Abstract

Background

   Colorectal cancer (CRC) is a global health burden because of its high
   mortalities. It is crucial to discover new biomarkers that can help
   predict the outcome of patients as well as identify new therapeutic
   targets to improve treatment options for CRC patients. EGLN1, a protein
   belonging to the family of prolyl hydroxylase domain-containing
   proteins, has been associated with the progression of various cancer
   types. However, its involvement in CRC remains uncertain.

Methods

   We conducted an analysis of EGLN1 expression levels in colorectal
   cancer (CRC) tissues using publicly available datasets. EGLN1
   expression was also evaluated in relation to patient survival. To
   explore the potential biological functions and signaling pathways
   associated with EGLN, we performed gene set enrichment analysis (GSEA).
   We also investigated the immune landscape of EGLN1 in CRC by examining
   its association with immune infiltration and immune-related markers.

Results

   We consistently observed a significant decrease in EGLN1 level in CRC
   tumor tissues when compared to normal tissues, indicating its potential
   role as a tumor suppressor gene specific to CRC. The diagnostic
   potential of EGLN1 was substantiated by its effective discrimination
   between normal and tumor tissues in patients with CRC. Functional
   enrichment analysis further uncovered EGLN1’s involvement in the
   regulation of crucial biological processes, cellular components, and
   molecular functions relevant to CRC development and progression. The
   correlation analysis revealed the potential immunomodulatory role of
   EGLN1, affecting immune checkpoint molecules, antigen presentation, and
   immune cell trafficking within the tumor microenvironment.
   Additionally, high level of EGLN1 exhibited a significant association
   with improved recurrence-free survival (RFS) and emerged as an
   independent prognostic factor. Knockdown experiments demonstrated
   EGLN1’s role in suppressing cell proliferation and migration in CRC.

Conclusion

   In summary, our findings position EGLN1 as a promising multi-faceted
   biomarker in CRC, with implications for prognosis, immune
   microenvironment modulation, and potential therapeutic targeting.

   Keywords: Colorectal cancer, EGLN1, Biomarker, Prognostic,
   Bioinformatics

Introduction

   Colorectal cancer (CRC) poses a significant challenge to public health.
   According to a recent study published in CA, there were approximately
   153,020 newly diagnosed cases of colorectal cancer (CRC) and about
   52,550 deaths for the year 2023 [[30]1]. Despite advancements in CRC
   diagnosis and treatment, the stage of the disease at the time of
   diagnosis still has a significant impact on patient prognosis, with
   higher survival rates observed among early-stage patients compared to
   those with advanced stages. Therefore, identifying new prognostic
   biomarkers and therapy targets for CRC holds significant clinical
   importance.

   EGLN1, also known as PHD2, is a crucial regulator of cellular response
   to hypoxia and belongs to the prolyl hydroxylase domain (PHD) enzyme
   famil [[31]2–[32]4]. Recently, there is growing interest in
   investigating EGLN1’s potential involvement in cancer, as its
   dysregulation is observed in several tumor types. However, EGLN1’s
   prognostic value and probable impact on CRC immune landscape remained
   largely unexplored.

   Bioinformatic analyses have been instrumental for elucidating the
   molecular mechanisms underlying cancer development and identifying
   potential prognostic biomarkers. In this study, our objective was to
   comprehensively explore the prognostic significance and immune
   landscape of EGLN1 in CRC by employing an extensive bioinformatics
   approach. Specifically, we conducted an analysis of EGLN1 expression
   levels in CRC tissues using publicly available datasets and examined
   its correlation with patient prognosis. We conducted gene set
   enrichment analysis (GSEA) in order to investigate the potential
   pathways related to EGLN1. Additionally, in order to explore the
   possible involvement of EGLN1 in modulating immune response, we
   investigated the correlation of EGLN1 level and immune infiltration in
   CRC. Our findings may help identify a novel prognostic biomarker and a
   possible therapy target for CRC.

Materials and methods

Data collection

   We obtained RNA sequencing data from publicly available databases,
   including the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas
   (TCGA), along with relevant clinical information for colorectal cancer
   patients. For the training cohort, we utilized the [33]GSE39582
   dataset, while the TCGA-COAD and [34]GSE14333 datasets served as the
   validation cohort.

Differential expression analysis

   “Limma” package was utilized to assess EGLN1 level among normal and
   tumor tissues [[35]5]. Additionally, we utilized a paired t-test to
   assess EGLN1 level among normal and matched tumor tissues. The
   visualization of the analysis results was carried out utilizing the
   “ggplot2” package. To assess the diagnostic capability of EGLN1 in
   differentiating between normal colon tissues and colon tumor tissues,
   we conducted receiver operating characteristic (ROC) curve analysis
   [[36]6].

Protein expression analysis

   Protein Expression Analysis was conducted using cProSite
   ([37]https://cprosite.ccr.cancer.gov/), an online tool developed and
   maintained by the Center for Cancer Research (CCR) at the National
   Cancer Institute (NCI). The data was derived from the Clinical
   Proteomic Tumor Analysis Consortium (CPTAC) datasets. The statistical
   significance of differences in protein expression among groups was
   evaluated using the Mann-Whitney U test.

GO and KEGG

   The patients were categorized into the low EGLN1 group and the high
   EGLN1 group based on the median. Differential expression analysis was
   performed using the “limma” package, with a significance threshold of
   p < 0.05 and an absolute log2 fold change > 0.3 applied to identify the
   genes that exhibited differential expression between the two groups.
   The functional annotation of significant differential expression genes
   (DEGs) was performed using “clusterProfiler” package, which facilitated
   Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
   enrichment analyses [[38]7, [39]8]. Statistically significant terms
   were enriched with p < 0.05.

Gene set enrichment analysis (GSEA)

   We performed gene set enrichment analysis (GSEA) used the “cluster
   profiler” package [[40]9]. The reference datasets were
   ‘c2.cp.go.v7.2.symbols.gmt’ and ‘c2.cp.kegg.v7.2.symbols.gmt downloaded
   from MSigD. The gene sets were ordered according to their correlation
   with the level of EGLN1. GSEA was conducted by using the enricher
   function with default settings and p < 0.05 as statistically
   significant.

Survival and prognostic analysis of EGLN1

   The optimal threshold for EGLN1 expression was determined using the
   “surv cutpoint” function available in R package “survminer”. This
   function identified the cutoff point that maximizes the statistical
   significance of the survival analysis. Overall survival (OS),
   recurrence-free survival (RFS) and disease-free survival (DFS) were
   analyzed using the “survival” package. Survival analysis was performed
   utilizing Kaplan-Meier (KM) survival curves. The statistical
   significance of the difference in survival was assessed using the
   log-rank test. The independent prognostic value of EGLN1 expression was
   assessed through univariate and multivariate Cox regression analysis.
   The nomogram served as a graphical tool for prediction of the
   individual survival probability of CRC patients. The purpose of
   calibration analysis was to evaluate the agreement between predicted
   probabilities from the nomogram and the observed outcomes. The C-index
   was calculated to measure the probability of concordance between
   predicted and observed survival outcomes [[41]10].

Cell culture

   The RKO colorectal cancer cell line and HEK293T were obtained from the
   American Type Culture Collection.The cell was cultured in a controlled
   environment at 37 °C and 5% CO2 using DMEM supplemented with 10% FBS
   and 1% penicillin/streptomycin. Sequences targeting EGLN1 were designed
   using an online shRNA design tool: shEGLN1 #1: 5’-3’
   CCGGGACGACCTGATACGCCACTGTCTCGAGACAGTGGCGTATCAGGTCGTCTTTTTG; shEGLN1 #2:
   5’-3’ CCGGACGCCACTGTAACGGGAAGCTCTCGAGAGCTTCCCGTTACAGTGGCGTTTTTTG. A
   scramble sequence, referred to as shNC, was used as a negative control:
   shNC: 5’-3’ CCGGCGCTGAGTACTTCGAAATGTCCTCGAGGACATTTCGAAGTACTCAGCGTTTTTG.
   For lentivirus production, HEK293T was simultaneously transfected with
   the lentivirus and packaging plasmids (psPAX2 and pMD2.G) using Lipo293
   Transfection Reagent. Virus supernatant was collected after 48 h and
   passed through a 0.45 μm filter. RKO cell was then infected with the
   obtained lentivirus particles. Following a 72-hour incubation, the
   infected cell was treated with puromycin (2 µg/mL) for a period of 7–10
   days, allowing the establishment of stable cell lines. All cell
   experiments adhered to institutional biosafety guidelines and the
   International Cell Line Authentication Committee (ICLAC) standards to
   ensure ethical use of cell lines.

Colony formation assay

   The stable cell line was grown for 10 days at a density of 500 cells
   per well in six-well plates and then fixed by treating them with a 4%
   paraformaldehyde solution. After fixation, cells were subjected to 0.1%
   crystalviolet staining. We captured images of the colonies using a
   digital camera. Image J was used to count colonies.

Scratch wound assay

   The stable cell line was seeded in 6-well plates and grown until
   confluence to assess effect of EGLN1 knockdown on cell migration. To
   create a defined cell-free area, the cell stratum was scraped with a
   sterile 10-µL pipette tip. PBS was then added to rinse cell debris and
   better visualize the scratched surface. The cells were then incubated
   in DMEM without serum for 0, 24, and 48 h. The area of the scratch was
   measured under a microscope at each time point.

Statistical analysis

   We assessed the characteristics of the tumor microenvironment in each
   patient using the ESTIMATE algorithm. This algorithm provided insights
   on tumor microenvironment composition based on gene expression data
   [[42]11]. To investigate the immune infiltration patterns, the
   CIBERSORT algorithm was employed [[43]12]. CIBERSORT is a powerful gene
   expression deconvolution tool that allows for the quantification of
   different cell types within complex tissues. The LM22 signature matrix
   was used for the analysis. This matrix contains 547 genes representing
   22 immune cell types. Pearson analysis was applied to determine the
   correlation of EGLN1 with immune-related markers.

   GraphPad Prism (v. 9.3) and R software (v. 4.3.0) were applied to
   perform statistical analysis. Wilcoxon rank-sum test or Kruskal-Wallis
   test was applied for evaluating group differences. Paired t-test was
   utilized to examine EGLN1 expression level in tumor and matched
   adjacent normal tissues. Chi-square test was performed to compare the
   clinical information with EGLN1 level. P-value <0.05 was considered
   statistically significant.

Results

Differential expression of EGLN1 in normal and tumor tissues

   To assess EGLN1 level in colorectal tumors, we conducted an analysis
   using the [44]GSE39582 dataset, which includes paired samples of normal
   and tumor tissues. Our analysis found significant higher EGLN1 level in
   normal tissues than tumor tissues (Fig. [45]1A). Additionally, we
   consistently observed higher level of EGLN1 in normal tissues compared
   to paired tumors (Fig. [46]1B). The ROC curve analysis revealed the AUC
   value was 0.955 (Fig. [47]1C), suggesting that EGLN1 expression levels
   could effectively differentiate between normal and tumor tissues. To
   validate our findings, we further analyzed the TCGA-COAD validation
   dataset. Consistent with the previous analysis (Fig. [48]1D–F), EGLN1
   level was higher in normal tissues than in colorectal tumors.

Fig. 1.

   [49]Fig. 1
   [50]Open in a new tab

   Differential level of EGLN1 between normal and tumor tissues. A Boxplot
   showing EGLN1 differential expression in the [51]GSE39582 dataset. B
   Boxplot showing EGLN1 differential expression between paired normal and
   tumor tissues in [52]GSE39582 dataset. C ROC curve analysis of EGLN1
   mRNA expression to discriminate normal and tumor tissues in
   [53]GSE39582 dataset. D Boxplot showing EGLN1 differential expression
   in the TCGA-COAD dataset. E Boxplot showing EGLN1 differential
   expression between paired normal and tumor tissues in TCGA-COAD
   dataset. F ROC curve analysis of EGLN1 mRNA expression to discriminate
   normal and tumor tissues in TCGA-COAD dataset. G Boxplot showing EGLN1
   differential expression at protein level in normal and tumor tissues
   using cProSite analysis. ****P < 0.0001

   These results further supported the conclusion that EGLN1 exhibited
   reduced expression in colorectal tumor tissues at the RNA level. To
   evaluate EGLN1 protein expression in CRC, we utilized the cProSite
   online analysis tool. Our analysis identified a notable elevation in
   EGLN1 protein levels in normal tissues compared to tumor tissues.
   (Fig. [54]1G). Overall, our bioinformatics analysis consistently
   demonstrated that EGLN1 level was markedly increased at both the mRNA
   and protein levels in normal tissues compared to colorectal tumor
   tissues.

Relationship between EGLN1 and clinical characteristics

   The low EGLN1 expression group exhibited a higher proportion of stage 3
   and 4, increased prevalence of pMMR (mismatch repair), and elevated
   CIN+ (chromosomal instability). Nevertheless, no statistically
   significant differences were observed among the remaining clinical
   characteristics (Fig. [55]2A; Table [56]1). These findings suggested
   that EGLN1 downregulation may contribute to tumor progression and
   aggressiveness in CRC. Furthermore, our subgroup analysis demonstrated
   that EGLN1 expression was particularly low in the CIN + and pMMR
   groups, suggesting a potential involvement of EGLN1 in modulating the
   microsatellite instability and mismatch repair (Fig. [57]2B–F). These
   findings provided significant insights into the involvement of EGLN1 in
   CRC and its potential clinical implications.

Fig. 2.

   [58]Fig. 2
   [59]Open in a new tab

   Correlation between EGLN1 and clinical characteristics. A Heatmap
   illustrating the correlation between EGLN1 and clinical features. B–F
   Boxplots of EGLN1 mRNA expression among different clinical subgroups in
   colorectal tumors. *P < 0.05, **P < 0.01, ***P < 0.001 and
   ****P < 0.0001

Table 1.

   Clinical characteristics of the sample according to EGLN1 expression
   status
   Characteristic EGLN1 high, N = 283^a EGLN1 low, N = 283^a P-value^b
   Gender
    Female        123 (43%)             133 (47%)            0.4
    Male          160 (57%)             150 (53%)
   Age
    <=65          110 (39%)             112 (40%)            0.9
    > 65          172 (61%)             171 (60%)
    Unknown       1                     0
   Stage
    Stage 1–2     163 (58%)             134 (48%)            0.018^c
    Stage 3–4     119 (42%)             146 (52%)
    Unknown       1                     3
   mmr.status
    dMMR          59 (23%)              16 (6.0%)            < 0.001^d
    pMMR          193 (77%)             251 (94%)
    Unknown       31                    16
   cin.status
    –             78 (33%)              32 (14%)             < 0.001^d
    +             162 (68%)             192 (86%)
    Unknown       43                    59
   [60]Open in a new tab

   EGLN1 Egl-9 family hypoxia-inducible factor 1, dMMR different mismatch
   repair, pMMR proficient mismatch repair, cin chromosomal instability

   ^an (%)

   ^bPearson’s Chi-squared test

   ^cP<0.05

   ^dP<0.001

Functional enrichment analysis

   To explore the functional role of EGLN1 in colorectal tumors, we
   performed a differential gene expression analysis comparing colorectal
   tumor samples exhibiting high EGLN1 expression to those displaying low
   EGLN1 expression. 825 DEGs were identified in the analysis, including
   141 downregulated genes and 684 upregulated genes. The heatmap showed
   the top 20 downregulated and upregulated genes (Fig. [61]3A). The
   volcano plot displayed a clear separation of the significant DEGs from
   the non-significant ones (Fig. [62]3B).

Fig. 3.

   [63]Fig. 3
   [64]Open in a new tab

   Differential Gene Expression of EGLN1. A Heatmap of expression profiles
   about the top 20 significantly downregulated and upregulated genes
   based on log2FC. B Volcano plot of the top 5 significantly
   downregulated and upregulated genes based on statistical significance
   (p-value)

   The GO-BP analysis of the DEGs revealed significant enrichment in
   multiple biological processes, including antigen processing and
   presentation of peptide antigen, immune response, response to oxygen
   levels and cell-cell adhesion (Fig. [65]4A). The GO-CC analysis
   identified several significantly cellular components among the DEGs,
   including the membrane raft, membrane microdomain, secretory granule
   membrane, and specific granule. (Fig. [66]4B) In the GO-MF analysis, we
   observed significant enrichment of molecular functions among the DEGs,
   including chemokine activity, CXCR chemokine receptor binding, MAP
   kinase tyrosine phosphatase activity and cytokine activity
   (Fig. [67]4C).

Fig. 4.

   [68]Fig. 4
   [69]Open in a new tab

   Function Enrichment Analysis of EGLN1. A GO-BP analysis according to
   differential expression genes. B GO-CC analysis according to
   differential expression genes. C GO-MF analysis according to
   differential expression genes. D KEGG pathway analysis according to
   differential expression genes

   Additionally, the KEGG pathway analysis unveiled several significantly
   enriched pathways associated with EGLN1 in colorectal tumors. These
   pathways include HIF-1, B cell receptor, MAPK, TNF, and Chemokine
   signaling pathways. (Fig. [70]4D). To validate the functional
   enrichment results, we performed GSEA using a reference dataset. The
   GSEA results were similar to the findings from the initial GO and KEGG
   analyses, providing additional support for the functional relevance of
   EGLN1-associated DEGs in colorectal tumors (Fig. [71]5A, B).

Fig. 5.

   [72]Fig. 5
   [73]Open in a new tab

   Functional analysis of EGLN1 in colorectal tumors using GSEA. A GO
   enrichment analysis results from GSEA. B KEGG pathway enrichment
   analysis results from GSEA

Correlation analysis of EGLN1 with immune-related markers

   Our analysis investigating the relationship between EGLN1 expression
   and immune-related markers in colorectal cancer (CRC) revealed several
   significant correlations. Specifically, EGLN1 expression was positively
   correlated with chemokines and their receptors, including CCL18
   (r = 0.24, p < 0.001), CCL26 (r = 0.15, p < 0.001), CXCL10 (r = 0.16,
   p < 0.001), CCR1 (r = 0.20, p < 0.001), CCR2 (r = 0.19, p < 0.001), and
   CXCR1 (r = 0.17, p < 0.001) (Fig. [74]6A, D). Moreover, EGLN1
   expression exhibited positive correlations with major
   histocompatibility complex (MHC) markers, such as B2M (r = 0.12,
   p = 0.003), HLA-DMA (r = 0.19, p < 0.001), and HLA-DRA (r = 0.17,
   p = 0.001) (Fig. [75]6B). This indicates that EGLN1 may play a role in
   antigen presentation and T cell-mediated immune activation in CRC.
   Additionally, EGLN1 expression showed positive correlations with immune
   checkpoint stimulatory factors, such as CD28 (r = 0.15, p < 0.001),
   CD86 (r = 0.25, p < 0.001), and IL-6 (r = 0.20, p < 0.001)
   (Fig. [76]6C). These results suggest a potential relationship between
   increased EGLN1 levels and elevated levels of immune stimulatory
   molecules, which are essential for immune activation and anti-tumor
   immune responses. In contrast, EGLN1 expression demonstrated negative
   correlations with immune checkpoint inhibitory markers, including
   KIR2DL1 (r = −0.17, p < 0.001) and PDCD1 (r = −0.26, p < 0.001)
   (Fig. [77]6E). These findings indicate a possible correlation between
   higher EGLN1 expression and reduced levels of immune checkpoint
   molecules, known to inhibit antitumor immune responses.

Fig. 6.

   [78]Fig. 6
   [79]Open in a new tab

   Correlation of EGLN1 mRNA with immune-related markers. A Correlation of
   EGLN1 mRNA expression with chemokine markers. B Correlation of EGLN1
   mRNA expression with MHC markers. C Correlation of EGLN1 mRNA
   expression with immune stimulator markers. D Correlation of EGLN1 mRNA
   expression with chemokine receptor makers. E Correlation of EGLN1 mRNA
   expression with immune inhibitor markers. *P < 0.05, **P < 0.01,
   ***P < 0.001

   These findings suggest a potential role for EGLN1 in modulating the
   trafficking, recruitment, and activation of immune cells within the
   tumor microenvironment.

ESTIMATE score and CIBERSORT analysis

   The results demonstrated a notable increase in ESTIMATE scores in the
   EGLN1 high expression group, suggesting a better tumor microenvironment
   (Fig. [80]7A). In particular, our findings indicated that samples
   exhibiting high EGLN1 expression had markedly elevated immune and
   stromal scores, while displaying lower tumor purity when compared to
   samples with low EGLN1 expression (Fig. [81]7B–D). These analyses
   showed that elevated EGLN1 level was related to increased immune
   infiltration and stromal cell content, but decreased tumor cell purity
   in colorectal cancer. High expression of EGLN1 in CRC was potentially
   linked to an improved tumor microenvironment.

Fig. 7.

   [82]Fig. 7
   [83]Open in a new tab

   Immunological Analysis of EGLN1 in Colorectal Tumors. A–D Boxplots
   illustrating the ESTIMATE, immune, stromal, and tumor purity scores
   between groups with low and high EGLN1 expression. E Boxplots
   illustrating the fraction of different immune cell types in EGLN1 low
   and high expression groups by CIBERSORT analysis. F Correlations
   between immune cell populations and EGLN1 expression based on CIBERSORT
   analysis. G–I Correlation analysis between EGLN1 expression and
   Dendritic cells, NK cells, and T cells. *P < 0.05, **P < 0.01, and
   ****P < 0.0001

   The CIBERSORT analysis showed that high EGLN1 level group exhibited
   higher fractions of T gamma delta cells, NK activated cells,
   macrophages M0, and neutrophils. Conversely, plasma cells, T regulatory
   cells, and resting dendritic cells exhibited lower fractions in the
   high EGLN1 expression group (Fig. [84]7E). The analysis based on
   CIBERSORT revealed several significant associations between EGLN1
   expression and specific immune cell populations. Notably, EGLN1
   expression showed positive correlations with Neutrophils (r = 0.28,
   p < 0.001), M0 Macrophages (r = 0.12, p = 0.004), NK activated cells
   (r = 0.11, p = 0.006), Dendritic activated cells (r = 0.09, p = 0.04),
   and T gamma delta cells (r = 0.09, p = 0.03). Conversely, EGLN1
   expression exhibited negative correlations with Treg cells (r = −0.26,
   p = 0.03), NK resting cells (r = −0.10, p = 0.01), Dendritic resting
   cells (r = −0.13, p = 0.002), CD8 + T cells (r = −0.13, p = 0.001) and
   plasma cells (r = −0.11, p = 0.01) (Fig. [85]7F–I).

Prognostic analysis of EGLN1

   The “surv cutpoint” method in R package was utilized to determine
   optimal cutoff values for EGLN1 expression. The Kaplan-Meier (KM)
   analysis revealed an association between high EGLN1 level and improved
   RFS (p < 0.001), while no statistically significant association with OS
   (p = 0.073) (Fig. [86]8A, B). Another analysis of the [87]GSE14333
   dataset, specifically examining DFS, also revealed a statistically
   significant discrepancy among the two EGLN1 expression groups
   (p = 0.018) (Fig. [88]8C). High EGLN1 expression linked to better RFS
   in univariate cox regression analysis (HR = 0.477, 95% CI: 0.281–0.809,
   p = 0.006) (Fig. [89]8D). Multivariate cox regression analysis
   adjusting for age, gender and tumor stage confirmed that high
   expression of EGLN1 was a significant and independent predictor for RFS
   (HR = 0.583, 95% CI: 0.347–0.988, p = 0.045) (Fig. [90]8E). The
   nomogram visually depicted the relationship between EGLN1 expression,
   along with other clinical factors, and the prediction of RFS
   (Fig. [91]8F). The calibration curve showed good agreement between the
   predicted and actual rates of RFS at 1 year, 3 years, and 5 years
   (Fig. [92]8G). The c-index value was found to be 0.669, indicating a
   moderate predictive ability of nomogram for patient survival.

Fig. 8.

   [93]Fig. 8
   [94]Open in a new tab

   Prognostic value of EGLN1 in CRC. A Kaplan-Meier curve for OS in
   [95]GSE39582. B Kaplan-Meier curve for RFS in [96]GSE39582. C
   Kaplan-Meier curve for DFS in [97]GSE14333. D, E The forest plot
   displays the univariate and multivariate analysis of RFS. D Nomogram
   predicting RFS based on EGLN1 expression and other clinical
   characteristics. G Calibration plot illustrating the agreement between
   observed and predicted RFS probabilities

EGLN1 knockdown promotes proliferation and migration in colorectal cancer
cells

   In this study, our objective was to examine the function of EGLN1 in
   colorectal cancer cell proliferation and migration. We designed two
   shRNA sequences targeting EGLN1 and generated stable cell lines with
   knockdown of EGLN1 expression. The number of colonies was markedly
   higher in EGLN1 knockdown cell than negative control cell (Fig. [98]9A,
   B). These results suggested that EGLN1 knockdown promoted colorectal
   cancer cell proliferation. The EGLN1 knockdown cells showed a
   significantly smaller area between the scratch edges than negative
   control cells (Fig. [99]9C, D). These results suggested that EGLN1
   knockdown promoted colorectal cancer cell migration.

Fig. 9.

   [100]Fig. 9
   [101]Open in a new tab

   Proliferation and migration experiments. A, B Colony formation assays
   of the stable EGLN1 low expression cell line and the control group. C,
   D Wound healing assays of the stable EGLN1 low expression cell line and
   the control group. (magnification, x100). **P < 0.01, ***P < 0.001 and
   ****P < 0.0001

Discussion

   EGLN1, a proline hydroxylase, degrades HIF (hypoxia-inducible factor)
   by catalyzing its hydroxylation under normoxic conditions. This
   facilitates HIF1α’s recognition by VHL, leading to its ubiquitination
   and degradation. Conversely, in hypoxic conditions, EGLN1 activity is
   suppressed, allowing HIF1α to accumulate and activate genes involved in
   cellular adaptation to low oxygen levels [[102]13–[103]16]. Mutations
   or abnormal expression of EGLN1 are linked with various diseases
   including congenital polycythemia [[104]17] cancer [[105]18]ischemic
   heart disease [[106]19] and pulmonary edema [[107]20]. Interestingly,
   EGLN1 can act as both a tumor suppressor and an oncogene depending on
   the context [[108]21]. For instance, it acts as a tumor suppressor by
   correlating with improved breast cancer survival [[109]22, [110]23],
   inhibiting hepatocarcinogenesis in mice [[111]24], and suppressing
   oncogenic pathways via hydroxylation of AKT [[112]25] and SFMBT1
   [[113]26]. Conversely, EGLN1 exhibits oncogenic activity by promoting
   nasopharyngeal carcinoma progression through p53 degradation [[114]27],
   sustaining clear cell ovarian cancer proliferation [[115]28], and
   cooperating with c-Myc to drive LSH-mediated lung tumorigenesis
   [[116]29]. Therapeutically, PHD2 depletion in cancer-associated
   fibroblasts reduces metastasis [[117]30], while its knockdown in breast
   cancer cells impedes TGF-β1 processing and growth [[118]31].

   In colorectal cancer (CRC), our findings indicate that EGLN1 acts as a
   tumor suppressor, evidenced by its elevated mRNA and protein levels in
   normal tissues compared to tumors. The high AUC value from ROC curve
   analysis suggests EGLN1 could serve as a diagnostic marker for CRC. Low
   EGLN1 expression correlates with advanced stages, pMMR status, and
   chromosomal instability, suggesting its role in tumor progression and
   aggressiveness [[119]32–[120]35].

   Functional enrichment analyses of EGLN1-associated differentially
   expressed genes (DEGs) revealed involvement in immune-related
   processes, such as antigen processing and presentation, chemokine
   production, and immune checkpoint modulation. Positive correlations
   between EGLN1 expression and MHC markers, chemokines, and stimulatory
   immune checkpoint molecules suggest an immunomodulatory role for EGLN1
   in enhancing antitumor immune responses. Additionally, ESTIMATE and
   CIBERSORT analyses showed that high EGLN1 expression is associated with
   increased immune infiltration and a favorable immune cell composition,
   potentially contributing to better patient outcomes.

   Prognostic analysis demonstrated that high EGLN1 levels significantly
   correlate with improved recurrence-free survival (RFS) and disease-free
   survival (DFS) in colorectal cancer (CRC) patients, though no
   significant difference was observed in overall survival (OS). High
   EGLN1 levels were identified as an independent prognostic indicator for
   RFS in Cox regression analysis, suggesting its potential as a
   prognostic biomarker in CRC. Patients with low EGLN1 expression may
   require more aggressive treatment strategies to improve survival.

   To investigate the functional impact of EGLN1 in CRC, we generated
   stable cell lines with EGLN1 knocked down and performed proliferation
   and migration assays. Results showed a significant increase in cell
   proliferation and migration upon EGLN1 knockdown, indicating EGLN1’s
   crucial role in suppressing these processes.

   While our study provides novel insights into EGLN1’s role in CRC,
   further mechanistic experiments are needed to elucidate how EGLN1
   regulates immune responses and whether its restoration could serve as a
   therapeutic strategy. Prospective clinical studies will also be
   essential to validate its diagnostic and prognostic utility.

Conclusion

   In summary, our findings position EGLN1 as a promising multi-faceted
   biomarker in CRC, with implications for prognosis, immune
   microenvironment modulation, and potential therapeutic targeting.

Acknowledgements