The association between chronic inflammation and cancer has reshaped our understanding of tumorigenesis and cancer therapy. Inflammatory responses can both promote and suppress cancer, depending on the context and timing. Key molecular players, such as nuclear factor kappa-light-chain-enhancer of activated B cells, interleukin-6, signal transducer and activator of transcription 3, and a variety of immune cell types, including tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, orchestrate an environment conducive to tumor survival, angiogenesis, metastasis, and immune evasion. In recent years, immunotherapy, particularly immune checkpoint inhibitors, has revolutionized cancer treatment. However, its success varies across tumor types and patients, underscoring the need to understand the tumor microenvironment and inflammatory context. This review examines the mechanistic underpinnings of inflammation-driven cancer, discusses translational research efforts targeting inflammatory pathways, and explores clinical applications, including the integration of immunotherapy with anti-inflammatory agents and biomarkers for personalized treatment. Future directions in the field include the application of artificial intelligence, microbiome research, single-cell technologies, and gene editing tools to further tailor therapies and overcome resistance mechanisms.

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment. Conventional computer-aided detection systems have shown limitations, including high false-positive rates and low sensitivity. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown great potential in improving the accuracy and reliability of nodule detection and classification. This study aimed to develop and evaluate an automatic method for lung nodule detection and classification using a CNN-based architecture applied to computed tomography images from the publicly available LIDC-IDRI database.

Many diseases are driven by proteins that are difficult or impossible to inhibit with conventional drugs. Instead of blocking their activity, an emerging therapeutic strategy aims to remove these proteins entirely from the cell by harnessing the cell’s own degradation machinery. In a new study, researchers at CeMM, AITHYRA and the Scripps Research Institute have now developed a systematic method to discover such protein-degrading compounds on a large scale. The approach, published in Nature Chemical Biology (DOI: 10.1038/s41589-025-02137-2) provides a powerful new route toward therapies for diseases such as certain aggressive forms of leukemia.Many diseases are driven by proteins that are difficult or impossible to inhibit with conventional drugs. Instead of blocking their activity, an emerging therapeutic strategy aims to remove these proteins entirely from the cell by harnessing the cell’s own degradation machinery. In a new study, researchers at CeMM, AITHYRA and the Scripps Research Institute have now developed a systematic method to discover such protein-degrading compounds on a large scale. The approach, published in Nature Chemical Biology (DOI: 10.1038/s41589-025-02137-2) provides a powerful new route toward therapies for diseases such as certain aggressive forms of leukemia.

Insulin resistance — when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels — is one of the fundamental causes of diabetes. In addition to diabetes, it is widely known that insulin resistance can lead to cardiovascular, kidney and liver diseases. While insulin resistance is tightly associated with obesity, it has been difficult to evaluate insulin resistance itself in the clinic. For the first time, researchers including those from the University of Tokyo applied a machine learning-based prediction model of insulin resistance to half a million participants from the UK Biobank and demonstrated that insulin resistance is a risk factor for 12 types of cancer. 

Salk Institute scientists find the genome’s dynamic 3D shape influences gene expression, and that the protein NIPBL is a key facilitator of genome structures that inform cell identity. Their findings may inform new therapeutics for disorders related to dysfunctional genome folding, including some cancers and developmental disorders such as autism-related disorders.

The intricate, lifelong conversation between blood vessels and immune system is fundamental to health, and its breakdown is pathogenic to many diseases. A review, published on February 10, 2026, in Immunity & Inflammation by Prof. Yihai Cao at the Karolinska Institute, Sweden, provides a systematic and mechanistic framework for understanding this dynamic crosstalk, offering a unified perspective on how vascular endothelial cells orchestrate immune responses and how their dysfunction leads to pathology.

A next-generation antibody–drug conjugate has transformed treatment for HER2-positive gastric cancer, yet many patients fail to benefit or relapse rapidly.