BACKGROUND: Growing evidence suggests that long non-coding RNAs (lncRNAs) exert pivotal roles in fostering chemoresistance across diverse tumors. Nevertheless, the precise involvement of lncRNAs in modulating chemoresistance within the context of gallbladder cancer (GBC) remains obscure. This study aimed to uncover how lncRNAs regulate chemoresistance in gallbladder cancer, offering potential targets to overcome drug resistance. METHODS: To elucidate the relationship between gemcitabine sensitivity and small nucleolar RNA host gene 1 ( SNHG1 ) expression, we utilized publicly available GBC databases, GBC tissues from Renji Hospital collected between January 2017 and December 2019, as well as GBC cell lines. The assessment of SNHG1, miR-23b-3p, and phosphatase and tensin homolog (PTEN) expression was performed using in situ  hybridization, quantitative real-time polymerase chain reaction, and western blotting. The cell counting kit-8 (CCK-8) assay was used to quantify the cell viability. Furthermore, a GBC xenograft model was employed to evaluate the impact of SNHG1 on the therapeutic efficacy of gemcitabine. Receiver operating characteristic (ROC) curve analyses were executed to assess the specificity and sensitivity of SNHG1. RESULTS: Our analyses revealed an inverse correlation between the lncRNA SNHG1 and gemcitabine resistance across genomics of drug sensitivity in cancer (GDSC) and Gene Expression Omnibus (GEO) datasets, GBC cell lines, and patients. Gain-of-function investigations underscored that SNHG1 heightened the gemcitabine sensitivity of GBC cells in both in vitro and in vivo  settings. Mechanistic explorations illuminated that SNHG1 could activate PTEN -a commonly suppressed tumor suppressor gene in cancers-thereby curbing the development of gemcitabine resistance in GBC cells. Notably, microRNA (miRNA) target prediction algorithms unveiled the presence of miR-23b-3p binding sites within SNHG1 and the 3'-untranslated region (UTR) of PTEN . Moreover, SNHG1 acted as a sponge for miR-23b-3p, competitively binding to the 3'-UTR of PTEN , thereby amplifying PTEN expression and heightening the susceptibility of GBC cells to gemcitabine. CONCLUSION The SNHG1/miR-23b-3p/PTEN axis emerges as a pivotal regulator of gemcitabine sensitivity in GBC cells, holding potential as a promising therapeutic target for managing GBC patients.
Humans ; Deoxycytidine/pharmacology* ; PTEN Phosphohydrolase/genetics* ; Gemcitabine ; RNA, Long Noncoding/metabolism* ; MicroRNAs/genetics* ; Gallbladder Neoplasms/genetics* ; Cell Line, Tumor ; Animals ; Mice ; Drug Resistance, Neoplasm/genetics* ; Mice, Nude ; Antimetabolites, Antineoplastic ; Gene Expression Regulation, Neoplastic

Noncoding RNAs (ncRNAs) are a class of RNA molecules with little or no protein-coding potential. Emerging evidence indicates that ncRNAs are frequently dysregulated and play pivotal roles in the pathogenesis of pancreatic cancer. Their aberrant expression can arise from chromosomal abnormalities, dysregulated transcriptional control, and epigenetic modifications. ncRNAs function as protein scaffolds or molecular decoys to modulate interactions between proteins and other biomolecules, thereby regulating gene expression and contributing to pancreatic cancer progression. In this review, we summarize the mechanisms underlying ncRNA dysregulation in pancreatic cancer, emphasize the biological significance of ncRNA-protein interactions, and highlight their clinical relevance. A deeper understanding of ncRNA-protein interactions is essential to elucidate molecular mechanisms and advance translational research in pancreatic cancer.
Humans ; Pancreatic Neoplasms/metabolism* ; RNA, Untranslated/metabolism* ; Gene Expression Regulation, Neoplastic/genetics*

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

The transcriptome sequencing based on Illumina Novaseq 6000 Platform was performed with the untreated seed embryo(DS), stratified seed embryo(SS), and germinated seed embryo(GS) of Zanthoxylum nitidum, aiming to explore the molecular mechanism regulating the seed dormancy and germination of Z. nitidum and uncover key differentially expressed genes(DEGs). A total of 61.41 Gb clean data was obtained, and 86 386 unigenes with an average length of 773.49 bp were assembled. A total of 29 290 DEGs were screened from three comparison groups(SS vs DS, GS vs SS, and GS vs DS), and these genes were annotated on 134 Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways. KEGG enrichment analysis revealed that the plant hormone signal transduction pathway is the richest pathway, containing 226 DEGs. Among all DEGs, 894 transcription factors were identified, which were distributed across 34 transcription factor families. These transcription factors were also mainly concentrated in plant hormone signal transduction and mitogen-activated protein kinase(MAPK) signaling pathways. Further real-time quantitative polymerase chain reaction(RT-qPCR) validation of 12 DEGs showed that the transcriptome data is reliable. During the process of seed dormancy release and germination, a large number of DEGs involved in polysaccharide degradation, protein synthesis, lipid metabolism, and hormone signal transduction were expressed. These genes were involved in multiple metabolic pathways, forming a complex regulatory network for dormancy and germination. This study lays a solid foundation for analyzing the molecular mechanisms of seed dormancy and germination of Z. nitidum.
Zanthoxylum/metabolism* ; Plant Dormancy/genetics* ; Seeds/metabolism* ; Gene Expression Regulation, Plant ; Plant Proteins/metabolism* ; Transcriptome ; Gene Expression Profiling ; Germination ; Transcription Factors/metabolism* ; Plant Growth Regulators/genetics* ; Signal Transduction

BACKGROUND: The transcription factor POU2F1 regulates the expression levels of microRNAs in neoplasia. However, the miR-29b1/a cluster modulated by POU2F1 in gastric cancer (GC) remains unknown. METHODS: Gene expression in GC cells was evaluated using reverse-transcription polymerase chain reaction (PCR), western blotting, immunohistochemistry, and RNA in situ hybridization. Co-immunoprecipitation was performed to evaluate protein interactions. Transwell migration and invasion assays were performed to investigate the biological behavior of GC cells. MiR-29b1/a cluster promoter analysis and luciferase activity assay for the 3'-UTR study were performed in GC cells. In vivo tumor metastasis was evaluated in nude mice. RESULTS: POU2F1 is overexpressed in GC cell lines and binds to the miR-29b1/a cluster promoter. POU2F1 is upregulated, whereas mature miR-29b-3p and miR-29a-3p are downregulated in GC tissues. POU2F1 promotes GC metastasis by inhibiting miR-29b-3p or miR-29a-3p expression in vitro and in vivo . Furthermore, PIK3R1 and/or PIK3R3 are direct targets of miR-29b-3p and/or miR-29a-3p , and the ectopic expression of PIK3R1 or PIK3R3 reverses the suppressive effect of mature miR-29b-3p and/or miR-29a-3p on GC cell metastasis and invasion. Additionally, the interaction of PIK3R1 with PIK3R3 promotes migration and invasion, and miR-29b-3p , miR-29a-3p , PIK3R1 , and PIK3R3 regulate migration and invasion via the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) pathway in GC cells. In addition, POU2F1 , PIK3R1 , and PIK3R3 expression levels negatively correlated with miR-29b-3p and miR-29a-3p expression levels in GC tissue samples. CONCLUSIONS The POU2F1 - miR-29b-3p / miR-29a-3p-PIK3R1 / PIK3R1 signaling axis regulates tumor progression and may be a promising therapeutic target for GC.
MicroRNAs/metabolism* ; Humans ; Stomach Neoplasms/pathology* ; Cell Line, Tumor ; Cell Movement/physiology* ; Phosphatidylinositol 3-Kinases/metabolism* ; Animals ; Mice ; Octamer Transcription Factor-1/metabolism* ; Mice, Nude ; Class Ia Phosphatidylinositol 3-Kinase/metabolism* ; Neoplasm Invasiveness ; Gene Expression Regulation, Neoplastic/genetics* ; Male ; Immunohistochemistry ; Female

OBJECTIVE: To explore the potential role of mRNA m7G modification in the pathogenesis of human adenocarcinoma of esophagogastric junction (AEG). METHODS: Pathological tissue specimens from four AEG patients who underwent surgical treatment at the People's Hospital Affiliated to Jiangsu University between 2018 and 2019 were selected. Tumor tissues and adjacent normal tissues were collected from these patients. RNA was extracted from both tissue types and subjected to m7G methylated RNA immunoprecipitation sequencing (m7G-MeRIP-seq) to analyze the patterns of m7G modification, the characteristics of differential m7G modification sites, the differentially expressed mRNA, and the correlation between m7G modification and mRNA expression levels. Differential m7G-modified genes (MSH6, BRCA1, and SOX9) were further validated using methylated RNA immunoprecipitation quantitative PCR (MeRIP-qPCR), while the expression of METTL1 and WDR4 genes was examined by real-time quantitative PCR (RT-qPCR). This study was approved by the Medical Ethics Committee of the People's Hospital Affiliated to Jiangsu University (Ethics No. 20150083). RESULTS: m7G-MeRIP-seq analysis revealed that m7G modifications in both AEG and adjacent normal tissues were predominantly located in the GC-rich region surrounding the internal start codon of mRNA. Differential m7G modification sites between the two groups were closely associated with cancer-related genes. mRNA library analysis showed that differentially expressed mRNA were predominantly upregulated in AEG tissues and downregulated in adjacent normal tissues. Cross-analysis indicated that genes with hypermethylation tended to exhibit upregulated expression, while genes with hypomethylation were typically downregulated in AEG tissues. MeRIP-qPCR validation confirmed that the mRNA expression of MSH6, BRCA1, and SOX9 were significantly upregulated in AEG tissues compared to adjacent normal tissues (AEG vs. normal, P < 0.05). RT-qPCR results demonstrated that the mRNA expression levels of METTL1 and WDR4 were also upregulated in AEG tissues (AEG vs. normal, P < 0.000 5). CONCLUSION These findings suggest that mRNA m7G modification plays a significant role in the development of AEG. Furthermore, proteins as METTL1 and WDR4 may facilitate AEG progression by regulating mRNA m7G modification. These results provide valuable insights into the molecular mechanisms underlying AEG and may inform future therapeutic strategies for this malignancy.
Humans ; RNA, Messenger/metabolism* ; Adenocarcinoma/pathology* ; Esophagogastric Junction/metabolism* ; Esophageal Neoplasms/metabolism* ; Gene Expression Regulation, Neoplastic ; Female ; Male ; Middle Aged ; DNA Methylation ; Methyltransferases/metabolism* ; Stomach Neoplasms/genetics*

OBJECTIVE: To evaluate the differential expression of RNA in blood monocytes in patients with Juvenile idiopathic arthritis (JIA) treated with TNF antagonists (TNFi), and to explore the effect and mechanism of gene expression on the efficacy of JIA. METHODS: A total of 29 children with JIA treated with methotrexate (MTX) and TNFi in Guangzhou Women and Children's Medical Center of Guangzhou Medical University from April 2021 to November 2023 were enrolled. After 6 months, the children were divided into two groups according to the treatment effect, i.e., 13 cases in the ineffective group and 16 cases in the effective group, the peripheral blood of the children was collected, the blood mononuclear cells were isolated for transcriptome sequencing, the differentially expressed genes between the groups were analyzed, the signaling pathways and metabolic pathways related to the efficacy of TNFi were analyzed by GO and KEGG enrichment, and the mechanism related to the efficacy of TNFi was explored. This study was approved by Medical Ethics Committee of the Guangzhou Women and Children's Medical Center of Guangzhou Medical University (Ethics No.: 2023-330B00). RESULTS: There was a statistically significant difference in the gender and age distribution between the two groups of children (P < 0.05), while no statistically significant differences were observed in disease duration, rheumatoid antibody levels, or JIA subtypes (P > 0.05). After sequencing data quality control and comparison of reference genomes, a total of 18 523 protein-coding genes were identified in all children's samples. A total of 705 differentially expressed genes (DEGs) were identified between the effective group and the invalid group through differential analysis, of which 579 were up-regulated in the effective group and 126 in the inactive group. GO function and KEGG pathway enrichment analysis showed that DEG was significantly enriched in 55 GO entries and 32 KEGG metabolic pathways, which were mainly related to IL-1β production and regulation, cytokine production and regulation, cytokine-cytokine receptor interaction, immune response regulation, and Toll-like receptor signaling pathway. CONCLUSION DEG between the effective and ineffective groups of TNFi treatment may be involved in the biological processes such as cytokine production and regulation, cytokine-receptor interaction, and immune response regulation, which will be helpful to predict the efficacy and prognosis of TNFi treatment for JIA.
Humans ; Arthritis, Juvenile/blood* ; Female ; Male ; Child ; Methotrexate/therapeutic use* ; Child, Preschool ; Tumor Necrosis Factor-alpha/antagonists & inhibitors* ; Transcriptome ; Adolescent ; RNA/genetics* ; Signal Transduction ; Gene Expression Profiling

The culture of suspended Vero cells is facing difficulties such as low cell viability and long doubling time. To investigate the main reasons for the slow growth and low viability of suspended Vero cells, this study conducted transcriptomic analysis of suspended Vero cells (Vero-XF) and adherent Vero cells (Vero-AD) to screen the differentially expressed genes (DEGs) affecting the growth of suspended cells. In addition, epidermal growth factor (EGF) was supplemented to the culture system to improve the growth of Vero-XF. The results showed that compared with the Vero-AD group, the Vero-XF group had 7 376 significant DEGs. Kyoto encyclopedia of genes and genomes enrichment analysis revealed that the DEGs were mainly enriched in the autophagy and mitophagy pathways. Eleven DEGs were selected and verified by quantitative real-time PCR, which showed up-regulated expression of ATG9B, WIPI2, LAMP2, OPTN, Rab7a, and DEPTOR and down-regulated expression of ATG4D, being consistent with the results of transcriptomic analysis. In addition, the Vero-XF group showed significantly up-regulated expression of ATG101, ATG2A, and STX17 and insignificant change in the expression of NBR1, compared with the Vero-AD group. The protein levels of LC3 and P62 in Vero-XF and Vero-AD were determined by Western blotting, which showed up-regulated expression of LC3Ⅱ/Ⅰ and down-regulated expression of P62 in Vero-XF, indicating a higher level of autophagy. Finally, the exogenous supplementation of EGF at 10, 20, and 30 μg/L in the culture system reduced the autophagy level of Vero-XF by 22.35%, 48.15%, and 71.29%, increased the specific growth rate by 15.48%, 33.33%, and 57.14%, and decreased the apoptosis rate by 2.84%, 15.46%, and 16.23%, respectively. The results of this study preliminarily reveal that the activation of autophagy is one of the reasons for the slow growth of Vero-XF, which provides reference for the subsequent culture of suspended Vero cells.
Animals ; Vero Cells ; Autophagy/genetics* ; Chlorocebus aethiops ; Epidermal Growth Factor/pharmacology* ; Gene Expression Profiling ; Transcriptome ; Cell Survival

Sucrose non-fermenting 1-related protein kinase 1 (SnRK1) is one of the highly conserved Ca²⁺ non-dependent serine/threonine protein kinases, playing a crucial role in regulating the stress responses, growth, and development of plants. SnRK1 is a three-subunit complex, and it is involved in responding to the signaling transduction induced by low-energy/low-sugar conditions. SnRK1 responds biotic and abiotic stress conditions (such as salt, drought, low/high temperatures, and diseases) through phosphorylation of key metabolic enzymes and regulatory proteins, regulation of transcription, and interactions with other proteins. Furthermore, SnRK1 is not only involved in hormone signaling pathways mediated by abscisic acid (ABA), jasmonic acid (JA) and salicylic acid (SA), but also regulates plant autophagy by inhibiting the activity of target of rapamycin (TOR). In this review, we summarized the current results of research on the discovery, structure, and classification of plant SnRK1 and its roles in the stress responses, growth, and development of plants. Furthermore, this article proposes the directions of future research. This review provides good genetic resources and a theoretical basis for the genetic improvement and biological breeding for enhancing the stress tolerance of crops.
Stress, Physiological/physiology* ; Protein Serine-Threonine Kinases/metabolism* ; Plant Development/genetics* ; Signal Transduction ; Gene Expression Regulation, Plant ; Plant Proteins/physiology* ; Plants/metabolism* ; Arabidopsis Proteins/physiology* ; Plant Growth Regulators/metabolism*

Small nuclear RNAs (snRNAs) refer to a class of highly abundant and functionally important non-coding small RNAs that are localized in the eukaryotic nucleus. These snRNAs are highly conserved in different eukaryotes during evolution and form complexes with specific chaperones to fulfill critical biological functions, including precursor messenger RNA (pre-mRNA) splicing and ribosomal RNA (rRNA) modification. Consequently, the regulation of snRNA gene expression is a crucial biological process for plants. In plants, the transcription and processing of snRNAs are regulated by RNA polymerase (Pol), snRNA-activating protein complex (SNAPc), defective in snRNA processing (DSP), and specific cis-elements in the snRNA promoter regions. Proper regulation of snRNA expression is essential for normal plant growth, development, and stress responses. This review summarizes the classification, structures, transcriptional regulation, and biological functions of plant snRNA genes, while outlining future research directions for snRNAs.
RNA, Small Nuclear/physiology* ; Gene Expression Regulation, Plant ; Transcription, Genetic ; Plants/metabolism* ; RNA, Plant/genetics*