Network-Based Logistic Classification with an EnhancedL1/2Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer

BioMed Research International ◽

10.1155/2015/713953 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Hai-Hui Huang ◽

Yong Liang ◽

Xiao-Ying Liu

Keyword(s):

Lung Cancer ◽

Regularization Method ◽

Predictive Accuracy ◽

Elastic Net ◽

Genomic Research ◽

Biological Processes ◽

Simulation Studies ◽

Expression Data ◽

Cancer Data ◽

Genomic Studies

Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based onL1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhancedL1/2penalized solver to penalize network-constrained logistic regression model called an enhancedL1/2net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperformsL1regularization, the oldL1/2penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy thanL1regularization, the oldL1/2penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected.

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TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification

BMC Bioinformatics ◽

10.1186/s12859-020-03869-9 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Yunxia Tang ◽

Yu Wang ◽

Jiaqian Wang ◽

Miao Li ◽

Linmin Peng ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Patient ◽

Binding Affinity ◽

Lung Cancer Patient ◽

Predictive Accuracy ◽

Recall Rate ◽

Biological Processes ◽

Data Set ◽

Patient Recall ◽

Positive Rate

Abstract Background Neoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Algorithms for the accurate prediction of neoantigens have played a pivotal role in such studies. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. However, the predictive accuracy of immunogenicity of these methods has been shown to be low. Thus, a ranking algorithm to select highly immunogenic neoantigens of patients is needed urgently in research and clinical practice. Results We develop TruNeo, an integrated computational pipeline to identify and select highly immunogenic neoantigens based on multiple biological processes. The performance of TruNeo and other algorithms were compared based on data from published literature as well as raw data from a lung cancer patient. Recall rate of immunogenic ones among the top 10-ranked neoantigens were compared based on the published combined data set. Recall rate of TruNeo was 52.63%, which was 2.5 times higher than that predicted by MHCflurry (21.05%), and 2 times higher than NetMHCpan 4 (26.32%). Furthermore, the positive rate of top 10-ranked neoantigens for the lung cancer patient were compared, showing a 50% positive rate identified by TruNeo, which was 2.5 times higher than that predicted by MHCflurry (20%). Conclusions TruNeo, which considers multiple biological processes rather than peptide-MHC binding affinity prediction only, provides prioritization of candidate neoantigens with high immunogenicity for neoantigen-targeting personalized immunotherapies.

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Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i5.2083 ◽

2020 ◽

Vol 4 (5) ◽

pp. 805-812

Author(s):

Riska Chairunisa ◽

Adiwijaya ◽

Widi Astuti

Keyword(s):

Breast Cancer ◽

Lung Cancer ◽

Ovarian Cancer ◽

Random Forest ◽

Classification And Regression Tree ◽

Classification Method ◽

Discrete Wavelet ◽

Prostate Tumors ◽

Cancer Data ◽

Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.

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Patients with Metastatic Lung Cancer and Oncologists’ Views on Achievement of Treatment Goals and Making the Right Treatment Decision: A Prospective Multicenter Study

Medical Decision Making ◽

10.1177/0272989x21998951 ◽

2021 ◽

pp. 0272989X2199895

Author(s):

Adinda Mieras ◽

Annemarie Becker-Commissaris ◽

Hanna T. Klop ◽

H. Roeline W. Pasman ◽

Denise de Jong ◽

...

Keyword(s):

Lung Cancer ◽

Multicenter Study ◽

Systemic Treatment ◽

Treatment Decision ◽

Treatment Goals ◽

Metastatic Lung Cancer ◽

Prospective Multicenter Study ◽

Cancer Data ◽

Metastatic Lung ◽

The Right

Background Previous studies have investigated patients’ treatment goals before starting a treatment for metastatic lung cancer. Data on the evaluation of treatment goals are lacking. Aim To determine if patients with metastatic lung cancer and their oncologists perceive the treatment goals they defined at the start of systemic treatment as achieved after treatment and if in hindsight they believe it was the right decision to start systemic therapy. Design and Participants A prospective multicenter study in 6 hospitals across the Netherlands between 2016 and 2018. Following systemic treatment, 146 patients with metastatic lung cancer and 23 oncologists completed a questionnaire on the achievement of their treatment goals and whether they made the right treatment decision. Additional interviews with 15 patients and 5 oncologists were conducted. Results According to patients and oncologists, treatment goals were achieved in 30% and 37% for ‘quality of life,’ 49% and 41% for ‘life prolongation,’ 26% and 44% for ‘decrease in tumor size,’ and 44% for ‘cure’, respectively. Most patients and oncologists, in hindsight, felt they had made the right decision to start treatment and also if they had not achieved their goals (72% and 93%). This was related to the feeling that they had to do ‘something.’ Conclusions Before deciding on treatment, the treatment options, including their benefits and side effects, and the goals patients have should be discussed. It is key that these discussions include not only systemic treatment but also palliative care as effective options for doing ‘something.’

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Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer

BMC Bioinformatics ◽

10.1186/s12859-021-04296-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Erica Ponzi ◽

Magne Thoresen ◽

Therese Haugdahl Nøst ◽

Kajsa Møllersen

Keyword(s):

Lung Cancer ◽

Biological Process ◽

Case Control ◽

Integrative Analysis ◽

Data Sources ◽

Omics Data ◽

Biological Processes ◽

Blood Samples ◽

Model Predictions

Abstract Background Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific (“individual”) patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as “shared” or “joint”. In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case–control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case–control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. Results Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. Conclusions In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

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Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning

IEEE Access ◽

10.1109/access.2021.3071466 ◽

2021 ◽

pp. 1-1

Author(s):

Dong Sui ◽

Maozu Guo ◽

Xiaoxuan Ma ◽

Julian Baptiste ◽

Lei Zhang

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Deep Learning ◽

Gene Expression Data ◽

Imaging Biomarkers ◽

Expression Data ◽

Data Correlation

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Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data

Journal of Physics Conference Series ◽

10.1088/1742-6596/1471/1/012043 ◽

2020 ◽

Vol 1471 ◽

pp. 012043

Author(s):

Yessi Jusman ◽

Zul Indra ◽

Roni Salambue ◽

Siti Nurul Aqmariah Mohd Kanafiah ◽

Muhammad Ahdan Fawwaz Nurkholid

Keyword(s):

Lung Cancer ◽

Radial Basis Function ◽

Basis Function ◽

Classification Performance ◽

Cancer Data ◽

Lung Cancer Data ◽

Radial Basis

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Proteomic and Genomic Studies on Lizard Venoms in the Last Decade

Proteomics Insights ◽

10.4137/pri.s3693 ◽

2010 ◽

Vol 3 ◽

pp. PRI.S3693 ◽

Cited By ~ 1

Author(s):

Hang Fai Kwok ◽

Craig Ivanyi ◽

Andrew Morris ◽

Chris Shaw

Keyword(s):

Natural Resources ◽

Potential Benefit ◽

Current Knowledge ◽

The State ◽

Bioactive Molecules ◽

Genomic Research ◽

Active Compounds ◽

Current Review ◽

Comprehensive Picture ◽

Genomic Studies

Traditionally man has looked to nature to provide cures for diseases. This approach still exists today in the form of ‘bio-prospecting’ for therapeutically-active compounds in venoms. For example, the venoms of many reptiles offer a spectacular laboratory of bioactive molecules, including peptides and proteins. In the last 10–15 years, there have been a number of major proteomic and genomic research breakthroughs on lizard venoms. In this current review, the key findings from these proteomic and genomic studies will be critically discussed and suggestions will be offered for future focused investigations. It is our intention that this article will not only provide a comprehensive picture of the state of current knowledge of the components of lizard venoms, but also engender awareness in readers of the need to protect and conserve such uniquely precious natural resources for several reasons, including the potential benefit of humankind.

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Circulating or tissue microRNAs and extracellular vesicles as potential lung cancer biomarkers: a systematic review

The International Journal of Biological Markers ◽

10.5301/ijbm.5000307 ◽

2017 ◽

Vol 33 (1) ◽

pp. 3-9 ◽

Cited By ~ 7

Author(s):

Yan Song ◽

Xiuli Yu ◽

Zongmei Zang ◽

Guijuan Zhao

Keyword(s):

Lung Cancer ◽

Systematic Review ◽

Cancer Patients ◽

Extracellular Vesicles ◽

Target Genes ◽

Enrichment Analysis ◽

Cancer Biomarkers ◽

Biological Processes ◽

Lung Cancer Patients ◽

Prediction Of Prognosis

For both lung cancer patients and clinical physicians, tumor biomarkers for more efficient early diagnosis and prediction of prognosis are always wanted. Biomarkers in circulating serum, including microRNAs (miRNAs) and extracellular vesicles, hold the greatest possibilities to partially substitute for tissue biopsy. In this systematic review, studies on circulating or tissue miRNAs and extracellular vesicles as potential biomarkers for lung cancer patients were reviewed and are discussed. Furthermore, the target genes of the miRNAs indicated were identified through the miRTarBase, while the relevant biological processes and pathways of miRNAs in lung cancer were analyzed through MiRNA Enrichment Analysis and Annotation (MiEAA). In conclusion, circulating or tissue miRNAs and extracellular vesicles provide us with a window to explore strategies for diagnosing and assessing prognosis and treatment in lung cancer patients.

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MicroRNAs: Key Regulators in Lung Cancer

MicroRNA ◽

10.2174/2211536610666210527102522 ◽

2021 ◽

Vol 10 ◽

Author(s):

Younes El Founini ◽

Imane Chaoui ◽

Hind Dehbi ◽

Mohammed El Mzibri ◽

Roger Abounader ◽

...

Keyword(s):

Lung Cancer ◽

Messenger Rna ◽

Recent Literature ◽

Immune Surveillance ◽

Rna Stability ◽

Noncoding Rnas ◽

Therapy Resistance ◽

Biological Processes ◽

Genome Wide ◽

Stable Forms

: Noncoding RNAs have emerged as key regulators of the genome upon gene expression profiling and genome-wide sequencing. Among these noncoding RNAs, microRNAs are short noncoding RNAs that regulate a plethora of functions, biological processes and human diseases by targeting the messenger RNA stability through 3’UTR binding, leading to either mRNA cleavage or translation repression, depending on microRNA-mRNA complementarity degree. Additionally, strong evidence has suggested that dysregulation of miRNAs contribute to the etiology and progression of human cancers, such as lung cancer, the most common and deadliest cancer worldwide. Indeed, by acting as oncogenes or tumor suppressors, microRNAs control all aspects of lung cancer malignancy, including cell proliferation, survival, migration, invasion, angiogenesis, cancer stem cells, immune-surveillance escape, and therapy resistance; and their expressions are often associated with clinical parameters. Moreover, several deregulated microRNAs in lung cancer are carried by exosomes, microvesicles and secreted in body fluids, mainly the circulation where they conserve their stable forms. Subsequently, seminal efforts have been focused on extracellular microRNAs levels as noninvasive diagnostic and prognostic biomarkers in lung cancer. In this review, focusing on recent literature, we summarize the deregulation, mechanisms of action, functions and highlight clinical applications of miRNAs for better management and design of future lung cancer targeted therapies.

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