scholarly journals The insertion and dysregulation of transposable elements in osteosarcoma and their association with patient event-free survival

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Chao Wang ◽  
Chun Liang
Keyword(s):  
Transposable Elements ◽  
Genome Project ◽  
Patient Specific ◽  
Cancer Genes ◽  
Rna Seq ◽  
Event Free Survival ◽  
Free Survival ◽  
Transcription Start Sites ◽  
Neuronal Processes ◽  
Flanking Regions

AbstractThe dysregulation of transposable elements (TEs) has been explored in a variety of cancers. However, TE activities in osteosarcoma (OS) have not been extensively studied yet. By integrative analysis of RNA-seq, whole-genome sequencing (WGS), and methylation data, we showed aberrant TE activities associated with dysregulations of TEs in OS tumors. Specifically, expression levels of LINE-1 and Alu of different evolutionary ages, as well as subfamilies of SVA and HERV-K, were significantly up-regulated in OS tumors, accompanied by enhanced DNA repair responses. We verified the characteristics of LINE-1 mediated TE insertions, including target site duplication (TSD) length (centered around 15 bp) and preferential insertions into intergenic and AT-rich regions as well as intronic regions of longer genes. By filtering polymorphic TE insertions reported in 1000 genome project (1KGP), besides 148 tumor-specific somatic TE insertions, we found most OS patient-specific TE insertions (3175 out of 3326) are germline insertions, which are associated with genes involved in neuronal processes or with transcription factors important for cancer development. In addition to 68 TE-affected cancer genes, we found recurrent germline TE insertions in 72 non-cancer genes with high frequencies among patients. We also found that +/− 500 bps flanking regions of transcription start sites (TSS) of LINE-1 (young) and Alu showed lower methylation levels in OS tumor samples than controls. Interestingly, by incorporating patient clinical data and focusing on TE activities in OS tumors, our data analysis suggested that higher TE insertions in OS tumors are associated with a longer event-free survival time.

scholarly journals Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models

2021 ◽  
Vol 22 (18) ◽  
pp. 9970
Author(s):  
Annabelle Nwaokorie ◽  
Dirk Fey
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Wnt Pathway ◽  
Predictive Power ◽  
Mechanistic Model ◽  
Patient Specific ◽  
Linear Regression Models ◽  
Event Free Survival ◽  
Novel Genes ◽  
Free Survival

Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these models should focus on the critical STNs for each cancer, include prognostic genes and proteins, and correctly predict patient-specific differences in STN activity. Focussing on colorectal cancer and the WNT STN, we used mechanism-based machine learning models to identify genes and proteins with significant associations to event-free patient survival and predictive power for explaining patient-specific differences of STN activity. First, we identified the WNT pathway as the most significant pathway associated with event-free survival. Second, we built linear-regression models that incorporated both genes and proteins from established mechanistic models in the literature and novel genes with significant associations to event-free patient survival. Data from The Cancer Genome Atlas and Clinical Proteomic Tumour Analysis Consortium were used, and patient-specific STN activity scores were computed using PROGENy. Three linear regression models were built, based on; (1) the gene-set of a state-of-the-art mechanistic model in the literature, (2) novel genes identified, and (3) novel proteins identified. The novel genes and proteins were genes and proteins of the extant WNT pathway whose expression was significantly associated with event-free survival. The results show that the predictive power of a model that incorporated novel event-free associated genes is better compared to a model focussing on the genes of a current state-of-the-art mechanistic model. Several significant genes that should be integrated into future mechanistic models of the WNT pathway are DVL3, FZD5, RAC1, ROCK2, GSK3B, CTB2, CBT1, and PRKCA. Thus, the study demonstrates that using mechanistic information in combination with machine learning can identify novel features (genes and proteins) that are important for explaining the STN heterogeneity between patients and their association to clinical outcomes.

scholarly journals GTFtools: a Python package for analyzing various modes of gene models

2018 ◽  
Author(s):  
Hong-Dong Li
Keyword(s):  
Intron Retention ◽  
Gc Content ◽  
Third Party ◽  
Rna Seq ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Different Types ◽  
Gene Models ◽  
Flanking Regions ◽  
Python Package

AbstractSummaryGene-centric bioinformatics studies frequently involve calculation or extraction of various features of genes such as gene ID mapping, GC content calculation and different types of gene lengths, through manipulation of gene models that are often annotated in GTF format and available from ENSEMBL or GENCODE database. Such computation is essential for subsequent analysis such as intron retention detection where independent introns may need to be identified, converting RNA-seq read counts to FPKM where gene length is required, and obtaining flanking regions around transcription start sites. However, to our knowledge, a software package that is dedicated to analyzing various modes of gene models directly from GTF file is not publicly available. In this work, GTFtools (implemented in Python and not dependent on any non-python third-party software), a stand-alone command-line software that provides a set of functions to analyze various modes of gene models, is provided for facilitating routine bioinformatics studies where information about gene models needs to be calculated.AvailabilityGTFtools is freely available at www.genemine.org/[email protected].

scholarly journals Are Fusion Transcripts in Relapsed/Metastatic Head and Neck Cancer Patients Predictive of Response to Anti-EGFR Therapies?

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Paolo Bossi ◽  
Marco Siano ◽  
Cristiana Bergamini ◽  
Maria Cossu Rocca ◽  
Andrea P. Sponghini ◽  
...  
Keyword(s):  
Head And Neck ◽  
Noncoding Rna ◽  
Growth Factor Receptor ◽  
Progression Free Survival ◽  
Cancer Genes ◽  
Rna Seq ◽  
Free Survival ◽  
Fusion Transcripts ◽  
Ras Activation ◽  
Patient’S Outcome

Prediction of benefit from combined chemotherapy and the antiepidermal growth factor receptor cetuximab is a not yet solved question in head and neck squamous cell carcinoma (HNSCC). In a selected series of 14 long progression-free survival (PFS) and 26 short PFS patients by whole gene and microRNA expression analysis, we developed a model potentially predictive of cetuximab sensitivity. To better decipher the “omics” profile of our patients, we detected transcript fusions by RNA-seq through a Pan-Cancer panel targeting 1385 cancer genes. Twenty-seven different fusion transcripts, involving mRNA and long noncoding RNA (lncRNA), were identified. The majority of fusions (81%) were intrachromosomal, and 24 patients (60%) harbor at least one of them. The presence/absence of fusions and the presence of more than one fusion were not related to outcome, while the lncRNA-containing fusions resulted enriched in long PFS patients (P=0.0027). The CD274-PDCD1LG2 fusion was present in 7/14 short PFS patients harboring fusions and was absent in long PFS patients (P=0.0188). Among the short PFS patients, those harboring this fusion had the worst outcome (P=0.0172) and increased K-RAS activation (P=0.00147). The associations between HNSCC patient’s outcome following cetuximab treatment and lncRNA-containing fusions or the CD274-PDCD1LG2 fusion deserve validation in prospective clinical trials.

scholarly journals Polo like kinase 1 expression in cervical cancer tissues generated from multiple detection methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10458
Author(s):  
Li Gao ◽  
Yu-Yan Pang ◽  
Xian-Yu Guo ◽  
Jing-Jing Zeng ◽  
Zhong-Qing Tang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Tissue Microarrays ◽  
Cervical Adenocarcinoma ◽  
Rna Seq ◽  
Event Free Survival ◽  
Survival Curves ◽  
Free Survival ◽  
Kaplan Meier ◽  
Plk1 Expression

Background Existing studies of PLK1 in cervical cancer had several flaws. The methods adopted by those studies of detecting PLK1 expression in cervical cancer were single and there lacks comprehensive evaluation of the clinico-pathological significance of PLK1 in cervical cancer. Methods A total of 303 cervical tissue samples were collected for in-house tissue microarrays. Immunohistochemistry was performed for evaluating PLK1 expression between cervical cancer (including cervical squamous cell carcinoma (CESC) and cervical adenocarcinoma) and non-cancer samples. The Expression Atlas database was searched for querying PLK1 expression in different cervical cancer cell lines and different tissues in the context of pan-cancer. Standard mean difference (SMD) was calculated and the summarized receiver’s operating characteristics (SROC) curves were plotted for integrated tissue microarrays, exterior high-throughput microarrays and RNA sequencing data as further verification. The effect of PLK1 expression on the overall survival, disease-free survival and event-free survival of cervical cancer patients was analyzed through Kaplan Meier survival curves for cervical cancer patients from RNA-seq and GSE44001 datasets. The gene mutation and alteration status of PLK1 in cervical cancer was inspected in COSMIC and cBioPortal databases. Functional enrichment analysis was performed for genes correlated with PLK1 from aggregated RNA-seq and microarrays. Results A total of 963 cervical cancer samples and 178 non-cancer samples were collected from in-house tissue microarrays and exterior microarrays and RNA-seq datasets. The combined expression analysis supported overexpression of PLK1 in CESC, cervical adenocarcinoma and all types of cervical cancer (SMD = 1.59, 95%CI [0.56–2.63]; SMD = 2.99, 95%CI [0.75–5.24]; SMD = 1.57, 95% CI [0.85–2.29]) and the significant power of PLK1 expression in distinguishing CESC or all types of cervical cancer samples from non-cancer samples (AUC = 0.94, AUC = 0.92). Kaplan-Meier survival curves showed that the event-free survival rate of cervical cancer patients with higher expression of PLK1 was shorter than that of patients with lower PLK1 (HR = 2.020, P = 0.0197). Genetic alteration of PLK1 including missense mutation and mRNA low occurred in 6% of cervical cancer samples profiled in mRNA expression. Genes positively or negatively correlated with PLK1 were mainly assembled in pathways such as DNA replication, cell cycle, mismatch repair, Ras signaling pathway, melanoma, EGFR tyrosine kinase inhibitor resistance and homologous recombination (P < 0.05). Conclusions Here, we provided sufficient evidence of PLK1 overexpression in cervical cancer. The overexpression of PLK1 in cervical cancer and the contributory effect of it on clinical progression indicated the hopeful prospect of PLK1 as a biomarker for cervical cancer.

Multivariate analysis of factors affecting overall survival, event free survival, and 60-day mortality among AML patients treated with CPX-351 or intensive chemotherapy.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7100-7100 ◽  
Author(s):  
Eric Jay Feldman ◽  
Jeffrey E. Lancet ◽  
Jonathan E. Kolitz ◽  
Donna Hogge ◽  
Martin S. Tallman ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Allogeneic Transplantation ◽  
Salvage Chemotherapy ◽  
Drug Uptake ◽  
Molar Ratio ◽  
Patient Specific ◽  
Newly Diagnosed ◽  
Event Free Survival ◽  
Free Survival ◽  
Factors Affecting

7100 Background: CPX-351 encapsulates cytarabine (CYT) and daunorubicin (DNR) at a 5:1 molar ratio within liposomes, enabling preferential drug uptake within leukemic blasts and intracellular release, potentially enhancing efficacy in AML. A pair of randomized Phase IIb studies in newly diagnosed older patients (pts) and younger 1st relapse AML pts reported improved rates of morphologic leukemia-free state, CR + CRi, and significant improvements in survival among previously untreated high risk (secondary) pts and among poor-risk 1st relapse pts. This report presents the results of the multivariate analyses performed on all pts treated in both studies. Methods: Patients 60-75 yo with newly diagnosed AML and ≤ 65 yo with 1st relapse AML and ECOG PS= 0-2, SCR < 2.0 mg/dL, total bilirubin < 2.0 mg/dL, ALT/AST <3x ULN, and LVEF ≥ 50% were eligible. Pts with APL, DNR exposure >368 mg/m2, active CNS leukemia, and uncontrolled infections were excluded. Pts were randomized 2:1 to receive up to 2 inductions and 2 consolidations with CPX-351 (100 u/m2; D 1, 3, 5) or CYT + DNR (7+3) for newly diagnosed pts or investigator’s choice of salvage chemotherapy for relapsed pts. Allogeneic transplantation was permitted. Univariate and multivariate Cox and logistic regression were used to assess associations between baseline characteristics and overall (OS) and event-free survival (EFS) and 60-day mortality for all pts. The multivariate employed stepwise selection to identify statistically significant prognostic factors after accounting for potential treatment effects. Results: Patient characteristics including cytogenetics were well balanced. Significant negative prognostic factors affecting OS, EFS, and 60-day mortality included relapsed disease (Study 205 participation, HR=2.13, p<0.001), adverse cytogenetics (HR=1.52, p=0.024), and low (<3g/dL) serum albumin (HR=1.82, p=0.005). CPX-351 treatment was a significant positive factor in EFS (HR=0.62, p=0.006). Conclusions: This analysis identified and quantitated disease specific (adverse cytogenetics) and patient specific (albumin<3gm/dL) factors that can be used to better design future studies. Clinical trial information: NCT00788892 and NCT00822094.

Sex Differences in Perioperative and Long-term Cardiac Event–Free Survival in Vascular Surgery Patients

Circulation ◽  
1995 ◽  
Vol 91 (4) ◽  
pp. 1044-1051 ◽  
Author(s):  
Robert C. Hendel ◽  
Ming Hui Chen ◽  
Gilbert J. L’Italien ◽  
John B. Newell ◽  
Sumita D. Paul ◽  
...  
Keyword(s):  
Sex Differences ◽  
Vascular Surgery ◽  
Cardiac Event ◽  
Event Free Survival ◽  
Free Survival

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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