Circulating tumor DNA (ctDNA) in peripheral blood is a “liquid biopsy” that contains representative tumor information including gene mutations. Additionally, repeated ctDNA samples can be easily obtained to monitor response to treatment and disease progression, which may be especially valuable to lung cancer patients with tumors that cannot be easily biopsied or removed. To investigate the changes in ctDNA after surgical tumor resection, tumor and blood samples obtained before and after surgery were collected prospectively from 41 non-small lung cancer (NSCLC) patients. Somatic driver mutations in tumor DNA (tDNA) and pre- and post-op plasma ctDNA sample pairs were identified by targeted sequencing in several genes including EGFR, KRAS, and TP53 with an overall study concordance of 78.1% and sensitivity and specificity of 69.2% and 93.3%, respectively. Importantly, the frequency of 91.7% of ctDNA mutations decreased after surgery and these changes were observed as little as 2 days post-op. Moreover, the presence of ctDNA had a higher positive predictive value than that of six tumor biomarkers in current clinical use. This study demonstrates the use of targeted sequencing to reliably identify ctDNA changes in response to treatment, indicating a potential utility of this approach in the clinical management of NSCLC.

The tumor microenvironment (TME) is a biological system with sophisticated constituents. In addition to tumor cells, tumor‐associated macrophages (TAMs) and microbiota are also dominant components. The phenotypic and functional changes of TAMs are widely considered to be related to most tumor progressions. The chronic colonization of pathogenic microbes and opportunistic pathogens accounts for the generation and development of tumors. As messengers of cell‐to‐cell communication, tumor‐derived extracellular vesicles (TDEVs) can transfer various malignant factors, regulating physiological and pathological changes in the recipients and affecting TAMs and microbes in the TME. Despite the new insights into tumorigenesis and progress brought by the above factors, the crosstalk among tumor cells, macrophages, and microbiota remain elusive, and few studies have focused on how TDEVs act as an intermediary. We reviewed how tumor cells recruit and domesticate macrophages and microbes through extracellular vehicles and how hijacked macrophages and microbiota interact with tumor‐promoting feedback, achieving a reciprocal coexistence under the TME and working together to facilitate tumor progression. It is significant to seek evidence to clarify those specific interactions and reveal therapeutic targets to curb tumor progression and improve prognosis.

BACKGROUND:The aim of this study was to clarify the potential function of LINC00922 in regulating the progression of lung cancer and its underling mechanism. MATERIAL AND METHODS:Relative levels of LINC00922 in lung cancer tissues and cell lines was ...

Background: The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. Methods: In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. Results: Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces, Aspergillus, and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. Conclusions: For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.