scholarly journals Association between the expression of secreted phosphoprotein - related genes and prognosis of human cancer

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yaqin Tu ◽  
Cai Chen ◽  
Guorun Fan
Keyword(s):  
Copy Number ◽  
Prognostic Value ◽  
Prognostic Markers ◽  
Human Cancer ◽  
Copy Number Variations ◽  
Cancer Prognosis ◽  
Clinical Markers ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Head Neck

Abstract Background While many studies have assessed the predictive value of secreted phosphoprotein (SPP) genes in cancer, the findings have been inconsistent. To resolve these inconsistencies, we systematically analyzed the available data to determine whether SPP1 and SPP2 are prognostic markers in the context of human cancer. Methods The expression of SPP1 and SPP2 was assessed by Oncomine analysis. The PrognoScan database was used to assess the prognostic value of SPP1 and SPP2, with cBioPortal used to assess copy number variations. The STRING database was used to generate a Protein - Protein Interaction (PPI) network for SPP genes. Results SPP1 was more likely to be over-expressed in breast, bladder, colorectal, head, neck, liver, lung, and esophageal cancers. SPP2 was expressed at lower levels in colorectal cancer, leukemia, liver cancer and pancreatic cancer. In addition, SPP1 and SPP2 mutations mainly occurred in cutaneous melanoma and endometrial cancer. Conclusions Our results suggest that SPP1 and SPP2 may be effective therapeutic or diagnostic targets in certain cancers. Further research is required to confirm these results and verify the value of SPP1 and SPP2 as clinical markers of cancer prognosis.

scholarly journals KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Kun Xie ◽  
Kang Liu ◽  
Haque A K Alvi ◽  
Yuehui Chen ◽  
Shuzhen Wang ◽  
...  
Keyword(s):  
Copy Number ◽  
Nearest Neighbor ◽  
Human Cancer ◽  
Gaussian Mixture ◽  
Disease Diagnosis ◽  
Copy Number Variations ◽  
Sequencing Data ◽  
K Nearest Neighbor ◽  
Data Types ◽  
Ngs Data

Copy number variation (CNV) is a well-known type of genomic mutation that is associated with the development of human cancer diseases. Detection of CNVs from the human genome is a crucial step for the pipeline of starting from mutation analysis to cancer disease diagnosis and treatment. Next-generation sequencing (NGS) data provides an unprecedented opportunity for CNVs detection at the base-level resolution, and currently, many methods have been developed for CNVs detection using NGS data. However, due to the intrinsic complexity of CNVs structures and NGS data itself, accurate detection of CNVs still faces many challenges. In this paper, we present an alternative method, called KNNCNV (K-Nearest Neighbor based CNV detection), for the detection of CNVs using NGS data. Compared to current methods, KNNCNV has several distinctive features: 1) it assigns an outlier score to each genome segment based solely on its first k nearest-neighbor distances, which is not only easy to extend to other data types but also improves the power of discovering CNVs, especially the local CNVs that are likely to be masked by their surrounding regions; 2) it employs the variational Bayesian Gaussian mixture model (VBGMM) to transform these scores into a series of binary labels without a user-defined threshold. To evaluate the performance of KNNCNV, we conduct both simulation and real sequencing data experiments and make comparisons with peer methods. The experimental results show that KNNCNV could derive better performance than others in terms of F1-score.

Overall Genomic Pattern Is a Predictor of Outcome in Neuroblastoma

2009 ◽  
Vol 27 (7) ◽  
pp. 1026-1033 ◽  
Author(s):  
Isabelle Janoueix-Lerosey ◽  
Gudrun Schleiermacher ◽  
Evi Michels ◽  
Véronique Mosseri ◽  
Agnès Ribeiro ◽  
...  
Keyword(s):  
Copy Number ◽  
Prognostic Significance ◽  
Genetic Alterations ◽  
Copy Number Variations ◽  
Comparative Genomic ◽  
Mycn Amplification ◽  
Clinical Markers ◽  
Prognostic Information ◽  
Stage 4 ◽  
Risk Of Relapse

Purpose For a comprehensive overview of the genetic alterations of neuroblastoma, their association and clinical significance, we conducted a whole-genome DNA copy number analysis. Patients and Methods A series of 493 neuroblastoma (NB) samples was investigated by array-based comparative genomic hybridization in two consecutive steps (224, then 269 patients). Results Genomic analysis identified several types of profiles. Tumors presenting exclusively whole-chromosome copy number variations were associated with excellent survival. No disease-related death was observed in this group. In contrast, tumors with any type of segmental chromosome alterations characterized patients with a high risk of relapse. Patients with both numerical and segmental abnormalities clearly shared the higher risk of relapse of segmental-only patients. In a multivariate analysis, taking into account the genomic profile, but also previously described individual genetic and clinical markers with prognostic significance, the presence of segmental alterations with (HR, 7.3; 95% CI, 3.7 to 14.5; P < .001) or without MYCN amplification (HR, 4.5; 95% CI, 2.4 to 8.4; P < .001) was the strongest predictor of relapse; the other significant variables were age older than 18 months (HR, 1.8; 95% CI, 1.2 to 2.8; P = .004) and stage 4 (HR, 1.8; 95% CI, 1.2 to 2.7; P = .005). Finally, within tumors showing segmental alterations, stage 4, age, MYCN amplification, 1p and 11q deletions, and 1q gain were independent predictors of decreased overall survival. Conclusion The analysis of the overall genomic pattern, which probably unravels particular genomic instability mechanisms rather than the analysis of individual markers, is essential to predict relapse in NB patients. It adds critical prognostic information to conventional markers and should be included in future treatment stratification.

scholarly journals Identification of rare germline copy number variations over-represented in five human cancer types

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Richard W Park ◽  
Tae-Min Kim ◽  
Simon Kasif ◽  
Peter J Park
Keyword(s):  
Copy Number ◽  
Human Cancer ◽  
Copy Number Variations ◽  
Cancer Types

scholarly journals Mutations and Copy Number Abnormalities of Hippo Pathway Components in Human Cancers

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhengjin He ◽  
Ruihan Li ◽  
Hai Jiang
Keyword(s):  
Cell Proliferation ◽  
Cancer Cells ◽  
Copy Number ◽  
Human Cancer ◽  
Hippo Pathway ◽  
Copy Number Variations ◽  
Tissue Homeostasis ◽  
Hippo Signaling ◽  
Core Components ◽  
The Hippo Pathway

The Hippo pathway is highly conserved from Drosophila to mammals. As a key regulator of cell proliferation, the Hippo pathway controls tissue homeostasis and has a major impact on tumorigenesis. The originally defined core components of the Hippo pathway in mammals include STK3/4, LATS1/2, YAP1/TAZ, TEAD, VGLL4, and NF2. However, for most of these genes, mutations and copy number variations are relatively uncommon in human cancer. Several other recently identified upstream and downstream regulators of Hippo signaling, including FAT1, SHANK2, Gq/11, and SWI/SNF complex, are more commonly dysregulated in human cancer at the genomic level. This review will discuss major genomic events in human cancer that enable cancer cells to escape the tumor-suppressive effects of Hippo signaling.

scholarly journals Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Guangda Yang ◽  
Liumeng Jian ◽  
Xiangan Lin ◽  
Aiyu Zhu ◽  
Guohua Wen
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Prognostic Value ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Trastuzumab Resistance ◽  
Hub Genes ◽  
Her2 Positive ◽  
Protein Protein Interaction ◽  
Stem Cell Pluripotency

Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

Comprehensive analysis of key proteins involved in radioresistance of prostate cancer by integrating protein-protein interaction networks.

2020 ◽  
Vol 15 ◽  
Author(s):  
Duocheng Qian ◽  
Quan Li ◽  
Yansong Zhu ◽  
Dujian Li
Keyword(s):  
Prostate Cancer ◽  
Kinase Activity ◽  
Bioinformatics Analysis ◽  
Prognostic Markers ◽  
Nuclear Division ◽  
Protein Interaction Networks ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Proteinprotein Interaction ◽  
Protein Protein Interaction Networks

Background: Radioresistance remains a significant obstacle in the treatment of prostate cancer (PCa). The mechanisms underlying the radioresistance in PCa remained to be further investigated. Methods: GSE53902 dataset was used in this study to identify radioresistance-related mRNAs. Proteinprotein interaction (PPI) network was constructed based on STRING analysis. DAVID system was used to predict the potential roles of radioresistance-related mRNAs. Results: We screened and re-annotated GSE53902 dataset to identify radioresistance-related mRNAs. A total of 445 up-regulated and 1036 downregulated mRNAs were identified in radioresistance PCa cells. Three key PPI network consisting of 81 proteins were further constructed in PCa. Bioinformatics analysis revealed these genes were involved in regulating MAP kinase activity, response to hypoxia, regulation of apoptotic process, mitotic nuclear division, and regulation of mRNA stability. Moreover, we observed radioresistance-related mRNAs, such as PRC1, RAD54L, PIK3R3, ASB2, FBXO32, LPAR1, RNF14, and UBA7, were dysregulated and correlated to the survival time in PCa. Conclusions: We thought this study will be useful to understand the mechanisms underlying radioresistance of PCa and identify novel prognostic markers for PCa.

scholarly journals Data Mining of Prognostic Microenvironment-Related Genes in Clear Cell Renal Cell Carcinoma: A Study with TCGA Database

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Bin Chen ◽  
Wei Chen ◽  
Jing Jin ◽  
Xueping Wang ◽  
Yifang Cao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Cancer Prognosis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent kidney malignancies. The tumor microenvironment (TME) is highly related to the oncogenesis, progress, and prognosis of ccRCC. The aim of this study was to infer the level of infiltrating stromal and immune cells and assess the prognostic value of them. The gene expression profile was obtained from TCGA and used for calculating the stromal and immune scores. Based on a cut-off value, patients were divided into low- and high-stromal/immune score groups. Survival analysis was performed to evaluate the prognostic value of stromal and immune scores. Moreover, differentially expressed genes (DEGs) that are highly related to TME were determined and applied for functional enrichment analysis and protein-protein interaction (PPI) network. The Kaplan-Meier plot demonstrated that patients with high-immune scores and stromal scores had poorer clinical outcome. In addition, a total of 89 DEGs were identified and mainly involved in 5 pathways. The top 5 degree genes were extracted from the PPI network; among them, IL10 and XCR1 were highly associated with prognosis of ccRCC. The results of the present study demonstrated that ESTIMATE algorithm-based stromal and immune scores may be a credible indicator of cancer prognosis and IL10 along with XCR1 may be a potential key regulator for the TME of ccRCC.

scholarly journals The signature of pharmaceutical sensitivity based on ctDNA mutation in eleven cancers

2020 ◽  
Vol 245 (8) ◽  
pp. 720-732
Author(s):  
Shumei Zhang ◽  
Mu Su ◽  
Zhongyi Sun ◽  
Haibo Lu ◽  
Yan Zhang
Keyword(s):  
Liquid Biopsy ◽  
Copy Number ◽  
Drug Sensitivity ◽  
Prognostic Markers ◽  
Gene Mutations ◽  
Drug Efficacy ◽  
Cancer Prognosis ◽  
Plasma Samples ◽  
Primary Tissue ◽  
Efficacy Prediction

Gene mutations are closely related to cancers and drug sensitivity. Noninvasive liquid biopsy was used to detect mutations of ctDNA in plasma, which is regarded as an indicator of chemotherapy reaction. In this study, we performed exon sequencing of 416 cancer-related genes for cancer primary tissue and plasma samples of 20 patients in 11 cancers. The comprehensive mutation landscape was obtained by bioinformatics tools. In all samples, a total of 0–135 genes involved somatic mutations, and 5–209 genes involved copy number variation. APC, KRAS, and TP53 were detected as frequently mutated genes. Nineteen genes with high-frequency copy-number amplification and 59 with frequent copy-number deletions were identified. By quantitatively assessing the degree of agreement, we found that liquid biopsy is reliable instead of tissues. Besides, 31 mutation prognostic markers in 7 cancers were screened by integrating the consistent mutations and enlarging samples in TCGA. Moreover, from drug-mutation network, 25 drugs connected with 9 mutations (B-Mut-9) were obtained which can be served as drug biomarkers in blood. This was proved by further integrating the mutation information of patients in TCGA into drug-mutation network. In summary, the variation in ctDNA can be used as the biomarkers for cancer prognosis and drug efficacy prediction. Impact statement Gene mutations are closely related to cancers and drug sensitivity and noninvasive liquid biopsy was used to detect mutations of ctDNA in plasma. In this study, we performed exon sequencing of 416 cancer-related genes for cancer primary tissue and plasma samples of 20 patients in 11 cancers and obtained the comprehensive mutation landscape. We found that liquid biopsy is reliable in place of tissue biopsy. And 31 potential unique mutation prognostic markers were screened in 7 cancer types. Moreover, the drug-mutation network (DMN) was constructed and 9 gene mutations (B-Mut-9) were confirmed that can be served as drug biomarkers in blood. Our study showed that the variation in ctDNA can be used as the biomarkers for cancer prognosis and drug efficacy prediction. This can provide a reference for clinical noninvasive testing.

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