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Mast cell‐based molecular subtypes and signature associated with clinical outcome in early‐stage lung adenocarcinoma

Xuanwen Bao, Run Shi, Tianyu Zhao, Yanfang Wang
2020 Molecular Oncology  
The meta-analysis confirmed the prognostic value of the mast cell-related gene signature.  ...  Regarding the impact of mast cells on the outcomes of patients with lung adenocarcinoma (LUAD) patient, several published studies have shown contradictory results.  ...  We constructed the neural network with the mast cell-related gene signature by the training dataset. The test dataset was applied to evaluate the accuracy of the neural network.  ... 
doi:10.1002/1878-0261.12670 pmid:32175651 fatcat:zq3sq7y3bba5feutkfmt7iuwli

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Grasieli de Oliveira, Paula Paccielli Freire, Sarah Santiloni Cury, Diogo de Moraes, Jakeline Santos Oliveira, Maeli Dal-Pai-Silva, Patrícia Pintor do Reis, Robson Francisco Carvalho
2020 Cancers  
Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk  ...  In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with  ...  Comparison with Prognostic Gene Signatures of Pancreatic Ductal Adenocarcinoma Comparison with Prognostic Gene Signatures of Pancreatic Ductal Adenocarcinoma Several prognostic gene signatures for PDAC  ... 
doi:10.3390/cancers12030716 pmid:32197468 fatcat:7aq6ugbcizf4ret6anus5pevoq

Potential Role of S-Palmitoylation in Cancer Stem Cells of Lung Adenocarcinoma

Yitong Zhang, Fenglan Li, Kexin Fu, Xiqing Liu, I-Chia Lien, Hui Li
2021 Frontiers in Cell and Developmental Biology  
However, stemness genes modulated by ZDHHCs in lung adenocarcinoma (LUAD) remain to be defined.  ...  Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA).  ...  In this study, LCE was employed to evaluate the prognostic value of ZDHHC5 and INCENP using meta-analysis.  ... 
doi:10.3389/fcell.2021.734897 pmid:34621750 pmcid:PMC8490697 fatcat:lc3fz2tdhrhh5deluuhaxecspe

Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer

Lei Cong, Wanbing Feng, Zhigang Yao, Xiaoming Zhou, Wei Xiao
2020 Journal of Cancer  
The identification and characteristics of malignant cells are essential for the diagnosis and treatment of primary or metastatic cancers.  ...  Deep learning is a new field of artificial intelligence, which can be used for computer aided diagnosis and scientific research of lung cancer pathology by analyzing and learning through establishment  ...  Zhenxiang Chen, Vice Dean of College of Information Science and Engineering, Jinan University, for his excellent suggestion and assist work for this manuscript.  ... 
doi:10.7150/jca.43268 pmid:32284758 pmcid:PMC7150458 fatcat:5lq3sjp2z5ecbomw43ab5howzy

An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Yidi Wang, Yaxuan Wang, Kenan Li, Yabing Du, Kang Cui, Pu Yu, Tengfei Zhang, Hong Liu, Wang Ma
2020 Bioscience Reports  
Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.  ...  AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD).  ...  Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms.  ... 
doi:10.1042/bsr20202338 fatcat:twforigpmnaahkkfrl4p6qyd3u

Editorial: Advances in Mathematical and Computational Oncology

George Bebis, Doron Levy, Russell Rockne, Ernesto Augusto Bueno Da Fonseca Lima, Panayiotis V. Benos
2022 Frontiers in Physiology  
networks with gene expression profiles.  ...  These 150 significant genes were further analyzed using DAVID, KEGG and other methods resulting in 24 hub-genes, which were then used for downstream analysis and validation.  ...  (Identification and Validation of Two Lung Adenocarcinoma-Development Characteristic Gene Sets for Diagnosing Lung Adenocarcinoma and Predicting Prognosis) identify and validate two Lung adenocarcinoma  ... 
doi:10.3389/fphys.2022.889198 pmid:35464082 pmcid:PMC9021698 fatcat:f6mocy3oebhvxmn675bzbyfvuq

Profiling cancer testis antigens in non–small-cell lung cancer

Dijana Djureinovic, Björn M. Hallström, Masafumi Horie, Johanna Sofia Margareta Mattsson, Linnea La Fleur, Linn Fagerberg, Hans Brunnström, Cecilia Lindskog, Katrin Madjar, Jörg Rahnenführer, Simon Ekman, Elisabeth Ståhle (+10 others)
2016 JCI Insight  
We thank Clinical Pathology at the Uppsala University Hospital and Simin Tahmasebpoor for assistance with tissue samples and for assistance with sample preparations.  ...  Acknowledgments We acknowledge the HPA team; support from Science for Life Laboratory, the National Genomics Infrastructure (NGI); and Uppmax for providing assistance in massive parallel sequencing and  ...  In accordance with the algorithm presented by Jiao et al. (66) for each gene, the average β value of probes for the TSS200 was calculated. Survival analysis and meta-analysis of public data sets.  ... 
doi:10.1172/jci.insight.86837 pmid:27699219 pmcid:PMC5033889 fatcat:tyzramce7zhgjg6c72dig2qqr4

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

Sandip Kumar Patel, Bhawana George, Vineeta Rai
2020 Frontiers in Pharmacology  
of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery.  ...  AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification  ...  Moarii et al. used a large data set of 672 cancerous and healthy methylomes gene expression and copy number profiles from TCGA and performed a meta-analysis to clarify the interplay between promoter methylation  ... 
doi:10.3389/fphar.2020.01177 pmid:32903628 pmcid:PMC7438594 fatcat:u7mdynhnwfazbn6jhvcagorp2a

Cancer Subtype Discovery Using Prognosis-Enhanced Neural Network Classifier in Multigenomic Data

Prasanna Vasudevan, Thangamani Murugesan
2018 Technology in Cancer Research and Treatment  
The proposed prognosis-enhanced neural network classifier algorithm produces higher accuracy results of 89.2% for 215 samples efficiently.  ...  The samples were classified into 4 different classes such as mesenchymal, classical, proneural, and neural subtypes owing to mutations and gene expression.  ...  The study constitutes constructing the decision tree by using the J48 Weka tool for lung cancer subtypes and predicts the lung cancer variety for unfamiliar class.  ... 
doi:10.1177/1533033818790509 pmid:30092720 pmcid:PMC6088521 fatcat:cjptqm3gdrgjzc754v3idtpyba

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

Giovanna Nicora, Francesca Vitali, Arianna Dagliati, Nophar Geifman, Riccardo Bellazzi
2020 Frontiers in Oncology  
The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework.  ...  data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice.  ...  ACKNOWLEDGMENTS We would like to acknowledge Simone Marini for his valuable help in the initial phases of the study.  ... 
doi:10.3389/fonc.2020.01030 pmid:32695678 pmcid:PMC7338582 fatcat:wr3auiukhrdm7o76ksgayy4aim

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

Ahmed Hosny, Chintan Parmar, Thibaud P. Coroller, Patrick Grossmann, Roman Zeleznik, Avnish Kumar, Johan Bussink, Robert J. Gillies, Raymond H. Mak, Hugo J. W. L. Aerts, Atul J. Butte
2018 PLoS Medicine  
This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.  ...  Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage.  ...  The deep learning network predictions on the surgery tuning dataset MUMC were linked to global gene expression patterns using a pre-ranked gene set enrichment analysis (GSEA).  ... 
doi:10.1371/journal.pmed.1002711 pmid:30500819 pmcid:PMC6269088 fatcat:mh5nqx6zqfdqrjikdvk6i4flkq

The prognostic landscape of interactive biological processes presents treatment responses in cancer

Bin He, Rui Gao, Dekang Lv, Yalu Wen, Luyao Song, Xi Wang, Suxia Lin, Qitao Huang, Ziqian Deng, Zifeng Wang, Min Yan, Feimeng Zheng (+4 others)
2019 EBioMedicine  
Gene-prognosis scores of 39 malignancies (GEO datasets) were used for examining the prognoses, and TCGA datasets were selected for validation.  ...  Differential gene expression patterns are commonly used as biomarkers to predict treatment responses among heterogeneous tumors.  ...  Author contributions Bin He and Rui Gao participated in design, acquisition, analysis and interpretation of data and manuscript preparation for the entire project.  ... 
doi:10.1016/j.ebiom.2019.01.064 pmid:30799199 pmcid:PMC6441875 fatcat:jtp6otyj6fhvfgktbc3altb2nm

Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

Chunyang Li, Haopeng Yu, Yajing Sun, Xiaoxi Zeng, Wei Zhang
2021 PeerJ  
The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy. GSE66229 from Gene Expression Omnibus (GEO) was used as training set.  ...  Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes.  ...  Then, artificial neural network algorithms were performed, and demonstrated that this 11-gene model could effectively discriminate between gastric cancer and normal tissues.  ... 
doi:10.7717/peerj.10682 pmid:33717664 pmcid:PMC7938783 fatcat:ackmbjzqnzanbczvrldcfgnffi

Genomic Copy Number Signatures Based Classifiers for Subtype Identification in Cancer [article]

Bo Gao, Michael Baudis
2020 bioRxiv   pre-print
Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes.  ...  of source data and derived CNV profiles pose great challenges for data integration and comparative analysis.  ...  Acknowledgments We thank Paula Carrio Cordo and Qingyao Huang for support with the data ontologies, and all current and previous members of the Baudis group for contributions to the Progenetix resource  ... 
doi:10.1101/2020.12.18.423278 fatcat:t2gonvbthjc35d72xwbsvrhwpm

A Novel Attention-Mechanism Based Cox Survival Model by Exploiting Pan-Cancer Empirical Genomic Information

Xiangyu Meng, Xun Wang, Xudong Zhang, Chaogang Zhang, Zhiyuan Zhang, Kuijie Zhang, Shudong Wang
2022 Cells  
In addition, we carry out some feature analysis experiments. Based on the experimental results, we concluded that our model is helpful for revealing cancer-related genes and biological functions.  ...  Given these problems, we introduced a novel framework called SAVAE-Cox for survival analysis of high-dimensional transcriptome data.  ...  Then Ching et al. designed a Coxnnet composed of two-layer neural networks, and successfully used Cox-nnet to make reasonable survival analysis recommendations for 10 different cancer gene expression data  ... 
doi:10.3390/cells11091421 pmid:35563727 pmcid:PMC9100007 fatcat:wqtemxio2nes7d6yu7ptogl7na
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