FGF19 is a biomarker associated with prognosis and immunity in colorectal cancer

doi:10.21203/rs.3.rs-4812212/v1

Download PDF

Article

FGF19 is a biomarker associated with prognosis and immunity in colorectal cancer

https://doi.org/10.21203/rs.3.rs-4812212/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The aim of this study was to investigate the relationship between fibroblast growth factor 19 (FGF19) and the prognosis and immune infiltration of colorectal cancer (CRC), and to find the related genes and pathways affecting the occurrence and development of CRC, providing an important molecular basis for the early diagnosis and immunotherapy of CRC. We performed Venn overlap analysis on prognosis-related genes of CRC and up-regulated differentially expressed genes (DEGs) of CRC and immune-related gene sets to obtain the final DEGs. We investigated the relationship between the target genes and pathological parameters, immune infiltration, and immune checkpoints. The relevant functions and signaling pathways of target genes were analyzed by enrichment analysis. We investigated the genetic variation of the target genes. We analyzed the association of target genes with tumor heterogeneity and drug sensitivity. Finally, we performed single-cell analysis of the target genes. The results indicate that FGF19 is a target gene associated with immunity and prognosis in CRC patients. By exploring the relationship between FGF19 and neutrophil extracellular traps (NETs), and the relationship between NETs and the immune microenvironment, we found that FGF19 may have an effect on the progression of CRC by promoting NETs expression leading to immune cell suppression.

Biological sciences/Cancer/Gastrointestinal cancer/Colorectal cancer

Health sciences/Oncology/Surgical oncology

Biological sciences/Immunology/Tumour immunology

CRC

prognosis

immunity

NETs

tumor heterogeneity

single cell analysis

CRC is one of the most prevalent malignant tumors of digestive tract worldwide, and its morbidity and mortality continue to rise, which seriously threatens human health, resulting in economic burden and disease burden that cannot be ignored. According to global cancer data estimates from 2020, there are 1.932 million new cases of CRC, accounting for about 10.0% of all new malignant tumors, and 935,000 deaths, accounting for 9.4% of all malignant tumor deaths, with morbidity and mortality ranking third and second among all malignant tumors¹. In recent years, the incidence of CRC has also gradually increased in China. According to the epidemic data of malignant tumors in China in 2020, the incidence and mortality of CRC rank second and fourth among all malignant tumors². In recent years, with the influence of environmental factors, the global incidence rate has been increasing year by year and younger. The occurrence and development of CRC are related to many factors. Studies have confirmed that the immune system is closely related to the occurrence and development of CRC. In the treatment of advanced CRC, immunotherapy has better effect and less toxic and side effects³.

FGF19 gene is located in q13 region of chromosome 11 and consists of 216 amino acid residues with a signal peptide sequence at the N-terminus, so it can act in an autocrine and paracrine manner.FGF19 is mainly expressed in the ileum and is also expressed in cartilage, skin, retina, kidney, and gallbladder⁴.Fibroblast growth factor receptors are tyrosine kinase receptors, including five subtypes: FGFR1, FGFR2, FGFR3, FGFR4, and FGFR5, which are composed of extracellular ligand-binding domains, intracellular tyrosine kinase domains, and a single transmembrane domain⁵.Signaling by fibroblast growth factors requires the Klotho family of single transmembrane proteins as coreceptors^6–9.FGF19 has been found to be closely related to FGFR4, and FGF19 must bind β-Klotho to form the receptor complex FGFR4-β-Klotho to function and enhance the affinity between ligands and receptors.FGF19 binds FGFR4 and then undergoes autophosphorylation and dimerization, which promotes the proliferation of tumor cells, promotes epithelial-mesenchymal transition, and inhibits tumor cell apoptosis by activating mitogen-activated extracellular signal-regulated kinase-extracellular regulated protein kinases, phosphatidylinositol 3 - kinase(PI3K)-serine/threonine kinase(AKT), glycogen synthase kinase-3 - β-catenin and other pathways¹⁰. FGF19 has been found to be closely associated with a variety of cancers, and overexpression of FGF19 and its receptor FGFR4 up-regulates early growth response gene-1, immediate early gene, interleukin-6, and connective tissue growth factor and induces hepatoma cell proliferation^11–13.Aberrant signaling pathways of the FGF19-FGFR4 complex have been demonstrated to be oncogenic drivers of hepatocellular carcinoma¹¹.In addition, FGF19 and FGFR4 can also promote gallbladder cancer progression dependent on the autocrine pathway of the G-protein coupled bile acid receptor 1-cyclic adenosine monophosphate-recombinant early growth response protein 1 axis¹⁴. FGF19 may also be a new diagnostic marker for screening lung cancer, and binding to FGFR4 drives the progression of lung squamous cell carcinoma¹⁵.In studies of pancreatic cancer, high mobility group a1 directly induced the expression of FGF19 and increased its protein secretion by recruiting active histone markers (H3K4me3, H3K27Ac), thereby driving pancreatic carcinogenesis and matrix formation¹⁶.FGF19 also has oncogenic driver functions in head and neck squamous cell carcinoma¹⁷. FGF19 is not only closely related to cancer development, but also to the immune microenvironment of tumor cells and tumor immune cells¹⁸.

The advent of the era of precision medicine has made targeted therapy and immunotherapy a hot topic in research, and with the rapid development of targeted drugs, the treatment concept of cancer also tends to be individualized and precise, and immunotherapy has become an effective clinical strategy for the treatment of malignant tumors because of its unique function^19,20. With the diversified precise treatment options for CRC to obtain a certain degree of benefit for clinical patients, but then gradually appear drug resistance and other problems, seriously restricting its efficacy and prognosis, so it becomes essential to find new immunotherapy targets. FGF19 is not only closely associated with the development of a variety of cancers, but also with the tumor immune microenvironment. At present, FGF19 has been studied in a variety of cancers, but there are few studies in CRC. Therefore, the aim of this study was to explore the prognosis of FGF19 in CRC, investigate the relationship between FGF19 and clinical pathology, and clarify the relationship between FGF19 and the immune microenvironment of CRC. It is hoped that this study can provide new ideas for CRC patients to develop new effective molecular targets and immunotherapy strategies for the benefit of more CRC patients.

Screening of Differential Genes Associated with Prognosis and Immunity in CRC

We first identified the DEGs of CRC cancer and normal tissues, and selected a total of 537 up-regulated DEGs (LogFC > 3, P < 0.05); then identified the DEGs of CRC prognosis, and selected a total of 1706 DEGs (P < 0.05); then we downloaded a total of 2438 immune-related genes from the ImmPort database; finally, we performed Venn overlap analysis of the selected up-regulated DEGs, prognostic DEGs of CRC, and immune-related genes (Fig. 1A), and selected a total of 6 intersection genes, which were TG, ULBP2, S100A7, FGF19, CXCL8, and GAST (Supplementary Table 1).We performed survival analysis of CRC for these six genes, and we found that TG, ULBP2, S100A7, FGF19, CXCL8, and GAST were all associated with overall survival (OS) in patients (P < 0.05), and we found that TG and CXCL8 were beneficial to the prognosis of CRC patients (Fig. 1B, Fig. 1C), and ULBP2, S100A7, FGF19, and GAST were not conducive to the prognosis of CRC patients (Fig. 1D-G). In addition, to verify whether the six genes were highly expressed, we performed volcano mapping using the datasets of TCGA from CRC cancer and normal tissues and GSE41328 and GSE71187 datasets, to verify whether the genes were up-regulated; we found that these six genes were up-regulated in the volcano mapping using the TCGA dataset (Fig. 1H), and only three genes, ULBP2, CXCL8, and FGF19, were up-regulated in the volcano mapping based on the GSE41328 dataset (Fig. 1I), and only GAST, ULBP2, GAST, and FGF19 were up-regulated in the volcano mapping based on the GSE71187 dataset (Fig. 1J).By comprehensively comparing the prognostic analysis and gene expression of CRC, we found that FGF19 was not only highly correlated with the prognosis of CRC patients, but also highly expressed in the TCGA database and GSE41328 and GSE71187 datasets, based on which, we finally selected FGF19 as our research target to explore its relationship with the development of CRC.

In CRC, high expression of FGF19 is strongly associated with poor prognosis

In order to investigate the expression level of FGF19 in normal and tumor tissues, we first analyzed the expression of FGF19 in pan-cancer tissues and normal tissues in the TCGA database using the TIMER2.0 database, and the results showed that FGF19 was up-regulated in colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) cancer tissues with a significant difference (P < 0.001) (Fig. 2A).In addition, we obtained RNA-seq data from normal and tumor tissues of 34 cancers from the TCGA database and the GTEx database and performed a significant difference analysis using the Wilcoxon test, and the results showed that FGF19 was up-regulated in COAD, COADREAD, and READ cancer tissues with significant differences (COAD: P = 1.4e-88, COADREAD: P = 1.7e-103, READ: P = 2.6e-6) (Fig. 2B). Based on the TCGA database, we found that FGF19 expression was up-regulated in CRC tissues compared with adjacent non-cancerous tissues, both by paired and unpaired differential analysis, and was significantly correlated (paired difference: P = 7.8e-09, unpaired difference: P = 5.7e-22) (Fig. 2C-D). In addition, the results of GSE41328, GSE110224, and GSE41328 databases also confirmed that FGF19 was up-regulated in both paired and unpaired difference analyses in cancer and normal tissues, and all were significantly correlated (paired difference analysis: P = 0.02 unpaired difference analysis: P = 0.0042) (Supplementary Fig. 1A-B). Based on the Human Protein Atlas (HPA) database, we found that FGF19 protein was highly expressed in CRC tissues (Fig. 2E). Based on the TCGA database, we plotted ROC curves and showed AUC = 0.904 (Supplementary Fig. 1C), suggesting FGF19 could be a potential diagnostic biomarker. According to our Kaplan-Meier survival curve analysis done for FGF19 (Fig. 1E), CRC patients with high FGF19 expression had lower overall survival.

Relationship between FGF19 expression and clinicopathological parameters

We used Chi-square test and single gene logistic analysis to analyze the association between clinicopathological factors and FGF19 expression (Table 1–2). Chi-square test showed that FGF19 was associated with N stage (P = 0.002), M stage (P < 0.001), pathological stage (P < 0.001), and degree of lymphatic invasion (P = 0.011) in CRC patients. By single gene logistic regression analysis, FGF19 was significantly associated with N stage (P = 0.004), M stage (P < 0.001), pathological stage (P = 0.004), and degree of lymphatic invasion (P = 0.007) in CRC patients. We also analyzed the relationship between FGF19 expression and clinical variable groupings and showed that FGF19 expression was associated with N0 and N2 stages in CRC patients (P < 0.001) (Fig. 3A), meaning that the number of lymph node metastases also increased as FGF19 expression increased. We also found a correlation between FGF19 expression and M0 and M1 stages in CRC patients (P < 0.001) (Fig. 3B), which means that as FGF19 expression increases, the risk of distant metastasis of cancer also increases. Interestingly, there was also a correlation between FGF19 expression and pathological stage in CRC patients (Fig. 3C), and the results showed a correlation between stage I and stage IV (P = 0.0031), stage II and stage IV (P = 3e-0.5), and stage III and stage IV (P = 0.01), which means that with increasing FGF19 expression, pathological stage is posterior and represents more severe disease. To investigate the impact of FGF19 expression and clinicopathological parameters on survival, we used univariate and multivariate cox regression analysis (Table 3). Among the variables of univariate cox regression model (P < 0.05), age, T stage, N stage, M stage, pathological stage, CEA level, and lymphatic invasion were all associated with overall survival of patients. Then, these variables in the univariate cox regression model were included in the multiple cox regression model for analysis. Eventually, we could find that age (P < 0.001), M stage (P = 0.035), pathological stage (P = 0.013), and lymphatic invasion (P = 0.006) were independent risk factors affecting the overall survival of CRC patients.

Table 1

FGF19 expression associated with clinicopathological characteristics (chi-square test)
Characteristics	Low expression of FGF19	High expression of FGF19	P value
n	322	322
Pathologic T stage, n (%)			0.107
T1	14 (2.2%)	6 (0.9%)
T2	50 (7.8%)	61 (9.5%)
T3	225 (35.1%)	211 (32.9%)
T4	32 (5%)	42 (6.6%)
Pathologic N stage, n (%)			0.002
N0	204 (31.9%)	164 (25.6%)
N1	74 (11.6%)	79 (12.3%)
N2	44 (6.9%)	75 (11.7%)
Pathologic M stage, n (%)			< 0.001
M0	248 (44%)	227 (40.2%)
M1	28 (5%)	61 (10.8%)
Pathologic stage, n (%)			< 0.001
Stage I	58 (9.3%)	53 (8.5%)
Stage II	137 (22%)	101 (16.2%)
Stage III	91 (14.6%)	93 (14.9%)
Stage IV	28 (4.5%)	62 (10%)
Gender, n (%)			0.305
Male	178 (27.6%)	165 (25.6%)
Female	144 (22.4%)	157 (24.4%)
CEA level, n (%)			0.814
<= 5	137 (33%)	124 (29.9%)
> 5	79 (19%)	75 (18.1%)
Lymphatic invasion, n (%)			0.011
Yes	105 (18%)	127 (21.8%)
No	196 (33.7%)	154 (26.5%)
P<0.05, and the results were statistically significant.

Table 2

FGF19expression associated with clinicopathological characteristics (logistic regression)
Characteristics	Total (N)	OR (95% CI)	P value
Pathologic T stage (T3&T4 vs. T1&T2)	641	0.977 (0.666–1.435)	0.906
Pathologic N stage (N0 vs. N1&N2)	640	0.632 (0.461–0.867)	0.004
Pathologic M stage (M1 vs. M0)	564	2.360 (1.457–3.823)	< 0.001
Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II)	623	1.606 (1.168–2.209)	0.004
CEA level ( < = 5 vs. > 5)	415	0.939 (0.630–1.399)	0.756
Lymphatic invasion (No vs. Yes)	582	0.631 (0.452–0.881)	0.007
Age ( < = 65 vs. > 65)	644	1.194 (0.874–1.633)	0.265
Gender (Female vs. Male)	644	1.206 (0.885–1.644)	0.236
P<0.05, and the results were statistically significant.

Table 3

Univariate and multivariate analyses of clinicopathological parameters in patients with CRC
Characteristics	Total(N)	Univariate analysis		Multivariate analysis
Characteristics	Total(N)	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Age	643
<= 65	276	Reference		Reference
> 65	367	1.939 (1.320–2.849)	< 0.001	3.944 (2.014–7.722)	< 0.001
Gender	643
Female	301	Reference
Male	342	1.054 (0.744–1.491)	0.769
Pathologic T stage	640
T1&T2	131	Reference		Reference
T3&T4	509	2.468 (1.327–4.589)	0.004	1.580 (0.548–4.554)	0.398
Pathologic N stage	639
N0	367	Reference		Reference
N1&N2	272	2.627 (1.831–3.769)	< 0.001	0.235 (0.053–1.052)	0.058
Pathologic M stage	563
M0	474	Reference		Reference
M1	89	3.989 (2.684–5.929)	< 0.001	2.172 (1.055–4.470)	0.035
Pathologic stage	622
Stage I&Stage II	348	Reference		Reference
Stage III&Stage IV	274	2.988 (2.042–4.372)	< 0.001	8.843 (1.584–49.370)	0.013
CEA level	414
<= 5	260	Reference		Reference
> 5	154	2.620 (1.611–4.261)	< 0.001	1.496 (0.808–2.770)	0.200
Lymphatic invasion	581
No	349	Reference		Reference
Yes	232	2.144 (1.476–3.114)	< 0.001	2.542 (1.304–4.954)	0.006
FGF19	643
Low	322	Reference		Reference
High	321	1.548 (1.089–2.202)	0.015	1.439 (0.799–2.592)	0.225
P<0.05, and the results were statistically significant.

Functional Enrichment Analysis of FGF19 in CRC

Based on the TCGA database, a total of 516 DEGs (| LogFC |>1,Padj < 0.05) were identified in CRC samples with high versus low FGF19 expression (Supplementary Table 2).We performed gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on 516 DEGs. We did molecular function, cellular component, and biological process of GO analysis (Fig. 3D-F). We analyzed the molecular function of FGF19 and found that FGF19 was mainly associated with signal receptor activator activity, receptor ligand activity, and protein heterodimerization activity. The cellular components of FGF19 are mainly associated with protein DNA complexes, intermediate filament cytoskeleton, and intermediate filaments. The biological process of FGF19 is mainly related to the detection of chemical stimuli participating in sensory perception, protein-DNA complex subunit organization, and chromatin assembly. By performing KEGG analysis of FGF19 (Fig. 3G), we found 10 relevant signaling pathways for FGF19. Notably, we found that the enriched pathways were mainly associated with NETs formation, alcoholism, systemic lupus erythematosus, staphylococcus aureus infection, estrogen signaling pathway, gastric cancer, taste transduction, complement and coagulation cascade system, cholesterol metabolism, and digestion and absorption of fat. Because it has been demonstrated that the neutrophil extracellular trapping net is associated with a variety of cancers, to verify whether the NETs is associated with FGF19 expression in CRC, we explored FGF19 association with NETs-related genes (Fig. 3H), and the results showed that in CRC, FGF19 was mostly associated with NETs-related genes, demonstrating that FGF19 has the potential to play a role in CRC by promoting NETs formation.

In addition, to explore the relevant pathways of FGF19 in CRC more comprehensively, we further performed gene set enrichment analysis (GSEA) using data from 516 DEGs. GSEA results showed that formation of the cornified envelope, keratinization, malignant pleural mesothelioma, Pi3Kakt signaling pathway, regulation of insulin-like growth factor Igf transport, and regulation of uptake of insulin-like growth factor-binding protein Igfbps were enhanced in samples with high FGF19 expression (Fig. 3I).Several pathways are inhibited, including systemic lupus erythematosus, chromatin-modifying enzymes, acetylated histones, late events in human cytomegalovirus, and histone deacetylases that deacetylate histones (Fig. 3J).Supplemental Table 3 shows more GSEA results. We also analyzed PPI network diagrams for FGF19-related genes (Supplementary Fig. 1D).

FGF19 gene alterations in CRC

We investigated FGF19 variants in CRC using the cBioPortal database, and selected four CRC datasets from the cBioPortal database including 2405 samples. The results showed that FGF19 mutations were present in 1.33% (32/2479) of CRC patients, and the proportion of gene mutations and gene amplifications was 0.67% (Fig. 4A). We found 18 mutation sites between amino acids 0 and 216, including 17 missense mutations and 1 truncated mutation, and in addition, we found that R43H was the most common mutation site, and the first exon of the gene was mutated the most, and the most important mutation type was missense mutation (Fig. 4B). We also investigated the relationship between typing and mutation count of cancers in four CRC datasets, and we found that the corresponding mutation count was the highest in CRC adenocarcinoma (Fig. 4C). We utilized the COSMIC database to further explore mutation types. The results showed that 62.96% of CRC samples showed missense substitutions and 29.63% of CRC samples showed synonymous substitutions (Fig. 4D). In addition, base substitutions were mainly C > T (46.15%), G > A (15.38%), and T > C (15.38%) (Fig. 4E).

Relationship between FGF19 expression and tumor immune infiltration

To examine the impact of FGF19 changes on the tumor microenvironment, we first analyzed the correlation between FGF19 and ESTIMATE scores in CRC and showed that this gene expression presented a significant negative correlation with immune infiltration in CRC (N = 373, R = -0.14, P = 5.7e-3) (Fig. 4F).We also used the ssGSEA algorithm to assess the relationship between the relative abundance of 24 immune cells and FGF19 expression in CRC (Fig. 4G).In addition, we also plotted scatter plots of FGF19 with immune cells (Fig. 5A-D, Supplementary Fig. 1E-L) and found that different types of immune cells were associated with FGF19 expression, and we found that FGF19 was positively correlated with NK cells (P < 0.001, r = 0.134) and FGF19 was negatively correlated with Tem cells (P = 0.042, r = − 0.08) and neutrophils (P = 0.011, r = − 0.1), FGF19 was significantly negatively correlated with regulatory T cells (P = 0.007, r = -0.107), Th1 cells (P < 0.001, r = -0.16), Th2 cells (P < 0.001, r = -0.257), B cells (P = 0.005, r = -0.11), helper T cells (P = 0.004, r = -0.113), activated dendritic cells (P < 0.001, r = -0.154), T cells (P < 0.001, r = -0.195), cytotoxic cells (P < 0.001, r = -0.202), and macrophages (P < 0.001, r = -0.167).In addition, we also analyzed the expression of FGF19 with multiple immune cell groupings (Fig. 5E-H, Supplementary Fig. 1M-S, Supplementary Fig. 2A), and the results showed that the proportion of NK cells in the FGF19 high expression group was significantly higher than that in the low expression group, and the proportion of macrophages, T cells, activated dendritic cells, helper T cells, B cells, Th1 cells, Th2 cells, regulatory T cells, neutrophils, Tem cells, and cytotoxic cells in the FGF19 high expression group was lower than that in the low expression group.

Relationship between FGF19 Expression and Immune Checkpoints

Because immune checkpoints are increasingly found to be aberrantly expressed on cancer cells, and the expression of immune checkpoint genes is closely related to the efficacy of immunotherapy, we investigated the correlation between FGF19 and 60 immune checkpoint-related genes in CRC, and we observed that FGF19 was negatively correlated with most immune checkpoints genes (Fig. 5I). We investigated several important immune checkpoints genes, so we plotted a scatter plot of FGF19 with eight immune checkpoints genes (Fig. 5J-O, Supplementary Fig. 2B-C), and the results showed that FGF19 was negatively correlated with CD274 (P < 0.001, R = − 0.226), FGF19 was negatively correlated with LAG3 (P < 0.001, R = − 0.174), FGF19 was negatively correlated with HAVCR2 (P < 0.001, R = − 0.220), FGF19 was negatively correlated with CTL4 (P = 0.004, R = − 0.113), FGF19 was negatively correlated with PDCD1LG2 (P < 0.001, R = − 0.219), and FGF19 was negatively correlated with TIGIT (P < 0.001, R = − 0.259). In addition to this, we found a positive correlation between FGF19 and SIGLEC5 (P < 0.001, R = -0.130), and FGF19 had little relationship with PDCD1.

FGF19 in Relation to Tumor Heterogeneity and Chemosensitivity

Tumor genomic heterogeneity is closely associated with tumor development, where Tumor mutation burden (TMB), microsatellite instability (MSI), Neoantigen (NEO), and mismatch repair (MMR) are considered promising biomarkers to predict the efficacy of immunotherapy ^21–24. We observed that FGF19 was negatively correlated with TMB expression in CRC (Fig. 6A) and FGF19 was negatively correlated with MSI expression in CRC (Fig. 6B). FGF19 negatively correlated with NEO expression in CRC (Fig. 6C). We observed a negative correlation between FGF19 and MSH6 of MMR-related genes (Fig. 6D). Overall, FGF19 may influence tumor immunity through TMB, MSI, NEO, MMR linkages. Mutant-allele tumor heterogeneity (MATH) was also strongly associated with tumor heterogeneity, and we observed a positive correlation between FGF19 and MATH in CRC (Fig. 6E). In addition, we analyzed the sensitivity of FGF19-related drugs. According to CTRP dataset analysis, we found that there was a correlation between FGF19 expression level and drug sensitivity, we found that the first three drugs positively correlated with FGF19 expression were BRD-K99006945, PI-103 and AT7867; the first three drugs negatively correlated with FGF19 expression were afatinib, lapatinib and linifanib (Fig. 6F, Supplementary Table 4), and plotted the network diagram of FGF19-related chemotherapeutic drugs (Fig. 6G). we plotted the network diagram of FGF19-related chemotherapeutic drugs, according to the drug sensitivity results of GDSC, the drug positively correlated with FGF19 expression was JNJ − 26854165, and the drug negatively correlated with FGF19 expression was VX − 11e (Fig. 6H, Supplementary Table 5). For visual analysis, we also plotted the network diagram of FGF19-related chemotherapeutic drugs (Supplementary Fig. 2D).

Single Cell Analysis of FGF19 in CRC

To understand the predominant cell types that express FGF19 in the cancer microenvironment, we performed single cell analysis on single cell datasets from CRC samples in the GSE178341 dataset. We found that the FGF19 gene is located on chromosome 11 and contains three exons with a total length of approximately 1821 bp; genes interacting with FGF19 are FXR1, FGF2, EGFR (Fig. 7A). Figure 7B shows the distribution of all cells in CRC, and Fig. 7C shows the distribution of FGF19 in all cells in CRC. By comparison, we found that FGF19 was mainly distributed in squamous epithelial cells, myeloid cells, stromal cells, T cells, NK cells, and innate lymphocytes (ILCs)(Supplementary Fig. 2E). In addition, we also explored the distribution of FGF19 in immune cells, Fig. 7D showed the distribution of immune cells in CRC, and Fig. 7E showed the distribution of FGF19 in CRC immune cells, and by comparison we found that FGF19 was mainly distributed in monocytes, cytotoxic T lymphocytes, promyelocytic leukemia zinc finger protein, dendritic cells, and macrophages ) (Supplementary Fig. 2F). Finally, we specifically explored the distribution of FGF19 in T cells, NK cells, and ILCs. Figure 7F shows the distribution of T cells, NK cells, and ILCs in CRC, and Fig. 7G shows the distribution of FGF19 in CRC immune cells. By comparison, we found that FGF19 was mainly distributed in cytotoxic T lymphocytes, CD4⁺ T cells, and promyelocytic leukemia zinc finger protein(Supplementary Fig. 2G). Because FGF19 is mainly distributed in monocytes and cytotoxic T lymphocytes, we performed cell-interaction analysis of monocytes and cytotoxic T lymphocytes in CRC. First, we did single-cell sequencing comparisons of different samples, and finally selected CRC-101-03-1A samples with high FGF19 expression in CRC single-cell sequencing (Supplementary Fig. 2H); because cytotoxic T lymphocytes are mainly composed of CD8 + T cells, so we analyzed the cellular interactions of monocytes and CD8⁺T cells in CRC-101-03-1A samples, circos plots of monocyte and CD8 ⁺ T cells interactions with other cells in CRC are shown in Fig. 7H, dot plots of monocyte interactions with other cells in CRC are shown in Fig. 7I, and dot plots of CD8 ⁺ T cells interactions with other cells in CRC are shown in Supplementary Fig. 2I.

For CRC, molecular targeted therapy and immunotherapy have played a certain therapeutic role in recent years²⁵. For example, development of immune checkpoint inhibitors has shown clinical efficacy. However, most CRC patients do not benefit from immune checkpoint inhibitors due to adverse events ²⁶. Therefore, further search for immune-related genes is necessary to improve the prognosis of CRC patients.

In TCGA, we obtained clinical and RNA sequencing data from 644 CRC patients, then DEGs in CRC was obtained by DESeq2 analysis, DEGs associated with CRC patient prognosis was analyzed by Survival package, and immune-related genes were downloaded from ImmPort database, and 6 genes were obtained by Venn overlap analysis of the three, and FGF19 was finally selected as the target gene by comparing the prognosis of CRC and the expression of 6 genes in cancer and adjacent non-cancerous tissues.FGF19 expression has been found to be upregulated in a variety of cancers and associated with adverse outcomes in these patients. However, FGF19 remains poorly investigated in CRC. According to the GTEx database and TCGA database, FGF19 expression levels were higher in CRC tissues compared with normal tissues, and we used GSE41328, GSE110224, and GSE41328 datasets for validation, and the results still showed that FGF19 expression was increased in CRC tissues. We also compared immunohistochemistry between normal and cancer tissues in the HPA database and found that FGF19 protein expression remained highly expressed in CRC tissues, indicating that FGF19 mRNA was consistent with FGF19 protein expression. We found that the overall survival time of CRC patients decreased with increasing FGF19 expression levels, indicating that FGF19 is not conducive to the prognosis of patients. By comparing the diagnostic efficacy of FGF19 in CRC, our analysis results confirmed that FGF19 had a good diagnostic efficacy for CRC (AUC = 0.904). We found that FGF19 expression in CRC was associated with T stage, N stage, M stage, and pathological stage. By comparing the relationship between high and low FGF19 expression and clinical parameters, we found that when FGF19 levels increased, N stage, M stage and pathological stage were more advanced. According to multivariate regression analysis, age, M stage, pathological stage, and lymphatic invasion were independent risk factors for the prognosis of CRC patients. Overall, we can conclude that FGF19 is highly expressed in CRC, and patient survival declines with increasing FGF19 levels; the effect of FGF19 on CRC prognosis may be achieved by affecting its expression in N stage, M stage, and pathological stage; in addition to the good diagnostic efficacy of FGF19, we believe that FGF19 can be used as a biomarker for CRC diagnosis and prognosis.

We have explored the impact of FGF19 on the prognosis and progression of CRC, so we wanted to explore through which pathway FGF19 impacts CRC. Through GO, KEGG, GSEA analysis of FGF19 related genes in CRC, we found that the molecular function of FGF19 was correlated with signal receptor activator activity, receptor ligand activity, and protein heterodimerization activity. Many activities in our body require signaling receptors, receptor ligands, and are essential during CRC formation. Protein dimerization often occurs in cells and plays an important role in various biological processes and cancer development^27–31, and proteomic studies have also pointed to a large proportion of mammalian proteins that function only as dimers or multimers in cells. Therefore, correct dimer formation is very important for a healthy proteome as well as the body³².Through KEGG and GSEA analysis of FGF19, we found that the pathways associated with tumors were mainly neutrophil extracellular trapping network, Pi3Kakt signaling pathway, regulation of insulin-like growth factor Igf transport, and regulation of uptake by insulin-like growth factor binding protein Igfbps. NETs are fibrous mesh-like structures released into the extracellular space by neutrophils³³.The main components of NETs include nuclear DNA, as well as granulin composed of matrix metalloproteinase-9, myeloperoxidase, neutrophil elastase, and cathepsin G³⁴.NETs are highly expressed in a variety of malignant tumor tissues, and tumors have systemic effects that regulate NETs. There are two neutrophil phenotypes associated with tumors, anti-tumor N1 and tumorigenic N2 neutrophils, and both N1 and N2 neutrophils can produce NETs³⁵.NETs are highly expressed in a variety of cancers, and promote the development of a variety of cancers ^36,37.NETs has also been associated with CRC progression and metastasis^38,39, and based on this, we explored the relationship between FGF19 and NETs related genes. We found a significant association between FGF19 and NETs related gene sets, so FGF19 may have an impact on CRC by affecting NETs. Pi3Kakt signaling pathway⁴⁰, insulin-like growth factor, and insulin-like growth factor binding protein have all been demonstrated to be associated with cancer progression, so FGF19 may have an impact on CRC development by affecting these three pathways. In addition to this, we explored the genes involved in FGF19 in CRC and found that the top three genes most associated with FGF19 were ALB, IL1B, H3C12.

In this study, we selected four CRC gene sets to explore FGF19 variants based on the cBioPortal database. We found a low frequency of FGF19 mutations in CRC, only 1.33%, and the proportion of mutations and gene amplification was consistent. We found 18 mutation sites between amino acids 0 and 216 and found missense mutations to be the most frequent mutations. We also analyzed the relationship between specific CRC classification and mutation count and found that simple CRC adenocarcinoma corresponded to the highest mutation count, and other types of CRC adenocarcinoma corresponded to fewer mutation counts. In addition, we explored FGF19 variants in the COSMIC database and found that 62.96% of CRC samples had missense substitutions and were also the most mutated, which was consistent with the mutation type in the cBioPortal database. Among them, base substitutions were mainly C > T (46.15%). Through genetic variation analysis of FGF19 in CRC, we found that FGF19 effects on CRC were not caused by genetic mutations.

With regard to the exploration of the association of FGF19 with immune infiltrating cells, we found that FGF19 was negatively correlated with most immune cells, but interestingly we found that FGF19 was positively correlated with NK cells, which are well-known to be critical cells for inhibiting cancer progression, and there are many immunosuppressive agents based on the emergence of NK cells. In response to this interesting phenomenon, we reasoned that there would be something that inhibited the action of NK cells, and through our review of the literature, NETs could inhibit the action of NK cells. We found that FGF19 was also negatively correlated with CD8 ⁺ T cells. NETs have also been found to encapsulate and coat tumor cells, protecting them from CD8 ⁺ T cells- and NK cells-mediated cytotoxicity, thus hindering the contact between immune cells and surrounding target tumor cells and further hindering the control of tumor metastasis by immune cells⁴¹.We found that FGF19 was also negatively correlated with macrophages and dendritic cells, which were found to be two major antigen-presenting cells and key innate immune cells regulating anti-tumor immune responses. It has been shown that NETs activates macrophages and DCs by up-regulating important costimulatory molecules (CD80, CD86) early (30 min), however, macrophages and DCs undergo apoptosis after prolonged incubation with NET (56).We found FGF19 also associated with Tem cells, neutrophils,TH1 cells, TH2 cells, Treg cells, B cells, T helpe cells, T cells, and Cytotoxic cells are negatively correlated, and the relationship between NET and these cells is unclear. Because NETs is associated with these immune cells in healthy humans and autoimmune diseases^42,43, so whether NETs may also have an effect on these cells during tumor development needs further validation. We explored the correlation between FGF19 and ESTIMATE scores in CRC and found that FGF19 showed a negative correlation with ESTIMATE scores, indicating that the proportion of immune cells decreased with increasing FGF19 expression levels. Overall, most immune cells decreased with increasing FGF19 levels, implying that FGF19 may contribute to CRC development and progression by suppressing immune cells. Because NETs has a significant relationship with immune cells, FGF19 may promote CRC development and progression by promoting NET expression and thus inhibiting immune cells.

We explored the relationship between FGF19 and immune checkpoints and found that FGF19 presented a negative correlation with most immune checkpoints. We subsequently analyzed eight important immune checkpoints, and we found that FGF19 was positively correlated with SIGLEC5, which has already been demonstrated to have an effect on CRC and has the potential to be an effective prognostic indicator in CRC. FGF19 is positively correlated with SIGLEC5, indicating that FGF19 and SIGLEC5 can be detected in combination and become effective prognostic indicators of CRC. In addition, we explored the relationship between FGF19 and tumor heterogeneity and found that FGF19 was negatively correlated with TMB, MSI, NEO, MMR, indicating that FGF19 was not highly sensitive to immunosuppressive agents. Next, we explored the relationship between FGF19 and MATH and found a positive correlation between FGF19 and MATH, indicating that with increasing FGF19 levels, the greater tumor heterogeneity in CRC, the less conducive to immunotherapy. Since FGF19 is not sensitive to immunosuppressive agents, we analyzed FGF19 sensitivity to drugs in the GDSA and CRTP databases and found that FGF19 is highly sensitive to BRD-K99006945, PI-103, and AT7867 in the CTRP database and JNJ − 26854165 in the GDSA database, so the scope of use of related drugs can be explored to verify the efficacy of drugs against CRC patients.

Finally, we analyzed the single cell of FGF19 and found that FGF19 mainly distributed in squamous epithelial cells, pith cells, stromal cells, T cells, NK cells and congenital lymphocytes. Myelocytes mainly include red cells, granulocytes, monocytes and macrophages. FGF19 may be distributed around granulocytes to promote the formation of NETs, and may be distributed around NK cells to inhibit NK cells. We also analyzed the distribution of FGF19 in CRC immunocytes and found that FGF19 mainly distributed around monocytes, cytotoxic T lymphocytes, promyelocytic leukemia zinc finger protein, dendritic cells and macrophages. Distribution around CD8 ⁺ T and macrophages may promote NET production and protect tumor cells. We also studied the distribution of FGF19 in T cells, NK cells and congenital lymphocytes, and found that FGF19 mainly distributed around cytotoxic T lymphocytes, CD4⁺T cells and promyelocytic leukemia zinc finger proteins.

In general, FGF19 is highly expressed in CRC compared with normal tissues and N stage, M stage and pathologic stage are later with the increase of FGF19 level, the prognosis of CRC is worse. FGF19 may promote the occurrence and progression of CRC by inhibiting immune cells by promoting NET expression. FGF19 is negatively correlated with most CRC immune cells, so it is feasible to study the inhibitors of FGF19 molecular targets. We believe that the study of FGF19 will benefit more CRC patients and eliminate their pain.

Screening of DEGs associated with prognosis and immunity in CRC

In TCGA (https://portal.gdc.cancer.gov/)⁴⁴, clinical and RNA sequencing data were obtained for 644 CRC patients, including 647 CRC tissues in the study, as well as 51 normal colon tissues, and also stage, age, N stage, gender, M stage, pathology, CEA level, and lymphatic invasion. Based on the CRC dataset of TCGA, tumor and normal tissue DEGs were obtained separately using the R software package DESeq2 (Log FC > 3, Padj < 0.05). We used the "Survival" software package to analyze DEGs associated with the prognosis of CRC patients (P < 0.05)⁴⁵, and CRC patients were divided into high and low expression groups according to the median expression of differential genes. We obtained immune-related genes in the Immport (https://www.immport.org/shared/home) database⁴⁶. Venn overlap analysis was then used to investigate the interaction between up-regulated DEGs and prognosis-related DEGs and immune-related genes between tumor and normal tissue. Subsequently, we used CRC survival data from the TCGA database and the survival package based on the Kaplan – Meier method [3.3.1] to analyze the correlation between mRNA expression of six selected DEGs(TG, ULBP2, S100A7, FGF19, CXCL8, GAST) and CRC prognosis, and the results were visualized with the survminer package as well as the ggplot2 package. We then downloaded GSE41328, GSE71187 datasets and obtained DEGs from tumor and normal tissues using the limma package. We performed volcano mapping of DEGs from CRC based on the TCGA database, GEO database via ggplot2 [3.3.6] software package. Finally, FGF19 was finally selected as the gene investigated by comprehensive comparison.

Analysis of FGF19 expression and prognosis in CRC

We used Timer2.0 (http://timer.comp-genomics.org/timer/), the database analyzed differential gene expression of FGF19 between pan-cancer tumor tissues and normal tissues⁴⁷.In addition to this, we obtained data from UCSC (https://xenabrowser.net/), a uniformly standardized pan-cancer dataset was downloaded from the database: TCGA TARGET GTEx (PANCAN, N = 19131, G = 60499), and further we extracted the expression data of ENSG00000162344 (FGF19) gene in each sample, and further we screened the samples from: Solid Normal, Primary Solid Tumor, Primary Tumor, Primary Tumor, Primary Tumor, Primary Derived Cancer- Bone Marrow, and Blood Derived Peripheral Blood - Normal Blood, and further performed a log2 (x + 0.001) transformation of each expression value, and finally we also removed the cancer species with less than 3 samples in a single cancer species, and finally obtained the expression data of 34 cancer species, and we used R software (version 3.6.4) to calculate the expression differences between normal and tumor samples in each tumor. Unpaired Wilcoxon Rank Sum and Signed Rank Tests were used for significance of differences. We performed unpaired and paired difference analysis based on Wilcoxon rank sum test and Wilcoxon signed rank test statistical methods for FGF19 expression in cancer and normal tissues, respectively, based on the CRC dataset of the TCGA database, and visualized the data with ggplot2 [3.3.6].In addition to this, we performed unpaired and paired difference analysis using the same method with a collection of three datasets, GSE41328, GSE110224, and GSE41328.We used Human Protein Atlas (https://www.proteinatlas.org/), a database to investigate the protein expression levels of FGF19 in CRC. In addition to this, based on the CRC dataset of TCGA, we also utilized the pROC package to perform ROC analysis of the data, and the results were visualized with ggplot2 [3.3.6].

Analysis of FGF19 versus clinicopathologic parameters

Based on the CRC dataset from TCGA, we analyzed the association between clinicopathological factors and FGF19 expression using the chi-square test, in addition to single-gene logistic analysis of FGF19 using the R package stats [4.2.1]. Based on the CRC dataset from TCGA, we analyzed the data using the R package stats [4.2.1], car [3.1-0], the Wilcoxon rank sum test for statistical methods, and ggplot2 [3.3.6] for visualization of the data. Finally, we used the survivall [3.4.0] package for proportional hazards hypothesis testing and Cox regression analysis and entered the multivariate Cox model if the sample met the set P-value threshold in the univariate (P < 0.1).

Functional Enrichment Analysis of FGF19

Based on the TCGA database, we performed single-gene DEGs for FGF19 in CRC and finally selected 516 related genes (|LogFC|>1, Padj < 0.05).Subsequently we used the R package clusterProfiler [4.4.4], org. Hs. Eg. Db performed GO, KEGG analysis of FGF19-related genes, and then visualized the analysis results with ggplot2 [3.3.6].Then we use the R package org. Hs. Eg. Db, clusterProfiler [4.4.4] performed gene set enrichment analysis (GSEA) on the data, reference gene set: c2.Cp.All.V2022.1.Hs.Symbols.Gmt [All Canonical Pathways] (3050), and we subsequently visualized the analysis results with ggplot2 [3.3.6].Finally, we did a network diagram between the selected DEGs using the cytoHubba plugin of Cytoscape software. We investigated the association of FGF19 with this gene set in CRC based on the literature for NETs associated genes⁴⁸, using Spearman statistics to validate the association and ggplot2 [3.3.6] for heat map presentation.

Genetic Variation Analysis of FGF19 in CRC

We used cBioPortal (https://www.cbioportal.org/), to analyze genetic alterations in FGF19. Based on datasets from MSK, Nature Medicine 2019⁴⁹, Sidra-LUMC AC-ICAM⁵⁰, Nat Med 2023, MSK, JNCI 2021⁵¹, TCGA, Nature 2012⁵², we calculated frequencies of FGF19 gene mutations and copy number alterations in the 'Cancer Type Summary' module. Mutation site maps for FGF19 were created using the 'Mutation' module. A plot of FGF19 mutation counts versus cancer type was created using the 'plots' module. We used COSMIC (https://www.example.com), to obtain the type and frequency of FGF19 mutations in CRC. We chose the 'tissue distribution' and 'mutation distribution' modules in colorectal tissue for analysis.

Immune Checkpoint Analysis for FGF19

We obtained data from UCSC (https://xenabrowser.net/), a uniformly standardized pan-cancer dataset was downloaded from the database: TCGA Pan-Cancer (PANCAN, N = 10535, G = 60499), and further we extracted the expression data of ENSG00000162344 (FGF19) gene and 60 marker genes of the two types of immune checkpoint pathway genes (Inhibitory, Tumor) in each sample, and further we screened samples from: Primary Derived Cancer- Peripheral Blood, Primary Stimulatory Samples, and we also filtered all normal samples, and further performed log2 (x + 0.001) transformation of each expression value, and next we calculated the spearman correlation of ENSG00000162344 (FGF19) and five types of immune pathway markers. Finally, we evaluated the association between FGF19 expression of ICP genes in CRC using spearman analysis.

Immunoinvasive assay for FGF19

We obtained data from UCSC (https://xenabrowser.net/), a uniformly standardized pan-cancer dataset was downloaded from the database: TCGA Pan-Cancer (PANCAN, N = 10535, G = 60499), and further we extracted the expression data of the ENSG00000162344(FGF19) gene in each sample, and further we screened the metastatic samples from: Primary Blood Derived Cancer- Peripheral Blood (TCGA-LAML), Primary Tumor, and TCGA-SKCM, and further performed a log2 (x + 0.001) transformation of each expression value, in addition to extracting the gene expression profiles of each tumor, mapping the expression profiles to Gene Symbol, and further using the R software package ESTIMATE ⁵³. ESTIMATE scores were calculated for each patient in CRC based on gene expression. Based on the ssGSEA algorithm provided in the R packet-GSVA [1.46.0]⁵⁴, we used the markers of 24 immune cells provided in the Immunity article⁵⁵ to calculate the immune infiltration corresponding to cloud data, performed spearman correlation analysis between the principal variables and immune infiltration matrix data in the data, and the analysis results were visualized with the ggplot2 package for rhoptry and scatter plots. Finally we assessed the enrichment of immune infiltrating cells in CRC patients with high versus low FGF19 expression using Wilcoxon rank sum test.

Drug Sensitivity Analysis of FGF19

GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/)⁵⁶ is a tumor genomic analysis platform that integrates genomic data from 33 tumor types from the TCGA repository and drug response data from GDSC, CTRP. We analyzed FGF19 drug sensitivity using GDSA and CRTP data. In addition, we used Cytoscape to make a network diagram between drugs.

Analysis of tumor heterogeneity

We obtained data from UCSC (https://xenabrowser.net/), a uniformly standardized pan-cancer dataset was downloaded from the database: TCGA Pan-Cancer (PANCAN, N = 10535, G = 60499), and further we extracted ENSG00000162344 (FGF19) gene expression data in each sample, and further we screened samples from which the samples originated from Primary Blood Derived Cancer- Peripheral Blood and Primary Tumor, in addition to we also extracted samples from GDC (https://portal.gdc.cancer.gov/), the Simple Nucleotide Variation dataset of level4 for all TCGA samples processed by MuTect2 software was downloaded, and we calculated MATH and Tumor mutation burden (TMB) for each tumor using the tmb and Heterogeneity functions of the R software package maftools (version 2.8.05), we obtained from a previous study NEO (immune neoantigen) data obtained for each tumor, from a previous study MSI scores obtained. For each tumor integrated MATH, TMB, Neoantigen, MSI, and gene expression data of the samples, respectively, and further log2 (x + 0.001) transformation was performed for each expression value. Finally, we also excluded cancer types with less than 3 samples in a single cancer type, and finally obtained expression data of 37 cancer types. Finally, we calculated their correlation in each tumor using the spearman method. Regarding the heat map of FGF19 in relation to mismatch repair (MMR)-related genes, we obtained data from the TCGA database (htps://portal.Gdc.Cancergov) Download and sort the RNA seq data of STAR process of TCGA-COAD and ICGA-READ projects and extract the data in FPKM format as well as clinical data, remove the normal group, process the data with log2 (value + 1), perform spearman correlation analysis of variables in the data with R (4.2.1) version R package: ggplot2 [3.3.6], and visualize the analysis results with a heat map.

Single Cell Analysis of FGF19 in CRC

We performed single cell analysis through the Single Cell database (https://singlecell.broadinstitute.org/single_cell)⁵⁷. Parameters analyzed were as follows: FGF19 (gene), major lineage (cell-type annotation), and CRC (cancer type). The distribution of different types of cells in CRC can be seen by cell type annotation, and the distribution of FGF19 in different types of cells in CRC can be seen; the expression level and distribution of FGF19 in each cell type in CRC can be quantified and visualized by violin plot. Data collection, processing, and cell annotation procedures are available in the documentation section of the Single Cell website (https://singlecell.zendesk.com/hc/en-us), presented in. In addition, we explored FGF19 in the CancerSCEM database (https://ngdc.cncb.ac.cn/cancerscem/)⁵⁸ of single-cell sequencing data, first we finally determined the dataset CRC-101-03-1A by comparing FGF19 expression in different sequencing data, and then we analyzed the cellular interactions of single-cell and CD8⁺ T cells in this dataset and visualized them with Circos plots and Dot pot plots. Data collection, processing, and cell annotation procedures are in the documentation section of the CancerSCEM database (https://ngdc.cncb.ac.cn/cancerscem/documents), presented.

CRC, Colorectal cancer; DEGs, Differentially Expressed Genes; NETs Neutrophil Extracellular Traps; PI3K, Phosphatidylinositol 3–Kinase; AKT, Serine/threonine Kinase; HPA, Human Protein Atlas; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GESA, Gene Set Enrichment Analysis; ILCs, Innate Lymphocytes.

Ethics approval and consent to participate

This study did not require ethical board approval because it did not include human or animal trials.

Consent for publication

Not applicable

Data availability statement

The datasets presented in this study can be found in the online repositories, including TCGA (https://portal.gdc.cancer.gov/), Immport (https://www.immport.org/shared/home), Timer2.0 (http://timer.comp-genomics.org/timer/), UCSC (https://xenabrowser.net/), cBioPortal (https://www.cbioportal.org/), COSMIC (https://www.example.com), GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/), GDC (https://portal.gdc.cancer.gov/), Single Cell database (https://singlecell.broadinstitute.org/single_cell), CancerSCEM database (https://ngdc.cncb.ac.cn/cancerscem/).

Competing of interest

The authors declare that they have no competing interests

Funding

This study was supported by Zhangiiakou City Key R&D Plan Project (No.2322088D and 2311038D), Medical Science Research Subject Plan Project of Hebei Provincial Health Commission (No.20240805 and 20240782), The natural science project of Hebei North University (No. XJ2024034 and XJ2024035), Hebei Health Commission Scientific Research Foundation Project (20240240), Hebei Provincial Administration of Traditional Chinese Medicine Research Project (2024062), Hebei Provincial Administration of Traditional Chinese Medicine Project (2022147).

Authors’ contributions

Weizheng Liang: Conceptualization, funding acquisition. Jun Xue and Xuejun Zhi: Supervision, funding acquisition. Peng Wang: Data curation, Writing-Original draft preparation. Zhenpeng Zhu: Methodology, Software. Chenyang Hou: Writing- Reviewing and Editing. Dandan Xu: Software, Validation, funding acquisition. Fei Guo: Supervision. All authors have reviewed the results and approved the final version of the manuscript.

Acknowledgements

Not applicable.

Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71, 209-249, doi:10.3322/caac.21660 (2021).
Zheng, R. S. et al. [Cancer incidence and mortality in China, 2022]. Zhonghua Zhong Liu Za Zhi 46, 221-231, doi:10.3760/cma.j.cn112152-20240119-00035 (2024).
Fan, A. et al. Immunotherapy in colorectal cancer: current achievements and future perspective. Int J Biol Sci 17, 3837-3849, doi:10.7150/ijbs.64077 (2021).
Holt, J. A. et al. Definition of a novel growth factor-dependent signal cascade for the suppression of bile acid biosynthesis. Genes Dev 17, 1581-1591, doi:10.1101/gad.1083503 (2003).
Liu, Q. et al. FGFR families: biological functions and therapeutic interventions in tumors. MedComm (2020) 4, e367, doi:10.1002/mco2.367 (2023).
Lan, T. et al. FGF19, FGF21, and an FGFR1/β-Klotho-Activating Antibody Act on the Nervous System to Regulate Body Weight and Glycemia. Cell Metab 26, 709-718.e703, doi:10.1016/j.cmet.2017.09.005 (2017).
Itoh, N., Ohta, H. & Konishi, M. Endocrine FGFs: Evolution, Physiology, Pathophysiology, and Pharmacotherapy. Front Endocrinol (Lausanne) 6, 154, doi:10.3389/fendo.2015.00154 (2015).
Motylewska, E. et al. Alteration in the serum concentrations of FGF19, FGFR4 and βKlotho in patients with thyroid cancer. Cytokine 105, 32-36, doi:10.1016/j.cyto.2018.02.013 (2018).
Li, X., Wang, C., Xiao, J., McKeehan, W. L. & Wang, F. Fibroblast growth factors, old kids on the new block. Semin Cell Dev Biol 53, 155-167, doi:10.1016/j.semcdb.2015.12.014 (2016).
Vainikka, S. et al. Signal transduction by fibroblast growth factor receptor-4 (FGFR-4). Comparison with FGFR-1. J Biol Chem 269, 18320-18326 (1994).
Urtasun, R. et al. Connective tissue growth factor autocriny in human hepatocellular carcinoma: oncogenic role and regulation by epidermal growth factor receptor/yes-associated protein-mediated activation. Hepatology 54, 2149-2158, doi:10.1002/hep.24587 (2011).
Zhou, M., Yang, H., Learned, R. M., Tian, H. & Ling, L. Non-cell-autonomous activation of IL-6/STAT3 signaling mediates FGF19-driven hepatocarcinogenesis. Nat Commun 8, 15433, doi:10.1038/ncomms15433 (2017).
Wu, A. L. et al. FGF19 regulates cell proliferation, glucose and bile acid metabolism via FGFR4-dependent and independent pathways. PLoS One 6, e17868, doi:10.1371/journal.pone.0017868 (2011).
Chen, T. et al. Correction: FGF19 and FGFR4 promotes the progression of gallbladder carcinoma in an autocrine pathway dependent on GPBAR1-cAMP-EGR1 axis. Oncogene 42, 3219, doi:10.1038/s41388-023-02834-z (2023).
Li, F. et al. Enhanced autocrine FGF19/FGFR4 signaling drives the progression of lung squamous cell carcinoma, which responds to mTOR inhibitor AZD2104. Oncogene 39, 3507-3521, doi:10.1038/s41388-020-1227-2 (2020).
Chia, L. et al. HMGA1 induces FGF19 to drive pancreatic carcinogenesis and stroma formation. J Clin Invest 133, doi:10.1172/jci151601 (2023).
Gao, L. et al. FGF19 amplification reveals an oncogenic dependency upon autocrine FGF19/FGFR4 signaling in head and neck squamous cell carcinoma. Oncogene 38, 2394-2404, doi:10.1038/s41388-018-0591-7 (2019).
Xie, M. et al. FGF19/FGFR4-mediated elevation of ETV4 facilitates hepatocellular carcinoma metastasis by upregulating PD-L1 and CCL2. J Hepatol 79, 109-125, doi:10.1016/j.jhep.2023.02.036 (2023).
Tang, W. et al. NRS2002 score as a prognostic factor in solid tumors treated with immune checkpoint inhibitor therapy: a real-world evidence analysis. Cancer Biol Ther 25, 2358551, doi:10.1080/15384047.2024.2358551 (2024).
Palecki, J. et al. T-Cell redirecting bispecific antibodies: a review of a novel class of immuno-oncology for advanced prostate cancer. Cancer Biol Ther 25, 2356820, doi:10.1080/15384047.2024.2356820 (2024).
Yi, M. et al. Biomarkers for predicting efficacy of PD-1/PD-L1 inhibitors. Mol Cancer 17, 129, doi:10.1186/s12943-018-0864-3 (2018).
Yarchoan, M., Hopkins, A. & Jaffee, E. M. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med 377, 2500-2501, doi:10.1056/NEJMc1713444 (2017).
Peng, M. et al. Neoantigen vaccine: an emerging tumor immunotherapy. Mol Cancer 18, 128, doi:10.1186/s12943-019-1055-6 (2019).
Lee, V., Murphy, A., Le, D. T. & Diaz, L. A., Jr. Mismatch Repair Deficiency and Response to Immune Checkpoint Blockade. Oncologist 21, 1200-1211, doi:10.1634/theoncologist.2016-0046 (2016).
Lopes, N. et al. Distinct metabolic programs established in the thymus control effector functions of γδ T cell subsets in tumor microenvironments. Nat Immunol 22, 179-192, doi:10.1038/s41590-020-00848-3 (2021).
Camidge, D. R., Doebele, R. C. & Kerr, K. M. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol 16, 341-355, doi:10.1038/s41571-019-0173-9 (2019).
Yan, C. et al. A cleaved METTL3 potentiates the METTL3-WTAP interaction and breast cancer progression. Elife 12, doi:10.7554/eLife.87283 (2023).
Zhang, J. et al. Src heterodimerically activates Lyn or Fyn to serve as targets for the diagnosis and treatment of esophageal squamous cell carcinoma. Sci China Life Sci 66, 1245-1263, doi:10.1007/s11427-022-2216-x (2023).
Zhou, L. et al. Circadian rhythms and cancers: the intrinsic links and therapeutic potentials. J Hematol Oncol 15, 21, doi:10.1186/s13045-022-01238-y (2022).
Song, Y. et al. Heterodimerization With 5-HT(2B)R Is Indispensable for β(2)AR-Mediated Cardioprotection. Circ Res 128, 262-277, doi:10.1161/circresaha.120.317011 (2021).
Mena, E. L. et al. Dimerization quality control ensures neuronal development and survival. Science 362, doi:10.1126/science.aap8236 (2018).
De Paolis, V. et al. Unusual Association of NF-κB Components in Tumor-Associated Macrophages (TAMs) Promotes HSPG2-Mediated Immune-Escaping Mechanism in Breast Cancer. Int J Mol Sci 23, doi:10.3390/ijms23147902 (2022).
Brinkmann, V. et al. Neutrophil extracellular traps kill bacteria. Science 303, 1532-1535, doi:10.1126/science.1092385 (2004).
Urban, C. F. et al. Neutrophil extracellular traps contain calprotectin, a cytosolic protein complex involved in host defense against Candida albicans. PLoS Pathog 5, e1000639, doi:10.1371/journal.ppat.1000639 (2009).
Fridlender, Z. G. et al. Polarization of tumor-associated neutrophil phenotype by TGF-beta: "N1" versus "N2" TAN. Cancer Cell 16, 183-194, doi:10.1016/j.ccr.2009.06.017 (2009).
Yang, L. et al. DNA of neutrophil extracellular traps promotes cancer metastasis via CCDC25. Nature 583, 133-138, doi:10.1038/s41586-020-2394-6 (2020).
Xia, X. et al. Neutrophil extracellular traps promote metastasis in gastric cancer patients with postoperative abdominal infectious complications. Nat Commun 13, 1017, doi:10.1038/s41467-022-28492-5 (2022).
Shang, A. et al. Exosomal KRAS mutation promotes the formation of tumor-associated neutrophil extracellular traps and causes deterioration of colorectal cancer by inducing IL-8 expression. Cell Commun Signal 18, 52, doi:10.1186/s12964-020-0517-1 (2020).
Okamoto, M. et al. Neutrophil Extracellular Traps Promote Metastases of Colorectal Cancers through Activation of ERK Signaling by Releasing Neutrophil Elastase. Int J Mol Sci 24, doi:10.3390/ijms24021118 (2023).
Wang, L., Li, S., Luo, H., Lu, Q. & Yu, S. PCSK9 promotes the progression and metastasis of colon cancer cells through regulation of EMT and PI3K/AKT signaling in tumor cells and phenotypic polarization of macrophages. J Exp Clin Cancer Res 41, 303, doi:10.1186/s13046-022-02477-0 (2022).
Yan, M., Gu, Y., Sun, H. & Ge, Q. Neutrophil extracellular traps in tumor progression and immunotherapy. Front Immunol 14, 1135086, doi:10.3389/fimmu.2023.1135086 (2023).
Li, X. et al. Role and Therapeutic Targeting Strategies of Neutrophil Extracellular Traps in Inflammation. Int J Nanomedicine 18, 5265-5287, doi:10.2147/ijn.S418259 (2023).
Melbouci, D., Haidar Ahmad, A. & Decker, P. Neutrophil extracellular traps (NET): not only antimicrobial but also modulators of innate and adaptive immunities in inflammatory autoimmune diseases. RMD Open 9, doi:10.1136/rmdopen-2023-003104 (2023).
Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113-1120, doi:10.1038/ng.2764 (2013).
Liu, J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173, 400-416.e411, doi:10.1016/j.cell.2018.02.052 (2018).
Dai, Y. et al. An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma. Cancer Immunol Immunother 70, 967-979, doi:10.1007/s00262-020-02743-0 (2021).
Li, T. et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 77, e108-e110, doi:10.1158/0008-5472.Can-17-0307 (2017).
Zhang, Y. et al. A signature for pan-cancer prognosis based on neutrophil extracellular traps. J Immunother Cancer 10, doi:10.1136/jitc-2021-004210 (2022).
Ganesh, K. et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat Med 25, 1607-1614, doi:10.1038/s41591-019-0584-2 (2019).
Roelands, J. et al. An integrated tumor, immune and microbiome atlas of colon cancer. Nat Med 29, 1273-1286, doi:10.1038/s41591-023-02324-5 (2023).
Cercek, A. et al. A Comprehensive Comparison of Early-Onset and Average-Onset Colorectal Cancers. J Natl Cancer Inst 113, 1683-1692, doi:10.1093/jnci/djab124 (2021).
Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330-337, doi:10.1038/nature11252 (2012).
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4, 2612, doi:10.1038/ncomms3612 (2013).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7, doi:10.1186/1471-2105-14-7 (2013).
Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782-795, doi:10.1016/j.immuni.2013.10.003 (2013).
Liu, C. J. et al. GSCALite: a web server for gene set cancer analysis. Bioinformatics 34, 3771-3772, doi:10.1093/bioinformatics/bty411 (2018).
Tarhan, L. et al. Single Cell Portal: an interactive home for single-cell genomics data. bioRxiv, doi:10.1101/2023.07.13.548886 (2023).
Zeng, J. et al. CancerSCEM: a database of single-cell expression map across various human cancers. Nucleic Acids Res 50, D1147-d1155, doi:10.1093/nar/gkab905 (2022).

No competing interests reported.

Download PDF

Reviews received at journal
27 Oct, 2024
Reviews received at journal
17 Oct, 2024
Reviewers agreed at journal
17 Oct, 2024
Reviewers agreed at journal
16 Oct, 2024
Reviewers invited by journal
30 Aug, 2024
Editor assigned by journal
29 Aug, 2024
Editor invited by journal
03 Aug, 2024
Submission checks completed at journal
03 Aug, 2024
First submitted to journal
27 Jul, 2024

You are reading this latest preprint version

FGF19 is a biomarker associated with prognosis and immunity in colorectal cancer

Status:

Version 1

Abstract

Figures

Introduction

Results

Screening of Differential Genes Associated with Prognosis and Immunity in CRC

In CRC, high expression of FGF19 is strongly associated with poor prognosis

Relationship between FGF19 expression and clinicopathological parameters

Functional Enrichment Analysis of FGF19 in CRC

FGF19 gene alterations in CRC

Relationship between FGF19 expression and tumor immune infiltration

Relationship between FGF19 Expression and Immune Checkpoints

FGF19 in Relation to Tumor Heterogeneity and Chemosensitivity

Single Cell Analysis of FGF19 in CRC

Discussion

Materials and Methods

Screening of DEGs associated with prognosis and immunity in CRC

Analysis of FGF19 expression and prognosis in CRC

Analysis of FGF19 versus clinicopathologic parameters

Functional Enrichment Analysis of FGF19

Genetic Variation Analysis of FGF19 in CRC

Immune Checkpoint Analysis for FGF19

Immunoinvasive assay for FGF19

Drug Sensitivity Analysis of FGF19

Analysis of tumor heterogeneity

Single Cell Analysis of FGF19 in CRC

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1