Low Level of PALMD Contributes to the Metastasis of Uveal Melanoma

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

BACKGROUND

Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration.

METHODS

PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149.

RESULTS

Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI.

CONCLUSIONS

This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

Machine Learning Methods for Gene Selection in Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Regulation of Cellular-Signaling Pathways by Mammalian Proteins Containing Bacterial EPIYA or EPIYA-Like Motifs Predicted to be Phosphorylated

The effector proteins of several pathogenic bacteria contain the Glu-Pro-Ile-Tyr-Ala (EPIYA) motif or other similar motifs. The EPIYA motif is delivered into the host cells by type III and IV secretion systems, through which its tyrosine residue undergoes phosphorylation by host kinases. These motifs atypically interact with a wide range of Src homology 2 (SH2) domain-containing mammalian proteins through tyrosine phosphorylation, which leads to the perturbation of multiple signaling cascades, the spread of infection, and improved bacterial colonization. Interestingly, it has been reported that EPIYA (or EPIYA-like) motifs exist in mammalian proteomes and regulate mammalian cellular-signaling pathways, leading to homeostasis and disease pathophysiology. It is possible that pathogenic bacteria have exploited EPIYA (or EPIYA-like) motifs from mammalian proteins and that the mammalian EPIYA (or EPIYA-like) motifs have evolved to have highly specific interactions with SH2 domain-containing proteins. In this review, we focus on the regulation of mammalian cellular-signaling pathways by mammalian proteins containing these motifs.

Linking Gene Fusions to Bone Marrow Failure and Malignant Transformation in Dyskeratosis Congenita

Dyskeratosis Congenita (DC) is a multisystem disorder intrinsically associated with telomere dysfunction, leading to bone marrow failure (BMF). Although the pathology of DC is largely driven by mutations in telomere-associated genes, the implications of gene fusions, which emerge due to telomere-induced genomic instability, remain unexplored. We meticulously analyzed gene fusions in RNA-Seq data from DC patients to provide deeper insights into DC's progression. The most significant DC-specific gene fusions were subsequently put through in silico assessments to ascertain biophysical and structural attributes, including charge patterning, inherent disorder, and propensity for self-association. Selected candidates were then analyzed using deep learning-powered structural predictions and molecular dynamics simulations to gauge their potential for forming higher-order oligomers. Our exploration revealed that genes participating in fusion events play crucial roles in upholding genomic stability, facilitating hematopoiesis, and suppressing tumors. Notably, our analysis spotlighted a particularly disordered polyampholyte fusion protein that exhibits robust higher-order oligomerization dynamics. To conclude, this research underscores the potential significance of several high-confidence gene fusions in the progression of BMF in DC, particularly through the dysregulation of genomic stability, hematopoiesis, and tumor suppression. Additionally, we propose that these fusion proteins might hold a detrimental role, specifically in inducing proteotoxicity-driven hematopoietic disruptions.

Palmdelphin Inhibits Ovarian Cancer Cell Stem Specification via Downregulating Ring Finger Protein 145

This study aimed to investigate the roles of PALMD in ovarian cancer. mRNA expression was detected using RT-qPCR. The aldehyde dehydrogenase (ALDH) activity and biomarkers of ovarian cancer stem cells were determined using flow cytometry. The stemness of ovarian cancer cells was determined using sphere formation assay. Cell viability was determined using CCK-8 assay. The number of colonies was determined using colony formation assay. Cell migration was detected using wound healing assay. Cell invasion was determined using transwell assay. The results showed that PALMD was downregulated in ovarian cancer. Overexpressed PALMD inhibited the proliferative, migrative, and invasive ability of ovarian cancer cells. Moreover, PALMD inhibited the stem-like properties of ovarian cancer cells. Additionally, PALMD downregulated ring finger protein 145 (RNF145) expression, overexpression of which contributed to the aggressiveness of ovarian cancer cells. Taken together, PALMD suppressed ovarian cancer cell stem specification via inhibiting RNF145 expression.