Distinct roles of Akt1 in regulating proliferation, migration and invasion in HepG2 and HCT 116 cells

Unraveling the mechanism of core prescription in primary liver cancer: integrative analysis through data mining, network pharmacology, and molecular simulation

This study aims to identify core Traditional Chinese Medicine compound prescriptions (TCM CPs) for Primary Liver Cancer (PLC) and their underlying mechanisms. A comprehensive search was conducted using China National Knowledge Infrastructure (CNKI) and the Chinese Medical Code V5.0, identifying 151 TCM CPs. Medication frequency and association rules were analyzed with TCMICS V3.0, while active compounds were identified via TCMSP and TCMIP V2.0. Targets were predicted using Swiss Target Prediction, and disease targets from DisGeNET, OMIM, and GeneCards were cross-referenced. A protein-protein interaction (PPI) network was constructed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using DAVID. In the process of studying active compounds, an orthogonal experiment was carried out on the extraction process of relevant herbs. The results of the orthogonal experiment and range analysis showed that for the extraction rate of the extract and the content of paeoniflorin, the decoction cycles had the most significant impact, followed by soaking time and water volume. The optimal extraction conditions were determined as soaking time of 30 min, water volume of tenfold, and 3 decoction cycles. Under these conditions, the extract yield reached 42.49%, and the paeoniflorin content was 73.60 mg/25.02 g crude herb (equivalent to 2.94 mg/g). ANOVA analysis further confirmed the significance of these factors. The results revealed 109 common targets between TCM component targets and disease targets, with key targets including STAT3, SRC, AKT1, HRAS, and PIK3CA. Molecular docking showed strong binding affinities of paeoniflorin and 3,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl) chromone to PLC targets, with ADME predictions favoring paeoniflorin. Furthermore, Molecular Dynamics (MD) simulations revealed that paeoniflorin maintains stable binding to the target proteins, demonstrating promising conformational stability. The CCK-8 assay demonstrated that the core TCM CP exerted a dose-dependent inhibitory effect on HepG2 cells. After 24 h of intervention, the IC50 values of paeoniflorin and the TCM CP on HepG2 cells were 17.58 μg/mL and 120.5 μg/mL, respectively, which confirmed their anti-proliferative activity against PLC. This study identifies key active compounds and investigates their roles in modulating the Ras/Raf/MEK/ERK, AKT/NF-κB, and JAK-STAT signaling pathways, offering valuable insights into the therapeutic potential of TCM for PLC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00352-2.

Effects of Viscum coloratum (Kom.) Nakai on collagen-induced rheumatoid arthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Viscum coloratum (Kom.) Nakai has been traditionally used in China for nearly a thousand years to treat rheumatic diseases. However, its efficacy and mechanisms in treating rheumatoid arthritis (RA) have not been demonstrated.

AIM OF THE STUDY

To investigate the anti-arthritic effects and molecular mechanisms of Viscum coloratum (Kom.) Nakai on collagen-induced arthritic mice through network pharmacology technology and experimental validation.

MATERIALS AND METHODS

First, the main ingredients of the extract of Viscum coloratum (Kom.) Nakai (EVC) were identified through chemical composition characterization using Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS). Then, the collagen-induced arthritis (CIA) model was established in DBA/1 J mice and the ameliorative effects of EVC on the progression of CIA mice were evaluated by oral treatment with different doses of the EVC for 28 days. After that, cytokine antibody microarray assay was used to detect the levels of multiple inflammation-related cytokines and chemokines in each group, and performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analysis. Subsequently, the potential target for the effective chemical components of EVC in treating RA was identified using various databases. Additionally, a drug-disease target protein-protein interaction network (PPI) was conducted using Cytoscape for visualization and clustering, while GO and KEGG enrichment analyses were performed with the Metascape database. Finally, identified phenotypes and targets by network pharmacology analysis were experimentally validated in vivo.

RESULTS

Treatment with EVC significantly suppressed the severity of CIA with a dramatic reduction of paw swelling, arthritis index, levels of IgGs (IgG, IgG1, IgG2a, and IgG2b), multi-inflammation-related cytokines and chemokines on the progression of CIA. Histopathological examinations showed EVC could markedly inhibit inflammatory cell infiltration, tartrate-resistant acid phosphatase (TRAP) activity of osteoclast, and bone destruction. Furthermore, GO and KEGG enrichment analyses revealed that EVC could ameliorate RA by inhibiting osteoclast differentiation and regulating multiple signaling pathways including Osteoclast differentiation, IL-17, and TNF. PPI network analysis demonstrated that AKT1, MMP9, MAPK3, and other genes were highly related to EVC in treating RA. Finally, we proved that EVC could inhibit the expression of NFTAc1, MMP9, Cathepsin K, and AKT which were closely related to osteoclast activity.

CONCLUSIONS

EVC could treat RA through multiple components, multiple targets, and multiple pathways. The present study demonstrated the therapeutic efficacy of EVC and its molecular mechanisms in treating RA, indicating that it would be a potent candidate as a novel botanical drug for further investigation.

Expression of AKT1 Related with Clinicopathological Parameters in Clear Cell Renal Cell Carcinoma

Pathways such as VEGF, EGF and mTOR are known to be one of the major mechanisms of tumorigenesis including kidney cancer. To identify potential signaling pathway proteins, we performed differential/correlation analyses of mTOR-associated genes from three public datasets. AKT1 protein, one of the PI3K/AKT/mTOR pathways, turned out to be the potential by showing a consistent discrepancy between ccRCC-associated conditions as well as strong correlation with other mTOR-associated genes across the datasets. Then, we analyzed how AKT1 alteration affects clear cell renal cell carcinoma. The pathology of 58 kidney cancer patients was constructed to analyze the relationship between the expression level of AKT1 through immunohistochemical staining and their clinicopathological data. Gender, age and TNM stage did not show significant results. AKT1 is a known oncogene. However, in this study, high expression of AKT1 showed a slight correlation with lower WHO/ISUP grade, longer recurrence-free and progression-free survival rates.

Data Mining, Network Pharmacology, and Molecular Docking Explore the Effects of Core Traditional Chinese Medicine Prescriptions in Patients with Rectal Cancer and Qi and Blood Deficiency Syndrome

Background

“Zheng” (syndrome) is the basic unit and the basis of traditional Chinese medicine (TCM) treatment. In clinical practice, we have been able to improve the survival time and quality of life for patients with rectal cancer through the treatment of “FuZhengXiaoJi” (strengthening the Qi and reducing accumulation).

Purpose

In this study, we elucidated the core prescriptions for patients with rectal cancer and Qi and blood deficiency syndrome, and we explored the potential mechanisms of the prescriptions using an integrated strategy that coupled data mining with network pharmacology.

Methods

A Bron–Kerbosch (BK) algorithm was applied to find the core prescriptions. The active ingredients, targets, activated signaling pathways, and biological functions of core prescriptions were analyzed using network pharmacology and directly associated proteins were docked using molecular docking technology to elucidate the multicomponent, multitarget, and inter-related components associated with TCM systematically.

Results

Data mining identified 3 core prescriptions, and most of the herbs consisted of “FuZhengXiaoJi” Fang. Network pharmacology identified 15 high-degree active ingredients among the 3 core prescriptions and 16 high-degree hub genes linked with both rectal cancer and the 3 core prescriptions. Additional Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of these 16 targets showed that the most significant pathways were MAPK, interleukin-17, tumor necrosis factor (TNF), and vascular endothelial growth factor (VEGF) pathways. From the 16 genes, TGFB1, IL1B, IL10, IL6, PTGS2, and PPARG closely interacted with the tumor microenvironment, and PPARG, MYC, and ERBB2 were closely linked to survival. In molecular docking, quercetin, kaempferol, and lauric acid showed good binding energy to each target.

Conclusion

Data mining, network pharmacology, and molecular docking may help identify core prescriptions, high-degree ingredients, and high-degree hub genes to apply to diseases and treatments. Furthermore, these studies may help discover hub genes that affect the tumor microenvironment and survival. The combination of these tools may help elucidate the relationship between herbs acting on “Zheng” (syndrome) and diseases, thus expanding the understanding of TCM mechanisms.

Systematic Elucidation of the Potential Mechanisms of Core Chinese Materia Medicas in Treating Liver Cancer Based on Network Pharmacology

Objective

In this study, the data mining method was used to screen the core Chinese materia medicas (CCMMs) against primary liver cancer (PLC), and the potential mechanisms of CCMMs in treating PLC were analyzed based on network pharmacology.

Methods

Traditional Chinese medicine (TCM) prescriptions for treating PLC were obtained from a famous TCM doctor in Shenzhen, China. According to the data mining technique, the TCM Inheritance Support System (TCMISS) was applied to excavate the CCMMs in the prescriptions. Then, bioactive ingredients and corresponding targets of CCMMs were collected using three different TCM online databases, and target genes of PLC were obtained from GeneCards and OMIM. Afterwards, common targets of CCMMs and PLC were screened. Furthermore, a network of CCMMs bioactive ingredients and common target gene was constructed by Cytoscape 3.7.1, and gene ontology (GO) and signaling pathways analyses were performed to explain the mechanism of CCMMs in treating PLC. Besides, protein-protein interaction (PPI) analysis was used to identify key target genes of CCMMs, and the prognostic value of key target genes was verified using survival analysis.

Results

A total of 15 high-frequency Chinese materia medica combinations were found, and CCMMs (including Paeoniae Radix Alba, Radix Bupleuri, Macrocephalae Rhizoma, Coicis Semen, Poria, and Curcumae Radix) were identified by TCMISS. A total of 40 bioactive ingredients (e.g., quercetin, kaempferol, and naringenin) of CCMMs were obtained, and 202 common target genes of CCMMs and PLC were screened. GO analysis indicated that biological processes of CCMMs were mainly involved in response to drug, response to ethanol, etc. Pathway analysis demonstrated that CCMMs exerted its antitumor effects by acting on multiple signaling pathways, including PI3K-Akt, TNF, and MAPK pathways. Also, some key target genes of CCMMs were determined by PPI analysis, and four genes (MAPK3, VEGFA, EGF, and EGFR) were found to be correlated with survival in PLC patients.

Conclusion

Based on data mining and network pharmacology methods, our results showed that the therapeutic effect of CCMMs on PLC may be realized by acting on multitargets and multipathways related to the occurrence and development of PLC.

Distinct functions of AKT isoforms in breast cancer: a comprehensive review

BACKGROUND

AKT, also known as protein kinase B, is a key element of the PI3K/AKT signaling pathway. Moreover, AKT regulates the hallmarks of cancer, e.g. tumor growth, survival and invasiveness of tumor cells. After AKT was discovered in the early 1990s, further studies revealed that there are three different AKT isoforms, namely AKT1, AKT2 and AKT3. Despite their high similarity of 80%, the distinct AKT isoforms exert non-redundant, partly even opposing effects under physiological and pathological conditions. Breast cancer as the most common cancer entity in women, frequently shows alterations of the PI3K/AKT signaling.

MAIN CONTENT

A plethora of studies addressed the impact of AKT isoforms on tumor growth, metastasis and angiogenesis of breast cancer as well as on therapy response and overall survival in patients. Therefore, this review aimed to give a comprehensive overview about the isoform-specific effects of AKT in breast cancer and to summarize known downstream and upstream mechanisms. Taking account of conflicting findings among the studies, the majority of the studies reported a tumor initiating role of AKT1, whereas AKT2 is mainly responsible for tumor progression and metastasis. In detail, AKT1 increases cell proliferation through cell cycle proteins like p21, p27 and cyclin D1 and impairs apoptosis e.g. via p53. On the downside AKT1 decreases migration of breast cancer cells, for instance by regulating TSC2, palladin and EMT-proteins. However, AKT2 promotes migration and invasion most notably through regulation of β-integrins, EMT-proteins and F-actin. Whilst AKT3 is associated with a negative ER-status, findings about the role of AKT3 in regulation of the key properties of breast cancer are sparse. Accordingly, AKT1 is mutated and AKT2 is amplified in some cases of breast cancer and AKT isoforms are associated with overall survival and therapy response in an isoform-specific manner.

CONCLUSIONS

Although there are several discussed hypotheses how isoform specificity is achieved, the mechanisms behind the isoform-specific effects remain mostly unrevealed. As a consequence, further effort is necessary to achieve deeper insights into an isoform-specific AKT signaling in breast cancer and the mechanism behind it.

The unconventional role of Akt1 in the advanced cancers and in diabetes-promoted carcinogenesis

Decades of research have elucidated the critical role of Akt isoforms in cancer as pro-tumorigenic and metastatic regulators through their specific effects on the cancer cells, tumor endothelial cells and the stromal cells. The pro-cancerous role of Akt isoforms through enhanced cell proliferation and suppression of apoptosis in cancer cells and the cells in the tumor microenvironment is considered a dogma. Intriguingly, studies also indicate that the Akt pathway is essential to protect the endothelial-barrier and prevent aberrant vascular permeability, which is also integral to tumor perfusion and metastasis. To complicate this further, a flurry of recent reports strongly indicates the metastasis suppressive role of Akt, Akt1 in particular in various cancer types. These reports emanated from different laboratories have elegantly demonstrated the paradoxical effect of Akt1 on cancer cell epithelial-to-mesenchymal transition, invasion, tumor endothelial-barrier disruption, and cancer metastasis. Here, we emphasize on the specific role of Akt1 in mediating tumor cell-vasculature reciprocity during the advanced stages of cancers and discuss how Akt1 differentially regulates cancer metastasis through mechanisms distinct from its pro-tumorigenic effects. Since Akt is integral for insulin signaling, endothelial function, and metabolic regulation, we also attempt to shed some light on the specific effects of diabetes in modulating Akt pathway in the promotion of tumor growth and metastasis.

Nodal pathway activation due to Akt1 suppression is a molecular switch for prostate cancer cell epithelial-to-mesenchymal transition and metastasis

Several studies have unraveled the negative role of Akt1 in advanced cancers, including metastatic prostate cancer (mPCa). Hence, understanding the consequences of targeting Akt1 in the mPCa and identifying its downstream novel targets is essential. We studied how Akt1 deletion in PC3 and DU145 cells activates the Nodal pathway and promotes PCa epithelial-to-mesenchymal transition (EMT) and metastasis. Here we show that Akt1 loss increases Nodal expression in PCa cells accompanied by activation of FoxO1/3a, and EMT markers Snail and N-cadherin as well as loss of epithelial marker E-cadherin. Treatment with FoxO inhibitor AS1842856 abrogated the Nodal expression in Akt1 deleted PCa cells. Akt1 deficient PCa cells exhibited enhanced cell migration and invasion in vitro and lung metastasis in vivo, which were attenuated by treatment with Nodal pathway inhibitor SB505124. Interestingly, Nodal mRNA analysis from two genomic studies in cBioportal showed a positive correlation between Nodal expression and Gleason score indicating the positive role of Nodal in human mPCa. Collectively, our data demonstrate Akt1-FoxO3a-Nodal pathway as an important mediator of PCa metastasis and present Nodal as a potential target to treat mPCa patients.

Phosphorylated Akt1 expression is associated with poor prognosis in cutaneous, oral and sinonasal melanomas

Melanomas are highly aggressive tumours derived from melanocytes, which occur most commonly in the skin. Occasionally, these tumours may appear in oral and sinonasal mucous membranes. In this study, we performed a comparative analysis of the Phosphorylated Akt1 (p-Akt1) expression in 144 patients affected by cutaneous (CM), 34 oral cavity (OM), and 31 sinonasal melanomas (SNM). Similar to the metastatic cutaneous melanomas, p-Akt1 was overexpressed in 17/34 of the oral cavity and 20/31 of the sinonasal melanomas. In addition, the p-Akt1-nuclear expression was associated with poorer cancer-specific survival in cutaneous (P < .0001), oral (P < .0001), and sinonasal (P = .001) melanomas. Multivariate analysis showed p-Akt1 to be an independent prognostic marker in oral (P = .041) and sinonasal (P < .0001) melanomas patients. In conclusion, p-Akt1 overexpression is an independent prognostic marker in mucosal melanomas and is significantly up-regulated in sinonasal melanomas. As both mucosal and metastatic cutaneous melanomas showed high frequency of p-Akt1 expression, these findings suggest that mucosal melanomas have a biological behaviour, similar to the aggressive cutaneous melanomas.

Akt1 inhibition promotes breast cancer metastasis through EGFR-mediated β-catenin nuclear accumulation

Background

Knockdown of Akt1 promotes Epithelial-to-Mesenchymal Transition in breast cancer cells. However, the mechanisms are not completely understood.

Methods

Western blotting, immunofluorescence, luciferase assay, real time PCR, ELISA and Matrigel invasion assay were used to investigate how Akt1 inhibition promotes breast cancer cell invasion in vitro. Mouse model of lung metastasis was used to measure in vivo efficacy of Akt inhibitor MK2206 and its combination with Gefitinib.

Results

Knockdown of Akt1 stimulated β-catenin nuclear accumulation, resulting in breast cancer cell invasion. β-catenin nuclear accumulation induced by Akt1 inhibition depended on the prolonged activation of EGFR signaling pathway in breast cancer cells. Mechanistic experiments documented that knockdown of Akt1 inactivates PIKfyve via dephosphorylating of PIKfyve at Ser³¹⁸ site, resulting in a decreased degradation of EGFR signaling pathway. Inhibition of Akt1 using MK2206 could induce an increase in the expression of EGFR and β-catenin in breast cancer cells. In addition, MK2206 at a low dosage enhance breast cancer metastasis in a mouse model of lung metastasis, while an inhibitor of EGFR tyrosine kinase Gefitinib could potentially suppress breast cancer metastasis induced by Akt1 inhibition.

Conclusion

EGFR-mediated β-catenin nuclear accumulation is critical for Akt1 inhibition-induced breast cancer metastasis.

Extracellular polyamines-induced proliferation and migration of cancer cells by ODC, SSAT, and Akt1-mediated pathway

High levels of polyamines were observed and were related to a poor prognosis in cancer patients. However, the mechanism is not obvious. The aim of this study is to mimic the extracellular polyamines in a tumor microenviroment and to explore the role of extracellular polyamines in the proliferation and migration of cancer cells. Three different concentrations of polyamines composed of putrescine, spermidine, and spermine were used. Colony formation assay, wound healing assay, and transwell migration assay were performed. Akt1-overexpression cells were constructed. The related protein expression was examined using a western blot. In this study, polyamines promoted colony formation and cell migration in a concentration-dependent and time-dependent manner. Polyamines upregulated the expression of ornithine decarboxylase (ODC), SSAT, Akt1, Akt, hypoxia-inducible factors-1α, vascular endothelial growth factor, and matrix metalloproteinases, and downregulated p27 expression. The effects of combination of polyamines and Akt1 overexpression on colony formation and migration were more obvious than the effects of Akt1 overexpression alone. In Akt1-overexpression cells, polyamines also upregulated the expression of ODC, SSAT, hypoxia-inducible factors-1α, vascular endothelial growth factor, and matrix metalloproteinases and downregulated p27 expression. In conclusion, extracellular polyamines induced proliferation and cancer cell migration by inducing ODC and SSAT expression, and the Akt1-mediated pathway.

Using machine learning algorithms to identify genes essential for cell survival

Background

With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive. One reason being that these diseases are driven by a network of genes that need to be targeted in order to understand and treat them effectively. Part of the solution lies in mining and integrating information from various disciplines. Here we propose a machine learning method to mining through publicly available literature on RNA interference with the goal of identifying genes essential for cell survival.

Results

A total of 32,164 RNA interference abstracts were identified from 10.5 million pubmed abstracts (2001 - 2015). These abstracts spanned over 1467 cancer cell lines and 4373 genes representing a total of 25,891 cell gene associations. Among the 1467 cell lines 88% of them had at least 1 or up to 25 genes studied in a given cell line. Among the 4373 genes 96% of them were studied in at least 1 or up to 25 different cell lines.

Conclusions

Identifying genes that are crucial for cell survival can be a critical piece of information especially in treating complex diseases, such as cancer. The efficacy of a therapeutic intervention is multifactorial in nature and in many cases the source of therapeutic disruption could be from an unsuspected source. Machine learning algorithms helps to narrow down the search and provides information about essential genes in different cancer types. It also provides the building blocks to generate a network of interconnected genes and processes. The information thus gained can be used to generate hypothesis which can be experimentally validated to improve our understanding of what triggers and maintains the growth of cancerous cells.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1799-1) contains supplementary material, which is available to authorized users.

MEK inhibitor, PD98059, promotes breast cancer cell migration by inducing β-catenin nuclear accumulation

Abnormal activation of the RAF/MEK/ERK signaling pathway has been observed in breast cancer. Thus, a number of MEK inhibitors have been designed as one treatment option for breast cancer. Although some studies have found that these MEK inhibitors inhibit the growth of a variety of human cancer cells, some trials have shown that the use of MEK inhibitors as a treatment for breast cancer does not adequately improve survival for unknown reasons. In the present study, MEK inhibitor PD98059 was used to evaluate its anticancer effects on human breast cancer MCF-7 and MDA-MB-231 cells and to explore the possible mechanism of action. Our results revealed that MEK inhibitor PD98059 exhibited antiproliferative effects in a dose- and time-dependent manner in MCF-7 and MDA-MB-231 breast cancer cells. Conversely, incubation of MCF-7 and MDA-MB-231 cells with PD98059 promoted their migration. Further investigation disclosed that the enhanced ability of migration promoted by PD98059 was dependent on β-catenin nuclear translocation in the MCF-7 and MDA-MB‑231 cells. Subsequent experiments documented that activation of EGFR signaling induced by PD98059 increased the amount of β-catenin in the nucleus. Taken together, our findings may elucidate a possible mechanism explaining the ineffectiveness of MEK inhibitors in breast cancer treatment and improve our understanding of the role of MEK in cancer.

Inhibition of AKT1 signaling promotes invasion and metastasis of non-small cell lung cancer cells with K-RAS or EGFR mutations

Accumulating evidence supports a role of the PI3K-AKT pathway in the regulation of cell motility, invasion and metastasis. AKT activation is known to promote metastasis, however under certain circumstances, it also shows an inhibitory activity on metastatic processes, and the cause of such conflicting results is largely unclear. Here we found that AKT1 is an important regulator of metastasis and down-regulation of its activity is associated with increased metastatic potential of A549 cells. Inhibition of AKT1 enhanced migration and invasion in KRAS- or EGFR-mutant non-small cell lung cancer (NSCLC) cells. The allosteric AKT inhibitor MK-2206 promoted metastasis of KRAS-mutated A549 cells in vivo. We next identified that the phosphorylation of Myristoylated alanine-rich C-kinase substrate (MARCKS) and LAMC2 protein level were increased with AKT1 inhibition, and MARCKS or LAMC2 knockdown abrogated migration and invasion induced by AKT1 inhibition. This study unravels an anti-metastatic role of AKT1 in the NSCLC cells with KRAS or EGFR mutations, and establishes an AKT1-MARCKS-LAMC2 feedback loop in this regulation.