Introduction: Migraine, as a complex neurological disease, brings heavy burden to patients and society. Despite the availability of established therapies, existing medications have limited efficacy. Thus, we aimed to find the drug targets that improve the prognosis of migraine. Method: We used Mendelian Randomization (MR) and Summary-data-based MR (SMR) analyses to study possible drug targets of migraine by summary statistics from FinnGen cohorts (nCase = 44,616, nControl = 367,565), with further replication in UK Biobank (nCase = 26,052, nControl = 487,214). Genetic instruments were obtained from eQTLGen and UKB-PPP to verify the drug targets at the gene expression and protein levels. The additional analyses including Bayesian co-localization, the heterogeneity in dependent instruments(HEIDI), Linkage Disequilibrium Score(LDSC), bidirectional MR, multivariate MR(MVMR), heterogeneity test, horizontal pleiotropy test, and Steiger filtering were implemented to consolidate the findings further. Lastly, drug prediction analysis and phenome-wide association study(PheWAS) were employed to imply the possibility of drug targets for future clinical applications. Result: The MR analysis of eQTL data showed that four drug targets (PROCR, GSTM4, SLC4A1, and TNFRSF10A) were significantly associated with migraine risk in both the FinnGen and UK Biobank cohorts. However, only GSTM4 exhibited consistent effect directions across the two outcomes(Discovery cohort: OR(95%CI) = 0.94(0.93–0.96); p = 2.70e − 10; Replication cohort: OR(95%CI) = 0.93(0.91–0.94); p = 4.21e − 17). Furthermore, GSTM4 passed the SMR at p < 0.05 and HEIDI test at p > 0.05 at both the gene expression and protein levels. The protein-level MR analysis revealed a strong correlation between genetically predicted GSTM4 with a lower incidence of migraine and its subtypes(Overall migraine: OR(95%CI) = 0.91(0.87–0.95); p = 6.98e-05; Migraine with aura(MA): OR(95%CI) = 0.90(0.85–0.96); p = 2.54e-03; Migraine without aura(MO): OR(95%CI) = 0.90(0.83–0.96); p = 2.87e-03), indicating a strong co-localization relationship (PPH4 = 0.86). Further analyses provided additional validation for the possibility of GSTM4 as a migraine treatment target. Conclusion: This study identifies GSTM4 as a potential druggable gene and promising therapeutic target for migraine.

Neuroblastoma (NB), an embryonic tumour originating from sympathetic crest cells, is the most common extracranial solid tumour type in children with poor overall prognosis. Accumulating evidence has demonstrated the involvement of long non‐coding RNA (lncRNA) in numerous biological processes and their associations with embryonic development and multiple diseases. Ectopic lncRNA expression is linked to malignant tumours. Previous studies by our team indicate that MEG3 attenuates NB autophagy through inhibition of FOXO1 and epithelial‐mesenchymal transition via the mTOR pathway in vitro. Moreover, MEG3 and EZH2 negatively regulate each other. In present study, we first collected 60 NB tissues and 20 adjacent tissues for Quantitative real‐time polymerase chain reaction (Q‐PCR) experiments and performed clinical correlation analysis of the results. At the same time, nude mice were used for subcutaneous tumour formation to detect the effect of MEG3 in vivo. Two NB cell lines, SK‐N‐AS and SK‐N‐BE(2)C, were overexpressed MEG3 and rescued with EZH2 and then were subjected to proliferation, migration, invasion, apoptosis and autophagy experiments. RNA‐binding protein immunoprecipitation (RIP) and Co‐Immunoprecipitation (Co‐IP) experiments were performed to explore the molecular mechanism of MEG3 and EZH2 interaction. Q‐PCR revealed that MEG3 expression was negatively correlated with INSS stage and risk grade of NB. Moreover, MEG3 overexpression was associated with inhibition of NB growth in vivo. MEG3 exerted an anti‐cancer effect via stimulatory effects on EZH2 ubiquitination leading to its degradation. Conversely, EZH2 interacted with DNMT1 and HDAC1 to induce silencing of MEG3. The EZH2 inhibitor, DZNep, and HDAC inhibitor, SAHA, displayed synergistic activity against NB. Combined treatment with DZNep and SAHA inhibited proliferation, migration and invasion of NB through suppression of the PI3K/AKT/mTOR/FOXO1 pathway. In conclusion, downregulation of MEG3 and upregulation of EZH2 forms a feedback loop that concertedly promotes the development of NB. Combined blockage of EZH2 and HDAC1 with the appropriate inhibitors may therefore present an effective treatment strategy for NB cases with low MEG3 and high EZH2 expression.

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.

Objective: The purpose of this study was to investigate whether the combination of abnormal systemic immune-inflammation index (SII) levels and hyperglycemia increased the risk of cognitive function decline and reduced survival rate in the United States. Methods: This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) database from 2011–2014 and enrolled 1,447 participants aged 60 years or older. Restricted cubic splines (RCS), linear regression and kaplan-meier(KM) curve were employed to explore the combined effects of abnormal SII and hyperglycemia on cognitive function and survival rate, and subgroup analysis was also conducted. Results: The RCS analysis revealed an inverted U-shaped relationship between lgSII levels and cognitive function. Linear regression analysis indicated that neither abnormal SII nor diabetes alone significantly contributed to the decline in cognitive function compared to participants with normal SII levels and blood glucose. However, when abnormal SII coexisted with diabetes (but not prediabetes), it resulted to a significant decline in cognitive function. After adjusting for various confounding factors, these results remained significant in Delayed Word Recall (β:-0.76, P<0.05) and Digit Symbol Substitution tests (β:-5.02, P<0.05). Nevertheless, these results showed marginal significance in Total Word Recall test as well as Animal Fluency test. Among all subgroup analyses performed, participants with both abnormal SII levels and diabetes exhibited the greatest decline in cognitive function compared to those with only diabetes. Furthermore, KM curve demonstrated that the combination of abnormal SII levels and diabetes decreased survival rate among participants. Conclusion: The findings suggest that the impact of diabetes on cognitive function/survival rate is correlated with SII levels, indicating that their combination enhances predictive power.