Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing.

INTRODUCTION: Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets. METHOD: The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments. RESULTS: An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro. CONCLUSION: The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.

Evolving understandings for the roles of non-coding RNAs in autoimmunity and autoimmune disease.

Autoimmune diseases (ADs) are characterized by aberrant generation of autoreactive immune cells and persistent inflammation, leading to tissue destruction. Although common definitive pathogenesis mechanisms of ADs remain elusive, increasing recent evidence has found that non-coding RNAs (ncRNAs) are extensively involved in ADs and AD-related immune responses. Recent advances in the comprehension of biological functions of ncRNAs have greatly evolved the understandings of epigenetic regulation of autoimmunity and ADs. In general, ncRNAs are involved in proliferation, activation, differentiation, apoptosis, and functions of immune cells, promoting or inhibiting immune responses through multiple pathways. Aberrant expression of ncRNAs in immune cells dysregulates immune homeostasis, and has been implicated in a variety of ADs. Therefore, these ncRNAs are promising biomarkers of AD diagnosis and potential therapeutic targets for AD treatment. Clarification of the critical functions and mechanisms for ncRNAs may provide insights into understanding AD pathogenesis and treatment. In this review, we focus on recent studies on the involvement of ncRNAs in autoimmunity and ADs.

ε2 allele and ε2-involved genotypes (ε2/ε2, ε2/ε3, and ε2/ε4) may confer the association of APOE genetic polymorphism with risks of nephropathy in type 2 diabetes: a meta-analysis.

BACKGROUND: Diabetic nephropathy (DN) contributes to end-stage renal failure. Microvascular injury resulted from reactive oxygen species is implicated in the pathogenesis of DN. Genetic polymorphism of Apolipoprotein E (APOE) influences the antioxidative properties of the protein. The relationship of APOE polymorphism with the risks of nephropathy in type 2 diabetes (T2DN) remains elusive. METHODS: An up-to-date meta-analysis was conducted on the basis of studies selected from PubMed, WanFang database, Embase, Vip database, Web of Science, Scopus, and CNKI database. RESULTS: A total of 33 studies conferring 3266 cases and 3259 controls were selected on the basis of criteria of inclusion and exclusion in this meta-analysis. For APOE alleles, the pooled odds ratio (OR) of ε2 vs. ε3 was 1.89 (95% confidence intervals [95% CI]: 1.49-2.38, P < 0.0001). With regard to APOE genotypes, ε2/ε2, ε2/ε3, and ε2/ε4 increased the risk of T2DN (ε2/ε2 vs. ε3/ε3: OR = 2.32, 95% CI: 1.52-3.56, P = 0.0001; ε2/ε3 vs. ε3/ε3: OR = 1.97, 95% CI: 1.50-2.59, P<0.0001; ε2/ε4 vs. ε3/ε3: OR = 1.69, 95% CI: 1.18-2.44, P = 0.0046). CONCLUSIONS: This meta-analysis found that the APOE ε2 allele and the ε2-involved genotypes (ε2/ε2, ε2/ε3, and ε2/ε4) are the risk factors of T2DN.

Three-Dimensional Chromosomal Landscape Revealing miR-146a Dysfunctional Enhancer in Lupus and Establishing a CRISPR-Mediated Approach to Inhibit the Interferon Pathway.

OBJECTIVE: The diminished expression of microRNA-146a (miR-146a) in systemic lupus erythematosus (SLE) contributes to the aberrant activation of the interferon pathway. Despite its significance, the underlying mechanism driving this reduced expression remains elusive. Considering the integral role of enhancers in steering gene expression, our study seeks to pinpoint the SLE-affected enhancers responsible for modulating miR-146a expression. Additionally, we aim to elucidate the mechanisms by which these enhancers influence the contribution of miR-146a to the activation of the interferon pathway. METHODS: Circular chromosome conformation capture sequencing and epigenomic profiles were applied to identify candidate enhancers of miR-146a. CRISPR activation was performed to screen functional enhancers. Differential analysis of chromatin accessibility was used to identify SLE-dysregulated enhancers, and the mechanism underlying enhancer dysfunction was investigated by analyzing transcription factor binding. The therapeutic value of a lupus-related enhancer was further evaluated by targeting it in the peripheral blood mononuclear cells (PBMCs) of patients with SLE through a CRISPR activation approach. RESULTS: We identified shared and cell-specific enhancers of miR-146a in distinct immune cells. An enhancer 32.5 kb downstream of miR-146a possesses less accessibility in SLE, and its chromatin openness was negatively correlated with SLE disease activity. Moreover, CCAAT/enhancer binding protein α, a down-regulated transcription factor in patients with SLE, binds to the 32.5-kb enhancer and induces the epigenomic change of this locus. Furthermore, CRISPR-based activation of this enhancer in SLE PBMCs could inhibit the activity of interferon pathway. CONCLUSION: Our work defines a promising target for SLE intervention. We adopted integrative approaches to define cell-specific and functional enhancers of the SLE critical gene and investigated the mechanism underlying its dysregulation mediated by a lupus-related enhancer.

Integrative Functional Genomics Identifies Systemic Lupus Erythematosus Causal Genetic Variant in the IRF5 Risk Locus.

OBJECTIVE: IRF5 plays a crucial role in the development of lupus. Genome-wide association studies have identified several systemic lupus erythematosus (SLE) risk single-nucleotide polymorphisms (SNPs) enriched in the IRF5 locus. However, no comprehensive genome editing-based functional analysis exists to establish a direct link between these variants and altered IRF5 expression, particularly for enhancer variants. This study was undertaken to dissect the regulatory function and mechanisms of SLE IRF5 enhancer risk variants and to explore the utilization of clustered regularly interspaced short palindromic repeat interference (CRISPRi) to regulate the expression of disease risk gene to intervene in the disease. METHODS: Epigenomic profiles and expression quantitative trait locus analysis were applied to prioritize putative functional variants in the IRF5 locus. CRISPR-mediated deletion, activation, and interference were performed to investigate the genetic function of rs4728142. Allele-specific chromatin immunoprecipitation-quantitative polymerase chain reaction and allele-specific formaldehyde-assisted isolation of regulatory element-quantitative polymerase chain reaction were used to decipher the mechanism of alleles differentially regulating IRF5 expression. The CRISPRi approach was used to evaluate the intervention effect in monocytes from SLE patients. RESULTS: SLE risk SNP rs4728142 was located in an enhancer region, indicating a disease-related regulatory function, and risk allele rs4728142-A was closely associated with increased IRF5 expression. We demonstrated that an rs4728142-containing region could act as an enhancer to regulate the expression of IRF5. Moreover, rs4728142 affected the binding affinity of zinc finger and BTB domain-containing protein 3 (ZBTB3), a transcription factor involved in regulation. Furthermore, in monocytes from SLE patients, CRISPR-based interference with the regulation of this enhancer attenuated the production of disease-associated cytokines. CONCLUSION: These results demonstrate that the rs4728142-A allele increases the SLE risk by affecting ZBTB3 binding, chromatin status, and regulating IRF5 expression, establishing a biologic link between genetic variation and lupus pathogenesis.

Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system.

Pancreatic cystic neoplasms (PCNs) are a group of heterogeneous diseases with distinct prognosis. Existing differential diagnosis methods require invasive biopsy or prolonged monitoring. We sought to develop an inexpensive, non-invasive differential diagnosis system for PCNs based on radiomics features and clinical characteristics for a higher total PCN screening rate. We retrospectively analyzed computed tomography images and clinical data from 129 patients with PCN, including 47 patients with intraductal papillary mucinous neoplasms (IPMNs), 49 patients with serous cystadenomas (SCNs), and 33 patients with mucinous cystic neoplasms (MCNs). Six clinical characteristics and 944 radiomics features were tested, and nine features were finally selected for model construction using DXScore algorithm. A five-fold cross-validation algorithm and a test group were applied to verify the results. In the five-fold cross-validation section, the AUC value of our model was 0.8687, and the total accuracy rate was 74.23%, wherein the accuracy rates of IPMNs, SCNs, and MCNs were 74.26%, 78.37%, and 68.00%, respectively. In the test group, the AUC value was 0.8462 and the total accuracy rate was 73.61%. In conclusion, our research constructed an end-to-end powerful PCN differential diagnosis system based on radiomics method, which could assist decision-making in clinical practice.