Construction and Validation of a Lung Cancer Diagnostic Model Based on 6-Gene Methylation Frequency in Blood, Clinical Features, and Serum Tumor Markers

Kang, Chunyan; Wang, Dandan; Zhang, Xiuzhi; Wang, Lingxiao; Chen, Jie

doi:10.1155/2021/9987067

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…CDKN2A was silenced in more than 70% of lung squamous cell carcinoma samples. 40 Chunkang et al 21 constructed a lung cancer diagnosis model with 6 genes including CDKN2A (p.16), which can effectively diagnose early lung cancer and indicate cancer risk. In addition, Wei Liu et al 19 reported that CDKN2A indicates a poor prognosis of lung cancer, which was consistent with our findings.…”

Section: Discussionmentioning

confidence: 99%

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Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study

Pan

et al. 2022

Technol Cancer Res Treat

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Background: This study is a retrospective study. The purpose of this study is to construct and validate an early warning model of lung cancer through machine learning. Methods: The CDKN2A gene expression profile and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database and divided into a tumor group and a normal group (n = 57). The top 5 somatic mutation-related genes were extracted from 567 somatic mutation data downloaded from TCGA database using random forest algorithm. Cox proportional hazard model and nomogram were constructed combining CDKN2A, 5 somatic mutation-related genes, gender, and smoking index. Patients were divided into high-risk and low-risk groups according to risk score. The predictability of the model in the prognosis of lung cancer was estimated by Kaplan–Meier survival analysis and receiver operating characteristics curve. Results: We constructed a prognostic model consisting of 5 somatic mutation-related genes (sphingosine 1-phosphate receptor 1 [S1PR1], dedicator of cytokinesis 7 [DOCK7], DEAD-box helicase 4 [DDX4], laminin subunit beta 3 [LAMB3], and importin 5 [IPO5]), cyclin-dependent kinase inhibitor 2A (CDKN2A), gender, and smoking indicators. The high-risk group had a lower overall survival rate compared to the low-risk group (hazard ratio = 2.14, P = 0 .0323). The area under the curve predicted for 3-year, 5-year, and 10-year survival rates are 0.609, 0.673, and 0.698, respectively. The accuracy, sensitivity, and specificity of the model for predicting the 10-year survival rate of lung cancer are 76.19%, 56.71%, and 86.23%. Conclusion: The lung cancer early warning model and nomogram may provide an essential reference for patients with lung cancer management in the clinic.

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Section: Discussionmentioning

confidence: 99%

“…The area under the curve (AUC) value was calculated to verify the reliability and the accuracy, sensitivity, and specificity were calculated as previously described. 21 …”

Section: Methodsmentioning

confidence: 99%

Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study

Pan

et al. 2022

Technol Cancer Res Treat

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“…The SVM model was validated calculating the area under the ROC curve and other statistical parameters. Based on these validation data (area under the ROC curve of 0.963 sensitivity of 0.900, specificity of 0.971, and accuracy of 0.936), the scientists proved the validity of the developed method, highlighting the crucial role of ML models as diagnostic tools for an early diagnosis of cancers that can contribute to increasing the survival rate of patients [242].…”

Section: Basic Researchmentioning

confidence: 90%

Artificial Intelligence in Translational Medicine

Brogi¹,

Calderone²

2021

Preprint

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The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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“…The SVM model was validated by calculating the area under the ROC curve and other statistical parameters. Based on these validation data (area under the ROC curve of 0.963, sensitivity of 0.900, specificity of 0.971, and accuracy of 0.936), the scientists proved the validity of the developed method, highlighting the crucial role of ML models as diagnostic tools for the early diagnosis of cancers that can contribute to increase the survival rate of patients [253].…”

Section: Basic Researchmentioning

confidence: 92%

Artificial Intelligence in Translational Medicine

Brogi

Calderone

2021

IJTM

View full text Add to dashboard Cite

The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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Construction and Validation of a Lung Cancer Diagnostic Model Based on 6-Gene Methylation Frequency in Blood, Clinical Features, and Serum Tumor Markers

Cited by 6 publications

References 24 publications

Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study

Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study

Artificial Intelligence in Translational Medicine

Artificial Intelligence in Translational Medicine

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