Background: Colon adenocarcinoma (COAD) is a gastrointestinal tumor with a high degree of malignancy. Its deterioration process is closely related to the tumor microenvironment, and transcription factors (TF) play a regulatory role in this process. Currently, there is a lack of exploration between the genes related to the COAD tumor microenvironment and the survival prognosis of patients. Models composed of multiple genes usually predict the survival prognosis of patients more accurately than single genes. We can analyze the multigene models that can predict the prognosis of COAD from the current database. Methods: The limma package of the R programming language is used for gene differential expression analysis. Kaplan-Meier curve is used to analyze the relationship between the patient risk score model and survival data. The hazard model is used to analyze the relationship between the risk score and the clinical data of COAD patients. The information of immune genes and immune cells is obtained from IMMPORT database and TIMER database. Receiver operating characteristic (ROC) curve is used to judge the stability of the model. Results: We found 7 immune genes, which can built a risk score model to predict the survival prognosis of COAD. According to univariate and multivariate analysis, the risk score can be used as an independent predictor. The content of some immune microenvironment cells will also increase as the risk score increases. Conclusions: We found 7 immune genes, such as SLC10A2 (solute carrier family 10 member 2), CXCL3 (C-X-C motif chemokine ligand 3), IGHV5-51 (immunoglobulin heavy variable 5-51), INHBA (inhibin subunit beta A), STC1 (stanniocalcin 1), UCN (urocortin), and OXTR (oxytocin receptor), can constitute a model for predicting the prognosis of COAD. They may provide potential therapeutic targets for clinical treatment of COAD.

Spinal cord malignant melanotic schwannoma (MMNST) is a rare central nervous system tumor that originates from the spinal cord or spinal myelin sheath cells and can produce melanin. This type of tumor is usually highly aggressive and malignant, with a poor prognosis. The clinical manifestations of spinal cord MMNST are mainly pain, paresthesia, muscle weakness, muscle atrophy, etc., and symptoms of spinal cord compression, such as intestinal and bladder dysfunction, paraplegia, etc. Early detection of tumor lesions can facilitate tumor removal, improve patients’ quality of life, and prolong patients’ survival. In this case report, a 27-year-old young woman was diagnosed with MMNST of the cervical spinal cord due to weakness of her limbs in our hospital, and underwent surgical resection. The patient’s limbs returned to normal after surgery. It is worth mentioning that the patient visited our hospital 7 months ago for “right upper limb pain for 3 days” and was diagnosed with a cervical spine space-occupying lesion at the same position this time, but the pathology report was “hemosiderosis”. This case report aims to raise awareness of the problem of spinal cord MMNST and contribute to greater knowledge of this rare tumor.

Parkinson’s disease (PD) is a neurodegenerative disorder linked to alpha-synuclein pathology and dopaminergic neuron loss. Exosomes play dual roles in PD progression, facilitating pathological protein spread or exerting neuroprotective effects. This study aimed to identify exosome-related gene biomarkers for PD diagnosis and therapy. Using multi-dataset analysis (Series: GSE42966, Platforms: GPL4133, Series: GSE99039, Platforms: GPL570, Series: GSE156926, Platforms: GPL19920) from the Gene Expression Omnibus (GEO), we integrated principal component analysis (PCA), differential gene screening (|logFC|>0.5, p < 0.05), and machine learning to identify PD-associated exosome-related genes. Functional enrichment revealed associations with oxidative phosphorylation, Huntington’s disease pathways, and immune responses. A predictive model comprising five genes—GNAS, TUBB2A, RPL22, RPL5, and WNT5A—was established and validated using ROC curves (Receiver Operating Characteristic Curve) and nomograms. Immune profiling linked these genes to B cells, MDSCs, and CD4+/CD8 + T cells. Drug-gene network analysis highlighted interactions with compounds like NSC94017 and phosphine, while molecular docking identified key binding residues (e.g., GLN294, ASP295). These genes may serve as early diagnostic biomarkers and therapeutic targets. Despite promising results, further validation in larger cohorts and mechanistic studies are needed to confirm their roles in PD pathogenesis and treatment. This study provides a foundation for developing precision gene therapies and non-invasive diagnostic strategies for PD.