scholarly journals Transcription Factors Leading to High Expression of Neuropeptide L1CAM in Brain Metastases from Lung Adenocarcinoma and Clinical Prognostic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xu Feng ◽  
Ning Guan ◽  
Enshi Xu ◽  
Ye Miao ◽  
Chenguang Li
Keyword(s):  
Transcription Factors ◽  
Brain Metastases ◽  
Lung Adenocarcinoma ◽  
Single Cell ◽  
Prediction Models ◽  
Roc Curves ◽  
Distant Metastases ◽  
Transcriptional Regulators ◽  
High Expression ◽  
Tissue Samples

Background. There is a lack of understanding of the development of metastasis in lung adenocarcinoma (LUAD). This study is aimed at exploring the upstream regulatory transcription factors of L1 cell adhesion molecule (L1CAM) and to construct a prognostic model to predict the risk of brain metastasis in LUAD. Methods. Differences in gene expression between LUAD and brain metastatic LUAD were analyzed using the Wilcoxon rank-sum test. The GRNdb (http://www.grndb.com) was used to reveal the upstream regulatory transcription factors of L1CAM in LUAD. Single-cell expression profile data (GSE131907) were obtained from the transcriptome data of 10 metastatic brain tissue samples. LUAD prognostic nomogram prediction models were constructed based on the identified significant transcription factors and L1CAM. Results. Survival analysis suggested that high L1CAM expression was negatively significantly associated with overall survival, disease-specific survival, and prognosis in the progression-free interval ( p < 0.05 ). The box plot indicates that high expression of L1CAM was associated with distant metastases in LUAD, while ROC curves suggested that high expression of L1CAM was associated with poor prognosis. FOSL2, HOXA9, IRF4, IKZF1, STAT1, FLI1, ETS1, E2F7, and ADARB1 are potential upstream transcriptional regulators of L1CAM. Single-cell data analysis revealed that the expression of L1CAM was found significantly and positively correlated with the expression of ETS1, FOSL2, and STAT1 in brain metastases. L1CAM, ETS1, FOSL2, and STAT1 were used to construct the LUAD prognostic nomogram prediction model, and the ROC curves suggest that the constructed nomogram possesses good predictive power. Conclusion. By bioinformatics methods, ETS1, FOSL2, and STAT1 were identified as potential transcriptional regulators of L1CAM in this study. This will help to facilitate the early identification of patients at high risk of metastasis.

scholarly journals A multistep computational procedure to identify candidate master Transcriptional Regulators (TRs) of glioblastoma (GBM)

2020 ◽  
Author(s):  
Michelangelo Cordenonsi ◽  
Silvio Bicciato ◽  
Stefano Piccolo
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Transcriptional Regulators ◽  
Computational Procedure ◽  
Tissue Specific ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract We describe a multistep computational procedure to identify candidate master Transcriptional Regulators (TRs) of glioblastoma (GBM) from glioblastoma single cell gene expression profiles and tissue-specific transcription factors.

scholarly journals Protocol for single-cell ATAC sequencing using combinatorial indexing in mouse lung adenocarcinoma

2021 ◽  
Vol 2 (2) ◽  
pp. 100583
Author(s):  
Isabella Del Priore ◽  
Sai Ma ◽  
Jonathan Strecker ◽  
Tyler Jacks ◽  
Lindsay M. LaFave ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Single Cell ◽  
Mouse Lung

scholarly journals Nomogram based on homogeneous and heterogeneous associated factors for predicting distant metastases in patients with colorectal cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Tianwen Luo ◽  
Yutong Wang ◽  
Xuefeng Shan ◽  
Ye Bai ◽  
Chun Huang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lung Metastases ◽  
Roc Curves ◽  
Distant Metastases ◽  
Good Prediction ◽  
Operating Characteristics ◽  
Good Prediction Performance ◽  
Different Types ◽  
Associated Risk Factors

Abstract Background The identification of the homogeneous and heterogeneous risk factors for different types of metastases in colorectal cancer (CRC) may shed light on the aetiology and help individualize prophylactic treatment. The present study characterized the incidence differences and identified the homogeneous and heterogeneous risk factors associated with distant metastases in CRC. Methods CRC patients registered in the SEER database between 2010 and 2016 were included in this study. Logistic regression was used to analyse homogeneous and heterogeneous risk factors for the occurrence of different types of metastases. Nomograms were constructed to predict the risk for developing metastases, and the performance was quantitatively assessed using the receiver operating characteristics (ROC) curve and calibration curve. Results A total of 204,595 eligible CRC patients were included in our study, and 17.07% of them had distant metastases. The overall incidences of liver metastases, lung metastases, bone metastases, and brain metastases were 15.34%, 5.22%, 1.26%, and 0.29%, respectively. The incidence of distant metastases differed by age, gender, and the original CRC sites. Poorly differentiated grade, more lymphatic metastasis, higher carcinoembryonic antigen (CEA), and different metastatic organs were all positively associated with four patterns of metastases. In contrast, age, sex, race, insurance status, position, and T stage were heterogeneously associated with metastases. The calibration and ROC curves exhibited good performance for predicting distant metastases. Conclusions The incidence of distant metastases in CRC exhibited distinct differences, and the patients had homogeneous and heterogeneous associated risk factors. Although limited risk factors were included in the present study, the established nomogram showed good prediction performance.

scholarly journals Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110180
Author(s):  
Xiao Lin ◽  
Meng Zhou ◽  
Zehong Xu ◽  
Yusheng Chen ◽  
Fan Lin
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Candidate Genes ◽  
Postoperative Recurrence ◽  
High Expression ◽  
Ppi Network ◽  
Hub Genes ◽  
Expression Levels

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein–protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

scholarly journals Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Xingmin Feng ◽  
Sachiko Kajigaya ◽  
Xujing Wang ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Transcription Factors ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transcription Regulation ◽  
Regulatory Networks ◽  
Stem Cell Differentiation ◽  
Hematopoietic Stem ◽  
Single Cell Rna Sequencing ◽  
Human And Mouse

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.

57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A62-A62
Author(s):  
Dattatreya Mellacheruvu ◽  
Rachel Pyke ◽  
Charles Abbott ◽  
Nick Phillips ◽  
Sejal Desai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Antigen Processing ◽  
Large Scale ◽  
Prediction Models ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
High Quality ◽  
Tissue Samples

BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

scholarly journals Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

2021 ◽  
Author(s):  
Yixuan Qiu ◽  
Jiebiao Wang ◽  
Jing Lei ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
R Package ◽  
Supplementary Information ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Correlation Pattern ◽  
Tissue Samples ◽  
Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 702: Single-cell expression landscape of SARS-CoV-2 receptorACE2and host proteases in human lung adenocarcinoma

2021 ◽  
Author(s):  
Guangchun Han ◽  
Ansam Sinjab ◽  
Kieko Hara ◽  
Warapen Treekitkarnmongkol ◽  
Patrick Brennan ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Single Cell ◽  
Human Lung ◽  
Human Lung Adenocarcinoma ◽  
Cell Expression
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