Blood Lipid Metabolic Profiles and Causal Links to Site-Specific Cancer Risks: A Mendelian Randomization Study.

Observational and Mendelian randomization (MR) studies have established links between dyslipidemia and select cancer susceptibilities. However, there is a lack of comprehensive exploration of causal relationships spanning diverse cancer types. Here, we conducted a two-sample MR analysis to elucidate the causative connections between 9 blood lipid metabolic profiles (namely, adiponectin, leptin, lipoprotein A, apolipoprotein A1, apolipoprotein B, cholesterol, triglycerides, LDL-cholesterol, and HDL-cholesterol) and 21 site-specific cancer risks. Our findings reveal genetically predicted adiponectin levels to be associated with a reduced ovarian cancer risk, while genetically determined leptin increases bladder cancer risk but decreases prostate cancer risk. Lipoprotein A elevates risk of prostate cancer while diminishing risk of endometrial cancer, while apolipoprotein A1 heightens risks of breast and cervical cancers. Furthermore, elevated levels of cholesterol are positively correlated with kidney cancer, and triglycerides demonstrate a positive association with non-melanoma skin cancer but a negative association with breast cancer. Protective effects of genetically predicted LDL-cholesterol on endometrial cancer and adverse effects of HDL-cholesterol on breast cancer are also observed. Our study conclusively establishes that blood lipid metabolic profiles exert causal effects on cancer susceptibility, providing more robust evidence for cancer prevention and prompting contemplation regarding the future health of the human populace.

Refining the 8th edition TNM classification for EBV related nasopharyngeal carcinoma.

The AJCC/UICC TNM classification describes anatomic extent of tumor progression and guides treatment decisions. Our comprehensive analysis of 8,834 newly diagnosed patients with non-metastatic Epstein-Barr virus related nasopharyngeal carcinoma (NPC) from six Chinese centers indicates certain limitations in the current staging system. The 8th edition of the AJCC/UICC TNM classification inadequately differentiates patient outcomes, particularly between T2 and T3 categories and within the N classification. We propose reclassifying cases of T3 NPC with early skull-base invasion as T2, and elevating N1-N2 cases with grade 3 image-identified extranodal extension (ENE) to N3. Additionally, we suggest combining T2N0 with T1N0 into a single stage IA. For de novo metastatic (M1) NPC, we propose subdivisions of M1a, defined by 1-3 metastatic lesions without liver involvement, and M1b, characterized by >3 metastatic lesions or liver involvement. This proposal better reflects responses of NPC patients to the up-to-date treatments and their evolving risk profiles.

Prognostic significance of Dickkopf-1 in head and neck squamous cell carcinoma.

BACKGROUND: Dickkopf-1 (DKK1) exhibits abnormal expression in various cancers and correlates with poor prognosis. This study investigates DKK1's prognostic relevance in head and neck squamous cell carcinoma (HNSC). METHODS: We conducted a comprehensive search across literature and sequencing databases to gather eligible studies and HNSC datasets. We calculated pooled standardized mean differences (SMD) and 95% confidence intervals (CI) for clinical characteristics, as well as hazard ratios (HR) with 95% CIs for overall survival (OS) and progression-free/disease-free survival (PFS/DFS). Sensitivity analysis gauged result stability, and Egger's test assessed publication bias. RESULTS: Pooled results indicated that HNSC patients with higher T-stage exhibited elevated DKK1 expression levels, and this elevated expression was associated with shorter OS and PFS/DFS. While sensitivity analysis identified some studies significantly affecting pooled results, most were unaffected, and no publication bias was detected. CONCLUSION: DKK1 holds promise as a potential biomarker for predicting poor prognosis in HNSC patients, but further research is needed for confirmation.

Efficacy of sequential chemoradiotherapy combined with toripalimab in de novo metastatic nasopharyngeal carcinoma: A phase II trial.

Locoregional radiotherapy added to chemotherapy has significantly improved survival in de novo metastatic nasopharyngeal carcinoma (mNPC). However, only 54% of de novo mNPC patients who received sequential chemoradiotherapy have complete or partial response 3 months after radiotherapy. This Simon's optimal two-stage design phase II study (NCT04398056) investigates whether PD-1 inhibitor could improve tumor control in combination with chemoradiation. The primary endpoint is objective response rate (ORR) at 3 months after radiotherapy. Twenty-two patients with primary mNPC are enrolled. The ORR at 3 months after radiotherapy is 81.8% (22.7% complete response, n = 5; 59.1% partial response, n = 13), and the disease control rate is 81.8%. The 3-year progression-free survival (PFS) rate is 44.9% (95% confidence interval 26.4%-76.3%). Fifteen patients (68.2%) experienced grade 3-4 adverse events. Patients with high baseline plasma Epstein-Barr virus DNA copy number (>104 cps/mL) show worse PFS. Addition of toripalimab to sequential chemoradiotherapy suggests promising tumor response in patients with primary mNPC.

An adaptive heuristic clustering algorithm for influence maximization in complex networks.

Influence maximization research in the real world allows us to better understand, accelerate spreading processes for innovations and products, and effectively analyze, predict, and control the spread of diseases, rumors, and computer viruses. In this paper, we first put forward a new path-based node similarity measure, named the dynamic local similarity index, which can be dynamically adjusted to the optimal mode according to network topology characteristics. Compared to the Katz index with high complexity and an LP index with a limited application range, the proposed index achieves an excellent balance between complexity and precision. Second, combining the extended neighborhood coreness with the minimum distance, a novel strategy is presented for selecting initial centers of clusters, which is helpful for speeding up clustering convergence and avoiding local optimum, especially in non-connected networks. Subsequently, we present an adaptive heuristic clustering algorithm, which can find the seed set with maximum collective influence through clustering. The empirical results on four real datasets show the effectiveness and efficiency of the proposed algorithm, which compares favorably to several state-of-the-art algorithms.

Painlevé classification of a generalized coupled Hirota system.

By means of singularity analysis, the integrability of a system of generalized coupled Hirota equations with parameter coefficients is tested. It is proven that the system passes the Painlevé test for integrability only in two distinct cases.