scholarly journals Identification of Key Genes Involved in the Progression of Renal Fibrosis and Associated with Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Qiming Xu ◽  
Jianrao Lu
Keyword(s):  
Plasma Membrane ◽  
Renal Fibrosis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Data Set ◽  
Extracellular Region ◽  
Core Genes

Abstract Purpose: Renal fibrosis (RF) is the necessary way for Chronic kidney disease (CKD) to develop to End Stage Renal Disease (ESRD). Patients with chronic kidney disease suffer from high morbidity and premature death due to various complications and even cancer. Therefore, this study aims to identify key genes in the pathogenesis of RF and Kidney Renal Clear Cell Carcinoma (KIRC).Method: We analyzed the gene expression characteristics of two databases (GSE6344 and GSE22459) and used geo2R tools to obtain the differentially expressed genes (DEG). Then, use Database for Annotation, Visualization and Integrated Discovery (DAVID) for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) path analysis. Subsequently, we used the STRING database and built the protein-protein-interactions (PPI) network, the cytoHubba plug-ins of Cytoscape were used to select the hub. Then, we used The Cancer Genome Atlas(TCGA) database to verify hub genes and further screen out core genes. Then, TargetScanHuman, miRTarbase and miRWalk databases were used to reverse-predict targeted miRNA regulated by core genes and screen out core miRNA. mRNA and miRNA mutual aid network were established. At the same time, Gene Expression Profiling Interactive Analysis（GEPIA）database was used for survival analysis of screened core genes to find genes related to prognosis. Tumor Immune Estimation Resource（TIMER）database was used to evaluate the correlation between the expression of core genes and immune cell penetration. Then use the Gene Set Enrichment Analysis (GSEA) tool to analyze the LYZ gene, and finally use the Human Protein Atlas (HPA) online database to verify the expression level of the identified central gene.Result: We filtered 2755 DEGs from the GSE6344 database, including 1292 upregulated DEGs and 1463 downregulated DEGs; 2552 DEGs were filtered from the GSE22459 database, including 2022 downregulated DEGs and 530 upregulated DEGs. We did functional enrichment analysis of down-regulated and up-regulated differential genes, Functional enrichment analysis of up-regulated genes shows that DEGs involves many functions and expression pathways. such as immune response, plasma membrane, membrane, integral component of plasma membrane, signal transduction, extracellular region and extracellular space. It is demonstrated in the PPI network constructed by 67 nodes (proteins) and 546 PPI edges (interactions); Functional enrichment analysis of down-regulated genes also shows that DEGs involves many functions and expression pathways. such as integral component of plasma membrane, plasma membrane, extracellular space and extracellular region. It is demonstrated in the PPI network constructed by 141 nodes (proteins) and 624 PPI edges (interactions). Then a gene LYZ was selected step by step in three rounds of validation through TCGA data set, GTEx data set, Timer database and HPA database. LYZ expression was significantly correlated with the immune infiltration levels of CD4+ T cells, CD8+ T cells, Macrophage, Myeloid dendritic, Neutrophil and B cell. The upstream hub miRNA that regulate this gene were identified: has-miR-4649-3p and has-miR-873-3p. Based on these findings, it is proposed that LYZ may be a potential novel diagnostic and prognostic biomarker of KIRC at the mRNA and protein levels, and has-miR-4649-3p and has-miR-873-3p at the molecular level, and can help us better manage the progression of renal fibrosis.Conclusion: Our findings suggest that immune response, inflammation and other pathways play an extremely important role in RF and KIRC. LYZ, has-miR-4649-3p and has-miR-873-3p may become potential prognostic biomarkers of KIRC and contribute to the prevention and treatment of renal fibrosis, which also shows us a new therapeutic idea that provides the possibility to treat renal fibrosis from the perspective of immunity.

scholarly journals STAT1, IGF1, RAC1, and MDM2 Are Associated with Recurrence of Giant Cell Tumor of Bone

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Shuxin Chen ◽  
Zepeng Du ◽  
Bingli Wu ◽  
Huiyang Shen ◽  
Chunpeng Liu ◽  
...  
Keyword(s):  
Giant Cell Tumor ◽  
Giant Cell ◽  
Clinical Characteristics ◽  
Multivariate Logistic Regression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Small Gtpase ◽  
Cell Tumor ◽  
Functional Enrichment ◽  
Ppi Network

Background. In our previous study, mouse double minute 2 homolog (MDM2), insulin-like growth factor 1 (IGF1), signal transducer and activator of transcription 1 (STAT1), and Rac family small GTPase 1 (RAC1) were correlated with the recurrence of giant cell tumor of bone (GCT). The aim of this study is to use a large cohort study to confirm the involvement of these four genes in GCT recurrence. Methods. The expression of these four genes was detected and compared between GCT patients with or without recurrence. The correlation between the expression of these four genes and clinical characteristics was evaluated. Protein-protein interaction (PPI) network was constructed for functional enrichment analysis. Results. It showed that the expression levels of MDM2, IGF1, STAT1, and RAC1 in GCT patients with recurrence were significantly higher than those in GCT patients without recurrence (P<0.05). Multivariate logistic regression analysis suggested that several clinical characteristics may influence prognosis. A PPI network was constructed using the four genes as hub genes. Functional enrichment analysis showed that this network involves many important biological progress mediated by these four genes, including immune response. Conclusion. MDM2, IGF1, STAT1, and RAC1 are associated with GCT recurrence, which might serve as biomarkers for GCT recurrence.

scholarly journals Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

2021 ◽  
Author(s):  
Nana Yang ◽  
Qianghua Wang ◽  
Biao Ding ◽  
Yinging Gong ◽  
Yue Wu ◽  
...  
Keyword(s):  
Iron Homeostasis ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

scholarly journals Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yong Liu ◽  
Hui Liu ◽  
Queqiao Bian
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Characteristic Curve ◽  
External Validation ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Microarray Datasets

Purpose. This work is aimed at identifying several molecular markers correlated with the diagnosis and development of basal cell carcinoma (BCC). Methods. The available microarray datasets for BCC were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between BCC and healthy controls. Afterward, the functional enrichment analysis and protein-protein interaction (PPI) network analysis of these screened DEGs were performed. An external validation for the DEG expression level was also carried out, and receiver operating characteristic curve analysis was used to evaluate the diagnostic values of DEGs. Result. In total, five microarray datasets for BCC were downloaded and 804 DEGs (414 upregulated and 390 downregulated genes) were identified. Functional enrichment analysis showed that these genes including CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1 were closely correlated with the cell process and PTCH1 played central roles in the BCC signaling pathway. Moreover, EGFR was a hub gene in the PPI network. The expression changes of six genes (CYFIP2, HOXB5, FOXN3, PTPN3, MARCKSL1, and FAS) were validated by an external GSE74858 dataset analysis. Finally, ROC analysis revealed that CYFIP2, HOXB5, PTPN3, MARCKSL1, PTCH1, and CDC20 could distinguish BCC and healthy individuals. Conclusion. Nine gene signatures (CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1) may serve as promising targets for BCC detection and development.

scholarly journals Identification of Potential Biomarkers and Related Transcription Factors in Peripheral Blood of Tuberculosis Patients

2020 ◽  
Vol 17 (19) ◽  
pp. 6993
Author(s):  
Longxiang Xie ◽  
Xiaoyu Chao ◽  
Tieshan Teng ◽  
Qiming Li ◽  
Jianping Xie
Keyword(s):  
Transcription Factors ◽  
T Cell Activation ◽  
Peripheral Blood ◽  
Cell Activation ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Peripheral Blood Mononuclear ◽  
Potential Biomarkers

Tuberculosis (TB), one major threat to humans, can infect one third of the worldwide population, and cause more than one million deaths each year. This study aimed to identify the effective diagnosis and therapy biomarkers of TB. Hence, we analyzed two microarray datasets (GSE54992 and GSE62525) derived from the Gene Expression Omnibus (GEO) database to find the differentially expressed genes (DEGs) of peripheral blood mononuclear cell (PBMC) between TB patients and healthy specimens. Functional and pathway enrichment of the DEGs were analyzed by Metascape database. Protein-protein interaction (PPI) network among the DEGs were constructed by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs was also constructed. A total of 190 DEGs including 36 up-regulated genes and 154 down-regulated genes were obtained in TB samples. Gene functional enrichment analysis showed that these DEGs were enriched in T cell activation, chemotaxis, leukocyte activation involved in immune response, cytokine secretion, head development, etc. The top six hub genes (namely, LRRK2, FYN, GART, CCR7, CXCR5, and FASLG) and two significant modules were got from PPI network of DEGs. Vital transcriptional factors, such as FoxC1 and GATA2, were discovered with close interaction with these six hub DEGs. By systemic bioinformatic analysis, many DEGs associated with TB were screened, and these identified hub DEGs may be potential biomarkers for diagnosis and treatment of TB in the future.

scholarly journals Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

2020 ◽  
Vol 9 (2) ◽  
pp. LMT30
Author(s):  
Chuanli Ren ◽  
Weixiu Sun ◽  
Xu Lian ◽  
Chongxu Han
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Kaplan Meier ◽  
Key Genes ◽  
Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

scholarly journals Xiaoyu Xiezhuo Drink Protects against Ischemia-Reperfusion Acute Kidney Injury in Aged Mice through Inhibiting the TGF-β1/Smad3 and HIF1 Signaling Pathways

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Qingqing Ye ◽  
Hongbo Chen ◽  
Hongzhen Ma ◽  
Xiaojun Xiang ◽  
Shouci Hu ◽  
...  
Keyword(s):  
Drug Targets ◽  
Ischemia Reperfusion ◽  
Kidney Injury ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Aged Mice ◽  
Tgf Β1

Acute kidney injury (AKI) is responsible for significant mortality among hospitalized patients that is especially troubling aged people. An effective self-made Chinese medicine formula, Xiaoyu Xiezhuo Drink (XXD), displayed therapeutic effects on AKI. However, the compositions and underlying mechanisms of XXD remain to be elucidated. In this study, we used the ultra-high-performance liquid chromatography method coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) to investigate the chemical components in XXD. Then, the absorbable components of XXD were identified based on the five principles and inputted into the SwissTargetPrediction and STITCH databases to identify the drug targets. AKI-related targets were collected from the GenCLiP 3, GeneCards, and DisGeNET databases. The crossover genes of XXD and AKI were identified for functional enrichment analysis. The protein-protein interaction (PPI) network of crossover genes was constructed, followed by the identification of hub genes. Subsequently, the effects and potential mechanisms of XXD on AKI predicted by the network pharmacology and bioinformatics analyses were experimentally validated in ischemia-reperfusion (I/R) injury-induced AKI aged mouse models. A total of 122 components in XXD were obtained; among them, 58 components were found that could be absorbed in the blood. There were 800 potential drug targets predicted from the 58 absorbable components in AKI which shared 36 crossover genes with AKI-related targets. The results of functional enrichment analysis indicated that crossover genes mostly associated with the response to oxidative stress and the HIF1 signaling pathway. In the PPI network analysis, 12 hub genes were identified, including ALB, IL-6, TNF, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, and HMOX1. In AKI aged mice, XXD prominently alleviated I/R injury-induced renal dysfunction, abnormal renal pathological changes, and cellular senescence, inflammation, and oxidative damage with a reduction in the expression level of the inflammatory mediator, α-SMA, collagen-1, F4/80, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, ICAM-1, TGF-β1, Smad3, and p-Smad3 and an increase of nephridial tissue p-H3, Ki67, HMOX1, MMP-9, and Smad7 levels. In summary, our findings suggest that XXD has renoprotective effects against AKI in aged mice via inhibiting the TGF-β1/Smad3 and HIF1 signaling pathways.

scholarly journals Using Weighted Gene Co-Expression Network Analysis to Identify Key Genes Related to the Onset of Preeclampsia

2020 ◽  
Author(s):  
Xinyang Shen ◽  
Zhijian Wang ◽  
Zhirui Zeng ◽  
Zhenqin Huang ◽  
Xiaowei Huang ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Immune Response Gene ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Ontology Term ◽  
Gene Data ◽  
Core Genes

Abstract Background: Preeclampsia is a form of hypertension in pregnancy, which induced by complicated factors. However, the pathogenesis of the disease is unclear. The present study was aimed to discover the critical biomarkers associated with the occurrence and development of preeclampsia. Methods：Gene data profile GSE75010 was downloaded from the Gene Expression Omnibus (GEO) database and used as discovery cohort to establish a WGCNA network determining significant modules which associated with clinical traits. Subsequently, functional enrichment analysis, pathway analysis and protein-protein interaction (PPI) network construction were performed on the core genes in significant modules to identify hub genes. Then, gene data profile GSE25906 was used as verified cohort to determine their diagnostic value of hub genes. The protein expression levels of these hub genes in preeclampsia and control placental tissues were verified using immunohistochemistry method. Finally, GSEA was performed to analyze their enrichment pathways. Results: Total 33 co-expression modules were identified after the establishment of WGCNA, of which 4 gene modules were identified as significant modules because they were related to multiple (>3) clinical traits. Total 75 core genes in significant modules were analyzed, and results showed that they were mainly enriched in adaptive immune response (Gene Ontology term) and platelet activation (Kyoto Encyclopedia of Genes and Genomes term). Finally, a total of 5 genes including TYROBP, PLEK, LCP2, HCK, ITGAM were identified as hub genes which scored high in PPI network and had high diagnostic value. Furthermore, the protein level of these 5 genes in placental tissues of preeclampsia was lower than that of the control group. Moreover, these 5 genes were all enriched in 17 pathways, including autoimmunity pathway. Conclusions：These 5 genes (TYROBP, PLEK, LCP2, HCK, ITGAM) may be closely related to the pathogenesis of preeclampsia, which may also help the diagnosis and therapy of preeclampsia.

scholarly journals Identification of an m6A-Related lncRNA Signature for Predicting the Prognosis in Patients With Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
JunJie Yu ◽  
WeiPu Mao ◽  
Si Sun ◽  
Qiang Hu ◽  
Can Wang ◽  
...  
Keyword(s):  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Prognostic Signature ◽  
Training Set ◽  
Testing Set

PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan–Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

scholarly journals Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiarong Yi ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
Xiazi Zouxu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

Identification Of Putative Gene Signatures Associated With Diagnosis And Prognosis Of Breast Cancer

2021 ◽  
Vol 44 (3) ◽  
pp. E45-54
Author(s):  
Chao Tan ◽  
Fang Zuo ◽  
Mingqian Lu ◽  
Sai Chen ◽  
Zhenzhen Tian ◽  
...  
Keyword(s):  
Risk Model ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Support Vector ◽  
Ppi Network ◽  
Hub Genes ◽  
Putative Gene ◽  
Network Analyses ◽  
Cluster 2

Purpose: This study aimed to identify potential diagnostic and therapeutic biomakers for the development ofbreast cancer (BC). Methods: GSE86374 dataset containing 159 samples was acquired from the Gene Expression Omnibus (GEO) database followed by differentially expressed genes (DEGs) identification and cluster analysis. Corresponding functional enrichment and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Prognostic evaluation using clinical information obtained from TCGA database and hub genes was conducted to screen for crucial indicators for BC progression. The risk model was established and validated. Results: In total, 186 DEGs were identified and grouped into four clusters: 96 in cluster 1; 69 in cluster 2; 16 in  cluster 3; and 5 in cluster 4. Functional enrichment analysis showed that DEGs, including ADH1B in cluster 1,  were dramatically enriched in the tyrosine and drug metabolism pathways, while genes in cluster 2, including  SPP1 and RRM2, played crucial roles in PI3K-Akt and p53 signalling pathway. SPP1 and RRM2 served as hub  genes in the PPI network, resulting in an support vector machine classifier with good accuracy and specificity.Ad ditionally, the results of prognostic analysis suggest that age, metastasis stage, SPP1 and ADH1B were correlated with risk of BC, which was validated by using the established risk model analysis. Conclusion: SPP1, RRM2 and ADH1B appear to play vital roles in the development of BC. Age and TNM stage  were also preferentially associated with risk of developing BC. Evaluation of the risk model based on larger sample size and further experimental validation are required.

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