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. 2026 Feb 25;15(3):180. doi: 10.21037/tcr-2025-aw-2408

Pan-cancer analysis reveals FOLH1 as a prognostic biomarker and immune modulator in human tumors

Weiming Fan 1,2,#, Xiaofei Guo 1,#, Yuan Wang 1, Shan He 1,✉, Ping Zhang 1,✉
PMCID: PMC13066985  PMID: 41969500

Abstract

Background

Folate hydrolase-1 (FOLH1) is the gene encoding prostate-specific membrane antigen (PSMA) and is highly associated with prostate cancer. However, its involvement in other tumorigenesis remains to be investigated. This study aims to elucidate its role in multiple cancers through various bioinformatics approaches.

Methods

In this study, we integrated the multi-omics data of TCGA (The Cancer Genome Atlas Program), GTEx (The Genotype-Tissue Expression), and CPTAC (Clinical Proteomic Tumor Analysis Consortium) to analyze the expression pattern, genetic variation, copy number alteration (CNA), and methylation characteristics of FOLH1 in 33 cancers by using tools such as TIMER 2.0, GEPIA 3, and cBioPortal. We also assessed its prognostic significance by survival analysis and combined the immune infiltration algorithm with the drug sensitivity database to reveal its relationship with the tumor microenvironment, including immune checkpoints, tumor mutation burden (TMB), microsatellite instability (MSI), and chemotherapy response.

Results

FOLH1 expression was significantly up-regulated in uterine corpus endometrial carcinoma (UCEC), adrenocortical carcinoma (ACC), and colon adenocarcinoma (COAD), and low in breast invasive carcinoma (BRCA) and liver hepatocellular carcinoma (LIHC). Survival analysis showed that high FOLH1 expression was significantly associated with shorter overall survival (OS) in patients with ACC and pancreatic adenocarcinoma (PAAD), but showed the opposite trend in stomach adenocarcinoma (STAD).

Conclusions

FOLH1 may be involved in tumor progression by regulating amino acid metabolic pathways and the immune microenvironment. It is a promising pan-cancer prognostic marker and synergistic target for immunotherapy.

Keywords: Folate hydrolase-1 (FOLH1), pan-cancer analysis, biomarker, immune microenvironment, bioinformatics

Highlight box.

Key findings

• Folate hydrolase-1 (FOLH1) expression is upregulated in adrenocortical carcinoma (ACC) and uterine corpus endometrial carcinoma (UCEC) but downregulated in breast and liver cancers.

• High FOLH1 predicts poor survival in ACC and pancreatic cancer but favorable prognosis in stomach adenocarcinoma.

What is known and what is new?

• FOLH1 encodes prostate-specific membrane antigen, a validated diagnostic and therapeutic target in prostate cancer.

• This study reveals FOLH1’s pan-cancer association with the tumor immune microenvironment, specifically correlating with cancer-associated fibroblasts and immune checkpoints in non-prostatic tumors like colon cancer.

What is the implication, and what should change now?

• FOLH1 shows potential as a prognostic biomarker and immunotherapy target beyond prostate cancer.

• Precision treatment strategies in FOLH1-high tumors (e.g., ACC, UCEC) should consider FOLH1-targeted or combination immunotherapies.

Introduction

Cancer remains one of the leading causes of death worldwide and continues to pose a major public health challenge (1,2). Immunotherapy has recently emerged as a promising strategy for treating multiple cancer types, including melanoma, lung cancer, and lymphoma (3-5). However, patient responses to immunotherapy exhibit considerable variability (6). This requires identifying predictive biomarkers and developing customized treatment strategies to improve therapeutic outcomes. Therefore, pan-cancer studies of target genes can help analyze molecular abnormalities and potential associations across different cancer types (7). This ultimately enables adjustments to the immune microenvironment, thereby advancing combination therapies and individualized treatments to improve patient outcomes.

Folate hydrolase-1 (FOLH1) is a protein-coding gene that encodes prostate-specific membrane antigen (PSMA), a type II transmembrane glycoprotein that functions as both a glutamate carboxypeptidase and folate hydrolase. PSMA is expressed not only in prostate cancer but also in non-prostatic tissues such as the salivary glands, liver, gastrointestinal tract, endocrine organs, breast, and adrenal glands (8). Notably, PSMA is expressed in some tumor-associated neovasculature, but the percentage and intensity vary among these tumor types. Tumor cells and the native vasculature lack expression in all types (9,10). At the messenger RNA (mRNA) level, FOLH1 expression is detectable in most tumor types. However, certain peaks in this pan-cancer expression profile, though relatively lower than prostate cancer, appear more valuable for the potential diagnostic and therapeutic applications of FOLH1 (11). Thus, the role of FOLH1 as a biomarker in tumors of the stomach, digestive tract, and other organs necessitates further investigation.

In this study, we applied multiple bioinformatics methods to perform a comprehensive pan-cancer analysis of FOLH1. Our workflow included gene expression profiling, functional annotation, prognostic association analysis, assessment of genetic variants and copy number alterations (CNAs), methylation profiling, immune-cell infiltration estimation, drug-sensitivity evaluation, and enrichment analysis of FOLH1-related gene sets. Our results show that FOLH1 expression correlates with immune-response mechanisms, suggesting its potential as a prognostic biomarker across various cancer types. Moreover, its prognostic significance varies with tumor site, pathological subtype, and additional clinical factors. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2408/rc).

Methods

Gene expression and functional analysis

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. RNA sequencing data from 17,382 samples and 948 donors (RP11-6N13.1) were utilized to identify the chromosomal location of the FOLH1 gene and its expression across 54 tissues in the GTEx (The Genotype-Tissue Expression) project (version 8) using the University of California Santa Cruz (UCSC) Genome Browser (Human Dec. 2013, GRCh38/hg38 assembly; http://genome.ucsc.edu/) (12).

The TIMER 2.0 platform (http://timer.cistrome.org/) (13) was employed via its “Exploration” module to assess differences in FOLH1 expression between tumor tissues and adjacent normal tissues in The Cancer Genome Atlas (TCGA) cohorts. Box plots were generated to visualize the distribution of gene expression levels.

The Human Protein Atlas (HPA) portal (version 23.0, https://www.proteinatlas.org/) was used to evaluate the normalized expression (NX) levels of FOLH1 across 55 normal tissue types under the “Tissue” module. Raw data were normalized to transcripts per million (TPM), and transcript expression values were represented as NX. “Low specificity” indicates NX ≥1 in ≥1 cell/region/tissue type but not significantly elevated in any specific cell/region/tissue. Additionally, immunohistochemistry (IHC) images of FOLH1 protein expression in normal tissues and two tumor types were downloaded and systematically analyzed. IHC was used to detect the presence of target antigens in cells or tissues.

The Gene Expression Profiling Interactive Analysis version 3 (GEPIA3) web server (https://gepia3.bioinfoliu.com/) (14) was applied to compare FOLH1 expression between normal and tumor tissues for specific cancer types lacking normal controls in the TCGA database, including adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS). Key parameters were configured (e.g., *P* value threshold =0.01 and |log2FC| cutoff =1), and the “Match TCGA normal and GTEx data” option in GEPIA3’s “Box Plots” module was selected to generate comparative box plots. Furthermore, uniformly preprocessed pan-cancer omics data (TCGA Pan-Cancer, N=10,535, G=60,499) were retrieved from the UCSC database for systematic analysis of the FOLH1 (ENSG00000086205) expression profile. Data standardization involved three steps: (I) filtering primary tumors and primary hematologic tumors derived from peripheral blood; (II) applying log2 (expression value +0.001) transformation to raw expression values; (III) excluding cancer types with fewer than three samples, resulting in 26 tumor types. Clinical stage-related differential analysis was conducted using the R platform (version 4.4.1). Unpaired t-tests were used for two-group comparisons, and analysis of variance (ANOVA) was applied for multi-group comparisons. Violin plots were constructed to illustrate FOLH1 expression across clinical stages for colon adenocarcinoma (COAD), colon and rectal adenocarcinoma (COADREAD), breast invasive carcinoma (BRCA), stomach and esophageal carcinoma (STES), and kidney renal papillary cell carcinoma (KIRP).

Finally, the “CPTAC Analysis” module of the UALCAN portal (The University of Alabama at Birmingham Cancer Data Analysis; http://ualcan.path.uab.edu/analysis-prot.html) (15) was utilized to evaluate differences in total FOLH1 protein expression between tumor and normal tissues.

Survival prognostic analysis

First, the “Survival Map” module of GEPIA3 was used to generate survival plots for FOLH1 expression across all TCGA tumor types by setting high (50%) and low (50%) cutoff values. This generated significance maps for overall survival (OS) and progression-free interval (PFI), facilitating the identification of high- and low-FOLH1 expression cohorts in specific cancer types (16).

Subsequently, the Kaplan-Meier Plotter (https://kmplot.com/analysis/) was employed to evaluate the prognostic significance of FOLH1 in colorectal and gastric cancers at the microarray level. Additionally, “FOLH1” was input into the “TCGA” module of UALCAN, and survival data from the TCGA database were analyzed using the “Survival” section to assess the impact of FOLH1 expression levels on cancer patient survival.

Genetic variation analysis

The cBio Cancer Genomics Portal (cBioPortal; http://www.cbioportal.org) (17-19), a multidimensional cancer genomics dataset platform, was utilized to explore, visualize, and analyze multivariate genetic alterations interactively. The “TCGA Pan-Cancer Atlas Studies” section of cBioPortal was employed to obtain data on mutation types, alteration frequencies, and CNAs of FOLH1 across TCGA tumors.

Copy number variation (CNV) and methylation analysis

The Gene Set Cancer Analysis (GSCA; https://guolab.wchscu.cn/GSCA/#/) database (20), a robust bioinformatics tool integrating TCGA-derived mRNA expression, mutation, immune infiltration, and methylation data, was used. The “Mutation” module of GSCA was applied to analyze FOLH1 CNVs, methylation patterns, and their correlations with mRNA expression levels.

Immune microenvironment and immune checkpoint analysis

TIMER 2.0’s three “Immune Gene” modules and the “Cancer-Associated Fibroblasts” subset were utilized. Algorithms including EPIC (13,21,22), MCPCOUNTER (13,21,23), XCELL (13,21,24), and TIDE (13,21,25), along with the “Purity Adjustment” option, were applied to evaluate immune infiltration of FOLH1 in multiple TCGA cancer types (26). Additionally, TISIDB (http://cis.hku.hk/TISIDB) was used to assess FOLH1 expression across immune subtypes in different cancers.

A uniformly standardized pan-cancer dataset integrating TCGA and GTEx databases was obtained from UCSC (https://xenabrowser.net/). The SangerBox online tool (http://sangerbox.com/tool.html) (27) was employed to identify correlations between FOLH1 expression and common immune checkpoint genes. Pearson correlation tests were performed, with P<0.05 considered statistically significant.

Drug sensitivity analysis of FOLH1

The Gene Set Cancer Analysis platform (GSCALite; https://guolab.wchscu.cn/GSCA/#/) (20) was used to analyze correlations between FOLH1 expression and drug sensitivity in the Cancer Therapeutics Response Portal (CTRP) database.

Correlation analysis of FOLH1 expression with tumor mutation burden (TMB), microsatellite instability (MSI), and tumor stemness

TMB, MSI, and mRNAsi/mDNAsi stemness scores are critical biomarkers (28). Spearman correlation coefficients were calculated to assess associations between FOLH1 expression and TMB, MSI, and mRNAsi/mDNAsi stemness-related genes.

Single-cell functional analysis of FOLH1

CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/) (29), a database designed for in-depth single-cell functional characterization of cancer cells, was utilized to evaluate the functional roles of FOLH1 across cancers. This analysis involved assessing average correlations between FOLH1 and various functional states, including invasion, metastasis, proliferation, epithelial-mesenchymal transition (EMT), angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, quiescence, and stemness. A correlation strength threshold of 0.3 and a significance level of P<0.05 were applied.

FOLH1-related gene enrichment analysis

The STRING database (v11.5, https://string-db.org/) was used to construct a protein-protein interaction (PPI) network for FOLH1. Parameters included: organism: *Homo sapiens*; query protein: “FOLH1”; minimum interaction confidence threshold: ≥0.150; maximum interactors: 50; only experimentally validated interactions were retained.

Subsequently, multidimensional analysis was performed using GEPIA3. The “Similar Genes Detection” module identified the top 100 FOLH1 co-expressed genes in TCGA tumors and normal tissues. The TIMER 2.0 “Gene Correlation” function was employed to systematically analyze expression correlations between these genes and FOLH1, visualized via heatmaps.

To refine candidate genes, Jvenn (http://bioinformatics.psb.ugent.be/webtools/Venn/), an interactive Venn diagram viewer, was used to intersect the top 100 FOLH1-associated genes with 10 FOLH1-binding proteins, yielding one overlapping gene: KLK3.

Functional enrichment analysis of FOLH1 was conducted using the Xiantao Academic Online Analysis Tool (https://www.xiantaozi.com/). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to elucidate FOLH1’s molecular mechanisms and signaling pathways.

Statistical analysis

The Wilcoxon rank-sum test was used to compare FOLH1 expression differences between tumor and normal tissues across TCGA cohorts and to evaluate intra- and inter-sample normalization of log2 spectral count ratios from CPTAC (Clinical Proteomic Tumor Analysis Consortium) datasets. For FOLH1 expression in tumor types lacking normal controls, one-way ANOVA with Tukey’s honest significant difference (HSD) post-hoc test was applied to combined TCGA and GTEx data in GEPIA3. Paired t-tests were performed using GraphPad Prism. Mantel-Cox tests in GEPIA3 assessed prognostic correlations between FOLH1 expression and OS/PFI in specific cancers. Kaplan-Meier analysis with log-rank tests evaluated the prognostic significance of FOLH1 expression in pan-cancer studies. Pearson correlation tests identified associations between FOLH1 expression and immune checkpoint genes. Spearman correlation coefficients quantified relationships between FOLH1 expression, immune cell infiltration, TMB, MSI, and mRNAsi/mDNAsi stemness scores. A threshold of P<0.05 was considered statistically significant. Given the exploratory nature of this pan-cancer analysis, all P values reported are nominal and were not adjusted for multiple comparisons [e.g., false discovery rate (FDR)]. Consequently, these findings should be interpreted as hypothesis-generating.

Results

Gene expression and functional analysis

Analysis of FOLH1 gene expression in 54 GTEx RNA-seq normal tissues (17,382 samples from 948 donors) using the UCSC Genome Browser (GRCh38/hg38) revealed significantly higher expression of FOLH1 in the prostate, cerebrospinal tissues (including the substantia nigra and hippocampus), and hypothalamus compared to other normal tissues (Figure 1A).

Figure 1.

Figure 1

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FOLH1 gene and RNA expression in human tissues and tumors. (A) FOLH1 gene expression in different tissues was analyzed using GTEx RNA-sequencing data. (B) A consensus FOLH1 mRNA dataset was created by combining HPA and GTEx transcriptomics datasets to analyze the normalized expression levels; (C) FOLH1 expression differences between different tumors and their adjacent normal tissues in The Cancer Genome Atlas cohort were analyzed using TIMER 2.0. *, P<0.05; **, P<0.01; ***, P<0.001. FOLH1, folate hydrolase-1; GTEx, The Genotype-Tissue Expression; HPA, Human Protein Atlas; TPM, transcripts per million.

A consensus dataset integrating HPA and GTEx transcriptomic data (55 tissue types) demonstrated elevated FOLH1 mRNA levels in the duodenum, small intestine, prostate, salivary glands, spinal cord, and liver, while no detectable expression was observed in over 20 other tissue types, indicating tissue-specific expression patterns (Figure 1B).

Box plots generated via TIMER2.0 (Figure 1C) systematically illustrated differential FOLH1 expression between malignant tumors and paired adjacent normal tissues in the TCGA database. Statistical analysis identified significant upregulation of FOLH1 in seven cancer types: KIRC, UCEC, lung squamous cell carcinoma (LUSC), thyroid carcinoma (THCA), COAD, lung adenocarcinoma (LUAD), and PRAD. Notably, esophageal carcinoma (ESCA) and pheochromocytoma and paraganglioma (PCPG) also exhibited pronounced FOLH1 overexpression. Conversely, FOLH1 expression was markedly downregulated in five tumor types: BRCA, glioblastoma multiforme (GBM), kidney chromophobe (KICH), KIRP, and liver hepatocellular carcinoma (LIHC) compared to adjacent normal tissues.

Box plots were generated using the “Box Plot” module of GEPIA3 to compare FOLH1 expression levels in several types of tumors and normal tissues. Figure 2A shows that the FOLH1 gene was significantly upregulated in ACC, KIRC, PRAD, PAAD, UCEC, and UCS tumor tissues compared to normal tissues, and the high expression of FOLH1 in these tissues may be associated with the development and progression of these cancers, suggesting the potential candidacy of FOLH1 as a biomarker for these cancer types.

Figure 2.

Figure 2

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Expression patterns of FOLH1 gene in different types of tumor tissues and pathological stages. (A) Differences in FOLH1 expression in certain tumor tissues (no normal controls in the TCGA database) and normal tissues were analyzed by combining data from the TCGA and GTEx databases using GEPIA 3. (B) Analysis of FOLH1 protein expression levels in primary tumors and normal tissues in OV, LUSC, PAAD, and LUAD using data from UALCAN. (C) FOLH1 expression was assessed in different pathological stages of COAD, COADREAD, BRCA, STES, and KIRP, using violin plots. (D) Representative immunohistochemistry images of FOLH1 in normal and tumor tissues of the endometrium and prostate sourced from HPA database. Image credit goes to the Human Protein Atlas. The links to the individual normal and tumor tissues of each protein are provided for normal endometrium (https://www.proteinatlas.org/ENSG00000086205-FOLH1/tissue/endometrium#img); endometrial cancer (https://www.proteinatlas.org/ENSG00000086205-FOLH1/cancer/endometrial+cancer#img); normal prostate (https://www.proteinatlas.org/ENSG00000086205-FOLH1/tissue/prostate#img); and prostate cancer (https://www.proteinatlas.org/ENSG00000086205-FOLH1/cancer/prostate+cancer#img), respectively. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. BRCA, breast invasive carcinoma; COAD, colon adenocarcinoma; COADREAD, colon and rectal adenocarcinoma; CPTAC, Clinical Proteomic Tumor Analysis Consortium; FOLH1, folate hydrolase-1; GTEx, The Genotype-Tissue Expression; HPA, Human Protein Atlas; KIRP, kidney renal papillary cell carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; STES, stomach and esophageal carcinoma; T, T stage; TCGA, The Cancer Genome Atlas; UCEC, uterine corpus endometrial carcinoma.

Differences in total FOLH1 protein expression levels between tumor and normal tissues from UALCAN were assessed. As shown in Figure 2B, ovarian serous cystadenocarcinoma (OV) and UCEC had significantly higher FOLH1 protein expression in primary tumor tissues compared to normal tissues. Meanwhile, head and neck squamous cell carcinoma (HNSCC) showed significantly lower FOLH1 protein expression in primary tumor tissues than in normal tissues. The results suggest that changes in FOLH1 protein expression in primary tissues of different cancer types may play an important role in cancer progression. These differential expression findings provide a foundation for further investigation of the specific functional role of FOLH1 in many cancer subtypes and its potential use as a biomarker.

Based on the “Pathological Staging Analysis” module of the GEPIA 3 platform, we systematically constructed FOLH1 expression profiles of TCGA cancer types with different pathological stages (Figure 2C). Quantitative analysis showed that the expression of FOLH1 in stage III samples of KIRP patients was significantly higher than that in stage I/II samples; the expression of FOLH1 in stage I and III samples of BRCA patients was significantly lower than that in stage II samples; and the expression level in stage IV samples of COAD patients was significantly up-regulated compared with that in stage I/II samples. Of particular interest, in the COADREAD cohort, the intensity of FOLH1 expression in stage III/IV patients was significantly higher than that in early (stage I/II) cases, whereas the gene expression in stage II/IV of STES patients was significantly higher than that in stage III samples. Additionally, IHC analysis verified the protein expression patterns in normal and tumor tissues (Figure 2D).

Survival prognostic analysis

To further evaluate the relationship between FOLH1 expression and patient prognosis, we generated survival maps for all TCGA tumor types using GEPIA3. We then analyzed OS and PFI in patient groups stratified by high versus low FOLH1 expression. As shown in Figure 3A, high FOLH1 expression correlated with reduced OS in ACC, kidney chromophobe (KICH), KIRP, PAAD, rectal adenocarcinoma (READ), and uveal melanoma (UVM). Likewise, Figure 3B demonstrates that high FOLH1 expression is linked to decreased PFI in ACC, KICH, KIRP, PAAD, thymoma (THYM), and UCS.

Figure 3.

Figure 3

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Prognostic correlation of FOLH1 expression with (A) overall survival and (B) progression-free interval across tumor types using GEPIA3. The Kaplan-Meier curves depict survival probability over time. Due to the visualization settings of the GEPIA3 platform, HR and numbers at risk were calculated and are listed as follows: for ACC [OS: HR =1.71 (95% CI: 0.78–3.78); PFI: HR =2.42 (95% CI: 1.26–4.66); N=38], KICH [OS: HR =4.43 (95% CI: 0.91–21.49); PFI: HR =3.51 (95% CI: 0.92–13.39); N=32], KIRP [OS: HR =3.38 (95% CI: 1.70–6.69); PFI: HR =2.45 (95% CI: 1.38–4.35); N=140], PAAD [OS: HR =1.28 (95% CI: 0.85–1.93); PFI: HR =1.04 (95% CI: 0.71–1.53); N=89], READ [OS: HR =0.64 (95% CI: 0.23–1.78); N=45], UVM [OS: HR =4.04 (95% CI: 1.48–11.07); N=38], THYM [PFI: HR =2.26 (95% CI: 0.90–5.67); N=59], UCS [PFI: HR =1.33 (95% CI: 0.69–2.56); N=28]. ACC, adrenocortical carcinoma; CI, confidence interval; FOLH1, folate hydrolase-1; HR, hazard ratio; KICH, kidney chromophobe; KIRP, kidney renal papillary cell carcinoma; OS, overall survival; PAAD, pancreatic adenocarcinoma; PFI, progression-free interval; READ, rectal adenocarcinoma; THYM, thymoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

Overall, our findings indicate that high FOLH1 expression is associated with poorer survival across multiple tumor types. Specifically, in KIRP, THYM, and UVM, high-expression patients exhibited a significantly increased hazard of death at all observed time points. Furthermore, in ACC, high-expression patients maintained a significantly elevated risk of death throughout a follow-up of ≤70 mouths. Besides, PFI analysis revealed that high FOLH1 expression correlated with increased mortality in ACC, KIRP, THYM, and UVM. Across all observation intervals, high-expression patients consistently exhibited a significantly higher mortality risk than low-expression patients. However, in PAAD, a significantly increased death risk emerged only after ≥50 mouths of follow-up.

We then assessed associations of OS, relapse-free survival (RFS), and post-progression survival (PPS) with FOLH1 expression in COAD and, stomach adenocarcinoma (STAD) via Kaplan-Meier Plotter using gene-chip data (Figure 4A). In COAD, high FOLH1 expression was associated with significantly reduced RFS, OS, and PPS compared to low expression. Conversely, in STAD, low FOLH1 expression correlated with decreased RFS, OS, and PPS relative to high expression. These findings demonstrate a significant association between FOLH1 expression and patient prognosis across multiple cancers.

Figure 4.

Figure 4

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Prognostic value of FOLH1 expression across cancers. (A) Using gene-chip data and Kaplan-Meier plotter, we evaluated the prognostic impact of FOLH1 expression across cancers. (B) Based on TCGA data, we analyzed how FOLH1 expression levels affect patient survival across tumor types. CI, confidence interval; FOLH1, folate hydrolase-1; HR, hazard ratio; KIRP, kidney renal papillary cell carcinoma; OS, overall survival; OV, ovarian serous cystadenocarcinoma; PPS, post-progression survival; RFS, relapse-free survival; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; UCEC, uterine corpus endometrial carcinoma.

Based on the TCGA multi-omics data, survival association validation was carried out through the UALCAN platform (Figure 4B). Quantitative analysis showed that patients with low expression in UCEC had a significant survival disadvantage, while KIRP showed the opposite trend. Time-series analysis showed that the survival risk of STAD patients after 1,700 days increased significantly with elevated FOLH1 expression, whereas patients with high FOLH1 expression in OV had a significantly lower survival probability than the intermediate/low expression group after 1,300 days. Cross-database analysis confirmed a significant dose-effect relationship between FOLH1 expression levels and survival prognosis in a variety of malignant tumors, suggesting its clinical translational potential as a prognostic biomarker across cancer types.

Genetic variation analysis

Given the role of genomic alterations in tumorigenesis, we used the “TCGA Pan-Cancer Atlas Studies” module in cBioPortal to profile FOLH1 genetic alterations across TCGA tumor types. As shown in Figure 5A, skin cutaneous melanoma (SKCM) exhibited the highest FOLH1 alteration frequency (>8.56%), exclusively due to mutations. Mutations represent the predominant form of FOLH1 genetic variation across the majority of TCGA tumor cohorts. SKCM, COADREAD, KICH, LIHC, KIRP, KIRC, acute myeloid leukemia (LAML), and THCA had only one type of genetic alteration, namely mutations. Amplification, on the other hand, was the second most common genetic variant, with the highest frequency of variants in OV and PCPG, where PCPG had only one type of genetic variant, amplification. In addition, Figure 5B shows the types, loci, and number of cases of FOLH1 mutations. Missense mutations were the most common, and translocation mutations caused by S501Vfs*12 alterations were found in 4 cases of STAD, 2 cases of UCEC, 1 case of COAD, and 1 case of BRCA, respectively.

Figure 5.

Figure 5

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Mutational features of FOLH1 across TCGA tumors from cBioPortal data. (A) Mutation‐type frequencies. (B) Mutation types, sites, and case counts. BRCA, breast invasive carcinoma; CNA, copy number alteration; COAD, colon adenocarcinoma; FOLH1, folate hydrolase-1; PTM, post-translational modification; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; UCEC, uterine corpus endometrial carcinoma.

Immune microenvironment and immune checkpoint analysis

Cancer progression depends on cancer‐cell behavior and interactions within the tumor microenvironment (30). Tumor‐infiltrating immune cells modulate the microenvironment and affect tumor progression and metastasis (31). To assess correlations between FOLH1 expression and cancer‐associated fibroblast infiltration across TCGA cancers, we applied four TIMER 2.0 algorithms (EPIC, MCPCOUNTER, XCELL, TIDE) to generate heatmaps (Figure 6A) and scatter plots (Figure 6B). Heatmaps revealed significant positive correlations between cancer‐associated fibroblast infiltration and multiple TCGA cancers [cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), HNSCC, PAAD, SKCM, testicular germ cell tumor (TGCT)]. Correlations in CESC and PAAD were further illustrated by scatter plots (Figure 6B).

Figure 6.

Figure 6

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Correlation of FOLH1 expression with immune infiltration and immune checkpoints. (A) Significant positive correlations between cancer-associated fibroblast infiltration and multiple TCGA cancer types. (B) Positive correlations between cancer-associated fibroblast infiltration and FOLH1 expression in COAD and CESC, shown in scatter plots. Analysis using EPIC, MCPCOUNTER, XCELL, and TIDE revealed that FOLH1 expression in CESC and PAAD was positively correlated with cancer-associated fibroblast infiltration. (C) Each square represents the correlation between FOLH1 expression and that of an immune checkpoint gene in a specific cancer type. The upper triangle of each box indicates the correlation coefficient; the lower triangle shows the P value of the correlation test (*, P<0.05). CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD, colon adenocarcinoma; FOLH1, folate hydrolase-1; PAAD, pancreatic adenocarcinoma; TCGA, The Cancer Genome Atlas; TPM, transcripts per million.

Aberrant expression of immune checkpoints, as key molecules regulating immune activation, can mediate tumor immune escape mechanisms (32). Based on the Sangerbox platform, this study systematically analyzed the co-expression characteristics of FOLH1 and 60 immune checkpoint genes (Figure 6C). The results showed that there was a significant positive correlation between FOLH1 expression and most of the immune checkpoint genes in most of the tumors, including COADREAD and OV, which suggests an inflamed tumor microenvironment, potentially indicating suitability for immune checkpoint blockade. Notably, TGCT showed a unique negative correlation pattern with a general down-regulation of immune checkpoint gene expression in its FOLH1 high-expression group, suggesting that patients with this type of tumor may be less sensitive to immune checkpoint inhibitor therapy, which is an immune-cold phenotype.

In contrast, the negative correlation in TGCT to investigate the relationship between FOLH1 expression and the immune environment of the tumor, we analyzed its association with immune subtypes (C1–C6) established across multiple cancer groups (Figure 7A-7L). The analysis revealed complex, cancer-specific association patterns. Notably, FOLH1 expression positively correlated with the C2 (IFN-γ dominant) subtype in BRCA, COAD, HNSCC, LUAD, READ, and UCEC. The C2 subtype features high M1/M2 macrophage polarization, strong CD8+ T-cell signaling, and elevated TCR diversity. This profile suggests that FOLH1 may link to pro-inflammatory, anti-tumor immune responses in these cancers. In KICH, LIHC, and UCEC, FOLH1 expression correlated with the C3 (inflammatory) subtype. The C3 subtype features elevated Th17 and Th1 gene expression, suggesting a potential role for FOLH1 in tumor microenvironment inflammation. FOLH1 expression also positively correlated with the C4 (lymphocyte depletion) subtype in cholangiocarcinoma (CHOL), HNSCC, KICH, and READ. The C4 subtype is defined by prominent macrophage features, Th1 suppression, and an enhanced M2 macrophage response.

Figure 7.

Figure 7

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Association of FOLH1 expression with tumor immune subtypes in the TISIDB dataset. Association of FOLH1 expression with tumor immune subtypes in the TISIDB dataset of BLCA (A), BRCA (B), CESC (C), CHOL (D), COAD (E), HNSCC (F), KICH (G), LIHC (H), LUAD (I), READ (J), STAD (K), UCEC (L) associations of tumor immunological subtypes in the TISIDB dataset. Immunologic subtypes include: C1, wound healing; C2, IFN-γ dominant; C3, inflammatory; C4, lymphocyte depletion; C5, immunologically quiet; C6, TGF-β dominant. BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; CPM, counts per million; FOLH1, folate hydrolase-1; HNSCC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; READ, rectal adenocarcinoma; STAD, stomach adenocarcinoma; TGF-β, transforming growth factor-beta; UCEC, uterine corpus endometrial carcinoma.

CNV and methylation analysis

In order to analyze the molecular mechanism of aberrant FOLH1 mRNA expression, the correlation between CNV and FOLH1 transcript levels was assessed in this study by the GSCA database system. Cross-cancer analysis showed that in the READ cohort, FOLH1 expression showed a significant positive correlation with CNV (Spearman cor. >−0.5, P<0.05), while PCPG patients showed a significant negative trend (Spearman cor. <0.5, P<0.05) (Figure 8A). CNV and FOLH1 expression did not reach the threshold of statistical significance in the remaining tumor types. This result suggests that CNV may be only partially involved in the regulation of FOLH1 aberrant expression, and that there are significant tumor type-specific differences in its mechanism of action.

Figure 8.

Figure 8

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Impact of CNV and DNA methylation on aberrant FOLH1 expression and drug-sensitivity correlations. (A) Correlation between CNV and FOLH1 mRNA levels in GSCA. (B) FOLH1 mRNA levels strongly correlated with DNA methylation in most tumors, especially HNSCC, LUSC, PRAD, THYM, and BRCA. BRCA, breast invasive carcinoma; CNV, copy number variation; FDR, false discovery rate; FOLH1, folate hydrolase-1; GSCA, Gene Set Cancer Analysis; HNSCC, head and neck squamous cell carcinoma; LUSC, lung squamous cell carcinoma; PRAD, prostate adenocarcinoma; THYM, thymoma.

DNA methylation, an epigenetic mechanism governing gene transcription, showed strong associations with mRNA expression across most tumor types—particularly HNSCC, LUSC, PRAD, THYM, and BRCA (Spearman cor. >−1, all P<0.05) (Figure 8B).

FOLH1 drug sensitivity analysis

GSCALite results showed that FOLH1 expression was positively correlated with dasatinib and negatively correlated with the remaining 29 drugs, such as adriamycin, pan-BET inhibitor 151 (I-BET151), and dinaciclib (Figure S1); these findings may be beneficial for drug screening and personalized therapy.

Correlation analysis of FOLH1 expression with TMB, MSI, and tumor stemness

As immunotherapy predictors, TMB and MSI possess significant clinical value in the tumor immune microenvironment. Multidimensional analysis (Figure 9A) revealed that FOLH1 expression positively correlated with MSI in CESC, COAD, KIRC, and TGCT, but inversely correlated with MSI in LIHC. In the TMB analysis (Figure 9B), high FOLH1 expression correlated positively with TMB in seven tumors—including ACC, BRCA, HNSCC, and OV—but negatively in ESCA and brain lower grade glioma (LGG).

Figure 9.

Figure 9

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Association of FOLH1 expression with genomic and transcriptomic instability across tumor types. (A) FOLH1 vs. MSI; (B) FOLH1 vs. TMB; (C) FOLH1 vs. DNA stemness score; (D) FOLH1 vs. RNA stemness score. *, P<0.05; **, P<0.01. FOLH1, folate hydrolase-1; MSI, microsatellite instability; TMB, tumor mutation burden.

For the assessment of tumor stem cell characteristics, DNA stemness score analysis (Figure 9C) revealed that FOLH1 expression was positively correlated with stem cell differentiation characteristics in 11 types of tumors, including BRCA, LGG, and KIRC, while TGCT and PCPG showed a significant negative correlation. RNA stemness score study (Figure 9D) further revealed that FOLH1 was positively correlated with stemness characteristics in 4 types of tumors, including LUSC and PRAD, and was positively correlated with stemness characteristics in LUSC and PRAD, and was positively correlated with stemness characteristics in ESCA and OV and PRAD, but showed extensive negative correlation in 14 types of tumors, including PAAD, THCA, and UCEC, suggesting its bidirectional regulatory function in the regulation of different tumor stem cells.

Single-cell functional analysis of FOLH1

Single-cell functional analysis of FOLH1 across cancers enables correlation of FOLH1 expression with diverse cellular states at single-cell resolution. The analysis revealed that FOLH1 expression positively correlates with differentiation but negatively with EMT, invasion, metastasis, and apoptosis (Figure 10A). We further examined FOLH1 associations with functional states in specific cancer types. In BRCA, FOLH1 expression positively correlates with DNA repair, DNA damage, apoptosis, and differentiation. However, in GBM, FOLH1 negatively correlates with metastasis, DNA repair, EMT, and invasion; and in UVM, with DNA repair, apoptosis, and DNA damage (Figure 10B).

Figure 10.

Figure 10

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Single-cell functional analysis in CancerSEA reveals FOLH1 roles. (A) Interactive bubble plots of FOLH1 versus cellular functional states in four cancers. (B) Correlations of FOLH1 with functional states in GBM, UVM, and BRCA. *, P<0.05; **, P<0.01; ***, P<0.001. BRCA, breast invasive carcinoma; CNS, central nervous system; EMT, epithelial-mesenchymal transition; FOLH1, folate hydrolase-1; GBM, glioblastoma multiforme; HNSCC, head and neck squamous cell carcinoma; UVM, uveal melanoma.

FOLH1-related gene enrichment analysis

We screened FOLH1 binding proteins and associated genes for functional enrichment to elucidate molecular mechanisms of FOLH1 in tumorigenesis and progression. Using STRING, we constructed a PPI network of FOLH1 binding proteins (Figure 11A). The analysis revealed a distinct interaction cluster enriched with prostate-specific and androgen-regulated proteins, including KLK3, FOLR1, and PSCA. This network structure suggests that FOLH1 is functionally coupled with hormone-response and folate-transport pathways. We then retrieved the top 100 FOLH1-associated genes from GEPIA3 using TCGA tumor and normal tissue data. Figure 11B illustrates the overlap between FOLH1-associated and co-expressed genes, highlighting KLK3 as a shared member. Across TCGA tumors, FOLH1 correlated strongly with eight genes—STEAP2, HOXB13, KLK2, SLC30A4, KLK3, RDH11, OR51A9P, and NKX3-1—as shown in Figure S2 (OR51A9P data unavailable). Functional enrichment of FOLH1 was performed using the Sendo Academic online database (Figure 11C). The collective genes assessed by the results of KEGG enrichment analysis were mainly enriched in the categories related to “alanine, aspartate and glutamate metabolism” and “prostate cancer”. The results of GO enrichment analysis were as follows: cellular component (CC) was enriched in basolateral plasma membrane, basolateral plasma membrane, and membrane-anchored component; molecular function (MF) was enriched in amino acid metabolism, cellular amino acid metabolism, and amino acid metabolism of the aspartate family; bioprocesses were enriched in amino acid metabolism, cellular amino acid metabolism, and amino acid metabolism of the aspartate family; and biological processes (BPs) were enriched in amino acid metabolism. The MF was enriched in amino acid metabolism, cellular amino acid metabolism and aspartic acid family amino acid metabolism, while the BP was enriched in ligase activity, formation of carbon-nitrogen bonds, hydrolytic enzyme activity, carbon-nitrogen bonding (not peptide bonding) and hydrolytic enzyme activity, carbon-nitrogen bonding (not peptide bonding) in the linear amide. This pattern of enrichment suggests that FOLH1 may play a key role in tumorigenesis and progression by participating in process-important biological pathways such as cellular amino acid metabolic processes and amino acid metabolism of the aspartic acid family. The enrichment of genes in these specific categories also implies that FOLH1 may be intrinsically related to cellular metabolism and general cellular activities, further highlighting its potential significance in cancer biology and disease progression.

Figure 11.

Figure 11

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FOLH1-associated gene enrichment analysis. (A) FOLH1-binding proteins obtained using the Search Tool for Retrieving Interacting Genes/Proteins web server tool; (B) Venn diagram showing the cross-tabulation analysis of FOLH1-binding and related genes; (C) bar graph of FOLH1 molecular function, determined using GO and KEGG analysis. BP, biological process; CC, cellular component; FOLH1, folate hydrolase-1; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

Discussion

FOLH1 is highly expressed in reproductive and urological tumors, which serves as a therapeutic target (33-35). Recent studies have also explored FOLH1-based radionuclide therapy (36). Our results showed significantly higher FOLH1 expression in prostate, brain, duodenum, small intestine tissues. In addition, FOLH1 expression was significantly elevated in endometrioid, adrenocortical, and LUSCs but reduced in invasive breast and hepatocellular carcinomas compared to normal controls.

FOLH1 expression correlates with the abundance and activity of tumor-infiltrating immune cells, potentially exerting effects by modulating the tumor immune microenvironment. Its glutamate metabolism, mediated through carboxypeptidase activity, alleviates microenvironmental acidosis (37) and inhibits malignant proliferation. FOLH1 expression is also associated with tumor invasiveness and prognosis. However, its underlying mechanisms remain unclear and may involve alternative splicing variants of FOLH1 (38). FOLH1 expression is regulated by factors such as androgen receptor signaling and epigenetic modifications (35,39), which may influence its role in tumorigenesis and progression. Our results showed that FOLH1 mRNA expression inversely correlates with DNA methylation in invasive breast carcinoma, gastric carcinoma, and hepatocellular carcinoma. This inverse relationship may explain FOLH1 downregulation relative to normal tissues.

Immune cell infiltration, another key tumor microenvironment component, influences tumor progression and immunotherapy sensitivity (40,41). In cancers such as colon, invasive breast, and pancreatic, FOLH1 expression positively correlates with key immune checkpoint targets. Furthermore, this study reveals a complex association between FOLH1 expression and TMB, MSI, and the stemness index, with this correlation exhibiting significant cancer specificity. This may indicate that FOLH1 enhances the efficacy of immune checkpoint inhibitors by increasing tumor immunogenicity (42).

In addition, the bioinformatics analysis in this study found that ACC, pancreatic cancer and uterine carcinosarcoma patients with high FOLH1 expression had lower OS and PFI than those with low FOLH1 expression, whereas patients with gastric cancer found that those with high FOLH1 expression possessed higher OS, RFS, and PPS. Expression may indicate poorer prognosis in ACC, pancreatic cancer, and uterine carcinosarcoma, but demonstrated the opposite result in gastric cancer, which may be related to the different mechanisms of action of FOLH1 in different cancer types.

In UCEC, FOLH1 appears to regulate tumor formation and progression through three mechanistic dimensions—signaling pathways, metabolic homeostasis, and immune modulation—suggesting that its high expression may associate with improved patient prognosis and warrants in-depth exploration. Firstly, at the level of signaling pathways, although FOLH1 usually promotes tumor progression through pathways such as PI3K-Akt (43), its specific role varies by cancer type. In endometrial cancers, its high expression may inhibit aberrant cell proliferation and EMT by negatively regulating mTOR signaling or antagonizing the Wnt/β-catenin pathway (44). Crucially, our PPI network analysis reinforces this signaling heterogeneity. We observed robust physical interactions between FOLH1 and androgen-regulated markers (e.g., KLK3), confirming its dependence on hormonal signaling in prostate-like contexts. In contrast, in non-hormonal tumors, the absence of these specific interactions suggests that FOLH1 likely shifts its function towards independent metabolic regulation. Secondly, regarding metabolic homeostasis, FOLH1 acts as a folate hydrolase to maintain intracellular folate homeostasis through hydrolysis of polyglutamylated folate (45). This activity may reduce DNA synthesis abnormalities and methylation disorders, thereby reducing genomic instability (46). This protective metabolic role is further supported by the close link between folate metabolism and trace elements. The antioxidant properties of selenium (Se) may inhibit cancer cell proliferation, while zinc (Zn) participates in cell cycle regulation and DNA repair; deficiency in these elements, which are metabolically linked to FOLH1 function, could enhance estrogen receptor signaling and promote proliferation (47). Thirdly, in terms of immunomodulation, FOLH1 expression positively correlates with key immune checkpoint targets. This correlation likely reflects an ‘inflamed’ tumor phenotype characterized by active immune recognition and compensatory checkpoint upregulation. Mechanistically, high FOLH1 expression may delay disease progression by enhancing the efficiency of antigen presentation (e.g., promoting tumor-associated antigen exposure) and reducing the infiltration of immunosuppressive cells [e.g., Tregs, myeloid-derived suppressor cells (MDSCs)] (48,49). Consequently, this dual role—increasing tumor immunogenicity while correlating with therapeutic targets—suggests that FOLH1-high tumors may be particularly sensitive to immune checkpoint inhibitors.

Based on the analyses presented in this study, FOLH1 is highly expressed in intra-abdominal tumors, including endometrioid carcinoma, ACC, uterine carcinosarcoma, as well as prostate and bladder cancers. In contrast, its expression is lower in BRCA, suggesting that the pathological role of FOLH1 may be associated with tumor anatomical location. Additionally, the high expression of FOLH1 in gastric precancerous intestinal metaplasia, despite its low expression in gastric carcinoma, suggests that FOLH1 may exert a protective role during the early stages of gastric carcinogenesis but shift to a pro-carcinogenic function during tumor progression. This dynamic expression pattern and its potential link to the digestive system warrant further investigation using time-series expression analysis and related approaches.

Finally, regarding clinical translation and therapeutic guidance, our findings suggest that FOLH1 expression patterns could refine precision treatment strategies. Clinical observations indicate that patients with high FOLH1 expression may be more sensitive to targeted therapies, similar to the efficacy pattern of PSMA-targeted therapy in prostate cancer. However, our analysis of GTEx data confirms high FOLH1 expression in normal salivary glands (Figure 1A), providing a transcriptomic basis for the xerostomia frequently observed during PSMA-targeted radioligand therapy. This underscores the need for strategies to protect salivary glands. In addition, for non-prostatic cancers, determining whether FOLH1 expression is localized on tumor cells or tumor-associated vascular endothelial cells will be essential for better guiding PSMA-targeted radioligand therapies beyond prostate cancer. Beyond radioligand therapy, our pharmacovigilance analysis revealed that high FOLH1 expression correlates with dasatinib sensitivity. Since dasatinib inhibits Src kinases, this suggests that FOLH1-high tumors may depend on Src signaling. However, these associations may be affected by tumor heterogeneity (e.g., PTEN/PIK3CA mutations), where FOLH1 function might be modulated by compensatory pathways (50). Therefore, FOLH1 expression serves as a promising complementary biomarker for optimizing molecular subtyping and precision treatment strategies, potentially identifying patients who would benefit from radioligand therapy or immune-combination strategies.

However, there are some limitations in this study. Firstly, the limited number of cases for each cancer type in databases such as TCGA, CPTAC, and GTEx, e.g., the PCPG normal group in TCGA contains only three samples, which may lead to biased results of CNV analysis and less reference value. Secondly, a major statistical limitation is that we did not apply multiple testing corrections (such as Bonferroni or Benjamini-Hochberg) across the diverse cancer types. While this approach maximizes the detection of potential biological signals, it inherently increases the risk of false positives. Therefore, the reported associations should be validated in independent clinical cohorts before clinical application. Thirdly, our prognostic conclusions rely on univariable Kaplan-Meier analyses. We acknowledge that without adjustment for clinical confounders such as age and tumor stage via multivariable Cox regression models, FOLH1 cannot yet be established as an independent prognostic factor. Thus, its prognostic utility requires further validation in clinically annotated cohorts. Finally, although we leveraged multiple independent databases (CPTAC, HPA, KM plotter) for cross-validation, this study relies mainly on in silico analysis. We did not validate the key findings in an independent, in-house clinical cohort using wet-lab methods such as reverse transcription-quantitative polymerase chain reaction (RT-qPCR) or Western blot. Consequently, rigorous biological validation in prospective clinical samples is required to verify the reported prognostic and immunological associations before clinical translation.

Conclusions

We present a comprehensive pan‑cancer bioinformatics characterization of FOLH1, providing insights into its roles in tumorigenesis, progression, and prognosis. Our results indicate that FOLH1 may serve as a prognostic biomarker across multiple cancers, particularly in endometrioid, adrenocortical, invasive breast, and gastric cancers.

Supplementary

The article’s supplementary files as

tcr-15-03-180-rc.pdf (225.6KB, pdf)
DOI: 10.21037/tcr-2025-aw-2408
tcr-15-03-180-coif.pdf (894.4KB, pdf)
DOI: 10.21037/tcr-2025-aw-2408
tcr-15-03-180-supplementary.pdf (453.7KB, pdf)
DOI: 10.21037/tcr-2025-aw-2408

Acknowledgments

None.

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 and its subsequent amendments.

Footnotes

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2408/rc

Funding: This research was funded by research grants from High-level Traditional Chinese Medicine Hospital Construction Project of the China Academy of Chinese Medical Sciences Wangjing Hospital Special Project for Clinical Evidence-based Research on Traditional Chinese Medicine (No. WJYY-XZKT-2023-07).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2408/coif). The authors have no conflicts of interest to declare.

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    Supplementary Materials

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    tcr-15-03-180-rc.pdf (225.6KB, pdf)
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    tcr-15-03-180-coif.pdf (894.4KB, pdf)
    DOI: 10.21037/tcr-2025-aw-2408
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    DOI: 10.21037/tcr-2025-aw-2408

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