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.

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.

Risk of intracranial hemorrhage with direct oral anticoagulants: a systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: We performed a systematic review and meta-analysis to compare the risk of intracranial hemorrhage (ICH) between direct oral anticoagulants (DOACs) and other antithrombotic drugs in detail across all diseases. METHODS: PubMed, EMBASE, Web of Science, and the Cochrane Library were searched for relevant randomized controlled trials (RCTs). Heterogeneity was examined using the I2 statistic. Risk ratio (RR) and 95% confidence interval (CI) were calculated using random-effects meta-analysis. RESULTS: Fifty-five RCTs were included in this meta-analysis. Compared with vitamin K antagonists (VKAs), dabigatran reduced the risk of ICH by 60% (RR 0.40; 95% CI 0.28-0.57), apixaban by 57% (RR 0.43; 95% CI 0.31-0.58), edoxaban by 56% (RR 0.44; 95% CI 0.29-0.67) and rivaroxaban by 41% (RR 0.59; 95%CI 0.44-0.80). Compared with low-molecular-weight heparins (LMWHs), apixaban, edoxaban and rivaroxaban had a similar risk of ICH. Compared with aspirin, dabigatran and apixaban had a similar risk of ICH, while rivaroxaban posed an increased risk of ICH (RR 2.12; 95% CI 1.31-3.44). For secondary prevention stroke, DOACs reduced the risk of ICH by 46% compared with warfarin (RR 0.54; 95% CI [0.42-0.70]) and had a similar risk of ICH compared with aspirin. CONCLUSION: All DOACs had a lower risk of ICH than VKAs. In terms of the risk of ICH, DOACs were overall as safe as LMWHs, and apixaban and dabigatran were as safe as aspirin, but rivaroxaban was not. For secondary prevention stroke, the risk of ICH with DOACs was overall lower than warfarin and similar to aspirin, but it should be noted that compared with aspirin, rivaroxaban may increase the risk of ICH. This is the first pair-wise meta-analysis that compares the risk of ICH between DOACs and other antithrombotic drugs in detail across all diseases, which may have certain significance for patients with high risk of ICH to choose antithrombotic drugs in clinical practice.