UNASSIGNED: Chronic low-grade inflammation of the pancreatic islets is the characteristic of type 2 diabetes (T2D), and some of the immune checkpoints may play important roles in the pancreatic islet inflammation. Thus, we aim to explore the immune checkpoint genes (ICGs) associated with T2D, thereby revealing the role of ICGs in the pathogenesis of T2D based on bioinformatic analyses.
UNASSIGNED: Differentially expressed genes (DEGs) and immune checkpoint genes (ICGs) of islets between T2D and control group were screened from datasets of the Gene Expression Omnibus (GEO). A risk model was built based on the coefficients of ICGs calculated by ridge regression. Functional enrichment analysis and immune cell infiltration estimation were conducted. Correlations between ICGs and hub genes, T2D-related disease genes, insulin secretion genes, and beta cell function-related genes were analyzed. Finally, we conducted RT-PCR to verify the expression of these ICGs.
UNASSIGNED: In total, pancreatic islets from 19 cases of T2D and 84 healthy subjects were included. We identified 458 DEGs. Six significantly upregulated ICGs (CD44, CD47, HAVCR2, SIRPA, TNFSF9, and VTCN1) in T2D were screened out. These ICGs were significantly correlated with several hub genes and T2D-related genes; furthermore, they were correlated with insulin secretion and β cell function-related genes. The analysis of immune infiltration showed that the concentrations of eosinophils, T cells CD4 naive, and T cells regulatory (Tregs) were significantly higher, but CD4 memory resting T cells and monocytes were lower in islets of T2D patients. The infiltrated immune cells in T2D pancreatic islet were associated with these six ICGs. Finally, the expression levels of four ICGs were confirmed by RT-PCR, and three ICGs were validated in another independent dataset.
UNASSIGNED: In conclusion, the identified ICGs may play an important role in T2D. Identification of these differential genes may provide new clues for the diagnosis and treatment of T2D.

BACKGROUND: Comprehensive and integrative analysis of hepatocellular carcinoma (HCC) is important. In this study, we explored Taiwanese HCCs using multi-omics analyses.
METHODS: We analyzed 254 HCCs by whole genome sequencing and total RNA sequencing, and then used bioinformatic tools to analyze genomic and transcriptomic alterations in coding and non-coding sequences to explore the clinical importance of each sequence.
RESULTS: The frequencies of the five most commonly mutated cancer-related genes were TERT, TP53, CTNNB1, RB1, and ARID1A. Genetic alteration frequencies influenced the etiology of HCC; some alterations were also correlated with clinicopathological conditions. Many cancer-related genes had copy number alterations (CNAs) and structure variants (SVs) that changed according to etiology and exhibited potential associations with survival. We also identified several alterations in histone-related genes, HCC-related long non-coding RNAs, and non-coding driver genes that may contribute to the onset and progression of HCC. Transcriptomic analysis revealed that 229 differentially expressed and 148 novel alternative splicing (AS) genes, as well as the presence of fusion genes, were associated with patient survival. Moreover, somatic mutations, CNAs, and SVs were associated with immune checkpoint gene expression and tumor microenvironment. Finally, we identified relationships among AS, immune checkpoint gene expression and tumor microenvironment.
CONCLUSIONS: This study shows that genomic alterations are associated with survival, including DNA-based and RNA-based data. Moreover, genomic alterations and their associations with immune checkpoint genes and the tumor microenvironment may provide novel insights for the diagnosis and treatment of HCC.

Increasing evidence has indicated that ferroptosis engages in the progression of Parkinson\'s disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD.
The microarray data of PD patients and healthy controls (HC) from the Gene Expression Omnibus (GEO) database was downloaded. Weighted gene co-expression network analysis (WGCNA) was processed to identify the significant modules related to PD in the GSE18838 dataset. Machine learning algorithms were used to screen the candidate biomarkers based on the intersect between WGCNA, FRGs and differentially expressed genes. Enrichment analysis of GSVA, GSEA, GO, KEGG, and immune infiltration, group comparison of ICGs were also performed. Next, candidate biomarkers were validated in clinical samples by ELISA and receiver operating characteristic curve (ROC) was used to assess diagnose ability.
In this study, FRGs had correlations with ICGs, immune infiltration. Then, plasma levels of LPIN1 in PD was significantly lower than that in healthy controls, while the expression of TNFAIP3 was higher in PD in comparison with HC. ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750-0.916). Moreover, each biomarker alone could discriminate the PD from HC (LPIN1: AUC = 0.754, 95% CI: 0.659-0.849; TNFAIP3: AUC = 0.754, 95% CI: 0.660-0.849). For detection of early PD from HC, the model of combination maintained diagnostic accuracy with an AUC of 0.831 (95% CI: 0.734-0.927), LPIN1 also performed well in distinguishing the early PD from HC (AUC = 0.817, 95% CI: 0.717-0.917). However, the diagnostic efficacy was relatively poor in distinguishing the early from middle-advanced PD patients.
The combination model composed of LPIN1 and TNFAIP3, and each biomarker may serve as an efficient tool for distinguishing PD from HC.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly malignant tumor with a very poor prognosis. Pyroptosis is an inflammatory form of cell death and plays an important role in cancer development. The prognostic value of pyroptosis-related genes (PRGs) in HCC has not been studied extensively.
METHODS: Unsupervised consensus clustering analysis was performed to identify two subtypes based on the expression profiles of prognostic PRGs in the The Cancer Genome Atlas (TCGA) database, and the differences between the two subtypes were compared. A prognostic model based on four PRGs was established by further least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox regression analysis.
RESULTS: Two subtypes (clusters 1 and 2) were identified by consensus clustering based on prognostic PRGs in HCC. Survival outcomes, biological function, genomic alterations, immune cell infiltration, and immune checkpoint genes were compared between the subtypes. Cluster 2 had a worse survival outcome than cluster 1. Cluster 2 was enriched for hallmarks of cancer progression, TP53 mutation, tumor-promoting immune cells, and immune checkpoint genes, which may contribute to the poor prognosis. A prognostic risk signature that predicted the overall survival (OS) of patients was constructed and validated. Consequently, a risk score was calculated for each patient. Combined with the clinical characteristics, the risk score was found to be an independent prognostic factor for survival of HCC patients. Further analysis revealed that the risk score was closely associated with the levels of immune cell infiltration and the expression profiles of immune checkpoint genes.
CONCLUSIONS: Collectively, our study established a prognostic risk signature for HCC and revealed a significant correlation between pyroptosis and the HCC immune microenvironment.

Autophagy related gene 101 (ATG101) plays a significant role in the occurrence and development of tumours by responding to stress. Our research aims to illustrate the correlation between the expression of ATG101 and tumor prognosis and its potential role and mechanism in tumor immunity and photodynamic therapy (PDT). First, integrated analysis of The Cancer Genome Atlas and Genotype-Tissue Expression portals were used to analyse the expression of ATG101. Then, Kaplan-Meier curves was applied in cholangiocarcinoma (CHOL) and liver hepatocellular carcinoma (LIHC) datasets for survival analysis. Next, the relationship between ATG101 expression and six immune cells, the immune microenvironment and immune checkpoints was analysed. Besides, the relationship between the expression of ATG101 and methyltransferase. GSEA was used to study the function and the related transcript factors of ATG101 in CHOL and LIHC. The effect of PDT on ATG101 was verified by microarray, qPCR and western blot. Then the effect of ATG101 and its regulatory factors on apoptosis were verified by siRNA, lentivirus transfection and Chip-qPCR. Comprehensive analysis showed that ATG101 was overexpressed in different tumours. Kaplan-Meier curves found that ATG101 was associated with poor prognosis in tumours (including CHOL and LIHC). We found that ATG101 can be used as a target and prognostic marker of tumour immunotherapy for different tumours. We also found that ATG101 regulates DNA methylation. GSEA analysis showed that ATG101 may play a critical role in CHOL and LIHC. Subsequent validation tests confirmed that the up-regulated ATG101 after PDT treatment is not conducive to the occurrence of apoptosis of cholangiocarcinoma cells. The high expression of ATG101 may be induced by the early stress gene EGR2. Our study highlights the significance of ATG101 in the study of tumour immunity and photodynamic therapy from a pan-cancer perspective.

The significant response of immunotherapy is currently limited to a small number of patients, which has a high degree of selectivity. Therefore, distinguishing the immune subtypes of the tumor is necessary for patients who may benefit from immune checkpoint therapy. Clinical data and RNA expression data from The Cancer Genome Atlas (TCGA) and GENE EXPRESSION OMNIBUS (GEO) databases were used to study the relationship of immune regulatory pathways with immune subtypes, and the effects on the prognosis of hepatocellular carcinoma (HCC) patients. Eight immune checkpoint coding genes (ICGs) were obtained that closely related to prognosis. Adaptive immune pathway genes (CD8A, CD68, GZMB, and NOS2) show significant positive correlation with most of ICGs. Therefore, adaptive immune pathway genes may play a certain regulatory role in the expression of ICGs. Among the four immune subtypes, the group with low expression level of PD-L1, IDO1 and CTLA4, and high expression of CD8A had showed the best prognosis. The prognosis was the worst in the group with low expression of PD-L1, IDO1 and CTLA4, and showed low expression of CD8A. This research proposed a method to analyze the gene expression profile of immune checkpoints. The present method can be applied to identify the relationship between immune subtype of HCC and the prognosis, providing basis for gene immunotherapy in HCC patients.