Biology subjects

Lischer, C.

Publications and source records attributed to Lischer, C..

4 recordsLinked to original sources

bioRxiv2025

Network-based analysis reveals microRNA regulation of oncogenic pathways in SOX10-depleted uveal melanoma

SOX10 is essential for melanocyte development and maintenance and plays a critical role in uveal melanoma (UM) initiation and progression. While SOX10s transcriptional regulation of protein-coding genes is well characterized, its role on microRNA (miRNA) regulatory landscape in UM remains unexplored. Here, we employed network-based modeling to systematically characterize miRNA regulatory functions following SOX10 depletion in UM. First, we profiled mRNA and miRNA expression levels in SOX10 wild-type and knockdown UM cells. Then, we integrated the transcriptomic data, a UM network, and a Bayesian model to quantify miRNAs regulatory activities and identify key miRNAs. Subsequently, we employed pathway enrichment analysis combined with literature mining to elucidate the functional roles of identified miRNAs through their target genes and associated signaling pathways in UM. We identified 17 miRNAs that show significant changes in regulatory activities following SOX10 knockdown in UM cells. These miRNAs regulate the expression of genes involved in cancer hallmark pathways, including cell cycle progression, mTORC1 signaling, and fatty acid metabolism. Notably, miR-34a, miR-25, miR-186, and miR-211 have tumor-suppressive potential by targeting genes involved in UM progression and metastasis. Our results suggested that SOX10 depletion in UM can activate tumor-suppressive mechanisms through regulating miRNAs.

cancer biology↗

bioRxiv2025

A computational SOX10 network-based selection strategy to identify new drug targets in uveal melanoma

Uveal melanoma (UM) is the most common intraocular malignancy in adults. In contrast to cutaneous melanoma (CM), effective treatment options for metastatic UM are limited. The transcription factor SOX10 is crucial for CM initiation and survival, making it an interesting candidate for new targeted therapies, but its relevance in UM was unclear. We found that SOX10 was widely expressed in UM and essential for proliferation, cell cycle progression, and survival. The effects were partially mediated by SOX10-related genes including MITF, highlighting high addiction of UM to the SOX10-MITF axis. Additionally, SOX10 knockdown induced massive transcriptomic changes. Due to a lack of specific inhibitors of SOX10 and MITF, a computational approach was used to identify druggable targets by curating a UM-specific protein interaction network to search for candidates downregulated upon SOX10 inhibition. Thereby, the E2F transcription factor family was identified and their potential as druggable target candidates in UM was confirmed using the pan-E2F inhibitor HLM006474, resulting in cell cycle arrest and apoptosis. Taken together, SOX10 is crucial for UM survival and SOX10-associated proteins may serve as promising targets for developing new therapeutic strategies in UM.

cancer biology↗

bioRxiv2023

An algorithm that combines machine learning ensemble modeling and network analysis to predict self-tolerant tumor-associated antigens for anti-cancer immunotherapy

Tumor-associated antigens (TAAs) and their derived peptides constitute the chance to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. Here, we present a computational pipeline that selects and ranks candidate antigens in a multi-pronged approach and applied it to the case of uveal melanoma. In addition to antigen expression in the tumor target and in healthy tissues, we incorporated a network analysis-derived antigen indispensability index motivated by computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model on peptide physicochemical characteristics.

bioinformatics↗

bioRxiv2020

Network and systems based re-engineering of dendritic cells with non-coding RNAs forcancer immunotherapy

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Through RNA sequencing of IKK{beta}-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates with miR-15a and miR-16 as the top ones. The results of our analysis are incorporated in a database which constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential (www.synmirapy.net/dc-optimization).

systems biology↗