MTF1 genetic variants are associated with lung cancer risk in the Chinese Han population.

BACKGROUND: Metal-regulatory transcription factor 1 (MTF1), a conserved metal-binding transcription factor in eukaryotes, regulates the proliferation of cancer cells by activating downstream target genes and then participates in the formation and progression of tumors, including lung cancer (LC). The expression level of MTF1 is down-regulated in LC, and high expression of MTF1 is associated with a good prognosis of LC. However, the association between MTF1 polymorphism and LC risk has not been explored. METHODS: The genotyping of MTF1 Single nucleotide polymorphisms (SNPs) including rs473279, rs28411034, rs28411352, and rs3748682 was identified by the Agena MassARRAY system among 670 healthy controls and 670 patients with LC. The odds ratio (OR) and 95% confidence intervals (CI) were calculated by logistics regression to assess the association of these SNPs with LC risk. RESULTS: MTF1 rs28411034 (OR 1.22, 95% CI 1.03-1.45, p = 0.024) and rs3748682 (OR 1.24, 95% CI 1.04-1.47, p = 0.014) were associated with higher LC susceptibility overall. Moreover, the effect of rs28411034 and rs3748682 on LC susceptibility was observed in males, subjects with body mass index (BMI) ≥ 24 kg/m2, smokers, drinkers, and patients with lung squamous carcinoma (OR and 95% CI > 1, p < 0.05). Besides, rs28411352 (OR 0.73, 95% CI 0.55-0.97, p = 0.028,) showed protective effect for reduced LC risk in drinkers. CONCLUSIONS: We were first who reported that rs28411034 and rs3748682 tended to be relevant to increased LC susceptibility among the Chinese Han population. These results of this study could help to recognize the pathogenic mechanisms of the MTF1 gene in LC progress.

Novel prognostic nomograms for postoperative patients with oral cavity squamous cell carcinoma in the central region of China.

BACKGROUND: Oral cavity squamous cell carcinoma (OCSCC) is the most common pathological type in oral tumors. This study intends to construct a novel prognostic nomogram model based on China populations for these resectable OCSCC patients, and then validate these nomograms. METHODS: A total of 607 postoperative patients with OCSCC diagnosed between June 2012 and June 2018 were obtained from two tertiary medical institutions in Xinxiang and Zhengzhou. Then, 70% of all the cases were randomly assigned to the training group and the rest to the validation group. The endpoint time was defined as overall survival (OS) and disease-free survival (DFS). The nomograms for predicting the 3-, and 5-year OS and DFS in postoperative OCSCC patients were established based on the independent prognostic factors, which were identified by the univariate analysis and multivariate analysis. A series of indexes were utilized to assess the performance and net benefit of these two newly constructed nomograms. Finally, the discrimination capability of OS and DFS was compared between the new risk stratification and the American Joint Committee on Cancer (AJCC) stage by Kaplan-Meier curves. RESULTS: 607 postoperative patients with OCSCC were selected and randomly assigned to the training cohort (n = 425) and validation cohort (n = 182). The nomograms for predicting OS and DFS in postoperative OCSCC patients had been established based on the independent prognostic factors. Moreover, dynamic nomograms were also established for more convenient clinical application. The C-index for predicting OS and DFS were 0.691, 0.674 in the training group, and 0.722, 0.680 in the validation group, respectively. Besides, the calibration curve displayed good consistency between the predicted survival probability and actual observations. Finally, the excellent performance of these two nomograms was verified by the NRI, IDI, and DCA curves in comparison to the AJCC stage system. CONCLUSION: The newly established and validated nomograms for predicting OS and DFS in postoperative patients with OCSCC perform well, which can be helpful for clinicians and contribute to clinical decision-making.

Effects of Ninjurin 2 polymorphisms on susceptibility to coronary heart disease.

OBJECTIVE: The aim of this study was to explore the effects of Ninjurin 2 (NINJ2) polymorphisms on susceptibility to coronary heart disease (CHD). METHODS: We conducted a case-control study with 499 CHD cases and 505 age and gender-matched controls. Five single nucleotide polymorphisms (SNPs) in NINJ2 (rs118050317, rs75750647, rs7307242, rs10849390, and rs11610368) were genotyped by the Agena MassARRAY platform. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression analysis to assess the association of NINJ2 polymorphisms and CHD risk-adjusted for age and gender. What's more, risk genes and molecular functions were screened via protein-protein interaction (PPI) network and functional enrichment analysis. RESULTS: Rs118050317 in NINJ2 significantly increased CHD risk in people aged more than 60 years and women. Rs118050317 and rs7307242 had strong relationships with hypertension risk in CHD patients. Additionally, rs75750647 exceedingly raised diabetes risk in cases under multiple models, whereas rs10849390 could protect CHD patients from diabetes in allele, homozygote, and additive models. We also observed two blocks in NINJ2. Further interaction network and enrichment analysis showed that NINJ2 played a greater role in the pathogenesis and progression of CHD. CONCLUSION: Our results suggest that NINJ2 polymorphisms are associated with CHD risk.

Heterogeneous effects of spanking on child protective services involvement in early childhood: A propensity score stratification analysis.

Background: Extant literature has primarily employed linear models to estimate the average effect of spanking on children. Less is known about child and parent characteristics that may predict differential risks of children's exposure to spanking (i.e., pre-treatment heterogeneity) and the effect of spanking on child outcomes that may differ by the propensity for spanking (i.e., post-treatment heterogeneity). Objective: The present study examines pre- and post-treatment heterogeneity in the association between maternal spanking at child age 3 and subsequent household Child Protective Services (CPS) involvement that were reported by mothers between child ages 3 to 5. Methods: The sample consisted of 2,422 families from the Fragile Families and Child Wellbeing Study, a birth cohort study that oversampled children born to unmarried families. A propensity score stratification analysis procedure examined pre- and post-treatment heterogeneity in the association between spanking and subsequent household CPS involvement. Children were stratified into three strata depending on their propensity for experiencing spanking. Subsequently, the association between spanking and household CPS involvement was examined for each stratum. Results: Disadvantaged socio-economic characteristics, negative psychosocial conditions of the mother, and dysregulated temperament of the child were associated with elevated risk of spanking. The odds of subsequent household CPS involvement increased by 2.4 times (ß = 0.869, p <.01) for children in the stratum with the lowest propensity for experiencing spanking. Conclusions: Results emphasize the importance of promoting alternative disciplinary strategies to families who would be considered low-risk before they may come into contact with the child welfare system.

Association study for the role of MMP8 gene polymorphisms in Colorectal cancer susceptibility.

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignant tumors, influenced by several genetic loci in its clinical phenotypes. The aim of this study was to determine the relationship between the MMP8 gene polymorphism and CRC risk in the Chinese Han population. METHOD: This study recruited 688 CRC patients and 690 healthy controls. The relationship between MMP8 polymorphism and CRC susceptibility was assessed by calculating the odds ratio (OR) and 95% confidence interval (CI) after stratifying by age, gender, body mass index (BMI), smoking, and alcohol consumption under a multi-genetic model. RESULTS: MMP8 rs3740938 was associated with increased CRC predisposition (p = 0.016, OR = 1.24, 95% CI: 1.04-1.48), and this association was detected particularly in subjects aged > 60 years, females, people with BMI > 24 kg/m2, smokers, and drinkers. Moreover, rs3740938 was found to be associated with the pathological type of rectal cancer. CONCLUSIONS: Our results first displayed that rs3740938 in MMP8 was a risk factor for CRC predisposition. This finding may provide a new biological perspective for understanding the role of the MMP8 gene in CRC pathogenesis.

Dishevelled Segment Polarity Protein 3: A Novel Prognosis-related Marker in Pan-driver-gene-negative Lung Adenocarcinoma.

Purpose: In this study, we aimed to investigate the potential prognostic molecular marker in patients with "pan-driver-gene-negative" lung adenocarcinoma (PDGN-LUAD). LUAD patients who were negative for mutations in EGFR, KRAS, BRAF, HER2, MET, ALK, RET and ROS1 were identified as PDGN-LUAD. Methods: In the screening phase, we profiled the mRNA expression levels in 52 paired PDGN-LUAD tumor tissues and adjacent normal tissues using a genome-wide microarray, and the results revealed that the expression level of dishevelled segment polarity protein 3 (DVL3) was higher in PDGN-LUAD tumor tissues than in normal lung tissues. Then, we enrolled 626 PDGN-LUAD patients from three independent hospital centers and divided them into a training cohort, an internal validation cohort and two external validation cohorts. In the training cohort, tissue microarrays (TMAs) were used to confirm the protein expression level of DVL3. Statistical methods were applied to explore the prognostic role of DVL3. Results: The results indicated that the level of DVL3 could be used to classify patients with PDGN-LUAD in the training cohort into a high-risk group (high expression level of DVL3) and a low-risk group (low expression level of DVL3). In the training cohort, high-risk patients had shorter overall survival (OS) times (hazard ratio [HR] 2.27; 95% CI, 1.57-2.97; p<0.001) than low-risk patients. In the validation phase, the performance of DVL3 as a prognostic marker was successfully validated in the internal and external cohorts. Conclusions: In conclusion, DVL3 is an important prognostic indicator for PDGN-LUAD and may provide new insights into the treatment of PDGN-LUAD.

Association of PTGER4 and PRKAA1 genetic polymorphisms with gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common malignancies, affected by several genetic loci in the clinical phenotype. This study aimed to determine the association between PTGER4 and PRKAA1 gene polymorphisms and the risk of GC. METHODS: A total of 509 GC patients and 507 age and sex-matched healthy controls were recruited to explore the association between PTGER4 and PRKAA1 genetic polymorphisms and GC susceptibility. Logistic regression analysis was used to study the correlation between these SNPs and GC, with odd ratio (OR) and 95% confidence interval (CI) as indicators. Multifactor dimensionality reduction was utilized to analyze the genetic relationships among SNPs. was conducted to predict gene expression, the impact of SNPs on gene expression, and the signaling pathways involved in PTGER4 and PRKAA1. RESULTS: Overall, rs10036575 in PTGER4 (OR = 0.82, p = 0.029), rs10074991 (OR = 0.82, p = 0.024) and rs13361707 (OR = 0.82, p = 0.030) in PRKAA1 were associated with susceptibility to GC. Stratification analysis revealed that the effects of these SNPs in PTGER4 and PRKAA1 on GC susceptibility were dependent on smoking and were associated with a reduced risk of adenocarcinoma (p < 0.05). Bioinformatics analysis showed an association between SNPs and corresponding gene expression (p < 0.05), and PRKAA1 may affect GC by mediating RhoA. CONCLUSION: This study suggests that PTGER4 and PRKAA1 SNPs might affect the susceptibility of GC, providing a new biological perspective for GC risk assessment, pathogenesis exploration, and personalized treatment.

Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI.

BACKGROUND: Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations. METHODS: This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n = 415 ASD patients (males n = 357), and n = 574 typical development (TD) controls (males n = 410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model. RESULTS: All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ∼75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%). CONCLUSIONS: The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

Nomograms for predicting overall survival and cancer-specific survival in elderly patients with epithelial ovarian cancer.

BACKGROUND: Epithelial ovarian cancer (EOC) is one of the most fatal gynecological malignancies among elderly patients. We aim to construct two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) in elderly EOC patients. METHODS: Elderly patients with EOC between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Enrolled patients were randomly divided into the training and validation set at a ratio of 2:1. The OS and CSS were recognized as endpoint times. The independent prognostic factors from the multivariate analysis were used to establish nomograms for predicting the 3-, 5- and 10-year OS and CSS of elderly EOC patients. The improvement of predictive ability and clinical benefits were evaluated by consistency index (C-index), receiver operating characteristic (ROC), calibration curve, decision curve (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Finally, the treatment efficacy of surgery and chemotherapy in low-, medium-, and high-risk groups were displayed by Kaplan-Meier curves. RESULTS: Five thousand five hundred eighty-eight elderly EOC patients were obtained and randomly assigned to the training set (n = 3724) and validation set (n = 1864). The independent prognostic factors were utilized to construct nomograms for OS and CSS. Dynamic nomograms were also developed. The C-index of the OS nomogram and CSS nomogram were 0.713 and 0.729 in the training cohort. In the validation cohort, the C-index of the OS nomogram and CSS nomogram were 0.751 and 0.702. The calibration curve demonstrated good concordance between the predicted survival rates and actual observations. Moreover, the NRI, IDI, and DCA curves determined the outperformance of the nomogram compared with the AJCC stage system. Besides, local tumor resection had a higher benefit on the prognosis in all patients. Chemotherapy had a better prognosis in the high-risk groups, but not for the medium- risk and low-risk groups. CONCLUSIONS: We developed and validated nomograms for predicting OS and CSS in elderly EOC patients to help gynecologists to develop an appropriate individualized therapeutic schedule.

Impact of NOTCH1 polymorphisms on liver cancer in a Chinese Han population.

NOTCH1, a member of the Notch family, is up-expression in advanced liver cancer (LC) patients and is associated with tumor sizes, tumor stage, metastasis, and invasion. A few studies have discovered the contribution of NOTCH1 variants to LC risk. Our purpose was to assess the relationship of NOTCH1 rs10521, rs2229971, and rs4489420 to LC risk. We enrolled 709 LC patients and 708 healthy controls. Genotyping was determined through the Agena MassARRAY system. Multiple genetic models by logistic regression were useful for odds ratios (ORs) with 95% confidence intervals (CIs). Rs10521-G (p = 0.009, OR = 0.75, 95% CI: 0.61-0.93), rs2229971-A (p = 0.023, OR = 0.81, 95% CI: 0.67-0.97), and rs4489420-A (p = 0.014, OR = 0.38, 95% CI: 0.16-0.85) might be protective factors for LC occurrence in the Chinese Han population, especially rs10521 and rs2229971 (false-positive report probability (FPRP) <0.2 and statistical power >90%). Interestingly, stratified analysis displayed that the contribution of NOTCH1 polymorphisms to LC risk might be associated with gender, age, smoking, and drinking. Our data first determined that NOTCH1 rs10521-G, rs2229971-A, and rs4489420-A might be protective factors for LC susceptibility.

Analysis of a machine learning-based risk stratification scheme for acute kidney injury in vancomycin.

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa.

Pseudomonas aeruginosa is a ubiquitous opportunistic bacterial pathogen, which is a leading cause of nosocomial pneumonia. Early identification of the risk factors is urgently needed for severe infection patients with P. aeruginosa. However, no detailed relevant investigation based on machine learning has been reported, and little research has focused on exploring relationships between key risk clinical variables and clinical outcome of patients. In this study, we collected 571 severe infections with P. aeruginosa patients admitted to the Xijing Hospital of the Fourth Military Medical University from January 2010 to July 2021. Basic clinical information, clinical signs and symptoms, laboratory indicators, bacterial culture, and drug related were recorded. Machine learning algorithm of XGBoost was applied to build a model for predicting mortality risk of P. aeruginosa infection in severe patients. The performance of XGBoost model (AUROC = 0.94 ± 0.01, AUPRC = 0.94 ± 0.03) was greater than the performance of support vector machine (AUROC = 0.90 ± 0.03, AUPRC = 0.91 ± 0.02) and random forest (AUROC = 0.93 ± 0.03, AUPRC = 0.89 ± 0.04). This study also aimed to interpret the model and to explore the impact of clinical variables. The interpretation analysis highlighted the effects of age, high-alert drugs, and the number of drug varieties. Further stratification clarified the necessity of different treatment for severe infection for different populations.

C2CD4A/B variants in the predisposition of lung cancer in the Chinese Han population.

BACKGROUND: The mRNA levels of C2CD4A and C2CD4B were dysregulation in lung cancer (LC). We aimed to evaluate the role of C2CD4A/B variants in LC susceptibility. METHODS: There were 710 cases with LC and 710 healthy controls enrolled in the study. The genotyping of twelve variants in C2CD4A/B was carried out by Agena MassARRAY system. Odds ratios (ORs) were calculated by logistic regression analysis to assess the relationship between these variants and LC predisposition. RESULTS: Rs8037894 (OR = 0.81, p = 0.005), rs7172432 (OR = 0.83, p = 0.013), rs11856307 (OR = 0.86, p = 0.043), and rs1436953 (OR = 0.79, p = 0.002) were related to the reduced risk of LC. Besides, the relation of rs7172432 with LC risk in subjects aged > 60 years was observed. Rs4502156 conferred to the increased LC risk, while rs1436953 was associated with the lower susceptibility to LC among males. Rs731820, rs4502156, rs11071657, rs7172432, and rs11856307 contributed to the predisposition of LC among subjects with BMI > 24 kg/m2, while rs7495253 was associated with an increased risk of LC in subjects with BMI ≤ 24 kg/m2. The increased LC risk was found in rs4502156, while the protective risk effect of rs8037894, rs7172432, rs11856307, and rs1436953 on the occurrence of LC was observed in smokers and non-drinkers. Moreover, rs7495253 and rs7495931 had a higher risk of lymphatic metastasis. Rs1436953 was related to the reduced risk of lung adenocarcinoma, while rs4502156, rs8037894, rs7172432, rs11856307, and rs1436953 were related to the risk of small cell carcinoma. CONCLUSIONS: Our results first display that C2CD4A/B polymorphisms served as protective factors for LC predisposition in a Chinese Han population. These findings could provide new biological insight into the understanding of C2CD4A/B genes on LC pathogenesis.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma.

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

An 8 miRNA-Based Risk Score System for Predicting the Prognosis of Patients With Papillary Thyroid Cancer.

BACKGROUND: Dysregulation of microRNAs (miRNAs) in papillary thyroid cancer (PTC) might influence prognosis of PTC. This study is aimed to develop a risk score system for predicting prognosis of PTC. METHODS: The miRNA and gene expression profiles of PTC were obtained from The Cancer Genome Atlas database. PTC samples were randomly separated into training set (n = 248) and validation set (n = 248). The differentially expressed miRNAs (DE-miRNAs) in the training set were screened using limma package. The independent prognosis-associated DE-miRNAs were identified for building a risk score system. Risk score of PTC samples in the training set was calculated and samples were divided into high risk group and low risk group. Kaplan-Meier curves and receiver operating characteristic (ROC) curve were used to assess the accuracy of the risk score system in the training set, validation set and entire set. Finally, a miRNA-gene regulatory network was visualized by Cytoscape software, followed by enrichment analysis. RESULTS: Totally, 162 DE-miRNAs between tumor and control groups in the training set were identified. An 8 independent prognosis-associated DE-miRNAs, (including miR-1179, miR-133b, miR-3194, miR-3912, miR-548j, miR-6720, miR-6734, and miR-6843) based risk score system was developed. The area under ROC curve in the training set, validation set and entire set was all above 0.93. A miRNA-gene regulatory network involving the 8 DE-miRNAs were built and functional enrichment analysis suggested the genes in the network were significantly enriched into 13 pathways, including calcium signaling pathway and hedgehog signaling pathway. CONCLUSION: The risk score system developed this study might be used for predicting the prognosis of PTC. Besides, the 8 miRNAs might affect the prognosis of PTC via hedgehog signaling pathway and calcium signaling pathway.

A Case-Control Study of ADCY9 Gene Polymorphisms and the Risk of Hepatocellular Carcinoma in the Chinese Han Population.

Background: Adenylyl cyclase type 9 (ADCY9) modulates signal transduction by producing the second messenger cyclic AMP. It has been reported that ADCY9 gene polymorphisms were associated with cancer development. The aim of this study was to investigate whether ADCY9 gene polymorphisms could contribute to the susceptibility of hepatocellular carcinoma (HCC) in the Chinese Han population. Methods: In the present study, five single-nucleotide polymorphisms (SNPs) in ADCY9 were genotyped using Agena MassARRAY platform in 876 subjects from China. Logistic regression was used to assess the effects of SNPs on HCC risk. Associations were also evaluated for HCC risk stratified by age and gender. False discovery rate (FDR) was used to correct multiple testing. Results: After adjusting for age and gender, we found a significant relationship between heterozygous genotypes of rs2531995 and HCC risk (OR = 1.34, 95% CI = 1.01-1.77, p = 0.045). ADCY9 rs2230742 had a strong relationship with lower risk of HCC in allele (p = 0.006), co-dominant (p = 0.023), dominant (p = 0.010), and additive (p = 0.006) models. Stratified analysis showed that rs879620 increased HCC risk and rs2230742 was associated with lower risk of HCC in the individuals aged 55 or younger, rs2531992 significantly decreased HCC risk in the elder group (age > 55). For women, rs2230742 and rs2230741 were significantly associated with HCC risk in multiple models (p < 0.05). FDR analysis showed that rs2230742 could protect individuals from HCC risk in the allele model (FDR-p = 0.030). In addition, haplotype analysis indicated that Crs879620Ars2230742Ars2230741 haplotype was a protective factor for HCC (OR = 0.67, 95% CI = 0.50-0.89, p = 0.007, FDR-p = 0.028). Conclusion: Our findings suggest that ADCY9 gene polymorphisms are associated with HCC risk in the Chinese Han population.

Increased Serum Calcium Level Promotes the Risk of Lymph Node Metastasis in Endometrial Cancer.

PURPOSE: The early predictive values of diagnostic markers for lymph node metastasis (LNM) in endometrial cancer (EC) are still unclear at present. The purpose of this study is to explore the relationship between serum calcium and LNM in EC. METHODS: We identified all patients with EC who underwent surgery between January 2012 and December 2016. Patient characteristics and various preoperative clinicopathologic data were obtained from medical records and were reviewed retrospectively. These patients were divided into two groups according to the pathology of their lymph node. Logistic regression models analyzed the relationship between the ionized calcium and LNM of EC patients, while adjusting for the potential confounders. RESULTS: A total of 448 patients were assessed. Univariate analysis showed that ionized calcium, CA125 level, tumor grade, peritoneal cytology, FIGO stage, histological type, LVSI, and myometrial invasion were positively correlated with LNM (all P<0.05). The risk of LNM increased with the promotion of serum ionized calcium (P for trend <0.01). Ionized calcium level was significant before and after the adjustment of cofounders (unadjusted: OR=11.9, 95% CI: 4.8-29.6, P< 0.01; model I: OR=11.3, 95% CI: 4.5-28.8, P< 0.01; model II: OR=5.2, 95% CI: 1.6-17.2, P< 0.05). Additionally, the risk of ionized calcium was especially evident in patients whose age was older than 60, BMI<28 kg/m2, grade 3, negative peritoneal cytology and endometrioid endometrial adenocarcinoma. CONCLUSION: Ionized calcium level was highly associated with LNM in EC and acted as a potential biomarker in predicting the risk of LNM in EC.

NT5C2 Gene Polymorphisms and the Risk of Coronary Heart Disease.

BACKGROUND: Increasing studies have reported that 5'-nucleotidase cytosolic II (NT5C2) has a strong relationship with coronary heart disease (CHD) development. This study was designed to examine the relationship between NT5C2 polymorphisms and CHD in the Chinese Han population. METHODS: We studied 501 CHD patients and 496 healthy controls from the Second Affiliated Hospital of Hainan Medical University in Hainan Province, China. Four single nucleotide polymorphisms (SNPs) in NT5C2 were selected and genotyped using Agena MassARRAY technology. Odds ratios and 95% confidence intervals were calculated using logistic regression after adjusting for age and gender. Stratification analysis was performed by age and gender in all individuals; we especially investigated the effects of NT5C2 SNPs on hypertension and diabetes among CHD patients. RESULTS: rs2148198 of NT5C2 was strongly associated with an increased risk of CHD (allele: p = 0.045; codominant: p = 0.007; additive: p = 0.016). Stratified analysis revealed that rs2148198 was associated with increased CHD risk in individuals aged ≤61 years and males. For CHD patients, rs2148198 significantly affected the risk of hypertension and diabetes (p < 0.05). Further, rs79237883 of NT5C2 was associated with decreased susceptibility to hypertension in multiple genetic models for individuals with CHD (allele: p = 0.007; codominant: p = 0.001; dominant: p = 0.001; additive: p = 0.008). CONCLUSION: This study reports the association of NT5C2 gene variants and CHD susceptibility in the Chinese Han population. Especially, NT5C2 rs2148198 was significantly associated with CHD risk in the subgroups of males, hypertension, and diabetes.

A 21­gene Support Vector Machine classifier and a 10­gene risk score system constructed for patients with gastric cancer.

Gastric cancer (GC) ranks fifth in terms of incidence and third in terms of tumor mortality worldwide. The present study was designed to construct a Support Vector Machine (SVM) classifier and risk score system for GC. The GSE62254 (training set) and GSE26253 (validation set 2) datasets were downloaded from the Gene Expression Omnibus database. Furthermore, the gene expression profile of GC (validation set 1) was obtained from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) between recurrent and non­recurrent samples were determined using the limma package. The feature genes were selected using the Caret package, and an SVM classifier was built using the e1071 package. Using the penalized package, the optimal predictive genes for constructing a risk score system were screened. Finally, stratification analysis of clinical factors and pathway enrichment analysis were performed using Gene Set Enrichment Analysis. A total of 239 DEGs were identified in GSE62254, among which 114 DEGs were significantly associated with both recurrence­free survival and overall survival. Subsequently, 21 feature genes were screened from the 114 DEGs, and an SVM classifier was built. A risk score system for survival prediction was constructed, following the selection of 10 optimal genes, including A­kinase anchoring protein 12, angiopoietin­like protein 1, cysteine­rich sequence 1, myeloid/lymphoid or mixed­lineage leukemia, translocated to chromosome 11, neuron navigator 3, neurobeachin, nephroblastoma overexpressed, pleiotrophin, tumor suppressor candidate 3 and zinc finger and SCAN domain containing 18. The stratification analysis revealed that pathological stage was an independent prognostic clinical factor in the high­risk group. Additionally, eight significant pathways were associated with the 10­gene signature. The SVM classifier and risk score system may be applied for classifying and predicting the prognosis of patients with GC, respectively.

Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma.

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.