Systematic analysis on multiple Gene Expression Omnibus data sets reveals fierce immune response in hepatitis B virus-related acute liver failure.

Acute liver failure (ALF) caused by hepatitis B virus (HBV) is common type of liver failure in the world, with high morbidity and mortality rates. However, the prevalence, genetic background and factors determining the development of HBV-related ALF are rarely studied. In this study, we examined three Gene Expression Omnibus (GEO) data sets by bioinformatics analysis to identify differentially expressed genes (DEGs), key biological processes and pathways. Immune infiltration analysis showed high immune cells infiltration in HBV-related ALF tissue. We then confirmed natural killer cells and macrophages infiltration in clinical samples by immunohistochemistry assay, implying these cells play a significant role in HBV-ALF. We found 1277 genes were co-up-regulated and that 1082 genes were co-down-regulated in the 3 data sets. Inflammation-related pathways were enriched in the co-up-regulated genes and synthetic metabolic pathways were enriched in the co-down-regulated genes. WGCNA also revealed a key module enriching in immune inflammation response and identified 10 hub genes, differentially expressed in an independent data set. In conclusion, we identified fierce immune inflammatory response to elucidate the immune-driven mechanism of HBV-ALF and 10 hub genes based on gene expression profiles.

The m6A demethylase FTO promotes keloid formation by up-regulating COL1A1.

Background: Keloid is a dermal fibrotic disease characterized by excessive proliferation of dermal fibroblasts and deposition of excessive collagen. N6-methyladenosine (m6A) plays a significant role in numerous physiological and pathological regulatory processes in the human body. Fat mass and obesity-associated protein (FTO) is one of the most essential m6A demethylases. However, whether FTO has a regulatory role in keloid development remains to be determined. Methods: In this study, we investigated the effects of the m6A demethylase FTO on keloid formation by performing hematoxylin and eosin (H&E) staining, m6A dot blotting, transwell migration experiment, and methylated RNA immunoprecipitation quantitative polymerase chain reaction (MeRIP-qPCR) tests, as well as real-time PCR (RT-PCR) and Western blot assays. Results: The H&E staining indicated abnormal arrangement and proliferation of fibroblasts in the keloid tissue. The m6A dot blotting and qPCR revealed lower levels of m6A modification and increased expression of the m6A demethylases FTO in keloid tissue. Furthermore, overexpression of FTO promoted fibroblast migration as well as the expression of collagen type I alpha 1 chain (COL1A1) and α-smooth muscle actin (α-SMA). Mechanistic experiments demonstrated that FTO enhances keloid formation by modulating COL1A1 m6A modification and messenger RNA (mRNA) stability. In addition, this study also revealed the role of FTO in the therapeutic effect of glucocorticoids on keloids. Conclusions: Our study demonstrates that FTO upregulates COL1A1 expression via regulating COL1A1 m6A modification and maintaining mRNA stability, hence promoting keloid development and providing a potential new therapeutic target for the treatment of keloids.

Cuproptosis-related risk score based on machine learning algorithm predicts prognosis and characterizes tumor microenvironment in head and neck squamous carcinomas.

Cuproptosis is a recently discovered type of programmed cell death that shows significant potential in the diagnosis and treatment of cancer. It has important significance in the prognosis of HSNC. This study aims to construct a cuproptosis-related prognostic model and risk score through new data analysis methods such as machine learning algorithms for the prognosis analysis of HSNC. Protein-protein interaction network and machine learning methods were employed to identify hub genes that were used to construct a TreeGradientBoosting model for predicting overall survival. The relationship between the risk scores obtained from the model and features such as tumor microenvironment (TME) and tumor immunity was explored. The C-indexes of the TreeGradientBoosting model in the training and validation cohorts were 0.776 and 0.848, respectively. The nomogram based on risk scores and clinical features showed good performance, and distinguished the TME and immunity between high-risk and low-risk groups. The cuproptosis-associated risk score can be used to predict prognoses, TME, and tumor immunity of HNSC patients.

Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a high-volume Chinese center.

Objective: This study aims to assess the effectiveness of the Gradient Boosting (GB) algorithm on glioma prognosis prediction and to explore new predictive models for glioma patient survival after tumor resection. Methods: A cohort of 776 glioma cases (WHO grades II-IV) between 2010 and 2017 was obtained. Clinical characteristics and biomarker information were reviewed. Subsequently, we constructed the conventional Cox survival model and three different supervised machine learning models, including support vector machine (SVM), random survival forest (RSF), Tree GB, and Component GB. Then, the model performance was compared with each other. At last, we also assessed the feature importance of models. Results: The concordance indexes of the conventional survival model, SVM, RSF, Tree GB, and Component GB were 0.755, 0.787, 0.830, 0.837, and 0.840, respectively. All areas under the cumulative receiver operating characteristic curve of both GB models were above 0.800 at different survival times. Their calibration curves showed good calibration of survival prediction. Meanwhile, the analysis of feature importance revealed Karnofsky performance status, age, tumor subtype, extent of resection, and so on as crucial predictive factors. Conclusion: Gradient Boosting models performed better in predicting glioma patient survival after tumor resection than other models.

DTYMK Expression Predicts Prognosis and Chemotherapeutic Response and Correlates with Immune Infiltration in Hepatocellular Carcinoma.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the most common malignant tumor of the liver. Identifying specific molecular markers that can predict HCC prognosis is extremely important. The protein deoxythymidylate kinase (DTYMK) has been reported to contribute to unfavorable prognosis in non-small cell lung cancer patients, but its role in the prediction of HCC patient prognosis has not been clarified. METHODS: Samples from the TCGA and GEO databases were consecutively enrolled for gene expression analysis, clinicopathology analysis, immune microenvironment analysis and chemotherapeutic response prediction. The results were validated using 86 samples from the First Affiliated Hospital of Sun Yat-sen University. Cox regression analysis was used to analyze the effect of DTYMK on progression-free survival (PFS) and overall survival (OS). Functional enrichment analysis was used to describe the marker pathways that were significantly related to DTYMK. TIMER (Tumor Immune Estimation Resource), TISIDB (Tumor and Immune System Interaction DataBase) and CIBERSORT (Cell type Identification By Estimating Relative Subsets Of RNA Transcripts) were used to explore the immune microenvironment. RESULTS: We found that DTYMK expression upregulation is associated with poor prognosis in HCC patients and tightly related to the pathways regulating the cell cycle and acid metabolism. Our findings revealed that hepatocellular carcinoma cell lines with high DTYMK expression were more sensitive to sorafenib and many other chemotherapeutic drugs. We also found an inhibiting effect of DTYMK on the immune microenvironment in the process of tumorigenesis. DISCUSSION: We found that DTYMK has potential as a new prognostic and chemotherapeutic response biomarker for HCC patients and correlates with the immune microenvironment in HCC. However, there are some deficiencies in our study. First, this is a retrospective study that may lead to selection bias. Second, the protein expression of DTYMK was investigated via immunohistochemical analysis. Finally, we did not explore the exact underlying molecular mechanisms of DTYMK in tumorigenesis in this study, which is needed to be clarified in future research.

Outcomes of Combined Liver and Pancreas Transplantation: A Review of the SRTR National Database and a Report of the Largest Single Center Series.

Purposes: This study was intended to summarize the characteristics and clinical outcome of Liver and Pancreas (LPTx) recipients in the Scientific Registry of Transplant Recipients (SRTR) database vs. the largest series from the First Affiliated Hospital (FAH), Sun Yat-sen University. Methods: The clinical data of 23 patients who underwent LPTx from 2000 to 2016 in the United States and 31 patients who underwent modified LPTx procedure (known as simplified multivisceral transplantation [SMT]) from 2008 to 2017 in our center were reviewed. The indications, surgical techniques, patient and graft survival, and complications were compared between the two groups. Results: All recipients in the FAH group were diagnosed with type 2 diabetes mellitus, while 10 of 23 recipients were diagnosed with type 1 diabetes mellitus in the SRTR group. The 1-, 3-, and 5-year cumulative patient survival rates were 81, 74, and 74% in the FAH group, respectively, and 51, 47, and 37% in the SRTR group, respectively (P = 0.023). No diabetes was observed during follow-up in the FAH group, while the diabetes recurrence rate was 22.2% in the SRTR group (P = 0.03). Conclusion: With multiple techniques modified and indications changed, the SMT procedure yielded a preferable outcome compared to that of the traditional LPTx procedure in records of SRTR. SMT has become a treatment option for patients with end-stage liver disease and concurrent diabetes.