ABSTRACT
BACKGROUND: Previous studies have hinted at a significant link between lung cancer and the gut microbiome, yet their causal relationship remains to be elucidated. METHODS: GWAS data for small cell lung cancer (SCLC) was extracted from the FinnGen consortium, comprising 179 cases and 218 613 controls. Genetic variation data for 211 gut microbiota were obtained as instrumental variables from MiBioGen. Mendelian randomization (MR) was employed to determine the causal relationship between the two, with inverse variance weighting (IVW) being the primary method for causal analysis. The MR results were validated through several sensitivity analyses. RESULTS: The study identified a protective effect against SCLC for the genus Eubacterium ruminantium group (OR = 0.413, 95% CI: 0.223-0.767, p = 0.00513), genus Barnesiella (OR = 0.208, 95% CI: 0.0640-0.678, p = 0.00919), family Lachnospiraceae (OR = 0.319, 95% CI: 0.107-0.948, p = 0.03979), and genus Butyricimonas (OR = 0.376, 95% CI: 0.144-0.984, p = 0.04634). Conversely, genus Intestinibacter (OR = 3.214, 95% CI: 1.303-7.926, p = 0.01125), genus Eubacterium oxidoreducens group (OR = 3.391, 95% CI: 1.215-9.467, p = 0.01973), genus Bilophila (OR = 3.547, 95% CI: 1.106-11.371, p = 0.03315), and order Bacillales (OR = 1.860, 95% CI: 1.034-3.347, p = 0.03842) were found to potentially promote the onset of SCLC. CONCLUSION: We identified potential causal relationships between certain gut microbiota and SCLC, offering new insights into microbiome-mediated mechanisms of SCLC pathogenesis, resistance, mutations, and more.
Subject(s)
Gastrointestinal Microbiome , Lung Neoplasms , Mendelian Randomization Analysis , Small Cell Lung Carcinoma , Humans , Mendelian Randomization Analysis/methods , Gastrointestinal Microbiome/genetics , Lung Neoplasms/microbiology , Lung Neoplasms/genetics , Small Cell Lung Carcinoma/microbiology , Small Cell Lung Carcinoma/genetics , Genome-Wide Association Study , Male , Female , Case-Control StudiesABSTRACT
BACKGROUND: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. MATERIALS AND METHODS: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. RESULTS: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). CONCLUSIONS: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.
Subject(s)
Cross Infection/epidemiology , Fungi/isolation & purification , Lung Neoplasms/microbiology , Mycoses/epidemiology , Neural Networks, Computer , Adenocarcinoma/microbiology , Adenocarcinoma/pathology , Adult , Aged , Aged, 80 and over , Carcinoma, Large Cell/microbiology , Carcinoma, Large Cell/pathology , Carcinoma, Non-Small-Cell Lung/microbiology , Carcinoma, Non-Small-Cell Lung/pathology , Carcinoma, Squamous Cell/microbiology , Carcinoma, Squamous Cell/pathology , Cross Infection/microbiology , Cross Infection/pathology , Female , Follow-Up Studies , Humans , Incidence , Logistic Models , Lung Neoplasms/pathology , Male , Middle Aged , Mycoses/microbiology , Mycoses/pathology , Neoplasm Staging , Prognosis , ROC Curve , Retrospective Studies , Risk Factors , Small Cell Lung Carcinoma/microbiology , Small Cell Lung Carcinoma/pathologyABSTRACT
Pneumocystis jirovecii causes pneumonia in immunosuppressed individuals. However, it has been reported the detection of low levels of Pneumocystis DNA in patients without signs and symptoms of pneumonia, which likely represents colonization. Several studies performed in animals models and in humans have demonstrated that Pneumocystis induces a local and a systemic response in the host. Since P jirovecii colonization has been found in patients with chronic pulmonary diseases it has been suggested that P jirovecii may play a role in the physiopathology and progression of those diseases. In this report we revise P. jirovecii colonization in different chronic pulmonary diseases such us, chronic obstructive pulmonary disease, interstitial lung diseases, cystic fibrosis and lung cancer.
