LAIR1-mediated resistance of hepatocellular carcinoma cells to T cells through a GSK-3ß/ß-catenin/MYC/PD-L1 pathway.

BACKGROUND: An increasing number of studies have reported the involvement of oncogenes in the regulation of the immune system. LAIR1 is an immunosuppressive molecule and its role in immune-related diseases has been mainly reported. To date, it is unclear whether LAIR1 in tumor cells is involved in immune regulation. Therefore, the aim of this study was to investigate the role of LAIR1 in the immune microenvironment of hepatocellular carcinoma (HCC) to seek the novel therapeutic discoveries. METHODS: Tumor Immune Dysfunction and Exclusion database was used to predict the response of LAIR1 expression to immune checkpoint blockade. CD8+ T cells were co-cultured with HCC cells, and the killing efficiency of leukocytes on HCC cells was detected by flow cytometry. Flow cytometry was also used to detect the expression of inhibitory receptors. In addition, Western blot, immunofluorescence, and nucleus/cytoplasm fractionation experiments were performed to explore the molecular mechanisms by which LAIR1 created a suppressive tumor microenvironment. RESULTS: LAIR1 expression in HCC was associated with worse immune prognosis and T-cell dysfunction. HCC cells overexpressing LAIR1 co-cultured with CD8+ T cells induced exhaustion of latter. Mechanism studies indicated that LAIR1 in HCC cells up-regulated the phosphorylation of ß-catenin by inducing the phosphorylation of GSK-3ß, leading to the impairment of the expression and the nuclear localization signal of ß-catenin. Low ß-catenin expression and nuclear localization signal inhibited MYC-mediated PD-L1 expression. Therefore, PD-L1 up-regulated by LAIR1 caused the exhaustion of infiltrating CD8+ T cells in HCC, which aggravated the malignant progression of HCC. CONCLUSION: LAIR1 increased PD-L1 expression through the GSK-3ß/ß-catenin/MYC/PD-L1 pathway and promoted immune evasion of HCC cells. Targeted inhibition of LAIR1 helped to enhance the immune killing effect of CD8+ T cells in HCC.

A modified immune cell infiltration score achieves ideal stratification for CD8+ T cell efficacy and immunotherapy benefit in hepatocellular carcinoma.

Immunotherapy, which aims to enhance the function of T cells, has emerged as a novel therapeutic approach for hepatocellular carcinoma (HCC). Nevertheless, the clinical utility of using flow cytometry to assess immune cell infiltration (ICI) is hindered by its cumbersome procedures, prompting the need for more accessible methods. Here, we acquired gene expression profiles and survival data of HCC from TCGA and GSE10186 datasets. The patients were categorized into two clusters of ICI, and a set of 11 characteristic genes responsible for the differentiation performance of these ICI clusters were identified. Subsequently, we successfully developed a modified ICI score (mICIS) by utilizing the expression levels of these genes. The efficacy of our mICIS was confirmed via mass cytometry, flow cytometry, and immunohistochemistry. Our research indicated that the favorable overall survival (OS) rate could be attributed to the improved function of anti-tumor leukocytes rather than their infiltration. Furthermore, we observed that the low score group exhibited lower expression levels of T-cell exhaustion-associated genes, which was confirmed in both HCC tissues from patients and mice, which demonstrated that the benefits of the low scores were due to enhanced active/cytotoxic CD8+ T cells and reduced exhausted CD8+ T cells. Additionally, our mICIS stratified the benefits derived from immunotherapies. Lastly, we observed a misalignment between CD8+ T-cell infiltration and function in HCC. In summary, our mICIS demonstrated proficiency in assessing the OS rate of HCC and offering significant stratified data pertaining to distinct responses to immunotherapy.

CSformer: Bridging Convolution and Transformer for Compressive Sensing.

Convolutional Neural Networks (CNNs) dominate image processing but suffer from local inductive bias, which is addressed by the transformer framework with its inherent ability to capture global context through self-attention mechanisms. However, how to inherit and integrate their advantages to improve compressed sensing is still an open issue. This paper proposes CSformer, a hybrid framework to explore the representation capacity of local and global features. The proposed approach is well-designed for end-to-end compressive image sensing, composed of adaptive sampling and recovery. In the sampling module, images are measured block-by-block by the learned sampling matrix. In the reconstruction stage, the measurements are projected into an initialization stem, a CNN stem, and a transformer stem. The initialization stem mimics the traditional reconstruction of compressive sensing but generates the initial reconstruction in a learnable and efficient manner. The CNN stem and transformer stem are concurrent, simultaneously calculating fine-grained and long-range features and efficiently aggregating them. Furthermore, we explore a progressive strategy and window-based transformer block to reduce the parameters and computational complexity. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing, which achieves superior performance compared to state-of-the-art methods on different datasets. Our codes is available at: https://github.com/Lineves7/CSformer.

An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma.

Lung adenocarcinoma is one of the most common malignant tumors worldwide. The purpose of this study was to construct a stable immune gene signature for prediction of prognosis (IGSPP) and response to immune checkpoint inhibitors (ICIs) therapy in LUAD patients. Five genes were screened by weighted gene coexpression network analysis, Cox regression and LASSO regression analyses and were used to construct the IGSPP. The survival rate of the IGSPP low-risk group was higher than that of the IGSPP high-risk group. Multivariate Cox regression analysis showed that IGSPP could be used as an independent prognostic factor for the overall survival of LUAD patients. IGSPP genes were enriched in cell cycle pathways. IGSPP gene mutation rates were higher in the high-risk group. CD4 memory-activated T cells, M0 and M1 macrophages had higher infiltration abundance in the high-risk group, which was associated with poor overall survival. In contrast, the abundance of resting CD4 memory T cells, monocytes, resting dendritic cells and resting mast cells associated with a better prognosis was higher in the low-risk group. TIDE scores and the expressions of different immune checkpoints showed that patients in the high-risk IGSPP group benefited more from ICIs treatment. In short, an IGSPP of LUAD was constructed and characterized. It could be used to predict the prognosis and benefits of ICIs treatment in LUAD patients.