Integrative analysis of the potential of DISP3 as a biomarker for thyroid cancer

Introduction

Dispatched RND transporter family member 3 (DISP3), also known as patched domain-containing 2 (PTCHD2), is a member of the Dispatched protein family. It primarily functions intracellularly in cholesterol transport and directly participates in the transmembrane transport of cholesterol-modified Hedgehog ligands (1-3). DISP3 is also closely associated with the expression of critical transcription factors and plays a pivotal role in neural crest cell migration (4-6). Despite its emerging significance, current direct research on DISP3 remains relatively limited, with its specific roles in tumors rarely reported. However, based on functional inferences from homologous proteins, DISP3 is hypothesized to influence cellular proliferation and differentiation by regulating ligand transport associated with the Hedgehog and Wnt signaling pathways (7,8). Given the critical roles of Hedgehog and Wnt signaling in the initiation and progression of various cancers, it is plausible that DISP3’s regulation within the tumor microenvironment may represent a significant biological function. Furthermore, DISP3 may interact with other membrane proteins to affect membrane stability and signal transduction, which is particularly crucial for tumor cell survival and migration (9,10).

Thyroid cancer (THCA) has emerged as one of the most prevalent endocrine malignancies worldwide, presenting significant health risks and economic burdens. The incidence of THCA has been steadily increasing, leading to escalating healthcare costs and challenges in patient management. Current diagnostic and therapeutic approaches such as surgery, radioactive iodine therapy, and targeted therapies have limitations, including treatment resistance and the need for better prognostic indicators. Consequently, further research is imperative to enhance our understanding of THCA and improve patient outcomes (11). Advances in molecular biology have endowed biomarker research with unprecedented power to reshape early diagnosis, prognostic stratification, and individualized therapy for THCA. The clinical deployment of molecular signatures such as the B-Raf proto-oncogene, serine/threonine kinase (BRAF) and Rat Sarcoma type GTPase family (RAS) mutations has already refined diagnostic accuracy and sharpened therapeutic targeting. Nevertheless, several limitations persist-most notably sub-optimal specificity and sensitivity of individual markers, together with the absence of universally standardized detection protocols. Consequently, the continued discovery and rigorous validation of novel biomarkers remain indispensable for the development of truly personalized treatment algorithms tailored to the unique molecular profile of each patient.

Our previous study found that DISP3 is significantly overexpressed in THCA tissues. Further exploration of its role in THCA may provide a new molecular marker for the diagnosis and prognosis of THCA. This study aimed to investigate the expression of DISP3 as a potential biomarker in THCA by employing comprehensive bioinformatics methodologies, including differential expression analysis, co-expression analysis, and functional enrichment analysis, immune infiltration and drug sensitivity analysis. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-856/rc).

Methods

Data acquisition and preprocessing

The mRNA expression data for both pan-cancer and normal tissues were sourced from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The preprocessing steps involved correction of batch effects using the ComBat algorithm, normalization through conversion from fragments per kilobase of transcript per million (FPKM) to transcripts per million (TPM), and removal of non-protein-coding genes. Additionally, immunohistochemical staining images of DISP3 in normal and tumor thyroid tissues were obtained from the Human Protein Atlas (HPA) database. The expression validation of DISP3 and PTCHD2 was conducted using the University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) platform. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Differential expression and co-expression analysis

To identify differentially expressed genes between high- and low-DISP3 THCA subgroups, the Limma package in R was employed, with the criteria of |log₂ fold change (logFC)| >0.4 and adjusted P value (P.adj) <0.05. Subsequently, co-expressed genes were determined through Spearman correlation analysis within the TCGA-THCA cohort, using the thresholds of |ρ| >0.3 and P.adj <0.05. Furthermore, protein-protein interaction (PPI) networks for the top 20 co-expressed genes were constructed using the STRING database and visualized using Cytoscape version 3.9.1.

Functional enrichment analysis

Functional enrichment analyses of DISP3-related genes were conducted using the xiantao platform, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Significant terms were identified based on adjusted P values (p.adj <0.05) and q-values (q-value <0.05). Additionally, Gene Set Enrichment Analysis (GSEA) was utilized to compare the high- and low-DISP3 expression groups, incorporating hallmark gene sets from the Molecular Signatures Database (MSigDB). Pathway significance was determined by normalized enrichment scores (NES) and a false discovery rate (FDR) threshold of <0.25.

Association with cell cycle and Hedgehog signaling pathway

The relationships between DISP3 and 27 cell cycle-related genes (including TP53, CDK1, AKT1) as well as components of the Hedgehog signaling pathway (such as PTCH1, SUFU, GLI1-3, SMO) were evaluated using Spearman correlation analysis. To visually represent the interactions between DISP3 and Hedgehog pathway genes, chord diagrams were generated using R package.

Clinicopathological correlation and survival analysis

The Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) and the TCGA database were utilized to evaluate the correlation between DISP3 expression and survival outcomes in THCA patients, taking into account clinicopathological features such as age, TNM stage, and overall survival (OS) events. The diagnostic performance was assessed using receiver operating characteristic (ROC) curves and quantified by calculating the area under the curve (AUC). Kaplan-Meier survival curves were generated and compared using the log-rank test, while univariate and multivariate Cox proportional hazards models were employed to determine the prognostic significance of DISP3 expression. Additionally, subgroup analyses were conducted to stratify patients based on various clinicopathological variables.

Prognostic model construction

A nomogram incorporating DISP3 expression, pathologic stage, and residual tumor status was constructed using the rms package in R. The predictive accuracy of the nomogram was evaluated using the concordance index (C-index).

Immune infiltration and checkpoint analysis

Immune cell infiltration scores were calculated using the CIBERSORT algorithm with the LM22 signature matrix. The relationship between DISP3 expression and 22 immune cell subtypes, as well as immune checkpoint genes (PDCD1, CD274, CTLA-4), was assessed using Spearman correlation analysis. Differences in immune cell infiltration between DISP3 expression groups were evaluated using the Mann-Whitney U test.

Genetic alteration and copy number variation analysis

The genetic alterations of DISP3, including mutations and copy number variations, were analyzed across various cancers using the cBioPortal database. Somatic copy number alterations (SCNAs) in THCA were classified into categories such as deep deletion, diploid, and gain using the TIMER database. Clinical prognosis based on the SCNAs of DISP3 was conducted using the TIDE database. Additionally, the UALCAN database was utilized to analyze the promoter methylation levels of DISP3 in both normal thyroid tissues and tumor tissues of THCA.

Drug sensitivity and expression correlation

The correlations between DISP3 expression and drug response (IC₅₀ values) were assessed using the Genomics of Drug Sensitivity in Cancer (GSCA) and Cancer Therapeutics Response Portal (CTRP) datasets. Significant associations between gene mRNA expression and drug IC₅₀ were identified through Pearson correlation coefficients, with P values adjusted for multiple testing using FDR. GSCA presents these correlations in a bubble plot format. In this plot, blue bubbles signify negative correlations, while red bubbles indicate positive correlations. The intensity of the color corresponds to the strength of the correlation, with deeper colors representing higher correlation values. Bubble size is proportional to the significance of the FDR. A black outline around a bubble denotes an FDR ≤0.05. The top 30 ranked drugs are displayed in the plot.

Statistical analysis

All statistical analyses were conducted using R (version 4.2.1). Multiple testing corrections were applied using the Benjamini-Hochberg method to control FDR. The significance threshold was set at P<0.05. For survival analysis, hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Data visualization was performed using the ggplot2, pheatmap, and forestplot packages in R.

Results

DISP3 expression in THCA

The expression of DISP3 across various cancers was analyzed using data obtained from TCGA and GTEx. The results indicated significant differences in DISP3 expression among multiple tumor types, including THCA (Figure 1A). Further evaluation of DISP3 expression in both unpaired and paired samples from THCA patients revealed that DISP3 levels were significantly elevated in tumor tissues compared to normal or paracancerous tissues (P<0.001) (Figure 1B,1C). Additionally, analysis using the UALCAN database showed that DISP3/PTCHD2 expression was higher in cancer tissues than in normal tissues (Figure 1D). Under normal physiological conditions, DISP3 is primarily expressed in nerve tissue and the testis (Figure 1E). However, in pathological contexts, its expression is significantly upregulated in various malignancies, including renal cancer, prostate cancer, hepatocellular carcinoma, cervical cancer, and THCA (Figure 1F). Immunohistochemical staining images of normal thyroid tissues and tumor tissues were retrieved fromHPA database. Figure 1G demonstrates that the expression level of DISP3 in THCA tissue is higher than that in normal tissue.

Figure 1 DISP3 expression in normal thyroid tissues and THCA tissues. (A) DISP3 differential expression of pan-cancer in TCGA datasets. (B,C) DISP3 expression in thyroid cancer unpaired and paired samples in the TCGA database. (D) DISP3 expression levels based on the UALCAN. (E,F) DISP3 expression levels in normal tissues and pan-cancer from Human Protein Atlas. (G) IHC staining of DISP3 in cancer and paracancerous tissues. Representative images were shown. Scale bars, 50 mm. *, P<0.05; **, P<0.01; ***, P<0.001. DISP3, dispatched RND transporter family member 3; NOS, not otherwise specified; TCGA, The Cancer Genome Atlas; THCA, thyroid cancer; TPM, transcripts per million; UALCAN, University of Alabama Cancer data analysis Portal.

Identification of related genes and functional enrichment of DISP3-related genes in THCA

We conducted differential expression analysis between low- and high-DISP3 expression groups in THCA using the Limma package, retaining only protein-coding genes. A total of 3,565 differentially expressed genes were identified, comprising 2,868 upregulated genes and 697 downregulated genes, based on the screening criteria of |log₂ fold change (logFC)| >0.4 and adjusted P value (P.adj) <0.05 (Figure 2A).

Figure 2 Identification of related genes. (A) A volcano plot of differentially expressed genes in THCA. (B) Heat map of correlations between DISP3 and the top 20 related-genes. (C) The protein-protein interactions network of the top 20 correlated genes. (D-H) Representative correlation scatter plot from GEPIA database. Cor, corelation; DISP3, dispatched RND transporter family member 3; GEPIA, Gene Expression Profiling Interactive Analysis; RSEM, RNA-Seq by Expectation Maximization; Sig., significant; THCA, thyroid cancer; TPM, transcripts per million.

Next, we analyzed co-expressed genes associated with DISP3 expression in the TCGA-THCA dataset. A total of 2,200 co-expressed genes were identified under the criteria of |Spearman correlation coefficient| >0.3 and adjusted P value (P.adj) <0.05, including 2,197 positively associated genes and 3 negatively associated genes. The top 20 positively correlated genes are shown in Figure 2B. The PPI network of these top 20 correlated genes is presented in Figure 2C. Representative scatter plots of individual gene correlations are displayed in Figures 2D-2I.

The 2,200 co-expressed genes were analyzed for GO terms and KEGG pathways using the clusterProfiler package in R. The co-expressed genes associated with DISP3 were involved in 204 biological processes, 46 cellular components, 53 molecular functions, and 21 KEGG pathways (P.adj <0.05 and q-value <0.05). Bubble plots displayed the top 5–7 enriched terms for biological processes, cellular components, molecular functions, and KEGG pathways, respectively (Figure 3A,3B). Among the GO terms, histone modification, endoplasmic reticulum to cytosol transport, regulation of cell development, regulation of cell growth, Ras protein signal transduction, neuron-to-neuron synapse, and protein serine/threonine kinase activity were significantly enriched. The enriched KEGG pathways included the Notch signaling pathway, phospholipase D signaling pathway, Hedgehog signaling pathway, and protein processing in the endoplasmic reticulum, among others. Additionally, we used GSEA to predict target pathways between DISP3 high-expression and low-expression groups. The results showed that high DISP3 expression was associated with pathways such as MAPK signaling, PDGF, phosphatidylinositol signaling, Hedgehog, Notch, and Wnt signaling (Figure 3C,3D). Conversely, low DISP3 expression was associated with pathways including programmed cell death, Th17 cell differentiation, CTLA4 signaling, DNA replication, citrate cycle, and natural killer cell-mediated cytotoxicity (Figure 3E,3F).

Figure 3 Functional enrichment analysis. (A) Bubble diagram of GO and KEGG enrichment analysis. (B) Clustering tree of GO/KEGG enrichment analysis represents the associations between different biological processes. (C-F) Gene cluster enrichment analysis (GSEA) showed that the signal pathway related to the expression of DISP3 in THCA. BP, biological process; CC, cellular component; DISP3, dispatched RND transporter family member 3; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; PID, Pathway Interaction Database; THCA, thyroid cancer.

Association of DISP3 expression level with cell cycle and Hedgehog pathway in THCA

The functional enrichment analysis of DISP3-related genes primarily highlighted their involvement in biological processes such as the regulation of cell growth and cell development. Given that the cell cycle is a critical driver of these processes, it is plausible that DISP3 expression may be associated with the cell cycle. To explore this, we investigated the correlation between DISP3 expression levels in THCA and 27 cell cycle-related genes. As shown in Figure 4A, DISP3 expression was significantly and positively correlated with several cell cycle-related genes, including TP53, MAPK11, MAPK12, CDK1, AKT1, ATR, YWHAB, WEE1, ATM, YWHAZ, MAPK14, CDC25C, MAPKAPK2, HUS1, YWHAE, YWHAQ, AKT2, CDC25A, MAP3K7, CDC25B, and CHEK1 (P<0.001). Figure 4B-4D present scatter plots of DISP3 expression against three representative cell cycle-related genes (Spearman r>0.3, P<0.001). Specifically, DISP3 expression was significantly positively correlated with TP53 (r=0.418, P<0.001), MAPK11 (r=0.364, P<0.001), and MAPK12 (r=0.311, P<0.001).

Figure 4 Correlations between DISP3 and cell cycle-related genes. (A) Heat map of correlations between DISP3 and 27 cell cycle-related genes. (B-D) The scatter plot of DISP3 expression with 3 cell cycle-related genes. DISP3, dispatched RND transporter family member 3; TPM, transcripts per million.

According to KEGG enrichment analysis, DISP3-related genes were related to Hedgehog pathway, and Hedgehog pathway was reported to be related to the dedifferentiation process of THCA. Our results showed that DISP3 were positively correlated with PTCH1, SUFU, GLI1, GLI2, GLI3, SMO (r=0.234–0.367, P<0.001) (Figure 5A-5F). The chord diagram showed the relationship between DISP3 and six key genes in the Hedgehog pathway (Figure 5G).

Figure 5 Correlations between DISP3 and Hedgehog pathway. (A-F) The scatter plot of DISP3 expression with 6 Hedgehog pathway-related genes. (G) The chord diagram of the relationship between DISP3 and 6 key genes in the Hedgehog pathway. The string width indicated the strength of the correlation. DISP3, dispatched RND transporter family member 3; TPM, transcripts per million.

Correlation between DISP3 expression levels and clinicopathological characteristics of THCA patients

The TCGA database was utilized to examine the correlation between DISP3 expression levels and various clinical characteristics in patients with THCA. Specifically, the relationship between DISP3 expression and ten clinicopathological features was investigated, including age, gender, race, T-stage, N-stage, M-stage, pathological stage, histological type, residual tumor, and OS events. The results indicated that high DISP3 expression was significantly associated with gender (P<0.05) (Figure 6A), N-stage (P<0.05) (Figure 6B), and OS events (P<0.05) (Figure 6C). To assess the diagnostic potential of DISP3 in THCA, ROC curves were generated (Figure 6D). The analysis revealed that DISP3 achieved an AUC of 0.724 (95% CI: 0.668–0.780) for THCA diagnosis, with a sensitivity of 0.604, specificity of 0.814, positive predictive value of 0.966, and negative predictive value of 0.191. These findings suggested that DISP3 may serve as an auxiliary diagnostic biomarker for THCA.

Figure 6 Analysis of the diagnostic value of DISP3 expression in THCA. (A-C) The expression difference of DISP3 in different clinicopathological features. (D) ROC curves for diagnosis of THCA. *, P<0.05. AUC, area under the curve; CI, confidence interval; DISP3, dispatched RND transporter family member 3; FPR, false positive rate; N, node; OS, overall survival; ROC, receiver operating characteristic; THCA, thyroid cancer; TPM, transcripts per million; TPR, true positive rate.

High DISP3 affects the prognosis of patients with THCA in different clinicopathological states

To investigate the association between DISP3 expression levels and the prognosis of THCA patients, Kaplan-Meier survival analysis was performed using data from the TCGA database. Patients were stratified into high and low DISP3 expression groups based on the median DISP3 expression value. The analysis revealed that high DISP3 expression was significantly associated with a poor prognosis in terms of OS, with a HR of 8.94 (95% CI: 2.02–39.52, P=0.004) (Figure 7A). The AUCs for OS at 3, 5, and 10 years were 0.681, 0.729, and 0.795, respectively (Figure 7B).

Figure 7 Analysis of the prognostic value of DISP3 expression in THCA. (A) The relationship between DISP3 expression and OS of THCA patients based on the TCGA database. (B) 3-, 5-, and 10-year time dependent ROC curves of the predictive value of DISP3 for OS of THCA. (C-L) Prognostic subgroup analyses of THCA patients with different clinicopathological status. AUC, area under the curve; CI, confidence interval; DISP3, dispatched RND transporter family member 3; FPR, false positive rate; HR, hazard ratio; M, metastasis; N, node; OS, overall survival; ROC, receiver operating characteristic; T, tumor; THCA, thyroid cancer; TPR, true positive rate.

Subgroup analyses were further conducted among THCA patients with different clinicopathological characteristics. The results showed that high DISP3 expression was significantly associated with poor prognosis in patients with the following characteristics: age >45 years (P=0.002), female gender (P=0.02), T2 stage (P=0.007), N1 stage (P=0.02), M0 stage (P=0.02), pathological stage I & II (P=0.02), pathological stage III & IV (P=0.02), R0 resection (P=0.02), classical histological type (P=0.002), and no extrathyroidal extension (P=0.003) (Figure 7C-7L).

Development of a prognostic model integrating DISP3 expression and clinical factors

Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify independent prognostic factors for OS in THCA patients, considering variables such as age, sex, T stage, N stage, M stage, pathologic stage, histological type, residual tumor, extrathyroidal extension, and DISP3 expression. In the univariate analysis, T stage (HR =3.002, 95% CI: 1.041–8.656, P=0.04), pathologic stage (HR =7.263, 95% CI: 2.337–22.573, P<0.001), residual tumor (HR =3.364, 95% CI: 1.010–11.207, P=0.048), and DISP3 expression (HR =8.938, 95% CI: 2.022–39.517, P=0.004) were identified as prognostic factors for OS in THCA patients (Figure 8A). In the multivariate analysis, high DISP3 expression emerged as an independent prognostic factor for OS in THCA patients (HR =8.429, 95% CI: 1.710–41.543, P=0.009) (Figure 8B).

Figure 8 Construction of a prognostic model integrating DISP3 expression and clinical factors in THCA. (A,B) Univariate and multivariate Cox risk regression analyses to identify independent prognostic factor for OS in THCA. (C) DISP3-based nomogram for 3-, 5-, and 10-year OS prediction in THCA. (D) The calibration curves of the prognostic model. CI, confidence interval; DISP3, dispatched RND transporter family member 3; HR, hazard ratio; M, metastasis; N, node; OS, overall survival; T, tumor; THCA, thyroid cancer.

A nomogram was developed for predicting 3-, 5-, and 10-year OS in THCA patients using data from the TCGA database. The nomogram incorporated pathologic stage, residual tumor status, and DISP3 expression level as key variables. The T-stage risk score was excluded due to statistical uncertainty, which led to a negative risk score. The nomogram visually depicts the relationship between these three variables and the 3-, 5-, and 10-year survival probabilities (Figure 8C). As shown in Figure 8D, the calibration curves for 3, 5, and 10 years demonstrated the consistency between the predicted values and actual outcomes, indicating that the DISP3-based nomogram performed well. The C-index of 0.818 (95% CI: 0.764–0.873) reflected substantial predictive accuracy. In summary, this nomogram may provide a more accurate model for predicting the survival of THCA patients compared to individual prognostic factors.

Correlation of DISP3 expression with immune characteristics

To investigate the relationship between DISP3 expression and immune cell infiltration, we analyzed the correlation between DISP3 and 22 immune cell subtypes using the CIBERSORT algorithm (Figure 9A). The results indicated that in THCA patients, DISP3 expression was positively correlated with plasmacytoid dendritic cells (pDCs), NK CD56bright cells (Figure 9B), central memory T (Tcm) cells, eosinophils, and NK cells. Conversely, DISP3 expression was negatively correlated with dendritic cells (DCs) (Figure 9C), Th17 cells (Figure 9D), B cells, regulatory T cells (Treg) (Figure 9E), activated dendritic cells (aDC), macrophages, and immature dendritic cells (iDCs).

Figure 9 Correlation analysis of DISP3 expression and immune infiltration in THCA. (A) The correlation between DISP3 and immune infiltrating cells in THCA. (B-E) Scatter plot of correlation between DISP3 and the representative of 4 types of immune cells. (F-K) Differential distribution of immune cells in patients with high DISP3 expression and low DISP3 expression. (L) Heat map of the correlation analysis of DISP3 expression and immune checkpoints [PDCD1 (PD1), CD274 (PD-L1) and CTLA4] in THCA by TCGA database. (M-O) The association between DISP3 and immune checkpoint in THCA by GEPIA database. *, P<0.05; **, P<0.01; ***, P<0.001. aDC, activated dendritic cell; DC, dendritic cell; DISP3, dispatched RND transporter family member 3; GEPIA, Gene Expression Profiling Interactive Analysis; iDC, immature dendritic cell; NK, natural killer; pDC, plasmacytoid dendritic cell; TCGA, The Cancer Genome Atlas; TFH, T follicular helper cell; THCA, thyroid cancer; TPM, transcripts per million.

Based on these findings, we further explored the differences in immune infiltration levels between high and low DISP3 expression groups. Significant differences (P<0.05) were observed in the infiltration levels of aDC, cytotoxic cells, DC, iDC, macrophages, NK CD56bright cells, pDC, Th17 cells, and Treg when patients were stratified by DISP3 expression levels (Figure 9F-9K).

PD1 (PDCD1) and PD-L1 (CD274), as well as CTLA-4, are critical immune checkpoints in the regulation of immune responses. Our analysis revealed that DISP3 expression in THCA was positively correlated with PD1 (PDCD1) but negatively correlated with PD-L1 (CD274) and CTLA-4 (Figure 9L-9O).

The genetic alteration and SCNA analysis of DISP3

We examined the genetic alterations of DISP3 across various cancers using the cBioPortal. As illustrated in Figure 10A, DISP3 mutations were detected in 28 different types of cancer. Specifically, in papillary THCA, DISP3 expression was altered in 2 out of 482 samples (0.4%) (Figure 10B). Mutations were identified as the primary type of genetic alterations in DISP3. Additionally, Figure 10C provided further details on the types and locations of these genetic alterations within the DISP3.

Figure 10 Genetic alteration and SCNAs analysis. (A) The genetic alterations of DISP3 across pan-cancer atlas analyzed by the cBioPortal database. (B) Genetic alteration in DISP3 in papillary thyroid cancer tissues, accounting for 0.4% of alterations (altered/profiled =2/482). (C) The mutation types, number and sites of the DISP3 genetic alterations. (D) The SCNAs of DISP3 in pan-cancer. (E) The clinical prognosis of SCNAs of DISP in THCA. (F) The promoter methylation level of DISP3 in normal and tumor tissues of THCA based on UALCAN database. CNA, copy number alteration; DISP3, dispatched RND transporter family member 3; MMPL, mycobacterial membrane protein large; OS, overall survival; SCNAs, somatic copy number alterations; TCGA, The Cancer Genome Atlas; THCA, thyroid cancer; UALCAN, University of Alabama Cancer data analysis Portal.

SCNAs are commonly observed in various tumors, including deep deletions, arm-level deletions, diploid/normal states, arm-level gains, and high-level amplifications. In THCA, the most frequent type of variation for DISP3/PTCHD2 is diploid/normal (Figure 10D). No significant differences in OS were found between the high SCNA group and the low SCNA group (P=0.37) (Figure 10E). Additionally, there were no significant differences in the promoter methylation levels of DISP3 between normal and tumor tissues in THCA (P>0.05) (Figure 10F).

The sensitivity of DISP3-related drugs in THCA

The GDSC dataset revealed correlations between DISP3 mRNA expression levels and drug sensitivity. The top four drugs positively associated with DISP3 expression were docetaxel, 17-AAG, trametinib, and bleomycin (50 µM), while those negatively associated with DISP3 expression were Vorinostat, Navitoclax, WZ3105, and PHA-793887 (Figure 11A).

Figure 11 Drug sensitivity of DISP3-related drugs in THCA. (A) Relationship between GSCA drug sensitivity and mRNA expression of DISP3. (B) Relationship between CTRP drug sensitivity and mRNA expression of DISP3. CTRP, Cancer Therapeutics Response Portal; DISP3, dispatched RND transporter family member 3; FDR, false discovery rate; GSCA, Genomics of Drug Sensitivity in Cancer; THCA, thyroid cancer.

The CTRP dataset showed that the top four drugs positively correlated with DISP3 expression were FGIN-1-27, simvastatin, MI-1, and BRD-K86535717, while those negatively correlated with DISP3 expression were GSK-J4, belinostat, PF-3758309, and SR8278 (Figure 11B).

Discussion

Researching the role of DISP3 in THCA has yielded significant insights into its potential as a biomarker and therapeutic target. The elevated expression of DISP3 in THCA tissues compared to normal or paracancerous tissues underscores its involvement in tumorigenesis. Specifically, the correlation of high DISP3 expression with adverse clinicopathological characteristics suggests its utility in clinical diagnostics and prognostics. The findings align with previous speculation that highlighted the role of DISP3 in various malignancies, further solidifying its relevance in cancer research.

The pathway analysis results revealed that DISP3 is significantly associated with critical biological processes, including cell cycle regulation and Hedgehog signaling pathway, Wnt signaling pathway. The Hedgehog signaling pathway and the Wnt/β-catenin signaling pathway play key roles in the formation and development of various tumors. Research indicated that the Hedgehog signaling pathway influences tumor development by influencing cell-to-cell interactions and the microenvironment, while the Wnt/β-catenin signaling pathway is important for maintaining stem cell characteristics and promoting tumor cell proliferation (12,13). In THCA, these two signaling pathways are closely linked to the proliferation, migration, and invasion capabilities of tumor cells. The activation of the Hedgehog signaling pathway is closely associated with the growth and metastasis of THCA cells. Some studies showed that in papillary thyroid carcinoma, the reduction of the Hedgehog signaling pathway may lead to a decrease in NIS expression, thereby impacting the efficacy of radioactive iodine treatment (14). At the same time, the Wnt/β-catenin signaling pathway is also seen as a key player in the progression of PTC, driving tumor development by enhancing cell proliferation and inhibiting apoptosis (15,16). Furthermore, some studies suggested there were interactions between these two pathways; for example, Wnt/β-catenin can affect the function of the Hedgehog signaling components, thereby working together to regulate THCA development (17,18). Future research will focus on elucidating the precise mechanisms by which DISP3 influences these pathways and exploring the potential of targeting these signaling cascades in therapeutic strategies.

Moreover, the correlation between DISP3 expression and immune cell infiltration highlighted the potential role of DISP3 in shaping the tumor microenvironment. The positive association with immune cell types, such as pDCs and natural killer cells, may suggest a complex interplay between DISP3 and the immune landscape of THCA (19). This relationship warrants further investigation to determine how DISP3 modulation could enhance immune responses against tumors.

The prognostic analysis that demonstrated that high DISP3 expression correlates with poor OS emphasized its potential as a prognostic indicator in THCA management. The findings suggested that incorporating DISP3 expression levels into clinical decision-making could improve patient stratification and treatment personalization. Future studies will aim to validate these findings in a cohort of our center, as robust prognostic models are crucial for improving treatment outcomes in THCA patients.

Finally, the drug sensitivity analysis indicated that DISP3 expression levels may influence the effectiveness of specific therapies. Identifying correlations between DISP3 and drug sensitivity could lead to personalized treatment regimens tailored to individual patients’ genetic profiles (20).

This study presented notable findings in the role of DISP3 in THCA. However, it was not without limitations. The absence of wet lab validation for these bioinformatics results raised concerns regarding the reproducibility and biological relevance of the findings. Additionally, the reliance on retrospective data from public databases introduced potential biases and confounding factors that could influence the observed associations. Furthermore, the lack of clinical validation analyses necessitated caution in the interpretation of DISP3 as a prognostic biomarker. Addressing these limitations in future studies will be essential for confirming the role of DISP3 in THCA or other tumor and its potential applications in clinical practice.

Conclusions

In conclusion, these findings underscored that DISP3 possibly served as an auxiliary diagnostic and prognostic biomarker for THCA. Further investigations were warranted to validate these findings and explore the mechanistic underpinnings of DISP3 in THCA biology.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-856/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-856/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-856/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 and its subsequent amendments.

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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