Background:Emerging observational and clinical studies have highlighted the role of gut microbiota in hematologic malignancies, including malignant lymphoma. However, conflicting findings persist regarding the causal direction of this relationship, as traditional studies are susceptible to confounding factors and reverse causality. Mendelian randomization (MR) analysis, leveraging genetic variants as instrumental variables (IVs), offers a robust approach to infer causality by minimizing these biases. Here, we investigate the bidirectional causal links between gut microbiota and malignant lymphoma, addressing controversies in existing population-based studies. Methods:Bidirectional two-sample MR analysis was used to examine the causal relationship between malignant lymphoma and gut microbiota. The summary-level data of gut microbiota was obtained from the MiBioGen Consortium, a large-scale genome-wide study, involving 18,340 participants from a multiethnic cohort. Summary statistics for malignant lymphoma were sourced from the OpenGWAS website, which contains data from 490,803 participants. Using the standard quality-controlled single-nucleotide polymorphism (SNP) as an IV, we examined the potential causative link between gut microbiota and malignant lymphoma via the inverse variance weighting, MR Egger, weighted median, weighted model, and simple mode. Reverse MR analysis was further conducted on bacterial taxa identified as causally associated with malignant lymphoma in the forward MR analysis. Results:Seven causal relationships between gut microbiota and malignant lymphoma were found, including the phylum Bacteroidetes [odds ratio (OR) =1.31; 95% confidence interval (CI): 1.02-1.68; P=0.03], the class Bacilli (OR =1.22; 95% CI: 1.00-1.49; P=0.048), the family Rikenellaceae (OR =1.27; 95% CI: 1.04-1.55; P=0.02), the genus Eubacterium nodatum group (OR =1.13; 95% CI: 1.00-1.27; P=0.046), the genus Oxalobacter (OR =1.23; 95% CI: 1.06-1.43; P=0.006), the genus Parabacteroides (OR =1.41; 95% CI: 1.00-1.99; P=0.049), and the genus Sellimonas (OR =1.18; 95% CI: 1.03-1.35; P=0.02). No significant level pleiotropy or heterogeneity was detected in the IV, and there was no reverse causality between gut microbiota and malignant lymphoma. Conclusions:We investigated the potential causal relationship between gut microbiota and malignant lymphoma. Our findings provide a theoretical foundation for future research on the relationship between gut microbiota and lymphoma, and may facilitate the development of diagnostic, therapeutic, and preventive strategies for lymphoma in clinical practice.

Background:Oxidative stress (OS) responses have been linked to oncogenesis and tumor progression and have recently been regarded as a potential strategy for tumor therapy. However, OS-related therapeutic targets have not been identified to date in the bladder cancer (BC).Methods:The mRNA expression and clinical data of BC were downloaded from the public database. Prognostic risk score signature was constructed using LASSO Cox regression analysis. External validation was performed in GSE15307 cohort. ESTIMATE, CIBERSORT, and ssGSEA algorithm were used to analyze immune cell infiltration and immune microenvironment. Next, functional enrichment analysis was performed to elucidate the mechanism underlying the signature. Additionally, we performed a nomogram to forecast the survival rate of individual BC patients.Results:An OS-related genes (OSRGs) signature was constructed. Overall survival was lower in the high-risk group than in the low-risk group, according to survival analyses. The area under the curve (AUC) of ROC curves further validated the prognostic signature's strong prediction performance in these two cohorts. The risk score was verified as an independent risk factor for BC by independent prognostic analysis. Moreover, as compared to TNM stage alone, a nomogram that integrated the risk score with TNM stage showed a much superior predictive value. Immune infiltration and tumor microenvironment studies indicated that immune cells and functions may play a significant role in carcinogenesis and development. The levels of expression of prognostic genes were shown to be substantially linked with drug sensitivity.Conclusion:We developed a novel OSRGs signature for predicting overall survival and impacting the immune status in patients with BC. New nomogram can help clinicians predict the survival rate of BC patients. These findings shed new light on the potential usage of OSRGs signature in BC patients.

ABSTRACT:Increasing evidence has shown that hypoxia is closely related to the development, progression, and prognosis of clear cell renal cell carcinoma (ccRCC). Nevertheless, reliable prognostic signatures based on hypoxia have not been well-established. This study aimed to establish a hypoxia-related prognostic signature and construct an optimized nomogram for patients with ccRCC.We accessed hallmark gene sets of hypoxia, including 200 genes, and an original RNA seq dataset of ccRCC cases with integrated clinical information obtained by mining the Cancer Genome Atlas database and the International Cancer Genome Consortium (ICGC) database. Univariate Cox regression analysis and multivariate Cox proportional hazards regression were performed to identify prognostic hub genes and further established prognostic model as well as visualized the nomogram. External validation of the optimized nomogram was performed in independent cohorts from the ICGC database.ANKZF1, ETS1, PLAUR, SERPINE1, FBP1, and PFKP were selected as prognostic hypoxia-related hub genes, and the prognostic model effectively distinguishes high-risk and low-risk patients with ccRCC. The results of receiver operating characteristic curve, risk plots, survival analysis, and independent analysis suggested that RiskScore was a useful tool and independent predictive factor. A novel prognosis nomogram optimized via RiskScore showed its promising performance in both the Cancer Genome Atlas-ccRCC cohort and an ICGC-ccRCC cohort.Our study reveals that the differential expressions of hypoxia-related genes are associated with the overall survival of patients with ccRCC. The prognostic model we established showed a good predictive and discerning ability in ccRCC patients. The novel nomogram optimized via RiskScore exhibited a promising predictive ability. It may be able to serve as a visualized tool for guiding clinical decisions and selecting effective individualized treatments.