Background: Tumor mutation burden (TMB) have been served as the most prevalent biomarkers to predict immunotherapy response. LRP1B (low-density lipoprotein receptor-related protein 1B) is frequently mutated in melanoma, non-small cell lung cancer (NSCLC) and other tumors; however, its association with TMB and survival in patients with immunotherapy remains unknown. Methods: We curated somatic mutation data and clinicopathologic information from 332 melanoma immunotherapy samples for discovery and 113 NSCLC samples for further corroboration. Bayesian variants non-negative matrix factorization was used to extract tumor mutational signatures. Multivariate Cox and logistic regression models were applied to adjust confounding factors. The CIBERSORT and GSEA algorithm were separately used to infer leukocyte relative abundance and significantly enriched pathways. Results: Patients with LRP1B mutation were identified to be associated with prolonged survival in both immunotherapy cohort. Higher tumor mutation burden was found in LRP1B mutated patients, and the association remained significant after controlling for age, gender, stage, mutations in TP53 and ATR , and mutational signatures. Immune response and cell cycle regulation circuits were among the top enriched pathways in samples with LRP1B mutations. Conclusion: Our studies suggested sequencing even a single, frequently mutated gene may provide insight into genome-wide mutational burden, and may serve as a biomarker to predict immune response.

BackgroundAs a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke.ObjectiveThe aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year.MethodsData from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual.ResultsThe 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension.ConclusionsWith statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.

Background Tuberculosis (TB) is difficult to diagnose under complex clinical conditions as electronic health records (EHRs) are often inadequate in making an affirmative diagnosis. As exosomal miRNAs emerged as promising biomarkers, we investigated the potential of using exosomal miRNAs and EHRs in TB diagnosis.MethodsA total of 370 individuals, including pulmonary tuberculosis (PTB), tuberculous meningitis (TBM), non-TB disease controls and healthy state controls, were enrolled. Exosomal miRNAs were profiled in the exploratory cohort using microarray and miRNA candidates were selected in the selection cohort using qRT-PCR. EHRs and follow-up information of the patients were collected accordingly. miRNAs and EHRs were used to develop diagnostic models for PTB and TBM in the selection cohort with the Support Vector Machine (SVM) algorithm. These models were further evaluated in an independent testing cohort.FindingsSix exosomal miRNAs (miR-20a, miR-20b, miR-26a, miR-106a, miR-191, miR-486) were differentially expressed in the TB patients. Three SVM models, "EHR+miRNA", "miRNA only" and "EHR only" were compared, and "EHR + miRNA" model achieved the highest diagnostic efficacy, with an AUC up to 0.97 (95% CI 0.80–0.99) in TBM and 0.97 (0.87–0.99) in PTB, respectively. However, "EHR only" model only showed an AUC of 0.67 (0.46–0.83) in TBM. After 2-month anti-tuberculosis therapy, overexpressed miRNAs presented a decreased expression trend (p= 4.80 × 10−5).InterpretationOur results showed that the combination of exosomal miRNAs and EHRs could potentially improve clinical diagnosis of TBM and PTB.FundFunds for the Central Universities, the National Natural Science Foundation of China.

Background: Gastric cancer (GC) is one common cancer which occurs in the stomach leading to high mortality around the world. Long non-coding RNAs (lncRNAs) were found overexpressed or silenced in the occurrence and progression of multiple cancers including GC. Method: The gene expression level in GC tissues and cells were analyzed by RT-qPCR. CCK-8, colony formation, flow cytometry and transwell assays were performed for the function analysis of HLA complex group 11 (HCG11). The mechanism study for HCG11 was conducted using RIP, RNA pull down and luciferase reporter assays. Results: HCG11 was discovered highly expressed in GC tissues and cells. Depletion experiments were used to evaluate HCG11 silence on cell proliferation, migration and apoptosis. Moreover, Wnt signaling pathway was found as a tumor promoter in GC. RIP assay, RNA pull down assay and luciferase reporter assay were performed to illustrate the relationship of HCG11, miR-1276 and CTNNB1. Rescue assays revealed that HCG11/miR-1276/CTNNB1 axis regulated the incidence and development of GC. Tumor formation in mice proved that HCG11 was negatively correlated with miR-1276 and had positively correlation with CTNNB1. Conclusion: Overall, HCG11 accelerated proliferation and migration in GC through miR-1276/CTNNB1 and Wnt signaling pathway, revealing that HCG11 could be a brand new target for GC.

Background Pregnane X receptor (PXR) regulates the expression of drug-metabolizing enzymes and transport enzymes. NF-κB not only plays a role in liver homeostasis and injury-healing processes by regulating inflammatory responses but may also regulate the transcription of PXR. Currently, genetic polymorphisms in PXR are associated with adverse drug effects. Because little is known about the association between NF-κB1 genetic polymorphisms and adverse drug reactions, we explored the association between PXR and NF-κB1 single nucleotide polymorphisms (SNPs) and susceptibility to anti-tuberculosis drug-induced liver injury (ATDILI). Materials and methods A total of 746 tuberculosis patients (118 with ATDILI and 628 without ATDILI) were prospectively enrolled at West China Hospital between December 2014 and April 2018. Nine selected SNPs (rs3814055, rs13059232, rs7643645 and rs3732360 in PXR and rs78872571, rs4647992, rs60371688, rs1598861 and rs3774959 in NF-κB1) were genotyped with a custom-designed 2x48-plex SNP Scan TM Kit. The frequencies of the alleles, genotypes and genetic models of the variants were compared between patients with or without ATDILI, while joint effect analysis of the SNP-SNP interactions was performed using multiplicative and additive models. The odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) were calculated. Results The T allele of rs3814055 in PXR was associated with a decreased risk for ATDILI (OR 0.61; 95% CI: 0.42–0.89, p = 0.0098). The T alleles of rs78872571 and rs4647992 in NF-κB1 were significantly associated with an increased risk for ATDILI (OR 1.91; 95% CI: 1.06–3.43, p = 0.028 and OR 1.81; 1.06–3.10, p = 0.029, respectively). The allele, genotype and genetic model frequencies were similar in the two groups for the other six SNPs (all P>0.05). There were no multiplicative or additive interactions between the SNPs. Conclusion Our study is the first to reveal that rs3814055 variants in PXR and rs78872571 and rs4647992 variants in NF-κB1 are associated with susceptibility to ATDILI caused by first-line anti-tuberculosis combination treatment in the Han Chinese population.