosorbent assay (ELISA) was employed to measure the expression levels of differentially expressed proteins in plasma. The expression of differential protein in LSCC cells was downregulated. Subsequently, the function of LSCC cells was evaluated using wound healing, transwell migration, incorporation of 5‐ethynyl‐2′‐deoxyuridine (EdU), and colony formation assays. Analysis of Plasma IGFBP2 Levels in Relation to Clinical Data and Evaluation of IGFBP2 as a Diagnostic and Prognostic Biomarker Using TCGA Database.ResultsA total of 16 differentially expressed proteins were identified in the plasma from patients with LSCC and healthy controls. Significant differences in protein expression patterns were observed between the LSCC patient group and the healthy control group. The expression of insulin‐like growth factor binding protein 2 (IGFBP2) in plasma of LSCC patients was significantly higher than that of healthy controls. After suppression of IGFBP2 expression, the migration, invasion, and proliferation abilities of LSCC cells were reduced and the expression of signal transduction and signal transducer and activator of transcription 3 (STAT3) was negatively regulated. The analysis of IGFBP2 expression in the TCGA‐LSCC data set showed a significant upregulation in tumor tissue compared to adjacent normal tissue. Furthermore, no significant differences in plasma IGFBP2 levels were observed with regard to gender, smoking status, history of malignancy, or tumor subsite. A positive correlation was observed between T stage and IGFBP2 levels. Kaplan–Meier survival analysis revealed that higher IGFBP2 expression levels were significantly associated with worse overall survival (OS) and progression‐free survival (PFS). Univariable Cox regression analysis identified high IGFBP2 expression as a significant predictor of poor prognosis (hazard ratio [HR] = 3.52, 95% confidence interval [CI]: 1.84–6.75, p &lt; 0.001). Furthermore, multivariable Cox regression analysis confirmed the independent prognostic value of IGFBP2 expression, with a hazard ratio (HR) of 3.32 (95% CI: 1.63–6.78, p &lt; 0.001).ConclusionsIGFBP2 may play a role in the occurrence and development of LSCC as an oncogene and may be used as a potential plasma biomarker for the diagnosis of LSCC. IGFBP2 may be related to STAT3 in mechanism.

Lactate-related genes signature as a novel prognostic landscape in laryngeal squamous cell carcinoma: insights from 156 machine learning algorithms and in vitro validation.

Lymph node (LN) metastasis is a key prognostic factor in laryngeal squamous cell carcinoma (LSCC). Emerging evidence implicates the role of the microbiome in cancer progression. This study aimed to investigate the microbial features associated with lymph node metastasis in LSCC and their potential for patient stratification. Using 16S rRNA gene sequencing, we characterized the microbiome of tumor tissues, adjacent normal tissues, lymph nodes, and oral rinses from 108 LSCC patients, including 36 with (LN+) and 72 without (LN-) cervical LN metastasis. Microbial functional potential was predicted using PICRUSt2. Based on repeated stratified 3 cross-validation, random forest models were used to identify metastasis-associated genera. Significant microbial differences were observed between LN + and LN- tumor tissues, with Ralstonia enriched in LN + tumors and Fusobacterium more abundant in LN- cases. All genera detected in lymph nodes were also found in tumor tissues. Functional predictions revealed enrichment of lipid biosynthesis, energy metabolism, and cell wall synthesis pathways in LN + patients, particularly in tumor and oral rinse samples, with low intra-group variability. Classifiers based on tumor, lymph node, and oral microbiota demonstrated the ability to distinguish LN + from LN- patients. The lymph node-derived classifier achieved an accuracy of 84.31% (95% confidence interval [CI]: 81.76% - 86.85%), followed by the tumor-based model (AUC = 84.11%, 95% CI: 81.75% - 86.46%) and oral rinse classifier (AUC = 79.88%, 95% CI: 77.09% - 83.11%). A tumor-specific 17 genera panel showed a discriminative efficacy of 84.11% (95% CI: 81.75% - 86.46%) in tumor tissues. These findings suggest that microbiome alterations may be associated with lymph node metastasis in LSCC. In addition, the oral microbiome showed potential as a non-invasive tool for occult lymph node metastasis detection. However, these results are preliminary and require validation in larger, independent cohorts.

Accurate prognostic prediction is crucial for patients with laryngeal squamous cell carcinoma (LSCC) to guide personalized treatment strategies. This study aimed to develop a comprehensive prognostic model leveraging clinical factors alongside radiomics and deep learning (DL) based on CT imaging to predict recurrence-free survival (RFS) in LSCC patients. We retrospectively enrolled 349 patients with LSCC from Center 1 (training set: n = 189; internal testing set: n = 82) and Center 2 (external testing set: n = 78). A combined model was developed using Cox regression analysis to predict RFS in LSCC patients by integrating independent clinical risk factors, radiomics score (RS), and deep learning score (DLS). Meanwhile, separate clinical, radiomics, and DL models were also constructed for comparison. Furthermore, the combined model was represented visually through a nomogram to provide personalized estimation of RFS, with its risk stratification capability evaluated using Kaplan-Meier analysis. The combined model achieved a higher C-index than did the clinical model, radiomics model, and DL model in the internal testing (0.810 vs. 0.634, 0.679, and 0.727, respectively) and external testing sets (0.742 vs. 0.602, 0.617, and 0.729, respectively). Additionally, following risk stratification via nomogram, patients in the low-risk group showed significantly higher survival probabilities compared to those in the high-risk group in the internal testing set [hazard ratio (HR) = 0.157, 95% confidence interval (CI): 0.063-0.392, p < 0.001] and external testing set (HR = 0.312, 95% CI: 0.137-0.711, p = 0.003). The proposed combined model demonstrated a reliable and accurate ability to predict RFS in patients with LSCC, potentially assisting in risk stratification.

BackgroundLymph node metastases (LNM) in laryngeal squamous cell carcinoma (LSCC) has been associated with lower survival, but current imaging methods, such as computed tomography (CT), have limited capabilities to identify them. Both conventional radiomics, involving data analysis of high-throughput quantitative features extracted from medical images, as well as deep learning networks, improved LNM diagnostic accuracy in LSCC, but the combination of both approaches has not been fully examined. In this study, we aimed to improve LNM identification in LSCC patients by developing a predictive nomogram, combining deep learning radiomics and clinical imaging features from CT images.MethodsA retrospective analysis of 235 LSCC patients, divided into training (164) and validation (71) sets, was conducted. Radiomics features were extracted from CT images, and 7 machine learning algorithms were used to develop 7 radiomics models, which were combined with deep learning features extracted from the ResNet50 deep learning network to form deep learning radiomics (DLR) models. The optimal DLR model was combined with significant clinical imaging features from CT scans to develop the predictive nomogram for LNM in LSCC.ResultsThe nomogram, under receiver operating characteristic (ROC) curve analyses, yielded areas under the curve (AUC) values of, respectively, 0.934 and 0.864 for training and validation sets, significantly higher than clinical imaging features (0.832 and 0.817), conventional radiomics (0.861 and 0.818), and DLR (0.913 and 0.864), indicating that it was significantly more accurate in predicting LNM in LSCC patients. Additionally, decision curve analysis found that the nomogram had significantly higher clinical utility than the other 3 models.ConclusionThe predictive nomogram, combining clinical imaging and DLR features, is able to accurately identify LNM in LSCC patients, providing valuable information for non-invasive LN staging and personalized treatment approaches.

Impact of ESRRG expression on proliferation and metastasis in laryngeal squamous cell carcinoma

An increasing number of studies have demonstrated that human papillomavirus (HPV) plays a crucial role in the occurrence and development of laryngeal cancer. The present study aims to identify the differentially expressed genes and pathways in HPV-positive and HPV-negative laryngeal squamous cell carcinoma (LSCC) cells for the diagnosis and treatment of HPV-related LSCC, and to determine the prevalence rate of HPV in laryngeal cancer in Northwest China. PCR-reverse dot blot was used to detect HPV genotypes in 115 LSCC patients' paraffin sections from Jan 2022 to Jun 2024.HPV-positive TU212 cells (TU212HPV) were constructed via lentiviral transfection. RT-qPCR and Western blot detected mRNA and protein levels. RNA-seq and TMT sequenced gene and protein differences. DAVID database was used for Gene Ontology and pathway enrichment analyses. STRING and Cytoscape screened key genes and further analyzed pathways. Among 115 patients, 64 were HPV-positive (HPV16 being the most common, 57 cases). The TU212HPV cell line was successfully constructed. RNA-seq identified 1,336 differentially expressed genes (797 upregulated, 539 downregulated). TMT found 236 differentially expressed proteins (124 upregulated, 112 downregulated). The key genes were discovered to be EGFR, CDC42, PXN, SLC2A1, GAPDH, FGF2, ICAM1, ITGB1, SFN, PGK1, and ISG15. Pathway enrichment showed involvement in neuroactive ligand-receptor interaction, Cytoskeleton in muscle cell, transcriptional misregulation in cancer, etc. (P < 0.05). HPV infection rate is 55.65% among laryngeal cancer patients in Northwest China. Ten key genes, namely EGFR, CDC42, PXN, SLC2A1, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), FGF2, ICAM1, ITGB1, PGK1 and ISG15, as well as pathways like proteoglycans in cancer, regulation of actin cytoskeleton and HIF-1 signaling pathway are demonstrated to be of significance in the occurrence and development of laryngeal squamous cell carcinoma. PXN, ITGB1, ISG15, SLC2A1 and ICAM1 are regarded as potential therapeutic targets for HPV-positive laryngeal cancer. PXN and PGK1 are considered as potential prognostic markers for HPV-positive laryngeal cancer.

Laryngeal squamous cell carcinoma (LSCC) presents significant treatment challenges, especially regarding recurrence after larynx-preservation therapy. We identified distinct microbial community structures between recurrence and non-recurrence groups, particularly highlighting the genera abundance of Fusobacterium and Serratia. However, larynx-preserving therapy did not significantly alter microbial diversity in recurrent patients. Survival analysis identified Fusobacterium and Serratia as independent prognostic factors for recurrence, leading to the development of a Serratia-Fusobacterium (SF) prognostic scoring model. The SF model achieved an AUC of 81.37% for predicting recurrence, outperforming the TNM staging system. LSCC patients classified as high-risk by the SF model exhibited significantly shorter disease-free survival (DFS) compared to low-risk patients in the LSCC cohort. Furthermore, the SF model demonstrated an AUC of 78.48% in the multi-center cohort for predicting recurrence. In conclusion, the Serratia-Fusobacterium prognostic scoring model can predict LSCC recurrence after larynx-preserving therapy and provide valuable insights to inform recommendations for LSCC surveillance.

Assessing the prognostic significance and predictive features of cervical occult metastasis in glottic laryngeal squamous cell carcinoma.

Aggressive laryngeal squamous cell carcinoma (LSCC) is characterized by a high metastatic potential, which is closely associated with epithelial-mesenchymal transition (EMT). Initiation of EMT is manifested by changes in the expression of some genes, including those associated with cytoskeleton reorganization. Currently, there are no effective methods for predicting metastasis in LSCC patients. In this regard, the study of SCC molecular characteristics remains relevant. In our study we assessed the relationship between the mRNA level of vimentin (VIM) and mRNA of cytoskeleton proteins: fascin-1 (FSCN1), ezrin (EZR), cofilin-1 (CFL1), profilin-1 (PFN1) and adenylyl cyclase-associated protein 1 (CAP1) in LSCC tumor tissue. The analysis was carried out using RT-PCR in paired samples from LSCC patients with and without lymph node metastases. The PFN1 mRNA level was found to be 6.3 times higher in LSCC patients with lymph node metastases than in patients without metastases. The EZR mRNA level was 17 times lower in patients with stage T3-4N0-2M0 LSCC than in patients with stage T1-2N0-1M0 LSCC. High VIM mRNA levels were associated with high FSCN1 and CAP1 mRNA levels and contributed to a stronger association between CFL1 and PFN1 mRNA levels. Thus, no direct relationship between the level of VIM as a marker of EMP and metastasis in a sample of LSCC patients was found. However, the detected relationships between the levels of cytoskeleton protein mRNA and vimentin mRNA may indicate an active reorganization of the cytoskeleton, which ensures high migration and proliferative activity of malignant cells of LSCC.

Habitat imaging radiomics increases the accuracy of a nomogram for predicting Ki-67-positivity in laryngeal squamous cell carcinoma.

To compare the clinicopathological features between laryngeal spindle cell carcinoma (LSpCC) and laryngeal squamous cell carcinoma (LSCC), and to develop and validate a prognostic model for LSpCC. A multicenter, retrospective cohort study. This study was conducted at a tertiary referral hospital (West China Hospital) and included data from the SEER database (2009-2022). A total of 37 LSpCC patients from West China Hospital and 432 LSpCC cases from the SEER database were included. LSCC patients (1:3 matched) were used as a comparison group. Clinical and demographic variables were analyzed using Kaplan-Meier survival curves and Cox regression models. A prognostic nomogram was developed to predict 1-, 3-, and 5-year overall survival (OS) and was validated using both internal and external cohorts. Model performance was evaluated with calibration plots, time-dependent receiver operating characteristic curves, and decision curve analysis (DCA). LSpCC patients were predominantly male, with an average age of 61.03 years. LSpCC had a significantly worse prognosis than LSCC (P = .043). Cox regression identified age, tumor stage, primary site, and treatment approach as significant prognostic factors for OS. The developed nomogram showed strong predictive accuracy, with AUC values ranging from 0.722 to 0.858 in both internal and external validation cohorts. DCA demonstrated the clinical utility of the model in predicting long-term outcomes. This study highlights the poor prognosis of LSpCC and provides a validated nomogram for individualized survival predictions. These findings underscore the need for tailored treatment approaches in managing this aggressive cancer subtype.