Data presented in this article are supplementary materials to the research article entitled "IGFBP2 regulates PD-L1 expression by activating the EGFR-STAT3 signaling pathway in malignant melanoma". Data for melanoma patients who did not receive anti-PD-1 treatment were obtained from Tianjin Medical University Cancer Institute & Hospital from February 1981 to May 2013. Kaplan-Meier was used for survival analysis. RNA sequencing data from 28 melanoma patients receiving anti-PD-1 therapy were download from GEO database (GSE78220). Cluster analysis of RNA expression was performed using R (package pheatmap). The difference of PD-L1 expression was analysed by the Boxplot (R ggplot2 package). Differences between each group were analyzed by Fisher exact test. Information of 13 melanoma patients who had failed prior chemotherapy and treated in the Tianjin Medical University Cancer Institute & Hospital between July 2015 and December 2018 was collected. The response was captured by Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1).
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http://dx.doi.org/10.1016/j.dib.2020.105523 | DOI Listing |
Br J Dermatol
January 2025
Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, the Netherlands.
Background: Patients with haematologic malignancies are at increased risk of developing skin cancer and often experience worse skin cancer-related outcomes. However, there is a lack of nationwide, population-based data with long-term follow-up on the incidence and risks of different skin cancer types across all haematologic malignancies.
Objectives: To assess population-based risk estimates for cutaneous squamous cell carcinoma (CSCC), malignant melanoma (MM), Merkel cell carcinoma (MCC), and basal cell carcinoma (BCC) among patients with haematologic malignancies, stratified by skin cancer type and haematologic malignancy subgroup.
J Eur Acad Dermatol Venereol
January 2025
Pathology Department, IHP Group, Nantes, France.
Background: There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly-supervised deep-learning approach, SmartProg-MEL, to predict survival outcomes in stages I to III melanoma patients from HE-stained whole slide image (WSI).
Methods: We designed a deep neural network that extracts morphological features from WSI to predict 5-y overall survival (OS), and assign a survival risk score to each patient.
J Pathol
January 2025
SIREDO Oncology Center (Care, Innovation and Research for Children and AYA with Cancer), Institut Curie, Université Paris Cité, Paris, France.
Rhabdoid tumours (RT) are an aggressive malignancy affecting <2-year-old infants, characterised by biallelic loss-of-function alterations in SWI/SNF-related BAF chromatin remodelling complex subunit B1 (SMARCB1) in nearly all cases. Germline SMARCB1 alterations are found in ~30% of patients and define the RT Predisposition Syndrome type 1 (RTPS1). Uveal melanoma (UVM), the most common primary intraocular cancer in adults, does not harbour SMARCB1 alterations.
View Article and Find Full Text PDFClin Pract
January 2025
Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800010 Galati, Romania.
Cutaneous squamous scell carcinoma (cSCC) is a frequent non-melanoma skin cancer that originates from keratinocytes with increased prevalence. cSCC can be either in situ, as in Bowen's disease, or extended. Advanced age, accumulated sun exposure, light pigmentation, and prior skin cancer diagnosis are all significant risk factors for cSCC.
View Article and Find Full Text PDFCells
January 2025
Institute for Population and Precision Health (IPPH), University of Chicago, Chicago, IL 60637, USA.
Arsenic (As) is a risk factor for non-melanoma skin cancer (NMSC). From a six-year follow-up study on 7000 adults exposed to As, we reported the associations of single-nucleotide variation in tumor tissue and gene expression. Here, we identify the associations of small deletions (DELs) and transcriptomic profiles in NMSC.
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