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BackgroundCholangiocarcinoma (CCA) is the malignancy originating from the biliary epithelium, including intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA) CCA. The prognosis of CCA is very poor, and the biomarkers of different CCA subsets should be investigated separately. Holliday junction recognition protein (HJURP) is a key component of the pre-nucleosomal complex, which is responsible for normal mitosis. The ectopic expression of HJURP has been reported in several cancers, but not CCA.Materials and methodsIn our study, we investigated the expression of HJURP in 127 CCA patients which were composed of 32 iCCAs, 71 pCCAs, and 24 dCCAs with immunohistochemistry and divided these patients into subgroups with a low or high expression of HJURP. With chi-square test and univariate and multivariate analyses, we evaluated the clinical relevance and prognostic significance of HJURP in iCCAs, pCCAs, and dCCAs.ResultsHJURP was ectopically upregulated in CCAs compared with the para-tumor tissues based on TCGA and other mRNA-seq databases. A high expression of HJURP was correlated with low overall survival rates of iCCA and pCCA, but not in dCCA. Moreover, HJURP was an independent prognostic biomarker in both iCCA and pCCA. Patients with high HJURP were more likely to suffer CCA-related death after operation.ConclusionsHJURP was an independent prognostic biomarker in both iCCA and pCCA, but not in dCCA. Our results provide more evidence of the molecular features of different CCA subsets and suggest that patients with high HJURP are more high-risk, which can guide more precision follow-up and treatment of CCA.

Although emerging evidence has revealed that LHPP, a histidine phosphatase protein, suppresses the progression of different cancers, a pan-cancer analysis still remains unavailable. Therefore, we first utilized different bioinformatics tools to explore the tumor inhibitory role of LHPP protein across 33 tumor types based on the TCGA project. Additionally, HGC-27 gastric cancer cells were used to evaluate the biological functions of LHPP after stable transfection with lentiviruses. Consequently, LHPP mRNA and protein expression were down-regulated in the most cancer tissues corresponding to normal tissues. The data showed that patients with higher LHPP performance had a better prognosis of overall survival (OS) and disease-free survival (DFS) in brain glioma and renal carcinoma. In addition, we found that enhancement of LHPP expression attenuated the proliferation, migration and invasion of gastric cancer cells. The expression levels of cell-cycle-related and EMT-related molecules, such as CDK4, CyclinD1, Vimentin and Snail, were clearly reduced. Moreover, a genetic alteration analysis showed that the most frequent mutation types in LHPP protein was amplification. The patients without LHPP mutation showed a better tendency of prognosis in UCEC, STAD and COAD. Cancer-associated fibroblast infiltration was also observed in head and neck squamous cell carcinoma, stomach adenocarcinoma and testicular germ cell tumors. In summary, our pancancer analysis among various tumor types could provide a comprehensive understanding of LHPP biological function in the progression of malignant diseases and promote the development of novel therapeutic targets.

BACKGROUND: To develop a predictive model for hepatotoxicity due to antituberculosis drugs using a machine learning approach combining general clinical features of the electronic medical record, laboratory indications and genetic features of key genes in the PXR/ALAS1/FOXO1 axis. METHODS: Using the occurrence of ATDH as the outcome variable, the data were screened for features and model construction based on general clinical features and laboratory test indications, combined with single nucleotide polymorphism characteristics of PXR, FOXO1 and ALAS1 genes, combined with Lasso regression and logistic regression to evaluate the model's goodness of fit, predictive efficacy, discrimination and consistency, and used clinical decision Curve analysis was used to assess the clinical applicability of the models. RESULTS: The best model had a discriminant efficacy C-index of 0.8164, sensitivity of 34.25%, specificity of 97.99%, positive predictive value of 78.13%, negative predictive value of 87.69%, consistency test Sp=0.896, maximum bias Emax=0.147, and mean bias Eave=0.017. In the validation set performance was close. The clinical decision curve shows the clinical applicability of the prediction model when the prediction risk threshold is between 0.1 and 0.8. CONCLUSION: The ATDH prediction model was constructed using a machine learning approach, combining general characteristics of the study population, laboratory indications and SNP features of PXR and FOXO1 genes with good fit and some predictive value, and has potential and value for clinical application.

BACKGROUND: To develop a predictive model for hepatotoxicity due to antituberculosis drugs using a machine learning approach combining general clinical features of the electronic medical record, laboratory indications and genetic features of key genes in the PXR/ALAS1/FOXO1 axis. METHODS: Using the occurrence of ATDH as the outcome variable, the data were screened for features and model construction based on general clinical features and laboratory test indications, combined with single nucleotide polymorphism characteristics of PXR, FOXO1 and ALAS1 genes, combined with Lasso regression and logistic regression to evaluate the model's goodness of fit, predictive efficacy, discrimination and consistency, and used clinical decision Curve analysis was used to assess the clinical applicability of the models. RESULTS: The best model had a discriminant efficacy C-index of 0.8164, sensitivity of 34.25%, specificity of 97.99%, positive predictive value of 78.13%, negative predictive value of 87.69%, consistency test Sp=0.896, maximum bias Emax=0.147, and mean bias Eave=0.017. In the validation set performance was close. The clinical decision curve shows the clinical applicability of the prediction model when the prediction risk threshold is between 0.1 and 0.8. CONCLUSION: The ATDH prediction model was constructed using a machine learning approach, combining general characteristics of the study population, laboratory indications and SNP features of PXR and FOXO1 genes with good fit and some predictive value, and has potential and value for clinical application.