Nongenic cancer-risk SNPs affect oncogenes, tumour-suppressor genes, and immune function

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Prioritization and Meta-analysis of regulatory SNPs identified IL6, TGFB1, TLR9 and MMP7 as significantly associated with cervical cancer

Cervical cancer is a leading women cancer globally with respect to both incidence and mortality. Its increased risk has been linked with HPV infection and genetic variations like single nucleotide polymorphisms (SNPs). Although, studies have been published which evaluates the effect of SNPs in a few candidate genes, however the role of number of regulatory SNPs (rSNPs) in cervical cancer is not available. As literature evidence has shown that non-coding rSNPs are related with increasing cervical cancer risk, we undertook this study to prioritize the important rSNPs and elucidate their role. A search was conducted in PubMed up to December 2020, which led to the identification of 263 articles and 969 SNPs in the non-coding region. These 969 SNPs were analysed through rSNPBase and RegulomeDB, leading to identification of 105 rSNPs. Afterwards, a regulatory module was constructed using protein-protein interaction data and a hub of highly interacting 23 target genes (corresponding to 34 rSNPs) was identified using MCODE. To further understand the mechanism of action of the 34 rSNPs, their transcription factor information with respect to cervical cancer was retrieved. To evaluate the pooled effect of these prioritized polymorphisms in cervical cancer patients, a meta-analysis was performed on 10,537 cases and 11,252 controls from 30 studies corresponding to 8 rSNPs. It led to identification of polymorphisms in IL6 (rs2069837), TGFB1 (rs1800469), TLR9 (rs187084) and MMP7 (rs11568818) which are significantly (p < 0.05) associated with increased cervical cancer risk at the population level. Overall, the study demonstrates that rSNPs targeting immune and inflammatory genes (IL1B, IL6, IL10, IL18, TGFB1, CCR5, CD40, TLR9, and MMP7) are associated with cervical cancer.

2q35-rs13387042 variant and the risk of breast cancer: a case-control study

BACKGROUND

Breast Cancer is the most frequent neoplasm diagnosed among women worldwide. Genetic background and lifestyle/environment play a significant role in the disease etiology. According to Genome-wide association studies, some single-nucleotide polymorphisms such as 2q35-rs13387042-(G/A) have been introduced to be associated with breast cancer risk and features. In this study, we aimed to evaluate the association between this variant and the risk of breast cancer in a cohort of Iranian women.

METHODS

Demographics and clinical information were collected by interview and using patients' medical records, respectively. DNA was extracted from 506 blood samples, including 184 patients and 322 controls, and genotyping was performed using allele specific-PCR. SPSS v16 was used for statistical analysis.

RESULT

Statistically significant association was observed between AA genotype and disease risk in all patients [p_adj = 0.048; OR_adj = 2.13, 95% CI (1.01-4.50)] and also ER-positive breast cancers [p_adj = 0.015; OR_adj = 2.12, 95% CI (1.16-3.88)]. There was no association between rs13387042 and histopathological characteristics of the disease. Furthermore, overall survival was not statistically associated with genotype and allelic models even after adjustment for stage and receptor status (p > 0.05).

CONCLUSION

There is a statistically significant association between 2q35-rs13387042 and breast cancer risk. rs13387042-AA genotype might be a risk-conferring factor for breast cancer development in the Iranian population. However, further consideration is suggested to confirm its role in risk assessment and probable association with other genetic markers.

Polymorphisms of an oncogenic gene, mesothelin, predict the risk and prognosis of gastric cancer in a Chinese Han population

Mesothelin (MSLN) is a cell surface protein associated with tumor invasion and metastasis. This study aims to explore the biological function of MSLN in gastric cancer and to evaluate the association of MSLN polymorphism (rs3764247, rs3764246, rs12597489, rs1057147, rs3765319) with the risk and prognosis of gastric cancer. Small interfering RNA (siRNA) transfection and MSLN overexpression were performed in human gastric cancer cell lines, respectively. The proliferation of tumor cells was evaluated by Cell counting kit 8(CCK-8) and colony formation assay. Wound healing assay and transwell assay were used to elucidate gastric cancer cell migration and invasion rates. We conducted a case-control study involving 860 patients with gastric cancer and 870 controls. All mutation sites were genotyped by PCR-LDR sequencing. First, our study revealed the cancer-promoting role of MSLN in gastric cancer. Second, we also demonstrated that rs3764247 and rs3764246 were associated with a reduced risk of gastric cancer (OR = 0.83, p = 0.010; OR = 0.84, p = 0.011; respectively). The clinicopathological analysis further showed that rs3764247 was closely related to T stage, vascular infiltration, and HER2 expression. In addition, in the survival analysis of 392 patients with gastric cancer, patients with rs3764247 CC genotype had poorer survival than patients with AA + AC genotype after adjusting for age, sex, TNM stage, and Lauren classification (HR = 2.07, p = 0.029). Our findings indicated that MSLN could be an oncogene whose polymorphisms were closely related to the risk and prognosis of gastric cancer.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Background

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.

Results

In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants.

Conclusions

Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01021-x.

Mapping of novel loci involved in lung and colon tumor susceptibility by the use of genetically selected mouse strains

Two non-inbred mouse lines, phenotypically selected for maximal (AIRmin) and minimal (AIRmax) acute inflammatory response, show differential susceptibility/resistance to the development of several chemically-induced tumor types. An intercross pedigree of these mice was generated and treated with the chemical carcinogen dimethylhydrazine, which induces lung and intestinal tumors. Genome wide high-density genotyping with the Restriction Site-Associated DNA genotyping (2B-RAD) technique was used to map genetic loci modulating individual genetic susceptibility to both lung and intestinal cancer. Our results evidence new common quantitative trait loci (QTL) for those phenotypes and provide an improved understanding of the relationship between genomic variation and individual genetic predisposition to tumorigenesis in different organs.

A catalog of curated breast cancer genes

Purpose

Decades of research have identified multiple genetic variants associated with breast cancer etiology. However, there is no database that archives breast cancer genes and variants responsible for predisposition. We set out to build a dynamic repository of curated breast cancer genes.

Methods

A comprehensive literature search was performed in PubMed and Google Scholar, followed by data extraction and harmonization for downstream analysis.

Results

Using a subset of 345 studies, we cataloged 652 breast cancer-associated loci across the genome. A majority of these were present in the non-coding region (i.e., intergenic (101) and intronic (345)), whereas only 158 were located within an exon. Using the odds ratio, we identified 429 loci to increase the disease risk and 198 to confer protection against breast cancer, whereas 25 were identified to both increase disease risk and confer protection against breast cancer. Chromosomal ideogram analysis indicated that chromosomes 17 and 19 have the highest density of breast cancer loci. We manually annotated and collated breast cancer genes in which a previous association between rare-monogenic variant and breast cancer has been documented. Finally, network and functional enrichment analysis revealed that steroid metabolism and DNA repair pathways were predominant among breast cancer genes and variants.

Conclusions

We have built an online interactive catalog of curated breast cancer genes (https://cbcg.dk). This will expedite clinical diagnostics and support the ongoing efforts in managing breast cancer etiology. Moreover, the database will serve as an essential repository when designing new breast cancer multigene panels.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06441-y.

Novel association between a transient receptor potential cation channel subfamily M member 5 expression quantitative trait locus rs35197079 and decreased susceptibility of gestational diabetes mellitus in a Chinese population

AIMS/INTRODUCTION

Emerging evidence suggests that expression quantitative trait loci (eQTLs) are more likely to associate with complex diseases. Transient receptor potential cation channel subfamily M member 5 (TRPM5) is a ubiquitously expressed voltage-gated cation channel that acts indispensably to trigger insulin secretion in pancreatic β-cells. The present study evaluated the association between TRPM5 eQTL single-nucleotide polymorphisms and the risk of gestational diabetes mellitus (GDM) in a Chinese population.

MATERIALS AND METHODS

A total of 380 unrelated Chinese pregnant women including 241 GDM patients and 139 controls were included in this study. The eQTL single-nucleotide polymorphisms of TRPM5 were obtained from the GTEx eQTL Browser, and were subsequently genotyped using the Agena MassARRAY iPLEX platform.

RESULTS

Logistic regression analysis and linear regression analysis showed that rs35197079 and rs74848824 were significantly associated with reduced GDM risk and lower fasting plasma glucose levels after adjusting confounder factors in dominant genetic models. Stratification analysis based on pre-pregnancy body mass index validated a strong association between rs35197079 and GDM susceptibility in underweight and normal weight individuals. Luciferase and electrophoretic mobility shift assays carried out in rat pancreatic β-cells showed that rs35197079 was functional.

CONCLUSIONS

The TRPM5 eQTL single-nucleotide polymorphism rs35197079 was associated with decreased GDM susceptibility in a Chinese population, especially in underweight and normal weight pregnant women, and it was functional in modulating gene transcription.

Intergenic SNPs in Obstructive Sleep Apnea Syndrome: Revealing Metabolic, Oxidative Stress and Immune-Related Pathways

There is strong evidence supporting the contribution of genetic factors to obstructive sleep apnea syndrome (OSAHS) susceptibility. In the current study we analyzed both in a clinical cohort and in silico, four single nucleotide polymorphisms SNPs, rs999944, rs75108997, rs35329661 and rs116133558 that have been associated with OSAHS. In 102 patients with OSAHS and 50 healthy volunteers, genetic testing of the above polymorphisms was performed. Polymorphism rs116133558 was invariant in our study population, whereas polymorphism rs35329661 was more than 95% invariant. Polymorphism rs999944 displayed significant (>5%) variance in our study population and was used in the binary logistic regression model. In silico analyses of the mechanism by which these three SNPs may affect the pathophysiology of OSAHS revealed a transcriptomic network of 274 genes. This network was involved in multiple cancer-associated gene signatures, as well as the adipogenesis pathway. This study, uncover a regulatory network in OSAHS using transcriptional targets of intergenic SNPs, and map their contributions in the pathophysiology of the syndrome on the interplay between adipocytokine signaling and cancer-related transcriptional dysregulation.

k-core genes underpin structural features of breast cancer

Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula: see text]) structures in cancer, and its relationship with biological functions. We found that in the Top-100,000-edges networks, the majority of interactions in breast cancer networks are intra-chromosome, meanwhile inter-chromosome interactions serve as connecting bridges between clusters. Moreover, core genes in the healthy network are strongly associated with processes such as metabolism and cell cycle. In breast cancer, only the core of Luminal A is related to those processes, and genes in its core are over-expressed. The intersection of the core nodes in all subtypes of cancer is composed only by genes in the chr8q24.3 region. This region has been observed to be highly amplified in several cancers before, and its appearance in the intersection of the four breast cancer k-cores, may suggest that local co-expression is a conserved phenomenon in cancer. Considering the many intricacies associated with these phenomena and the vast amount of research in epigenomic regulation which is currently undergoing, there is a need for further research on the epigenomic effects on the structure and function of gene co-expression networks in cancer.

Risks and Function of Breast Cancer Susceptibility Alleles

Family history remains one of the strongest risk factors for breast cancer. It is well established that women with a first-degree relative affected by breast cancer are twice as likely to develop the disease themselves. Twins studies indicate that this is most likely due to shared genetics rather than shared epidemiological/lifestyle risk factors. Linkage and targeted sequencing studies have shown that rare high- and moderate-penetrance germline variants in genes involved in the DNA damage response (DDR) including BRCA1, BRCA2, PALB2, ATM, and TP53 are responsible for a proportion of breast cancer cases. However, breast cancer is a heterogeneous disease, and there is now strong evidence that different risk alleles can predispose to different subtypes of breast cancer. Here, we review the associations between the different genes and subtype-specificity of breast cancer based on the most comprehensive genetic studies published. Genome-wide association studies (GWAS) have also been used to identify an additional hereditary component of breast cancer, and have identified hundreds of common, low-penetrance susceptibility alleles. The combination of these low penetrance risk variants, summed as a polygenic risk score (PRS), can identify individuals across the spectrum of disease risk. However, there remains a substantial bottleneck between the discovery of GWAS-risk variants and their contribution to tumorigenesis mainly because the majority of these variants map to the non-protein coding genome. A range of functional genomic approaches are needed to identify the causal risk variants and target susceptibility genes and establish their underlying role in disease biology. We discuss how the application of these multidisciplinary approaches to understand genetic risk for breast cancer can be used to identify individuals in the population that may benefit from clinical interventions including screening for early detection and prevention, and treatment strategies to reduce breast cancer-related mortalities.

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

Exploring the link between chronobiology and drug delivery: effects on cancer therapy

Circadian clock is an impressive timing system responsible for the control of several metabolic, physiological and behavioural processes. Nowadays, the connection between the circadian clock and cancer occurrence and development is consensual. Therefore, the inclusion of circadian timing into cancer therapy may potentially offer a more effective and less toxic approach. This way, chronotherapy has been shown to improve cancer treatment efficacy. Despite this relevant finding, its clinical application is poorly exploited. The conception of novel anticancer drug delivery systems and the combination of chronobiology with nanotechnology may provide a powerful tool to optimize cancer therapy, instigating the incorporation of the circadian timing into clinical practice towards a more personalized drug delivery. This review focuses on the recent advances in the field of cancer chronobiology, on the link between cancer and the disruption of circadian rhythms and on the promising targeted drug nanodelivery approaches aiming the clinical application of cancer chronotherapy.

Molecular Alterations in Gastric Intestinal Metaplasia

Gastric cancer (GC) remains one of the most common causes of mortality worldwide. Intestinal metaplasia (IM) is one of the preneoplastic gastric lesions and is considered an essential predisposing factor in GC development. Here we present a review of recent most relevant papers to summarize major findings on the molecular alterations in gastric IM. The latest progress in novel diagnostic methods allows scientists to identify various types of molecular alterations in IM, such as polymorphisms in various genes, changes in the expression of micro-RNAs and long noncoding RNAs, and altered microbiome profiles. The results have shown that some of these alterations have strong associations with IM and a potential to be used for screening, treatment, and prognostic purposes; however, one of the most important limiting factors is the inhomogeneity of the studies. Therefore, further large-scale studies and clinical trials with standardized methods designed by multicenter consortiums are needed. As of today, various molecular alterations in IM could become a part of personalized medicine in the near future, which would help us deliver a personalized approach for each patient and identify those at risk of progression to GC.

The Panorama of Cancer Genetics

Cancer, a disease of the genome, is caused by a combination of germline predisposing variants and acquired somatic mutations. A unified view of heritable and acquired genetic factors will improve our understanding of cancer occurrence and progression. Fanfani and colleagues provide new insight into heritable cancer risk through a computational method that identifies genes and loci that contribute strongly to cancer heritability; many of these loci also harbor somatic drivers. Beyond improving cancer clinical outcomes, these methods will also be valuable across complex disorders by identifying regions responsible for missing heritability.See related article by Fanfani et al., p. 2588.

Novel Biomarkers in the Diagnosis of Benign and Malignant Gastrointestinal Diseases

BACKGROUND

Various noninvasive biomarkers have been used in the diagnosis, prognosis, and treatment of different gastrointestinal (GI) diseases for years. Novel technological developments and profound perception of molecular processes related to GI diseases over the last decade have allowed researchers to evaluate genetic, epigenetic, and many other potential molecular biomarkers in different diseases and clinical settings. Here, we present a review of recent and most relevant articles in order to summarize major findings on novel biomarkers in the diagnosis of benign and malignant GI diseases.

SUMMARY

Genetic variations, noncoding RNAs (ncRNAs), cell-free DNA (cfDNA), and microbiome-based biomarkers have been extensively analyzed as potential biomarkers in benign and malignant GI diseases. Multiple single-nucleotide polymorphisms have been linked with a number of GI diseases, and these observations are further being used to build up disease-specific genetic risk scores. Micro-RNAs and long ncRNAs have a large potential as noninvasive biomarkers in the management of inflammatory bowel diseases and GI tumors. Altered microbiome profiles were observed in multiple GI diseases, but most of the findings still lack translational clinical application. As of today, cfDNA appears to be the most potent biomarker for early detection and screening of GI cancers. Key Messages: Novel noninvasive molecular biomarkers show huge potential as useful tools in the diagnostics and management of different GI diseases. However, the use of these biomarkers in real-life clinical practice still remains limited, and further large studies are needed to elucidate the ultimate role of these potential noninvasive clinical tools.

Molecular Dynamics Simulations Predict That rSNP Located in the HNF‑1α Gene Promotor Region Linked with MODY3 and Hepatocellular Carcinoma Promotes Stronger Binding of the HNF‑4α Transcription Factor

Our study aims to investigate the impact of the Maturity-onset diabetes of the young 3 disease-linked rSNP rs35126805 located in the HNF-1α gene promotor on the binding of the transcription factor HNF-4α and consequently on the regulation of HNF-1α gene expression. Our focus is to calculate the change in the binding affinity of the transcription factor HNF-4α to the DNA, caused by the regulatory single nucleotide polymorphism (rSNP) through molecular dynamics simulations and thermodynamic analysis of acquired results. Both root-mean-square difference (RMSD) and the relative binding free energy ΔΔG_bind reveal that the HNF-4α binds slightly more strongly to the DNA containing the mutation (rSNP) making the complex more stable/rigid, and thereby influencing the expression of the HNF-1α gene. The resulting disruption of the HNF-4α/HNF-1α pathway is also linked to hepatocellular carcinoma metastasis and enhanced apoptosis in pancreatic cancer cells. To the best of our knowledge, this represents the first study where thermodynamic analysis of the results obtained from molecular dynamics simulations is performed to uncover the influence of rSNP on the protein binding to DNA. Therefore, our approach can be generally applied for studying the impact of regulatory single nucleotide polymorphisms on the binding of transcription factors to the DNA.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure.. [DOI: 10.1101/2020.10.06.328567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Genetic variants associated with alcohol dependence co-ordinate regulation of ADH genes in gastrointestinal and adipose tissues

GWAS studies have identified genetic variants associated with Alcohol Dependence (AD), but how they link to genes, their regulation and disease traits, remains largely unexplored. Here we integrated information on the 3D genome organization with expression quantitative loci (eQTLs) analysis, using CoDeS3D, to identify the functional impacts of single nucleotide polymorphisms associated with AD (p < 1 × 10-6). We report that 42% of the 285 significant tissue-specific regulatory interactions we identify were associated with four genes encoding Alcohol Dehydrogenase - ADH1A, ADH1B, ADH1C and ADH4. Identified eQTLs produced a co-ordinated regulatory action between ADH genes, especially between ADH1A and ADH1C within the subcutaneous adipose and gastrointestinal tissues. Five eQTLs were associated with regulatory motif alterations and tissue-specific histone marks consistent with these variants falling in enhancer and promoter regions. By contrast, few regulatory connections were identified in the stomach and liver. This suggests that changes in gene regulation associated with AD are linked to changes in tissues other than the primary sites of alcohol absorption and metabolism. Future work to functionally characterise the putative regulatory regions we have identified and their links to metabolic and regulatory changes in genes will improve our mechanistic understanding of AD disease development and progression.