Characteristics of GTF2I L424H Mutated Thymoma and its Prognostic Impact: A Comprehensive Study

doi:10.21203/rs.3.rs-4860772/v1

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Characteristics of GTF2I L424H Mutated Thymoma and its Prognostic Impact: A Comprehensive Study

https://doi.org/10.21203/rs.3.rs-4860772/v1

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Thymic Epithelial Tumor (TET), a rare thoracic tumor, including thymoma and thymic carcinoma, has limited research on thymoma prognostic markers compared to thymic carcinoma. Using the cBiportal database, we analyzed gene expression, methylation, and mutation data in TETs. We explored the relationship between the GTF2I L424H mutation and thymoma pathology through differential gene expression, pathway enrichment analyses, and COX regression to develop a thymoma risk score. Compared with GTF2I wild-type, patients harboring GTF2I L424H mutation displayed distinctive gene expression and methylation profiles, resembling differences between pathology low-risk and high-risk patients. Importantly, patients with the GTF2I L424H mutation demonstrated a better prognosis than wild-type patients, but no such distinction was noted between low-risk and high-risk patients. Pathway analysis suggested that the mutation potentially regulates tumor development-related pathways, including the P53, Hippo, and TGFβ signaling pathways, ECM-receptor interaction, and tumor immune cell infiltration. Additionally, ten hub genes identified by cytoHubba, FGF20, FGF10, EGF, and TWIST1 were selected by stepwise multivariate Cox regression to construct a risk score model for thymoma. These findings highlight the potential role of the GTF2I L424H mutation as a prognostic factor, advocating for genetic profiling in personalized treatment strategies.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

Biological sciences/Genetics

Health sciences/Biomarkers

thymoma

GTF2I L424H

prognosis

pathways

hub gene

The GTF2I L424H mutation in thymic epithelial tumors, particularly thymoma, emerges as a key prognostic factor with distinct survival outcomes.
Analysis reveals the mutation's impact on crucial signaling pathways, including P53 and TGFβ, influencing tumor development and immune response in the thymic microenvironment.
Identification of four hub genes enables the construction of a robust Risk-Score model, offering a promising tool for personalized prognostication and potential therapeutic strategies in thymoma.

Thymic Epithelial Tumors (TETs) are rare mediastinal tumors that arise from the thymus gland, accounting for approximately 20% of all such tumors [1]. Histologically, according to the World Health Organization (WHO) classification standard, TETs can be classified into two categories: thymoma (types A, AB, B1, B2, and B3) and thymic carcinoma (type C or TC). According to the difference in the biological behavior of tumor subtypes, further simplification of the histological classification has led to the identification of three subgroups: the low-risk group (including types A, AB, and B1), the high-risk group (comprising types B2 and B3), and the TC group (type C). Each subgroup requires specific treatment approaches and has a unique prognostic outcome [2]. Although surgical resection remains the primary treatment approach for TETs, the optimal management of this condition is still controversial, and the prognosis can vary significantly depending on both the tumor's histological subtype and stage.

Identifying innovative treatment strategies for TETs can be facilitated by analyzing associated mutant genes. Certain genes have shown prognostic significance in thymic carcinoma, such as TP53 mutations, which are associated with shorter disease-free survival (DFS) and overall survival (OS) [3]. Patients with thymic squamous cell carcinoma harboring EGFR pathway mutations exhibit a significantly shorter OS [4]. However, only a few biomarkers have been studied for thymomas. Studies demonstrated that Missense mutation (p.L424H) of the General Transcription Factor IIi (GTF2I) gene is mostly observed in type A thymomas [5, 6]. This mutation exhibited a better prognosis, with a 10-year survival rate of 96% %compared to 70% in individuals with wildtype GTF2I [5]. Although a mouse model was generated with GTF2I L424H knocked in [7], the mechanism underlying the prognostic difference is unclear.

In this study, we analyzed thymic tumor data on gene mutation, expression, and methylation obtained from the cbiportal database to investigate the underlying mechanism of GTF2I mutation and develop a prognostic model for thymoma.

Datasets

All data on thymic tumors utilized in this study were sourced from the publicly accessible cbiportal database (https://www.cbioportal.org/datasets). Specifically, 151 TET patients with comprehensive genome-wide mutation data, 119 patients with available RNAseq data, and 124 patients with accessible DNA methylation data based on Illumina HumanMethylation450 BeadChip were carefully selected for inclusion in the analysis. Corresponding clinical data were downloaded to facilitate subsequent matching analysis. All patients with TET were grouped according to the pathological classification and mutation status of GTF2I.

Analysis of the difference in expression and methylation profiles

Differentially methylated genes (DMGs) were identified using the R package “minfi,” while differentially expressed genes (DEGs) were screened out using the R package “limma.” The Benjamini and Hochberg (BH) procedure was used to control the False Discovery Rate (FDR) [20]. Subsequently, the log2-fold change (log2FC) was calculated, and an adjusted P-value < 0.05 and |log2FC| ≥ 1.5 were considered as the cutoff values for the screening of DMGs and DEGs.

Mutation and pathway analysis in TETs

Genetic mutations in TETs were visualized using waterfall plots generated using the R package “maftools” and the oncoplot function. Genes were organized into signaling pathways using curated pathway templates. To assess differences in sex, age, Masaoka stage, and histological type, chi-squared and non-parametric tests were performed on TET patients in the low-risk, high-risk, and TC groups. The same analysis was performed between GTF2I mutant and wild-type groups.

Enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were conducted using the R package “Cluster Profile.” We utilized gene sets (c2. cp. kegg.v6.2.-symbols) with Gene Set Enrichment (GSEA) software (https://www.gsea-msigdb.org/gsea/login.jsp) to investigate differences across pathway activity between GTF2I L424H mutation and wild-type TET patients. Statistical significance was set at P < 0.05 and an FDR of < 0.25 were considered statistically significant.

PPI network construction and module analysis

The PPI network of the identified DEGs was constructed using an online tool, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; https://string-db.org/), with an interaction score > 0.4. Based on the highest degree of connectivity, the top 10 genes were selected as target hub genes using the cytoHubba plugin in Cytoscape (version 3.7.2).

Investigation of the immune cell infiltration and immune checkpoints in thymoma patients

IOBR [8] is a computational tool for immune tumor biology research. In our study, based on the expression profile, the ESTIMATE, TIMER, and IPS methods were used to calculate the thymoma immune-infiltrating cell score. We identified 59 immune checkpoints according to a previous study [9] and computed the relative percentage of infiltrating immune cell infiltration and checkpoints. To assess the differences between GTF2I mutant and wild-type thymoma patients, we used the Wilcox test function, along with the "ggplot2" and “ggpubr” R packages. The expression profile of the checkpoints was visualized using the “pheatmap” function.

Survival analysis and thymoma-related risk signature construction

Furthermore, we performed univariate Cox proportional hazard regression analysis on all variables and included those with a P < 0.1 into multivariate Cox proportional hazard regression analysis to determine the independent prognostic factors of thymoma. A multivariate Cox regression hazard analysis (backward stepwise) was used to establish a predictive model. The accuracy of the model was evaluated using the C-index and ROC with the “pROC” package.

Statistical methods

The independent Student’s t-test was used to compare continuous data with normal distribution, and the χ2 test for categorical data was used for pairwise comparisons between subgroups. The Kruskal–Wallis test (one-way ANOVA on ranks) was performed to determine whether there were statistically significant differences between multiple groups. The Mann–Whitney U test was used to compare differences between two independent groups when the dependent variable was either ordinal or continuous but not normally distributed. All statistical analyses were performed using R programming language (Version 4.2.2). A difference of p < 0.05 indicated statistical significance unless specified otherwise.

Clinicopathological, expression/methylation profile, and prognostic differences in TETs.

As indicated in Table S1, significant differences were observed in clinical pathological characteristics, such as age at diagnosis, WHO histological type, and Masaoka stage, among the low-risk, high-risk, and TC groups (P < 0.05). However, no sex differences were observed. The age at diagnosis in the low-risk group of TET patients was significantly higher than that in the high-risk group. However, no significant differences were observed between the low-risk and TC groups, as well as between the high-risk and TC groups (Fig. 1A). For tumor mutation burden (TMB), patients in the TC group had significantly higher TMB values than those in the low-risk and high-risk groups (P < 0.01) (Fig. 1B). However, MSI scores were similar, and no differences were observed.

The “limma” package was utilized to filter for DEGs and DMGs between distinct groups. In comparison to the high- and low-risk groups, the TC group demonstrated a significantly higher number of DEGs, with 2267 genes upregulated and 1857 genes downregulated. DEGs were also identified between the high- and low-risk groups, with 509 upregulated genes and 576 downregulated genes (Fig. S1 A-B and Table S2). This trend was also observed for DMGs (Fig. S1 C-D and Table S3). The heat map effectively illustrated the clustering patterns between the TC group and low/high-risk groups, including DEGs and DMGs (Fig. 1C and 1D). However, no clustering was observed between the high- and low-risk groups in terms of expression or methylation profiles.

Survival analysis revealed that patients in the TC group exhibited lower overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) rates than those in the low-risk and high-risk groups, especially DFS (P < 0.05) (Fig. 1E-1G). However, there was no significant difference in survival prognosis between the low- and high-risk groups.

Clinicopathological, expression profile, and prognostic differences between the GTF2I L424H mutation group and wild-type group

To further explore the association between the GTF2I mutation and pathological classification, we categorized the patients into two distinct groups: those harboring the GTF2I L424H mutation and those harboring the wild-type GTF2I. As illustrated in Figs. 2A and 2B and summarized in Table S3, patients in the GTF2I L424H mutation group exhibited significantly higher values for both age and TMB than those in the GTF2I wild-type group (p < 0.05). This pattern mirrors the observed distinctions between the low-risk and high-risk groups, implying a potential link between the GTF2I L424H mutation, advanced age, elevated TMB values, and the overall pathological classification.

“Limma” analysis revealed significant differences in expression and methylation profiles between the GFT2I mutation group and the wild-type group. In comparison to the GTF2I wild-type, the GTF2I L424H mutation group exhibited 1750 significantly upregulated genes, including 553 upregulated genes in the low-risk group compared to the high-risk group (553/576), and 880 significantly downregulated genes, including 406 downregulated genes in the low-risk group compared to the high-risk group (416/509) (Table S4 and Fig.S2). Interestingly, the heatmap results revealed distinct clustering between the two groups, regardless of whether the DEGs or DMGs were examined (Fig. 2C and 2D). Survival analysis revealed that individuals with the GTF2I L424H mutation had a longer survival rate than those in the GTF2I wild-type group (Fig. 2. E-G). This difference was particularly pronounced in terms of the PFS time (p < 0.05).

GTF2I L424H mutation potentially related to tumor suppression

We further explored the potential mechanism between GTF2I L424H mutation and the prognostic impact of TETs. We performed a comprehensive analysis of the gene mutation landscape. The waterfall plot depicted in Fig. 3A and 3B highlights GTF2I as the most frequently mutated gene, accounting for 44% (66 out of 151) of cases, primarily observed in the low-risk group, especially in type A and AB thymomas. In a comparative mutation analysis between the GTF2I L424H mutation and GTF2I wild-type groups, we observed significant differences in the mutation frequencies of two genes: HRAS (15% vs. 1%) and TP53 (0% vs. 8%) (Fig. 3C and 3D) (P < 0.05). HRAS mutations were notably associated with GTF2I and TTN mutations, while TP53 mutations demonstrated significant mutual exclusivity with GTF2I mutations (Fig. 3E).

Mutation analysis of the tumor signaling pathways was conducted to compare the GTF2I mutation and wild-type groups. The plot illustrating oncogenic signaling pathways (Fig. 3F) indicates that the P53 and Hippo signaling pathways are predominantly mutated in the GTF2I wild-type group. Detailed gene mutations in the P53 and Hippo signaling pathways are presented in Fig. S3. Notably, the top three enriched mutation pathways in the GTF2I mutation group were transcription factors, MAPK signaling, and genomic integrity, whereas the wild-type GTF2I group exhibited mutations in genomic integrity, MAPK signaling, and RTK signaling (Fig. 3G and 3H).

Differential gene expression profiles between the GTF2I L424H mutation group and wild-type group in thymoma patients

They considered the substantial overlap in DEGs between patients with and without GTF2I L424H mutation in thymoma patients (low-risk and high-risk groups), and their effective clustering into distinct groups. A “Limma” analysis was also performed between the GTF2I L424H mutation group and the wild-type group in thymomas. As shown in Fig. 4A and 4C and Table S4, 1870 genes were significantly upregulated in the GTF2I L424H group, among which 555 genes were also significantly upregulated in the low-risk group compared to the high-risk group (555/576). Additionally, 845 genes were significantly downregulated, including 419 genes that were also significantly downregulated in the low-risk group compared to the high-risk group (419/509). Cluster analysis revealed significant differences in the gene expression profiles between the GTF2I L424H mutation and wild-type groups (Fig. 4B). After eliminating interference from patients with TC, we observed that the GTF2I L424H mutation remained associated with a better prognosis, particularly in terms of PFS (Fig. 4D and Fig.S4).

GTF2I L424H mutation may regulate pathways linked to tumor development

To further study the potential effect of GTF2I L424H mutation on tumorigenesis, GO and KEGG enrichment analyses were performed using DEGs in thymoma patients. Enrichment analysis results between the GTF2I L424H mutation group and the wild-type group exhibited overlaps and differences compared to those between the low- and high-risk groups, as illustrated in Fig. 5A and 5B. Biological process (BP) enrichment analysis showed that GTF2I L424H mutation-related genes were mainly involved in cell-cell adhesion via plasma-membrane adhesion molecules, sensory organ morphogenesis, and synape organization. Molecular function (MF) enrichment analysis showed that the role of GTF2I L424H mutation in tumor pathogenesis was mainly related to the external matrix structure constituent, gated channel activity, and heparin binding. We found that GTF2I L424H mutation-related genes were enriched in the collagen-containing extracellular matrix, synaptic membrane, and ion channel complex in the cellular component (CC) enrichment analysis. In addition, KEGG pathway analysis showed that GTF2I L424H mutation participates in some pathways, such as the calcium signaling pathway, neuroactive ligand-receptor interaction, and ECM-receptor interaction (Fig. 5B). Subsequently, GSEA enrichment analysis revealed that GTF2I L424H mutation mainly participated in the TGFβ signaling pathway, ECM receptor interaction, focal adhesion, citrate cycle, TCA cycle, oxidative phosphorylation, etc. (Fig. 5C).

In contrast to the findings between the low-risk and high-risk groups, the cytoHubba plugin identified ten genes (CDH2, CXCL12, EGF, FGF5, FGF10, FGF17, FGF20, FN1, PXDNL and TWIST1) with the highest degree scores as hub genes for GTF2I L424H mutation-related TET, as depicted in Fig. 5D and 5E.

Estimation of immune cell infiltration and immune checkpoints between GTF2I mutant and wild-type thymoma patients

Given the discovery that the GTF2I L424H mutation is related to tumor immune microenvironment pathways such as the TGF-β pathway and ECM-receptor interaction [8, 9] in pathway analysis, we conducted analyses related to immune infiltration and immune checkpoints. There was no significant difference (p > 0.05) in the estimated scores between the GTF2I L424H mutation and wild-type groups (Fig. 6A). The stromal score in the GTF2I L424H mutation group was significantly higher than that in the GTF2I wild-type group (p < 0.01) but lower in the immune score (P < 0.001) (Fig. 6A). The timer score of macrophages in the GTF2I L424H mutation group was significantly higher than that in the GTF2I wild-type group (p < 0.001) but lower in the DC (P < 0.01) (Fig. 6A). The IPS scores of the CP (p < 0.001) and AZ (p < 0.05) in the GTF2I L424H mutation group were significantly higher than those in the GTF2I wild-type group (Fig. 6A). In terms of inhibitory immune checkpoints, CD276 (p < 0.0001), EDNRB (P < 0.0001), IL12A (P < 0.0001), KIR2DL1 (P < 0.05), KIR2DL3 (P < 0.01), and VEGFA (P < 0.0001) showed significantly higher expression levels in GTF2I L424H mutation than in the GTF2I wild-type group, but lower in ADORA2A (P < 0.01), BTLA (P < 0.0001), CD274 (P < 0.05), and PDCD1 (p < 0.001). In terms of stimulatory immune checkpoints, BTN3A1 (P < 0.05), CD40 (P < 0.0001), CX3CL1 (P < 0.001), CXCL10 (p < 0.001), CXCL9 (p < 0.01), ENTPD1 (p < 0.01), ICAM1 (p < 0.0001), IL1A (p < 0.05), TLR4 (p < 0.001), and TNFSF9 (p < 0.001) showed significantly higher expression levels in GTF2I L424H mutation group than in the GTF2I wild-type group, but lower expression of CD27 (p < 0.0001), CD28 (p < 0.0001), CD40LG (P < 0.0001), ICOS (p < 0.001), ITGB2 (p < 0.01), PRF1 (p < 0.05), and TNFSF4 (p < 0.05). The detailed expression profiles of immune checkpoints are shown in Fig. S5.

Survival analysis and construction of multivariate Cox regression model

Thymoma patients with GTF2I L424H mutation exhibited an upregulation of six hub genes and downregulation of four hub genes, as shown in Fig. 7A, relative to the GTF2I wild-type patients. Univariate Cox regression analysis of the expression of the 10 hub genes revealed significant correlations with the prognosis of thymoma (Fig. 7B). High expression levels of CXCL12, EGF, FGF10, FGF20, and PXDNL were identified as negatively affecting thymoma prognosis, particularly for EGF, FGF10, and FGF20 with p-values < 0.1. Conversely, the expression of CDH2, FGF17, FGF5, FN1, and TWIST1 was associated with a more favorable prognosis in thymoma. Notably, CDH2 and TWIST1 exhibited p-values of < 0.1. Kaplan-Meier survival curves demonstrated similar outcomes (Fig. S6). Diagnostic age, sex, WHO classification, Masaoka stage, GTF2I status, and TMB were included in univariate Cox regression analysis. However, only TMB exhibited a p-value of < 0.1 (Fig. S7).

The stepwise multivariable Cox method was used to assess the prognostic significance of the six features selected from the univariate Cox regression analysis in 106 patients with thymoma (Table S5). Notably, FGF20, FGF10, EGF, and TWIST1 were identified using stepwise multivariate Cox regression to construct a risk score model (Fig. 7C). The overall C-index of the Risk-Score model was 0.983, as evidenced by the log-rank test (P < 0.001), score test (P < 0.001), and Wald test (Wald test < 0.05). We then utilized the R package “pROC” (version 1.17.0.1) for ROC analysis to obtain the AUC. Using patient follow-up time and model scores, we applied the ROC function at 1095 and 1825 days, evaluating the AUC and confidence intervals with the CI function. The final AUC results were 0.99 for 1095 days and 1.0 for 1825 days OS (Fig. 7D) suggested successful model construction. The optimal risk score cutoff (6.018) was determined using the R package “maxstat” (maximally selected rank statistics with severe p-value approximations version 0.7–25). Patients were stratified into high- and low-risk groups based on this cut-off. Prognostic differences were analyzed using the survival function in the R package “survival.” Significance was assessed using the log-rank test, revealing a significantly poorer prognosis in the high-risk group than in the low-risk group (2721 days vs. 4575 days, p < 0.001) (Fig. 7E).

We concurrently examined the association between varied risk scores and patient follow-up duration, events, and gene expression alterations. As the risk scores increased, a noticeable decrease in patient survival rates was observed (Fig. 7F). TWIST1 has emerged as a protective factor, displaying downregulated expression with increasing risk scores. Conversely, FGF20, EGF, and FGF10 were identified as risk factors that exhibited an upregulation trend with escalating risk scores.

Thymoma is a rare tumor originating from the epithelial tissue of the thymic gland and is often accompanied by myasthenia gravis or other autoimmune diseases; its treatment and prognosis depend on the histological subtype and staging of the tumor [2]. Consistent with previous studies [5, 6], GTF2I was the most frequently mutated gene in thymomas, especially in low-risk thymomas. In this study, we found that patient age was related to the GTF2I L424H mutation, which is consistent with previous research findings [12]. We also found that GTF2I L424H was associated with a higher TMB score. There are few studies on TMB in thymic tumors, but in one case report, it was found that a patient with thymic carcinoma had a high TMB [13], indicating that GTF2I L424H may potentially improve the effectiveness of immunotherapy. The opposite result of gene expression and methylation profiles for GTF2I mutation status may also explain this phenomenon.

Preclinical studies have shown that GTF2I mutations cause the genesis and development of thymoma [7]; however, the underlying mechanism remains unclear. This study found that P53, Hippo signaling pathways, and genomic integrity were only and mainly mutated in the GTF2I wildtype group. In human cancers, TP53 mutations are associated with poor prognosis, such as thymoma [3], breast cancer [14], head and neck squamous cell carcinoma [15], lung adenocarcinoma [16], and liver hepatocellular carcinoma [17]. Few studies have investigated the relationship between Hippo pathway mutations and tumor prognosis. In esophageal cancer, patients with Hippo pathway-related mutations have a poorer prognosis than those in the wild-type group [18]. In addition, there are relatively few studies on genome integrity and prognosis of thymomas. A pan-cancer analysis suggested that more genomic instability (valued as CNA burden) is associated with poor overall survival (OS) and disease-specific survival (DSS) in a range of cancers, including breast, endometrial, renal, thyroid, and colorectal cancer [19]. The results of previous studies on the relationship between P53, Hippo pathways, and genomic integrity mutation and prognosis may also confirm that GTF2I mutations have better PFS prognosis.

Further results of GO, KEGG, and GSEA enrichment showed that the tumor-related pathways TGFβ and ECM-receptor interaction were significantly downregulated in the GTF2I mutant group. Although TGF-β and ECM-receptor interactions have not been studied in thymoma, they play a key role in the genesis and development of tumors in other cancers. In colorectal cancer, TGFβ signaling pathways are related to tumorigenesis and progression [20], and TGFβ has been shown to improve colorectal cancer cell migration in vitro [21]. In pancreatic adenocarcinoma, higher levels of ECM result in remarkably poorer OS times [22]. These findings suggest that although TGFβ and ECM-receptor interactions have not received much attention in thymoma research, they play key roles in other types of cancer and may also play important roles in the initiation and progression of thymoma. TGFβ and ECM-receptor interactions as well as immune-related [10, 11], our study showed that the GTF2I L424H mutation leads to significantly higher scores in certain immune infiltration indicators, such as stromal score and macrophage, and in the expression levels of certain immune checkpoints, such as CD276 and CD40. This indicated that patients with GTF2I L424H may benefit from immunotherapy. These findings may provide new directions for the future research and treatment of thymomas.

Using stepwise multivariate regression analysis, we identified four signature genes (FGF20, FGF10, EGF, and TWIST1). Research has found that FGF20 derived from glioma cells can inhibit macrophage activity by activating β-catenin [23]. The tumor-promoting role of FGF10 has been substantiated in various malignant models and cancers, and it was identified as a poor prognostic biomarker in gastric adenocarcinoma, where its expression was significantly associated with lymph node invasion and distant metastasis [24–25]. EGF is one of the most important ligands of EGFR, which is produced mainly in a paracrine fashion. A high EGF concentration in the serum is a poor prognostic factor for advanced lung cancers [26]. TWIST1 is known to be an important inducer of epithelial–mesenchymal transition (EMT), and TWIST1 overexpression has been reported in a variety of epithelial cancer cells, with clinical correlation with poor prognosis [27–29]. By combining the functions of these genes and their relationship with prognosis, we established a prognostic model with good predictive ability. So far, prognostic models for thymoma have mainly been established based on pathological features, such as the Masaoka Koga staging system, the WHO histological classification system, and a recurrence predictive model [30]. Although a new signature based on lncRNA pairs has been constructed to effectively predict tumor recurrence [31], to our knowledge, our model is the first comprehensive thymoma prognosis model that combines biomarkers.

This study had several limitations. The findings of this study are mainly based on public databases, and further clinical research is needed to confirm these findings. Second, research should expand the sample size to ensure the accuracy of the results. Therefore, the prognostic model needs to be validated clinically.

Taken together, our findings provide new insights into the clinicopathological, molecular, and prognostic differences in thymomas, particularly regarding the GTF2I L424H mutation. These findings may have important implications for the development of targeted therapies and personalized treatment strategies for patients with thymomas. Further validation of these findings in larger, independent cohorts is warranted to confirm the clinical utility of the GTF2I L424H mutation as a prognostic and predictive biomarker for thymoma.

Funding:

This work was supported by the Key Research and Development Program of Zhejiang province [2023C03057]; Zhejiang Leading Talent Entrepreneurship Project [2021R02019]; the Jiaxing Leading Talent Entrepreneurship Project; and the Key Technology Innovation Projects of Jiaxing [2024BZ20002].

Institutional Review Board Statement:

Not applicable

Informed Consent Statement:

Not applicable

Data availability statement

The original contributions of this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Acknowledgments:

Not applicable

Conflict of Interest

Xiaokai Zhao, Pengmin Yang, Caihong Zhou, Xuejiao Hu, Jieyi Li, Daoyun Zhang, and Ziying Gong were employed by Jiaxing Yunying Medical Inspection Co., Ltd., and Zhejiang Yunying Medical Technology Co., Ltd. The remaining authors declare that the research was conducted without commercial or financial relationships that could be construed as potential conflicts of interest.

Author Contributions

Shaojie Li: Investigation,Writing-original draft; Xiaokai Zhao: Methodology, Formal analysis, Visualization, Writing-review & editing; Pengmin Yang: Formal analysis, Visualization, Writing-original draft; Xia liu: Methodology, Formal analysis, Caihong Zhou Writing-review and editing; Xuejiao Hu: Writing-review & editing; Jieyi Li: Visualization; Ziying Gong: Methodology, Formal analysis, Funding acquisition; Daoyun Zhang: Conceptualization, Supervision, Project administration, Funding acquisition; Sheng Tan: Conceptualization, Supervision, Writing-review & editing. All authors contributed to the manuscript revision, and read, and approved the submitted version.

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No competing interests reported.

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Characteristics of GTF2I L424H Mutated Thymoma and its Prognostic Impact: A Comprehensive Study

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Abstract

Figures

Key points

Introduction

Materials and method

Datasets

Analysis of the difference in expression and methylation profiles

Mutation and pathway analysis in TETs

Enrichment analysis

PPI network construction and module analysis

Investigation of the immune cell infiltration and immune checkpoints in thymoma patients

Survival analysis and thymoma-related risk signature construction

Statistical methods

Results

Survival analysis and construction of multivariate Cox regression model

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1