A comparative study of microbial changes in dental plaque before and after single- and multiappointment treatments in patients with severe early childhood caries.

BACKGROUND: The status of dental caries is closely related to changes in the oral microbiome. In this study, we compared the diversity and structure of the dental plaque microbiome in children with severe early childhood caries (S-ECC) before and after general anaesthesia and outpatient treatment. METHODS: Forty children aged 3 to 5 years with S-ECC who had completed whole-mouth dental treatment under general anaesthesia (C1) or in outpatient settings (C2) were selected, 20 in each group. The basic information and oral health status of the children were recorded, and the microbial community structure and diversity of dental plaque before treatment (C1, C2), the day after treatment(C2_0D), 7 days after treatment (C1_7D, C2_7D), 1 month after treatment (C1_1M, C2_1M), and 3 months after treatment (C1_3M, C2_3M) were analysed via 16 S rRNA high-throughput sequencing technology. RESULTS: (1) The alpha diversity test showed that the flora richness in the multiappointment group was significantly greater at posttreatment than at pretreatment (P < 0.05), and the remaining alpha diversity index did not significantly differ between the 2 groups (P > 0.05). The beta diversity analysis revealed that the flora structures of the C1_7D group and the C2_3M group were significantly different from those of the other time points within the respective groups (P < 0.05). (2) The core flora existed in both the pre- and posttreatment groups, and the proportion of their flora abundance could be altered depending on the caries status of the children in both groups. Leptotrichia abundance was significantly (P < 0.05) lower at 7 days posttreatment in both the single- and multiappointment groups. Corynebacterium and Corynebacterium_matruchotii were significantly more abundant in the C1_1M and C1_3M groups than in the C1 and C1_7D groups (P < 0.05). Streptococcus, Haemophilus and Haemophilus_parainfluenzae were significantly more abundant in the C1_7D group than in the other groups (P < 0.05). CONCLUSION: A single session of treatment under general anaesthesia can cause dramatic changes in the microbial community structure and composition within 7 days after treatment, whereas treatment over multiple appointments may cause slow changes in oral flora diversity.

Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature.

BACKGROUND: The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression. METHODS: The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts. RESULTS: The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples. CONCLUSION: Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Oxaliplatin related lncRNAs prognostic models predict the prognosis of patients given oxaliplatin-based chemotherapy.

BACKGROUND: Oxaliplatin-based chemotherapy is the first-line treatment for colorectal cancer (CRC). Long noncoding RNAs (lncRNAs) have been implicated in chemotherapy sensitivity. This study aimed to identify lncRNAs related to oxaliplatin sensitivity and predict the prognosis of CRC patients underwent oxaliplatin-based chemotherapy. METHODS: Data from the Genomics of Drug Sensitivity in Cancer (GDSC) was used to screen for lncRNAs related to oxaliplatin sensitivity. Four machine learning algorithms (LASSO, Decision tree, Random-forest, and support vector machine) were applied to identify the key lncRNAs. A predictive model for oxaliplatin sensitivity and a prognostic model based on key lncRNAs were established. The published datasets, and cell experiments were used to verify the predictive value. RESULTS: A total of 805 tumor cell lines from GDSC were divided into oxaliplatin sensitive (top 1/3) and resistant (bottom 1/3) groups based on their IC50 values, and 113 lncRNAs, which were differentially expressed between the two groups, were selected and incorporated into four machine learning algorithms, and seven key lncRNAs were identified. The predictive model exhibited good predictions for oxaliplatin sensitivity. The prognostic model exhibited high performance in patients with CRC who underwent oxaliplatin-based chemotherapies. Four lncRNAs, including C20orf197, UCA1, MIR17HG, and MIR22HG, displayed consistent responses to oxaliplatin treatment in the validation analysis. CONCLUSION: Certain lncRNAs were associated with oxaliplatin sensitivity and predicted the response to oxaliplatin treatment. The prognostic models established based on the key lncRNAs could predict the prognosis of patients given oxaliplatin-based chemotherapy.