Abstract
Background
Pancreatic cancer remains one of the most aggressive malignancies, characterized by late diagnosis, limited treatment options, and a dismal prognosis, with a 5-year survival rate consistently below 13%. The molecular mechanisms driving its progression are incompletely understood, highlighting the need to identify novel regulators of oncogenic pathways. This study focused on keratin 13 (KRT13), a cytoskeletal protein, and investigated its role in pancreatic cancer pathogenesis, particularly its regulatory effect on peroxisome proliferator-activated receptor gamma (PPARG) through the PI3K/AKT signaling axis.
Material/Methods
We integrated in vitro cellular assays with molecular biology approaches and bioinformatic analyses to systematically examine KRT13 expression and function. Pancreatic cancer cell lines were used for functional studies, including proliferation, migration, and pathway modulation experiments. Gene expression levels were assessed via quantitative PCR. Expression of KRT13 was further validated via immunohistochemistry on tissue sections from 85 patients. Public transcriptomic datasets (eg, TCGA) were interrogated to evaluate clinical correlations. Mechanistic insights were gained through pharmacological inhibition and genetic silencing of key pathway components. Further in vitro investigation of drug sensitivity was performed with patient-derived organoids (PDOs).
Results
KRT13 expression was markedly upregulated in pancreatic tumor tissues compared to adjacent normal samples. This result was also confirmed in a set of 85 clinical samples, showing a strong positive correlation with PPARG levels. Clinically, elevated KRT13 was significantly associated with reduced overall survival (log-rank P < 0.001). Importantly, analysis of The Cancer Genome Atlas (TCGA) database revealed that KRT13 expression was significantly associated with tumor TNM stage and other clinical parameters. Functional experiments demonstrated that KRT13 activates the PI3K/AKT signaling cascade, leading to subsequent upregulation of PPARG. This activation promoted pancreatic cancer cell proliferation and migration, effects that were attenuated upon PI3K/AKT pathway inhibition. Drug sensitivity assays using patient-derived organoids (PDOs) demonstrated that KRT13 significantly enhanced the sensitivity of pancreatic cancer to Navitoclax.
Conclusions
Our findings establish KRT13 as a critical contributor to pancreatic cancer progression, via the PI3K/AKT–PPARG regulatory axis. These results underscore the potential of KRT13 as a prognostic biomarker and a candidate therapeutic target, offering new insights into the molecular pathogenesis of this lethal disease.
Keywords: Bioinformatics, Keratin-13, Oncology, Pancreatic Neoplasms, Peroxisome Proliferator-Activated Receptors, Phosphatidylinositol 3-Kinases, Prognosis
Introduction
Pancreatic cancer is a highly aggressive malignancy with a persistently poor prognosis, characterized by rising global incidence and mortality rates [1]. The 5-year survival rate remains below 13%, largely due to late-stage diagnosis and the limited efficacy of conventional therapies such as surgery, radiotherapy, and chemotherapy [1–3]. This underscores an urgent need to identify novel molecular drivers and therapeutic targets.
Keratins (KRTs), a family of intermediate filament proteins, are now recognized as dynamic regulators in cancer beyond their traditionally recognized structural role [4,5]. They modulate key oncogenic processes, including cell proliferation, survival, and metastasis [5]. Keratin 13 (KRT13), in particular, exhibits context-dependent expression and function across various malignancies [5,6]. Its expression is aberrant in multiple cancers, and it participates in signaling pathways such as c-Myc, influencing tumor stemness and progression [5–10]. Emerging evidence also highlights its diagnostic and prognostic potential in cancers such as gastric and oral squamous cell carcinoma [11–13].
Research on KRT13 in pancreatic cancer is still in the early stages, but studies indicate it is upregulated in pancreatic cancer stem cells and is associated with radioresistance [14]. Furthermore, a distinct tumor subpopulation co-expressing KRT13 and FABP5 has been identified, suggesting a role in cellular reprogramming [15]. A key mechanistic avenue involves the PI3K/AKT signaling pathway, through which KRT13 may influence cellular behavior [10]. This pathway is a potent regulator of peroxisome proliferator-activated receptor gamma (PPARG), a central transcription factor in lipid metabolism with complex, context-dependent roles in tumorigenesis [16–18]. The specific regulatory mechanism linking KRT13 to PPARG via PI3K/AKT signaling in pancreatic cancer remains unexplored.
Therefore, this study investigated the functional role of KRT13 in pancreatic cancer pathogenesis, with a focus on elucidating its regulation of PPARG via the PI3K/AKT pathway. By integrating cellular experiments, molecular biology techniques, and bioinformatics analyses, this study seeks to provide a mechanistic foundation for targeting the KRT13-PI3K/AKT–PPARG axis as a novel therapeutic strategy [6,16].
Material and Methods
Data Collection, Processing, and Expression
Differential expression analysis was performed on 179 pancreatic cancer tissues and 4 normal pancreatic tissues from The Cancer Genome Atlas (TCGA) database using R software (version 4.3.3) and the edgeR package. Differential expression analysis was performed using samples from the TCGA dataset. Although the number of normal samples is limited, the use of patient-matched adjacent tissues as controls allows for the specific identification of molecular alterations in tumors relative to their altered adjacent microenvironment, offering unique insights into local tumor progression. Differentially expressed genes (DEGs) were initially identified using a threshold of |log fold change (logFC)| > 3 and P value < 0.05. The results were visualized using the ggplot2 package. To assess statistical robustness, FDR-adjusted P values were also calculated using the Benjamini-Hochberg method. Given the exploratory nature of this analysis and the reduced power from limited normal samples, genes of interest (particularly KRT13) were selected for further investigation based on a combination of large effect size (logFC), nominal significance, compelling biological relevance to the study context, and their known biological relevance to cancer progression pathways as documented in the literature. The key gene KRT13 was identified through this analysis. For unpaired comparisons, data visualization was conducted with ggplot2, and statistical significance was assessed using the Mann-Whitney U test (Wilcoxon rank-sum test). Two datasets (GSE28735 and GSE62452) from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) were utilized to validate differential KRT13 expression between pancreatic cancer and normal tissues. Data were visualized with ggplot2, and the Mann-Whitney U test was applied for statistical analysis.
Patients and Specimens
Eighty-five pancreatic cancer tissue samples and 16 of the 85 pancreatic cancer tissue samples were paired with adjacent normal tissues randomly selected from the Department of Hepatobiliary and Pancreatic Surgery at the First Hospital of Shanxi Medical University. Following fixation and embedding, tissue microarrays were constructed. All procedures adhered to the ethical standards of the institutional and national committees on human experimentation and the Declaration of Helsinki (1964) and its subsequent amendments. Informed consent or equivalent approval was obtained from all participants. The study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University.
Immunohistochemical Staining (IHC)
IHC was performed on pancreatic cancer and normal tissues using a universal kit (ZSGB-BIO, Beijing, China). Briefly, sections were deparaffinized, rehydrated, and blocked for endogenous peroxidase activity. They were sequentially incubated with primary antibodies, reaction enhancers, and enzyme-labeled rabbit immunoglobulin polymers. Color development was achieved with DAB, and nuclei were counterstained with hematoxylin.
RNA Extraction and qRT-PCR
Total RNA was extracted using Trizol™ reagent (Invitrogen, Carlsbad, CA). Reverse transcription was performed with the BeyoRT™ II First Strand cDNA Synthesis Kit and gDNA Eraser (Beyotime, Shanghai, China), and qRT-PCR was conducted on a QuantStudio 3 Real-Time PCR System (Applied Biosystems, Carlsbad, CA) using BeyoFast™ SYBR Green qPCR Mix (Beyotime). GAPDH served as the internal control, and relative expression was quantified using the 2−ΔΔCT method. Primer sequences were as follows:
GAPDH-F: 5′-GAACGGGAAGCTCACTGG-3′,
GAPDH-R: 5′-GCCTGCTTCACCACCTTCT-3′,
KRT13-F: 5′-CCCCAGGCATTGACCTGAC-3′,
KRT13-R: 5′-GTGTTGGTAGACACCTCCTTG-3′.
PPARG-F: 5′-GGGATCAGCTCCGTGGATCT-3′,
PPARG-R: 5′-TGCACTTTGGTACTCTTGAAGTT-3′.
PI3K-F: 5′-TGCCTGCGACAGATGAGTGATG-3′,
PI3K-R: 5′-GCCCTATCCTCCGATTACCAAGTG-3′.
AKT-F: 5′-ACTGTCATCGAACGCACCTTCC-3′,
AKT-R: 5′-TCTCCTCCTCCTCCTGCTTCTTG-3′.
Transfection
Small interfering RNA (siRNA) was introduced into cells using liposome-mediated transfection with Lipofectamine 2000. Specifically, 1 day prior to transfection, cells in the logarithmic growth phase were harvested, counted, and seeded into 6-well plates at a density of 5 × 104 to 1.5 × 105 cells per well in complete medium without antibiotics. After 24 hours of culture, transfection was performed when cells reached approximately 70% confluence. The siRNA powders were reconstituted with RNase-free water before use. KRT13-overexpressing lentiviruses (Haixing Biotechnology, China) were transfected into pancreatic cancer cells according to the manufacturer’s instructions. The lentiviral expression plasmid used was pLVX-Puro. Lentiviral transduction was performed without the use of polybrene (hexadimethrine bromide). Stable clones were selected with 5 μg/mL puromycin 48 hours after lentiviral transduction. Transfection efficiency was validated by qRT-PCR. The SiRNA sequences were:
KRT13-siRNA1: sense 5′-GGUGUCUCUACCUGUUCAATT-3′,
antisense 5′-UUGAACAGGUAGAGACACCTT-3′;
KRT13-siRNA2: sense 5′-CGGACGACUUCAGGCUCAATT-3′,
antisense 5′-UUGAGCCUGAAGUCGUCCGTT-3′;
KRT13-siRNA3: sense 5′-CCAUGAUUCAGACCAGCAATT-3′,
antisense 5′-UUGCUGGUCUGAAUCAUGGTT-3′.
Cell Proliferation Assay
For the CCK-8 (Boster Biotechnology, China) assay, 48 hours after transfection, cells were digested, resuspended, counted, and seeded into 96-well plates at a density of 3 × 103 cells per well. After complete cell attachment, the experiment was performed with a negative control (NC) group and 3 KRT13 siRNA transfection groups (si-KRT13#1, #2, and #3), each containing 5 technical replicates. The cells were continuously cultured in an incubator for 5 days, and measurements were taken at 24, 48, 72, 96, and 120 hours after seeding. After adding 20 μL of CCK-8 solution to each well, cells were incubated for 1.5 hours, and absorbance was measured at 450 nm. For the colony formation assay, 1 × 103 cells were seeded per well in 12-well plates and cultured for 2 weeks. After completion of the culture period, the culture medium was removed, and cells were gently washed 2 to 3 times with PBS, followed by fixation with 4% paraformaldehyde at room temperature for 20 minutes. After fixation, cells were washed with PBS and stained with 0.5% crystal violet solution for 20 minutes. Subsequently, the cells were thoroughly rinsed with PBS and allowed to air-dry at room temperature. Finally, photographs were taken of the cell colonies formed in the wells and statistically analyzed using Image J software.
Cell Migration and Invasion Assays
Cell migration was assessed using wound healing and Transwell assays. For wound healing, pancreatic cancer cells were grown to confluence in 6-well plates and a scratch was made with a 20-μL pipette tip. Images were acquired at fixed reference positions under an inverted microscope at 0 and 24 hours after scratching. For Transwell migration and invasion assays, cells were seeded in the upper chamber (with Matrigel-coated inserts for invasion; Corning, NY). The upper chamber contained serum-free basal medium, while the lower chamber was filled with complete medium containing 20% fetal bovine serum (FBS) as a chemoattractant. After 48 hours, cells on the lower membrane were fixed with 4% paraformaldehyde, stained with crystal violet, and counted under an inverted microscope.
Protein Expression and Survival Analysis
KRT13 protein expression levels were obtained from the Human Protein Atlas (HPA; https://www.proteinatlas.org/) and UALCAN (https://ualcan.path.uab.edu/index.html) databases. The diagnostic value of KRT13 expression was evaluated using ROC analysis (pROC package) and time-dependent ROC analysis (timeROC package), with results visualized using ggplot2. Survival analysis was performed with the survival package, and Kaplan-Meier curves were generated with log-rank tests. Univariate and multivariate Cox regression analyses were conducted to assess the prognostic significance of KRT13, and results were visualized using forest plots (ggplot2). A nomogram was constructed with the rms package, and calibration analysis was performed.
Clinical Correlation Analysis
The relationship between KRT13 expression and pathological stage or histological grade was analyzed using TCGA data. Survival associations were evaluated with Kaplan-Meier curves and log-rank tests. Clinical parameters were correlated with KRT13 expression to assess prognostic relevance.
Mutation and Methylation Analysis
KRT13 mutation profiles were analyzed using cBioPortal. Methylation analysis was performed with MethSurv, MEXPRESS, and OncoDB databases.
Enrichment Analysis
Differential expression analysis of protein-coding genes was conducted on TCGA data using the DESeq2 package (|logFC| > 0.5, P value < 0.05). Correlation analysis was performed with the psych package (Spearman > 0.5, P value < 0.05). GO and KEGG pathway enrichment analyses were visualized with ggplot2. FDR-adjusted P values for enrichment terms are reported.
Correlation Analysis
Genes associated with the PI3K/AKT signaling pathway and lipid metabolism were obtained from GeneCards. Venn diagrams were used to identify overlapping genes with KRT13-correlated genes (Spearman > 0.5), revealing PPARG. The correlation between KRT13 and PPARG was assessed using Spearman’s method and visualized with ggplot2. Co-expression heatmaps were generated to illustrate their relationship.
PPARG Analysis
Differential expression analysis was performed using paired samples from the TCGA dataset (n = 179 tumor tissues, n = 4 adjacent non-tumor tissues). Although the number of normal samples was limited, the use of patient-matched adjacent tissues as controls allowed for the specific identification of molecular alterations in tumors relative to their altered adjacent microenvironment, offering unique insights into local tumor progression, with visualization via ggplot2 and statistical testing using the Mann-Whitney U test. A supplementary analysis was subsequently performed using data from the Genotype-Tissue Expression (GTEx) project. Diagnostic value was evaluated with ROC and time-dependent ROC analyses. Survival analysis included Kaplan-Meier curves, Cox regression, and nomogram construction.
Rescue Experiments
The AKT pathway inhibitor (KRX-0401) was applied to KRT13-overexpressing stable cell lines. Rescue experiments included CCK-8 proliferation assays, colony formation assays, and Transwell migration and invasion assays.
Drug Sensitivity Analysis
Pan-cancer drug sensitivity analysis for KRT13 was performed using the GDSC database. This model established the relationship between gene expression and drug response. A preliminary screening identified compounds exhibiting a positive correlation between KRT13 expression levels and drug sensitivity across multiple cancer types. Significant drugs were further analyzed in the pancreatic cancer TCGA cohort using R packages. To apply this model to TCGA tumor samples and our patient-derived organoids, we first normalized their RNA-seq gene expression data using the same pipeline as applied to the GDSC expression data. The trained model was then directly used to predict the drug response (IC50) for each TCGA sample and organoid based on their normalized expression profiles. This mapping approach ensured a consistent analytical framework across datasets.
Statistical Analysis
All statistical analyses and graphical representations of the experimental data in this study were performed using GraphPad Prism software (version 10.0). Data that followed a normal distribution are presented as the mean ± standard deviation (SD), and comparisons between 2 independent groups were conducted using the 2-sample t test. For data that did not meet the assumption of normality, non-parametric methods were employed; specifically, the Mann-Whitney U test (also referred to as the Wilcoxon rank-sum test) was used for comparisons between 2 independent groups. All experiments were performed with 3 independent biological replicates. A P value of less than 0.05 was considered statistically significant.
Results
KRT13 Is Upregulated in Pancreatic Cancer
To characterize KRT13 expression in pancreatic cancer, a multi-level analysis was performed. Initial screening of the TCGA PAAD cohort identified KRT13 among the significantly upregulated genes (Figure 1A). Among them, KRT13 exhibited the most dramatic fold change (logFC = 5.2, nominal P = 0.027). Although KRT13 did not remain statistically significant after FDR correction (adj. P = 0.3), it was selected for subsequent analysis due to its extreme fold change and pivotal biological relevance, as illustrated below. The P values and FDR-adjusted P values for genes selected using a logFC threshold of 5.2 to 5.4 are shown below. The P values for the genes KRT13, DMBT1, LRRC31, MUCL3, and HOXC10 were 0.0267, 0.0069, 0.0045, 0.0162, and 0.0173, respectively, with corresponding adjusted P values of 0.308, 0.1601, 0.1214, 0.2463, and 0.2551. Consistently, KRT13 mRNA levels were markedly elevated in tumor versus normal tissues in the TCGA dataset (Figure 1B). This upregulation was validated in 2 independent GEO cohorts (GSE28735 and GSE62452; Figure 1C, 1D). At the cellular level, qRT-PCR analysis confirmed higher KRT13 expression in multiple pancreatic cancer cell lines, reaching statistical significance in MIA-PaCa-2 and BxPC-3 cells (Figure 1E). These convergent findings demonstrate that KRT13 is consistently overexpressed in pancreatic cancer, implicating it as a potential oncogene.
Figure 1. Upregulation of KRT13 expression in pancreatic cancer.
(A) Identification of KRT13 as a key gene in pancreatic cancer using the TCGA database. (B) Analysis of KRT13 mRNA expression in pancreatic cancer tissues based on the TCGA database. (C) Evaluation of KRT13 mRNA expression in pancreatic cancer tissues using the GEO dataset (GSE28735). (D) Assessment of KRT13 mRNA expression in pancreatic cancer tissues using the GEO dataset (GSE62452). (E) Analysis of KRT13 mRNA expression in pancreatic cancer cell lines. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.
KRT13 Promotes Pancreatic Cancer Cell Proliferation, Migration, and Invasion In Vitro
To assess the functional role of KRT13 in pancreatic cancer, its expression was knocked down via siRNA in BxPC-3 and PANC-1 cell lines. Efficient silencing was confirmed by qRT-PCR (Figure 2A, 2D). Functional assays demonstrated that KRT13 depletion significantly impaired cellular proliferation (Figure 2B, 2E), as well as migration and invasion capabilities in Transwell assays (Figure 2C, 2F). This inhibitory effect on cell motility was further corroborated by wound healing assays (Figure 2G) (* P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001).
Figure 2. KRT13 promotes pancreatic cancer cell proliferation, migration, and invasion in vitro.
(A) qRT-PCR showing the knockdown efficiency of KRT13 in BxPC-3 cells. (B) CCK-8 assay showing the proliferation curve of BxPC-3 cells after KRT13 knockdown. (C) Transwell migration and invasion assays showing the migratory and invasive capabilities of BxPC-3 cells at 48 hours after KRT13 knockdown. (D) qRT-PCR showing the knockdown efficiency of KRT13 in PANC-1 cells. (E) CCK-8 assay showing the proliferation curve of PANC-1 cells after KRT13 knockdown. (F) Transwell migration and invasion assays showing the migratory and invasive capabilities of PANC-1 cells at 48 hours after KRT13 knockdown. (G) Wound healing assay showing the migratory capabilities of BxPC-3 and PANC-1 cells at 0 and 24 hours. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.
KRT13 Protein Expression is Upregulated in Pancreatic Cancer
Pan-cancer analysis via the UALCAN database revealed significantly elevated KRT13 protein expression in multiple cancer types, including pancreatic adenocarcinoma, compared to normal tissues (Figure 3A). This finding was corroborated by data from the Human Protein Atlas (HPA), which showed higher KRT13 protein levels and more intense immunohistochemical staining in tumor samples (Figure 3B–3D). Further validation using an independent clinical cohort from the First Hospital of Shanxi Medical University confirmed the upregulation of KRT13 protein in pancreatic cancer tissues (Figure 3E). Quantitative assessment using the H-score system demonstrated that strong positive staining was predominant in tumor samples, whereas normal pancreatic tissues were mostly negative or only weakly positive (Figure 3F).
Figure 3. Upregulation of KRT13 protein expression in pancreatic cancer.
(A) Analysis of KRT13 protein expression across various cancer types using the UALCAN database. (B) Validation of KRT13 protein expression in normal pancreatic tissue sections and pathological sections using the HPA database. (C) Validation of KRT13 protein expression in normal pancreatic tissues using the HPA database. (D) Validation of KRT13 protein expression in pancreatic cancer tissues using the HPA database. (E) Immunohistochemical (IHC) analysis revealed significantly elevated expression of KRT13 in cancerous tissues. (F) Comparative positive staining grades of KRT13 in normal pancreatic tissues and pancreatic cancer tissues based on IHC scoring.
Survival and Prognostic Analysis of KRT13
Analysis of the TCGA pancreatic cancer cohort established the clinical significance of KRT13. ROC analysis revealed its significant diagnostic value for pancreatic cancer (AUC = 0.819; Figure 4A), with time-dependent ROC curves confirming stable performance for 1-, 3-, and 5-year survival prediction (AUCs of 0.686, 0.829, and 0.787, respectively; Figure 4B). Survival analysis demonstrated that high KRT13 expression was significantly associated with poorer overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (Figure 4C). Univariate Cox regression analysis identified multiple clinicopathological factors associated with survival (Figure 4D). The following covariates were included in the multivariate Cox proportional hazards model analysis, all of which were identified as independent prognostic factors: Pathologic T stage (T1/T2 vs T3/T4) with a hazard ratio (HR) of 2.053 (95% Confidence Interval [95% CI]: 1.089–3.871); Pathologic N stage (N0 vs N1) with an HR of 2.114 (95% CI: 1.259–3.549); Pathologic stage (Stage I vs Stage II/III/IV) with an HR of 2.326 (95% CI: 1.067–5.069); Residual tumor status (R0 vs R1/R2) with an HR of 1.630 (95% CI: 1.051–2.528); Histologic grade (G1 vs G2/G3/G4) with an HR of 2.191 (95% CI: 1.155–4.159); Radiation therapy (No vs Yes) with an HR of 0.501 (95% CI: 0.294–0.854); and KRT13 expression level (Low vs High) with an HR of 2.297 (95% CI: 1.506–3.503). Multivariate Cox regression analysis confirmed KRT13 as an independent prognostic risk factor (Figure 4E). A prognostic nomogram integrating KRT13 and other independent factors was constructed for individualized prediction (Figure 4F), with calibration curves indicating high predictive accuracy (Figure 4G).
Figure 4. Survival and prognostic analysis of KRT13.
(A) Receiver operating characteristic (ROC) analysis of the relationship between KRT13 expression and diagnosis of pancreatic cancer patients from the TCGA database. (B) Time-dependent ROC analysis of the relationship between KRT13 expression and diagnosis of pancreatic cancer patients from the TCGA database. (C) Kaplan-Meier analysis of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) for pancreatic cancer patients with high or low KRT13 expression from the TCGA database. (D) Univariate Cox analysis of risk factors affecting pancreatic cancer from the TCGA database. (E) Multivariate Cox analysis of independent risk factors affecting pancreatic cancer from the TCGA database. (F) Prognostic nomogram analysis of independent prognostic factors for pancreatic cancer from the TCGA database. (G) Calibration plot for the prognostic nomogram prediction. (H) Analysis of KRT13 mRNA expression by tumor N, M, and T stages in the TCGA PAAD database. (I) Analysis of KRT13 mRNA expression by tumor histological grade (G grade) and residual tumor status (R classification) in the TCGA PAAD database. (J) Analysis of KRT13 mRNA expression by OS, DSS, and PFI events in the TCGA PAAD database.
Relationship Between KRT13 and Pathological Features
Analysis of the TCGA PAAD cohort revealed that elevated KRT13 mRNA expression was significantly associated with key clinicopathological features of pancreatic cancer progression. Specifically, higher KRT13 levels correlated with advanced N stage (lymph node metastasis), M stage (distant metastasis), and T stage (primary tumor invasion depth) (Figure 4H). Its expression was also markedly increased in poorly differentiated tumors (G3–G4 vs G1–G2) and in cases with residual tumor (R1 vs R0) after surgery (Figure 4I). Furthermore, KRT13 expression showed significant associations with worse overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (Figure 4J). These data collectively identify KRT13 as a potential biomarker for malignant progression in pancreatic cancer.
Impact of KRT13 on Survival
Analysis of the TCGA PAAD cohort revealed that elevated KRT13 mRNA expression was significantly associated with reduced overall survival in pancreatic cancer patients. This poor prognostic correlation persisted across key clinicopathological subgroups. Specifically, high KRT13 expression was correlated with worse survival in patients with advanced pathological stages (II–IV; HR = 2.13,95% CI: 1.38–3.29, P < 0.001; Figure 5A), higher T stage (T2–T4; HR = 2.40, 95% CI: 1.57–3.69, P < 0.001; Figure 5B), and higher histological grade (G2–G4; HR = 1.62, 95% CI: 1.04–2.51, P = 0.032; Figure 5C). Notably, its prognostic value remained significant even in patients without distant metastasis (M0; HR = 2.56, 95% CI: 1.32–4.96, P = 0.006; Figure 5D), with lymph node involvement (N1; HR = 1.77, 95% CI: 1.11–2.81, P = 0.017; Figure 5E), and most prominently, in those with residual tumor (R1/R2; HR = 3.62, 95%CI: 1.66–7.89, P = 0.001; Figure 5F). Furthermore, subgroup analyses confirmed that high KRT13 expression predicted inferior survival in patients with specific clinical histories, including alcohol consumption, absence of chronic pancreatitis, no prior radiotherapy, age ≤ 65 years, no family cancer history, female sex, and a history of diabetes (log-rank P < 0.05 for all; Figure 5G). These consistent findings across diverse subgroups identify KRT13 as a robust, independent biomarker for poor prognosis in pancreatic cancer.
Figure 5. Impact of KRT13 on survival.
(A) Survival analysis based on high and low KRT13 mRNA expression in patients with pathological stage 2, 3, and 4 tumors from the TCGA PAAD database. (B) Survival analysis based on high and low KRT13 mRNA expression in patients with T2, T3, and T4 stage tumors from the TCGA PAAD database. (C) Survival analysis based on high and low KRT13 mRNA expression in patients with G2, G3, and G4 grade tumors from the TCGA PAAD database. (D) Survival analysis based on high and low KRT13 mRNA expression in patients with M0 stage tumors from the TCGA PAAD database. (E) Survival analysis based on high and low KRT13 mRNA expression in patients with N1 stage tumors from the TCGA PAAD database. (F) Survival analysis based on high and low KRT13 mRNA expression in patients with R1 or R2 residual tumor status from the TCGA PAAD database. (G) Survival analysis based on high and low KRT13 mRNA expression in patients with alcohol consumption, absence of chronic pancreatitis, no prior radiotherapy, age ≤ 65 years, no family cancer history, female sex, and a history of diabetes from the TCGA PAAD database.
Mutation and Methylation Analysis of KRT13
Analysis of the cBioPortal database indicated that gene amplification is the predominant genomic alteration of KRT13 in pancreatic cancer (Figure 6A). Patients harboring KRT13 alterations exhibited significantly worse overall survival compared to those without such alterations (Figure 6B). Furthermore, methylation analysis via the MethSurv database detailed CpG site-specific methylation patterns of KRT13 across TCGA samples (Figure 6C). Interrogation of the OncoDB database revealed differential methylation at specific probes in tumor versus normal tissues; notably, the probe cg07381973 showed significantly higher methylation in cancer samples (mean beta: 0.87 vs 0.80 in normals, P = 3.4e-02), while other sites did not reach statistical significance (Figure 6D). Multivariate Cox regression analysis confirmed KRT13 as an independent prognostic risk factor in pancreatic cancer.
Figure 6. Mutation and methylation analysis, GO and KEGG analysis of KRT13.
(A) Analysis of KRT13 mutations across various cancers using the cBioPortal database. (B) Survival analysis based on the presence or absence of KRT13 mutations in pancreatic cancer from the cBioPortal database. (C) Analysis of KRT13 methylation levels in pancreatic cancer using the MethSurv database. (D) Analysis of KRT13 methylation sites in pancreatic cancer and normal tissues using the OncoDB database. (E) GO-BP, CC, and MF enrichment analysis of differentially expressed genes related to KRT13. (F) KEGG pathway enrichment analysis of genes correlated with KRT13. (G) Venn diagram showing the overlap among KRT13-correlated genes, lipid metabolism-related genes, and PI3K/AKT signaling pathway-related genes. (H) Scatter plot showing the correlation between KRT13 and PPARG. (I) Heatmap showing the correlated expression between KRT13 and PPARG.
GO and KEGG Analysis of KRT13 in Pancreatic Cancer
To elucidate the potential functions of KRT13 in pancreatic cancer, functional enrichment analysis of its co-expressed genes was conducted. GO terms were significantly enriched for processes such as cell proliferation, migration, and lipid transport; cellular components such as the keratin cytoskeleton; and molecular functions like fatty acid binding (Figure 6E). KEGG analysis further implicated these genes in cancer-related pathways, notably the PI3K/AKT signaling pathway (Figure 6F). The P values and FDR-corrected P values for the above partial enrichment results were as follows: the P values for lipid transport, fatty acid transport, long-chain fatty acid transport, collagen-containing extracellular matrix, membrane microdomain, membrane raft, and PI3K-AKT signaling pathway were 0.0027, 0.0038, 0.0009, 0.000006, 0.0046, 0.0045, and 0.0004, respectively, with corresponding FDR-corrected P values of 0.0327, 0.0437, 0.0009, 0.0003, 0.0504, 0.0504, and 0.0105. Venn analysis revealed a substantial overlap among KRT13-correlated genes, lipid metabolism-associated genes, and PI3K/AKT pathway-related genes (Figure 6G). Spearman correlation and heatmap analyses confirmed a significant positive correlation between KRT13 and PPARG expression (R = 0.514, P < 0.001; Figure 6H, 6I), suggesting a role for KRT13 in pancreatic cancer progression via regulation of lipid metabolism and the PI3K/AKT pathway.
PPARG is Upregulated in Pancreatic Cancer
By analyzing the TCGA database, we systematically evaluated the expression pattern and clinical significance of PPARG in pancreatic cancer. The results showed that there is a consistent trend of increased PPARG expression in tumors (Figure 7A). The analysis using an expanded validation set that included GTEx normal tissues confirmed that PPARG expression is significantly higher in tumors (Figure 7B). Further ROC curve analysis revealed that PPARG expression exhibited excellent diagnostic performance in distinguishing pancreatic cancer patients from normal controls, with an area under the curve (AUC) of 0.974 (95% confidence interval: 0.957–0.991) (Figure 7C). Time-dependent ROC analysis indicated that PPARG had predictive ability for 1-year, 2-year, and 3-year survival rates, with corresponding AUC values of 0.584, 0.626, and 0.660, respectively (Figure 7D). Survival analysis showed that high PPARG expression was closely associated with poor patient prognosis. Kaplan-Meier curve analysis demonstrated that patients with high PPARG expression had significantly shorter overall survival (OS) than those with low expression (hazard ratio [HR] = 1.70, 95% confidence interval 1.12–2.57, P = 0.012) (Figure 7E). Analysis of progression-free interval (PFI) and disease-specific survival (DSS) also indicated poorer prognosis in patients with high PPARG expression (Figure 7F, 7G). Multivariate Cox regression analysis confirmed PPARG as an independent prognostic factor for pancreatic cancer. A prognostic nomogram was constructed based on PPARG expression and other clinicopathological factors to predict 1-year, 2-year, and 3-year survival probabilities (Figure 7H).
Figure 7. Upregulation of PPARG expression in pancreatic cancer.
(A) Analysis of PPARG mRNA expression in pancreatic cancer tissues using the combined TCGA databases. (B) The expression of PPARG mRNA in pancreatic cancer tissues was analyzed using the TCGA and GTEx databases. (C) Receiver operating characteristic (ROC) analysis of the relationship between PPARG expression and diagnosis of pancreatic cancer patients from the TCGA database. (D) Time-dependent ROC analysis of the relationship between PPARG expression and diagnosis of pancreatic cancer patients from the TCGA database. (E) Kaplan-Meier analysis of overall survival (OS) for pancreatic cancer patients with high or low PPARG expression from the TCGA database. (F) Kaplan-Meier analysis of disease-specific survival (DSS) for pancreatic cancer patients with high or low PPARG expression from the TCGA database. (G) Kaplan-Meier analysis of progression-free interval (PFI) for pancreatic cancer patients with high or low PPARG expression from the TCGA database. (H) Prognostic nomogram analysis of independent prognostic factors for pancreatic cancer from the TCGA database.
Rescue Experiments
To elucidate the functional role of KRT13 in pancreatic cancer, a stable overexpression model was generated in PANC-1 cells, with transfection efficiency confirmed by qRT-PCR (Figure 8A). KRT13 overexpression led to a marked upregulation in the mRNA levels of PPARG, PI3K, and AKT (Figure 8B), suggesting its potential role in activating the PI3K/AKT signaling pathway. Functional assays demonstrated that KRT13 overexpression significantly enhanced the malignant phenotype of PANC-1 cells, including increased proliferative capacity (CCK-8 assay, Figure 8C), clonogenicity (colony formation assay, Figure 8D). This pro-tumorigenic effect was corroborated in a patient-derived pancreatic cancer organoid model, where KRT13 overexpression potentiated organoid formation (Figure 8E), and migratory and invasive abilities (wound Transwell assays, Figure 8F, healing assays, Figure 8G). Rescue experiments using an AKT inhibitor were performed to investigate the dependence of these phenotypes on the PI3K/AKT pathway. The enhanced proliferation (Figure 8C), clonogenicity (Figure 8D), migration, and invasion (Figure 8F) induced by KRT13 overexpression were all significantly attenuated upon AKT inhibition (* P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001).
Figure 8. KRT13 regulates PPARG via the PI3K/AKT signaling pathway to promote pancreatic cancer cell proliferation, migration, and invasion in vitro.
(A) qRT-PCR showing the overexpression efficiency of KRT13 in PANC-1 cells. (B) qRT-PCR showing PPARG, PI3K, and AKT expression after KRT13 overexpression in PANC-1 cells. (C) CCK-8 assay showing the proliferation curve of PANC-1 cells after KRT13 overexpression followed by treatment with AKT inhibitor. (D) Colony formation assay assessing the proliferative capacity of PANC-1 cells after KRT13 overexpression followed by treatment with AKT inhibitor. (E) Representative images of pancreatic cancer organoids overexpressing KRT13. (F) Transwell migration and invasion assay showing the migratory capabilities of PANC-1 cells at 48 hours after KRT13 overexpression followed by treatment with AKT inhibitor. (G) Wound healing assay showing the migratory capabilities of PANC-1 cells at 0 and 24 hours after KRT13 overexpression. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.
Drug Sensitivity Analysis
Pan-cancer drug sensitivity analysis linked KRT13 expression to therapeutic response (Figure 9A). In TCGA PAAD cohorts, high KRT13 expression was correlated with increased sensitivity to Navitoclax, I-BET-762, PI-103, AR-42, QL-X-138, and JW-7-24-1 (Figure 9B–9G). This association was functionally validated in vitro; KRT13-overexpressing pancreatic cancer organoids exhibited markedly enhanced sensitivity to Navitoclax (IC50 = 0.7743 μM vs 15.23 μM in controls, Figure 9B). Conversely, low KRT13 expression was associated with greater sensitivity to 17-AAG, Lapatinib, and WZ-1-84 (Figure 9H–9J). These findings nominate KRT13 as a potential biomarker for predicting drug response in pancreatic cancer.
Figure 9. Drug sensitivity analysis of KRT13.
(A) Pan-cancer drug sensitivity analysis for KRT13 using the GDSC database. (B) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to Navitoclax in the TCGA PAAD database and this bioinformatic finding was subsequently validated through in vitro drug sensitivity assays using a KRT13-overexpressing pancreatic cancer organoid model. (C) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to I-BET-762 in the TCGA PAAD database. (D) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to PI-103 in the TCGA PAAD database. (E) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to AR-42 in the TCGA PAAD database. (F) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to QL-X-138 in the TCGA PAAD database. (G) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to JW-7-24-1 in the TCGA PAAD database. (H) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to Lapatinib in the TCGA PAAD database. (I) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to WZ-1-84 in the TCGA PAAD database. (J) Analysis of the sensitivity of high and low KRT13 mRNA expression groups to 17-AAG in the TCGA PAAD database.
Discussion
Pancreatic cancer remains a major global health challenge due to its aggressive nature, frequently late diagnosis, and limited treatment efficacy, underscoring the urgent need for reliable biomarkers [19–27]. This study investigated the role of keratin 13 (KRT13) in this context. We found consistent and significant upregulation of KRT13 at mRNA and protein levels in pancreatic cancer tissues compared to normal controls, a finding validated across multiple independent databases and in pancreatic cancer cell lines [5,28]. Functionally, siRNA-mediated knockdown of KRT13 robustly inhibited the proliferation, migration, and invasion of pancreatic cancer cells, confirming its direct pro-tumorigenic role.
Clinically, elevated KRT13 expression is correlated strongly with advanced pathological features, including lymph node metastasis (N stage), distant metastasis (M stage), and poorer tumor differentiation [29]. Critically, high KRT13 expression is an independent prognostic factor, predicting significantly worse overall, disease-specific, and progression-free survival [29]. Mechanistically, enrichment analysis has linked KRT13-co-expressed genes to the oncogenic PI3K/AKT signaling pathway [30]. Furthermore, the significant positive correlation between KRT13 and PPARG (R = 0.514) suggests its potential involvement in lipid metabolic reprogramming. Genomic and epigenetic analyses revealed that KRT13 amplification is a primary genetic alteration in pancreatic cancer, and differential methylation at specific CpG sites (eg, cg07381973) may further contribute to its dysregulation in tumors [31].
This study used an exploratory, hypothesis-generating approach for the initial genome-wide expression screening, due to the inherent challenge of limited normal sample size. While we report FDR-adjusted P values for transparency, our candidate gene selection (KRT13) prioritized large-magnitude changes and biological context over strict statistical correction alone. This approach is justified by the subsequent validation of KRT13’s unique and biologically informative expression pattern across different “normal” tissue definitions, which is a core finding of this work. Future studies with larger cohorts are needed to confirm these findings at a more stringent statistical level. Our integrative bioinformatic and functional data suggest a potential link between KRT13 overexpression and activation of the PI3K/AKT signaling pathway and lipid metabolism in pancreatic cancer rather than definitive regulatory mechanisms. While our computational pipeline identified several candidate drugs, the experimental validation in patient-derived organoids was performed for Navitoclax as a proof-of-concept. Therefore, the correlation observed between the predicted sensitivity and the actual organoid response provides preliminary evidence supporting the pipeline’s performance for Navitoclax and highlights the potential feasibility of this approach. However, these findings should be considered exploratory, and further validation in independent cohorts and in vivo models is necessary before drawing definitive conclusions. Conclusions regarding other predicted drugs are presented as preliminary computational findings that require future experimental confirmation. The potential of KRT13 as a predictive biomarker for drug sensitivity also warrants prospective clinical validation.
A key limitation of this study lies in its reliance on in vitro experimental systems. All findings, including the functional characterization of KRT13 and the assessment of Navitoclax sensitivity, were derived from cell-based assays. While these models provide valuable mechanistic insights, they cannot fully recapitulate the complex tumor microenvironment. Future studies utilizing patient-derived xenografts (PDXs) or genetically engineered mouse models (GEMMs) will be essential to validate the role of KRT13 in tumor progression and to confirm the therapeutic potential of Navitoclax in a physiologically relevant context. It should be acknowledged that the absence of such in vivo confirmation limits the strength of our conclusions and underscores the need for cautious interpretation of the presented data.
Collectively, our comprehensive exploratory analysis indicates that KRT13 likely acts as an oncogene in pancreatic cancer, with its pro-tumorigenic effects possibly linked to the PI3K/AKT–PPARG axis. We further preliminarily assessed its diagnostic potential, relevance for therapeutic targeting, and association with drug sensitivity, which all require future clinical confirmation. These findings position KRT13 as a promising diagnostic and therapeutic biomarker and a strong independent prognostic factor, highlighting its potential value in the evolving field of personalized oncology.
Conclusions
In this study, we preliminarily determined that KRT13 is significantly overexpressed in pancreatic cancer. Its upregulation may promote tumor cell proliferation, migration, and invasion, potentially via activation of the PI3K/AKT–PPARG axis. High KRT13 expression was strongly correlated with poor patient prognosis and demonstrated considerable diagnostic value. Multivariate Cox regression analysis established KRT13 as an independent risk factor. Furthermore, its expression level may predict sensitivity to certain targeted agents. In summary, KRT13 may function as a pivotal oncogene that facilitates pancreatic cancer progression, likely via the PI3K/AKT–PPARG axis, highlighting its potential as both a diagnostic biomarker and a therapeutic target.
Abbreviations
- PAAD
pancreatic adenocarcinoma
- KRT13
Keratin 13
- PPARG
peroxisome proliferator-activated receptor gamma
- TCGA
The Cancer Genome Atlas
- GEO
Gene Expression Omnibus
- KEGG
Kyoto Encyclopedia of Genes and Genomes
- IHC
immunohistochemistry
Footnotes
Financial support: This work was supported by the Shanxi Provincial Graduate Education Innovation Program of the Shanxi Provincial Education Department (Grant No. 2024KY374)
Conflict of interest: None declared
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher
Patient Consent: Informed consent or its equivalent was obtained from all patients included in the study.
Ethics Approval and Consent to Participate: All procedures performed in this study were in accordance with the ethical standards of the responsible institutional and national committees on human experimentation and with the Declaration of Helsinki (1964) and its later amendments. Informed consent or its equivalent was obtained from all patients included in the study. This study was approved by the Ethics Committee of Shanxi Medical University/The First Affiliated Hospital of Shanxi Medical University. All institutional and national guidelines for the care and use of laboratory materials were followed.
Declaration of Figures’ Authenticity: All figures submitted have been created by the authors who confirm that the images are original with no duplication and have not been previously published in whole or in part.
Availability of Data and Materials
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.