Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer

- Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer.
- Cancer is a disease involving multiple components of both tumor cells and stromal cells (Mao et al., 2013).
- Molecular events in which active stromal cells affect cancer cells can be determined so that biomarkers and therapeutic targets can be identified (Valkenburg et al., 2018).
- Breast cancer is usually seen in the breast epithelium, but there is some important evidence that breast stromal cells also play an important role in tumor formation (Mao et al., 2013).
- Ovarian cancer, which proceeds from cell transformation through normal tissue invasion, is also connected to communication with the stromal microenvironment (Schauer et al., 2011).
- example, BRCA1 and BRCA2 are common susceptibility genes for breast and ovarian cancer (King et al., 2003)..
- Advances in microarray and high-throughput sequencing technologies have provided effective applications to help develop more reliable biomarkers for diagnosis, survival and prognosis (Gov et al., 2017a)..
- In several studies, the identification of molecular signatures to understand disease mechanism and explore the drug targets was studied such as key genes of three different ovarian diseases by using integrative systems biology analysis perspective (Kori et al., 2016), tissue-specific molecular biomolecules in ovarian cancer (Gov et al., 2017b), T2 diabetes (Calimlioglu et al., 2015), head and neck cancer (Islam et al., 2018) and Alzheimer disease (Rahman et al., 2020), as well as ovarian cancer stem cells (Gov, 2020)..
- Significant proinflammatory factors expressed by CAFs have been reported in some types of cancer such as breast and ovarian cancer (Erez et al., 2013).
- It was reported that infiltrated immune and inflammatory cells affect the molecular biology and clinical status of breast cancer (Karn et al., 2015).
- Planche et al.
- In the present study, we performed an analysis of transcriptome datasets of ovarian cancer stroma and breast cancer stroma through an integrative systems biology perspective to identify common critical molecular signatures at multiomics levels.
- The raw data of three transcriptome datasets related to breast cancer stroma [GSE26910 (Planche et al., 2011), GSE8977 (Karnoub et al., 2007) and GSE10797 (Casey et al., 2009)] and two datasets related to ovarian cancer stroma [GSE40595 (Yeung et al., 2013) and GSE38666 (Lili et al., 2013)] are obtained from Gene Expression Omnibus (GEO) (Barrett et al., 2013).
- Identification of differentially expressed genes For identification of differentially expressed genes (DEG), CEL microarray raw data files were downloaded and affy package (Gautier et al., 2004) of the R (version 3.6) was employed.
- The executed dataset was normalized through robust multiarray (RMA) techniques (Bolstad et al., 2003) and linear models for microarray data (LIMMA) method (Smyth et al., 2003) were examined in the advanced statistical analysis of each dataset.
- Gene enrichment analysis was carried out via the ConsensusPathDB functional annotation tool (Kamburov et al., 2013) to determine the down and upregulated biological pathways statistically significant associated with DEGs.
- Reactome (Croft et al., 2011) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al., 2012) were preferable used databases and enrichment results with a p-value of <0.01 were accepted.
- Protein-protein interaction network reconstruction The previously reconstructed comprehensive protein- protein interaction (PPI) network of Homo sapiens (Karagoz et al., 2016) which consists of 288,033 physical interactions between 21,052 proteins, was utilized.
- The networks were analyzed and visualized through Cytoscape (v3.6) (Smoot et al., 2011).
- hub protein) of PPI networks the dual-metric approach considering degree and betweenness centrality metrics simultaneously was used (Gov et al., 2017b)..
- Reporter molecules were determined via employing the hypergeometric probability density function by using the physical interaction of TFs, miRNAs and receptors with core DEGs obtained from breast and ovarian cancer stroma datasets..
- via Cytoscape (v3.6) which is an open-source software platform (Smoot et al., 2011)..
- Cox proportional hazards regression analysis was executed through the SurvExpress validation tool (Aguirre-Gamboa et al., 2013).
- Cox-survival analysis of hub miRNAs was performed by using the Kaplan–Meier plotter tool (Nagy et al., 2018)..
- Mutual tumor microenvironment signatures for breast and ovarian cancer.
- The microarray datasets obtained from the stroma of breast and ovarian cancer were analyzed.
- These core DEGs were considered as mutual tumor stroma signatures for breast and ovarian cancer.
- The hub proteins including EGFR, STAT1, VDR, NCOA1, CTBP2, MET, EIF3B, LEF1, KIF1B and CIRBP were identified by using degree (local-based) and betweenness centrality (global- based) metrics (Kori et al., 2016)..
- To identify common breast and ovarian cancer stroma response map was constructed using core DEGs and reporter biomolecules.
- The results have been mapped by using core DEGs reporter biomolecules interaction and it was reconstructed tumor microenvironment generic network of breast and ovarian cancer including 105 nodes and 251 edges (Figure 3)..
- a) The distribution of DEGs in stromal cells of breast and ovarian cancer.
- b) Venn diagram representation for the comparison of DEGs among stromal cells of breast and ovarian cancer.
- Statistically significant biological pathways in each datasets containing breast and ovarian cancer stroma samples.
- Reporter molecules in the mutual tumor stroma of breast and ovarian cancer..
- hub biomolecules using by GeneCards database (Safran et al., 2010).
- According to the results, the relatively high stromal score was found in breast carcinoma and high-grade serous ovarian carcinoma (Yoshihara et al., 2013).
- It was suggested that hub biomolecules of the tumor microenvironment generic network obtained from breast and ovarian cancer stroma samples represent prognostic biomolecule potentials in patients with breast and ovarian cancer..
- studied differential gene expression in breast and ovarian cancer stroma with the common regulatory patterns, common key pathways, cancer stroma associated PPI and tumor microenvironment generic network to identify central molecular signatures that may serve as potential.
- prognostic or therapeutic targets in breast and ovarian cancer.
- Henshall et al.
- (2001) reported that AR expression in tumor epithelium and stroma that is associated with a poor clinical outcome in prostate cancer, on the other hand, AR is emerging as a potential new therapeutic target for the treatment of breast cancer (Giovannelli et al., 2018).
- GATA2 gene has been identified in stroma-related studies in colon cancer prognosis (Uddin et al., 2019), and also reported as a molecular signature in ovarian cancer via network medicine perspective (Gov et al., 2017).
- Wang et al.
- Various research has shown that miR‐124 may act as a tumor suppressive by regulating different target genes in several cancers such as prostate cancer (Shi et al., 2013), and head and neck cancer (Zhao et al., 2017)..
- It is widely known that the use of inhibitors of ER (ESR1) in the treatment of patients with estrogen-positive breast cancer has offered a good prognosis (Tong et al., 2018)..
- Moreover, the ESR1 gene is frequently methylated in many types of gynecological malignancies such as highly expressed in epithelial ovarian cancer (Giannopoulou et al., 2018).
- On the other hand, that upregulated expression of EGFR protein has been reported to occur in 16%–36% of breast cancers (Bhargava et al., 2005).
- Zellmer et al.
- STAT1 is a tumor suppressor gene in breast cancer (Koromilas and Sexl, 2013) and upregulated STAT1 expression with better response to chemotherapy in patients with ovarian cancer (Josahkian et al., 2018).
- Hu et al.
- (2013) reported that miR- 192 expression is significantly downregulated in breast cancer tissue and the miR-192/215 family is upregulated in mucinous ovarian tumor samples (Agostini et al., 2018).
- GATA3 takes a crucial role in normal mammary gland development, and its expression demonstrates high correlation with the estrogen receptor α (ERa) in human breast tumors (Eeckhoute et al., 2007).
- Moreover, it was showed that GATA3 expression is related to poor prognosis of high-grade serous ovarian carcinoma patients (Chen et al., 2018).
- Recently it was reported that COL1A1 secreted by fibroblasts promoted stromal cells and facilitates the metastasis of ovarian cancer, which may provide a novel approach for ovarian cancer therapeutics (Li et al., 2020)..
- Considering the potential role of identified molecular signatures in the tumor microenvironment, two biomolecules, GATA2 and TORYAIP1, might be a novel candidate for the treatment in the breast and ovarian cancer.
- Agostini A, Brunetti M, Davidson B, Tropé CG, Eriksson AGZ et al.
- doi: 10.1038/s .
- Aguirre-Gamboa R, Gomez-Rueda H, Martínez-Ledesma E, Martínez-Torteya A, Chacolla-Huaringa R et al.
- Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF et al.
- doi: 10.1093/.
- Bhargava R, Gerald WL, Li AR, Pan Q, Lal P et al.
- Calimlioglu B, Karagoz K, Sevimoglu T, Kilic E, Gov E et al.
- Casey T, Bond J, Tighe S, Hunter T, Lintault L et al.
- doi: 10.1007/s Chen HJ, Huang RL, Liew PL, Su PH, Chen LY et al.
- Croft D, O’Kelly G, Wu G, Haw R, Gillespie M et al.
- doi: 10.1093/nar/.
- Eeckhoute J, Keeton EK, Lupien M, Krum SA, Carroll JS et al.
- doi: 10.1016/j..
- doi: 10.1093/bioinformatics/btg405.
- Giannopoulou L, Mastoraki S, Buderath P, Strati A, Pavlakis K et al.
- Giovannelli P, Di Donato M, Galasso G, Di Zazzo E, Bilancio A et al.
- RNA-based ovarian cancer research.
- Multiomics analysis of tumor microenvironment reveals Gata2 and miRNA-124-3p as potential novel biomarkers in ovarian cancer.
- doi: 10.1089/.
- Henshall SM, Quinn DI, Lee CS, Head DR, Golovsky D et al.
- Hu F, Meng X, Tong Q, Liang L, Xiang R et al.
- Islam T, Rahman R, Gov E, Turanli B, Gulfidan G et al.
- Josahkian JA, Saggioro FP, Vidotto T, Ventura HT, Reis FJCD et al.
- doi: 10.1097/IGC .
- Karnoub AE, Dash AE, Vo AP, Sullivan A, Brooks MW et al.
- doi: 10.1038/.
- Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2..
- doi: 10.4161/.
- Li M, Wang J, Wang C, Xia L, Xu J et al.
- doi: /10.1007/s .
- doi: 10.1038/s y.
- Planche A, Bacac M, Provero P, Fusco C, Delorenzi M et al.
- Rahman MR, Islam T, Zaman T, Shahjaman M, Karim MR et al..
- doi: 10.1016/j.ygeno .
- Safran M, Dalah I, Alexander J, Naomi R, Tsippi IS et al.
- doi: 10.1093/database/baq020.
- Shi XB, Xue L, Ma AH, Tepper CG, Edwards RG et al.
- doi: 10.1038/s Wang Y, Chen L, Wu Z, Wang M, Jin F et al.
- doi: 10.1186/s .
- Yeung TL, Leung CS, Wong KK, Samimi G, Thompson MS et al.
- TGF-β modulates ovarian cancer invasion by upregulating CAF-derived versican in the tumor microenvironment.
- Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H et al.
- doi: 10.1038/ncomms3612.
- Zellmer VR, Schnepp PM, Fracci SL, Tan X, Howe EN et al.
- doi MCR-16-0312 Zhao Y, Ling Z, Hao Y, Pang X, Han X et al

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