Genetic variants in GTF2H1 and risk of lung cancer: A case–control analysis in a Chinese population

Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma

DNA repair is a critical factor in tumor progression as it impacts tumor mutational burden, genome stability, PD-L1 expression, immunotherapy response, and tumor-infiltrating lymphocytes (TILs). In this study, we present a prognostic model for hepatocellular carcinoma (HCC) that utilizes genes related to the DNA damage response (DDR). Patients were stratified based on their risk score, and groups with lower risk scores demonstrated better survival rates compared to those with higher risk scores. The prognostic model's accuracy in predicting 1-, 3-, and 5-year survival rates for HCC patients was analyzed using receiver operator curve analysis (ROC). Results showed good accuracy in predicting survival rates. Additionally, we evaluated the prognostic model's potential as an independent factor for HCC prognosis, along with tumor stage. Furthermore, nomogram was employed to determine the overall survival year of patients with HCC based on this independent factor. Gene set enrichment analysis (GSEA) revealed that in the high-risk group, apoptosis, cell cycle, MAPK, mTOR, and WNT cascades were highly enriched. We used training and validation datasets to identify potential molecular subtypes of HCC based on the expression of DDR genes. The two subtypes differed in terms of checkpoint receptors for immunity and immune cell filtration capacity.Collectively, our study identified potential biomarkers of HCC prognosis, providing novel insights into the molecular mechanisms underlying HCC.

Impact of GTF2H1 and RAD54L2 polymorphisms on the risk of lung cancer in the Chinese Han population

Background

Repair pathway genes play an important role in the development of lung cancer. The study aimed to assess the correlation between single nucleotide polymorphisms (SNPs) in DNA repair gene (GTF2H1 and RAD54L2) and the risk of lung cancer.

Methods

Five SNPs in GTF2H1 and four SNPs in RAD54L2 in 506 patients with lung cancer and 510 age-and gender-matched healthy controls were genotyped via the Agena MassARRAY platform. The influence of GTF2H1 and RAD54L2 polymorphisms on lung cancer susceptibility was assessed using logistic regression analysis by calculating odds ratios (ORs) and their corresponding 95% confidence intervals (CIs).

Results

RAD54L2 rs9864693 GC genotype increased the risk of lung cancer (OR = 1.33, 95%CI: 1.01–1.77, p = 0.045). Stratified analysis found that associations of RAD54L2 rs11720298, RAD54L2 rs4687592, RAD54L2 rs9864693 and GTF2H1 rs4150667 with lung cancer risk were found in subjects aged ≤ 59 years. Precisely, a protective effect of RAD54L2 rs11720298 on the occurrence of lung cancer was observed in non-smokers and drinkers. GTF2H1 rs4150667 was associated with a decreased risk of lung cancer in subjects with BMI ≤ 24 kg/m2. RAD54L2 rs4687592 was associated with an increased risk of lung cancer in drinkers. In addition, GTF2H1 rs3802967 was associated with a reduced risk of lung squamous cell carcinoma.

Conclusion

Our study first revealed that RAD54L2 rs9864693 was associated with an increased risk of lung cancer in the Chinese Han population. This study may increase the understanding of the effect of RAD54L2 and GTF2H1 polymorphisms on lung cancer occurrence.

Prediction of lung cancer risk in Chinese population with genetic-environment factor using extreme gradient boosting

BACKGROUND

Detecting early-stage lung cancer is critical to reduce the lung cancer mortality rate; however, existing models based on germline variants perform poorly, and new models are needed. This study aimed to use extreme gradient boosting to develop a predictive model for the early diagnosis of lung cancer in a multicenter case-control study.

MATERIALS AND METHODS

A total of 974 cases and 1005 controls in Shanghai and Taizhou were recruited, and 61 single nucleotide polymorphisms (SNPs) were genotyped. Multivariate logistic regression was used to calculate the association between signal SNPs and lung cancer risk. Logistic regression (LR) and extreme gradient boosting (XGBoost) algorithms, a large-scale machine learning algorithm, were adopted to build the lung cancer risk model. In both models, 10-fold cross-validation was performed, and model predictive performance was evaluated by the area under the curve (AUC).

RESULTS

After FDR adjustment, TYMS rs3819102 and BAG6 rs1077393 were significantly associated with lung cancer risk (p < 0.05). For lung cancer risk prediction, the model predicted only with epidemiology attained an AUC of 0.703 for LR and 0.744 for XGBoost. Compared with the LR model predicted only with epidemiology, further adding SNPs and applying XGBoost increased the AUC to 0.759 (p < 0.001) in the XGBoost model. BAG6 rs1077393 was the most important predictor among all SNPs in the lung cancer prediction XGBoost model, followed by TERT rs2735845 and CAMKK1 rs7214723. Further stratification in lung adenocarcinoma (ADC) showed a significantly elevated performance from 0.639 to 0.699 (p = 0.009) when applying XGBoost and adding SNPs to the model, while the best model for lung squamous cell carcinoma (SCC) prediction was the LR model predicted with epidemiology and SNPs (AUC = 0.833), compared with the XGBoost model (AUC = 0.816).

CONCLUSION

Our lung cancer risk prediction models in the Chinese population have a strong predictive ability, especially for SCC. Adding SNPs and applying the XGBoost algorithm to the epidemiologic-based logistic regression risk prediction model significantly improves model performance.

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Background

Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment.

Material/methods

422 LC and adjacent normal tissues with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan–Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens.

Results

The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4 (replication factor C subunit 4), ZWINT (ZW10 interacting kinetochore protein), UPF3B (UPF3B regulator of nonsense mediated mRNA decay), NCBP2 (nuclear cap binding protein subunit 2), ADA (adenosine deaminase), SF3A3 (splicing factor 3a subunit 3) and GTF2H1 (general transcription factor IIH subunit 1). On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7 to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P < 0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 [95% CI (confidence interval) 1.523–3.132] and log-rank P-value < 0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted.

Conclusions

Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

Genetic variants in N-myc (and STAT) interactor and susceptibility to glioma in a Chinese Han population

Glioma is one of the most common and lethal brain tumors. N-myc (and STAT) interactor (NMI) gene has been reported in tumorigenesis, and our previous study further showed its implication in glioma progression. To elucidate its involvement in the etiology of glioma, we conducted a case-control study of 875 patients and 1040 controls in a Chinese Han population by genotyping 7 representative single nucleotide polymorphisms (SNPs) in NMI. Allele and genotype frequency distribution of five loci (rs2278089, rs2194492, rs6734376, rs3854012, and rs11730) were significantly different between the cases and controls. Unconditional logistic regression showed that the variant genotypes of rs2278089 [adjusted odds ratio (OR) = 1.57, P = 4.23 × 10(-6)], rs2194492 (adjusted OR = 1.49, P = 1.20 × 10(-4)), and rs6734376 (adjusted OR = 0.06, P = 8.65 × 10(-13)) significantly affected glioma risk compared with the major homozygotes, while the minor homozygotes of rs3854012 (adjusted OR = 0.54, P = 4.64 × 10(-6)) and rs11730 (adjusted OR = 0.60, P = 1.50 × 10(-4)) showed significant protective effects. Further stratified analyses indicated that these associations remained significant in subgroups of low-grade glioma (LGG) and high-grade glioma (HGG). Additionally, haplotype and diplotype analyses showed consistent results. The Bonferroni correction was applied for all these analyses. Moreover, luciferase reporter gene assays revealed enhanced promoter activity of the C risk allele of rs2194492 in several cell lines compared with the G major allele, suggesting its potential function in transcriptional activation of NMI. Taken together, these results revealed that NMI polymorphisms may contribute to genetic susceptibility to glioma.

Evolutionary conservation of TFIIH subunits: implications for the use of zebrafish as a model to study TFIIH function and regulation

Transcriptional factor IIH (TFIIH) is involved in cell cycle regulation, nucleotide excision repair, and gene transcription. Mutations in three of its subunits, XPB, XPD, and TTDA, lead to human recessive genetic disorders such as trichothiodystrophy and xeroderma pigmentosum, the latter of which is sometimes associated with Cockayne's syndrome. In the present study, we investigate the sequence conservation of TFIIH subunits among several teleost fish species and compare their characteristics and putative regulation by transcription factors to those of human and zebrafish. We report the following findings: (i) comparisons among protein sequences revealed a high sequence identity for each TFIIH subunit analysed; (ii) among transcription factors identified as putative regulators, OCT1 and AP1 have the highest binding-site frequencies in the promoters of TFIIH genes, and (iii) TFIIH genes have alternatively spliced isoforms. Finally, we compared the protein primary structure in human and zebrafish of XPD and XPB - two important ATP-dependent helicases that catalyse the unwinding of the DNA duplex at promoters during transcription - highlighting the conservation of domain regions such as the helicase domains. Our study suggests that zebrafish, a widely used model for many human diseases, could also act as an important model to study the function of TFIIH complex in repair and transcription regulation in humans.

HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors

MOTIVATION

Identification of expression Quantitative Trait Loci (eQTL), the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis.

METHODS

We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model is a combined multivariate regression and factor analysis, where the complete likelihood of the model is used to derive a ridge estimator for simultaneous factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects; it provides P-values and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of single nucleotide polymorphisms on a standard 8 core 2.6 G desktop.

RESULTS

By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and significantly outperforms all related hidden factor methods for eQTL analysis when there are eQTL with univariate and multivariate (pleiotropic) effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in the human lung for a study that included presumptive hidden factors. HEFT identified all of the cis-eQTL found by other hidden factor methods and 91 additional cis-eQTL. HEFT also identified a number of eQTLs with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer.

AVAILABILITY

Software is available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Lack of association between GTF2H4 genetic variants and AERD development and FEV1 decline by aspirin provocation

Aspirin-exacerbated respiratory disease (AERD) is prevalent in about 10% of asthma patients and is characterized by a severe decline in forced expiratory volume in 1-s (FEV(1) ), an important phenotype for total lung capacity, upon ingestion of aspirin. The general transcription factor IIH subunit 4 (GTF2H4) is positioned at 6p21.33, a part of the major histocompatibility complex (MHC) class II region that contains a number of genes that play an important role in the immune system. In addition, genetic variants in another general transcription factor IIH gene have revealed significant association with lung disease. To investigate whether GTF2H4 genetic variants could be a causative factor for AERD development and FEV(1) decline by aspirin provocation, five common single-nucleotide polymorphisms (SNPs) were genotyped in 93 patients with AERD and 96 aspirin-tolerant asthma (ATA) controls. As a result, when adjusted for age, gender, smoking status and atopy as covariates, the rs1264307 variant and two haplotypes showed nominal signals in the association with AERD (P = 0.02-0.04), but the significances disappeared after corrections for multiple testing (corrected P > 0.05). In further multiple regression analysis, no genetic variants of GTF2H4 showed significant associations with FEV(1) decline by aspirin provocation in asthmatics (P > 0.05). Despite the need for replications in larger cohorts, our preliminary findings suggest that GTF2H4 variants may not be associated with susceptibility to AERD and obstructive symptoms in asthmatics.

Genome-wide association study identifies two novel regions at 11p15.5-p13 and 1p31 with major impact on acute-phase serum amyloid A

Elevated levels of acute-phase serum amyloid A (A-SAA) cause amyloidosis and are a risk factor for atherosclerosis and its clinical complications, type 2 diabetes, as well as various malignancies. To investigate the genetic basis of A-SAA levels, we conducted the first genome-wide association study on baseline A-SAA concentrations in three population-based studies (KORA, TwinsUK, Sorbs) and one prospective case cohort study (LURIC), including a total of 4,212 participants of European descent, and identified two novel genetic susceptibility regions at 11p15.5-p13 and 1p31. The region at 11p15.5-p13 (rs4150642; p = 3.20×10⁻¹¹¹) contains serum amyloid A1 (SAA1) and the adjacent general transcription factor 2 H1 (GTF2H1), Hermansky-Pudlak Syndrome 5 (HPS5), lactate dehydrogenase A (LDHA), and lactate dehydrogenase C (LDHC). This region explains 10.84% of the total variation of A-SAA levels in our data, which makes up 18.37% of the total estimated heritability. The second region encloses the leptin receptor (LEPR) gene at 1p31 (rs12753193; p = 1.22×10⁻¹¹) and has been found to be associated with CRP and fibrinogen in previous studies. Our findings demonstrate a key role of the 11p15.5-p13 region in the regulation of baseline A-SAA levels and provide confirmative evidence of the importance of the 1p31 region for inflammatory processes and the close interplay between A-SAA, leptin, and other acute-phase proteins.

An elevated level of acute-phase serum amyloid A (A-SAA), a sensitive marker of the acute inflammatory state with high heritability estimates, causes amyloidosis and is a risk factor for atherosclerosis and its clinical complications, type 2 diabetes, as well as various malignancies. This study describes the first genome-wide association study on baseline A-SAA concentrations. In a meta-analysis of four genome-wide scans totalling 4,212 participants of European descent, we identified two novel genetic susceptibility regions on chromosomes 11 and 1 to be associated with baseline A-SAA concentrations. The chromosome 11 region contains the serum amyloid A1 gene and the adjacent genes and explains a high percentage of the total estimated heritability. The chromosome 1 region is a known genetic susceptibility region for inflammation. Taken together, we identified one region, which seems to be of key importance in the regulation of A-SAA levels and represents a novel potential target for the investigation of related clinical entities. In addition, our findings indicate a close interplay between A-SAA and other inflammatory proteins, as well as a larger role of a known genetic susceptibility region for inflammatory processes as it has been assumed in the past.