SNHG1, a KLF4-upregulated gene, promotes glioma cell survival and tumorigenesis under endoplasmic reticulum stress by upregulating BIRC3 expression

Integrated mRNA-seq and miRNA-seq analysis reveals key transcription factors of HNF4α and KLF4 in ADPKD

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent genetic disorder affecting the kidneys. Understanding epigenetic regulatory mechanisms and the role of microRNAs (miRNAs) is crucial for developing therapeutic interventions. Two mRNA datasets (GSE7869 and GSE35831) and miRNA expression data (GSE133530) from ADPKD patients were used to find differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs), with a focus on genes regulated by hub transcription factors (TFs) and their target genes. The expression of hub TFs was validated in human kidneys and animal models through Western Blot (WB) and RT-PCR analysis. The location of the hub TF proteins in kidney cells was observed by a laser confocal microscope. A total of 2037 DEGs were identified. DEM analysis resulted in 59 up-regulated and 107 down-regulated miRNAs. Predicted target DEGs of DEMs indicated two top dysregulated TFs: hepatocyte nuclear factor 4 alpha (HNF4α) and Kruppel-like factor 4 (KLF4). RT-PCR, WB, and immunochemistry results showed that mRNA and protein levels of HNF4α were significantly decreased while KLF4 levels were significantly up-regulated in human ADPKD kidneys and Pkd1 conditional knockout mice compared with normal controls. Laser confocal microscopy revealed that KLF4 was mainly located in the cytoplasm while HNF4α was in the nucleus. Functional enrichment analysis indicated that genes regulated by HNF4α were mainly associated with metabolic pathways, while KLF4-regulated genes were linked to kidney development. Drug response prediction analysis revealed potential drug candidates for ADPKD treatment, including BI-2536, Sepantronium, and AZD5582. This integrated analysis provides new epigenetic insights into the complex miRNA-TF-mRNA network in ADPKD and identifies HNF4α and KLF4 as key TFs. These findings offer valuable resources for further research and potential drug development for ADPKD.

LncRNA SNHG1 facilitates colorectal cancer cells metastasis by recruiting HNRNPD protein to stabilize SERPINA3 mRNA

Metastasis continues to negatively impact individuals diagnosed with colorectal cancer (CRC). Research has revealed the important role of long noncoding RNAs (lncRNAs) in CRC metastasis, but the underlying mechanisms remain unclear. Here, we revealed that the lncRNA small nucleolar RNA host gene 1 (SNHG1) is expressed at higher levels in metastatic CRC tissues than in primary CRC tissues, and that high lncRNA SNHG1 expression indicates poor patient outcomes. We found that lncRNA SNHG1 promotes the migration and invasion of tumor cells both in vivo and in vitro. Moreover, lncRNA SNHG1 increases serpin family A member 3 (SERPINA3) mRNA stability by interacting with the heterogeneous nuclear ribonucleoprotein D (HNRNPD) protein, and subsequently upregulates SERPINA3 expression. Moreover, HNRNPD and SERPINA3 reversed the effects of lncRNA SNHG1 knockdown on CRC cell metastasis. In conclusion, we report that the lncRNA SNHG1 recruits HNRNPD, in turn upregulating SERPINA3 expression and ultimately facilitating CRC cell migration and invasion. Targeting the lncRNA SNHG1/HNRNPD/SERPINA3 signaling pathway might be a therapeutic option for preventing CRC metastasis.

Deciphering the nexus between long non-coding RNAs and endoplasmic reticulum stress in hepatocellular carcinoma: biomarker discovery and therapeutic horizons

Hepatocellular carcinoma (HCC) remains a significant global health challenge with few effective treatment options. The dysregulation of endoplasmic reticulum (ER) stress responses has emerged as a pivotal factor in HCC progression and therapy resistance. Long non-coding RNAs (lncRNAs) play a crucial role as key epigenetic modifiers in this process. Recent research has explored how lncRNAs influence ER stress which in turn affects lncRNAs activity in HCC. We systematically analyze the current literature to highlight the regulatory roles of lncRNAs in modulating ER stress and vice versa in HCC. Our scrutinization highlights how dysregulated lncRNAs contribute to various facets of HCC, including apoptosis resistance, enhanced proliferation, invasion, and metastasis, all driven by ER stress. Moreover, we delve into the emerging paradigm of the lncRNA-miRNA-mRNA axis, elucidating it as the promising avenue for developing novel biomarkers and paving the way for more personalized treatment options in HCC. Nevertheless, we acknowledge the challenges and future directions in translating these insights into clinical practice. In conclusion, our review provides insights into the complex regulatory mechanisms governing ER stress modulation by lncRNAs in HCC.

Comprehensive analysis of an endoplasmic reticulum stress-related gene prediction model and immune infiltration in idiopathic pulmonary fibrosis

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease. This study aimed to investigate the involvement of endoplasmic reticulum stress (ERS) in IPF and explore its correlation with immune infiltration.

Methods

ERS-related differentially expressed genes (ERSRDEGs) were identified by intersecting differentially expressed genes (DEGs) from three Gene Expression Omnibus datasets with ERS-related gene sets. Gene Set Variation Analysis and Gene Ontology were used to explore the potential biological mechanisms underlying ERS. A nomogram was developed using the risk signature derived from the ERSRDEGs to perform risk assessment. The diagnostic value of the risk signature was evaluated using receiver operating characteristics, calibration, and decision curve analyses. The ERS score of patients with IPF was measured using a single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Subsequently, a prognostic model based on the ERS scores was established. The proportion of immune cell infiltration was assessed using the ssGSEA and CIBERSORT algorithms. Finally, the expression of ERSRDEGs was validated in vivo and in vitro via RT-qPCR.

Results

This study developed an 8-ERSRDEGs signature. Based on the expression of these genes, we constructed a diagnostic nomogram model in which agouti-related neuropeptide had a significantly greater impact on the model. The area under the curve values for the predictive value of the ERSRDEGs signature were 0.975 and 1.000 for GSE70866 and GSE110147, respectively. We developed a prognostic model based on the ERS scores of patients with IPF. Furthermore, we classified patients with IPF into two subtypes based on their signatures. The RT-qPCR validation results supported the reliability of most of our conclusions.

Conclusion

We developed and verified a risk model using eight ERSRDEGs. These eight genes can potentially affect the progression of IPF by regulating ERS and immune responses.