Sage Journals: Discover world-class research

Abstract

Background

The traditional Chinese medicine prescription Ping-Wei-San decoction (PWS) has been used historically in East Asian countries. This study aimed to investigate the ingredients of the decoction and explore its potential role in the adjuvant treatment of pulmonary malignancies.

Objectives

The primary objective was to analyze the ingredients of PWS and understand its potential in the treatment of pulmonary malignancy.

Methods

Medical records of 143 394 patients using PWS over a decade were collected from the Affiliated Hospital of Qingdao University. UPLC-QE-Orbitrap-MS detected 13 chemical components, including 12 known components. Lung adenocarcinoma data were obtained from the TCGA-LUAD dataset, and differential genes were screened using R studio. The EVENN website and the STRING database were used to identify drug action and disease targets.

Setting

The study was conducted at the Affiliated Hospital of Qingdao University.

Participants

The study included 3147 patients with “pulmonary malignancies.”

Intervention

PWS was used as an adjuvant treatment.

Results

Network analysis revealed 10 core targets (CCNA2, CHEK1, TOP2A, CDK1, CCNB1, CCNB2, AURKA, AURKB, KIF11, and MELK) for the treatment of lung adenocarcinoma and associated pathways.

Conclusion

PWS may offer multifunctional efficacy in the treatment of pulmonary malignancies based on constituent analysis. Further research and clinical trials are needed to explore its potential.

Keywords

ingredients pulmonary malignancy network pharmacology

Introduction

Traditional Chinese medicine (TCM) prescription Ping-Wei-San decoction (PWS) is a common clinical prescription and has been used for centuries in East Asian countries. The therapeutic effects of PWS (containing Atractylodis, Magnolia officinalis var., Pericarpium Citri Reticulata., classified at www.theplantlist.org or http://herb.ac.cn/) depend on the active ingredients. The constituents of PWS have been reported in the past and the content of the constituents varies widely due to different extraction methods.^1,2 In China, PWS is usually administered orally to patients using an aqueous decoction of mixed herbs, so this trial first tested the ingredients of the aqueous decoction.

Next, we found that PWS was the most commonly used adjuvant treatment for lung malignancy in the statistics of the Affiliated Hospital of Qingdao University in the past 10 years. Traditional Chinese medicine is one of the most popular alternative treatments for cancer.³ A study examined 111 564 lung malignancy patients using data from the National Health Insurance Program database. Moreover, 21.31% of patients used traditional Chinese medicine for adjuvant treatment of lung malignancy to improve their overall survival of lung malignancy patients.⁴ These observations led to a desire to explore the molecular biological mechanisms. In recent years, with the application and development of technologies such as sequencing, the data basis for the development of bioinformatics has been laid.^5,6 Bioinformatics based on big data provides a more accurate and faster method for screening cancer healing factors, identifying cancer-initiating factors, and exploring various mechanisms of cancer occurrence. It can also be used to explore the corresponding drug development, which greatly shortens the progress of drug development and provides practical exploration methods for Chinese medicine in clinical applications.^7–10 We then analyzed the molecular mechanism of PWS in the treatment of lung malignancy using bioinformatics based on the measured components.

Materials and Methods

Clinical Data and Methods

The medical records of patients who used PWS in outpatient and inpatient prescriptions for about 10 years from 2013 to 2021 were collected using the Data Process & Application Platform (DPAP) of the Affiliated Hospital of Qingdao University. The keywords “Cang Zhu (Atractylodis), Chen Pi (Magnolia officinalis var), Hou Pu (Pericarpium Citri Reticulata.)” were used to search for the top 15 internal diseases and the top five surgical diseases adjunctively treated with PWS. All experimental protocols were approved by the committee of the Affiliated Hospital of Qingdao University.

UPLC-QE-Orbitrap-MS Materials and Methods

Materials

Atractylodes macrocephala (lot expiration date: 201201∼2024-03-02) and Pericarpium citriodora (lot expiration date: 200701∼2023-07-01) were purchased from Linqu Pharmaceutical Co. Ltd. and Glycyrrhiza glabra (batch number validity: 20051404∼2023-06-13) were purchased from ShangPharma Holdings Qingdao Co. The control products, huperzine (lot no. 210112034, purity ≥ 98.0%), and huperzine (lot no. 19032102, purity ≥ 98.0%), hesperidin (lot no. 20110303, purity ≥ 98.0%), were purchased from Sichuan Pfid Biotechnology Co. Pepsin and trypsin were purchased from Solarbio, Beijing, China. Acetonitrile and methanol were chromatographically pure, Merck, USA; formic acid was chromatographic alcohol, Thermo Fisher, USA. Simulated gastric juice: 1.0 g of sodium chloride and 1.6 g of pepsin were weighed, dissolved with concentrated hydrochloric acid and water, adjusted the pH of the solution to 2.0, and fixed the volume to 500 mL for use. Simulated intestinal fluid: weigh 3.4 g of potassium dihydrogen phosphate, 0.3 g of sodium hydroxide, and 5 g of trypsin, dissolve with water, adjust the solution pH to 7.0, volume to 500 mL, and reserve.

Water decoction solution: According to the conversion of ancient and modern units, 12 g of A macrocephala, 9 g of M officinalis, 6 g of Pericarpium Citri Reticulata and 3 g of G glabra (all passed through 50 mesh sieve) were weighed, mixed with four times of water, heated and extracted at reflux for 120 min, then filtered to obtain the water decoction solution, which was passed through 0.22 μm microporous membrane before feeding into the sample. The decoction solution was mixed with simulated gastric juice and simulated intestinal juice in the ratio of 1:1 (10 mL:10 mL), placed in a shaker at a constant temperature of 37 °C and 100 r/min, and the appropriate amount of the solution was taken at 0 h, 2 h and 4 h for detection, and passed through 0.22 μm microporous membrane before sampling. The solutions were weighed precisely and prepared with methanol to 210, 280, and 265 µg/mL, respectively, for mass spectrometric detection.

Thermo Scientific Q-Exactive Orbitrap system, Waters Acquity H class-type high-performance liquid chromatograph, Waters Corporation, USA; Thermo Q Exactive Orbitrap-type mass spectrometer with Nano ion source, Xcalibur mass spectrometry workstation, Waters Corporation, USA. Thermo Scientific; MS105DU 1-in-100 000 balance, Mettler-Toledo Ltd., Switzerland; H1650-W benchtop high-speed centrifuge, Hunan Changsha Xiangyi Centrifuge Co., Ltd.; KQ-500V ultrasonic cleaner, Kunshan Ultrasonic Instruments Co.

UPLC-QE-Orbitrap-MS Methods

First, the samples were separated using liquid chromatography. A 50-mg liquid sample was accurately weighed, and the metabolites were extracted using a 400 µL methanol:water (4:1, v/v) solution with 0.02 mg/mL L-2-chlorophenylalanin as the internal standard. The samples were placed at −20 °C for 30 min. After centrifugation at 13 000g at 4 °C for 15 min, the supernatant was carefully transferred to sample vials for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. 2 µL of sample was separated by 120EC-C18 column (150 × 2.1 mm, 2.7 µm) and then entered into mass spectrometry detection. The mass spectrometric data was collected using a Thermo UHPLC-Q Exactive HF-X Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in either positive or negative ion mode. The mass spectrometric data was collected using a Thermo UPLC-Q Exactive Orbitrap-Mass Spectrometer equipped with an ESI source operating in either positive or negative ion mode.

Chromatographic column: Infinity Lab Poroshell 120EC-C18 (150 × 2.1 mm, 2.7 μm) column, mobile phase: 0.1% formic acid water (A)—acetonitrile (B); flow rate: 0.2 mL/min; injection volume: 2 µL; column temperature: 35 °C; elution method: 0-5 min, 10% B; 5-10 min, 10%-21% B; 10-20 min, 21% B; 20-30 min, 21%-70% B; 30-35 min, 70% B.

Mass spectrometry conditions: positive and negative ion mode detection, sheath gas flow rate 40 psi (1 psi = 6.895 kPa), auxiliary gas flow rate 10 psi; spray voltage 3.8 kV; capillary temperature 320 °C; auxiliary gas temperature 350 °C; mass scan range m/z 100-1500, detection resolution 70 000 FWHM. Ionization energies 20, 30, 40 eV, resolution 17 500.

Network Pharmacology Methods

Drug Component Structure Acquisition and Component Target of Action Prediction

The drug structures of the 12 known active ingredients screened by mass spectrometry were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/).¹¹ And the 12 drug structures were imported into SwissTargetPrediction database (http://swisstargetprediction.ch/) for prediction of their targets of action,¹² the species was selected Homo sapiens.

Screening of Disease-Related Differentially Expressed Genes

Lung adenocarcinoma data were downloaded from the TCGA-LUAD dataset of the TCGA database,¹³ which included 501 patients in the disease group and 54 patients in the control group. The differentially expressed genes of lung adenocarcinoma were screened by differential gene expression analysis, and this step was implemented by the Wilcoxon rank-sum test in R studio software with the logFC filter condition set to 1 and the corrected P-value filter condition set to .05.

Drug Action Disease Target Screening

This step was implemented through the EVENN website (http://www.ehbio.com/test/venn/#/)¹⁴ by screening the therapeutic targets involved in the action of PWS in lung adenocarcinoma using a Venn diagram.

Gene Interaction Network Construction

In this step, the gene interaction network was constructed through the STRING database (https://string-db.org/)¹⁵ to understand the interrelationship between the therapeutic targets, the species was selected H sapiens. The cytohubba plugin of Cytoscape software¹⁶ was used to rank the importance of the obtained gene interaction networks.

Enrichment Analysis

We used gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis to understand the relevant mechanisms and related pathways involved in the treatment of lung adenocarcinoma with PWS. This step was implemented by R studio software with the P-value filter set at .05 and the q-value filter set at .05.

Results

Clinical Results

A total of 143 394 patients were treated concomitantly with the formula containing PWS, generating 512 413 medical records, and the breakdown of outpatient and inpatient data is shown in Table 1. The first diagnosis of “pulmonary malignancy” was present in 36 439 medical records, with 3147 patients with pulmonary malignancy, of whom 1482 (47.1%) were female, 1675 (52.9%) were male; 1117 (35.5%) were older than 65 years, 1982 (62.9%) were between 41 and 65 years of age, and 50 (1.6%) were between 18 and 40 years of age.

Table 1.

Diseases Treated with PWS.

Disease		Number of medical record (patient-times)	Percentage
1	Pulmonary malignancy	36 439	7.11
2	Breast malignancy	22 550	4.40
3	Hypertension	20 112	3.92
4	Mixed hemorrhoids	17 012	3.32
5	Coronary artery atherosclerotic heart disease	16 811	3.28
6	Eczema	14 478	2.83
7	Chronic gastritis	13 085	2.55
8	Malignant tumor of stomach	11 753	2.29
9	Thyroid malignancy	9362	1.83
10	Diabetes mellitus	9068	1.77
11	Rectal malignancy	9039	1.76
12	Breast malignancy	8175	1.60
13	Colon malignancy	6766	1.32
14	Liver malignancy	5559	1.08
15	Acne	3841	0.75

Results of UPLC-QE-Orbitrap-MS

The total ion flow diagrams of the UPLC-QE-Orbitrap-MS of the PWS decoction, simulated gastric fluid, and simulated intestinal fluid test samples were analyzed and shown in Figure 1. Based on the ion peak information of the liquid-quality fragments and comparison with the Compound Discover database and literature, a total of 13 constituents were identified and the results are shown in Table 2.

Figure 1.

Total ion flow diagram of PWS. A, PWS aqueous decoction. B. PWS aqueous decoction simulating gastric juice digestion for 2 h. C. PWS aqueous decoction simulating intestinal juice digestion for 4 h; a. Positive ion pattern diagram; b. Negative ion pattern diagram. D. Heat map of the changes in the simulated digestive components of PWS.

Table 2.

Ion Peaks of Mass Spectrometry of PWS Aqueous Decoction Components.

No.	Formula	RT (min)	[M + H]+ Mass accuracy (ppm)	[M−H] ⁻ Mass accuracy (ppm)	Positive ions [M + H]+ and proposed CID fragment ions	Negative ions [M−H]⁻ and proposed CID fragment ions	Compounds
1	C₁₄H₂₀N₂O₃	4.54	265.1547	263.1401	265.1535[M + H]⁺ 177.0539 145.0279	263.1396[M−H]⁻ 248.1158	N-feruloylputrescine¹⁷
2	C₁₉H₂₃NO₃	5.76	314.1740	312.1603	314.1740 [M + H]⁺	312.1603 [M−H]⁻ 166.9928 146.9649	Magnocurarine^17,18
3	C₃₅H₄₆O₂₀	9.33	809.2444	785.2501	809.2444[M + Na]⁺ 633.2001 325.0907 163.0384	785.2501[M−H]⁻ 623.2178 477.1606 161.0234	Magnoloside B¹⁸
4	C₂₀H₂₃NO₄	9.80	342.1696	340.1551	342.1688[M + H]⁺ 297.1110 282.0872 265.0849 237.0899	340.1551[M−H]⁻	Magnoflorine¹⁷
5	C₃₅H₄₆O₂₀	10.72	809.2444	785.2504	809.2444[M + Na]⁺ 325.0908 163.0385	785.2504[M−H]⁻ 212.9986	Unknown
6	C₂₉H₃₆O₁₅	11.24	647.1923	623.1972	647.1923[M + Na]⁺ 471.1482 163.0385 325.0907	623.1973[M−H]⁻ 461.1661 315.1084 161.0234 135.0441	Magnoloside A^17,18
7	C₂₉H₃₆O₁₅	13.32	647.1919	623.1973	647.1919[M + Na]⁺ 449.1763 325.0905 163.0384	623.1973[M−H]⁻	Verbascoside¹⁸
8	C₂₉H₃₆O₁₅	14.41	647.1918	623.1971	647.1918[M + Na]⁺ 325.0918 163.0384	623.1973[M−H]⁻	Isoacteoside¹⁸
9	C₂₈H₃₄O₁₅	15.68	633.1761	609.1815	633.1761[M + Na]⁺ 449.1422 303.0851	609.1815[M−H]⁻	Hesperidin¹⁹
10	C₂₈H₃₄O₁₄	24.99	617.1818	593.1817	617.1818[M + Na]⁺ 595.2000 287.0903	593.1817[M−H]⁻	Poncirin¹⁹
11	C₁₅H₁₈O₂	30.65	231.238	229.1234	231.1371[M + H]+ 203.1423 189.0902 175.0747 163.0747	229.1234[M−H]⁻	Atractylenolide I²⁰
12	C₁₈H₁₈O₂	31.86	—	265.1232	—	265.1232[M−H]⁻ 212.9987 166.9929	Honokiol^17,18
13	C₁₈H₁₈O₂	32.93	267.138	265.1234	267.1367[M + H]+ 239.1057 226.0979 211.0746 197.0592	265.1230[M−H]⁻ 247.1126	Magnolol^17,18

We tested the chemical composition of PWS after simulated digestion, and the simulated digested components are shown in Table 3. The heat map with mass spectral peak area to visualize the change in response value of each component (Figure 1D). The intensity of the extracted ions of each peak at 2 h and 4 h compared with that at 0 h, and based on the intensity ratio, the changes of the compounds in the digest can be judged.

Table 3.

Differences in Composition After Simulated Digestion of PWS (Positive Ion Mode).

Compound		Apex RT （min）	Area/area (simulation of digestion)
Compound		Apex RT （min）	In gastric fluid 2 h/0 h	In intestinal fluid 4 h/0 h
1	N-feruloylputrescine	4.54	0.61	1.04
2	Magnocurarine	5.76	1.08	1.11
3	Magnoloside B	9.33	0.70	0.70
4	Magnoflorine	9.80	1.04	1.09
5	Unknown	10.72	0.66	1.28
6	Magnoloside A	11.24	0.84	1.13
7	Verbascoside	13.32	1.08	0.91
8	Isoacteoside	14.41	1.11	1.00
9	Hesperidin	15.68	1.06	1.18
10	Poncirin	24.99	1.40	1.71
11	Atractylenolide I	30.65	0.80	0.86
12	Honokiol	31.86	0.38	0.53
13	Magnolol	32.93	0.51	0.65

It can be seen that magnoflorine, verbascoside, atractylenolide I, hesperidin, and isoacteoside showed no significant change or a slight increase in response values after simulated digestion. N-feruloylputrescine and compound 5 showed decreased response values after simulated gastric juice digestion; after simulated intestinal juice digestion, the response values did not decrease or increase slightly. The responses of magnoloside B, honokiol, and magnolol were all decreased to some extent after simulated gastric and intestinal juice digestion.

Results of Network Pharmacology

Screening of Therapeutic Targets of PWS for the Treatment of Lung Adenocarcinoma

A total of 2071 differentially expressed genes were screened for lung adenocarcinoma-related differential genes (Figure 2A), 572 action targets were screened for the 12 drug targets, and the Venn diagram showed that 81 targets were involved in the treatment of lung adenocarcinoma with PWS (Figure 2B).

Figure 2.

Network pharmacology results. A, Differential gene expression analysis presented by heat map. B, Venn diagram showing identical targets of ping gastric san therapeutic targets and differential targets of lung adenocarcinoma. C, Gene interaction network constructed by Gene Mania database. D, Ranking of gene importance based on gene interaction network. E, GO enrichment analysis. F, KEGG enrichment analysis.

Gene Interaction Network Construction

We constructed a gene interaction network using the STRING database for the 81 obtained therapeutic targets obtained (Figure 2C), and the cytohubba plug-in ranked the network and found that CCNA2, CHEK1, TOP2A, CDK1, CCNB1, CCNB2, AURKA, AURKB, KIF11, and MELK played a central role in the network (Figure 2D).

Enrichment Analysis Results

We analyzed the results of GO enrichment analysis and KEGG enrichment analysis to obtain the therapeutic targets. Biological processes were mainly enriched in the regulation of inflammatory response, second messenger-mediated signaling, and response to alcohol. Cellular components were mainly enriched in Membrane Raft, Membrane Microdomain, and Membrane Region. Molecular functions were mainly enriched in endopeptidase activity, protein serine/threonine kinase activity, and metallopeptidase activity (Figure 2E). The results of the KEGG enrichment analysis indicated that the therapeutic targets were mainly enriched in the cellular senescence, lipid and atherosclerosis, and human T-cell leukemia virus 1 infection pathways (Figure 2F).

Discussion

From the clinical application of 143 394 cases of PWS in our hospital over the past 10 years, the number of patients treated has increased year by year, mainly in the age group of 41-65 years, and there are more women than men. The majority of patients are in the age group 41-65 years and there are more female than male patients. The top 10 diagnoses treated with PWS include various conditions such as lung and breast cancer, hypertension, hemorrhoids, coronary artery disease, eczema, chronic gastritis, and thyroid malignancies. Symptoms treated include weakness, dry mouth, cough, back pain, chest tightness, bitter mouth, anorexia, poor sleep, sweating, acid reflux, and heartburn. The first disease treated with adjuvant PWS is pulmonary malignancy, probably due to its high morbidity and the presence of comorbid digestive symptoms. Patients treated with adjuvant PWS often experience fatigue, loss of appetite, nausea, and vomiting. Weakness is common in pulmonary malignancies and is associated with weight loss, loss of appetite, anorexia, acid reflux, mild nausea, bloating, and constipation.^21–23 Patients with pulmonary malignancies present with chronic bowel dysmotility and may even develop chronic pseudointestinal obstruction in the form of abdominal and gastric distension, and most chemotherapy regimens can cause varying degrees of nausea and vomiting. Administration of PWS has been found to alleviate chronic fatigue and weakness in patients with pulmonary malignancies. This effect has been attributed to the analysis of 13 gastrointestinal compounds associated with PWS. Several studies have used structured physical activity interventions as a nursing intervention to improve cancer-related fatigue and quality of life in patients with advanced pulmonary malignancies.^24–26 Although structured physical activity interventions have been used to improve cancer-related fatigue and quality of life, they may require more resources and time compared to the administration of PWS.

In this experiment, the ultra-performance liquid chromatography conditions were mapped and the mobile phase, flow rate, column temperature, gradient, and other related conditions were investigated. Finally, it was found that the chromatographic conditions in the paper had better separation and smoother baseline, which were suitable for the determination of the components of PWS. The chromatographic method was used to detect simulated intestinal fluid digestion and there were several decreases in peak reactivity between 11-13 and 14-17 min RT time. After comparison with the control group, the components of magnolol and hesperidin were first determined. After mass spectrometry, 13 compounds were identified by literature review and database comparison, and the variation of each compound in the digestion could be assessed based on the intensity ratios in Table 3. The disappearance of peaks at around 4.3 min and 8.7 min and the new generation of compounds at around 23.3 min and 29.2 min for the aqueous decoction simulating gastric digestion, and the disappearance of peaks at around 4.3 min and 8.7 min and the new generation of compounds at around 29.2 min for the intestinal digestion need further in-depth study. Compound 5 is an unknown compound that could not be retrieved from the available literature reports and databases. We speculate that it may be a new constituent not found in previous experiments or a new compound produced by decoction of a mixture of three herbs. Previous studies on the efficacy of PWS aqueous decoction found that the optimal dose for storage was 1000 mg/kg/day and that PWS decoction packets had anti-inflammatory effects when stored at 4 °C and −20 °C for 6 months.¹⁵ In this study, the compositional changes of PWS were assumed to be more pronounced for different compounds within it by simulating the changes in body fluids at 37 °C.

Previous studies using lyophilized powder made of PWS extracted with boiling water showed a dose-dependent inhibition of indomethacin-induced gastric mucosal cell death, which attenuated indomethacin-induced gastric ulcers in male Wistar rats in vivo.²⁷ The present PWS contained magnolia arrow poison, magnoflorine, hesperidin, thickoside A, and N-feruloylputrescine, which have high response values in the intestinal tract. The active substances detected in this experiment have been found in other plants²⁸ and pharmacological studies on them are still rare. Given the uncertainty of the dose, we used bioinformatics to predict the molecular function prediction. MELK is an essential mitotic kinase in mitotic progression, and inhibition of MELK induces cell cycle arrest and apoptosis in tumor cells. A study from Japan suggested that lung malignancy was associated with apoptosis induced by MELK inhibition.²⁹ CCNA2 is a critical regulator of NSCLC cells, and some scholars suggested that targeted treatment of CCNA2-expressing cancer serves as a new therapeutic target for NSCLC.³⁰ Bioinformatics results suggest that PWS aqueous decoction extract may target CDK1/CCNB1 involved in cell cycle induction block; and mitotic block regulatory process through protein kinase PLK1, CCNA2/CCNE1 cell cycle proteins. KIF11 transcript was found to be overexpressed in the vast majority of metastatic lymph nodes from advanced lung malignancies and primary lung tumors. Inhibition of KIF11 expression effectively suppressed the growth of lung malignant lung cells.³¹ The compounds in the PWS are involved in the regulation of the oncogenic gene KIF11, the potential biomarker for immunotherapy biomarker TOP2A and other key proteins involved in the adjuvant therapy of squamous cell lung cancer. In addition, bioinformatics results suggest that PWS is involved in molecular functional signaling pathways regulating inflammatory response, second messenger mediated signaling, membrane microdomains, endopeptidase activity, and cellular senescence in lung adenocarcinoma.

Conclusion

Using the extensive data support of the DPAP platform and the application of bioinformatics techniques, we have discovered in clinical practice that the role and value of PWS in the adjuvant therapy of lung malignancies far exceeds our previous understanding. Mass spectrometry analysis and bioinformatics exploration have shed light on the multicomponent, multitarget nature of PWS, indicating its efficacy in the treatment of multiple diseases. In this study, we assert that in a comprehensive healthcare model, PWS treatment can actively participate in the processes of treating, alleviating, or prolonging life, resulting in positive and effective preventive or therapeutic outcomes. This also provides bioinformatics data support for further PWS clinical trials and subsequent PWS drug development.

Future Perspective

However, the process of drug-activated disease is not a single aspect, but rather intervenes in the disease from multiple levels, including gut flora and metabolomics, which means that the present study has some limitations, and we plan to explore the intervention mechanism of PWS from multiple perspectives in subsequent clinical trials. Overall, the study highlights the promising role of PWS in the adjuvant therapy of lung malignancies and suggests its potential applicability in the treatment of a range of diseases. Further research and clinical trials are warranted to fully explore its therapeutic mechanisms and potential benefits in different healthcare contexts.

Footnotes

Author Contributions

DL: Bioinformatic analysis, clinical analysis, software and article writing-original draft; RYY and GJH: methodology, investigation, data curation, and formal analysis; XQM: UPLC-QE-Orbitrap-MS detection and analysis guide; XY: analysis guide and supervision; XYL and ZY: conceptualization, funding acquisition, project administration, writing review, and final approval of article.

Availability of Data and Materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

Not applicable

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Xiao-yuan Li

Statement of Informed Consent

Not applicable

References

Riedlinger

Tan

. Ping Wei San, a Chinese medicine for gastrointestinal disorders. Ann Pharmacother. 2001;35(2):228-235.

You

Yao

Xie

. Mechanisms of the Ping-Wei-San plus herbal decoction against Parkinson's disease: multiomics analyses. Front Nutr. 2023;9:945356. doi:10.3389/fnut.2022.945356

Liao

Lin

Chiang

. Traditional Chinese medicine as adjunctive therapy improves the long-term survival of lung cancer patients. J Cancer Res Clin Oncol. 2017;143(12):2425-2435.

Liao

Lin

Chiang

. Traditional Chinese medicine as adjunctive therapy improves the long-term survival of lung cancer patients. J Cancer Res Clin Oncol. 2017;143(12):2425-2435.

Woollard

Mehta

Vamathevan

Van Horn

Bonde

Dow

. The application of next-generation sequencing technologies to drug discovery and development. Drug Discov Today. 2011;16(11–12):512-519.

Pareek

Smoczynski

Tretyn

. Sequencing technologies and genome sequencing. J Appl Genet. 2011;52(4):413-435.

Behl

Kaur

Sehgal

, et al. Bioinformatics accelerates the major tetrad: a real boost for the pharmaceutical industry. Int J Mol Sci. 2021;22(12):6184. doi:10.3390/ijms22126184

Behl

Kumar

Vishakha , et al. Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer. Int J Biol Macromol. 2023;253(Pt 1):126595.

Diniz

WJS

Canduri

. Bioinformatics: an overview and its applications. Genet Mol Res. 2017;16(1). doi:10.4238/gmr16019645

10.

Toro-López

Importance of behavior in health status. J Cancer Res Rev Rep. doi:10.47363/JCRR/2023(5)171

11.

Kim

Chen

Cheng

, et al. Pubchem 2023 update. Nucleic Acids Res. 2023;51(D1):D1373-d1380.

12.

Daina

Michielin

Zoete

. Swisstargetprediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357-w364.

13.

Carrot-Zhang

Yao

Devarakonda

, et al. Whole-genome characterization of lung adenocarcinomas lacking alterations in the RTK/RAS/RAF pathway. Cell Rep. 2021;34(8):108784.

14.

Chen

Zhang

Liu

Huang

. EVenn: easy to create repeatable and editable Venn diagrams and Venn networks online. J Genet Genomics. 2021;48(9):863-866.

15.

Szklarczyk

Kirsch

Koutrouli

, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638-d646.

16.

Shannon

Markiel

Ozier

, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-2504.

17.

Zhao

Yan

Zou

, et al. Identification of chemical constituents in Magnoliae officinalis cortex by UFLC-triple TOF-MS/MS. 基于UFLC-triple TOF-MS/MS技术分析厚朴化学成分. Chinese Pharm J. 2017 2017;52(20):1824-1831. 1001-2494(2017)52:20 < 1824:jyuttm>2.0.tx;2-6

18.

Fan

Gao

Tian

Wang

Niu

. Simultaneous determination of twenty six components in Yangwei decoction by UPLC-MS/MS. Uplc-ms/ms法同时测定养胃汤中26个成分的含量. Chinese J of Pharm Anal. 2021;41(11):1875-1884. 0254-1793(2021)41:11 < 1875:ummfts>2.0.tx;2-b

19.

Zhang

Fan

Chen

Xiao

Zhang

. Comprehensive chemical analysis of peels of Ganpu tea,Citrus reticulata cv. Chachiensis and Citrus tachibana based on UPLC-Q-TOF-MS. 基于Uplc-q-tof-ms的柑普茶外果皮、鲜陈皮和鲜砂糖橘皮的全成分对比分析. Acta Sci Nat Univ Sunyatseni. 2021;60(6):128-141.

20.

Zhang

Fan

, et al. Qualitative and quantitative determination of Atractylodes rhizome using ultra-performance liquid chromatography coupled with linear ion trap-orbitrap mass spectrometry with data-dependent processing. Biomed Chromatogr. 2019;33(3):e4443.

21.

Prommer

. Oncology update: anamorelin. Palliat Care. 2017;10:1178224217726336. doi:10.1177/1178224217726336

22.

Harle

ASM

Blackhall

Molassiotis

, et al. Cough in patients with lung cancer: a longitudinal observational study of characterization and clinical associations. Chest. 2019;155(1):103-113.

23.

Nolden

Joseph

Kober

, et al. Co-occurring gastrointestinal symptoms are associated with taste changes in oncology patients receiving chemotherapy. J Pain Symptom Manage. 2019;58(5):756-765.

24.

Himbert

Klossner

Coletta

, et al. Exercise and lung cancer surgery: a systematic review of randomized-controlled trials. Crit Rev Oncol Hematol. 2020;156:103086. doi:10.1016/j.critrevonc.2020.103086

25.

Ester

Culos-Reed

Abdul-Razzak

, et al. Feasibility of a multimodal exercise, nutrition, and palliative care intervention in advanced lung cancer. BMC Cancer. 2021;21(1):159.

26.

Ligibel

Bohlke

May

, et al. Exercise, diet, and weight management during cancer treatment: ASCO guideline. J Clin Oncol. 2022;40(22):2491-2507.

27.

Tiong

Liang

Liu

Wang

Chang

Wang

. Gastroprotective effects of Ping-Wei San on indomethacin-induced gastric ulcer. J Food Drug Anal. 2011;19(4):509-516.

28.

Bala

Kumar

Pratap

Verma

Padwad

Singh

. Bioactive isoquinoline alkaloids from Cissampelos pareira (dagger). Nat Prod Res. 2019;33(5):622-627.

29.

Inoue

Kato

Olugbile

, et al. Effective growth-suppressive activity of maternal embryonic leucine-zipper kinase (MELK) inhibitor against small cell lung cancer. Oncotarget. 2016;7(12):13621-13633.

30.

Ruan

Zhou

Yang

Wang

Jiang

Wang

. CCNA2 facilitates epithelial-to-mesenchymal transition via the integrin alpha v beta 3 signaling in NSCLC. Int J Clin Exp Pathol. 2017;10(8):8324-8333.

31.

Xue

Chen

Kong

Zhang

. Cyclovirobuxine D inhibits growth and progression of non-small cell lung cancer cells by suppressing the KIF11-CDC25C-CDK1-CyclinB1 G(2)/M phase transition regulatory network and the NFκB/JNK signaling pathway. Int J Oncol. 2023;62(5):57. doi:10.3892/ijo.2023.5505

Bioinformatic Analysis of Ping-Wei-San Decoction for Pulmonary Malignancy Based on UPLC-QE-Orbitrap-MS Detection

Abstract

Background

Objectives

Methods

Setting

Participants

Intervention

Results

Conclusion

Keywords

Introduction

Materials and Methods

Clinical Data and Methods

UPLC-QE-Orbitrap-MS Materials and Methods

Materials

UPLC-QE-Orbitrap-MS Methods

Network Pharmacology Methods

Drug Component Structure Acquisition and Component Target of Action Prediction

Screening of Disease-Related Differentially Expressed Genes

Drug Action Disease Target Screening

Gene Interaction Network Construction

Enrichment Analysis

Results

Clinical Results

Results of UPLC-QE-Orbitrap-MS

Results of Network Pharmacology

Screening of Therapeutic Targets of PWS for the Treatment of Lung Adenocarcinoma

Gene Interaction Network Construction

Enrichment Analysis Results

Discussion

Conclusion

Future Perspective

Footnotes

Author Contributions

Availability of Data and Materials

Declaration of Conflicting Interests

Ethical Approval

Funding

ORCID iD

Statement of Informed Consent

References