scholarly journals Screening of Prognostic Factors in Early-Onset Breast Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303381989367 ◽  
Author(s):  
Zhun Yu ◽  
Qi He ◽  
Guoping Xu
Keyword(s):  
Breast Cancer ◽  
Transcription Factor ◽  
Differentially Expressed Genes ◽  
Early Onset ◽  
Differentially Expressed ◽  
Peroxisome Proliferator ◽  
Early Onset Breast Cancer ◽  
Microrna Target ◽  
Peroxisome Proliferator Activated Receptor ◽  
Protein Protein Interaction

Background: Gene expression profiles from early-onset breast cancer and normal tissues were analyzed to explore the genes and prognostic factors associated with breast cancer. Methods: GSE109169 and GSE89116 were obtained from the database of Gene Expression Omnibus. We firstly screened the differentially expressed genes between tumor samples and normal samples from patients with early-onset breast cancer. Based on database for annotation, visualization and intergrated discovery (DAVID) tool, functional analysis was calculated. Transcription factor-target regulation and microRNA-target gene network were constructed using the tool of transcriptional regulatory relatitionships unraveled by sentence-based text mining (TRRUST) and miRWalk2.0, respectively. The prognosis-related survival information was compiled based on The Cancer Genome Atlas breast cancer clinical data. Results: A total of 708 differentially expressed genes from GSE109169 data sets and 358 differentially expressed genes from GSE89116 data sets were obtained, of which 122 common differentially expressed genes including 102 uniformly downregulated genes and 20 uniformly upregulated genes were screened. Protein–protein interaction network with a total of 83 nodes and 157 relationship pairs was obtained, and genes in protein–protein interaction, such as peroxisome proliferator-activated receptor γ, FGF2, adiponectin, and PCK1, were recognized as key nodes in protein–protein interaction. In total, 66 transcription factor–target relationship pairs were obtained, and peroxisome proliferator-activated receptor γ was the only one downregulated transcription factor. MicroRNA-target gene network contained 368 microRNA-target relationship pairs. Moreover, 16 differentially expressed genes, including 2 upregulations and 14 downregulations, were related to a significant correlation with the prognosis, including SQLE and peroxisome proliferator-activated receptor γ. Conclusions: SQLE and peroxisome proliferator-activated receptor γ might be important prognostic factors in breast cancers, and adiponectin might be important in breast cancer pathogenesis regulated by peroxisome proliferator-activated receptor γ.

scholarly journals Key Genes and Prognostic Analysis in HER2+ Breast Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382098329
Author(s):  
Yujie Weng ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Zhongxian Li ◽  
Rong Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Differentially Expressed ◽  
Protein Kinase Activity ◽  
Protein Protein Interaction ◽  
Risk Of Death ◽  
Her2 Breast Cancer ◽  
Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

scholarly journals Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

2020 ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7 , KIF18A , STIL , and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

scholarly journals Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

scholarly journals Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

2020 ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract Background: Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field.Methods: We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype.Results: We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis.Conclusions: NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

scholarly journals Differential expression of the peroxisome proliferator activated receptor gamma in cancers of the breast.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Survival Data ◽  
Differentially Expressed Gene ◽  
Normal Breast ◽  
Differentially Expressed ◽  
Peroxisome Proliferator ◽  
Significant Differential Expression ◽  
Peroxisome Proliferator Activated Receptor ◽  
Primary Tumors

Breast cancer affects women at relatively high frequency (1). We mined published microarray datasets (2, 3) to determine in an unbiased fashion and at the systems level genes most differentially expressed in the primary tumors of patients with breast cancer. We report here significant differential expression of the gene encoding the peroxisome proliferator activated receptor gamma, PPARG, when comparing primary tumors of the breast to the tissue of origin, the normal breast. PPARG mRNA was present at significantly lower quantities in tumors of the breast as compared to normal breast tissue. Analysis of human survival data revealed that expression of PPARG in primary tumors of the breast was correlated with overall survival in patients with luminal A and luminal B subtype cancer, demonstrating a relationship between primary tumor expression of a differentially expressed gene and patient survival outcomes influenced by molecular subtype. PPARG may be of relevance to initiation, maintenance or progression of cancers of the female breast.

scholarly journals Selective Activation of CNS and Reference PPARGC1A Promoters Is Associated with Distinct Gene Programs Relevant for Neurodegenerative Diseases

2021 ◽  
Vol 22 (7) ◽  
pp. 3296
Author(s):  
Markus Kwik ◽  
Stefan Hainzl ◽  
Jan Oppelt ◽  
Boris Tichy ◽  
Ulrich Koller ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Differentially Expressed Genes ◽  
Reference Gene ◽  
Transcriptional Activation ◽  
Gene Promoter ◽  
Differentially Expressed ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptor ◽  
Exon 2 ◽  
Exon Usage

The transcriptional regulator peroxisome proliferator activated receptor gamma coactivator 1A (PGC-1α), encoded by PPARGC1A, has been linked to neurodegenerative diseases. Recently discovered CNS-specific PPARGC1A transcripts are initiated far upstream of the reference promoter, spliced to exon 2 of the reference gene, and are more abundant than reference gene transcripts in post-mortem human brain samples. The proteins translated from the CNS and reference transcripts differ only at their N-terminal regions. To dissect functional differences between CNS-specific isoforms and reference proteins, we used clustered regularly interspaced short palindromic repeats transcriptional activation (CRISPRa) for selective endogenous activation of the CNS or the reference promoters in SH-SY5Y cells. Expression and/or exon usage of the targets was ascertained by RNA sequencing. Compared to controls, more differentially expressed genes were observed after activation of the CNS than the reference gene promoter, while the magnitude of alternative exon usage was comparable between activation of the two promoters. Promoter-selective associations were observed with canonical signaling pathways, mitochondrial and nervous system functions and neurological diseases. The distinct N-terminal as well as the shared downstream regions of PGC-1α isoforms affect the exon usage of numerous genes. Furthermore, associations of risk genes of amyotrophic lateral sclerosis and Parkinson’s disease were noted with differentially expressed genes resulting from the activation of the CNS and reference gene promoter, respectively. Thus, CNS-specific isoforms markedly amplify the biological functions of PPARGC1A and CNS-specific isoforms and reference proteins have common, complementary and selective functions relevant for neurodegenerative diseases.

scholarly journals Positive Effects of Walnut Meal Extracts on Memory Impairment of Alzheimer's Disease Mice Induced by D-Galactose

2021 ◽  
Author(s):  
Honglin Lv ◽  
Dan Chen ◽  
Chengmei Xu ◽  
Yage Ma ◽  
Jingjuan Yang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transcription Factor ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Memory Impairment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Peroxisome Proliferator ◽  
Positive Effects

Abstract Walnut kernel was a traditional Chinese medicine, as well as a brain power boosting food. Walnut meal (WM) was prepared from walnut kernel by cold-press deoil, which was rich in polyphenols. In this study, we investigated the positive effects of walnut meal extracts on memory impairment of Alzheimer's disease (AD) mice induced by D-galactose. After 6 weeks of WMP treatment, the behavioral results showed that WMP could significantly improve the learning and memory of D-galactose Model (MOL) mice. The biochemical assay showed WMP could increase the amount of Acetylcholine (Ach) and reduce the oxidative stress and inflammation. Meanwhile, WMP improved the expression of neural stem cells, restored the number and the shape of neurons. The RNA-seq analysis revealed 284 differentially expressed genes in the hippocampus were regulated by WMP treatment, among which Gzma, Apol11b, H2-Q6 were up-regulated, while Rny1, Scgn, Col6a3 were down-regulated. Further analysis disclosed the differentially expressed genes were relevant to PI3K-Akt signaling pathway, FoxO signaling pathway, PPAR signaling pathway, neuroactive ligand-receptor interaction, cellular senescence, and particularly strongly interacted with the ribosomal family genes (Rpl35a, Rps27rt, Rpl3l, Rpl21, Rpl26). In addition, transcription factor Ep300 regulated genes of Cdkn1a, Spp1, and Tnfsf10 distinctly in the hippocampus, which were involved in inflammation and protein kinase. Transcription factor Pparg regulated genes of Angptl4, Fabp4, and Plin4, which were mainly expressed in PPAR (Peroxisome Proliferator-activated receptors) signaling pathway. This study might assist in identifying new targets to restore memory impairment in Alzheimer's disease.

scholarly journals High likelihood of actionable pathogenic variant detection in breast cancer genes in women with very early onset breast cancer

2021 ◽  
pp. jmedgenet-2020-107347
Author(s):  
D Gareth Evans ◽  
Elke Maria van Veen ◽  
Helen J Byers ◽  
Sarah J Evans ◽  
George J Burghel ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Onset ◽  
Ductal Carcinoma ◽  
Cancer Genes ◽  
Early Onset Breast Cancer ◽  
Younger Women ◽  
Pathogenic Variants ◽  
Younger Age ◽  
Predisposition Genes

BackgroundWhile the likelihood of identifying constitutional breast cancer-associated BRCA1, BRCA2 and TP53 pathogenic variants (PVs) increases with earlier diagnosis age, little is known about the correlation with age at diagnosis in other predisposition genes. Here, we assessed the contribution of known breast cancer-associated genes to very early onset disease.MethodsSequencing of BRCA1, BRCA2, TP53 and CHEK2 c.1100delC was undertaken in women with breast cancer diagnosed ≤30 years. Those testing negative were screened for PVs in a minimum of eight additional breast cancer-associated genes. Rates of PVs were compared with cases ≤30 years from the Prospective study of Outcomes in Sporadic vs Hereditary breast cancer (POSH) study.ResultsTesting 379 women with breast cancer aged ≤30 years identified 75 PVs (19.7%) in BRCA1, 35 (9.2%) in BRCA2, 22 (5.8%) in TP53 and 2 (0.5%) CHEK2 c.1100delC. Extended screening of 184 PV negative women only identified eight additional actionable PVs. BRCA1/2 PVs were more common in women aged 26–30 years than in younger women (p=0.0083) although the younger age group had rates more similar to those in the POSH cohort. Out of 26 women with ductal carcinoma in situ (DCIS) alone, most were high-grade and 11/26 (42.3%) had a PV (TP53=6, BRCA2=2, BRCA1=2, PALB2=1). This PV yield is similar to the 61 (48.8%) BRCA1/2 PVs identified in 125 women with triple-negative breast cancer. The POSH cohort specifically excluded pure DCIS which may explain lower TP53 PV rates in this group (1.7%).ConclusionThe rates of BRCA1, BRCA2 and TP53 PVs are high in very early onset breast cancer, with limited benefit from testing of additional breast cancer-associated genes.

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