Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma

Molecular signature of immune-related new survival predictions for subtype of renal cell carcinomas

Background

Kidney renal papillary cell carcinoma (KIRP), kidney chromophobe (KICH), and kidney renal clear cell carcinoma (KIRC) are three most common subtypes of renal cell carcinomas (RCC), and its development is a multifaceted process that intricately involves the interplay of numerous genes. Despite recent advances in research on renal cell carcinoma, the prognosis of KIRC patients remains dismal. Therefore, there is an urgent need to explore new prognostic biomarkers and treatment strategies to help clinicians choose more effective treatment methods and accurately predict long-term efficacy. Our study aimed to systematically evaluate the gene expression profiles of three RCC subtypes, especially KIRC, and to identify survival-related biomarker.

Methods

In our present study, we systematically evaluate the genes expression profile difference among three subtypes of RCC, and identify the survival-related key genes signature based on GEPIA2. GeneMANIA was used to identify the functionality-related differentially expressed genes (DEGs). Furthermore, focusing on KIRC, we intersected functionality-related and survival-related DEGs based on two datasets.

Results

We ascertained five DEGs (ANK3, FREM2, KIF13B, MPP7 and SOX6) as key survival-related genes in KIRC. High levels of these five DEGs expressions were strongly associated with favorable prognosis, but not correlated to metastasis. Downregulation of these five DEGs expressions was closely associated with immunomodulators, chemokines, and infiltrating levels of different immune cells, which indicated that these five DEGs were key immune-related novel prognostic biomarkers for KIRC.

Conclusions

The five identified DEGs serve as potential novel prognostic biomarkers for KIRC. However, the crucial factors that lead to the downregulation and functional inactivation of these five key genes need to be explored in future studies.

Non-coding transcriptome profiles in clear-cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is a common urological malignancy with an increasing incidence. The development of molecular biomarkers that can predict the response to treatment and guide personalized therapy selection would substantially improve patient outcomes. Dysregulation of non-coding RNA (ncRNA) has been shown to have a role in the pathogenesis of ccRCC. Thus, an increasing number of studies are being carried out with a focus on the identification of ncRNA biomarkers in ccRCC tissue samples and the connection of these markers with patients' prognosis, pathological stage and grade (including metastatic potential), and therapy outcome. RNA sequencing analysis led to the identification of several ncRNA biomarkers that are dysregulated in ccRCC and might have a role in ccRCC development. These ncRNAs have the potential to be prognostic and predictive biomarkers for ccRCC, with prospective applications in personalized treatment selection. Research on ncRNA biomarkers in ccRCC is advancing, but clinical implementation remains preliminary owing to challenges in validation, standardization and reproducibility. Comprehensive studies and integration of ncRNAs into clinical trials are essential to accelerate the clinical use of these biomarkers.

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma

BACKGROUND

Zinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown.

METHODS

Through the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration.

RESULTS

It was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments.

CONCLUSIONS

Anoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Identification and validation of shared gene signature of kidney renal clear cell carcinoma and COVID-19

Background

COVID-19 is a severe infectious disease caused by the SARS-CoV-2 virus, and previous studies have shown that patients with kidney renal clear cell carcinoma (KIRC) are more susceptible to SARS-CoV-2 infection than the general population. Nevertheless, their co-pathogenesis remains incompletely elucidated.

Methods

We obtained shared genes between these two diseases based on public datasets, constructed a prognostic risk model consisting of hub genes, and validated the accuracy of the model using internal and external validation sets. We further analyzed the immune landscape of the prognostic risk model, investigated the biological functions of the hub genes, and detected their expression in renal cell carcinoma cells using qPCR. Finally, we searched the candidate drugs associated with hub gene-related targets from DSigDB and CellMiner databases.

Results

We obtained 156 shared genes between KIRC and COVID-19 and constructed a prognostic risk model consisting of four hub genes. Both shared genes and hub genes were highly enriched in immune-related functions and pathways. Hub genes were significantly overexpressed in COVID-19 and KIRC. ROC curves, nomograms, etc., showed the reliability and robustness of the risk model, which was validated in both internal and external datasets. Moreover, patients in the high-risk group showed a higher proportion of immune cells, higher expression of immune checkpoint genes, and more active immune-related functions. Finally, we identified promising drugs for COVID-19 and KIRC, such as etoposide, fulvestrant, and topotecan.

Conclusion

This study identified and validated four shared genes for KIRC and COVID-19. These genes are associated with immune functions and may serve as potential prognostic biomarkers for KIRC. The shared pathways and genes may provide new insights for further mechanistic research and treatment of comorbidities.

Combination of bulk RNA sequencing and scRNA sequencing uncover the molecular characteristics of MAPK signaling in kidney renal clear cell carcinoma

The MAPK signaling pathway significantly impacts cancer progression and resistance; however, its functions remain incompletely assessed across various cancers, particularly in kidney renal clear cell carcinoma (KIRC). Therefore, there is an urgent need for comprehensive pan-cancer investigations of MAPK signaling, particularly within the context of KIRC. In this research, we obtained TCGA pan-cancer multi-omics data and conducted a comprehensive analysis of the genomic and transcriptomic characteristics of the MAPK signaling pathway. For in-depth investigation in KIRC, status of MAPK pathway was quantitatively estimated by ssGSEA and Ward algorithm was utilized for cluster analysis. Molecular characteristics and clinical prognoses of KIRC patients with distinct MAPK activities were comprehensively explored using a series of bioinformatics algorithms. Subsequently, a combination of LASSO and COX regression analyses were utilized sequentially to construct a MAPK-related signature to help identify the risk level of each sample. Patients in the C1 subtype exhibited relatively higher levels of MAPK signaling activity, which were associated with abundant immune cell infiltration and favorable clinical outcomes. Single-cell RNA sequencing (scRNA-seq) analysis of KIRC samples identified seven distinct cell types, and endothelial cells in tumor tissues had obviously higher MAPK scores than normal tissues. The immunohistochemistry results indicated the reduced expression levels of PAPSS1, MAP3K11, and SPRED1 in KIRC samples. In conclusion, our study represents the first integration of bulk RNA sequencing and single-cell RNA sequencing to elucidate the molecular characteristics of MAPK signaling in KIRC, providing a solid foundation for precision oncology.

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma

OBJECTIVE

Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC).

MATERIALS AND METHODS

A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation.

RESULTS

A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance.

CONCLUSIONS

In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.

ETNK2 Low-Expression Predicts Poor Prognosis in Renal Cell Carcinoma with Immunosuppressive Tumor Microenvironment

Background

The ethanolamine kinase 2 (ETNK2) gene is implicated in carcinogenesis, but its expression and involvement in kidney renal clear cell carcinoma (KIRC) remain unknown.

Methods

Initially, we conducted a pan-cancer study in which we searched the Gene Expression Profiling Interactive Analysis, the UALCAN, and the Human Protein Atlas databases to determine the expression level of the ETNK2 gene in KIRC. The Kaplan-Meier curve was then used to calculate the overall survival (OS) of KIRC patients. We then used the differentially expressed genes (DEGs) and enrichment analysis to explain the mechanism of the ETNK2 gene. Finally, the immune cell infiltration analysis was performed.

Results

Although the ETNK2 gene expression was lower in KIRC tissues, the findings illustrated a link between the ETNK2 gene expression and a shorter OS time for KIRC patients. DEGs and enrichment analysis revealed that the ETNK2 gene in KIRC involved multiple metabolic pathways. Finally, the ETNK2 gene expression has been linked to several immune cell infiltrations.

Conclusions

According to the findings, the ETNK2 gene plays a crucial role in tumor growth. It can potentially serve as a negative prognostic biological marker for KIRC by modifying immune infiltrating cells.

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network

Background

Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network.

Results

By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments.

Conclusion

A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.

The Role of Long Noncoding RNA (lncRNAs) Biomarkers in Renal Cell Carcinoma

Renal cell carcinoma is one of the common cancers whose incidence and mortality are continuously growing worldwide. Initially, this type of tumour is usually asymptomatic. Due to the lack of reliable diagnostic markers, one-third of ccRCC patients already have distant metastases at the time of diagnosis. This underlines the importance of establishing biomarkers that would enable the prediction of the disease's course and the risk of metastasis. LncRNA, which modulates genes at the epigenetic, transcriptional, and post-transcriptional levels, appears promising. The actions of lncRNA involve sponging and sequestering target miRNAs, thus affecting numerous biological processes. Studies have confirmed the involvement of RNAs in various diseases, including RCC. In this review, we focused on MALAT1 (a marker of serious pathological changes and a factor in the promotion of tumorigenesis), RCAT1 (tumour promoter in RCC), DUXAP9 (a plausible marker of localized ccRCC), TCL6 (exerting tumour-suppressive effects in renal cancer), LINC00342 (acting as an oncogene), AGAP2 Antisense1 (plausible predictor of RCC progression), DLEU2 (factor promoting tumours growth via the regulation of epithelial-mesenchymal transition), NNT-AS1 (sponge of miR-22 contributing to tumour progression), LINC00460 (favouring ccRCC development and progression) and Lnc-LSG1 (a factor that may stimulate ccRCC metastasis).

Development of genomic instability-associated long non-coding RNA signature: A prognostic risk model of clear cell renal cell carcinoma

Purpose

Renal clear cell carcinoma (ccRCC) is the most lethal of all pathological subtypes of renal cell carcinoma (RCC). Genomic instability was recently reported to be related to the occurrence and development of kidney cancer. The biological roles of long non-coding RNAs (lncRNAs) in tumorigenesis have been increasingly valued, and various lncRNAs were found to be oncogenes or cancer suppressors. Herein, we identified a novel genomic instability-associated lncRNA (GILncs) model for ccRCC patients to predict the overall survival (OS).

Methods

The Cancer Genome Atlas (TCGA) database was utilized to obtain full transcriptome data, somatic mutation profiles, and clinical characteristics. The differentially expressed lncRNAs between the genome-unstable-like group (GU) and the genome-stable-like group (GS) were defined as GILncs, with |logFC| > 1 and an adjusted p-value< 0.05 for a false discovery rate. All samples were allocated into GU-like or GS-like types based on the expression of GILncs observed using hierarchical cluster analyses. A genomic instability-associated lncRNA signature (GILncSig) was constructed using parameters of the included lncRNAs. Quantitative real-time PCR analysis was used to detect the in vitro expression of the included lncRNAs. Validation of the risk model was performed by the log-rank test, time-dependent receiver operating characteristic (ROC) curves analysis, and multivariate Cox regression analysis.

Results

Forty-six lncRNAs were identified as GILncs. LINC00460, AL139351.1, and AC156455.1 were employed for GILncSig calculation based on the results of Cox analysis. GILncSig was confirmed as an independent predictor for OS of ccRCC patients. Additionally, it presented a higher efficiency and accuracy than other RCC prognostic models reported before.

Conclusion

GILncSig score was qualified as a critical indicator, independent of other clinical factors, for prognostic prediction of ccRCC patients.

DHRS7 is an immune-related prognostic biomarker of KIRC and pan-cancer

Renal clear cell carcinoma (KIRC) is one malignancy whose development and prognosis have been associated with aberrant DHRS7 expression. However, the catalytic activity and pathophysiology of KIRC are poorly understood, and no sensitive tumor biomarkers have yet been discovered. In our study, we examined the significant influence of DHRS7 on the tumor microenvironment (TME) and tumor progression using an overall predictable and prognostic evaluation approach. We found novel cancer staging, particularly in KIRC, as well as potential therapeutic drugs out of 27 drug sensitivity tests. Using Perl scripts, it was possible to determine the number of somatic mutations present in 33 tumors, as well as the relative scores of 22 immune cells using CIBERSORT, the relationship between immune infiltration and differential expression using TCGA data, and the immune microenvironment score using the estimate technique. Our results show that DHRS7 is abnormally expressed in pan-cancer patients, which influences their survival. Low DHRS7 expression was associated with late clinical stages and a low survival rate in KIRC patients, suggesting a poor prognosis and course of treatment, in HNSG, MESO, and KIRC patients. We also found that DHRS7 was associated with TMB and MSI in certain tumors. Using KIRC as an example, we discovered a negative correlation between DHRS7 expression and immunological assessments, suggesting that this substance might be used as a tumor biomarker.

Prognostic, Clinicopathological, and Function of Key Cuproptosis Regulator FDX1 in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cancer. Cuproptosis is suggested to be a novel therapy target for cancer treatment. However, the function of cuproptosis and its key regulator FDX1 in ccRCC remains unclear. In this study, we adequately explored the prognostic factors, clinicopathological characteristics, and function of FDX1 in ccRCC. We found that the expression of FDX1 was significantly downregulated in ccRCC samples. Patients with a higher FDX1 expression had a significantly better prognosis, including overall survival (OS) (Hazard ratio (HR): 2.54, 95% confidence interval (CI): 1.82−3.53, p < 0.001), disease-specific survival (DSS) (HR: 3.04, 95% CI: 2.04−4.54, p < 0.001), and progression-free survival (PFS) (HR: 2.54, 95% CI: 1.82−3.53, p < 0.001). FDX1 was a clinical predictor to stratify patients into the high or low risk of poor survival, independent of conventional clinical features, with the area under the ROC curve (AUC) of 0.658, 0.677, and 0.656 for predicting the 5-year OS, DSS, and PFS. The nomogram model based on FDX1 had greater predictive power than other individual prognostic parameters. FDX1 mainly participated in the oxidative-related process and mitochondrial respiration-related processes but was not associated with immune infiltration levels. In conclusion, the cuproptosis key regulator FDX1 could serve as a potential novel prognostic biomarker for ccRCC patients.

Oncogenic roles of the lncRNA LINC00460 in human cancers

Long noncoding RNAs (lncRNAs) represent an important group of endogenous RNAs with limit protein-encoding capability, with a length of more than 200 nucleotides. Emerging evidence have demonstrated that lncRNAs are greatly involved in multiple cancers by playing critical roles in tumor initiation and progression. Long intergenic non-protein coding RNA 460 (LINC00460), a novel cancer-related lncRNA, exhibits abnormal expression and oncogenic function in multiple cancers, and positively correlates with poor clinical characteristics of cancer patients. LINC00460 has also been shown to be a promising biomarker for diagnosis as well as prognostic evaluation in cancer patients. In this review, we briefly summarized recent knowledge on the expression, functional roles, molecular mechanisms, and diagnostic and prognostic values of LINC00460 in human malignancies.

Molecular Subtyping Based on Cuproptosis-Related Genes and Characterization of Tumor Microenvironment Infiltration in Kidney Renal Clear Cell Carcinoma

The incidence of kidney renal clear cell carcinoma (KIRC) is rising worldwide, and the prognosis is poor. Cuproptosis is a new form of cell death that is dependent on and regulated by copper ions. The relationship between cuproptosis and KIRC remains unclear. In the current study, changes in cuproptosis-related genes (CRGs) in TCGA-KIRC transcriptional datasets were characterized, and the expression patterns of these genes were analyzed. We identified three main molecular subtypes and discovered that multilayer CRG changes were associated with patient clinicopathological traits, prognosis, elesclomol sensitivity, and tumor microenvironment (TME) cell infiltration characteristics. Then, a CRG score was created to predict overall survival (OS). The CRG score was found to be strongly linked to the TME. These findings may help elucidate the roles of CRGs in KIRC, potentially enhancing understanding of cuproptosis and supporting the development of more effective immunotherapy strategies.

FMR1 is identified as an immune-related novel prognostic biomarker for renal clear cell carcinoma: A bioinformatics analysis of TAZ/YAP

WW domain-containing transcription regulator 1 (TAZ, or WWTR1) and Yes-associated protein 1 (YAP) are both important effectors of the Hippo pathway and exhibit different functions. However, few studies have explored their co-regulatory mechanisms in kidney renal clear cell carcinoma (KIRC). Here, we used bioinformatics approaches to evaluate the co-regulatory roles of TAZ/YAP and screen novel biomarkers in KIRC. GSE121689 and GSE146354 were downloaded from the GEO. The limma was applied to identify the differential expression genes (DEGs) and the Venn diagram was utilized to screen co-expressed DEGs. Co-expressed DEGs obtained the corresponding pathways through GO and KEGG analysis. The protein-protein interaction (PPI) network was constructed using STRING. The hub genes were selected applying MCODE and CytoHubba. GSEA was further applied to identify the hub gene-related signaling pathways. The expression, survival, receiver operating character (ROC), and immune infiltration of the hub genes were analyzed by HPA, UALCAN, GEPIA, pROC, and TIMER. A total of 51 DEGs were co-expressed in the two datasets. The KEGG results showed that the enriched pathways were concentrated in the TGF-β signaling pathway and endocytosis. In the PPI network, the hub genes (STAU2, AGO2, FMR1) were identified by the MCODE and CytoHubba. The GSEA results revealed that the hub genes were correlated with the signaling pathways of metabolism and immunomodulation. We found that STAU2 and FMR1 were weakly expressed in tumors and were negatively associated with the tumor stages. The overall survival (OS) and disease-free survival (DFS) rate of the high-expressed group of FMR1 was greater than that of the low-expressed group. The ROC result exhibited that FMR1 had certainly a predictive ability. The TIMER results indicated that FMR1 was positively correlated to immune cell infiltration. The abovementioned results indicated that TAZ/YAP was involved in the TGF-β signaling pathway and endocytosis. FMR1 possibly served as an immune-related novel prognostic gene in KIRC.

Inhibition of the lncRNA MIAT prevents podocyte injury and mitotic catastrophe in diabetic nephropathy

Podocyte damage is strongly associated with the progression of diabetic nephropathy. Mitotic catastrophe plays an essential role in accelerating podocyte loss and detachment from the glomerular basement membrane. In the current study, we observed that the long non-coding RNA (lncRNA) MIAT was noticeably upregulated in the plasma and kidney tissues of patients with diabetic nephropathy, and this upregulation was accompanied by higher albumin/creatinine ratios and serum creatinine levels. By generating CRISPR-Cas9 Miat-knockout (KO) mice in vivo and employing vectors in vitro, we found that the depletion of Miat expression significantly restored slit-diaphragm integrity, attenuated foot process effacement, prevented dedifferentiation, and suppressed mitotic catastrophe in podocytes during hyperglycemia. The mechanistic investigation revealed that Miat increased Sox4 expression and subsequently regulated p53 ubiquitination and acetylation, thereby inhibiting the downstream factors CyclinB/cdc2 by enhancing p21^cip1/waf1 activity, and that Miat interacted with Sox4 by sponging miR-130b-3p. Additionally, the inhibition of miR-130b-3p with an antagomir in vivo effectively enhanced glomerular podocyte injury and mitotic dysfunction, eventually exacerbating proteinuria. Based on these findings, MIAT may represent a therapeutic target for diabetic nephropathy.

Identification and Validation of a Ferroptosis-Related Long Non-Coding RNA (FRlncRNA) Signature to Predict Survival Outcomes and the Immune Microenvironment in Patients With Clear Cell Renal Cell Carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is increasing worldwide, contributing to 70–85% of kidney cancer cases. Ferroptosis is a novel type of programmed cell death and could predict prognoses in cancers. Here, we developed a ferroptosis-related long non-coding RNA (FRlncRNA) signature to improve the prognostic prediction of ccRCC.

Methods: The transcriptome profiles of FRlncRNAs and clinical data of ccRCC were obtained from The Cancer Genome Atlas and ICGC databases. Patients were randomly assigned to training cohorts, testing cohorts, and overall cohorts. The FRlncRNA signature was constructed by Lasso regression and Cox regression analysis, and Kaplan–Meier (K-M) analysis was used to access the prognosis of each group. The accuracy of this signature was evaluated by the receiver operating characteristic (ROC) curve. The visualization of functional enrichment was carried out by the gene set enrichment analysis (GSEA). Internal and external datasets were performed to verify the FRlncRNA signature.

Results: A FRlncRNA signature comprising eight lncRNAs (AL590094.1, LINC00460, LINC00944, AC024060.1, HOXB-AS4, LINC01615, EPB41L4A-DT, and LINC01550) was identified. Patients were divided into low- and high-risk groups according to the median risk score, in which the high-risk group owned a dramatical shorter survival time than that of the low-risk group. Through ROC analysis, it was found that this signature had a greater predictive capability than traditional evaluation methods. The risk score was an independent risk factor for overall survival suggested by multivariate Cox analysis (HR = 1.065, 95%CI = 1.036–1.095, and p < 0.001). We constructed a clinically predictive nomogram based on this signature and its clinical features, which is of accurate prediction about the survival rate of patients. The GSEA showed that primary pathways were the P53 signaling pathway and tumor necrosis factor–mediated signaling pathway. The major FRlncRNAs (LINC00460, LINC00944, LINC01550, and EPB41L4A-DT) were verified with the prognosis of ccRCC in the GEPIA and K-M Plotter databases. Their major target genes (BNIP3, RRM2, and GOT1) were closely related to the stage, grade, and survival outcomes of ccRCC by the validation of multiple databases. Additionally, we found two groups had a significant distinct pattern of immune function, immune checkpoint, and immune infiltration, which may lead to different survival benefits.

Conclusions: The FRlncRNA signature was accurate and act as reliable tools for predicting clinical outcomes and the immune microenvironment of patients with ccRCC, which may be molecular biomarkers and therapeutic targets.

LncRNA LINC00460: Function and mechanism in human cancer

Long non-coding RNAs (LncRNAs), which are more than 200 nucleotides in length and with limited protein-coding potential, play vital roles in the pathogenesis, tumorigenesis, and angiogenesis of cancers. Aberrant expression of lncRNAs has been detected in various carcinomas and may be correlated with oncogenesis by affecting related genes expression. Recently, an increasing number of studies have reported on long intergenic non-protein coding RNA 460 (LINC00460) in human tumor fields. LINC00460 is upregulated in diverse cancer tissues and cells. The upregulated expression level of LINC00460 is correlated with larger tumor size, tumor node metastasis (TNM) stage, lymph node metastasis, and shorter overall survival. The regulatory mechanism of LINC00460 was complex and diverse. LINC00460 could act as a competitive endogenous RNA (ceRNA), directly bind with proteins or regulate multiple pathways, which affected tumor progression. Moreover, LINC00460 was also identified to increase drug resistance, and therefore, weaken the effectiveness of tumor treatment. It has become increasingly important to investigate the roles of LINC00460 in various cancers by different mechanisms. Therefore, a more comprehensive understanding of LINC00460 is crucial to expound on the cellular function and molecular mechanism of human cancers. In this review, we refer to studies concerning LINC00460 and provide the basis for the evaluation of LINC00460 as a predicted biomarker or potential therapeutic target in malignancies, and also provide ideas for the future research of lncRNAs similar to LINC00460.

A computational prognostic model of lncRNA signature for clear cell renal cell carcinoma with genome instability

Long non-coding RNAs (lncRNAs) play a critical role in genomic instability and prognosis of cancer patients, but the methods to identify genomic instability-related lncRNAs have yet to be established. In the present study, to assess the prognostic value of lncRNAs associated with genomic instability in clear cell renal cell carcinoma (ccRCC).A computational framework was established based on the mutation hypothesis and combined lncRNA expression and somatic mutation profiles of the ccRCC genome. Furthermore, a prognostic model was developed using the genome instability-derived lncRNA signature GILncSig based on three lncRNA genes (LINC02471, LINC01234, and LINC00460) and verified using multiple independent patient cohorts.This study established an effective computational method to study the role of lncRNAs in genomic instability, with potential applications in identifying new genomic instability-related cancer biomarkers.

MMP25-AS1/hsa-miR-10a-5p/SERPINE1 axis as a novel prognostic biomarker associated with immune cell infiltration in KIRC

Long non-coding RNAs (lncRNAs) play a significant role in multiple human cancers as competing endogenous RNAs (ceRNAs). However, a systematic mRNA-microRNA (miRNA)-lncRNA network linked to kidney renal clear cell carcinoma (KIRC) prognosis has not been described. In this study, we aimed to identify the prognosis-related ceRNA regulatory network and analyzed its relationship with immune cell infiltration to predict KIRC patient survival. The MMP25-AS1/hsa-miR-10a-5p/SERPINE1 ceRNA network related to the prognosis of KIRC was obtained through bioinformatics analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Meanwhile, we constructed a three-gene-based survival predictor model, which could be referential for future clinical research. Methylation analyses suggested that the abnormal upregulation of the SERPINE1 likely resulted from hypomethylation. Furthermore, the immune infiltration analysis showed that the MMP25-AS1/hsa-miR-10a-5p/SERPINE1 axis could affect the changes in the tumor immune microenvironment and the development of KIRC by affecting the expression of chemokines (CCL4, CCL5, CXCL13, and XCL2). Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that the high expression of SERPINE1 might be related to tumor immune evasion in KIRC. In summary, the current study constructing the MMP25-AS1/hsa-miR-10a-5p/SERPINE1 ceRNA network might be a novel significant prognostic factor associated with the diagnosis and prognosis of KIRC.

Aberration of lncRNA LINC00460 is a Promising Prognosis Factor and Associated with Progression of Clear Cell Renal Cell Carcinoma

Purpose

Long noncoding RNAs have been studied more and more as potential prognostic markers. However, the prognostic of LINC00460 in clear cell renal cell carcinoma (ccRCC) has not been explored. In this study, the potential role of LINC00460 was investigated in ccRCC.

Patients and Methods

One hundred thirteen pairs of ccRCC tissues and para-normal tissues were collected. The expressions of LINC00460 in these tissues and ccRCC cells were evaluated via qRT-PCR. The prognostic value of LINC00460 was accessed with the use of Kaplan–Meier analysis and Cox proportional hazards model analysis. The influence of LINC00460 on ccRCC cell proliferation, migration, and invasion was determined via cell counting kit-8 (CCK-8) and Transwell assays.

Results

The results revealed that LINC00460 was significantly enhanced in ccRCC tissues, as well as in ccRCC cell lines. The overexpression of LINC00460 was significantly associated with lymph node metastasis and TNM stage, and lead to poor overall survival. Knockdown of LINC00460 reduces the cell ability of proliferation, migration, and invasion. LINC00460 could sponge to miR-149-5p.

Conclusion

LINC00460 may be developed as a prognostic biomarker and molecular therapy target for ccRCC.

LncRNAs as Therapeutic Targets and Potential Biomarkers for Lipid-Related Diseases

Lipid metabolism is an essential biological process involved in nutrient adjustment, hormone regulation, and lipid homeostasis. An irregular lifestyle and long-term nutrient overload can cause lipid-related diseases, including atherosclerosis, myocardial infarction (MI), obesity, and fatty liver diseases. Thus, novel tools for efficient diagnosis and treatment of dysfunctional lipid metabolism are urgently required. Furthermore, it is known that lncRNAs based regulation like sponging microRNAs (miRNAs) or serving as a reservoir for microRNAs play an essential role in the progression of lipid-related diseases. Accordingly, a better understanding of the regulatory roles of lncRNAs in lipid-related diseases would provide the basis for identifying potential biomarkers and therapeutic targets for lipid-related diseases. This review highlighted the latest advances on the potential biomarkers of lncRNAs in lipid-related diseases and summarised current knowledge on dysregulated lncRNAs and their potential molecular mechanisms. We have also provided novel insights into the underlying mechanisms of lncRNAs which might serve as potential biomarkers and therapeutic targets for lipid-related diseases. The information presented here may be useful for designing future studies and advancing investigations of lncRNAs as biomarkers for diagnosis, prognosis, and therapy of lipid-related diseases.

Identification of autophagy-related long non-coding RNA prognostic and immune signature for clear cell renal cell carcinoma

BACKGROUND

Studies over the past decade have shown that long non-coding RNAs (lncRNAs) play an essential role in the tumorigenesis and progression of kidney renal clear cell carcinoma (KIRC). Meanwhile, autophagy has been demonstrated to regulate KIRC pathogenesis and targeting therapy resistance. However, the prognostic value of autophagy-related lncRNAs in KIRC patients has not been reported before.

METHODS

In this study, we obtained transcriptome data of 611 KIRC cases from the TCGA database and 258 autophagy-related mRNAs from the HADb database to identify autophagy-related lncRNAs by co-expression network. A prognostic model was then established basing on these autophagy-related lncRNAs, dividing patients into high-risk and low-risk groups. Survival analysis, clinical variables dependent receiver operating characteristic (ROC) analyses, univariate/multivariate Cox analyses, and clinical correlation analysis were performed based on risk signature with R language. Gene set enrichment analysis (GSEA) was then performed to investigate the potential mechanism of the risk signature promoting KIRC progression with GSEA software. CIBERSORT algorithm was performed to assess the impact of these lncRNAs on the infiltration of immune cells.

RESULTS

A total of 17 lncRNAs were screened out and all these lncRNAs were found significantly related to KIRC patients' overall survival in subsequent survival analyses. Besides, the overall survival time in the high-risk group was much poorer than in the low-risk group. The ROC analysis revealed that the prognostic value of risk signature was better than age, gender, grade, and N stage. Univariate/multivariate analyses suggested that the risk signature was an independent predictive factor for KIRC patients. Immune and autophagy related pathways were dramatically enriched in high-risk and low-risk groups, respectively, and lncRNAs related immune cells were identified by CIBERSORT.

CONCLUSIONS

In summary, our identified 17 autophagy-related lncRNAs had prognostic value for KIRC patients which may function in immunomodulation.

Genome instability-related long non-coding RNA in clear renal cell carcinoma determined using computational biology

Background

There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear.

Method

TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. “GSVA” package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. “Survival” package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model.

Results

In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model’s genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway.

Conclusion subsections

These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08356-9.

LncRNA Gas5 regulates Fn1 deposition via Creb5 in renal fibrosis

Aim: Although studies on lncRNAs in renal fibrosis have focused on target genes and functions of lncRNAs, a comprehensive interaction analysis of lncRNAs is lacking. Materials & methods: Differentially expressed genes in renal fibrosis were screened, and the interaction between lncRNAs and miRNAs was searched. Results: We constructed a ceRNA network associated with renal fibrosis, by which we found the transcription factor Creb5, a target gene of lncRNA Gas5 that might regulate extracellular Fn1 deposition. Conclusion: Our study not only provides a theoretical basis for the ceRNA regulation mechanism of Gas5 but also provides experimental evidence supporting the use of Gas5 targeting in the treatment of renal fibrosis.

LINC00460 Is a Dual Biomarker That Acts as a Predictor for Increased Prognosis in Basal-Like Breast Cancer and Potentially Regulates Immunogenic and Differentiation-Related Genes

Breast cancer (BRCA) is a serious public health problem, as it is the most frequent malignant tumor in women worldwide. BRCA is a molecularly heterogeneous disease, particularly at gene expression (mRNAs) level. Recent evidence shows that coding RNAs represent only 34% of the total transcriptome in a human cell. The rest of the 66% of RNAs are non−coding, so we might be missing relevant biological, clinical or regulatory information. In this report, we identified two novel tumor types from TCGA with LINC00460 deregulation. We used survival analysis to demonstrate that LINC00460 expression is a marker for poor overall (OS), relapse-free (RFS) and distant metastasis-free survival (DMFS) in basal-like BRCA patients. LINC00460 expression is a potential marker for aggressive phenotypes in distinct tumors, including HPV-negative HNSC, stage IV KIRC, locally advanced lung cancer and basal-like BRCA. We show that the LINC00460 prognostic expression effect is tissue-specific, since its upregulation can predict poor OS in some tumors, but also predicts an improved clinical course in BRCA patients. We found that the LINC00460 expression is significantly enriched in the Basal-like 2 (BL2) TNBC subtype and potentially regulates the WNT differentiation pathway. LINC00460 can also modulate a plethora of immunogenic related genes in BRCA, such as SFRP5, FOSL1, IFNK, CSF2, DUSP7 and IL1A and interacts with miR-103-a-1, in-silico, which, in turn, can no longer target WNT7A. Finally, LINC00460:WNT7A ratio constitutes a composite marker for decreased OS and DMFS in Basal-like BRCA, and can predict anthracycline therapy response in ER-BRCA patients. This evidence confirms that LINC00460 is a master regulator in BRCA molecular circuits and influences clinical outcome.

Construction of a Five-Super-Enhancer-Associated-Genes Prognostic Model for Osteosarcoma Patients

Osteosarcoma is a malignant tumor most commonly arising in children and adolescents and associated with poor prognosis. In recent years, some prognostic models have been constructed to assist clinicians in the treatment of osteosarcoma. However, the prognosis and treatment of patients with osteosarcoma remain unsatisfactory. Notably, super-enhancer (SE)-associated genes strongly promote the progression of osteosarcoma. In the present study, we constructed a novel effective prognostic model using SE-associated genes from osteosarcoma. Five SE-associated genes were initially screened through the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, as well as univariate and multivariate Cox regression analyses. Meanwhile, a risk score model was constructed using the expression of these five genes. The excellent performance of the five-SE-associated-gene-based prognostic model was determined via time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves. Inferior outcome of overall survival (OS) was predicted in the high-risk group. A nomogram based on the polygenic risk score model was further established to validate the performance of the prognostic model. It showed that our prognostic model performed outstandingly in predicting 1-, 3-, and 5-year OS of patients with osteosarcoma. Meanwhile, these five genes also belonged to the hub genes associated with survival and necrosis of osteosarcoma according to the result of weighted gene co-expression network analysis based on the dataset of GSE39058. Therefore, we believe that the five-SE-associated-gene-based prognostic model established in this study can accurately predict the prognosis of patients with osteosarcoma and effectively assist clinicians in treating osteosarcoma in the future.