Dataset Information

Proteomic analyses identify major vault protein as a prognostic biomarker for fatal prostate cancer.

ABSTRACT: The demographic shift toward an older population will increase the number of prostate cancer cases. A challenge in the treatment of prostate cancer is to avoid undertreatment of patients at high risk of progression following curative treatment. These men can benefit from early salvage treatment. An explorative cohort consisting of tissue from 16 patients who underwent radical prostatectomy, and were either alive or had died from prostate cancer within 10 years postsurgery, was analyzed by mass spectrometry analysis. Following proteomic and bioinformatic analyses, major vault protein (MVP) was identified as a putative prognostic biomarker. A publicly available tissue proteomics dataset and a retrospective cohort of 368 prostate cancer patients were used for validation. The prognostic value of the MVP was verified by scoring immunohistochemical staining of a tissue microarray. High level of MVP was associated with more than 4-fold higher risk for death from prostate cancer (hazard ratio = 4.41, 95% confidence interval: 1.45-13.38; P = 0.009) in a Cox proportional hazard models, adjusted for Cancer of the Prostate Risk Assessments Post-surgical (CAPRA-S) score and perineural invasion. Decision curve analyses suggested an improved standardized net benefit, ranging from 0.06 to 0.18, of adding MVP onto CAPRA-S score. This observation was confirmed by receiver operator characteristics curve analyses for the CAPRA-S score versus CAPRA-S and MVP score (area under the curve: 0.58 versus 0.73). From these analyses, one can infer that MVP levels in combination with CAPRA-S score might add onto established risk parameters to identify patients with lethal prostate cancer.

SUBMITTER: Ramberg H

PROVIDER: S-EPMC8163044 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:We examine the advantages of going beyond sequence similarity and use both protein three-dimensional (3D) structure prediction and then quaternary structure (docking) of inferred 3D structures to help evaluate whether comparable sequences can fold into homologous structures with sufficient lateral associations for quaternary structure formation. Our test case is the major vault protein (MVP) that oligomerizes in multiple copies to form barrel-like vault particles and is relatively widespread among eukaryotes. We used the iterative threading assembly refinement server (I-TASSER) to predict whether putative MVP sequences identified by BLASTp and PSI Basic Local Alignment Search Tool are structurally similar to the experimentally determined rodent MVP tertiary structures. Then two identical predicted quaternary structures from I-TASSER are analyzed by RosettaDock to test whether a pair-wise association occurs, and hence whether the oligomeric vault complex is likely to form for a given MVP sequence. Positive controls for the method are the experimentally determined rat (Rattus norvegicus) vault X-ray crystal structure and the purple sea urchin (Strongylocentrotus purpuratus) MVP sequence that forms experimentally observed vaults. These and two kinetoplast MVP structural homologs were predicted with high confidence value, and RosettaDock predicted that these MVP sequences would dock laterally and therefore could form oligomeric vaults. As the negative control, I-TASSER did not predict an MVP-like structure from a randomized rat MVP sequence, even when constrained to the rat MVP crystal structure (PDB:2ZUO), thus further validating the method. The protocol identified six putative homologous MVP sequences in the heterobolosean Naegleria gruberi within the excavate kingdom. Two of these sequences are predicted to be structurally similar to rat MVP, despite being in excess of 300 residues shorter. The method can be used generally to help test predictions of homology via structural analysis.

Project description:Ovarian cancer (OC) is one of the most malignant tumors whose mortality rate ranks first in gynecological tumors. Although immunotherapy sheds new light on clinical treatments, the low response still restricts its clinical use because of the unique characteristics of OC such as immunosuppressive microenvironment and unstable genomes. Further exploration on determining an efficient biomarker to predict the immunotherapy response of OC patients is of vital importance. In this study, integrative analyses were performed systematically using transcriptome profiles and somatic mutation data from The Cancer Genome Atlas (TCGA) based on the immune microenvironment and genomic instability of OC patients. Firstly, intersection analysis was conducted to identify immune-related differentially expressed genes (DEGs) and genomic instability-related DEGs. Secondly, Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3A (APOBEC3A) was recognized as a protective factor for OC, which was also verified through basic experiments such as quantitative reverse transcription PCR (RT-qPCR), immunohistochemistry (IHC), Cell Counting Kit-8 (CCK-8), and transwell assays. Thirdly, the correlation analyses of APOBEC3A expression with tumor-infiltrating immune cells (TICs), inhibitory checkpoint molecules (ICPs), Immunophenoscores (IPS), and response to anti-PD-L1 immunotherapy were further applied along with single-sample GSEA (ssGSEA), demonstrating APOBEC3A as a promising biomarker to forecast the immunotherapy response of OC patients. Last, the relationship between APOBEC3A expression with tumor mutation burden (TMB), DNA damage response (DDR) genes, and m6A-related regulators was also analyzed along with the experimental verification of immunofluorescence (IF) and RT-qPCR, comprehensively confirming the intimate association of APOBEC3A with genomic instability in OC. In conclusion, APOBEC3A was identified as a protective signature and a promising prognostic biomarker for forecasting the survival and immunotherapy effect of OC patients, which might accelerate the clinical application and improve immunotherapy effect.

Dataset Information

Proteomic analyses identify major vault protein as a prognostic biomarker for fatal prostate cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets