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Objective: The pathogenesis of periprosthetic bone loss in aseptic and septic prosthesis loosening is unclear. There is considerable evidence that macrophages and osteoclasts play a key role in focal bone erosion and osteolysis around the prosthesis. RANKL (receptor activator of nuclear factor kappaB ligand) was shown to be a potent osteoclastogenic factor, and to be involved in bone destruction of myeloma and rheumatoid arthritis patients. Osteoprotegerin (OPG) is the natural RANKL inhibitor and may prevent periprosthetic bone loss.Methods: The presence and distribution of RANKL, its receptor RANK and OPG in the periprosthetic interface of septically (n = 5) and aseptically (n = 6) loosened prostheses was examined by immunohistochemistry and immunoblotting. Additionally, the immunophenotype of the inflammatory infiltrate was determined [CD3, CD68, Ki-67, tartrate-resistant acid posphatase (TRAP)].Results: Aseptic and septic cases revealed a different histopathologic pattern. However, in all cases RANKL and RANK could be demonstrated in macrophages and giant cells. In addition, RANKL detected by immunoblot analysis proved to have the same molecular weight as a recombinant RANKL used as a control (31 kD and approximately 48 kD). OPG was detected in aseptic loosening, where macrophages showed a strong staining, but multinucleated giant cells were only weakly stained. A weak OPG staining was also observed in septic loosening.Conclusion: The pathogenesis of bone loss in septic loosening remains unclear, because the septic membrane bears few macrophages and giant cells, and half of them express OPG. In aseptic loosening, macrophages might not be stimulated by RANKL as a result of OPG expression. But multinucleated giant cells may be activated, as they hardly express OPG. They might be responsible for periprosthetic bone loss in aseptic loosening as a result of their RANKL and RANK expression. [ABSTRACT FROM AUTHOR]

Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 ) and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

Purpose: Single-cell transcriptional profiling reveals cell heterogeneity and clinically relevant traits in intra-operatively collected patient-derived tissue. So far, single-cell studies have been constrained by the requirement for prospectively collected fresh or cryopreserved tissue. This limitation might be overcome by recent technical developments enabling single-cell analysis of FFPE tissue., Methods: We benchmark single-cell profiles from patient-matched fresh, cryopreserved and archival FFPE cancer tissue., Results: We find that fresh tissue and FFPE routine blocks can be employed for the robust detection of clinically relevant traits on the single-cell level. Specifically, single-cell maps of fresh patient tissues and corresponding FFPE tissue blocks could be integrated into common low-dimensional representations, and cell subtype clusters showed highly correlated transcriptional strengths of signaling pathway, hallmark, and clinically useful signatures, although expression of single genes varied due to technological differences. FFPE tissue blocks revealed higher cell diversity compared to fresh tissue. In contrast, single-cell profiling of cryopreserved tissue was prone to artifacts in the clinical setting., Conclusion: Our analysis highlights the potential of single-cell profiling in the analysis of retrospectively and prospectively collected archival pathology cohorts and increases the applicability in translational research., (© 2024. The Author(s).)

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer cells in single-cell data. We find that our method and others based on gene expression or allelic imbalances identify overlapping sets of colorectal cancer versus normal colon epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible., (© 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Background: The aim of this study was to analyse transcriptomic differences between primary and recurrent high-grade serous ovarian carcinoma (HGSOC) to identify prognostic biomarkers., Methods: We analysed 19 paired primary and recurrent HGSOC samples using targeted RNA sequencing. We selected the best candidates using in silico survival and pathway analysis and validated the biomarkers using immunohistochemistry on a cohort of 44 paired samples, an additional cohort of 504 primary HGSOCs and explored their function., Results: We identified 233 differential expressed genes. Twenty-three showed a significant prognostic value for PFS and OS in silico. Seven markers (AHRR, COL5A2, FABP4, HMGCS2, ITGA5, SFRP2 and WNT9B) were chosen for validation at the protein level. AHRR expression was higher in primary tumours (p < 0.0001) and correlated with better patient survival (p < 0.05). Stromal SFRP2 expression was higher in recurrent samples (p = 0.009) and protein expression in primary tumours was associated with worse patient survival (p = 0.022). In multivariate analysis, tumour AHRR and SFRP2 remained independent prognostic markers. In vitro studies supported the anti-tumorigenic role of AHRR and the oncogenic function of SFRP2., Conclusions: Our results underline the relevance of AHRR and SFRP2 proteins in aryl-hydrocarbon receptor and Wnt-signalling, respectively, and might lead to establishing them as biomarkers in HGSOC., (© 2024. The Author(s).)

Purpose: We evaluated additional mutations in RAS wild-type (WT) metastatic colorectal cancer (mCRC) as prognostic and predictive biomarkers for the efficacy of added panitumumab to a 5-fluorouracil plus folinic acid (FU/FA) maintenance as pre-specified analysis of the randomized PanaMa trial., Patients and Methods: Mutations (MUT) were identified using targeted next-generation sequencing (NGS; Illumina Cancer Hotspot Panel v2) and IHC. RAS/BRAF V600E/PIK3CA/AKT1/ALK1/ERBB2/PTEN MUT and HER2/neu overexpressions were negatively hyperselected and correlated with median progression-free survival (PFS) and overall survival (OS) since start of maintenance treatment, and objective response rates (ORR). Univariate/multivariate Cox regression estimated hazard ratios (HR) and 95% confidence intervals (CI)., Results: 202 of 248 patients (81.5%) of the full analysis set (FAS) had available NGS data: hyperselection WT, 162 (80.2%); MUT, 40 (19.8%). From start of maintenance therapy, hyperselection WT tumors were associated with longer median PFS as compared with hyperselection MUT mCRC (7.5 vs. 5.4 months; HR, 0.75; 95% CI, 0.52-1.07; P = 0.11), OS (28.7 vs. 22.2 months; HR, 0.53; 95% CI, 0.36-0.77; P = 0.001), and higher ORR (35.8% vs. 25.0%, P = 0.26). The addition of panitumumab to maintenance was associated with significant benefit in hyperselection WT tumors for PFS (9.2 vs. 6.0 months; HR, 0.66; 95% CI, 0.47-0.93; P = 0.02) and numerically also for OS (36.9 vs. 24.9 months; HR, 0.91; 95% CI, 0.61-1.36; P = 0.50), but not in hyperselection MUT tumors. Hyperselection status interacted with maintenance treatment arms in terms of PFS (P = 0.06) and OS (P = 0.009)., Conclusions: Extended molecular profiling beyond RAS may have the potential to improve the patient selection for anti-EGFR containing maintenance regimens., (©2024 American Association for Cancer Research.)

Motivation: Cell fate decisions, such as apoptosis or proliferation, are communicated via signaling pathways. The pathways are heavily intertwined and often consist of sequential interaction of proteins (kinases). Information integration takes place on the protein level via n -to-1 interactions. A state-of-the-art procedure to quantify information flow (edges) between signaling proteins (nodes) is network inference. However, edge weight calculation typically refers to 1-to-1 interactions only and relies on mean protein phosphorylation levels instead of single cell distributions. Information theoretic measures such as the mutual information (MI) have the potential to overcome these shortcomings but are still rarely used., Results: This work proposes a Bayesian nearest neighbor-based MI estimator (BannMI) to quantify n -to-1 kinase dependency in signaling pathways. BannMI outperforms the state-of-the-art MI estimator on protein-like data in terms of mean squared error and Pearson correlation. Using BannMI, we analyze apoptotic signaling in phosphoproteomic cancerous and noncancerous breast cell line data. Our work provides evidence for cooperative signaling of several kinases in programmed cell death and identifies a potential key role of the mitogen-activated protein kinase p38., Availability and Implementation: Source code and applications are available at: https://github.com/zuiop11/nn&#95;info and can be downloaded via Pip as Python package: nn-info., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press.)