# Molecular Targets of Shenqi Dihuang, A Traditional Chinese Herbal

Authors: Xinglin Chen; Tongtong Zhang; Xiangyang Zhan; Xinyue Zang; Xinyu Zhai; Zhong Wan; Minyao Ge; Mingyue Tan; Jianyi Gu; Dongliang Xu
Journal: Genetics Research
 (2023)

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## Abstract

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.


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## Body

## 1. 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]. The 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
efficacy 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]. Therefore, it is
clinically significant to determine which patients will benefit 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, effectively 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 efficacy of traditional Chinese
medicine (TCM) in treating cancer. TCM is widely accepted in China as an effective
complementary and alternative therapy for cancer patients [11]. Chinese medicine has been used throughout Asia since
ancient times. The most common application category of TCM is Chinese herbal
medicine (CHM), which includes herbal slices and proprietary products [12]. Because CHM is effective and has fewer
side effects, 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. There is evidence
that the traditional Chinese herbal formula has fewer side effects and is more
cost-effective than other treatments [15].
Previous studies demonstrated that their effects are mediated by immune cell
activation and reprogramming metabolic-related inflammatory responses [16].With the development of bioinformatics analysis, many researchers have started
exploring the potential prognostic factors for multiple tumors. The present study
aimed to investigate the potential correlation between SQDH and RCC. In addition,
immune infiltration 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.


## 2. Methods

### 2.1. 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 The Cancer Genome Atlas
(TCGA) database.


### 2.2. Differentially Expressed Analysis

The Cancer Genome Atlas (TCGA) database (https://portal.gdc.com) was
used to obtain RNAseq data and associated clinical information. The Limma R
software package was used to investigate mRNA expression differences. A
threshold differential expression screen for mRNA was defined as “P<0.05 and log 2 (fold change) > 2 or
log 2 (fold change) < −2.”


### 2.3. Functional Analysis Based on Gene Ontology (GO) and Kyoto Encyclopedia
of Genes and Genomes (KEGG) Enrichment Pathways

The data were analyzed using feature enrichment to confirm 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, ClusterProfiler in R was used to
analyze the GO functions and KEGG pathway.


### 2.4. Protein-Protein Network (PPI) Analysis Based on SQDH-Related
Genes

The 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.


### 2.5. Immune Cell Infiltration

To investigate the correlation between the built MRGS and immune cell
infiltration, we estimated the infiltration 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
infiltration. This analysis revealed information about immune infiltration, such
as immune cell species, immune functions, and immune-related pathways.


### 2.6. Construction of the Prognostic Prediction Model Based on the SQDH Target
Genes

The 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 low- and high-risk groups. Furthermore,
an area under the receiver curve (AUC) was determined using the receiver
operating characteristic curve (ROC).


### 2.7. Statistical Analysis

Statistical analysis was performed using R software. The difference between
groups was statistically significant, with aP value <0.05.


## 2.1. 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 The Cancer Genome Atlas
(TCGA) database.


## 2.2. Differentially Expressed Analysis

The Cancer Genome Atlas (TCGA) database (https://portal.gdc.com) was
used to obtain RNAseq data and associated clinical information. The Limma R
software package was used to investigate mRNA expression differences. A
threshold differential expression screen for mRNA was defined as “P<0.05 and log 2 (fold change) > 2 or
log 2 (fold change) < −2.”


## 2.3. Functional Analysis Based on Gene Ontology (GO) and Kyoto Encyclopedia
of Genes and Genomes (KEGG) Enrichment Pathways

The data were analyzed using feature enrichment to confirm 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, ClusterProfiler in R was used to
analyze the GO functions and KEGG pathway.


## 2.4. Protein-Protein Network (PPI) Analysis Based on SQDH-Related
Genes

The 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.


## 2.5. Immune Cell Infiltration

To investigate the correlation between the built MRGS and immune cell
infiltration, we estimated the infiltration 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
infiltration. This analysis revealed information about immune infiltration, such
as immune cell species, immune functions, and immune-related pathways.


## 2.6. Construction of the Prognostic Prediction Model Based on the SQDH Target
Genes

The 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 low- and high-risk groups. Furthermore,
an area under the receiver curve (AUC) was determined using the receiver
operating characteristic curve (ROC).


## 2.7. Statistical Analysis

Statistical analysis was performed using R software. The difference between
groups was statistically significant, with aP value <0.05.


## 3. Results

### 3.1. The 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 identified as SQDH target genes. The GO and KEGG
enrichment analyses were performed to investigate potential pathways closely
associated with SQDH. The 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, DNA-binding transcription factor
binding, and ubiquitin-like protein ligase binding (Figure 1(c)). The 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)).Figure 1
(a) GO BP enrichment analysis based on the SQDH-related genes. (b) GO CC
enrichment analysis based on the SQDH-related genes. (c) GO MF
enrichment analysis based on the SQDH-related genes. (d) PPI network
based on the SQDH-related genes.
(a)(b)(c)(d)


### 3.2. Exploration of the Differentially Expressed Genes between RCC Patients
and Normal People

A total of 532 RCC patients and 72 normal people were included in the TCGA
cohort. The fold change was set into 2 to explore the genes closely associated
with RCC. The differentially expressed analysis revealed 695 differentially
expressed genes, including 278 upregulated and 417 downregulated genes (Figures2(a)–2(b)). The pathways enrichment analysis demonstrated that
some immune-related pathways, such as regulation of T cell activation,
regulation of T cell proliferation, and Th1 and Th2 cell differentiation, are
closely linked to the differentially expressed genes. In addition, the most
enriched pathways are renal tubule development, renal system development, kidney
morphogenesis, and kidney epithelium development. The 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)). The Venn diagram
demonstrated that ten genes, including HK2, VEGFA, IGFBP3, CAV1, ALOX5, CCND1,
DIO1, NR3C2, ADH1B and PTGER3, are closely related to the differentially
expressed genes in the RCC cohort and SQDH targets genes (Figure 2(d)).Figure 2
(a) The differential expression analysis based on the RCC cohort in the
TCGA database. (b) The heat map demonstrated the differentially
expressed genes between RCC and normal tissue. (c) The GO and KEGG
enrichment analysis based on differentially expressed genes. (d) The
Venn diagram displayed the genes that are closely associated with RCC
and SQDH.
(a)(b)(c)(d)


### 3.3. 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. The 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 (Figure3(a)). The multivariate
COX regression analysis revealed that the prognostic prediction model was built
using CCND1 and NR3C2. The risk score is: risk score =
−0.0197007190065525
∗ CCND1 + −0.167437463401867
∗ NR3C2. Then, we performed a survival analysis based on the
expression level of ALOX5, CCND1, NR3C2, and PTGER3. The 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. The results
demonstrated that the 1-year, 3-year, and 5-year AUC are >0.6, indicating
that the model has a good predictive value (Figure 3(g)). Furthermore, the clinical-related ROC curve
demonstrated that the prognostic prediction model and clinical characteristics
could be used as predictive factors (Figure 3(h)).Figure 3
(a) The univariate COX regression analysis based on the OS of RCC
patients. (b) Survival analysis between low- and high-expression of
ALOX5 groups. (c) The survival analysis between low- and high-expression
of CCND1 groups. (d) The survival analysis between low- and
high-expression of NR3C2 groups. (e) The survival analysis between low-
and high-expression of PTGER3 groups. (f) The survival analysis between
low- and high-risk groups. (g) The clinical-related ROC curve
demonstrated the predictive value of clinical characteristics and risk
score. (h) The time-dependent ROC curve demonstrated the predictive
value of prognostic prediction model.
(a)(b)(c)(d)(e)(f)(g)(h)


### 3.4. The SQDH-Based Prognostic Prediction Model Is Closely Associated with
Many Immune Cells

The immune cell infiltration analysis was then performed using the SQDH-related
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 (Figures4(a) and 4(b)). In addition, the distribution of
some immune cells is linked to the OS of RCC patients. The 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)).Figure 4
(a)–(b) The difference in immune cells between low- and high-risk
groups. (c) Survival analysis based on the resting T cells expression
level. (d) Survival analysis based on the resting mast cells expression
level. (e) Survival analysis based on the expression monocytes level.
(f) Survival analysis based on the regulatory T cells expression level.
(g) Survival analysis based on the activated T cells expression
level.
(a)(b)(c)(d)(e)(f)(g)


### 3.5. Some Immune-Related Functions Are Closely Associated with the SQDH-Based
Prognostic Prediction Model

We then compared immune-related function between low- and high-risk groups using
the risk score for RCC patients and immune cell infiltration 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 significantly different (Figure5(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 inflammation-promoting factors
and T cell co-inhibition and co-stimulation factors are correlated with a poorer
OS in RCC patients (Figures 5(b)–5(e)).Figure 5
(a) The correlation between SQDH-based prognostic prediction model of
immune-related functions. (b) The survival analysis between low- and
high-HLA groups. (c) The survival analysis between low- and
high-inflammation-promoting groups. (d) The survival analysis between
low- and high-co-inhibition T cell groups. (e) The survival analysis
between low- and high-co-stimulation T cell groups.
(a)(b)(c)(d)(e)


### 3.6. 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.
The 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 (Figure6(a)). In addition,
transporter activity, beta-catenin 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)).Figure 6
(a) The GSVA analysis of low- and high-CCND1 groups. (b) The GSVA
analysis of low- and high-NR3C2 groups.
(a)(b)


## 3.1. The 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 identified as SQDH target genes. The GO and KEGG
enrichment analyses were performed to investigate potential pathways closely
associated with SQDH. The 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, DNA-binding transcription factor
binding, and ubiquitin-like protein ligase binding (Figure 1(c)). The 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)).Figure 1
(a) GO BP enrichment analysis based on the SQDH-related genes. (b) GO CC
enrichment analysis based on the SQDH-related genes. (c) GO MF
enrichment analysis based on the SQDH-related genes. (d) PPI network
based on the SQDH-related genes.
(a)(b)(c)(d)


## 3.2. Exploration of the Differentially Expressed Genes between RCC Patients
and Normal People

A total of 532 RCC patients and 72 normal people were included in the TCGA
cohort. The fold change was set into 2 to explore the genes closely associated
with RCC. The differentially expressed analysis revealed 695 differentially
expressed genes, including 278 upregulated and 417 downregulated genes (Figures2(a)–2(b)). The pathways enrichment analysis demonstrated that
some immune-related pathways, such as regulation of T cell activation,
regulation of T cell proliferation, and Th1 and Th2 cell differentiation, are
closely linked to the differentially expressed genes. In addition, the most
enriched pathways are renal tubule development, renal system development, kidney
morphogenesis, and kidney epithelium development. The 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)). The Venn diagram
demonstrated that ten genes, including HK2, VEGFA, IGFBP3, CAV1, ALOX5, CCND1,
DIO1, NR3C2, ADH1B and PTGER3, are closely related to the differentially
expressed genes in the RCC cohort and SQDH targets genes (Figure 2(d)).Figure 2
(a) The differential expression analysis based on the RCC cohort in the
TCGA database. (b) The heat map demonstrated the differentially
expressed genes between RCC and normal tissue. (c) The GO and KEGG
enrichment analysis based on differentially expressed genes. (d) The
Venn diagram displayed the genes that are closely associated with RCC
and SQDH.
(a)(b)(c)(d)


## 3.3. 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. The 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 (Figure3(a)). The multivariate
COX regression analysis revealed that the prognostic prediction model was built
using CCND1 and NR3C2. The risk score is: risk score =
−0.0197007190065525
∗ CCND1 + −0.167437463401867
∗ NR3C2. Then, we performed a survival analysis based on the
expression level of ALOX5, CCND1, NR3C2, and PTGER3. The 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. The results
demonstrated that the 1-year, 3-year, and 5-year AUC are >0.6, indicating
that the model has a good predictive value (Figure 3(g)). Furthermore, the clinical-related ROC curve
demonstrated that the prognostic prediction model and clinical characteristics
could be used as predictive factors (Figure 3(h)).Figure 3
(a) The univariate COX regression analysis based on the OS of RCC
patients. (b) Survival analysis between low- and high-expression of
ALOX5 groups. (c) The survival analysis between low- and high-expression
of CCND1 groups. (d) The survival analysis between low- and
high-expression of NR3C2 groups. (e) The survival analysis between low-
and high-expression of PTGER3 groups. (f) The survival analysis between
low- and high-risk groups. (g) The clinical-related ROC curve
demonstrated the predictive value of clinical characteristics and risk
score. (h) The time-dependent ROC curve demonstrated the predictive
value of prognostic prediction model.
(a)(b)(c)(d)(e)(f)(g)(h)


## 3.4. The SQDH-Based Prognostic Prediction Model Is Closely Associated with
Many Immune Cells

The immune cell infiltration analysis was then performed using the SQDH-related
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 (Figures4(a) and 4(b)). In addition, the distribution of
some immune cells is linked to the OS of RCC patients. The 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)).Figure 4
(a)–(b) The difference in immune cells between low- and high-risk
groups. (c) Survival analysis based on the resting T cells expression
level. (d) Survival analysis based on the resting mast cells expression
level. (e) Survival analysis based on the expression monocytes level.
(f) Survival analysis based on the regulatory T cells expression level.
(g) Survival analysis based on the activated T cells expression
level.
(a)(b)(c)(d)(e)(f)(g)


## 3.5. Some Immune-Related Functions Are Closely Associated with the SQDH-Based
Prognostic Prediction Model

We then compared immune-related function between low- and high-risk groups using
the risk score for RCC patients and immune cell infiltration 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 significantly different (Figure5(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 inflammation-promoting factors
and T cell co-inhibition and co-stimulation factors are correlated with a poorer
OS in RCC patients (Figures 5(b)–5(e)).Figure 5
(a) The correlation between SQDH-based prognostic prediction model of
immune-related functions. (b) The survival analysis between low- and
high-HLA groups. (c) The survival analysis between low- and
high-inflammation-promoting groups. (d) The survival analysis between
low- and high-co-inhibition T cell groups. (e) The survival analysis
between low- and high-co-stimulation T cell groups.
(a)(b)(c)(d)(e)


## 3.6. 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.
The 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 (Figure6(a)). In addition,
transporter activity, beta-catenin 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)).Figure 6
(a) The GSVA analysis of low- and high-CCND1 groups. (b) The GSVA
analysis of low- and high-NR3C2 groups.
(a)(b)


## 4. 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]. The incidence of RCC has increased over
time. Although surgery remains the primary treatment option for patients with
locally or locally advanced disease, a significant proportion of patients will
eventually experience disease recurrence [18]. Chemo- and radiotherapy are ineffective in treating RCC. Immunotherapy
has recently been implemented due to a better understanding of RCC biology [19]. The 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 significantly delayed tumor
progression and had a high treatment response rate. The ORR in both trials was
42%, with a median time to disease progression (TTP) of 8.7 months
[21].Furthermore, sunitinib was more effective in phase III clinical study of patients
with metastatic RCC than IFN-α [22]. It is also likely to cause serious side effects such as
nausea, vomiting, diarrhea, rash, hand-foot syndrome, and others that will
significantly 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 effects 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 [25–27].SQDH consists of ginseng,Astragalus membranaceus, rehmannia, yam,
tuckahoe, paeonol, and dogwood. The 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, influence
tumor cell autophagy, and inhibit tumor angiogenesis [30]. The present study aimed to explore the association between
RCC and SQDH using a network pharmacology approach.The main ingredients and target genes of SQDH were obtained from the TCMSP database,
and relative pathways closely associated with the development and progression of
ccRCC were identified. The PPI network based on the SQDH-related genes also revealed
that these genes are highly correlated. In addition, the TCGA database was searched
for RCC differentially expressed genes. The Venn diagram was then used to
investigate the genes linked to RCC and SQDH. The SQDH-based prognostic prediction
model reveals that two SQDH target genes (CCND1 and NR3C2) were closely associated
with RCC patient prognosis. The survival analysis and the ROC curve demonstrated
that the model has an excellent predictive value for RCC patients. Finally, we
investigate the role of the SQDH-based model in the immune response of RCC patients.
The findings revealed that certain immune checkpoints, immune cells, and functions
are highly correlated with the risk model. Our results indicated that CD4+ T
cells and macrophages are closely linked to the SQDH-based prognostic prediction
model. In addition, the HLA and immune checkpoint are also closely associated with
the model, implying that SQDH may play an important role in the immunotherapy of RCC
patients. Further investigation revealed that CCND1 and NR3C2 are the key
SQDH-target genes closely related to the RCC.In conclusion, we identified the close relationship between SQDH and RCC. In
addition, the SQDH-based prognostic prediction model reveals that SQDH may influence
the immune response of RCC patients. In addition, two key genes (CCND1 and NR3C2)
may play an important role in the immunotherapy process for SQDH and RCC patients.
By further investigating its mechanism, it may be possible to develop a basis for
combining TCM and advanced renal cancer.



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*Source: 2355891-2023-01-25.xml*