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. 2025 Dec 8;26:208. doi: 10.1186/s40360-025-01020-z

Repurposing DPP-4 inhibitors as anticancer agents in KRAS-mutated pancreatic ductal adenocarcinoma

Prasanna Srinivasan Ramalingam 1,#, Md Sadique Hussain 2,#, Gayathri Chellasamy 3, Sujatha Elangovan 1, Divya Sharma 1, Premkumar T 4, Rudra Awdhesh Kumar Mishra 5, Gothandam Kodiveri Muthukaliannan 5, Tajamul Hussain 6, Salman Alrokayan 7, Purushothaman Balakrishnan 8, Janaki Ramaiah Mekala 9,✉, Sivakumar Arumugam 1,✉
PMCID: PMC12687489  PMID: 41361888

Abstract

Background

Cancer has become a global health threat with increasing incidence & mortality rates, and despite significant advancements in diagnostics and therapeutics, they limited for aggressive cancers like pancreatic ductal adenocarcinoma (PDAC). To note, KRAS-mutated PDAC is very frequent and limited by potent therapeutics due to their aggressiveness. Drug repurposing has become a potent strategy due to cost-effective, established safety & toxicity profiles. Sitagliptin and Linagliptin are Dipeptidyl Peptidase-4 (DPP-4) inhibitors, which are being used to manage Type 2 Diabetes Mellitus (T2DM). Recent studies have indicated that they have the potential to induce apoptotic-mediated cell death in cancer.

Methods

In the current study, we examined the therapeutic potential of these DPP-4 inhibitors in proliferation, wound healing, and colony formation, ROS induction, DNA fragmentation, apoptosis induction, and regulation of gene expression in KRAS G12C-mutated MIA PaCa-2 & KRAS G12D-mutated PANC-1 PDAC cells. Additionally, the network pharmacology, Gene Ontology (GO) & KEGG pathways enrichment were also studied for DPP-4 inhibitors in PDAC.

Results

The results indicated that both drugs inhibited the proliferation, migration, & colony formation; elevated intracellular ROS levels; induced DNA fragmentation, regulated MAPK & apoptosis-related gene expression, and induced apoptosis confirmed by flow cytometry. In addition, the network pharmacology analysis supported that the identified hub genes plays a role in apoptosis.

Conclusions

Overall, we report that Sitagliptin and Linagliptin have significant anticancer potential towards KRAS-mutated PDAC. Furthermore, we recommend repurposing of more drugs to examine their anti-cancer potential towards these aggressive cancers and to overcome clinical resistance in the near future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40360-025-01020-z.

Keywords: DPP-4, Diabetes mellitus, Drug repurposing, Cancer, Targeted therapy

Introduction

Cancer continues to pose a major global health threat, with an increasing incidence and mortality rates of 20 million & 9.7 million cases worldwide respectively, and lung, colon, breast, and pancreas are the common cancers [1]. Despite significant advancements in cancer diagnosis and treatment strategies have been developed, they are limited for aggressive cancers such as non-small cell lung cancer (NSCLC), glioblastoma (GBM), and pancreatic ductal adenocarcinoma (PDAC) [2–4]. Conventional chemotherapy is also hindered by suboptimal efficacy, toxicity, and drug resistance among patients, highlighting the importance of novel and effective therapeutic strategies [5–8]. Target-specific potent inhibitors/modulators, siRNA candidates, cancer vaccines, PROteolysis TArgeting Chimeras (PROTACs), and immunotherapies like CAR-T cell therapies are being developed and studied against various cancers in preclinical and clinical settings [9–13]. In this landscape, drug repurposing has become a potent strategy due to cost-effective, established safety & toxicity profiles, approved to be safe, and also accelerates the drug development process to other disease conditions [14–16]. Metformin, Aspirin, Chloroquine, Artesunate, Propranolol, Doxycycline, & Diclofenac are some of the common drugs that have been repurposed as potent treatment strategies against various cancers [17, 18].

Recent epidemiological and mechanistic studies have underscored a complex, bidirectional relationship between type 2 diabetes mellitus (T2DM) and cancer, and even diabetes patients have a high risk of various solid cancers, including pancreatic, colon, and liver [19–21]. Notablty, a study indicated that 50% of T2DM have a high chance for pancreatic ductal adenocarcinoma (PDAC) and are associated with impaired glucose tolerance [22, 23]. Obesity, inflammation, oxidative stress, hyperglycemia, and hyperinsulinemia are some of the common risk factors that correlate T2DM with cancer [20]. Dipeptidyl Peptidase-4 (DPP-4) stimulates insulin secretion and suppresses glucagon release via degrading incretin hormones such as glucagon-like peptide-1 (GLP-1) and Glucose-dependent Insulinotropic Polypeptide (GIP) and regulates glucose regulation in T2DM patients [24, 25]. Sitagliptin and Linagliptin are commonly available DPP-4 inhibitors that are anti-hyperglycemic medications used for T2DM that improve glycemic control, induce insulin secretion, and suppress glucagon release in T2DM patients [25]. Several reports indicated that Sitagliptin and Linagliptin induced apoptosis-mediated cell death in various cancers, including lung, glioblastoma, and colon respectively [26–28]. On the other hand, the KRAS mutated PDAC is very frequent and limited by potent therapeutics due to their aggressiveness [29]. From these understandings, we planned to examine the therapeutic potential of Sitagliptin and Linagliptin in KRAS mutated MIA PaCa-2 and PANC-1 PDAC cells to provide foundational evidence for supporting of DPP-4 inhibitors repurposing towards KRAS mutated PDAC cancer.

Materials and methods

Cell culture and reagents

The KRASG12C mutated MIA PaCa-2 and KRASG12C mutated PANC-1 pancreatic ductal adenocarcinoma cancer cells were purchased from NCCS, India. Both cells were cultured in DMEM (Gibco), supplemented with 10% FBS (HiMedia), & Antibiotic/Antimycotic Solution. The cells were properly maintained at 37 °C at 5% CO2 for further study. Trypsin-EDTA (Gibco), DPBS (Thermo Fisher), MTT (Sigma), Dichlorofluorescin diacetate (Sigma), and DAPI dihydrochloride (Sigma) were also purchased. The compounds Sitagliptin and Linagliptin were purchased from MedChemExpress.

Cell proliferation assay

The cell proliferation of the drug treatment on the PDAC was determined by the MTT assay [30]. About 1 × 105 cells/ml were seeded in a 96-well plate & maintained in 5% CO2 at 37 °C for 24 h. Then, the cells were treated with Sitagliptin and Linagliptin to determine their cytotoxic potential towards PDAC cells. After drug treatment, the cells were incubated under the same conditions for 24 h. Following this, the suspension was removed, 20 µL of MTT (5 mg/ml) was added & incubated at 37 °C in the dark for 4 h. Then, it was solubilized with 100 µL of DMSO & incubated for 5–10 min, and finally, the absorbance was measured at 570 nm in a BioTek Epoch microplate reader (Agilent).

Scratch assay

Both MIA PaCa-2 and PANC-1 PDAC cells were seeded in 6-well plates at a density of 5 × 10⁵ cells/well, and when the monolayer reached 90% confluence, 200 µL micropipette tip was used to make the wound. The cells were washed twice with PBS to remove debris, then incubated with serum-free medium containing Sitagliptin or Linagliptin at their respective sub-cytotoxic doses (25% of IC50 concentration). The wound healing was observed at various time intervals like 0, 24, 48 h, using an inverted microscope (Labomed TCM-400, USA) [31].

Colony formation assay

Both MIA PaCa-2 and PANC-1 PDAC cells were seeded in 6-well plates (2000 cells/well) & incubated in 5% CO2 at 37 °C for 24 h. Sitagliptin and Linagliptin were treated at their respective IC50 concentrations, and the cells were incubated under the same conditions. After treatment, the medium was replaced with fresh drug-free complete medium and cells were incubated for 10–14 days to allow colony formation. Then the cells were fixed with methanol: acetic acid (3:1) & stained with 0.5% crystal violet, and the visible colonies were observed [32].

ROS induction assay

The reactive oxygen species levels were determined using the DCFH-DA assay [33]. Briefly, cells were seeded in 6-well plates & incubated in 5% CO2 at 37 °C for 24 h. Sitagliptin and Linagliptin were treated at their respective IC50 concentrations, and the cells were incubated under the same conditions. Then the suspension was removed, and 1 mL of 10 µM DCFH-DA (5 mg/mL) was added to each well and incubated at 37 °C for 30 min. Then the suspension was removed, washed with DMEM & 1X PBS, and the cells were visualized at 485/535 nm (Excitation/Emission) in a fluorescent microscope (WESWOX FM-3000, India).

DNA fragmentation assay

The DNA fragmentation of the cells were determined by the DAPI assay [34]. Briefly, cells were seeded in 6-well plates & incubated in 5% CO2 at 37 °C for 24 h. Sitagliptin and Linagliptin were treated at their respective IC50 concentrations, and the cells were incubated under the same conditions. Then the suspension was removed, and 1 mL of 10 µM DAPI (5 mg/mL) was added & incubated for 10 min at 37 °C. Then the suspension was removed, washed with DMEM & 1X PBS, and the cells were visualized at 358/460 nm (Excitation/Emission) in a fluorescent microscope (WESWOX FM-3000, India).

Apoptosis analysis

The apoptosis analysis was determined by the Annexin V-FITC/PI apoptosis detection kit (Elabscience) as per the manufacturer’s protocol [35]. Briefly, cells were seeded in 6-well plates & incubated in 5% CO2 at 37 °C for 24 h. Sitagliptin and Linagliptin were treated at their respective IC50 concentrations, and the cells were incubated under the same conditions. Then the suspension was removed, cells were trypsinized, spin at 300 g for 5 min, and repeated with PBS, and the supernatant was removed. The cell pellet was resuspended with 500 µL of 1×Annexin V binding buffer, and 5µL of Annexin V-FITC & PI were added, & incubated at room temperature for 15–20 min in the dark. Then the cells were counted at 518/620 nm (annexin V/PI) using a flow cytometer and the untreated cells served as negative controls (CytoFLEX, Beckman Coulter, USA).

RNA isolation and qRT-PCR analysis

Sitagliptin and Linagliptin were treated at their respective IC50 concentrations in both PDAC cells for 24 h, and using Trizol reagent (Takara), RNA was isolated as per the manufacturer’s protocol. The isolated RNA was quantified (at 260 nm), and the cDNA strand was synthesized using PrimeScript RT reagent kit (Takara) as per the manufacturer’s protocol. Then the gene expression was performed in CFX96 Touch Real-Time PCR Detection System, BioRad, using the TB Green® Advantage® qPCR Premix (Takara) as per the manufacturer’s protocol. GAPDH was used as a control, and the sequences of all the primers used in the study are given in Table 1. The mRNA expression levels were determined by the 2-ΔΔCT method, & the data were normalized to GAPDH, and all the reactions were carried out in triplicates to ensure the reliability of the gene expression levels [36, 37].

Table 1.

List of primers used in the study

Gene name Forward primer (5’-3’) Reverse primer (5’-3’)
GAPDH GGTGAAGGTCGGTGTGAACG CTCGCTCCTGGAAGATGGTG
BAX GCCCTTTTGCTTCAGGGTTTC GCAGGGTAGATGAATCGGGG
BCL-2 GAACTGGGGGAGGATTGTGG CATCCCAGCCTCCGTTATCC
CASP3 GACTCTAGACGGCATCCAGC TAGGCTAGGGTTCTGGTGCT
PARP CTCAGGGGAGGGTCTGATGA CTTTGACACTGTGCTTGCCC
KRAS TACATGAGGACTGGGGAGGG AGGCATCATCAACACCCAGA
BRAF ATTCCGGAGGAGGTGTGGA TCTCTGCTAAGGACGCCTCT
MEK GGCAACAGGACAGTTTCCCT TCCGTTCACAGTGTCTGTCG
ERK ACTCCAAAGCCCTTGACCTG CTTCAGCCGCTCCTTAGGTA
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Statistical analysis

All the statistical tests were performed using GraphPad Prism v.8 software. The data collected from different experiments were analyzed by ANOVA & multiple comparison tests. All the experiments were executed at least in triplicates (n = 3), and represented in mean ± SD. The statistical significance was considered when P < 0.05. * indicates P < 0.05, ** indicates P < 0.01, *** indicates P < 0.005, **** indicates P < 0.001 respectively.

Network pharmacology analysis

Identification of common drug targets

To elucidate the potential apoptotic mechanisms of Sitagliptin and Linagliptin in PDAC, a comprehensive network pharmacology-based approach was employed. This study utilized the GeneCards database to discover the disease-associated targets, using the specific keyword “pancreatic ductal adenocarcinoma [38]. " The initial retrieved list comprised a total of 6654 genes along with their corresponding UniProt and GeneCards identifiers. Putative protein targets for Sitagliptin and linagliptin were independently predicted using the Swiss target prediction tool database, restricting the search to Homo sapiens (http://www.swisstargetprediction.ch/) [39]. To assess the intersection between drug targets and PDAC-associated genes, a comparative analysis was conducted using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). This revealed overlapping targets for Sitagliptin and Linagliptin, suggesting potential therapeutic relevance in PDAC.

Hub gene identification and construction of PPI network

To explore the functional interconnectivity among the overlapping targets, a protein-protein interaction (PPI) network was constructed using the STRING database with a high-confidence interaction threshold (score >0.7) [40]. The resulting interaction networks were further visualized and analyzed in Cytoscape (v3.9.1). The topological parameters were calculated using the cytohubba plugin, incorporating four centrality measures- Maximal clique centrality (MCC), degree, closeness, and betweenness- to identify key regulatory nodes [41]. Each centrality algorithm identified the top 10 key hub genes for both sitagliptin and linagliptin that play a significant role in apoptosis and various signaling pathways. Subsequently, a second Venn diagram analysis for each drug was conducted to identify hub genes consistently across all four centrality measures. This integrative analysis yielded consensus hub genes for sitagliptin and linagliptin, enhancing the robustness of candidate genes for downstream biological evaluation.

Gene ontology and functional enrichment analysis

To elucidate the mechanistic relevance of these common hub genes identified in the context of PDAC, a comprehensive functional enrichment analysis was performed. These genes were analyzed using the ENrichr database (https://maayanlab.cloud/Enrichr/) to assess their biological significance [42]. Gene ontology (GO) classification was conducted to annotate the gene set across three fundamental domains: Biological process (BP), which highlights the involvement of genes in critical cellular and physiological pathways; Cellular component (CC), which defines the spatial localization of the gene products within the cell; and Molecular function (MF), which describe the biochemical activities mediated by the gene products. Additionally, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was performed using the ShinyGO platform (https://bioinformatics.sdstate.edu/go/), which integrates data from GO, KEGG, and Reactome to provide high-throughput functional annotations [43]. Significantly enriched pathways were selected based on a false discovery rate (FDR)- adjusted p-value threshold of < 0.05, ensuring statistical robustness. These analyses emphasized key molecular mechanisms and potential signaling cascades mediated by the hub genes, underscoring their relevance in PDAC pathophysiology and therapeutic modulation.

Results

DPP-4 inhibitors induces cytotoxicity in KRAS-mutated PDAC cells

Initially, the cytotoxic potential of Sitagliptin and Linagliptin in KRAS mutated PDAC cells was examined. The IC50 of Sitagliptin in MIA PaCa-2 & PANC-1 was determined as 1.6 ± 0.56 µM & 0.6 ± 0.12 µM, and the IC50 of Linagliptin in MIA PaCa-2 and PANC-1 was determined as 20 ± 1.75 µM & 8.5 ± 1.15 µM respectively as shown in Fig. 1. The DPP-4 inhibitors exhibited potential cytotoxicity towards the KRAS mutated cells in a dose-dependent manner, indicating that they could induce cytotoxicity in PDAC.

Fig. 1.

Fig. 1

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IC50 plots of Sitagliptin (A) and Linagliptin (C) in MIA PaCa-2 and PANC-1 cells and 2D structures Sitagliptin (B) and Linagliptin (D) were depicted. The viability percentage was normalized to the control. All the experiments were performed in double triplicates (n = 6) and the data were shown as mean ± SD respectively

DPP-4 inhibitors inhibits proliferation, migration, and colony formation in PDAC

After the examination of anti-proliferation, Sitagliptin and Linagliptin were tested at their sub cytotoxic doses (25% of IC50 concentration) for their role in the migration of KRAS mutated PDAC cells by scratch assay. In MIA PaCa-2, after 24 h, the wound closure of Sitagliptin and Linagliptin was observed to be 22% & 25%, and after 48 h it showed 49% & 57% respectively. While in the PANC-1 cells, after 24 h, the wound closure was observed to be 33% & 29%, and after 48 h it showed 47%, & 54% for Sitagliptin and Linagliptin respectively as shown in Fig. 2. Notably, in both cells, the DPP-4 inhibitors exhibited potential anti-migration properties as compared to the control group in a time-dependent manner. Alongside, the clonogenic assay indicated that colony formation is inhibited in both cells. In MIA PaCa-2, Sitagliptin and Linagliptin showed 18% & 25% colony formation, and in PANC-1 cells, they showed 29% & 38% colony formation respectively. Comparatively, drug-treated groups significantly reduced colony formation than the control group as shown in Fig. 3.

Fig. 2.

Fig. 2

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Wound healing of Sitagliptin and Linagliptin in MIA PaCa-2 (A & C) and PANC-1 (B & D) cells. The wound closure was measured at 0, 24, and 48 h. All the experiments were performed in triplicates (n = 3) and the data were shown as mean ± SD respectively

Fig. 3.

Fig. 3

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Colony formation of Sitagliptin and Linagliptin in MIA PaCa-2 (A) and PANC-1 (B) cells. All the experiments were performed in triplicates (n = 3) and the data were shown as mean ± SD respectively

DPP-4 inhibitors increases ROS production and DNA fragmentation in PDAC

The intracellular ROS production & DNA fragmentation of drug-treated cells were determined by DCFH-DA and DAPI assay respectively. In MIA PaCa-2 and PANC-1 cells, the Sitagliptin and Linagliptin elevated ROS levels by 21.2, fold & 16.3 fold and 11.3 fold & 7.6 fold higher than the control, respectively, as shown in Fig. 4. Additionally, In MIA PaCa-2 and PANC-1 cells, the Sitagliptin and Linagliptin induced DNA fragmentation by 10.4 fold & 7.7 fold and 10.5 fold & 7 fold higher than the control, as shown in Fig. 4. From the results, it is observed that Sitagliptin (comparatively high) and Linagliptin have more potential in inducing ROS levels & DNA fragmentation in both cells. Elevated ROS levels and DNA fragmentation in cancer cells are also associated with apoptosis & could induce apoptotic-mediated cell death in KRAS-mutated PDAC cells.

Fig. 4.

Fig. 4

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Intracellular ROS production (DCFH-DA) and DNA fragmentation (DAPI) of Sitagliptin and Linagliptin in MIA PaCa-2 (A & B) and PANC-1 (C & D) cells respectively. All the experiments were performed in triplicates (n = 3) and the data were shown as mean ± SD respectively

DPP-4 inhibitors induces apoptosis-mediated cell death in PDAC

The apoptosis induction potential of the drug-treated cells was determined using flow cytometry analysis by Annexin FITC/PI dual staining. In MIA PaCa-2, Sitagliptin and Linagliptin induced apoptosis in 87.38% & 84.03% of the population respectively as shown in Fig. 5. Likewise, in PANC-1, apoptosis was observed in 83.67%, and 88.82% of the population treated with Sitagliptin and Linagliptin. Additionally, it also indicated that the compounds specifically induce apoptosis in KRAS G12C mutated and KRAS G12D mutated PDAC cells, & no considerable necrosis was observed in all treatment groups of both cells as depicted in Fig. 5.

Fig. 5.

Fig. 5

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Apoptosis induction potential of Sitagliptin and Linagliptin in MIA PaCa-2 (A) and PANC-1 cells (B). The apoptosis induced cells in the control and treated groups were determined by employing FITC detection in x-axis and PI detection in y-axis respectively. In the plot, LL, LR, UR, & UL represents the amount of cells in the normal, early apoptotic, late apoptotic, and necrosis states respectively. All the experiments were performed in triplicates (n = 3) and the data were shown as mean ± SD respectively

DPP-4 inhibitors regulates the expression of MAPK and apoptotic-related genes

The role of Sitagliptin and Linagliptin in the regulation of gene expression of KRAS pathway relates genes (KRAS, BRAF, MEK, ERK), pro-apoptotic related genes (BAX, CASP3), anti-apoptotic related genes (BCL-2), and cleaved PARP respectively were analyzed by the qRTPCR analysis. The mRNA expression levels were determined by the 2-ΔΔCT method, & the data were normalized to GAPDH. It is clearly observed that Sitagliptin and Linagliptin significantly upregulated the pro-apoptotic genes, downregulated the KRAS pathway genes, and anti-apoptotic genes in both cells as depicted in Fig. 6. In MIA PaCa-2, Sitagliptin and Linagliptin significantly upregulated the pro-apoptotic genes BAX (3.5-fold increase & 1.9-fold increase), CASP3 (1.9-fold increase & 1.8-fold increase), PARP (1.8-fold increase & 1.6-fold increase); downregulated anti-apoptotic genes BCL-2 (0.6-fold decrease & 0.2-fold decrease) respectively. Likewise, in PANC-1, Sitagliptin and Linagliptin significantly upregulated the pro-apoptotic genes BAX (2-fold increase & 1.8-fold increase), CASP3 (1.9-fold increase & 1.7-fold increase), PARP (1.9-fold increase & 1.5-fold increase); downregulated anti-apoptotic genes BCL-2 (0.2-fold decrease & 0.1-fold decrease) respectively. Notably, Sitagliptin and Linagliptin significantly downregulated the KRAS expression by 0.2-fold decrease & 0.6-fold decrease in KRAS G12C mutated MIA PaCa-2, and by 0.2-fold decrease & 0.3-fold decrease in KRAS G12D mutated PANC-1 cells respectively. Additionally, they have also downregulated KRAS downstream effectors such as the BRAF, MEK, & ERK in both PDAC cells, indicating that it inhibits the KRAS-related genes and could promote apoptosis by modulating gene expression levels.

Fig. 6.

Fig. 6

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mRNA expression analysis of Sitagliptin and Linagliptin in MIA PaCa-2 (A) and PANC-1 cells (B). All the mRNA expression levels were normalized with GAPDH, and the normalized expression levels of BAX, BCL-2, CASP3, PARP, KRAS, BRAF, MEK, and ERK were calculated by 2-ΔΔCT method. All the experiments were performed in triplicates (n = 3) and the data were shown as mean ± SD respectively

Network Pharmacology analysis highlights the significance of apoptosis regulation of DPP-4 inhibitors

To further support the significance of Sitagliptin and Linagliptin in the regulation of apoptosis, we have performed the network pharmacology analysis. The probable potential targets of PDAC, Sitagliptin, and Linagliptin were observed and shown in the Supplementary File 1. For Sitagliptin, out of 6654 PDAC-associated genes, 6591 were unique to PDAC, while 37 targets were exclusive, identifying 63 overlapping targets (0.9%). For Linagliptin, 6579 were unique to PDAC, while 25 were exclusive, revealing 75 common targets (1.1%). To further assess the biological relevance of these shared targets, a high-confidence PPI network was constructed using STRING. For Sitagliptin, the resulting PPI network comprised 63 nodes and 51 edges, exhibiting an average node degree of 1.62 and a local clustering coefficient of 0.459. The expected number of edges was 23, whereas the observed connectivity yielded a statistically significant PPI enrichment p- p-value of < 2.87e-07, indicating non-random functional associations. Similarly, the Linagliptin-PDAC network yielded 75 nodes and 135 edges, with an average node degree of 3.6 and a local clustering coefficient of 0.487. The enrichment p- p-value < 1.0e-16, affirming the functional associations of these targets and their modulatory effects within the PDAC molecular network as shown in the Fig. 7.

Fig. 7.

Fig. 7

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Commonly shared targets of pancreatic ductal adenocarcinoma (PDAC) with Sitagliptin (A1) and Linagliptin (A2) were showed in Venn diagram. The protein-protein interaction (PPI) network of 63 commonly shared targets of Sitagliptin (B1) and 75 commonly shared targets of Linagliptin (B2) were showed. Then the hub genes were identified by using parametes like MCC, degree, betweenness, & closeness for Sitagliptin (C1) and Linagliptin (C2) and showed in Venn diagram. The PPI interaction network of TOP hub genes were shown, for Sitagliptin (D1) and Linagliptin (D2) respectively

To further analyze the regulatory mechanism of the drug-disease interactome, the STRING-derived PPI networks were visualized in Cytoscape v3.9.1, in a circular layout to emphasize the network modularity and interaction centrality. Key nodes within the network were identified using the Cytohubba plugin, which implements multiple centrality algorithms, including MCC, degree, closeness, and betweenness. Each algorithm ranks the top 10 hub genes, and a consensus analysis via Venn diagram identified shared hub genes across all centrality measures. For Sitagliptin, 10 interconnected hub genes (43.5%) were consistently identified, while Linagliptin yielded 9 such genes (42.9%) as shown in the Fig. 7. Top hub genes were shown in the Table 2. Notably, BID, BAX, BCL, CASP3, and AKT1 were common for both drug target networks, underscoring their relevance in apoptotic regulation and tumor suppression. Functional enrichment analysis was further performed to gain deeper insights into the molecular functions of the identified hub genes associated with Sitagliptin and Linagliptin. In addition, the GO and KEGG pathway enrichment were performed, and the enrichment terms such as the biological process (BP), cellular component (CC), & molecular function (MF), and enriched KEGG pathways of Apoptosis & pancreatic cancer were shown in Figs. 8 and 9 respectively.

Table 2.

Top 10 hub target proteins

Gene name Associated protein UniProt ID Mechanism related to cancer
BCL2 Associated X, Apoptosis Regulator BAX Q07812 Apoptosis
BCL2 Apoptosis Regulator BCL2 P10415 Apoptosis
Mitogen-Activated Protein Kinase 14 MAPK14 Q16539 MAPK pathway
Mitogen-Activated Protein Kinase 3 MAPK3 P27361 MAPK pathway
BH3 Interacting Domain Death Agonist BID P55957 Apoptosis
Matrix Metallopeptidase 9 MMP9 P14780 Invasion & metastasis
Caspase 3 CASP3 P42574 Apoptosis
Janus kinase 3 JAK2 O60674 Signalling pathway
Glycogen Synthase Kinase 3 Beta GSK3B P49841 Survival & proliferation
Poly(ADP-Ribose) Polymerase 1 PARP1 P09874 Apoptosis
Matrix Metallopeptidase 1 MMP1 P03956 Invasion and metastasis
CREB Binding Protein CREBBP Q92793 Metastasis
AKT Serine/ Threonine Kinase 1 AKT1 P31749 Signalling pathway
MDM2 Proto-Oncogene MDM2 Q00987 Apoptosis
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Fig. 8.

Fig. 8

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Gene Ontology (GO) enrichment terms like biological process (BP), cellular component (CC) and molecular function (MF) of top hub genes were shown in p-value ranking

Fig. 9.

Fig. 9

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The KEGG pathway enrichment of Apoptosis (A) and Pancreatic cancer (B) indicates the hub genes in red color

Discussion

Cancer continues to pose a major global health threat, with an increasing incidence and mortality rates of 20 million & 9.7 million cases worldwide respectively, and lung, colon, breast, and pancreas are the common cancers [1, 44]. Lung, colorectal, breast, and pancreatic cancers are some of the commonly diagnosed malignancies that possess several challenges, including early diagnosis & detection, improper & poor treatment response, and poor survival rates respectively [2, 45, 46]. Although, significant progress has been made to combat cancer such as early diagnostic methods & potent therapeutic interventions such as chemotherapy, radiotherapy, immunotherapy and target based therapies, these strategies and limited for aggressive cancers such as pancreatic ductal adenocarcinoma (PDAC) which exhibits poor treatment response due to frequent mutations [47–51]. Presently, several therapeutics such as inhibitors/modulators, siRNA candidates, cancer vaccines, PROteolysis TArgeting Chimeras (PROTACs), and immunotherapies like CAR-T cell therapies are being developed and studied against various cancers in preclinical and clinics to combat aggressive cancers. Alongside, drug repurposing has emerged as a potent strategy due to cost-effective, established safety & toxicity profiles [14, 47, 52–54]. Several drugs have been successfully repurposed for other diseases, including cancer. Antidiabetic drugs such as Biguanides (Metformin); Sulfonylureas (Glyburide, Glipizide); SGLT2 inhibitors (Canagliflozin, Empagliflozin); DPP-4 inhibitors (Saxagliptin, Alogliptin); Thiazolidinediones (Pioglitazone, Troglitazone); α-Glucosidase inhibitors (Acarbose, Voglibose) have been repurposed for various and reported to exhibit potent anticancer properties (Dhas Y et al., 2024).

On the other hand, KRAS mutations are prevalent in several such as PDAC, NSCLC, & CRC respectively [55]. KRAS G12C and KRAS G12D mutations are more commonly observed and have a frequency of more than 90% in PDAC [56]. Although Sotorasib & Adagrasib exhibit significant activity against KRAS mutated PDAC, they are limited by toxicity and drug resistance, and there is a huge need for potent therapeutic strategies [57]. Several reports indicated that the DPP‑4 inhibitors have the potential to exhibit significant anticancer potential against several solid cancers and hematological malignancies beyond their role in glycemic control [58]. DPP‑4 differential expression is associated with cancer aggressiveness and growth. A study reported that the restoration of DPP-4 expression in NSCLC cells contributes to inhibiting cell progression in vitro and in vivo [59]. While, in ovarian cancer, DPP-4 expression is associated with lymph node metastasis and a worse stage in tumor samples [60]. A retrospective and single center study demonstrated the association of DPP4-inhibitor treatment and better prognosis of CRC patients with significantly enhanced 5-year disease-free survival, indicating the potential of DPP4 inhibitors in cancer treatment [61]. In NSCLC, the DPP4 inhibitor Anagliptin was reported to reduce tumor-associated macrophages and enhances enhance PD-L1 blockade efficacy by inhibiting macrophage differentiation and M2 macrophage polarization [62]. These studies highlight the possible significant potential of DPP4 inhibitors in cancer treatment, including solid cancers like PDAC. Sitagliptin and Linagliptin are DPP-4 inhibitors used as anti-diabetic medication and have also been reported to induce apoptotic-mediated cell death in cancer cells [63]. Previous reports indicated that Sitagliptin & Linagliptin have the potential to exhibit anticancer properties by various molecular mechanisms, including the elevation of ROS levels, activation of NF-κB, induction of p21 & p27 levels, activation of PCNA, and DNA damage in various cancers including liver, lung and colon respectively [64, 65]. Thus with this understanding, we have explored the anti-cancer potential of Sitagliptin and Linagliptin in KRAS G12C mutated Mia Paca-2 and KRAS G12D PANC-1 PDAC cells. Initially, we found that the IC50 of Sitagliptin in MIA PaCa-2 & PANC-1 was determined as 1.6 ± 0.56 µM & 0.6 ± 0.12 µM, and the IC50 of Linagliptin in MIA PaCa-2 and PANC-1 was determined as 20 ± 1.75 µM & 8.5 ± 1.15 µM respectively as shown in Fig. 1. They exhibited dose-dependent inhibition, which helps to determine the drug’s potency, target specificity, and mechanistic efficacy [66].

Mechanistic assays scratch and colony formation, have been used to determine the efficacy of the drug in the migration and proliferation capacity of cancer cells, which also pave a path in providing mechanical insights [67]. We observed that both Sitagliptin and Linagliptin exhibited significant inhibition of proliferation, colony formation, & migration of KRAS-mutated PDAC cells. For instance, in the wound healing assay, Sitagliptin exhibited 49% & 47% wound closure in MIA PaCa-2 & PANC-1, and Linagliptin exhibited 57% & 54% wound closure in MIA PaCa-2 & PANC-1, even after 48 h, as shown in Fig. 2. Likewise, in the clonogenic assay, Sitagliptin and Linagliptin showed 18% & 25% colony formation in MIA PaCa-2, and 29% & 38% colony formation in PANC-1 respectively as shown in Fig. 3. Notably, in both cells, the DPP-4 inhibitors exhibited potential anti-migration properties and inhibition of colony formation as compared to the control group.

Elevated ROS levels & DNA fragmentation are the key factors involved in apoptotic-mediated cell death in cancer cells [68, 69]. Increased ROS levels alter the gene expression of apoptotic-related genes and promote DNA fragmentation, which is usually observed in the late apoptotic stage, confirming cancer cell death [70]. An interesting study reported that the T2DM medication drug Glyburide elevated ROS levels in human gastric cancer cells and promoted apoptosis mediated via JNK activation [71]. Metformin was heavily reported to induce apoptosis, elevated ROS levels, and regulate signaling pathways, and also regulates the AMPK/LKB1 pathway activation in various cancers, including colon & PDAC [72]. In contrast, metformin was reported to inhibit pancreatic cancer cell survival by reducing the ROS levels by elevating MnSOD & suppressing NOX2/4 levels [73]. In our study, we observed that both Sitagliptin and Linagliptin have elevated ROS levels and induced DNA fragmentation in KRAS-mutated PDAC cells, indicating their potency to induce apoptosis as shown in Fig. 4. Further, the flow cytometry analysis confirmed the apoptosis-inducing potential of DPP-4 inhibitors in PDAC cells. In MIA PaCa-2, Sitagliptin and Linagliptin induced apoptosis in 87.38% & 84.03%, and in PANC-1, apoptosis was observed in 83.67%, and 88.82% of the population respectively. Additionally, it also indicated that the compounds specifically induce apoptosis in KRAS G12C mutated and KRAS G12D mutated PDAC cells, & no considerable necrosis was observed in all treatment groups of both cells as depicted in Fig. 5. Apoptosis-mediated cell death of cancer cells is achieved by upregulating pro-apoptotic genes & downregulating anti-apoptotic genes respectively [74–76]. From our study, we observed that both Sitagliptin and Linagliptin regulated the gene expression of KRAS pathway-related genes (KRAS, BRAF, MEK, ERK), pro-apoptotic related genes (BAX, CASP3), anti-apoptotic related genes (BCL-2), and cleaved PARP, respectively confirmed by qRTPCR analysis. In both PDAC cells, they upregulated anti-apoptotic genes, downregulated anti-apoptotic genes & MAPK pathway genes respectively as depicted in Fig. 6, indicating that it inhibits the KRAS-related genes and could promote apoptosis by modulating gene expression levels in KRAS-mutated PDAC cells.

DPP-4 inhibitors have been reported to alter redox-sensitive signaling cascades [77]. The observed pro-apoptotic effects of Sitagliptin and Linagliptin in KRAS-mutant PDAC cells appear to be closely associated with oxidative stress and downstream signaling disruptions. Elevated intracellular ROS levels, as quantified in both MIA PaCa-2 and PANC-1 cell lines, likely serve as a primary trigger for the activation of the intrinsic apoptosis pathway, and also ROS accumulation may intersect with mitogen-activated pathways, thereby contributing to PDAC growth [34]. Although direct inhibition of KRAS was not the mechanism targeted in this study, the differential response in KRAS-mutated versus wild-type PDAC cells (as demonstrated by comparative IC50 values) suggests that oncogenic KRAS may sensitize cells to oxidative imbalance and apoptotic triggers. This supports the hypothesis that DPP-4 inhibition can amplify cellular stress in KRAS-driven tumors, making them more vulnerable to apoptosis. Together, these findings propose a multi-layered mechanism where DPP-4 inhibitors promote apoptosis via ROS elevation, mitochondrial destabilization, and MAPK pathway disruption, offering a promising therapeutic route in KRAS-mutated PDAC.

To comprehensively understand the pharmacodynamic landscape of Sitagliptin and Linagliptin within the context of PDAC, a network pharmacology-based knowledge graph was constructed [78, 79]. This integrative strategy facilitated the exploration of complex interactions between bioactive compounds, their molecular targets, and associated biological pathways implicated in oncogenesis [80–82]. Our analysis resulted in the identification of hub genes for Sitagliptin and Linagliptin in PDAC. Notably, BID, BAX, BCL, CASP3, and AKT1 were common for both drug target networks, underscoring their relevance in apoptotic regulation and tumor suppression as depicted in Fig. 7 [83, 84]. Additional targets included MMPs, MAPKs, PARP, GSK3B, MDM, and JAK family kinases, all of which are implicated in key oncogenic processes such as epithelial-mesenchymal transition (EMT), signaling pathways, apoptosis, immune escape, inflammation, and cell survival [85–87]. The high connectivity and functional annotations of common genes suggest that sitagliptin and linagliptin may exert anti-cancer effects through modulation of apoptotic pathways, inhibition of proliferative and migration pathways, and immune signaling, warranting further mechanistic exploration.

The biological process domain revealed significant enrichment in apoptotic signaling pathways, response to oxidative stress, and regulation of cell proliferation, indicating the role of these genes in modulating tumor cell fate [86]. Within the cellular component domain, enriched components include the mitochondrial membrane, cytosol, and nucleus, suggesting spatial regulation of signaling cascades. The molecular function analysis highlighted key activities such as protein kinase binding, caspase activation, and transcription factor binding, reflecting their involvement in apoptotic and proliferative regulation. KEGG pathway analysis revealed multiple significantly enriched oncogenic pathways, including PI3K-AKT signaling, p53 signaling, FoXo signaling, JAK STAT and MAPK pathways, with FDR-adjusted p-values < 0.05. These cascades are critically involved in cellular processes such as apoptosis, DNA damage response, immune modulation, and tumor proliferation [88]. Genes such as AKT1, GSKB3, MAPK1, MAPK14, BAX, BID, BCL2, CASP3, JAK2, and STAT2 were functionally enriched across multiple pathways. The presence of BAX, BID, and CASP3 in both apoptosis and p53 pathways underscores their role in regulating programmed cell death and maintaining genomic integrity [89]. Similarly, enrichment of AKT1 and GSK3B in the PI3K-AKT axis highlights potential involvement in cellular proliferation, metabolic regulation, and survival signaling [90]. The JAK-STAT and NF-KB pathway enriched via JAK2, STAT3, and BCL2, suggests immune and inflammatory modulation mechanisms as additional therapeutic dimensions.

Furthermore, the observed enrichment in the TNF signaling and cellular senescence pathways supports the compound’s potential role in altering tumor microenvironment dynamics and apoptotic-associated tumor suppression. In addition to KEGG, several other pathway databases offer comparable functional insights, each employing distinct data curation methodologies. For instance, the Reactome pathway database provides a curated interaction network encompassing a broad spectrum of signaling pathways (Supplementary File). Moreover, the qRT-PCR analysis provide a direct experimental validation of the hub genes and enriched pathways identified via network pharmacology. Upregulation of BAX, CASP3, and PARP, along with the suppression of BCL-2, aligns with the observed apoptotic phenotype in DPP-4 inhibitor treated PDAC cells. Simultaneously, the downregulation of KRAS, BRAF, MEK, and ERK transcripts supports inhibition of the MAPK cascade, a key survival pathway in KRAS-driven PDAC. This integrative approach not only validates the computational predictions but also provides deeper insight into the mechanistic underpinnings of Sitagliptin and Linagliptins anticancer activity. Collectively, the consistent enrichment of these pathways across both drug-target genes validates the therapeutic relevance for the potential repositioning of Sitagliptin and Linagliptin as modulators in PDAC pathophysiology. Collectively, we observed that Sitagliptin and Linagliptin inhibited migration & colony formation, elevated intracellular ROS induction, induced DNA fragmentation, induced apoptosis, and regulated gene expression of apoptosis and MAPK-related genes in KRAS-mutated PDAC cells. Furthermore, we recommend repurposing of more drugs against these aggressive types of cancers, including PDAC in the near future.

Conclusion

Cancer has become a global health threat with increasing incidence & mortality rates and despite significant advancements in diagnostics and therapeutics, they limited for aggressive cancers like pancreatic ductal adenocarcinoma (PDAC). KRAS mutated PDAC (KRAS G12C and KRAS G12D mutations) is very frequent and limited by potent therapeutics due to their aggressiveness. We have repurposed DPP-4 inhibitors Sitagliptin and Linagliptin, and found that they inhibited the proliferation, migration, & colony formation; elevated intracellular ROS levels; induced DNA fragmentation, regulated MAPK & apoptosis-related gene expression levels, and induced apoptosis in both the KRAS G12C mutated MIA PaCa-2 & KRAS G12D mutated PANC-1 cells. Overall, we conclude that Sitagliptin and Linagliptin have significant anticancer potential towards KRAS-mutated PDAC via regulating apoptosis. Additionally, the network pharmacology and enrichment analysis supported the significance of Sitagliptin and Linagliptin in apoptosis regulation. Additionally, the network pharmacology and enrichment analysis supported the significance of Sitagliptin and Linagliptin in apoptosis regulation. While our current study provides preliminary in vitro evidence supporting the pro-apoptotic and anti-proliferative roles of Sitagliptin and Linagliptin in KRAS mutated PDAC, we suggest to further study in in vivo settings for clinical translation. Furthermore, we recommend repurposing of more drugs to examine their anti-cancer potential towards these aggressive cancers and to overcome clinical resistance in the near future.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (894.9KB, xlsx)

Acknowledgements

The authors would like to thank their respective institutes/organizations for providing the necessary facilities to carry out this work. Sivakumar Arumugam and Janaki Ramaiah Mekala specially thank Vellore Institute of Technology, Vellore for providing necessary facilities needed for the work. Prasanna Srinivasan Ramalingam would like to thank the Council for Scientific and Industrial Research (CSIR) for providing him the Senior Research Fellowship (File No: 09/0844(18240)/2024-EMR-I). This work was supported by the Gachon University research fund of 2024(GCU-202403950001,202405290001) and Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2021R1A6A1A03038996).

Abbreviations

BP

Biological process

CC

Cellular component

DPP

4-Dipeptidyl Peptidase-4

EMT

Epithelial-mesenchymal transition

FDR

False discovery rate

GBM

Glioblastoma

GIP

Glucose-dependent Insulinotropic Polypeptide

GLP

1-Glucagon-like peptide-1

GO

Gene Ontology

IC50

Half maximal inhibitory concentration

KEGG

Kyoto Encyclopedia of Genes and Genomes

KRAS

Kirsten rat sarcoma virus

MCC

Maximal clique centrality

MF

Molecular function

NSCLC

Non-small cell lung cancer

PDAC

Pancreatic ductal adenocarcinoma

PPI

Protein-protein interaction

PROTACs

PROteolysis TArgeting Chimeras

ROS

Reactive oxygen species

T2DM

Type 2 Diabetes Mellitus

Author contributions

Prasanna Srinivasan Ramalingam: Conceptualization, Formal analysis, Data curation, Writing-original draft, Writing- review & editing. Md Sadique Hussain: Formal analysis, Data curation, Writing- review & editing. Gayathri Chellasamy: Methodology, Formal analysis, Resources, Writing- review & editing. Sujatha Elangovan: Formal analysis, Data curation, Software. Divya Sharma: Formal analysis, Data curation, Software. Premkumar T: Formal analysis, Data curation, Writing-original draft. Rudra Awdhesh Kumar Mishra: Formal analysis, Data curation, Writing-original draft. Gothandam Kodiveri Muthukaliannan: Resources, Software, Writing- review & editing. Tajamul Hussain: Resources, Validation, Writing- review & editing. Salman Alrokayan: Resources, Validation, Writing- review & editing. Purushothaman Balakrishnan: Resources, formal analysis, Writing- review & editing. Janaki Ramaiah Mekala: Validation, Supervision, Writing- review & editing. Sivakumar Arumugam: Validation, Supervision, Writing- review & editing. All the authors proofread and accepted for the submission of the manuscript.

Funding

Authors would like to thank and acknowledge Ongoing Research Funding program - Research Chairs (ORF-RC-2025-2600), King Saud University, Riyadh, Saudi Arabia.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Prasanna Srinivasan Ramalingam and Md Sadique Hussain contributed equally to this work.

Contributor Information

Janaki Ramaiah Mekala, Email: janakiramaiah.m@vit.ac.in.

Sivakumar Arumugam, Email: siva_kumar.a@vit.ac.in.

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Associated Data

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Supplementary Materials

Supplementary Material 1 (894.9KB, xlsx)

Data Availability Statement

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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