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. 2023 Feb 17;102(7):e32999. doi: 10.1097/MD.0000000000032999

Reverse predictive analysis of Rhizoma Pinelliae and Rhizoma Coptidis on differential miRNA target genes in lung adenocarcinoma

Tianwei Meng a, Jiawen Liu a, Hong Chang b,*, Rui Qie c
PMCID: PMC9936040  PMID: 36800601

Abstract

To use bioinformatics and network analysis to reveal the mechanism of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair in the treatment of lung adenocarcinoma. The target and pathway of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair in the treatment of lung adenocarcinoma were explored by online databases and network analysis tools, and the potential biomarkers of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair in the treatment of lung adenocarcinoma were predicted in reverse. A total of 59 traditional Chinese medicine compounds and 510 drug targets were screened in this study. A total of 25 micro-RNAs and 15,323 disease targets were obtained through GEO2R software analysis. In the end, 294 therapeutic targets and 47 core targets were obtained. A total of 186 gene ontology enrichment assays were obtained, and core therapeutic targets play multiple roles in biological processes, molecular functions, and cellular composition. Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that the core targets were mainly enriched in cancer-related pathways, immune-related pathways, endocrine-related pathways, etc, among which the non-small cell lung cancer pathway was the most significant core pathway. Molecular docking shows that the compound and the target have good binding ability. “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair plays a mechanism of action in the treatment of lung adenocarcinoma through multiple targets and pathways. miR-5703, miR-3125, miR-652-5P, and miR-513c-5p may be new biomarkers for the treatment of lung adenocarcinoma.

Keywords: bioinformatics, lung adenocarcinoma, miRNA, molecular docking, Rhizoma Coptidis, Rhizoma Pinelliae

1. Introduction

Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer and has the highest mortality rate of all cancers.[1] According to statistics, about 1.6 million people die of lung cancer every year worldwide, of which LUAD accounts for more than 40%.[2] Although treatments for LUAD are being gradually updated, the prognosis for patients with LUAD is still unsatisfactory, with a 5-year survival rate of  < 20%.[3] At present, the clinical treatment of LUAD is mainly based on surgical resection of the lesion, and patients with poor prognosis are supplemented by radiotherapy, chemotherapy, and targeted therapy drugs epidermal growth factor receptor (EGFR)-(TKI and ALK-TKI).[4] However, clinical response is usually short-lived, and most cancer patients develop resistance to targeted drugs within a few months,[5] and these drugs often have more severe side effects. Traditional Chinese medicine is a cultural treasure developed in China for thousands of years, not only has low toxicity, and small side effects, but also multi-ingredient, multi-target characteristics are also advantages in treating diseases.[6]

The tuberous root of Pinellia ternata (Thunb.) 10. ex Breitenb is the traditional Chinese herb Rhizoma Pinelliae. Rhizoma Coptidis is the dried rhizome of Coptis Chinensis Franch. They are a group of herbal combinations commonly found in traditional Chinese medicine compounds.[7] In recent years, the antitumor biological activity of Rhizoma Pinelliae and Rhizoma Coptidis has been confirmed.[8,9] Modern research has found that the mechanism of action of Rhizoma Pinelliae inducing apoptosis of tumor cells is mainly achieved by activating the mononuclear phagocyte system, increasing the level of antioxidant enzymes, and scavenging excessive free radicals.[10] Rhizoma Coptidis exerts antitumor effects by inhibiting tumor extracellular matrix degradation, regulating the cell cycle, and inhibiting angiogenesis.[11–13] However, the molecular mechanism of the combination of Rhizoma Pinelliae and Rhizoma Coptidis in the treatment of LUAD has not been clarified. In this study, the material basis and mechanism of Rhizoma Pinelliae and Rhizoma Coptidis in the treatment of LUAD were explored by bioinformatics. In addition, the differential micro-RNA (miRNAs) of Rhizoma Pinelliae and Rhizoma Coptidis in the treatment of LUAD were screened by network analysis.

2. Materials and methods

2.1. Screening and target prediction of active ingredients of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair

In this study, the HERB database (http://herb.ac.cn/) searched for the compound composition of Rhizoma Pinelliae and Rhizoma Coptidis. The HERB database integrates multiple traditional Chinese medicine databases (Sym Map, TCMID, TCMSP, and TCMD), and high-throughput experiments (microarray and RNA-seq experiments) were performed on the collected data to determine the active ingredients in traditional Chinese medicine. Therefore, the database contains the most comprehensive list of traditional Chinese medicines and ingredients so far.[14] The compounds of Rhizoma Pinelliae and Rhizoma Coptidis are screened by the Swiss ADME platform (http://www.swissadme.ch/).[15] Screening criteria include: Following Lipinski rules, that is, “molecular weight < 500,” “Rotatable bonds ≤ 10,” “H-bond acceptors ≤ 10,” “H-bond donors ≤ 5,” and “lipid-water partition coefficient (Log Po/w) ≤ 5”; In pharmacokinetics, GI absorption is “High” and the blood-brain barrier (BBB permeant) is “Yes”; Drug likeness satisfies 3 or more “Yes.” Through the Swiss Target Prediction platform, the potential targets of drug compounds are predicted, and targets with “Probability” greater than “0” are screened as potential targets of drugs.

2.2. Screening of differential miRNAs and acquisition of related targets for LUAD

Using “Lung adenocarcinoma” as the keyword, the GEO database (https://www.ncbi.nlm.nih.gov/geo/)[16] retrieved microarray datasets for studies related to LUAD. Through comparison and screening, the chips GSE135918 and GSE128311 were finally included in the study. Two sample sets were analyzed by GEO2R software, and miRNAs were screened with P < .05 and |log2FC|≥1. The 2 sets of differentially expressed miRNAs were imported into the FunRich3.1.3 software to take the intersection, and the intersecting miRNAs were analyzed by the TargetScanHuman platform (https://www.targetscan.org/vert_72/),[17] and finally, the relevant target genes of LUAD were obtained.

2.3. Target screening and network construction of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair in the treatment of LUAD

The targets of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair were combined and intersected with the targets of LUAD. Protein-protein interaction (PPI) of intersecting partial targets is constructed through the String database (https://string-db.org/).[18] The threshold above “0.900” was used as the screening criterion, and the target of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair for the treatment of LUAD was finally screened. Through Cytoscape 3.8.0 software,[19] the network diagram of “traditional Chinese medicine - active ingredient - therapeutic target - miRNA - disease” was constructed. The top 10 compounds with degree values were screened as core compounds, and it was believed that these compounds played an important role in the treatment of LUAD by “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair.

2.4. Core target screening

The PPI is imported into the Cytoscape software, and the MCODE algorithm is applied to screen out the highly relevant modules and construct a visual network diagram. The algorithm starts the search by weighting the nodes in the network, then selecting the node with high weight as the initial node, and finally filtering out the core targets in the dense area of the original PPI network. The MCODE algorithm parameters are set as follows “degree = 2,” “node score cutoff = 0.2,” “k-core = 2, max.” “Depth = 100.”[20] The target protein corresponding to the screening module is considered to be the core target of Rhizoma Pinelliae and Rhizoma Coptidis in the treatment of LUAD.

2.5. Gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis

The core target was imported into the DAVID database, the attribute was set to “Homo sapiens,” GO enrichment analysis was performed with P < .05 as the condition, and the analysis results of biological processes, molecular functions, and cellular components were displayed through bubble charts. The BioMart platform (http://asia.ensembl.org/biomart/martview/c8e74f900745aa80be4b62038e25607a)[21] was used to convert the gene ID of the core target to Ensemblegene ID. Through Omicshare platform (http://www.omicshare.com/tools/index.php/) conducts KEGG pathway enrichment analysis on core targets. In the KEGG enrichment analysis results, the entries with P < .001 were screened, and the top 20 items were plotted as a circle chart. The suitable pathway was screened out in the enrichment pathway, and the enriched target in the pathway was considered to be the key target.

2.6. Molecular docking verification

Download the mol2 format of the 3D structure of core compounds in Banxia and Coptis via the Pubchem database (https://pubchem.ncbi.nlm.nih.gov/).[22] Protein structures of key targets are screened in the PDB database (https://www.rcsb.org/)[23] under the condition that resolution is less than 2.5 and small molecule compounds are attached. Use the DockThor platform (https://dockthor.lncc.br/v2/)[24] to build a suitable docking box and perform molecular docking. Use PyMol 2.4.0 software to visualize the top results.

3. Results

3.1. Acquisition of active ingredients and targets of herbs

A total of 180 active ingredients in Rhizoma Pinelliae and 77 active ingredients in Rhizoma Coptidis were retrieved through the HERB database. After screening, 32 active ingredients of Rhizoma Pinelliae were obtained (Table 1). There are 27 active ingredients in Rhizoma Coptidis were obtained (Table 2). A total of 5715 targets were predicted using the SwissTargetPrediction platform, and a total of 510 targets were obtained by removing “Probability” to “0” and removing duplicate values.

Table 1.

Active ingredients of Pinellia.

Component Formula Molecular weight Rotatable bonds H-Bond acceptors H-Bond donors Consensus log P Gi absorption Bbb permeant Lipinski Ghose Veber Egan Muegge
(-)-Citronellal C10H18O 154.3 5 1 0 2.94 High Yes Yes No Yes Yes No
2-Undecanone C11H22O 170.3 8 1 0 3.48 High Yes Yes Yes Yes Yes No
3-Phenylpropionic acid C9H10O2 150.2 3 2 1 1.78 High Yes Yes No Yes Yes No
4-Methoxycyclohexanoic acid C8H8O3 152.2 2 3 1 1.49 High Yes Yes No Yes Yes No
6-Shogaol C17H24O3 276.4 9 3 1 3.76 High Yes Yes Yes Yes Yes Yes
9-Oxononanoic acid C9H16O3 172.2 8 3 1 1.71 High Yes Yes Yes Yes Yes No
Anethole C10H12O 148.2 2 1 0 2.79 High Yes Yes No Yes Yes No
Benzaldehyde C7H6O 106.1 1 1 0 1.57 High Yes Yes No Yes Yes No
Bis (4-hydroxybenzyl) ether C14H14O3 230.3 4 3 2 2.34 High Yes Yes Yes Yes Yes Yes
Butyl vinyl ether C6H12O 100.2 4 1 0 1.82 High Yes Yes No Yes Yes No
Catechol C6H6O2 110.1 0 2 2 0.97 High Yes Yes No Yes Yes No
Cavidine C21H23NO4 353.4 2 5 0 3.2 High Yes Yes Yes Yes Yes Yes
Cedrol C15H26O 222.4 0 1 1 3.55 High Yes Yes Yes Yes Yes No
Citral C10H16O 152.2 4 1 0 2.71 High Yes Yes No Yes Yes No
Coniine C8H17N 127.2 2 1 1 1.99 High Yes Yes No Yes Yes No
Conimine C22H36N2 328.5 1 2 2 3.84 High Yes No Yes Yes Yes Yes
Crysophanol C15H10O4 254.2 0 4 2 2.38 High Yes Yes Yes Yes Yes Yes
Ephedrine C10H15NO 165.2 3 2 2 1.46 High Yes Yes Yes Yes Yes No
Ferulic acid C10H10O4 194.2 3 4 2 1.36 High Yes Yes Yes Yes Yes No
Furfural C5H4O2 96.08 1 2 0 0.69 High Yes Yes No Yes Yes No
Hydroquinone C6H6O2 110.1 0 2 2 0.87 High Yes Yes No Yes Yes No
Methyl 2-chloropropenoate C4H5ClO2 120.5 2 2 0 1.17 High Yes Yes No Yes Yes No
Methylpyrazine C5H6N2 94.11 0 2 0 0.62 High Yes Yes No Yes Yes No
N-(5-methylisoxazol-3-Yl) acetamide C6H8N2O2 140.1 2 3 1 0.61 High Yes Yes No Yes Yes No
Nonanal C9H18O 142.2 7 1 0 2.78 High Yes Yes No Yes Yes No
Norharman C11H8N2 168.2 0 1 1 2.41 High Yes Yes Yes Yes Yes No
O-methyl ferulic acid C11H12O4 208.2 4 4 1 1.83 High Yes Yes Yes Yes Yes Yes
P-coumaric acid C9H8O3 164.2 2 3 2 1.26 High Yes Yes Yes Yes Yes No
Protocatechuic aldehyde C7H6O3 138.1 1 3 2 0.8 High Yes Yes No Yes Yes No
Scopoletin C10H8O4 192.2 1 4 1 1.52 High Yes Yes Yes Yes Yes No
Spantol C10H13NO2 179.2 5 2 1 1.8 High Yes Yes Yes Yes Yes No
Valeraldoxime C5H11NO 101.2 3 2 1 1.32 High Yes Yes No Yes Yes No
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Table 2.

Active ingredients of Coptis.

Component Formula Molecular weight Rotatable bonds H-Bond acceptors H-Bond donors Consensus log P GI absorption BBB permeant Lipinski Ghose Veber Egan Muegge
(R)-Canadine C20H21NO4 339.4 2 5 0 2.97 High Yes Yes Yes Yes Yes Yes
2,4-Heptadienal C7H10O 110.2 3 1 0 1.67 High Yes Yes No Yes Yes No
2,4-Octadienal C8H12O 124.2 4 1 0 2.07 High Yes Yes No Yes Yes No
2-Octenal C8H14O 126.2 5 1 0 2.27 High Yes Yes No Yes Yes No
5,8-Dihydroxy-2-(2-phenylethyl) chromone C17H14O4 282.3 3 4 2 2.91 High Yes Yes Yes Yes Yes Yes
Berberine C20H18NO4 336.4 2 4 0 2.53 High Yes Yes Yes Yes Yes Yes
Berlambine C20H17NO5 351.4 2 5 0 3.04 High Yes Yes Yes Yes Yes Yes
Citral C10H16O 152.2 4 1 0 2.71 High Yes Yes No Yes Yes No
Columbamine C20H20NO4 338.4 3 4 1 2.33 High Yes Yes Yes Yes Yes Yes
Coptisine C19H14NO4 320.3 0 4 0 2.4 High Yes Yes Yes Yes Yes Yes
Corydaldine C11H13NO3 207.2 2 3 1 1.34 High Yes Yes Yes Yes Yes Yes
Epiberberine C20H18NO4 336.4 2 4 0 2.5 High Yes Yes Yes Yes Yes Yes
Ethyl Caffeate C11H12O4 208.2 4 4 2 1.82 High Yes Yes Yes Yes Yes Yes
Fagarine C13H11NO3 229.2 2 4 0 2.59 High Yes Yes Yes Yes Yes Yes
Ferulic acid C10H10O4 194.2 3 4 2 1.36 High Yes Yes Yes Yes Yes No
Groenlandicine C19H16NO4 322.3 1 4 1 2.18 High Yes Yes Yes Yes Yes Yes
Isovanillin C8H8O3 152.2 2 3 1 1.12 High Yes Yes No Yes Yes No
Jatrorrhizine C20H20NO4 338.4 3 4 1 2.31 High Yes Yes Yes Yes Yes Yes
Magnoflorine C20H24NO4 342.4 2 4 2 0.65 High Yes Yes Yes Yes Yes Yes
Magnograndiolide C15H22O4 266.3 0 4 2 1.7 High Yes Yes Yes Yes Yes Yes
Matsutake alcohol C8H16O 128.2 5 1 1 2.21 High Yes Yes No Yes Yes No
O-Methylferulic acid C11H12O4 208.2 4 4 1 1.83 High Yes Yes Yes Yes Yes Yes
Palmatine C21H22NO4 352.4 4 4 0 2.64 High Yes Yes Yes Yes Yes Yes
P-Coumaric acid C9H8O3 164.2 2 3 2 1.26 High Yes Yes Yes Yes Yes No
Phellodendrine C20H24NO4 342.4 2 4 2 0.45 High Yes Yes Yes Yes Yes Yes
Worenine C20H16NO4 334.4 0 4 0 2.69 High Yes Yes Yes Yes Yes Yes
Zosimin C19H20O5 328.4 4 5 0 3.4 High Yes Yes Yes Yes Yes Yes
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3.2. Differential miRNA expression profile selection and related gene acquisition

The dataset GSE135918 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135918) has a total of 10 samples, including 2 samples from the LUAD group and 2 samples from the control group. GPL19730 in the dataset GSE128311 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128311) is an expression profile analysis of miRNA arrays, with a total of 77 samples, including 35 samples in the LUAD group, and 32 samples in the control group. Both datasets were analyzed by miRNA microarrays to obtain differential miRNA expression profiles. Using P < .05 and |log2FC|≥1 as screening criteria, a total of 652 miRNAs were obtained in the dataset GSE135918. A total of 93 miRNAs were obtained in the dataset GSE128311. Differential miRNA expression is represented as a volcano plot (Fig. 1A and B). The 2 datasets were intersected to obtain 25 differential miRNAs (Fig. 1C). Through the analysis and screening of the target scan human platform, 60,326 related genes were obtained, and 15,323 related genes were obtained after removing duplicates.

Figure 1.

Figure 1.

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The differential miRNA volcano map of the dataset GSE135918, with a total of 380 up-regulated miRNAs and 272 down-regulated miRNAs (A). The differential miRNA volcano map of the dataset GSE128311, with a total of 78 miRNAs up-regulated and 15 miRNAs down-regulated (B). The Venn plot of the intersection of differential miRNAs in 2 datasets (C). miRNA = micro RNA.

3.3. Therapeutic target screening and related network construction

A total of 417 targets were obtained by intersecting the targets of the “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair and LUAD targets (Fig. 2A). Through the String database, protein interaction analysis was conducted on intersection targets, and 1050 interactions were obtained, including 294 protein nodes, namely therapeutic targets. The “Traditional Chinese Medicine-Active Ingredient-Therapeutic-Therapeutic Target-miRNA-Disease” network (Fig. 2B) was constructed using Cytoscape software and network topology analysis was performed, and core compounds were screened out based on the top 10 degree values (Table 3). The most important modules and core targets were screened out by the analysis of protein interactions of therapeutic targets by MCODE plug-ins (Fig. 2C).

Figure 2.

Figure 2.

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The Venn diagram of the herb and disease intersection target (A). The network diagram of “Traditional Chinese Medicine-Active Ingredient-Therapeutic Target-miRNA-Disease” (B). The module analysis and core target screening network diagram, the darker the color, the higher the importance of the target in the network (C). miRNA = micro RNA.

Table 3.

Topological analysis of core compounds.

Name Degree Average shortest path length Betweenness centrality Closeness centrality
Cavidine 74 2.413793103 0.017390208 0.414285714
2-Undecanone 70 2.440318302 0.017644867 0.409782609
Zosimin 64 2.461538462 0.012928191 0.40625
Berlambine 61 2.4933687 0.010971259 0.40106383
6-Shogaol 58 2.514588859 0.011055308 0.397679325
Bis (4-hydroxybenzyl) ether 54 2.535809019 0.010722681 0.394351464
(-)-Citronellal 51 2.546419098 0.009951934 0.392708333
Palmatine 46 2.557029178 0.00573066 0.391078838
Berberine 44 2.583554377 0.004843539 0.387063655
9-Oxononanoic acid 43 2.631299735 0.005664846 0.380040323
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3.4. Enrichment analysis results

A total of 1124 GO enrichment analysis results were obtained with P < .05 as the screening condition. There are 109 biological processes, including bidirectional regulation of RNA polymerase II promoter transcription, cell signaling, and cell differentiation regulation (Fig. 3A). There are 56 molecular functions, including RNA polymerase II transcription factor activity, ligand activation sequence-specific DNA binding, ion, transcription factor, and protease binding (Fig. 3B); There are 21 cell compositions, including peroxisome matrix, RNA polymerase II transcription factor complex, and polymer complexes (Fig. 3C).

Figure 3.

Figure 3.

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The bubble plot of biological process results in GO enrichment analysis (A). The bubble plot of molecular function results in GO enrichment analysis (B). The bubble plot of cell composition results in GO enrichment analysis. The color from red to blue in the bubble chart indicates that the “P” value is getting higher, and the larger the bubble, the more genes are enriched in entry (C). The top 30 KEGG pathways are significantly the most relevant, with the length of the bar corresponding to the number of background genes, the depth of the color corresponding to the “P” value, and the darker the color, the smaller the value (D). GO = gene ontology. KEGG = Kyoto encyclopedia of genes and genomes.

A total of 69 KEGG enrichment analysis results were screened with P < .001, and the results showed that cancer-related pathways were the most involved, accounting for 27%, of which the most relevant cancer pathway was non-small cell lung cancer. In addition, the signaling pathway of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair for the treatment of LUAD is also enriched in immune-related pathways, such as C-type lectin receptor signaling pathway and T cell receptor signaling pathway; Endocrine-related pathways, such as thyroid hormone signaling pathway and estrogen signaling pathway; Cell cycle signaling pathways, such as the forkhead box O signaling pathway; Cell metabolic regulatory pathways, such as the cyclic adenosine monophosphate signaling pathway; Tumor vascular endothelial generating pathways, such as VEGF signaling pathways. These signaling pathways may be the main pathway for the treatment of LUAD with “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair (Fig. 3D).

3.5. Molecular docking results

In the KEGG results, the non-small cell lung cancer pathway was the most significant, so the 10 targets enriched on this pathway (EGFR, Janus Kinase 3, mitogen-activated protein kinase 1, phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit alpha, phosphoinositide-3-kinase regulatory subunit 1, protein kinase C alpha, retinoic acid receptor beta, retinoid x receptor alpha, retinoid x receptor gamma, signal transducer and activator of transcription 3) were used as key targets for molecular docking with core compounds. According to the heat map of docking results (Fig. 4A), all docking scores were  < −5.0 kcal/mol, the average docking score was −7.55 kcal/mol, and the docking score of ≤ −7.0 kcal/mol accounted for 73.0%, so the core compounds in the “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair had the good binding ability to key targets. Among them, the compounds with better binding ability were Palmatine, Berberine, and Berlambine. Visualize the result with the highest score of the 3 combinations through PyMol (Fig. 4B–D).

Figure 4.

Figure 4.

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The heat map of molecular docking results, the color is from yellow to purple, and the docking score is gradually reduced (A). The schematic diagram of the binding of the compound Palmatine to the target JAK3, and aspartic acid forms a hydrogen bond at position 154 (B). The schematic diagram of the binding of the compound Berberine to the target JAK3, forming 2 hydrogen bonds at position 140 arginine and 1 hydrogen bond at position 92 isoleucine (C). The schematic diagram of the combination of compound Berlambine and target PIK3CA forms one hydrogen bond at position 519 arginine and cysteine at position 695, respectively (D). JAK3 = Janus Kinase 3. PIK3CA = phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha.

4. Discussion

As one of the deadliest malignant tumors in the world, LUAD has always attracted the attention of scholars from all over the world. When most patients are diagnosed, they are already in the advanced stage of LUAD, because of its insidious onset and small lesions. At present, modern medicine has limited means of treating LUAD, and many patients turn their attention to the medicine of the motherland. As a cultural treasure of the Chinese nation, Chinese medicine has a significant effect on the treatment of intractable diseases, and its natural advantages of multiple ingredients have gradually been recognized by many scholars.[25] In this study, the commonly used anti-tumor Chinese herbal medicine combination: “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair, analyzed the mechanism of action of the 2 in the treatment of LUAD by integrating network technology, and obtained the difference miRNA and target genes between “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair in the treatment of LUAD by reverse matching, and verified the results through molecular docking.

The unbalanced expression of miRNAs is a key factor to promote the rapid occurrence and development of tumors, and the expression profiles of miRNAs are generally characterized in tumor tissues, and the expression profiles of these tumor cells are significantly different from the normal cell expression profiles in the same tissue, so the expression of miRNAs is more accurate to distinguish the type of tumor than the mRNA encoded by proteins.[26] The difference between Rhizoma Pinelliae and Rhizoma Coptidis in the treatment of LUAD in this study found a total of 25 miRNAs, and through searching domestic and foreign literature, it was found that these miRNAs were all related to cancer, of which 10 (miR-126-5P,[27] miR-22-5P,[28] miR-183-5P,[29] miR-144-3P,[30] miR-340-5P,[31] miR-142-5P,[3] miR-200A-3P,[32] miR-429,[33] miR-200B-3P,[34] miR-3648[35]) are directly associated with LUAD. At the same time, the pathway with the highest significance of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair in the treatment of LUAD found in this study is the non-small cell lung cancer pathway, and the key target with the highest degree of freedom value is EGFR. At present, EGFR is considered to be the most common driver gene for LUAD,[36] which coincides with the results of this study. Therefore, miRNAs with a strong correlation with EGFR in this study are expected to become potential biomarkers affecting the development of LUAD, and in addition to miRNAs that have been reported to be directly related to LUAD, 4 miRNAs that are expected to be biomarkers for the identification of the occurrence and progression of LUAD are screened in this study.

miR-5703 is a driver of cell proliferation, miR-5703 promotes adenocarcinoma cell proliferation by binding to CKLF Like MARVEL transmembrane domain containing 4 and downregulates its expression.[37] Inhibition of miR-5703 has a down-regulating effect on adenocarcinoma-derived exosomes, which directly inhibits the proliferation of adenocarcinoma cells.[38] Similarly, overexpression of miR-3125 reduces levels of the pyruvate dehydrogenase kinase 1 protein in cells, increasing the chance of adenocarcinoma.[39] miR-652-5P has been demonstrated in several studies for its potential as a tumor marker.[40] Interestingly, the expression of miR-652-5P in plasma samples from patients with adenocarcinoma was higher than that of normal people,[41] while the reverse was true for patients with squamous cell carcinoma.[40] miR-513c-5p is a tumor suppressor gene that regulates cell proliferation, migration, and invasion.[42] The study found that miR-513c-5p targets lysophosphatidic acid receptor 5 and inhibits its expression. High expression of lysophosphatidic acid receptor 5 tends to predict higher rates of lymphatic metastasis and distant metastases, as well as lower overall survival of adenocarcinoma.[43] This result may make miR-513c-5p a potential biomarker of the degree of metastasis of cancer cells. All 4 miRNAs affected the proliferation and migration of adenocarcinoma, and they had a strong correlation with EGFR in this study. Therefore, miR-5703, miR-3125, miR-652-5P, and miR-513c-5p are promising potential biomarkers for identifying different stages of LUAD development and determining cancer types.

Molecular docking results showed that the compounds with outstanding binding ability were Palmatine, Berberine, and Berlambine, which all belonged to alkaloids. Alkaloids exert significant antitumor effects mainly by inhibiting tumor cell proliferation, promoting apoptosis and autophagy, and regulating tumor angiogenesis and tumor metastasis.[44] In addition, studies have proved that alkaloid-containing traditional Chinese medicines have good pharmacological effects on targeting the tumor microenvironment.[45] Modern pharmacology shows that Berberine has the characteristics of low toxicity to healthy cells and high toxicity to cancer cells, which makes Berberine one of the most promising natural anticancer drugs.[46] Berberine can effectively inhibit the proliferation and migration of LUAD.[47] The study found that berberine blocked DNA replication in LUAD cells by down-regulating DNA polymerase epsilon 2 and DNA primase subunit 1 in LUAD cells. At the same time, Berberine has a down-regulating effect on the main gene expression biomarker (forkhead box M1) that can reflect the poor prognosis of pan-cancer,[47] which indicates that berberine has a good inhibitory effect on the development of LUAD cells throughout the cycle. Palmatine is a class of isoquinoline alkaloids with highly effective biological activity and good water solubility and has shown a good effect in promoting apoptosis of adenocarcinoma cells in previous studies.[48,49] Studies have shown that palmatine inhibits the activation of collagen type 1 alpha 1 and survivin mediated by glioma-associated oncogene 1, thereby inhibiting adenocarcinoma cell proliferation and promoting apoptosis.[50] At the same time, palmatine can inhibit lung metastasis of adenocarcinoma cells and prevent lung structural changes, which is achieved by upregulating the expression of tumor suppressor p53 and thus inhibiting the transcription of metastasis-associated protein 1.[51]

5. Conclusion and outlook

In this study, the differential miRNAs of LUAD were mined by bioinformatics technology, and the targets and related pathways enriched by “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair were screened out by network analysis, and the differential miRNA in the treatment of LUAD were reverse-matched by the herb. It is ultimately believed that miR-5703, miR-3125, miR-652-5P, and miR-513c-5p may become new biomarkers for the treatment of LUAD. Through the analysis of molecular docking results, alkaloids may be the main components of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair for the treatment of LUAD, mainly by inhibiting tumor cell proliferation, promoting apoptosis, and regulating tumor metastasis.

In summary, this study explores the targets, herb components, differential miRNAs, and pathways of “Rhizoma Pinelliae-Rhizoma Coptidis” herb pair in the treatment of LUAD, which provides a key reference basis and data support for elucidating the mechanism of action of traditional Chinese medicine in the treatment of LUAD and assisting in prevention in the future, and also lays a preliminary foundation and provides direction for follow-up research. In addition, the differential miRNAs and core targets screened by bioinformatics in this study need to be verified by subsequent experiments to ensure the accuracy and reliability of the results.

Author contributions

Funding acquisition: Hong Chang.

Supervision: Rui Qie.

Visualization: Tianwei Meng.

Writing – original draft: Jiawen Liu.

Writing – review & editing: Rui Qie.

Abbreviations:

EGFR
epidermal growth factor receptor
FoxO
forkhead box O
GO
gene ontology
KEGG
Kyoto encyclopedia of genes and genomes
LUAD
lung adenocarcinoma
miRNA
micro RNA
PPI
protein-protein interaction

TM and JL contributed equally to this work.

This article is supported by the National Natural Science Foundation of China (82060784).

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

The present study is a Bioinformatics-based analysis, so ethical and consent permission is unnecessary.

How to cite this article: Meng T, Liu J, Chang H, Qie R. Reverse predictive analysis of Rhizoma Pinelliae and Rhizoma Coptidis on differential miRNA target genes in lung adenocarcinoma. Medicine 2023;102:7(e32999).

Contributor Information

Tianwei Meng, Email: mtw19950813@163.com.

Jiawen Liu, Email: 1664485352@qq.com.

Rui Qie, Email: qierui@hljucm.edu.cn.

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