Abstract
Background
Triple negative breast cancer (TNBC) is a more aggressive subtype of
breast cancer that usually progresses rapidly, develops drug
resistance, metastasis, and relapses, and remains a challenge for
clinicians to treat. Programmed cell death (PCD), a conserved mechanism
of cell suicide controlled by various pathways, contributed to
carcinogenesis and cancer progression. Nevertheless, the prognostic
significance of PCD-related genes in TNBC remains largely unclear, and
more accurate prognostic models are urgently needed.
Methods
Gene expression profiles and clinical information of TNBC patients were
obtained from The Cancer Genome Atlas (TCGA) and Gene Expression
Omnibus (GEO) database. Least absolute shrinkage and selection operator
(LASSO) and multivariate Cox regression analysis were used to establish
the PCD-related gene signature. Kaplan-Meier plotter, receiver
operating characteristic curves, and nomogram were applied to validate
the prognostic value of the gene signature. Gene set enrichment
analysis was carried out to investigate the pathways and molecular
functions.
Results
Five PCD-related genes including SEPTIN3, SCARB1, CHML, SYNM, and
COL5A3 were identified to establish the PCD-related risk score for TNBC
patients. Patients stratified into high-risk or low-risk group showed
significantly different survival outcome, immune infiltration, and drug
susceptibility. Kaplan-Meier and receiver operating characteristic
curves showed a good performance for survival prediction in different
cohorts. Gene set enrichment analysis revealed that the five-gene
signature was associated with tumor metabolism, cancer cell
proliferation, invasion and metastasis, and tumor microenvironment.
Nomogram including the five-gene signature was established.
Conclusion
A novel five PCD-related gene signature and nomogram could be used for
prognostic prediction in TNBC. The present work might offer useful
insights in digging sensitive and effective biomarkers for TNBC
prognosis prediction and establishing accurate prognostic model in
clinical management.
Keywords: Triple negative breast cancer, Programmed cell death,
Biomarkers, Apoptosis, Prognostic model
Introduction
Breast cancer is the most common malignancy among women worldwide and a
leading cause of cancer-related death ([32]Giaquinto et al., 2022).
Triple negative breast cancer (TNBC) accounts for approximately 10–20%
of breast cancer cases and lacks expression of the estrogen receptor,
progesterone receptor, and human epidermal growth factor receptor-2.
Substantial strides in regular screening, surgical resection, adjuvant
chemotherapy, precise radiotherapy, and immune regulators have resulted
in decreasing the 5-year mortality of TNBC over the last three decades
([33]Korde et al., 2021; [34]Lee, 2023). However, TNBC is a more
aggressive subtype of breast cancer that usually progresses rapidly,
develops drug resistance, metastasis, and relapses, and remains a
challenge for clinicians to treat. These, along with the fact that TNBC
represents a heterogeneous group of diverse subtypes with own
biological and molecular characteristics ([35]Jiang et al., 2019),
highlight the need to identify more sensitive and effective prognostic
biomarkers as surrogates of clinical and pathological features
([36]Duffy et al., 2015).
Cell death includes accidental cell death and programmed cell death
(PCD). Unlike accidental cell death caused by extreme physical,
chemical, or mechanical damage, PCD is a conserved mechanism of cell
suicide that is controlled by various pathways, including apoptosis,
ferroptosis, cuproptosis, and so on ([37]Wang et al., 2024; [38]Chen et
al., 2024). Apoptosis is the most extensively studied form of PCD and
is morphologically characterized by cell shrinkage, organelles
destruction, cytoplasmic condensation, and nuclear fragmentation
([39]Saraste & Pulkki, 2000). Ferroptosis is characterized by
disordered intracellular iron ion flow and significant accumulation of
reactive oxygen species and lipid peroxide levels without the need for
caspases ([40]Stockwell et al., 2017). Cuproptosis is a new form of PCD
triggered by accumulation of intracellular copper level and aggregation
of mitochondrial lipoylated proteins ([41]Tsvetkov et al., 2022). With
the deepening of PCD research, various PCD modes have been discovered
and studied, such as autophagy, necroptosis, pyroptosis,
disulfidptosis, oxeiptosis, alkaliptosis, parthanatos, netotic cell
death, entotic cell death, and lysosome-dependent cell death ([42]Chen
et al., 2024).
Mounting evidence showed that PCD contributed to carcinogenesis and
cancer progression, and thus have attracted increasing attention in
oncology research. Studies with regard to PCD-related gene signature as
a prediction model are emerging. For example, [43]Chen et al. (2024)
screened 149 PCD-related differentially expressed genes, of which
INHBA, LRRK2, HSP90AA1, HSPB8, and EIF2AK2 were identified as the hub
genes of esophageal squamous cell carcinoma. [44]Cao et al. (2023)
constructed a 16 PCD-related gene model with potential in predicting
prognosis and response to immune checkpoint inhibitors in cancer.
[45]Dong et al. (2024) identified seven PCD-related genes to establish
the PCD-related risk score for the advanced non-small cell lung cancer
model, effectively stratifying overall survival in patients with
advanced non-small cell lung cancer. However, the implication of
PCD-related genes in TNBC is still unknown. It is therefore necessary
to elucidate the molecular significance of PCD-related genes in TNBC
and their correlations with survival outcomes and treatment efficacy.
In this study, five PCD-related genes associated with prognosis were
identified, and a risk model was constructed to assess the prognosis
and accurately stratify TNBC patients to enhance survival outcomes. The
findings of the present work might offer useful insights in digging
sensitive and effective biomarkers for TNBC prognosis prediction and
establishing accurate prognostic model in clinical management.
Materials and Methods
Data collection
The transcriptomic, clinicopathological, and survival information of
TNBC patients were obtained from The Cancer Genome Atlas (TCGA)
database. Patients with unavailable prognostic information or
incomplete gene expression data were excluded. [46]GSE58812,
[47]GSE21653, and [48]GSE135565 were downloaded from the Gene
Expression Omnibus (GEO) database and used as validation cohorts.
PCD-related genes including 580 apoptosis genes, 87 ferroptosis genes,
15 entotic cell death genes, 454 autophagy genes, nine parthanatos
genes, 14 cuproptosis genes, 52 pyroptosis genes, eight netotic cell
death genes, 220 lysosome-dependent cell death genes, five oxeiptosis
genes, and seven alkaliptosis genes were retrieved from publications
studying PCD in cancer, and a total of 1,160 PCD-related genes were
finally listed after duplication elimination, according to other
researchers’ study protocol ([49]Chen et al., 2024; [50]Cao et al.,
2023; [51]Dong et al., 2024) ([52]Table S1).
Clustering and identification of PCD-related genes
In the present study, R language was used for statistical computing and
graphics. Consensus clustering was performed by using the
ConsensusClusterPlus package in R language. The optimal cluster “K” was
obtained by using the cumulative distribution function (CDF), and
number of groups was determined according to the relative CDF delta
area plot stability as previously reported ([53]Cancer Genome Atlas
Research Network, 2017). Differentially expressed genes (DEGs) between
selected PCD clusters were identified by using the limma package in R
language. They were defined as follows: |log[2]FC| > 1.5 and false
discovery rate < 0.05.
Cell culture and PCR
TNBC cell line MDA-MB-231 and human mammary epithelial cell line
MCF-10A were purchased from Procell (Wuhan, China). MDA-MB-231 was
cultured in dulbecco’s modified eagle medium (DMEM; Gibco, CA, USA)
supplemented with 10% fetal bovine serum (Gibco, CA, USA) and 1%
penicillin-streptomycin (Gibco, CA, USA). MCF-10A was maintained in
DMEM medium supplemented with 5% horse serum, 20 ng/mL epidermal growth
factor, and 1% penicillin-streptomycin from Procell (Wuhan, China).
Both types of cells were cultured in a humidified incubator at 37 °C
with 5% CO2. Total RNA was isolated from cultured cells by using TRIzol
reagent (Invitrogen, CA, USA). Reverse transcription was performed
according to the manufacturer’s instructions (Vazyme, Jiangsu, China).
Expressions of the selected genes were further examined by using the
SYBR Green method (Vazyme, Jiangsu, China). The QuantStudioTM 5
Real-Time PCR System (Thermo Fisher, MA, USA) was employed to conduct
the data analysis. Cyclic threshold (CT) (2^−ΔΔCT) method was used to
calculate the data. All reactions, including the negative controls,
were tested in triplicate.
Construction and validation of a prognostic model based on PCD-related genes
Differentially expressed PCD-related genes were narrowed down via the
least absolute shrinkage and selection operator (LASSO) method by using
the glmnet package in R language. Risk score for each patient was
obtained according to a linear combination of expression values
(weighted by the coefficient of a multivariate Cox regression
analysis).
[MATH: RiskScore=∑i=1
nCoe
fficient
i×Expre<
/mi>ssioni :MATH]
. Coefficient i stands for the coefficient of relative prognostic
PCD-related genes in the multivariate Cox regression model, and
expression i represents the expression level of each PCD-related genes.
Based on the optimal cut-off value (−0.918997797) of the risk score,
patients were divided into high-risk group and low-risk group.
Likewise, patients in the [54]GSE58812, [55]GSE21653, and [56]GSE135565
cohorts were also stratified into high-risk group and low-risk group
for validation, followed by Kaplan-Meier analysis and receiver
operating characteristic (ROC) curve analysis.
Analysis of biological functions and pathway
Selected genes were uploaded to the GeneMANIA database to obtain
protein-protein interaction information. Kyoto Encyclopedia of Genes
and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analysis
were carried out by using the clusterProfiler package in R language.
Infiltration of immune cells in high-risk group and low-risk group was
performed by using the immunocor package in R language. Seven immune
infiltration algorithms including MCP-counter, xCell, CIBERSORT,
CIBERSORT abs.mode, EPIC, quanTIseq, and TIMER were used to evaluate
the association of five PCD-related genes and risk score with immune
infiltration. Box plots were applied to show the differences in drug
sensitivity between high-risk group and low-risk group by using the
oncoPredict package in R language.
Establishing and validating the predictive nomogram
Independent prognostic factors were obtained from univariate and
multivariate Cox regression analysis, and a predictive nomogram was
then established by using the nomoR package in R language. ROC curves
were generated by using the timeroc package in R language to check the
survival rates at 365 days, 1,095 days, and 1,825 days. Calibration
plots of nomogram were used to describe the predicted 365 days, 1,095
days, and 1,825 days survival events and the actual observed results.
Statistical analysis
All statistical analysis were conducted by using R software (version
4.1.0, [57]http://www.R-project.org/) with the selected packages
including limma (version 3.58.0), clusterProfiler (version 4.10.0),
maxstat (version 0.7–25), pROC (version 1.18.5), ConsensusClusterPlus
(version 1.68.0), glmnet (version 4.1–7), oncoPredict (version 0.2.3),
nomoR (version 1.0.3), timeroc (version 0.5.1), and survival (version
3.5–7). Student’s t test or Chi-square test was used to determine
differences between variables. Wilcoxon test was applied to compare the
proportional differences of tumor-infiltrating immune cells. Pearson
correlation analysis was employed to discern relationships between
distinct variables. Kaplan-Meier method was utilized for survival
analysis. Univariate and multivariate Cox regression analysis were
performed to obtain significant prognostic factors and their
independence. ROC curve was applied to examine the accuracy of nomogram
predictions. Statistical significance was assumed when P < 0.05.
Result
Clustering and identification of PCD-related genes
Information of 109 TNBC patients was downloaded from the TCGA database
and compared with 1160 PCD-related genes from previous publications
([58]Fig. 1). Univariate Cox regression analysis was used to screen
PCD-related genes associated with survival, and a total of 29 genes
(VDAC1, ATP6V0D2, DAPK2, ZNF385A, MILR1, SPTLC2, PLA2G15, LAMP3,
ATP6V0D1, MT1G, LIPT1, MUL1, LGALS8, HPS6, PINK1, SVIP, CREB3L1, ACKR3,
SERPINE1, LRSAM1, GPR137, BCL2A1, BCL2L10, NCK2, CTSD, CDKN1A, FZD9,
RRP8, and TPCN1) were enrolled. CDF was applied to categorize the
optimal number of clusters. When K was identified as 2, clustering
results were relatively stable and CDF delta displayed the slowest
decreasing trend, demonstrating that the differences were most
significant when TNBC samples were divided into two groups ([59]Figs.
2A–[60]2B). As shown in the present work, samples were clustered into
two groups: C1 and C2, as exhibited in the heat map ([61]Fig. 2C). In
addition, clustering consistency plots with other Ks (from 3 to 10)
samples showed lower average intra-group consistency, when compared
with the optimal number of clusters ([62]Fig. 2D). Kaplan-Meier curve
was used to analyze the survival difference between two groups
([63]Fig. 2E). Subgroup C1 had remarkable better prognosis with respect
to subgroup C2, indicating that survival difference could be related to
the differentially expressed PCD-related genes between C1 and C2.
Therefore, the volcano map and heat map of the differentially expressed
PCD-related genes between C1 and C2 were drawn by using the limma
package in R language ([64]Figs. 2F–[65]2G).
Figure 1. Overview of the research process.
[66]Figure 1
[67]Open in a new tab
Figure 2. Clustering and identification of PCD-related genes.
[68]Figure 2
[69]Open in a new tab
(A) Relative change in area under CDF curve. (B) CDF curves of Ks from
2 to 10. (C) Heat map showing sample clustering results, with consensus
K identified as 2. (D) Clustering consistency plots for Ks from 2 to
10. (E) Kaplan—Meier curves for cluster C1 and C2. (F) Volcano map of
the differentially expressed PCD-related genes between C1 and C2.
Significantly up-regulated or down-regulated genes are respectively
shown in red or green. (G) Heat map of the differentially expressed
PCD-related genes between C1 and C2.
Construction of PCD-related prognostic model for TNBC patients
According to the preliminarily obtained gene dataset, a prognostic
model was constructed via LASSO analysis by using the glmnet package in
R language. After picking the optimal penalty parameter, λ associated
with the minimum five fold cross-validation, a total of five
PCD-related genes, namely, SEPTIN3, SCARB1, CHML, SYNM, and COL5A3,
were then chosen as candidate genes ([70]Figs. 3A, [71]3B). Survival
information of these genes were evaluated by using the survival package
in R language. In particular, SEPTIN3, SCARB1, CHML, and SYNM were
associated with better prognosis; whereas COL5A3 was responsible for
poor prognosis ([72]Fig. 3C). Based on the LASSO analysis and
multivariate Cox regression analysis, these five PCD-related genes were
finally used for prognostic model construction. Risk score for each
patient was calculated as follows: risk score = −0.0799972791070521 *
SCARB1 + 0.196866693004799 * COL5A3 − 0.153621818676914 * CHML −
0.141523874769071 * SEPTIN3 − 0.0623684500004447 * SYNM. Then, 109 TNBC
patients in the TCGA cohort were stratified into high-risk group and
low-risk group, according to the optimal cut-off value (−0.918997797)
of risk score calculated by using the maxstat package in R language.
When compared with the patients in the low-risk group, patients in the
high-risk group showed a significantly poor survival probability
([73]Fig. 3D). By using the pROC package in R language, time-dependent
ROC analysis was carried out to further evaluate the prediction
efficiency of the constructed gene signature, with the areas under
curve (AUC) of 365 days, 1,095 days, and 1,825 days being 0.90, 0.89,
and 0.89 ([74]Fig. 3E), respectively. Relationship among risk score,
gene expression, and survival status was also checked. Increased
expressions of SCARB1, SEPTIN3, CHML, and SYNM were observed in
patients from low-risk group, and elevated level of COL5A3 was found in
patients from high-risk group ([75]Figs. 3F–[76]3G), indicating that
SCARB1, SEPTIN3, CHML, and SYNM were protective factors and COL5A3 was
a risk factor.
Figure 3. Construction of PCD-related prognostic model for TNBC patients.
[77]Figure 3
[78]Open in a new tab
(A) LASSO regression analysis of five PCD-related genes associated with
prognosis. (B) Optimal penalty parameter λ identified by five fold
cross-validation. (C) Prognostic values of SEPTIN3, SCARB1, CHML,
COL5A3, and SYNM in TNBC were analyzed. (D) Kaplan—Meier curves showing
the overall survival of patients in high-risk group and low-risk group.
(E) ROC curves of the five-gene signature prediction model. (F)
Correlations among risk score, heat map of gene expression, and
survival status of TNBC patients. (G) E xpression s of five PCD-related
genes between high—risk group and low—risk group.
Validation of PCD-related prognostic model for TNBC patients
To solidify the accuracy of PCD-related prognostic model, the
[79]GSE58812, [80]GSE21653, and [81]GSE135565 were used as validation
cohorts. Patients from these datasets were therefore divided into
high-risk group and low-risk group, based on the optimal cut-off value
of calculated risk score as described above. Correlations among risk
score, gene expression, and survival status were also evaluated. As
shown in the Kaplan-Meier curves, patients in the high-risk group had a
significantly worse overall survival, with respect to patients in
low-risk group ([82]Fig. 4). Moreover, patients in the low-risk group
expressed increased levels of SCARB1, SEPTIN3, CHML, and SYNM; whereas
patients in the high-risk group displayed elevated levels of COL5A3
([83]Fig. 4). The results collectively showed that the constructed
PCD-related prognostic model was accurate for survival outcome
prediction in TNBC patients.
Figure 4. External validation of the PCD-related prognostic model.
[84]Figure 4
[85]Open in a new tab
Kaplan—Meier curve s show ing the overall survival of TNBC patients in
high-risk group and low-risk group in [86]GSE58812 (A), [87]GSE21653
(C) and [88]GSE135565 (E). Correlations among risk score, heat map of
gene expression, and survival status of TNBC patients in [89]GSE58812
(B), [90]GSE21653 (D) and [91]GSE135565 (F).
Elucidation of pathways and molecular functions in the TCGA cohort
To better understand the differences in signaling pathways and
molecular functions between the high-risk group and the low-risk group,
gene set enrichment analysis was carried out. Volcano map of the
differentially expressed genes between the high-risk group and the
low-risk group were drawn by using the limma package in R language
([92]Fig. 5A). KEGG analysis indicated that many metabolism-related
molecular pathways were enriched, including protein and fat digestion
and absorption, glycerolipid, glycerophospholipid, and arachidonic acid
metabolism ([93]Fig. 5B). Pathways related to tumor cell invasion and
metastasis, such as the extracellular matrix (ECM)-receptor
interaction, and relaxin signaling pathway, were also significantly
enriched. GO analysis of biological processes, cell components, and
molecular functions showed that the differentially expressed genes
between the high-risk group and the low-risk group were mainly involved
in cell proliferation, extracellular matrix, protein binding, and
collage binding ([94]Figs. 5C–[95]5E). Interestingly, several cell
components responsible for intercellular communication were also
detected, such as extracellular region, exosomes, and extracellular
vesicles. GeneMANIA database was applied to predict the genes with
similar functions to these five genes. A total of 20 genes were
selected, and function analysis indicated that they took part in
cholesterol transport, collagen, and metabolism process ([96]Fig. 5F).
Gene set enrichment analysis demonstrated that the high-risk group was
significantly enriched in coagulation, angiogenesis, P53 pathway,
apoptosis, and epithelial-mesenchymal transition ([97]Fig. S1).
Collectively, the above results showed that the differences between the
high-risk group and the low-risk group classified by the PCD-related
prognostic model were mainly associated with tumor metabolism, cancer
cell proliferation, invasion and metastasis, and tumor
microenvironment.
Figure 5. Elucidation of pathways and molecular functions.
[98]Figure 5
[99]Open in a new tab
(A) Volcano map of the differentially expressed genes between high-risk
group and low-risk group. (B) KEGG analysis showing many
metabolism-related pathways. GO analysis showing enrichment of
biological processes (C), cell components (D), and molecular functions
(E). (F) Prediction and analysis of genes that were functionally
similar to these five genes by using GeneMANIA database.
Immune infiltration, and drug susceptibility analysis
Immune cells play a significant role in oncogenesis, progression, and
prognosis of TNBC. CIBERSORT algorithms were further performed to
explore the immune cell landscape of TNBC patients in the TCGA cohort.
Analysis of immune cell infiltration patterns revealed substantial
variations in the distribution of 22 distinct immune cell types in the
high-risk group and the low-risk group ([100]Fig. 6A). Such variations
in the proportions of tumor-infiltrating immune cells might represent
an intrinsic feature that could characterize individual differences.
Moreover, the proportions of different subpopulations of
tumor-infiltrating immune cells were weakly to moderately correlated
([101]Fig. 6B). Seven immune infiltration algorithms were used to
evaluate the association of five PCD-related genes and risk score with
immune infiltration. The results indicated that risk score was
positively correlated with immune infiltration; whereas COL5A3 and CHML
were negatively correlated with immune infiltration ([102]Figs. 6C,
[103]6D). Box plots were applied to show the differences in drug
sensitivity between the high-risk group and the low-risk group by using
the oncoPredict package in R language. Low-risk group was more
sensitive to most chemotherapy drugs, with respect to high-risk group,
suggesting a potential factor for better prognosis in the low-risk
group ([104]Fig. 7A). Remarkably, the high-risk group was relatively
sensitive to ABT737, Bl2536, and Daporinad, when compared to the
low-risk group ([105]Fig. 7B). This would provide a new way to study
more effective chemotherapy regimen for high-risk TNBC patients.
Figure 6. Immune infiltration, and drug susceptibility analysis of the
five-gene signature.
[106]Figure 6
[107]Open in a new tab
(A) Bar graph of immune cell infiltration showing distribution of 22
distinct immune cell types in high-risk group and low-risk group. (B)
Immune cell correlation matrix. Correlation between SCARB1, COL5A3,
CHML, SEPTIN3, SYNM (C), risk score (D) and immune infiltration in TNBC
patients.
Figure 7. Drug sensitivity in TNBC.
[108]Figure 7
[109]Open in a new tab
Box plot show ing differences in chemotherapy sensitivity between
high-risk group and low-risk group.
Expression levels of PCD-related genes involved in prognostic model
Expression levels of the five PCD-related genes in the TNBC cell line
and the mammary epithelial cell line were analyzed by PCR.
Specifically, SCARB1, SEPTIN3, CHML, and SYNM were down-regulated,
while COL5A3 was significantly upregulated in MDA-MB-231 compared with
that in MCF-10A ([110]Fig. 8). These results, along with the
bioinformatics analysis data, demonstrated that the prognostic model is
meaningful.
Figure 8. Expression levels of the selected PCD-related genes.
[111]Figure 8
[112]Open in a new tab
Expression levels of SCARB1 (A), SEPTIN3 (B), CHML (C), COL5A3 (D), and
SYNM (E). *P < 0.05; **P < 0.01; ***P < 0.001.
Construction of a nomogram
Univariate Cox regression analysis indicated that overall survival was
significantly correlated with risk score, T, N, and stage in the TCGA
cohort ([113]Fig. 9A), and multivariate Cox regression analysis
revealed that risk score and N stage were the independent prognostic
factors ([114]Fig. 9B). To build a useful predictive method, a
prognostic nomogram that integrated independent prognostic factors,
namely, PCD-related risk score and N stage, was generated ([115]Fig.
9C). Based on the time-dependent ROC curves, the AUCs of nomogram
achieved 0.99, 0.85, and 0.87 at 365 days, 1,095 days, and 1,825 days,
respectively ([116]Fig. 9D). Calibration curves showed the prediction
value of the nomogram and demonstrated high accuracy of the predicted
survival ([117]Fig. 9E). These results demonstrated that the nomogram
could be a promising method for predicting overall survival in TNBC
patients.
Figure 9. Construction of a nomogram for TNBC patients based on the
PCD-related risk score and clinical features.
[118]Figure 9
[119]Open in a new tab
(A) Forrest plot of the univariate Cox regression analysis. (B) Forrest
plot of the multivariate Cox regression analysis. (C) Nomogram for
predicting 365 days, 1,095 days, and 1,825 days overall survival of
TNBC patients. (D) ROC curves for 365 days, 1,095 days, and 1,825 days
overall survival of the nomogram. (E) Calibration curves for predicting
365 days, 1,095 days, and 1,825 days overall survival of TNBC patients.
Discussion
Breast cancer is the most common malignancy among women worldwide, and
the subtype of the particular note is TNBC, which lacks expression of
estrogen receptor, progesterone receptor, and human epidermal growth
factor receptor-2. Both clinical and pathological features have been
widely used to predict therapeutic response and long-term results.
Since TNBC represents a heterogeneous group of diverse subtypes with
its own biological and molecular characteristics ([120]Jiang et al.,
2019), identification of novel prognostic biomarkers and establishment
of more accurate prognostic models remain imperative and warranted in
TNBC research ([121]Duffy et al., 2015).
PCD is a common gene regulated cell death mode in multicellular
organisms. With the deepening of PCD research, various PCD modes are
being discovered and studied ([122]Wang et al., 2024; [123]Chen et al.,
2024). Recently, signatures based on the differentially expressed
PCD-related genes have gained much attention and shown great potential
in prognosis prediction of cancer ([124]Chen et al., 2024; [125]Cao et
al., 2023; [126]Dong et al., 2024; [127]Cancer Genome Atlas Research
Network, 2017; [128]Cheng et al., 2023; [129]Sha et al., 2022; [130]Liu
et al., 2023). [131]Chen et al. (2024) screened 149 PCD-related
differentially expressed genes, of which INHBA, LRRK2, HSP90AA1, HSPB8,
and EIF2AK2 were identified as the hub genes of esophageal squamous
cell carcinoma. [132]Cao et al. (2023) constructed a 16 PCD-related
gene model with potential in predicting prognosis and response to
immune checkpoint inhibitors in cancer. [133]Dong et al. (2024)
identified seven PCD-related genes to establish the PCD-related risk
score for the advanced non-small cell lung cancer model, effectively
stratifying overall survival in patients with advanced non-small cell
lung cancer. In the present work, a novel five-gene signature including
SEPTIN3, SCARB1, CHML, SYNM, and COL5A3 was identified from a number of
PCD-related genes and applied for prognosis prediction in TNBC
patients. Specifically, SEPTIN3, SCARB1, CHML, and SYNM were associated
with better prognosis; whereas COL5A3 was responsible for poor outcome.
Risk score based on the five-gene signature was an independent
prognostic factor of TNBC and patients in high-risk group showed a
significantly worse overall survival, with respect to patients in the
low-risk group. Results from survival curves and AUCs values indicated
that the constructed PCD-related prognostic model was accurate for
survival prediction in TNBC patients not only from the TCGA cohort but
also from the [134]GSE58812, [135]GSE21653, and [136]GSE135565 cohorts.
A prognostic nomogram that integrated PCD-related risk score and
clinical N stage was therefore generated. All these results
demonstrated a good performance of PCD-related prognostic model and the
prediction model could be a promising indicator for TNBC survival.
Gene SCARB1 encodes the scavenger receptor class B type I (SR-BI)
glycoprotein and regulates cholesterol exchange between cells and
high-density lipoproteins ([137]Gutierrez-Pajares et al., 2016).
Mounting studies have reported the significant role of SCARB1 gene and
SR-BI protein in cancer proliferation and progression. In the context
of breast cancer, over-expression of SR-BI could increase high-density
lipoproteins-mediated proliferation of breast cancer cells via
PI3K/AP-1 pathway ([138]Cao et al., 2004). Down-regulation of SR-BI in
breast cancer cells was associated with decreased cellular cholesterol
content and reduced tumor aggressiveness ([139]Danilo et al., 2013).
COL5A3, a member of the collagen triple helical repeat family, takes
part in cell growth and migration. [140]Amrutkar et al. (2019)
confirmed that COL5A3 promoted chemoresistance of pancreatic cancer
cells to gemcitabine treatment. COL5A3 has also been shown to
participate in breast cancer brain metastasis, assess the infiltration
of cancer-associated fibroblasts, and predict immune and chemotherapy
responses ([141]Zhang et al., 2021; [142]Song et al., 2024). CHML, also
known as Rab escort protein 2, is one of the key factors for Rab
proteins prenylation. Researchers have found that CHML was related to
the development of urothelial carcinoma, multiple myeloma,
hepatocellular carcinoma, and lung cancer ([143]Li et al., 2008;
[144]Zhang et al., 2019; [145]Chen et al., 2019; [146]Dong et al.,
2021). CHML could promote proliferation, inhibit apoptosis, and induce
metastasis of tumor cells, and high expression of CHML is associated
with poor survival ([147]Li et al., 2008; [148]Zhang et al., 2019;
[149]Chen et al., 2019; [150]Dong et al., 2021). SEPTIN3, a member of
the septin family primarily expressed in brain and testis, is a
membrane-bound presynaptic protein connected to autophagy ([151]Rosa et
al., 2020). [152]Wang, Yang & Gao (2023) have found that SEPTIN3 was
overexpressed in TNBC and was related to poor prognosis ([153]Yang,
Wang & Gao, 2024). Moreover, SEPTIN3 was observed to favor cell growth
and oncogenesis. SEPTIN3 promoted TNBC cell aggressiveness and
proliferation via activation of Wnt signaling pathway ([154]Wang, Yang
& Gao, 2023). SYNM is a type IV intermediate filament that has been
reported to modulate cell adhesion and motility. Aberrant promoter
methylation of gene SYNM was associated with lymph node metastases and
advanced tumor grade ([155]Noetzel et al., 2010). Besides, SYNM was
positively correlated with CD8 T cells and monocytes, but was
negatively correlated with γδ T cells and M1 macrophages, suggesting
that SYNM could affect breast cancer cells by modulating
immune-infiltrating cells ([156]Bao & He, 2022). Given that only a few
publications report the roles of these genes on TNBC are currently
available, more thorough researches are now needed to clarify the
significance and mechanism of these five genes.
Besides resisting cell death and metastasis of malignant cells, tumor
metabolism and immune microenvironment are two hallmarks of cancer
([157]Hanahan, 2022). In the present study, KEGG pathway and GO
enrichment analysis were carried out by evaluating the differentially
expressed genes between high-risk and low-risk groups classified by the
five-gene signature. Several metabolism-related pathways including
protein and fat digestion and absorption, glycerolipid,
glycerophospholipid, and arachidonic acid metabolism were significantly
enriched. Moreover, immune infiltration analysis revealed substantial
variations in the distribution of 22 distinct immune cell types in the
high-risk group and the low-risk group. These gene enrichment analyses
collectively suggested that PCD might be closely related to tumor
metabolism and immune microenvironment of TNBC.
To the best of our knowledge, the five PCD-related gene signature and
nomogram have not been reported previously and could be a promising
prognostic model for TNBC patients. In particular, all these five
genes, namely, SEPTIN3, SCARB1, CHML, SYNM, and COL5A3, were first
applied in the prognosis prediction model for TNBC. The constructed
nomogram combining the present PCD-related gene signature with
patient’s N stage showed improved performance in predicting overall
survival, and were relatively higher than some other published
nomograms for TNBC prediction ([158]Cheng et al., 2023; [159]Sha et
al., 2022). Our study has several limitations. First, the five
PCD-related gene signature and prognostic model was established by
analyzing retrospective data from public databases, leading to
inevitable bias. Second, few publications reporting the functions and
mechanisms of these genes on TNBC are currently available. Third, TNBC
usually progresses rapidly, develops metastasis and relapses, so
disease-free survival, progression-free survival, and short-term
survival should be included as well. Fourth, genomic and transcriptomic
landscape of TNBC were quite different ([160]Jiang et al., 2019). Given
that TNBC subtypes were not classified in the selected TCGA and GEO
database, we are unable to classify the transcriptomic,
clinicopathological, and survival information of TNBC patients in the
present study. Fifth, the external validity of the prognostic model and
its applicability to diverse clinical settings are not evaluated, and
taking the aforementioned limitations together suggest that our
findings need to be validated with prospective large cohort studies,
and more thorough cellular research should be performed in this field.
Conclusion
In summary, this study established a new five-gene prognostic model and
nomogram to predict overall survival of TNBC patients, which can not
only be applied for patients management, but also provide a new
direction for exploring therapeutic targets of TNBC.
Supplemental Information
Supplemental Information 1. 1160 PCD-related genes.
[161]peerj-13-19359-s001.xlsx^ (31.9KB, xlsx)
DOI: 10.7717/peerj.19359/supp-1
Supplemental Information 2. Supplementary MIQE checklist.
[162]peerj-13-19359-s002.xls^ (33.5KB, xls)
DOI: 10.7717/peerj.19359/supp-2
Supplemental Information 3. Raw PCR data.
[163]peerj-13-19359-s003.xls^ (1.7MB, xls)
DOI: 10.7717/peerj.19359/supp-3
Supplemental Information 4. Gene set enrichment analysis showing
different pathways.
[164]peerj-13-19359-s004.pdf^ (959.1KB, pdf)
DOI: 10.7717/peerj.19359/supp-4
Funding Statement
This present work was supported by grants from the Excellent
Post-doctoral Program of Jiangsu Province (2022ZB820), the Changzhou
Science and Technology Program (ZD202225), the Top Talent of Changzhou
“The 14th Five-Year Plan” High-Level Health Talents Training Project
(2022CZBJ065), the Post-doctoral Foundation of China (2022M720543 and
2019M661677), the Post-doctoral Foundation of Jiangsu Province
(2019K161). The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Additional Information and Declarations
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Quanfeng Shao conceived and designed the experiments, performed the
experiments, analyzed the data, prepared figures and/or tables,
authored or reviewed drafts of the article, and approved the final
draft.
Hai-yan Gao conceived and designed the experiments, analyzed the data,
prepared figures and/or tables, and approved the final draft.
Zi-ying Wang performed the experiments, prepared figures and/or tables,
authored or reviewed drafts of the article, and approved the final
draft.
Yu-ling Qian performed the experiments, authored or reviewed drafts of
the article, and approved the final draft.
Wei-xian Chen conceived and designed the experiments, analyzed the
data, authored or reviewed drafts of the article, and approved the
final draft.
Data Availability
The following information was supplied regarding data availability:
Raw data is available in the [165]Supplemental Files.
References