Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer.

Gastric cancer presents a formidable challenge, marked by its debilitating nature and often dire prognosis. Emerging evidence underscores the pivotal role of tumor stem cells in exacerbating treatment resistance and fueling disease recurrence in gastric cancer. Thus, the identification of genes contributing to tumor stemness assumes paramount importance. Employing a comprehensive approach encompassing ssGSEA, WGCNA, and various machine learning algorithms, this study endeavors to delineate tumor stemness key genes (TSKGs). Subsequently, these genes were harnessed to construct a prognostic model, termed the Tumor Stemness Risk Genes Prognostic Model (TSRGPM). Through PCA, Cox regression analysis and ROC curve analysis, the efficacy of Tumor Stemness Risk Scores (TSRS) in stratifying patient risk profiles was underscored, affirming its ability as an independent prognostic indicator. Notably, the TSRS exhibited a significant correlation with lymph node metastasis in gastric cancer. Furthermore, leveraging algorithms such as CIBERSORT to dissect immune infiltration patterns revealed a notable association between TSRS and monocytes and other cell. Subsequent scrutiny of tumor stemness risk genes (TSRGs) culminated in the identification of CDC25A for detailed investigation. Bioinformatics analyses unveil CDC25A's implication in driving the malignant phenotype of tumors, with a discernible impact on cell proliferation and DNA replication in gastric cancer. Noteworthy validation through in vitro experiments corroborated the bioinformatics findings, elucidating the pivotal role of CDC25A expression in modulating tumor stemness in gastric cancer. In summation, the established and validated TSRGPM holds promise in prognostication and delineation of potential therapeutic targets, thus heralding a pivotal stride towards personalized management of this malignancy.

GNPNAT1 promotes the stemness of breast cancer and serves as a potential prognostic biomarker.

Glucosamine­phosphate N­acetyltransferase 1 (GNPNAT1) is a member of the acetyltransferase superfamily, related to general control non­depressible 5 (GCN5). It has been documented that GNPNAT1 expression is increased in lung cancer, whereas its involvement in breast cancer (BC) remains to be further investigated. The present study aimed to evaluate the expression levels of GNPNAT1 in BC and its effect on BC stem cells (BCSCs). The Cancer Genome Atlas (TCGA) database was used for the analysis of the expression of GNPNAT1 and its clinical significance. Cox regression and logistic regression analyses were used to evaluate prognosis­related factors. The GNPNAT1­binding protein network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) application. The biological signaling pathways implicated in GNPNAT1 were investigated through function enrichment analysis including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis. The single­sample GSEA method was used to investigate the connection between the level of immune infiltration and GNPNAT1 expression in BC. GNPNAT1 expression was upregulated in patients with BC and was significantly associated with a poor prognosis. GNPNAT1 and its co­expressed genes were mostly enriched in nuclear transport, Golgi vesicle transport, ubiquitin­like protein transferase activity and ribonucleoprotein complex binding, as determined using functional enrichment analysis. GNPNAT1 expression was positively associated with Th2 cells and T­helper cells, and negatively associated with plasmacytoid dendritic cells, CD8+ T­cells and cytotoxic cells. Additionally, the GNPNAT1 expression levels were considerably increased in BCSCs. GNPNAT1 knockdown markedly decreased the stemness ability of SKBR3 and Hs578T cells, including the production of CSC markers and mammosphere or clone formation, while GNPNAT1 overexpression increased the stemness level. Hence, the findings of the present study demonstrate that GNPNAT1 may be exploited as a novel prognostic biomarker and therapeutic target for BC.

CDCA5 promotes the progression of breast cancer and serves as a potential prognostic biomarker.

Cell division cycle­associated 5 (CDCA5) plays a critical role in the progression of various human cancers by regulating cell cycle­related proteins; however, the function of CDCA5 in breast cancer (BC) is poorly understood. The aim of the present study was to investigate the expression level of CDCA5 in BC and its effect on BC progression. CDCA5 was found to be highly expressed in patients with BC, as well as in BC cell lines. It was also found that a high CDCA5 expression in BC was significantly associated with a shorter survival rate. In addition, the expression level of CDCA5 was significantly increased in stem cells derived from suspension­cultured BC cells, as compared to adherent­cultured cells. CDCA5 knockdown in MCF7 and SKBR3 cells significantly reduced cell proliferation, migration and clone formation. At the same time, the stemness capacity of BC cells, determined by analyzing cancer stem cell marker expression and mammosphere formation, was also markedly diminished following the knockdown of CDCA5. In addition, in vivo experiments demonstrated that CDCA5 knockdown in MCF7 cells markedly reduced tumor growth. On the whole, the present study demonstrates that CDCA5 may be used as a prognostic biomarker and therapeutic target for BC.