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The spindle and kinetochore-associated protein complex (Ska) is an essential component in chromosome segregation. It comprises three proteins (Ska1, Ska2, and Ska3) with theorized roles in chromosomal instability and tumor development, and its overexpression has been widely reported in a variety of tumors. However, the prognostic significance and immune infiltration of Ska proteins in hepatocellular carcinoma (HCC) are not completely understood. The bioinformatics tools Oncomine, UALCAN, gene expression profiling interactive analysis 2 (GEPIA2), cBioPortal, GeneMANIA, Metascape, and TIMER were used to analyze differential expression, prognostic value, genetic alteration, and immune cell infiltration of the Ska protein complex in HCC patients. We found that the mRNA expression of the Ska complex was markedly upregulated in HCC. High expression of the Ska complex is closely correlated with tumor stage, patient race, tumor grade, and TP53 mutation status. In addition, high expression of the Ska complex was significantly correlated with poor disease-free survival, while the high expression levels of Ska1 and Ska3 were associated with shorter overall survival. The biological functions of the Ska complex in HCC primarily involve the amplification of signals from kinetochores, the mitotic spindle, and (via a MAD2 invasive signal) unattached kinetochores. Furthermore, the expression of the complex was positively correlated with tumor-infiltrating cells. These results may provide new insights into the development of immunotherapeutic targets and prognostic biomarkers for HCC. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a chronic degenerative disease of the bone and joints. Immune-related genes and immune cell infiltration are important in OA development. We analyzed immune-related genes and immune infiltrates to identify OA diagnostic markers. The datasets GSE51588, GSE55235, GSE55457, GSE82107, and GSE114007 were downloaded from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and differentially expressed immune-related genes (DEIRGs), and functional correlation analysis was conducted. Second, CIBERSORT was used to evaluate infiltration of immune cells in OA tissue. Finally, the least absolute shrinkage and selection operator logistic regression algorithm and support vector machine-recurrent feature elimination algorithm were used to screen and verify diagnostic markers of OA. A total of 711 DEGs and 270 DEIRGs were identified in this study. Functional enrichment analysis showed that the DEGs and DEIRGs are closely related to cellular calcium ion homeostasis, ion channel complexes, chemokine signaling pathways, and JAK-STAT signaling pathways. Differential analysis of immune cell infiltration showed that M1 macrophage infiltration was increased but that mast cell and neutrophil infiltration were decreased in OA samples. The machine learning algorithm cross-identified 15 biomarkers (BTC, PSMD8, TLR3, IL7, APOD, CIITA, IFIH1, CDC42, FGF9, TNFAIP3, CX3CR1, ERAP2, SEMA3D, MPO, and plasma cells). According to pass validation, all 15 biomarkers had high diagnostic efficacy (AUC > 0.7), and the diagnostic efficiency was higher when the 15 biomarkers were fitted into one variable (AUC = 0.758). We developed 15 biomarkers for OA diagnosis. The findings provide a new understanding of the molecular mechanism of OA from the perspective of immunology. [ABSTRACT FROM AUTHOR]