Current Cancer Drug Targets - Online First

Lenvatinib is an oral tyrosine kinase inhibitor that selectively inhibits receptors involved in tumor angiogenesis and tumor growth. It is an emerging first-line treatment agent for hepatocellular carcinoma (HCC). However, there is no intravenous administration of Lenvatinib.

This study aimed to construct nanocomposites that can efficiently support Lenvatinib and target liver cancer tissues and cells.

In this study, ferric oxide-viral-like mesoporous silica nanoparticles-folic acid (Fe3O4-vMSN-FA) nanocomposites loaded with Lenvatinib were constructed, and their anti-hepatocellular carcinoma effects were evaluated.

The hydrothermal method was used to synthesize ferric oxide (Fe3O4). Ferric oxide-viral-like mesoporous silica nanoparticles (Fe3O4-vMSN) were synthesized using a two-phase method. Then, Fe3O4-vMSN was modified with folic acid (Fe3O4-vMSN-FA) to better target tumor cells.

The experimental data showed that Fe3O4-vMSN-FA nanocomposites were successfully synthesized and could be loaded with Lenvatinib (Len@ Fe3O4-vMSN-FA). Fe3O4-vMSN-FA had good stability and biocompatibility, and it can release the loaded Lenvatinib faster in an acidic environment (pH 5.5). CCK8 assay and flow cytometry showed that HepG2 cells in the Len@ Fe3O4-vMSN group had the lowest cell viability and the highest apoptosis rate, confirming the anticancer properties of Len@ Fe3O4-vMSN-FA in vitro. In addition, transwell experiments showed that the migration and invasion ability of HepG2 cells in the Len@ Fe3O4-vMSN-FA group were significantly inhibited. In vivo fluorescence imaging in mice confirmed the enhanced tumor-targeting ability of Fe3O4-vMSN-FA. The tumor volume of the Len@ Fe3O4-vMSN-FA group was significantly reduced, and there was no significant effect on body weight. Moreover, serum liver function index (ALT and AST) and HE staining showed that Len@ Fe3O4-vMSN-FA did not cause obvious damage to organ tissue.

Len@ Fe3O4-vMSN-FA has a good anti-liver cancer effect. Fe3O4-vMSN-FA can be used as an alternative platform for drug delivery, providing more options for cancer therapy.

Nasopharyngeal cancer [NPC] is prevalent in Southeast Asia and North Africa, and is generally associated with limited treatment options and poor patient prognosis.

Ferroptosis is a recently observed cell death modality and has been shown to link to the efficacy of different anti-cancer treatments, thus offering opportunities for the development of novel therapies. This study aims to investigate the potentiating effects of COX-2 inhibitors on ferroptosis in nasopharyngeal cancer.

The inhibitory effects of COX-2 inhibitors celecoxib and rofecoxib on nasopharyngeal cancer cells were assessed with MTT, colony formation, sphere formation, Transwell, and wound healing assays. The status of COX-2 with celecoxib and rofecoxib treatment was investigated by Western blotting and immunofluorescence experiments. Ferroptosis was induced with the GPX4 inhibitor RSL3 with or without COX-2 inhibition and was monitored by fluorescence microscopy. Transcriptomic profiling was conducted with 5-8F cells treated with DMSO as control or celecoxib, and ferroptosis-related candidates were validated by RT-PCR analysis.

Celecoxib and rofecoxib effectively inhibited the growth and migration of nasopharyngeal cancer cells. Both inhibitors evidently sensitized nasopharyngeal cancer cells to ferroptosis induction by RSL3, with celecoxib outperforming rofecoxib. Celecoxib treatment resulted in significantly differentially expressed genes in 5-8F cells, among which CHAC1 was validated as a ferroptosis-related target.

The COX-2 inhibitor celecoxib effectively sensitized nasopharyngeal cancer cells to ferroptosis induction.

Gastrointestinal (GI) cancers represent some of the most common and lethal malignancies globally, underscoring the urgent need for improved diagnostic strategies. Traditional diagnostic methods, while effective to some degree, are often invasive and unsuitable for regular screenings.

This review article explores integrating machine learning (ML) with liquid biopsy techniques as a revolutionary approach to enhance the detection and monitoring of GI cancers. Liquid biopsies offer a non-invasive alternative for cancer detection through the analysis of circulating tumor DNA (ctDNA) and other biomarkers, which when combined with ML, can significantly improve diagnostic accuracy and patient outcomes.

We conducted a comprehensive review of recent advancements in liquid biopsy and ML, focusing on their synergistic potential in the early detection of GI cancers. The review addresses the application of next-generation sequencing and digital droplet PCR in enhancing the sensitivity and specificity of liquid biopsies.

Machine learning algorithms have demonstrated remarkable ability in navigating complex datasets and identifying diagnostically significant patterns in ctDNA and other circulating biomarkers. Innovations such as machine learning-enhanced “fragmentomics” and tomographic phase imaging flow cytometry illustrate significant strides in non-invasive cancer diagnostics, offering enhanced detection capabilities with high accuracy.

The integration of ML in liquid biopsy represents a transformative step in the early detection and personalized treatment of GI cancers. Future research should focus on overcoming current limitations, such as the heterogeneity of tumor-derived genetic materials and the standardization of liquid biopsy protocols, to fully realize the potential of this technology in clinical settings.

Data sourced from the Surveillance, Epidemiology, and End Results (SEER) database encompassing liver cancer diagnoses from 2000 to 2017 were utilized. The standardized mortality rate (SMR) was computed using general population reference data, and multivariate competing risk models were employed for analysis.

Analysis of 70,733 liver cancer patient records revealed 1,954 instances of CVM. The overall CVM SMR for liver cancer patients was 12.01 (95% CI: 11.48-12.55). Various demographic and clinical factors, including sex, race, age, year of diagnosis, pathological type, general stage, treatment modalities, and matrimonial status, emerged as liver cancer patients` independent predictors of CVM.

Liver cancer patients have a notably heightened susceptibility to cardiovascular mortality (CVM) in contrast to the general populace. It is imperative to promptly recognize high-risk subcategories and execute tailored cardiovascular interventions as crucial measures to bolster survival rates within this cohort of patients.

Ferroptosis, a regulated cell death initiated by Fe-dependent lipoperoxidation, is closely linked to the development of lung adenocarcinoma (LUAD). LncRNA human leukocyte antigen complex P5 (HCP5) has been confirmed as oncogenic in LUAD, but its function in ferroptosis is unknown.

Based on the previous bioinformatics mining of the ceRNA (competitive endogenous RNA) network HCP5/miR-17-5p/ Homeobox A7 (HOXA7) related to ferroptosis in LUAD, in this study, we characterized the cell-based experiments to validate the binding between the HCP5/miR-17-5p/HOXA7 axis and ferroptosis.

The HCP5/miR-17-5p/HOXA7 linkage was identified by a two-luciferase reporter. Cell Counting Kit-8 (CCK-8) and Transwell assay were employed for the detection of viability, invasion, and migration of A549 cells, respectively. ACSL4 and SLC7A11 were associated with ferroptosis, MMP 9, vimentin, and E-cadherin, which were associated with migration and invasion and were assessed by WB and qRT-PCR. Fe2+ and malondialdehyde (MDA) were analyzed using kits.

Over-expression of HCP5 enhances the growth, invasion, and migration of A549 cells by adjusting miR-17-5P to increase the expression of HOXA7. In addition, the knockdown of HCP5 elevated miR-17-5p, which inhibited HOXA7 expression and suppressed ferroptosis and EMT in A549 cells.

HCP5/miR-17-5p/HOXA7 can affect ferroptosis as well as the biological behavior of A549 cells.

PANoptosis, a novelty mechanism of cell death involving crosstalk between apoptosis, pyroptosis, and necroptosis, is strongly associated with tumor cell death and immunotherapy efficacy. However, its relevance in lung adenocarcinoma (LUAD) remains to be elucidated.

In this study, we acquired 18 PANoptosis-related differentially expressed gene (PRDEG) of LUAD. Based on these genes, LUAD samples were identified with different subtypes by unsupervised clustering. Next, we compared the differences between the subtypes, including clinical features, immune microenvironment, and potentially sensitive drugs. Furthermore, we used machine learning to identify hub prognostic PRDEGs, construct a risk score, and validate it on other external datasets. We incorporated the patient's clinical information and risk score into the proportional hazards model and lasso-cox models to find key prognostic features and constructed five prognostic models. The best model was identified via the area under the curve and validated on an external dataset.

LUAD patients were divided into two clusters named C1 and C2, respectively. The C2 cluster exhibited shorter survival time, more advanced tumor stage, higher suppressive immune cell scores, such as dendritic cells, and higher expression of inhibitory immune checkpoints, such as LAG3 and CD86. TIMP1, CAV1, and CD69 were recognized as key prognostic factors, and risk scores predicted survival with significant differences in the external validation set. Risk score and N-stage were identified as critical prognostic features. The Coxph model outperformed other machine learning clinical models. The 1-, 3-, and 5-year time-ROCs in the external validation set were 0.55, 0.59, and 0.60, respectively.

We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of patients with LUAD as well as the tumor microenvironment.