Integrating p53-associated genes and infiltrating immune cell characterization as a prognostic biomarker in multiple myeloma.

Background: Tumor genetic anomalies and immune dysregulation are pivotal in the progression of multiple myeloma (MM). Accurate patient stratification is essential for effective MM management, yet current models fail to comprehensively incorporate both molecular and immune profiles. Methods: We examined 776 samples from the MMRF CoMMpass database, employing univariate regression with LASSO and CIBERSORT algorithms to identify 15 p53-related genes and six immune cells with prognostic significance in MM. A p53-TIC (tumor-infiltrating immune cells) classifier was constructed by calculating scores using the bootstrap-multicox method, which was further validated externally (GSE136337) and through ten-fold internal cross-validation for its predictive reliability and robustness. Results: The p53-TIC classifier demonstrated excellent performance in predicting the prognosis in MM. Specifically, patients in the p53low/TIChigh subgroup had the most favorable prognosis and the lowest tumor mutational burden (TMB). Conversely, those in the p53high/TIClow subgroup, with the least favorable prognosis and the highest TMB, were predicted to have the best anti-PD1 and anti-CTLA4 response rate (40 %), which can be explained by their higher expression of PD1 and CTLA4. The three-year area under the curve (AUC) was 0.80 in the total sample. Conclusions: Our study highlights the potential of an integrated analysis of p53-associated genes and TIC in predicting prognosis and aiding clinical decision-making in MM patients. This finding underscores the significance of comprehending the intricate interplay between genetic abnormalities and immune dysfunction in MM. Further research into this area may lead to the development of more effective treatment strategies.

Spatial Transcriptomics Analysis Reveals that CCL17 and CCL22 are Robust Indicators of a Suppressive Immune Environment in Angioimmunoblastic T Cell Lymphoma (AITL).

BACKGROUND: T cell lymphoma is a complex and highly aggressive clinicopathological entity with a poor outcome. The angioimmunoblastic T-cell lymphoma (AITL) tumor immune microenvironment is poorly investigated. METHODS: Here, to the best of our knowledge, spatial transcriptomics was applied for the first time to study AITL. RESULTS: Using this method, we observed that AITL was surrounded by cells bearing immune-suppressive markers. CCL17 and CCL22, the dominant ligands for CCR4, were up-regulated, while the expression of natural killer (NK) cell and CD8+ cytotoxic T lymphocyte (CTL) markers decreased. Colocalization of Treg cells with the CD4+ TFH-GC region was also deduced from the bioinformatic analysis. The results obtained with spatial transcriptomics confirm that AITL has a suppressive immune environment. Chemotherapy based on the CHOP regimen (cyclophosphamide, doxorubicin, vincristine plus prednisone) induced complete remission (CR) in this AITL patient. However, the duration of remission (DoR) remains a concern. CONCLUSIONS: This study demonstrates that AITL has an immune suppressive environment and suggests that anti-CCR4 therapy could be a promising treatment for this lethal disease.

CXCL1 Clone Evolution Induced by the HDAC Inhibitor Belinostat Might Be a Favorable Prognostic Indicator in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most lethal subtype of breast cancer due to its lack of treatment options. Patients with TNBC frequently develop resistance to chemotherapy. As epigenetic-based antineoplastic drugs, histone deacetylase inhibitors (HDACis) have achieved particular efficacy in lymphoma but are less efficacious in solid tumors, and the resistance mechanism remains poorly understood. In this study, the GSE129944 microarray dataset from the Gene Expression Omnibus database was downloaded, and fold changes at the transcriptome level of a TNBC line (MDA-MB-231) after treatment with belinostat were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to identify the critical biological processes. Construction and analysis of the protein-protein interaction (PPI) network were performed to screen candidate genes related to cancer prognosis. A total of 465 DEGs were identified, including 240 downregulated and 225 upregulated genes. The cytokine-cytokine receptor pathway was identified as being significantly changed. Furthermore, the expression of CXCL1 was implicated as a favorable factor in the overall survival of breast cancer patients. With in vitro approaches, we also showed that belinostat could induce the expression of CXCL1 in another 2 TNBC cell lines (BT-549 and HCC-1937). We speculate that belinostat-induced CXCL1 expression could be one of the results of the stress clone evolution of cells after HDACi treatment. These findings provide new insights into clone evolution during HDACi treatment, which might guide us to a novel perspective that various mutation-targeted treatments should be implemented during the whole treatment cycle.

Identification of Key Genes and Pathways Associated with Hepatosplenic T-Cell Lymphoma (HSTCL) by Bioinformatics Analysis.

BACKGROUND: Prognosis of Hepatosplenic T-Cell Lymphoma (HSTCL) is very poor, while the molecular mechanism of this disease has rarely been investigated and remains mysterious. The aim of the study is to screen differentially expressed genes (DEGs) of patients with HSTCL and normal controls, explore the pathogenesis, and provide guidance for the gene diagnosis and precise treatment of HSTCL. METHODS: The genetic chip data GSE57520 of HSTCL was searched from the GEO database, and the quality control and DEGs screening were performed through BART online tools. In addition, FunRich software was used to perform gene enrichment and pathway analysis on the screened DEGs. Subsequently protein interaction network (PPI) was constructed via the STRING database and analyzed using the visual module of Cytoscape software. RESULTS: A total of 4,759 DEGs were obtained, including 2,501 up-regulated genes and 2,258 down-regulated genes (p < 0.05). The analysis of gene ontology (GO) showed that DEGs in cytology component (CC) mainly involved cytoplasm, nucleus, plasma membrane, Golgi apparatus, lysosome, and endoplasmic reticulum. Besides, DEGs in molecular function (MF) mainly included transcription factor activity, catalytic activity, transporter activity, transcription regulator activity, receptor signaling pathway complex, receptor activity. Moreover, DEGs in biological processes (BP) are mainly involved in base regulation, transport, energy pathways, metabolism, protein metabolism, and apoptosis. The results of the Kyoto Gene and Genome Encyclopedia (KEGG) analysis showed that the DEGs mainly include TRAIL, Beta1 integrin, integrin family, proteoglycan, S1P, and ErbB. Combined with Cytoscape software cytoHubba plug-in, protein interaction network (PPI) analysis showed that KIF20A, DLGAP5, PBK, TOP2A, ASPM, NEK2, KIF14, and DEPDC1B were the most abundant core genes. Module analysis showed that the three gene modules with the highest scores were mainly related to mitosis, epithelial cell adhesion and signal transduction, and the process of DNA damage. CONCLUSIONS: The DEGs of HSTCL patients versus healthy control groups were obtained through a variety of bioinformatics methods. KIF20A and DLGAP5 may become potential therapeutic targets for HSTCL. Also, the most abundant signaling pathway in DEGs was the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) - related pathway. Besides, related genes and expression characteristics of HSTCL pathogenesis were reanalyzed from distinctive perspectives, which might provide specific diagnostic markers and targeted therapy for HSTCL.

Discovery of Dysimmunity in Large Granular Lymphocytic Leukemia (LGLL) using Bioinformatic Analysis.

BACKGROUND: Large granular lymphocytic leukemia (LGLL) is a chronic lymphoproliferative disorder characterized by the clonal proliferation of large granular lymphocytes (LGL), classified as T and NK subtypes. Although JAK/STAT pathway gene mutation, such as STAT3/STAT5B, is the dominant driver in the proliferation of LGLL, immune abnormality remains an unsolved puzzle in the pathogenesis. METHODS: By means of bioinformatic method through the GEO dataset GSE39838, we performed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, as well as protein-protein interaction network (PPI) module calculation. RESULTS: As a consequence, differentially expressed genes (DEGs) involved in immune regulation were detected to be related with LGLL, including C1QA, C1QC and CD163 etc. Among all the DEGs, 147 genes were up-regulated, while the number of down-regulated genes was 1,296. In the KEGG pathway of LGLL, infection and immunity were the primary alteration, including tuberculosis and rheumatoid arthritis (RA). However, meticulous experiments are required to validate. CONCLUSIONS: To sum up, dysimmunity might be another internal anomaly of LGLL, thus it is a reminder that immune regulation of LGLL should be paid more attention. Moreover, immune microenvironment studies in LGLL covering T, B, and NK cells probably contribute to the molecular pathology, aiming to contribute to the molecular pathology of the LGLL. Additionally, pharmaceutical development directed at immune molecules might be pre-dictive of targeted therapy era in LGLL.