Introduction

As of June 2024, there have been more than 775 million confirmed cases of coronavirus disease 2019 (COVID-19) worldwide and more than 7 million deaths (Covid19.who.int/). Currently, there are no effective treatment for COVID-19. In addition, it has been reported that the risk of COVID-19 infection among hospitalized cancer patients is 2.31 times higher than that of the general population1. Gastric cancer (GC) is the fifth most prevalent cancer and the third leading cause of cancer-related deaths, with over one million cases diagnosed annually2. In the majority of the world, GC has a mortality rate of 75% 3. The treatment of GC patients who acquire COVID-19 is often complicated by their poor nutritional status and immune response4. Effective therapeutic options for such patients are needed.

Lentinus edodes (dubbed “the king of mushrooms”) is a medicinal and edible fungus. Its main active substance is lentinan5. Lentinan is a macromolecule with a main chain of β-1,3-D-glucopyranose that branches every five glucose units consisting of two β-1,6-bonded glucopyranose residues6. Lentinan can induce antitumor immune responses that involved activated macrophages, T lymphocytes, B lymphocytes, natural killer cells, and other immune cells7. Lentinan also has anticancer activity, which involves controlling caspase-3 expression8, telomerase activity9, and cytochrome P450 (CYP) production10. Moreover, lentinan exerts pronounced antiviral activity against infectious hematopoietic cell necrosis virus11, herpes simplex virus type 1 12, and human immunodeficiency virus13. However, the activity of lentinan against COVID-19 and whether can be used to treat patients with both GC and COVID-19 (GC/COVID-19) are unclear.

Network pharmacology14,15 is an efficient identification technique for key targets, molecular functions, and biological processes involved in the treatment of clinical diseases with bioactive compounds. In this study, we investigated the potential mechanism of lentinan intervention in GC/COVID-19 using a network pharmacology approach. The findings reveal a potentially valuable complementary treatment option for GC/COVID-19 patients (Fig. 1).

Fig. 1
figure 1

Workflow of this study.

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Materials and methods

Identification of GC/COVID-19–associated genes

To identify genes associated with GC/COVID-19, transcriptomic data from TCGA (https://portal.gdc.cancer.gov/) was downloaded, comprising 375 tumor samples and 32 normal tissue samples. As well, data of the clinical characteristic for 443 GC patients was obtained16. The “limma” package in R (3.6.3) was used to screen and obtain DEGs from GC patients with a false discovery rate < 0.05 and |logfold change (FC)| > 1. Genes associated with COVID-19 were obtained from the Genecard, OMIM, and NCBI gene databases17. The resulting GC/COVID-19 genes were compared, and the intersection was considered.

Development and validation of a GC/COVID-19 prognostic model

A Perl script was used to integrate the GC/COVID-19-related genes with the survival time and survival status data of GC patients. The “survival” package of R (3.6.3) was used to perform univariate and multifactor Cox analyses on the integrated data18. Kaplan–Meier survival curves were used to verify the relationship between risk class and overall survival of patients. ROC curves were used to assess the accuracy of prognostic models. Independent prognostic analyses of clinical factors and risk scores were performed on univariate and multifactorial bases. Finally, the constructed prognostic model was validated in each clinical subgroup.

Determination of lentinan-pharmacological targets in GC/COVID-19

We obtained the target genes of lentinan from the Herb19, Swiss Target Prediction20 and PharmMapper21 databases. The targets obtained from the latter database were annotated using UniProt. The targets obtained from the three databases were compared with GC/COVID-19 related genes, and intersections were considered.

Enrichment analyses

GO and KEGG22,23,24 enrichment of lentinan/GC/COVID-19-associated genes were performed using “org.Hs.eg.db” and “clusterProfiler” packages of R (3.6.3)25. GO terms with p < 0.05 were considered statistically significant. Associations between lentinan/GC/COVID-19-related genes, GO terms, and KEGG pathways were visualized using Cytoscape 3.9.0 (https://cytoscape.org/) .

PPI network map

All lentinan/GC/COVID-19-related genes were entered into the STRING database (https://string-db.org/). The combined score was set at > 0.4 to obtain the protein network interactions of the intersecting genes. The results were imported into Cytoscape 3.9.0 for visualization to construct the PPI network.

Molecular docking

Molecular docking can be used to predict the binding and interactions between proteins and small molecules. The crystal structure of the target protein was downloaded from the PDB database. PyMol 2.5 (https://pymol.org/) was used to delete the water molecules and ligands in the crystal structure. AutoDockTools (1.5.6) was used for hydroprocessing. The final results were saved as a pdbqt file. The SDF format file of the two-dimensional structure of lentinan was obtained from the PubChem database, converted to the three-dimensional structure using Chem3D (14.0), and energy-minimized by molecular dynamics calculations using the MM2 method exported as a mol2 format file. AutoDockTools (1.5.6) was used to process the mol2 file. AutoDock vina26 was used for molecular docking. Each docking generated a total of nine conformations. The conformation with the highest affinity was chosen as the final docking conformation and was represented in PyMoL 2.5.

Results

Identification of GC/COVID-19-associated genes

We compiled 4672 COVID-19-associated genes from the Online Mendelian Inheritance in Man (OMIM), National Center for Biotechnology Information (NCBI), and Genecard databases (Fig. 2A). The Cancer Genome Atlas (TCGA) database was screened for 8893 differentially expressed genes (DEGs) between tumor and normal tissues of GC patients. Aligning these two gene sets revealed 683 overlapping genes between GC and COVID-19 (Fig. 2A). Of the 683 overlapping genes, 552 genes were upregulated, and 131 genes were downregulated in tumor tissues of GC patients (Fig. 2B).

Fig. 2
figure 2

Analysis of intersecting genes in GC/COVID-19. (A) Venn diagram depicting intersecting genes in GC/COVID-19. (B) Volcano-plot represents the expression of DEGs in tumor tissues of GC patients.

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Development and validation of GC/COVID-19 prognostic model

To determine the relationship between GC/COVID-19-related genes and the clinicopathological features of patients with GC/COVID-19, univariate and multifactorial Cox analyses were performed on the 683 overlapping genes. Univariate Cox analysis identified 19 genes that were significantly associated with GC/COVID-19 (P < 0.05; Table 1). Of these genes, multivariate Cox analysis revealed a significant association of GC/COVID-19 and five genes (F5, SERPINE1, F2, RNASE3, and KYNU; Table 2). Based on the coefficient values of the multivariate Cox analysis, patients were divided into a high-risk and low-risk group (Table 2; Fig. 3A). Patients with higher risk values had lower survival rates (Fig. 3B) and were associated with elevated expression levels of F5, SERPINE1, F2, RNASE3, and KYNU (Fig. 3C). Survival analysis revealed that the overall survival rate of patients in the low-risk group was higher than that of the patients in the high-risk group (Fig. 3D). The prognostic model was highly accurate in predicting 5-year survival, based on area under the receiver operation characteristic (ROC) curve (AUC > 0.7; Fig. 3E). Independent prognostic univariate (Fig. 4A) and multifactorial (Fig. 4B) analyses revealed the risk score as an independent risk factor affecting patient prognosis (Table 3). The patients were subgrouped according to age, sex, grade, and stage. The prognostic model was validated among the different subgroups. Survival of patients in the low-risk group was significantly longer than that of patients in the high-risk group in the different subgroups, except for the distant tumor metastasis (M1) subgroup (Fig. 5A-N). The negative results in the M1 subgroup may have been caused by the low number of cases.

Table 1 Univariate Cox proportional hazards regression analysis of GC/Covid-19 genes.
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Table 2 Multivariate Cox proportional hazards regression analysis of GC/Covid-19 genes.
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Fig. 3
figure 3

Prognostic value of GC/COVID-19- associated genes. (A) Risk scores for patients in the high- and low-risk groups. (B) Survival of patients in the high- and low-risk groups. (C) Heat map of the expression of F5, SERPINE1, F2, RNASE3, and KYNU in tumor tissues of patients in high- and low-risk groups. (D) Survival analysis of patients in the high- and low-risk groups. (E) Receiver operating characteristic (ROC) curve of the prognostic model.

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Fig. 4
figure 4

Independent prognostic analysis of risk scores and clinical factors in patients with GC/COVID-19. (A and B) Univariate (A) and multivariate (B) analysis of the effects of risk score and clinical factors, including age, gender, grade, and stage, on the prognosis of patients with GC/COVID-19.

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Table 3 Univariate and multivariate analysis of the association between prognosis-related genes and overall survival of patients.
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Fig. 5
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Subgroup survival analysis of prognosis-related genes. The subgroups are: >65-years-of-age(A), <=65-years-of-age (B), females (C), males (D), G1-2 (E), G3 (F), Stage I-II (G), Stage III-IV (H), T1-2 (I), T3-4 (J), N0 (K), N1-3 (L), M0 (M), and M1 (N).

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Identification of core targets of lentinan against GC/COVID-19

We obtained 240 lentinan target genes from the Herb, Swiss Target Prediction, and PharmMapper databases. A comparison of the GC/COVID-19-related genes with the target genes of lentinan identified 22 overlapping genes (Fig. 6A). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on these 22 genes. The analyses revealed the involvement of lentinan in the regulation of a series of biological processes that included neutrophil activation (including activation involved in the immune response), neutrophil-mediated immunity, neutrophil degranulation, collagen metabolic process, leukocyte migration, collagen catabolic process, regulation of inflammatory response, covalent chromatin modification, and response to oxidative stress (Figs. 6B and 7). Ten KEGG pathways related to all core targets were found. They included the nucleotide-binding and oligomerization domain (NOD)-like receptor signaling pathway, prostate cancer, interleukin-17 (IL-17) signaling pathway, rheumatoid arthritis, fluid shear stress and atherosclerosis, measles, and Th17 cell differentiation (Figs. 6C and 7).

Fig. 6
figure 6

Functional data of lentinan against GC/COVID-19 intersecting genes. (A-C) Venn diagram (A), GO enrichment analysis (B), and KEGG enrichment analysis (C) of intersecting genes of lentinan and GC/COVID-19.

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Fig. 7
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Interaction network showing core biotargets, pharmacological functions, and signaling pathways of lentinan against GC/COVID-19.

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Molecular docking of lentinan with potential core targets

The STRING database was used to obtain the interactions between the lentinan/GC/COVID-19- associated genes. Cytoscape software was used to visualize and construct the protein–protein interaction (PPI) network (Fig. 8). Based on the degree value of the PPI network, we identified C-X-C motif chemokine ligand 8 (CXCL8) and vascular endothelial growth factor A (VEGFA) as the core targets of lentinan against GC/COVID-19. In addition, F2 and ribonuclease 3 (RNASE3) were identified as lentinan anti-GC/COVID-19 related genes and key prognostic genes in GC/COVID-19 patients. To determine the possible binding of lentinan to proteins encoded by CXCL8, VEGFA, F2, and RNASE3, a molecular docking analysis was performed. The molecular structures of proteins encoded by CXCL8, VEGFA, F2, and RNASE3 were obtained from the PDB database (6n2u, 6zfl, 6e09, and 4 × 08, respectively). Lower binding energy of the ligand to the receptor indicated a tighter binding conformation between them. The lowest binding energies of lentinan to proteins encoded by CXCL8, VEGFA, F2, and RNASE3 were − 5.9, -7.6, -8.4, and − 10.3 kcal/mol, respectively. Lentinan may have a strong binding effect on these proteins (Table 4; Fig. 9).

Fig. 8
figure 8

PPI network map of lentinan/GC/COVID-19 related genes.

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Table 4 Clinical correlation analysis.
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Fig. 9
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Molecular docking analysis of lentinan binding to the core targets. (A-D) Binding of lentinan and CXCL8-encoded protein (A), lentinan and VEGFA-encoded protein (B), lentinan and F2-encoded protein (C), and lentinan and RNASE3-encoded protein (D).

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Discussion

GC patients have an older average age, more complications, repeated hospital visits due to treatment needs, and are often immunocompromised due to antitumor therapy. If they become infected with severe acute respiratory syndrome coronavirus 1, which causes COVID-19, the disease will progress faster and can be more severe, which increases mortality27,28,29,30. In this study, we screened the DEGs in GC patients and aligned them with COVID-19-related genes to obtain 683 intersecting genes. Among these genes, 552 were upregulated and 131 genes were downregulated in the tumor tissues of GC/COVID-19 patients. Based on independent prognostic and survival analyses, a few important DEGs, including F5, SERPINE1, F2, RNASE3, and KYNU, were implicated as effective biomarkers for the screening and identification of GC/COVID-19 patients at different risk stages.

Lentinan has significant antitumor effects against GC31. Given the antiviral activity of lentinan32, we speculated that it may have a powerful pharmacological effect in GC/COVID-19 patients. Using network pharmacology, we further screened 22 intersecting genes of lentinan/GC/COVID-19. GO enrichment analysis showed that in GC/COVID-19, lentinan mainly regulates several neutrophil-related biological processes, including neutrophil activation (including the activation involved in immunity), neutrophil-mediated immunity, neutrophil degranulation, among others. The most enriched KEGG pathways were the NOD-like receptor signaling pathway, prostate cancer, IL-17 signaling pathway, rheumatoid arthritis, and others. NOD-like receptors are important pattern recognition receptors in innate immune responses33, which can directly participate in the signal transduction of viral invasion34,35 and affect tumor progression by regulating tumor necrosis factor, Toll-like receptor, and other signaling pathways36,37. IL-17, the main effector of Th17 cells38, induces a systemic increase in the leukocyte chemokine granulocyte-colony stimulating factor, leading to massive neutrophil infiltration around the tumor, inhibition of CD8 + cytotoxic T lymphocytes, and promotion of tumor cell metastasis39. Notably, COVID-19 patients also have increased neutrophils in their blood and lungs40. Activated neutrophils induce the production of reactive oxygen species and cause lung tissue damage41,42. Dysregulation of neutrophil elastase also disrupts the alveolar–capillary barrier43,44. Additionally, viral infection can induce the release of neutrophil extracellular traps45. Elevated plasma levels of neutrophil extracellular traps in COVID-19 patients reportedly lead to lung injury and microvascular thrombosis, which can increase the severity of COVID-19 46. The increase and activation of neutrophils have been regarded as an indicator of poor prognosis in COVID-19 patients47. The results of the enrichment analysis showed that the biological processes and signaling pathways involved in the 22 genes related to lentinan/GC/COVID-19 were strongly correlated with GC/COVID-19. The findings implicate these 22 genes as effective targets of lentinan in the treatment of GC/COVID-19.

Analysis of the degree values of the PPI network revealed that the main targets of lentinan intervention in GC/COVID-19 were IL-8 (CXCL8) and VEGFA. IL-8 is involved in the IL-17 signaling pathway (Supplementary Table 2) and has a strong catalytic effect on neutrophils48. VEGFA is a key cytokine that regulates tumor angiogenesis49. RNASE3 and F2 were also lentinan anti-GC/COVID-19 related genes and key prognostic genes in GC/COVID-19 patients. RNASE3 is a member of the RNase A superfamily involved in host immunity. RNASE3 regulation of macrophages has an anti-infection role50. F2 encodes prothrombin. Coagulation disorders are frequently in critically ill COVID-19 patients and have become a useful way to differentiate the severity of COVID-19 51. Through molecular docking analysis, we demonstrated the stable binding of lentinan to proteins encoded by CXCL8, VEGFA, RNASE3, and F2.

The collective findings suggest that lentinan may be an effective agent for treating patients with GC/COVID-19.

Conclusion

In this study, key prognostic genes were identified in patients with GC/COVID-19 through the construction of a prognostic model. Pharmacological functions and signaling pathways of lentinan against GC/COVID-19 were revealed. These included the regulation of neutrophils and NOD-like receptor signaling pathways. The findings provide the first published evidence of the potential value of lentinan as a complementary therapy for GC/COVID-19. Further clinical experiments are required to verify the conclusions of this study.