Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Multi-omics and immunogenomics analysis revealed PFKFB3 as a targetable hallmark and mediates sunitinib resistance in papillary renal cell carcinoma: in silico study with laboratory verification

Glycolysis-related metabolic reprogramming is a central hallmark of human cancers, especially in renal cell carcinoma. However, the regulatory function of glycolytic signature in papillary RCC has not been well elucidated. In the present study, the glycolysis-immune predictive signature was constructed and validated using WGCNA, glycolysis-immune clustering analysis. PPI network of DEGs was constructed and visualized. Functional enrichments and patients' overall survival were analyzed. QRT-PCR experiments were performed to detect hub genes' expression and distribution, siRNA technology was used to silence targeted genes; cell proliferation and migration assays were applied to evaluate the biological function. Glucose concentration, lactate secretion, and ATP production were measured. Glycolysis-Immune Related Prognostic Index (GIRPI) was constructed and combined analyzed with single-cell RNA-seq. High-GIRPI signature predicted significantly poorer outcomes and relevant clinical features of pRCC patients. Moreover, GIRPI also participated in several pathways, which affected tumor immune microenvironment and provided potential therapeutic strategy. As a key glycolysis regulator, PFKFB3 could promote renal cancer cell proliferation and migration in vitro. Blocking of PFKFB3 by selective inhibitor PFK-015 or glycolytic inhibitor 2-DG significantly restrained renal cancer cells' neoplastic potential. PFK-015 and sunitinib could synergistically inhibit pRCC cells proliferation. Glycolysis-Immune Risk Signature is closely associated with pRCC prognosis, progression, immune infiltration, and therapeutic response. PFKFB3 may serve as a pivotal glycolysis regulator and mediates Sunitinib resistance in pRCC patients.

LncRNA MAGI2-AS3 inhibites tumor progression by up-regulating STAM via interacting with miR-142-3p in clear cell renal cell carcinoma

Revealing the role of non-coding RNAs (ncRNAs) in inducing dysregulated pathological responses to external signals may identify therapeutic targets for inhibiting the progression of clear cell renal cell carcinoma (ccRCC). Non-coding RNAs belong to a class of RNA molecules that do not encode proteins but possess diverse biological functions, playing essential roles in the occurrence and development of metastatic and proliferative tumors. To investigate the impact of the upstream interaction between miR-142-3p and lncRNA MAGI2-AS3 on the tumor-suppressive activity of the STAM gene, we firstly conducted bioinformatics analysis to predict the upstream miRNAs of STAM and the upstream lncRNAs of the miRNAs through online databases (miRanda, miRDB, TargetScan, LncBase v2), which were further validated by the starBasev2.0 database. Subsequently, multiple experimental techniques were employed to validate these findings, including RT-qPCR, Western blotting, measurement of cellular functional activity, and luciferase reporter assays. Through these experimental methods, we provided compelling evidence regarding the role of miR-142-3p and MAGI2-AS3 in regulating STAM gene expression and functionality, revealing their potential significance in tumor suppression. Our research demonstrates the importance of the MAGI2-AS3/miR-142-3p/STAM signaling pathway axis in ccRCC. MAGI2-AS3 competes for binding with miR-142-3p, resulting in upregulated STAM gene expression. This upregulation inhibits tumor proliferation and metastasis in ccRCC cells. Conversely, overexpression of miR-142-3p or silencing of MAGI2-AS3 promotes tumor behavior, while downregulation of miR-142-3p inhibits the development of ccRCC. Targeting the MAGI2-AS3/miR-142-3p/STAM axis holds promise as a therapeutic strategy for ccRCC treatment.

Unveiling the multi-target compounds of Rhazya stricta: Discovery and inhibition of novel target genes for the treatment of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a prevalent kidney malignancy with a pressing need for innovative therapeutic strategies. In this context, emerging research has focused on exploring the medicinal potential of plants such as Rhazya stricta. Nevertheless, the complex molecular mechanisms underlying its potential therapeutic efficacy remain largely elusive. Our study employed an integrative approach comprising data mining,network pharmacology,tissue cell type analysis, and molecular modelling approaches to identify potent phytochemicals from R. stricta, with potential relevance for ccRCC treatments. Initially, we collected data on R. stricta's phytochemical from public databases. Subsequently, we integrated this information with differentially expressed genes (DEGs) in ccRCC, which were derived from microarray datasets(GSE16441,GSE66270, and GSE76351). We identified potential intersections between R. stricta and ccRCC targets, which enabled us to construct a compound-genes-pathway network using Cytoscape software. This helped illuminate R. stricta's multi-target pharmacological effects on ccRCC. Moreover, tissue cell type analysis added another layer of insight into the cellular specificity of potential therapeutic targets in the kidney. Through further Kaplan-Meier survival analysis, we pinpointed MMP9,ACE,ERBB2, and HSP90AA1 as prospective diagnostic and prognostic biomarkers for ccRCC. Notably, our study underscores the potential of R. stricta derived compounds-namely quebrachamine,corynan-17-ol, stemmadenine,strictanol,rhazinilam, and rhazimolare-to impede ccRCC progression by modulating the activity of MMP9,ACE,ERBB2, and HSP90AA1 genes. Further, molecular docking and dynamic simulations confirmed the plausible binding affinities of these compounds. Despite these promising findings, we recognize the need for comprehensive in vivo and in vitro studies to further investigate the pharmacokinetics and biosafety profiles of these compounds.

A novel oxidative-stress related lncRNA signature predicts the prognosis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a primary malignant tumour of tubular epithelial origin and is most common in the urinary tract. Growing evidence suggests that oxidative stress (OS), generates high levels of reactive oxygen species (ROS) and free radicals, and plays a critical role in cancer in humans. However, the predictive value of OS-related long non-coding RNAs (lncRNAs) in ccRCC remains unclear. We constructed a predictive signature of survival based on OS-related lncRNAs that were obtained from The Cancer Genome Atlas (TCGA-KIRC), to predict the prognosis of patients with ccRCC. The signature comprised seven lncRNAs: SPART-AS1, AL162586.1, LINC00944, LINC01550, HOXB-AS4, LINC02027, and DOCK9-DT. OS-related signature of lncRNAs had diagnostic efficiency higher than that of clinicopathological variables, with an area of 0.794 under the receiver operating characteristic curve. Additionally, the nomogram based on risk scores and clinicopathological variables (age, gender, grade, stage, M-stage, and N-stage) showed strong predictive performance. Patients with high-risk were found to be more sensitive to the therapeutic drugs ABT.888, AICAR, MS.275, sunitinib, AZD.2281, and GDC.0449. Our constructed the predictive signature can independently predict the prognosis of patients with ccRCC; however, the underlying mechanism needs further investigation.

Long noncoding RNAs: glycolysis regulators in gynaecologic cancers

The three most common gynaecologic cancers that seriously threaten female lives and health are ovarian cancer, cervical cancer, and endometrial cancer. Glycolysis plays a vital role in gynaecologic cancers. Several long noncoding RNAs (lncRNAs) are known to function as oncogenic molecules. LncRNAs impact downstream target genes by acting as ceRNAs, guides, scaffolds, decoys, or signalling molecules. However, the role of glycolysis-related lncRNAs in regulating gynaecologic cancers remains poorly understood. In this review, we emphasize the functional roles of many lncRNAs that have been found to promote glycolysis in gynaecologic cancers and discuss reasonable strategies for future research.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

High expression of CCNB1 driven by ncRNAs is associated with a poor prognosis and tumor immune infiltration in breast cancer

BACKGROUNDS

Breast cancer (BC) is the most frequent cancer diagnosed in women throughout the world. The purpose of this study was to explore new biomarkers for breast cancer diagnosis. CyclinB1 (CCNB1) is found in abundance in a wide range of human malignancies.

MATERIAL AND METHODS

We evaluated the transcriptional, survival data and expression levels in tissue data of CCNB1 in patients with breast cancer from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The Human Protein Atlas (THAP) and Genome Tissue Expression (GTEx) database. A series of in silico analyses were used to investigate at noncoding RNAs (ncRNAs), gene ontology (GO) annotation analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Protein-Protein Interaction (PPI) network. A quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate CCNB1 in BC cell lines.

RESULTS

CCNB1 expression was higher in BC tissues than in normal breast tissues. It was significantly related to survival time, tumor mutation burden (TMB), methylated, immune cell infiltration, and the expressed in estrogen receptor (ER) (-), lymphnode (+), and p53 (+) groups in BC. Moreover, The AC026401.3/CCNB1-miR-139-5p axis was discovered as the most promising upstream ncRNA-related pathway of CCNB1 in BC.

CONCLUSION

CCNB1 can be used as an independent predictive factor for BC, indicating that this would be a target for highly precise therapy and a biomarker for the disease.

Systematic Investigation of Immune-Related lncRNA Landscape Reveals a Potential Long Non-Coding RNA Signature for Predicting Prognosis in Renal Cell Carcinoma

Background: Renal cell carcinoma (RCC) is the predominant type of malignant tumor in kidney cancer. Finding effective biomarkers, particularly those based on the tumor immune microenvironments (TIME), is critical for the prognosis and diagnosis of RCC. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) play a crucial role in cancer immunity. However, the comprehensive landscape of immune infiltration-associated lncRNAs and their potential roles in the prognosis and diagnosis of RCC remain largely unexplored.

Methods: Based on transcriptomic data of 261 RCC samples, novel lncRNAs were identified using a custom pipeline. RCC patients were classified into different immune groups using unsupervised clustering algorithms. Immune-related lncRNAs were obtained according to the immune status of RCC. Competing endogenous RNAs (ceRNA) regulation network was constructed to reveal their functions. Expression patterns and several tools such as miRanda, RNAhybrid, miRWalk were used to define lncRNAs-miRNAs-mRNAs interactions. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed on the training set to construct a tumorigenesis-immune-infiltration-related (TIR)-lncRNA signature for predicting the prognosis of RCC. Independent datasets involving 531 RCC samples were used to validate the TIR-lncRNA signature.

Results: Tens of thousands of novel lncRNAs were identified in RCC samples. Comparing tumors with controls, 1,400 tumorigenesis-related (TR)-lncRNAs, 1269 TR-mRNAs, and 192 TR-miRNAs were obtained. Based on the infiltration of immune cells, RCC patients were classified into three immune clusters. By comparing immune-high with immune-low groups, 241 TIR-lncRNAs were identified, many of which were detected in urinary samples. Based on lncRNA-miRNA-mRNA interactions, we constructed a ceRNA network, which included 25 TR-miRNAs, 28 TIR-lncRNAs, and 66 TIR-mRNAs. Three TIR lncRNAs were identified as a prognostic signature for RCC. RCC patients in the high-risk group exhibited worse OS than those in the low-risk group in the training and testing sets (p < 0.01). The AUC was 0.9 in the training set. Univariate and multivariate Cox analyses confirmed that the TIR-lncRNA signature was an independent prognostic factor in the training and testing sets.

Conclusion: Based on the constructed immune-related lncRNA landscape, 241 TIR-lncRNAs were functionally characterized, three of which were identified as a novel TIR-lncRNA signature for predicting the prognosis of RCC.

Expression and Clinical Significance of lncRNA NEAT1 in Patients with Spinal Tuberculosis

Background

Spinal tuberculosis (STB) often leads to irreversible neurological injury, resulting in serious social and economic problems. With the emergence of drug resistance, the management becomes even more challenging, given the treatment courses are generally longer for skeletal than pulmonary tuberculosis (PTB). The development and validation of nonsputum biomarkers for diagnosis and tailoring of treatment duration to enable personalized and evidence-based management of such diseases to improve treatment outcomes is being called for globally. Studies have demonstrated that lncRNA NEAT1 was highly expressed in pulmonary tuberculosis (TB) and was related to its progression and recovery. However, the expression and clinical significance of lncRNA NEAT1 in STB remains unclear.

Methods

The relative expression of lncRNA NEAT1 was quantified by relative real-time reverse transcription PCR (RT-PCR). The prognostic value was assessed by receiver-operating characteristic (ROC) curve analysis. Pearson and Spearman correlation coefficient and chi-square test were used to analyze the correlation between the lncRNA NEAT1 expression and the clinical characteristics. Univariate and multivariate logistic regression analyses were used to analyze independent predictors of STB recurrence.

Results

Compared with normal healthy individuals, the expression level of lncRNA NEAT1 in peripheral blood and granulomatous tissues of STB patients was significantly increased. The results of the in vitro Mycobacterium tuberculosis- (Mtb-) infected cell model showed that the expression level of lncRNA NEAT1 was significantly upregulated in macrophages infected with Mtb, and the difference was statistically significant compared with Mtb-uninfected group. The expression level of lncRNA NEAT1 in granulomatous tissue of STB was significantly higher than that in peripheral blood. The expression of lncRNA NEAT1 was related to segments of the lesions, paraspinal abscesses, anti-TB treatment, drug resistance, interleukin-6 (IL-6), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). Multivariate analysis results showed that relatively high expression of lncRNA NEAT1_1, the shorter transcript of the NEAT1 gene, was an independent prognostic factor of STB outcome.

Conclusion

LncRNA NEAT1 was highly expressed in peripheral blood mononuclear cells (PBMCs) and granulomatous tissue from patients with STB, as well as in Mtb-infected THP-1 cell lines. LncRNA NEAT1 expression was significantly associated with clinical characteristics (paraspinal abscesses, segments of the lesions and anti-TB treatment, IL-6, CRP, and ESR) of patients in STB. Increased expression of lncRNA NEAT1_1 predicted good prognosis of STB and might become a prognostic biomarker for STB.