Introduction
Cancer is the second most lethal disease worldwide,
with colorectal cancer (CRC) being the third most common and deadly
cancer in western countries (1).
Late-stage metastasis is the main cause of death among patients
with CRC. CRC demonstrates significant heterogeneity both within
and between tumors. Despite the numerous types and subtypes of
colon cancer, once distant metastasis occurs, the long-term
survival prospects for patients are very limited. Effective
treatment options for late-stage colon cancer remain scarce. While
immune checkpoint inhibitor (ICI) can induce remission in CRC with
high microsatellite instability (MSI) (2), there are certain limitations,
including individual variations in treatment sensitivity and the
potential for post-treatment drug resistance. Therefore, accurately
predicting the prognosis of patients with CRC is essential for
effective treatment and clinical management of this disease.
Programmed cell death (PCD) is an evolutionarily
conserved process of cellular suicide that is controlled by the
interplay of multiple genes and signaling pathways. PCD encompasses
numerous biological processes of cell death, including apoptosis,
pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis,
parthanatos, entotic cell death, netotic cell death,
lysosome-dependent cell death, alkaliptosis, and oxeiptosis. A
recent study has demonstrated that several types of atypical PCD,
including ferroptosis, pyroptosis, necroptosis, parthanatos and
oxeiptosis, are closely associated with the occurrence and
development of cancers such as colon cancer (3). PCD can have a dual role in tumor
development, either promoting or inhibiting tumor growth depending
on the cellular contents released during the process. Additionally,
PCD is intricately linked to the immune system, as it can regulate
the effect or enrichment of immune cells, thereby participating in
the fine-tuning of anti-tumor immunity within the tumor
microenvironment (TME). Liu et al (4) reported that apoptosis, necroptosis,
pyroptosis, ferroptosis, and autophagy play crucial roles in
regulating the immunosuppressive TME and influencing clinical
outcomes of cancer treatments. Wu et al (5) elucidated the impact of cytokines
produced by inflammasomes and pyroptosis on the breast cancer TME,
and improved immunotherapy efficiency for breast cancer based on
insights from pyroptosis. Some researchers have constructed risk
models using gene combinations associated with PCD to predict the
efficacy of immunotherapy for lung squamous cell carcinoma
(6). These findings highlight the
significant interaction between tumor immunity and PCD.
In the present study, the crosstalk between cellular
immunity and PCD was explored. The objective was to develop a risk
prediction model for CRC based on the effects of both cellular
immunity and PCD. This model aimed to assess patient prognosis,
enhance risk stratification, and improve clinical management of
patients with CRC. The critical relationship between cellular
immunity and PCD was uncovered, identifying key genes that
influence patient prognosis, and providing novel therapeutic
targets for patients with CRC.
Materials and methods
Data acquisition
The gene expression data and related clinical
information for The Cancer Genome Atlas (TCGA)-colon adenocarcinoma
(COAD) were sourced from TCGA database (https://portal.gdc.cancer.gov/). Immune-related genes
(IRGs) were downloaded from the IMMPORT (https://www.immport.org/home) database, while
PCD-related genes (PRGs) were referenced from the recent study by
Zou et al (7). Genetic
mutation data were obtained from the Genomic Data Commons (GDC;
https://portal.gdc.cancer.gov/) database
and analyzed using the ‘maftools’ package. Transcriptome datasets
GSE17538, GSE29621, GSE39582, GSE44076 and GSE74602 were retrieved
from the Gene Expression Omnibus (GEO) database. Additionally,
GSE161277 was downloaded for single-cell RNA sequencing (scRNA-seq)
data. Due to the small sample sizes of GSE39582, GSE44076 and
GSE74602, they were merged and normalized into the GEO-Meta dataset
for model validation.
Computational model for evaluating the
levels of PCD and immunity in CRC
Zhou et al (8) developed the apoptosis index and
epithelial-mesenchymal transition (EMT) index to assess the
prognosis of COAD. Based on their study, the Immune Potential Index
(IPI) and PCD potential index (PPI) were introduced in the present
study. Using the TCGA-COAD dataset, immune and PCD genes were
utilized as independent variables, with overall survival (OS) as
the target variable, to identify significant genes associated with
prognosis through univariate Cox regression analysis. In the
TCGA-COAD dataset, differentially expressed genes (DEGs) between
tumors and normal tissues were first identified. These DEGs were
then combined with significant genes from the univariate Cox
regression analysis to pinpoint IRGs and PRGs. At the same time,
these genes were intersected with those from the GSE17538,
GSE29621, GSE39582, GSE44076 and GSE74602 datasets to identify IRGs
and PRGs that present across all datasets.
Principal component analysis (PCA) was employed to
quantify the levels of immunity and PCD in each tumor tissue, with
main components 1 and 2 serving as the primary dimensions.
Referring to previous research (9), the IPI and PPI were defined
respectively, where the formula for calculating IPI or PPI is ∑
(|PC1i| + |PC2i|), with i representing the gene expression matrix
of IRGs or PRGs. Patients in the TCGA-COAD and GSE17538 datasets
were divided into two molecular subtypes based on the median values
of IPI: IPI_high and IPI_low, while they are also divided into
another two molecular subtypes based on the median values of PPI:
PPI_high and PPI_low. Combing IPI and PPI, patients were divided
into four molecular subtypes: IPI_high + PPI_high, IPI_high +
PPI_low, IPI_low + PPI_high, and IPI_low + PPI_low. Kaplan-Meier
(K-M) curve analysis was used to compare OS differences among these
four molecular subtypes.
Crosstalk of IRG and PRG and
development of risk scoring model
Based on the merged expression matrix of IRGs and
PRGs, clustering analysis on the GSE17538 and TCGA-COAD datasets
was conducted using the R package ‘ConsensusClusterPlus’. The
association between the identified clustering categories and OS was
then investigated.
To develop a high-precision and stable risk
assessment model, the study of Liu et al (10) was referenced, and a combination of
101 algorithms was constructed by integrating 10 machine learning
algorithms. The machine learning algorithms include random survival
forest, elastic network, least absolute shrinkage and selection
operator, Ridge, stepwise Cox, CoxBoost, partial least squares
regression for Cox, supervised principal components, generalized
boosted regression modeling and survival support vector machine.
Details of the hyperparameters used in the machine learning models
are provided in Table SI. A
leave-one-out cross-validation framework was employed to fit these
101 prediction models, using a merged gene set of IRGs and PRGs as
independent variables, with the TCGA-COAD dataset serving as the
training set. At the same time, the consistency index (C-index) for
each model was calculated across all validation datasets (GSE17538
and GSE29621). The model with the highest weighted average C-index
in the validation set was selected as the best model. The code for
model training and validation has been uploaded to https://github.com/wuqiumei/101-ML.git.
Using this optimal model, a risk score was
calculated for each sample, referred to as ‘Score’. Samples from
different datasets were divided into high and low groups based on
the median value of Score. The value of Score was validated in the
GSE17538, GSE29621 and TCGA-COAD datasets. Based on the TCGA-COAD
dataset, a nomogram integrating the Score with other clinical
factors was constructed to evaluate its predictive ability for
1-year, 3-year and 5-year survival rates. Additionally, the Score
was explored in relation to various aspects such as TME, immune
markers and genetic mutations based on the TCGA dataset.
Tumor immune-related analysis
The ‘estimate’ R package is used to calculate the
Immune Score and Stromal Score of patients. The ‘xCell’ R package
is used to assess the infiltration of various immune cell types in
both groups with high and low scores. Additionally, Bagaev et
al (11) proposed 29
functional gene expression signatures (Fges) to represent the
cellular and functional attributes of the TME.
Investigation of immunotherapy
response
Based on the TCGA-COAD dataset, the Tumor Immune
Dysfunction and Exclusion (TIDE) tool (http://tide.dfci.harvard.edu/login/) was employed to
assess the potential individual response to ICI treatment (12). Individual scores such as Exclusion
score, myeloid-derived suppressor cells' (MDSCs) score,
tumor-associated M2 macrophages' (TAM) score and cancer-associated
fibroblasts' (CAFs) score were also obtained.
Investigation of genes included in the
model
Based on the TCGA dataset, the differential
expression of genes included in the model between tumors and normal
tissues was investigated. Underlying reasons for these differential
expression levels from the perspectives of genetic mutations, copy
number variations (CNV) and methylation were explored.
Additionally, the expression association of the included genes with
tumor immune genes was explored from multiple aspects such as
chemokines, immune inhibitors, Major Histocompatibility Complex
(MHC), immune stimulants and receptor-related genes.
Single-cell analysis
The single-cell sequencing dataset GSE161277 was
retrieved from the GEO database. Low-quality cells were excluded
based on the criteria of nFeature_RNA >500, nFeature_RNA
<10,000, and percent.mt <25. The ‘harmony’ package was used
to preprocess and integrate four single-cell samples. The
CellCycleScoring function was used to calculate the cell cycle
scores, and the influence of the cell cycle on clustering was
subsequently removed. The FindVariableFeatures function in the
‘Seurat’ R package was used to identify 2,000 highly variable genes
for subsequent dimension reduction based on PCA. According to
principal components 1 to 20, the t-distributed stochastic neighbor
embedding method was employed to visualize visualizing single-cell
clustering. Subsequently, the ‘singleR’ R package was used to
annotate cells based on different markers on the cell surface.
Specimen collection and LTB4R
expression detection
Patients who received treatment for CRC in the
Department of Gastrointestinal Surgery at Zhanjiang Central
People's Hospital from November 2021 to November 2022 were selected
as research subjects. The patients ranged in age from 34 to 78
years, with male and female patient percentages of 72.5 and 27.5%,
respectively. Inclusion criteria were as follows: i) Age ≥18 years;
ii) Confirmed diagnosis of CRC through pathological examination,
without other tissue-originated malignant tumors; iii) no
peripheral organ metastasis before surgery and underwent radical
tumor surgery with 1-year follow-up. Patients with clear peripheral
organ metastasis or recurrence within 1 year of follow-up, as
evidenced by imaging or pathology, were classified into the
transfer group. Otherwise, they were classified into the
non-transfer group. From the transfer group, 10 patients were
randomly selected and matched with 10 patients from the
non-metastasis group based on propensity scores considering factors
such as sex, age, body mass index and tumor staging. The operation
process of propensity score matching was completed using SPSS 21.0
software (IBM Corp.). CRC specimens from these two groups were
obtained retrospectively from the specimen library for
immunohistochemistry (IHC) and western blotting to assess LTB4R
expression. The present study adhered to the Helsinki Declaration
and was approved by the Institutional Review Committee of Zhanjiang
Central People's Hospital (approval no. 2023-002-013; Zhanjiang,
China). Written informed consent was provided by all
participants.
IHC
CRC tissue samples were collected, including 10
cases from the transfer group and 10 matched cases from the
non-transfer group. The tissue was typically embedded in paraffin
and sectioned (4-µm thick). Deparaffinization was performed using
xylene, followed by rehydration with a gradient of alcohols. The
sections were then placed in TRIS-EDTA antigen retrieval solution,
covered, and microwaved for 10 min on high power and 5 min on
medium power. After stopping the heating, the sections were cooled
to room temperature and washed several times with distilled water.
Endogenous peroxidase activity was blocked using 3%
H2O2 at room temperature for 10 min. The
sections were incubated overnight at 4˚C with a primary antibody
against LTB4R (1:25; cat. no. R30118; Zen-Bio, Inc.). The next day,
the sections were rewarmed at room temperature for 45 min, washed
three times with PBS, and incubated with a biotinylated secondary
antibody. The staining was visualized using DAB, followed by
counterstaining with hematoxylin and differentiation with
hydrochloric acid alcohol. Finally, the sections were dehydrated in
a gradient of ethanol, cleared in xylene, and mounted for
microscopic examination.
Western blot analysis
Total protein was extracted from CRC tissue. Protein
concentration was determined using the BCA method, and the samples
were stored at -80˚C. A total of, 20 µg proteins were separated by
10% SDS-PAGE gel electrophoresis and transferred onto a PVDF
membrane using a constant current of 300 mA. The membrane was
blocked with a blocking solution at room temperature for 1 h. The
blocking solution was prepared by dissolving non-fat milk in TBST
(containing 0.05% Tween-20) to a final concentration of 5%. The
membrane was then incubated overnight at 4˚C with diluted primary
antibodies: LTB4R (1:1,000) and GAPDH (1:50,000; cat. no.
60004-1-Ig; Proteintech Group, Inc.). After washing with TBST, the
membrane was incubated with an HRP-conjugated secondary antibody
(1:50,000; cat. no. BL003A; Biosharp Life Sciences) at room
temperature for 1-2 h. Following additional washes,
chemiluminescent detection was performed using a chemiluminescence
substrate (cat. no. BL520A; Biosharp Life Sciences). Protein band
intensities were analyzed using ImageJ software (version: 1.8.0;
National institutes of Health), with GAPDH as the internal
reference for comparing protein expression differences between
different groups.
Statistical analysis
The Wilcoxon test was used to compare differences
between continuous variables. while the chi-square test was
employed for inter-group comparisons of categorical variables. The
log rank test was utilized to analyze inter-group differences in
survival rates. Depending on the data characteristics, Pearson
correlation coefficient or Spearman correlation coefficient was
used for correlation analysis. P<0.05 was considered to indicate
a statistically significant difference (*P<0.05;
**P<0.01; ***P<0.001;
****P<0.0001).
Results
Identifying the differentially
expressed IRGs and PRGs associated with OS
A total of 2,483 IRGs were downloaded from the
IMMPORT database (Table SII), and
referred to the research by Zou et al (7) to obtain 1277 genes related to 12
types of PCD (Table SIII). Using
the TCGA-COAD dataset, univariate Cox analysis was performed with
OS time as the target variable and 135 immune genes (Table SIV) and 102 PCD genes (Table SV) that are related to prognosis
were identified. The final IRGs and PRGs were obtained by selecting
genes that exist in all datasets including GSE17538, GSE29621,
GSE39582, GSE44076 and GSE74602.
Based on the TCGA-COAD dataset, the heatmaps of DEGs
were generated for IRGs and PRGs between tumor tissues and adjacent
normal tissues (Fig. 1A and
B). Similarly, heatmaps of DEGs
between tumors and adjacent tissues were obtained from the GEO-Meta
dataset (Fig. 1C and D). After combining the gene expression
matrix for IRGs and PRGs, consensus clustering analysis on the
GSE17538 dataset was performed (Fig.
1G). The results revealed significant differences in OS rates
among different clusters (Fig.
1H). The same analysis conducted on the TCGA-COAD dataset
(Fig. 1E and F) also demonstrated significant
differences in OS among different clusters (P<0.05). These
findings suggested that the gene set comprising IRGs and PRGs can
effectively distinguish the overall prognosis of colon cancer to a
certain extent.
Construction of IPI and PPI
To further investigate the crosstalk between IRGs
and PRGs, PCA was performed to quantify the immune level and PCD
level in each tumor tissue, which are defined as IPI and PPI,
respectively. The distributions of IPI and PPI are shown in
Fig. 2A and B; the correlation analysis between IPI
and PPI is presented in Fig. 2C.
Survival analysis of the TCGA-COAD dataset revealed that the
IPI_high group had a significantly lower survival rate compared
with the IPI_low group (P<0.01; Fig. 2D). Similarly, the PPI_high group
had a significantly reduced survival rate compared with the PPI_low
group (P<0.01; Fig. 2E),
indicating that both high PPI and high IPI are associated with
poorer survival outcomes.
Based on IPI and PPI, patients in the TCGA-COAD
dataset were categorized into four molecular subtypes: IPI_high +
PPI_high, IPI_high + PPI_low, IPI_low + PPI_high, and IPI_low +
PPI_low. K-M analysis demonstrated that the survival rates of the
IPI_high + PPI_high, IPI_high + PPI_low, and IPI_low + PPI_high
subtypes were significantly lower than those of the IPI_low +
PPI_low subtype (P<0.001; Fig.
2F). After merging the first three subtypes into a single
category called the ‘Others group’, a comparison with the IPI_low +
PPI_low subtype revealed consistent results (Fig. 2G). The results of the multivariate
Cox regression analysis (Fig. S1)
based on TCGA-COAD data indicated that this molecular
classification is an independent prognostic factor, unaffected by
variables such as age, tumor stage, and microsatellite status.
Survival analysis results for IPI and PPI based on the GSE17538
dataset are depicted in Fig. 2H
and I, respectively. As shown in
Fig. 2J, the IPI_low + PPI_low
group has a higher survival rate. However, due to limitations in
the dataset samples, this difference has not yet reached
statistical significance. The analysis across various datasets
indicates a crosstalk between immune and PCD. The combination of
IPI and PPI effectively distinguishes OS rates and other prognostic
factors in patients with CRC.
Development of machine learning
models
After merging IRGs and PRGs, an expression matrix
containing 104 genes was constructed. Using this combined gene set
as the independent variable and the TCGA-COAD dataset as the
training set, 101 prediction models were fitted, and the C-index
was calculated for each model in the validation datasets (GSE17538
and GSE29621). The performance of these prediction models is
presented in Fig. 3A, which
reveals that the CoxBoost + Ridge model achieved the highest
weighted average C-index in the validation set. This model includes
13 gene features: VEGFA, CCRL2, LTB4R, RORC, INHBB, FABP4, NOS2,
C8G, CD24, HSPB1, SFRP2, STK25 and NOL3. The risk score for each
sample was calculated. Using the median value of Score, samples
from different datasets were classified into high and low groups.
The K-M analysis results for the TCGA-COAD, GSE17538 and GSE29621
datasets are depicted in Fig. 3B-D
respectively, demonstrating that the prognosis of the high group
was significantly worse. To explore the performance of Score in
predicting OS in the GSE17538 dataset, it was found that AUC for
1-year, 3-year and 5-year OS was 0.792, 0.716 and 0.758,
respectively (Fig. 3F). In the
GSE29621 dataset, the AUCs for 1-year, 3-year and 5-year survival
rates were 0.645, 0.604 and 0.623, respectively (Fig. 3E). Simultaneously, a multivariate
Cox regression analysis based on the TCGA-COAD dataset revealed
that the Score is a significant prognostic factor, independent of
tumor grading, age and MSI (Fig.
3G). A nomogram incorporating the Score and other clinical
factors was also constructed (Fig.
3H). The receiver operating characteristic curve areas for
predicting 1-year, 3-year, and 5-year OS were 0.759, 0.797 and
0.791, respectively (Fig. 3I),
indicating strong predictive performance.
 | Figure 3Performance of machine learning
models and Score in prognostic prediction. (A) Consistency index
comparison across different machine learning models in three
datasets. (B) K-M analysis results for high and low Score subgroups
in the TCGA-COAD dataset. (C) K-M analysis results for high and low
Score subgroups in the GSE17538 dataset. (D) K-M analysis results
for high and low Score subgroups in the GSE29621 dataset. (E) ROC
curves of Score for predicting 1-year, 3-year, and 5-year OS rates
in the GSE17538 dataset. (F) ROC curves of Score for predicting
1-year, 3-year, and 5-year survival rates in the GSE29621 dataset.
(G) Forest plot showing multivariate Cox regression results of
Score and other clinical variables in the TCGA-COAD dataset, with
time OS as the target variable. (H) Nomogram constructed using
Score and other clinical variables. (I) ROC curves of nomogram for
predicting 1-year, 3-year and 5-year survival in the TCGA-COAD
dataset. KM, Kaplan-Meier; TCGA, The Cancer Genome Atlas; COAD,
colon adenocarcinoma; ROC, Receiver Operating Characteristic; OS,
overall survival; AUC, area under the curve. |
TME of high and low score groups
TME is a complex environment comprising tumor cells,
immune cells and stromal cells, all of which significantly affect
tumor cell invasion and metabolism (13). Analysis of the TCGA-COAD dataset
using the ESTIMATE algorithm revealed the Immune Score of the low
Score group was significantly higher than that of the high Score
group (P<0.05, Fig. 4A),
suggesting greater immune cell infiltration in the low Score group.
As shown in Fig. 4B, the Stromal
Score of the low Score group was significantly lower than the high
group (P<0.01). Using the XCELL algorithm to assess and compare
immune cell infiltration between the two groups, it was observed
that the low Score group had higher abundances of activated
dendritic cells, B cells, CD4+ memory T cells,
CD8+ naive T cells, CD8+ T cells,
CD8+ Tcm cells, CD4+ T cells, CD4+
Tem cells, CD8+ memory T cells, natural killer (NK)
cells, and others (Fig. 4C).
Further correlation analysis indicated that the Score was
negatively associated with the infiltration of 12 types of immune
cells, including macrophages, activated dendritic cells, memory B
cells, T cells, Th2 cells, CD8+ Tcm cells and NK cells
(Fig. 4D). This analysis supports
the conclusion that a higher Score is linked to reduced immune cell
infiltration.
The scores of 29 Fges were compared between the two
groups, and it was found that the low Score group exhibited
significantly higher scores in antitumor immune-related Fges,
including M1 signature, myeloid immune suppression, checkpoint
molecules, Th2 signature, neutrophil signature, antitumor
cytokines, NK cells, Th1 signature and T cells (Fig. 4E). The TCGA-COAD dataset defines
three immune phenotypes: Desert phenotype (phenotype 1 and 2),
excluded phenotype (phenotype 3 and 4) and inflamed phenotype
(phenotype 5 and 6). As depicted in Fig. 4F, the proportions of desert
phenotype (40%) and excluded phenotype (33%) in the high Score
group were higher than those in the low Score group (48 and 22%,
respectively), while the proportion of inflamed phenotype (31%) was
lower compared with the low Score group (26%). A chi-square test
performed on the two groups of immune phenotypes yielded a P-value
of 0.036, indicating a significant difference in the proportion of
immune phenotypes between the two groups (P<0.05).
The present study further investigated the
association between Score and immunotherapy response. Given the
correlation between immunotherapy response and MSI, the association
between Score and MSI was explored. As demonstrated in Fig. 4G, there was no significant
difference in Score between the microsatellite stable (MSS)/MSI low
subtype and the MSI high (MSI-H) subtype in the TCGA-COAD dataset.
The high Score group exhibited significantly higher scores for
MDSC, CAF, TAM.M2, Exclusion and Dysfunction compared with the low
Score group (P<0.05; Fig.
4H-L). This indicates that the high Score group is more prone
to tumor immune evasion. Furthermore, the TIDE score of the high
Score group was significantly higher than that of the low Score
group (P<0.001; Fig. 4M),
suggesting a lower response to ICI treatment in the high Score
group.
Genetic alterations
Somatic mutations in the high and low Score groups
in the TCGA-COAD dataset were further investigated. The waterfall
plots for the two groups of mutated genes are shown in Fig. 5A and B, respectively. The results revealed that
somatic mutations were present in 93.97% of samples in the low
Score group and 98.02% of samples in the high Score group. The top
8 genes with the highest mutation frequencies were identical in
both groups, including APC, TP53, PIK3CA, TTN, CRAS, MUC16, SYNE1
and FAT4. As depicted in Fig. 5C,
there were notable differences in somatic gene mutations between
the two groups. In the high Score group, mutations in TP53
(P<0.001), SNX1 (P<0.01), ZNF214 (P<0.01), ELAPOR1
(P<0.01), AP2M1 (P<0.01), LMX1A (P<0.01) and NACC2
(P<0.01) were notably higher compared with the low Score group.
On the other hand, mutations in PCDH10 (P<0.001), WDR19
(P<0.001), SCN5A (P<0.01), ATP2 (P<0.01) and EFCAB13
(P<0.01) were greatly increased in the low Score group. The
tumor-related signaling pathways were enriched by the mutated genes
in the high and low Score groups, respectively 9 Fig. 5D and E). Both groups exhibited enrichment in
largely the same signaling pathways, including RTK-RAS, NOTCH, WNT,
Hippo, PI3K, Cell Cycle, MYC, TGF-β, TP53 and NRF2. Regarding tumor
mutation burden, although the high Score group had a higher burden
than the low Score group, this difference did not reach statistical
significance (Fig. 5F).
Features genes included in the
model
In total, 13 feature genes included in the model
were analyzed. Using the expression matrix of these genes, the
consensus clustering method was applied to the TCGA-COAD dataset.
The optimal number of clusters k was determined to be 2, resulting
in the division of the dataset samples into 2 clusters (Fig. 6A). The comparison of the OS time
between different clusters is presented in Fig. 6B. Similarly, the GSE17538 dataset
was clustered using the same method, and the optimal number of
clusters k was also found to be 2 (Fig. 6C). Although differences in OS time
between the clusters were observed, they did not reach statistical
significance (Fig. 6D). In the
TCGA-COAD dataset, K-M analysis of the 13 feature genes indicated
that all were related to patient survival rate (Fig. 6E). Univariate Cox analysis revealed
that VEGFA, LTB4R, INHBB, FABP4, C8G, HSPB1, STK25, and NOL3 were
significant risk factors, whereas the remaining 5 feature genes
were identified as significant protective factors (Fig. 6F). In the GSE17538 dataset,
univariate Cox analysis showed that VEGFA, FABP4, HSPB1 and SFRP2
were significant risk factors, while RORC and NOS2 were significant
protective factors (Fig. 6G). The
differential expression of feature genes between tumor tissues and
normal tissues in the TCGA-COAD dataset were further investigated.
In cancer tissues, genes such as VEGFA, LTB4R, INHBB, STK25 and
NOL3 were significantly upregulated, while protective factors
including CCRL2, RORC, CD24 and SFRP2 were significantly
downregulated (Fig. 6H). In the
GSE17538 dataset, risk factors VEGFA and HSPB1 were significantly
upregulated in cancer tissues, while protective factors CCRL2,
RORC, NOS2, C8G, CD24 and STK25 were significantly downregulated
(Fig. 6I). To explore the driving
factors of abnormal expression of these feature genes in CRC
tissues, the correlation was analyzed between gene expressions and
mutations, methylation, and CNVs using the cBioPortal database. The
results of this correlation analysis are shown in Fig. S2A-C. Additionally, the correlation
analysis of feature gene expression is presented in Fig. S2D. The results indicated a broad
positive correlation among VEGFA, LTB4R, INHBB, STK25 and NOL3,
while CCRL2, RORC and CD24 also showed a positive correlation.
Association of feature genes with
immunity
In the present study, the correlation between the
expression of the 13 selected feature genes and various
immune-related gene categories was analyzed, including chemokines,
immune inhibitors, MHC-related genes, immune stimulation-related
genes and receptor-related genes. The correlation analysis results,
depicted in Fig. 7A-E, reveal a
significant association between the expression of these 13 feature
genes and these five types of immune genes. Specifically, genes
such as SFRP2, NSO2 and FABP4 exhibit a positive correlation with
the expression of most chemokines, immune inhibitors, MHC-related
genes, immune stimulation-related genes and receptor-related genes.
Conversely, the expression of genes like LTB4R shows a negative
correlation with the expression of the majority of these IRGs.
Single-cell analysis results
Preprocessing, integration, dimensionality reduction
and clustering analysis was performed on GSE161277. Using the R
package ‘singleR’, cells were annotated based on various cell
surface markers, ultimately identifying 5 distinct cell types:
epithelial cells, T cells, B cells, monocytes and endothelial cells
(Fig. 8A). Comparison of the Score
for each cell type (Fig. 8B)
reveals obvious differences. Endothelial cells scored the highest,
while B cells scored the lowest. This suggests that endothelial
cells and B cells may play crucial roles in the crosstalk between
cellular immunity and PCD. The expression levels of 13 feature
genes in each cell subgroup are illustrated in Fig. 8C. Notably, VEGFA, STK25 and NOL3
are predominantly expressed in epithelial cells, while CD24 and
other genes are mainly expressed in B cells and epithelial
cells.
Analysis of LTB4R expression
IHC analysis revealed that the staining of LTB4R was
markedly more intense in colon cancer tissues in the transfer group
(Fig. 9A), while the staining was
lighter in the non-transfer group (Fig. 9B). The results of the IHC analysis
showed that the expression of LTB4R in the transfer group was
significantly higher than that in the non-transfer group. Western
blotting analysis showed that the LTB4R protein expression level
was significantly higher in the transfer group compared with the
non-transfer group (Fig. 9C and
D), with a statistically
significant difference (P<0.01) (Fig. 9E). These findings suggested that
LTB4R expression levels may be useful in distinguishing between CRC
recurrence and metastasis.
Discussion
PCD encompasses various processes, including
apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis,
cuproptosis, parthanatos, entotic cell death, netotic cell death,
lysosome-dependent cell death, alkaliptosis and oxeiptosis. The
interplay among various PCD mechanisms is highly complex,
characterized by numerous crosstalk interactions (14,15).
Not only do they operate through distinct regulatory mechanisms
individually, but they also mutually constrain and influence each
other (15). These PCD pathways
form a dynamic molecular interaction network, synergistically
modulating cellular processes in cancer (16,17).
Therefore, in the present study, various PCDs were investigated as
a cohesive whole. Recent studies indicated that different forms of
PCD, such as pyroptosis, necroptosis, parthanatos and oxeiptosis,
play crucial roles in eliminating unnecessary cells and maintaining
the health and stability of the body's microenvironment (4,18).
However, the effects of PCD on tumor occurrence, invasion and
immunity have not been thoroughly understood, and there remains
considerable controversy in this area. On the one hand, PCD can
promote the occurrence and progression of tumors under certain
conditions. For example, ferroptosis-related genes can increase the
survival rate of tumor cells by activating the TGFB pathway, and at
the same time accelerate the decomposition of the extracellular
matrix, promoting tumor progression and invasion (18). On the other hand, PCD can regulate
the enrichment of effector immune cells or regulatory immune cells,
thereby playing a key role in regulating immunity, affecting TME,
and influencing the clinical outcomes of cancer treatment (4). Currently, numerous studies have
confirmed the close association among PCD, immunity and colon
cancer prognosis (4,19). Exploring the crosstalk between PCD
and cellular immunity in CRC can enhance the understanding of how
these processes affect the prognostic mechanisms of CRC. The
present study uncovered key genes that impact patient outcomes and
identify potential therapeutic targets for CRC treatment.
In the present study, PCA algorithm was employed to
reduce the dimensionality of expression matrix of IRGs and PRGs.
IPI and PPI were then constructed to quantify immune levels and PCD
levels in colon cancer tumor tissues. Based on IPI and PPI results,
four molecular subtypes were classified and analyzed survival rate
differences among these subtypes. It was found that the OS rate of
the IPI_low + PPI_low group was significantly higher compared with
the other groups. This analysis indicates a crosstalk between
cellular immunity and PCD, and that combining IPI and PPI can
effectively distinguish OS rates and other prognostic for patients
with CRC. Based on the combined gene expression matrix of IRGs and
PRGs, 101 machine learning models were constructed and a risk score
named ‘Score’ was derived from the best-performing model. The Score
demonstrated excellent prognostic performance in the TCGA-COAD,
GSE17538 and GSE29621 datasets, indicating its effectiveness in
reflecting the impact of the crosstalk between PCD and cellular
immunity on CRC prognosis. This reflects that the model Score, by
comprehensively considering both cellular immunity and PCD, as well
as their crosstalk, has achieved significant effectiveness in
prognostic prediction.
The present study investigated the relationship
between Score, immunotherapy response, and MSI. MSI has been
reported to correlate with immunotherapy response in patients with
CRC. In MSS-type CRC, only 5-20% of patients experience tumor
regression following combination immunotherapy (20). However, ~50% of patients with MSI-H
CRC also exhibit primary resistance to immunotherapy, particularly
in metastatic cases (21). Due to
the heterogeneity of CRC, MSI is not an ideal biomarker for
predicting immunotherapy response. As a result, numerous research
teams are actively seeking more promising biomarkers. In the
present study, the proposed biomarker, Score, shows a strong
association with immunotherapy response in patients with CRC
(Fig. 4M), but no significant
correlation with microsatellite stability status (Fig. 4G), suggesting its potential as an
MSI-independent predictor of immunotherapy response.
The present study further explored the relationship
between Score and TME, as well as the response to immunotherapy.
Immune cell infiltration was compared and it was found that the
abundance of immune cells, including CD4+ memory T
cells, CD8+ naive T cells, CD8+ T cells,
CD4+ T cells, CD8+ memory T cells and NK
cells, was higher in the low Score group (Fig. 4C). Correlation analysis revealed a
negative relationship between the Score and the infiltration of 12
different immune cell types (Fig.
4D). Additionally, analysis of 29 Fges showed that the scores
of antitumor immunity Fges were significantly higher in the low
Score group compared with the high Score group (Fig. 4E). A significant difference in the
proportions of immune phenotypes between the two groups was
demonstrated in Fig. 4F.
Furthermore, the scores of MDSCs, CAFs, TAM.M2 Exclusion and
Dysfunction were notably higher in the high Score group compared
with the low Score group (P<0.05; Fig. 4H-K). These findings suggested that
the interaction between PCD and cellular immunity may affect
prognosis by acting on TME. In the single-cell analysis of the
GSE161277 dataset (Fig. 8B), it
was observed that endothelial cells had the highest Score, while B
cells had the lowest, suggesting that endothelial cells and B cells
might be key carriers of the crosstalk between cellular immunity
and PCD. Furthermore, TIDE score was significantly higher in the
high Score group compared with the low Score group (Fig. 4L), indicating that the high Score
group has a poorer response to ICI immunotherapy. There was no
significant correlation between the Score and MSI (Fig. 4G), indicating that the Score serves
as a signature for reflecting the degree of immunotherapy response
and is not dependent on MSI. In the analysis of gene variations, an
increase in mutations of genes such as TP53, SNX1 and ELAPOR1 was
observed in the high score group. The mutation of the TP53 tumor
suppressor gene is a clear driver of CRC development and is a poor
prognostic factor for metastatic CRC (22). Additionally, TP53 gene mutations
indicate a poor response to immunotherapy in patients (23). As tumor suppressors, mutations in
SNX1 and ELAPOR1 can promote cell proliferation and invasion in CRC
(24,25). In the low Score group, mutations in
genes such as SCN5A increased. It has been reported that silencing
SCN5A suppresses the cell cycle and EMT, reducing proliferation,
migration and invasion, while enhancing apoptosis induced by 5-FU
(26).
The present study also aimed to identify key genes
that impact the prognosis of patients with CRC, focusing on 13
feature genes involved in the crosstalk between cellular immunity
and PCD. Among these, LTB4R, VEGFA, FABP4, HSPB1 and SFRP2 are
considered as risk factors in prognosis analysis, while RORC, NOS2,
and other genes are recognized as significant protective factors.
VEGFA, a member of the PDGF/VEGF growth factor family, promotes the
proliferation and migration of vascular endothelial cells and plays
a crucial role in both physiological and pathological angiogenesis.
It has been presviously indicated that VEGFA protein expression is
significantly positively correlated with the infiltration of B
cells and M2 macrophages, affecting CRC prognosis by influencing
the abundance of B cells and macrophages in TME (27). FABP4 is related to tumor
progression and poor prognosis across various cancers, with its low
expression linked to the infiltration of B cells, CD4+ T
cells, CD8+ T cells, myeloid dendritic cells,
macrophages and neutrophils (28).
The heat shock protein β-1 (HSPB1) gene encodes a protein involved
in the differentiation of various cell types. Its expression is
correlated with poor clinical outcomes in multiple human cancers,
as its encoded protein may promote cancer cell proliferation and
metastasis while protecting them from apoptosis. HSPB1 is also
regarded as a negative regulator of ferroptosis in cancer cells.
Protein kinase C-mediated HSPB1 phosphorylation reduces the
production of iron-mediated lipid reactive oxygen species, thereby
protecting cancer cells from ferroptosis (29). The gene signature composed of HSPB1
has been proven to reflect the TME and tumor immunity in liver
cancer (30), colorectal
adenocarcinoma (31) and glioma
(32). Methylation of SFRP2 gene
is a potential biomarker for CRC, and its upregulation can weaken
WNT signaling in human colon cancer cell lines (33). Upregulating SFRP2 results in the
weakness of WNT signal pathway transmission in human colon cancer
cell lines (33). Multiple studies
have revealed the interactions between tumor cells and the TME
mediated by SFRP2 (34-36),
highlighting the potential of SFRP2 as a prognostic biomarker
(37,38). RORC and NOS2, as significant
protective factors, are significantly downregulated in colon cancer
tissues. Among them, RORC encodes a DNA-binding transcription
factor and is a member of the nuclear hormone receptor NR1
subfamily. It has been revealed that RORC exhibits either high or
low methylation in various cancers and is closely associated with
immune cell infiltration and immunomodulators in these cancers
(39). NOS2 produces low levels of
nitric oxide in tumor cells, which significantly impacts tumor cell
development (40,41). The spatial expression of NOS2 and
COX2, as well as their interactions with T cells and macrophages,
is currently a significant focus in TME and tumor immunity
research. The present study also investigated the general
expression correlation of these genes with chemokines, immune
inhibitors, MHC-related genes, immune stimulation-related genes,
and receptor-related genes. Additionally, these genes exhibit
differential expression in tumor and paracancerous tissues of CRC.
The present study further analyzed the driving factors behind
abnormal expression of these feature genes, including gene
mutations, methylation and CNVs.
The present study focuses on LTB4R, which is one of
the feature genes involved in the crosstalk. The LTB4R gene encodes
the leukotriene B4 receptor, which serves as a high-affinity
receptor for leukotriene B4. The gene exerts various effects on
different cell types, with the most notable being its chemotactic
influence on neutrophils and macrophages (42). Previous studies have found that
LTB4R is expressed in various cells in TME, including both immune
cells (such as macrophages, activated T cells and dendritic cells)
and non-immune cells (such as smooth muscle cells and endothelial
cells) (43-45),
highlighting its direct role in controlling adaptive immune
responses. In analyzing the TCGA-COAD dataset, it was found that
LTB4R expression was significantly upregulated in colon cancer
tissues and is negatively correlated with the expression of most
chemokines, immune inhibitors, MHC-related genes, immune
stimulation-related genes and receptor-related genes. Although
current research predominantly explores the association between
LTB4R expression and tumor proliferation and development in animal
models (46), clinical evidence
remains insufficient. The present study further examines the
relationship between LTB4R expression and CRC progression,
revealing a significant positive correlation with cancer recurrence
and metastasis. These findings suggest potential new targets for
CRC treatment.
The present study explored the combined impact of
cellular immunity and PCD on CRC prognosis and developed a machine
learning model-based risk score combing both cellular immunity and
PCD to accurately assess patient outcomes, facilitate risk
stratification, and improve clinical management for patients with
CRC. Subsequently, the research identified feature genes involved
in the crosstalk between cellular immunity and PCD and provided an
in-depth analysis of these genes in terms of prognosis and tumor
immunity, offering new tumor markers and therapeutic strategies for
CRC. However, the present study has certain limitations, notably
the need for further experimental validation of the specific roles
of these 13 genes in the biological processes of CRC. Additionally,
due to the relatively small sample size included in the biological
experiments of the present study, the investigation into genes such
as LTB4R was not conducted in depth. Subsequent studies will
continue to investigate the biological effects of genes such as
LTB4R and elucidate the mechanisms through which they influence the
prognosis of CRC. Despite this, the research provides significant
insights and potential value.
Supplementary Material
Forest plot showing multivariate Cox
regression results of IPI_PPI_group and other clinical variables in
the The Cancer Genome Atlas-colon adenocarcinoma dataset, with
overall survival time as the target variable. IPI, immune potential
index; PPI, PCD potential index; MSI, microsatellite
instability.
(A) Correlation analysis results
between expression of feature genes and gene mutations in the
TCGA-COAD dataset. (B) Correlation analysis results between
expression of feature genes and copy number variations in the
TCGA-COAD dataset. (C) Correlation analysis results between
expression of feature genes and methylation in the TCGA-COAD
dataset. (D) Graph showing expression correlations among the 13
feature genes in the TCGA-COAD dataset. TCGA, The Cancer Genome
Atlas; COAD, colon adenocarcinoma.
R package and hyperparameters in R for
each model.
List of immune-related genes
List of programmed cell death-related
genes
The significant results of univariate
Cox analysis for immune-related genes in The Cancer Genome
Atlas-colon adenocarcinoma dataset.
Significant results of univariate Cox
analysis for programmed cell death-related genes in The Cancer
Genome Atlas-colon adenocarcinoma dataset.
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
The data generated in the present study may be
requested from the corresponding author.
Authors' contributions
CW, SC, YH and QW conceived and designed the study;
and critically revised the manuscript for important intellectual
content. YC conceived and designed the study; acquired, analyzed
and interpreted the data; and drafted the manuscript. CW and QW
confirm the authenticity of all the raw data. All authors read and
approved the final version of the manuscript.
Ethics approval and consent to
participate
The present study was carried out in compliance with
the Declaration of Helsinki and was approved (approval no.
2023-002-013) by the Institutional Review Committee of Zhanjiang
Central People's Hospital (Zhanjiang, China). Written informed
consent was provided by all participants.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
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