Comprehensive molecular biomarker identification in breast cancer brain metastases

Schulten, Hans-Juergen; Bangash, Mohammed; Karim, Sajjad; Dallol, Ashraf; Hussein, Deema; Merdad, Adnan; Althoubaity, Fatma; Al-Maghrabi, Jaudah; Jamal, Awatif; Alghamdi, Fahad; Choudhry, Hani; Baeesa, Saleh; Chaudhary, Adeel; Al‐Qahtani, Mohammed

doi:10.1186/s12967-017-1370-x

Cited by 193 publications

(382 citation statements)

References 89 publications

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“…), and one from Weigelt and colleagues (40), which could not find any global significant differences over chance expected between primary and metastasic breast cancer samples. Studies subsequent to the Weigelt and colleagues' study have been able to define widespread differences associated with breast cancer metastasis versus primary tumors (5,8,9). Interestingly, when surveying TCGA data, none of the Ramaswamy signature genes showed consistent high or low expression patterns in metastasis across the different cancer types (Supplementary Data S2).…”

Section: Discussionmentioning

confidence: 99%

“…Molecular comparisons between primary tumors and metastases can potentially provide insights into the pathways and processes involved with cancer disease progression (4,5). Numerous independent studies have carried out gene expression profiling of metastasis versus primary cancer for individual cancer types (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). In addition to individual studies by cancer type, "pancancer" molecular analyses would allow for examining similarities and differences among the molecular alterations that may be associated with metastasis across diverse cancer types.…”

Section: Introductionmentioning

confidence: 99%

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Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas

Chen

Zhang

Varambally

et al. 2019

Molecular Cancer Research

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Tumor metastasis is a major contributor to mortality of cancer patients, but the process remains poorly understood. Molecular comparisons between primary tumors and metastases can provide insights into the pathways and processes involved. Here, we systematically analyzed and cataloged molecular correlates of metastasis using The Cancer Genome Atlas (TCGA) datasets across 11 different cancer types, these data involving 4,473 primary tumor samples and 395 tumor metastasis samples (including 369 from melanoma). For each cancer type, widespread differences in gene transcription between primary and metastasis samples were observed. For several cancer types, metastasis-associated genes from TCGA comparisons were found to overlap extensively with external results from independent profiling datasets of metastatic tumors. Although some differential expression patterns associated with metastasis were found to be shared across multiple cancer types, by and large each cancer type showed a metastasis signature that was distinctive from those of the other cancer types. Functional categories of genes enriched in multiple cancer type-specific metastatic overexpression signatures included cellular response to stress, DNA repair, oxidationreduction process, protein deubiquitination, and receptor activity. The TCGA-derived prostate cancer metastasis signature in particular could define a subset of aggressive primary prostate cancer. Transglutaminase 2 protein and mRNA were both elevated in metastases from breast and melanoma cancers. Alterations in miRNAs and in DNA methylation were also identified.Implications: Our findings suggest that there are different molecular pathways to metastasis involved in different cancers. Our catalog of alterations provides a resource for future studies investigating the role of specific genes in metastasis.(Continued.) B, For each indicated cancer type, numbers of genes overlapping between the TCGA metastasis signature genes (left, genes overexpressed in metastasis; right, genes underexpressed in metastasis) and the genes significantly high or low in metastasis (P < 0.05, t test) in the published external datasets corresponding to the given cancer type. Significance of overlap (by one-sided Fisher exact test; x 2 test for SKCM genes) is indicated for TCGA genes found in one or more external datasets (blue bars) and in two or more external datasets (red bars). Selected top genes overlapping between TCGA and results from other datasets are listed (BRCA overexpressed: TCGA P < 1E-6 and P < 1E-6 for E-MTAB-4003 dataset; BRCA underexpressed: TCGA P < 1E-6 and P < 1E-6 for E-MTAB-4003 dataset; CRC underexpressed: TCGA FDR < 10% and P < 0.05 for two or more external datasets; PAAD overexpressed: TCGA FDR < 10% and P < 0.01 for GSE42952 dataset; PAAD underexpressed: TCGA FDR < 10% and P < 0.05 for one or more external datasets; PRAD overexpressed: TCGA FDR < 10% and P < 0.01 for all three external datasets; PRAD underexpressed: TCGA FDR < 10% and P < 0.05 for all three external datasets; SKCM overexpressed:...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas

Chen

Zhang

Varambally

et al. 2019

Molecular Cancer Research

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“…As a transcription target of MYC, BAG1 cooperates with HSP70 molecular chaperone to selectively mediate MYC overexpression and affect the survival of OS cells [55]. The abnormal expression of BNIP3 is associated with the prognosis of renal cancer [56], breast cancer [57], and salivary adenoid cystic carcinoma [16]. Abnormal expression of AMBRA1 is associated with prognosis of bile duct cancer and cutaneous melanoma [58,59].…”

Section: Discussionmentioning

confidence: 99%

Identification of a novel autophagy-related gene signature for predicting metastasis and survival in patients with osteosarcoma

Zhang

Deng

et al. 2020

Preprint

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Background: Osteosarcoma (OS) is a bone malignant tumor that occurs in children and adolescents. Due to a lack of reliable prognostic biomarkers, the prognosis of OS patients is often uncertain. This study aimed to construct an autophagy-related gene signature to predict the prognosis of OS patients.Methods: The gene expression profile data of OS and normal muscle tissue samples were downloaded separately from the Therapeutically Applied Research To Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases . The differentially expressed autophagy-related genes (DEARGs) in OS and normal muscle tissue samples were screened using R software, before being subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A protein-protein interaction (PPI) network was constructed and hub autophagy-related genes were screened. Finally, the screened autophagy-related genes were subjected to univariate Cox regression, Lasso Cox regression, survival analysis, and clinical correlation analysis.Results: A total of 120 DEARGs and 10 hub autophagy-related genes were obtained. A prognostic autophagy-related gene signature consisting of 9 genes ( BNIP3 , MYC , BAG1 , CALCOCO2 , ATF4 , AMBRA1 , EGFR , MAPK1 , and PEX ) was constructed. This signature was significantly correlated to the prognosis ( P <0.0001) and distant metastasis of OS patients ( P = 0.013).Conclusion: This signature based on 9 autophagy-related genes could predict metastasis and survival in patients with OS.

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“…SNORA47 is involved in lung cancer tumorigenesis [26] and promotes tumorigenesis by regulating EMT markers in hepatocellular carcinoma [27]. SNORA71C is overexpressed in breast cancer brain metastases [28]. Furthermore, SNHG3 facilitates cell proliferation and migration in oral squamous cell carcinoma via targeting nuclear transcription factor Y subunit gamma [29].…”

Section: Discussionmentioning

confidence: 99%

Identification and analysis of small nucleolar RNAs (snoRNAs) associated co-expression and ceRNA regulatory networks in esophageal carcinoma

Jiang¹,

Zhuang²,

Yang³

et al. 2020

Preprint

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Background: This study aimed to investigate the role of small nucleolar RNAs (snoRNAs) and their host genes (SNHGs) in tumorigenesis and progression of esophageal carcinoma (EC). Methods: RNA-seq data and clinical information for EC patients were obtained from The Cancer Genome Atlas database. Differentially expressed snoRNAs (DE-snoRNAs) and SNHGs (DE-SNHGs) between EC samples and normal controls were screened with the edgeR package, followed by the trend analysis with STEM. Survival analysis was performed using the Kaplan–Meier method with the log-rank test. Identification of co-expressed genes and functional enrichment analyses were performed with Pearson’s correlation analysis and enrichment analysis, respectively. Lastly, the co-expression and ceRNA networks were constructed. Results: A total of 19 DE-snoRNAs and nine DE-SNHGs were identified between EC samples and normal controls. Moreover, two snoRNA clusters related to the clinic stage were identified through trend analysis. Survival analysis results demonstrated that SNORA70D was significantly associated with the overall survival of EC patients (P=0.034). In addition, a co-regulation network that included six snoRNAs and 29 mRNAs was constructed. A ceRNA network comprised of five SNHGs, 11 mRNAs, and 38 miRNAs was constructed based on the top 50 relationship pairs. KEGG pathway enrichment analysis revealed that SNORA14B, SNORA47, SNORA71C, SNORD12B, and SNORD14E in the co-expression network were mainly enriched in the NOD−like receptor signaling pathway, and neuroactive ligand−receptor interaction pathway. SNHG23, SNHG3, SNHG17, SNHG4, and SNHG8 in the ceRNA regulation network were mainly enriched in calcium signaling pathway, cytokine−cytokine receptor interaction, and the extracellular matrix−receptor interaction pathway. Conclusion: The data revealed a series of snoRNAs, SNHGs, and pathways involved in EC carcinogenesis, which will provide a basis for understanding the underlying molecular mechanism of EC development.

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Comprehensive molecular biomarker identification in breast cancer brain metastases

Cited by 193 publications

References 89 publications

Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas

Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas

Identification of a novel autophagy-related gene signature for predicting metastasis and survival in patients with osteosarcoma

Identification and analysis of small nucleolar RNAs (snoRNAs) associated co-expression and ceRNA regulatory networks in esophageal carcinoma

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