Glycosylphosphatidylinositol anchor biosynthesis pathway-based biomarker identification with machine learning for prognosis and T cell exhaustion status prediction in breast cancer.

As the primary component of anti-tumor immunity, T cells are prone to exhaustion and dysfunction in the tumor microenvironment (TME). A thorough understanding of T cell exhaustion (TEX) in the TME is crucial for effectively addressing TEX in clinical settings and promoting the efficacy of immune checkpoint blockade therapies. In eukaryotes, numerous cell surface proteins are tethered to the plasma membrane via Glycosylphosphatidylinositol (GPI) anchors, which play a crucial role in facilitating the proper translocation of membrane proteins. However, the available evidence is insufficient to support any additional functional involvement of GPI anchors. Here, we investigate the signature of GPI-anchor biosynthesis in the TME of breast cancer (BC)patients, particularly its correlation with TEX. GPI-anchor biosynthesis should be considered as a prognostic risk factor for BC. Patients with high GPI-anchor biosynthesis showed more severe TEX. And the levels of GPI-anchor biosynthesis in exhausted CD8 T cells was higher than normal CD8 T cells, which was not observed between malignant epithelial cells and normal mammary epithelial cells. In addition, we also found that GPI -anchor biosynthesis related genes can be used to diagnose TEX status and predict prognosis in BC patients, both the TEX diagnostic model and the prognostic model showed good AUC values. Finally, we confirmed our findings in cells and clinical samples. Knockdown of PIGU gene expression significantly reduced the proliferation rate of MDA-MB-231 and MCF-7 cell lines. Immunofluorescence results from clinical samples showed reduced aggregation of CD8 T cells in tissues with high expression of GPAA1 and PIGU.

Attention guided neural ODE network for breast tumor segmentation in medical images.

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer. Doctors make diagnoses and further instructions through the sizes, shapes and textures of the breast masses showed on medical images, so automatic tumor segmentation via deep neural networks can to some extent assist doctors. Compared to some challenges which the popular deep neural networks have faced, such as large amounts of parameters, lack of interpretability, overfitting problem, etc., we propose a segmentation network named Att-U-Node which uses attention modules to guide a neural ODE-based framework, trying to alleviate the problems mentioned above. Specifically, the network uses ODE blocks to make up an encoder-decoder structure, feature modeling by neural ODE is completed at each level. Besides, we propose to use an attention module to calculate the coefficient and generate a much refined attention feature for skip connection. Three public available breast ultrasound image datasets (i.e. BUSI, BUS and OASBUD) and a private breast DCE-MRI dataset are used to assess the efficiency of the proposed model, besides, we upgrade the model to 3D for tumor segmentation with the data selected from Public QIN Breast DCE-MRI. The experiments show that the proposed model achieves competitive results compared with the related methods while mitigates the common problems of deep neural networks.

S100A6 promotes the development of thyroid cancer and inhibits apoptosis of thyroid cancer cells through the PI3K/AKT/mTOR pathway.

High levels of S100A6 have been associated with progression in some types of human cancers. Cancers related to S100A6 have been reported to include lung cancer, cervical cancer, pancreatic cancer, gastric cancer, colon cancer, etc., but its role in the molecular pathogenesis of these cancers is largely unknown. This study investigated the expression and functional roles of S100A6 in human thyroid cancer. The expression level of S100A6 in thyroid cancer cells was determined by bioinformatics and transcriptomic analysis. Furthermore, the potential functions of S100A6 in tumorigenesis were analyzed by cell proliferation, migration, invasion, and Western blot assays in human thyroid cancer cells. Public database queries revealed high S100A6 expression in thyroid cancer. In addition, we also found that high expression of S100A6 was positively correlated with malignant clinicopathological characteristics of thyroid cancer in The Cancer Genome Atlas database. qPCR results confirmed the high expression of S100A6 in thyroid cancer cells. S100A6 silencing inhibited cell proliferation, migration, and invasion. Western blot assays and response experiments showed that S100A6 promotes cell proliferation and tumorigenicity partly through the PI3K/AKT/mTOR signaling pathway. These results suggest that S100A6 affects the progression of thyroid cancer and can be used as a target in the future treatment of thyroid cancer.

PDZK1IP1 gene promotes proliferation, migration, and invasion in papillary thyroid carcinoma.

Thyroid cancer is a common malignant tumor for the adult and the potential molecular mechanism of papillary thyroid cancer cell metastasis is still unclear. We used sequencing techniques to analyze paired papillary thyroid carcinoma (PTC) and adjacent thyroid tissue and identified a gene, PDZK1IP1, that was significantly overexpressed in thyroid cancer. We found It has been detected to play an important role in many malignant tumors. But the role in papillary thyroid cancer was still unknown, we decided to find a new marker and therapeutic target for the disease. The present study shows that PDZK1IP1 may be a potential gene that leads to thyroid cancer. In our study, silencing PDZK1IP1 can inhibit PTC cell proliferation, migration, invasion, apoptosis, and cell cycle arrest. This study surmised that PDZK1IP1 was an oncogene that correlated with tumor development.

The ETNK2 gene promotes progression of papillary thyroid carcinoma through the HIPPO pathway.

Thyroid cancer is a disease with an extremely high incidence rate and is divided into papillary, follicular, medullary, and undifferentiated thyroid cancers. Among them, papillary carcinoma is the most common subtype. We assessed expression of ETNK2 in public databases and found that ETNK2 is upregulated in PTC. Cohort and RNA sequencing data were used to verify this discovery. To further determine the relationship between ETNK2 and papillary thyroid carcinoma, we performed an in vitro experiment. In a PTC cell line, silencing ETNK2 inhibited cell proliferation, weakened cell migration and invasion ability, promoted apoptosis, and blocked the cell cycle. In addition, western blotting suggested that ETNK2 is related to the HIPPO pathway and may activate the EMT pathway through the HIPPO pathway to promote the development of thyroid cancer. These results revealed that ETNK2 is related to the occurrence and development of papillary thyroid carcinoma, suggesting that ETNK2 may be an oncogene associated with PTC.

Revealing the selective mechanisms of inhibitors to PARP-1 and PARP-2 via multiple computational methods.

BACKGROUND: Research has shown that Poly-ADP-ribose polymerases 1 (PARP-1) is a potential therapeutic target in the clinical treatment of breast cancer. An increasing number of studies have focused on the development of highly selective inhibitors that target PARP-1 over PARP-2, its closest isoform, to mitigate potential side effects. However, due to the highly conserved and similar binding sites of PARP-1 and PARP-2, there is a huge challenge for the discovery and design of PARP-1 inhibitors. Recently, it was reported that a potent PARP-1 inhibitor named NMS-P118 exhibited greater selectivity to PARP-1 over PARP-2 compared with a previously reported drug (Niraparib). However, the mechanisms underlying the effect of this inhibitor remains unclear. METHODS: In the present study, classical molecular dynamics (MD) simulations and accelerated molecular dynamics (aMD) simulations combined with structural and energetic analysis were used to investigate the structural dynamics and selective mechanisms of PARP-1 and PARP-2 that are bound to NMS-P118 and Niraparib with distinct selectivity. RESULTS: The results from classical MD simulations indicated that the selectivity of inhibitors may be controlled by electrostatic interactions, which were mainly due to the residues of Gln-322, Ser-328, Glu-335, and Tyr-455 in helix αF. The energetic differences were corroborated by the results from aMD simulations. CONCLUSION: This study provides new insights about how inhibitors specifically bind to PARP-1 over PARP-2, which may help facilitate the design of highly selective PARP-1 inhibitors in the future.

Exploration of the Prognostic and Immunotherapeutic Value of B and T Lymphocyte Attenuator in Skin Cutaneous Melanoma.

B and T lymphocyte attenuator (BTLA) is a newly identified immune checkpoint molecular belonging to the CD28 immunoglobulin superfamily. However, the expression and clinical value of BTLA in skin cutaneous melanoma (SKCM) has not been widely characterized. We found that BTLA levels were upregulated in metastatic melanoma compared to normal skin tissues and primary melanoma. Higher BTLA was also correlated with improved prognosis in SKCM based on several datasets. The multivariate Cox regression model revealed that BTLA was an independent survival indicator in metastatic melanoma. Tumor microenvironment analysis indicated BTLA was positively associated with the infiltrating levels of different immune cells and the activity of the anti-cancer immunity cycle. Importantly, BTLA accurately predicted the outcome of melanoma patients treated with MAGE-A3 blocker or first-line anti-PD-1. The present findings disclose that BTLA is a reliable biomarker for prognosis and immunotherapeutic response and might contribute to developing novel SKCM immunological treatment strategies.