缺氧相关长链非编码RNA作为肝癌预后预测标志物的潜在价值

肝细胞癌(hepatocellular carcinoma,简称肝癌)是一种常见的恶性肿瘤。缺氧是肝癌等实体肿瘤的一个重要特征,同时也是诱导肿瘤恶性进展的重要因素。然而,肝癌缺氧相关的长链非编码RNA(long non-coding RNA,lncRNA)的鉴定及其在临床生存预后等方面的价值仍未得到系统的研究。本研究旨在通过肝癌转录组的整合分析鉴定肝癌缺氧相关的lncRNA,并评估其在肝癌预后中的价值。基于癌症基因组图谱(The Cancer Genome Atlas,TCGA)计划的肝癌转录组数据的整合分析,初步鉴定到233个缺氧相关的候选lncRNA。进一步筛选具有预后价值的候选者,基于其中12个缺氧相关lncRNA(AC012676.1、PRR7-AS1、AC020915.2、AC008622.2、AC026401.3、MAPKAPK5-AS1、MYG1-AS1、AC015908.3、AC009275.1、MIR210HG、CYTOR和SNHG3)建立了肝癌预后风险模型。Cox比例风险回归分析显示,基于该模型计算的缺氧风险评分作为肝癌患者新的独立预后预测指标,优于传统的临床病理因...     

Long non-coding RNA SOS1-IT1 promotes endometrial cancer progression by regulating hypoxia signaling pathway

Endometrial cancer (EC) is one of the most common types of gynecological cancer. Hypoxia is an important clinical feature and regulates various tumor processes. However, the prognostic value of hypoxia-related lncRNA in EC remains to be further elucidated. Here, we aimed to characterize the molecular features of EC by the development of a classification system based on the expression profile of hypoxia-related lncRNA. Based on univariate Cox regression analysis, we identified 17 hypoxia-related lncRNAs significantly associated with overall survival. Next, the least absolute shrinkage and selection operator Cox regression model was utilized to construct a multigene signature in the TCGA EC cohort. The risk score was confirmed as an independent predictor for overall survival in multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis. Besides, the survival time of EC patients in different risk group was significantly correlated to clinicopathologic factors, such as age, stage and grade. Furthermore, hypoxia-related lncRNA associated with the high-risk group were involved in various aspects of the malignant progression of EC via Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Gene Set Enrichment Analysis. Moreover, the risk score was closely correlated to immunotherapy response, microsatellite instability and tumor mutation burden. Finally, we select one hypoxia-related lncRNA SOS1-IT1 to validate its role in hypoxia and EC progression. Interestingly, we found SOS1-IT1 was overexpressed in tumor tissues, and closely correlated with clinicopathological parameters of EC. The expression level of SOS1-IT1 was significantly increased under hypoxia condition. Additionally, the important hypoxia regulatory factor HIF-1α can directly bind SOS1-IT1 promoter region, and affect its expression level. In summary, this study established a new EC classification based on the hypoxia-related lncRNA signature, thereby provide a novel sight to understand the potential mechanism of human EC development.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00651-1.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.     

METTL14 promotes tumorigenesis by regulating lncRNA OIP5-AS1/miR-98/ADAMTS8 signaling in papillary thyroid cancer

BackgroundPapillary thyroid cancer (PTC) is the most common type of cancer of the endocrine system. Long noncoding RNAs (lncRNAs) are emerging as a novel class of gene expression regulators associated with tumorigenesis. Through preexisting databases available for differentially expressed lncRNAs in PTC, we uncovered that lncRNA OIP5-AS1 was significantly upregulated in PTC tissues. However, the function and the underlying mechanism of OIP5-AS1 in PTC are poorly understood.MethodsExpression of lncRNA OIP5-AS1 and miR-98 in PTC tissue and cells were measured by quantitative real-time PCR (qRT-PCR). And expression of METTL14 and ADAMTS8 in PTC tissue and cells were measured by qRT-PCR and western blot. The biological functions of METTL14, OIP5-AS1, and ADAMTS8 were examined using MTT, colony formation, transwell, and wound healing assays in PTC cells. The relationship between METTL14 and OIP5-AS1 were evaluated using RNA immunoprecipitation (RIP) and RNA pull down assay. And the relationship between miR-98 and ADAMTS8 were examined by luciferase reporter assay. For in vivo experiments, a xenograft model was used to investigate the effects of OIP5-AS1 and ADAMTS8 in PTC.ResultsFunctional validation revealed that OIP5-AS1 overexpression promotes PTC cell proliferation, migration/invasion in vitro and in vivo, while OIP5-AS1 knockdown shows an opposite effect. Mechanistically, OIP5-AS1 acts as a target of miR-98, which activates ADAMTS8. OIP5-AS1 promotes PTC cell progression through miR-98/ADAMTS8 and EGFR, MEK/ERK pathways. Furthermore, RIP and RNA pull down assays identified OIP5-AS1 as the downstream target of METTL14. Overexpression of METTL14 suppresses PTC cell proliferation and migration/invasion through inhibiting OIP5-AS1 expression and regulating EGFR, MEK/ERK pathways.ConclusionsCollectively, our findings demonstrate that OIP5-AS1 is a METTL14-regulated lncRNA that plays an important role in PTC progression and offers new insights into the regulatory mechanisms underlying PTC development.Subject terms: Tumour biomarkers, Oncogenes     

Hypoxia-related LncRNAs signature predicts prognosis and is associated with immune infiltration and progress of head and neck squamous cell carcinoma

BackgroundDisclosing prognostic information is necessary to enable good treatment selection and improve patient outcomes. Previous studies suggest that hypoxia is associated with an adverse prognosis in patients with HNSCC and that long non-coding RNAs (lncRNAs) show functions in hypoxia-associated cancer biology. Nevertheless, the understanding of lncRNAs in hypoxia related HNSCC progression remains confusing.MethodsData were downloaded from TCGA and GEO database. Bioinformatic tools including R packages GEOquery, limma, pheatmap, ggplot2, clusterProfiler, survivalROC and survcomp and LASSO cox analysis were utilized. Si-RNA transfection, CCK8 and real-time quantified PCR were used in functional study.ResultsGEO data (GSE182734) revealed that lncRNA regulation may be important in hypoxia related response of HNSCC cell lines. Further analysis in TCGA data identified 314 HRLs via coexpression analysis between differentially expressed lncRNAs and hypoxia-related mRNAs. 23 HRLs were selected to build the prognosis predicting model using lasso Cox regression analyses. Our model showed excellent performance in predicting survival outcomes among patients with HNSCC in both the training and validation sets. We also found that the risk scores were related to tumor stage and to tumor immune infiltration. Moreover, LINC01116 were selected as a functional study target. The knockdown of LINC01116 significantly inhibited the proliferation of HNSCC cells and effected the hypoxia induced immune and the NF-κB/AKT signaling.ConclusionsData analysis of large cohorts and functional experimental validation in our study suggest that hypoxia related lncRNAs play an important role in the progression of HNSCC, and its expression model can be used for prognostic prediction.     

An eight-long noncoding RNA expression signature for colorectal cancer patients’ prognosis

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.     

An Immune-Related Six-lncRNA Signature to Improve Prognosis Prediction of Glioblastoma Multiforme

Recent studies have demonstrated the utility and superiority of long non-coding RNAs (lncRNAs) as novel biomarkers for cancer diagnosis, prognosis, and therapy. In the present study, the prognostic value of lncRNAs in glioblastoma multiforme was systematically investigated by performing a genome-wide analysis of lncRNA expression profiles in 419 glioblastoma patients from The Cancer Genome Atlas (TCGA) project. Using survival analysis and Cox regression model, we identified a set of six lncRNAs (AC005013.5, UBE2R2-AS1, ENTPD1-AS1, RP11-89C21.2, AC073115.6, and XLOC_004803) demonstrating an ability to stratify patients into high- and low-risk groups with significantly different survival (median 0.899 vs. 1.611 years, p = 3.87e?09, log-rank test) in the training cohort. The six-lncRNA signature was successfully validated on independent test cohort of 219 patients with glioblastoma, and it revealed superior performance for risk stratification with respect to existing lncRNA-related signatures. Multivariate Cox and stratification analysis indicated that the six-lncRNA signature was an independent prognostic factor after adjusting for other clinical covariates. Further in silico functional analysis suggested that the six-lncRNA signature may be involved in the immune-related biological processes and pathways which are very well known in the context of glioblastoma tumorigenesis. The identified lncRNA signature had important clinical implication for improving outcome prediction and guiding the tailored therapy for glioblastoma patients with further prospective validation.     

A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non-coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy-related lncRNA in lung adenocarcinoma. In this study, autophagy-related mRNAs-lncRNAs were screened from lung adenocarcinoma and a co-expression network of autophagy-related mRNAs-lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy-related lncRNAs and finally obtained a survival model composed of 11 autophagy-related lncRNAs. Through Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and time-dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low-risk and high-risk groups. These 11 lncRNAs were GAS6-AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA-DQB1-AS1, LINC01116, LINC01806, FAM83A-AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196-1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149-1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy-related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy-related therapeutic targets.     

The lncRNA ELF3-AS1 promotes bladder cancer progression by interaction with Krüppel-like factor 8

Accumulating evidence has shown the critical role of long non-coding RNAs (lncRNAs) during cancer progression. However, the involvement of ELF3-AS1 in bladder cancer (BC) remains largely unclear. By lncRNA profiling, we identified ELF3-AS1 as a novel oncogenic lncRNA during bladder cancer development. ELF3-AS1 was highly expressed in bladder cancer and correlated with poor prognosis. ELF3-AS1 could increase viability and migration of bladder cancer cells in vitro and promoted xenograft tumor growth in vivo. Furthermore, ELF3-AS1 could interact with KLF8 to stabilize KLF8 by protecting it from proteasome-mediated degradation. KLF8 in turn could bind ELF3-AS1 promoter and transactivate ELF3-AS1 expression. The positive feedback loop between ELF3-AS1 and KLF8 enhanced KLF8 signaling by increasing MMP9 expression. Collectively, our study has unraveled a novel mechanism of ELF3-AS1-mediated oncogenesis in bladder cancer by reinforcement of ELF3-AS1/KLF8 signaling with potential implications for therapeutic intervention.     

Long non-coding RNA OIP5-AS1 promotes pancreatic cancer cell growth through sponging miR-342-3p via AKT/ERK signaling pathway

Opa-interacting protein 5 antisense RNA 1 (OIP5-AS1), a long non-coding RNA (lncRNA), has been reported to link with the progression of some cancers. However, its biological functions and underlying molecular mechanisms in pancreatic cancer are largely unknown. The aim of this study was to investigate the role of lncRNA OIP5-AS1 in pancreatic cancer. Quantitative real-time PCR analysis revealed that OIP5-AS1 is highly expressed in pancreatic cancer tissues versus adjacent non-tumor tissues. In vitro functional assays showed that downregulation of OIP5-AS1 or overexpression of miR-342-3p inhibited the proliferation, decreased Ki67 expression, and induced cell cycle arrest in pancreatic cancer cells. The expression of cyclinD1, CDK4, and CDK6 was decreased by knockdown of OIP5-AS1. Moreover, we found that OIP5-AS1 acted as a miR-342-3p sponge to suppress its expression and function. Dual-luciferase assay confirmed the interaction of OIP5-AS1 and miR-342-3p and verified anterior gradient 2 (AGR2) as a direct target of miR-342-3p. Results showed that depletion of miR-342-3p abolished the inhibitory effects of OIP5-AS1 knockdown on pancreatic cancer cell growth. The expression of Ki67, AGR2, cyclinD1, CDK4, CDK6, p-AKT, and p-ERK1/2 was reversed by silencing of miR-342-3p in pancreatic cancer cells with OIP5-AS1 knockdown. Further, knockdown of OIP5-AS1 suppressed tumor growth in a xenograft mouse model of pancreatic cancer. OIP5-AS1 induced pancreatic cancer progression via activation of AKT and ERK signaling pathways. Therefore, we demonstrate that OIP5-AS1 functions as oncogene in pancreatic cancer and its downregulation inhibits pancreatic cancer growth by sponging miR-342-3p via targeting AGR2 through inhibiting AKT/ERK signaling pathway.

     

Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer

Currently, traditional predictors of prognosis (tumor size, nodal status, progesterone receptor [PR], estrogen receptor [ER], or human epidermal growth factor receptor-2 [HER2]) are insufficient for precise survival prediction for triple-negative breast cancer (TNBC). Long noncoding RNAs (lncRNAs) have been observed to exert critical functions in cancer, including in TNBC. Nevertheless, systematically tracking expression-based lncRNA biomarkers based on the sequence data for the prediction of prognosis in TNBC has not yet been investigated. To ascertain whether biomarkers exist that can distinguish TNBC from adjacent normal tissue or nTNBC, we implemented a comprehensive analysis of lncRNA expression profiles and clinical data of 1097 BC samples from The Cancer Genome Atlas database. A total of 1510 differentially expressed lncRNAs in normal and TNBC samples were extracted. Similarly, 672 differentially expressed lncRNAs between nTNBC and TNBC samples were detected. The receiver operating characteristic curve analysis indicated that three upregulated lncRNAs (AC091043.1, AP000924.1, and FOXCUT) may be of strong diagnostic value for predicting the existence of TNBC in the training and validation sets (area under the curve (AUC > 0.85). Kaplan-Meier analysis demonstrated that the other three lncRNAs (AC010343.3, AL354793.1, and FGF10-AS1) were associated with the prognosis of TNBC patients (P < 0.05). We used the three overall survival (OS)-related lncRNAs to establish a three-lncRNA signature. Multivariate Cox regression analysis suggested that the three-lncRNA signature was a prognostic factor independent of other clinical variables ( P < 0.01) for predicting OS in TNBC patients that could be utilized to classify patients into high- or low-risk subgroups. Our results might provide efficient signatures for clinical diagnosis and prognostic evaluation of TNBC.     

Cell-free long non-coding RNAs (LY86-AS1 & HCG27_201and GAS5) as biomarkers for pre-diabetes and type 2 DM in Egypt

BackgroundIncreasing interest has been focused on lncRNAs as potential markers in the pathogenesis and progression of numerous diseases.AimWe aimed to investigate the expression pattern and role of cell-free lncRNAs (GAS5, HCG27_201 and LY86-AS1) in pre-diabetic, diabetic and T2DM groups.Subjects & methodsQuantification of the expression level of cell-free lncRNAs (GAS5, HCG27_201 and LY86-AS1) was performed by real-time PCR in 210 individuals classified in diabetic (T2DM), pre-diabetic and control groups.ResultsSignificant differences were observed in the relative expression level of lncRNAs (GAS5, LY86-AS1 and HCG27_201) among the three studied groups. The LncRNA expression levels decreased gradually from the control to the pre-diabetic group and reached the lowest values in the T2DM group. The A receiver operating characteristic curve (ROC) was applied to identify a cut-off value for each of the three genes among our groups. The three lncRNAs showed promising results in discriminating between the diabetic patients and controls, with HCG27_201 gene expression having the best performance. Furthermore, lncRNA expression was able to predict the future development of DM in the pre-diabetics because ROC analysis among diabetics and pre-diabetics revealed considerable results. GAS5 gene expression showed the best performance. Additionally, HCG27_201 expression was the most valuable biomarker for differentiating between pre-diabetics and controls and presented a sensitivity of 91% and specificity of 64%.ConclusionsWe concluded that cell free lncRNAs (GAS5, LY86-AS1 and HCG27_201) could be considered promising diagnostic and predictive biomarkers for DM and that HCG27_201 could act as a potential diagnostic biomarker for pre-diabetes.     

An innovative prognostic model based on autophagy-related long noncoding RNA signature for low-grade glioma

Autophagy is a highly conserved lysosomal degradation process essential in tumorigenesis. However, the involvement of autophagy-related long noncoding RNAs (lncRNAs) in low-grade glioma (LGG) remains unclear. In this study, we established an autophagy-related lncRNA prognostic signature for patients with LGG and assess its underlying functions. We used univariate Cox, least absolute shrinkage and selection operator and multivariate Cox regression models to establish an autophagy-related lncRNA prognostic signature. Kaplan–Meier survival analysis, receiver operating characteristic curve, nomogram, C-index, calibration curve and clinical decision-making curve were used to assess the predictive capability of the identified signature. A signature comprising nine autophagy-related lncRNAs (AL136964.1, ARHGEF26-AS1, PCED1B-AS1, AS104072.1, PRKCQ-AS1, LINC00957, AS125616.1, PSMB8-AS1 and AC087741.1) was identified as a prognostic model. Patients with LGG were divided into the high- and low-risk cohorts based on the median model-based risk score. The survival analysis revealed a 10-year survival rate of 9.3% (95% CI 1.91–45.3%) and 13.48% (95% CI 4.52–40.2%) in high-risk patients in the training and validation sets, respectively, and 48.4% (95% CI 24.7–95.0%) and 48.4% (95% CI 28.04–83.4%) in low-risk patients in the training and validation sets, respectively. This finding suggested a relatively low survival in high-risk patients. In addition, the lncRNA signature was independently prognostic and potentially associated with the progression of LGG. Therefore, the 9-autophagy-related-lncRNA signature may play a crucial role in the diagnosis and treatment of LGG, which may offer new avenues for tumour-targeted therapy.

     

SBF2-AS1: An oncogenic lncRNA in small-cell lung cancer

The long noncoding RNAs (lncRNAs) SBF2 antisense RNA 1 (SBF2-AS1) was found to act as an oncogenic lncRNA in non–small-cell lung cancer (NSCLC), but the role of SBF2-AS1 in small-cell lung cancer (SCLC) was still unclear. The purpose of this study was to provide the clinical significance and biological function of SBF2-AS1 in SCLC. In our results, SBF2-AS1 was found to be upregulated in SCLC tissues compared with NSCLC tissues or adjacent normal lung tissues. Besides, SBF2-AS1 expression was also elevated in SCLC cell lines compared with the normal bronchial epithelial cell line or NSCLC lines. Moreover, high expression of SBF2-AS1 was associated with clinical stage, tumor size, lymph node metastasis and distant metastasis in SCLC patients. Survival analysis showed SCLC patients with high expression of SBF2-AS1 had shorter overall survival than patients with low expression of SBF2-AS1, and high expression of SBF2-AS1 acted as an independent poor prognostic factor for overall survival in SCLC patients. The study in vitro suggested inhibition of SBF2-AS1 obviously depressed cell proliferation, migration, and invasion in SCLC. In conclusion, SBF2-AS1 acts as a novel oncogenic lncRNA in SCLC.     

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.     

Analysis of competing endogenous RNA network identifies a poorly differentiated cancer-specific RNA signature for hepatocellular carcinoma

Plenty of evidence has suggested that long noncoding RNAs (lncRNAs) play a vital role in competing endogenous RNA (ceRNA) networks. Poorly differentiated hepatocellular carcinoma (PDHCC) is a malignant phenotype. This paper aimed to explore the effect and the underlying regulatory mechanism of lncRNAs on PDHCC as a kind of ceRNA. Additionally, prognosis prediction was assessed. A total of 943 messenger RNAs (mRNAs), 86 miRNAs, and 468 lncRNAs that were differentially expressed between 137 PDHCCs and 235 well-differentiated HCCs were identified. Thereafter, a ceRNA network related to the dysregulated lncRNAs was established according to bioinformatic analysis and included 29 lncRNAs, 9 miRNAs, and 96 mRNAs. RNA-related overall survival (OS) curves were determined using the Kaplan-Meier method. The lncRNA ARHGEF7-AS2 was markedly correlated with OS in HCC (P = .041). Moreover, Cox regression analysis revealed that patients with low ARHGEF7-AS2 expression were associated with notably shorter survival time (P = .038). In addition, the area under the curve values of the lncRNA signature for 1-, 3-, and 5-year survival were 0.806, 0.741, and 0.701, respectively. Furthermore, a lncRNA nomogram was established, and the C-index of the internal validation was 0.717. In vitro experiments were performed to demonstrate that silencing ARHGEF7-AS2 expression significantly promoted HCC cell proliferation and migration. Taken together, our findings shed more light on the ceRNA network related to lncRNAs in PDHCC, and ARHGEF7-AS2 may be used as an independent biomarker to predict the prognosis of HCC.