HER2-low breast cancer (BC) has a poor prognosis, making the development of more suitable treatment an unmet clinical need. While chemotherapy is the main method of treatment for HER2-low BC, not all patients benefit from it. Antineoplastic therapy without chemotherapy has shown promise in clinical trials and is being explored further. As quantitative detection techniques become more advanced, they assist in better defining the expression level of HER2 and in guiding the development of targeted therapies, which include directly targeting HER2 receptors on the cell surface, targeting HER2-related intracellular signaling pathways and targeting the immune microenvironment. A new anti-HER2 antibody-drug conjugate called T-DM1 has been successfully tested and found to be highly effective in clinical trials. With this progress, it could eventually be transformed from a disease without a defined therapeutic target into a disease with a defined therapeutic molecular target. Furthermore, efforts are being made to compare the sequencing and combination of chemotherapy, endocrine therapy, and HER2-targeted therapy to improve prognosis to customize the subtype of HER2 low expression precision treatment regimens. In this review, we summarize the current and upcoming treatment strategies, to achieve accurate management of HER2-low BC.

Background: Determining the benign or malignant status of indeterminate pulmonary nodules (IPN) with intermediate malignancy risk is a significant clinical challenge. Oral microbiota-lung cancer (LC) interactions have qualified oral microbiota as a promising non-invasive predictive biomarker in IPN. Materials and methods: Prospectively collected saliva, throat swabs, and tongue coating samples from 1040 IPN patients and 70 healthy controls across three hospitals. Following up, the IPNs were diagnosed as benign (BPN) or malignant pulmonary nodules (MPN). Through 16S rRNA sequencing, bioinformatics analysis, fluorescence in situ hybridization (FISH), and seven machine learning algorithms (support vector machine, logistic regression, naïve Bayes, multi-layer perceptron, random forest, gradient-boosting decision tree, and LightGBM), we revealed the oral microbiota characteristics at different stages of HC-BPN-MPN, identified the sample types with the highest predictive potential, constructed and evaluated the optimal MPN prediction model for predictive efficacy, and determined microbial biomarkers. Additionally, based on the SHAP algorithm interpretation of the ML model’s output, we have developed a visualized IPN risk prediction system on the web. Results: Saliva, tongue coating, and throat swab microbiotas exhibit site-specific characteristics, with saliva microbiota being the optimal sample type for disease prediction. The saliva-LightGBM model demonstrated the best predictive performance (AUC = 0.887, 95%CI: 0.865–0.918), and identified Actinomyces, Rothia, Streptococcus, Prevotella, Porphyromonas, and Veillonella as biomarkers for predicting MPN. FISH was used to confirm the presence of a microbiota within tumors, and external data from a LC cohort, along with three non-IPN disease cohorts, were employed to validate the specificity of the microbial biomarkers. Notably, coabundance analysis of the ecological network revealed that microbial biomarkers exhibit richer interspecies connections within the MPN, which may contribute to the pathogenesis of MPN. Conclusion: This study presents a new predictive strategy for the clinic to determine MPNs from BPNs, which aids in the surgical decision-making for IPN.

Micro-aspiration of oral microorganisms results in considerable enrichment within the lower respiratory tract (LRT), constituting an early event in lung cancer pathogenesis. To explore the correlation between malignant risk of pulmonary nodules (PNs) and oral commensals enrichment in LRT, oral saliva and bronchial alveolar lavage fluid samples from 22 low-risk PN patients, 17 intermediate-risk PN patients, and 11 high-risk PN patients were analyzed using 16S rRNA gene sequencing. Alpha and beta diversity analyses reveal minimal variation in oral microbial diversity and abundance among patients with different risks of PN. In contrast, a significant reduction in the diversity of LRT microbiota is observed in patients at high risk of PN. Based on multigroup comparative analysis of species differences and the linear discriminant analysis effect size method, Synergistes and Tannerella were identified as the dominant bacterial genera in the oral and LRT of high-risk PN patients, respectively. The study found that the LRT microbiota of PN patients seemed to originate from the oral, and the high enrichment of oral microbiota in the lower respiratory tract was most common in high-risk PN patients. The predominant bacterial genera present in the oral cavity and LRT of patients with PN were identified through abundance variance analysis. Eight key microbial genera were found in both the oral cavity and LRT: Streptococcus , Granulicatella , Porphyromonas , Bacillus , Neisseria , Alloprevotella , Prevotella , and Leptotrichia . Notably, receiver operating characteristic analysis identified Streptococcus , Granulicatella , and Leptotrichia as reliable biomarkers to differentiate high-risk PN. Spearman correlation analysis confirmed that the accumulation of oral microorganisms in the LRT played an important role in the process of PN cancerization. The co-occurrence network showed that the coexistence of Veillonella and Streptococcus in the oral and LRT may be involved in the occurrence of PN, while the LRT cluster of Rothia occurred in high-risk PN patients. Correlation analysis among species identified microbial communities predominantly composed of Veillonella , which may facilitate pulmonary carcinogenesis. IMPORTANCE: This study is the first to elucidate the composition and interrelationships of oral and lower respiratory tract (LRT) microbiota in patients with pulmonary nodule (PN) across varying malignancy risk levels. We conducted an analysis to investigate the correlation between the malignant potential of PNs and the enrichment of oral microbiota within the LRT. Additionally, we explored the feasibility of utilizing oral-lower respiratory commensal microbiota as biomarkers to assess the benign and malignant nature of pulmonary nodules. This study aims to provide evidence supporting early diagnosis and intervention strategies for lung cancer.

Early diagnosis of lung cancer (LC) is challenging, treatment options are limited, and treatment resistance leads to poor prognosis and management in most patients. The Wnt/β-catenin signaling pathway plays a vital role in the occurrence, progression, and therapeutic response of LC. Recent studies indicate that non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) function as epigenetic regulators that can promote or inhibit Wnt/β-catenin signaling by interacting with Wnt proteins, receptors, signaling transducers, and transcriptional effectors, thereby affecting LC cell proliferation, metastasis, invasion, and treatment resistance. Deepening our understanding of the regulatory network between ncRNAs and the Wnt/β-catenin signaling pathway will help overcome the limitations of current LC diagnosis and treatment methods. This article comprehensively reviews the regulatory mechanisms related to the functions of ncRNAs and the Wnt/β-catenin pathway in LC, examining their potential as diagnostic and prognostic biomarkers and therapeutic targets, aiming to offer new promising perspectives for LC diagnosis and treatment.

Astragalus laxmannii is a traditional Chinese medicine. The complete chloroplast genome sequence is 122,844 bp in length, contains 110 complete genes, including 75 protein-coding genes (75 PCGs), 8 ribosomal RNA genes (4 rRNAs), and 30 tRNA genes (30 tRNAs). The overall GC content of cp DNA is 34.1%. Phylogenetic tree shows that A. laxmannii is a sister to A. strictus.