Identification and Mechanism of the PD-1/PD-L1 Genomic Signature SORL1 as Protective Factor in Bladder Cancer

Characterization of ligand-receptor pair in acute myeloid leukemia: a scoring model for prognosis, therapeutic response, and T cell dysfunction

Introduction

The significance of ligand-receptor (LR) pair interactions in the progression of acute myeloid leukemia (AML) has been the focus of numerous studies. However, the relationship between LR pairs and the prognosis of AML, as well as their impact on treatment outcomes, is not fully elucidated.

Methods

Leveraging data from the TCGA-LAML cohort, we mapped out the LR pair interactions and distinguished specific molecular subtypes, with each displaying distinct biological characteristics. These subtypes exhibited varying mutation landscapes, pathway characteristics, and immune infiltration levels. Further insight into the immune microenvironment among these subtypes revealed disparities in immune cell abundance.

Results

Notably, one subtype showed a higher prevalence of CD8 T cells and plasma cells, suggesting increased adaptive immune activities. Leveraging a multivariate Lasso regression, we formulated an LR pair-based scoring model, termed "LR.score," to classify patients based on prognostic risk. Our findings underscored the association between elevated LR scores and T-cell dysfunction in AML. This connection highlights the LR score's potential as both a prognostic marker and a guide for personalized therapeutic interventions. Moreover, our LR.score revealed substantial survival prediction capacities in an independent AML cohort. We highlighted CLEC11A, ICAM4, ITGA4, and AVP as notably AML-specific.

Discussion

qRT-PCR analysis on AML versus normal bone marrow samples confirmed the significant downregulation of CLEC11A, ITGA4, ICAM4, and AVP in AML, suggesting their inverse biomarker potential in AML. In summary, this study illuminates the significance of the LR pair network in predicting AML prognosis, offering avenues for more precise treatment strategies tailored to individual patient profiles.

Urine proteome profile of firefighters with exposure to emergency fire-induced smoke: A pilot study to identify potential carcinogenic effects

Firefighters are frequently exposed to a variety of chemicals formed from smoke, which pose a risk for numerous diseases, including cancer. Comparative urine proteome profiling could significantly improve our understanding of the early detection of potential cancer biomarkers. In this study, for the first time, we conducted a comparative protein profile analysis of 20 urine samples collected from ten real-life firefighters prior to and following emergency fire-induced smoke. Using a label-free quantitative proteomics platform, we identified and quantified 1325 unique protein groups, of which 45 proteins showed differential expressions in abundance in response to fire-smoke exposure (post) compared to the control (pre). Pathway analysis showed proteins associated with epithelium development (e.g., RHCG, HEG1, ADAMTSL2) and Alzheimer's disease (SORL1) were significantly increased in response to smoke exposure samples. A protein-protein-network study showed a possible link between these differentially abundant proteins and the known cancer gene (TP53). Moreover, a cross-comparison analysis revealed that seven proteins-ALDH1A1, APCS, POMC, COL2A1, RDX, DDAH2, and SDC4 overlapped with the previously published urine cancer proteome datasets, suggesting a potential cancer risk. Our findings demonstrated that the discovery proteomic platform is a promising analytical technique for identifying potential non-invasive biomarkers associated with fire-smoke exposure in firefighters that may be related to cancer.

Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer analysis

We aimed to develop endoplasmic reticulum (ER) stress-related risk signature to predict the prognosis of melanoma and elucidate the immune characteristics and benefit of immunotherapy in ER-related risk score-defined subgroups of melanoma based on a machine learning algorithm. Based on The Cancer Genome Atlas (TCGA) melanoma dataset (n = 471) and GTEx database (n = 813), 365 differentially expressed ER-associated genes were selected using the univariate Cox model and LASSO penalty Cox model. Ten genes impacting OS were identified to construct an ER-related signature by using the multivariate Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. Thereafter, the immune features, CNV, methylation, drug sensitivity, and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups, were analyzed. We further validated the gene signature using pan-cancer analysis by comparing it to other tumor types. The ER-related risk score was constructed based on the ARNTL, AGO1, TXN, SORL1, CHD7, EGFR, KIT, HLA-DRB1 KCNA2, and EDNRB genes. The high ER stress-related risk score group patients had a poorer overall survival (OS) than the low-risk score group patients, consistent with the results in the GEO cohort. The combined results suggested that a high ER stress-related risk score was associated with cell adhesion, gamma phagocytosis, cation transport, cell surface cell adhesion, KRAS signalling, CD4 T cells, M1 macrophages, naive B cells, natural killer (NK) cells, and eosinophils and less benefitted from ICI therapy. Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics, CNV, methylation, and the clinical benefit of ICI therapy. KEY MESSAGES: Melanoma is the cutaneous tumor with a high degree of malignancy, the highest fatality rate, and extremely poor prognosis. Model usefulness should be considered when using models that contained more features. We constructed the Endoplasmic Reticulum stress-associated signature using TCGA and GEO database based on machine learning algorithm. ER stress-associated signature has excellent ability for predicting prognosis for melanoma.

Improvement of Gene Delivery by Minimal Bacteriophage Particles

Direct delivery of therapeutic genes is a promising approach for treating cancers and other diseases. The current human viral vectors, however, suffer from several drawbacks, including poor cell-type specificity and difficult large-scale production. The M13 phage provides an alternative vehicle for gene therapy with engineerable specificity, but the low transduction efficiency seriously limits its translational application. In this work, we discovered important factors of cells and phages that greatly influence the phage transduction. The up-regulation of PrimPol or the down-regulation of DMBT1 in cells significantly enhanced the phage transduction efficiency. Furthermore, we found that the phage transduction efficiency was inversely correlated with the phage size. By carefully reconstructing the phage origin with the gene of interest, we designed "TransPhage" with a minimal length and maximal transduction efficiency. We showed that TransPhage successfully transduced the human cells with an excellent efficiency (up to 95%) comparable to or superior to that of the adeno-associated virus vectors. Moreover, we showed that TransPhage's tropism was specific to the cells that overexpress the target antigen, whereas adeno-associated viruses (AAVs) promiscuously infected many cell types. Using TransPhage as a gene therapy vehicle, we invented an NK-cell-mediated immunotherapy in which a membrane-bound fragment crystallizable region was introduced to cancer cells. We showed in vitro that the cancer cells expressing the membrane-bound fragment crystallizable (Fc) were effectively killed by CD16+ NK cells through an antibody-dependent cell-mediated cytotoxicity (ADCC)-like mechanism. In the xenograft mouse model, the administration of TransPhage carrying the membrane-bound Fc gene greatly suppressed tumor growth.

Construction of Exosome SORL1 Detection Platform Based on 3D Porous Microfluidic Chip and its Application in Early Diagnosis of Colorectal Cancer

Exosomes are promising new biomarkers for colorectal cancer (CRC) diagnosis, due to their rich biological fingerprints and high level of stability. However, the accurate detection of exosomes with specific surface receptors is limited to clinical application. Herein, an exosome enrichment platform on a 3D porous sponge microfluidic chip is constructed and the exosome capture efficiency of this chip is ≈90%. Also, deep mass spectrometry analysis followed by multi-level expression screenings revealed a CRC-specific exosome membrane protein (SORL1). A method of SORL1 detection by specific quantum dot labeling is further designed and the ensemble classification system is established by extracting features from 64-patched fluorescence images. Importantly, the area under the curve (AUC) using this system is 0.99, which is significantly higher (p < 0.001) than that using a conventional biomarker (carcinoembryonic antigen (CEA), AUC of 0.71). The above system showed similar diagnostic performance, dealing with early-stage CRC, young CRC, and CEA-negative CRC patients.