For the identification of COVID-19-associated targets, the keywords “coronavirus COVID-19,” “coronavirus Disease 2019,” “severe acute respiratory syndrome coronavirus 2,” and “COVID-19” were subjected to different databases, including the Genecards database (17), Online Mendelian Inheritance in Man (OMIM) database (https://omim.org/), Therapeutic Target Database (TTD) (18), Comparative Toxicogenomics Database (CTD) (19), and National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/), genes with the relevance score >1 were obtained from the databases. To identify LUAD-associated targets, the transcriptome data of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) on 26 July 2021. Using the limma package in R on Bioconductor software, genes with FDR < 0.05, and |logfold change| > 2 were considered as differentially expressed genes (DEGs) (20).

The pharmacological targets of curcumol were determined using various online tools and databases, including Swiss Target Prediction database and Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM) (21). The target genes were subjected to UniProt for human database correction. The common deregulated genes between COVID-19 and LUAD, that were previously identified, were intersected and compared with the targets of curcumol. Interactions between common targets were analyzed using the STRING database (version 11.0) (22) and Cytoscape software (version 3.6.1) (23). The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 was used for the gene ontology (GO) function enrichment analysis and KEGG enrichment analysis, to understand the functional roles of targets and signaling pathways controlled by the genes.

Molecular docking analysis was used to investigate the possible binding between curcumol and its predicted targets. The protein structures of the core targets were searched for from the Protein Data Bank (PDB) database (24). Identified protein structures were then docked with curcumol using the AutoDock Vina program and docking analysis was conducted (25).

To determine the pathological roles of the core targets in LUAD, Cox proportional hazards models were applied in univariate analysis of survival as a function of clinical variables and gene expression.

The human lung adenocarcinomic cell lines of A549 were incubated with high glucose dulbecco's modified eagle medium (DMEM) medium (ThermoFisher, Cat. No. 11965118), supplemented with 0.5% penicillin–streptomycin (ThermoFisher, Cat. No. 15140122) and 5% fetal bovine serum (ThermoFisher, Cat. No. 10082147) under 5% CO₂ at 37°C.

The cells were seeded in a 96-well plate at a cell density of 2 × 10⁴ cells per well, with eight replicate wells. The cells were treated with different concentrations of curcumol (0.1–100 μM) for 48 h. After the incubation, the cell viability was measured by the CCK-8 assay (Data Inventory Biotechnology) as described previously (26). The colorimetric product formed was measured at an absorbance of 450 nm and 600 nm, ΔOD = OD_450nm – OD_600nm.

After the treatment of the cell with 100 μM curcumol for 48 h, the total RNA of the cell was extracted using Trizol reagent (Thermofisher) following the manufacturer's instruction. The RNA quality and quantity were assessed by using Bioanalyzer 2,100 and RNA 6,000 Nano LabChip Kit (Agilent), high-quality RNA samples with RNA integrity number (RIN) number higher than 7.0 were used to construct sequencing library. The average insert size for the final complementary DNA (Cdna) library was about 300 bp. Then 2 × 150 bp paired-end sequencing (PE150) was performed on an Illumina Novaseq™. The high-quality clean reads were mapped to the Human genome reference (Homo sapiens Ensembl v96) using HISAT2 software (version: hisat2-2.0.4) (27). StringTie and ballgown were used to determine the gene expression level (28). The genes with a 1.5 < fold change (treatment/control) < 0.75 and –log10 (q-value) > 1.3 were considered as DEGs. The DEGs were subjected to the DAVID v6.8 analysis (29) and IPA (https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis) to delineate the molecular mechanism underlying the effect of curcumol for treating LUAD.

Using the relevant databases, we identified a total of 8,339 targets associated with COVID-19 (Figure 1A). Using the TCGA database, we found 5,538 differential expressed genes associated with LUAD (Figure 1A). When we compared the COVID-19- and LUAD-associated targets, we found 882 shared targets (Figure 1A), among which 216 were downregulated and 666 were upregulated, in patients with LUAD (Figure 1B). To understand the pharmacology of curcumol, a network pharmacology analysis was conducted. We identified 151 curcumol-associated targets using the mentioned databases (Figure 1A) and, after comparison of the curcumol-associated targets with the COVID-19/ LUAD-associated targets, we found 28 targets shared by curcumol, COVID-19, and LUAD (Figure 1A). The molecular network analysis using Cytoscape highlighted seven core targets of curcumol, namely, AURKA, CDK1, CCNB1, CCNB2, CCNE1, CCNE2, and TTK in COVID-19 and LUAD (Figure 1C and Table 1).

Three protein structures, CDK1, TTK, and AURKA, out of the seven core targets were available on the PDB database (AURKA, ID:2J50; CDK1, ID:5HQ0, and TTK, ID:6N6O). These protein structures were subjected to docking analysis with curcumol using the AutoDock Vina program. The results were displayed using PyMOL (version 2.3), which showed that curcumol formed a hydrogen bond with LYS-162 (3.2 Å) of AURKA (PDB ID: 2J50) (Figure 2A), and the binding affinity of curcumol for AURKA was −6.4 kcal/mol. A similar bindings were observed between curcumol and the amino acid residue LEU-83 (2.2 Å) of CDK1 (PDB ID: 5HQ0) (Figure 2B) and between curcumol and the amino acid residue LYS-529 (3.0 Å) of TTK (PDB ID: ID:6N6O) (Figure 2C). The binding affinities of curcumol for CDK1 and TTK were −3.2 kcal/mol and −4.7 kcal/mol, respectively. Our data suggested the direct binding of curcumol to its targets.

The results of the hazards models showed that the expression of curcumol targets in COVID-19 and LUAD was significantly associated with the relative risk of survival [AURKA (p = 0.001, hazard ratio, 1.086–1.399); CDK1 (p < 0.001, hazard ratio, 1.099–1.401); CCNB1 (p < 0.001, hazard ratio, 1.144–1.499); CCNB2 (p < 0.001, hazard ratio, 1.089–1.388); CCNE1 (p = 0.012, hazard ratio, 1.032–1.283); CCNE2 (p = 0.026, hazard ratio, 1.019–1.341); TTK (p = 0.004, hazard ratio, 1.053–1.307)] in patients with LUAD (Figure 3A). The results of survival analysis using the Kaplan–Meier estimator also showed that patients with LUAD with greater expression of the genes, AURKA, CDK1, CCNB1, CCNB2, CCNE2, and TTK, had poorer overall survival rates (Figure 3B). In addition, the correlation analysis highlighted that increased expression of AURKA was associated with advanced stages of LUAD and the increased number of lymph nodes containing tumor (Figure 3C).

To further understand the biological roles of curcumol in the treatment of COVID-19 and LUAD, the identified core targets were subjected to GO enrichment analysis and KEGG pathway enrichment analysis. The results of GO analysis showed that curcumol targets (AURKA, CDK1, and CCNB1) could mediate biological processes related to DNA damage response by controlling DNA integrity and the DNA damage checkpoint (Figure 4A and Supplementary Table 1), or by regulating cyclin-dependent protein kinase activity, which could lead to cell cycle arrest at different cell cycle checkpoints, such as cell cycle G1/S phase transition and cell cycle G2/M phase transition (Figure 4B and Supplementary Table 1). These responses are controlled by the seven curcumol targets (Figure 4B and Supplementary Table 1). The other possible outcome of mediation of biological processes related to the DNA damage response by these targets is the alteration of cellular assembly processes, such as organelle fission, mitotic nuclear envelope disassembly, chromosome segregation, and mitotic spindle organization (Figure 4C and Supplementary Table 1). In addition, curcumol targets were found to regulate many important processes involved in LUAD carcinogenesis, such as histone phosphorylation, oxidative phosphorylation, and cellular respiration (Figure 4D and Supplementary Table 1).

The GO cellular components and molecular functions analyses showed that the curcumol targets play roles in many enzymatic complexes related to cell cycle control, such as in cyclin-dependent protein kinase holoenzyme complex and in serine/threonine protein kinase complex (Figure 5A and Supplementary Table 2). More importantly, the targets are involved in chromosome organization, specifically, in organization of centromeric region of chromosomes, kinetochores, mitotic spindle pores, and telomeric regions (Figure 5A and Supplementary Table 2). The results of the molecular function analysis further highlighted the effects of curcumol on cyclin-dependent protein serine/threonine kinase regulator activity, histone kinase activity, and cyclin binding (Figure 5A and Supplementary Table 2). KEGG pathway analysis was used to investigate the pharmacological effect of curcumol on the regulation of cell-signaling pathways. The results showed that curcumol could target many cell-signaling pathways related to cancer development (Figure 5B and Supplementary Table 3), including the p53 signaling pathway, FoxO signaling pathway, and PI3K–Akt signaling pathway (Figure 5B and Supplementary Table 3). The curcumol targets were also found to play a role in immune response to viral infections, like T-cell leukemia virus 1 infection, papillomavirus infection, and immunodeficiency virus 1 infection (Figure 5B and Supplementary Table 3).

To investigate the effect of curcumol on lung cancer, an in vitro LUAD model A549 was used. Our result showed that the treatment of curcumol caused a significant dose-dependent inhibition of cell proliferation in LUAD as compared to the control group (Figure 6A). Then comparative transcriptomic analysis was conducted to delineate the corresponding molecular mechanism. By comparing the gene expression profile of control and curcunol treatment group, we found 348 DEGs, including 206 upregulated genes and 142 downregulated genes (Figure 6B and Supplementary Table 4). The DEGs were used for the DAVID and IPA analysis to understand the biological alteration and gene network mediated by curcumol treatment in LUAD. The result of GO enrichment showed that the curcumol treatment controlled biological processes related to cell cycle and gene transcription of LUAD (Figure 6C and Supplementary Table 5). More importantly, curcumol could trigger DNA damage response, leading to cell death and cell apoptosis in LUAD (Figure 6C and Supplementary Table 5). These results further supported the above findings from network pharmacology. Finally, gene networking of IPA highlighted the involvement of transcription and translation factors, enzymes, kinases, phosphatases, and receptors in curcumol-mediated cell cycle and DNA response (Figure 6D and Table 2).