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Biomedicines
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Genetic and Molecular Mechanisms of Cardiometabolic Diseases and Cancers
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Genetic and Molecular Mechanisms of Cardiometabolic Diseases and Cancers

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular Genetics and Genetic Diseases".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 7086

Special Issue Editors

Prof. Dr. Dafang Chen

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Interests: molecular epidemiology of chronic diseases; data mining
Prof. Dr. Tao Wu

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Interests: genetic epidemiology; cardiometabolic disease

Special Issue Information

Dear Colleagues,

Cardiometabolic diseases (CMDs) and cancers, the two most prevalent non-communicable diseases globally, constitute major concerns worldwide. Recent evidence suggests close co-morbid linkages and shared risk factors between CMDs and cancers, calling for an integrated and systematic interrogation into their intercorrelation. Nevertheless, the mechanisms whereby genetic and other factors contribute to their co-occurrence remain unclear. Further research is therefore expected to provide novel insights into the genetic and molecular mechanisms underlying their correlations. Possible perspectives may involve shared genetic factors like genetic pleiotropy. In addition, complex interactions between genes and the environment, such as inter-generational genetic and environment transmission, interacting phenotypes influenced by genotypes, as well as their effects on the differentiated co-morbidity risks, require more comprehensive assessment.

In this Special Issue, we intend to take CMDs and cancers as examples to elucidate the intricate interconnections among complex diseases, and welcome the submission of original high-quality research and review articles focused on: 1) research on disease comorbidities; 2) genetic pleiotropy and heterogeneity; 3) genetic overlap and causality between CMD and cancers; and 4) indirect genetic effects.

Prof. Dr. Dafang Chen
Prof. Dr. Tao Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cardiometabolic disease
  • oncology
  • comorbidity
  • genetic pleiotropy
  • direct genetic effects
  • indirect genetic effects
  • parent-of-origin

Published Papers (5 papers)

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Research

23 pages, 4313 KiB  
Article
Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer
by Irina Ponomarenko, Konstantin Pasenov, Maria Churnosova, Inna Sorokina, Inna Aristova, Vladimir Churnosov, Marina Ponomarenko, Yuliya Reshetnikova, Evgeny Reshetnikov and Mikhail Churnosov
Biomedicines 2024, 12(4), 818; https://doi.org/10.3390/biomedicines12040818 - 08 Apr 2024
Viewed by 498
Abstract
The purpose of this work was to find a link between the breast cancer (BC)-risk effects of sex hormone-binding globulin (SHBG)-associated polymorphisms and obesity. The study was conducted on a sample of 1498 women (358 BC; 1140 controls) who, depending on the presence/absence [...] Read more.
The purpose of this work was to find a link between the breast cancer (BC)-risk effects of sex hormone-binding globulin (SHBG)-associated polymorphisms and obesity. The study was conducted on a sample of 1498 women (358 BC; 1140 controls) who, depending on the presence/absence of obesity, were divided into two groups: obese (119 BC; 253 controls) and non-obese (239 BC; 887 controls). Genotyping of nine SHBG-associated single nucleotide polymorphisms (SNP)—rs17496332 PRMT6, rs780093 GCKR, rs10454142 PPP1R21, rs3779195 BAIAP2L1, rs440837 ZBTB10, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs8023580 NR2F2, and rs12150660 SHBG—was executed, and the BC-risk impact of these loci was analyzed by logistic regression separately in each group of obese/non-obese women. We found that the BC-risk effect correlated by GWAS with the SHBG-level polymorphism rs10454142 PPP1R21 depends on the presence/absence of obesity. The SHBG-lowering allele C rs10454142 PPP1R21 has a risk value for BC in obese women (allelic model: CvsT, OR = 1.52, 95%CI = 1.10–2.11, and pperm = 0.013; additive model: CCvsTCvsTT, OR = 1.71, 95%CI = 1.15–2.62, and pperm = 0.011; dominant model: CC + TCvsTT, OR = 1.95, 95%CI = 1.13–3.37, and pperm = 0.017) and is not associated with the disease in women without obesity. SNP rs10454142 PPP1R21 and 10 proxy SNPs have adipose-specific regulatory effects (epigenetic modifications of promoters/enhancers, DNA interaction with 51 transcription factors, eQTL/sQTL effects on five genes (PPP1R21, RP11-460M2.1, GTF2A1L, STON1-GTF2A1L, and STON1), etc.), can be “likely cancer driver” SNPs, and are involved in cancer-significant pathways. In conclusion, our study detected an obesity-dependent association of the rs10454142 PPP1R21 with BC in women. Full article
(This article belongs to the Special Issue Genetic and Molecular Mechanisms of Cardiometabolic Diseases and Cancers)
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17 pages, 3039 KiB  
Article
PDE3A and GSK3B as Atrial Fibrillation Susceptibility Genes in the Chinese Population via Bioinformatics and Genome-Wide Association Analysis
by Zechen Zhou, Yu Wang, Xiaoyi Li, Yinan Zhang, Lichuang Yuan, Dafang Chen and Xuedong Wang
Biomedicines 2023, 11(3), 908; https://doi.org/10.3390/biomedicines11030908 - 15 Mar 2023
Viewed by 1523
Abstract
Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia, with uncovered genetic etiology and pathogenesis. We aimed to screen out AF susceptibility genes with potential pathogenesis significance in the Chinese population. Methods: Differentially expressed genes (DEGs) were screened by the Limma package [...] Read more.
Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia, with uncovered genetic etiology and pathogenesis. We aimed to screen out AF susceptibility genes with potential pathogenesis significance in the Chinese population. Methods: Differentially expressed genes (DEGs) were screened by the Limma package in three GEO data sets of atrial tissue. AF-related genes were identified by combination of DEGs and public GWAS susceptibility genes. Potential drug target genes were selected using the DrugBank, STITCH and TCMSP databases. Pathway enrichment analyses of AF-related genes were performed using the databases GO and KEGG databases. The pathway gene network was visualized by Cytoscape software to identify gene–gene interactions and hub genes. GWAS analysis of 110 cases of AF and 1201 controls was carried out through a genome-wide efficient mixed model in the Fangshan population to verify the results of bioinformatic analysis. Results: A total of 3173 DEGs were identified, 57 of which were found to be significantly associated with of AF in public GWAS results. A total of 75 AF-related genes were found to be potential therapeutic targets. Pathway enrichment analysis selected 79 significant pathways and classified them into 7 major pathway networks. A total of 35 hub genes were selected from the pathway networks. GWAS analysis identified 126 AF-associated loci. PDE3A and GSK3B were found to be overlapping genes between bioinformatic analysis and GWAS analysis. Conclusions: We screened out several pivotal genes and pathways involved in AF pathogenesis. Among them, PDE3A and GSK3B were significantly associated with the risk of AF in the Chinese population. Our study provided new insights into the mechanisms of action of AF. Full article
(This article belongs to the Special Issue Genetic and Molecular Mechanisms of Cardiometabolic Diseases and Cancers)
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13 pages, 2269 KiB  
Article
Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population
by Kexin Ding, Zechen Zhou, Yujia Ma, Xiaoyi Li, Han Xiao, Yiqun Wu, Tao Wu and Dafang Chen
Biomedicines 2022, 10(12), 3093; https://doi.org/10.3390/biomedicines10123093 - 01 Dec 2022
Viewed by 1257
Abstract
The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a [...] Read more.
The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177–13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250–5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes APOA5, BUD13, ZNF259, and WNT4. Functional analysis revealed an enrichment of top genes in metabolism-related biological pathways and expression in the heart, brain, arteries, and kidneys. Our findings provide directions for future attempts at risk stratification and evidence-based management in populations with metabolic abnormalities from a systematic perspective. Full article
(This article belongs to the Special Issue Genetic and Molecular Mechanisms of Cardiometabolic Diseases and Cancers)
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10 pages, 282 KiB  
Article
Association of SLC22A1, SLC22A2, SLC47A1, and SLC47A2 Polymorphisms with Metformin Efficacy in Type 2 Diabetic Patients
by Peixian Chen, Yumin Cao, Shenren Chen, Zhike Liu, Shiyi Chen and Yali Guo
Biomedicines 2022, 10(10), 2546; https://doi.org/10.3390/biomedicines10102546 - 12 Oct 2022
Cited by 5 | Viewed by 1605
Abstract
Response to metformin, first-line therapy for type 2 diabetes mellitus (T2DM), exists interindividual variation. Considering that transporters belonging to the solute carrier (SLC) superfamily are determinants of metformin pharmacokinetics, we evaluated the effects of promoter variants in organic cation transporter 1 (OCT1) ( [...] Read more.
Response to metformin, first-line therapy for type 2 diabetes mellitus (T2DM), exists interindividual variation. Considering that transporters belonging to the solute carrier (SLC) superfamily are determinants of metformin pharmacokinetics, we evaluated the effects of promoter variants in organic cation transporter 1 (OCT1) (SLC22A1 rs628031), OCT2 (SLC22A2 rs316019), multidrug and toxin extrusion protein 1 (MATE1) (SLC47A1 rs2289669), and MATE2 (SLC47A2 rs12943590) on the variation in metformin response. The glucose-lowering effects and improvement of insulin resistance of metformin were assessed in newly diagnosed, treatment-naive type 2 diabetic patients of Han nationality in Chaoshan China (n = 93) receiving metformin. Fasting plasma glucose (FPG), fasting insulin (FINS), glycated hemoglobin A1 (HbA1C), homeostasis model assessment-insulin sensitivity (HOMA-IS), and homeostasis model assessment-insulin resistance (HOMA-IR) were the main metformin efficacy measurements. There were significant correlations between both SLC47A1 rs2289669 and SLC47A2 rs12943590 and the efficacy of metformin in individuals with T2DM. In normal weight T2DM patients, significant associations between the AA and GG genotypes of the rs2289669 variant of SLC47A1 and a greater reduction in FINS and HOMA-IR were detected. A significant correlation was observed between the AG genotype of the rs12943590 polymorphism of SLC47A2 and a greater reduction in HOMA-IR. Gene–environment interaction analysis showed that in the FINS interaction model, the second-order of dose30_g-SLC47A2 rs12943590 was statistically significant. The variants of SLC47A1 rs2289669 and SLC47A2 rs12943590 could be predictors of insulin resistance in type 2 diabetic patients treated with metformin. The second-order interaction of dose30_g-SLC47A2 rs12943590 may have a significant effect on FINS in patients with T2DM on metformin treatment. These findings suggest that promoter variants of SLC47A1 and SLC47A2 are important determinants of metformin transport and response in type 2 diabetes mellitus. Full article
(This article belongs to the Special Issue Genetic and Molecular Mechanisms of Cardiometabolic Diseases and Cancers)
18 pages, 2275 KiB  
Article
Genetic Susceptibility to Insulin Resistance and Its Association with Estimated Longevity in the Hungarian General and Roma Populations
by Peter Piko, Nardos Abebe Werissa and Roza Adany
Biomedicines 2022, 10(7), 1703; https://doi.org/10.3390/biomedicines10071703 - 14 Jul 2022
Viewed by 1487
Abstract
Diabetes mellitus is a major public health problem with a wide range of prevalence among different ethnic groups. Early recognition of pre-diabetes is important to prevent the development of the disease, its complications, co-morbidities, and consequently early death. Insulin resistance (IR) is considered [...] Read more.
Diabetes mellitus is a major public health problem with a wide range of prevalence among different ethnic groups. Early recognition of pre-diabetes is important to prevent the development of the disease, its complications, co-morbidities, and consequently early death. Insulin resistance (IR) is considered a condition that precedes type 2 diabetes; thus, understanding its underlying causes (genetic and non-genetic factors) will bring us closer to preventing it. The present study aimed to investigate the genetic susceptibility to IR and its impact on estimated longevity in populations with different ethnic origins using randomly selected samples of 372 Hungarian general (HG, as a reference with Caucasian origin) and 334 Roma participants (largest ethnic minority in Europe, with a northern India origin). In the present study, we used the Homeostasis Model Assessment—Insulin Resistance (HOMA—IR) to identify people with IR (>3.63) at the population level. To investigate the genetic predisposition to IR, 29 single nucleotide polymorphisms (SNPs) identified in a systematic literature search were selected and genotyped in sample populations. In the analyses, the adjusted p < 0.0033 was considered significant. Of these 29 SNPs, the commutative effects of 15 SNPs showing the strongest association with HOMA—IR were used to calculate an optimized genetic risk score (oGRS). The oGRS was found nominally significantly (p = 0.019) higher in the Roma population compared to HG one, and it was more strongly correlated with HOMA—IR. Therefore, it can be considered as a stronger predictor of the presence of IR among the Roma (AUCRoma = 0.673 vs. AUCHG = 0.528). Furthermore, oGRS also showed a significant correlation with reduced estimated longevity in the Roma population (β = −0.724, 95% CI: −1.230–−0.218; p = 0.005), but not in the HG one (β = 0.065, 95% CI: −0.388–0.518; p = 0.779). Overall, IR shows a strong correlation with a genetic predisposition among Roma, but not in the HG population. Furthermore, the increased genetic risk of Roma is associated with shorter estimated longevity, whereas this association is not observed in the HG one. Increased genetic susceptibility of Roma to IR should be considered in preventive programs targeting the development of type 2 diabetes, which may also reduce the risk of preventable premature death among them. Full article
(This article belongs to the Special Issue Genetic and Molecular Mechanisms of Cardiometabolic Diseases and Cancers)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: The Role of Mineralocorticoid receptor gene (NR3C2) in Cardiometabolic Diseases and Cancers
Authors: Mahyar Heydarpour; Gordon H Williams
Affiliation: Department of Medicine, Division of Endocrinology, diabetes, and Hypertension, Mass General Brigham (MGB), Harvard Medical School, 221 Longwood Ave., Boston MA 02115
Abstract: NR3C2, or the mineralocorticoid receptor gene, has been identified as a key regulator of cardiometabolic diseases and cancer. Studies have demonstrated that NR3C2 plays an important role in the regulation of a broad range of metabolic processes, such as glucose and lipid metabolism, and has been linked to several metabolic diseases, including type 2 diabetes (T2DM), obesity and cardiovascular diseases (CVD). The NR3C2 gene is located on the long arm of chromosome 4 and is responsible for the production of the mineralocorticoid receptor (MR). The MR is a protein that binds to aldosterone and is responsible for its physiological actions. Aldosterone is a hormone produced by the adrenal gland that regulates sodium and potassium levels in the body. The association between NR3C2 gene and aldosterone levels is of particular interest in the context of hypertension. Furthermore, NR3C2 has been implicated in the development and progression of certain cancers, such as colorectal cancer, lung cancer and breast cancer. Several studies have demonstrated that genetic variants in NR3C2 are associated with an increased risk of T2DM, obesity, and CVD. For example, in a meta-analysis of study identified two common single nucleotide polymorphisms (SNPs) in NR3C2 that were associated with an increased risk of T2DM. Similarly, a study of Han Chinese individuals identified two other SNPs in NR3C2 that were associated with an increased risk of CVD. In addition to its role in metabolic diseases, NR3C2 has been shown to play a role in the development and progression of certain cancers. A meta-analysis studies identified a significant association between NR3C2 variants and an increased risk of colorectal cancer. Furthermore, a study of lung cancer patients identified three SNPs in NR3C2 that were significantly associated with an increased risk of lung cancer. Lastly, several studies have identified NR3C2 variants that are associated with an increased risk of breast cancer. Overall, NR3C2 plays an important role in the regulation of metabolic processes, and genetic variants in NR3C2 have been associated with an increased risk of various cardiometabolic diseases and certain cancers. This study provides a comprehensive overview of the role of NR3C2 in cardiometabolic diseases and cancers and highlights the potential of NR3C2 as a therapeutic target.

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