Comprehensive FGFR3 alteration-related transcriptomic characterization is involved in immune infiltration and correlated with prognosis and immunotherapy response of bladder cancer

Abstract

Background: Bladder cancer (BC) threatens the health of human beings worldwide because of its high recurrence rate and mortality. As an actionable biomarker, fibroblast growth factor receptor 3 (FGFR3) alterations have been revealed as a vital biomarker and associated with favorable outcomes in BC. However, the comprehensive relationship between the FGFR3 alteration associated gene expression profile and the prognosis of BC remains ambiguous.

Materials and methods: Genomic alteration profile, gene expression data, and related clinical information of BC patients were downloaded from The Cancer Genomics database (TCGA), as a training cohort. Subsequently, the Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to identify the hub modules correlated with FGFR3 alteration. The univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to obtain an FGFR3 alteration-related gene (FARG) prognostic signature and FARG-based nomogram. The receiver operating characteristic (ROC) curve analysis was used for evaluation of the ability of prognosis prediction. The FARG signature was validated in four independent datasets, namely, GSE13507, GSE31684, GSE32548, and GSE48075, from Gene Expression Omnibus (GEO). Then, clinical feature association analysis, functional enrichment, genomic alteration enrichment, and tumor environment analysis were conducted to reveal differential clinical and molecular characterizations in different risk groups. Lastly, the treatment response was evaluated in the immunotherapy-related dataset of the IMvigor210 cohort and the frontline chemotherapy dataset of GSE48276, and the chemo-drug sensitivity was estimated via Genomics of Drug Sensitivity in Cancer (GDSC).

Results: There were a total of eleven genes (CERCAM, TPST1, OSBPL10, EMP1, CYTH3, NCRNA00201, PCDH10, GAP43, COLQ, DGKB, and SETBP1) identified in the FARG signature, which divided BC patients from the TCGA cohort into high- and low-risk groups. The Kaplan-Meier curve analysis demonstrated that BC patients in the low-risk group have superior overall survival (OS) than those in the high-risk group (median OS: 27.06 months vs. 104.65 months, p < 0.0001). Moreover, the FARG signature not only showed a good performance in prognosis prediction, but also could distinguish patients with different neoplasm disease stages, notably whether patients presented with muscle invasive phenotype. Compared to clinicopathological features, the FARG signature was found to be the only independent prognostic factor, and subsequently, a FARG-based prognostic nomogram was constructed with better ability of prognosis prediction, indicated by area under ROC curve (AUC) values for 1-, 3-, and 5-year OS of 0.69, 0.71, and 0.79, respectively. Underlying the FARG signature, multiple kinds of metabolism- and immune-related signaling pathways were enriched. Genomic alteration enrichment further identified that FGFR3 alterations, especially c.746C>G (p.Ser249Cys), were more prevalent in the low-risk group. Additionally, FARG score was positively correlated with ESTIMATE and TIDE scores, and the low-risk group had abundant enrichment of plasma B cells, CD8+ T cells, CD4+ naive T cells, and helper follicular T cells, implying that patients in the low-risk group were likely to make significant responses to immunotherapy, which was further supported by the analysis in the IMvigor210 cohort as there was a significantly higher response rate among patients with lower FARG scores. The analysis of the GDSC database finally demonstrated that low-risk samples were more sensitive to methotrexate and tipifarnib, whereas those in the high-risk group had higher sensitivities in cisplatin, docetaxel, and paclitaxel, instead.

Conclusion: The novel established FARG signature based on a comprehensive FGFR3 alteration-related transcriptomic profile performed well in prognosis prediction and was also correlated with immunotherapy and chemotherapy treatment responses, which had great potential in future clinical applications.

Keywords: FGFR3 alteration-related genes (FARGs); bladder cancer; chemotherapy response; fibroblast growth factor receptor 3; immune infiltration; immunotherapy response; nomogram; overall survival.

Figure 1

Screening FGFR3 alterations related to modules by the Weighted Gene Co-expression Network Analysis (WGCNA). (A) Hierarchical clustering based on mRNA expressions of 282 BC patients from TCGA. (B) Determination of power value in the WGCNA analysis. (C) The association between power value and connection degree. (D) Clustering and merging the co-expression modules. (E) Clustering of module eigengenes. (F) The heatmap of co-expression module-FGFR3 alteration correlation; red and green represent positive and negative correlation, respectively.

Figure 2

The Kaplan–Meier curve analysis for BC patients with distinct expression pattern when exploring the relationship between eight key modules and overall survival (OS) of BC patients from TCGA. (A) The expression of black module was positively correlated with OS of BC patients. (B, C) The expression of blue (B) or yellow (C) module was negatively correlated with OS of BC patients. (D–H) No statistically significant difference in OS of BC patients between high and low expression of brown (D), red (E), turquoise (F), green (G), or gray (H) module.

Figure 3

The construction and validation of a novel FGFR3 alteration-related gene (FARG) signature. (A) A total of 11 FARGs were involved in the prognostic signature by the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. (B) The heatmap of these 11 FARGs’ expression among BC patients and their coefficients were exhibited. (C) The Kaplan–Meier curve analysis for high- and low-risk BC patients, which were divided by the median value of the FARG score. (D) The receiver operating characteristic (ROC) curve analysis for evaluation of the novel constructed FARG signature. (E–H) The FARG signature was further validated in four independent cohorts: GSE13507 (E), GSE31684 (F), GSE32548 (G), and GSE48075 (H). (I) The area under ROC curve (AUC) values at 1-, 3-, and 5-year OS further verified the good performance of the FARG signature in prognosis prediction.

Figure 4

The prognostic merits of 11 FARGs involved in the signature. (A) The correlation analysis of FGFR3 expression and 11 signature-related FARGs (CERCAM, TPST1, OSBPL10, EMP1, CYTH3, NCRNA00201, PCDH10, GAP43, COLQ, DGKB, and SETBP1) at the transcriptional level (× indicated correlations were not significant). (B) The Kaplan–Meier curve analysis for BC patients with high and low expression of these 11 signature-related FARGs. (C) The comparison of expression of 11 signature-related FARGs between BC patients in different neoplasm disease stages. * indicated p < 0.05, ** indicated p < 0.01, **** indicated p < 0.0001, ns indicated no statistically significant difference.

Figure 5

The correlation analysis between the FARG signature and clinical features. (A) The clinical association analysis demonstrated that more advanced BC patients were enriched in the high-risk group. (B) The FARG signature score was positively correlated with advanced clinical status. (C) Muscle-invasive BC (MIBC) patients had higher FARG scores than NMIBC patients in GSE13507. (D) MIBC patients had higher FARG scores than NMIBC patients in GSE31684. (E) MIBC patients had higher FARG scores than NMIBC patients in GSE32548. (F) The comparison analysis of FARG score between MIBC and NMIBC groups by integrating MIBC or NMBIC patients from GSE13507, GSE31684, and GSE32548. (G) Kaplan–Meier curve analysis, in the cohort of merging GSE13507, GSE31684, and GSE32548 datasets, for high- and low-risk groups among MIBC patients or NMIBC patients. (H) The stratification analysis revealed the robust ability of the FARG signature in prognosis prediction, which also exhibited the potential to predict OS for early-stage BC patients. ** indicated p < 0.01, *** indicated p < 0.001, **** indicated p < 0.0001, ns indicated no statistically significant difference.

Figure 6

The construction and evaluation of a FARG-based nomogram. (A) Based on the independent prognostic factor of the FARG score together with clinical features of diagnosis age, and T, N, M stages, a novel nomogram was constructed. (B) The receiver operating characteristic curve analysis for evaluation of the FARG-based nomogram in prognosis prediction. (C) The calibration plots further estimated the ability of the FARG-based nomogram in prognosis prediction. (D) The decision curve analysis for the evaluation of the FARG-based nomogram in prognosis prediction.

Figure 7

The functional enrichment analysis. (A) The KEGG pathway enrichment analysis for identification of biological functions associated with FARG signature. (B) The GO enrichment analysis for identification of biological processes (GOBP) associated with FARG signature. (C) The GO enrichment analysis for identification of cellular components (GOCC) associated with FARG signature. (D) The GO enrichment analysis for identification of molecular functions (GOMF) associated with FARG signature.

Figure 8

The differentiation analysis of genomic alteration landscapes between high- and low-risk groups. (A) The oncoplot demonstrated the genomic alteration profile of high-risk samples. (B) The oncoplot demonstrated the genomic alteration profile of low-risk samples. (C) The genomic alteration enrichment analysis between high- and low-risk groups. (D) The lollipop plot demonstrated the alteration sites of high- and low-risk samples. ** indicated p < 0.01, *** indicated p < 0.001.

Figure 9

The analysis of the tumor environment between high- and low-risk groups. (A) Based on the ESTIMATE algorithm, it was found that low-risk BC patients had lower stromal, immune, and ESTIMATE scores. (B) Further analysis showed that low-risk BC patients had lower dysfunction, exclusion, and T-cell dysfunction and exclusion (TIDE) scores. (C) The evaluation of tumor purity of high- and low-risk BC patients. (D) The estimate of 22 common tumor-infiltrated lymphocytes between high- and low-risk groups. * indicated p < 0.05, *** indicated p < 0.001, **** indicated p < 0.0001, ns indicated no statistically significant difference.

Figure 10

The evaluation of immunotherapy response by the FARG signature in the IMvigor210 cohort. (A) The Kaplan–Meier curve analysis between high- and low-risk groups in the IMvigor210 cohort. (B) The proportion of BC patients made complete/partial response (CR/PR) or kept a stable/progressive disease (SD/PD) in high- and low-risk groups. (C) The comparison of FARG score between patients making complete/partial response (CR/PR) and those who kept a stable/progressive disease (SD/PD). (D) The lower expression of TPST1 and EMP1 was significantly associated with the better immunotherapy response of anti-PD-L1/PD-1 treatment in the analysis of the IMvigor210 cohort. ** indicated p < 0.01, ns indicated no statistically significant difference.

Figure 11

The evaluation of chemotherapy response based on the analysis of Genomics of Drug Sensitivity in Cancer (GDSC). (A) The comparison of IC₅₀ values between high- and low-risk groups. (B) The comparison of IC₅₀ values between patients with FGFR3 alterations or with wild-type FGFR3. (C) The comparison of IC₅₀ values between high- and low-risk groups of patients with FGFR3 alterations. (D) The comparison of IC₅₀ values between high- and low-risk groups of patients with wild-type FGFR3. * indicated p < 0.05, ** indicated p < 0.01, *** indicated p < 0.001, **** indicated p < 0.0001, ns indicated no statistically significant difference.