Pepsinogen C expression-related lncRNA/circRNA/mRNA profile and its co-mediated ceRNA network in gastric cancer

Identification and validation of DHCR7 as a diagnostic biomarker involved in the proliferation and mitochondrial function of breast cancer

BACKGROUND

Energy metabolism has a complex intersection with pathogenesis and development of breast cancer (BC). This allows for the possibility of identifying energy-metabolism-related genes (EMRGs) as novel prognostic biomarkers for BC. 7-dehydrocholesterol reductase (DHCR7) is a key enzyme of cholesterol biosynthesis involved in many cancers, and in this paper, we investigate the effects of DHCR7 on the proliferation and mitochondrial function of BC.

METHODS

EMRGs were identified from the Gene Expression Omnibus (GEO) and MSigDB databases using bioinformatics methods. Key EMRGs of BC were then identified and validated by functional enrichment analysis, interaction analysis, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Cox analysis, and immune infiltration. Western blot, qRT-PCR, immunohistochemistry (IHC), MTT assay, colony formation assay and flow cytometry assay were then used to analyze DHCR7 expression and its biological effects on BC cells.

RESULTS

We identified 31 EMRGs in BC. These 31 EMRGs and related transcription factors (TFs), miRNAs, and drugs were enriched in glycerophospholipid metabolism, glycoprotein metabolic process, breast cancer, and cell cycle. Crucially, DHCR7 was a key EMRG in BC identified and validated by WGCNA, LASSO regression and receiver operating characteristic (ROC) curve analysis. High DHCR7 expression was significantly associated with tumor immune infiltration level, pathological M, and poor prognosis in BC. In addition, DHCR7 knockdown inhibited cell proliferation, induced apoptosis and affected mitochondrial function in BC cells.

CONCLUSIONS

DHCR7 was found to be a key EMRG up-regulated in BC cells. This study is the first to our knowledge to report that DHCR7 acts as an oncogene in BC, which might become a novel therapeutic target for BC patients.

IGF2 is upregulated by its antisense RNA to potentiate pancreatic cancer progression

Pancreatic cancer is a deadly cancer. More and more long noncoding RNAs (lncRNAs) have received confirmation to be dysregulated in tumors and exert the regulatory function. Studies have suggested that lncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) participates in the development of some cancers. Thus, we attempted to clarify its function in pancreatic cancer. Reverse-transcription quantitative polymerase chain reaction was applied for testing IGF2-AS expression in pancreatic cancer cells. Colony formation and Transwell wound experiments were applied for determining cell proliferative, migratory, and invasive capabilities. The alteration of epithelial-mesenchymal transition (EMT)-related gene level was tested via western blot. The mice model was established for measuring the tumor growth and metastasis. RIP validated the interaction of RNAs. IGF2-AS displays high expression in pancreatic cancer cells. IGF2-AS depletion repressed PC cell proliferative, migratory, invasive capabilities, and EMT process. Furthermore, pancreatic cancer tumor growth and metastasis were also inhibited by IGF2-AS depletion. Additionally, IGF2-AS positively regulated IGF2 level via recruiting HNRNPC. IGF2 overexpression counteracted the functions of IGF2-AS deficiency on pancreatic cancer cell behaviors. Moreover, IGF2R deletion was found to inhibit the positive effect of IGF2 on pancreatic cancer progression. IGF2-AS potentiates pancreatic cancer cell proliferation, tumor growth, and metastasis by recruiting HNRNPC via the IGF2-IGF2R regulatory pathway. These discoveries might offer a novel insight for treatment of PC, which may facilitate targeted therapies of PC in clinical practice.

Identification of m7G-related hub biomarkers and m7G regulator expression pattern in immune landscape during the progression of osteoarthritis

BACKGROUND

Accumulating evidence has shown that aberrant N7-methylguanosine (m7G) RNA methylation played an important role in the occurrence and development of cancer. However, knowledge of m7G modifications in inflammatory diseases is limited. Osteoarthritis (OA) is the most common arthritic disease with poor prognosis. Our research aimed to identify m7G-related hub biomarkers and investigate m7G regulator expression pattern in immune landscape of OA patients.

METHODS

Gene expression profiles and their clinical information were obtained from the Gene Expression Omnibus (GEO) database, and differential analysis of 14 m7G-related regulators between elective OA and normal samples was performed. M7G-related hub genes for OA were mined based on single-sample gene set enrichment analysis (ssGSEA) and the random forest (RF) algorithm, and qRT-PCR was performed to confirm the abnormal expression of hub genes. Enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory analysis based on m7G hub genes were performed. Then we predicted several candidate drugs related to m7G hub genes using DSigDB database. Moreover, we comprehensively evaluated m7G methylation patterns in OA samples and systematically correlated these modification patterns with the characteristics of immune cell infiltration. The m7G score was generated to quantify m7G methylation patterns for individual OA patients by the application of principal component analysis (PCA) algorithm.

RESULTS

We constructed an OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1). Two m7G methylation patterns in OA were discovered to show distinct biological characteristics, and an m7G score were generated. M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype.

CONCLUSIONS

Our study was the first to comprehensively investigate the m7G methylation dysregulations in immune landscape during the progression of OA. These 4 m7G gene-related signatures can be used as novel OA biomarkers to predict the occurrence of OA. Evaluating the m7G methylation patterns of OA individuals will contribute to enhancing our cognition of immune infiltration characterization and guiding more effective immunotherapy strategies.

CircUSP48 promotes malignant behavior by regulating CYR61 via miR-365 in osteosarcoma

Even though circular RNAs (circRNAs), a class of non-coding endogenous RNA, play a crucial role in the progression of osteosarcoma (OS), the specific function of hsa_circ_0000028 (circUSP48) remains unclear. This study aims to elucidate the mechanism by which circUSP48 regulates OS. We employed qRT-PCR and western blot techniques to quantify circDOCK1, miR-186, and DNMT3A levels. Cell proliferation was assessed using the cell counting kit-8 (CCK-8), 5-Ethynyl-20-deoxyuridine (EdU) assay, and colony formation assay. Cell migration and invasion were evaluated through Transwell and cell scratch assays. Furthermore, we performed dual-luciferase reporter, RIP, and RNA pull-down assays to investigate the association between circUSP48, miR-365, and CYR61. In addition, an in vivo xenograft model was utilized to assess the functional role of circUSP48. High levels of circUSP48 and CYR61 were observed in OS tissues and cells, while miR-365 levels were low. Knockdown of circUSP48 suppressed the multiplication, motility, and invasion of OS cells, thereby reducing carcinoma growth. Moreover, inhibition of miR-365 reversed the OS cell-suppressive effect caused by circUSP48 knockdown through direct interaction with circUSP48. Additionally, circUSP48 upregulated the expression of CYR61 by sponging miR-365. The findings suggest that circUSP48 promotes malignant behavior in OS by regulating the expression of CYR61 through miR-365, making it a potential therapeutic target for OS.

Long non-coding RNA in coronary artery disease: the role of PDXDC1-AS1 and SFI1-AS1

This study investigates the interaction between long non-coding RNAs (lncRNAs) and metabolic risk factors that contribute to coronary artery disease (CAD). A total transcriptome high throughput sequencing study was conducted on peripheral blood mononuclear cells from five patients with CAD and five healthy controls. Validation assay by qRT-PCR was conducted among 270 patients and 47 controls. Finally, to evaluate the lncRNAs' diagnostic value for CAD, the Spearman correlation test and receiver operating characteristic curve (ROC) analysis were utilized. Additionally, univariate and multivariate logistic regression along with crossover analyses were conducted to identify the interaction between lncRNA and environmental risk factors. A total of 2149 of 26,027 lncRNAs identified by RNA sequencing were differentially expressed in CAD patients compared to controls. Validation by qRT-PCR showed significantly different relative expression levels for lncRNAs PDXDC1-AS1, SFI1-AS1, RP13-143G15.3, DAPK1-IT1, PPIE-AS1, and RP11-362A1.1 between the two groups (all P<0.05). The area under the ROC values of PDXDC1-AS1 and SFI1-AS1 is 0.645 (sensitivity=0.443 and specificity=0.920) and 0.629 (sensitivity=0.571 and specificity=0.909), especially. Multivariate logistic regression analyses showed that lncRNAs PDXDC1-AS1 (OR=2.285, 95%CI=1.390-3.754, p=0.001) and SFI1-AS1 (OR=1.163, 95%CI=1.163-2.264, p=0.004) were protective factors against CAD. Under the additive model, cross-over analyses demonstrated significant interactions between lncRNAs PDXDC1-AS1 and smoking in relation to CAD risk (S=3.871, 95%CI=1.140-6.599). PDXDC1-AS1 and SFI1-AS1 were sensitive and specific biomarkers for CAD and exhibited synergistic effects with certain environmental factors. These results highlighted their potential use as CAD diagnostic biomarkers for future research.

ITGA3 acts as a purity-independent biomarker of both immunotherapy and chemotherapy resistance in pancreatic cancer: bioinformatics and experimental analysis

Contribution of integrin superfamily genes to treatment resistance remains uncertain. Genome patterns of thirty integrin superfamily genes were analyzed of using bulk and single-cell RNA sequencing, mutation, copy number, methylation, clinical information, immune cell infiltration, and drug sensitivity data. To select the integrins that are most strongly associated with treatment resistance in pancreatic cancer, a purity-independent RNA regulation network including integrins were constructed using machine learning. The integrin superfamily genes exhibit extensive dysregulated expression, genome alterations, epigenetic modifications, immune cell infiltration, and drug sensitivity, as evidenced by multi-omics data. However, their heterogeneity varies among different cancers. After constructing a three-gene (TMEM80, EIF4EBP1, and ITGA3) purity-independent Cox regression model using machine learning, ITGA3 was identified as a critical integrin subunit gene in pancreatic cancer. ITGA3 is involved in the molecular transformation from the classical to the basal subtype in pancreatic cancer. Elevated ITGA3 expression correlated with a malignant phenotype characterized by higher PD-L1 expression and reduced CD8+ T cell infiltration, resulting in unfavorable outcomes in patients receiving either chemotherapy or immunotherapy. Our findings suggest that ITGA3 is an important integrin in pancreatic cancer, contributing to chemotherapy resistance and immune checkpoint blockade therapy resistance.

Circular RNA circDLG1 contributes to HCC progression by regulating the miR-141-3p/WTAP axis

This study aims to explore novel and reliable biomarkers for predicting hepatocellular carcinoma (HCC) prognosis. Circular RNAs (circRNAs) were determined by analysis of human circRNA arrays and quantitative reverse transcription polymerase reactions. To test for an interaction between circDLG1, we used luciferase reporter assays, RNA immunoprecipitation, and fluorescence in situ hybridization assays that were employed to test the interaction between circDLG1, miR-141-3p, and WTAP. q-RT-PCR and western blot were used to evaluate the target regulation of miR-141-3p and WTAP. shRNA-mediated knockdown of circDLG1, proliferation, migration, and invasion experiment of metastasis were used to evaluate the function of circDLG. CircDLG1 rather than lining DLG1 was upregulated in HCC tissues, from HCC patients as well as HCC cell lines compared to normal controls. circDLG1 high expression in HCC patients was correlated with shorter overall survival. Knockdown of circDLG1 and miR-141-3p mimic could inhibit the tumorigenesis of HCC cells in vivo and in vitro. Importantly, we demonstrated that circDLG1 could act as a sponge of miR-141-3p to regulate the expression of WTAP, and further suppress the tumorigenesis of HCC cells. Our study reveals that circDLG1 can serve as a novel potential circulating biomarker for the detection of HCC. circDLG1 participates in the progression of HCC cells by sponging miR-141-3p with WTAP, providing new insight into the treatment of HCC.

Bioinformatics Prediction and Experimental Validation Identify a Novel Cuproptosis-Related Gene Signature in Human Synovial Inflammation during Osteoarthritis Progression

Osteoarthritis (OA) is the one of most common joint diseases worldwide. Cuproptosis, which had been discovered lately, is a novel form of cell death induced by copper. Our purpose is to study the relationship between cuproptosis-related genes (CRGs) and inflammatory microenvironments in patients with OA and identify characteristic cuproptosis-related biomarkers. First, the combinatory analysis of OA transcriptome data from five datasets identified differentially expressed CRGs associated with OA. Then, we applied single-sample gene set enrichment analysis (ssGSEA) to evaluate immune-cell infiltration and immune-function levels in OA patients and normal controls, respectively. Hub CRGs for OA were mined based on the random forest (RF) model, and a nomogram prediction model was constructed based on them. In total, four differentially expressed CRGs were identified through bioinformatics analysis and confirmed by RT-qPCR. FDX1 and LIPT1 were expressed at a high level in OA, while DBT and DLST were expressed higher in the normal group. In total, 10 CRGs were found to be significantly correlated with immune landscape. Four hub CRGs were subsequently obtained by the RF analysis as potential biomarkers for OA. We constructed an OA predictive model based on these four CRGs (DBT, DLST, FDX1, and LIPT1).

Effects of PAMK on lncRNA, miRNA, and mRNA expression profiles of thymic epithelial cells

Polysaccharides from Atractylodes macrocephala Koidz (PAMK) can promote the proliferation of thymocytes and improve the body's immunity. However, the effect of PAMK on thymic epithelial cells has not been reported. Studies have shown that miRNAs and lncRNAs are key factors in regulating cell proliferation. In this study, we found that PAMK could promote the proliferation of mouse medullary thymic epithelial cell line 1 (MTEC1) cells through CCK-8 and EdU experiments. To further explore its mechanism, we detected the effect of PAMK on the expression profiles of lncRNAs, miRNAs, and mRNAs in MTEC1 cells. The results showed that PAMK significantly affected the expression of 225 lncRNAs, 29 miRNAs, and 800 mRNAs. Functional analysis showed that these differentially expressed genes were significantly enriched in cell cycle, cell division, NF-kappaB signaling, apoptotic process, and MAPK signaling pathway. Finally, we used Cytoscape to visualize lncRNA-miRNA-mRNA(14 lncRNAs, 17 miRNAs, 171 mRNAs) networks based on ceRNA theory. These results suggest that lncRNAs and miRNAs may be involved in the effect of PAMK on the proliferation of MTEC1 cells, providing a new research direction for exploring the molecular mechanism of PAMK promoting the proliferation of thymic epithelial cells.