Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Novel peptides derived from S. hispanica seeds induce selective cytotoxicity on human cancer cells

Cancer prevails as one of the major health concerns worldwide due to the consistent rise in incidence and lack of effective therapies. Previous studies identified the peptides KLKKNL, MLKSKR, and KKYRVF from Salvia hispanica seeds and stated their selective anticancer activity. Thus, this study aimed to determine the cell death pathway induced by these peptides on five cancer cell lines (MCF-7, Caco2, HepG2, DU145, and HeLa). Based on the results of this work, it is possible to suggest that KLKKNL primarily induces selective cancer cell death through the apoptotic pathway in the Caco2 and HeLa lines. On the other hand, the peptide KKYRVF reported the highest statistical (p < 0.05) selective cytotoxic effect on the MCF-7, Caco2, HepG2, and DU145 cancer cell lines by induction of the necrotic pathway. These findings offer some understanding of the selective anticancer effect of KLKKNL, MLKSKR, and KKYRVF.

mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations

Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the past, several web-based machine learning (ML) tools have been proposed to assist researchers in identifying potential ACPs for further testing. Notably, our meta-approach method, mACPpred, introduced in 2019, has significantly advanced the field of ACP research. Given the exponential growth in the number of characterized ACPs, there is now a pressing need to create an updated version of mACPpred. To develop mACPpred 2.0, we constructed an up-to-date benchmarking dataset by integrating all publicly available ACP datasets. We employed a large-scale of feature descriptors, encompassing both conventional feature descriptors and advanced pre-trained natural language processing (NLP)-based embeddings. We evaluated their ability to discriminate between ACPs and non-ACPs using eleven different classifiers. Subsequently, we employed a stacked deep learning (SDL) approach, incorporating 1D convolutional neural network (1D CNN) blocks and hybrid features. These features included the top seven performing NLP-based features and 90 probabilistic features, allowing us to identify hidden patterns within these diverse features and improve the accuracy of our ACP prediction model. This is the first study to integrate spatial and probabilistic feature representations for predicting ACPs. Rigorous cross-validation and independent tests conclusively demonstrated that mACPpred 2.0 not only surpassed its predecessor (mACPpred) but also outperformed the existing state-of-the-art predictors, highlighting the importance of advanced feature representation capabilities attained through SDL. To facilitate widespread use and accessibility, we have developed a user-friendly for mACPpred 2.0, available at https://balalab-skku.org/mACPpred2/.

MLASM: Machine learning based prediction of anticancer small molecules

Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research.

Prediction of leukemia peptides using convolutional neural network and protein compositions

Leukemia is a type of blood cell cancer that is in the bone marrow's blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy.

Peptides for microbe-induced cancers: latest therapeutic strategies and their advanced technologies

Cancer is a significant global health concern associated with multiple distinct factors, including microbial and viral infections. Numerous studies have elucidated the role of microorganisms, such as Helicobacter pylori (H. pylori), as well as viruses for example human papillomavirus (HPV), hepatitis B virus (HBV), and hepatitis C virus (HCV), in the development of human malignancies. Substantial attention has been focused on the treatment of these microorganism- and virus-associated cancers, with promising outcomes observed in studies employing peptide-based therapies. The current paper provides an overview of microbe- and virus-induced cancers and their underlying molecular mechanisms. We discuss an assortment of peptide-based therapies which are currently being developed, including tumor-targeting peptides and microbial/viral peptide-based vaccines. We describe the major technological advancements that have been made in the design, screening, and delivery of peptides as anticancer agents. The primary focus of the current review is to provide insight into the latest research and development in this field and to provide a realistic glimpse into the future of peptide-based therapies for microbe- and virus-induced neoplasms.

Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells

Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources. We developed a precise prediction model based on their sequence and structural features, and the model's evaluation results suggest its strong predictive ability for anticancer activity. To enhance reliability, we integrated the results of this model with those from other available methods. In total, we identified 176 potential ACPs, some of which were synthesized and further evaluated using the MTT colorimetric assay. All of these putative ACPs exhibited significant anticancer effects and selective cytotoxicity against specific tumor cells. In summary, we present a strategy for identifying and characterizing natural peptides with selective cytotoxicity against cancer cells, which could serve as novel therapeutic agents. Our prediction model can effectively screen new molecules for potential anticancer activity, and the results from in vitro experiments provide compelling evidence of the candidates' anticancer effects and selective cytotoxicity.

Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine-Learning for Tumor Therapy

Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme-based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) has provided a useful tool to enhance the accuracy and efficiency in developing synergistic substances. In this study, ML models to mine low-cytotoxicity oncolytic peptides are applied. The filtering Pipeline is constructed using a traversal design and the Autogluon framework. Through the Pipeline, 37 novel peptides with high oncolytic activity against cancer cells and low cytotoxicity to normal cells are identified from a library of 25,740 sequences. Combining dataset testing with cytotoxicity experiments, an 80% accuracy rate is achieved, verifying the reliability of ML predictions. Peptide C2 is proven to possess membranolytic functions specifically for tumor cells as targeted by Pipeline. Then Peptide C2 with CoFe hollow hydroxide nanozyme (H-CF) to form the peptide/H-CF composite is integrated. The new composite exhibited acid-triggered membranolytic function and potent peroxidase-like (POD-like) activity, which induce ferroptosis to tumor cells and inhibits tumor growth. The study suggests that this novel ML-assisted design approach can offer an accurate and efficient paradigm for developing both oncolytic peptides and synergistic peptides for catalytic materials.

Exploration of novel cationic amino acid-enriched short peptides: design, SPPS, biological evaluation and in silico study

Antimicrobial resistance (AMR) represents a critical challenge worldwide, necessitating the pursuit of novel approaches to counteract bacterial and fungal pathogens. In this context, we explored the potential of cationic amino acid-enriched short peptides, synthesized via solid-phase methods, as innovative antimicrobial candidates. Our comprehensive evaluation assessed the antibacterial and antifungal efficacy of these peptides against a panel of significant pathogens, including Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pyogenes, Candida albicans, and Aspergillus niger. Utilizing molecular docking techniques, we delved into the molecular interactions underpinning the peptides' action against these microorganisms. The results revealed a spectrum of inhibitory activities, with certain peptide sequences displaying pronounced effectiveness across various pathogens. These findings underscore the peptides' potential as promising antimicrobial agents, with molecular docking offering valuable insights into their mechanisms of action. This study enriches antimicrobial peptide (AMP) research by identifying promising candidates for further refinement and development toward therapeutic application, highlighting their significance in addressing the urgent issue of AMR.

Konozy E, Abu Zeid IM, Ahmad A, Altayb HN. Prediction of anticancer peptides derived from the true lectins of Phoenix dactylifera and their synergetic effect with mitotane

Background and aims: Cancer continues to be a significant source of both illness and death on a global scale, traditional medicinal plants continue to serve as a fundamental resource of natural bioactive compounds as an alternative source of remedies. Although there have been numerous studies on the therapeutic role of Phoenix dactylifera, the study of the role of peptides has not been thoroughly investigated. This study aimed to investigate the anticancer activity of lectin peptides from P. dactylifera using in silico and in vivo analysis. Methods: Different computational tools were used to extract and predict anticancer peptides from the true lectins of P. dactylifera. Nine peptides that are bioactive substances have been investigated for their anticancer activity against MCF-7 and T47D (two forms of breast cancer). To counteract the unfavorable effects of mitotane, the most potent peptides (U3 and U7) were combined with it and assessed for anticancer activity against MCF-7 and HepG2. Results: In silico analysis revealed that nine peptides were predicted with anticancer activity. In cell lines, the lowest IC50 values were measured in U3 and U7 against MCF-7 and T47D cells. U3 or U7 in combination with mitotane demonstrated the lowest IC50 against MCF-7 and HepG2. The maximum level of cell proliferation inhibition was 22% when U3 (500 µg/mL) and 25 µg/mL mitotane were combined, compared to 41% when 25 µg/mL mitotane was used alone. When mitotane and U3 or U7 were combined, it was shown that these bioactive substances worked synergistically with mitotane to lessen its negative effects. The combination of peptides and mitotane could be regarded as an efficient chemotherapeutic medication having these bioactive properties for treating a variety of tumors while enhancing the reduction of side effects.

Design of Bioengineered Peptides/Proteases as Anti-cancer Reagents with Integrated Omics and Machine Learning Approaches

Cancer is a heterogeneous disorder of uncontrolled growth of cells, which has proven to be a major burden worldwide. Many treatment options are available for cancer therapy, yet side effects and drug resistance remain major hurdles. Therefore, it is necessary to develop novel drugs for cancer therapy. Anti-cancer peptides (ACPs) are attractive candidates with remarkable potency, low toxicity, and high specificity advantages. However, traditional experimental identification of ACPs is time-consuming and expensive. Integrated omics combined with machine learning (ML) is considered a new powerful and cost-effective strategy to discover ACPs from natural products. In this chapter, we describe in detail experimental procedures for collecting both transcriptomic and proteomic data from venoms, followed by descriptive approaches to ML prediction.

Dual-action potential of cationic cryptides against infections and cancers

Cryptides are a subfamily of bioactive peptides embedded latently in their parent proteins and have multiple biological functions. Cationic cryptides could be used as modern drugs in both infectious diseases and cancers because their mechanism of action is less likely to be affected by genetic mutations in the treated cells, therefore addressing a current unmet need in these two areas of medicine. In this review, we present the current understanding of cryptides, methods to mine them sustainably using available online databases and prediction tools, with a particular focus on their antimicrobial and anticancer potential, and their potential applicability in a clinical setting.

A novel portable immuno-device for the recognition of lymphatic vessel endothelial hyaluronan receptor-1 biomarker using GQD-AgNPrs conductive ink stabilized on the surface of cellulose

Lymphatic vessel endothelium expresses various lymphatic marker molecules. LYVE-1, the lymphatic vessel endothelial hyaluronan (HA) receptor, a 322-residue protein belonging to the integral membrane glycoproteins which is found on lymph vessel wall and is completely absent from blood vessels. LYVE-1 is very effective in the passage of lymphocytes and tumor cells into the lymphatics. As regards cancer metastasis, in vitro studies indicate LYVE-1 to be involved in tumor cell adhesion. Researches show that, in neoplastic tissue, LYVE-1 is limited to the lymphovascular and could well be proper for studies of tumor lymphangiogenesis. So, the monitoring of LYVE-1 level in human biofluids has provided a valuable approach for research into tumor lymphangiogenesis. For the first time, an innovative paper-based electrochemical immune-platform was developed for recognition of LYVE-1. For this purpose, graphene quantum dots decorated silver nanoparticles nano-ink was synthesized and designed directly by writing pen-on paper technology on the surface of photographic paper. This nano-ink has a great surface area for biomarker immobilization. The prepared paper-based biosensor was so small and cheap and also has high stability and sensitivity. For the first time, biotinylated antibody of biomarker (LYVE-1) was immobilized on the surface of working electrode and utilized for the monitoring of specific antigen by simple immune-assay strategy. The designed biosensor showed two separated linear ranges in the range of 20-320 pg ml-1 and 0.625-10 pg ml-1, with the acceptable limit of detection (LOD) of 0.312 pg ml-1. Additionally, engineered immunosensor revealed excellent selectivity that promises its use in complex biological samples and assistance for biomarker-related disease screening in clinical studies.

Anticancer peptides from induced tumor-suppressing cells for inhibiting osteosarcoma cells

Osteosarcoma (OS) is the most frequent primary bone cancer, which is mainly suffered by children and young adults. While the current surgical treatment combined with chemotherapy is effective for the early stage of OS, advanced OS preferentially metastasizes to the lung and is difficult to treat. Here, we examined the efficacy of ten anti-OS peptide candidates from a trypsin-digested conditioned medium that was derived from the secretome of induced tumor-suppressing cells (iTSCs). Using OS cell lines, the antitumor capabilities of the peptide candidates were evaluated by assaying the alterations in metabolic activities, proliferation, motility, and invasion of OS cells. Among ten candidates, peptide P05 (ADDGRPFPQVIK), a fragment of aldolase A (ALDOA), presented the most potent OS-suppressing capabilities. Its efficacy was additive with standard-of-care chemotherapeutic agents such as cisplatin and doxorubicin, and it downregulated oncoproteins such as epidermal growth factor receptor (EGFR), Snail, and Src in OS cells. Interestingly, P05 did not present inhibitory effects on non-OS skeletal cells such as mesenchymal stem cells and osteoblast cells. Collectively, this study demonstrated that iTSC-derived secretomes may provide a source for identifying anticancer peptides, and P05 may warrant further evaluations for the treatment of OS.

In Silico Prospecting for Novel Bioactive Peptides from Seafoods: A Case Study on Pacific Oyster (Crassostrea gigas)

Pacific oyster (Crassostrea gigas), an abundant bivalve consumed across the Pacific, is known to possess a wide range of bioactivities. While there has been some work on its bioactive hydrolysates, the discovery of bioactive peptides (BAPs) remains limited due to the resource-intensive nature of the existing discovery pipeline. To overcome this constraint, in silico-based prospecting is employed to accelerate BAP discovery. Major oyster proteins were digested virtually under a simulated gastrointestinal condition to generate virtual peptide products that were screened against existing databases for peptide bioactivities, toxicity, bitterness, stability in the intestine and in the blood, and novelty. Five peptide candidates were shortlisted showing antidiabetic, anti-inflammatory, antihypertensive, antimicrobial, and anticancer potential. By employing this approach, oyster BAPs were identified at a faster rate, with a wider applicability reach. With the growing market for peptide-based nutraceuticals, this provides an efficient workflow for candidate scouting and end-use investigation for targeted functional product preparation.

ApInAPDB: a database of apoptosis-inducing anticancer peptides

ApInAPDB (Apoptosis-Inducing Anticancer Peptides Database) consists of 818 apoptosis-inducing anticancer peptides which are manually collected from research articles. The database provides scholars with peptide related information such as function, binding target and affinity, IC50 and etc. In addition, GRAVY (grand average of hydropathy), net charge at pH 7, hydrophobicity and other physicochemical properties are calculated and presented. Another category of information are structural information includes 3D modeling, secondary structure prediction and descriptors for QSAR (quantitative structure-activity relationship) modeling. In order to facilitate the browsing process, three types of user-friendly searching tools are provided: top categories browser, simple search and advanced search. Overall ApInAPDB as the first database presenting apoptosis-inducing anticancer peptides can be useful in the field of peptide design and especially cancer therapy. Researchers can freely access the database at http://bioinf.modares.ac.ir/software/ApInAPDB/ .

iDVIP: identification and characterization of viral integrase inhibitory peptides

Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.

Peptides as Potentially Anticarcinogenic Agent from Functional Canned Meat Product with Willow Extract

The aim of the study was to demonstrate canned pork as a functional meat product due to the presence of potentially anti-cancer factors, e.g., (a) bioactive peptides with potential activity against cancer cells; (b) lowering the content of sodium nitrite and with willow herb extract. In silico (for assessing the anticancer potential of peptides) and in vitro (antiproliferation activity on L-929 and CT-26 cell lines) analysis were performed, and the obtained results confirmed the bioactive potential against cancer of the prepared meat product. After 24 h of incubation with peptides obtained from meat product containing lyophilized herb extract at a concentration of 150 mg/kg, the viability of both tested cell lines was slightly decreased to about 80% and after 72 h to about 40%. On the other hand, after 72 h of incubation with the peptides obtained from the variant containing 1000 mg/kg of freeze-dried willow herb extract, the viability of intestinal cancer cells was decreased to about 40%, while, by comparison, the viability of normal cells was decreased to only about 70%.

Hunting for Novel Routes in Anticancer Drug Discovery: Peptides against Sam-Sam Interactions

Among the diverse protein binding modules, Sam (Sterile alpha motif) domains attract attention due to their versatility. They are present in different organisms and play many functions in physiological and pathological processes by binding multiple partners. The EphA2 receptor contains a Sam domain at the C-terminus (EphA2-Sam) that is able to engage protein regulators of receptor stability (including the lipid phosphatase Ship2 and the adaptor Odin). Ship2 and Odin are recruited by EphA2-Sam through heterotypic Sam-Sam interactions. Ship2 decreases EphA2 endocytosis and consequent degradation, producing chiefly pro-oncogenic outcomes in a cellular milieu. Odin, through its Sam domains, contributes to receptor stability by possibly interfering with ubiquitination. As EphA2 is upregulated in many types of tumors, peptide inhibitors of Sam-Sam interactions by hindering receptor stability could function as anticancer therapeutics. This review describes EphA2-Sam and its interactome from a structural and functional perspective. The diverse design strategies that have thus far been employed to obtain peptides targeting EphA2-mediated Sam-Sam interactions are summarized as well. The generated peptides represent good initial lead compounds, but surely many efforts need to be devoted in the close future to improve interaction affinities towards Sam domains and consequently validate their anticancer properties.

Pseudanabaena galeata CCNP1313—Biological Activity and Peptides Production

Even cyanobacteria from ecosystems of low biodiversity, such as the Baltic Sea, can constitute a rich source of bioactive metabolites. Potent toxins, enzyme inhibitors, and anticancer and antifungal agents were detected in both bloom-forming species and less commonly occurring cyanobacteria. In previous work on the Baltic Pseudanabaena galeata CCNP1313, the induction of apoptosis in the breast cancer cell line MCF-7 was documented. Here, the activity of the strain was further explored using human dermal fibroblasts, African green monkey kidney, cancer cell lines (T47D, HCT-8, and A549ACE2/TMPRSS2) and viruses (SARS-CoV-2, HCoV-OC43, and WNV). In the tests, extracts, chromatographic fractions, and the main components of the P. galeata CCNP1313 fractions were used. The LC-MS/MS analyses of the tested samples led to the detection of forty-five peptides. For fourteen of the new peptides, putative structures were proposed based on MS/MS spectra. Although the complex samples (i.e., extracts and chromatographic fractions) showed potent cytotoxic and antiviral activities, the effects of the isolated compounds were minor. The study confirmed the significance of P. galeata CCNP1313 as a source of metabolites with potent activity. It also illustrated the difficulties in assigning the observed biological effects to specific metabolites, especially when they are produced in minute amounts.

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.

Anticancer peptide Q7 suppresses the growth and migration of human endometrial cancer by inhibiting DHCR24 expression and modulating the AKT-mediated pathway

Endometrial cancer is one of the most common malignancy affecting women in developed countries. Resection uterus or lesion area is usually the first option for a simple and efficient therapy. Therefore, it is necessary to find a new therapeutic drug to reduce surgery areas to preserve fertility. Anticancer peptides (ACP) are bioactive amino acids with lower toxicity and higher specificity than chemical drugs. This study is to address an ACP, herein named Q7, which could downregulate 24-Dehydrocholesterol Reductase (DHCR24) to disrupt lipid rafts formation, and sequentially affect the AKT signal pathway of HEC-1-A cells to suppress their tumorigenicity such as proliferation and migration. Moreover, lipo-PEI-PEG-complex (LPPC) was used to enhance Q7 anticancer activity in vitro and efficiently show its effects on HEC-1-A cells. Furthermore, LPPC-Q7 exhibited a synergistic effect in combination with doxorubicin or paclitaxel. To summarize, Q7 was firstly proved to exhibit an anticancer effect on endometrial cancer cells and combined with LPPC efficiently improved the cytotoxicity of Q7.