Liver hepatocellular carcinoma (LIHC) remains a global health challenge with poor prognosis and high mortality. was first discovered as a receptor for the immunosuppressant drug FK506 in immune cells and is critical for various tumors and cancers. However, the relationships between expression, cellular distribution, tumor immunity, and prognosis in LIHC remain unclear. Here, we investigated the expression level of and its prognostic value in LIHC via multiple datasets including ONCOMINE, TIMER, GEPIA, UALCAN, HCCDB, Kaplan-Meier plotter, LinkedOmics, and STRING. Human liver tissue microarray was employed to analyze the characteristics of FKBP1A protein including the expression level and pathological alteration in cellular distribution. expression was significantly higher in LIHC and correlated with tumor stage, grade and metastasis. The expression level of the FKBP1A protein was also increased in LIHC patients along with its accumulation in endoplasmic reticulum (ER). High expression was correlated with a poor survival rate in LIHC patients. The analysis of gene co-expression and the regulatory pathway network suggested that is mainly involved in protein synthesis, metabolism and the immune-related pathway. expression had a significantly positive association with the infiltration of hematopoietic immune cells including B cells, CD8 T cells, CD4 T cells, macrophages, neutrophils, and dendritic cells. Moreover, M2 macrophage infiltration was especially associated with a poor survival prognosis in LIHC. Furthermore, expression was significantly positively correlated with the expression of markers of M2 macrophages and immune checkpoint proteins such as , , and . Our study demonstrated that could be a potential prognostic target involved in tumor immune cell infiltration in LIHC.
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http://dx.doi.org/10.3390/ijms232112797 | DOI Listing |
Brief Bioinform
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Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.
Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions.
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School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
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Department of Clinical and Molecular Sciences (DISCLIMO), Università Politecnica delle Marche, Ancona, Italy.
Introduction: Psoriasis is characterized by aberrant keratinocyte activity and immune cell infiltration, driven by immune-mediated pathways. MicroRNAs (miRNAs) play crucial roles in regulating these processes, offering insights into disease mechanisms and therapeutic targets.
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Cell Tissue Res
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Department of Anatomy, Faculty of Science, Mahidol University, 272 Rama VI Road, Ratchathewi District, Bangkok, 10400, Thailand.
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View Article and Find Full Text PDFNeurochem Res
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Department of Biochemistry and Molecular Biology, Federal University of Santa Maria (UFSM), Santa Maria, RS, Brazil.
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