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Abstract Background The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large‐scale meta‐analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine‐learning classifier. Methods In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony‐related functions. Results The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top‐ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota‐associated functions between cancer‐affected and healthy individuals that were primarily associated with metabolic disturbances. Conclusions GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC‐specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.

Abstract Breast cancer is the most common cancer in women and the second leading cause of cancer‐related deaths after lung cancer. Metastasis of the central nervous system is a terrible event for breast cancer patients, affecting their survival and quality of life. Compared with hormone receptor‐positive/human epidermal growth factor receptor 2‐negative breast cancer patients, brain metastases are more likely to affect patients with triple‐negative breast cancer and human epidermal growth factor receptor 2‐positive breast cancer. The treatment of breast cancer has improved greatly in the last two decades. However, brain metastases from breast cancer remain the leading cause of morbidity and mortality. Patients with breast cancer brain metastasis have been in an inferior position due to the lack of clinical research in this field, and they are often explicitly excluded from almost all clinical trials. The occurrence and progression of brain metastases will result in severe cognitive impairment and adverse physical consequences, so we must have a good understanding of the molecular mechanisms of breast cancer brain metastasis. In this article, we have retrieved the latest literature of molecules and pathways associated with breast cancer brain metastasis, summarized common therapy strategies, and discussed the prospects and clinical implications of targeting the molecules involved.

Abstract The treatment of breast cancer (BC) has improved greatly in recent years, however, the limitations of current therapeutic modalities underscore the need to define new prognostic tools and develop highly targeted therapies. The aims of the present study were to explore the effects of circulating blood lymphocyte subsets on the survival of metastatic breast cancer (MBC) patients and to evaluate their predictive and prognostic value. The clinical data of 482 patients with MBC were retrospectively analyzed, and patients were grouped according to molecular types of BC. The distribution of peripheral blood lymphocyte subsets at the time of first metastasis was examined by flow cytometry, and the distribution of lymphocyte subsets in each group was categorized into ‘‘high or low’’ subgroups using the upper quartile point as the cutoff. The relationship between the distribution of lymphocyte subsets and progression‐free survival (PFS) as well as overall survival (OS) was evaluated in diverse molecular MBCs. In multivariate analysis, CD4+ was a negative independent predictor of PFS (hazard ratio [HR] = 0.538, 95% confidence interval [CI] = 0.313‐0.926, P = 0.025) and CD3+ was a poor independent prognostic factor for OS (HR = 0.437, 95% CI = 0.248‐0.772, P = 0.004) in the human epidermal growth factor receptor 2 (HER2)‐positive group. Neither the CD8+, CD19+, and CD56+ lymphocyte subsets nor the CD4+/CD8+ ratio in peripheral blood was significant predictive or prognostic factors. In conclusion, higher circulating levels of CD4+ and CD3+ at first diagnosis in HER2‐overexpressing MBC were significantly associated with worse survival outcomes. Low levels of plasma CD4+ and CD3+ were associated with increased anti‐HER2 benefit in HER2‐positive MBC. The present results indicate that these factors can be used as predictive and prognostic indicators of the outcome of patients with MBC.