Molecular Targets of Shenqi Dihuang, A Traditional Chinese Herbal Medicine, and Its Potential Role in Renal Cell Carcinoma Therapy

Chinese herbal medicine (CHM), which includes herbal slices and proprietary products, is widely used in China. Shenqi Dihuang (SQDH) is a traditional Chinese medicine (TCM) formula with ingredients that affect tumor growth. Despite recent advances in prognosis, patients with renal cell carcinoma (RCC) cannot currently receive curative treatment. The present study aimed to explore the potential target genes closely associated with SQDH. The gene expression data for SQDH and RCC were obtained from the TCMSP and TCGA databases. The SQDH-based prognostic prediction model reveals a strong correlation between RCC and SQDH. In addition, the immune cell infiltration analysis indicated that SQDH might be associated with the immune response of RCC patients. Based on this, we successfully built the prognostic prediction model using SQDH-related genes. The results demonstrated that CCND1 and NR3C2 are closely associated with the prognosis of RCC patients. Finally, the pathways enrichment analysis revealed that response to oxidative stress, cyclin binding, programmed cell death, and immune response are the most enriched pathways in CCND1. Furthermore, transcription regulator activity, regulation of cell population proliferation, and cyclin binding are closely associated with the NR3C2.


Introduction
Renal cell carcinoma (RCC) is the second most lethal tumor of the urinary system's malignant tumors [1]. Clear-cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (chRCC) are the most common subtypes of RCC [2]. Te most common type of RCC in the United States is ccRCC, which accounted for 85% of all cases in 2019 [3]. In addition, approximately 74,000 new cases of ccRCC were diagnosed in 2019. Currently, two major surgical approaches for treating RCC are laparoscopic partial nephrectomy and radical nephrectomy [4]. However, approximately, 30% of patients with ccRCC developed distant metastases that could not be removed surgically. Because ccRCC patients are resistant to radiotherapy, hormones, and cytotoxic treatments [5], several targeted therapies have been approved for metastatic ccRCC, including sunitinib, sorafenib, lenvatinib, and nivolumab [6]. However, the efcacy of these drugs remains limited. Although an increasing number of PD-1/PD-L1 blocking immunotherapy drugs have been approved for the treatment of ccRCC, not all patients respond to them [7]. Terefore, it is clinically signifcant to determine which patients will beneft from immunotherapy.
Despite recent advances in prognosis over the past decade, patients with metastatic RCC cannot currently receive curative treatment. Cytokine radiation and hormonal therapies have all been studied in combination to reduce relapse rates [8]. Several antiangiogenic medicines, including VEGF pathway inhibitors sunitinib and sorafenib, efectively treat patients with metastatic RCC [9]. Adjuvant sunitinib or sorafenib was superior to placebo in a phase three trial with locally advanced RCC [10].
In recent years, many studies have demonstrated the efcacy of traditional Chinese medicine (TCM) in treating cancer. TCM is widely accepted in China as an efective complementary and alternative therapy for cancer patients [11]. Chinese medicine has been used throughout Asia since ancient times. Te most common application category of TCM is Chinese herbal medicine (CHM), which includes herbal slices and proprietary products [12]. Because CHM is efective and has fewer side efects, it is used as an alternative therapy by many cancer patients [13]. Shenqi Dihuang (SQDH) is a TCM formula containing ingredients that inhibit tumor growth [14]. Ginseng, Astragalus membranaceus, rehmannia, yam, tuckahoe, paeonol, and dogwood are among the ingredients in SQDH. Tere is evidence that the traditional Chinese herbal formula has fewer side efects and is more cost-efective than other treatments [15]. Previous studies demonstrated that their efects are mediated by immune cell activation and reprogramming metabolic-related infammatory responses [16].
With the development of bioinformatics analysis, many researchers have started exploring the potential prognostic factors for multiple tumors. Te present study aimed to investigate the potential correlation between SQDH and RCC. In addition, immune infltration analysis was used to reveal the relationship between the immune response of RCC patients and SQDH. Furthermore, the prognostic prediction model was developed to investigate the genes closely associated with the prognosis of RCC patients. Finally, the pathway enrichment analysis was performed to explore the potential pathways closely linked to the SQDH. Our study aims to investigate the role of SQDH in RCC immunotherapy.

Datasets Downloaded. Traditional Chinese Medicine
System Pharmacology Database and Analysis Platform (TCMSP) (http://tcmspw.com/tcmsp.php) was used to obtain SQDH composition and molecular target data. Furthermore, the expression data and clinical characteristics of RCC patients were downloaded from Te Cancer Genome Atlas (TCGA) database.

Diferentially Expressed Analysis. Te Cancer Genome
Atlas (TCGA) database (https://portal.gdc.com) was used to obtain RNAseq data and associated clinical information. Te Limma R software package was used to investigate mRNA expression diferences. A threshold diferential expression screen for mRNA was defned as "P < 0.05 and log 2 (fold change) > 2 or log 2 (fold change) < −2."

Functional Analysis Based on Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Pathways. Te data were analyzed using feature enrichment to confrm the possible functions of potential targets. Using GO, it is a common practice to annotate genes with functions, particularly molecular function (MF), biological pathway (BP), and cellular component (CC). An enrichment analysis based on KEGG can be used to investigate gene function and related high-level genomic information. To better understand the oncogenic role of target genes, ClusterProfler in R was used to analyze the GO functions and KEGG pathway.

Protein-Protein Network (PPI) Analysis
Based on SQDH-Related Genes. Te PPI network was then constructed to investigate the potential correlation between the proteins encoded by key genes. STRING was used to perform an interactive analysis of a gene PPI network. Furthermore, Cytoscape 3.7.2 was used to analyze and visualize PPI networks when interactions with composite ratings exceeded 0.9.

Immune Cell Infltration.
To investigate the correlation between the built MRGS and immune cell infltration, we estimated the infltration levels of 22 immune cell subtypes in the RCC cohort using CIBERSORT. Enrichment scores calculated by ssGSEA of R's Gene Set Variation Analysis package were used to quantify immune cell infltration. Tis analysis revealed information about immune infltration, such as immune cell species, immune functions, and immune-related pathways.

Construction of the Prognostic Prediction Model
Based on the SQDH Target Genes. Te prognostic prediction model was built using univariate and multivariate COX regression analyses. In addition, the survival analysis was used to compare the overall survival (OS) of RCC patients in lowand high-risk groups. Furthermore, an area under the receiver curve (AUC) was determined using the receiver operating characteristic curve (ROC).

Statistical Analysis.
Statistical analysis was performed using R software. Te diference between groups was statistically signifcant, with a P value <0.05.

Te Potential SQDH Target Genes Pathways and the Protein-Protein Network Based on SQDH-Related Proteins.
Based on the ingredients in SQDH, Codonopsis pilosula, Poria cocos, and Astragalus membranaceus were considered the most important ingredients. Subsequently, the TCMSP database was used to obtain the target genes of Codonopsis pilosula, Poria cocos, and Astragalus membranaceus. A total of 108 genes were identifed as SQDH target genes. Te GO and KEGG enrichment analyses were performed to investigate potential pathways closely associated with SQDH. Te results demonstrated that most GO BP pathways are cellular responses to chemical stress, ketone, a steroid hormone, oxidative stress, and oxygen levels (Figure 1(a)). Regarding CC, membrane raft, postsynaptic membrane, membrane microdomain, synaptic membrane, and transcription regulator complex are closely associated with SQDH-related genes (Figure 1(b)). In addition, the GO MF enrichment analysis revealed that the most enriched pathways involved in SQDH-related genes are ligand-activated transcription factor activity, nuclear receptor activity, DNAbinding transcription factor binding, and ubiquitin-like protein ligase binding (Figure 1(c)). Te results of the PPI network revealed that 81 SQDH-related genes were closely related to one another. Furthermore, some genes, known as hub genes, had more than 20 interactive counts with other genes, including ESR1, RELA, FOS, AR, CCND1, NCOA1, MAPK8, EGFR, HIF1A, NR3C1, MDM2, and PRKCA ( Figure 1(d)).

Exploration of the Diferentially Expressed Genes between RCC Patients and Normal People.
A total of 532 RCC patients and 72 normal people were included in the TCGA cohort. Te fold change was set into 2 to explore the genes closely associated with RCC. Te diferentially expressed analysis revealed 695 diferentially expressed genes, including 278 upregulated and 417 downregulated genes (Figures 2(a)-2(b)). Te pathways enrichment analysis demonstrated that some immune-related pathways, such as regulation of T cell activation, regulation of T cell proliferation, and T1 and T2 cell diferentiation, are closely linked to the diferentially expressed genes. In addition, the most enriched pathways are renal tubule development, renal system development, kidney morphogenesis, and kidney epithelium development. Te target genes and active ingredients of Codonopsis pilosula, Astragalus membranaceus, and Poria cocos were then obtained from the TCMSP dataset. Finally, 108 target genes associated with Codonopsis pilosula, Astragalus membranaceus, and Poria cocos were downloaded (Figure 1(c)). Te Venn diagram demonstrated that ten genes, including HK2, VEGFA, IGFBP3, CAV1, ALOX5, CCND1, DIO1, NR3C2, ADH1B and PTGER3, are closely related to the diferentially expressed genes in the RCC cohort and SQDH targets genes ( Figure 2(d)).

Construction of the SQDH-Related Prognostic Prediction
Model. Based on the previous analysis, ten genes were thought to be closely related to the prognosis of RCC patients. Te expression matrix of RCC patients was obtained by combining the expression data and the clinical information of RCC patients. Subsequently, the univariate COX regression analysis reveals that ALOX5, CCND1, NR3C2, and PTGER3 are strongly linked to the prognosis of RCC patients (Figure 3(a)). Te multivariate COX regression analysis revealed that the prognostic prediction model was built using CCND1 and NR3C2. Te risk score is: risk score � −0.0197007190065525 * CCND1 + −0.167437463401867 * N R3C2. Ten, we performed a survival analysis based on the expression level of ALOX5, CCND1, NR3C2, and PTGER3. Te results demonstrated that the high-expression levels of CCND1, NR3C2, and PTGER3 are associated with a better OS in RCC patients. However, the high ALOX5 expression is associated with poorer OS in RCC patients (Figures 3(b)-3(e)). In addition, the survival analysis based on the risk score revealed that RCC patients in the high-risk group have a poorer OS (Figure 3(f)). Finally, we performed the ROC curve. Te results demonstrated that the 1-year, 3-year, and 5year AUC are >0.6, indicating that the model has a good predictive value (Figure 3(g)). Furthermore, the clinicalrelated ROC curve demonstrated that the prognostic prediction model and clinical characteristics could be used as predictive factors (Figure 3(h)).

Te SQDH-Based Prognostic Prediction Model Is Closely
Associated with Many Immune Cells. Te immune cell infltration analysis was then performed using the SQDHrelated prognostic prediction model. Some immune cells were closely associated with the risk score, including plasma cells, CD8 T cells, CD4 memory resting T cells, follicular helper T cell, regulatory T cell, monocyte, and M0, M1, and M2 macrophages (Figures 4(a) and 4(b)). In addition, the distribution of some immune cells is linked to the OS of RCC patients. Te results demonstrated that the RCC tissues with high resting dendritic cells, resting mast cells, and monocytes have a better OS. However, the higher number of T regulatory cells and activated memory CD4 T cells is associated with worse OS (Figures 4(c)-4(g)).

Some Immune-Related Functions Are Closely Associated with the SQDH-Based Prognostic Prediction Model.
We then compared immune-related function between low-and highrisk groups using the risk score for RCC patients and immune cell infltration analysis. Some immune-related functions, such as aDCs, immune checkpoint, human leukocyte antigen (HLA), type I and type II IFN responses, T cell co-stimulation and co-inhibition factors, are found to be signifcantly different ( Figure 5(a)). In addition, some immune-related functions are linked to the OS of RCC patients. RCC patients with higher HLA have a better OS. However, the higher levels of infammation-promoting factors and T cell coinhibition and co-stimulation factors are correlated with a poorer OS in RCC patients ( Figures 5(b)-5(e)).

CCND1 and NR3C2 Were Closely Associated with Many
Enriched Pathways Involved in RCC Patients. CCND1 and NR3C2 build the SQDH-based prognostic prediction model. Subsequently, we aimed to explore the potential pathways closely linked to CCND1 and NR3C2. Te most enriched pathways for CCND1 are a response to oxidative stress, central nervous system development, carbohydrate metabolic process, regulation of cell population proliferation, cyclin binding, programmed cell death, and immune response ( Figure 6(a)). In addition, transporter activity, betacatenin binding, transcription regulator activity, identical protein binding, regulation of cell population proliferation, and cyclin binding are all closely associated with the NR3C2 expression ( Figure 6(b)).

Discussion
RCC is the sixth most common malignancy in men and the tenth most common in women, accounting for 5% and 3% of all cancers, respectively [17]. Te incidence of RCC has Genetics Research 3 increased over time. Although surgery remains the primary treatment option for patients with locally or locally advanced disease, a signifcant proportion of patients will eventually experience disease recurrence [18]. Chemo-and radiotherapy are inefective in treating RCC. Immunotherapy has recently been implemented due to a better understanding of RCC biology [19]. Te antitumor activity of sunitinib can be attributed to its multichannel nature as a tyrosine kinase inhibitor [20]. A phase II study conducted independently in two separate groups revealed that sunitinib signifcantly delayed tumor progression and had a high treatment response rate. Te ORR in both trials was 42%, with a median time to disease progression (TTP) of 8.7 months [21]. Furthermore, sunitinib was more efective in phase III clinical study of patients with metastatic RCC than IFN-α [22]. It is also likely to cause serious side efects such as nausea, vomiting, diarrhea, rash, hand-foot syndrome, and others that will signifcantly impair the patient's ability to adhere to their treatment and live a good life [23]. TCM treatment is also used to reduce the side efects of targeted drug therapy and promote recovery of patients' body function in advanced cancer patients [24]. In addition, analysis of the online dataset has been widely applied in the various of human diseases [25][26][27].
SQDH consists of ginseng, Astragalus membranaceus, rehmannia, yam, tuckahoe, paeonol, and dogwood. Te SQDH boosts the body's humoral and cellular immunity, accelerates tumor cell apoptosis, inhibits angiogenesis, regulates cytokines, and slows metastasis [28]. Polysaccharides have immune-regulatory and antitumor properties in Codonopsis pilosula [29]. A variety of polysaccharides, saponins, and other compounds found in Astragalus membranaceus can regulate tumor immunity, infuence tumor cell autophagy, and inhibit tumor angiogenesis [30]. Te present study aimed to explore the association between RCC and SQDH using a network pharmacology approach.

Data Availability
Te data used to support the fndings of this study are included within the article.