Identification of the six-hormone secretion-related gene signature as a prognostic biomarker for colon adenocarcinoma

Abstract

BACKGROUND:

Colon adenocarcinoma (COAD) is a globally prevalent cancer, with hormone secretion playing a crucial role in its progression. Despite this, there is limited understanding of the impact of hormone secretion on COAD prognosis. This study aimed to establish a prognostic signature based on hormone secretion-related genes and to elucidate the potential functional mechanisms of these genes in COAD.

METHODS:

Using data from The Cancer Genome Atlas COAD cohort (TCGA-COAD), six hormone secretion-related genes were identified (CYP19A1, FOXD1, GRP, INHBB, SPP1, and UCN). These genes were used to develop a Hormone secretion score (HSS), which was then evaluated using the Kaplan-Meier curve and multivariable Cox analysis. The HSS model was further validated with external GEO cohorts (GSE41258, GSE39582, and GSE87211). Functional enrichment analyses were performed, and the CIBERSORT and TIDE algorithms were used to assess tumor infiltration.

RESULTS:

The study developed a prognostic signature, dividing patients into HSS-high and HSS-low groups. The HSS-high group showed a notably worse prognosis within the TCGA-COAD dataset and in three independent datasets: GSE41258, GSE39582, and GSE87211. Moreover, the HSS-high group predicted a shorter overall survival rate in patients maintaining microsatellite stability (MSS). The functional analysis associated HSS-high with the hypoxic, epithelial-mesenchymal transition (EMT), and TGF- $\beta$ signaling pathways and correlated with distant and lymph node metastases. The tumor immune microenvironment analysis revealed an elevated CIBERSORT score in the HSS-high group, suggesting an association with tumor metastasis. Further, the HSS-high group showed a higher TIDE score, indicating that patients with high HSS scores are less likely to benefit from Immune Checkpoint Inhibitor (ICI) therapy.

CONCLUSIONS:

This study demonstrated the prognostic significance of a HSS signature based on six hormone secretion-related genes in COAD. The findings suggest that this gene signature may serve as a reliable biomarker for predicting survival outcomes in COAD patients.

Keywords

Colon adenocarcinoma hormone secretion prognosis hypoxia pathway tumor microenvironment

1. Introduction

Colorectal cancer (CRC), one of the most common malignant tumors globally, significantly impacts both men and women and serves as a leading contributor to cancer-related deaths in China [1, 2, 3]. Colon adenocarcinoma (COAD), the most prevalent histological subtype, constitutes approximately 90% of all CRC cases [4]. CRC displays considerable heterogeneity, as shown by the range of subtypes defined by anatomical location or molecular alterations [5]. An alarming rise in CRC incidence among young adults has been noted in recent years [6]. Despite substantial advancements in COAD treatments, including chemotherapy, radiation therapy, and immunotherapy. Over the past decades, the prognosis for patients with COAD, particularly those with lymph node metastases, remains poor [7, 8]. Therefore, the urgent and unfulfilled need to decode the molecular underpinnings of COAD’s pathogenesis remains central to identifying innovative therapeutic avenues and prognostic biomarkers.

The hormone secretion pathway holds significant sway in the development of cancer. Previously study indicated that hormones, which regulate the standard growth of target organs, can also foster conditions conducive to neoplastic transformation [9, 10]. Both endogenous and exogenous hormones stimulate cell proliferation, thereby elevating the likelihood of accumulating errors in random inheritance [11]. This process can lead to the onset of malignant phenotypes, as observed in various types of cancer such as breast [12, 13, 14], endometrial [15, 16], prostate [17, 18], and ovarian cancers [19, 20]. The significant advancements in hormone therapy have notably improved survival rates in the treatment of localized and metastatic breast, ovarian, endometrial, and prostate cancers [21].

Previous study has demonstrated that hormones also play a role in CRC [18, 22, 23]. For instance, Harbs et al. reported that elevated levels of circulating testosterone and sex hormone-binding globulin could heighten the risk of CRC in men [18]. Moreover, oral supplementation with the gut-derived peptide hormone uroguanylin has been found to inhibit polyp formation in mice and to suppress CRC proliferation without inducing serious side effects [22, 23]. However, the prognostic implications of hormones in CRC remain inadequately defined.

In this study, we leveraged public datasets to formulate and validate a COAD prognostic signature predicated on hormone secretion-related genes. Furthermore, we investigated the correlations between the devised hormone signature and clinicopathological characteristics. These findings could provide a fundamental basis for elucidating associations that are critical to the diagnosis and treatment of COAD, thereby influencing future strategic planning in this field.

2. Materials and methods

2.1 Data collection

Gene expression profiles of COAD and matched non-cancerous samples were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) portal. The clinical characteristics including age, gender, stage, overall survival (OS), and the outcome of COAD patients were obtained. The MSI MANTIS Score of patients was downloaded from cBioPortal (http://www.cbioportal.org/). The independent microarray data and clinical information of GSE41258 ( $n=$ 185), GSE39582 ( $n=$ 562), and GSE87211 ( $n=$ 196) were downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/). Human hormone secretion pathways were downloaded from the AmiGO 2.5.17 (http://amigo.geneontology.org /amigo). All datasets have been listed in Supplement Table 1.

2.2 Differentially expressed genes analysis

Expressions of 314 hormone secretion-related genes were extracted from the TCGA expression profile data, 66 genes were not found, and genes with expression greater than 0 in more than half of the samples were retained. Finally, 248 genes were included for subsequent analyses. The raw read counts were utilized to identify differentially expressed genes (DEG) between normal and tumor samples by R package “DEseq2”. The up-regulated and down-regulated gene was defined with a $p$ -value $<$ 0.05 and the log ${}_{2}$ (fold change [FC]) $>$ 1 and $<$ $-$ 1 respectively. The heatmap of gene expression profiles was generated after the $z$ -score normalization of transcripts per million (TPM) by the “pheatmap” package of R.

2.3 Overall survival analysis and validation of candidate genes

To detect the prognosis-related genes in COAD, we used TPM and Kaplan – Meier curves (R package “survival” and “survminer”) to analyze the expression and prognosis value of 176 hormone secretion members in TCGA-COAD. Among them, six hormone secretion-related genes (CYT19A1, FOXD1, GRP, INHBB, SPP1, UCN) whose expression levels were related to the OS were further validated with GEO datasets. Finally, all these six genes were used to construct a prognostic signature for future analyses.

2.4 Calculation of gene signature score of the hormone secretion members

The hormone secretion signature (HSS) score was generated by summing the Z scores of six HSS genes as previously reported [17, 24, 25]. TCGA-COAD samples were grouped as either HSS-high (top 25% of HSS scores) or HSS-low [15, 26]. To identify the effect of the HSS score on the prognosis of COAD, Kaplan–Meier plotter was applied to explore the association of the HSS score with prognosis in TCGA-COAD patients.

2.5 Gene set enrichment analysis and Gene Ontology analysis

The gene sets of the latest Hallmark (v7.4) from the MsigDB database (http://www.gsea-msigdb.org/gsea/ msigdb/index.jsps) based on which GSEA analysis was implemented using GSEA software (v4.0.3, https://www.gsea-msigdb.org/). Besides this, the Z-score of ssGSEA in the enriched pathway was calculated using the ssGSEA algorithm for normalization. Normalized Enrichment Score (NES) and false discovery rate (FDR) were both calculated. The significant pathway was defined as NES $\geqslant$ 2 and FDR $<$ 0.05. The crosstalk between the EMT process and the TGF- $\beta$ signal pathway was investigated based on GSVA. The Gene Ontology (GO) annotations including biological process, cellular component, and molecular function analysis were performed by R package “clusterProfiler” and “GSVA”.

2.6 Multivariate cox analysis

Multivariate analysis with the Cox proportional hazards model was adopted to identify independent factors associated with OS in COAD. This analysis was performed with R packages (“survival”, “survminer” and “forestplot”).

2.7 Statistics analysis

Statistical analyses were conducted using the R platform (R v4.0.3) to assess significant differences. These assessments utilized a two-tailed Student’s $t$ -test, Log-Rank test, and the Wilcoxon test. $P$ -values less than 0.05, 0.01, and 0.001 were considered significant, indicated by one, two, or three asterisks, respectively. The False Discovery Rate (FDR) is an estimate of the probability that a gene set, and was calculated using the ‘gseKEGG’ package, where an FDR value less than 0.05 was deemed significant. All these statistical procedures were executed on the R platform (R v4.0.3).

Figure 1.

Workflow of this study. TCGA, The Cancer Genome Atlas; COAD, colon adenocarcinoma; DEG, differentially expressed genes; FC, fold change.

Figure 2.

Forty-five hormone-related DEGs in COAD based on the TCGA-COAD dataset. TCGA, The Cancer Genome Atlas; COAD, colon adenocarcinoma; DEG, differentially expressed genes.

3. Results

3.1 Screening of hormone secretion-related differentially expressed genes with prognostic significance from TCGA

This study utilized a total of 519 patients from The Cancer Genome Atlas colon adenocarcinoma (TCGA-COAD) dataset and three COAD validation datasets from the GEO database. The workflow is depicted in Fig. 1. The TCGA-COAD dataset contained 41 normal samples and 478 tumor samples. Due to the presence of multiple COAD samples from a single patient, 455 COAD patients were ultimately included in this study. By comparing COAD tumor samples with normal samples, 45 hormone secretion-related genes were identified as up-regulated differentially expressed genes (DEGs) ( $p$ -value $<$ 0.05 and $|$ log2FC $|$ $>$ 1) (Fig. 2).

Subsequently, Kaplan–Meier survival analysis was performed to identify DEGs significantly correlated to OS, 6 genes were risk factors with hazard ratios (HR) $>$ 1 and with $p$ -value $<$ 0.05 (Fig. 3A). This group of hormone secretion-related genes including CYT19A1, FOXD1, GRP, INHBB, SPP1, and UCN at the RNA level was significantly overexpressed in COAD tumor samples compared with normal samples (Fig. 3B). Microsatellite instability-high (MSI-H) tumors represent a subset of COAD tumors, which are sensitive to immunotherapy. Compared with normal samples, the six genes were overexpressed in microsatellite stability (MSS, Figure S1A) MSI-H (Figure S1B) tumors according to the TCGA-COAD dataset.

Figure 3.

Overall survival analysis based on the TCGA-COAD dataset. (A) Kaplan-Meier Curves indicating the associations of six hormone-related DEGs with overall survival in COAD. (B) The expression of six hormone related DEGs in tumor and normal samples. All $p$ -values are detailed within the figures, as determined by the Log-Rank test (KM Curves) and Wilcoxon test (boxplot).

3.2 Construction of a six-gene-based prognostic signature in the TCGA-COAD

The six prognosis-related genes were subjected to z-score analysis. According to the sum of the $z$ -score of six genes, patients in the TCGA-COAD dataset were separated into the HSS-high group (top 25% $z$ -score) and the HSS-low group ( $z$ -score bottom 75%). Patients with higher HSS scores have a poor OS ( $p$ -value $<$ 0.001, HR $>$ 2) (Fig. 4A), suggesting the prognostic value of the hormone secretion-related signature in COAD.

Figure 4.

The prognostic value of six hormone-secretion related genes signature in the TCGA-COAD and GEO-COAD datasets. (A) Kaplan-Meier Curves indicating the associations of six hormone-secretion related genes signature (hormone signature score, HSS 25% vs. 75%) with overall survival in the TCGA-COAD dataset; (B) Kaplan-Meier Curves indicating the associations of the signature with overall survival in the GSE41258 dataset; (C) Kaplan-Meier Curves indicating the associations of the signature with overall survival in the GSE39582 dataset; (D) Kaplan-Meier Curves indicating the associations of the signature with overall survival in the GSE87211 dataset. TCGA, The Cancer Genome Atlas; COAD, colon adenocarcinoma; GEO, Gene Expression Omnibus. All $p$ -values are detailed within the figures, as determined by the Log-Rank test.

3.3 Validation of six genes signature in the external cohorts

In addition, the data from GSE41258 including 185 COAD patients ( $p=$ 0.028, HR $=$ 1.59, Fig. 4B), GSE39582 including 562 COAD samples ( $p=$ 0.027, HR $=$ 1.42, Fig. 4C), and GSE87211 including 196 COAD cases ( $p=$ 0.017, HR $=$ 2.43, Fig. 4D) further displayed that HSS-high predicted a poor OS in COAD. HSS score (25% vs. 75%) were also compared with OS in the TCGA-MSS population and MSI population, which showed a significant association between HSS score and OS in the MSS population ( $p<$ 0.001, HR $=$ 2.53, Figure S2A) rather than MSI population ( $p=$ 0.077, HR $=$ 2.51, Figure S2B). Multivariate cox analysis of clinical information (age, gender, stage, and MSI status) and HSS score (High vs. Low revealed that six gene expression based HSS score was a significant independent factor associated with OS in COAD ( $p=$ 0.0334, HR $=$ 1.66, Fig. 5).

Figure 5.

Multivariate analysis with the cox proportional hazards model on the independent factors associated with overall survival in the TCGA-COAD dataset. TCGA, The Cancer Genome Atlas; COAD, colon adenocarcinoma; MSI-H, microsatellite instability-high; MSS, microsatellite stability. All $p$ -values are detailed within the figures, as determined by the Log-Rank test.

3.4 Functional enrichment analysis with hormone secretion signature genes

Next, we performed Hallmark enrichment analysis using the gene sets from MSigDB between the HSS-high and HSS-low score groups (Fig. 6A). Given the results, we observed that the hypoxic pathway (Fig. 6A) and EMT pathway (Fig. 6A) were significantly enriched in the HSS-high group. In addition, the KEGG analysis supported that the HSS score was positively correlated with the transforming growth factor- $\beta$ (TGF- $\beta$ ) signaling pathway (Fig. 6B). Through GO functional annotation, we observed that endocrine hormone secretion was enriched in the biological process (BP) term (Figure S3A), while hormone activity was in the molecular function (MF) term (Figure S3B). For the cellular component (CC) term, the membrane was significantly involved (Figure S3C).

Figure 6.

Functional enrichment analysis of the HSS-high group. (A) Hallmark enrichment analysis of DEGs identified in HSS-high vs. HSS-low group; (B) KEGG enrichment analysis of DEGs identified in HSS-high vs. HSS-low group; (C) the difference of EMT score between HSS-high and HSS-low group calculated using the GSVA algorithm; (D) the difference of TGF- $\beta$ score between HSS-high and HSS-low group calculated using the GSVA algorithm; (E) the difference of the proportion of patients with distant metastases between HSS-high and HSS-low group; (F) the difference of the proportion of patients with lymph node metastases between HSS-high and HSS-low group; (G) Crosstalk of gene-gene interaction in hypoxia, hormone, immune checkpoint, EMT and TGF- $\beta$ gene sets. KEGG, Kyoto Encyclopedia of Genes and Genomes; EMT, epithelial-mesenchymal transition; HSS, hormone signature score. All $p$ -values are detailed within the figures, as determined by the Wilcoxon test.

To further uncover the correlations between HSS score and related pathways, we also conducted a GSEA analysis (Figure S3D-3F). Encouragingly, the results showed significant enrichment of the hypoxic pathway, EMT pathway, and estrogen response pathway in the HSS-high group, proving that this six-gene prognostic signature could predict the status in COAD patients.

3.5 The association of six genes set expression with the EMT pathway

EMT is a cellular process implicated in cancer metastasis, while the TGF- $\beta$ signaling pathway, often activated in late-stage cancers, can encourage tumorigenesis, including metastasis and chemoresistance. In the present study, we calculated the EMT (Fig. 6C) and TGF- $\beta$ (Fig. 6D) scores using the GSVA algorithm and compared the differences between groups with high and low hormone secretion scores (HSS-high and HSS-low, respectively). The analysis showed that the HSS-high group had significantly higher GSVA scores in both the EMT and TGF- $\beta$ pathways. We also examined the proportion of patients with distant (TNM-M, Fig. 6E) or lymph node (TNM-N, Fig. 6F) metastases in the HSS-high and HSS-low score groups. Results indicated a higher percentage of patients with M1 and N2 status in the HSS-high group, suggesting that the expression of the six genes might influence COAD metastasis. Additionally, we analyzed the correlations between the expressions of the six genes and TGF- $\beta$ and EMT-related genes. Notably, SPP1 and GRP expression was positively correlated with the expression of all EMT-related genes and most TGF- $\beta$ -related genes (Figure S4). Lastly, the STRING tool was used to evaluate interactions of hormone-related genes with Hypoxia, TGF- $\beta$ , and EMT gene sets. The crosstalk among these gene sets is depicted in Fig. 6G.

3.6 Tumor-infiltrating analysis between the HSS-high and HSS-low score groups

Figure 7.

Tumor microenvironment analysis. (A) CIBERSORT analysis in the HSS-high and HSS-low groups; (B) TIDE analysis in the HSS-high and HSS-low groups. All $p$ -values are detailed within the figures, as determined by the Student’s $t$ -test.

Subsequent analyses assessed the influence of the HSS score on the tumor microenvironment. The CIBERSORT algorithm was utilized to estimate the proportions of tumor-infiltrating immune cells in COAD patients. The analysis showed that the group with a high HSS score (HSS-high) had a lower proportion of CD4 and CD8 cells than the group with a low HSS score (HSS-low), as depicted in Fig. 7A. Additionally, the proportion of M2 macrophages was significantly higher in the HSS-high group, suggesting a correlation between a high HSS score and tumor metastasis. Using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, the potential clinical efficacy of immunotherapy in different HSS score groups was examined. A higher TIDE prediction score suggests an increased likelihood of immune evasion and a reduced likelihood of benefiting from immune checkpoint inhibitor (ICI) treatment. Results indicated that the HSS-high group had a higher TIDE score than the HSS-low group, suggesting that patients with a high HSS score are less likely to benefit from ICI treatment (Fig. 7B). Furthermore, the HSS-high group had a higher cancer-associated fibroblast (CAF) score and increased T cell exclusion and dysfunction scores (Fig. 7B). These findings suggest that the HSS-low group, with its higher levels of tumor-infiltrating cells, might be more likely to benefit from ICI treatment.

4. Discussion

Despite the progress in therapeutic approaches, there is still a lack of effective prognostic indicators [27]. The utilization of hormone therapy in breast and prostate cancers has changed the prognosis and treatment of patients, however, the link between CRC and hormones is less well known. In this study, the six hormone secretion gene signature (CYT19A1, FOXD1, GRP, INHBB, SPP1, and UCN) was constructed. The associations of the signature with survival outcomes in COAD were explored. Moreover, the potential molecular mechanisms of the signature implicated in COAD tumorigenesis were also investigated based on public datasets.

The CYP19A1 is recognized as an influential lipid metabolism-related gene (LMG) in colon cancer, as revealed in the previously described [28]. The study aimed to create a lipid metabolism-related prognostic risk score (LMrisk) and utilized differentially expressed LMGs, including CYP19A1. The CYP19A1 was identified as an independent prognostic factor and was found to be positively correlated with PD-L1 expression in human colon cancer tissues. Inhibition of CYP19A1 was observed to enhance CD8+ T cell-mediated antitumor immune response, induce normalization of tumor blood vessels, and boost the efficacy of anti-PD-1 therapy in colon cancer models. This was achieved through the downregulation of PD-L1, IL-6 and TGF- $\beta$ levels via the GPR30-AKT signaling pathway. In conclusion, CYP19A1 plays a key role in colon cancer, potentially influencing the tumor immune microenvironment and affecting the effectiveness of immunotherapeutic strategies. This study proposes the combined use of CYP19A1 inhibition and PD-1 blockade as a promising approach for colon cancer immunotherapy.

FOXD1, or foxhead box D1, has been identified as a critical player in various aspects of CRC. According to several studies, FOXD1 is involved in the mechanism of CXCL5’s role in tumor angiogenesis in CRC. Its influence is traced to the activation of the AKT/NF- $\kappa$ B/FOXD1/VEGF-A pathway in a CXCR2-dependent manner [29]. However, when the expression of CXCR2 and FOXD1 was suppressed or the AKT and NF- $\kappa$ B pathways were inhibited, the stimulatory impact of rhCXCL5 on HUVEC tube formation, proliferation, and migration was significantly reduced. This points to potential therapeutic applications for CXCL5 in anti-angiogenesis cancer treatments. In a separate study, FOXD1 was reported as the most differentially expressed factor among transcription factors (TFs), covering the majority of downstream differentially expressed genes (DEGs) and serving as an independent risk prognostic factor [30]. High FOXD1 expression, evaluated in 131 CRC patients’ tissues, was strongly correlated with TNM stage and pathological differentiation. It was confirmed as an independent prognostic risk factor for overall survival and disease-free survival, with patients expressing high FOXD1 more likely to have poor outcomes. Additionally, the same study discovered that suppression of FOXD1 resulted in reduced CRC cell proliferation, migration, and invasion, while its overexpression enhanced these traits [17]. These outcomes were mediated by the activation of the ERK 1/2 signaling pathway, with its inhibition mitigating the tumor-promoting effects induced by FOXD1 overexpression. This evidence underscores the role of FOXD1 in driving tumorigenesis and the progression of CRC through the activation of the ERK 1/2 signaling pathway, thereby positioning it as a potential candidate for therapeutic intervention.

The gastrin-releasing peptide (GRP) and its receptor (GRPR) play significant roles in the development and progression of CRC. Notably, both amidated and non-amidated forms of GRP were found to accelerate CRC cell proliferation and migration, mediated through the GRP receptor. These effects were inhibited by a GRP receptor antagonist, indicating the potential therapeutic applications for GRP receptor antagonists in CRC management [31]. GRPR is ectopically expressed in over 60% of colon cancers, and its expression is correlated with increased colon cancer cell migration. Upon activation, GRP stimulates the small GTPase RhoA through G13 heterotrimeric G-protein signaling. The regulator of G-protein signaling, postsynaptic density 95/disk-large/ZO-1 (PDZ)-RhoGEF (PRG), has been identified as the main activator of RhoA downstream of GRPR [32]. This PRG-RhoA-ROCK pathway contributes to GRP-stimulated colon cancer cell migration and Cox-2 expression, which in turn increases prostaglandin-E2 (PGE2) production, further enhancing cell migration. GRP and GRPR can also act as morphogens in CRC, influencing cell motility and enhancing cell-matrix attachment, processes mediated by focal adhesion kinase (FAK) [33]. Interestingly, GRP-induced FAK activation upregulates the expression of ICAM-1, a molecule implicated in enhancing cell motility and attachment to the extracellular matrix. Therefore, GRP is impact on CRC cell behavior and progression involves multiple molecular pathways and interactions.

Inhibin subunit beta B (INHBB), a protein-coding gene involved in the synthesis of the transforming growth factor- $\beta$ (TGF- $\beta$ ) family members, has been identified as having potential clinical significance for patients with CRC [34]. Various bioinformatics analysis methods have shown that INHBB expression is elevated in CRC tissue, and overexpression is significantly associated with invasion depth, distant metastasis, and advanced TNM stage. Although hypermethylation was observed in the promoter region of INHBB, it did not correlate with INHBB expression. High expression of INHBB was correlated with worse overall survival and disease-free survival. INHBB was found to be involved in various cancer-promoting signaling pathways, including the Hedgehog signaling pathway and TGF- $\beta$ signaling pathway, among others. Interestingly, INHBB expression promoted macrophage infiltration while inhibiting memory T cell, mast cell, and dendritic cell infiltration, suggesting a possible role in immune response modulation in CRC. Lastly, a novel 8-gene signature, including INHBB, was developed to predict both OS and RFS, showing potential to improve personalized treatment and survival of CRC patients [19]. Overall, these findings propose INHBB as a potential prognostic biomarker and a novel therapeutic target for CRC.

Secreted phosphoprotein 1 (SPP1), also known as osteopontin, plays a significant role in CRC. In multiple studies, SPP1 was found to be overexpressed in CRC tissues, correlating with invasion and metastasis. In the tumor microenvironment, SPP1 enriched macrophages were found to interact with fibroblast activation protein (FAP)+ fibroblasts, potentially contributing to the formation of an immune-excluded structure that inhibits T cell infiltration. Inhibiting this interaction could potentially improve the efficacy of immunotherapy [35, 36]. Furthermore, a study demonstrated that overexpression of SPP1 can promote the process known as epithelial-mesenchymal-transition (EMT), which is associated with increased cancer cell invasiveness and metastasis [37]. Lastly, a seven-gene prognostic model, including SPP1, was constructed and validated, capable of predicting disease-specific survival in CRC patients [28]. This model also revealed an immunosuppressive tumor microenvironment in high-risk CRC patients. The presence of SPP1+ macrophages, found to be highly expressed in a subset of macrophages with strong senescence-associated secretory phenotype (SASP) features, was linked with poor prognosis in CRC. Overall, these findings indicate that SPP1 could serve as a potential therapeutic target in CRC.

Urocortin (UCN) is a part of several immune gene models which have been developed to predict the prognosis of COAD and colon cancer. In one study, UCN was identified as one of twelve immune-related genes that were used to construct a risk model for colon cancer [28]. This model was verified as an independent prognostic variable with good prognostic ability, and the increase in risk score was associated with a rise in macrophages, myeloid dendritic cells, and CD4+ T cells. Another study identified UCN as one of seven immune genes that can be used to form a model for predicting the prognosis of COAD, potentially providing potential therapeutic targets for clinical treatment [38]. These studies suggest that UCN plays a significant role in the immune response within the tumor microenvironment of COAD and colon cancer, and could be a valuable biomarker for prognosis and treatment planning.

This study reveals a significant increase in the expression of six hormone secretion-related genes in COAD tumor samples compared to normal tissue. By summing the z-score of each gene, a hormone secretion score (HSS) is computed. Analysis of COAD datasets from TCGA and three GEO repositories indicates that patients with a high HSS suffer from significantly shorter overall survival than those with a low HSS. Further exploration of the molecular mechanisms related to these six hormone secretion genes in COAD identifies several hallmark pathways. These pathways, significantly enriched in the high HSS group compared to the low HSS group, include the hypoxia pathway, the TGF- $\beta$ signaling pathway, and the epithelial-mesenchymal transition (EMT) process.

Hypoxia, a defining characteristic in CRC development [39], triggers the expression of genes linked to inflammatory response, tumor vascularization, and metastasis by stabilizing the HIF1a transcription factor [1, 38, 40]. The EMT is another crucial physiological process pivotal to embryonic development and wound healing [41]. It’s also a notable feature associated with cancer invasion and metastasis [42, 43]. Previous studies have revealed a significant link between the upregulation of SPP1, a gene enriched in the EMT pathway, and enhanced cell growth, adherence, and invasion in CRC [37, 42]. Functioning as a cytokine, SPP1 amplifies the production of interferon-gamma (IFN- $\gamma$ ) and interleukin-12 (IL-12) while mitigating the production of interleukin-10 (IL-10), playing a vital role in facilitating type I immunity. Recent evidence also suggests that hypoxia could potentially influence the activation of the IL-10 signal in a variety of cancers [44], indicating a possible impact of hypoxic conditions on tumor immunity.

Recent research reveals that CRC exhibits significant stromal TGF- $\beta$ activity, as evidenced by the presence of phosphorylated SMAD3 (pSMAD3) and the expression of CALD1 and IGFBP7. These markers are expressed in cancer-associated fibroblasts (CAFs), which are predictors of poor prognosis [44]. The TGF- $\beta$ family members are known to stimulate the epithelial-mesenchymal transition (EMT) process. Global chromatin alterations, removal of histone variants like H2A.Z, and the addition of new chromatin modifiers such as UTX, Rad21, PRMT5, RbBP5, and others are recognized as transcription factors critical to EMT regulation [38]. Previous study suggests a close relationship between INHBB and EMT [45]. It’s worth noting that activin B, a homodimer of INHBB, functions as a multifaceted cytokine within the TGF- $\beta$ family.

These findings suggest that the six identified hormone-secretion-related genes contribute to the progression and metastasis of COAD. Specifically, HSS-high could potentially intensify the EMT process via the activation of the TGF- $\beta$ signaling pathway. This might lead to hypoxic conditions that promote COAD metastasis, reduce the presence of tumor-infiltrating immune cells, and result in a worse prognosis for COAD patients.

However, the study has certain limitations. First, the conclusions are largely based on bioinformatics analysis and need further verification through in vivo and in vitro studies. Second, the study does not incorporate key risk factors for CRC, such as dietary habits and family history.

In conclusion, this study identified a signature of six hormone-secretion-related genes that influence the TGF- $\beta$ pathway and the tumor immune microenvironment. Validation across different datasets implies that the HSS score, based on these six genes, serves as an independent prognostic factor for COAD patients with worse outcomes. Future studies should investigate the potential use of this six-gene signature as a prognostic risk biomarker and as a guide for COAD treatment in a large, prospective cohort.

Author contribution

Conception: Yongkun Sun, Yin Guan.

Interpretation or analysis of data: Xiongjie Jia, Tao Zhang, Xinze Lv, Haiwei Du, Yongkun Sun, Yin Guan.

Preparation of the manuscript: Xiongjie Jia, Tao Zhang, Xinze Lv, Haiwei Du.

Revision for important intellectual content: Yongkun Sun, Yin Guan.

Supervision: Yongkun Sun, Yin Guan.

Supplementary data

The supplementary files are available to download from https://dx-doi-org.web.bisu.edu.cn/10.3233/CBM-230126.

sj-xlsx-1-cbm-10.3233_CBM-230126.xlsx - Supplemental material

Supplemental material, sj-xlsx-1-cbm-10.3233_CBM-230126.xlsx

Footnotes

Conflict of interest

Xinze Lv and Haiwei Du were employed by the company Burning Rock Biotech Ltd. at the time of writing this report. The other authors have no conflicts of interest to declare.

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