Comprehensive bioinformatics analysis of co-mutation of FLG2 and TP53 reveals prognostic effect and influences on the immune infiltration in ovarian serous cystadenocarcinoma

Introduction

Ovarian serous cystadenocarcinoma (OV) is a type of ovarian cancer that poses a significant threat to women’s health worldwide, especially the high-grade variant, which is known for its high malignancy, invasiveness, and late detection (1,2). The incidence and mortality rates of ovarian cancer are among the top ten female malignancies, and the mortality rate has been increasing year by year (3). The 5-year survival rate for ovarian cancer patients is only about 30%, indicating the challenges faced in treating this disease (4). OV is primarily treated with a multimodal approach that combines surgery, chemotherapy, and, in some cases, radiation therapy (5). In recent years, targeted therapies and immunotherapy have shown promising results in the treatment of ovarian cancer (6,7). However, the efficacy of immunotherapy in high-grade serous ovarian cancer remains limited (8).

TP53 is a tumor suppressor gene that regulates cell cycle, DNA repair, and apoptosis (9). The TP53 mutation is commonly identified and ranks among the top five mutations observed in prevalent human cancers (10). TP53 mutation contributes significantly to the development and progression in tumors (9). FLG2, also known as filaggrin 2 or ifapsoriasin (IFPS), encodes a protein called filaggrin-like protein 2, which is regulated by calcium and hydrolyzed by calpain 1 protease. The encoded protein plays a crucial role in epithelial homeostasis and is essential for normal keratinization of the skin (11). At present, there are few studies on FLG2 gene mutation in ovarian cancer, and this previous study only simply verified that FLG2 mutation affects the prognosis of OV (12).

In previous studies, it has been reported that the single gene mutations of TP53 and FLG2 have predictive value for the prognosis of OV (9,12). The highly significant co-occurrence of TP53 and FLG2 gene mutations was observed in ovarian cancer, but no study has confirmed the role of the co-mutation of these two genes (12,13). This study aimed to analyze the prognostic implications and predictive value for precision treatment selection of FLG2 and TP53 co-mutation in OV by evaluating multiple immunotherapy biomarkers and characterizing the tumor microenvironment (TME). Since immune cell infiltration is associated with immunotherapy for ovarian cancer, we sought to explain the immune cell infiltration in the immune microenvironment to explain the benefits that co-mutants can benefit from immunotherapy (14). Finally, a prognostic model was developed to enhance the clinical applicability of the FLG2/TP53 co-mutation. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1596/rc).

Methods

Data collection

This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The clinical data of 585 OV samples, including tumor mutation burden (TMB) microsatellite instability (MSI), and mutations data, were retrieved from the cBioPortal {Ovarian Serous Cystadenocarcinoma; [The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov); PanCancer Atlas]} (https://www.cbioportal.org/) database and the samples with missing information were excluded. Three hundred samples with RNA sequencing (RNA-seq) data had undergone normalization processing.

Differentially expressed genes (DEGs) analysis

Limma is a differential expression screening method based on generalized linear models. Here, we employed the R software package limma (version 3.40.6) to perform differential analysis in order to identify DEGs between the group of co-mutation and the non-co-mutation (NCM) group based on FLG2 and TP53 mutations status. Specifically, using the obtained expression profiling dataset, we utilized the lmFit function to conduct multiple linear regression. Subsequently, the eBays function was employed to compute moderated t-statistics, moderated F-statistics, and log-odds of differential expression through empirical Bayes moderation of the standard errors towards a common value. Ultimately, this allowed us to obtain the significance of the differences for each gene.

Functional enrichment

To compare the differential signaling pathways and biological effects between the two groups, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The ‘clusterProfiler’ package in R software was utilized to assess the GO and KEGG pathways, ensuring rigorous evaluation. These GO and KEGG enrichment analyses were conducted with a significance threshold of P<0.05, providing a robust foundation for identifying relevant biological processes and pathways.

Consensus cluster analysis

Cluster analysis was performed using ConsensusClusterPlus, using agglomerative partition around medoids (PAM) clustering with a 1 − Pearson correlation distance and resampling 80% of the samples for 10 repetitions. The optimal number of clusters was determined using the empirical cumulative distribution function plot.

Estimation of immune infiltration in OV

IOBR is a computational tool for immuno-oncological biology research. Here, using 300 samples of OV expression profiles, we employed the R software package IOBR to select the CIBERSORT method to calculate the scores of 22 immune infiltrating cells in each sample.

Construction and evaluation of the nomogram

Using the R package rms, we constructed a nomogram based on the Cox proportional hazards model, which integrated survival time, survival status, and six other features. This nomogram was built to evaluate the prognostic significance of these features in a cohort of 571 samples with overall survival (OS) information. The overall performance of the model was assessed, yielding a C-index of 0.662, with a 95% confidence interval ranging from 0.623 to 0.701. The P value associated with the model was 3.883e−16, indicating its statistical significance.

Statistical analysis

The Wilcoxon rank-sum test was used for comparisons between two groups, while the Kruskal-Wallis test was applied for comparisons involving three or more groups. We used the survfit function from the R package survival to analyze the prognostic differences between different groups of samples, and assessed the significance of these differences using the log-rank test.

Results

The mutational landscape of OV

According to the waterfall plot, the top 20 most frequently mutated genes in OV cohort were TP53 (91.9%), TTN (35.8%), MUC16 (12.4%), CSMD3 (9.3%), FLG (8.8%), DNAH3 (8.6%), SYNE1 (8.4%), HMCN1 (8.4%), FAT3 (8.4%), USH2A (8.4%), KMT2C (8.1%), RYR2 (8.1%), PRUNE2 (7.9%), FLG2 (7.6%), DYNC1H1 (7.6%), RNF213 (7.6%), APOB (7.6%), FCGBP (7.4%), MACF1 (7.4%), and NF1 (7.2%) (Figure 1A). The genetic mutations affecting the expression of TP53 and FLG2 in patients with OV from TCGA database were extensively analyzed utilizing the cBioPortal online tool. A total of 383 mutation sites were found in p53 protein domains, including 229 missense mutations, 105 truncating mutations, 12 inframe insertion-site mutations, 36 splice-site mutations, one fusion mutation, and notably, R248Q/W emerged as the most frequently occurring mutation site (Figure 1B). Forty-three missense mutations, five truncating mutations, and one inframe insertion-site mutation were identified in FLG2 protein domains, and S1631A/C was the locus exhibiting the highest frequency of mutations (Figure 1C).

Figure 1 The mutational landscape in 585 OV patients. (A) Waterfall chart for genetic mutation analysis. (B,C) Mutation diagram of TP53 and FLG2 across protein domains. #, numbers; OV, ovarian serous cystadenocarcinoma.

The co-mutation of FLG2 and TP53 showed significant prognostic value

First, we analyzed the prognostic effects of individual mutations of TP53 and FLG2. There was no significant difference in prognosis of ovarian cancer patients with TP53 single mutation (OS; P>0.05), progression-free survival (PFS; P>0.05), disease-specific survival (DSS; P>0.05) (Figure 2A-2C). Patients with FLG2 mutation had longer OS (P=0.04) and DSS (P=0.02); however, no significant differences were observed in PFS (P=0.25) (Figure 2D-2F). The 585 samples of OV, including a sample with missing mutation information, were divided into four groups based on the mutation status of FLG2 and TP53: 191 patients with FLG2 wild-type and TP53 wild-type (FLG2⁻/TP53⁻), 322 patients with FLG2 wild-type and TP53 mutant (FLG2⁻/TP53⁺), 16 patients with FLG2 mutant and TP53 wild-type (FLG2⁺/TP53⁻), 55 patients with FLG2 mutant and TP53 mutant (FLG2⁺/TP53⁺). In the comprehensive prognostic analysis of OS, the group of FLG2⁺/TP53⁺ exhibited significantly improved prognosis compared to the group of FLG2⁻/TP53⁻, demonstrating a marked difference in outcomes (P=0.02). Similarly, the group of FLG2⁺/TP53⁺ exhibited notably superior OS in comparison to both the groups of FLG2⁻/TP53⁺ (P=0.004) and FLG2⁺/TP53⁻ (P=0.005) (Figure 2G). From Figure 2H, it can be observed that the group of FLG2⁺/TP53⁺ exhibited better PFS compared to the FLG2⁻/TP53⁺ (P=0.03) and FLG2⁺/TP53⁻ (P=0.01). In the DSS analysis, the group of FLG2⁺/TP53⁺ also showed better prognosis compared to the other three groups (Figure 2I). To better analyze the prognostic value of co-mutation in the FLG2 and TP53 genes, the three groups of FLG2⁻/TP53⁻, FLG2⁻/TP53⁺, and FLG2⁺/TP53⁻ were combined into a NCM group. In the Kaplan-Meier (KM) survival curve analysis of the two groups, the co-mutation group exhibited better OS, PFS, and DSS (Figure 2J-2L). Taken together, these results suggested that patients with the co-mutation of FLG2/TP53 have a better prognosis.

Figure 2 Analysis of the survival status of the OV samples. (A-C) Analysis of TP53 gene mutation on OS, PFS, and DSS. (D-F) Kaplan-Meier survival curves were established by FLG2 mutation on OS, PFS, and DSS. (G-I) The survival curve of pairwise comparison of four groups (FLG2⁺/TP53⁺, FLG2⁺/TP53⁻, FLG2⁻/TP53⁺, FLG2⁻/TP53⁻) in OS, PFS, DSS. (J-L) The survival curve of comparison of co-mutation group and NCM group in OS, PFS, DSS. *, P<0.05; **, P<0.01; ***, P<0.001. FLG2⁺/TP53⁺, group of patients with FLG2 and TP53 mutation; FLG2⁺/TP53⁻, group of patients with FLG2 mutation and TP53 wild-type; FLG2⁻/TP53⁺, group of patients with FLG2 wild-type and TP53 mutation; FLG2⁻/TP53⁻, group of patients with FLG2 and TP53 wild-type. NCM, non-co-mutation; OV, ovarian serous cystadenocarcinoma; OS, overall survival; PFS, progression-free survival; DSS, disease-specific survival.

The relationship between the mutation status of FLG2 and TP53 and TMB MSI

TMB and MSI are both closely related to tumor initiation and progression, and they can independently predict the efficacy of tumor immunotherapy (15). There was no significant difference in TMB score between TP53 mutant group and FLG2 mutant group (Figure 3A). Since the prognosis effect of the co-mutation group was significant, further analysis revealed that the TMB of the co-mutation group was significantly higher than other groups (Figure 3B). In the TMB analysis comparing the two groups of samples, the co-mutation group displayed a notably higher level (Figure 3C). Based on the optimal cut-off value, the group of co-mutation was divided into high and low groups according to the TMB, the result showed that patients with high TMB score in the co-mutation group had a better prognosis (P=0.04; Figure 3D). We used the same method for prognostic analysis of the NCM group, and there was no significant difference in high and low TMB scores between the two groups (P=0.19; Figure 3E). In a prognostic analysis of all patients, the high TMB group had a longer survival than the low TMB group (P=0.005; Figure 3F). The co-mutant group only had higher MSI scores than the wild-type (FLG2⁻/TP53⁻) group, and there was no significant difference from the other two groups (Figure 3G). There was no significant difference was observed between the two groups in MSI (Figure 3H). We employed the TMB grouping method outlined above to categorize co-mutants into high and low MSI groups; however, our findings did not align with those observed for TMB (Figure 3I). Based on the analysis of TMB and OS, the patients with a co-mutation and high TMB score have a better prognosis, while those with a low TMB score have a worse prognosis, suggested that patients with FLG2⁺/TP53⁺ may hold considerable potential for immunotherapy.

Figure 3 The correlation between FLG2/TP53 mutation status and TMB and MSI. (A) The comparison of TMB between TP53- and FLG2-mutated groups. (B,C) FLG2/TP53 mutation status was correlated in TMB of four groups (B) and two groups (C). (D-F) The survival curve between high- and low-TMB in terms of co-mutation samples, NCM samples, and all samples. (G,H) The analysis of FLG2/TP53 mutation status MSI between four and two groups. (I) Comparison of prognosis between high and low MSI of co-mutation samples. ***, P<0.001; ****, P<0.0001; ns, no significance. FLG2⁺/TP53⁺, group of patients with FLG2 and TP53 mutation; FLG2⁺/TP53⁻, group of patients with FLG2 mutation and TP53 wild-type; FLG2⁻/TP53⁺, group of patients with FLG2 wild-type and TP53 mutation; FLG2⁻/TP53⁻, group of patients with FLG2 and TP53 wild-type. TMB, tumor mutation burden; NCM, non-co-mutation; L, low; H, high; MSI, microsatellite instability.

DEGs analysis between the group of co-mutation and the NCM

Out of a total of 585 samples, 300 were selected for RNA-seq. Subsequently, these 300 samples were further categorized into two distinct groups, based on FLG2 and TP53 mutation status, the co-mutation group and the NCM. In the KM curve prognostic analysis with these 300 samples, the co-mutation group also exhibited better OS and DSS, while there was no significant difference in PFS between the two groups (Figure S1A-S1C). Through the differential analysis between two sets of samples, the DEGs were visualized using volcano plots, which illustrated the up-regulated and down-regulated genes (Figure 4A). DEGs were screened [|log₂fold change (FC)| >0.58, P<0.01], including 315 upregulated genes and 12 downregulated genes. Their expression profiles were visualized in a heatmap (Figure 4B). The GO analysis showed that the DEGs between the two groups were enriched in cellular component (CC) such as mitochondrial inner membrane, mitochondrial protein-containing complex, ribosomal subunit, etc. In the KEGG analysis, these genes were involved in the thermogenesis, Parkinson’s disease (PD), and oxidative phosphorylation signaling pathway (Figure 4C). We subsequently utilized consensus clustering to delineate the 327 DEGs clusters of OV. Following k-means clustering, two distinct clusters within the cohort were identified, exhibiting unique expression patterns of DEGs (Figure 4D). C1 and C2 were clearly distinguished in the principal component analysis (PCA) and C1 had a better OS (Figure 4E,4F).

Figure 4 DEGs analysis between the group of co-mutation and the NCM in 300 RNA-seq OV samples. (A) Volcano plot showing DEGs. (B) Heatmap of DEGs between co-mutation and NCM groups. (C) GO/KEGG enrichment analyses based on DEGs. (D) Heatmap depicts consensus clustering solution (k=2) for 327 DEGs in OV samples. (E) PCA analysis of cluster 1 and 2. (F) Kaplan-Meier curves of OS in two clusters. NCM, non-co-mutation; CC, cellular component; KEGG, Kyoto Encyclopedia of Genes and Genomes; C1, cluster 1; C2, cluster 2; PCA, principal component analysis; DEGs, differential expressed genes; RNA-seq, RNA sequencing; OV, ovarian serous cystadenocarcinoma; GO, Gene Ontology; OS, overall survival.

Analysis of immune characteristics between the group of co-mutation and the NCM

The association between the 300 samples with OV and the infiltration of 22 types of immune cells identified by the CIBERSORT algorithm was analyzed. The analysis of immune cell proportions showed that T cells accounted for the highest percentage (Figure 5A). There was a significant negative correlation between resting mast cells and activated mast cells. Monocytes and M2 macrophages showed a significant positive correlation (Figure 5B). An analysis of the immune cell composition revealed a significantly higher proportion of T follicular helper (TFH) cells, T gamma delta cells, M2 macrophages, and eosinophils in the co-mutation group (Figure 5C). Immunomodulatory genes such as CCR9, TMEM173, TNFRSF13B, TNFRSF17 exhibited significant differences between the two groups, but there were no significant differences in some commonly used immune checkpoint genes (Figure 5D). The difference in MSI between the two groups was analyzed, and the results showed no significant difference (Figure S2A). We further divided the samples into four groups to explore the difference in MSI, and the results showed that the MSI scores in the co-mutation group were only higher than the group of wild-type (FLG2⁻/TP53⁻; Figure S2B). The group of co-mutation had a higher TMB score compared to the NCM (Figure S2C). The study revealed that the group of co-mutation was associated with immune-infiltrating cells, as well as immune regulatory genes.

Figure 5 The analysis of immune characteristics between the group of co-mutation and the NCM. (A) The analysis of immune cell proportions in 300 OV samples. (B) The correlation between 22 immune cells. (C,D) The correlation between the co-mutation and NCM in immune cells (C), and immunomodulatory genes (D). *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; –, no significance. NCM, non-co-mutation; OV, ovarian serous cystadenocarcinoma.

The clinical applications of FLG2 and TP53 gene mutation

After performing both univariate and multivariate Cox analyses on 585 samples with six clinical characteristics including FLG2/TP53 mutation status, diagnosis age, MSI, TMB, histologic grade, and stage, we found that FLG2⁺/TP53⁺, diagnosis age, stage III, and stage IV serve as independent prognostic factors (Table 1). Given the significant difference in TMB scores in terms of mutation status, we have included it with the three independent prognostic factors in the construction of the nomogram (Figure 6A). The calibration curve indicated that the nomogram model had a high predictive accuracy (Figure 6B). All samples were divided into two groups based on the optimal cutoff nomogram score, with the low-risk score group having a better prognosis (P<0.001; Figure 6C). The area under the curve (AUC) values of this model for 1-, 3-, and 5-year were 0.74, 0.70, and 0.67, respectively (Figure 6D). The decision curve analysis (DCA) results show that the nomogram model had the highest prognostic accuracy at 1-, 3-, and 5-year OS (Figure 6E-6G). These results indicated that the model had a clinical practical value.

Figure 6 Relationship between FLG2/TP53 mutation status and other clinical information. (A) Nomogram for predicting the probability of 1-, 3-, and 5-year OS for OV patients. (B) Calibration plot of the nomogram for predicting the probability of OS at 1, 3, and 5 years. (C) Kaplan-Meier curves of OS in nomogram risk score. (D) Time-dependent ROC curve analyses of the nomogram model. (E-G) DCA in 1-, 3-, and 5-year OS. FLG2⁺/TP53⁺, group of patients with FLG2 and TP53 mutation; FLG2⁺/TP53⁻, group of patients with FLG2 mutation and TP53 wild-type; FLG2⁻/TP53⁺, group of patients with FLG2 wild-type and TP53 mutation; FLG2⁻/TP53⁻, group of patients with FLG2 and TP53 wild-type. TMB, tumor mutation burden; L, low; H, high; AUC, area under the curve; CI, confidence interval; OS, overall survival; OV, ovarian serous cystadenocarcinoma; ROC, receiver operating characteristic curve; DCA, decision curve analysis.

Discussion

OV poses a significant challenge in women’s health, with high incidence and mortality rates. Further research is needed to develop more effective treatment options for this devastating disease. In this study, we identified a unique molecular subtype of OV with good prognosis and related with immune infiltration, which was the FLG2/TP53 co-mutation.

In the mutational landscape of OV, we found TP53 had the highest mutation frequency, which is consistent with previous research (12,16), and FLG2 mutation frequency was 7.6%. Whether the 585 samples were divided into four groups or two groups, FLG2⁺/TP53⁺ showed better outcomes in terms of OS, PFS, and DSS. This suggested that the co-mutation of FLG2/TP53 played an important anti-tumor role in OV, which was significant in its mechanism research. We observed an interesting phenomenon that patients of OV only with TP53 mutation had a poor prognosis, while only with FLG2 mutation, on the contrary, had a good prognosis value (12,17,18). However, in our study, we found no significant difference in prognosis between TP53 single gene mutation and the wild type. We postulate that the functionalities of p53 are distinctively modulated based on the specific site of mutation within the gene, and this hypothesis has been corroborated by the research conducted by Antoun et al. (19). TMB and MSI are both significant biomarkers in the field of oncology and cancer immunotherapy (20). TMB reflects the tumor’s ability to generate new antigens, which is associated with the sensitivity of the tumor to programmed cell death protein 1 (PD-1) immunotherapy (21). MSI, on the other hand, reflects the instability of the tumor’s genome, which is related to the deficiency of the mismatch repair (MMR) mechanism and the effectiveness of immunotherapy (22). After analyzing the relationship between the co-mutation and TMB and MSI, we found that FLG2⁺/TP53⁺ showed a high TMB and MSI score. Patients with co-mutations exhibiting high TMB scores also had a better prognosis. This indicated that patients with co-mutation may have a good response to immunotherapy.

Among 300 samples that underwent RNA-seq, we further confirmed that FLG2⁺/TP53⁺ co-mutation had good prognostic value. Through limma analysis, 327 DEGs were identified in the group of co-mutation and NCM. Subsequently, using KEGG analysis, three signaling pathways were enriched, including thermogenesis, PD, and oxidative phosphorylation. Previous reports have indicated that there is a significant negative correlation between the polygenic risk score (PRS) for ovarian cancer and PD. The association between PD and ovarian cancer primarily driven by rs183211, which is located in an intron region of the NSF gene (17q21.31) (23). However, a few studies found that there is no strong relationship between OV and PD, but they represent a huge public health burden, the potential link between them is worth further exploration (24,25). Current literature suggests that oxidative stress and mitochondrial dysfunction may exacerbate the pathological features of both PD and ovarian cancer, but the precise mechanisms by which these interactions occur are still not fully elucidated. For instance, lactate—a byproduct of anaerobic metabolism—has been shown to stimulate mitochondrial oxidative phosphorylation, yet its role in the context of PD and ovarian cancer remains poorly defined (26). In ovarian cancer, particularly in cases harboring mutations in TP53 and FLG2, the regulation of oxidative phosphorylation may be further complicated by aberrations in mitochondrial dynamics and bioenergetics (27). Alterations in oxidative phosphorylation have been noted in ovarian cancer, suggesting a potential therapeutic approach of targeting this process for the management of the disease (28-30).

Up until now, clinical trials evaluating PD-1 and programmed death-ligand 1 (PD-L1) inhibitors for patients with recurrent ovarian cancer have often been disappointing, which may be due to the fact that the efficacy of immunotherapy is influenced by various factors, such as the TME and the patient’s immune status (31). In the immune infiltration analysis of ovarian cancer, the results showed that the co-mutation group had higher levels of immune cells. FLG2/TP53 mutation status showed different correlations with TFH cells, T gamma delta cells, M2 macrophages, and eosinophils, which may explain the potential ability of FLG2/TP53 co-mutation regulate tumor-associated immune cells. TFH cells, characterized by their role in facilitating B cell maturation and antibody production, are crucial for adaptive immunity. Their presence has been associated with favorable outcomes in several malignancies, as they promote effective humoral responses (32). In addition to TFH cells, T gamma delta cells play a pivotal role in anti-tumor activity (33). The differential correlation of γδ T cells with FLG2/TP53 mutations may indicate a unique immunological profile that enhances anti-tumor immunity, further supporting the notion that these mutations could be harnessed for therapeutic strategies. M2 macrophages were significantly associated with poor prognosis among patients with high-grade, type II OV (34). The observed relationship between M2 macrophages and FLG2/TP53 co-mutations may reflect a complex interplay where these mutations modulate macrophage polarization. The tumor-promoting function of M2 macrophages in co-mutated patients was offset by other tumor-suppressing immune cells, such as TFH cells and T gamma delta cells, thus forming an anti-tumor immune microenvironment. The analysis of immune cell markers in the context of ovarian cancer reveals critical insights into the TME, particularly concerning the implications of FLG2/TP53 co-mutation. Notably, the expression levels of PD-1 and PD-L1, which are pivotal in the regulation of immune responses and are frequently targeted in immunotherapy, did not exhibit significant alterations in patients harboring these mutations. This finding suggests a potential resistance mechanism to PD-1/PD-L1 blockade therapies in this subset of patients, aligning with previous studies that have indicated the complexity of immune evasion in tumors with specific genetic alterations (35,36). The lack of change in these markers may indicate that alternative pathways are being utilized by the tumor to escape immune surveillance, necessitating further investigation into the underlying mechanisms. CCR9 is the only specific receptor for CCL25. The increased expression of CCR9 has been detected in various solid tumors, and the high expression of CCR9 plays an important role in tumor development, progression, and metastasis (37). CCR9 can inhibit the formation of the TME and is associated with poor prognosis (38). The expression of CCR9 in the co-mutation group was significantly downregulated. The co-mutation group may promote the formation of a tumor immune-activated microenvironment by downregulating CCR9, enhance anti-tumor immune responses, inhibit tumor development, and thereby have a favorable prognosis. Elevated TMB has been associated with improved responses to immunotherapy, particularly in the context of checkpoint inhibitors. Therefore, while the PD-1/PD-L1 axis may not be altered, the increased TMB in FLG2/TP53 co-mutated ovarian cancer patients presents an opportunity for novel immunotherapeutic strategies that leverage the neoantigen landscape.

This discovery presents a challenge to the outcomes of molecular typing and has the potential to prevent unnecessary treatment in certain patients, pending further validation. Our study offers novel perspectives on the OV immune microenvironment and immune-related treatments. Nonetheless, the retrospective nature of our research imposes limitations, necessitating validation through prospective studies. Furthermore, conducting functional and mechanistic investigations on the mutation of the two genes both independently and in conjunction is essential to bolster their clinical utility.

Conclusions

We have successfully identified and validated a distinct subtype characterized by mutations in FLG2/TP53⁺, which holds independent prognostic value for ovarian cancer patients and is indicative of the overall immune response within the ovarian cancer microenvironment.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1596/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1596/prf

Funding: This study was supported by the National Natural Science Foundation of China (No. 81602020), the Tianjin Medical University Cancer Institute&Hospital Research Project (No. 1805), and the Tianjin Binhai New Area Health Research Project (No. 2024BWKZ09).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1596/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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