Different prognosis in cutaneous early-onset and late-onset Merkel cell carcinoma: a population-based retrospective study.

BACKGROUND: Merkel cell carcinoma (MCC) is a rare and highly aggressive form of skin cancer. However, there is limited research on the clinicopathological features of early-onset MCC (EOMCC) and the differences between EOMCC and late-onset MCC (LOMCC). Our objective was to evaluate the clinicopathological features and cancer-specific survival (CSS) of EOMCC. METHODS: Our cohort study analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2018, to December 31, 2020. Data from 1941 patients who were diagnosed with primary cutaneous MCC were included. We then divided the patients with MCC into two groups: those with EOMCC (526 patients) and those with LOMCC (1415 patients). CSS is used as the primary outcome. RESULTS: The EOMCC group exhibited trends toward advanced tumor progression, an expanded surgical scope, increased lymph node retrieval, intensified radiotherapy, greater utilization of systemic therapy, and a better prognosis. Multivariate analysis revealed that LOMCC (HR 3.305 [2.002-5.456], P < 0.001), advanced T stage (HR 1.430 [1.139-1.797], P = 0.002), advanced N stage (HR 1.522 [1.221-1.897], P < 0.001), M1 stage (HR 2.587 [1.480-4.521], P < 0.001), and radiation (HR 0.586 [0.410-0.837], P = 0.003) were significantly associated with CSS. Among these factors, EOMCC/LOMCC was most strongly associated with CSS, indicating that LOMCC is an independent risk factor for CSS. Interestingly, we found that regional EOMCC and localized or in situ LOMCC had almost completely overlapping survival curves (Plog-rank = 0.620). Additionally, we observed that the TNM staging + age model was a more accurate predictor of CSS among MCC patients than using TNM staging alone. CONCLUSIONS: We found that EOMCC has distinct clinicopathological features compared to LOMCC. EOMCC is associated with better CSS. The combination of TNM staging and age was more accurate for predicting patient outcomes than TNM staging alone.

Potential crosstalk between SPP1 + TAMs and CD8 + exhausted T cells promotes an immunosuppressive environment in gastric metastatic cancer.

BACKGROUND: Immunotherapy brings new hope to patients with advanced gastric cancer. However, liver metastases can reduce the efficacy of immunotherapy in patients. Tumor-associated macrophages (TAMs) may be the cause of this reduction in efficacy. SPP1 + TAMs are considered to have immunosuppressive properties. We aimed to investigate the involvement of SPP1 + TAMs in the metastasis of gastric cancer. METHODS: The single-cell transcriptome was combined with batched BULK datasets for analysis. Animal models were used to verify the analysis results. RESULTS: We reveal the interaction of SPP1 + TAMs with CD8 + exhausted T cells in metastatic cancer. Among these interactions, GDF15-TGFBR2 may play a key immunosuppressive role. We constructed an LR score to quantify interactions based on ligands and receptors. The LR score is highly correlated with various immune features and clinical molecular subtypes. The LR score may also guide the prediction of the efficacy of immunotherapy and prognosis. CONCLUSIONS: The crosstalk between SPP1 + TAMs and CD8 + exhausted T cells plays a key immunosuppressive role in the gastric metastatic cancer microenvironment.

Development and validation of a novel diagnostic model for initially clinical diagnosed gastrointestinal stromal tumors using an extreme gradient-boosting machine.

INTRODUCTION: Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal soft tissue tumor. Clinical diagnosis mainly relies on enhanced CT, endoscopy and endoscopic ultrasound (EUS), but the misdiagnosis rate is still high without fine needle aspiration biopsy. We aim to develop a novel diagnostic model by analyzing the preoperative data of the patients. METHODS: We used the data of patients who were initially diagnosed as gastric GIST and underwent partial gastrectomy. The patients were randomly divided into training dataset and test dataset at a ratio of 3 to 1. After pre-experimental screening, max depth = 2, eta = 0.1, gamma = 0.5, and nrounds = 200 were defined as the best parameters, and in this way we developed the initial extreme gradient-boosting (XGBoost) model. Based on the importance of the features in the initial model, we improved the model by excluding the hematological features. In this way we obtained the final XGBoost model and underwent validation using the test dataset. RESULTS: In the initial XGBoost model, we found that the hematological indicators (including inflammation and nutritional indicators) examined before the surgery had little effect on the outcome, so we subsequently excluded the hematological indicators. Similarly, we also screened the features from enhanced CT and ultrasound gastroscopy, and finally determined the 6 most important predictors for GIST diagnosis, including the ratio of long and short diameter under CT, the CT value of the tumor, the enhancement of the tumor in arterial period and venous period, existence of liquid area and calcific area inside the tumor under EUS. Round or round-like tumors with a CT value of around 30 (25-37) and delayed enhancement, as well as liquid but not calcific area inside the tumor best indicate the diagnosis of GIST. CONCLUSIONS: We developed a model to further differential diagnose GIST from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results of clinical examinations that most patients should have completed before surgical resection.