Development of diagnostic model of lung cancer based on multiple tumor markers and data mining

Wang, Zhaoxian; Feng, Feifei; Zhou, Xiaoshan; Duan, Lei; Wang, Jing; Wu, Yongjun; Wang, Na

doi:10.18632/oncotarget.21935

Cited by 8 publications

(8 citation statements)

References 46 publications

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“…In the present study, increase in the number of markers (peptides) in high-dimensional classifier resulted in improved performance, which is in good agreement with previous studies showing a clear advantage of using multiple features as compared to single markers for predictive disease modeling 28–30 . This observation seems consistent, provided that the variables are truly associated with the investigated outcome to positively influence the model training phase.…”

Section: Discussionsupporting

confidence: 92%

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Krochmal

Kessel

Zwarthoff

et al. 2019

Sci Rep

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Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MS-based urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profiling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n = 98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high significance in BC relapse prognosis [HR = 5.76, p-value = 0.0001, c-index = 0.64]. Urinary peptide profiles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC.

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Section: Discussionsupporting

confidence: 92%

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Krochmal

Kessel

Zwarthoff

et al. 2019

Sci Rep

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“…Estimates show that COPD becomes the third leading cause of death in 2017, worldwide. Recent studies have indicated that the occurrence of lung cancer is a multiple-factors and multiple-step process, and it is the result of interaction between genetic and environmental exposure factors (13). COPD is a complex disease that is influenced by genetic factors, environmental influences, and genotype-environment interactions.…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Genes Associated with Chronic Obstructive Pulmonary Disease Using Different Penalized Logistics Regression with High-Dimensional Biological Data from Human Sputum Cells

Gohari

Kazemnejad

Mostafaei

et al. 2021

Preprint

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Background: Comparison of LASSO, smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) logistic classifiers in order to reconnaissance of related genes with COPD disease and assessing the genes effects on the progression of the disease based on one of the main classes of cells involved in the disease, Sputum Cells. We used a genome-wide expression profiling to define gene networks relevant to the disease. The data retrieved from Gene Expression Omnibus (GEO) with accession numbers "GSE22148". From 143 samples in GOLD stage 2-4 COPD ex-smokers, 54,675 probes primary were assessed. After normalization, LASSO, SCAD and MCP logistic regressions were applied. K-fold cross-validation scheme was used to evaluate the performance of two methods. All of the computational processes were done using "ncvreg", "Affy," "Limma" and "SVA" R packages. Results: The results of LASSO (AUC=0.95, sensitivity= 0.91, specificity= 0.86) and SCAD (AUC=0.97, sensitivity= 0.95, specificity= 0.85) logistic regression were almost similar. There were 23 and 22 significantly associated genes for LASSO and SCAD, respectively. The only difference between these models is related to "stromal interaction molecule 2". Comparing to MCP approach, the most conservative method, we detected only 7 significant genes (AUC= 0.94, sensitivity= 0.94, specificity= 0.82). Conclusions: In the present study, the relative expressions of thousands of the genes were assessed and identified as associated genes with the progression of COPD. Differential analysis of gene expression data is able to reduce the number of genes but in a limited manner. In order to find an efficient and small subset of genes, we should use alternative approaches like logistic regression. Regularization solves the high dimensionality problem in using this kind of regression.

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“…Recent publications [ 13 , 14 ] have shown that models based on data mining improve the diagnostic effect and profound significance for diagnostic of early stage lung cancer. A Chinese group [ 14 ] showed that the AUC level of each discriminative model was improved by about 10% based on multiple tumor markers and data mining compared with the diagnostic model based on different tumour markers, which indicates that the sensitivity and specificity of diagnosis can be substantially improved through combining different tumor markers compared to an individual tumor marker. However, the information about data mining based decision support systems for survival in lung cancer is sparse.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have shown the capacity of data mining-based models to predict the onset of lung cancer [ 13 , 14 ]. However, the study of the survival outcome with this approach is sparse.…”

Section: Introductionmentioning

confidence: 99%

A data mining based clinical decision support system for survival in lung cancer

Pontes

Núñez

Rubio

et al. 2021

Rep Pract Oncol Radiother.

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Background A clinical decision support system (CDSS ) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients. Materials and methods Prospective multicenter data from 543 consecutive (2013–2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared. Results Overall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001). Conclusions The CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.

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Development of diagnostic model of lung cancer based on multiple tumor markers and data mining

Cited by 8 publications

References 46 publications

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Identification of Novel Genes Associated with Chronic Obstructive Pulmonary Disease Using Different Penalized Logistics Regression with High-Dimensional Biological Data from Human Sputum Cells

A data mining based clinical decision support system for survival in lung cancer

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