Background Breast cancer is one of the most common malignant tumors among women worldwide. This study aimed to screen key genes and pathways for breast cancer diagnosis and treatment. Material/Methods We obtained public data from the NCBI GEO database. The data were divided into a control group (normal breast tissue) and a treatment group (breast cancer tissue). We screened 32 differentially expressed genes (DEGs) between normal breast and cancerous tissues and used GO analysis and GSEA to identify the key pathways. We then combined LASSO and SVM-RFE analyses to screen key genes, and used CIBERSORT to obtain the proportion of 22 types of immune cells. The relationships between key genes and immune-infiltrating cells were further explored. Results We screened 32 DEGs from the 2 groups, including 27 downregulated genes and 5 upregulated genes. GO analysis indicated that the DEGs were mainly correlated with collagen-containing extracellular matrix (ECM), Wnt signaling pathway, and glycosaminoglycan binding. GSEA indicated that the treatment group was correlated with chromosome segregation and cell cycle while the control group was correlated with cornification, intermediate filament, and nuclear transcription. Through machine learning, SYNM , TGFBR3 , and COL10A1 were screened as key genes. Numbers of CD8 T cells, gamma delta T cells, and M1 macrophages were significantly higher, while monocytes and follicular helper-T cells were significantly lower in the treatment group. The downregulated genes, SYNM and TGFBR3 , were positively correlated with CD8 T cells and monocytes, but were negatively correlated with gamma delta T cells and M1 macrophages. The upregulated gene, COL10A1 , was positively correlated with gamma delta T cells and M1 macrophages, and was negatively correlated with CD8 T cells, monocytes, and follicular helper-T cells. Conclusions SYNM , TGFBR3 , and COL10A1 are diagnostic genes of breast cancer. They affect breast cancer cells by modulating immune-infiltrating cells.

Background Skin cutaneous melanoma (SKCM) is one of the most highly prevalent and complicated malignancies. Glycolysis and cholesterogenesis pathways both play important roles in cancer metabolic adaptations. The main aims of this study are to subtype SKCM based on glycolytic and cholesterogenic genes and to build a clinical outcome predictive algorithm based on the subtypes. Methods A dataset with 471 SKCM specimens was downloaded from The Cancer Genome Atlas (TCGA) database. We extracted and clustered genes from the Molecular Signatures Database v7.2 and acquired co-expressed glycolytic and cholesterogenic genes. We then subtyped the SKCM samples and validated the efficacy of subtypes with respect to simple nucleotide variations (SNVs), copy number variation (CNV), patients’ survival statuses, tumor microenvironment, and proliferation scores. We also constructed a risk score model based on metabolic subclassification and verified the model using validating datasets. Finally, we explored potential drugs for high-risk SKCM patients. Results SKCM patients were divided into four subtype groups: glycolytic, cholesterogenic, mixed, and quiescent subgroups. The glycolytic subtype had the worst prognosis and MGAM SNV extent. Compared with the cholesterogenic subgroup, the glycolytic subgroup had higher rates of DDR2 and TPR CNV and higher proliferation scores and MK167 expression levels, but a lower tumor purity proportion. We constructed a forty-four-gene predictive signature and identified MST-321, SB-743921, Neuronal Differentiation Inducer III, romidepsin, vindesine, and YM-155 as high-sensitive drugs for high-risk SKCM patients. Conclusions Subtyping SKCM patients via glycolytic and cholesterogenic genes was effective, and patients in the glycolytic-gene enriched group were found to have the worst outcome. A robust prognostic algorithm was developed to enhance clinical decisions in relation to drug administration.

Background. To explore the effect of age on the prognosis of patients with early stage breast cancer after breast-conserving surgery (BCS) and to provide references for young patients. Methods. All clinical data of patients with early breast cancer undergoing BCS who were treated at Shengjing Hospital of China Medical University from January 2011 to May 2016 were obtained. The primary endpoints were local recurrence (LR) and distant recurrence, and the secondary endpoint was breast cancer-specific survival (BCSS). Chi-squared tests and Fisher’s exact tests were used for statistical analysis. Disease-free survival (DFS) and BCSS were calculated by Kaplan–Meier survival analysis and compared using log-rank tests. Logistic regression was used for multivariable analysis of the effect of age in different subgroups. Propensity score matching (PSM) was used to reduce the bias confounding factors on oncological outcomes. Results. Younger patients had higher Ki-67 expression ( P = 0.048 ) and larger tumors ( P = 0.042 ) compared to older patients. No other clinical features were significantly different between age groups. There was no significant difference between the two groups in BCSS ( P = 0.186 ); however, DFS was significantly different before PSM ( P = 0.012 ). Triple-negative breast cancer and Ki-67 positivity combined with younger age at diagnosis were associated with a higher risk of recurrence ( P = 0.018 and P = 0.046 , respectively). After PSM, there were no significant differences in BCSS nor DFS between the two age groups ( P = 0.559 and P = 0.261 , respectively). Conclusion. BCS for young patients is not associated with increased DFS nor BCSS. However, young patients with triple-negative breast cancer and/or Ki-67 positivity have a poor prognosis. In sum, BCS may be appropriate for a subgroup of young patients.

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