Abstract
Introduction
Cervical cancer remains a significant challenge in oncology with an
escalating demand for novel therapeutic strategies that can navigate
the complexities of its pathophysiology. This study elucidated the
antineoplastic effects of cryptotanshinone, a derivative of danshen
(Salvia miltiorrhiza), a herb widely utilized in traditional Chinese
medicine practices.
Methods
Employing a comprehensive multi-omics approach, including
transcriptomic, proteomic, and bioinformatics analyses, we investigated
the potential effects of cryptotanshinone on cervical cancer through
data mining and computational analysis.
Results and Discussion
Our results demonstrated that the potential of cryptotanshinone to
disrupted cancer cell proliferation and induced apoptosis may be
ascribed to its modulation of gene expression and interaction with
specific protein networks. Furthermore, network pharmacology and
pathway enrichment analyses identified critical hubs and signaling
pathways, suggesting a multi-targeted mechanism of action. Furthermore,
the establishment of a prognostic model, which is founded upon
differentially expressed genes linked to cryptotanshinone treatment,
underscores its promising role as both a prognostic biomarker and a
therapeutic agent. These insights pave the way for the integration of
cryptotanshinone into therapeutic regimens, offering a promising avenue
for enhancing the efficacy of cervical cancer treatment and patient
outcomes.
Keywords: cryptotanshinone, cervical cancer, multi-omics analysis,
network pharmacology, bioinformatics, prognostic biomarkers
1 Introduction
Cervical cancer remains a critical public health issue and is
characterized by significant global prevalence and mortality rates.
This malignancy, primarily manifesting as squamous cell carcinoma and
adenocarcinoma, originates in the cervix and lower part of the uterus.
Human Papillomavirus (HPV) infection has been definitively recognized
as the primary contributory factor leading to the development of
cervical cancer, underscoring the importance of preventive measures
such as regular screening and HPV vaccination ([39]Lei et al., 2020).
The severity of the disease is particularly pronounced in low- and
middle-income economies, where limited availability of preventive
healthcare and treatment options often exacerbates the situation. It is
crucial to decrease the impact of cervical cancer by making progress in
comprehending the biological processes involved and creating successful
treatment choices. The advancement of innovative diagnostic and
therapeutic methodologies is crucial for enhancing patient outcomes as
early detection significantly improves prognosis ([40]Sundstrom and
Elfstrom, 2020).
Danshen (Salvia miltiorrhiza), a widely used herb in traditional
Chinese medicine, and its bioactive compound, cryptotanshinone, have
garnered attention for their therapeutic potential in oncology,
including cervical cancer treatment ([41]Jin et al., 2021).
Historically utilized for the therapy of cardiovascular and
cerebrovascular diseases, Danshen exhibits anti-inflammatory,
antioxidant, and antitumor properties. Cryptotanshinone has been the
subject of research due to its potential in inhibiting the
proliferation of cancer cells and triggering apoptosis. ([42]Zhang et
al., 2020). The pharmacological effects of danshen and cryptotanshinone
highlight their relevance in cancer research and offer a promising
avenue for novel treatment strategies that target the complex
pathophysiology of cervical cancer ([43]Liu et al., 2020). Their
incorporation into cancer treatment regimens underscores the
integration of traditional medicine into modern oncological practices,
potentially enhancing therapeutic outcomes and patient quality of life
([44]Li et al., 2021).
In our preliminary investigations using the HERB and SymMap databases,
we identified Danshen and Lei Gong Teng as potential botanical agents
implicated in the etiology and progression of cervical cancer. These
findings highlight the intricate interplay between specific herbal
compounds and the pathophysiological mechanisms of cervical cancer and
suggest a promising direction for future research. This exploration of
traditional medicinal herbs using contemporary bioinformatic approaches
provides novel insights into their potential therapeutic roles, paving
the way for innovative treatment strategies against cervical cancer.
The burgeoning resistance to conventional chemotherapeutic agents and
adverse side effects associated with current cervical cancer treatments
underscore the urgent need for innovative therapeutic strategies.
Natural compounds, such as danshen and cryptotanshinone have emerged as
promising candidates owing to their multi-targeted therapeutic
potential and low toxicity profiles. Their capacity to regulate
critical signaling pathways implicated in cancer cell proliferation,
apoptosis, and metastasis positions them as viable complements or
alternatives to traditional treatments ([45]Wang et al., 2024; [46]Song
et al., 2023). This paradigm shift toward incorporating phytochemicals
into cancer management could significantly improve treatment outcomes
and patient quality of life, requiring further exploration of their
clinical applicability.
Network drug analysis represents a cutting-edge approach in cancer
research that leverages the power of systems biology to unravel the
complex interactions within the cellular networks exploited by cancer
cells ([47]Theodoris et al., 2023). This approach aids in the discovery
of new drug targets and understanding of drug mechanisms by examining
the interconnected pathways and genetic networks that are modified in
cancer.Moreover, it offers the potential to repurpose existing drugs
for cervical cancer treatment by revealing previously unrecognized
anticancer activities within their pharmacological profiles. This
innovative strategy holds promise for accelerating the development of
more effective and targeted therapies for cervical cancer, thereby
enhancing the precision of treatment interventions.
This research aimed to investigate the therapeutic efficacy and
underlying mechanisms of danshen and cryptotanshinone in treating
cervical cancer. By focusing on these natural compounds, we aimed to
elucidate their impact on cancer cell biology, their potential to
inhibit tumor growth, and their ability to improve patient outcomes.
2 Materials and methods
2.1 Data download
Utilizing the TCGAbiolinks package in R ([48]Colaprico et al., 2016) we
retrieved datasets specific to cervical endocervical adenocarcinoma and
squamous cell carcinoma (CESC) from The Cancer Genome Atlas
(TCGA-CESC). This data was used as the primary test set. Following the
exclusion of samples that lacked comprehensive clinical details, the
final count included 306 CESC samples and three normal controls, all
sequenced in count format. These data have been standardized by FPKM
per thousand bases, and the relevant clinical information was obtained
from the UCSC Xena database ([49]Goldman et al., 2020)
([50]https://xena.ucsc.edu/). For additional details, please see
[51]Table 1.
TABLE 1.
Overall baseline data sheet.
Characteristics Overall
Pathologic T stage, n (%)
T1 142 (53.8%)
T2 74 (28.0%)
T3 21 (8.0%)
T4 10 (3.8%)
TX 17 (6.4%)
Pathologic N stage, n (%)
N0 136 (51.5%)
N1 62 (23.5%)
NX 66 (25.0%)
Pathologic M stage, n (%)
M0 116 (44.8%)
M1 11 (4.2%)
MX 132 (51.0%)
Age, n (%)
≤ 50 189 (61.2%)
>50 120 (38.8%)
[52]Open in a new tab
For validation purposes, we employed the GEOquery package ([53]Davis
and Meltzer, 2007; [54]Barrett et al., 2013) to access cervical cancer
datasets [55]GSE7803 ([56]Zhai et al., 2007) and [57]GSE9750
([58]Scotto et al., 2008) from the GEO database
([59]https://www.ncbi.nlm.nih.gov/geo/). These datasets consisted of
cervical tissue samples analyzed on [60]GPL96 microarrays. The
[61]GSE7803 dataset included 21 CESC and 10 normal samples, whereas
[62]GSE9750 comprised 33 CESC and 24 normal samples. Comprehensive
sample details are available in [63]Supplementary Table S1.
To address batch effects during the integration of TCGA-CESC and GEO
datasets, we utilized the sva package ([64]Leek et al., 2012)
specifically utilizing the ComBat method within an empirical Bayes
framework. We initially used Principal Component Analysis (PCA) to
detect potential discrepancies in the datasets, enabling us to visually
assess batch effects before integration. After correction with ComBat,
PCA was reapplied, confirming effective batch effect removal through
clearer clustering of samples. This resulted in a harmonized dataset
comprising 54 CESC and 34 normal samples. Subsequently, the limma
package ([65]Ritchie et al., 2015) was used to standardize this
combined dataset, including probe annotations and processing steps. The
standardized dataset was then visualized in two- or three-dimensional
PCA plots, providing a reduced-dimensionality perspective that
demonstrated successful integration.
2.2 Cryptotanshinone target prediction
Initially, an exploration of Cryptotanshinone’s potential target genes
was conducted by accessing the PubChem database ([66]Kim et al., 2021)
([67]https://pubchem.ncbi.nlm.nih.gov), a repository rich in chemical
data pertinent to drug discovery. The search term “Cryptotanshinone”
yielded 46 cryptotanshinone-related genes (CTSRGs). Further predictive
analysis was performed using the SwissTargetPrediction tool ([68]Daina
et al., 2019) ([69]http://swisstargetprediction.ch/), which suggested
an additional 100 CTSRGs. Complementing these methods, the DGIdb
database ([70]Freshour et al., 2021) ([71]https://dgidb.org/), which
catalogs potential drug-gene interactions, was queried using
“Cryptotanshinone” as the keyword, identifying 14 unique CTSRGs. A
comprehensive set of 133 CTSRGs was assembled from these sources, and
their interactions were visualized using a network map created using
Cytoscape software ([72]Shannon et al., 2003).
2.3 Cryptotanshinone-related differentially expressed genes associated with
cervical cancer
Analysis of the TCGA-CESC dataset, which segregates samples into CESC
and normal controls, was conducted using the DESeq2 package ([73]Love
et al., 2014) Differentially expressed genes (DEGs) were identified
using strict criteria, with |logFC| > 3.0 and adjusted p-value <0.05.
Upregulated genes exhibited logFC >3.0 and adj. p < 0.05, while
downregulated genes presented with |logFC| < −3.0 and adj. p < 0.05.
The Benjamini–Hochberg method was applied for p-value correction. These
differential expression results were graphically depicted using the
ggplot2 package in R.
To discern Cryptotanshinone-related differentially expressed genes
(CTSRDEGs) pertinent to cervical cancer, DEGs from TCGA-CESC dataset
that met the criteria of |logFC| > 3.0 and P-adj <0.05 were intersected
with the CTSRGs, and the intersections were visualized using a Venn
diagram. Using the pheatmap package in R to create a heatmap of the
CTSRDEGs further elucidates the gene expression changes associated with
Cryptotanshinone expression in cervical cancer.
2.4 Protein–protein interaction (PPI) network and hub gene screening
The PPI network encompasses a complex web of interacting proteins that
play critical roles in various biological functions including signal
transduction, gene regulation, and essential life processes like
metabolic pathways and cell cycle control. Analyzing these interactions
provides profound insights into protein functions, biological signaling
mechanisms, and metabolic processes under specific physiological
conditions, including disease states. For constructing the PPI network
related to CTSRDEGs, we utilized the STRING database ([74]Szklarczyk et
al., 2019) ([75]https://string-db.org/) to map out both known and
predicted protein interactions, setting a minimum confidence score
threshold of 0.40. Regions within the PPI network demonstrating high
connectivity often indicate the presence of protein complexes linked to
specific biological functions. We employed several algorithms via the
CytoHubba ([76]Chin et al., 2014) plugin in Cytoscape to determine the
centrality of nodes within the network: Maximal Clique Centrality
(MCC), Degree, Maximum Neighborhood Component (MNC), Edge-Percolated
Component (EPC), and Closeness ([77]Yang et al., 2019). The top 10
CTSRDEGs were identified based on their network scores, and the overlap
of results from these algorithms highlighted key hub genes associated
with cryptotanshinone.
2.5 Enrichment analysis of gene ontology (GO) and kyoto encyclopedia of genes
and genomes (KEGG) pathway
Functional enrichment analysis is pivotal for understanding the roles
of genes within biological contexts. GO analysis ([78]Mi et al., 2019)
and the Kyoto Encyclopedia of Genes and Genomes (KEGG) ([79]Kanehisa
and Goto, 2000) are instrumental in elucidating the biological
processes, cellular components, molecular functions, and pathway
interactions of genes. We used the clusterProfiler software package
([80]Engebretsen and Bohlin, 2019) to conduct a comprehensive
enrichment analysis of the hub genes associated with cryptotanshinone.
Criteria for significant enrichment included a p-value of less than
0.05 and an FDR (q value) of less than 0.25. Results were visualized
using Cytoscape, creating a network map that integrates
cryptotanshinone, its related hub genes, significant GO terms, and
enriched KEGG pathways. Furthermore, pathway illustrations based on
KEGG analysis were generated using the Pathview package ([81]Luo and
Brouwer, 2013), providing a visual representation of the pathways
involved.
2.6 Differential expression verification and receiver operating
characteristic (ROC) curve analysis of cryptotanshinone-related hub genes
We investigated the expression differences of genes responsive to
cryptotanshinone between cervical cancer samples (CESC) and normal
controls across both the TCGA-CESC and Combined GEO datasets. Use the
Mann-Whitney U test to analyze expression discrepancies, resulting in a
visual comparison of gene expression levels between groups. Subsequent
analysis of the diagnostic capabilities of these genes was performed
using the pROC package in R to plot ROC curves and determine the Area
Under Curve (AUC) values for each gene. The diagnostic performance was
interpreted based on AUC values, where an AUC close to 1 indicates
excellent diagnostic accuracy. Specifically, AUC values ranging from
0.5 to 0.7 indicates a low diagnostic accuracy, while those ranging
from 0.7 to 0.9 suggest a moderate level of accuracy, and values
exceeding 0.9 reflect high levels of accuracy.
2.7 Construction of prognostic risk model and prognostic analysis of cervical
cancer
In TCGA-CESC dataset, a prognostic risk model was developed using the
survival package in R ([82]Therneau, 2023). Initial univariate Cox
regression analyses identified cryptotanshinone-sensitive DEGs
(CTSRDEGs) with a significant impact on prognosis (p < 0.10). These
genes underwent additional Least Absolute Shrinkage and Selection
Operator (LASSO) regression, utilizing the glmnet package in R with a
Cox family parameter, to refine the model and enhance its predictive
robustness ([83]Engebretsen and Bohlin, 2019). The LASSO regression,
which penalizes the regression model by incorporating a lambda
parameter to reduce overfitting, generated a RiskScore computed as the
summation of gene coefficients multiplied by their corresponding mRNA
expressions. The LASSO RiskScore was calculated as follows:
[MATH:
riskScore<
/mi>=∑iCoeffic
ient genei∗m<
mi>RNA Exp<
/mi>ression genei
:MATH]
The prognostic model underwent further validation through multivariate
Cox regression analysis, incorporating the identified CTSRDEGs from the
LASSO model. The impact of these genes on survival was graphically
represented in a Forest Plot. Utilizing the median LASSO RiskScore,
cervical cancer samples were categorized into high- and low-risk
groups.
The comparison of survival between these groups was conducted using the
Kaplan-Meier curve analysis with the survival package ([84]Rich et al.,
2010), and a time-dependent ROC curve was generated with the Surviroc
package to assess the precision of the prognostic model in forecasting
1-, 3-, and 5-year survival rates ([85]Park et al., 2004). The AUC
values derived from the ROC analysis provided a quantitative measure of
the model’s predictive accuracy, where higher values indicate superior
prognostic performance.
2.8 Validation of cervical cancer prognostic risk models
The relationship between the LASSO RiskScore expression and clinical
outcomes was investigated through univariate Cox regression analysis,
incorporating the LASSO RiskScore alongside age and three clinical
staging parameters (T stage, N stage, and M stage). The outcomes of
both univariate and multivariate Cox regression analyses were
graphically depicted using forest plots to elucidate the impact of
LASSO RiskScore and other clinical factors. A nomogram ([86]Wu et al.,
2020), constructed with the rms package, represented the multifactorial
relationships and predicted the 1-, 3-, and 5-year survival
probabilities based on variables included in the multivariate Cox
regression model.
The model’s predictive performance was evaluated using a calibration
curve, which compares the actual outcomes with those predicted by the
model across various scenarios. This curve was crucial for assessing
the precision and reliability of the prognostic model over 1-, 3-, and
5-year periods, as indicated by the nomogram. Additionally, the
decision curve analysis (DCA), implemented with the ggDCA package, was
utilized to assess the clinical utility of the nomogram predictions for
these time frames.
2.9 Analysis of differential expression and correlation in risk groups
The TCGA-CESC dataset was divided into high-risk and low-risk groups
based on the median LASSO RiskScore. The risk stratification was
similarly utilized for the combined GEO dataset using the LASSO
RiskScore calculated from the risk coefficients. Further investigation
was performed to examine the variation in gene expression related to
the prognostic risk model within both high and low-risk groups of
TCGA-CESC and combined GEO datasets. Expression comparison graphs were
created to visualize these differences. Correlations among the genes
related to the prognostic risk model in the TCGA-CESC and combined GEO
datasets were analyzed using the Spearman correlation method. The
correlation chord graphs produced by the igraph and ggraph packages
were used to depict the associations among gene expression levels. The
strength of the correlation was classified based on the correlation
coefficient (r value): values below 0.3 indicated negligible to weak
correlation, between 0.3 and 0.5 indicated weak correlation, between
0.5 and 0.8 suggested moderate correlation, and above 0.8 indicated
strong correlation.
2.10 Immuno-infiltration analysis of cervical cancer
Utilizing the CIBERSORT algorithm ([87]Newman et al., 2015), which is
grounded in linear support vector regression, we decomposed the
transcriptomic data to assess immune cell compositions and abundances
within mixed cellular contexts. Applying the LM22 gene signature, we
processed the data to retain only samples with nonzero immune cell
fractions, leading to the derivation of the immune cell infiltration
matrix for the TCGA-CESC dataset. The ggplot2 package in R was utilized
to visualize variations in immune cell profiles between CESC and normal
samples, highlighting significant variances in LM22-defined immune cell
types. Spearman’s rank correlation was employed to both assess
inter-immune cell correlations and link these cells with model genes,
identifying statistically significant relationships (p < 0.05).
Visualization of these correlations was achieved through correlation
heatmaps and bubble plots, generated with the pheatmap and ggplot2
packages, respectively.
2.11 Analysis of immune profiles in high vs. low-risk cervical cancer groups
Through Single-Sample Gene-Set Enrichment Analysis (ssGSEA) ([88]Xiao
et al., 2020), we quantified the degree of immune cell infiltration,
encompassing activated CD8 T cells, dendritic cells, and other immune
subsets, within the CESC samples of the TCGA-CESC dataset. These
measurements provided a relative abundance score for each immune type,
forming the basis for constructing an immune cell invasion matrix.
Comparative analyses of immune cell abundance between high and low-risk
groups were illustrated utilizing ggplot2, with significant immune cell
variances noted for further analysis. Spearman’s correlation was again
utilized to explore both intra-immune cell relationships and their
associations with model genes. The findings were presented through
heatmaps and bubble maps, crafted using pheatmap and ggplot2.
2.12 Statistical analysis
All analytical procedures were executed in R software (Version 4.2.2).
Statistical significance for data with a normal distribution was
assessed by employing the Student’s t-test to compare two continuous
variables. For data not following normal distribution, the Mann-Whitney
U test, or the Wilcoxon Rank Sum Test, was applied. Multiple group
comparisons were conducted using the Kruskal–Wallis test. Spearman’s
correlation coefficient was computed to determine the relationships
among diverse biomolecules. Statistical significance was set at a
bilateral p-value of less than 0.05, unless specified otherwise.
3 Results
3.1 Technology roadmap and target prediction of cryptotanshinone
Bioinformatics analysis of cryptotanshinone is shown in [89]Figure 1A.
Cryptotanshinone (CTS) was used as a keyword to search the PubChem and
DGIdb databases to identify the CTS-related targets. The target of CTS
was predicted by SwissTargetPrediction website, and CTS-related targets
identified by the three methods were combined to obtain 133 CTSRGs. The
Cytoscape network diagram of CTS- and PPPPC-related genes (CTSRGs) is
shown in [90]Figure 1B. Detailed information is presented in
[91]Supplementary Table S2.
FIGURE 1.
FIGURE 1
[92]Open in a new tab
Comprehensive Analysis and Interaction Network of
Cryptotanshinone-Related Differentially Expressed Genes (CTSRDEGs). (A)
Flow Chart for analysis of CTSRDEGs. This panel presents the systematic
workflow for analyzing CTSRDEGs in cervical endocervical adenocarcinoma
and squamous cell carcinoma (CESC) using The Cancer Genome Atlas (TCGA)
data. It covers the steps to identify differentially expressed genes
(DEGs) upon Cryptotanshinone (CTS) treatment, followed by subsequent
functional enrichment analysis (Gene Ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathways), differential
expression profiling (Exp Diff), and validation through Receiver
Operating Characteristic (ROC) analysis and LASSO regression. Also
shown are the methods for constructing the protein–protein interaction
(PPI) network. (B) Cryptotanshinone and Targets Interaction Network.
This panel illustrates the interaction network of CTS (yellow oval)
with its predicted targets: DGIdb prediction targets (red circles),
PubChem predicted targets (orange circles), and SwissTargetPrediction
targets (purple circles). This network provides insights into the
potential mechanisms of action of CTS in targeting gene expressions in
CESC.
3.2 Differentially expressed genes associated with cryptotanshinone in
cervical cancer
The dataset from The Cancer Genome Atlas (TCGA-CESC) was segregated
into two groups: cases of cervical endocervical adenocarcinoma and
squamous cell carcinoma (CESC) and normal controls. We conducted an
analysis of differential gene expression between these groups utilizing
the limma package in R.This analysis identified 3,024 differentially
expressed genes, adhering to the criteria of an absolute log fold
change (|logFC|) greater than 3.0 and an adjusted p-value (adj.P) below
0.05. Specifically, there was an upregulation of 2,114 genes and a
downregulation of 911 genes under these conditions. We illustrated
these findings in a volcano plot ([93]Figure 2A).
FIGURE 2.
[94]FIGURE 2
[95]Open in a new tab
Integrated Analysis of Cryptotanshinone-Associated Gene Expression and
Protein Interactions in Cervical Cancer. (A) Volcano plot illustrating
the differential gene expression between the cervical cancer (CESC)
group and the control (Normal) group in TCGA-CESC dataset. (B) Venn
diagram showing the intersection of differentially expressed genes
(DEGs) and cryptotanshinone-associated genes (CTSRGs) in cervical
cancer. (C) Heat map of cryptotanshinone-associated differentially
expressed genes (CTSRDEGs) showing gene expression levels in the
cervical cancer dataset (TCGA-CESC), with high expression denoted by
red and low expression by blue. (D) Protein–protein interaction (PPI)
network of CTSRDEGs, analyzed utilizing the STRING database and
visualized to highlight the top 10 hub genes as determined by five
CytoHubba algorithms: MCC, MNC, Degree, EPC, and Closeness. (E–I) The
PPI networks of the top 10 cryptotanshinone-related differentially
expressed genes (CTSRDEGs) were constructed using five different
algorithms from the CytoHubba plugin, revealing unique interactions
among the genes. (J) The Venn diagram illustrates the overlap among the
top genes identified by these algorithms in the context of cervical
cancer. Color coding: orange represents the cervical cancer (CESC)
group and green the control (Normal) group.
In order to detect Cryptotanshinone-Related Differentially Expressed
Genes (CTSRDEGs), we intersected the genes meeting the differential
expression criteria with known Cryptotanshinone-Sensitive/Responsive
Genes (CTSRGs). This intersection yielded 17 significant CTSRDEGs,
including TOP2A, PLK1, KIF11, CDH1, CTSV, ADAMTS5, S1PR1, KDR, CA2,
BCHE, PTGER2, TNF, ABCB11, MMP13, NPY5R, CA4, and IDO1. To visualize
the expression patterns of these CTSRDEGs across the diverse groups
within the TCGA-CESC dataset, we employed the pheatmap package in R to
generate heat maps highlighting the differential expression
([96]Figures 2B,C).
3.3 Construction of PPI networks and screening of hub genes
Initially, an analysis to discern protein protein interactions (PPI)
was conducted, leading to the creation of a PPI network for 17 core
CTSRDEGs utilizing the STRING database, as depicted in [97]Figure 2D.
This analysis led to the retention of key interacting CTSRDEGs. The PPI
network revealed 14 connected CTSRDEGs: MMP13, TNF, CA4, KDR, TOP2A,
S1PR1, PLK1, ADAMTS5, BCHE, CA2, CDH1, CTSV, IDO1, and KIF11. The
relevance of these genes was further quantified using five distinct
algorithms provided by the CytoHubba plugin in Cytoscape, ranking the
genes based on their interaction scores. The algorithms employed were
MCC, Degree, MNC, EPC, and Closeness. The PPI networks of the top 10
cryptotanshinone-related differentially expressed genes (CTSRDEGs) were
constructed using five different algorithms from the CytoHubba plugin,
revealing unique interactions among the genes ([98]Figures 2E–I), where
the gradation from red to yellow in the nodes denotes scores from high
to low. A synthesis of the results from the five algorithms highlighted
in [99]Figure 2J identified seven hub genes critical to CESC: TNF,
MMP13, KDR, CDH1, TOP2A, ADAMTS5, and S1PR1.
3.4 Enrichment analysis of GO and KEGG pathway
Subsequent GO and KEGG pathway analyses were employed to delineate the
biological processes (BP), cellular components (CC), molecular
functions (MF), and pathway involvements of the seven identified hub
genes linked to cryptotanshinone. As illustrated in [100]Table 2, these
analyses elucidated significant enrichment of the hub genes across
diverse biological processes, including endothelial cell
differentiation, endothelium development, and vascular wound healing;
cellular components including membrane raft and external side of plasma
membrane; and molecular functions like metalloendopeptidase activity
and integrin binding. Key pathways identified included the IL-17
signaling pathway, sphingolipid signaling pathway, and fluid shear
stress and atherosclerosis, among others detailed in the Rap1 signaling
pathway and proteoglycans in cancer. These results were visually
represented in a bubble chart ([101]Figure 3A) and further supported by
a drug-target-pathway network diagram ([102]Figure 3B). Additional
comparisons in the KEGG enrichment analysis were made for the
sphingolipid signaling pathway, as well as the fluid shear stress and
atherosclerosis pathway, as shown in [103]Supplementary Figures S1A, B.
The visualization of these pathways was enhanced with the use of the R
package Pathview for [104]Supplementary Figures S1C–E.
TABLE 2.
Results of GO and KEGG enrichment analysis for CTSRDEGs.
Ontology ID Description BgRatio p-value p.adjust qvalue
BP GO:0045446 endothelial cell differentiation 121/18903 8.79E-06
4.75E-03 1.69E-03
BP GO:0003158 endothelium development 139/18903 1.33E-05 4.75E-03
1.69E-03
BP GO:0022411 cellular component disassembly 483/18903 1.39E-05
4.75E-03 1.69E-03
BP GO:0061042 vascular wound healing 22/18903 2.71E-05 6.52E-03
2.32E-03
BP GO:0050927 positive regulation of positive chemotaxis 25/18903
3.51E-05 6.52E-03 2.32E-03
CC GO:0045121 membrane raft 326/19869 2.40E-06 4.12E-05 2.81E-05
CC GO:0098857 membrane microdomain 327/19869 2.42E-06 4.12E-05 2.81E-05
CC GO:0009897 external side of plasma membrane 462/19869 4.08E-04
4.62E-03 3.15E-03
MF GO:0004222 metalloendopeptidase activity 112/18432 7.53E-04 3.09E-02
1.27E-02
MF GO:0005178 integrin binding 156/18432 1.45E-03 3.09E-02 1.27E-02
MF GO:0008237 metallopeptidase activity 184/18432 2.01E-03 3.09E-02
1.27E-02
MF GO:0045295 gamma-catenin binding 13/18432 4.93E-03 3.68E-02 1.51E-02
MF GO:0032794 GTPase activating protein binding 15/18432 5.68E-03
3.68E-02 1.51E-02
KEGG hsa04657 IL-17 signaling pathway 94/8644 1.71E-03 1.10E-01
8.29E-02
KEGG hsa04071 Sphingolipid signaling pathway 121/8644 2.81E-03 1.10E-01
8.29E-02
KEGG hsa05418 Fluid shear stress and atherosclerosis 139/8644 3.69E-03
1.10E-01 8.29E-02
KEGG hsa05205 Proteoglycans in cancer 205/8644 7.88E-03 1.44E-01
1.09E-01
KEGG hsa04015 Rap1 signaling pathway 210/8644 8.26E-03 1.44E-01
1.09E-01
[105]Open in a new tab
GO, gene ontology; BP, biological process; CC, cellular component; MF,
molecular function; KEGG, kyoto encyclopedia of genes and genomes;
CTSRDEGs, Cryptotanshinone-Related Differentially Expressed Genes.
FIGURE 3.
[106]FIGURE 3
[107]Open in a new tab
Comprehensive Analysis of Cryptotanshinone-Induced Gene Expression and
Batch Effects Correction in Cervical Cancer Data Sets. (A) Gene
Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathway enrichment analyses of cryptotanshinone-related differentially
expressed genes (CTSRDEGs) depicted via bubble maps for Biological
Process (BP), Cellular Component (CC), Molecular Function (MF), and
biological pathways (KEGG). The horizontal axes represent GO and KEGG
terms. (B) Drug-target-pathway network illustrating interactions among
cryptotanshinone (CTS), hub genes, GO entries, and KEGG pathways. The
network uses yellow ovals for drugs, purple circles for genes, light
brown for KEGG pathways, light green for CC entries, dark green for BP
entries, and dark brown for MF entries. (C) Boxplot distribution of
combined GEO datasets [108]GSE7803 and [109]GSE9750, illustrating batch
effects before and after correction. (D) Principal Component Analysis
(PCA) plots of the integrated GEO datasets, comparing data clustering
before and after batch effect removal, with the cervical endocervical
adenocarcinoma and squamous cell carcinoma (CESC) dataset [110]GSE7803
in green and [111]GSE9750 in orange. The screening criteria for GO and
KEGG pathway enrichment analysis were p-value <0.05 and FDR value (q
value) <0.25. In the network diagram, yellow ovals represent drugs,
purple circles represent genes, light brown represents KEGG pathways,
light green circles represent CC entries, dark green represents BP
entries, and dark brown represents MF entries.
3.5 Consolidation of cervical cancer datasets
Initially, the sva package in R was utilized to combine the datasets
[112]GSE7803 and [113]GSE9750, aiming to rectify batch effects present
within the CESC samples. The consistency of expression values pre- and
post-adjustment was visually inspected using box plots ([114]Figure
3C). Additionally, the dimensional reduction achieved through this
correction was depicted using PCA, validating the effective alleviation
of batch effects., as illustrated in [115]Figure 3D.
3.6 Differential expression verification and ROC curve analysis of
cryptotanshinone- related hub genes
We conducted an analysis to identify expression discrepancies of seven
cryptotanshinone-sensitive hub genes within the CESC and control
cohorts within the TCGA-CESC dataset, employing the Mann-Whitney U
test. This analysis revealed statistically significant disparities (p <
0.05) for six of these genes: TNF, KDR, CDH1, TOP2A, ADAMTS5, and
S1PR1, as shown in the comparative plot ([116]Figure 4A). We then
evaluated the diagnostic potential of these genes using ROC curve
analyses, which demonstrated a high level of diagnostic accuracy (AUC >
0.9) in discriminating between the groups. ([117]Figures 4B–D).
FIGURE 4.
[118]FIGURE 4
[119]Open in a new tab
Differential Expression Validation and ROC Curve Analysis. (A) Grouping
comparison diagram of cryptotanshinone-associated hub genes in cervical
cancer (CESC) group and control (Normal) group in the Cervical cancer
dataset (TCGA-CESC). (B–D) ROC curves of cryptotanshinone-associated
hub genes TNF and KDR (B), CDH1 and TOP2A (C), ADAMTS5 and S1PR1 (D) in
TCGA-CESC dataset. (E) Grouping comparison diagram of
cryptotanshinone-associated hub genes in cervical cancer (CESC) group
and control (Normal) group in the integrated GEO dataset (Combined
Datasets). (F-H) ROC curves of cryptotanshinone-associated hub genes
MMP13 (F), CDH1 (G), TOP2A, and ADAMTS5 (H) in an integrated GEO
dataset (Combined Datasets). CESC, Cervical Endocervical Adenocarcinoma
and Squamous Cell Carcinoma; CTSRDEGs, Cryptotanshinone-Related
Differentially Expressed Genes; DCA, Decision Curve Analysis; ROC,
Receiver Operating Characteristic; AUC, Area Under the Curve. ns
indicates p-values ≥0.05, which were not statistically significant. *p
< 0.05, statistical significance; **p < 0.01, highly statistically
significant; ***p < 0.001, highly statistically significant. The AUC
has high accuracy above 0.9, and the AUC has some accuracy between 0.7
and 0.9. Orange represents cervical cancer (CESC) group and green
represents control (Normal) group.
Following this, expression variances of the same hub genes were
examined in the integrated GEO dataset, identifying significant
differences for four genes: MMP13, CDH1, TOP2A, and ADAMTS5 (p < 0.05),
as documented in [120]Figure 4E. ROC curves based on these genes
demonstrated substantial diagnostic utility, with TOP2A showing high
accuracy (AUC > 0.9) and MMP13, CDH1, and ADAMTS5 exhibiting moderate
accuracy (0.7 < AUC <0.9) for classifying CESC and normal samples
([121]Figures 4F–H).
3.7 Construction and prognostic analysis of cervical cancer prognostic model
We utilized univariate Cox regression analysis, incorporating 17
CTSRDEGs, to devise a prognostic model specifically tailored for
cervical squamous cell carcinoma and endocervical adenocarcinoma
(CESC).
Variables exhibiting a p-value less than 0.10 were subsequently
illustrated in forest plots ([122]Figure 5A). This analysis identified
three significant CTSRDEGs: CA2, TNF, and IDO1. To refine our
prognostic model, we applied LASSO regression to these genes,
visualized using both a model diagram and a variable trace plot
([123]Figures 5B,C). This step confirmed the inclusion of CA2, TNF, and
IDO1 in our LASSO model. Subsequent multivariate Cox regression
analysis, focused on these genes, was conducted to explore their
relationship with clinical outcomes and their prognostic efficacy.
Results were depicted in another Forest Plot ([124]Figure 5D). The
formula for the LASSO RiskScore was established as:
[MATH: riskScore=CA2* 0.0895+<
mtext
mathvariant="italic">TNF* 0.3250+<
mtext>IDO1* ‐0.1470<
/mrow> :MATH]
FIGURE 5.
[125]FIGURE 5
[126]Open in a new tab
LASSO and Cox Regression Analysis. (A) Forest Plot of three
cryptotanshinone-related differentially expressed genes (CTSRDEGs) in a
univariate Cox regression model. The prognostic risk model plot (B) and
variable locus plot (C) of the (B, C). LASSO regression model. (D)
Forest Plot of three prognostic risk model-associated genes in
multifactor Cox regression model. (E) Risk factor plot of LASSO
RiskScore (F) Prognostic KM curve between high and low groups of the
LASSO risk Score (RiskScore) and overall survival (OS) of cervical
cancer (CESC). G-I. 1 year (G), 3 years (H), and 5 years (I) LASSO risk
scores depend on the ROC curve. TCGA, The Cancer Genome Atlas; CESC,
Cervical Endocervical Adenocarcinoma and Squamous Cell Carcinoma;
CTSRDEGs, Cryptotanshinone-Related Differentially Expressed Genes;
LASSO, Least Absolute Shrinkage and Selection Operator; OS, Overall
Survival; KM, Kaplan-Meier; ROC, Receiver Operating Characteristic
Curve; AUC, Area Under the Curve. AUC has some accuracy at 0.7 to 0.9.
The color green is employed to denote the low-risk group, whereas
orange is utilized to signify the high-risk group.
Using this score, we constructed a risk factor map ([127]Figure 5E),
employing the ggplot2 package, and stratified the CESC samples from
TCGA-CESC dataset into high and low-risk categories based on the median
RiskScore. The impact of this stratification on overall survival (OS)
was assessed through the analysis of Kaplan-Meier (KM) curves, using
the survival package ([128]Figure 5F), and revealed significant
differentiation in prognostic outcomes (p < 0.05). Time-dependent ROC
curves for 1-, 3-, and 5-year forecasts were generated to assess the
predictive accuracy of the RiskScore ([129]Figures 5G–I), with the
1-year AUC reaching 0.753, indicating strong predictive power.
3.8 Validation of cervical cancer prognostic model
The prognostic model’s reliability was further examined through
calculations involving the LASSO RiskScore, based on gene expression
levels and coefficients from the CESC dataset. Univariate Cox
regression analyses were conducted utilizing the RiskScore, alongside
age and stages (T, N, M), where all factors with p-values below 0.10
advanced to multivariate analysis ([130]Table 3). The results, shown in
forest plots ([131]Figures 6A,B), affirmed the significance of the
clinical stage variables and the RiskScore in predicting clinical
outcomes. A nomogram integrating the RiskScore with T, N, and M stages
was crafted to depict their prognostic relationships ([132]Figure 6C),
underscoring the superior prognostic value of the RiskScore over other
variables. Calibration of the prognostic model at 1-, 3-, and 5-year
intervals was performed, with calibration curves demonstrating close
alignment with ideal predictions, especially at the 1-year mark
([133]Figures 6D–F). Finally, the clinical usefulness of the model was
assessed across different time frames through decision curve analysis
(DCA). ([134]Figures 6G–I). The analysis showed the model’s net benefit
to be most substantial for predictions at 3 years, followed by 1 year,
and 5 years, highlighting its effective prognostic capability across
these intervals.
TABLE 3.
Results of cox analysis.
Characteristics Total (N) Univariate analysis Multivariate analysis
Hazard ratio (95% CI) p-value Hazard ratio (95% CI) p-value
M Stage 256
M0 116 Reference Reference
M1 11 3.651 (1.226–10.872) 0.020 1.370 (0.393–4.775) 0.621
MX 129 1.973 (1.112–3.501) 0.020 1.544 (0.816–2.922) 0.182
N Stage 256
N0 131 Reference Reference
N1 60 2.872 (1.461–5.648) 0.002 2.818 (1.382–5.744) 0.004
NX 65 3.850 (1.971–7.517) <0.001 1.288 (0.501–3.313) 0.600
T Stage 256
T1 137 Reference Reference
T2 72 1.145 (0.559–2.345) 0.711 0.788 (0.366–1.697) 0.543
T3 21 2.687 (1.158–6.239) 0.021 2.182 (0.818–5.820) 0.119
T4 10 8.174 (3.459–19.317) <0.001 4.566 (1.432–14.558) 0.010
TX 16 3.471 (1.395–8.637) 0.007 2.194 (0.729–6.610) 0.162
Age 256 1.011 (0.990–1.032) 0.323
LASSO RiskScore 256 4.111 (2.283–7.403) <0.001 3.604 (1.815–7.156)
<0.001
[135]Open in a new tab
HR, hazard ratio, general HR > 1 indicates that the variable is a risk
factor and HR < 1 is a protective factor. A single factor p-value <0.1
was included in the analysis.
FIGURE 6.
[136]FIGURE 6
[137]Open in a new tab
Validation of Prognostic Model. (A) Forest Plot of three clinical stage
variables (T stage, N stage, M stage), Age, and LASSO RiskScore in
univariate Cox regression model. (B) Forest Plot of three clinical
stage variables (T stage, N stage, M stage), LASSO risk score
(RiskScore) in multivariate Cox regression model. (C) Nomogram of three
clinical stage variables (T stage, N stage, M stage) and LASSO
RiskScore in a single multifactor Cox regression model. (D-F) 1-year
(D), 3-year (E), and 5-year (F) calibration curves of CESC prognostic
risk model; G-I. 1-year (G), 3-year (H), and 5-year (I) decision curve
analysis (DCA) graph of cervical cancer (CESC) prognostic risk model.
TCGA, The Cancer Genome Atlas; CESC, Cervical Endocervical
Adenocarcinoma and Squamous Cell Carcinoma; LASSO, Least Absolute
Shrinkage and Selection Operator; OS, Overall Survival.
3.9 Differential expression validation and correlation analysis in high- and
low-risk groups
CESC specimens from TCGA-CESC dataset were segregated into high and
low-risk categories based on median LASSO RiskScore derived from the
prognostic risk model for CESC. To investigate differential gene
expression linked to the prognostic risk model, a comparison chart
([138]Figure 7A) displayed variations in expression of three key genes
associated with risk in both high- and low-risk CESC groups. Analysis
revealed statistically significant differential expression for genes
CA2, TNF, and IDO1 across high- and low-risk groups (p < 0.001).
Furthermore, CESC samples from the combined GEO dataset were
categorized similarly using LASSO RiskScore. Differential expression of
the same three genes in these groups was illustrated in a comparison
figure ([139]Figure 7B), indicating a highly significant variation in
IDO1 expression between high- and low-risk groups (p < 0.001), with CA2
and TNF also showing significant differences (p < 0.05). A correlation
analysis of these genes was conducted across samples from both
TCGA-CESC and combined GEO datasets, visualized in a correlation chord
diagram ([140]Figures 7C,D). The analysis demonstrated a positive
correlation between IDO1 and TNF, and CA2 and TNF, with no significant
correlation observed between IDO1 and CA2.
FIGURE 7.
[141]FIGURE 7
[142]Open in a new tab
Differential Expression Validation and Correlation Analysis. (A, B)
High risk of prognostic risk model-associated genes in cervical cancer
(CESC) samples (A) from the cervical cancer dataset (TCGA-CESC) and
cervical cancer (CESC) samples (B) from the Combined GEO Datasets
(High) Subgroup comparison graph in the High-Risk group and the
Low-Risk group. (C, D) Prognostic risk model-associated genes in
cervical cancer (CESC) samples (C) from the Cervical cancer dataset
(TCGA-CESC) and cervical cancer (CESC) samples (D) from the integrated
GEO dataset (Combined Datasets). TCGA, The Cancer Genome Atlas; CESC,
Cervical Endocervical Adenocarcinoma and Squamous Cell Carcinoma. *p <
0.05, which has statistical significance; ***p < 0.001, which is highly
statistically significant. A correlation coefficient (r value) with an
absolute value below 0.3 suggests weak or no correlation, while values
between 0.3 and 0.5 indicate a weak correlation, those between 0.5 and
0.8 suggest moderate correlation, and those above 0.8 indicate strong
correlation. Red and blue represent positive and negative correlation,
respectively. Orange and green represent the cervical cancer (CESC) and
control (Normal) groups, respectively.
3.10 Immuno-infiltration analysis of cervical cancer
The TCGA-CESC dataset served as a basis for evaluating the correlation
between 22 immune cell types and the classification of samples into
CESC and normal groups utilizing the CIBERSORT algorithm. An
immuno-infiltration histogram ([143]Supplementary Figure S2A) depicted
the proportions of immune cells, and differences in immune cell
abundance between CESC and normal groups were highlighted in a subgroup
comparison graph ([144]Supplementary Figure S2B). Significant
differences (p < 0.05) were found in 10 types of immune cells,
including plasma cells, CD4^+ T cells, resting memory CD4^+ T cells,
resting NK cells, monocytes, and various macrophage subsets among
others. Correlation strengths among these immune cells were depicted in
a heatmap ([145]Supplementary Figure S2C), showing a strong positive
correlation between resting dendritic cells and resting memory CD4^+ T
cells (r = 0.44), and a notable negative correlation between resting
memory CD4^+ T cells and macrophages M1 (r = −0.46).
Additionally, a correlation bubble map ([146]Supplementary Figure S2D)
detailed was employed to delve into the intricate relationships
existing between critical prognostic genes and immune cell
infiltration. The gene IDO1 was positively correlated with macrophage
M1 (r > 0.0, p-value < 0.05) and negatively correlated with resting
memory CD4^+ T cells (r < 0.0, p < 0.05).
3.11 Analysis of immune infiltration in high- and low-risk groups
We evaluated the immune cell infiltration in the cervical endocervical
adenocarcinoma and squamous cell carcinoma (CESC) samples from
TCGA-CESC dataset, employing the ssGSEA algorithm to quantify the
presence of 28 distinct immune cell types within high- and low-risk
CESC cohorts. Initial analyses demonstrated notable differences in the
infiltration levels of 15 distinct immune cell types between these
groups, as evidenced by a group comparison plot ([147]Supplementary
Figure S3A). This plot highlighted significant variances (p < 0.05)
across several immune cells, including activated B cells, both CD4 and
CD8 T cells (activated, central memory, and effector memory), activated
dendritic cells, eosinophils, immature cells (B cells and dendritic
cells), macrophages, myeloid-derived suppressor cells (MDSC),
monocytes, natural killer cells, natural killer T cells, and
neutrophils.
To further analyze these differences, we constructed correlation heat
maps ([148]Supplementary Figures S3B, C) to display the relationships
among the immune cells within the defined risk groups. These maps
showed predominantly strong positive correlations among the immune cell
populations in both risk categories. Additionally, correlation bubble
maps ([149]Supplementary Figures S3D, E) were used to clarify the
intricate connections between prognostic risk model genes and the
abundance of immune cells in the CESC samples, segregated by risk
group.
Moreover, to clarify the intricate connections between prognostic risk
model genes and the varying abundances of immune cells in the CESC
samples, segregated by risk category, correlation bubble maps
([150]Supplementary Figures S3D, E) were utilized.
Notably, in the high-risk group, IDO1 exhibited a substantial positive
correlation with effector memory CD8^+ T cells (r = 0.618; p < 0.05),
whereas in the low-risk group, the strongest positive correlation was
between IDO1 and activated CD4^+ T cells (r = 0.696, p < 0.05). These
findings underscore the intricate associations between immune cell
dynamics and the molecular underpinnings of risk stratification in
CESC.
4 Discussion
This study analyzed cervical cancer network drugs, focusing on
cryptotanshinone and its related genes. Data from TCGA and GEO
databases were used to identify cryptotanshinone-related gene
expression changes in cervical cancer samples. Using various
bioinformatics tools and methods, such as DESeq2 for examining
differential expression, the STRING database for protein–protein
interaction networks, and Cox regression analysis for prognostic
modeling, this study identified key genes and pathways associated with
cervical cancer. We additionally explored the prognostic significance
of these genes in cervical cancer and validated our findings through
immuno-infiltration and survival analyses.
Comparative analysis with the existing literature reveals that our
research offers unique perspectives on the anticancer effects of
cryptotanshinone on cervical cancer. While previous research has
primarily focused on the cytotoxic effects of cryptotanshinone against
various cancer cell lines, our work focuses on the underlying molecular
dynamics, uncovering specific gene expression and protein–protein
interaction networks influenced by cryptotanshinone ([151]Yen et al.,
2019). Notably, our discovery of essential genes and pathways offers a
more detailed mechanistic understanding of the anticancer activity of
cryptotanshinone and establishes a foundation for future therapeutic
strategies for cervical cancer treatment.
The mechanistic insights derived from our study highlight the
biological significance of the identified hub genes and pathways,
providing a deeper understanding of the anticancer effects of
cryptotanshinone in cervical cancer. These hub genes and pathways are
intricately involved in cellular processes, encompassing apoptosis,
cell cycle regulation, and immune response modulation ([152]Kim et al.,
2018; [153]Su et al., 2021; [154]Luo et al., 2019). The influence of
cryptotanshinone on these genes and pathways suggests a multi-targeted
approach that disrupts cancer cell proliferation and survival, while
enhancing immune surveillance against tumor cells. This comprehensive
analysis revealed the potential molecular mechanisms by which
cryptotanshinone exerts its therapeutic effects, offering promising
directions for targeted cancer therapy development.
The IL-17 signaling pathway plays a key role in immune modulation
within the tumor microenvironment, promoting inflammation, tumor cell
survival, and immune evasion (PMID: 39219271, PMID: 36053326, PMID:
35376994). In cervical cancer, IL-17 upregulation may drive immune
escape and tumor proliferation, making it a critical target in our
study. Our findings suggest that cryptotanshinone may exert anti-tumor
effects by modulating the IL-17 pathway, potentially inhibiting cancer
cell growth and metastasis through the regulation of IL-17 expression
and its downstream signals. Further exploration of these mechanisms may
clarify cryptotanshinone’s therapeutic potential in targeting IL-17 in
cervical cancer. Our findings highlight the promise of cryptotanshinone
as a new and effective therapeutic choice for treating cervical cancer.
By demonstrating the influence of cryptotanshinone on key molecular
pathways and hub genes associated with tumor progression and immune
evasion, our study suggests new approaches for more effective and
targeted treatment ([155]Shin et al., 2009; [156]Wang et al., 2020). In
addition, the identified biomarkers offer valuable prognostic tools,
potentially enabling the development of personalized medical approaches
that optimize treatment outcomes for patients with cervical cancer.
This study paves the way for future clinical trials exploring the
efficacy and safety of cryptotanshinone-based therapies in clinical
settings.
Cryptotanshinone’s demonstrated efficacy in modulating key molecular
pathways highlights its translational potential as an adjunctive or
alternative therapy for cervical cancer. Current therapeutic
approaches, primarily based on chemoradiotherapy, often face
limitations such as adverse side effects, resistance, and limited
efficacy in advanced-stage or recurrent cervical cancer. Compared to
conventional therapies, cryptotanshinone offers a multi-targeted
approach by simultaneously influencing apoptotic, proliferative, and
immune-modulatory pathways, as observed in our study. These advantages
underscore cryptotanshinone’s potential to enhance treatment outcomes
while reducing the burden of side effects. Future research should
prioritize clinical trials to validate cryptotanshinone’s safety and
efficacy in clinical settings, with particular attention to its role in
multi-drug regimens. Additionally, exploring optimal dosing strategies
and delivery mechanisms for cryptotanshinone may further establish its
position within cervical cancer treatment paradigms, potentially
improving patient outcomes and offering a valuable tool in oncologic
care.
However, certain limitations warrant further investigation. The small
sample size in the TCGA-CESC dataset, which includes only three normal
samples, may limit the generalizability of our findings. This
constraint highlights the need for validation using larger and more
diverse cohorts to enhance the robustness and applicability of the
results in broader clinical settings. Future studies should aim to
address this limitation by incorporating multi-center data or
additional high-throughput datasets. Additionally, Our bioinformatics
analysis suggests that cryptotanshinone may interact with multiple
signaling pathways related to the inhibition of cancer cell
proliferation and induction of apoptosis. However, as these conclusions
are primarily based on computational data, further experimental studies
are necessary to explore and validate the specific mechanisms of
cryptotanshinone’s action. These limitations highlight the need for
further research involving larger, more diverse cohorts, and the
application of more comprehensive analytical methods to validate and
extend our findings. Future studies should clarify the detailed
molecular mechanisms through which cryptotanshinone acts on cancer
cells, particularly by exploring its interaction with the novel targets
identified in our study. Expanding our understanding of the
pharmacodynamics of this drug and optimizing its delivery mechanisms
could significantly enhance its clinical application and pave the way
for novel targeted treatments for cervical cancer.
5 Conclusion
This report presents a comprehensive analysis of cervical cancer and
its associated genes, with a specific focus on the impact of
cryptosalvianols. Using TCGA and GEO databases, we identified DEGs in
cervical cancer and employed bioinformatic tools to analyze their roles
and prognostic value. The objective of this study was to lay the
foundation for the advancement of targeted therapeutic approaches in
the treatment of cervical cancer.
Acknowledgments