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.

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/).

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.).

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.

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.

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.).

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′.

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.).

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).

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).

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.

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.

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.

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).

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).

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.

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).

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.

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.

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.

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).

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.

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).