Unveiling the multifaceted role of the FLNC gene: implications for cancer diagnosis and prognosis

Abstract

Background

The Filamin C (FLNC) gene, pivotal for cellular structure and function, holds significance in cancer biology.

Methods

We conducted a comprehensive study using various analytical approaches, including survival analysis, immune response assessment, methylation profiling, and single-cell analysis.

Results

Our findings indicate that abnormal FLNC expression correlates with unfavorable prognosis across multiple cancer types, suggesting its potential as a prognostic biomarker. FLNC dysfunction influences the tumor microenvironment, promoting immunosuppressive conditions and disease progression. Methylation profiling reveals a strong correlation between increased methylation, FLNC downregulation, and patient survival, highlighting the role of epigenetic regulation in cancer development. Single-cell analysis uncovers spatial and temporal variations in FLNC expression, identifying distinct cancer cell subsets with unique functional implications. Finally, immunohistochemistry was used to verify the results in different tumors.

Conclusions

In conclusion, our research provides insights into the versatile role of FLNC in cancer biology, emphasizing its clinical significance and potential applications as a diagnostic, prognostic, and therapeutic target across diverse cancer types. These findings offer valuable insights for personalized therapeutic interventions and precision medicine approaches.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-025-02876-x.

Introduction

Despite rising global cancer incidence, mortality and extensive clinical research, precise diagnosis and personalized treatment have not achieved expected 5-year overall survival rates [1, 2]. While cancer development involves dysregulation of numerous genes and pathways, research increasingly highlights the fundamental contribution of the tumor microenvironment (TME) to tumorigenesis and disease progression [3, 4].

TME critically determines immunotherapy success. Tumor cell metabolic gene regulation alterations can affect TME, suppress immune responses, and challenge cancer treatment [5–8]. However, tumor and TME complexity hinders translating discoveries into clinical benefits [9, 10]. Deeper understanding of TME, immune regulatory mechanisms, immunophenotypes from tumor–immune interactions, and validation of novel immune-related therapeutic targets is crucial [11–13].

Filamins (FLNs) are conserved, multi-domain proteins bridging the extracellular matrix (ECM) and cytoskeleton while interacting with structural and signaling proteins [14]. Filamin scaffolds over 90 binding partners, including channels, receptors, signaling molecules, and transcription factors [15–17]. The FLN family consists of three members, namely, FLNA, FLNB, and FLNC [18]. FLNA is widely expressed and has dual roles: cytoplasmic pro-tumor effects via signaling interactions, and nuclear anti-tumor effects via transcription factor binding [19, 20]. FLNB, concentrated in endothelial cells, regulates endothelial migration and angiogenesis [21]. FLNC, a major actin cross-linking protein, maintains structural integrity and facilitates cell contraction. Although FLNC exhibits constitutive low-level expression in certain non-muscle cell types [22], compensatory upregulation of FLNC occurs following knockdown of other filamin isoforms, indicating that maintenance of critical filamin stoichiometry is essential for cellular homeostasis [23]. Members of the FLN protein family are implicated in diverse malignancies. In particular, the overexpression of FLNA has been associated with tumor progression and an unfavorable prognosis in cervical cancer [24], colon cancer [25], bladder cancer [26], and lung adenocarcinoma [27]. FLNB has established connections with both prostate cancer [28] and breast cancer [29]. While FLNC has shown increased expression in prostate cancer [30], gastric cancer [31], hepatocellular carcinoma [32], and glioma [33], potentially linked to the mitogen-activated extracellular signal-regulated kinase 1/2 (MEK1/2) and extracellular signal-regulated kinase 1/2 (ERK1/2) pathways [34]. The precise function of FLNC in tumor cell invasion and metastasis remains uncertain and may encompass the regulation of cell migration [35]. This study systematically investigated FLNC molecular alterations, prognostic significance, and therapeutic potential across 33 malignancies, revealing widespread dysregulation characterized by transcriptional and genomic aberrations.

Methods

FLNC expression analysis in pan-cancer

We sourced the data from the Cancer Genome Atlas (TCGA) database and the University of California Santa Cruz (UCSC) Xena platform (https://xenabrowser.net/datapages//, Accessed on 23 August 2024). The complete data set was processed using the RMA function within the R package. This involved filtering the data set, eliminating missing and duplicated entries, and then converting the data to log2 (TPM + 1). Patient-related information, including age, gender, tumor stage, clinical stage, and various clinical data, was collected from the portal. To assess the variations in gene expression between tumor and normal tissue, we employed the TIMER Database (http://cistrome.dfci.harvard.edu/TIMER/, Accessed on 23 August 2024) [36]. Furthermore, the level of FLNC protein expression in tumors was investigated using the Human Protein Atlas (HPA) database through the examination of immunofluorescence microscopy images (https://www.proteinatlas.org/search/FLNC, Accessed on 23 August 2024).

Clinical features analysis of FLNC

Kaplan–Meier survival curves with log-rank tests were generated using the survival package in R, with optimal expression cutoffs determined via maximally selected rank statistics. Clinicopathological correlations employed Wilcoxon rank-sum tests. Clinical variables included T stage (tumor size), N stage (lymph node involvement), pathological stage, and WHO histologic grade.

Establishment and evaluation of the nomogram models

Tumors significantly impacting survival data (univariate Cox p < 0.05) with sample sizes > 500 were selected for modeling. Univariate Cox regression first identified candidate variables (FLNC expression, stage, grade, age, gender), followed by multivariate Cox proportional hazards modeling with backward elimination. Nomograms were constructed using rms package with 1000 bootstrap resamples for internal validation. Calibration curves assessed 1-/3-/5 year concordance between predicted and observed outcomes along 45° reference lines.

FLNC and immune cell infiltration and related genes

To comprehensively characterize the tumor immune microenvironment, we performed integrated computational analyses. Stromal, immune, and ESTIMATE scores were calculated using the ESTIMATE algorithm which employs specific gene signatures. This analysis was conducted on normalized expression data, with scores computed through single-sample Gene Set Enrichment Analysis implemented in estimate package of R [37]. Subsequently, Spearman correlation analysis was utilized to examine the association between FLNC and various immune cell types, followed by the generation of scatter diagrams and heatmaps to visualize these relationships. Further investigation of FLNC heatmaps associated with immunomodulatory genes was conducted.

Correlation between FLNC and genomic instability biomarker-related genes

Data on tumor mutational burden (TMB) and microsatellite instability (MSI) were obtained from the TCGA database. TMB was calculated by tallying the total number of nonsynonymous mutations per sample, normalized by the exonic region size (typically – 38 Mb) and expressed as mutations per million bases. Microsatellite instability (MSI) was determined by quantifying insertion/deletion (indel) events in microsatellite regions, normalized by the total number of microsatellite loci and scaled to mutations per million. Statistical differences in TMB and MSI between predefined groups were assessed using the Wilcoxon rank-sum test. In addition, rank correlation analysis of Spearman was employed to evaluate the association between FLNC expression and TMB and MSI levels. Results were visualized using boxplots and scatter plots, with significance thresholds set at p < 0.05. All statistical analyses and visualizations were performed in R using the "ggplot2" and stats packages.

FLNC methylation profile in pan-cancer

We explored alterations in the FLNC gene within tumors by utilizing the cBioPortal database. To delve deeper into the connection between CNV, methylation, FLNC mRNA, and OS, we conducted our analysis using GSCA (http://bioinfo.life.hust.edu.cn/GSCA/#/, Accessed on 25 August 2024) [38]. The distribution of methylation probes across chromosomes was visualized using the Shiny Methylation Analysis Resource Tool (SMART, http://www.bioinfo-zs.com/smartapp/, Accessed on 25 August 2024) [39]. In addition, we harnessed MethSurv (https://Biit.cs.ut.ee/MethSurv/, Accessed on 25 August 2024) for the analysis of CpG methylation and its correlation with survival outcomes [40].

Single-cell function and drug response in FLNC

The tumor immune single-cell Hub 2 (TISCH2, http://tisch.comp-genomics.org/home/; Accessed on 26 August 2024) is a comprehensive analytical platform designed for exploring single-cell transcriptomic data within the tumor immune microenvironment. This resource enables systematic characterization of cell-type-specific gene expression patterns and functional states across multiple cancer types. Concurrently, drug sensitivity analysis was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database to assess the association between FLNC expression and therapeutic response.

Gene enrichment analysis for FLNC

To comprehensively characterize FLNC-associated molecular networks, we performed systematic bioinformatics analyses using multiple computational approaches. The GeneMANIA database (http://genemania.org/; Accessed 28 August 2024) was employed to construct an interaction network for FLNC, identifying 100 co-expressed genes with significant associations [41, 42]. Subsequent functional annotation of these genes was conducted through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the R package “clusterProfiler” [43]. Biological process, molecular function, and cellular component terms were systematically analyzed, with statistical significance defined as p < 0.05. Pathway visualization was implemented using the “tidyr” and “ggplot2” packages to generate figures. Furthermore, to elucidate potential biological mechanisms, we performed gene set enrichment analysis (GSEA) through the “Cluster Profile” package, which enabled identification of significantly enriched gene sets in predefined molecular signatures. All statistical analyses were conducted in R.

Immunohistochemistry

The slides were placed in a 60° oven for 2 h; before dewaxing, they were heated in a microwave for 10 min. Then the slides were soaked in xylene solution for 10 min each for three times. After that soak the slides in anhydrous ethanol, 95, 85, and 75% alcohol for 5 min each, then rinsed twice with distilled water for 5 min each time. Then the dewaxed slides were taken out, the tissue slices around were dried with tissue paper, an immunohistochemistry pen was used to outline the tissue according to shape. After placing the slides in a humid chamber, filling the tissue with 3% hydrogen peroxide, and reacting at room temperature for 10 min, the slides were washed with distilled water three times for 5 min each. Whereupon we placed a heat-resistant box containing citrate repair solution in the microwave to heat until boiling; after boiling, placed the dewaxed slides in an organic stripping rack, immerse completely in boiling citrate repair solution for 10 min, then boiled for another 5 min. After cooling naturally to room temperature, we took out the slides, washed with PBS on a slow rocking shaker three times, 5 min each time. Then we added 5% BSA, blocked at room temperature for 20 min, and washed with PBS three times, 5 min each. The primary antibody with antibody dilution solution was diluted according to the recommended ratio (1:100) in the instructions. We added the antibody to cover the tissue on each slide, placed in a humid chamber in the refrigerator at 4 °C overnight, and washed with PBS three times the next day, 5 min each. Then we diluted the secondary antibody with antibody dilution solution according to the recommended ratio (1:200); added the antibody to cover the tissue on each slide, incubated at room temperature for 20 min, and washed with PBS three times, 5 min each. After preparing fresh 3,3′-diaminobenzidine staining solution, the slides were held in hand to avoid light. Then we added 50ul staining solution to cover the tissue on each slide, observed the color change of the tissue under a microscope to control staining, stopped staining by inserting the slides into distilled water when the target tissue turns brown. The stained slides were placed in hematoxylin staining solution (staining cell nuclei) at room temperature for 2 min, then rinsed with tap water for 10 min, followed by decolorization with 1% hydrochloric acid ethanol for 1–2 s, then rinsed with tap water, observed the staining under a microscope and if the color is too light, and repeated staining with hematoxylin for 1 min. After that soaked the slides in 75% ethanol, 85% ethanol, 95% ethanol, 100% ethanol for 3 min each, then in xylene for 10 min * 2 times, and finally air dried in a fume hood for about 3–4 h. The xylene was allowed to completely evaporate, the tissue slices were sealed with neutral gum, and air dried in a ventilated place for 2–3 days.

Results

The expression of FLNC in pan-cancer

We conducted an examination of FLNC expression in both pan-cancer and normal tissues. Our findings revealed that FLNC expression was significantly upregulated in four cancer types (GBM, KIRP, CHOL, and LIHC) compared to normal tissues, where its levels were markedly reduced. Conversely, FLNC demonstrated decreased expression in 15 cancer types, which included PRAD, STAD, ESCA, READ, COAD, HNSC, BLCA, LUAD, LUSC, BRCA, UCEC, KIRC, CESC, KICH, and THCA (as depicted in Fig. 1A). Since some tumors in TCGA do not have normal tissues, we also analyzed the integrated TCGA_GTEx data collected from UCSC (Fig. 1B). Our findings revealed differential FLNC expression across a majority of tumors, with some exhibiting elevated expression and others displaying reduced expression levels (as illustrated in Fig. 1B). This trend generally aligns with the outcomes observed in the TCGA analysis. According to data from the HPA database, FLNC exhibited elevated expression levels (the intensity in HPA is strong) compared to normal tissues in BRCA, CESC, KIRC, PAAD, STAD, and THCA (as depicted in Fig. 1C). Inversely, other tumor types above include PRAD, STAD, ESCA, READ, COAD, HNSC, BLCA, LUAD, LUSC, BRCA, UCEC, and KICH showed no significant upregulation of FLNC expression in the HPA compared to normal tissues. In addition, the subcellular distribution of FLNC was determined by immunofluorescent localization of nuclei, microtubules, and the endoplasmic reticulum (ER) in BJ (human fibroblast), U-2OS, and Rh30 cells. In all three cell types, FLNC was located in the plasma membrane and cytosol (Fig. 1D).

Fig. 1.

FLNC expression patterns in pan-cancer and subcellular localization. A Differential FLNC mRNA expression across 33 cancers in TCGA: upregulated in GBM, KIRP, CHOL and LIHC; (B) differential FLNC mRNA expression across 33 cancers in TCGA + GTEX: upregulated in GBM, TGCT, KIRP, PAAD, LGG, CHOL and LIHC; (C) immunohistochemical images of FLNC expression in BRCA, CESA, KIRC, PAAD, STAD and THCA from HPA; (D) immunofluorescence staining of the subcellular localization in U2OS, Rh30 and BJ of FLNC from HPA database. (*p < 0.05; **p < 0.01; ***p < 0.001, ns: no statistical differences)

FLNC expression level and prognosis value in pan-cancer

Overall survival (OS)

To investigate the prognostic significance of FLNC across various cancer types, we conducted Kaplan–Meier survival analysis to evaluate the connection between FLNC expression and clinical outcomes. Initially, we scrutinized the association between FLNC expression and OS in 33 different cancer types (Fig. 2A, B). The findings indicated that elevated FLNC expression was linked to reduced OS in BLCA (p < 0.001), GBM (p = 0.012), KIRC (p = 0.014), LGG (p < 0.001), LIHC (p = 0.019), LUAD (p = 0.035), LUSC (p = 0.018), OV (p = 0.008), SKCM (p = 0.012), and UVM (p = 0.005). Conversely, high FLNC expression was associated with extended OS in the case of LAML (p = 0.013). Notably, in LUSC, while the low-FLNC expression group exhibited better OS than the high-expression group, the survival curves ultimately converged to zero probability.

Fig. 2.

Prognostic significance of FLNC for OS. A OS forest map of 33 tumors, upregulated in 10 different cancer types and downregulated in LAML; (B) OS Kaplan–Meier curves in BLCA, GBM, KIRC, LIHC, LAML, LGG, LUAD, LUSC, OV, SKCM and UVM

Disease-specific survival (DSS)

We also conducted an examination of the connection between FLNC expression and DSS through Cox regression analysis. The outcomes of this analysis echoed the patterns observed in the OS analysis, revealing significant hazard effects in BLCA (HR = 2.023, 95% CI 0.396–2.932, p < 0.001), GBM(HR = 1.576, 95% CI 1.094–2.271, p = 0.015), KIRC(HR = 1.920, 95% CI 1.300–2.836, p = 0.001), LGG (HR = 2.652, 95% CI 1.814–3.878, p < 0.001), OV (HR = 1.419, 95% CI 1.071–1.880, p = 0.015), PAAD (HR = 1.748, 95% CI 1.095–2.782, p = 0.019), SKCM (HR = 1.421, 95% CI 1.066–1.896, p = 0.017), and UVM (HR = 5.074, 95% CI 1.844–13.962, p = 0.002) (Fig. S1A, B). Of particular importance is the fact that in certain cancer types, hazard ratios could not be calculated due to the absence of relevant data. In this subsequent survival analysis, cancer types characterized by high FLNC expression were associated with an unfavorable prognosis when compared to those with low expression.

Progression-free interval (PFI)

In our final analysis, we delved into the connection between FLNC expression and the PFI. Specifically, heightened FLNC expression was linked to a less favorable PFI in BLCA(HR = 1.670, 95% CI 1.236–2.256, p < 0.001), COAD (HR = 1.608, 95% CI 1.131–2.288, p = 0.008), KIRC (HR = 1.539, 95% CI 1.124–2.106, p = 0.007), LGG (HR = 2.428, 95% CI 1.823–3.232, p < 0.001), LUSC(HR = 1.426, 95% CI 1.029–1.975, p = 0.033), OV (HR = 1.295, 95% CI 1.020–1.643, p = 0.034), and UVM (HR = 6.964, 95% CI 2.743–17.680, p < 0.001), but conversely, it correlated with an improved PFI in PRAD (HR = 0.642, 95% CI 0.425–0.970, p = 0.035) (Fig. S2A, B).

Relationship between FLNC and clinicopathological parameters

Afterward, we investigated the connection between FLNC and pathological characteristics. Concerning the T stage, we observed elevated FLNC expression in higher T stages for BLCA (p < 0.001), KICH (p < 0.05), KIRC (p < 0.001), LUSC (p < 0.05), and STAD (p < 0.001), while lower expression was noted in higher T stages for PRAD (p < 0.01) (as shown in Fig. 3A), while among the remaining tumors, T stage showed no significant association with FLNC expression. Moreover, in cases of lymph node metastasis, FLNC expression levels were higher in BLCA (p < 0.001), CHOL (p < 0.01), COAD (p < 0.05), KICH (p < 0.05), and KIRC (p < 0.001) when compared to patients without lymph node metastasis. However, in PRAD (p < 0.05), FLNC expression levels were diminished in patients with lymph node metastasis compared to those without such metastasis (Fig. 3B). A higher expression was observed in later stages in BLCA (mean_early vs mean_later: 1.12 vs 1.99, p < 0.001), KICH (mean_early vs mean_later: 0.41 vs 0.95, p < 0.05), KIRC (mean_early vs mean_later: 1.04 vs 1.53, p < 0.001), and THCA (mean_early vs mean_later: 0.46 vs 0.59, p < 0.01) (Fig. 3C). In addition, we also found that among the 33 tumors, FLNC expression was higher in higher histological grades in BLCA (mean_low-grade vs mean_high-grade: 0.43 vs 1.77, p < 0.001), KIRC (mean _G1&G2 vs mean _G3&G4: 0.97 vs 1.43, p < 0.001), LIHC (mean_G1&G2 vs mean_G3&G4: 0.70 vs 1.36, p < 0.001), STAD (mean_high-grade vs mean_low-grade: 2.52 vs 3.31, p < 0.01), and UCEC (mean_G1&G2 vs mean_G3: 1.05 vs 1.31, p < 0.001) (Fig. 3D).

Fig. 3.

Relationship of FLNC expression with clinical features in cancers. A FLNC increases with advanced T-stage in BLCA, KICH, KIRC, LUSC, and STAD but decreases in PRAD; (B) higher FLNC in node-positive BLCA, CHOL, COAD, KICH, and KIRC but decreases in PRAD; (C) elevated FLNC in late-stage BLCA, KICH, KIRC, and THCA; (D) increased FLNC in high-grade BLCA, KIRC, LIHC, STAD and UCEC (*p < 0.05; **p < 0.01; ***p < 0.001, ns: no statistical differences)

Construction and evaluation of nomogram models in kidney renal clear cell carcinoma and low-grade glioma

To delve deeper into the influence of FLNC expression on the prognosis of cancers, we conducted univariate Cox regression analysis for five tumors in which FLNC might impact OS. Subsequently, based on the outcomes of this univariate Cox regression analysis, we selected samples with a sample size exceeding 500 for both KIRC and LGG. This selection allowed us to create nomogram models, validate the prognostic value, and evaluate the prediction accuracy of these nomogram models over 1-, 3-, and 5 year time frames through calibration curves. The results showed that FLNC made a significant contribution to the prognosis in the nomogram models and demonstrated good prediction ability for OS in KIRC (Fig. 4A) and LGG (Fig. 4B). The calibration curves pertaining to the predictions of 1, 3, and 5 year survival convincingly demonstrated the high precision of the nomogram in predicting OS (Fig. 4C). The multivariate cox model displayed in Supplementary data.

Fig. 4.

Nomogram, calibration curve and receiver operating characteristic curves of FLNC. A Nomogram models integrating FLNC with clinicopathological variables predict 1/3/5 year OS of gender, stage, age, and FLNC in KIRC; (B) nomogram models integrating FLNC with clinicopathological variables predict 1/3/5 year OS of grade, IDH status, age and FLNC in LGG; (C) calibration curve of FLNC in KIRC and LGG with high accuracy between predicted and observed survival

Correlation between FLNC and immune cell infiltration

Immune cell infiltration

We extended our analysis to assess the connection between FLNC expression and the infiltration of 24 immune cell types. FLNC displayed a positive correlation with immune cell infiltration in most tumors, notably showing increased levels of NK cell infiltration in 26 different cancer types (Fig. 5A). FLNC expression demonstrated significant positive correlations with NK cell infiltration in 26 cancers, notably in BLCA, STAD, PRAD, and TGCT. Notably, BLCA demonstrates significant positive correlations with a broad spectrum of immune cell subsets, whereas SARC exhibits predominantly negative associations with these populations. Although TGCT show a remarkable positive association with NK cells, they display negative expression patterns across most immune cell lineages. Linear regression plots indicated that the high expression of FLNC was moderate associated (R > 0.5) with the increased infiltration of certain immune cells, especially NK cells. For example, macrophages (R = 0.634, p < 0.001), NK cells (R = 0.568, p < 0.001) and mast cells (R = 0.634, p < 0.001) infiltrates in BLCA, exactly as NK cells (R = 0.531, p < 0.001) in BRCA, NK cells (R = 0.526, p < 0.00) in COAD, macrophages (R = 0.560, p < 0.001) in LGG, NK cells (R = 0.518, p < 0.00) in OV, neutrophils (R = 0.522, p < 0.001) and mast cells (R = 0.617, p < 0.001) in PRAD, NK cells (R = 0.537, p < 0.001) in READ, mast cells (R = 0.506, p < 0.001) and NK cells (R = 0.680, p < 0.001) in STAD, NK CD56bright cells (R = 0.616, p < 0.001) in TGCT, and NK cells (R = 0.628, p < 0.001) in THYM (Fig. S3). In addition, strong correlations (R > 0.7) were found in NK cells with PRAD (R = 0.794, p < 0.001) and TGCT (R = 0.746, p < 0.001).

Fig. 5.

Correlation between FLNC and immune cells or immune regulator genes. A Correlation between FLNC and 24 types of immune cells demonstrate positive FLNC–NK cell association in 26 cancers; (B) correlation between FLNC and immune score, stromal score and estimate score reveal strong stromal links in BLCA, BRCA, STAD, SARC and TGCT; (C) correlation between FLNC and chemokines receptors, positive in BLCA and KICH but negative in TGCT; (D) correlation between FLNC and immune checkpoints, positive in ACC, BLCA and KICH but negative in TGCT; (E) correlation between FLNC and chemokines, positive in ACC, BLCA, LGG and KICH but negative in SARC and TGCT; (F) correlation between FLNC and immunostimulator genes, positive in ACC, BLCA, LGG, KICH, PAAD and PCPG but negative in SARC and TGCT; (G) correlation between FLNC and MHC genes, positive in BLCA and LGG but negative in OV, SARC, TGCT and UCS; (H) correlation between FLNC and immunoesuppressive genes, positive in KICH and LGG but negative in TGCT and UCS. (*p < 0.05; **p < 0.01; ***p < 0.001, ns: no statistical differences)

Immune scores

We applied the ESTIMATE algorithm to compute the association between FLNC expression and stromal scores as well as immune scores across 33 cancer types. As shown in Fig. S4, FLNC expression with stromal scores, immune scores, and ESTIMATE scores exhibited a positive correlation in BLCA (R_{stromal scores} = 0.732, R_{immune scores} = 0.426, R_{ESTIMATE scores} = 0.621, all p < 0.001), BRCA(R_{stromal scores} = 0.683, R_{immune scores} = 0.230, R_{ESTIMATE scores} = 0.465, all p < 0.001), COAD (R_{stromal scores} = 0.507, R_{immune scores} = 0.309, R_{ESTIMATE scores} = 0.440, all p < 0.001), ESCA(R_{stromal scores} = 0.506, R_{immune scores} = 0.353, R_{ESTIMATE scores} = 0.486, all p < 0.001), KICH(R_{stromal scores} = 0.481, R_{immune scores} = 0.413, R_{ESTIMATE scores} = 0.465, all p < 0.001), KIRC(R_{stromal scores} = 0.405, R_{immune scores} = 0.192, R_{ESTIMATE scores} = 0.311, all p < 0.001), LGG(R_{stromal scores} = 0.559, R_{immune scores} = 0.456, R_{ESTIMATE scores} = 0.506, all p < 0.001), LIHC(R_{stromal scores} = 0.254, R_{immune scores} = 0.182, R_{ESTIMATE scores} = 0.230, all p < 0.001), LUAD(R_{stromal scores} = 0.372, R_{immune scores} = 0.117, R_{ESTIMATE scores} = 0.263, all p < 0.001), LUSC(R_{stromal scores} = 0.410, R_{immune scores} = 0.143, R_{ESTIMATE scores} = 0.281, all p < 0.001), PAAD(R_{stromal scores} = 0.491, R_{immune scores} = 0.273, R_{ESTIMATE scores} = 0.391, all p < 0.001), PCPG(R_{stromal scores} = 0.532, R_{immune scores} = 0.370, R_{ESTIMATE scores} = 0.481, all p < 0.001), PRAD(R_{stromal scores} = 0.520, R_{immune scores} = 0.262, R_{ESTIMATE scores} = 0.403, all p < 0.001), READ(R_{stromal scores} = 0.368, R_{immune scores} = 0.170, R_{ESTIMATE scores} = 0.298, all p < 0.001), STAD(R_{stromal scores} = 0.591, R_{immune scores} = 0.161, R_{ESTIMATE scores} = 0.403, all p < 0.001), THCA(R_{stromal scores} = 0.539, R_{immune scores} = 0.205, R_{ESTIMATE scores} = 0.357, all p < 0.001), and UVM(R_{stromal scores} = 0.392, R_{immune scores} = 0.261, R_{ESTIMATE scores} = 0.319, all p < 0.001). The observed patterns of stromal, immune, and ESTIMATE scoring systems mirrored the immune cell infiltration results. Specifically, BRCA, STAD, and BLCA exhibited significantly elevated scores across all three metrics, while TGCT and SARC consistently demonstrated substantially lower values in these assessments (Fig. 5B).

Correlation of FLNC expression with expression of some immune-related genes

We investigated the potential association between FLNC expression, and the expression of genes related to the immune system. The correlation analysis, focusing on checkpoint gene expression across various cancer types, revealed a notable connection between FLNC and TNF-related genes in most cases (Fig. 5C–H). As demonstrated in Fig. 5C, FLNC expression shows statistically significant correlations with tumor chemokine receptors. Specifically, BLCA and KICH exhibit positive correlations between FLNC and chemokine receptor expression, whereas TGCT displays inverse relationships. Figure 5D indicates regulatory relationships between FLNC and immune checkpoints, with ACC, BLCA, KICH, and LGG showing FLNC-mediated upregulation of these checkpoints. Conversely, TGCT demonstrates downregulatory effects. Figure 5E–H collectively illustrates FLNC's associations with chemokines, immunostimulators, MHC molecules, and immunosuppressive genes. Similarly, FLNC upregulates these immune modulators in ACC, BLCA, and LGG, while predominant downregulation occurs in SARC, UCS, particular in TGCT. The significant co-expression of FLNC with a greater number of immune checkpoint genes implies that FLNC plays a role in influencing tumor immune responses by modulating the activity of immune checkpoints. It is worth highlighting that in TGCT, FLNC expression exhibited a negative correlation with most immune checkpoints, immunostimulatory genes, MHC genes, and immunosuppressive genes.

Relationship between FLNC and TMB and MSI

Within COAD (cor = 0.109, p = 0.030), KICH (cor = 0.355, p = 0.004), KIRP (cor = 0.146, p = 0.145), LGG (cor = 0.183, p < 0.001), and THYM (cor = 0.422, p < 0.001), FLNC expression displayed a positive correlation TMB, whereas in BLCA (cor = − 0.129, p < 0.001), BRCA (cor = − 0.138, p < 0.001), PRAD (cor = − 0.396, p < 0.001), SKCM (cor = − 0.151, p = 0.001), and STAD (cor = − 0.253, p < 0.001), it exhibited a negative correlation with TMB (Fig. 6A). In COAD (cor = 0.221, p < 0.001), OV (cor = 0.189, p = 0.002), TGCT (cor = 0.258, p = 0.001), and UVM (cor = 0.272, p = 0.014), there was a positive correlation between FLNC expression and MSI. Conversely, in ESCA (cor = − 0.283, p < 0.001), HNSC (− 0.138, p = 0.002), PRAD (cor = − 0.090, p = 0.465), SKCM (cor = − 0.093, p = 0.045), and STAD (cor = − 0.116, p = 0.025), FLNC expression exhibited a negative correlation with MSI (Fig. 6B). Figure 6C illustrates the association between FLNC and MMR genes, with EPCAM showing a negative correlation in 18 different tumors.

Fig. 6.

Relationships with genomic instability biomarkers. A Correlation of FLNC expression with TMB, positive in COAD, KICH, KIRP, LGG and THYM, negative in BLCA, BRCA, PRAD, SKCM and STAD. B Correlation of FLNC expression with MSI, positive in COAD, OV, TGCT and UVM, negative in ESCA, HNSC, PRAD and STAD. C Correlation of FLNC expression with MMR genes, negative FLNC–EPCAM correlation in 18 cancers. (*p < 0.05; **p < 0.01; ***p < 0.001, ns: no statistical differences)

Mutation analysis and methylation

Genetic variations

We discussed the genetic alterations of FLNC in tumors utilizing the cBioPortal database. The frequency of genetic variations in FLNC exceeded 10% in melanoma (22.75%), endometrial cancer (13.04%), and non-small cell lung cancer (10.06%), mainly in the form of mutations. Among them, melanoma had the highest frequency (Fig. 7A). We also examined the FLNC methylation status (Fig. 7B) and copy number variation (CNV) (Fig. 7C) in pan-cancer. Significant epigenetic relationships between FLNC and DNA methylation were observed in UCS, LGG, PRAD, KIRP, SARC, and STAD. Notably, an inverse correlation predominated with FLNC exhibiting negative associations with methylation levels across most tumor types. Complementarily, FLNC expression demonstrated positive correlations with tumor copy number variations, with particularly pronounced co-amplification in SARC, LGG and KIRP. Figure S5A reveals a significant correlation in some cancer types between CNV and survival. The methylation status of FLNC displayed a significant association with the expression of FLNC mRNA in all 33 cancer types. Particularly in OV (cor = − 0.85, p < 0.001), UCS (cor = − 0.79, p < 0.001), LGG (cor = − 0.57, p < 0.001), and PRAD (cor = − 0.62, p < 0.001), the correlation is significantly evident. Figure S5B shows the top four with the highest correlation scores. Survival analysis revealed that OS in LAML (p = 0.012), LGG (p < 0.001), and UVM (p = 0.004) were significantly related to methylation (Fig. 7D–F). Survival analyses further revealed elevated FLNC methylation predicted improved clinical outcomes in solid tumors, while it conferred poorer prognosis in hematopoietic malignancies. We also analyzed genes related to m1A, m5C, and m6A modifications (Fig. 7G). In PRAD, STAD and BRCA, FLNC expression demonstrated significant negative associations with these epigenetic regulators. TRMT10C and NSUN6 were identified as key regulators among the implicated genes.

Fig. 7.

Correlation of FLNC with mutation, methylation, CNV and m¹A, m⁵C, m⁶A regulatory genes. A FLNC mutation types in different cancers, highest mutation frequency in melanoma (22.75%); (B) correlation between methylation and FLNC mRNA expression. Hypermethylation in UCS, LGG, PRAD, KIRP, SARC, and STAD; (C) correlations of CNV with FLNC mRNA expression. Low CNVs in SARC, LGG and KIRP with FLNC expression; (D–F) OS survival curves of FLNC methylation in LAML (D), LGG (E) and UVM (F). Protective in UVM and LGG but harmful in LAML; (G) correlation of FLNC with m1A, m5C, m6A regulatory genes. A significant negative association observed in PRAD, STAD and BRCA; (H) CpG-aggregated methylation of FLNC in different cancers, downregulate in eight cancer types and upregulate in four cancer types. (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns: no statistical differences)

DNA methylation

MethSurv was used to examine the DNA methylation levels of each CpG site in FLNC and survival outcomes. As shown in Fig. S5C, FLNC is related to 44 methylation probes. CpG-aggregated methylation value is significantly expressed in BLCA (p < 0.0001), BRCA (p < 0.0001), CHOL (p < 0.01), COAD (p < 0.0001), HNSC (p < 0.0001), LIHC (p < 0.0001), LUAD (p < 0.0001), LUCS (p < 0.0001), PRAD (p < 0.001) and UCEC (p < 0.01) (Fig. 7H). High methylation of cg01940964 indicated good prognosis for UVM (HR = 8.693, 95% CI 1.157–65.318, p = 0.036), ACC (HR = 2.318, 95% CI 1.08–4.972, p = 0.031), UCEC (HR = 1.761, 95% CI 1.101–2.817, p = 0.018), and HNSC (HR = 1.76, 95% CI 1.235–2.507, p = 0.002), but was correlated with poor prognosis in STAD (HR = 0.662, 95% CI 0.448–0.98, p = 0.039), KIRC (HR = 0.568, 95% CI 0.342–0.945, p = 0.029), GBM (HR = 0.549, 95% CI 0.337–0.893, p = 0.016), SARC (HR = 0.536, 95% CI 0.354–0.813, p = 0.003), and LGG (HR = 0.368, 95% CI 0.256–0.529, p < 0.001) (Supplemental Fig. 5D).

Single-cell function

For a more in-depth exploration of the possible functions of FLNC in tumors, we employed TISCH to examine its roles at the single-cell level (Fig. S6). The findings showed that FLNC was positively correlated with malignant cells, fibroblasts and endothelial cells, especially in Glioma (malignant cell, GSE14882), Non-Hodgkin Lymphoma (smooth-muscle-cell, GSE128531), and PRAD (fibroblasts, endothelial cell, CRA001160) (Fig. S7).

Drug response analysis and FLNC

We also discussed the drug sensitivity of FLNC expression in tumors, such as AS605240, AT-7519, BHG712, BMS345541, EKB-569, GSK2126458, GSK690693, I-BET-762, KIN001-102, Methotrexate, NPK76-II-72-1, Navitoclax, PHA-793887, PIK-93, Phenformin, TAK-715, THZ-2-102-1, TPCA-1, Vorinostat, WZ3105, and XMD13-2. The 50% inhibitory concentration (IC50) value of ZSTK474 was positively correlated with FLNC expression. FLNC expression is associated with AG-014699, Bleomycin (50 µM), CHIR-99021, and Dasatinib. Nonetheless, the IC50 values of Docetaxel, Elesclomol, Midostaurin, and TGX221 were negatively correlated (Fig. S8A) with FLNC expression.

Protein interaction network and enrichment analysis of FLNC

To explore the potential processes in which FLNC is involved in cancer development, we employed the GeneMANIA online tool to construct a protein–protein interaction (PPI) network for FLNC, which is depicted in Fig. S8B. This analysis revealed notable physical interactions between FLNC and FBLIM1, ANK3, SGCG, and SGCD. To gain further insight into the role of FLNC, we utilized GSEA to perform GO and KEGG enrichment. This approach allowed us to uncover the underlying biological functions of FLNC in tumors. The findings highlighted that FLNC was primarily linked to calcium signaling pathway, cGMP–PKG signaling pathway, and the PPAR signaling pathway, as illustrated in Fig. S8C, D.

Immunohistochemical validation

To investigate the effect of FLNC, we employed immunohistochemistry to validate the experimental results in LUAD, LIHC, GBM, BLCA, OV, and KIRC (Fig. 8). Our findings indicate that the staining intensities for cancer stages I and II are significantly lighter compared to those at stages III and IV, suggesting a progressive increase in FLNC protein expression from early stages (I and II) to advanced stages (III and IV) in these cancer types. This indicates that FLNC expression is upregulated in late-stage tumors (III/IV), whereas its levels are comparatively lower in early stages (I/II). This observation may imply a correlation between cancer progression and an increase in FLNC protein expression, which could contribute to tumor advancement.

Fig. 8.

Representative IHC images of FLNC in different stages of cancer. Progressive FLNC upregulation from early (I/II) to advanced stages (III/IV) in KIRC, OV, GBM, LIHC, LUAD and BLCA

Discussion

We systematically analyzed the molecular alterations of FLNC, prognostic impact, and therapeutic potential across 33 different cancer types. FLNC is overexpressed in many malignancies [44]. Elevated FLNC correlated with poor prognosis, advanced stage, lymph node metastasis, and higher grade, indicating its value as a clinical outcome predictor [45]. In LUSC, OS analysis revealed a counterintuitive survival pattern (Fig. 2B). The proposed mechanisms include several important considerations. First, there may be a discrepancy between short-term advantage and long-term prognosis in low-FLNC groups, where the potential long-term survival benefits could be offset by the inherent aggressiveness of tumors or the limited efficacy of second-line therapies. Second, treatment heterogeneity bias in high-FLNC cohorts might occur as patients in these groups could have received more intensive treatments, where the associated toxicities may counterbalance the putative protective effects of FLNC. Third, molecular subtype confounding could also play a role in these observations. FLNC also shows paradoxical expression, like in BLCA, where levels decrease in tumors vs normal tissue but rise with higher grade, highlighting its duality. In BLCA, early downregulation may reflect loss of tumor-suppressive functions facilitating invasion, while later upregulation could support metastasis through enhanced mechanical resilience. Similar biphasic roles exist in other cytoskeletal proteins. For example, NEDD9 promotes invasion and metastasis [46] but its knockout enhances invasiveness post-tumor formation [47, 48]. Collectively, these findings position FLNC as a prognostic risk factor that is significantly associated with tumor metastasis. Elevated FLNC expression correlates with advanced metastatic potential and poor clinical outcomes. Our analysis reveals distinct patterns of FLNC dysregulation. While FLNC expression was significantly upregulated in GBM, KIRP, CHOL, and LIHC compared to normal tissues. This variability extends to its prognostic impact, with elevated FLNC correlating with reduced OS in cancers, such as BLCA, GBM, LGG, and LIHC (Fig. 2A, B). Similarly, its relationship with clinicopathological parameters exhibited cancer-specificity. For instance, higher T stage associated with increased FLNC in BLCA, KICH, KIRC, LUSC, and STAD (Fig. 3A). Lymph node metastasis showed analogous divergent correlations (Fig. 3B). This complex landscape underscores the functional role of FLNC is highly context-dependent, varying across tumor types and stages.

FLNC expression positively correlated with NK cells in 26 cancers and macrophages in most tumors, linking it to anti-tumor immunity. It is also associated with stromal and immune scores in 17 cancers, indicating TME interactions. FLNC correlates with immunomodulatory genes and immune checkpoint markers (PD-1, CTLA-4), affecting prognosis. In hepatocellular carcinoma, FLNC may remodel immunosuppressive microenvironments via ECM signatures and immune checkpoint upregulation [49]. These findings propose FLNC as a potential target for immunosuppressive drugs, given its dual role in both structural remodeling and immune modulation. DNA methylation, a common epigenetic alteration, has a fundamental role in gene expression, genomic integrity, and the process of tumorigenesis [50]. Hypermethylation of the FLNC promoter in gastric cancer was shown to suppress its expression, correlating with chemoresistance via impaired DNA damage repair. In addition, FLNC methylation has been associated with various malignancies, including PRAD [51], gallbladder carcinoma [52], OV [53], and STAD [54]. Notably, hypermethylation of the FLNC promoter in gastric cancer has been demonstrated to be associated with Helicobacter pylori (H. pylori) infection, where FLNC methylation levels decrease but persist following H. pylori eradication [55]. Furthermore, FLNC exhibits a higher methylation frequency in metastatic gastric cancers compared to their primary counterparts [56]. We also noticed a significant association of OS with methylation in LAML, LGG, and UVM compared to normal tissue. RNA methylation is implicated in tumor initiation, progression, and prognosis [57–60]. These observations suggest that FLNC may promote tumorigenesis via RNA methylation. Elevated FLNC CNV mRNA in SARC, LGG, and KIRP suggests diagnostic potential. In addition, diverse CpG roles aid prognosis and personalized therapy [58, 61]. Enrichment analysis links FLNC to tumor progression via calcium, cGMP–PKG, and PPAR pathways. FLNC stabilizes structure by linking actin to ECM via calcium signaling, the impairment will facilitate tumor detachment [62–64]. Furthermore, FLNC participates in mechanical stress responses [65, 66], influencing proliferation and invasion via pathways like YAP/TAZ [67–70]. The cGMP–PKG pathway regulates calcium homeostasis [71, 72] and synergizes with FLNC to control actin reorganization and migration [73, 74] promoting malignancy in cancers, such as pancreatic ductal adenocarcinoma [75]and gliomas [76]. FLNC also functionally links to PPAR signaling [77, 78], and may coordinate with redox homeostasis regulation. Other pathways involve FLNC enhancing MEK1/2 and ERK1/2 activation to promote migration [79] and associating with microvascular invasion in hepatocellular carcinoma [79].

Based on the above-mentioned research, FLNC interacts dynamically with the TME, regulating immune evasion via immunosuppressive cell infiltration and checkpoint expression. Contradictory to variable biomarkers (PD-L1 demonstrates high sensitivity in non-small cell lung cancer but low sensitivity in colorectal cancer), FLNC broadly remodels TMEs across cancers, correlating with immune and stromal scores. Furthermore, FLNC correlates with multiple immune checkpoints in most tumors (Fig. 5D), particularly in TGCT. In addition, whereas classic tumor suppressor genes such as TP53 exhibit mutation profiles characterized by substantial inter-tumor heterogeneity that closely links to tumorigenesis and drug resistance, FLNC demonstrates consistent associations with epigenetic alterations and CNV across diverse cancers. This consistency indicates its stable involvement in tumor progression. Integrating FLNC expression and methylation status with established biomarkers may optimize immunotherapy patient stratification. Single-cell analysis demonstrated a positive correlation between FLNC and malignant cells, fibroblasts and endothelial cells in various tumors. The accumulation of FLNC in fibrosis reflects defective BAG3-dependent autophagy [80], promoting fibroblast overactivation and stromal remodeling. FLNC also functions in tumor-associated endothelium, underscoring the role of vascular remodeling and metastasis in PRAD [81] and hepatocellular carcinoma [82]. To further translate the prognostic value of FLNC into clinically applicable tools, we developed and evaluated nomogram models for KIRC and LGG (Fig. 4). These models integrated FLNC expression with other significant clinicopathological variables identified via univariate Cox regression (selected from tumors, where FLNC impacted OS). The nomograms (Fig. 4A for KIRC, Fig. 4B for LGG) visually represent the contribution of each factor, including FLNC, to the total points, which are then translated into predicted 1-, 3-, and 5 year overall survival probabilities. Critically, the calibration curves (Fig. 4C) demonstrated excellent agreement between the nomogram-predicted survival probabilities and the actual observed survival fractions at 1, 3, and 5 years for both KIRC and LGG. This high predictive accuracy confirms the robustness and clinical utility of these FLNC-integrated nomograms as practical instruments for individualized prognosis assessment in these specific cancers.

Using GDSC, FLNC positively correlated with IC50 values for several drugs. Elevated FLNC associated with reduced IC50 for EKB-569, methotrexate, metformin, and vorinostat, suggesting prioritization in FLNC-high tumors. Given FLNC–immune checkpoint associations, combining checkpoint inhibitors with FLNC-targeted regimens is recommended. FLNC promoter hypermethylation correlates with poor prognosis in LGG and UVM, warranting demethylating agent evaluation. Conversely, FLNC expression inversely correlated with bleomycin, midostaurin, docetaxel, and dasatinib efficacy, urging caution in FLNC-high tumors. Pharmacogenomic profiling is advised. The dichotomous expression (elevated in GBM, LIHC and KIRP, reduced in PRAD and STAD) suggests oncogenicity may arise from post-translational modifications, isoform-specific interactions, or microenvironmental pressures. To mitigate therapeutic risks, we propose the following strategies. Tissue-specific targeting approaches, such as antibody–drug conjugates or nanoparticle-based delivery systems, could selectively deliver FLNC inhibitors to tumor tissues, minimizing systemic exposure to muscle cells. Isoform-specific inhibition strategies prioritize FLNC isoforms or phosphorylation states unique to cancer cells, thereby avoiding disruption of normal FLNC functions. In addition, combining FLNC-targeted agents with immunotherapies, such as checkpoint inhibitors, might lower required dosages and reduce off-target effects.

While this study provides comprehensive insights into the multifaceted roles of FLNC in pan-cancer, several limitations should be acknowledged. Analyses relied on public bioinformatics data, risking batch and heterogeneity biases. Drug sensitivity data requires pharmacological and clinical confirmation. Spatial and temporal FLNC heterogeneity was unaddressed, potentially affecting prognostic and therapeutic accuracy. In summary, this pan-cancer analysis reveals elevated FLNC expression and prognostic value, alongside its involvement in signaling and immune pathways that influence TME interactions and progression. Therapeutic insights emerge from observed roles in methylation, drug sensitivity, and single-cell biology. Further research is required to elucidate underlying mechanisms in tumor development, progression, and treatment response.

Abbreviations

ACC

Adrenocortical carcinoma

BLCA

Bladder urothelial carcinoma

BRCA

Breast invasive carcinoma

CESC

Cervical squamous cell carcinoma

CHOL

Cholangiocarcinoma

CNV

Copy number variation

COAD

Colon adenocarcinoma

DSS

Disease-specific survival

ER

Endoplasmic reticulum

ERK 1/2

Extracellular signal-regulated kinase 1/2

ESCA

Esophageal carcinoma

ESTIMATE

Estimation of stromal and immune cells in malignant tumours using expression data

FLNA

Filamin A

FLNB

Filamin B

FLNC

Filamin C

GBM

Glioblastoma multiforme

GDSC

Genomics of drug sensitivity in cancer

GO

Gene ontology

GSCA

Gene set cancer analysis

GSEA

Gene set enrichment analysis

GTEx

Genotype-tissue expression

HNSC

Head and neck squamous cell carcinoma

HPA

Human protein atlas

IC50

Half maximal inhibitory concentration

ICIs

Immune checkpoint inhibitors

KEGG

Kyoto encyclopedia of genes and genomes

KICH

Kidney chromophobe

KIRC

Kidney renal clear cell carcinoma

KIRP

Kidney renal papillary cell carcinoma

LAML

Acute myeloid leukemia

LGG

Low grade glioma

LIHC

Liver hepatocellular carcinoma

LUAD

Lung adenocarcinoma

LUSC

Lung squamous cell carcinoma

MEK1/2

Mitogen-activated extracellular signal-regulated kinase 1/2

MHC

Major histocompatibility complex

MMR

Mismatch repair

MSI

Microsatellite instability

OS

Overall survival

OV

Ovarian serous cystadenocarcinoma

PAAD

Pancreatic adenocarcinoma

PFI

Progression-free interval

PPI

Protein–protein interaction

PRAD

Prostate adenocarcinoma

READ

Rectum adenocarcinoma

SKCM

Skin cutaneous melanoma

SMART

Shiny methylation analysis resource tool

STAD

Stomach adenocarcinoma

TCGA

The cancer genome atlas

TGCT

Testicular germ cell tumors

THCA

Thyroid carcinoma

TIMER

Tumor immune estimation resource

TISCH2

Tumor immune single-cell Hub 2

TMB

Tumor mutational burden

TME

Tumor microenvironment

TPM

Transcripts per kilobase million

UCEC

Uterine corpus endometrial carcinoma

UCSC

University of California Santa Cruz

UVM

Uveal melanoma

Author contributions

All authors contributed to the study conception and design. Writing—original draft preparation: [Ni Peizan]; Writing—review and editing: [Yaru Zhu, Duanyu Wang, Changqian Wang, Qianzi Kou]; Conceptualization: [Yaru Zhu, Nov Pengkhun, Wen Fu]; Methodology: [Peizan Ni, Kunpeng Du, Nov Pengkhun, Changqian Wang, Ying Li]; Formal analysis and investigation: [Lilin Li, Yangfeng Zhang, Chongyang Zheng]; Funding acquisition: [Jiqiang Li]; Resources: [Jiqiang Li]; Supervision: [Jiqiang Li],and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This project is supported by the National Natural Science Foundation of China (No.: 82374168), the Natural Science Foundation of Guangdong Province (No.: 2023A1515012548) and Medical Scientific Research Foundation of Guangdong Province (Grant No. B2021041).

Availability of data and materials

The data from the Cancer Genome Atlas (TCGA) database and the University of California Santa Cruz (UCSC) Xena platform (https://xenabrowser.net/datapages//, accessed on 23 August 2024); TIMER Database (http://cistrome.dfci.harvard.edu/TIMER/, accessed on 23 August 2024); (HPA) database through the examination of immunofluorescence microscopy images (https://www.proteinatlas.org/search/FLNC, accessed on 23 August 2024).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.