Dataset Information

DNA repair-related genes and adipogenesis: Lessons from congenital lipodystrophies.

ABSTRACT: Classical and progeroid congenital lipodystrophies are a collection of rare diseases displaying a large genetic heterogeneity. They occur due to pathogenic variants in genes associated with adipogenesis, DNA repair pathways, and genome stability. Subjects with lipodystrophy exhibit an impairment in the homeostasis of subcutaneous white adipose tissue (sWAT), resulting in low leptin and adiponectin levels, insulin resistance (IR), diabetes, dyslipidemia, ectopic fat deposition, inflammation, mitochondrial and endoplasmic reticulum commitments, among others. However, how pathogenic variants in adipogenesis-related genes modulate DNA repair in some classical congenital lipodystrophies has not been elucidated. In the same way, no data is clarifying how pathogenic variants in DNA repair genes result in sWAT loss in different types of progeroid lipodystrophies. This review will concentrate on the main molecular findings to understand the link between DNA damage/repair and adipogenesis in human and animal models of congenital lipodystrophies. We will focus on classical and progeroid congenital lipodystrophies directly or indirectly related to DNA repair pathways, highlighting the role of DNA repair-related proteins in maintaining sWAT homeostasis.

SUBMITTER: Campos JTAM

PROVIDER: S-EPMC9648094 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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DNA repair-related genes and adipogenesis: Lessons from congenital lipodystrophies.

Campos Julliane Tamara Araújo de Melo JTAM Oliveira Matheus Sena de MS Soares Luisa Pessoa LP Medeiros Katarina Azevedo de KA Campos Leonardo René Dos Santos LRDS Lima Josivan Gomes JG

Genetics and molecular biology 20221107 3 Suppl 1

Classical and progeroid congenital lipodystrophies are a collection of rare diseases displaying a large genetic heterogeneity. They occur due to pathogenic variants in genes associated with adipogenesis, DNA repair pathways, and genome stability. Subjects with lipodystrophy exhibit an impairment in the homeostasis of subcutaneous white adipose tissue (sWAT), resulting in low leptin and adiponectin levels, insulin resistance (IR), diabetes, dyslipidemia, ectopic fat deposition, inflammation, mito ...[more]

PMID: 36354755

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Project description:BackgroundLiver 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/methods422 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.ResultsThe 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.ConclusionsOur 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.

Project description:Backgrounds: Colorectal cancer (CRC) with high incidence, has the third highest mortality of tumors. DNA damage and repair influence a variety of tumors. However, the role of these genes in colon cancer prognosis has been less systematically investigated. Here, we aim to establish a corresponding prognostic signature providing new therapeutic opportunities for CRC. Method: After related genes were collected from GSEA, univariate Cox regression was performed to evaluate each gene's prognostic relevance through the TCGA-COAD dataset. Stepwise COX regression was used to establish a risk prediction model through the training sets randomly separated from the TCGA cohort and validated in the remaining testing sets and two GEO datasets (GSE17538 and GSE38832). A 12-DNA-damage-and-repair-related gene-based signature able to classify COAD patients into high and low-risk groups was developed. The predictive ability of the risk model or nomogram were evaluated by different bioinformatics- methods. Gene functional enrichment analysis was performed to analyze the co-expressed genes of the risk-based genes. Result: A 12-gene based prognostic signature established within 160 significant survival-related genes from DNA damage and repair related gene sets performed well with an AUC of ROC 0.80 for 5 years in the TCGA-CODA dataset. The signature includes CCNB3, ISY1, CDC25C, SMC1B, MC1R, LSP1P4, RIN2, TPM1, ELL3, POLG, CD36, and NEK4. Kaplan-Meier survival curves showed that the prognosis of the risk status owns more significant differences than T, M, N, and stage prognostic parameters. A nomogram was constructed by LASSO regression analysis with T, M, N, age, and risk as prognostic parameters. ROC curve, C-index, Calibration analysis, and Decision Curve Analysis showed the risk module and nomogram performed best in years 1, 3, and 5. KEGG, GO, and GSEA enrichment analyses suggest the risk involved in a variety of important biological processes and well-known cancer-related pathways. These differences may be the key factors affecting the final prognosis. Conclusion: The established gene signature for CRC prognosis provides a new molecular tool for clinical evaluation of prognosis, individualized diagnosis, and treatment. Therapies based on targeted DNA damage and repair mechanisms may formulate more sensitive and potential chemotherapy regimens, thereby expanding treatment options and potentially improving the clinical outcome of CRC patients.

Dataset Information

DNA repair-related genes and adipogenesis: Lessons from congenital lipodystrophies.

Publications

DNA repair-related genes and adipogenesis: Lessons from congenital lipodystrophies.

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