An in silico model of LINE-1-mediated neoplastic evolution

2020 ◽  
Vol 36 (14) ◽  
pp. 4144-4153
Author(s):  
Jack LeBien ◽  
Gerald McCollam ◽  
Joel Atallah
Keyword(s):  
Population Dynamics ◽  
Cancer Progression ◽  
Sequence Data ◽  
Human Cancer ◽  
Lesion Size ◽  
Supplementary Information ◽  
Driver Genes ◽  
Protein Coding ◽  
Somatic Evolution ◽  
Cancer Types

Abstract Motivation Recent research has uncovered roles for transposable elements (TEs) in multiple evolutionary processes, ranging from somatic evolution in cancer to putatively adaptive germline evolution across species. Most models of TE population dynamics, however, have not incorporated actual genome sequence data. The effect of site integration preferences of specific TEs on evolutionary outcomes and the effects of different selection regimes on TE dynamics in a specific genome are unknown. We present a stochastic model of LINE-1 (L1) transposition in human cancer. This system was chosen because the transposition of L1 elements is well understood, the population dynamics of cancer tumors has been modeled extensively, and the role of L1 elements in cancer progression has garnered interest in recent years. Results Our model predicts that L1 retrotransposition (RT) can play either advantageous or deleterious roles in tumor progression, depending on the initial lesion size, L1 insertion rate and tumor driver genes. Small changes in the RT rate or set of driver tumor-suppressor genes (TSGs) were observed to alter the dynamics of tumorigenesis. We found high variation in the density of L1 target sites across human protein-coding genes. We also present an analysis, across three cancer types, of the frequency of homozygous TSG disruption in wild-type hosts compared to those with an inherited driver allele. Availability and implementation Source code is available at https://github.com/atallah-lab/neoplastic-evolution. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Transporter associated with antigen processing 1 (TAP1) expression and prognostic analysis in breast, lung, liver, and ovarian cancer

2021 ◽  
Author(s):  
Anika Tabassum ◽  
Md. Nazmus Samdani ◽  
Tarak Chandra Dhali ◽  
Rahat Alam ◽  
Foysal Ahammad ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Cancer Progression ◽  
Antigen Processing ◽  
Human Cancer ◽  
Analytical Data ◽  
Significant Negative Correlation ◽  
Cancer Tissue ◽  
Human Cancer Cell Lines ◽  
Prognostic Analysis ◽  
Cancer Types

Abstract Transporter associated with antigen processing 1 (TAP1) is a transporter protein that represent tumor antigen in the MHC I or HLA complex. Any defect in the TAP1 gene resulting in inadequate tumor tracking. TAP1 influences multidrug resistance (MDR) in human cancer cell lines and hinders the treatment during chemotherapeutic. The association of TAP1 in cancer progression remains mostly unknown and further study of the gene in relation with cancer need to conduct. Thus, the study has designed to analyze the association between the TAP1 with cancer by computationally. The expression pattern of the gene has determined by using ONCOMINE, GENT2, and GEPIA2 online platforms. The protein level of TAP1 was examined by the help of Human Protein Atlas. Samples with different clinical outcomes were investigated to evaluate the expression and promoter methylation in cancer vs. normal tissues by using UALCAN server. The copy number alteration, mutation frequency, and expression level of the gene in different cancer were analyzed by using cBioPortal server. The PrognoScan and KM plotter platforms were used to perform the survival analysis and represented graphically. Additionally, pathway and gene ontology (GO) features correlated to the TAP1 gene were analyzed and presented by bar charts. After arranging the data in a single panel like correlating expression to prognosis, mutational and alterations characteristic, and pathways analysis, we observed some interesting insights that emphasized the importance of the gene in cancer progression. The study found the relationship between the TAP1 expression pattern and prognosis in different cancer tissues and shows how TAP1 affects the clinical characteristics. The analytical data presented in the study is vital to learn about the effect of TAP1 in tumor tissue, where previously studies showing contradicting expression of TAP1 in cancer tissue. The analyzed data can also be utilized further to evade the threats against chemotherapy. Overall, the study provided a new aspect to consider the role of TAP1 gene in cancer progression and survival status. Key messages • This study demonstrated, for the first time, a correlation between the TAP1 gene and tumor progression. • An upregulation of TAP1 mRNA was demonstrated in various cancer types. • This study reported a significant negative correlation for TAP1 gene expression and the survival rate in different cancer types.

scholarly journals Mutational analysis of driver genes with tumor suppressive and oncogenic roles in gastric cancer

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3585 ◽  
Author(s):  
Tianfang Wang ◽  
Yining Liu ◽  
Min Zhao
Keyword(s):  
Gastric Cancer ◽  
Cancer Progression ◽  
Complex Disease ◽  
Mutational Analysis ◽  
Driver Mutations ◽  
Driver Genes ◽  
Protein Coding ◽  
Mirna Genes ◽  
Genetic Mechanisms ◽  
New Treatment

Gastric cancer (GC) is a complex disease with heterogeneous genetic mechanisms. Genomic mutational profiling of gastric cancer not only expands our knowledge about cancer progression at a fundamental genetic level, but also could provide guidance on new treatment decisions, currently based on tumor histology. The fact that precise medicine-based treatment is successful in a subset of tumors indicates the need for better identification of clinically related molecular tumor phenotypes, especially with regard to those driver mutations on tumor suppressor genes (TSGs) and oncogenes (ONGs). We surveyed 313 TSGs and 160 ONGs associated with 48 protein coding and 19 miRNA genes with both TSG and ONG roles. Using public cancer mutational profiles, we confirmed the dual roles of CDKN1A and CDKN1B. In addition to the widely recognized alterations, we identified another 82 frequently mutated genes in public gastric cancer cohort. In summary, these driver mutation profiles of individual GC will form the basis of personalized treatment of gastric cancer, leading to substantial therapeutic improvements.

scholarly journals Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers

2020 ◽  
Vol 36 (20) ◽  
pp. 5027-5036 ◽  
Author(s):  
Mingzhou Song ◽  
Hua Zhong
Keyword(s):  
Clustering Algorithm ◽  
Human Cancer ◽  
Clustering Algorithms ◽  
Search Space ◽  
R Package ◽  
Supplementary Information ◽  
Diagnostic Biomarkers ◽  
Cancer Types ◽  
Molecular Patterns ◽  
Pan Cancer

Abstract Motivation Chromosomal patterning of gene expression in cancer can arise from aneuploidy, genome disorganization or abnormal DNA methylation. To map such patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optimality and reproducibility. Results We present the chromosome clustering method, establish its optimality and runtime and evaluate its performance. It uses dynamic programming enhanced with an algorithm to reduce search-space in-place to decrease runtime overhead. Using the method, we delineated outstanding genomic zones in 17 human cancer types. We identified strong continuity in dysregulation polarity—dominance by either up- or downregulated genes in a zone—along chromosomes in all cancer types. Significantly polarized dysregulation zones specific to cancer types are found, offering potential diagnostic biomarkers. Unreported previously, a total of 109 loci with conserved dysregulation polarity across cancer types give insights into pan-cancer mechanisms. Efficient chromosomal clustering opens a window to characterize molecular patterns in cancer genome and beyond. Availability and implementation Weighted univariate clustering algorithms are implemented within the R package ‘Ckmeans.1d.dp’ (4.0.0 or above), freely available at https://cran.r-project.org/package=Ckmeans.1d.dp. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Somatic Evolution Introduces Distinctive Changes in Proteome Composition in Cancer Types

2018 ◽  
Author(s):  
Viktoriia Tsuber
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Human Cancer ◽  
Multiple Cancer ◽  
Somatic Evolution ◽  
Proteome Level ◽  
Cancer Types ◽  
Exogenous Factors ◽  
Gain Loss ◽  
Gains And Losses

AbstractOn the proteome level, somatic evolution in cancer is asymmetric, with several amino acids being irreversibly lost and the others gained across cancer types. However, though the gains and losses of amino acids are consistent in multiple cancer types, their magnitudes and prevalences differ. Here, we analyzed data on circa three million amino acid substitutions in the Catalogue of Somatic Mutations in Cancer (COSMIC) database to investigate profiles of gains and losses of amino acids in 23 human cancer types. We found characteristic differences in amino acid gain/loss profiles in the cancer types, with several of them demonstrating very specific profiles of proteome alterations associated with distinct endogenous or exogenous factors.

scholarly journals SGK1 in Human Cancer: Emerging Roles and Mechanisms

2021 ◽  
Vol 10 ◽  
Author(s):  
Yiwen Sang ◽  
Piaoping Kong ◽  
Shizhen Zhang ◽  
Lingyu Zhang ◽  
Ying Cao ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Kinase Activity ◽  
Human Cancer ◽  
Therapy Resistance ◽  
Cancer Cell Proliferation ◽  
Serine Threonine Kinase ◽  
Aberrant Expression ◽  
Threonine Kinase ◽  
Cancer Types

Serum and glucocorticoid-induced protein kinase 1 (SGK1) is a member of the “AGC” subfamily of protein kinases, which shares structural and functional similarities with the AKT family of kinases and displays serine/threonine kinase activity. Aberrant expression of SGK1 has profound cellular consequences and is closely correlated with human cancer. SGK1 is considered a canonical factor affecting the expression and signal transduction of multiple genes involved in the genesis and development of many human cancers. Abnormal expression of SGK1 has been found in tissue and may hopefully become a useful indicator of cancer progression. In addition, SGK1 acts as a prognostic factor for cancer patient survival. This review systematically summarizes and discusses the role of SGK1 as a diagnostic and prognostic biomarker of diverse cancer types; focuses on its essential roles and functions in tumorigenesis, cancer cell proliferation, apoptosis, invasion, metastasis, autophagy, metabolism, and therapy resistance and in the tumor microenvironment; and finally summarizes the current understanding of the regulatory mechanisms of SGK1 at the molecular level. Taken together, this evidence highlights the crucial role of SGK1 in tumorigenesis and cancer progression, revealing why it has emerged as a potential target for cancer therapy.

scholarly journals The landscape of long noncoding RNA-involved and tumor-specific fusions across various cancers

2020 ◽  
Vol 48 (22) ◽  
pp. 12618-12631
Author(s):  
Mengbiao Guo ◽  
Zhen-Dong Xiao ◽  
Zhiming Dai ◽  
Ling Zhu ◽  
Hang Lei ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Chimeric Proteins ◽  
Protein Coding ◽  
Cellular Processes ◽  
Cancer Types ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Somatic Copy Number Alterations

Abstract The majority of the human genome encodes long noncoding RNA (lncRNA) genes, critical regulators of various cellular processes, which largely outnumber protein-coding genes. However, lncRNA-involved fusions have not been surveyed and characterized yet. Here, we present a systematic study of the lncRNA fusion landscape across cancer types and identify >30 000 high-confidence tumor-specific lncRNA fusions (using 8284 tumor and 6946 normal samples). Fusions positively correlated with DNA damage and cancer stemness and were specifically low in microsatellite instable (MSI)-High or virus-infected tumors. Moreover, fusions distribute differently among cancer molecular subtypes, but with shared enrichment in tumors that are microsatellite stable (MSS), with high somatic copy number alterations (SCNA), and with poor survival. Importantly, we find a potentially new mechanism, mediated by enhancer RNAs (eRNA), which generates secondary fusions that form densely connected fusion networks with many fusion hubs targeted by FDA-approved drugs. Finally, we experimentally validate functions of two tumor-promoting chimeric proteins derived from mRNA-lncRNA fusions, KDM4B–G039927 and EPS15L1–lncOR7C2–1. The EPS15L1 fusion protein may regulate (Gasdermin E) GSDME, critical in pyroptosis and anti-tumor immunity. Our study completes the fusion landscape in cancers, sheds light on fusion mechanisms, and enriches lncRNA functions in tumorigenesis and cancer progression.

scholarly journals Expression of Oncogenic Drivers in 3D Cell Culture Depends on Nuclear ATP Synthesis by NUDT5

Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1337 ◽  
Author(s):  
Katherine E. Pickup ◽  
Felicitas Pardow ◽  
José Carbonell-Caballero ◽  
Antonios Lioutas ◽  
José Luis Villanueva-Cañas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Cancer Progression ◽  
Breast Cancer Cells ◽  
3D Culture ◽  
Driver Genes ◽  
Increased Risk ◽  
Cancer Types

The growth of cancer cells as oncospheres in three-dimensional (3D) culture provides a robust cell model for understanding cancer progression, as well as for early drug discovery and validation. We have previously described a novel pathway in breast cancer cells, whereby ADP (Adenosine diphosphate)-ribose derived from hydrolysis of poly (ADP-Ribose) and pyrophosphate (PPi) are converted to ATP, catalysed by the enzyme NUDT5 (nucleotide diphosphate hydrolase type 5). Overexpression of the NUDT5 gene in breast and other cancer types is associated with poor prognosis, increased risk of recurrence and metastasis. In order to understand the role of NUDT5 in cancer cell growth, we performed phenotypic and global expression analysis in breast cancer cells grown as oncospheres. Comparison of two-dimensional (2D) versus 3D cancer cell cultures from different tissues of origin suggest that NUDT5 increases the aggressiveness of the disease via the modulation of several key driver genes, including ubiquitin specific peptidase 22 (USP22), RAB35B, focadhesin (FOCAD) and prostagladin E synthase (PTGES). NUDT5 functions as a master regulator of key oncogenic pathways and of genes involved in cell adhesion, cancer stem cell (CSC) maintenance and epithelial to mesenchyme transition (EMT). Inhibiting the enzymatic activities of NUDT5 prevents oncosphere formation and precludes the activation of cancer driver genes. These findings highlight NUDT5 as an upstream regulator of tumour drivers and may provide a biomarker for cancer stratification, as well as a novel target for drug discovery for combinatorial drug regimens for the treatment of aggressive cancer types and metastasis.

scholarly journals DriverGroup: A novel method for identifying driver gene groups

2020 ◽  
Author(s):  
Vu VH Pham ◽  
Lin Liu ◽  
Cameron P Bracken ◽  
Gregory J Goodall ◽  
Jiuyong Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Single Gene ◽  
Gene Interaction ◽  
Computational Method ◽  
Supplementary Information ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Critical Nodes ◽  
Novel Method

AbstractMotivationIdentifying cancer driver genes is a key task in cancer informatics. Most exisiting methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesise that there are driver gene groups that work in concert to regulate cancer and we develop a novel computational method to detect those driver gene groups.ResultsWe develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (1) Constructing the gene network, (2) Discovering critical nodes of the constructed network, and (3) Identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify coding and non-coding driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup.Availability and implementationDriverGroup is available at https://github.com/pvvhoang/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Characterization of Glycolysis-Associated Molecules in the Tumor Microenvironment Revealed by Pan-Cancer Tissues and Lung Cancer Single Cell Data

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1788
Author(s):  
Jinfen Wei ◽  
Kaitang Huang ◽  
Zixi Chen ◽  
Meiling Hu ◽  
Yunmeng Bai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Progression ◽  
Single Cells ◽  
Gene Expression Signature ◽  
Human Lung Cancer ◽  
Driver Genes ◽  
Molecular Features ◽  
Cancer Types ◽  
Cancer Tissues ◽  
Pan Cancer

Altered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. There is however still a lack of molecular characterization glycolysis and comprehensive studies related to tumor glycolysis in the pan-cancer landscape. Here, we applied a gene expression signature to quantify glycolysis in 9229 tumors across 25 cancer types and 7875 human lung cancer single cells and verified the robustness of signature using defined glycolysis samples from previous studies. We classified tumors and cells into glycolysis score-high and -low groups, demonstrated their prognostic associations, and identified genome and transcriptome molecular features associated with glycolysis activity. We observed that glycolysis score-high tumors were associated with worse prognosis across cancer types. High glycolysis tumors exhibited specific driver genes altered by copy number aberrations (CNAs) in most cancer types. Tricarboxylic acid (TCA) cycle, DNA replication, tumor proliferation and other cancer hallmarks were more active in glycolysis-high tumors. Glycolysis signature was strongly correlated with hypoxia signature in all 25 cancer tissues (r > 0.7) and cancer single cells (r > 0.8). In addition, HSPA8 and P4HA1 were screened out as the potential modulating factors to glycolysis as their expression were highly correlated with glycolysis score and glycolysis genes, which enables future efforts for therapeutic options to block the glycolysis and control tumor progression. Our study provides a comprehensive molecular-level understanding of glycolysis with a large sample data and demonstrates the hypoxia pressure, growth signals, oncogene mutation and other potential signals could activate glycolysis, thereby to regulate cell cycle, energy material synthesis, cell proliferation and cancer progression.

scholarly journals DriverGroup: a novel method for identifying driver gene groups

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i583-i591
Author(s):  
Vu V H Pham ◽  
Lin Liu ◽  
Cameron P Bracken ◽  
Gregory J Goodall ◽  
Jiuyong Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Single Gene ◽  
Gene Interaction ◽  
Computational Method ◽  
Supplementary Information ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Critical Nodes ◽  
Novel Method

Abstract Motivation Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. Results We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. Availability and implementation DriverGroup is available at https://github.com/pvvhoang/DriverGroup Supplementary information Supplementary data are available at Bioinformatics online.

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