Causal relationships between gut microbiota and programmed cell death protein 1/programmed cell death-ligand 1: A bidirectional Mendelian randomization study.

Background: Multiple clinical studies have indicated that the gut microbiota influences the effects of immune checkpoint blockade (ICB) therapy comprising PD-1/PD-L1 inhibitors, but the causal relationship is unclear. Because of numerous confounders, many microbes related to PD-1/PD-L1 have not been identified. This study aimed to determine the causal relationship between the microbiota and PD-1/PD-L1 and identify possible biomarkers for ICB therapy. Method: We used bidirectional two-sample Mendelian randomization with two different thresholds to explore the potential causal relationship between the microbiota and PD-1/PD-L1 and species-level microbiota GWAS to verify the result. Result: In the primary forward analysis, genus_Holdemanella showed a negative correlation with PD-1 [ßIVW = -0.25; 95% CI (-0.43 to -0.07); PFDR = 0.028] and genus_Prevotella9 showed a positive correlation with PD-1 [ßIVW = 0.2; 95% CI (0.1 to 0.4); PFDR = 0.027]; order_Rhodospirillales [ßIVW = 0.2; 95% CI (0.1 to 0.4); PFDR = 0.044], family_Rhodospirillaceae [ßIVW = 0.2; 95% CI (0 to 0.4); PFDR = 0.032], genus_Ruminococcaceae_UCG005 [ßIVW = 0.29; 95% CI (0.08 to 0.5); PFDR = 0.028], genus_Ruminococcus_gnavus_group [ßIVW = 0.22; 95% CI (0.05 to 0.4); PFDR = 0.029], and genus_Coprococcus_2 [ßIVW = 0.4; 95% CI (0.1 to 0.6); PFDR = 0.018] were positively correlated with PD-L1; and phylum_Firmicutes [ßIVW = -0.3; 95% CI (-0.4 to -0.1); PFDR = 0.031], family_ClostridialesvadinBB60group [ßIVW = -0.31; 95% CI (-0.5 to -0.11), PFDR = 0.008], family_Ruminococcaceae [ßIVW = -0.33; 95% CI (-0.58 to -0.07); PFDR = 0.049], and genus_Ruminococcaceae_UCG014 [ßIVW = -0.35; 95% CI (-0.57 to -0.13); PFDR = 0.006] were negatively correlated with PD-L1. The one significant species in further analysis was species_Parabacteroides_unclassified [ßIVW = 0.2; 95% CI (0-0.4); PFDR = 0.029]. Heterogeneity (P > 0.05) and pleiotropy (P > 0.05) analyses confirmed the robustness of the MR results.

A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening.

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy. Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs. Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability. Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.