Current Cancer Drug Targets - Online First

The primary objective of this review is to provide updated mechanisms that regulate ferroptosis sensitivity in cancer cells and recent advancements in drug targeting for ferroptosis as an antitumor therapy.

To achieve these objectives, a comprehensive literature review was conducted, analyzing recent studies on ferroptosis, including its cellular, molecular, and gene-level characteristics. The review involved an evaluation of advancements in ferroptosis drug research across various medical domains, with particular attention to novel therapeutic approaches in nanomedicine, TCM, and Western medicine. The review also included an assessment of how ferroptosis influences cancer treatment, including its role in tumor drug resistance and immunotherapy, and provided a detailed analysis of pharmacological activators of ferroptosis.

The review highlights several key findings, like primary mechanisms that regulate cancer cell sensitivity to ferroptosis, and provides an overview of the latest advancements in ferroptosis drug research. The review reveals that ferroptosis has both beneficial and detrimental effects on human cancer, reflecting its complex role in cancer progression and treatment. The review also emphasizes the dual nature of ferroptosis, noting its potential as both a tumor suppressor and an oncogenic factor. Additionally, it provides a comprehensive examination of various pharmacological agents that activate ferroptosis and their potential therapeutic applications.

In conclusion, ferroptosis represents a promising target for cancer therapy, given its distinctive characteristics and significant role in tumor biology. The review underscores the need for further research to clarify the complex roles of ferroptosis in carcinogenesis and to optimize the development of novel therapeutics targeting this form of cell death. It also highlights current challenges and opportunities in the field, including the potential for overcoming cancer metastasis through ferroptosis modulation and the need for continued exploration of pharmacological activators to advance therapeutic strategies.

Cyclin-Dependent Kinase 8 (CDK8) is a paracrine transcriptional regulator involved in regulating cellular stress response, growth, and neurological functions in conjunction with mediator complex subunits 12 (MED12), MED13, and cyclin C.

Studying the relationship between CDK8 and cervical squamous cell carcinoma (CESC) has significant clinical implications in diagnosis, treatment, and prognosis. Therefore, we analyzed the relationship between CDK8 and poor prognosis of CESC.

Differentially expressed genes and their functional enrichment were analyzed using limma R software package. Immune cell infiltration was measured by TIMER and “GSVA” R packaging.

The results indicated that CDK8 was overexpressed in CESC, and the survival rate of patients in the CDK8 hypermethylation group was higher than that in the CDK8 hypomethylation group. In addition, CDK8 was associated with immune cell infiltration in tumor tissues.

Overall, these findings provide more evidence for the relationship between CDK8 and a patients overall survival, which could provide insights into clinical diagnosis and prognosis prediction for CESC.

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.

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.