KRT14 activated by transcription factor E2F1 promotes bladder cancer progression

Introduction

Bladder cancer is the ninth most common malignant tumors globally, with ~614,000 new cases and 220,000 mortalities reported in 2022, ranking second in the most common cancers of the urinary and reproductive systems, with its prevalence increasing year by year (1,2). Upon histological examination, urothelial-origin bladder cancer is categorized as either muscle-invasive bladder cancer or non-muscle-invasive bladder cancer (3).

In early treatment, transurethral resection is the most commonly used method for patients with bladder cancer (4). However, patients have a high risk of tumor progression and recurrence within 5 years after initial treatment, accounting for 50–70% of all those treated (5,6). It is recommended to perform lifelong follow-up for patients with bladder cancer who have moderate or high risk of recurrence or progression (7). Although regular cystoscopy is the most widely used measure in the treatment of bladder cancer and is regarded as the gold standard, it must be performed regularly. Due to its invasiveness, it may bring inconvenience and discomfort to patients (8). Certain patients may present with pink urine or hematuria after cystoscopy, and may even develop fever due to infection (9–11). Doctors often use follow-up cytology tests which have good specificity but low sensitivity. Moreover, the results of cytological examination are largely influenced by the subjectivity of the observer (12). Therefore, a non-invasive and efficient detection method is urgently needed to avoid follow-up cystoscopy and cytology for patients with bladder cancer.

The development of improved diagnosis, treatment and prevention methods for cancer is closely associated with an improved understanding of the genetic changes in tumors (13). The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) are two core public omics resources that employ standardized high-throughput genomic profiling pipelines to dissect molecular landscapes of human malignancies, accelerating the discovery of novel diagnostic biomarkers and therapeutic targets for patients with cancer (13). TCGA systematically collects multi-omics and matched long-term clinical data across 33 major cancer types, while GEO serves as the central National Center for Biotechnology Information repository for depositing, retrieving and reanalyzing microarray and RNA-sequencing (RNA-seq) transcriptomic datasets worldwide (14). Collectively, these platforms provide open-access multi-layered data covering tumor genetics, epigenetics and transcriptomics, enabling global researchers to systematically mine candidate oncogenic signatures and druggable targets and translate bulk cancer genomics findings into individualized clinical management strategies (15). The continuous expansion of data resources hosted by TCGA and GEO has greatly enriched public functional genomics datasets for diverse malignancies and has laid a solid foundation for systematic bioinformatic screening of tumor progression-related molecular markers. Zhang et al (16) used bioinformatics analysis to demonstrate that follistatin-like 1 exhibits notable upregulation in bladder tumors, promotes cancer cell movement through differentiation mechanisms and may regulate key components of the tumor microenvironment. In addition, Aizezi et al (17) used TCGA database, reporting that insulin-like growth factor-like family member 2 (IGFL2) is highly expressed in bladder cancer, which may be associated with a poor prognosis. Therefore, IGFL2 may be a potential biomarker and an important therapeutic target for patients with bladder cancer (17). However, the public microarray database of bladder cancer needs more exploration to help to identify potential biomarkers associated with disease progression.

In the present study, the key differentially expressed gene keratin 14 (KRT14) in bladder cancer was screened from the microarray dataset GSE77883. KRT14 serves an important role in the occurrence and development of several cancers. It has been reported to be upregulated in numerous cancers and is considered to be a biomarker of certain malignant tumors, such as ovarian cancer, breast cancer and renal cell carcinoma (18–20). However, the expression and potential role of KRT14 in bladder cancer are unknown. The present study aimed to demonstrate the role of KRT14 in bladder cancer tissues and cells, and evaluate the potential value of KRT14 in the development of bladder cancer.

Materials and methods

Dataset processing and hub gene extraction

The GEO dataset GSE77883 (21), which is associated with bladder cancer, was downloaded from the GEO database. GSE77883 is based on the GPL17077 platform (Agilent-039494 SurePrint G3 Human GE v2 8×60K Microarray 039381; Agilent Technologies, Inc.), which includes 3 samples from a test group and 3 samples from a control group. This dataset profiled transcriptional differences between parental T24 bladder cancer cells and gemcitabine-resistant T24 sublines (3 biological replicates per group). This dataset was selected for two reasons: i) Previous studies (22–24) have demonstrated that genes driving bladder cancer malignant progression frequently overlap with mediators of chemotherapy resistance; oncogenic factors overexpressed in drug-resistant bladder cancer cells often simultaneously promote cell proliferation, migration and invasion, which are core malignant phenotypes of primary bladder tumors; and ii) transcriptomic alterations in drug-resistant cell sublines can capture low-abundance oncogenic transcripts that are easily masked in bulk primary tumor-normal tissue comparisons, enabling the mining of weakly expressed but functionally critical tumor-promoting genes. The matched clinical and survival data from TCGA database [TCGA-Bladder Urothelial Carcinoma (BLCA)] was also downloaded, and 403 patients with bladder cancer were finally included to form a training set using TCGA data. The raw readings of the aforementioned data were processed and normalized in R software (version 4.2.1; Posit Software, PBC). Differentially expressed genes between the test and control groups of bladder cancer samples from the GEO dataset GSE77883 were screened using National Center for Biotechnology Information GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE77883). GEO2R performs statistical computations through the ‘limma’ R package (https://bioconductor.org/packages/release/bioc/html/limma.html). Log2 fold multiples ≥1 and adjusted P<0.05 were considered as screening criteria. The top 20 differentially expressed genes were sorted based on logFoldChange (FC) values, protein-protein interaction (PPI) network analysis was performed on differentially expressed genes using the STRING website (https://cn.string-db.org/), and then hub genes were extracted using Cytoscape visualization (version 3.10.1; Cytoscape Consortium). KRT14 ranked within the top 3 nodes by Degree metrics.

Bioinformatics analysis for KRT14

Based on the gene expression data of TCGA, the expression of KRT14 in bladder cancer and normal samples was determined. The online Kaplan Meier website (https://www.kmplot.com/analysis/) was used to evaluate the association between KRT14 expression and the prognosis of bladder cancer (parameters: Pan-cancer; Bladder Carcinoma; KRT14 gene; auto select best cut-off: percentiles) for the TCGA-BLCA dataset. The receiver operating characteristic (ROC) curve was applied to analyze the accuracy and efficacy of KRT14 in predicting the prognosis of bladder cancer. The ROC curve can reflect the relationship between sensitivity and specificity. The area under curve (AUC) of the ROC curve was used to evaluate predictive performance, with AUC typically ranging from 0.5–1; a value closer 1 indicates a greater predictive performance. Single-cell sequencing profiles of KRT14 in bladder cancer samples was also downloaded from the Single Cell Portal (https://singlecell.broadinstitute.org/single_cell) database for cell type-specific expression analysis. ‘Bladder organ’ was used to filter the results, so only one study met the criteria. The study sample included the RNA-seq data of 2,800 IMvigor patients with bladder cancer, as well as the single cell data of 200 patient samples and healthy donors. By exploring the KRT14 gene, single-cell sequencing results were obtained. The specific analysis pipeline was: Clustering selected X_umap; annotation selected cell_type_custom; substamping selected All Cells; continuous color scale selected Viridis; order expression selected high. The expression of KRT14 in bladder cancer was analyzed in a pathological tissue microarray from the Human Protein Atlas (HPA; http://www.proteinatlas.org/). Furthermore, immunohistochemical results were obtained from the HPA. The upstream transcription factors of KRT14 were predicted through the GeneCards website (https://www.genecards.org/) and the binding motif was predicted through JASPAR (https://jaspar.elixir.no/).

Drug sensitivity and molecular docking analysis

Query drug sensitivity correlations for KRT14 mRNA expression were assessed using the Gene Set Cancer Analysis (GSCA) platform (https://guolab.wchscu.cn/GSCA/#/). The top-correlated small-molecule compounds were selected for the target gene and protein-ligand docking was performed employing AutoDock Vina (version 1.2.2; Scripps Institute Molecular Graphics Laboratory) to evaluate binding affinity and interaction patterns between candidate compounds and target genes. The full-length 3D structure of human KRT14 was retrieved from the AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/search/text/krt14; entry ID no. AF-P02533-F1). The AlphaFold model was processed using ChimeraX (version 1.10; University of California; San Francisco, USA) in which water molecules and low-confidence residues (predicted Local Distance Difference Test, <70) were removed, polar hydrogens were added and Gasteiger partial charges were assigned before conversion to PDBQT format for docking. 3D conformations of afatinib, erlotinib and vandetanib were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/), optimized with MMFF94 force field, protonated at physiological pH 7.4 and exported as PDBQT files. A cubic binding pocket of 40 Å side length was centered on the conserved globular domain of KRT14, with grid spacing set to 0.05 nm. The exhaustiveness parameter was set to 8 to balance sampling efficiency and accuracy. A total of three independent docking replicates with different random seeds were run for each ligand-receptor complex. The pose with the lowest binding free energy (kcal/mol) was selected for subsequent interaction analysis. Protein-ligand hydrogen bonds, hydrophobic and π-interactions were analyzed using PLIP (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) and LigPlus (version 2.2.9; EMBL-European Bioinformatics Institute); binding complex structures were visualized in PyMOL 2.5 (Schrödinger, Inc.).

Tissue samples

Samples were prospectively collected for the present study. Samples of bladder tumor tissue and adjacent healthy tissue (3 cm away from malignant lesions) were obtained from three patients with bladder cancer. The inclusion criteria encompassed patients receiving total cystectomy, transurethral resection of bladder tumor or partial cystectomy. The exclusion criteria encompassed patients with previous or concomitant malignancies. The cohort consisted of 2 men and 1 woman, aged 62, 83 and 77 years old, respectively. The recruitment date was March 2026. All samples were collected immediately before transurethral bladder tumor resection or bladder resection in Nantong City No.1 People's Hospital (Nantong, China). The use of patient samples was approved by the hospital ethics committee (approval no. 2026-KT102-01). Prior to collecting samples from the subjects, written informed consent was obtained from each individual.

Cell culture and transfection

The cell lines used in the present study were purchased from the cell bank of the Chinese Academy of Medical Sciences and Zhejiang Meisen Cell Technology Co., Ltd. SV-HUC-1, RT4 and T24 cells were cultured in RPMI 1640 (HyClone™; Cytiva) medium containing 10% FBS (Gibco; Thermo Fisher Scientific, Inc.) and 1% penicillin/streptomycin (Gibco; Thermo Fisher Scientific, Inc.) solution in a moist atmosphere at 37°C and 5% CO2. 5637 and J82 cells were cultured in MEM-EBSS (Gibco; Thermo Fisher Scientific, Inc.) containing 10% FBS and 1% penicillin/streptomycin solution at 37°C in a moist atmosphere with 5% CO2.

The present study used small interfering (si)-RNA (3rd generational system; Guangzhou RiboBio Co., Ltd.) for transfection and Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. The 293T cell line was used for lentivirus packaging, as purchased from the cell bank of the Chinese Academy of Medical Sciences. The lentiviral overexpression backbone vector used in the present study was pLVX-EF1a (empty vector as control, target gene overexpression vector). For lentivirus packaging in a 10 cm cell culture dish, the total transfection plasmid dosage was 16 µg, including 8 µg overexpression backbone plasmid, 6 µg packaging plasmid psPAX2 and 2 µg envelope plasmid pMD2.G. The mass ratio of backbone plasmid: psPAX2: pMD2.G was maintained at 4:3:1. All plasmids were transfected into 293T cells using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) following the standard manufacturer's protocol. The cell culture and transfection environment was set at 37°C with 5% CO2. The transfection complex-containing medium was discarded 6 h after transfection and fresh complete culture medium was added for continuous incubation. The cell supernatant containing lentiviral overexpression particles was separately collected at 48 h and 72 h after plasmid transfection. The collected supernatant was centrifuged at 4°C, 3,000 × g for 15 min and filtered with a 0.45 µm sterile filter to eliminate cell debris and impurities. The target cells were infected with the purified lentivirus at a multiplicity of infection of 10 for stable overexpression cell line construction. When the fusion degree of 5637 and RT4 cells reached 70–80% and they were in logarithmic growth phase, siRNA against KRT14 or scrambled siRNA [si-negative control (siNC)] at a concentration of 50 nM was transfected using liposomes. Following transfection, the culture plate was placed in a CO2 incubator at 37°C and incubated for 48 h prior to subsequent experiments.

The target KRT14 sequence was 5′-GCACCAAGGTCATGGATGT-3′. The target siNC sequence was 5′-TTCCCGAACGTGTCACGTT-3′. The E2F1 overexpression vector and the empty pENTER vector were purchased from GeneCopoeia, Inc. and transfected using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) following the standard manufacturer's protocol. For one well of a 6-well plate, 2.5 µg plasmid DNA (E2F1 overexpression vector or empty pENTER vector) was used. The DNA-transfection reagent mixture was incubated at room temperature for 20 min to form lipoplexes. Cells were maintained at 37°C with 5% CO2 during the entire transfection process. Culture medium was refreshed 6 h after adding complexes. Cells were harvested or subjected to downstream experiments 48 h post-transfection.

Western blotting

Total protein was extracted using a Protein Isolation Kit (Beyotime Biotechnology). The protein concentration was then measured using the Pierce™ Rapid Gold BCA Protein Assay Kit (Beyotime Biotechnology). The protein sample was then subjected to 10% SDS-PAGE (Beyotime Biotechnology), initially at 120 V for 20 min and then at 100 V for 1 h. Subsequently, the protein was transferred onto a PVDF membrane (Merck KGaA) and then incubated with the following antibodies according to standard procedures: Anti-KRT14 (1:1,000; cat. no. ab181595; Abcam) and anti-GAPDH (1:1,000; cat. no. sc-137179; Santa Cruz Biotechnology, Inc.), which was loaded as a control with the same sample. The secondary antibody used was goat anti-mouse IgG HRP (1:1,000; cat. no. A0216; Beyotime Biotechnology). Incubation was conducted for 2 h at room temperature. The antibody dilution buffer was purchased from Beyotime Biotechnology. ECL reagent (Tanon Science and Technology Co., Ltd.) was used to detect protein bands and imaged on a ChemiDoc MP System (Bio-Rad Laboratories, Inc.).

Reverse transcription-quantitative PCR (RT-qPCR)

Total RNA content was extracted from sample tissues or cells (SV-HUC-1, 5637, J82, RT4 and T24) using the RNeasy Mini assay kit (TransGen Biotech Co., Ltd.) and then reverse transcribed to cDNA using the PrimeScript RT assay kit (TransGen Biotech Co., Ltd.) with a gDNA eraser according to the manufacturer's protocol. The CFX96 instrument (Bio-Rad Laboratories, Inc.) and iTAPTM Universal SYBR Green SuperMix (TransGen Biotech Co., Ltd.) were used to quantify mRNA expression levels using qPCR. In qPCR, GAPDH was used as the reference gene for mRNA. The primers used for qPCR were designed using Premier Primer software (version 5.0; PREMIER Biosoft) and synthesized by Sangon Biotech Co., Ltd. The relative mRNA expression levels were calculated using the 2−ΔΔCq method (25). The qPCR program was as follows: Pre denaturing at 95°C for 2 min; cycle steps: 30 sec at 60°C, 30 sec annealing at 72°C, 60 sec extension, with a total of 35 cycles; extend to 72°C for 10 min; maintain at 4°C. The following primers for qPCR were used: KRT14 forward: 5′-CTACTTCAAGACCATTGAG-3′ and reverse, 5′-CAACTCTGTCTCATACTTG-3′; and GAPDH forward, 5′-GGAGCGAGATCCCTCCAAAAT-3′ and reverse, 5′-GGCTGTTGTCATACTTCTCATGG-3′.

Cell viability assay

Cells were cultured at a density of 2,000 cells per well onto a 96-well plate. Cell proliferation was detected every 24 h using the Cell Counting Kit-8 (CCK-8) kit (Vazyme Biotech Co., Ltd.) for a total of 4 times (at 0, 24, 48 and 72 h). CCK-8 solution (10 µl) was added to each well of the 96 well culture plate and incubated for 2 h, then absorbance was measured at 450 nm using the Multiskan GO system (Thermo Fisher Scientific, Inc.).

Transwell assay

The transfected cells (5×104) were added into an upper Transwell chamber using serum-free medium with or without Matrigel. Matrigel was stored at −20°C on ice or in a refrigerator at 4°C in advance, before being thawed. Subsequently, 50 µl diluted Matrigel was added to the Transwell plate chamber, before the entire polycarbonate film was covered, and incubated at 37°C for 30 min to polymerize into a gel. Culture medium containing 10% FBS was then added into the bottom chamber. After incubating at 37°C for 24 h, the cells attached to the surface of the membrane were carefully removed with a cotton swab. Cells on the lower side of the chamber were fixed in 4% paraformaldehyde at room temperature for 20 min and stained with 0.1% crystal violet at room temperature for 15 min before counting. Finally, images were captured of the field of view under an inverted microscope (Olympus Corporation) and the number of migrating cells was calculated through ImageJ software (version 1.8.0; National Institutes of Health).

Colony formation assay

Both 5637 and RT4 cell lines were cultured at a density of 1,000 cells per well onto a 6-well plate and placed in a moist incubator at 37°C for 2 weeks. After fixing with 4% paraformaldehyde at room temperature for 20 min, the cells were stained with 0.1% crystal violet at room temperature for 30 min. Finally, images of the cells were captured and the number of colonies (defined as a cluster containing ≥50 cells) was quantified using ImageJ software (version 1.8.0; National Institutes of Health).

Dual luciferase reporter assay

The 293T cell line was cultured in a 24-well plate and transfected with E2F1 overexpression and luciferase reporter vectors containing a KRT14 promoter region. Mutant E2F1 was used as a negative control. After 48 h transfection using Lipofectamine 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.), luciferase activity was measured using the Luciferase Reporter assay system (Promega Corporation). The firefly luciferase reporter gene vector pGL3 Basic Vector was provided by Promega Corporation. The ratio of the target luciferase value to the internal reference value was used as the final result to reduce experimental errors.

Chromatin immunoprecipitation (ChIP)

ChIP was performing using the EZ-Magna ChIP chromatin immunoprecipitation kit (MilliporeSigma; Merck KGaA). 5637 and RT4 cells were cultured in 10 cm plates and cross-linked with 1% formaldehyde at room temperature for 10 min. Cells were then collected and lysed in SDS lysis buffer, followed by chromatin sonication on ice. The lysate was centrifuged at 12,000 × g for 10 min at 4°C to collect the supernatant. For immunoprecipitation, 100 µl anti-E2F1 antibody (1:100; cat. no. 3742; Cell Signaling Technology, Inc.) or anti-IgG antibody (1:100; cat. no. 2729; Cell Signaling Technology, Inc.) was added to the supernatant, followed by incubation with protein A/G magnetic beads overnight at 4°C with gentle rotation. In parallel, 20 µl supernatant from each sample was retained as the input control. Finally, RT-qPCR was carried out to detect and quantify the enrichment of target amplified DNA products.

Statistical analysis

Western blotting, colony formation assays and migration/invasion assays were repeated at least three times in the present study. These experiments were performed to demonstrate representative phenotypic changes and the data presented are qualitative observations rather than quantitative measurements for statistical comparison. No quantitative statistical analyses were applied to these results. All data are presented as mean ± SD. The results were analyzed using SPSS 25.0 (IBM Corp.) and GraphPad Prism 8.0 (Dotmatics) analysis software. For two-group comparisons, an unpaired Student's t-test was applied for unpaired data and a paired Student's t-test was used for paired data. One-way ANOVA followed by Tukey's post-hoc test was performed for multiple-group comparisons. P<0.05 was considered to indicate a statistically significant difference.

Results

Differentially expressed genes in the GSE77883 dataset

GSE77883 is a data set for the study of bladder cancer. The data set contains six samples, which are divided into a control group (GSM2060946, GSM2060947 and GSM2060948) and an experimental group (GSM2060949, GSM2060950 and GSM2060951). Firstly, principal component analysis was performed on the dataset to evaluate the repeatability of the data within the group and confirm the positive repeatability of the data in GSE77883 (Fig. 1A). Subsequently, the gene expression data of the control group and experimental group in GSE77883 were analyzed using the GEO2R online website. According to the volcano map, a total of 831 differentially expressed genes were identified, of which 495 genes were upregulated and 336 genes were downregulated (Fig. 1B). The upregulated and downregulated genes in the experimental and control groups are shown in Fig. 1C. The number of genes with abnormally high expression in the experimental group was significantly higher than that in the control group, and the difference was statistically significant (Fig. 1D).

Figure 1. Analysis of GEO dataset GSE77883. (A) Principal component analysis of dataset GSE77883. (B) Volcano plot of differentially expressed genes. The red dots represent significantly upregulated genes, while the blue dots represent significantly downregulated genes. (C) Heat map of partially differentially expressed genes, with blue representing the control group and red representing the experimental group. (D) Box plot of gene expression in 6 samples of the GSE77883 dataset. GEO, Gene Expression Omnibus; PC, principal component; ref, reference.

Identification of hub oncogene KRT14 in bladder cancer

To identify the core functional genes from the differentially expressed genes, the top 20 differentially expressed genes were sorted based on logFC values and PPI network analysis was performed using Cytoscape software. The specific ranking of hub-gene and degree scores are shown in Table SI. The results demonstrated that KRT14 ranked within the top 1 nodes by Degree metrics, serving an important role in bladder cancer (Fig. 2A). Furthermore, the findings of the validation of TCGA-BLCA dataset and paired sample analysis revealed that the expression of KRT14 in the bladder cancer group was significantly higher than that in the normal control group (Fig. 2B and C).

Figure 2. Identification of the oncogene KRT14 in bladder cancer. (A) Protein-protein interaction network screening vital differentially expressed genes in bladder cancer. (B) Expression of KRT14 in bladder cancer in paired samples. (C) Expression of RNA-sequencing data of KRT14 in bladder cancer. (D) OS of KRT14 in bladder cancer (P=0.013). (E) RFS of KRT14 in bladder cancer (P=0.043). (F) ROC curve of TCGA-BLCA according to the expression of KRT14 (P<0.001). **P<0.01; ***P<0.001. KRT14, keratin 14; OS, overall survival; RFS, regression-free survival; ROC, receiver operating characteristic; TCGA-BLCA, The Cancer Genome Atlas-Bladder Cancer; TPM, transcripts per million; HR, hazard ratio; CI, confidence interval; AUC, area under the curve; TPR, true positive rate; FPR, false positive rate.

Based on the aforementioned results, we hypothesized that the high level of KRT14 in bladder cancer is associated with the survival and prognosis of bladder cancer. Therefore, the overall survival (OS) and recurrence-free survival (RFS) of KRT14 in bladder cancer were assessed using the online Kaplan Meier mapping database. The results demonstrated that the OS [hazard ratio (HR), 1.45; 95% confidence interval (CI), 1.08–1.94; Fig. 2D] and RFS (HR, 2.1; 95% CI, 1.01–4.39; Fig. 2E) of patients with bladder cancer with a high expression level of KRT14 were shorter, indicating that the prognosis is worse. Moreover, the ROC curve was used to validate the prognostic ability of KRT14 in TCGA-BLCA cohort, demonstrating that the AUC of KRT14 was 0.79 (Fig. 2F). Therefore, combined with high sensitivity and specificity, KRT14 can be used as a candidate prognostic biomarker for bladder cancer.

Analysis of the expression patterns of KRT14 in single-cell sequencing and the HPA datasets

Publicly available single-cell RNA-seq data was analyzed from 3,000 patients with BLCA (884,573 cells). In the initial study, 10 cell types were annotated with marker genes, including nine cell types (B cell, Cancer Associate, Cancer/Epithelial, EC, Mast cell, Myeloid, Normal Fibroblast, Pericyte and T cell) and one other unclassified mixed cell cluster shown in grey (Fig. 3A). The results showed that the expression of KRT14 was concentrated in cell types identified by the analysis output as ‘cancer-associated cell types’ (Fig. 3B). In order to further explore the expression of KRT14 in bladder cancer, the HPA online website was used to analyze the expression of KRT14 in the pathological tissue chip of bladder cancer. Consistent with the aforementioned results, the expression level of KRT14 in bladder cancer was significantly higher than that in the normal control group (Fig. 3C).

Figure 3. Analysis of the expression patterns of KRT14 in single-cell sequencing and the Human Protein Atlas datasets. (A) UMAP visualization of integrated bladder cancer single-cell datasets. A total of 10 cell subtypes were annotated by canonical lineage markers. (B) The specific expression of KRT14 in various cell types. (C) Analysis of differential expression of KRT14 proteins in bladder cancer tissues and normal bladder tissues (scale bar, 100 µm). Images are available from https://www.proteinatlas.org/ENSG00000186847-KRT14/tissue/urinary+bladder. KRT14, keratin 14; UMAP, Uniform Manifold Approximation and Projection.

High expression of KRT14 in bladder cancer tissues and cells

The aforementioned experiments adopted bioinformatics methods to predict the role of KRT14 in bladder cancer. In order to confirm these results, the role and function of KRT14 in bladder cancer was assessed further though several experiments. First, in order to verify the expression of KRT14 in bladder cancer tissue, three pairs of tumor and adjacent normal tissue samples were obtained from patients with bladder cancer. The results demonstrated that KRT14 protein expression was markedly elevated in bladder tumor tissues compared with that in the paired normal adjacent tissues (Fig. 4A). In addition, the mRNA expression level of KRT14 in bladder cancer tissue was significantly higher than that in the adjacent normal tissue, and the difference was significant (Fig. 4B). Subsequently, the expression of KRT14 at the level of bladder cancer cells was assessed. First, the protein expression level of KRT14 was compared between human normal bladder epithelial cells (SV-HUC-1) and four human bladder cancer cells (5637, J82, RT4 and T24). Consistent with the aforementioned results, the expression level of KRT14 in bladder cancer cells was significantly higher than that in the normal control cells (Fig. 4C). Similarly, the mRNA expression level of KRT14 in bladder cancer cells was significantly higher than that in the normal control cells, and the difference was statistically significant (Fig. 4D). Moreover, the findings revealed that KRT14 had the highest expression level in 5637 and RT4 bladder cancer cells. Therefore, these two bladder cancer cells were selected for further experimentation.

Figure 4. High expression of KRT14 in bladder cancer tissues and cells. (A) Protein expression levels of KRT14 in bladder cancer tissues. (B) mRNA expression levels of KRT14 in bladder cancer tissues. (C) Protein expression levels of KRT14 in bladder cancer cell lines. (D) mRNA expression level of KRT14 in bladder cancer cell lines. SV-HUC-1 served as the normal control cell line. **P<0.01; ***P<0.001. KRT14, keratin 14.

Silencing KRT14 inhibits the proliferation of bladder cancer cells

In order to further explore the effect of KRT14 on the biological behavior of bladder cancer cells, KRT14 expression was silenced in 5637 and RT4 bladder cancer cells (Fig. 5A and B). The results demonstrated that KRT14 expression was significantly inhibited in the two bladder cancer cell lines. In order to further evaluate the impact of KRT14 on the progress of bladder cancer cells, the invasive ability of cells was assessed using Transwell assays. The results revealed that, compared with that in the control group cells, the silencing of KRT14 in cells markedly attenuated migration (Fig. 5C). Cell clone formation assays further revealed that knocking down KRT14 simultaneously inhibited the colony numbers of 5637 and RT4 cells compared with that of the control group cells (Fig. 5D and E). Consistent with the aforementioned results, CCK-8 assay results demonstrated that the cell viability of KRT14-silenced bladder cancer cells 5637 and RT4 was significantly reduced at 24, 48 and 72 h. Furthermore, the results revealed a time-dependent effect (Fig. 5F and G). These results indicate that inhibition of KRT14 can significantly inhibit the invasion and proliferation of bladder cancer cells.

Figure 5. Silencing of KRT14 inhibits the proliferation of bladder cancer cells. Silencing efficiency of KRT14-interfering lentivirus in (A) 5637 and (B) RT4 cells. (C) Transwell assays were used to assess the migration of 5637 and RT4 cells. Colony forming assays were performed in (D) 5637 and (E) RT4 cells. Cell Counting Kit-8 assay in (F) 5637 and (G) RT4 cells. *P<0.05; **P<0.01. KRT14, keratin 14; si, small interfering RNA; NC, negative control.

Overexpression of KRT14 promotes the progression of bladder cancer

Subsequently, KRT14 was overexpressed in 5637 and RT4 cells, and the findings revealed that KRT14 was markedly upregulated in the two bladder cancer cells (Fig. 6A and B). In order to further explore the impact of KRT14 on the progress of bladder cancer cells, the migratory ability of cells was assessed using Transwell assays. The results revealed that the migratory ability of cells overexpressing KRT14 was notably higher (Fig. 6C). Cell clone formation assays further demonstrated that overexpression of KRT14 increased the number of colonies of 5637 and RT4 cells simultaneously compared with that in the control group (Fig. 6D and E). Consistent with the aforementioned results, CCK-8 assay results revealed that the cell viability of KRT14-overexpressed bladder cancer cells 5637 and RT4 significantly increased at 24, 48 and 72 h, and revealed a time-dependent effect (Fig. 6F and G). These results indicate that KRT14 can significantly promote the invasion and proliferation of bladder cancer cells.

Figure 6. Overexpression of KRT14 promotes the progression of bladder cancer. Overexpression efficiency of KRT14 in (A) 5637 cells and (B) RT4 cells. (C) Transwell assays were used to assess the migration of 5637 and RT4 cells. Colony forming assay was performed in (D) 5637 and (E) RT4 cells. Cell Counting Kit-8 assays were performed in (F) 5637 and (G) RT4 cells. *P<0.05. KRT14, keratin 14; OE, overexpression; NC, negative control.

KRT14 is directly regulated by transcription factor E2F1

By searching the Genecards database, potential upstream transcription factors that may regulate KRT14 were identified. E2F1 was selected for further research as it serves an important role in human malignant tumors (26). The binding site of KRT14 promoter and E2F1 were predicted using the meta site MOTIF. The E2F1 binding sites from −734 to −724 on the KRT14 gene were identified (Fig. 7A). To assess whether the transcriptional regulatory relationship between E2F1 and KRT14 observed in vitro exists in human bladder cancer clinical tissues, co-expression correlation analysis was performed using transcriptomic data from TCGA-BLCA cohort. The results demonstrated that E2F1 and KRT14 exhibited a statistically significant positive co-expression correlation in bladder tumor tissues (Fig. 7B).

Figure 7. KRT14 is directly regulated by transcription factor E2F1. (A) MOTIF was used to predict the binding site of KRT14 promoter and E2F1. (B) Co-expression scatter plot of E2F1 and KRT14 mRNA expression in TCGA-BLCA patient samples. (C) Schematic diagram of the binding effects of KRT14 promoter and E2F1. (D) Luciferase assay for assessing binding effects. (E) 5637 and (F) RT4 cells were transfected with an E2F1 overexpression vector or empty pENTER vector. The mRNA levels of KRT14 were assessed using reverse transcription-quantitative PCR. (G) Protein levels of KRT14 were assessed using western blotting. Chromatin immunoprecipitation-PCR was performed in (H) 5637 and (I) RT4 cells using specific antibodies against E2F1. (J) Cell viability was assessed using Cell Counting Kit-8 assays in 5637 cells transfected with KRT14 siRNA or E2F1 overexpression vector. (K) Cell migration and invasion were measured using CCK-8 assays. *P<0.05; **P<0.01; ***P<0.001. KRT14, keratin 14; E2F1, E2F transcription factor 1; TCGA-BLCA, The Cancer Genome Atlas-Bladder Cancer; si, small interfering RNA; NC, negative control; TPM, transcripts per million; mut, mutant; ns, not significant.

In order to further assess the aforementioned findings, the KRT14 promoter with binding sites was cloned into a pGL3 basic vector (Fig. 7C). A significant increase in luciferase activity was observed in cells transfected with KRT14 promoter and E2F1 overexpression vector, and revealed strong luciferase activity compared with that in cells transfected with E2F1 mutant vectors (Fig. 7D). In order to provide a more comprehensive explanation of the association between E2F1 and KRT14, E2F1 was overexpressed in bladder cancer cells. Findings revealed that E2F1 was significantly upregulated in the two bladder cancer cells (Fig. S1). In 5637 and RT4 cells, overexpression of E2F1 significantly upregulated the mRNA and protein levels of KRT14 (Fig. 7E-G). In addition, ChIP-PCR detection results revealed that the promoter amplification of KRT14 at the E2F1 binding site were significantly enriched using anti-E2F1 antibodies (Fig. 7H and I). Further research analyzed the effect of E2F1 on KRT14 mediated phenotype of bladder cancer cells. The results revealed that KRT14 silencing significantly reduced the survival rate of 5637 cells, while transfection of cells with E2F1 overexpression vectors significantly improved the survival rate. In addition, overexpression of exogenous E2F1 did not effectively restore cell proliferation (Fig. 7J), migration and invasion (Fig. 7K) abilities under KRT14 silencing conditions. Collectively, these phenotypic rescue data functionally support that E2F1 exerts its tumor-promoting effects through transcriptionally upregulating KRT14 as a downstream effector gene.

Drug sensitivity analysis and molecular docking

The aforementioned results demonstrated the cancer promoting effect of KRT14 in bladder cancer. Furthermore, in order to explore the small molecular compounds that could be applied to target KRT14, the drug sensitivity of KRT14 was analyzed. GSCA correlation analysis was performed to screen small molecules whose drug sensitivity status exhibited significant correlation with KRT14 mRNA abundance in bladder cancer. The top three correlated compounds (afatinib, erlotinib and vandetanib) were selected for further in silico docking simulation (Fig. 8A). The structure of potential compounds was determined based on Spearman correlation analysis. To further validate the binding activity between KRT14 and these three compounds, the potential of small molecules were predicted as candidate compounds through computer simulation of molecular docking. Subsequent molecular docking simulations predicted favorable theoretical binding energy and potential intermolecular interactions between each candidate compound and the KRT14 protein structure, providing preliminary computational evidence for their possible binding capacity with KRT14 (Fig. 8B-D).

Figure 8. Drug sensitivity analysis and molecular docking. (A) Gene Set Cancer Analysis correlation analysis was used to screen candidate small molecules with predicted potential binding to KRT14. (B) Structure of afatinib and its molecular docking with KRT14. (C) Structure of erlotinib and its molecular docking with KRT14. (D) Structure of vandetanib and its molecular docking with KRT14. KRT14, keratin 14; FDR, false discovery rate.

Discussion

Bladder cancer is one of the most common types of cancer in the world, with ~610, 000 new cases and ~220,000 mortalities each year (1). Therefore, seeking effective treatment, improving the prognosis and survival rate of patients with bladder cancer has become an urgent clinical issue.

The occurrence and development of tumors is a complex process that is regulated by numerous genes (27). The construction of high-throughput sequencing technology and genomic databases, such as TCGA and GEO, has provided new information about genomics and transcriptomics (28), and bioinformatics and data mining are increasingly being applied in medical research (29). Moreover, fully utilizing public databases is a reliable method for assessing tumor progression and prognosis. The present study first identified differentially expressed genes from the GSE77883 dataset, and subsequently validated KRT14 as a robust prognostic marker and oncogenic driver across independent clinical tissue datasets and multiple cell models.

KRT14 is a major cytoplasmic intermediate filament protein and belongs to the type I keratin family KRT. Cancer cells expressing KRT14 have been observed in several types of invasive tumors, such as bladder and breast cancer (30–35), which are specifically associated with invasive cancer cells and contain invasive components (36–38). Therefore, KRT14 may serve a specific role in the invasion process beyond the basic maintenance of cytoskeleton integrity. Cancer-related studies have reported that subpopulations of cells expressing KRT14 regulate the formation of polyclonal tumor deposits in vitro and in vivo (34,35). KRT14 serves an important role in the occurrence and development of several cancers. It is reported to be upregulated in numerous cancers and is considered to be a biomarker of certain malignant tumors, such as lung adenocarcinoma (39) and breast cancer (40). KRT14 contributes to chemoresistance in bladder cancer not only via its structural roles, but also by directly regulating translational machinery through eukaryotic translation initiation factor 4H, leading to upregulation of the metabolic enzyme acyl-CoA oxidase 2 (41). Previous research reported that cisplatin-resistant ovarian cancer cell lines had elevated KRT14 expression, and KRT14 knockdown reduced cisplatin resistance by lowering low-density lipoprotein receptor-related protein 11 expression. Therefore, KRT14 may serve a crucial role in mediating cisplatin resistance in ovarian cancer (42).

However, little is known about the expression and potential role of KRT14 in bladder cancer. The results of the present study demonstrated that KRT14 is highly expressed in bladder cancer. Kaplan Meier Plotter survival curve analysis revealed that the prognosis of patients with bladder cancer with high expression of KRT14 was worse than those with low KRT14 expression. In addition, ROC curve analysis suggested that KRT14 could be an effective indicator to predict the poor prognosis of bladder cancer. Moreover, single cell sequencing demonstrated that KRT14 is typically at high levels in tumor associated cells. KRT14 was also revealed to be highly expressed in a pathological tissue microarray of bladder cancer. Furthermore, in order to confirm the role of KRT14 in bladder cancer in vitro, the high expression of KRT14 in bladder cancer at the tissue and cell levels was assessed. The results revealed that silencing KRT14 could significantly inhibit cell invasion and proliferation, and overexpression of KRT14 could significantly promote the invasion and proliferation of 5637 and RT4 cells.

E2F1 is a widely studied transcription factor that can transcribe and regulate SH3 domain binding kinase 1 (43), RCC1 domain containing 1 (44) and pyruvate dehydrogenase kinase 1 (45), mediating several malignant behaviors by regulating cell cycle progression, tumor metastasis and treatment response through these target genes. In the research of bladder cancer, E2F1 has been reported to induce myelin and lymphocyte protein 2 transcriptional activation, thus promoting the progress of bladder cancer and inhibiting sunitinib sensitivity (46). In addition, as a pivotal transcription factor, E2F1 promotes the transcription of disks large-associated protein 5 (DLGAP5) and establishes a positive feedback loop between DLGAP5 and E2F1, thus accelerating the progress of bladder cancer (47). This indicates the carcinogenic effect of E2F1 and its potential as a molecular drug target. In the present work, multiple lines of molecular evidence including motif prediction, dual-luciferase reporter assay and ChIP-PCR consistently demonstrated that E2F1 acts as an upstream transcriptional activator of KRT14: E2F1 directly binds the −734 to −724 region of the KRT14 promoter and markedly elevates KRT14 mRNA and protein abundance in bladder cancer cells. Notably, phenotypic rescue experiments demonstrated that the tumor-promoting capacity of E2F1 was highly dependent on intact KRT14 expression.

Several limitations of the present study should be acknowledged. First, the initial differentially expression gene screening relied on GSE77883, a cell-line microarray dataset comparing gemcitabine-resistant T24 cells with parental T24 cells, rather than primary human bladder tumor and adjacent normal tissues. Transcriptional changes identified in this dataset may partially reflect adaptive transcriptomic remodeling induced by long-term gemcitabine exposure, which is specifically associated with chemoresistance rather than general bladder tumorigenesis. Another limitation is the potential confounding effect of altered cell proliferation on Transwell migration and invasion readouts. As KRT14 knockdown robustly suppressed cell proliferation and viability, the reduced number of migrated/invaded cells may have partially arisen from slower cell growth rather than a specific impairment of cell motility. Follow-up experiments such as time-lapse live cell imaging to track single-cell migration speed, or normalization of Transwell counts to parallel viable cell numbers, will be performed in future work to fully decouple cell motility from proliferation. Finally, the paired human bladder tumor and adjacent normal tissue cohort used for RT-qPCR and western blot analysis only contained 3 patient pairs, which provides limited statistical power for drawing definitive tissue-level expression conclusions. This small sample size restricts the generalizability of the tissue western blot and RT-qPCR results. Future validation with an expanded clinical cohort containing at least 10–15 paired tissue samples will be performed to further consolidate the tissue expression pattern of KRT14.

In conclusion, KRT14 is upregulated in bladder cancer and is associated with an unfavorable clinical prognosis. KRT14 depletion suppresses multiple malignant phenotypes of bladder cancer cells, highlighting its candidacy as a promising prognostic biomarker and therapeutic target. Mechanistically, the present study defined a linear oncogenic cascade where upstream transcription factor E2F1 directly binds the KRT14 promoter to transcriptionally activate KRT14 expression. Functional rescue assays further demonstrated that the oncogenic activity of E2F1 relies on downstream KRT14 to drive bladder cancer progression. These findings advance the mechanistic insight into KRT14-mediated bladder tumorigenesis and provide novel clues for developing targeted therapeutic strategies against bladder cancer.

Supplementary Material

Supporting Data

Supporting Data

Acknowledgements

Not applicable.

Funding

The present study was funded by Nantong University Special Research Fund for Clinical Medicine (grant no. 2025LQ024), the Scientific Research Development Fund Project of Kangda College of Nanjing Medical University (grant no. KD2025KYJJ180), and the Nantong Science and Technology Program Guidance Project (grant no. MSZ2024128).

Availability of data and materials

The data generated in the present study may be requested from the corresponding author.

Authors' contributions

CLW and XC designed the study, and drafted and revised the manuscript. XHC, SNL and XSZ performed the experiments and analyzed the data. CLW and XC oversaw the manuscript and gave approval for the final submitted version. CLW and XHC confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

All experimental procedures were performed in accordance with the ethical standards of the Declaration of Helsinki and were approved by the Ethics Committee of Nantong City No. 1 People's Hospital (approval no. 2026-KT102-01). All patients signed a written informed consent form, which included consent to participate in the present study and use of their medical data for research purposes.

Patient consent for publication

All patients signed a written informed consent form, which included consent to for the publication of anonymized findings.

Competing interests

The authors declare that they have no competing interests.

References