Four long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Development and validation of a combined cuproptosis and immunogenic cell death prognostic model for diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma worldwide with a high degree of heterogeneity. Cuproptosis and immunogenic cell death (ICD) have been considered to be vital for tumor progression. However, current understanding of cuproptosis and immunogenic cell death in DLBCL is still very limited. We aim to explore a prognostic model combining cuproptosis and immunogenic cell death in DLBCL.

METHODS

Pearson's correlation analysis was utilized to acquire lncRNAs associated with cuproptosis and immunogenic cell death. Prognostic biomarker identification and model construction involved the use of univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox regression. We assessed the predictive capability of the risk model by conducting Kaplan-Meier analysis and time-dependent ROC analysis. The analysis and comparison of immune infiltration and drug sensitivity were conducted in this study. Moreover, RT-qPCR was employed to validate the expression of lncRNAs associated with cuproptosis and immunogenic cell death in DLBCL cell lines.

RESULTS

We identified 4 prognosis-related lncRNAs (ANKRD10-IT1, HOXB-AS1, LINC00520 and LINC01165) that were correlated with cuproptosis and immunogenic cell death. The model was verified to have a good and independent predictive ability in the prognostic prediction of DLBCL patients. Moreover, significant difference was observed in immune infiltration and drug sensitivity between high- and low-risk groups.

CONCLUSION

Our discoveries could enhance the comprehension of the role of cuproptosis and ICD in DLBCL, potentially offering novel viewpoints and knowledge for personalized and precise treatment of DLBCL individuals.

A novel autophagy-related long non-coding RNAs prognostic risk score for clear cell renal cell carcinoma

BACKGROUND

As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.

METHODS

The RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores.

RESULTS

A total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC.

CONCLUSIONS

We established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.

Identification of a novel prognostic signature for HCC and analysis of costimulatory molecule-related lncRNA AC099850.3

Costimulatory molecules are involved in initiation of anti-tumor immune responses while long non‐coding RNAs (lncRNAs) regulate the development of various cancers. However, the roles of lncRNA in hepatocellular carcinoma (HCC) have not been fully established. In this study, we aimed at identifying lncRNAs-related costimulatory molecules in HCC and to construct a prognostic signature for predicting the clinical outcomes for HCC patients. Data were downloaded from The Cancer Genome Atlas database for bioinformatics analyses. Costimulatory molecules were obtained from published literature. The R software, SPSS, and GraphPad Prism were used for statistical analyses. A risk model that is based on five costimulatory molecule-related lncRNAs was constructed using lasso and Cox regression analyses. Multivariate regression analysis revealed that the risk score could predict the prognostic outcomes for HCC. Samples in high- and low-risk groups exhibited significant differences in gene set enrichment and immune infiltration levels. Through colony formation and CCK8 assays, we found that AC099850.3 was strongly associated with HCC cell proliferation. We identified and validated a novel costimulatory molecule-related survival model. In addition, AC099850.3 was found to be closely associated with clinical stages and proliferation of HCC cells, making it a potential target for HCC treatment.

Aberrant lncRNA Profiles Are Associated With Chronic Benzene Poisoning and Acute Myelocytic Leukemia

OBJECTIVE

This study investigates the mechanisms of benzene hematotoxicity.

METHODS

We used microarray to detect expression profiles of long non-coding RNAs (lncRNAs) and mRNAs in peripheral lymphocytes from chronic benzene poisoning, acute myelocytic leukemia, and healthy controls. The lncRNAs and mRNAs were validated using real-time quantitative PCR (RT-qPCR). Cytokinesis-block micronucleus assay was used to analyze chromosomal aberration.

RESULTS

We found 173 upregulated and 258 downregulated lncRNAs, and 695 upregulated and 804 downregulated mRNAs. The lncRNA CUST_40243 and mRNA PDGFC and CDKN1A associated with chronic benzene poisoning. Relevant inflammatory response, hematopoietic cell lineage, and cell cycle may be important pathways for the sifted lncRNAs and mRNAs. Furthermore, micronuclei frequency was significantly higher in off-post chronic benzene poisoning patients.

CONCLUSIONS

Chromosomal aberration induced by benzene exposure is irreversible. The lncRNA CUST_40243 and mRNA PDGFC and CDKN1A are related to chronic benzene poisoning.

A ten N6-methyladenosine-related long non-coding RNAs signature predicts prognosis of triple-negative breast cancer

Background

Patients with triple‐negative breast cancer (TNBC) face a major challenge of the poor prognosis, and N6‐methyladenosine‐(m6A) mediated regulation in cancer has been proposed. Therefore, this study aimed to explore the prognostic roles of m6A‐related long non‐coding RNAs (LncRNAs) in TNBC.

Methods

Clinical information and expression data of TNBC samples were collected from TCGA and GEO databases. Pearson correlation, univariate, and multivariate Cox regression analysis were employed to identify independent prognostic m6A‐related LncRNAs to construct the prognostic score (PS) risk model. Receiver operating characteristic (ROC) curve was used to evaluate the performance of PS risk model. A competing endogenous RNA (ceRNA) network was established for the functional analysis on targeted mRNAs.

Results

We identified 10 independent prognostic m6A‐related LncRNAs (SAMD12‐AS1, BVES‐AS1, LINC00593, MIR205HG, LINC00571, ANKRD10‐IT1, CIRBP‐AS1, SUCLG2‐AS1, BLACAT1, and HOXB‐AS1) and established a PS risk model accordingly. Relevant results suggested that TNBC patients with lower PS had better overall survival status, and ROC curves proved that the PS model had better prognostic abilities with the AUC of 0.997 and 0.864 in TCGA and GSE76250 datasets, respectively. Recurrence and PS model status were defined as independent prognostic factors of TNBC. These ten LncRNAs were all differentially expressed in high‐risk TNBC compared with controls. The ceRNA network revealed the regulatory axes for nine key LncRNAs, and mRNAs in the network were identified to function in pathways of cell communication, signaling transduction and cancer.

Conclusion

Our findings proposed a ten‐m6A‐related LncRNAs as potential biomarkers to predict the prognostic risk of TNBC.

Long noncoding RNA ZFPM2-AS1 acts as a miRNA sponge and promotes cell invasion through regulation of miR-139/GDF10 in hepatocellular carcinoma

Background

Emerging evidence has shown that dysregulated expression of long noncoding RNAs (lncRNAs) is implicated in liver hepatocellular carcinoma (HCC). However, the role and molecular mechanism of differentially expressed lncRNAs in HCC has not been fully explained.

Methods

The expression profiles of lncRNAs in HCC samples were derived from microarrays analysis or downloaded from The Cancer Genome Atlas (TCGA), and their correlation with prognosis and clinical characteristics were further analyzed. Silencing of lncRNA ZFPM2-AS1 was conducted to assess the effect of ZFPM2-AS1 in vitro. The miRcode and Target Scan databases were used to determine the lncRNA-miRNA-mRNA interactions. The biological functions were demonstrated by luciferase reporter assay, western blotting, PCR and rescue experiments.

Results

The expression level of lncRNA ZFPM2-AS1 was significantly higher in HCC tissues than in adjacent normal tissues, and higher ZFPM2-AS1 was remarkably related to poor survival. Functionally, silencing of lncRNA ZFPM2-AS1 inhibited cell proliferation, migration, invasion and promoted cell apoptosis in vitro. Bioinformatics analysis based on the miRcode and TargetScan databases showed that lncRNA ZFPM2-AS1 regulated GDF10 expression by competitively binding to miR-139. miR-139 and downregulated GDF10 reversed cell phenotypes caused by lncRNA ZFPM2-AS1 by rescue analysis.

Conclusions

ZFPM2-AS1, an upregulated lncRNA in HCC, was associated with malignant tumor phenotypes and worse patient survival. ZFPM2-AS1 regulated the progression of HCC by acting as a competing endogenous RNA (ceRNA) to competitively bind to miR-139 and regulate GDF10 expression. Our study provides new insight into the posttranscriptional regulation mechanism of lncRNA ZFPM2-AS1 and suggests that ZFPM2-AS1/miR-139/GDF10 may act as a potential therapeutic target and prognostic biomarker for HCC.

A novel lncRNA PLK4 up-regulated by talazoparib represses hepatocellular carcinoma progression by promoting YAP-mediated cell senescence

A growing number of studies recognize that long non‐coding RNAs (lncRNAs) are essential to mediate multiple tumorigenic processes, including hepatic tumorigenesis. However, the pathological mechanism of lncRNA‐regulated liver cancer cell growth remains poorly understood. In this study, we identified a novel function lncRNA, named polo‐like kinase 4 associated lncRNA (lncRNA PLK4, GenBank Accession No. RP11‐50D9.3), whose expression was dramatically down‐regulated in hepatocellular carcinoma (HCC) tissues and cells. Interestingly, talazoparib, a novel and highly potent poly‐ADP‐ribose polymerase 1/2 (PARP1/2) inhibitor, could increase lncRNA PLK4 expression in HepG2 cells. Importantly, we showed that talazoparib‐induced lncRNA PLK4 could function as a tumour suppressor gene by Yes‐associated protein (YAP) inactivation and induction of cellular senescence to inhibit liver cancer cell viability and growth. In summary, our findings reveal the molecular mechanism of talazoparib‐induced anti‐tumor effect, and suggest a potential clinical use of talazoparib‐targeted lncRNA PLK4/YAP‐dependent cellular senescence for the treatment of HCC.

RNA sequencing reveals the long noncoding RNA and mRNA profiles and identifies long non-coding RNA TSPAN12 as a potential microvascular invasion-related biomarker in hepatocellular carcinoma

Emerging evidence demonstrates that abnormally expressed long noncoding RNAs (lncRNAs) are involved in the progression of various cancers. However, the expression profiles and functions of lncRNAs in hepatocellular carcinoma (HCC) with microvascular invasion (MVI) remain largely unknown. In this study, we revealed the differential expression profiles of lncRNA and messenger RNA in four pairs of HCC with MVI and adjacent nontumor liver tissues by using high-throughput RNA sequencing. Among these dysregulated lncRNAs, lnc-TSPAN12 was the most significantly upregulated lncRNA in HCC. The results of real time-PCR showed that lnc-TSPAN12 was highly expressed in HCC, including HCC with MVI, and its high expression was associated with unfavorable clinicopathological features and poor prognosis. Moreover, multivariate Cox regression analysis verified that lnc-TSPAN12 was an independent prognostic predictor for overall and recurrence-free survival. Receiver operating characteristic curve analysis indicated that lnc-TSPAN12 could serve as a potential diagnostic biomarker for HCC with MVI. In addition, a loss-of-function experiment demonstrated that lnc-TSPAN12 knockdown inhibited HCC cell migration and invasion in vitro. Our findings suggest that lnc-TSPAN12 may function as an oncogene in HCC progression and could serve as a novel diagnostic/prognostic biomarker and potential therapeutic target for HCC with MVI.

Integrative analysis of dysregulated lncRNA-associated ceRNA network reveals potential lncRNA biomarkers for human hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is an aggressive cancer with a poor prognosis and a high incidence. The molecular changes and novel biomarkers of HCC need to be identified to improve the diagnosis and prognosis of this disease. We investigated the current research concentrations of HCC and identified the transcriptomics-related biomarkers of HCC from The Cancer Genome Atlas (TGCA) database.

Methods

We investigated the current research concentrations of HCC using literature metrology analysis for studies conducted from 2008 to 2018. We identified long noncoding RNAs (lncRNAs) that correlated with the clinical features and survival prognoses of HCC from The Cancer Genome Atlas (TGCA) database. Differentially expressed genes (lncRNAs, miRNAs, and mRNAs) were also identified by TCGA datasets in HCC tumor tissues. A lncRNA competitive endogenous RNA (ceRNA) network was constructed from lncRNAs based on intersected lncRNAs. Survival times and the association between the expression levels of the key lncRNAs of the ceRNA network and the clinicopathological characteristics of HCC patients were analyzed using TCGA. Real-time polymerase chain reaction (qRT-PCR) was used to validate the reliability of the results in tissue samples from 20 newly-diagnosed HCC patients.

Results

Analysis of the literature pertaining to HCC research revealed that current research is focused on lncRNA functions in tumorigenesis and tumor development. A total of 128 HCC dysregulated lncRNAs were identified; 66 were included in the co-expressed ceRNA network. We analyzed survival times and the associations between the expression of 66 key lncRNAs and the clinicopathological features of the HCC patients identified from TCGA. Twenty-six lncRNAs were associated with clinical features of HCC (P < 0.05) and six key lncRNAs were associated with survival time (log-rank test P < 0.05). Six key lncRNAs were selected for the validation of their expression levels in 20 patients with newly diagnosed HCC using qRT-PCR. Consistent fold changes in the trends of up and down regulation between qRT-PCR validation and TCGA proved the reliability of our bioinformatics analysis.

Conclusions

We used integrative bioinformatics analysis of the TCGA datasets to improve our understanding of the regulatory mechanisms involved with the functional features of lncRNAs in HCC. The results revealed that lncRNAs are potential diagnostic and prognostic biomarkers of HCC.

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma

Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC.

Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan–Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases.

Results: The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model.

Conclusions: Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value.

A five lncRNA signature for prognosis prediction in hepatocellular carcinoma

This study was conducted to screen prognosis-associated long-noncoding RNAs (lncRNAs) and a prognosis assessment model in hepatocellular carcinoma (HCC). lncRNA- and mRNA-sequencing data of early-stage HCC samples were downloaded from The Cancer Genome Atlas database. The samples were divided into training set and validation set. Differentially expressed lncRNAs (DELs) between poor prognosis and good prognosis samples were screened with DEseq and edgeR. Cox regression analysis was conducted to identify prognosis-associated lncRNAs in the training set. A prognosis risk assessment model was established to calculate the risk score for each patient in the training set, and the prognosis prediction function was tested and validated in the validation dataset. The connection between the risk assessment model and clinical features was analyzed. A co-expression network between lncRNAs and corresponding genes was constructed, and functional enrichment was performed for these genes. A total of 81 DELs were screened between poor and good prognosis samples in the training set, and 43 prognosis-associated lncRNAs were observed. Of these DELs, five were used to construct the risk assessment model (RP11-325L7.2, DKFZP434L187, RP11-100L22.4, DLX2-AS1 and RP11-104L21.3). Low-risk samples exhibited longer survival time compared with the high-risk samples. The five lncRNAs exhibited significant differences in expression levels between different prognosis groups. Risk score was an independent prognostic factor for HCC. In the entire set, the low-risk group demonstrated significantly better prognosis compared with the high-risk group, even across all age, sex and alcohol consumption subgroups. ‘Nucleoside-triphosphatase regulator activity’, ‘GTPase regulator activity’, ‘enzyme binding’, ‘peroxisome proliferator-activated receptor signaling pathway’ and ‘fatty acid metabolism’ were the most significantly enriched functional terms. The signature lncRNAs screened in this study may have constitute novel strategies and biomarkers that predict the prognosis of HCC, and these may also contribute to a deeper understanding of the mechanisms underlying HCC development.