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. 2024 Sep 27;103(39):e39862. doi: 10.1097/MD.0000000000039862

Identification of potential core genes in lung cancer and therapeutic traditional Chinese medicine compounds using bioinformatics analysis

Yue Zhang a, Yaguang Wang b, Xuepu Zhang c, Jiansheng Liu d,*
PMCID: PMC11441908  PMID: 39331864

Abstract

Lung cancer (LC) remains the leading cause of cancer-related death. We identified potential therapeutic targets and traditional Chinese medicine (TCM) compounds for LC treatment. GSE43346 and GSE18842 were derived from the Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Protein–protein interactions were analyzed using STRING and Cytoscape software. Hub gene expression was validated using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. Kaplan–Meier survival analysis was conducted to evaluate the prognostic value of hub genes in patients with LC. Therapeutic TCM compounds were screened using the Comparative Toxicogenomics Database, and DEGs were largely enriched in biological processes, including cell division and mitotic nuclear division, such as the cell cycle and p53 signaling pathways. Elevated expression of hub genes was observed in LC samples. Overexpression of CDC20, CCNB2, and TOP2A is an unfavorable prognostic factor for postprogressive survival in patients with LC. Paclitaxel, quercetin, and rotenone have been identified as active substances in TCM. CDC20, CCNB2, and TOP2A are novel hub genes associated with LC. Paclitaxel, quercetin, and rotenone can be used as therapeutic agents in TCM.

Keywords: bioinformatics analysis, hub genes, lung cancer, traditional Chinese medicine compounds

1. Introduction

The incidence and mortality rates of lung cancer (LC) have been increasing worldwide.[1] Identifying prognostic biomarkers will be helpful in stratifying patients with lung adenocarcinoma (LUAD) with different prognoses.[2] The mainstay treatment for LC is drug efficacy and drug resistance.[3] LC accounts for 18.4% of cancer-related deaths and is the leading cause of cancer-related deaths.[4] Thus, there is an urgent need for novel therapeutic targets and advanced therapeutic strategies for the effective diagnosis and treatment of LC.

Bioinformatics can help identify valuable therapeutic targets, meaningful genes, and conduct new research. In the present study, 2 mRNA microarray datasets were analyzed to identify differentially expressed genes (DEGs) in LC and normal tissues.

In this study, we identified novel indicators of poor prognosis in patients with LC and potential therapeutic targets for this challenging disease. To detect DEGs between LC and healthy human tissues, bioinformatics methods were used to analyze gene expression profiling data downloaded from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the screened DEGs. Next, we established a protein–protein interaction (PPI) network to identify hub genes related to LC. Hub gene expression was validated using Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA). Survival analysis of these hub genes was performed using the online Kaplan–Meier plotter database. Therapeutic TCM compounds were screened using the Comparative Toxicogenomics Database (CTD).

2. Materials and methods

2.1. Data source

The gene expression datasets analyzed in this study were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/nih.gov/geo/). After a careful review, 2 gene expression profiles (GSE43346 and GSE18842) were selected.[5,6] GSE18842 and GSE43346 were based on the platform GPL570 ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). Both profiles are freely available online. This study did not include any experiments involving humans or animals. The data were sourced as previously described.[7]

2.2. Data processing of DEGs

The GEO2R online analysis tool was used to detect DEGs in the LC and normal samples, and the adjusted P-value and |logFC| were calculated. Genes that met the cutoff criteria, adjusted P < .05, and |logFC|≥2.0, were considered DEGs. Statistical analysis was performed for each dataset, and the intersecting parts were identified using the Venn diagram web tool (bioinformatics.psb.ugent.be/web tools/Venn/).[7]

2.3. GO and KEGG pathway analysis of DEGs

GO analysis is a commonly used method for large-scale functional enrichment research, and gene functions can be classified into biological processes (BP), molecular functions (MF), and cellular components (CC). KEGG is a widely used database that stores a large amount of data on genomes, biological pathways, diseases, chemical substances, and drugs. GO annotation and KEGG pathway enrichment analyses of DEGs were performed using Database for Annotation, Visualization, and Integrated Discovery (DAVID) tools (https://david.ncifcrf.gov/). GO (P < .01, gene counts ≥ 20) and KEGG (P < .01, gene counts ≥ 5) were considered statistically significant.

2.4. PPI network construction and hub gene identification

The Search Tool for the Retrieval of Interacting Genes (STRING) database (http://string-db.org/) was used to analyze the PPI information. To evaluate potential PPI relationships, previously identified DEGs were mapped to the STRING database. PPI pairs with a combined score of > 0.9 were extracted. Subsequently, the PPI network was visualized using the Cytoscape software (www.cytoscape.org/). Nodes with a higher degree of connectivity tend to be more essential for maintaining the stability of the entire network. CytoHubba, a Cytoscape plugin, was used to calculate the degree and mean correlation coefficient (MCC) of each protein node.

To further screen out key targets that played an important role, nodes with topological importance in the PPI network were filtered through a series of parameters using the Cytoscape plugin CytoNCA.[8] We selected 3 parameters were selected (DC), betweenness centrality (BC), and closeness centrality (CC). After a series of screenings, we obtained a PPI network containing core genes. In addition, interactions between the important proteins were observed. Furthermore, we matched the results of plugins cytoHubba and CytoNCA using the Venn Diagram Platform (https://bioinfogp.cnb.csic.es/tools/venny/) to obtain “real” hub genes.

2.5. Validation hub genes

GEPIA (http://gepia.cancer-pku.cn/detail.php/, |logFC|:1, P-value: 0.01) was used for visual analysis of expression and clinical relevance. The Human Protein Atlas (HPA; https://www.proteinatlas.org/) provides location and qualitative data for proteins. We used GEPIA and HPA to validate the hub genes.[9,10]

2.6. Survival analysis of hub genes

The Kaplan–Meier plotter (http://kmplot.com/analysis/) is an online tool used to assess the effect of 54,000 genes on survival in 14,912 cancer samples (7830 breast, 2190 ovarian, 3452 lung, and 1440 gastric cancers). The Kaplan–Meier plotter mRNA LC database was used to evaluate the prognostic value of hub genes in patients with LC. Probes of genes were selected based on the “only JetSet best probe set.”[7] For each gene, patients with cancer were divided into 2 groups according to the median value of mRNA expression. P < .01 was considered to indicate a statistically significant.

2.7. Screening of active ingredients of TCM targeting core genes

The following method was used: Click the “Analyze” “VennViewer” option in the Search drop-down list, Select your input type: genes. The hub genes in the “Gene” input box successively obtained the possible active ingredients of TCM and Venn View (CTD, http://ctdbase.org/).

3. Results

3.1. Identification of DEGs

Gene expression profiles (GSE43346 and GSE18842) were selected for this study. GSE43346 contained 23 LC samples and 1 normal sample, and GSE18842 included 46 LC specimens and 45 normal specimens (Table 1). Based on the criteria of P < .05 and |logFC|≥2, 575 DEGs were identified from GSE43346, including 380 upregulated genes and 195 downregulated genes. In the gene chip GSE18842, 752 DEGs were identified: 310 genes were upregulated and 442 genes were downregulated. All DEGs were identified by comparing the LC and normal samples. Subsequently, Venn analysis was performed to determine the intersection of DEG profiles (Fig. 1). Finally, 162 DEGs were found to be significantly and differentially expressed between the 2 groups, of which 90 were significantly upregulated and 72 were downregulated.

Table 1.

Statistics of the 2 microarray databases derived from the GEO database.

Dataset ID Lung cancer Normal Total number
GSE43346 23 1 24
GSE18842 46 45 91
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GEO = Gene Expression Omnibus.

Figure 1.

Figure 1.

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Venn diagram of DEGs common to both GEO datasets. (A) Upregulated genes. (B) Downregulated genes. DEG = differentially expressed gene; GEO = Gene Expression Omnibus.

3.2. Functional enrichment analyses of DEGs

GO functional enrichment analysis of the DEGs was performed using DAVID (Table 2, Fig. 2A). Enriched GO terms were divided into CC, BP, and MF ontologies. GO analysis indicated that DEGs were largely enriched in BPs, including cell division and mitotic nuclear division. For cell components, DEGs were enriched in the nucleus, nucleoplasm, cytosol, cytoplasm, and cell membrane. For MF, DEGs were enriched for protein and ATP binding. In addition, KEGG pathway analysis revealed that DEGs were largely enriched in the cell cycle, p53 signaling pathway, oocyte meiosis, malaria, progesterone-mediated oocyte maturation, HTLV-I infection, and tumor necrosis factor signaling pathways (Table 3, Fig. 2B).

Table 2.

Significantly enriched GO terms pathways of DEGs.

Description Category Count P-value
Cell division BP 32 2.54E−22
Mitotic nuclear division BP 26 1.82E−19
Nucleus CC 84 7.65E−11
Nucleoplasm CC 55 4.16E−10
Cytosol CC 51 7.38E−06
Cytoplasm CC 69 1.27E−05
Membrane CC 31 3.95E−03
Protein binding MF 112 4.35E−10
ATP binding MF 27 3.53E−04
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BP = biological processes, CC = cellular components, DEGs = differentially expressed genes, GO = gene ontology, MF = molecular functions.

Figure 2.

Figure 2.

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Functional classification of DEGs among different samples. (A) GO functional classification. (B) KEGG pathway. GO = gene ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.

Table 3.

Significantly enriched KEGG pathways of DEGs.

Description Count P-value
Cell cycle 14 1.53E−10
p53 signaling pathway 8 3.66E−06
Oocyte meiosis 9 1.18E−05
Malaria 6 1.11E−04
Progesterone-mediated oocyte maturation 6 0.001607
HTLV-I infection 9 0.003359
TNF signaling pathway 6 0.003973
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DEGs = differentially expressed genes, KEGG = Kyoto Encyclopedia of Genes and Genomes.

3.3. PPI network construction and hub gene identification

Protein interactions among the DEGs were predicted using Cytoscape software. A total of 87 nodes and 901 edges were identified in the PPI network (Fig. 3A). The top 10 genes evaluated based on the degree of connectivity (Table 4, Fig. 3B) and MCC (Fig. 3C) in the PPI network were identified. The results demonstrated that cyclin-dependent kinase 1 (CDK1) was the most prominent gene with connectivity degree = 61, followed by cell division cycle protein 20 homolog (CDC20; degree = 52), Cyclin-A2 (CCNA2; degree = 51), G2/mitotic-specific cyclin-B2 (CCNB2; degree = 49), G2/mitotic-specific cyclin-B1 (CCNB1; degree = 49), DNA topoisomerase 2-alpha (TOP2A; degree = 48), mitotic checkpoint serine/threonine-protein kinase BUB1 (BUB1; degree = 48), mitotic checkpoint serine/threonine-protein kinase BUB1 beta (BUB1B; degree = 46), kinesin-like protein KIF11 (KIF11; degree = 45), and borealin (DLGAP5; degree = 43). These hub genes were upregulated in LC.

Figure 3.

Figure 3.

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(A) PPI network constructed using DEGs. Red nodes represent upregulated genes and green nodes represent downregulated genes. (B) Top 10 hub nodes in the PPI network of DEGs by degree. (C) Top 10 hub nodes in the PPI network of DEGs identified by MCC. PPI = protein–protein interaction, DEGs = differentially expressed genes, MCC = mean correlation coefficient.

Table 4.

Top 10 hub genes with higher degree of connectivity.

Gene symbol Gene description Degree
CDK1 Cyclin-dependent kinases 1 61 Up
CDC20 Cell division cycle protein 20 homolog 52 Up
CCNA2 Cyclin-A2 51 Up
CCNB2 G2/mitotic-specific cyclin-B2 49 Up
CCNB1 G2/mitotic-specific cyclin-B1 49 Up
TOP2A DNA topoisomerase 2-alpha 48 Up
BUB1 Mitotic checkpoint serine/threonine-protein kinase BUB1 48 Up
BUB1B Mitotic checkpoint serine/threonine-protein kinase BUB1 beta 46 Up
KIF11 Kinesin-like protein KIF11 45 Up
DLGAP5 DLG associated protein 5 43 Up
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Next, we used the degree, betweenness, and closeness as the 3 main parameters for critical target screening.[11] In the first topological analysis, based on the criteria of DC ≥ 10.968, BC ≥ 0.101, CC ≥ 30 (Fig. 4A) were obtained, and among the 91 nodes, 35 yellow nodes represented the core targets obtained in the topology analysis, whereas 56 blue nodes represented the non-core targets. After the second topological analysis, with the criteria of DC ≥ 3.099, BC ≥ 0.895, and CC ≥ 60, the results demonstrated that there were 35 nodes (20 yellow nodes representing the core targets and 15 blue nodes representing the non-core targets) and 956 edges (Fig. 4B). The PPI network (Fig. 4C) was finally obtained, containing 20 nodes marked in yellow and 380 edges, indicating that 15 core targets were identified after topological analysis: CCNB1, CDC20, NCAPG, AURKA, BUB1, ASPM, CDK1, TOP2A, RRM2, UBE2C, KIF11, DLGAP5, BIRC5, MAD2L1, CCNB2, BUB1B, CCNA2, TTK, NDC80, and NUSAP1. They displayed equal degrees, betweenness, and closeness. The common hub genes in topological analysis, degree, and MCC were regarded as “real” hub genes: CDK1, CDC20, CCNA2, CCNB2, TOP2A, BUB1, BUB1B, and DLGAP5 (Fig. 4D). Thus, these genes may play major roles in LC treatment.

Figure 4.

Figure 4.

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By taking the intersection of cytoHubba and CytoNCA (A, B, and C), 8 common elements (D) in “degree,” “MCC,” and “Topology” were identified: CDK1, CDC20, CCNA2, CCNB2, TOP2A, BUB1, BUB1B, and DLGAP5. (A) DC ≥ 10.968, BC ≥ 0.101, CC ≥ 30, 91 nodes and 1852 edges. (B) DC ≥ 3.099, BC ≥ 0.895, and closeness centrality (CC) ≥ 60. (C) Thirty-five nodes and 956 edges and 20 nodes and 380 edges were identified. D.Venn diagram of DEGs common in topological analysis, degree, and MCC. DC = degree centrality, BC = betweenness centrality, CC = closeness centrality.

3.4. Expression of hub genes in LC

To further evaluate the expression of hub genes in LC, we examined hub gene expression using the GEPIA (Fig. 5) and HPA databases (Fig. 6). The expression of hub genes (CDK1, CDC20, CCNA2, CCNB2, TOP2A, BUB1, BUB1B, and DLGAP5) was higher in the LC samples than in the normal samples. This result is consistent with that of the GEO database.

Figure 5.

Figure 5.

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The expression of hub genes in lung cancer patients compared with healthy normal controls. (A) CDK1, B:CDC20, (C) CCNA2, (D) CCNB2, E:TOP2A, F:BUB1, G:BUB1B, (H) DLGAP5.

Figure 6.

Figure 6.

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Immunohistochemistry confirmed the differential expression of hub genes in lung cancer and normal tissues in the Human Protein Atlas. (A) CDK1 had a low expression in normal lung tissue; (B) CDK1 had a high expression in lung cancer tissue; (C) CDC20 had a low expression in normal lung tissue; (D) CDC20 had a high expression in lung cancer tissue; (E) CCNA2 had a low expression in normal lung tissue; (F) CCNA2 had a high expression in lung cancer tissue; (G) CCNB2 had a low expression in normal lung tissue; (H) CCNB2 had a high expression in lung cancer tissue; (I) TOP2A had a low expression in normal lung tissue; (J) TOP2A had a high expression in lung cancer tissue; (K) DLGAP5 had a low expression in normal lung tissue; (L) DLGAP5 had a high expression in lung cancer tissue. No results were found for BUB1 and BUB1B genes.

3.5. Survival analysis of hub genes

The Kaplan–Meier plotter bioinformatics analysis platform was used to investigate the prognostic value of the 8 potential hub genes. We found that high expression of these hub genes was associated with unfavorable overall survival in LC (Fig. 7). In addition, overexpression of CDC20, CCNB2, and TOP2A is an unfavorable prognostic factor for postprogression survival (PPS) in patients with LC (Fig. 8).

Figure 7.

Figure 7.

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Kaplan–Meier overall survival analyses for the top 8 hub genes expressed in lung cancer patients. The results showed that the patients with high expression levels of 8 hub genes had a poorer prognosis than those with low expression levels of 8 hub genes. HR, hazard ratio; Ssk1:BUB1B.

Figure 8.

Figure 8.

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Kaplan–Meier postprogression survival (PPS) analyses for CDC20, CCNB2, and TOP2A expression in lung cancer patients.

3.6. Active ingredients in TCM for hub genes

The CDC20, CCNB2, and TOP2A core DEGs were imported into the CTD database, and 108 intersection compounds were obtained, among which paclitaxel, quercetin, and rotenone were screened as the active substances of TCM (Fig. 9).

Figure 9.

Figure 9.

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The number of chemicals in common for CDC20, CCNB2, and TOP2A.

4. Discussion

In the present study, gene and protein–protein expression analyses based on publicly available databases were performed to identify potential key genes associated with LC. DEGs between LC and healthy human tissues were screened based on the gene expression profiling data from the GEO database. Ninety upregulated and 72 downregulated DEGs were identified. These DEGs were associated with GO terms, such as cell division and mitotic nuclear division, and were significantly enriched in KEGG pathways in the cell cycle. Previous studies have demonstrated that the mitotic cell cycle (mitotic) may play a key role in the prognosis of small cell LC.[12] Erastin and APAP promote cancer cell death by regulating nuclear translocation of nuclear factor erythroid 2-related factor 2 (Nrf2).[13] TP53 is the most frequently mutated gene in LC.[14] Cancer cells exhibit cell cycle dysregulation, which leads to uncontrolled cell proliferation. These results suggest that GO and KEGG terms are potentially important events in LC.

A PPI network was constructed to investigate the interrelationships between DEGs and 8 hub genes: CDC20, BUB1, CDK1, TOP2A, DLGAP5, CCNB2, BUB1B, and CCNA2. These genes are upregulated in LC. Previous studies have demonstrated that these hub genes can serve as biomarkers and therapeutic targets in LC.[15–23] Finally, the online survival analysis tool was used to predict the relationship between the expression of hub genes and the prognosis of patients with LC. Overexpression of all the above genes is related to an unfavorable prognosis for overall survival in patients with LC. In particular, CDC20, CCNB2, and TOP2A were unfavorable prognostic factors for postprogression survival in patients with LC. In line with previous studies, the functional analysis conducted in this study suggests that the 8 hub genes serve as biomarkers and therapeutic targets in LC.[15–23]

Differential gene expression analysis using GEO datasets (GSE162102) revealed that they were associated with oocyte meiosis, the cell cycle, the p53 signaling pathway, and malaria in patients with LC.[24,25] The initial screening results of our hub genes were similar to those previously reported,25 and network verification was performed for our results.

In addition, the intersection of genes obtained by different algorithms and survival analysis were used to screen out the “real” hub genes. Next, we identified the active substances in TCM using “real” hub genes. The results demonstrated that paclitaxel, quercetin, and rotenone were intricately related to “real” hub genes. Paclitaxel is a well-known anticancer agent with a unique mechanism of action. It has great potential for use in the treatment of several cancers.[26,27] At present, different dosage forms of paclitaxel are used for the treatment of LC, displaying good efficacy and drug resistance.[28,29] Quercetin is a natural compound that is used in cancer treatment.[30,31] In TCM, quercetin is the main effective component of Yang-Yin-Qing-Fei-Tang, which has potent inhibitory activity against non-small cell LC.[32] Rotenone is widely used for the treatment of other cancers.[33,34] However, rotenone has not been used extensively in LC research. These studies have demonstrated that these active substances have therapeutic effects on cancers, including LC. However, the effects and mechanisms of action of quercetin and rotenone in LC remain relatively unknown. Therefore, the combined application or use of these 3 active substances in LC research is worth investigating.

5. Conclusions

In summary, we identified 3 core genes (CDC20, CCNB2, and TOP2A) related to poor prognosis and 3 effective components (paclitaxel, quercetin, and rotenone) in LC via bioinformatics analysis. In the future, core genes and effective components for the treatment of LC need to be experimentally confirmed.

Acknowledgments

We thank the Gene Expression Omnibus database for the datasets GSE43346 and GSE18842.

Author contributions

Data curation: Jiansheng Liu.

Methodology: Xuepu Zhang.

Resources: Xuepu Zhang.

Software: Yue Zhang.

Validation: Yue Zhang.

Writing – original draft: Yaguang Wang.

Writing – review & editing: Yaguang Wang, Jiansheng Liu.

Abbreviations:

BC
betweenness centrality
BP
biological processes
CC
cellular components
CC
closeness centrality
CTD
Compartive Toxicogenomics Database
DC
degree centrality
DEGs
differentially expressed genes
GEO
Gene Expression Omnibus
GEPIA
Gene Expression Profiling Interactive Analysis
GO
gene ontology
HPA
the Human Protein Atlas
KEGG
Kyoto encyclopedia of and genomes
LC
lung cancer
log2FC
log2FoldChange
LUAD
lung adenocarcinoma
MCC
mean correlation coefficient
MF
molecular functions
PPI
protein–protein interaction
TCM
traditional Chinese medicine

The authors have no funding and conflicts of interest to disclose.

Consent for publication is not applicable to this article.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Zhang Y, Wang Y, Zhang X, Liu J. Identification of potential core genes in lung cancer and therapeutic traditional Chinese medicine compounds using bioinformatics analysis. Medicine 2024;103:39(e39862).

YZ and YW contributed to this article equally.

Contributor Information

Yue Zhang, Email: 34916851@qq.com.

Yaguang Wang, Email: 49365960@qq.com.

Xuepu Zhang, Email: 34916851@qq.com.

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