Abstract

Background

   Diseases are often caused by multiple factors, angiogenesis-related
   genes (ARGs) have been shown to be associated with cancer, however,
   their role in colon cancer had not been fully explored. This study
   investigated potential biomarkers based on ARGs to improve prognosis
   and treatment effect in colon cancer.

Methods

   ARGs associated with colon cancer prognosis were identified using Cox
   regression analysis and LASSO analysis. Furthermore, a prognostic model
   was constructed in colon cancer based on the 3 ARGs, and its biological
   function were analyzed. We evaluated the differences in tumor immune
   microenvironment based on prognostic signature. Finally, cell
   experiments confirmed the function of genes in colon cancer.

Results

   The prognostic value of ARGs in colon cancer patients has been
   comprehensively analyzed for the first time and identified 3 ARGs with
   prognostic values. A prognosis risk model was constructed based on 3
   ARGs and its prognostic value was validated on an independent external
   colon cancer dataset. In colon cancer patients, this prognostic feature
   was an independent risk factor and was significantly correlated with
   clinical feature information of colon cancer patients. This feature was
   also related to the immune microenvironment of colon cancer. Cell
   experiments showed that high expression of TNF Receptor Superfamily
   Member 1B (TNFRSF1B) significantly promoted apoptosis and inhibited
   proliferation of colon cancer cells. Therefore, TNFRSF1B may become an
   important regulatory factor in the progression of colon cancer by
   participating in intracellular functional regulation.

Conclusions

   This study constructed a prognostic risk model based on three ARGs and
   for the first time discovered that TNFRSF1B may become an important
   regulatory factor in cancer progression by participating in
   intracellular functional regulation.

Supplementary Information

   The online version contains supplementary material available at
   10.1007/s12672-025-01835-6.

   Keywords: Colon cancer, Angiogenesis, TNFRSF1B, Prognosis, Immune

Introduction

   Colon cancer was a common malignant tumor of the digestive tract that
   occurs in the colon, ranking third among gastrointestinal tumors
   [[34]1]. The common sites of occurrence are the rectum and the junction
   between the rectum and the sigmoid colon [[35]2]. Many early colon
   cancer patients may have no symptoms in clinical practice, but as the
   disease progresses and the lesions continue to grow, a series of common
   symptoms of colon cancer can occur, such as increased frequency of
   bowel movements, bloody and mucous stools, abdominal pain, diarrhea or
   constipation, intestinal obstruction, as well as general fatigue,
   weight loss, and anemia [[36]3]. In recent years, chemotherapy and
   immunotherapy have further improved the prognosis of colon cancer.
   However, only a small percentage of colon cancer patients will respond
   well to this therapy [[37]4]. Finding novel indicators to forecast
   patient survival and responsiveness to immunotherapies and targeted
   medicines was therefore urgently needed.

   Angiogenesis was a process of generating new blood vessels and
   lymphatic vessels based on the existing vascular system of the body's
   tissues [[38]5]. This process plays an important role in the body's
   functions such as wound healing and embryonic development [[39]6]. The
   dynamic balance between proangiogenic and anti-angiogenic factors was
   disrupted in the tumor tissue microenvironment, shifting towards the
   direction of promoting angiogenesis [[40]7]. This mechanism of
   neovascularization in tumor tissue was known as the "angiogenesis
   effect" of tumors [[41]8].

   Angiogenesis was a complex process involving multiple molecules and
   cells [[42]9]. Angiogenesis was a necessary process for the repair and
   growth of normal tissue damage, and it was crucial for various
   processes including tumor growth, organ growth, and wound healing.
   Although angiogenesis was beneficial for tissue growth and
   regeneration, the structure formed by angiogenesis in pathological
   states of diseases was usually functionally abnormal, which can promote
   malignant diseases and inflammation, and be utilized by tumor cells to
   kill and metastasize cancer patients [[43]10]. Angiogenesis involves a
   large number of disease state processes. Anti- angiogenesis has become
   one of the important strategies for the treatment of tumors and other
   diseases [[44]11].

   Angiogenesis-related genes (ARGs) have been previously demonstrated to
   have therapeutic potential in various cancers [[45]12]. However, the
   role of these ARGs in colon cancer was not fully understood. This study
   analyzed the differential expression of ARGs in colon cancer patients.
   Then construct a prognostic model for colon cancer patients. Finally,
   cell experiments showed that high expression of TNF Receptor
   Superfamily Member 1B (TNFRSF1B) significantly promoted apoptosis and
   inhibited proliferation of colon cancer cells.

Materials and methods

Colon cancer dataset preprocessing

   Two independent datasets ([46]GSE39582 and [47]GSE38832), containing
   684 colon cancer samples. The enrollment criteria for the prognostic
   model were as follows: datasets containing 684 colon cancer samples,
   series presented with OS time and survival status, and transcriptome
   profiling as the experiment type. The GEO dataset can be downloaded
   from the Gene Expression Comprehensive Database (GEO). [48]GSE39582 was
   used as the training set, while [49]GSE38832 was used as the validation
   set for subsequent analysis. The raw data processing of microarrays and
   the evaluation of gene expression levels were carried out using the R
   4.2.1 tool [[50]13].

Identification of ARGs with potential prognostic value in colon cancer
patients

   The surve_cutpoint function in R package “surviminer” was used to
   calculate the optional cutoff value of ARGs in all samples, and the
   samples were classified into high- and low-expression groups
   accordingly. Univariate, LASSO, and multivariate Cox regression
   analyses were applied to identify the most significant ARGs to build a
   prognostic model. Univariate Cox regression analysis was then performed
   to identified genes linked to survival in [51]GSE39582 dataset,
   validated using [52]GSE38832 dataset. ARGs were considered significant
   when the p-value was < 0.05 in univariate Cox regression analysis.
   Overlapping molecules of genes associated with survival and ARGs were
   considered candidates. The prognosis of the selected candidates was
   evaluated using Kaplan–Meier curves followed by log-rank tests through
   the “survminer” package. Subsequently, we used Least Absolute Shrinkage
   and Selection Operator (LASSO) of the "glmnet" software-penalized Cox
   regression to filter out less relevant factors., and ten-fold crossover
   was used to validate the ideal values for determining penalty
   parameters. Finally, multivariate Cox regression analysis was applied
   to optimize the model [[53]13, [54]14].

Consensus clustering analysis of survival related ARGs

   Furthermore, we performed consensus clustering of the selected genes to
   investigate their function and prognostic value in colon cancer using
   the parameters as follows: k = 10 and 1000 repeats. Then the "ggplot2"
   package was used for principal component analysis (PCA) to confirm the
   number of clusters, and Kaplan Meier curves were plotted to confirm the
   different subtypes’ prognostic value in colon cancer patients after
   clustering classification [[55]15].

A ARGs prognostic model constructed and validated

   Multivariate Cox proportional hazards regression was used to analyze
   the coefficients of ARGs, and the risk scores for prognostic signature
   of patients were constructed based on the coefficients. The detailed
   formula was as follows: Risk score = β1 * Exp1 + β2 * Exp2 + β, where
   Exp and β represent expression levels and coefficients of selected
   ARGs, respectively. Patients were classified into high- and low-risk
   subtypes according to the median risk scores. Kaplan–Meier analysis
   followed by log-rank tests was employed for prognostic analysis using
   the “survminer” package [[56]16].

Pathway enrichment analysis

   Genes with correlation coefficients | R |> 0.3 obtained from spearman
   correlation analysis were considered to be significantly correlated
   with risk scores. The clusterProfiler package was used to perform Kyoto
   Encyclopedia of Genes and Genomes (KEGG) functional analysis and Gene
   Ontology (GO) functional analysis on these genes significantly
   correlated with risk scores. Normalized P-value < 0.05 and false
   discovery rate (FDR) < 0.2 were the thresholds for selecting
   differential pathways [[57]17].

Differences in tumor immune microenvironment among different risk groups

   The CIBERSORT software package was used to analyze the infiltration of
   22 immune cells in colon cancer tissues in the [58]GSE39582 dataset.
   Wilcoxon rank sum test was used to compare the differences in immune
   cell infiltration between colon cancer tissues of different risk
   groups. Finally, we analyzed the Spearman relationship between immune
   cells and 3 ARGs and plotted a network diagram. By the pheatmap
   packages and ggplot2 of the R software, the results were visualized.
   P < 0.05 was considered significant [[59]18]. *P < 0.05, **P < 0.01,
   ***P < 0.001, ****P < 0.0001, ns P ≥ 0.05.

Estimation of therapeutic drugs

   The oncoPredict software package was used to predict colon cancer drug
   activity and evaluate the differences in drug activity among low-risk
   score group and high-risk score group [[60]19].

Construction of TNFRSF1B overexpression colon cancer cell model

   HTC116 cells were sourced from the cell bank of The First Affiliated
   Hospital of Anhui Medical University in Anhui Province. HTC116 cells
   were cell lines of colon cancer. HTC116 cells were inoculated in DMEM
   medium containing 10% fetal bovine serum (containing 100 U/mL
   penicillin and 100 mg/mL streptomycin) and cultured at 37 ℃ in 5% CO^2
   incubator. When the adherent parietal cell grows into a compact
   monolayer, it is subcultured. Partial stably growing colon cancer cells
   were randomly divided into two groups: the empty control group
   (HTC116 + OE-NC) and the TNFRSF1B overexpression group
   (HTC116 + OE-TNFRSF1B). The TNFRSF1B overexpression plasmid vector and
   the nonsense sequence TNFRSF1B plasmid vector were transfected into
   colon cancer cells, respectively. In addition, Quantitative Real-time
   PCR (qPCR) was used to verify the overexpression effect of TNFRSF1B in
   HTC116 + OE-NC group cells and HTC116 + OE-TNFRSF1B group cells.
   Student’s t-test (unpaired) was used to check the statistical
   significance while comparing the means of two groups, each group has
   three replicates. P < 0.05 was considered significant [[61]20].
   *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns P ≥ 0.05.

Colon cancer apoptosis experiment

   Pre-cooled PBS was used to wash the collected HTC116 cells
   (HTC116 + OE-NC group cells and HTC116 + OE-TNFRSF1B group cells)
   (1 × 10^6 cells/time). Then, The cells were resuspended by 1 ml of 1X
   binding buffer, and after reaching a density of 1 × 10^6 cells/ml in
   the test tube, in the dark and at room temperature conditions, 5 μL
   Annexin V-FITC was added to the test tube and gently mixed for 10 min.
   Finally, 5 μL propidine iodide was added to the test tube and incubated
   in the dark for 5 min before being detected within 1 h by flow
   cytometry. After counting total cells and apoptotic cells, we analyzed
   three independent repeated data and plotted them. Student’s t-test
   (unpaired) was used to check the statistical significance while
   comparing the means of two groups. P < 0.05 was considered significant
   [[62]21]. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns
   P ≥ 0.05.

Colon cancer cell cycle experiment

   Transfer HTC116 cells (HTC116 + OE-NC group cells and
   HTC116 + OE-TNFRSF1B group cells) and culture medium into centrifuge
   tubes. After centrifugation at 4 °C for 5 min (1000 rpm), remove the
   supernatant from the centrifuge tube. Then, pre-cooled 75% alcohol was
   added to the centrifuge tube to fix and resuspend the cells, and place
   the centrifuge tube in a refrigerator at 4 °C overnight to secure it.
   After centrifugation at 4 °C for 5 min (1000 rpm), remove the
   supernatant from the centrifuge tube. Finally, propidine iodide
   staining solution was added to the centrifuge tube, stain in the dark
   at 37 °C for 30 min, and then perform flow cytometry detection. We
   analyzed three independent repeated data and plotted them, Student’s
   t-test (unpaired) was used to check the statistical significance while
   comparing the means of two groups. P < 0.05 was considered significant.
   P < 0.05 was considered significant [[63]22]. *P < 0.05, **P < 0.01,
   ***P < 0.001, ****P < 0.0001, ns P ≥ 0.05.

Statistical analysis

   The Wilcoxon rank sum test and Student t-test were utilized to compare
   two groups and the Kruskal–Wallis test to compare multiple groups.
   Univariate, LASSO, and multivariate Cox regression analyses were
   applied to identify the most significant ARGs to build a prognostic
   model. ARGs were considered significant when the p-value was < 0.05 in
   univariate Cox regression analysis. Subsequently, LASSO-penalized Cox
   regression was used to filter out less relevant factors. Finally,
   multivariate Cox regression analysis was applied to optimize the model.
   The surve_cutpoint function in R package “surviminer” was used to
   calculate the optional cutoff value of per gene in all samples. Kaplan
   Meier method was used for log-rank test and survival analysis. The
   Spearman analysis method was used for correlation analysis. P < 0.05
   was considered significant [[64]13].

Results

Identification of ARGs with potential prognostic value in colon cancer
patients

   Based on [65]GSE39582 dataset, a total of 3 ARGs (CD36 [hazard ratio
   (HR): 1.36, 95% confidence interval (95%CI) 1.20–1.54], ERAP1 [HR:
   0.77, 95%CI 0.66–0.91] and TNFRSF1B [HR: 0.63, 95%CI 0.48–0.83]) were
   associated with prognosis significantly in colon cancer patients and
   validated them in the [66]GSE38832 dataset (Figures S1, S2). the
   Kaplan–Meier curves confirmed 3 AGRs (CD36, ERAP1 and TNFRSF1B) s's
   prognostic value (Fig. [67]1A-B), P < 0.05 were considered significant.
   In colon cancer, high expression of CD36 had a poor prognosis, while
   high expression of these genes (ERAP1 and TNFRSF1B) had a better
   prognosis.

Fig. 1.

   [68]Fig. 1
   [69]Open in a new tab

   Survival analysis for 3 ARGs. A The Kaplan–Meier curves for the 3 ARGs
   in colon cancer from the [70]GSE39582 dataset; B The Kaplan–Meier
   curves for the 3 ARGs in colon cancer from [71]GSE38832 dataset.
   P < 0.05 were considered significant

Consensus clustering analysis of prognostic survival related ARGs

   The consensus clustering analysis results indicate that k = 2 seems to
   be more stable than k = 3–6 (Fig. [72]2A-B). PCA was further used to
   verify the reliability of the clustering analysis results, which showed
   that when k = 2, the samples showed clustered together and high
   similarity (Figure S3). Thus, we stratified the colon cancer patients
   into 2 clusters. Colon cancer patients in cluster 2 had a better
   prognosis than cluster 1 (Fig. [73]2C).

Fig. 2.

   [74]Fig. 2
   [75]Open in a new tab

   A ARGs prognostic model constructed and validated. A The cumulative
   distribution function of consensus clustering from k = 2–6; B The
   relative change in area under the CDF curve from k = 2–6; C Colon
   cancer patients in cluster 2 had a better prognosis than cluster 1

A ARGs prognostic model constructed and validated

   Risk scores for prognostic signature of colon cancer patients were
   constructed based on the coefficients and expression levels of 3 ARGs.
   The detailed formula was shown below: risk
   score = (0.2085*CD36) + (− 0.2166*ERAP1) + (− 0.4180*TNFRSF1B). Based
   on the median risk score of prognostic characteristics of colon cancer
   patients, colon cancer patients were divided into different risk groups
   (low-risk group and high-risk group), Kaplan–Meier curves showed that
   colon cancer patients with high-risk had worse prognosis compared with
   those with low-risk (Fig. [76]3A). The prognostic characteristics were
   then validated in [77]GSE38832 dataset, showing the similar result of
   [78]GSE39582 dataset (Figure S4). These analysis results demonstrate
   the stability and reliability of the prognostic features of colon
   cancer, and the risk score was identified as an independent risk factor
   for the prognosis of colon cancer patients by multivariate Cox
   regression analysis (Fig. [79]3B). These results demonstrate the
   reliability and stability of the prognostic characteristics. Dividing
   patients into subgroups based on clinical variables, we found that
   colon cancer patients with pM, pT, TNM and MMR had different risk
   scores (Fig. [80]3C).

Fig. 3.

   [81]Fig. 3
   [82]Open in a new tab

   Relationship between the prognostic model and clinical pathological
   factors of colon cancer patients. A Kaplan–Meier curves showed that
   patients with high-risk had worse prognosis compared with those with
   low-risk; B These analysis results demonstrate the stability and
   reliability of the prognostic features of colon cancer; C Dividing
   patients into subgroups based on clinical variables, we found that
   colon cancer patients with pM, pT, TNM and MMR had different risk
   scores

Identification of the prognostic model-related biological pathways

   246 significantly negatively correlated genes and 62 significantly
   positively correlated genes were selected. The results of the KEGG
   pathways and GO are shown in Figure S5. The GO functional analysis
   results were mainly enriched in nuclear division, while KEGG functional
   analysis results were mainly enriched in Ubiquitin mediated
   proteolysis.

Differences in tumor immune microenvironment among different risk groups and
its relationship with drug activity

   The plots show a significant difference in immune cell infiltration
   between low-risk patients and high-risk patients and the differences in
   various immune cells' proportions are complex (Fig. [83]4A-B). 3 ARGs
   were significantly associated with immune cells in colon cancer
   (Fig. [84]4C-D). Using the oncoPredict software package in R, we
   evaluated and calculated 198 drugs' drug sensitivity in colon cancer.
   We identified 6 drugs with significant differences in IC50 between the
   low-risk and high-risk groups (Cytarabine, AZD7762, PD0325901,
   Bortezomib, Dihydrorotenone, BMS.754807) (Figure S6).

Fig. 4.

   [85]Fig. 4
   [86]Open in a new tab

   The relationship between tumor immune microenvironment. A-B The plots
   show a significant difference in immune cell infiltration between
   low-risk patients and high-risk patients and the differences in various
   immune cells' proportions are complex; C-D 3 ARGs associated with
   immune cells, which may have an impact on patient prognosis. P < 0.05
   was considered significant. *P < 0.05, **P < 0.01, ***P < 0.001,
   ****P < 0.0001, ns P ≥ 0.05

Construction of TNFRSF1B overexpression HTC116 cell model

   The qPCR results showed that compared with the control group, the cells
   transfected with HTC116 + OE-TNFRSF1B had the significant
   overexpression effect on TNFRSF1B and named HTC116 + OE-TNFRSF1B
   (Figure S7).

Colon cancer apoptosis experiment

   Cell apoptosis' results showed that compared with the control group
   (HTC116 + OE-NC) cells, the colon cancer apoptosis rate of the
   HTC116 + OE-TNFRSF1B group colon cancer cells were significantly
   increased (Fig. [87]5A-B).

Fig. 5.

   [88]Fig. 5
   [89]Open in a new tab

   Colon cancer apoptosis experiment. Cell apoptosis' results showed that
   compared with the control group (HTC116 + OE-NC) cells, the colon
   cancer apoptosis rate of the HTC116 + OE-TNFRSF1B group colon cancer
   cells were significantly increased (A-B). *P < 0.05, **P < 0.01,
   ***P < 0.001, ****P < 0.0001, ns P ≥ 0.05

Colon cancer cell cycle experiment

   Cell cycle's results showed that compared with the control group
   (HTC116 + OE-NC) cells, the HTC116 + OE-TNFRSF1B group showed
   significant inhibition of colon cancer cells in the G1 phase
   (Fig. [90]6A-B).

Fig. 6.

   [91]Fig. 6
   [92]Open in a new tab

   Colon cancer cell cycle experiment. Cell cycle's results showed that
   compared with the control group (HTC116 + OE-NC) cells, the
   HTC116 + OE-TNFRSF1B group showed significant inhibition of colon
   cancer cells in the G1 phase (A-B). *P < 0.05, **P < 0.01,
   ***P < 0.001, ****P < 0.0001, ns P ≥ 0.05

Discussion

   At present, the advancement of medical technology had improved the
   prognosis of colon cancer patients. However, most colon cancer patients
   face the risk of drug resistance and recurrence during treatment
   [[93]23]. Therefore, there was an urgent need to find new therapeutic
   targets and prognostic indicators in clinical practice to improve the
   prognosis and survival rate of colon cancer patients [[94]24]. ARGs had
   been reported to play a crucial role in the occurrence and development
   of various cancers, however, their function in colon cancer had not
   been fully explored [[95]25]. In this research, we systematically
   evaluated the role of ARGs in colon cancer and selected 3 ARGs with
   prognostic and potential therapeutic value. Then, new prognostic
   features of colon cancer patients were first constructed and validated
   based on these 3 ARGs. We found a significant correlation between the
   newly constructed prognostic features and the clinical characteristics
   of colon cancer patients. In addition, the new prognostic features of
   colon cancer are significantly correlated with the immune
   microenvironment of colon cancer, which may provide valuable clues for
   selecting immunotherapy patients and predicting patient prognosis,
   which may provide a fundamental theoretical basis for improving the
   diagnosis and treatment of colon cancer in clinical practice.

   At present, in clinical practice, the prognosis of colon cancer
   patients is mainly predicted based on their clinical and pathological
   characteristics, such as tumor size, depth of invasion, etc. [[96]26].
   Although pathology played an important role in evaluating the prognosis
   of diseases such as tumors, its predictive accuracy was still limited.
   Clinical pathological evaluation usually only focuses on changes at the
   tissue or cellular level and may not fully consider the overall
   condition of the patient [[97]27]. Cancer cells originate from normal
   tissue cells and may be the result of genetic factors leading to
   certain gene changes [[98]28]. In addition, as our research on cancer
   mechanisms deepens, gene changes have become increasingly important in
   the pathology and immune mechanisms of cancer [[99]29]. Therefore,
   exploring the genetic level of cancer was particularly important.
   Previous studies had reported that genetic prognostic markers can help
   doctors assess a patient's disease risk and take more effective
   prevention and treatment measures [[100]13]. We constructed a
   prognostic risk model based on 3 ARGs, which was a novel prognostic
   tool aimed at improving survival prediction for colon cancer patients,
   and its prognostic value was validated on an independent external colon
   cancer dataset, demonstrating the stability and reliability of cancer
   prognostic features and potentially supplementing existing clinical
   pathological feature methods. However, this study relies on
   retrospective data, so further prospective cohort validation studies
   will be our next research direction.

   Our study found that compared to the low-risk group, the infiltration
   degree of CD8 + T cells was significantly reduced in the high-risk
   group. TNFRSF1B was significantly positively correlated with the degree
   of immune infiltration of CD8 + T cells. CD8 + T cells, as the core of
   the adaptive immune system, played a role in cancer that not only
   involves specific recognition and direct killing of tumor cells, but
   also inhibits tumor growth by regulating the tumor microenvironment
   [[101]30]. Numerous studies have shown that infiltration of CD8 + T
   cells into the tumor microenvironment was a favorable prognostic
   feature for many malignant tumors. CD8 + T cells recognize specific
   tumor associated antigens expressed on tumor cells, release cytotoxic
   molecules granzyme B and perforin into tumor cells, induce caspase
   activation, and ultimately lead to tumor cell apoptosis [[102]31].
   Inducing tumor cell apoptosis had always been various tumor
   immunotherapies' fundamental goal [[103]32]. Colon cancer cells
   confirmed that expression of TNFRSF1B can significantly promote colon
   cancer cell apoptosis.Therefore, in colon cancer patients, TNFRSF1B may
   effectively enhance T cell-mediated anti-tumor immunity by promoting
   immune infiltration of CD8 + T cells, thereby inducing cancer cell
   apoptosis. However, further research was needed to determine the
   specific mechanism.

   In colon cancer, high expression of TNFRSF1B had a better prognosis,
   based on [104]GSE38832 and [105]GSE39582, TNFRSF1B was an independent
   prognostic risk factor in colon cancer. The TNFRSF1B gene encodes
   member 1B of the tumor necrosis factor receptor superfamily, which was
   an important immune regulatory molecule involved in regulating
   processes such as cell survival, proliferation, and apoptosis
   [[106]33]. The variation of this gene was associated with various
   diseases, including cardiovascular disease, autoimmune diseases, and
   certain cancers [[107]34]. The current research focus was on exploring
   how the polymorphism of TNFRSF1B gene affects disease susceptibility,
   disease progression, and response to specific treatments, especially
   the expression of TNFRSF1B gene and the regulatory mechanism of
   cellular function, which are the focus of research [[108]35]. In cell
   experiments, the absence of TNFRSF1B induces regulatory changes in
   several key pathways related to inflammation, apoptosis, and cell
   proliferation [[109]36]. TNFRSF1B may therefore become an important
   regulatory factor in colon cancer's progression by participating in
   intracellular functional regulation. The colon cancer cell experiment
   confirmed that the gene expression of TNFRSF1B can significantly
   inhibit the cell cycle progression of colon cancer cells and
   significantly promote apoptosis of colon cancer cells. Therefore,
   TNFRSF1B was a potential therapeutic target for colon cancer.

   This study inevitably had several limitations. The dataset for this
   study was from a public database, which were retrospective and had
   potential confounding factors. Prospective cohort studies were needed
   for further exploration. The expression levels of ARGs may be
   influenced by clinical parameters, and the clinical information data
   available to colon cancer patients in this study is limited. Therefore,
   the expression levels of ARGs in some cancer patients may not be
   accurate. Our study only used one colon cancer cell line for research,
   and the experimental results of a single cancer cell line may have
   limitations and cannot fully reflect the heterogeneity and complexity
   of cancer cells. In order to gain a more accurate and comprehensive
   understanding of the biological characteristics of genes in cancer
   cells, biological experiments on multiple cancer cell lines will be one
   of our next research goals. In addition, in vitro cell experiments do
   not involve the regulation of the neuroendocrine system or interactions
   between cells in vivo, and the characteristics of cell-derived tissues
   cannot be fully represented by in vitro experiments. Therefore, further
   animal experiments were necessary.

Conclusion

   In summary, the prognostic value of ARGs in colon cancer patients had
   been comprehensively analyzed for the first time and identified 3 ARGs
   with prognostic values. A prognosis risk model was constructed based on
   3 ARGs and its prognostic value was validated on an independent
   external colon cancer dataset. In colon cancer patients, this
   prognostic feature was an independent risk factor and was significantly
   correlated with clinical feature information of colon cancer patients.
   This feature was also related to the immune microenvironment of colon
   cancer. The high expression of TNFRSF1B can significantly promote cell
   apoptosis and inhibit the colon cancer cells cycle progression.
   Therefore, TNFRSF1B may become an important regulatory factor in the
   progression of colon cancer by participating in intracellular
   functional regulation. This research provides a fundamental theoretical
   basis for improving the diagnosis and treatment of colon cancer in
   clinical practice.

Supplementary Information

   [110]Additional file 1.^ (1.4MB, docx)

Author contributions

   Fei Liu: Conceived and designed the experiments; Fei Liu and Yi Wang:
   Performed the experiments; Fei Liu and Leiming Xia: Analyzed and
   interpreted the data; Yunhong Xia: Contributed reagents, materials,
   analysis tools or data; Fei Liu, Chen Sun and Yun Li: Wrote the paper.

Funding

   Not applicable.

Data availability

   Gene expression microarray data sets, including [111]GSE38832 and
   [112]GSE39582, are downloaded from the Gene Expression Integrated
   Database (GEO)（ [113]https://www.ncbi.nlm.nih.gov/geo/)".

Declarations

Ethics approval and consent to participate

   Not applicable.

Consent for publication

   Not applicable.

Competing interests

   The authors declare no competing interests.

Footnotes

   Publisher's Note

   Springer Nature remains neutral with regard to jurisdictional claims in
   published maps and institutional affiliations.

References