Dataset Information

Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Breast Invasive Carcinoma.

ABSTRACT:

Background

Bone is the most common metastatic site of Breast invasive carcinoma (BRCA). In this study, the bone metastasis-specific regulation network of BRCA was constructed based on prognostic stemness-related signatures (PSRSs), their upstream transcription factors (TFs) and downstream pathways.

Methods

Clinical information and RNA-seq data of 1,080 primary BRCA samples (1,048 samples without bone metastasis and 32 samples with bone metastasis) were downloaded from The Cancer Genome Atlas (TCGA). The edgeR method was performed to identify differential expressed genes (DEGs). Next, mRNA stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR). To analyze DEGs by classification, similar genes were integrated into the same module by weighted gene co-expression network analysis (WGCNA). Then, univariate and multivariate Cox proportional hazard regression were applied to find the PSRSs. Furthermore, PSRSs, 318 TFs obtained from Cistrome database and 50 hallmark pathways quantified by GSVA were integrated into co-expression analysis. Significant co-expression patterns were used to construct the bone metastasis-specific regulation network. Finally, spatial single-cell RNA-seq and chromatin immunoprecipitation sequence (ChIP-seq) data and multi-omics databases were applied to validate the key scientific hypothesis in the regulation network. Additionally, Connectivity Map (CMap) was utilized to select the potential inhibitors of bone metastasis-specific regulation network in BRCA.

Results

Based on edgeR and WGCNA method, 43 PSRSs were identified. In the bone metastasis-specific regulation network, MAF positively regulated CD248 (R = 0.435, P < 0.001), and hallmark apical junction was the potential pathway of CD248 (R = 0.353, P < 0.001). This regulatory pattern was supported by spatial single-cell RNA sequence, ChIP-seq data and multi-omics online databases. Additionally, alexidine was identified as the possible inhibitor for bone metastasis of BRCA by CMap analysis.

Conclusion

PSRSs played important roles in bone metastasis of BRCA, and the prognostic model based on PSRSs showed good performance. Especially, we proposed that CD248 was the most significant PSRS, which was positively regulated by MAF, influenced bone metastasis via apical junction pathway. And this axis might be inhibited by alexidine, which providing a potential treatment strategy for bone metastasis of BRCA.

SUBMITTER: Huang R

PROVIDER: S-EPMC7875018 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Breast Invasive Carcinoma.

Huang Runzhi R Li Zhenyu Z Zhang Jiayao J Zeng Zhiwei Z Zhang Jiaqi J Li Mingxiao M Wang Siqao S Xian Shuyuan S Xue Yuna Y Chen Xi X Li Jie J Cheng Wenjun W Wang Bin B Yan Penghui P Yang Daoke D Huang Zongqiang Z

Frontiers in oncology 20210127

<h4>Background</h4>Bone is the most common metastatic site of Breast invasive carcinoma (BRCA). In this study, the bone metastasis-specific regulation network of BRCA was constructed based on prognostic stemness-related signatures (PSRSs), their upstream transcription factors (TFs) and downstream pathways.<h4>Methods</h4>Clinical information and RNA-seq data of 1,080 primary BRCA samples (1,048 samples without bone metastasis and 32 samples with bone metastasis) were downloaded from The Cancer G ...[more]

PMID: 33585235

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Project description:BackgroundBladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play an important role in the occurrence and progression of BLCA, however, its roles in bone metastasis and the prognostic stemness biomarkers have not been identified in BLCA.MethodIn order to identify the roles of CSC in the tumorigenesis, bone metastasis and prognosis of BLCA, the RNA sequencing data of patients with BLCA were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between mRNAsi and the tumorigenesis, bone metastasis, clinical stage and overall survival (OS) were also established. The key prognostic stemness-related genes (PSRGs) were screened by Lasso regression, and based on them, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score. Additionally, in order to explore the key regulatory network, the relationship among differentially expressing TFs, PSRGs, and absolute quantification of 50 hallmarks of cancer were also identified by Pearson correlation analysis. To verify the identified key TFs and PSRGs, their expression levels were identified by our clinical samples via immunohistochemistry (IHC).ResultsA total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples. According to the clinical stage, mRNAsi and bone metastasis of BLCA, 2,383 stage-related DEGs, 3,680 stemness-related DEGs and 716 bone metastasis-associated DEGs were uncovered, respectively. Additionally, compared with normal tissue, mRNAsi was significantly upregulated in the primary BLCA and also associated with the prognosis (P = 0.016), bone metastasis (P < 0.001) and AJCC clinical stage (P < 0.001) of BLCA patients. A total of 20 PSRGs were further screened by Lasso regression, and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). Moreover, we found two key TFs (EPO, ARID3A), four key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and five key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response) in the regulatory network. The tissue microarray of BLCA and BLCA bone metastasis also revealed high expression of the key TFs (EPO, ARID3A) and PRSGs (SSX3) in BLCA.ConclusionOur study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of patients with BLCA and provides a well-applied model for predicting the OS for patients with BLCA based on 20 PSRGs. Besides, we also identified the regulatory network between key PSRGs and cancer gene sets in mediating the BLCA bone metastasis.

Project description:BackgroundHepatocellular carcinoma (HCC) most commonly develops in patients who have a viral infection, especially in the case of hepatitis B virus (HBV), and in patients with a chronic liver disease. HCC patients with bone metastasis (BM) suffer from pain and other symptoms that significantly reduce their quality of life. Identification of patients who are at high risk for BM after undergoing potentially curative treatment for HCC remains challenging. Here, we aimed to identify HCC BM-related genes and proteins to establish prediction biomarkers.MethodsRNA was extracted from 48 pairs of intratumoral and peritumoral formalin-fixed, paraffin-embedded tissue from HCC patients with and without BM. A cDNA-mediated annealing, selection, extension and ligation assay containing 502 cancer-related genes was used to identify novel BM-associated genes. An additional independent study with 350 HCC patients who had undergone hepatectomy was conducted to evaluate the expression of candidate genes at the protein level using immunohistochemistry on tissue microarrays (TMAs). Of the 350 patients, 273 (78.0%) were infected with HBV.ResultsSeven intratumoral genes and 17 peritumoral genes were overexpressed in patients with BM, whereas 15 intratumoral genes and 28 peritumoral genes were underexpressed in patients with BM. We selected the following four genes for further analysis because they were differentially expressed in the cancer gene-specific microarray and were previously reported to be associated with BM: connective tissue growth factor (CTGF), matrix metalloproteinase-1 (MMP-1), transforming growth factor β1 (TGF-β1), and interleukin-11 (IL-11). We assessed the protein expression of these selected genes using immunohistochemistry on TMAs including 350 HCC patient specimens. We determined that expression of intratumoral CTGF, intratumoral IL-11, and peritumoral MMP-1 were independent prognostic factors for developing BM in HCC patients. Combining intratumoral CTGF and IL-11 expression was also an independent risk factor for BM development.ConclusionsSixty-seven genes were differentially expressed in HCC patients with and without BM. High intratumoral CTGF, positive IL-11, and high peritumoral MMP-1 expression were associated with BM after hepatectomy. Intratumoral CTGF expression combined with IL-11 expression may serve as a useful predictive biomarker for HCC BM.

Dataset Information

Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Breast Invasive Carcinoma.

Background

Methods

Results

Conclusion

Publications

Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Breast Invasive Carcinoma.

Similar Datasets

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