Advances in cutaneous squamous cell carcinoma

TL;DR: This article provides a comprehensive overview of current knowledge of cSCC pathogenesis, emphasizing dysregulated signaling pathways and the significance of molecular profiling, and explores resistance mechanisms to molecularly targeted therapies and proposes methods to overcome them.

TL;DR: MALDI-MSI combined with machine learning achieves 92.3% predictive accuracy in identifying cutaneous squamous cell carcinoma, enabling detailed metabolomic analysis and potential improvements in diagnosis and therapy, with implications for clinical diagnosis of SCC and related keratinocyte carcinomas.

TL;DR: Keratinocytes play a crucial role in skin cancer initiation and progression, particularly in non-melanoma skin cancers and melanoma, through dysregulation of proliferation and differentiation, and may serve as biomarkers or therapeutic targets.

TL;DR: Lymphocyte activation-gene 3 is expressed on SCC infiltrating T lymphocytes at a higher percentage than PD-1 and LAG-3 mRNA expression is significantly higher in SCC tumors.

TL;DR: In this review the usual methods applied in systematic reviews and meta-analyses are outlined, and the most common procedures for combining studies with binary outcomes are described, illustrating how they can be done using Stata commands.

TL;DR: The four articles in this special section onMeta-analysis illustrate some of the complexities entailed in meta-analysis methods and contributes both to advancing this methodology and to the increasing complexities that can befuddle researchers.

TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.

TL;DR: Tumors of epithelioma are composed of two discrete but interdependent compartments: the malignant cells themselves and the stroma that they induce and in which they are dispersed.