Anti‐cancer targets of formononetin and molecular mechanisms in osteosarcoma: Findings of bioinformatic and experimental assays

Abstract In current study, a bioinformatic‐based network pharmacology was used to identify the osteosarcoma (OGS)‐pathological targets and formononetin (FN)‐treated targets before the main core predictive biotargets were screened. In addition, all core targets were selected through a number of bioinformatic databases, followed by identification of predominant biological processes and signalling pathways of FN anti‐OGS. Further, top three core targets of FN anti‐OGS were determined as oestrogen receptor 1 (ESR1), tumour protein p53 (TP53), receptor tyrosine‐protein kinase erbB‐2 (ERBB2) respectively. In clinical biochemical data, the plasma samples of OGS showed the increased trends of alkaline phosphatase, triglyceride, blood glucose, lactate dehydrogenase, high‐sensitive C‐reactive protein and some immune cell counts when referenced to medical criteria. In clinicopathological examination, histological OGS sections resulted in increased positive cell counts of neoplastic ESR1, TP53, ERBB2. To further validate these corn proteins in experimental study in vivo, FN‐treated tumour‐bearing nude mice showed intracellular reductions of ESR1, TP53, ERBB2 positive expressions, accompanied with visibly reduced tumour weights. Collectively, our bioinformatic and experimental findings disclosed main core targets, biological processes and signalling pathways of FN anti‐OGS. Interestingly, the top core targets were representatively validated following FN treatment in vivo. Therefore, we reasoned that these predictive targets might be the potential biomarkers for screening and treating osteosarcoma.

before the main core predictive biotargets were screened. In addition, all core targets were selected through a number of bioinformatic databases, followed by identification of predominant biological processes and signalling pathways of FN anti-OGS.
Further, top three core targets of FN anti-OGS were determined as oestrogen receptor 1 (ESR1), tumour protein p53 (TP53), receptor tyrosine-protein kinase erbB-2 (ERBB2) respectively. In clinical biochemical data, the plasma samples of OGS showed the increased trends of alkaline phosphatase, triglyceride, blood glucose, lactate dehydrogenase, high-sensitive C-reactive protein and some immune cell counts when referenced to medical criteria. In clinicopathological examination, histological OGS sections resulted in increased positive cell counts of neoplastic ESR1, TP53, ERBB2.
To further validate these corn proteins in experimental study in vivo, FN-treated tumour-bearing nude mice showed intracellular reductions of ESR1, TP53, ERBB2 positive expressions, accompanied with visibly reduced tumour weights. Collectively, our bioinformatic and experimental findings disclosed main core targets, biological processes and signalling pathways of FN anti-OGS. Interestingly, the top core targets were representatively validated following FN treatment in vivo. Therefore, we reasoned that these predictive targets might be the potential biomarkers for screening and treating osteosarcoma.

K E Y W O R D S
bioinformatics, biomarkers, formononetin, mechanism, osteosarcoma is to pursue a safe and effective medication for combating OGS.
Historically, traditional Chinese medicine (TCM) is demonstrated with therapeutic efficacy against cancers, as revealed by less side effects. 5 TCM in the treatment of bone cancer pain is used in China   before the TCM-rich component is isolated for anti-cancer therapy. 6 Formononetin (FN), a bioactive chemical from Astragalus root, is found to play many benefits, such as anti-neuropathy, anti-hyperlipaemia and anti-cardiopathy effects. 7 In another pharmacological activity, FN can prevent cancers through regulating oestrogen-dependent signalling pathway, including breast, prostate and colon cancers. 8 Additionally, our previous data showed that FN promotes cell apoptosis of human bone cancer in vitro and in vivo through the molecular mechanism of regulating intracellular Bcl-2, Bax and MiR-375 expressions. 9 However, detailed molecular targets of FN anti-OGS have not yet totally identified. Recently, network pharmacology, a bioinformatic method, can be used to elucidate the predictive targets and pharmacological mechanisms of TCM-isolated component. 10 Methodologically, the data of network pharmacology can help pharmacologist to disclose the therapeutic biomarkers and mechanisms of FN anti-OGS. And currently no report has explored this topic using network pharmacology for studying FN anti-OGS. As a result, our present study was designed to use the method of network pharmacology to construct a network of FN anti-OGS in targets-OGSpathways interactions for predicting the therapeutic targets and pharmacological mechanisms respectively. Further, these predictive targets would be validated by human OGS samples, and FN-treated tumour-bearing nuke mice model. First of all, the flowchart of investigative designs in this study was revealed in Figure 1.

| Collection of pharmacological targets in FN anti-OGS
The herbal ingredients targets (HIT) database was employed to collect the associated targets of FN. In addition, treated targets of FN were predicted via two different databases of chemical association networks (STITCH) and Swiss TargetPrediction. Further, a database of gene-disease associations (DisGeNET) was utilized to screen the OGS-diseased genes, and the optimal genes were identified through the designed parameters and scoring index.
Subsequently, the optimal targets of FN were pooled with the disease genes of OGS before producing pharmacological targets of FN anti-OGS.

| Confirmation of protein-protein interaction (PPI) network and customization of core targets
The pick-up targets were re-assay using a database of functional protein association networks (STRING) to further produce a PPI F I G U R E 1 Flowchart of bioinformatic study of FN anti-OGS using a network pharmacology strategy The detailed network of OGS-diseased targets and FN-associated targets network of FN anti-OGS. Following computerized setting, correlative proteins with a confidence score >0.9 were identified via a Cytoscape software for eliminating duplicates, mean degree of freedom and maximum degree of freedom. And a target-associated protein network of FN anti-OGS was produced. After collecting a target-related protein network, core targets of FN anti-OGS were produced through grading values in software parameters.

| Data of core targets-associated biological functions and pathway
Following by Funrich software, the core targets of FN anti-OGS were identified the correlative biological processes and signalling pathways via enrichment analyses. Among these data, top 20 biological processes and signalling pathways were isolated according to the degree of importance for further discussion.

| Human study of clinical cases of OGS
In hospitalization cases, five adult patients were medically iden- Hospital Ethics Committee has approved this human study. This human study was implemented according to the required principles of the Declaration of Helsinki. 11

| Pharmacological study of FN against tumourbearing mice
Commercially available BALB/c-nu nude mice, aged 6 weeks, were

| The characteristics of biological targets
As a result, total 200 genes from DisGeNET database were harvested widely. Meanwhile, other 189 diseased genes of OGS were pick-up as potential targets for re-analysis. In addition, 53 FN-associated genes from the HIT database were identified. As a result, 12 therapeutic targets of FN anti-OGS were finally identified through pooled data of diseased and pharmacological targets (Figure 2). In PPI data and network topology assay, the results showed that the main core targets were produced as ESR1, TP53, ERBB2, Jun, EGFR, TNF, RELA respectively ( Figure. 3).

| Main biological processes and signalling pathway of FN anti-OGS
As shown in Figure 4

| Clinicopathological characteristics of OGS patients
In baseline data, the adult patients with OGS exhibited that the average age was (30.60 ± 17.5) years and the sexual proportion was Male:Female (4:1). As shown in And the positive cells in OGS cases were greater than those in OGSfree sections (P < 0.05) ( Figure 5).

| Anti-OGS effects of FN on tumour-bearing nude mice
To characterize the pharmacological activities of FN on OGS, the transplantable tumour mice were used to validate the predictive targets. Beneficially, FN-treated mice resulted in reduced tumour mass in a dose-dependent manner (P < 0.05) ( Figure 6A). In addition, immunofluorescence staining assay showed that dose-dependent reductions of intracellular ESR1, TP53, ERBB2 positive cells were observed following FN treatments respectively. And these positive cell counts in FN groups were lower than those in controls (P < 0.05) ( Figure 6B).

| D ISCUSS I ON
A primary osteosarcoma is a neoplastic growth in bone tissue, followed by malignant proliferation and metastasis. 15  TP53 has many mechanisms of anti-cancer function and plays a role in apoptosis, genomic stability and inhibition of angiogenesis.
However, TP53 mutation has been associated with tumourigenesis and carcinomatous metastasis. 18,19 ERBB2 (receptor tyrosine-protein kinase erbB-2) is a member of the epidermal growth factor receptor that promotes cell proliferation and opposes apoptosis, and therefore must be tightly regulated to prevent uncontrolled cell growth. 20 In human study, elevated positive proteins of neoplas- Beneficially, these predictive biotargets might be likely the potential biomarkers for screening and treating OGS.

| CON CLUS ION
Taken together, these bioinformatic findings from network pharmacology discolse the main biotargets and molecular mechanisms of FN anti-OGS. In addition, further clinicopathological and experimental data are validated to characterize the top core targets. Therefore, the network pharmacology may be used for development of TCMisolated component.

ACK N OWLED G EM ENTS
The current study was supported by Natural Science Foundation of Guangxi (2016GXNSFBA380026).

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interest and are responsible for the contents of this study.