Identification of novel drug targets for multiple sclerosis by integrating plasma genetics and proteomes.

BACKGROUND: Genome-wide association studies (GWAS) have revealed numerous loci associated with multiple sclerosis (MS). However, the challenge lies in deciphering the mechanisms by which these loci influence the target traits. Here, we employed an integrative analytical pipeline to efficiently transform genetic associations to identify novel proteins for MS. METHODS: We systematically integrated MS GWAS data (N = 115,803) with human plasma proteome data (N = 7213) and conducted proteome-wide association studies (PWAS) to identify MS-associated pathogenic proteins. Following this, we employed Mendelian randomization and Bayesian colocalization analyses to verify the causal relationship between these significant plasma proteins and MS. Lastly, we utilized the Drug-Gene Interaction Database (DGIdb) to identify potential drug targets for MS. RESULTS: The PWAS identified 25 statistically significant cis-regulated plasma proteins associated with MS at a false discovery rate of P < 0.05. Further analysis revealed that the abundance of 7 of these proteins (PLEK, TNXB, CASP3, CD59, CR1, TAPBPL, ATXN3) was causally related to the incidence of MS. Our findings indicated that genetically predicted higher levels of TNXB and CD59 were associated with a lower risk of MS, whereas higher levels of PLEK, CASP3, CR1, TAPBPL, and ATXN3 were associated with an increased risk of MS. Three plasma proteins (PLEK, CR1, CD59) were validated by colocalization analysis. Among these, CR1 was prioritized as a target for Eculizumab due to its significant association with MS risk. Additionally, PLEK, CR1, and CD59 were identified as druggable target genes. CONCLUSIONS: Our proteomic analysis has identified PLEK, CR1, and CD59 as potential drug targets for MS treatment. Developing pharmacological inducers or inhibitors for these proteins could pave the way for new therapeutic approaches, potentially improving outcomes for MS patients.

Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Ultrasound-guided Microwave Ablation.

BACKGROUND: Ultrasound-guided microwave ablation (MWA) is recommended as a first-line treatment for early liver cancer due to its minimally invasive, efficient, and cost-effective nature. It utilizes microwave radiation to heat and destroy tumor cells as a local thermal therapy and offers the benefits of being minimally invasive, repeatable, and applicable to tumors of various sizes and locations. However, despite the efficacy of MWA, early recurrence after treatment remains a challenge, particularly when it occurs within a year and has a significant impact on the prognosis of the patient. OBJECTIVE: This study aimed to identify the risk factors for early recurrence after MWA in patients with hepatocellular carcinoma (HCC) and establish a predictive model. METHODS: A total of 119 patients with hepatocellular carcinoma (HCC) treated in the Department of Ultrasound at the First Affiliated Hospital of the Air Force Medical University from January, 2020 to April, 2022 were included in this study. Patients were categorized into the early recurrence group and the non-early recurrence group based on whether recurrence occurred within 1 year. We conducted univariate analysis on 29 variables. A predictive model was developed using multiple-factor logistic regression analysis, and a risk column graph was created. RESULTS: A total of 28 patients were included in the early recurrence group, with an early recurrence rate of 23%. Tumor size ≥ 3cm, multiple tumors, AST > 35 U/L, low pathological differentiation, CD34 positive, Ki67 level, quantitative parameters mean transit time (mTT), and rise time (RT) were confirmed as risk factors affecting early recurrence after ablation (P < 0.05). Furthermore, the model constructed based on these 5 predictive factors, including tumor size, tumor number, pathological differentiation, CD34, and quantitative analysis parameter mTT, demonstrated good predictive ability, with an AUC of 0.93 in the training set and 0.86 in the validation set. CONCLUSION: Our research indicates that the risk column graph can be utilized to predict the risk of early postoperative recurrence in patients after MWA. This contributes to guiding personalized clinical treatment decisions and provides important references for improving the prognosis of patients.

Genetic Evidence Supporting a Causal Association Between mTOR-Dependent EIF-4E Circulating Protein Level and Osteoporosis.

INTRODUCTION: The mechanistic target of rapamycin (mTOR) regulates bone homeostasis, a crucial factor in osteoporosis (OP) development. However, most research is based on observational studies, and the causality remains uncertain. Therefore, we analyzed two samples of mendelian randomization (MR) to determine whether there is a causal relationship between mTOR-dependent circulating proteins and OP. METHODS: Mendelian weighting (weighted median [WM], inverse variance weighting [IVW], and MR-Egger regression) were applied to analyze the causality between bone phenotypes (bone mineral density [BMD] in forearm, femoral neck, lumbar spine, and heel) and mTOR-dependent circulating proteins (RP-S6K, 4EBP, EIF-4E, EIF-4A, and EIF-4G). Horizontal pleiotropy and heterogeneities were detected using Cochran's Q test, MR-Pleiotropy RE-Sidual Sum and Outlier (MR-PRESSO), and "leave-one-out" analysis. The proteomics-GWAS INTERVAL study was used to select the instrumental variables (IVs) for mTOR proteins. RESULTS: As phenotypes for OP, estimations of BMD were taken in four different sites: forearm (FA) (n = 8143), femoral neck (FN) (n = 32,735), lumbar spine (LS) (n = 28,498), and heel (eBMD) (n = 426,824). Based on IVW analysis, EIF4E is causally related to FA-BMD (OR = 0.938, 95% CI 0.887, 0.991, p = 0.024) but not to BMD elsewhere. CONCLUSION: MR analysis revealed a causal relationship between EIF-4E and FA-BMD, which may provide new insights into the underlying pathogenesis of OP and a new therapeutic target for OP.

Genetically Determined Levels of mTOR-Dependent Circulating Proteins and Risk of Multiple Sclerosis.

BACKGROUND: Results from observational studies indicate an association between circulating levels of mammalian target of rapamycin (mTOR)-dependent circulating proteins and the risk of multiple sclerosis (MS). However, a causal association has not been fully elucidated. Mendelian randomization (MR) is used to overcome limitations inherent to observational studies, assess the causal association, and minimize bias due to confounding and reverse causation. METHODS: To explore the causal association between seven mTOR-dependent proteins (AKT, RP-S6K, eIF4E-BP, eIF4A, eIF4E, eIF4G, and PKC-α) and MS, we obtained summary statistics from the genome-wide association study (GWAS) meta-analysis of the International Multiple Sclerosis Genetics Consortium (47,429 patients and 68,374 controls) and the INTERVAL study (genetic associations with 2994 plasma proteins from 3301 healthy individuals). MR analyses were conducted using inverse variance weighted, weighted median estimator, and MR-Egger regression methods/models. Sensitivity analyses were performed to ensure the reliability of the findings. Single nucleotide polymorphisms (SNPs) that are independent (r2 < 0.01) and strongly associated to minerals (p < 1e-5) were selected as instrumental variables. RESULTS: The results of the MR analyses revealed that among the seven mTOR-dependent proteins selected for study, the circulating level of PKC-α (odds ratio [OR] 0.90, 95% confidence interval [CI] 0.82-0.98; P = 0.017) and RP-S6K (OR 1.12, 95% CI 1.00-1.25; P = 0.045) were associated with MS risk and that there was no sign of pleiotropy or heterogeneity. PKC-α was negatively related to MS, while RP-S6K was positively related to MS. No significant causation was found between the other proteins studied (AKT, eIF4E-BP, eIF4A, eIF4E, eIF4G) and MS. CONCLUSION: Molecules in the mTOR signaling pathway may bidirectionally regulate the occurrence and development of MS. PKC-α is a protective factor, while RP-S6K is a risk factor. Further explorations of pathways underlying the association between mTOR-dependent proteins and MS are required. PKC-α and RP-S6K might be used as future therapeutic targets for screening high-risk individuals and potentially improving opportunities for targeted prevention strategies.

Genetic Evidence Supporting Causal Roles of mTOR-Dependent Proteins in Rheumatic Fever: A Two-Sample Randomized Mendelian Study.

BACKGROUND: The expression of signaling molecules downstream of the mammalian target of rapamycin (mTOR) is dysregulated in patients with rheumatic fever (RF), but the causality of mTOR on RF remains unknown. This study aimed to investigate the causal effects of the mTOR-dependent proteins in RF. METHODS: The summary data for targets of the mTOR signaling were acquired from the publicly available INTERVAL study GWAS data. Data on RF have been obtained from the Integrated Epidemiology Unit GWAS database (38,209 cases and 156,711 healthy controls). A two-sample Mendelian randomization (MR) study was conducted to examine the association of RF risk and mTOR-dependent proteins (EIF4EBP2, EIF-4E, EIF-4G, EIF-4A, RP-S6K, and ATG7), including the inverse-variance weighted (IVW) method, MR-Egger, and weighted median, which was followed by sensitivity analyses. RESULTS: RP-S6K is associated with a lowered risk of RF with an odds ratio (OR) of 0.97, 95% confidence interval (95% CI) of 0.94-0.99 (p = 0.027). In contrast, ATG7 accounts for higher risk of RF with an OR of 1.05 (95% CI = 1.00-1.12, p = 0.047). No apparent heterogeneity and no horizontal pleiotropy were observed in the sensitivity analysis (p > 0.05). No statistical significance was identified for levels of EIF4A, EIF4G, EIF4E-BP2, and RP-S6K with RF risk (p > 0.05). CONCLUSION: MR found robust evidence of a causal association between RF and mTOR. RP-S6K and ATG7 may be targeted for intervention by repurposing existing therapeutics to reduce the risk of RF.

Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus.

OBJECTIVES: To leverage the high clinical heterogeneity of systemic lupus erythematosus (SLE), we developed and validated a new stratification scheme by integrating genome-scale transcriptomic profiles to identify patient subtypes sharing similar transcriptomic markers and drug targets. METHODS: A normalized compendium of transcription profiles was created from peripheral blood mononuclear cells (PBMCs) of 1046 SLE patients and 86 healthy controls (HCs), covering an intersection of 13 689 genes from six microarray datasets. Upregulated differentially expressed genes were subjected to functional and network analysis in which samples were grouped using unsupervised clustering to identify patient subtypes. Then, clustering stability was evaluated by the stratification of six integrated RNA-sequencing datasets using the same method. Finally, the Xgboost classifier was applied to the independent datasets to identify factors associated with treatment outcomes. RESULTS: Based on 278 upregulated DEGs of the transcript profiles, SLE patients were classified into three subtypes (subtype A-C) each with distinct molecular and cellular signatures. Neutrophil activation-related pathways were markedly activated in subtype A (named NE-driving), whereas lymphocyte and IFN-related pathways were more enriched in subtype B (IFN-driving). As the most severe subtype, subtype C [NE-IFN-dual-driving (Dual-driving)] shared functional mechanisms with both NE-driving and IFN-driving, which was closely associated with clinical features and could be used to predict the responses of treatment. CONCLUSION: We developed the largest cohesive SLE transcriptomic compendium for deep stratification using the most comprehensive microarray and RNA sequencing datasets to date. This result could guide future design of molecular diagnosis and the development of stratified therapy for SLE patients.

The value of contrast-enhanced ultrasound quantitative parameters in the prognosis prediction of hepatocellular carcinoma after thermal ablation: a retrospective cohort study.

Background: The postoperative recurrence rate is the main factor affecting the prognosis of hepatocellular carcinoma (HCC) patients, this study sought to investigate the value of contrast-enhanced ultrasound (CEUS) quantitative parameters in predicting the recurrence and the survival of HCC patients after thermal ablation. Methods: The data of 97 patients with pathologically diagnosed HCC who underwent thermal ablation were retrospectively included in this study. The patients had an average age of 46.6 years (range, 23-79 years), and 79 were male and 18 were female. CEUS follow-up was performed at 1- and 3-month after thermal ablation, then at 6-month intervals thereafter for 5 years. CEUS was performed before thermal ablation, and the results were analyzed quantitatively using CEUS perfusion software (VueBox®, Bracco, Italy). The ratios of the CEUS quantitative parameters between the HCC lesions and reference liver parenchyma were calculated. The parameters included the average contrast signal intensity (MeanLin), peak enhancement (PE), rising time (RT), fall time (FT), time to peak (TTP), mean transit time (mTT), perfusion index (PI), Wash-in Area Under the Curve (WiAUC), Wash-in Rate (WiR), Wash-in Perfusion Index (WiPI), Wash-out Area Under the Curve (WoAUC), Wash-out Rate (WoR), and WiAUC + WoAUC (WiWoAUC). The correlations between the preoperative CEUS quantitative parameter ratios, the blood laboratory indexes, postoperative recurrence, and survival were analyzed using log-rank tests and a Cox regression model. Results: The average follow-up duration period was 79 months (range, 5-145 months). The average recurrence time after ablation was 1-127 months, and the median disease-free survival time was 21 months. The 1-, 3- and 5-year survival rates were 96.9%, 92.3%, and 80.6%, respectively. The log-rank tests showed that tumor size, prothrombin time, and WiAUC, WoAUC, and WiWoAUC ratios were predictors of survival, and aspartate aminotransferase was a predictor of recurrence. The Cox regression analysis showed that tumor size [odds ratio (OR): 6.421; 95% CI: 1.434-28.761] and alanine transaminase (OR: 0.88; 95% CI: 0.010-0.742) were predictors of a poor prognosis. Conclusions: CEUS quantitative parameters before thermal ablation and blood laboratory indexes provide potential clinical value for predicting the postoperative recurrence and survival of HCC patients.