Comprehensive characterization of the molecular feature of T cells in laryngeal cancer: evidence from integrated single-cell and bulk RNA sequencing data using multiple machine learning approaches

BACKGROUND

The clinical relevance of T cell-related molecules at single-cell resolution in laryngeal cancer (LC) has not been clarified.

MATERIALS AND METHODS

Three LC tissues and matching adjoining normal tissues from the hospital were used to perform 10X single-cell RNA sequencing. Hub T cell-related genes (TCRGs) were detected by applying ten machine learning (ML) techniques based TCGA and GEO databases, which were also utilized to create a prediction model (TCRG classifier) and a multicenter validation model. Lastly, we conducted a comprehensive analysis of the TCRG's correlation with immunological properties.

RESULTS

The analysis of single-cell RNA-seq data revealed that T cells are the primary components of the tumor microenvironment (TME), are significantly involved in cell differentiation pathways, and play a considerable role in intercellular communication. Based on 10 ML approaches, TCRG classifier were identified to develop and validate. The TCRG classifier exhibited excellent prognostic values with a mean C-index of 0.66 in six cohorts, serving as an independent risk factor (p < 0.01). Additionally, the TCRG exhibited a significant relationship with immune score, immune cell infiltration, immune-associated pathways, immune checkpoint inhibitors, human leukocyte antigen, and immunogenicity. Lastly, IPS, TCIC, TIDE, and IMvigor210 cohort analysis illustrated that the immunotherapy response may be accurately predicted using TCRG.

CONCLUSION

A TCRG classifier is an excellent resource for predicting a patient's prognosis, potentially guiding the preservation of laryngeal function, and identifying patients who may have a positive response to immunotherapy, which might have profound effects on therapeutic practice.

Pyroptosis in lung cancer: The emerging role of non-coding RNAs

Lung cancer remains an intractable malignancy worldwide, prompting novel therapeutic modalities. Pyroptosis, a lethal form of programmed cell death featured by inflammation, has been involved in cancer progression and treatment response. Simultaneously, non-coding RNA has been shown to have important roles in coordinating pattern formation and oncogenic pathways, including long non-coding RNA (lncRNAs), microRNA (miRNAs), circular RNA (circRNAs), and small interfering RNA (siRNAs). Recent studies have revealed that ncRNAs can promote or inhibit pyroptosis by interacting with key molecular players such as NLRP3, GSDMD, and various transcription factors. This dual role of ncRNAs offers a unique therapeutic potential to manipulate pyroptosis pathways, providing opportunities for innovative cancer treatments. In this review, we integrate current research findings to propose novel strategies for leveraging ncRNA-mediated pyroptosis as a therapeutic intervention in lung cancer. We explore the potential of ncRNAs as biomarkers for predicting patient response to treatment and as targets for overcoming resistance to conventional therapies.

T cell effects and mechanisms in immunotherapy of head and neck tumors

Head and neck tumors (HNCs) are a common tumor in otorhinolaryngology head and neck surgery, accounting for 5% of all malignant tumors in the body and are the sixth most common malignant tumor worldwide. In the body, immune cells can recognize, kill, and remove HNCs. T cell-mediated antitumor immune activity is the most important antitumor response in the body. T cells have different effects on tumor cells, among which cytotoxic T cells and helper T cells play a major killing and regulating role. T cells recognize tumor cells, activate themselves, differentiate into effector cells, and activate other mechanisms to induce antitumor effects. In this review, the immune effects and antitumor mechanisms mediated by T cells are systematically described from the perspective of immunology, and the application of new immunotherapy methods related to T cells are discussed, with the objective of providing a theoretical basis for exploring and forming new antitumor treatment strategies. Video Abstract.

Anticalin® proteins: from bench to bedside

INTRODUCTION

Anticalin proteins are engineered versions of lipocalins that constitute a novel class of clinical-stage biopharmaceuticals. The lipocalins exhibit a central β-barrel with eight antiparallel β-strands and an α-helix attached to its side. Four structurally variable loops at the open end of the β-barrel form a pronounced binding pocket, which can be reshaped to generate specificities toward diverse disease-relevant molecular targets.

AREAS COVERED

This article reviews the current status of Anticalin engineering, from the basic principles to the development of Anticalins with high target affinity and specificity via combinatorial protein design and directed evolution, including examples of Anticalin-based drug candidates under preclinical and clinical development.

EXPERT OPINION

Combinatorial gene libraries together with powerful molecular selection techniques have enabled the expansion of the natural ligand specificities of lipocalins from small molecules to peptides and proteins. This biomolecular concept has been validated by structural analyses of a series of Anticalin•target complexes. Promising Anticalin lead candidates have reached different preclinical and clinical development stages in the areas of (immuno)oncology, metabolic, and respiratory diseases, as antidotes to treat intoxications and as novel antibiotics. Thus, Anticalins offer an alternative to antibodies with promising and potentially superior features as next-generation biologics.

Radiotherapy, Lymphopenia, and Host Immune Capacity in Glioblastoma: A Potentially Actionable Toxicity Associated With Reduced Efficacy of Radiotherapy

Radiotherapy is cytotoxic to tumor cells and is therefore a critical component of therapy for many malignancies, including glioblastoma (GBM). We now appreciate the value of the immunomodulatory effects of radiation that may be important to overall therapeutic success in some patients with this primary brain tumor. Although potentially beneficial immune-stimulating properties of radiotherapy treatment have been the focus of recent study, this modality is actually at the same time associated with the depletion of lymphocytes, which are crucial to the defense against neoplastic development and progression. In this review, we describe the association of systemic lymphopenia with poor tumor outcome, present evidence that radiotherapy is an important contributing cause of lymphodepletion, describe the systemic immune context of tumor and brain injury that contributes to immunosuppression, describe other contributing factors to lymphopenia including concomitant medications and treatments, and speculate about the role of the normal physiologic response to brain injury in the immunosuppressive dynamics of GBM. Radiotherapy is one significant and potentially actionable iatrogenic suppressor of immune response that may be limiting the success of therapy in GBM and other tumor types. Altered strategies for radiotherapy more permissive of a vigorous antineoplastic immune response may improve outcome for malignancy.

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.