Fibroblast Growth Factor 11 Enables Tumor Cell Immune Escape by Promoting T Cell Exhaustion and Predicts Poor Prognosis in Patients with Lung Adenocarcinoma

Fibroblast growth factor 11 (FGF11) accelerates tumor proliferation in a variety of cancer types. This study aimed to examine the link between FGF11 and the prognosis of lung adenocarcinoma. FGF11 was searched in the Tumor Cancer Genome Atlas (TCGA) and ImmProt databases. The link between FGF11 and lung cancer clinical data was investigated using TCGA and Kaplan–Meier (KM)-plotter databases, and we developed a prediction model. Putative mechanisms of action were investigated using Gene Ontology (GO) and KEGG enrichment analyses. The GeneMANIA and STRING databases were used to search for genes that interact with FGF11, and the Tumor Immune Estimation Resource (TIMER) database was used to discover connections between FGF11 and immune cells, as well as any correlations with immune-related genes. We found that FGF11 expression was higher in the lung adenocarcinoma tissue than in the paracancerous tissue, and patients with high FGF11 expression had a lower overall survival, progression-free survival, and disease specific survival rate than those with low FGF11 expression. The expression of FGF11 was inversely linked to six types of infiltrating immune cells in the TIMER database and was associated with EGFR, VEGFA, BRAF, and MET expressions. The FGF11 gene is negatively correlated with the expression of most immune cells, mainly with various functional T cells including Th1, Th1-like, Treg, and Resting Treg characterization genes. These results indicate that FGF11 has the potential to be a new lung adenocarcinoma biomarker. It increases tumor cell immune escape by boosting T cell exhaustion in the tumor microenvironment, contributing to the poor prognosis of the patients with lung adenocarcinoma. These results provide incentive to further research FGF11 as a possible biomarker and drug target for patients with lung adenocarcinoma.


Introduction
Lung cancer remains prevalent and is a leading cause of cancer-related mortality globally, accounting for 18.4 percent of all cancer-related deaths [1,2]. Lung adenocarcinoma is a prominent type that accounts for more than half of all lung cancer cases [3]. Lung cancer begins in the bronchial epithelium and mucous glands of the major bronchi. Although it has a lower incidence than squamous cell carcinoma or undiferentiated carcinoma, it typically develops at a younger age. Small bronchial adenocarcinomas are the most common type of adenocarcinoma and manifest as peripheral lung cancer. In the early stages, there are usually no noticeable symptoms, and they are often discovered during chest radiography. On imaging, the tumor appears as a slow-developing round or oval mass. Although hematogenous metastasis may develop throughout the progression of cancer, lymphatic metastasis is more common later in cancer development [4,5]. Lung adenocarcinoma has a 5-year survival rate of approximately 20.0%-30.0% [6,7]. Previous treatment approaches, such as minimally invasive surgery, radiation, and chemotherapy, have progressively improved, and the survival time of lung adenocarcinoma patients has accordingly increased [8]. Te risk of postoperative recurrence and death in patients with early-stage lung adenocarcinoma can be reduced by immediate surgery [9]. Terefore, the identifcation of reliable target molecules for early detection and therapy is critical.
By combining the Cancer Genome Atlas (TCGA) database analysis with immune infltration-related information, we were able to identify the diferentially expressed gene FGF11, which has been linked to lung adenocarcinoma (LUAD) prognosis. In this study, we explore the efect of FGF11 expression on tumor cell behavior and the activities in the tumor microenvironment. Relevant bioinformatics analysis validation was also performed to ensure accuracy.

Screening of Diferentially Expressed Genes.
We downloaded the dataset of diferentially expressed genes in lung adenocarcinoma from the TCGA database (https://tcgadata.nci.nih.gov/). From the gene list module of the immunology database and the analysis portal ImmPort database (ImmPort Private Data [nih.gov]), a total of 2483 immune genes was downloaded. We arranged the intersection of these data sets as a Wayne diagram and take the log2FC absolute value greater than 1 and P value less than 0.05 as the parameter to determine diferentially expressed genes (DEGs).

Relationship between FGF11 in UALCAN Database and
Clinical Data of Lung Adenocarcinoma. UALCAN (https:// ualcan.path.uab.edu/) is a web-based program that analyzes transcriptome data from the Cancer Genome Atlas (TCGA). Te association between FGF11 and clinicopathological characteristics of lung cancer was examined using UALCAN [17,18].

Te Relationship between FGF11 in KM-Plotter Database
and Clinical Data of Lung Adenocarcinoma. Te impact of clinical parameters and FGF11 expression on the clinical outcome of lung cancer was investigated using the Kaplan-Meier (KM)-plotter database (https://kmplot.com/ analysis/index.php?p=service).

Nomogram Construction and Evaluation.
We created a nomogram based on multivariate examination and expected survival rates of 1, 3, and 5 years. A nomogram showing clinical characteristics related to FGF11 and calibration plots were created using the rms package in R software. Calibration and discrimination are the most used methods for evaluating the performance of the models. In this study, the calibration curve was evaluated by mapping the nomogram prediction probability to the observed ratio with the 45°line representing the best prediction value. Te consistency index (C-index) was used to determine the discrimination of nomographs, which was calculated using the bootstrap method with 1000 resamplings. In addition, the prediction accuracy of the nomogram and individual prognostic factors was compared using the C-index.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) Pathway Enrichment Analysis and Gene
Set Enrichment Analysis (GSEA). Te biological function of FGF11 in lung cancer was investigated by GO and KEGG analyses. FGF11-related biological procedures (BPs), cellular mechanisms (CCs), and molecular activities were identifed using GO analysis. Te underlying mechanism of FGF11 expression was investigated using GSEA. GO, KEGG, and GSEA analyses were performed using the R package cluster profler.

Correlation between FGF11
and Immune Cells in the TIMER Database. Te Tumor Immune Estimation Resource (TIMER) database (https://cistrome.shinyapps.io/ timer/) is an interactive portal that can comprehensively analyze the infltration levels of diferent immune cells. In this study, FGF11 expression in various types of cancer was evaluated through the "dif exp" module. Te correlation between FGF11 and immune cell infltration in lung adenocarcinoma was analyzed using TIMER. Te "gene" module was used with the TCGA database to study the relationship between FGF11 expression and the immune cell infltration level (B cells, CD8+ T cells, CD4+ T cells, and others). TIMER was also used to evaluate the relationship between FGF11 expression and diferent gene marker sets of immune cells by using the "correlation" module. Te correlation between FGF11 expression and immune infltration was investigated using partial Spearman correlation and statistical signifcance related to tumor purity.

Increased FGF11 Expression in Lung Adenocarcinoma.
First, we obtained data from TCGA database and ImmPort database and analyzed by Venn diagram, and we found that FGF11 is one of 170 diferentially expressed genes, which is related to the prognosis of lung adenocarcinoma (Figure 1(a)). FGF11 was found to be substantially expressed in most tumor tissues after pan-cancer investigation ( Figure 1(b)). Te expression of FGF11 was also upregulated in lung adenocarcinoma tumor tissues according to the TCGA database (Figures 1(c) and 1(d)). Te area under the curve was 0.912 in the receiver operating characteristic (ROC) curve, indicating that the high expression of FGF11 can further predict the poor prognosis of lung adenocarcinoma patients (Figure 1(e)).

Te Relationship between FGF11 Expression and Clinical
Data of Patients with Lung Adenocarcinoma. By analyzing the clinical data of bladder cancer in the UALCAN database, we found that the expression of FGF11 is diferent in patients with diferent TNM stages, diferent pathological stages, diferent ages, and diferent genders, but the expression in tumor tissues is higher than that in normal tissue. Meanwhile, the high expression of FEF11 is also consistent with the epidemiology that lung adenocarcinoma occurs more frequently in women and patients with a history of smoking ( Figure 2(a)). Based on these clinical data, a forest map is drawn, and in most of the groups, FGF11 played the role of "risk factor," which was consistent with the abovementioned results ( Figure 2(b)).

Correlation between FGF11 Expression and Prognosis of Lung Adenocarcinoma Using the KM-Plotter Database.
Using the lung cancer information within the KM-plotter database, we examined the prognosis of patients with varying FGF11 levels. Patients with lung adenocarcinoma with high FGF11 expression correlated with a shorter OS, PFS, and DSS than those with low FGF11 expression (Figure 3(a)). Further stratifed patient analysis revealed that the patients with a lower FGF11 expression had a better prognosis than female patients, those with TMN staging of T2, M0, N0, stage I LUAD, and patients with a smoking history (Figure 3(b)).

Nomogram Construction.
We created a nomogram based on multivariate analysis predicting the expected survival in patients with LUAD at 1, 3, and 5 years. Tis nomogram had a C-index of 0.679 (0.653-0.704). (Figure 4(a)). Te bias correction line in the calibration plot is close to the ideal curve (45°line), indicating that the anticipated predicted values should be consistent with the realworld data ( Figure 4(b)). Tese data indicate that the prediction model has a certain prediction accuracy.

Identifcation of FGF11-Interacting Genes and Proteins.
A gene-gene interaction network of FGF11 and altered adjacent genes was created by GeneMania ( Figure 5(a)). Te STRING database was used to create a protein-protein interaction (PPI) network for FGF11 ( Figure 5(b)).

GO and KEGG Analyses of the FGF11 Pathway and Its
Coexpressed Genes in Lung Adenocarcinoma Using the TCGA. Genes that were positively or negatively linked with FGF11 coexpression were identifed using data from the TCGA database. We found the top 50 genes in lung adenocarcinoma that are positively and negatively correlated with FGF11 levels (Figures 6(a) and 6(b)). To uncover FGF11related pathways and biological activities, we analyzed 600 FGF11-related genes using KEGG and GO enrichment analyses ( Figure 6(c)).
the immunological checkpoint-related molecule CD274 (P < 0.05), but there was no signifcant correlation between CTLA-4 and PDCD1 expression (Figure 7(d)). Based on these results, we hypothesized that the FGF11 expression is linked to immune cell infltration. Tese results suggest that in lung adenocarcinoma, FGF11 may play a key role in the immune escape of tumor cells, and these data indicate future directions for research.

Correlation between FGF11 Expression and Drug Target
Molecules in the TCGA Database. Tere are targeted therapies available for patients with advanced lung adenocarcinoma, and the most recent National Comprehensive Cancer Network (NCCN) lung cancer recommendations advocate for further identifcation of relevant drug targets, including mutant EGFR (19DEL, L858R). Osimertinib is the frst-line therapy option for patients with cancer, followed by erlotinib, afatinib, geftinib, dacomitinib, erlotinib + ramucirumab, and erlotinib plus bevacizumab. Te ALK mutation is known as the "diamond mutation," and treatments for ALK rearrangement-positive nonsmall cell lung cancer recommend alectinib, brigatinib, or lorlatinib as frst-line therapy, with ceritinib or ceritinib as secondary alternatives. Tus, we conducted a correlation study between FGF11 and EGFR (19DEL, L858R), EGFR (Exon 20ins), KRAS (G12C), ALK, ROS1, BRAF, NTRK1/2/3, MET, and RET, which are all recommended by the NCCN guidelines for the diagnosis of nonsmall cell lung cancer. FGF11 was signifcantly associated with EGFR, VEGFA, BRAF, and MET (P < 0.05). However, there was no signifcant diference in the association between FGF11 and ALK, KRAS, ROS1, NTRK1, NTRK2, NTRK3, or RET ( Figure 8).

Correlation between FGF11 Expression and Immune Cell
Markers. We used the TIMER database to further evaluate the interaction between FGF11 and drug responses. We discovered a link between FGF11 expression and immune cell markers in lung adenocarcinoma. B cells, T cells, CD8+ T cells, monocytes, tumor-associated macrophages (TAMs), M1 macrophages, M2 macrophages, neutrophils, NK cells, and dendritic cells were used because we previously used them to discover immune-related genes in Table 1. Te development of immune cell penetration resistance in clinical surgeries remains prejudiced through tumor purity. FGF11 expression remained substantially related to the greatest immunological indicators within distinct kinds of immune cells within lung adenocarcinoma after controlling for tumor purity (Table 1). We also examined the relationship between the FGF11 expression and other types of T cells, such as T1, T1-like, and T2. After controlling for tumor purity, we discovered that FGF11 expression levels were strongly linked to 12 T cell markers in lung adenocarcinomas using the TIMER database (Table 2).
T cell depleting therapies may concern patients with possibilities of chronic infections and future malignancies. T lymphocytes are abundant in patients with tumors, even though most of them are functionally exhausted [19,20].   FGF11 has been shown to be negatively linked to the infltration of dendritic cells, macrophages, neutrophils, T cells, T1 cells, and immune cell genes. FGF11 expression was not correlated with functional T cell diferentiation genes, such as T1, T1-like, Treg, and resting Tregs.
Terefore, we hypothesize that FGF11 in the lung adenocarcinoma microenvironment increases tumor immune escape by increasing T cell depletion and exhaustion, contributing to the poor prognosis in patients with lung adenocarcinoma.

Discussion
Many factors, including local invasion, distant metastasis, and treatment resistance, lead to poor outcomes in patients with lung adenocarcinoma [21]. In the United States, 228,000 individuals were diagnosed with lung cancer in 2019 and approximately 160,000 patients died [22]. Lung cancer has a high rate of morbidity, mortality, and poor prognosis. Of all nonsmall cell lung cancers (NSCLCs), lung adenocarcinoma is the most diagnosed subtype and entails a poor prognosis [23,24]. As a result, modern lung adenocarcinoma research faces the challenge of identifying how LUAD develops, how it invades, and the process for distant metastasis. Targeted therapy, immunosuppressive therapy, and other treatment methods are now widely used in clinical practice, owing to many breakthroughs and advances in bioinformatics, molecular biology, immunology, and other felds, but these breakthroughs have not signifcantly decreased the mortality of patients with LUAD [25,26]. Terefore, understanding the mechanisms of lung cancer incidence and progression is critical to uncovering signifcant biomarkers and identifying novel treatment options. Te development of sequencing and omics technologies nowadays has provided more opportunities to further understand the mechanism of lung adenocarcinoma and explore diagnostic and therapeutic targets [27]. We analyzed the TCGA database and other bioinformatics sources to determine whether FGF11 is associated with lung adenocarcinoma initiation and progression. We discovered that FGF11 expression is higher in lung adenocarcinoma tissues than in normal tissues. We also found that that patients with lung adenocarcinomas with a high FGF11 expression experienced lower OS, PFS, and DSS compared to those of patients with a low FGF11 expression. We used multivariate analysis of these data to build an accurate prediction model to assess the 1-, 3-, and 5-year survival likelihood of those patients with high levels of FGF11. Using the TIMER database, we discovered that higher FGF11 expression was correlated with decreased tumor infltration of immune cells. Tese cells included B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells. Additionally, we found that the higher expression of FGF11 is correlated with higher levels of the immune checkpoint-related molecule CD274. Tus, by decreasing immune cell infltration and restricting the immune anticancer activity, FGF11 may play a role in increasing tumor cell immune escape in the tumor microenvironment of LUAD. We then examined the relationship between FGF11 and targets for drug therapy and discovered that FGF11 levels are linked to EGFR, VEGFA, BRAF, and MET levels in LUAD, of which LUAD is the most predominant subtype. According to these fndings, FGF11 leads to a lower immune cell infltration and can also alter medication selection and their efectiveness in LUAD.
Our data indicate the FGF11 function in immune aspect. Multiple regimens are now employed in clinical practice to enhance the prognosis of patients with progressive lung cancer, and recent clinical trials have used immune-focused therapies in their treatment plans. Tese regimens include immune therapy combined with chemotherapy, targeted drugs, and other immunotherapies. Potential future  PNMA5  NTS  MAGEA4  SPDYC  MYO18B  LIN28A  SLC1A6  MAEL  CTCFL  SOHLH2  UPK2  TAC3  MKRN9P  MAGEA10  COL2A1  MEG3  PSG4  LINC00958  GABRQ  CLDN6  TKTL1  CD177  AC020661.3 SLC7A3  Te anti-PD-1 antibody pembrolizumab was used in combination with chemoradiation treatment for unresectable, locally advanced, stage III NSCLC [28]. Te KEYLYNK-012 trial is investigating a treatment strategy for unresectable stage III NSCLC using PARP inhibitors combined with pembrolizumab and chemoradiotherapy [29]. Te updated data from the CheckMate 9LA study show that when compared to four cycles of chemotherapy alone, dual immunotherapy (nivolumab and ipilimumab) combined with short-course chemotherapy treatment-naïve patients with progressive NSCLC increased patient OS, overcoming the low response rate of a single immunotherapy while maximizing the long-tail efect of immunotherapy [30]. Furthermore, the CheckMate 816 study [31] corroborated the efectiveness of neoadjuvant combination immunotherapy (nivolumab) with chemotherapy in resectable NSCLC, indicating that another treatment may be possible for the patients with NSCLC. Te therapeutic and scientifc relevance of our research is refected in the immunity-focused results. We examined the relationship between FGF11 and immunological markers and found that FGF11 inversely correlated with all genes that characterize immune cells, including functional T cell characterization genes such as T1, T1-like, Treg, and resting Tregs. Tis prompted us to investigate whether   FGF11 in the immunological milieu of lung adenocarcinoma increases tumor cell immune escape by boosting T cell exhaustion, thereby contributing to the poor prognosis of lung adenocarcinoma. T cell exhaustion indicates decreased T cell function in patients with prevalent chronic illnesses or malignancies. Exhausted T cells eventually lose the efector activity and memory T cell properties because of extended exposure to antigens or chronic infammation. However, this exhaustion may be partially restored by blocking inhibitory mechanisms, such as PD-1 and IL-10. Te persistent exposure of T cells to antigens is a characteristic element of chronic infection or malignancy, and both high antigen load and extended antigen exposure contribute to more severe T cell exhaustion [32,33]. Te STAT3-induced cytokine, IL-10, produces T cell exhaustion and reduces T cell activation. T cell exhaustion was prevented and/or reversed when IL-10 was blocked. Dendritic cells, B cells, monocytes, CD8+ T cells, and nonregulatory CD4+ T cells are among the immune cell types that may release IL-10 [34]. T cells may be directly afected by IL-10 via STAT3, indirectly afected by APC modulation, or both. Neutralizing this IL-10 efect with antibodies combined with immunotherapy may improve CD8+ and CD4+ T cell efector responses [35]. IL-2 is a critical cytokine required for T cell survival and activation as well as for increasing infection and tumor immune responses. It belongs to a family of cytokines that counteract T cell depletion. IL-2 levels are increased after the use of a microRNA targeting the mRNA of T cell inhibitory receptors PD-1, TIM-3, BTLA, and Foxp1, indicating its positive role on anticancer immune responses [36,37]. FGF11 was shown to have a positive correlation with IL-10 and a negative correlation with IL-2, indicating how it negatively modulates immune reactions in the TME. Tese fndings support our hypothesis that FGF11 increases T cell exhaustion.   Table 2: Correlation analysis between FGF11 and T cell gene markers using the TIMER data.

Conclusion
Tis study adds to the body of data supporting FGF11's role in LUAD formation and as a potential biomarker of LUAD. FGF11 may increase tumor cell immune escape by increasing T cell exhaustion in the LUAD tumor microenvironment, contributing to the poor prognosis for patients with LUAD. Tese results suggest that FGF11 is a possible target for future lung adenocarcinoma anticancer treatments.

Data Availability
Te datasets used or analyzed to support the fndings of this study are available from the corresponding author on reasonable request.

Conflicts of Interest
Te authors declare that they have no conficts of interest.