Germline Variations of Apurinic/Apyrimidinic Endonuclease 1 (APEX1) Detected in Female Breast Cancer Patients

Ali, Kashif; Mahjabeen, Ishrat; Sabir, Maimoona; Baig, Ruqia Mehmood; Zafeer, Maryam; Faheem, Muhammad; Kayani, Mahmood Akhtar

doi:10.7314/apjcp.2014.15.18.7589

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Germline nucleotide variants in OBSCN (fifth by MUFFINN yet below 6000th by the gene-centric methods in papillary kidney carcinoma (KIRP) samples) were found in highly aggressive tumors such as glioblastoma, melanoma, and pancreatic carcinoma [ 43 ] and involved in cancer predispositions. Likewise, germline variants in APEX1 (14th by MUFFINN yet below 13,000th by the gene-centric methods in head and neck squamous cell carcinoma (HNSC) samples) is known to increase the risk of breast cancer development by contributing apurinic/apyrimidinic (AP) site accumulation in DNA [ 44 ]. Indeed, the genes functionally associated with APEX1 are enriched for a relevant pathway, “transcription-coupled nucleotide excision repair” (GO biological process, P = 7.45e-05; Fig.…”

Section: Resultsmentioning

confidence: 99%

MUFFINN: cancer gene discovery via network analysis of somatic mutation data

et al. 2016

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A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0989-x) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

MUFFINN: cancer gene discovery via network analysis of somatic mutation data

et al. 2016

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“…Eleven positively regulated genes have all been documented and con rmed to be closely associated with tumor progression. VSIG4 is a B7 family related protein expressed by resting macrophages [33] that inhibits the activation of macrophages to endotoxin and negatively regulates T cell immunity [34] . It has been reported in the literature that high expression of VSIG4 promotes primary lung tumorigenesis and is associated with poor prognosis in high-grade gliomas [35,36] .…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a novel prognosis prediction model for M2-type macrophage of Clear cell carcinoma of kidney

Sun

Zhang

2023

Preprint

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Background The aim of this study is to establish a prognostic risk assessment model for coexpressed M2 related genes and to elucidate the role of M2 macrophages within the ccRCC (Clear cell carcinoma of the kidney) immune microenvironment, which may have the potential to enhance the efficacy of ccRCC treatment.Method Transcriptome data, clinical data, and mutation data were obtained from TCGA-KIRC. CIBERSORT was used to calculate the proportion of M2 macrophage cells of each of the 539 samples. Genes associated with macrophage M2 in TCGA-KIRC with the external dataset E-MTAB-1980 from the Arrayexpress database were determined by intersection, and a coexpression network was established. Following lasso regression, a prognostic model was constructed, factors with significant findings were entered into a Cox regression analysis. Next, we used the external dataset E-MTAB-1980 from the ArrayExpress database for validation. Lastly, risk score was evaluated by stroma immune infiltration, GSEA, TMB and drug sensitivity.Results We obtained the top 46 genes most strongly correlated with macrophage M2 in TCGA-KIRC, which are enriched in immune receptor activity, leukocyte and mononuclear cell migration. A model of twelve genes related to the coexpressed macrophage M2 gene was established, we demonstrated that it has good prognostic capacity.Conclusion We proposed a twelve-gene Cox proportional hazard regression model associated with M2 ccRCC macrophage that could provide a measurement method to generate prognostic scores in patients with ccRCC. We discovered that the M2 macrophage infiltration was closely related to tumor metabolism and inversely correlated with risk score in ccRCC. The observations we report here have the potential to provide meaningful candidate biomarkers for the treatment and surveillance of ccRCC.

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“…The second lumB-specific gene is APEX1. Genetic alterations in genes that code for proteins that play a role in DNA repair pathways and in homologous recombination of DNA such as APEX1, BRCA1, BRCA2, XRCC2, XRCC3, ATM, CHEK2, PALB2, RAD51, and XPD have been implicated in BC [40]. APEX1 is a multifunctional protein that plays a central role in the base excision repair pathway.…”

Section: Discussionmentioning

confidence: 99%

Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles

et al. 2021

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Background and Objectives: Breast cancer is a heterogeneous disease categorized into four subtypes. Previous studies have shown that copy number alterations of several genes are implicated with the development and progression of many cancers. This study evaluates the effects of DNA copy number alterations on gene expression levels in different breast cancer subtypes. Materials and Methods: We performed a computational analysis integrating copy number alterations and gene expression profiles in 1024 breast cancer samples grouped into four molecular subtypes: luminal A, luminal B, HER2, and basal. Results: Our analyses identified several genes correlated in all subtypes such as KIAA1967 and MCPH1. In addition, several subtype-specific genes that showed a significant correlation between copy number and gene expression profiles were detected: SMARCB1, AZIN1, MTDH in luminal A, PPP2R5E, APEX1, GCN5 in luminal B, TNFAIP1, PCYT2, DIABLO in HER2, and FAM175B, SENP5, SCAF1 in basal subtype. Conclusions: This study showed that computational analyses integrating copy number and gene expression can contribute to unveil the molecular mechanisms of cancer and identify new subtype-specific biomarkers.

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Germline Variations of Apurinic/Apyrimidinic Endonuclease 1 (APEX1) Detected in Female Breast Cancer Patients

Cited by 8 publications

References 31 publications

MUFFINN: cancer gene discovery via network analysis of somatic mutation data

MUFFINN: cancer gene discovery via network analysis of somatic mutation data

Development and validation of a novel prognosis prediction model for M2-type macrophage of Clear cell carcinoma of kidney

Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles

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