DNA damage-repair machinery participates in maintaining genomic integrity and affects tumorigenesis. Molecular signatures based on DNA damage-repair-related genes (DRGs) capable of comprehensively indicating the prognosis, tumor immunometabolic profile and therapeutic responsiveness of breast cancer (BRCA) patients are still lacking. Integrating public datasets and bioinformatics algorithms, we developed a robust prognostic signature based on 27 DRGs. Multiple patient cohorts identified significant differences in various types of survival between high- and low-risk patients stratified by the signature. The signature correlated well with clinicopathological factors and could serve as an independent prognostic indicator for BRCA patients. Furthermore, low-risk tumors were characterized by more infiltrated CD8⁺ T cells, follicular helper T cells, M1 macrophages, activated NK cells and resting dendritic cells, and fewer M0 and M2 macrophages. The favorable immune infiltration patterns of low-risk tumors were also accompanied by specific metabolic profiles, decreased DNA replication, and enhanced antitumor immunity. Low-risk patients may respond better to immunotherapy, and experience improved outcomes with conventional chemotherapy or targeted medicine. Real-world immunotherapy and chemotherapy cohorts verified the predictive results. Additionally, four small molecule compounds promising to target high-risk tumors were predicted. In vitro experiments confirmed the high expression of GNPNAT1 and MORF4L2 in BRCA tissues and their association with immune cells, and the knockdown of these two DRGs suppressed the proliferation of human BRCA cells. In summary, this DNA damage-repair-related signature performed well in predicting patient prognosis, immunometabolic profiles and therapeutic sensitivity, hopefully contributing to precision medicine and new target discovery of BRCA.

Background: Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes.Methods: The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC).Results: Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC.Conclusions: We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

Background: Lung adenocarcinoma (LUAD) represents a significant global health burden. The absence of reliable biomarkers and the heterogeneity in treatment responses continue to hinder improvements in patient prognosis. This study aimed to identify novel biomarkers capable of predicting patient outcomes and therapeutic responsiveness, while also assessing their potential as intervention targets for LUAD.Methods: Multiple cohorts from public databases were employed to screen key prognostic genes, followed by external validations. Clinicopathological indicators were integrated to analyze the independent prognostic role of the key gene UBE2N and its association with LUAD progression. Functional enrichment analysis elucidated the biological mechanisms regulated by UBE2N. Differences in immune microenvironment components, immunoregulatory gene expression, and immune functional activities between subgroups stratified by UBE2N expression levels. The role of UBE2N in predicting tumor therapeutic susceptibility was characterized using bioinformatics algorithms combined with publicly available CRISPR screening datasets and immunotherapy cohorts. Immunohistochemistry, cell viability, and apoptosis experiments were conducted to verify the oncogenic effects of UBE2N.Results: UBE2N was identified as an independent prognostic biomarker for LUAD. Elevated UBE2N expression correlated with poorer patient survival rates and advanced disease stages. Genes associated with UBE2N were significantly enriched in critical cellular processes, including DNA replication, nucleosome assembly, and neutrophil extracellular trap formation. High-UBE2N tumors exhibited enhanced cell cycle, DNA replication, and oxidative phosphorylation activities. Low-UBE2N tumors exhibit elevated proportions of intratumoral NK cells, dendritic cells, effector T cells, and enhanced antigen processing and presentation. UBE2N was a potential promoter of immune evasion and drug resistance, with its high expression suggestive of low responsiveness to cancer immunotherapy and targeted therapies. Three potential UBE2N-inhibiting compounds were identified. Tissue microarrays confirmed UBE2N overexpression in LUAD, correlating with tumor size, while UBE2N knockdown suppressed tumor cell viability and induced apoptosis.Conclusions: UBE2N may serve as a promising prognostic biomarker and therapeutic target for LUAD. Inhibition of UBE2N is expected to suppress LUAD progression and enhance therapeutic efficacy.