Clinical application and mechanism of traditional Chinese medicine in treatment of lung cancer

Su, Xiaolin; Jiang-wei, Wang; Che, Hui; Wang, Changfu; Jiang, Hai; Xia, Liangyu; Zhao, Wei; Kuang, Hai‐Xue; Wang, Qiuhong

doi:10.1097/cm9.0000000000001141

Cited by 88 publications

(56 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the last decade, NSCLC treatment has achieved certain efficacy through the use of targeted therapy, but all of the commonly used drugs have a single pathway and are subject to increasing drug resistance. Thus, the use of Chinese medicine in combination with the commonly used drugs and treatments can enhance the effectiveness of conventional treatment, reduce drug resistance, reduce adverse effects and toxicity, alleviate patient suffering, and improve the quality of life ( Su et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (Cremastra appendiculata) Against Non-Small Cell Lung Cancer

et al. 2021

View full text Add to dashboard Cite

Background: In recent years, the incidence and mortality rates of non-small cell lung cancer (NSCLC) have increased significantly. Shan Ci Gu is commonly used as an anticancer drug in traditional Chinese medicine; however, its specific mechanism against NSCLC has not yet been elucidated. Here, the mechanism was clarified through network pharmacology and molecular docking.Methods: The Traditional Chinese Medicine Systems Pharmacology database was searched for the active ingredients of Shan Ci Gu, and the relevant targets in the Swiss Target Prediction database were obtained according to the structure of the active ingredients. GeneCards were searched for NSCLC-related disease targets. We obtained the cross-target using VENNY to obtain the core targets. The core targets were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins database, and Cytoscape software was used to operate a mesh chart. R software was used to analyze the Gene Ontology biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The core targets and active compounds were molecularly docked through Auto-Dock Vina software to predict the detailed molecular mechanism of Shan Ci Gu for NSCLC treatment. We did a simple survival analysis with hub gene to assess the prognosis of NSCLC patients.Results: Three compounds were screened to obtain 143 target genes and 1,226 targets related to NSCLC, of which 56 genes were related to NSCLC treatment. Shan Ci Gu treatment for NSCLC involved many BPs and acted on main targets including epidermal growth factor receptor (EGFR), ESR1, and SRC through signaling pathways including the endocrine resistance, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathways. Shan Ci Gu might be beneficial for treating NSCLC by inhibiting cell proliferation and migration. Molecular docking revealed that the active compounds β-sitosterol, stigmasterol, and 2-methoxy-9,10-dihydrophenanthrene-4,5-diol had good affinity with the core target genes (EGFR, SRC, and ESR1). Core targets included EGFR, SRC, ESR1, ERBB2, MTOR, MCL1, matrix metalloproteinase 2 (MMP2), MMP9, KDR, and JAK2. Key KEGG pathways included endocrine resistance, EGFR tyrosine kinase inhibitor resistance, ErbB signaling, PI3K-Akt signaling, and Rap1 signaling pathways. These core targets and pathways have an inhibitory effect on the proliferation of NSCLC cells.Conclusion: Shan Ci Gu can treat NSCLC through a multi-target, multi-pathway molecular mechanism and effectively improve NSCLC prognosis. This study could serve as a reference for further mechanistic research on wider application of Shan Ci Gu for NSCLC treatment.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (Cremastra appendiculata) Against Non-Small Cell Lung Cancer

et al. 2021

View full text Add to dashboard Cite

show abstract

“…TCM is a promising and effective adjuvant therapy in the treatment of lung cancer. Compared with chemotherapy and radiotherapy, it has the advantages of availability, effectiveness, and low toxicity [ 35 ], although its various mechanisms deserve further study [ 36 , 37 ]. In this study, the tongue parameters, including TB-a, TC-a, TB-Cr, and TC-Cr of Qi deficiency syndrome and Yin deficiency syndrome, represent the red value of tongue body and tongue coating.…”

Section: Discussionmentioning

confidence: 99%

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

Shi

Liu

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

Objective. To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. Methods. We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome ( n = 163 ), patients with Yin deficiency syndrome ( n = 174 ), and healthy controls ( n = 185 ) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. Results. Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. Conclusions. It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

show abstract

“…Statistical Analysis of tongue and pulse data of Qi de ciency and Yin de ciency TCM is a promising and effective adjuvant therapy in the treatment of lung cancer. Compared with chemotherapy and radiotherapy, it has the advantages of availability, effectiveness and low toxicity 33 , its various mechanisms deserve further study [34][35] . In this study, the tongue parameters TB-a, TC-a, TB-Cr, TC-Cr of Qi de ciency syndrome and Yin de ciency syndrome, thy all represent the red value of tong body and tongue coating, the larger the value, the more redder or magenta the tongue is.…”

Section: Discussionmentioning

confidence: 99%

Study on the Syndrome Characteristics and Classification Model of Non- Small Cell Lung Cancer Based on Tongue and Pulse Data

Shi

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Lung cancer is a common malignant tumor that affects people's health seriously. Traditional Chinese medicine (TCM) is one of the effective methods for the treatment of advanced lung cancer, accurate TCM syndrome differentiation is essential to treatment. When the symptoms are not obvious, the traditional symptom-based syndrome differentiation cannot be carried out. There is a close relationship between syndrom and index of western medicine, the combination of micro index and macro symptom can assist syndrome differentiation effectively.Methods: Tongue and pulse data of non-small cell lung cancer (NSCLC) patients with Qi deficiency syndrome (n=163), patients with Yin deficiency syndrome (n=174) and healthy controls (n=185) were collected by using intelligent Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument, respectively. The characteristics of tongue and pulse data were analyzed, the correlation analysis was also made on tongue and pulse data. And four machine learning methods, namely Random Forest, Logistic Regression, Support Vector Machine and Neural Network, were used to establish the classification models based on symptoms, tongue & pulse data, and symptoms & tongue & pulse data, respectively.Results: Significant difference indexes of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll and the tongue coating texture indexes including TC-Con, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indexes of pulse diagnosis were t4 and t5. The classification performance of each model based on different data sets was as follows: model of tongue & pulse data <model of symptom < model of symptom & tongue & pulse data. The Neural Network model had a better classification performance for the symptom & tongue & pulse data, with an area under the ROC curve and accuracy rate were 0.9401 and 0.8806.Conclusions:This study explored the characteristics of tongue and pulse data of NSCLC with Qi deficiency syndrome and Yin deficiency syndrome, and established syndromes classification model. It was feasible to use tongue and pulse data as one of the objective diagnostic indexes in Qi deficiency syndrome and Yin deficiency syndrome of NSCLC.Name of the registry: Chinese Clinical Trial RegistryTrial registration number: ChiCTR1900026008; ChiCTR-IOR-15006502 Date of registration: Jun. 04th, 2015URL of trial registry record: http://www.chictr.org.cn/showprojen.aspx?proj=11119;http://www.chictr.org.cn/edit.aspx?pid=38828&htm=4 (This is a retrospective registration)

show abstract

Clinical application and mechanism of traditional Chinese medicine in treatment of lung cancer

Cited by 88 publications

References 81 publications

Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (Cremastra appendiculata) Against Non-Small Cell Lung Cancer

Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (Cremastra appendiculata) Against Non-Small Cell Lung Cancer

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

Study on the Syndrome Characteristics and Classification Model of Non- Small Cell Lung Cancer Based on Tongue and Pulse Data

Contact Info

Product

Resources

About