Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Dang HH
PROVIDER: S-EPMC8471503 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundGastric cancer is one of the most common malignant cancers worldwide. Despite substantial developments in therapeutic strategies, the five-year survival rate remains low. Therefore, novel biomarkers and therapeutic targets involved in the progression of gastric tumors need to be identified.MethodsWe obtained the mRNA microarray datasets GSE65801, GSE54129 and GSE79973 from the Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). We used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to analyze DEG pathways and functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape to obtain the protein-protein interaction (PPI) network. Next, we validated the hub gene expression levels using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA), and conducted stage expression and survival analysis.ResultsFrom the three microarray datasets, we identified nine major hub genes: COL1A1, COL1A2, COL3A1, COL5A2, COL4A1, FN1, COL5A1, COL4A2, and COL6A3.ConclusionOur study identified COL1A1 and COL1A2 as potential gastric cancer prognostic biomarkers.
| S-EPMC7255341 | biostudies-literature
Project description:PurposeThe aim of this study was to identify critical genes in lung cancer progression.MethodsWe downloaded and reanalyzed gene expression profiles from different public data-sets using comprehensive bioinformatics analysis. Differentially expressed genes (DEGs) were identified in lung adenocarcinoma tissues compared with adjacent nonmalignant lung tissues. The overlapping DEGs identified from different datasets were used for functional and pathway enrichment analyses and protein-protein interaction (PPI) analysis. Moreover, transcription factors (TFs) and miRNAs that regulated the overlapping DEGs were predicted, followed by a TF-miRNA-target network construction. Furthermore, survival analysis of genes was performed. Several genes were further validated by quantitative real-time PCR (qRT-PCR).ResultsA total of 647 overlapping upregulated genes and 979 overlapping downregulated genes were identified. The overlapping upregulated genes and downregulated genes were involved in different functions, such as cell cycle, p53 signaling pathway, immune response, and cell adhesion molecules (CAMs). Several genes belonging to the cyclin family, including CCNB1, CCNB2, and CCNA2, were hubs of the PPI network and TF-miRNA-target network. Additionally, genes, including NPAS2, GNG7, CHIA, and SLC2A1, were predicted to be prognosis-related DEGs. Gene expression profiles determined by bioinformatics analysis and qRT-PCR were highly comparable.ConclusionCCNB1, CCNB2, CCNA2, NPAS2, GNG7, CHIA, and SLC2A1 are promising targets for the clinical diagnosis and therapy of lung adenocarcinoma.
| S-EPMC6204853 | biostudies-literature
Project description:Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.
| S-EPMC7796196 | biostudies-literature
Project description:Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. We used integrated bioinformatics analysis to identify potential biomarkers in breast carcinoma in the present study. Microarray data, 127breast tumor samples and 23 non-tumor samples, received from the Gene Expression Omnibus (GEO) dataset; 121 differentially expressed genes (DEGs) were selected. Functional analysis using DAVID revealed that these DEGs were highly gathered in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. We used Cytoscape software and STRING dataset to structure a protein-protein interaction (PPI) network. The expression of CD24, MMP1, SDC1, and SPP1 was much higher in breast carcinoma tissue than in Para cancerous tissues analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and ONCOMINE. Overexpression ofCD24, MMP1, SDC1, and SPP1 indicated the poor prognosis in breast carcinoma patients analyzed by Kaplan-Meier (KM) Plotter. Immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor sections than in Para cancerous tissues. Hub genes expression at the protein level was correlated tothe breast cancer subtype and grade. Furthermore, immunity analysis showed that CD24, MMP1, SDC1, and SPP1 were potentially associated with five immune cell types infiltration (CD8+ T cells, CD4+ T cells, neutrophils, macrophages,and dendritic cells) by TIMER. Thus, this study indicates potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, further studies are required for verifying these results in vivo and vitro.
| S-EPMC8819662 | biostudies-literature
Project description:Low-grade gliomas (LGGs) is a primary invasive brain tumor that grows slowly but is incurable and eventually develops into high malignant glioma. Novel biomarkers for the tumorigenesis and lifetime of LGG are critically demanded to be investigated. In this study, the expression levels of procollagen-lysine, 2-oxoglutarate 5-dioxygenases (PLODs) were analyzed by ONCOMINE, HPA and GEPIA. The GEPIA online platform was applied to evaluate the interrelation between PLODs and survival index in LGG. Furthermore, functions of PLODs and co-expression genes were inspected by the DAVID. Moreover, we used TIMER, cBioportal, GeneMINIA and NetworkAnalyst analysis to reveal the mechanism of PLODs in LGG. We found that expression levels of each PLOD family members were up-regulated in patients with LGG. Higher expression of PLODs was closely related to shorter disease-free survival (DFS) and overall survival (OS). The findings showed that LGG cases with or without alterations were significantly correlated with the OS and DFS. The mechanism of PLODs in LGG may be involved in response to hypoxia, oxidoreductase activity, Lysine degradation and immune cell infiltration. In general, this research has investigated the values of PLODs in LGG, which could serve as biomarkers for diagnosis, prognosis and potential therapeutic targets of LGG patients.
| S-EPMC7840023 | biostudies-literature
Project description:Background: Breast cancer is a heterogeneous neoplasm. Distinct subtypes of breast cancer have been defined, suggesting the existence of molecular differences contributing to their clinical outcomes. However, the molecular differences between HER2 positive and negative breast cancer tumors remain unclear. Objective: The aim of this study was to identify a gene expression profile for breast tumors based on HER2 status. Material and methods: The HER2 status was determined by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) in 54 breast tumor samples. Using Affymetrix microarray data from these breast tumors, we established the expression profiling of breast cancer based on HER2 IHC and FISH results. To validate microarray experiment data, real-time quantitative reverse transcription-PCR was performed. Results: We found significant differences between the HER2-positive and HER2-negative breast tumor samples, which included overexpression of HER2, as well as other genes located on 17q12, and genes functionally related to migration. Conclusion: Our study shows the potential of integrated genomics profiling to shed light on the molecular knowledge of HER2-positive breast tumors. The tumor samples under study correspond to 54 primary breast carcinomas. They included 15 cases with a HER2 IHC3+ score with HER2 gene amplification, 13 cases with IHC2+ score with amplification and 13 without HER2 gene amplification, and 13 cases IHC0/1+ score without HER2 gene amplification. 12 samples of breast normal tissues from breast cancer patients were also included as a reference. Neither overexpression nor amplification of HER2 was observed.
| S-ECPF-GEOD-29431 | biostudies-other
Project description:Background. Bladder cancer (BLCA) is a highly malignant tumor that develops in the urinary system. Identification of biomarkers in progression and prognosis is crucial for the treatment of BLCA. BLCA-related differentially expressed genes (DEGs) were authenticated by screening the DEGs and weighted gene coexpression network analysis (WGCNA). LASSO and SVM-RFE algorithms were utilized to screen the feature genes in BLCA. Survival analysis was performed using the Kaplan-Meier curve provided by the 'survival' R package. The BLCA samples were clustered by hclust based on the immune score matrix calculated by the single-sample GSEA (ssGSEA) algorithm. The immune, stromal, and ESTIMATE scores of each BLCA patient were calculated by applying the ESTIMATE algorithm. ssGSEA was conducted to explore the function of characteristic genes in BLCA. The expression of characteristic genes in clinical cancer tissue, and the pericancerous tissue of BLCA patients was verified using qRT-PCR assays. A total of 189 BLCA-related DEGs were identified. Fourteen feature genes were defined by LASSO and SVM-RFE algorithms. Five characteristic genes, including SMYD2, GAPDHP1, ATP1A2, CILP, and THSD4, were related to the OS of BLCA. The correlation analysis of five characteristic genes and clinicopathological factors showed that five genes played a role in the progression of BLCA. Additionally, the expression of five characteristic genes in clinical cancer tissues and pericarcinomatous tissues from BLCA patients was verified by qRT-PCR, which was consistent with the result from the public database. Finally, we discovered five prognostic genes linked to BLCA progression, which might serve as a theoretical basis for prognosis and treatment targets for BLCA patients.
| S-EPMC9042640 | biostudies-literature
Project description:Background: Gastric cancer (GC) is one of the most common and poor prognosis malignancy in the world. The Family with sequence similarity 83 (FAM83) comprises of eight members of A-H. Accumulating evidence confirmed important roles for FAM83 family in tumorigenesis and progression. However, the prognostic values of FAM83 family in GC still have not been clarified. Methods: ONCOMINE, UALCAN, GEPIA, THE HUMAN PROTEIN ATLAS, Kaplan-Meier Plotter, cBioPortal, DAVID, STRING and TIMER databases and R software were adopted in this study. Results: In this study, we demonstrated that the mRNA levels of FAM83 B/C/D/H were significantly up-regulated in stomach adenocarcinoma (STAD), but the protein level of FAM83G/H were remarkable lowly in STAD. Next, FAM83C/D/G/H were significantly associated with tumor stages in STAD patients. Then, the mutation rate of FAM83 family members in STAD patients was 46%, and the highest mutation rate was FAM83H (23%). Furthermore, the functions of FAM83 family and their 259 co-expression genes were primarily related to Shigellosis, RNA degradation and Ribosome biogenesis in eukaryotes pathway. Besides, we have established the prognostic model of FAM83 family in STAD, including the prognostic model of STAD patients (FAM83C/D/G), STAD with lymph node metastasis (FAM83C/D/G/H) and STAD with ERBB2 high expression (FAM83G/H). FAM83C/D high expression with a poor prognosis, while FAM83G/H high expression with a favorable prognosis of STAD. Additionally, we found that the expression of FAM83C/D/G/H were significantly correlated with the infiltration of six types of immune cells [B cells, CD8+T cells, CD4+T cells, macrophages and Myeloid dendritic cells (DC)], whereas CD4+T cells and Macrophage cells have higher risk scores (HR > 1) when FAM83C lowly expression and FAM83D highly expression. The risk score of NK cells was significantly reduced when FAM83G lowly expression and FAM83H highly expression (HR < 1). Conclusion: These findings suggested that FAM83C/D/G/H might play key roles in STAD tumorigenesis and progression, and FAM83C/D might be risk factors but FAM83G/H might be favorable prognostic factors for STAD patients. In addition, CD4+T cells and Macrophage cells may be the promoters of FAM83D in progression of STAD, while NK cells may promote the protective effect of FAM83H on STAD patients.
| S-EPMC8640971 | biostudies-literature
Project description:Background: Breast cancer is a heterogeneous neoplasm. Distinct subtypes of breast cancer have been defined, suggesting the existence of molecular differences contributing to their clinical outcomes. However, the molecular differences between HER2 positive and negative breast cancer tumors remain unclear. Objective: The aim of this study was to identify a gene expression profile for breast tumors based on HER2 status. Material and methods: The HER2 status was determined by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) in 54 breast tumor samples. Using Affymetrix microarray data from these breast tumors, we established the expression profiling of breast cancer based on HER2 IHC and FISH results. To validate microarray experiment data, real-time quantitative reverse transcription-PCR was performed. Results: We found significant differences between the HER2-positive and HER2-negative breast tumor samples, which included overexpression of HER2, as well as other genes located on 17q12, and genes functionally related to migration. Conclusion: Our study shows the potential of integrated genomics profiling to shed light on the molecular knowledge of HER2-positive breast tumors. The tumor samples under study correspond to 54 primary breast carcinomas. They included 15 cases with a HER2 IHC3+ score with HER2 gene amplification, 13 cases with IHC2+ score with amplification and 13 without HER2 gene amplification, and 13 cases IHC0/1+ score without HER2 gene amplification. 12 samples of breast normal tissues from breast cancer patients were also included as a reference. Neither overexpression nor amplification of HER2 was observed.
2011-12-22 | E-GEOD-29431 | biostudies-arrayexpress
Project description:BackgroundThe FAM83 family plays a key role in tumorigenesis and cancer progression. However, the role of the FAM83 family in the development of breast tumors is unclear to date. This report explores the expression, prognostic significance, and function of the FAM83 family members in breast cancer using public databases.MethodsUALCAN database was used to explore the expression of FAM83 family members in breast cancer. Furthermore, we validated the expression of FAM83 family members in twenty pairs of breast cancer and normal tissues by RT-PCR. Kaplan-Meier plotter database was used to explore the prognostic significance of FAM83 family members in breast cancer. GeneMANIA and DAVID databases were used for functional and pathway enrichment analysis of genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G. MEXPRESS and UALCAN databases were used to analyze the level of DNA promoter methylation of FAM83A, FAM83D, FAM83F, and FAM83G in breast cancer. TIMER database was utilized to explore the relationships between immune cell infiltration and FAM83A, FAM83D, FAM83F, and FAM83G expression.ResultsAmong FAM83 family members, FAM83A, FAM83D, FAM83F, and FAM83G were higher expressed in breast cancer than in normal tissues. We also validated the significant high expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA in breast cancer than in normal samples. Their increased expression has an adverse prognostic effect on breast cancer patients. These genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G might take part in cell proliferation, G2/M transition of the mitotic cell cycle, regulation of apoptosis process and other cancer-related biological processes. In addition, they were mainly enriched in the Hippo signaling pathway, Hedgehog signaling pathway, PI3K/AKT signaling pathway, and other cancer-related pathways. We also found that promoter DNA methylation might regulate the expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA in most CpG islands. At last, we found the expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA was significantly related to immune cell infiltration.ConclusionsFAM83A, FAM83D, FAM83F, and FAM83G were highly expressed in breast cancer tissues and had an adverse effect on the survival outcomes of breast cancer patients. Also, they were involved in breast cancer-related signal pathways. Therefore, they might serve as potential therapeutic targets for breast cancer clinical treatment.
| S-EPMC9158143 | biostudies-literature