Ferroptosis and WDFY4 as novel targets for immunotherapy of lung adenocarcinoma

Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer

Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction.

Oleic Acid Inhibits SDC4 and Promotes Ferroptosis in Lung Cancer Through GPX4/ACSL4

INTRODUCTION

As a common malignancy, lung cancer has a relatively poor prognosis and a low survival rate. In recent years, ferroptosis, as an emerging filed, has great promise in the potential treatment of cancer. Brucea javanica oil (BJO) is often used to treat various cancers. Oleic acid (OA) is the main ingredient of BJO. In this study, we investigated the role and molecular mechanism of OA in lung cancer treatment by promoting ferroptosis.

METHODS

In this study, A549 cells and H1299 cells were used for in vitro experiments, and a CCK-8 test, scratch test, and MTT experiment were carried out. We examined reactive oxygen species (ROS), the JC-1 probe, glutathione (GSH) expression, lipid peroxidation, SDC4 mRNA levels, and ACSL4, SLC7A11, GPX4, and SDC4 protein levels.

RESULTS

The results showed that OA could inhibit the proliferation and migration of A549 cells and H1299 cells, SDC4 was a potential therapeutic target of OA against lung cancer, and OA treatment significantly inhibited the expression of SDC4 in A549 cells and H1299 cells. OA induces ferroptosis in A549 cells and H1299 cells, decreases GSH levels, increases lipid peroxidation levels, and decreases SDC4 mRNA expression; in addition, OA upregulates ACSL4 expression and decreases SLC7A11, GPX4, and SDC4 expression.

CONCLUSION

This study confirmed that OA could inhibit SDC4 expression and promote the occurrence of ferroptosis in A549 cells and H1299 cells through the GPX4/ACSL4 pathway, providing an effective basis for the use of drugs targeting ferroptosis in lung cancer treatment.

METTL Family in Healthy and Disease

Transcription, RNA splicing, RNA translation, and post-translational protein modification are fundamental processes of gene expression. Epigenetic modifications, such as DNA methylation, RNA modifications, and protein modifications, play a crucial role in regulating gene expression. The methyltransferase-like protein (METTL) family, a constituent of the 7-β-strand (7BS) methyltransferase subfamily, is broadly distributed across the cell nucleus, cytoplasm, and mitochondria. Members of the METTL family, through their S-adenosyl methionine (SAM) binding domain, can transfer methyl groups to DNA, RNA, or proteins, thereby impacting processes such as DNA replication, transcription, and mRNA translation, to participate in the maintenance of normal function or promote disease development. This review primarily examines the involvement of the METTL family in normal cell differentiation, the maintenance of mitochondrial function, and its association with tumor formation, the nervous system, and cardiovascular diseases. Notably, the METTL family is intricately linked to cellular translation, particularly in its regulation of translation factors. Members represent important molecules in disease development processes and are associated with patient immunity and tolerance to radiotherapy and chemotherapy. Moreover, future research directions could include the development of drugs or antibodies targeting its structural domains, and utilizing nanomaterials to carry miRNA corresponding to METTL family mRNA. Additionally, the precise mechanisms underlying the interactions between the METTL family and cellular translation factors remain to be clarified.

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy

BACKGROUND

Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized.

METHODS

We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database.

RESULTS

We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups.

CONCLUSION

Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.