Epidermal growth factor receptor­targeted antibody nimotuzumab combined with chemoradiotherapy improves survival in patients with locally advanced head and neck squamous cell carcinoma: a propensity score matching real-world study.

Patients with locally advanced head and neck squamous cell carcinoma (LA-HNSCC) have poor survival outcomes. The real-world efficacy of nimotuzumab plus intensity modulated radiotherapy (IMRT)-based chemoradiotherapy in patients with LA-HNSCC remains unclear. A total of 25,442 HNSCC patients were screened, and 612 patients were matched by propensity score matching (PSM) (1:1). PSM was utilized to balance known confounding factors. Patients who completed at least five doses of nimotuzumab were identified as study group. The primary end point was 3-year overall survival (OS) rate. Log-rank test examined the difference between two survival curves and Cloglog transformation test was performed to compare survival at a fixed time point. The median follow-up time was 54.2 (95% confidence interval [CI]: 52.7-55.9) months. The study group was associated with improved OS (hazard ratio [HR] = 0.75, 95% CI: 0.57-0.99, p = 0.038) and progression-free survival (PFS) (HR = 0.74, 95% CI: 0.58-0.96, p = 0.021). Subgroup analysis revealed that aged 50-60 year, IV, N2, radiotherapy dose ≥ 60 Gy, without previous surgery, and neoadjuvant therapy have a trend of survival benefit with nimotuzumab. Nimotuzumab showed favorable safety, only 0.2% had nimotuzumab-related severe adverse events. Our study indicated the nimotuzumab plus chemoradiotherapy provides survival benefits and safety for LA-HNSCC patients in an IMRT era.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction.

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

A nomogram based on nutritional and inflammatory parameters to predict DMFS and identify beneficiaries of adjuvant chemotherapy in IVA-stage nasopharyngeal carcinoma.

OBJECTIVE: This study aims to develop a nomogram integrating inflammation (NLR), Prognostic Nutritional Index (PNI), and EBV DNA (tumor burden) to achieve personalized treatment and prediction for stage IVA NPC. Furthermore, it endeavors to pinpoint specific subgroups that may derive significant benefits from S-1 adjuvant chemotherapy. METHODS: A total of 834 patients diagnosed with stage IVA NPC were enrolled in this study and randomly allocated into training and validation cohorts. Multivariate Cox analyses were conducted to identify independent prognostic factors for constructing the nomogram. The predictive and clinical utility of the nomogram was assessed through measures including the AUC, calibration curve, DCA, and C-indexes. IPTW was employed to balance baseline characteristics across the population. Kaplan-Meier analysis and log-rank tests were utilized to evaluate the prognostic value. RESULTS: In our study, we examined the clinical features of 557 individuals from the training cohort and 277 from the validation cohort. The median follow-up period was 50.1 and 49.7 months, respectively. For the overall cohort, the median follow-up duration was 53.8 months. The training and validation sets showed 3-year OS rates of 87.7% and 82.5%, respectively. Meanwhile, the 3-year DMFS rates were 95.9% and 84.3%, respectively. We created a nomogram that combined PNI, NRI, and EBV DNA, resulting in high prediction accuracy. Risk stratification demonstrated substantial variations in DMFS and OS between the high and low risk groups. Patients in the high-risk group benefited significantly from the IC + CCRT + S-1 treatment. In contrast, IC + CCRT demonstrated non-inferior 3-year DMFS and OS compared to IC + CCRT + S-1 in the low-risk population, indicating the possibility of reducing treatment intensity. CONCLUSIONS: In conclusion, our nomogram integrating NLR, PNI, and EBV DNA offers precise prognostication for stage IVA NPC. S-1 adjuvant chemotherapy provides notable benefits for high-risk patients, while treatment intensity reduction may be feasible for low-risk individuals.

Machine learning-based survival prediction nomogram for postoperative parotid mucoepidermoid carcinoma.

Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I-IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model's clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.

The causal impact of complement C3d receptor 2 on head and neck cancer microenvironment and its implications for immunotherapy response prediction.

This research dives into the intricate immune landscape of head and neck cancer (HNC), with a keen focus on the roles of specific immune cell subpopulations and their linked genes. We used tumour RNA-seq (in-house cohort: n = 192, TCGA-HNSC: n = 546) and Mendelian randomization to pinpoint key SNPs in immune cells that have a causal connection to HNC. Our discoveries unveil a spectrum of tumour immune phenotypes that either offer protection against or increase the risk of HNC. We underscore the therapeutic promise of Complement C3d Receptor 2 (CR2), a gene closely tied to immune cells, with its increased expression in tumour tissues linked to a more favourable prognosis. This is correlated with heightened immune pathway activity, stronger resistance to radiochemotherapy, and improved immunotherapy responses. Our research emphasises the pivotal role of CR2 in immune regulation and the significance of immune cells in tumour progression, highlighting the potential of CR2-targeted therapeutic interventions.

Comorbidity profiling identifies potential subtype of elderly patients with nasopharyngeal carcinoma.

BACKGROUND: Few studies have assessed the comprehensive associations among comorbid diseases in elderly patients with nasopharyngeal carcinoma (NPC). This study sought to identify potential comorbidity patterns and explore the relationship of comorbidity patterns with the mortality risk in elderly patients with NPC. METHODS: A total of 452 elderly patients with NPC were enrolled in the study. The network analysis and latent class analysis were applied to mine comorbidity patterns. Propensity score matching was used for adjusting confounders. A restricted cubic spline model was used to analyze the nonlinear association between age and the risk of all-cause mortality. RESULTS: We identified 2 comorbidity patterns, metabolic disease-related comorbidity (MDRC) and organ disease-related comorbidity (ODRC) in elderly patients with NPC. Patients in MDRC showed a significantly higher risk of all-cause mortality (71.41% vs 87.97%, HR 1.819 [95% CI, 1.106-2.994], P = .031) and locoregional relapse (68.73% vs 80.88%, HR 1.689 [95% CI, 1.055-2.704], P = .042). Moreover, in patients with MDRC pattern, we observed an intriguing inverted S-shaped relationship between age and all-cause mortality among patients aged 68 years and older. The risk of mortality up perpetually with age increasing in ODRC group, specifically within the age range of 68-77 years (HR 4.371, 1.958-9.757). CONCLUSION: Our study shed light on the potential comorbidity patterns in elderly patients with NPC, thereby providing valuable insights into the development of comprehensive health management strategies for this specific population.

Non-coding RNA-related FCGBP downregulation in head and neck squamous cell carcinoma: a novel biomarker for predicting paclitaxel resistance and immunosuppressive microenvironment.

In head and neck squamous cell carcinoma (HNSC), chemoresistance is a major reason for poor prognosis. Nevertheless, there is a lack of validated biomarkers to screen for patients for categorical chemotherapy. Fc gamma binding protein (FCGBP) is a mucus protein associated with mucosal epithelial cells and has immunological functions that protect against tumors and metastasis. However, the effect of FCGBP on HNSC is unclear. In pan-cancer tissues, the expression of FCGBP and the survival status of patients were analyzed using information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Correlation analysis and Cox regression analysis were conducted to confirm the relationship and survival outcome. Bioinformatics analysis was utilized to predict the probable upstream non-coding RNA. FCGBP functioned as a potential tumor suppressor gene in HNSC. Notably, FCGBP expression was negatively correlated with enriched tumor-infiltrating macrophages and paclitaxel resistance. Cox regression with gene, clinical, and immune factors showed that FCGBP was a risk factor acting in an independent manner. In HNSC, the utmost possibly upstream non-coding RNA-related pathway of FCGBP was also discovered to be the PART1/AC007728.2/LINC00885/hsa-miR-877-5p/FCGBP axis. According to the present study, non-coding RNA-related low levels of FCGBP are a prognostic indicator and are linked to an HNSC-related immunosuppressive state.

Identification of novel molecular subtypes to improve the classification framework of nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) treatment is largely based on a 'one-drug-fits-all' strategy in patients with similar pathological characteristics. However, given its biological heterogeneity, patients at the same clinical stage or similar therapies exhibit significant clinical differences. Thus, novel molecular subgroups based on these characteristics may better therapeutic outcomes. METHODS: Herein, 192 treatment-naïve NPC samples with corresponding clinicopathological information were obtained from Fujian Cancer Hospital between January 2015 and January 2018. The gene expression profiles of the samples were obtained by RNA sequencing. Molecular subtypes were identified by consensus clustering. External NPC cohorts were used as the validation sets. RESULTS: Patients with NPC were classified into immune, metabolic, and proliferative molecular subtypes with distinct clinical features. Additionally, this classification was repeatable and predictable as validated by the external NPC cohorts. Metabolomics has shown that arachidonic acid metabolites were associated with NPC malignancy. We also identified several key genes in each subtype using a weighted correlation network analysis. Furthermore, a prognostic risk model based on these key genes was developed and was significantly associated with disease-free survival (hazard ratio, 1.11; 95% CI, 1.07-1.16; P < 0.0001), which was further validated by an external NPC cohort (hazard ratio, 7.71; 95% CI, 1.39-42.73; P < 0.0001). Moreover, the 1-, 3-, and 5-year areas under the curve were 0.84 (95% CI, 0.74-0.94), 0.81 (95% CI, 0.73-0.89), and 0.82 (95% CI, 0.73-0.90), respectively, demonstrating a high predictive value. CONCLUSIONS: Overall, we defined a novel classification of nasopharyngeal carcinoma (immune, metabolism, and proliferation subtypes). Among these subtypes, metabolism and proliferation subtypes were associated with advanced stage and poor prognosis of NPC patients, whereas the immune subtype was linked to early stage and favorable prognosis.