CD56 polysialylation promotes the tumorigenesis and progression via the Hedgehog and Wnt/β-catenin signaling pathways in clear cell renal cell carcinoma

Comprehensive analysis of α2,3-sialyltransferases as prognostic biomarkers and immunotherapy targets in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC), a therapy-resistant aggressive kidney cancer, exhibits resistance to immune checkpoint inhibitors. Altered sialylation is involved in tumor development, affecting immune microenvironment dynamics. In the study, through systematic bioinformatics analysis and experimental verification, we demonstrated that ST3Gal5 expression was elevated in tumor tissues of KIRC patients, correlating with poor prognosis, and ST3Gal1 was downregulated and associated with a better prognosis. Immunohistochemistry analysis confirmed the expression patterns of ST3Gal1 and ST3Gal5 in 30 KIRC patients. Furthermore, KIRC patients were stratified into two clusters based on ST3Gal1 and ST3Gal5 levels using consensus clustering to investigate their roles in KIRC tumorigenesis, immune characteristics and treatment sensitivity. KIRC patients in Cluster 2, characterized by increased ST3Gal5 and downregulated ST3Gal1 expression, exhibited increased expression of immune checkpoints, immune cell infiltration, immune escape scores, and worse prognosis. Knockdown of ST3Gal5 in KIRC cell lines (786-O and 769-P) resulted in reduced tumor proliferation, migration, and invasion in vivo and in vitro. Together, the dysregulation of sialyltransferases (ST3Gal1 and ST3Gal5) in KIRC influences tumorigenesis and immune responses. These findings underscore the potential of ST3Gal1 and ST3Gal5 as prognostic factors and immunotherapy targets for KIRC.

Comparison of Bone Marrow Biopsy and Flow Cytometry in Demonstrating Bone Marrow Metastasis of Neuroblastoma

OBJECTIVE

This study aimed to compare bone marrow aspirate (BMA) multicolor flow cytometry (MFC) analysis and bone marrow biopsy (BMB) in detecting bone marrow (BM) involvement in children with neuroblastoma (NB) at diagnosis and during follow-up.

MATERIALS AND METHODS

A total of 132 BM samples from 39 patients (M/F ratio: 19/20; median age: 38 months) with neuroblastoma were simultaneously obtained for evaluation. The samples were investigated for BM involvement using BMB and MFC.

RESULTS

A comparison between MFC (n: 60) and BMB (n: 60) was possible for 120 samples. When BMB was considered as the reference standard, MFC had diagnostic sensitivity, specificity, positive predictive value, and negative predictive value of 86%, 58%, 54%, and 88%, respectively, and values of 90%, 57%, 60%, and 89%, respectively, at diagnosis. The median proportion of CD45-/CD56+ cells in MFC was 0.028% (range 0-35%). The event-free survival (EFS) rates for MFC (+) and MFC (-) patients according to the analysis results of the BM samples at the time of diagnosis were 70.6% and 81.8%, respectively (p = 0.607), and the overall survival (OS) rates were 88.2% in MFC (+) patients and 90.9% in MFC (-) patients (p = 0.583).

CONCLUSION

Multicolor flow cytometry may be used as an adjunct to cytomorphology to achieve more sensitive and accurate results as an objective, quantitative method with fast results in detecting bone marrow involvement in children with NB.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment

PURPOSE

We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.

METHODS

The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.

RESULTS

At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).

CONCLUSION

The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

Spatial proteomic profiling of tumor and stromal compartments in non-small-cell lung cancer identifies signatures associated with overall survival

Objectives

Non-small-cell lung carcinoma (NSCLC) is the most prevalent and lethal form of lung cancer. The need for biomarker-informed stratification of targeted therapies has underpinned the need to uncover the underlying properties of the tumor microenvironment (TME) through high-plex quantitative assays.

Methods

In this study, we profiled resected NSCLC tissues from 102 patients by targeted spatial proteomics of 78 proteins across tumor, immune activation, immune cell typing, immune-oncology, drug targets, cell death and PI3K/AKT modules to identify the tumor and stromal signatures associated with overall survival (OS).

Results

Survival analysis revealed that stromal CD56 (HR = 0.384, P = 0.06) and tumoral TIM3 (HR = 0.703, P = 0.05) were associated with better survival in univariate Cox models. In contrast, after adjusting for stage, BCLXL (HR = 2.093, P = 0.02) and cleaved caspase 9 (HR = 1.575, P = 0.1) negatively influenced survival. Delta testing indicated the protective effect of TIM-3 (HR = 0.614, P = 0.04) on OS. In multivariate analysis, CD56 (HR = 0.172, P = 0.001) was associated with better survival in the stroma, while B7.H3 (HR = 1.72, P = 0.008) was linked to poorer survival in the tumor.

Conclusions

Deciphering the TME using high-plex spatially resolved methods is giving us new insights into compartmentalised tumor and stromal protein signatures associated with clinical endpoints in NSCLC.

Induction of Endoplasmic Reticulum Stress by Prodigiosin in Yeast Saccharomyces cerevisiae

Prodigiosin, a red pigment produced by numerous bacterial species, exerts various antibiotic effects on prokaryotic and eukaryotic organisms. For instance, human carcinoma cell lines appear to suffer from endoplasmic reticulum (ER) stress in the presence of prodigiosin. Here, we demonstrated that prodigiosin also triggers the unfolded-protein response (UPR), which is a cytoprotective response against ER stress, in yeast Saccharomyces cerevisiae. An S. cerevisiae mutant carrying a UPR-deficient mutation was hypersensitive to prodigiosin. Our observations cumulatively indicate that protein folding in the ER is impaired by prodigiosin, illustrating a new mode of action.