Prediction of fucosterol related targets
A diagrammatic overview displaying the different steps of network pharmacology and molecule was illustrated in Fig. 1. We identified 210 predictive targets from the Pharmmapper database, and ultimately, we obtained 135 official symbols of fucosterol related targets for limiting to “Homo sapiens” in the UniProtKB database after filtering by z-score > 0 (Table 1).
Table 1
Prediction targets of fucosterol
Pharma Model | Gene symbol | z-score |
3f0r_v | HDAC8 | 2.82743 |
1qyx_v | HSD17B1 | 2.70536 |
1o6u_v | SEC14L2 | 2.68742 |
1n83_v | RORA | 2.39228 |
1a28_v | PGR | 2.2175 |
1ya3_v | NR3C2 | 2.13319 |
1s9j_v | MAP2K1 | 2.12046 |
1q22_v | SULT2B1 | 1.98504 |
1pic_v | PIK3R1 | 1.88125 |
1lv2_v | HNF4G | 1.83771 |
1rhr_v | CASP3 | 1.80882 |
1osh_v | NR1H4 | 1.79715 |
830c_v | MMP13 | 1.78365 |
1rbp_v | RBP4 | 1.71609 |
1lho_v | SHBG | 1.70322 |
1mrq_v | AKR1C1 | 1.70301 |
1sz7_v | TRAPPC3 | 1.6669 |
3czr_v | HSD11B1 | 1.65102 |
2piw_v | AR | 1.6069 |
1fd0_v | RARG | 1.59522 |
1d4p_v | F2 | 1.55496 |
1gse_v | GSTA1 | 1.54788 |
1uhl_v | NR1H3 | 1.52908 |
1hrk_v | FECH | 1.52472 |
2iiv_v | DPP4 | 1.50916 |
1s0z_v | VDR | 1.49636 |
1dkf_v | RARA | 1.49513 |
2i6b_v | ADK | 1.38413 |
1o1v_v | FABP6 | 1.35079 |
2shp_v | PRKACA | 1.28799 |
2fjm_v | PTPN11 | 1.27676 |
2j14_v | PTPN1 | 1.27187 |
1 × 0n_v | PPARD | 1.2585 |
3bbt_v | GRB2 | 1.25573 |
1l8j_v | ERBB4 | 1.23987 |
2vx0_v | PROCR | 1.23974 |
1he3_v | EPHB4 | 1.19363 |
1pq2_v | BLVRB | 1.19205 |
1j99_v | CYP2C8 | 1.08311 |
1cbs_v | SULT2A1 | 1.06101 |
1sm2_v | CRABP2 | 1.04514 |
1upw_v | ITK | 1.03078 |
1qpd_v | NR1H2 | 1.00159 |
1uym_v | LCK | 0.997933 |
1hov_v | HSP90AB1 | 0.978531 |
1h9u_v | MMP2 | 0.976261 |
1u59_v | RXRB | 0.97084 |
1fe3_v | ZAP70 | 0.963876 |
19gs_v | FABP7 | 0.95545 |
2hzi_v | GSTP1 | 0.954306 |
1w6k_v | ABL1 | 0.954084 |
1yvj_v | LSS | 0.922657 |
1 m48_v | JAK3 | 0.917557 |
1t84_v | IL2 | 0.909365 |
1dxo_v | WAS | 0.886825 |
1j78_v | NQO1 | 0.867991 |
2p4i_v | GC | 0.803103 |
1iz2_v | TEK | 0.79593 |
1oiz_v | SERPINA1 | 0.795317 |
1ln3_v | TTPA | 0.794336 |
1t4e_v | PCTP | 0.78729 |
1xap_v | MDM2 | 0.775562 |
1qkt_v | RARB | 0.767258 |
1r9o_v | ESR1 | 0.742597 |
1qip_v | CYP2C9 | 0.733894 |
1hmt_v | GLO1 | 0.731703 |
1bl6_v | FABP3 | 0.730803 |
2rfn_v | MAPK14 | 0.721237 |
1i7g_v | MET | 0.707586 |
1pmn_v | PPARA | 0.678619 |
1e7a_v | MAPK10 | 0.644744 |
2oi0_v | ALB | 0.634049 |
1cg6_v | ADAM17 | 0.623444 |
1ma0_v | MTAP | 0.608206 |
1g3m_v | ADH5 | 0.594411 |
1xjd_v | SULT1E1 | 0.557908 |
2uwd_v | PRKCQ | 0.555214 |
1oj9_v | HSP90AA1 | 0.540688 |
1njs_v | MAOB | 0.529254 |
1xvp_v | GART | 0.502645 |
2pjl_v | NR1I3 | 0.488399 |
1nhz_v | ESRRA | 0.486782 |
1j96_v | NR3C1 | 0.454473 |
2ipw_v | AKR1C2 | 0.434142 |
1p49_v | AKR1B1 | 0.426598 |
1xbb_v | STS | 0.421996 |
1okl_v | SYK | 0.418201 |
2qu2_v | CA2 | 0.398063 |
1ih0_v | BACE1 | 0.394868 |
1sa4_v | TNNC1 | 0.387846 |
1l6l_v | FNTA | 0.385162 |
2fgi_v | APOA2 | 0.36325 |
2pe0_v | FGFR1 | 0.355588 |
3hvc_v | PDPK1 | 0.353361 |
2h8h_v | SRC | 0.34725 |
2itp_v | EGFR | 0.342717 |
1xor_v | PDE4D | 0.333997 |
1jqe_v | HNMT | 0.312737 |
1shj_v | CASP7 | 0.309471 |
2iku_v | REN | 0.285831 |
2pg2_v | KIF11 | 0.27567 |
3bgp_v | PIM1 | 0.259029 |
2ywp_v | CHEK1 | 0.245709 |
1t46_v | KIT | 0.241973 |
1s95_v | PPP5C | 0.227167 |
1itu_v | DPEP1 | 0.226319 |
2b53_v | CDK2 | 0.222033 |
2jbp_v | MAPKAPK2 | 0.216487 |
3cjg_v | KDR | 0.212231 |
1n7i_v | PNMT | 0.205326 |
1egc_v | ACADM | 0.198463 |
1so2_v | PDE3B | 0.191704 |
1l9n_v | TGM3 | 0.19101 |
2bxr_v | MAOA | 0.187567 |
1hw8_v | HMGCR | 0.158181 |
1tt6_v | TTR | 0.155605 |
2o9i_v | NR1I2 | 0.148658 |
1 × 89_v | LCN2 | 0.147746 |
1j4i_v | FKBP1A | 0.145396 |
1xlz_v | PDE4B | 0.141599 |
1s8c_v | HMOX1 | 0.137498 |
1tjj_v | GM2A | 0.121157 |
1 mx1_v | CES1 | 0.098769 |
2g01_v | MAPK8 | 0.092468 |
1reu_v | BMP2 | 0.090768 |
1fzv_v | PGF | 0.088164 |
1s1p_v | AKR1C3 | 0.08785 |
1r7y_v | ABO | 0.071475 |
1pme_v | MAPK1 | 0.068987 |
1g4k_v | MMP3 | 0.067658 |
1h6g_v | CTNNA1 | 0.067305 |
1vj5_v | EPHX2 | 0.059538 |
2nn7_v | CA1 | 0.049744 |
2pin_v | THRB | 0.011683 |
1gzr_v | IGF1 | 0.0063 |
Cluster analysis of NSCLC network
Although we selected high-relevance score of NSCLC targets from GeneCards, the NSCLC bio-network is still huge. In order to further analyze biological processes of the function module in NSCLC network, we obtained protein-protein interaction network (PPI) data for NSCLC from the String database, and Cytoscape3.7.2 plug-in MCODE was used for clustering to find out the topology module of NSCLC’ s PPI network. The cluster 1 was retained since it is the most significant for PPI network of NSCLC (Fig. 2). We imported the data of cluster 1 into the DAVID6.8 to discover its GO biological processes which were shown in a bubble chart (Fig. 3). Cluster 1 is mainly involved in cell proliferation, apoptosis, cell cycle, angiogenesis, NSCLC gene expression, invasion and migration, signal transduction, and NSCLC related signaling pathways.
Analysis of fucosterol-NSCLC PPI network
In order to construct the interaction network between proteins and dig out the core regulatory genes, fucosterol-NSCLC PPI network of candidate targets was constructed by Cytoscape3.7.2 based on the String database (Fig. 4). Fucosterol shares 37 targets with NSCLC, and the network consists of 36 nodes (one of which does not interact with other target proteins) and 177 edges. The color of each node is related to its degree; the darker nodes have the larger value of Degree. The size of the node is linked to its Edge; the bigger nodes have the larger value of Edge Between. Based on the network topology analysis, which business centrality is 0.0261, average node degree is 9.83, average closeness centrality is 0.535, which suggests the presence of a central hub between candidate targets. Cytoscape3.7.2 plug-in cytoHubba was utilized to obtain six hub genes, which between centrality, node degree and closeness centrality as screening conditions. We speculate that these hub genes play a significant role in NSCLC treated fucosterol: EGFR, MAPK8, MAPK1, GRB2, SRC, IGF1 (Table 2).
Table 2
Hub genes of fucosterol against NSCLC
Name | Betweenness Centrality | Closeness Centrality | Degree |
GRB2 | 0.03441407 | 0.614035 | 16 |
EGFR | 0.06936033 | 0.648148 | 19 |
MAPK1 | 0.12911792 | 0.729167 | 22 |
SRC | 0.03183796 | 0.660377 | 18 |
IGF1 | 0.09323304 | 0.660377 | 18 |
MAPK8 | 0.18473443 | 0.7 | 20 |
Gene ontology analysis of candidate targets
GO enrichment allows a better understand of the gene function and biological significance of the candidate targets of fucosterol for NSCLC treatment on a systematic level. To obtain the biological processes, molecular functions, and cellular components of the candidate targets, we performed a GO enrichment analysis which was displayed the top 20 significantly terms(p-value ≤ 0.05) of each module in Fig. 5. It is suggested that the candidate targets could act through protein tyrosine kinase activity, protein phosphatase binding, negative regulation of apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation in the nucleus, cytosol, extracellular space, nucleoplasm, extracellular region. Among them, 14 vital biological processes directly affecting the most significant cluster 1 of NSCLC disease module were presented with candidate targets independently (Fig. 6). This network diagram reveals candidate targets were mainly involved in cell proliferation and apoptosis, angiogenesis, cell migration and signal transduction, meanwhile it illustrates that hub genes were strongly associated with various biological processes.
Evaluation of target-pathway network
As showed in Table 3, 37 candidate targets were further mapped to 73 pathways for more intuitive explain the mechanism of fucosterol in treating NSCLC at the pathway level. Cytoscape3.7.2 was utilized to construct the T-P network which showed the relationship between 37 candidate targets and the pathways that getting rid of generalized and disease terms (Fig. 7). It is found that some targets are mapped to multiple pathways and multiple targets also regulated various pathways, which suggest that candidate targets may mediate interaction and crosstalk of different pathways. These pathways may be the major factor for fucosterol's resistance to non-small cell lung cancer, such as PI3K-Akt signaling pathway, VEGF signaling pathway, ErbB signaling pathway. The PI3K-Akt signaling pathway is widely recognized as a prominent cancer signaling pathway, which is closely related to affect the proliferation, survival and apoptosis of NSCLC cells[32–34]. Also, VEGF signaling pathway involves in tumor cell-dependent continuous vascular supply, which has a profound effect on tumor cell growth and metastasis[35]. In addition, for interaction of ErbB receptors with many signal transduction molecules which can activate multiple intracellular pathways, the ErbB signaling pathway plays a significant role in the development of cancer[36]. According to T-P network analysis, we discover fucosterol maybe to regulate NSCLC therapy through PI3K-Akt signaling pathway, VEGF signaling pathway, ErbB signaling pathway by apoptosis, tumor angiogenesis, and cell cycle arrest.
Table 3
KEGG analysis of candidate targets of fucosterol against NSCLC
Term | Pathways | Count | PValue |
hsa05200 | Pathways in cancer | 21 | 1.21E-16 |
hsa05205 | Proteoglycans in cancer | 17 | 3.47E-16 |
hsa04151 | PI3K-Akt signaling pathway | 15 | 4.36E-10 |
hsa04014 | Ras signaling pathway | 13 | 5.06E-10 |
hsa05215 | Prostate cancer | 11 | 9.84E-12 |
hsa04015 | Rap1 signaling pathway | 11 | 5.35E-08 |
hsa04068 | FoxO signaling pathway | 10 | 1.42E-08 |
hsa04510 | Focal adhesion | 10 | 5.84E-07 |
hsa04914 | Progesterone-mediated oocyte maturation | 9 | 8.41E-09 |
hsa04012 | ErbB signaling pathway | 9 | 8.41E-09 |
hsa04915 | Estrogen signaling pathway | 9 | 2.36E-08 |
hsa04550 | Signaling pathways regulating pluripotency of stem cells | 9 | 3.57E-07 |
hsa05203 | Viral carcinogenesis | 9 | 6.39E-06 |
hsa05218 | Melanoma | 8 | 4.93E-08 |
hsa04917 | Prolactin signaling pathway | 8 | 4.93E-08 |
hsa04912 | GnRH signaling pathway | 8 | 2.79E-07 |
hsa04722 | Neurotrophin signaling pathway | 8 | 1.85E-06 |
hsa05161 | Hepatitis B | 8 | 6.56E-06 |
hsa04010 | MAPK signaling pathway | 8 | 2.33E-04 |
hsa05206 | MicroRNAs in cancer | 8 | 4.92E-04 |
hsa05230 | Central carbon metabolism in cancer | 7 | 6.71E-07 |
hsa05214 | Glioma | 7 | 7.37E-07 |
hsa05120 | Epithelial cell signaling in Helicobacter pylori infection | 7 | 8.84E-07 |
hsa05220 | Chronic myeloid leukemia | 7 | 1.36E-06 |
hsa04668 | TNF signaling pathway | 7 | 1.38E-05 |
hsa05219 | Bladder cancer | 6 | 1.66E-06 |
hsa05223 | Non-small cell lung cancer | 6 | 8.03E-06 |
hsa05221 | Acute myeloid leukemia | 6 | 8.03E-06 |
hsa04370 | VEGF signaling pathway | 6 | 1.23E-05 |
hsa05211 | Renal cell carcinoma | 6 | 1.81E-05 |
hsa04664 | Fc epsilon RI signaling pathway | 6 | 2.10E-05 |
hsa04066 | HIF-1 signaling pathway | 6 | 1.11E-04 |
hsa04660 | T cell receptor signaling pathway | 6 | 1.35E-04 |
hsa05231 | Choline metabolism in cancer | 6 | 1.41E-04 |
hsa04114 | Oocyte meiosis | 6 | 2.21E-04 |
hsa04919 | Thyroid hormone signaling pathway | 6 | 2.60E-04 |
hsa04650 | Natural killer cell mediated cytotoxicity | 6 | 3.43E-04 |
hsa05169 | Epstein-Barr virus infection | 6 | 3.43E-04 |
hsa04380 | Osteoclast differentiation | 6 | 4.76E-04 |
hsa05160 | Hepatitis C | 6 | 5.10E-04 |
hsa04062 | Chemokine signaling pathway | 6 | 2.30E-03 |
hsa04810 | Regulation of actin cytoskeleton | 6 | 3.89E-03 |
hsa05213 | Endometrial cancer | 5 | 1.28E-04 |
hsa05210 | Colorectal cancer | 5 | 2.54E-04 |
hsa05131 | Shigellosis | 5 | 2.87E-04 |
hsa05212 | Pancreatic cancer | 5 | 3.05E-04 |
hsa04115 | p53 signaling pathway | 5 | 3.43E-04 |
hsa04520 | Adherens junction | 5 | 4.29E-04 |
hsa04540 | Gap junction | 5 | 9.67E-04 |
hsa04750 | Inflammatory mediator regulation of TRP channels | 5 | 1.45E-03 |
hsa05142 | Chagas disease (American trypanosomiasis) | 5 | 1.80E-03 |
hsa04620 | Toll-like receptor signaling pathway | 5 | 1.93E-03 |
hsa04071 | Sphingolipid signaling pathway | 5 | 3.04E-03 |
hsa04910 | Insulin signaling pathway | 5 | 5.01E-03 |
hsa04630 | Jak-STAT signaling pathway | 5 | 5.97E-03 |
hsa05202 | Transcriptional misregulation in cancer | 5 | 9.76E-03 |
hsa05164 | Influenza A | 5 | 1.12E-02 |
hsa05152 | Tuberculosis | 5 | 1.19E-02 |
hsa04320 | Dorso-ventral axis formation | 4 | 3.25E-04 |
hsa04621 | NOD-like receptor signaling pathway | 4 | 2.78E-03 |
hsa04662 | B cell receptor signaling pathway | 4 | 5.03E-03 |
hsa05133 | Pertussis | 4 | 6.34E-03 |
hsa05145 | Toxoplasmosis | 4 | 1.80E-02 |
hsa04670 | Leukocyte transendothelial migration | 4 | 2.02E-02 |
hsa04110 | Cell cycle | 4 | 2.46E-02 |
hsa04611 | Platelet activation | 4 | 2.78E-02 |
hsa05162 | Measles | 4 | 2.95E-02 |
hsa04921 | Oxytocin signaling pathway | 4 | 4.00E-02 |
hsa04960 | Aldosterone-regulated sodium reabsorption | 3 | 1.66E-02 |
hsa04930 | Type II diabetes mellitus | 3 | 2.45E-02 |
hsa04150 | mTOR signaling pathway | 3 | 3.48E-02 |
hsa04730 | Long-term depression | 3 | 3.70E-02 |
Analysis of an Integrated pathway map
To further explicate the complex mechanism of fucosterol in the treatment of NSCLC, an integrated pathway map (Fig. 8) was constructed by integrating the non-small cell lung cancer disease pathway map and the key signal transduction pathways map that obtained from evaluating of T-P network. The key signal transduction pathways map comprises of three signaling pathways: hsa04151: PI3K-Akt signaling pathway, hsa04012: ErbB signaling pathway, hsa04370: VEGF signaling pathway. As shown in Fig. 8, the signaling transduction pathways and NSCLC disease pathway reflect multiple modules such as cell proliferation, apoptosis, migration and angiogenesis. From the perspective of non-small cell lung cancer disease pathway (Fig. 8A), it was found that the fucosterol treatment of NSCLC can crosstalk the inflammatory module and the tumor module, involving the joint action of multiple pathways. Fucosterol can directly activate the Ras signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway, and indirectly activate the MAPK signaling pathway to regulate cell proliferation and apoptosis to achieve the therapeutic effect on NSCLC. From the perspective of the key signal transduction pathways(Fig. 8B), it was found that both VEGF signaling pathway and ErbB signaling pathway can activate downstream signals to control angiogenesis, affect cell proliferation and migration, and play a crucial role in the development and metastasis of tumors[37–39]. Meanwhile, the PI3K-Akt signaling pathway participates in the regulation of cell cycle progression[40–42]. According to this map, we found the three signaling pathways can jointly activate the Raf / MEK / ERK signaling pathway which was reported playing an anti-proliferation role in curing cancer[43]. More importantly, the downstream pathway-Raf / MEK / ERK signaling pathway mainly activated by GRB2 to regulate cell proliferation and migration. We will further discuss GRB2 which is a significant target in fucosterol for treating NSCLC.
The expression of GRB2 and immune infiltrates in NSCLC
The docking mode and hydrogen bonding residues of fucosterol with GRB2 after docking are shown in Fig. 9. Then,the binding sites of ligands and GRB2 and the surrounding residues are shown by pymol in Fig. 9A. In the docking model of fucosterol and GRB2, the action site are ASP-16, LYS-11,LYS-21,Ser-33,ASN-30, which forms hydrogen bonds with Ser-33, while the interaction between ligands and surrounding residues is analyzed in Fig. 9B. It was able to be seen that the connection between ligands and GRB2 mainly depends on hydrophobic interaction, only Ser-33 forms hydrogen bonds, and the binding energy is-9.9 kcal/mol, indicating that the binding effect between them is still decent. Besides, we conducted the TIMER database to analyze the relationship GRB2 expression and immune infiltrates. As shown in Fig. 10A-B, the expression of GRB2 was significantly negatively associated with tumor purity while significantly positively correlated with infiltrating levels of immune cells, CD4+T cells (r = 0.385729 P = 1.28e-18), CD8+T cells(r = 00.307506, P = 3.99e-12), B cells(r = -0.196239, P = 1.37e-05), neutrophils(r = 0.541071, P = 4.43e-38), macrophages(r = 0.404903, P = 1.47e-20) and dendritic cells(r = 0.544328, P = 5.45e-39), suggesting that the GRB2 expression was mainly related to the immune infiltration of CD4+T cells, CD8+T cells, B cells, neutrophils, macrophages and dendritic cells. Besides, the result in KM plotter showed that the lower expression level of GRB2 has a better overall survival rate and gene GRB2 was an independent prognosis indicator for OS of patients with LUAD( Fig. 10C-D). Hence, we speculated that GRB2 exerted a more significant effect on the prognosis of LUAD, for it was highly associated with various immune cells in LUAD.