Downregulation of lncRNA MIR181A2HG by high glucose impairs vascular endothelial cell proliferation and migration through the dysregulation of the miRNAs/AKT2 axis

LncRNA MIR181A2HG negatively regulates human keratinocytes proliferation by binding SRSF1

Psoriasis is a common chronic inflammatory skin disease. Abnormal proliferation of keratinocytes plays an important role in the pathogenesis of psoriasis. Long non-coding RNAs (lncRNAs) are involved in the regulation of a variety of cell biological processes. The purpose of this study was to investigate the potential role of lncRNA MIR181A2HG in the proliferation of human keratinocytes. qRT-PCR and Western blotting were performed to measure the expression levels of MIR181A2HG, SRSF1, KRT6, and KRT16 in tissue specimens and HaCaT keratinocytes. The effects of MIR181A2HG on HaCaT keratinocytes proliferation were evaluated using Cell Counting Kit-8 (CCK-8) assays, 5-Ethynyl-2'-deoxyuridine (EdU) incorporation, and cell-cycle assays. RNA pulldown-mass spectrometry (MS) was applied to identify the proteins interacting with MIR181A2HG. RNA pull-down-Western blotting and RNA immunoprecipitation coupled with real-time quantitative reverse transcription-PCR (RIP-qRT-PCR) assays were used to determine the interactions between MIR181A2HG and its RNA-binding proteins (RBPs). MIR181A2HG was down-regulated in psoriasis tissues. MIR181A2HG overexpression induced G0/G1 and G2/M phase cell cycle arrest and decreased the protein levels of KRT6, KRT16, Cyclin D1, CDK4, and Cyclin A2 in HaCaT keratinocytes. MIR181A2HG knockdown showed the opposite effect. By using RNA pulldown-MS, 356 proteins were identified to interact with MIR181A2HG potentially. Bioinformatics analysis showed that NOP56 and SRSF1 may be RNA binding proteins (RBPs) that may be interact with MIR181A2HG. Furthermore, by using RNA pull-down-Western blotting and RIP-qRT-PCR, SRSF1 was determined to interact with MIR181A2HG. Moreover, silencing of SRSF1 inhibited keratinocytes proliferation, which could be reversed with the knockdown of MIR181A2HG. Our findings indicated that MIR181A2HG can negatively regulate HaCaT keratinocytes proliferation by binding SRSF1, suggesting that MIR181A2HG and SRSF1 may serve as potential targets for the treatment of psoriasis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-024-00621-6.

Gynura divaricata (L.) DC. promotes diabetic wound healing by activating Nrf2 signaling in diabetic rats

THE ETHNOPHARMACOLOGICAL SIGNIFICANCE

Diabetic chronic foot ulcers pose a significant therapeutic challenge as a result of the oxidative stress caused by hyperglycemia. Which impairs angiogenesis and delays wound healing, potentially leading to amputation. Gynura divaricata (L.) DC. (GD), a traditional Chinese herbal medicine with hypoglycemic effects, has been proposed as a potential therapeutic agent for diabetic wound healing. However, the underlying mechanisms of its effects remain unclear.

AIM OF THE STUDY

In this study, we aimed to reveal the effect and potential mechanisms of GD on accelerating diabetic wound healing in vitro and in vivo.

MATERIALS AND METHODS

The effects of GD on cell proliferation, apoptosis, reactive oxygen species (ROS) production, migration, mitochondrial membrane potential (MMP), and potential molecular mechanisms were investigated in high glucose (HG) stimulated human umbilical vein endothelial cells (HUVECs) using CCK-8, flow cytometry assay, wound healing assay, immunofluorescence, DCFH-DA staining, JC-1 staining, and Western blot. Full-thickness skin defects were created in STZ-induced diabetic rats, and wound healing rate was tracked by photographing them every day. HE staining, immunohistochemistry, and Western blot were employed to investigate the effect and molecular mechanism of GD on wound healing in diabetic rats.

RESULTS

GD significantly improved HUVEC survival, decreased apoptosis, lowered ROS production, restored MMP, improved migration ability, and raised VEGF expression. The use of Nrf2-siRNA completely abrogated these effects. Topical application of GD promoted angiogenesis and granulation tissue growth, resulting in faster healing of diabetic wounds. The expression of VEGF, CD31, and VEGFR was elevated in the skin tissue of diabetic rats after GD treatment, which upregulated HO-1, NQO-1, and Bcl-2 expression while downregulating Bax expression via activation of the Nrf2 signaling pathway.

CONCLUSION

The findings of this study indicate that GD has the potential to serve as a viable alternative treatment for diabetic wounds. This potential arises from its ability to mitigate the negative effects of oxidative stress on angiogenesis, which is regulated by the Nrf2 signaling pathway. The results of our study offer valuable insights into the therapeutic efficacy of GD in the treatment of diabetic wounds, emphasizing the significance of directing interventions towards the Nrf2 signaling pathway to mitigate oxidative stress and facilitate the process of angiogenesis.

Construction of a cuproptosis-associated lncRNA prognostic signature for bladder cancer and experimental validation of cuproptosis-related lncRNA UBE2Q1-AS1

Introduction

Bladder cancer (BLCA) is the ninth most common malignancy worldwide and the fourth most common cancer in men. Copper levels are significantly altered in patients with thyroid, breast, lung, cervical, ovarian, pancreatic, oral, gastric, bladder, and prostate cancers. Outcomes can be predicted by constructing signatures using lncRNA-related genes associated with outcomes.

Methods

We identified lncRNAs related to outcomes, those differentially expressed in bladder cancer, and cuproptosis-related lncRNAs from TCGA. We identified the intersection to obtain 12 genes and established a prognostic risk signature consisting of eight genes using LASSO-penalized multivariate Cox analysis. We constructed a training set, performed survival analysis on the high-and low-risk groups, and performed validation in the test and full sets. There existed a substantial contrast in the likelihood of survival among the cohorts of high and low risk. An in-depth analysis of the gene mutations associated with tumors was conducted to evaluate the risk of developing cancer. We also performed gene analysis on neoadjuvant chemotherapy. We conducted experimental validation on the key gene UBE2Q1-AS1 in our prognostic signature.

Results

The risk signature we constructed shows significant differences between the high-risk group and the low-risk group. Univariate survival analysis of the eight genes in our signature showed that each gene distinguished between high- and low-risk groups. Sub-group analysis revealed that our risk score differed significantly in tumor stage, age, and gender. The analysis results of the tumor mutation burden (TMB) showed a significant difference in the TMB between the low- and high-risk groups, which had a direct impact on the outcomes. These findings highlight the importance of TMB as a potential prognostic marker in cancer detection and prevention. We analyzed the immune microenvironment and found significant differences in immune function, validation responses, immunotherapy-related positive markers, and critical steps in the tumor immunity cycle between the high- and low-risk groups. We found that the effect of anti-CTLA4 and PD-1 was higher in the high-risk group than in the low-risk group.Gene analysis of neoadjuvant chemotherapy revealed that the treatment effect in the high-risk group was better than in the low-risk group. The key gene UBE2Q1-AS1 in our prognostic signature can significantly influence the cell viability, migration, and proliferation of cancer cells.

Discussion

We established a signature consisting of eight genes constructed from cuproptosis-related lncRNAs that have potential clinical applications for outcomes prediction, diagnosis, and treatment.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.

Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score.

Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways.

Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

Construction and validation of a novel algorithm based on oncosis-related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis

Colorectal cancer (CRC) has high morbidity and mortality, particularly if diagnosed at an advanced stage. Although there have been several studies on CRC, few have investigated the relationship between oncosis and CRC. Thus, the purpose of the present study was to identify oncosis-related long noncoding RNAs (lncRNAs) and to establish a clinical prognostic model. Original data were acquired from The Cancer Genome Atlas database and PubMed. Differentially expressed oncosis-related lncRNAs (DEorlncRNAs) were identified and were subsequently formed into pairs. Next, a series of tests and analyses, including both univariate and multivariate analyses, as well as Lasso and Cox regression analyses, were performed to establish a receiver operating characteristic curve. A cut-off point was subsequently used to divide the samples into groups labelled as high- or low-risk. Thus, a model was established and evaluated in several dimensions. Six pairs of DEorlncRNAs associated with prognosis according to the algorithm were screened out and the CRC cases were divided into high- and low-risk groups. Significant differences between patients in the different risk groups were observed for several traits, including survival outcomes, clinical pathology characteristics, immune cell infiltration status and drug sensitivity. In addition, PCR and flow cytometry were performed to further verify the model. In summary, a new risk model algorithm based on six pairs of DEorlncRNAs in CRC, which does not require specific data regarding the level of gene expression, was established and validated. This algorithm may be used to predict patient prognosis, immune cell infiltration and drug sensitivity.

Comprehensive analysis of cuproptosis-related lncRNAs in the prognosis and therapy response of patients with bladder cancer

Background

Cuproptosis is the recently defined regulatory cell death (RCD) that plays essential roles in tumorigenesis and progression. Long noncoding RNAs (lncRNAs) regulate the gene expression through various means. However, the clinical value of cuproptosis-related lncRNAs in bladder cancer (BLCA) remains poorly described.

Methods

We downloaded the transcriptome sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and lasso Cox regression analyses were performed to construct the prognostic risk signature, the predictive accuracy of which was validated in the subsequent independence and stratification analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the underlying molecular mechanisms involved in the signature to explore therapeutic vulnerabilities and potential targets in BLCA. Tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) were used to estimate the response to immune checkpoint inhibitors (ICIs). We further explored the potential new drug-target candidates based on the half maximal inhibitory concentration for this patient population.

Results

Fifteen cuproptosis-related lncRNAs significantly associated with survival were identified to construct the risk signature based on the normalized expression level and regression coefficient of each gene. The patients with BLCA and high-risk scores defined by the signature were associated with worse survival outcomes. The differentially expressed genes (DEGs) between the 2 risk groups had different biological activity. Furthermore, the patients in the low-risk group exhibited a higher TMB index and a lower TIDE score. The sensitivity of multiple antitumor drugs was negatively related to risk score, including AR-42, AS605240, FK866, TAK-715, and tubastatin A, while the sensitivity of some antitumor drugs, such as AMG-706, BX-795, and RO-3306, were positively correlated with risk score.

Conclusions

Our study established and verified a novel clinical risk signature with cuproptosis-related lncRNAs that may predict therapy response and prognosis with robust and stable accuracy in patients with BLCA and enhance the personalized management of this patient population.

Recent advances in molecular biology of metabolic syndrome pathophysiology: endothelial dysfunction as a potential therapeutic target

Current advances in molecular pathobiology of endotheliocytes dysfunctions are promising in finding the pathogenetic links to the emergence of insulin resistance syndrome. Physiologically, human organism homeostasis is strictly controlled to maintain metabolic processes at the acquainted level. Many factors are involved in maintaining these physiological processes in the organism and any deviation is undoubtedly accompanied by specific pathologies related to the affected process. Fortunately, the body’s defense system can solve and compensate for the impaired function through its multi-level defense mechanisms. The endothelium is essential in maintaining this homeostasis through its ability to modulate the metabolic processes of the organism. Pathological activity or impairment of physiological endothelium function seems directly correlated to the emergence of metabolic syndrome. The most accepted hypothesis is that endothelium distribution is due to endoplasmic reticulum stress and unfolded protein response development, which includes inhibition of long non-coding RNAs expression, cytokines disbalance, Apelin dysregulation, glycocalyx degradation, and specific microparticles. Clinically, the enhancement or restoration of normal endothelial cells can be a target for novel therapeutic strategies since the distribution of its physiological activity impairs homeostasis and results in the progression of metabolic syndrome, and induction of its physiological activity can ameliorate insulin resistance syndrome. Novel insights on the molecular mechanisms of endothelial cell dysfunction are concisely represented in this paper to enhance the present therapeutic tactics and advance the research forward to find new therapeutic targets.

Lactate Metabolism-Associated lncRNA Pairs: A Prognostic Signature to Reveal the Immunological Landscape and Mediate Therapeutic Response in Patients With Colon Adenocarcinoma

Background

Lactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).

Methods

A gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.

Results

In total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.

Conclusion

The newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients’ immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.