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PURPOSE: Studies have shown that the gut microbiota may affect anti-tumor immunity by regulating the host immune system and tumor microenvironment. To date, little is known about whether the gut microbiota underlies the occurrence of diffuse large B-cell lymphoma (DLBCL) and drug resistance. METHODS: In the present study, we compared the gut microbiota structure of fecal samples from 26 patients with primary DLBCL, 28 patients with relapsed and refractory (RR) DLBCL, and 30 healthy people. RESULTS: Notably, Fusobacteria (from phylum to species) was enriched in the primary group. A decrease of Fusobacterium and an increase of Enterococcus were found in the RR group. PICRUSt analysis found that genes related to cytochrome P450 were upregulated in the RR group compared to the primary group, which likely contributes to the occurrence of DLBCL and the formation of drug resistance. CONCLUSIONS: Our study provides further evidence for the relationship between gut microbiota and DLBCL and the formation of drug resistance, highlighting the potential significance of the bacterial variations may be used as new biomarkers of DLBCL.
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BACKGROUND: The combination of preoperative chemotherapy and surgical treatment has been shown to significantly enhance the prognosis of colorectal cancer with liver metastases (CRLM) patients. Nevertheless, as a result of variations in clinicopathological parameters, the prognosis of this particular group of patients differs considerably. This study aimed to develop and evaluate Cox proportional risk regression model and competing risk regression model using two patient cohorts. The goal was to provide a more precise and personalized prognostic evaluation system. METHODS: We collected information on individuals who had a pathological diagnosis of colorectal cancer between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) Database. We obtained data from patients who underwent pathological diagnosis of colorectal cancer and got comprehensive therapy at the hospital between January 1, 2010, and June 1, 2022. The SEER data collected after screening according to the inclusion and exclusion criteria were separated into two cohorts: a training cohort (training cohort) and an internal validation cohort (internal validation cohort), using a random 1:1 split. Subgroup Kaplan-Meier (K-M) survival analyses were conducted on each of the three groups. The data that received following screening from the hospital were designated as the external validation cohort. The subsequent variables were chosen for additional examination: age, gender, marital status, race, tumor site, pretreatment carcinoembryonic antigen level, tumor size, T stage, N stage, pathological grade, number of tumor deposits, perineural invasion, number of regional lymph nodes examined, and number of positive regional lymph nodes. The primary endpoint was median overall survival (mOS). In the training cohort, we conducted univariate Cox regression analysis and utilized a stepwise regression approach, employing the Akaike information criterion (AIC) to select variables and create Cox proportional risk regression models. We evaluated the accuracy of the model using calibration curve, receiver operating characteristic curve (ROC), and area under curve (AUC). The effectiveness of the models was assessed using decision curve analysis (DCA). To evaluate the non-cancer-related outcomes, we analyzed variables that had significant impacts using subgroup cumulative incidence function (CIF) and Gray's test. These analyses were used to create competing risk regression models. Nomograms of the two models were constructed separately and prognostic predictions were made for the same patients in SEER database. RESULTS: This study comprised a total of 735 individuals. The mOS of the training cohort, internal validation cohort, and QDU cohort was 55.00 months (95%CI 46.97-63.03), 48.00 months (95%CI 40.65-55.35), and 68.00 months (95%CI 54.91-81.08), respectively. The multivariate Cox regression analysis revealed that age, N stage, presence of perineural infiltration, number of tumor deposits and number of positive regional lymph nodes were identified as independent prognostic risk variables (p < 0.05). In comparison to the conventional TNM staging model, the Cox proportional risk regression model exhibited a higher C-index. After controlling for competing risk events, age, N stage, presence of perineural infiltration, number of tumor deposits, number of regional lymph nodes examined, and number of positive regional lymph nodes were independent predictors of the risk of cancer-specific mortality (p < 0.05). CONCLUSION: We have developed a prognostic model to predict the survival of patients with synchronous CRLM who undergo preoperative chemotherapy and surgery. This model has been tested internally and externally, confirming its accuracy and reliability.
Assuntos
Neoplasias Colorretais , Neoplasias Hepáticas , Modelos de Riscos Proporcionais , Programa de SEER , Humanos , Neoplasias Colorretais/patologia , Neoplasias Colorretais/tratamento farmacológico , Neoplasias Colorretais/mortalidade , Neoplasias Hepáticas/secundário , Neoplasias Hepáticas/tratamento farmacológico , Neoplasias Hepáticas/mortalidade , Masculino , Feminino , Pessoa de Meia-Idade , Prognóstico , Idoso , Estimativa de Kaplan-Meier , Nomogramas , Curva ROC , Estudos de Coortes , Adulto , Estudos RetrospectivosRESUMO
Accumulating evidences shed light on the important roles of Circular RNAs (circRNAs) acting as competing endogenous RNAs (ceRNAs) in cervical cancer (CC) biology. The present study aimed to identify a novel circRNA-related prognostic signature for CC. The expression data and clinical information of CC were downloaded from the Gene Expression Omnibus (GEO) datasets to identify the differential circRNAs expression. Based on the targeted miRNA prediction, circRNA-related miRNAs were detected in training group and validation group of The Cancer Genome Atlas (TCGA) dataset to construct the novel prognostic signature of CC with least absolute shrinkage and selection operator (LASSO). Moreover, the Kaplan-Meier (K-M) analysis was applied to test the model. In the present study, three differentially expressed circRNAs (hsa_circ_0001498, hsa_circ_0066147, and hsa_circ_0006948) were identified in GSE102686 and GSE107472. Then, with the criteria 25 predicted miRNAs were analyzed in TCGA datasets to calculate the prognostic signature. Furthermore, we developed a six-miRNA signature (hsa-miR-217, hsa-miR-30b-3p, hsa-miR-136-5p, hsa-miR-185-3p, hsa-miR-501-5p and hsa-miR-658) based on their expression level and coefficients. We performed a Pearson correlation analysis to screen 47 mRNAs which are negatively regulated by these six miRNAs. Functional enrichment analysis indicated these mRNAs were mainly enriched in cancer-related biology, such as regulation of transcription, signal transduction, and cell cycle. The present study provides novel insight for better understanding of circRNA-related ceRNA network in CC and facilitates the identification of potential biomarkers for prognosis.