Multiomics integration-based immunological characterizations of adamantinomatous craniopharyngioma in relation to keratinization.

Although adamantinomatous craniopharyngioma (ACP) is a tumour with low histological malignancy, there are very few therapeutic options other than surgery. ACP has high histological complexity, and the unique features of the immunological microenvironment within ACP remain elusive. Further elucidation of the tumour microenvironment is particularly important to expand our knowledge of potential therapeutic targets. Here, we performed integrative analysis of 58,081 nuclei through single-nucleus RNA sequencing and spatial transcriptomics on ACP specimens to characterize the features and intercellular network within the microenvironment. The ACP environment is highly immunosuppressive with low levels of T-cell infiltration/cytotoxicity. Moreover, tumour-associated macrophages (TAMs), which originate from distinct sources, highly infiltrate the microenvironment. Using spatial transcriptomic data, we observed one kind of non-microglial derived TAM that highly expressed GPNMB close to the terminally differentiated epithelial cell characterized by RHCG, and this colocalization was verified by asmFISH. We also found the positive correlation of infiltration between these two cell types in datasets with larger cohort. According to intercellular communication analysis, we report a regulatory network that could facilitate the keratinization of RHCG+ epithelial cells, eventually causing tumour progression. Our findings provide a comprehensive analysis of the ACP immune microenvironment and reveal a potential therapeutic strategy base on interfering with these two types of cells.

Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model's proficiency in linear trend analysis and the deep learning models' capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index's opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.