Screening of Differentially Expressed Genes among Various TNM Stages of Lung Adenocarcinoma by Genomewide Gene Expression Profile Analysis

Liu, Ming; Pan, Hong; Zhang, Na; Zhang, Yong-Biao; Zhang, Yang; Xia, Han; Zhu, Jing; Fu, Weiling; Zhang, Xiaoli

doi:10.7314/apjcp.2013.14.11.6281

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…This corroborates well with the previous findings that indicated a pro-tumorigenic role for ZMIZ2. 73 , 74 Staining of ZMIZ2 on a second cohort of ovarian cancer patients, reiterated the upregulation of ZMIZ2 in poor prognosis patients. Functionally, this is validated by the findings that the expression of ZMIZ2 is correlated with the Wnt/beta-catenin signaling 74 and Wnt-signaling is often dysregulated in ovarian cancer.…”

Section: Discussionmentioning

confidence: 98%

Circulating Tumor Cell-Free DNA Genes as Prognostic Gene Signature for Platinum Resistant Ovarian Cancer Diagnosis

et al. 2022

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Clinical management of gynecological cancer begins by optimal debulking with first-line platinum-based chemotherapy. However, in ~80% patients, ovarian cancer will recur and is lethal. Prognostic gene signature panel identifying platinum-resistance enables better patient stratification for precision therapy. Retrospectively collected serum from 11 “poor” (<6 months progression free interval [PFI]) and 22 “favorable” (>24 months PFI) prognosis patients, were evaluated using circulating cell-free DNA (cfDNA). DNA from both groups showed 50 to 10 000 bp fragments. Pairwise analysis of sequenced cfDNA from patients showed that gene dosages were higher for 29 genes and lower for 64 genes in poor than favorable prognosis patients. Gene ontology analysis of higher dose genes predominantly grouped into cytoskeletal proteins, while lower dose genes, as hydrolases and receptors. Higher dosage genes searched for cancer-relatedness in Reactome database indicated 15 genes were referenced with cancer. Among them 3 genes, TGFBR2, ZMIZ2, and NRG2, were interacting with more than 4 cancer-associated genes. Protein expression analysis of tumor samples indicated that TGFBR2 was downregulated and ZMIZ2 was upregulated in poor prognosis patients. Our results indicate that the cfDNA gene dosage combined with protein expression in tumor samples can serve as gene signature panel for prognosis determination amongst ovarian cancer patients.

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Section: Discussionmentioning

confidence: 98%

Circulating Tumor Cell-Free DNA Genes as Prognostic Gene Signature for Platinum Resistant Ovarian Cancer Diagnosis

et al. 2022

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“…In case of expected tumors sampled it is possible to use technics on the basis of gene activity pattern in affected cells (Han et al, 2013;Liu et al, 2013). Thus gene expression levels can be used as classification attributes which characterize production rate of protein in lung tumor cells compared with healthy cells (Cheng et al, 2012).…”

Section: Evaluation Of Machine Learning Algorithm Utilization For Lunmentioning

confidence: 99%

Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels

Podolsky¹,

Барчук²,

Kuznetcov³

et al. 2016

Asian Pacific Journal of Cancer Prevention

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Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

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“…на современном этапе морфологический диагноз все больше опирается на молекулярно-генетический состав опухоли, определяемый широким спектром методик [23]. В частности, используется гибридизация на олигонуклеотидном микрочипе [6,17] с последующей алгоритмической обработкой, что позволяет оценить уровень экспрессии генов в исследуемом образце.…”

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Automated Diagnosis in a Population-Based Screening for Lung Cancer

Барчук¹,

Атрощенко²,

Gaydukov³

et al. 2017

Problems in oncology

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Oncologists nowadays are faced with big amount of heterogeneous medical data of diagnostic studies. Possible errors in determining the nature and extent of spread the tumor process will inevitably reduce the effectiveness of treatment and increase the unnecessary costs to it. To reduce the burden on clinicians, various computer-aided solutions based on machine learning algorithms are being developed. We made an attempt to evaluate effectiveness of thirteen machine learning algorithms in the tasks of classification of pathologic tissue samples in cancerous thorax based on gene expression levels. For a preliminary study we used open data set of molecular genetics composition of lung adenocarcinoma and pleural mesothelioma. Effectiveness of machine learning algorithms was evaluated by Matthews correlation coefficient and Area Under ROC Curve. Best results were showed by two methods: Bayesian logistic regression and Discriminative Multinomial Naive Bayes classifier. Nevertheless, all of the methods were effective at automatic discrimination of two types of cancer. That proves machine learning algorithms are applicable in lung cancer classification. In the future studies it will be carried out a similar analysis of the diagnostic value of methods for other malignancies with more complex differential morphological diagnosis. Similar methods can be applied to other diagnostic studies including computerized tomography image analysis in the differential diagnosis of lung nodules.

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Screening of Differentially Expressed Genes among Various TNM Stages of Lung Adenocarcinoma by Genomewide Gene Expression Profile Analysis

Cited by 5 publications

References 22 publications

Circulating Tumor Cell-Free DNA Genes as Prognostic Gene Signature for Platinum Resistant Ovarian Cancer Diagnosis

Circulating Tumor Cell-Free DNA Genes as Prognostic Gene Signature for Platinum Resistant Ovarian Cancer Diagnosis

Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels

Automated Diagnosis in a Population-Based Screening for Lung Cancer

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