A novel seven-lncRNA signature for prognosis prediction in hepatocellular carcinoma

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Featured lncRNA-based signature for discriminating prognosis and progression of hepatocellular carcinoma

Long non-coding RNAs (lncRNAs) have been implicated in carcinogenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify a robust lncRNA signature for predicting the survival of HCC patients. We performed an integrated analysis of the lncRNA expression profiling in The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma database to identify the prognosis-related lncRNA for the HCC. The HCC cohort was randomly divided into a training set (n = 250) and a testing set (n = 113). Following a two-step screening, we identified an 18-lncRNA signature risk score. The high-risk subgroups had significantly shorter survival time than the low-risk group in both the training set (P < 0.0001) and the testing set (P = 0.005). Stratification analysis revealed that the prognostic value of the lncRNA-based signature was independent of the tumor stage and pathologic stage. The area under the receiver operating characteristic curve (AUROC) of the 18-lncRNA signature risk score was 0.826 (95%CI, 0.764-0.888), 0.817 (95%CI, 0.759-0.876), and 0.799 (95%CI, 0.731-0.867) for 1-year, 3-year, and 5-year follow-up, respectively. Bioinformatics analyses indicated that the 18 lncRNA might mediate cell cycle, DNA replication processes, and canonical cancer-related pathways, in which MCM3AP-AS1 was a potential target for HCC. In conclusion, the 18-lncRNA signature was a robust predictive biomarker for the prognosis and progression of HCC.

A nomogram based on 4-lncRNAs signature for improving prognostic prediction of hepatocellular carcinoma

PURPOSE

Long noncoding RNAs (lncRNAs) with abnormal expression are frequently seen in hepatocellular cancer patients (HCC). Previous studies have reported the correlation between lncRNA and prognosis processes of HCC patients. In this research, a graphical nomogram with lncRNAs signatures, T, M phases was developed using the rms R package to estimate the survival rates of HCC patients in year 1, 3, and 5.

METHODS

To find the prognostic lncRNA and create the lncRNA signatures, univariate Cox survival analysis and multivariate Cox regression analysis were chosen. The rms R software package was used to build a graphical nomogram based on lncRNAs signatures to predict the survival rates in of HCC patients in 1, 3, and 5 years. Using "edgeR", "DEseq" R packages to find the differentially expressed genes (DEGs).

RESULTS

Firstly, a total of 5581 DEGs including 1526 lncRNAs and 3109 mRNAs were identified through bioinformatic analysis, of which 4 lncRNAs (LINC00578, RP11-298O21.2, RP11-383H13.1, RP11-440G9.1) were identified to be strongly related to the prognosis of liver cancer (P < 0.05). Moreover, we constructed a 4-lncRNAs signature by using the calculated regression coefficient. 4-lncRNAs signature is identified to significantly correlated with clinical and pathological characteristics (such as T stage, and death status of HCC patients).

CONCLUSIONS

A prognostic nomogram on the base of 4-lncRNAs markers was built, which is capable to accurately predict the 1-year, 3-year, and 5-year survival of HCC patients after the construction of the 4-lncRNAs signature linked with prognosis of HCC.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Intra-tumor heterogeneity and prognostic risk signature for hepatocellular carcinoma based on single-cell analysis

Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the "Enricher" function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.

Regulatory Roles of Noncoding RNAs in the Progression of Gastrointestinal Cancers and Health Disparities

Annually, more than a million individuals are diagnosed with gastrointestinal (GI) cancers worldwide. With the advancements in radio- and chemotherapy and surgery, the survival rates for GI cancer patients have improved in recent years. However, the prognosis for advanced-stage GI cancers remains poor. Site-specific GI cancers share a few common risk factors; however, they are largely distinct in their etiologies and descriptive epidemiologic profiles. A large number of mutations or copy number changes associated with carcinogenesis are commonly found in noncoding DNA regions, which transcribe several noncoding RNAs (ncRNAs) that are implicated to regulate cancer initiation, metastasis, and drug resistance. In this review, we summarize the regulatory functions of ncRNAs in GI cancer development, progression, chemoresistance, and health disparities. We also highlight the potential roles of ncRNAs as therapeutic targets and biomarkers, mainly focusing on their ethnicity-/race-specific prognostic value, and discuss the prospects of genome-wide association studies (GWAS) to investigate the contribution of ncRNAs in GI tumorigenesis.

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Background

Accumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC.

Methods

Expression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs.

Results

Two robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients (P < 0.001). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I–II) and late (III–IV) HCC detection were 0.88 and 0.93, respectively.

Conclusions

The two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.

A Panel of E2F Target Gene Signature Predicting the Prognosis of Hepatocellular Carcinoma

Hepatocellular carcinoma is one of the most malignant tumors, and the therapeutic effects of traditional treatments are poor. It is urgent to explore and identify new biomarkers and therapeutic targets to develop novel treatments which are individualized and effective. Three hallmarks, including E2F targets, G2M checkpoint and DNA repair, were collected by GSEA analysis. The panel of E2F-related gene signature consisted of five genes: HN1, KIF4A, CDCA3, CDCA8 and SSRP1. They had various mutation rates ranging from 0.8 to 5% in hepatocellular carcinoma, and patients with gene mutation had poorer prognosis. Among these genes, HN1 has the greatest mutation rate, and SSRP1 has the greatest impact on the model with a B (COX) value of 0.8842. Patients with higher expression of these genes had poorer prognosis. Kaplan-Meier curves in stratified survival analysis confirmed that patients with high risk scores had poor prognosis (p < 0.05). The results of univariate and multivariate COX survival analysis showed that risk score was closely related to the overall survival of patients with hepatocellular carcinoma. For clinical validation, we found that all the genes in the model were upregulated in hepatocellular carcinoma tissues compared to normal liver tissues, which was consistent with the previous results we obtained. Our study demonstrated that a panel of E2F target genes signature including five genes could predict the prognosis of hepatocellular carcinoma. This panel gene signature can facilitate the development of individualized and effective treatment for hepatocellular carcinoma.

Testing lncRNAs signature as clinical stage–related prognostic markers in gastric cancer progression using TCGA database

LncRNA expression can be conducive to gastric cancer (GC) prognosis. The objective of this study is to ascertain five specific lncRNAs involved in tumor progression of GC and their role as prognostic markers to diagnose clinical stage-wise GC. High-throughput RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database and performed genome-wide lncRNA expression analysis using edgeR package, Bioconductor.org , and R-statistical computing to analyze differentially expressed lncRNA analysis. Cutoff parameters were FDR < 0.05 and |Log2FC| > 2. Total 351 tumor samples with differentially expressed lncRNAs were divided into group-1 lncRNAs such as AC019117.2 and LINC00941, and group-2 lncRNAs such as LINC02410, AC012317.2, and AC141273.1 by 2:1. The Spearman correlation coefficients ( p < 0.05) and correlation test function (cor.test ()) were performed for lncRNAs as per clinical stage. Cytoscape software was used to construct lncRNA–mRNA interaction networks. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ( p < 0.05) analysis were conducted using the clusterProfiler package. Kaplan–Meier survival analysis was performed to determine the overall survival of patients based on the expression of five lncRNAs in different clinical stages of GC. AC019117.2 and LINC00941 of group 1 inferred a positive correlation with clinical stages of stage I to stage IV, and their expressions were higher in tumor tissues than normal tissues. On the contrary, LINC02410, AC012317.2, and AC141273.1 of group 2 exhibited a negative correlation with clinical stage, and they exhibited more expression in normal tissues compared to tumor tissues. GO and KEGG pathway analysis reported that AC019117.2 may interact with LINC00941 via ITGA3 and trophoblast glycoprotein (TPBG) to foster tumor progression. Tumor-specific group-1 lncRNAs were conducive to the poor overall survival and exhibited a positive correlation with the clinical stages of stage I to stage IV in GC as per the lncRNA–mRNA networking analysis. These five lncRNAs could be considered as clinically useful lncRNA-based prognostic markers to predict clinical stage-wise GC progression.

Validation of the GALAD model for early diagnosis and monitoring of hepatocellular carcinoma in Chinese multicenter study

BACKGROUND

GALAD is an algorithm model estimating the presence of hepatocellular carcinoma (HCC). However, the participants enrolled in the GALAD differ from those of Chinese subjects whose HCCs are mainly hepatitis B virus infection related. Therefore, the cross-sectional as well as longitudinal multicenter study was designed to assess the clinical performances of GALAD in the Chinese population.

METHODS

A case-control study of 602 patients with HCC (34.10% within Barcelona Clinic Liver Cancer 0-A stage) and 923 subjects without HCC from five Chinese medical centres was conducted. Longitudinally the performances of GALAD identifying HCC were assessed using receiver operating characteristic curves analyses. Furthermore, the surveillance performance of GALAD for 204 HCC patients after radical surgery and for the early detection of HCC prospectively in an independent cohort of chronic hepatitis B were analysed, respectively.

RESULTS

We found the GALAD identified early stage HCC at an area under the receiver operating characteristic curve (AUC) above 0.85 and outperformed significantly than AFP, PIVKAII, AFP-L3 and BALAD-2 respectively. Meanwhile the GALAD could stratify HCC into two distinct subgroups with high or low risks of overall survival and recurrence. The GALAD could detection HCC 24 (AUC: 0.848) or even 48 (AUC: 0.833) weeks before clinical diagnosis.

CONCLUSIONS

Our study indicates that the GALAD exhibits outstanding performance in the early diagnosis, prognosis prediction as well as risk monitoring of HCC in our cross-sectional and longitudinal multicenter study of 1561 patients. GALAD should be implanted into clinical practice early so as to improve the clinical efficacy of individual biomarkers in HCC early monitoring and prognosis prediction.

Construction of an HCC recurrence model based on the investigation of immune-related lncRNAs and related mechanisms

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and play fundamental roles in immune regulation. Growing evidence suggests that immune-related genes and lncRNAs can serve as markers to predict the prognosis of patients with cancers, including hepatocellular carcinoma (HCC). This study aimed to contract an immune-related lncRNA (IR-lncRNA) signature for prospective assessment to predict early recurrence of HCC. A total of 319 HCC samples under radical resection were randomly divided into a training cohort (161 samples) and a testing cohort (158 samples). In the training dataset, univariate, lasso, and multivariate Cox regression analyses identified a 9-IR-lncRNA signature closely related to disease-free survival. Kaplan-Meier analysis, principal component analysis, gene set enrichment analysis, and nomogram were used to evaluate the risk model. The results were further confirmed in the testing cohort. Furthermore, we constructed a competitive endogenous RNA regulatory network. The results of the present study indicated that this 9-IR-lncRNA signature has important clinical implications for improving predictive outcomes and guiding individualized treatment in HCC patients. These IR-lncRNAs and regulated genes may be potential biomarkers associated with the prognosis of HCC.

Comprehensive analysis of N6 -methyladenosine-related long non-coding RNAs for prognosis prediction in liver hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is a lethal cancer. This study aimed to identify the N⁶‐methyladenosine (m⁶A)‐targeted long non‐coding RNA (lncRNA) related to LIHC prognosis and to develop an m⁶A‐targeted lncRNA model for prognosis prediction in LIHC.

Methods

The expression matrix of mRNA and lncRNA was obtained, and differentially expressed (DE) mRNAs and lncRNAs between tumor and normal samples were identified. Univariate Cox and pathway enrichment analyses were performed on the m⁶A‐targeted lncRNAs and the LIHC prognosis‐related m⁶A‐targeted lncRNAs. Prognostic analysis, immune infiltration, and gene DE analyses were performed on LIHC subgroups, which were obtained from unsupervised clustering analysis. Additionally, a multi‐factor Cox analysis was used to construct a prognostic risk model based on the lncRNAs from the LASSO Cox model. Univariate and multivariate Cox analyses were used to assess prognostic independence.

Results

A total of 5031 significant DEmRNAs and 292 significant DElncRNAs were screened, and 72 LIHC‐specific m⁶A‐targeted binding lncRNAs were screened. Moreover, a total of 29 LIHC prognosis‐related m⁶A‐targeted lncRNAs were obtained and enriched in cytoskeletal, spliceosome, and cell cycle pathways. An 11‐m⁶A‐lncRNA prognostic model was constructed and verified; the top 10 lncRNAs included LINC00152, RP6‐65G23.3, RP11‐620J15.3, RP11‐290F5.1, RP11‐147L13.13, RP11‐923I11.6, AC092171.4, KB‐1460A1.5, LINC00339, and RP11‐119D9.1. Additionally, the two LIHC subgroups, Cluster 1 and Cluster 2, showed significant differences in the immune microenvironment, m⁶A enzyme genes, and prognosis of LIHC.

Conclusion

The m⁶A‐lncRNA prognostic model accurately and effectively predicted the prognostic survival of LIHC. Immune cells, immune checkpoints (ICs), and m⁶A enzyme genes could act as novel therapeutic targets for LIHC.

Molecular classification of hepatocellular carcinoma: prognostic importance and clinical applications

Hepatocellular carcinoma (HCC) is a lethal human malignancy with a very low overall and long-term survival rate. Poor prognostic outcomes are predominantly associated with HCC due to a huge landscape of heterogeneity found in the deadliest disease. However, molecular subtyping of HCC has significantly improved the knowledge of the underlying mechanisms that contribute towards the heterogeneity and progression of the disease. In this review, we have extensively summarized the current information available about molecular classification of HCC. This review can be of great significance for providing the insight information needed for development of novel, efficient and personalized therapeutic options for the treatment of HCC patients globally.

Long noncoding RNA ZFPM2-AS1 promotes the proliferation, migration, and invasion of hepatocellular carcinoma cells by regulating the miR-576-3p/HIF-1α axis

Long noncoding RNA (LncRNA) zinc finger protein multitype 2 antisense RNA 1 (ZFPM2-AS1) is highly expressed in a variety of tumors and is involved in promoting the malignant biological behaviors of cancer cells. However, the mechanism of ZFPM2-AS1 in the progression of hepatocellular carcinoma (HCC) remains to be explored. The ZFPM2-AS1 expression in HCC was measured by quantitative real-time PCR (qRT-PCR); cell counting kit-8, 5-bromo-2'-deoxyuridine (BrdU), and transwell assays were used to confirm the biological functions of ZFPM2-AS1 in regulating the malignant biological behaviors of HCC cells; the luciferase reporter gene assay was employed to detect whether ZFPM2-AS1 could bind to microRNA (miR)-576-3p; the regulatory relationship between ZFPM2-AS1 and miR-576-3p was probed by qRT-PCR; the effects of ZFPM2-AS1 and miR-576-3p on the expression of hypoxia-inducible factor 1α (HIF-1α) were detected by qRT-PCR and Western blot. The expression of ZFPM2-AS1 in HCC tissues, compared with that in normal liver tissues, was significantly upregulated. Knockdown of ZFPM2-AS1 markedly inhibited HCC cell proliferation, migration, and invasion while the overexpression of ZFPM2-AS1 worked oppositely. miR-576-3p could reverse the effects of ZFPM2-AS1 on the biological behaviors of HCC cells. Besides, ZFPM2-AS1 could bind to miR-576-3p and positively regulate the expression of HIF-1α, a target gene of miR-576-3p, by adsorbing miR-576-3p. ZFPM2-AS1 is abnormally highly expressed in HCC and facilitates proliferation, migration, and invasion of HCC cells by adsorbing miR-576-3p and upregulating HIF-1α expression.

LncRNA-HEIH is a Novel Diagnostic and Predictive Biomarker in Gastric Cancer

Background: Gastric cancer (GC) is associated with a high mortality rate. Long noncoding RNA (lncRNA)-high expressed in hepatocellular carcinoma (HEIH) has recently gained interest as a marker for the detection of several cancer types. This study was designed to uncover the function of lncRNA-HEIH in GC. Materials and Methods: Oncomine was used to analyze HEIH expression in cancerous and paired noncancerous tissues of GC patients. Subsequently, the expression levels of HEIH in GC cells was determined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). In addition, the effects of HEIH expression level on clinicopathological parameters and prognosis were further studied by statistical analysis and Kaplan-Meier survival curves. GC cell proliferation and the influence of HEIH on the sensitivity of cells to oxaliplatin following HEIH knockdown were assessed using sulforhodamine blue (SRB) assays in the MKN45 and AGS cell lines. In addition, the expression levels of p53 were detected by RT-qPCR following knockdown of HEIH. Results: The lncRNA-HEIH was highly expressed in both GC tissues and GC cell lines. Patients with high HEIH expression were associated with medium-high differentiation (p = 0.0058), distant metastasis (M, p = 0.0378), lymph node metastasis (N, p = 0.0083), and a deeper tumor invasion (T, p = 0.0204). The elevated expression levels of HEIH in GC patients were associated with a worse prognosis compared to GC patients with low HEIH expression. This finding was supported by the parameters overall survival (p = 3.3e-06), first progression (p = 0.00028), and postprogression (p = 1.5e-08). Downregulation of HEIH expression inhibited cell proliferation, enhanced oxaliplatin sensitivity, and induced the expression of p53 in MKN45 and AGC cells. Conclusion: These findings provide evidence that HEIH may be useful as a prognostic biomarker in GC. This lncRNA may also serve as a potential therapeutic target in GC patients.

Application of Molecular Nanoprobes in the Analysis of Differentially Expressed Genes and Prognostic Models of Primary Hepatocellular Carcinoma

Analyzing hub genes related to tumorigenesis based on biological big data has recently become a hotspot in biomedicine. Nanoprobes, nanobodies and theranostic molecules targeting hub genes delivered by nanocarriers have been widely applied in tumor theranostics. Hepatocellular carcinoma (HCC) is one of the most common cancers, with a poor prognosis and high mortality. Identifying hub genes according to the gene expression levels and constructing prognostic signatures related to the onset and outcome of HCC will be of great significance. In this study, the expression profiles of HCC and normal tissue were obtained from the GEO database and analyzed by GEO₂R to identify DEGs. GO terms and KEGG pathways were enriched in DAVID software. The STRING database was consulted to find protein-protein interactions between proteins encoded by the DEGs, which were visualized by Cytoscape. Then, overall survival associated with the hub genes was calculated by the Kaplan-Meier plotter online tool, and verification of the results was carried out on TCGA samples and their corresponding clinical information. A total of 603 DEGs were obtained, of which 479 were upregulated and 124 were downregulated. PPI networks including 603 DEGs and 18 clusters were constructed, of which 7 clusters with MCODE score ≥3 and nodes ≥5 were selected. The 5 genes with the highest degrees of connectivity were identified as hub genes, and a prognostic model was constructed. The expression and prognostic potential of this model was validated on TCGA clinical data. In conclusion, a five-gene signature (TOP2A, PCNA, AURKA, CDC20, CCNB2) overexpressed inHCC was identified, and a prognostic model was constructed. This gene signature may act as a prognostic model for HCC and provide potential targets of nanotechnology.

Construction of a ceRNA Network and a Prognostic lncRNA Signature associated with Vascular Invasion in Hepatocellular Carcinoma based on Weighted Gene Co-Expression Network Analysis

Background: Understanding risk factors for vascular invasion (VI) is crucial for assessing the risk of recurrence and overall prognosis of hepatocellular carcinoma (HCC). This study aimed to construct a prognostic long non-coding RNA (lncRNA) signature and a ceRNA Network associated with vascular invasion in HCC. Methods: Differentially expressed genes (DEGs) of HCC patients associated with VI were identified by analyzing data from TCGA. Weighted gene co-expression network analysis (WGCNA) was used to identify associations between gene expression modules and clinical features. A VI-related prognostic lncRNA signature was then established using univariate, LASSO and multivariate Cox proportional hazards regression analyses. Based on the hub modules identified by the WGCNA, we constructed a VI-related lncRNA-miRNA-mRNA ceRNA network and screened hub lncRNAs for further research. Finally, we conducted in vitro and in vivo experiments to determine the biological roles of the identified hub gene BBOX1-AS1. Results: The key module related to VI and OS was identified using WGCNA, after which a prognostic model consisting of eight lncRNAs was established, and verified using time-dependent receiver operating characteristic (ROC) curve analysis. BBOX1-AS1 was confirmed to be highly expressed in HCC tissues, and its expression was significantly correlated with a poor prognosis. Silencing BBOX1-AS1 in vitro significantly suppressed the proliferation, migration and invasion of HCC cells. In vivo experiments demonstrated that knocking down of BBOX1-AS1 could result in significant decrease of tumor volume and tumor weight. Conclusions: The VI-related lncRNA signature established in this study can be used to predict the clinical outcomes of HCC patients. In addition, we constructed a VI-related lncRNA-miRNA-mRNA ceRNA network and demonstrated that BBOX1-AS1 might be a novel biomarker associated with VI in HCC.

Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC.

Methods

The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort.

Results

We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

A new prognostic strategy based on four DNA repair-associated lncRNAs for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant tumour with poor prognosis. The effect of DNA repair on prognosis cannot be ignored; and long non-coding RNA (lncRNA) can regulate the DNA repair process.

OBJECTIVE

To obtain DNA repair-associated lncRNA (DR-lncRNA) prognostic signature for improved ability to prediction of HCC prognosis.

METHODS

Our study used the Cancer Genome Atlas database. Gene set variation analysis was performed to differentiate high and low levels of DNA repair to identify DR-lncRNAs. By performing univariate Cox regression, LASSO regression, and multivariate Cox regression analyses, we finally obtained a DR-lncRNA prognostic signature and constructed a nomogram prognostic model. Time-dependent receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact curves were used to assess predictive ability and clinical utility. Differentially expressed genes (DEGs) functional enrichment analysis was performed to further explore the underlying mechanisms that influence HCC prognosis.

RESULTS

We obtained a DR-lncRNA prognostic signature-AP002478.1, AC116351.1, LINC02580, and LINC00861. The ROC curves and calibration plots showed good discrimination and calibration properties. Combining the DR-lncRNA prognostic signature and tumour stages, we established a nomogram prognostic model. DCA and clinical impact curves showed the clinical utility of the nomogram prognostic model. DEGs of high-risk and low-risk groups predicted by the DR-lncRNA prognostic were significantly associated with cell cycle and various metabolic pathways and biological processes such as the oxidation-reduction process and cell division.

CONCLUSION

We identified a DR-lncRNA prognostic signature and constructed a nomogram prognostic model, which could be a beneficial prognostic strategy for HCC.

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Background

Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment.

Material/methods

422 LC and adjacent normal tissues with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan–Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens.

Results

The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4 (replication factor C subunit 4), ZWINT (ZW10 interacting kinetochore protein), UPF3B (UPF3B regulator of nonsense mediated mRNA decay), NCBP2 (nuclear cap binding protein subunit 2), ADA (adenosine deaminase), SF3A3 (splicing factor 3a subunit 3) and GTF2H1 (general transcription factor IIH subunit 1). On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7 to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P < 0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 [95% CI (confidence interval) 1.523–3.132] and log-rank P-value < 0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted.

Conclusions

Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

Identification of immune-related lncRNA signature for predicting immune checkpoint blockade and prognosis in hepatocellular carcinoma

BACKGROUND

An increasing body of evidence has supported that long non-coding RNAs (lncRNAs) can play as essential roles of various physiological process and pathological diseases. We aimed to construct a robust immune-associated lncRNA signature associated with the prognosis for HCC survival prediction.

METHODS

7 immune-associated lncRNAs presenting significant correlation with survival were screened through stepwise univariate Cox regression and LASSO algorithm, and multivariate Cox regression. Kaplan-Meier analysis, proportional hazards model, and ROC analyses further conducted. Gene set enrichment analysis (GSEA) was applied for functional annotation. We conducted quantitative real-time polymerase chain reaction to determine NRAV expression and preliminarily explored the latent role of NRAV in prognosis of HCC patients.

RESULTS

Finally, 7 immune-related lncRNA signature composed of AC007405.3, AC023157.3, NRAV, CASC19, MSC-AS1, GASAL1, and LINC00942 were validated. This lncRNAs signature can serve as an independent predictive biomolecular factor. This signature was further confirmed in the validation group and the entire cohort. We demonstrated that NRAV was significantly upregulated in HCC cell lines and it may serve as a key regulator in HCC. Our signature was associated to apoptosis and immunologic characteristics. This signature mediated immune cell infiltration (i.e., Dendritic, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., CD274, etc.,).

CONCLUSION

This immune-related lncRNA signature possesses promising prognostic value in HCC and may have the potentiality to predict clinical outcome of ICB immunotherapy.

STAT1-induced regulation of lncRNA ZFPM2-AS1 predicts poor prognosis and contributes to hepatocellular carcinoma progression via the miR-653/GOLM1 axis

Long noncoding RNAs (lncRNAs) have drawn growing attention owing to their important effects in various tumors, including hepatocellular carcinoma (HCC). Recently, a newly identified lncRNA, ZFPM2 antisense RNA 1 (ZFPM2-AS1), was reported to serve as an oncogene in gastric cancer. However, its function in tumors remains largely unknown. In this study, we identified ZFPM2-AS1 as a novel HCC-related lncRNA, which was observed to be distinctly upregulated in HCC tissues and associated with shorter overall survival. Luciferase reporter and chromatin immunoprecipitation assays suggested that overexpression of ZFPM2-AS1 was induced by STAT1. Functional investigations suggested that the inhibition of ZFPM2-AS1 suppressed cell proliferation, metastasis, cell cycle progression while accelerated cell apoptosis. Mechanistic studies showed that there were two binding sites of miR-653 within the sequence of ZFPM2-AS1 and the levels of ZFPM2-AS1 were negatively correlated with miR-653. In addition, ZFPM2-AS1 could reverse the suppressor effects of miR-653 on the proliferation and metastasis of HCC cells by the modulation of GOLM1, a target gene of miR-653. To conclude, we provided a better understanding of the interaction mechanism between ZFPM2-AS-miR-653-GOLM1 axis, which may help develop prognostic biomarkers and therapeutic target for HCC.

A Practical Model is Equivalent to the BALAD or BALAD-2 Score in Predicting Long-term Survival after Hepatectomy in Chinese Patients with Hepatocellular Carcinoma

Aim: To evaluate the predictive value of the BALAD and BALAD-2 scores on long-term survival after hepatectomy in Chinese hepatocellular carcinoma (HCC) patients and to attempt to establish a more practical or effective model.

Methods: A total of 251 HCC patients underwent hepatectomy were recruited. The BALAD and BALAD-2 scores were calculated with total bilirubin, albumin, alpha-fetoprotein, Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein and des-gamma-carboxyprothrombin. The associations of the two scores and their components with the overall survival were analyzed. Finally, three prediction models were explored and constructed.

Results: We observed that HCC patients had 5-year survival rates that worsened with increasement of BALAD and BALAD-2 scores. The BALAD and BALAD-2 scores demonstrated fine value in predicting overall survival with Harrell-C statistics of 0.665 (0.618-0.712) and 0.603 (0.554-0.636). After two variables, largest tumor size and BMI, were included in BALAD [0.720 (0.671-0.769)] or BALAD-2 [0.701 (0.649-0.751)] multivariate models, the Harrell-C statistic increased significantly than BALAD (P=0.048) or BALAD-2 (P<0.001) alone. Taking into account availability and expense, an equivalent BAA-BS model was established based on total bilirubin, albumin, AFP, BMI and largest tumor size. The Harrell-C statistic of BAA-BS model [0.723(0.674-0.772)] was similar to that of BALAD (P=0.820) or BALAD-2 (P=0.209) multivariate model. And, the continuous net reclassification index and integrated discriminatory improvement were not statistically different. Finally, a nomogram of the equivalent BAA-BS model was constructed to assist surgeons and patients in predicting 5-year survival rates.

Conclusion: Both BALAD and BALAD-2 scores were highly suitable for predicting long-term survival after hepatectomy in Chinese HCC patients. A significant increase in predictive efficacy was observed after the addition of largest tumor size and BMI to BALAD or BALAD-2 score. Even if AFP-L3 and DCP are not detected, an equivalent BAA-BS model also obtained an excellent discriminatory performance.

Four Immune-Related Long Non-coding RNAs for Prognosis Prediction in Patients With Hepatocellular Carcinoma

BACKGROUND

Long non-coding RNA (LncRNA) plays an important role in the occurrence and development of hepatocellular carcinoma (HCC). This study aims to establish an immune-related LncRNA model for risk assessment and prognosis prediction in HCC patients.

METHODS

Hepatocellular carcinoma patient samples with complete clinical data and corresponding whole transcriptome expression were obtained from the Cancer Genome Atlas (TCGA). Immune-related genes were acquired from the Gene Set Enrichment Analysis (GSEA) website and matched with LncRNA in the TCGA to get immune-related LncRNA. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for screening the candidate LncRNAs and calculating the risk coefficient to establish the prognosis model. Patients were divided into a high-risk group and a low-risk group depending on the median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort. GSEA and principal component analysis were used for function evaluation.

RESULTS

A total of 319 samples met the screening criteria and were randomly distributed across the training cohort and the validation cohort. After comparison with the IMMUNE_RESPONSE gene set and the IMMUNE_SYSTEM_PROCESS gene set, a total of 3094 immune-related LncRNAs were screened. Ultimately, four immune-related LncRNAs were used to construct a formula using LASSO regression. According to the formula, the low-risk group showed a higher survival rate than the high-risk group in the validation cohort and the whole cohort. The receiver operating characteristic curves data demonstrated that the risk score was more specific than other traditional clinical characteristics in predicting the 5-year survival rate for HCC.

CONCLUSION

The four-immune-related-LncRNA model can be used for survival prediction in HCC and guide clinical therapy.

A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients.

Methods

International Cancer Genome Consortium (ICGC) database was searched for the target information in our study. Lasso regression, univariate and multivariate cox analysis were applied during the analysis process. And a nomogram integrating model scoring and clinical characteristic was drawn.

Results

Six mRNAs were screened out by Lasso regression to make a model for predicting the OS of HCC patients. And this model was proved to be an independent prognostic model predicting OS in HCC patients. The area under the ROC curve (AUC) of this model was 0.803. TCGA database validated the significant value of this 6-mRNA model. Eventually a nomogram including 6-mRNA risk score, gender, age, tumor stage and prior malignancy was set up to predict the OS in HCC patients.

Conclusions

We established an independent prognostic model of predicting OS for 1–3 years in HCC patients, which is available to all populations. And we developed a nomogram on the basis of this model, which could be of great help to precisely individual treatment measures.

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma

Background

Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients.

Methods

The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram.

Results

Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally.

Conclusion

Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

Four Autophagy-Related lncRNAs Predict the Prognosis of HCC through Coexpression and ceRNA Mechanism

Abnormally expressed long noncoding RNAs (lncRNAs) have been reported to affect the occurrence and progression of hepatocellular carcinoma (HCC) by modulating the autophagy axis. However, none of studies has explored the clinical significance of these autophagy-related lncRNAs in HCC comprehensively. In this study, the RNA-seq, miRNA-seq, and clinical data of normal and HCC patients from the TCGA database and autophagy genes from the Human Autophagy Database were extracted. Subsequently, we screened out 78 differentially expressed autophagy-related lncRNAs, and four prognostic-related lncRNAs (LUCAT1, AC099850.3, ZFPM2-AS1, and AC009005.1) were eventually used to develop the prognostic model. This signature could be regarded as an independent prognostic signature for HCC patients and has the highest prediction efficiency than other clinicopathological factors for the 1-, 3-, and 5-year survival (AUC = 0.764, 0.738, and 0.717, respectively). Additionally, regardless of whether the clinical information is complete for HCC patients, the autophagy-related lncRNA model shows a good predictive power for the overall survival. Importantly, the coexpression network of 4 lncRNAs and 11 autophagy-related genes was constructed. Moreover, based on the bioinformatic analyses, our results found that LUCAT1 and ZFPM2-AS1 may affect the autophagic activity in HCC through the hsa-miR-495-3p/DLC1 and hsa-miR-515-5p/DAPK2 axis, respectively. In conclusion, we establish an effective prognostic model for HCC patients and shed new light on the autophagy-related regulatory mechanisms of the identified lncRNAs.

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 three-mRNA status risk score has greater predictive ability compared with a lncRNA-based risk score for predicting prognosis in patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents the fifth most common cause of cancer-associated mortality in men, and the seventh in women, worldwide. The aim of the present study was to identify a reliable and robust RNA-based risk score for the survival prediction of patients with hepatocellular carcinoma (HCC). Gene expression data from HCC and healthy control samples were obtained from The Cancer Genome Atlas to screen differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Univariate and multivariate Cox proportional-hazards regression models and the LASSO algorithm for the Cox proportional-hazards model (LASSO Cox-PH model) were used to identify the prognostic mRNAs and lncRNAs among differentially expressed mRNAs (DEMs) and differentially expressed lncRNAs (DELs), respectively. Prognostic risk scores were generated based on the expression level or status of the prognostic lncRNAs and mRNAs, and the predictive abilities of these RNAs in TCGA and validation datasets were compared. Functional enrichment analyses were also performed. The results revealed a total of 154 downregulated and 625 upregulated mRNAs and 18 upregulated lncRNAs between tumor and control samples in TCGA dataset. A three-mRNA and a five-lncRNA expression signatures were identified using the LASSO Cox-PH model. Three-mRNA and five-lncRNA expression and status risk scores were generated. Using likelihood ratio P-values and area under the curve values from TCGA and the validation datasets, the three-mRNA status risk score was more accurate compared with the other risk scores in predicting the mortality of patients with HCC. The three identified mRNAs, including hepatitis A virus cellular receptor 1, MYCN proto-oncogene BHLH transcription factor and stratifin, were associated with the cell cycle and oocyte maturation pathways. Therefore, a three-mRNA status risk score may be valuable and robust for risk stratification of patients with HCC. The three-mRNA status risk score exhibited greater prognostic value compared with the lncRNA-based risk score.

Discovery of a novel three-long non-coding RNA signature for predicting the prognosis of patients with gastric cancer

Background

Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play a predictive role in the prognosis of gastric cancer (GC). The present study aims to construct a lncRNA-based model via mining data of The Cancer Genome Atlas (TCGA).

Methods

Differentially expressed lncRNAs were first identified, followed by univariate Cox analysis, Robust likelihood-based survival model and multivariate Cox analysis to construct a signature composed of lncRNAs.

Results

A three-lncRNA based predictive signature (OVAAL, FLJ16779, FAM230D) was established to stratify GC patients into high- and low-risk groups. Patients in the high-risk group had markedly shorter overall survival (OS) than those in the low-risk group, which was verified by the ROC curve. Then, we validated the predictive power of the scoring system in other two cohorts. Multivariate Cox analysis also indicated that the 3-lncRNA signature was an independent prognostic factor for survival prediction in GC patients. Moreover, Gene Set Enrichment Analysis (GSEA) revealed that diverse metabolic pathways significantly clustered in the low-risk group, which might explain how the 3-lncRNA signature promoted gastric carcinogenesis.

Conclusions

We established a robust three-lncRNA model to predict the OS of GC patients, which might benefit the clinical decision making for personalized treatment and prognostic prediction for GC patients.

Risk Scoring System based on lncRNA Expression for Predicting Survival in Hepatocellular Carcinoma with Cirrhosis

Objective:

This study aims to explore the roles of long non-coding RNAs (lncRNAs) for predicting survival in hepatocellular carcinoma (HCC) patients with cirrhosis.

Methods:

A set of lncRNAs differentially expressed between HCC patients with or without cirrhosis was identified using expression profiles of The Cancer Genome Atlas database, and these lncRNAs were screened for their risk scoring system to predict recurrence-free survival (RFS) or overall survival (OS). Predictive ability of risk scoring systems was confirmed using uni- and multivariate Cox analyses while adjusting for clinical features. Predictive lncRNAs were analyzed by functional enrichment analysis.

Results:

Our screen identified 22 lncRNAs that were upregulated in the presence of cirrhosis and 59 that were downregulated. To predict OS of HCC patients with cirrhosis, a risk scoring system was developed with four lncRNAs (LINC02086, LINC00880, LINC01549 and AC136475.3); to predict RFS in these patients, the risk scoring system contained five lncRNAs (SH3RF3-AS1, AC104117.3, AC136475.3, LINC00239 and MRPL23-AS1). All risk scoring systems were associated with an area under the receiver operating characteristic curve > 0.7. Based on uni- and multivariate Cox analyses, the risk scoring system could serve as a significant independent predictor for OS in HCC patients with cirrhosis. Functional enrichment analysis suggested that the lncRNAs in the risk scoring systems are involved primarily in the pathway of Wnt signal and cytokine-cytokine receptor interaction.

Conclusion:

Risk scoring systems based on lncRNAs can effectively predict OS of HCC patients with cirrhosis. The system should be further developed and validated in larger, preferably multi-site patient populations.

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.

Identification of a Novel Eight-lncRNA Prognostic Signature for HBV-HCC and Analysis of Their Functions Based on Coexpression and ceRNA Networks

Studies have demonstrated the prognosis potential of long noncoding RNAs (lncRNAs) for hepatocellular carcinoma (HCC), but specific lncRNAs for hepatitis B virus- (HBV-) related HCC have rarely been reported. This study was aimed at identifying a lncRNA prognostic signature for HBV-HCC and exploring their underlying functions. The sequencing dataset was collected from The Cancer Genome Atlas database as the training set, while the microarray dataset was obtained from the European Bioinformatics Institute database (E-TABM-36) as the validation set. Univariate and multivariate Cox regression analyses identified that eight lncRNAs (TSPEAR-AS1, LINC00511, LINC01136, MKLN1-AS, LINC00506, KRTAP5-AS1, ZNF252P-AS1, and THUMPD3-AS1) were significantly associated with overall survival (OS). These eight lncRNAs were used to construct a risk score model. The Kaplan-Meier survival curve results showed that this risk score can significantly differentiate the OS between the high-risk group and the low-risk group. Receiver operating characteristic curve analysis demonstrated that this risk score exhibited good prediction effectiveness (area under the curve (AUC) = 0.990 for the training set; AUC = 0.903 for the validation set). Furthermore, this lncRNA risk score was identified as an independent prognostic factor in the multivariate analysis after adjusting other clinical characteristics. The crucial coexpression (LINC00511-CABYR, THUMPD3-AS1-TRIP13, LINC01136-SFN, LINC00506-ANLN, and KRTAP5-AS1/TSPEAR-AS1/MKLN1-AS/ZNF252P-AS1-MC1R) or competing endogenous RNA (THUMPD3-AS1-hsa-miR-450a-TRIP13) interaction axes were identified to reveal the possible functions of lncRNAs. These genes were enriched into cell cycle-related biological processes or pathways. In conclusion, our study identified a novel eight-lncRNA prognosis signature for HBV-HCC patients and these lncRNAs may be potential therapeutic targets.

Multifaceted Role of PRDM Proteins in Human Cancer

The PR/SET domain family (PRDM) comprise a family of genes whose protein products share a conserved N-terminal PR [PRDI-BF1 (positive regulatory domain I-binding factor 1) and RIZ1 (retinoblastoma protein-interacting zinc finger gene 1)] homologous domain structurally and functionally similar to the catalytic SET [Su(var)3-9, enhancer-of-zeste and trithorax] domain of histone methyltransferases (HMTs). These genes are involved in epigenetic regulation of gene expression through their intrinsic HMTase activity or via interactions with other chromatin modifying enzymes. In this way they control a broad spectrum of biological processes, including proliferation and differentiation control, cell cycle progression, and maintenance of immune cell homeostasis. In cancer, tumor-specific dysfunctions of PRDM genes alter their expression by genetic and/or epigenetic modifications. A common characteristic of most PRDM genes is to encode for two main molecular variants with or without the PR domain. They are generated by either alternative splicing or alternative use of different promoters and play opposite roles, particularly in cancer where their imbalance can be often observed. In this scenario, PRDM proteins are involved in cancer onset, invasion, and metastasis and their altered expression is related to poor prognosis and clinical outcome. These functions strongly suggest their potential use in cancer management as diagnostic or prognostic tools and as new targets of therapeutic intervention.

YY1-induced lncRNA ZFPM2-AS1 facilitates cell proliferation and invasion in small cell lung cancer via upregulating of TRAF4

Background

Newly identified lncRNA zinc finger protein, FOG family member 2 antisense RNA 1 (ZFPM2-AS1) is identified as an oncogenic gene. However, the role of ZFPM2-AS1 in small cell lung cancer (SCLC) is poorly comprehended.

Methods

The expression of genes in SCLC tissues and cells was measured by qRT-PCR. Colony formation, EdU, CCK-8, transwell and wound healing as well as in vivo assays revealed the function of ZFPM2-AS1 in SCLC. ChIP, luciferase reporter, RIP and RNA pull down assays demonstrated the binding relation among genes.

Results

ZFPM2-AS1 was significantly upregulated in SCLC tissues and cells. ZFPM2-AS1 deficiency attenuated SCLC cell proliferation, invasion and migration. In addition, ZFPM2-AS1 was transcriptionally activated by Yin Yang 1 (YY1) factor. Further, miR-3612 was confirmed as downstream miRNA of ZFPM2-AS1. Moreover, TNF receptor associated factor 4 (TRAF4) was the target gene of miR-3612 in SCLC. ZFPM2-AS1, miR-3612 and TRAF4 jointly constituted a competing endogenous RNA (ceRNA) network in SCLC. Finally, TRAF4 could countervail ZFPM2-AS1 downregulation-mediated function on SCLC cell proliferation and invasion in vitro and tumor growth in vivo.

Conclusion

Our study elucidated the oncogenic effect of ZFPM2-AS1 in SCLC progression, indicating it may be a therapeutic target for SCLC.

Bioinformatics analyses and biological function of lncRNA ZFPM2-AS1 and ZFPM2 gene in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains one of the most lethal malignant tumors worldwide; however, the etiology of HCC still remains poorly understood. In the present study, cancer-omics databases, including The Cancer Genome Atlas, GTEx and Gene Expression Omnibus, were systematically analyzed in order to investigate the role of the long non-coding RNA (lncRNA) zinc finger protein, FOG family member 2-antisense 1 (ZFPM2-AS1) and the zinc finger protein, FOG family member 2 (ZFPM2) gene in the occurrence and progression of HCC. It was identified that the expression levels of lncRNA ZFPM2-AS1 were significantly increased in HCC tissues, whereas expression levels of the ZFPM2 gene were significantly decreased in HCC tissues compared with normal liver tissues. Higher expression levels of ZFPM2-AS1 were significantly associated with a less favorable prognosis of HCC, whereas higher expression levels of the ZFPM2 gene were associated with a more favorable prognosis of HCC. Genetic alterations in the ZFPM2 gene may contribute to a worse prognosis of HCC. Validation of the GSE14520 dataset also demon stared that ZFPM2 gene expression levels were significantly decreased in HCC tissues (P<0.001). The receiver operating characteristic (ROC) analysis of the ZFPM2 gene indicated high accuracy of this gene in distinguishing between HCC tissues and non-tumor tissues. The areas under the ROC curves were >0.8. Using integrated strategies, the present study demonstrated that lncRNA ZFPM2-AS1 and the ZFPM2 gene may contribute to the occurrence and prognosis of HCC. These findings may provide a novel understanding of the molecular mechanisms underlying the occurrence and prognosis of HCC.

A Robust 6-lncRNA Prognostic Signature for Predicting the Prognosis of Patients With Colorectal Cancer Metastasis

Objective: Our study aimed to construct a robust long non-coding RNA (lncRNA) prognostic signature for colorectal cancer (CRC) metastasis.

Methods: Differentially expressed lncRNAs were identified between metastatic CRC and non-metastatic CRC samples from The Cancer Genome Atlas Database (TCGA) using the edgeR package. The differentially expressed lncRNAs with prognosis of patients with CRC metastasis were identified by univariate Cox regression analysis, followed by a stepwise multivariate Cox regression model. The survminer package in R was used to identify the optimal cutoff point for high-risk and low-risk groups. The receiver operating characteristic (ROC) curves were plotted to assess this signature. To explore potential signaling pathways associated with these lncRNAs, Gene Set Enrichment Analysis (GSEA) was performed.

Results: A 6-lncRNA signature was built based on the lncRNA expression profile for CRC metastasis. The optimal cutoff value was used to classify high-risk and low-risk groups using the survminer package. The high-risk groups could have poorer survival time than the low-risk groups. ROC curve result indicated that this lncRNA signature had high sensitivity and accuracy. GSEA analysis results showed that the six lncRNAs were significantly enriched in several CRC metastasis-related signaling pathways such as “cell cycle,” “DNA replication,” “mismatch repair,” “oxidative phosphorylation,” “regulation of autophagy,” and “insulin signaling pathway.”

Conclusion: Our study constructed a 6-lncRNA model for predicting the survival outcomes of patients with CRC metastasis, which could become potential prognostic biomarkers, and therapeutic targets for CRC metastasis.

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

Patients with hepatocellular carcinoma (HCC) have different prognoses depending on whether or not they also have fibrosis. Since long non-coding RNAs (lncRNAs) affect tumor formation and progression, the present study aimed to investigate whether their expression might help predict the survival of patients with HCC. Expression profiles downloaded from The Cancer Genome Atlas database were examined to identify lncRNAs differentially expressed (DElncRNAs) between HCC patients with or without fibrosis. These DElncRNAs were then used to develop a risk scoring system to predict overall survival (OS) or recurrence-free survival (RFS). A total of 142 significant DElncRNAs were identified using data from 135 patients with fibrosis and 72 without fibrosis. For HCC patients with fibrosis, a risk scoring system to predict OS was constructed based on five lncRNAs (AL359853.1, Z93930.3, HOXA-AS3, AL772337.1 and AC012640.3), while the risk scoring system to predict RFS was based on 12 lncRNAs (PLCE1-AS1, Z93930.3, LINC02273, TRBV11-2, HHIP-AS1, AC004687.1, LINC01857, AC004585.1, AP000808.1, CU638689.4, AC090152.1 and AL357060.1). For HCC patients without fibrosis, the risk scoring system to predict OS was established based on seven lncRNAs (LINC00239, AC104971.4, AP006285.2, HOXA-AS3, AC079834.2, NRIR and LINC01929), and the system to predict RFS was based on five lncRNAs (AC021744.1, NRIR, LINC00487, AC005858.1 and AC107398.3). Areas under the receiver operating characteristic curves for all risk scoring systems exceeded 0.7. Uni- and multivariate Cox analyses showed that the risk scoring systems were significant independent predictors of OS for HCC patients with fibrosis, or of OS and RFS for HCC patients without fibrosis, after adjusting for clinical factors. Functional enrichment analysis suggested that, depending on the risk scoring system, highly associated genes were involved in pathways mainly associated with the cell cycle, chemokines, Th17 cell differentiation or thermogenesis. The findings of the present study indicate that risk scoring systems based on lncRNA expression can effectively predict the OS of HCC patients with fibrosis as well as the OS or RFS of HCC patients without fibrosis.

A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC.

METHODS

We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice.

RESULTS

Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice.

CONCLUSION

This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

Long noncoding RNA ZFPM2-AS1 is involved in lung adenocarcinoma via miR-511-3p/AFF4 pathway

Lung cancer is the dominating cause of cancer-induced death and can be classified into small cell lung cancer and non-small cell lung cancer (NSCLC). Lung adenocarcinoma (LUAD) is the most common histological subtype of NSCLC and its pathology remains unclear. Mounting reports have revealed that lncRNAs could regulate cellular activities in cancers. Yet the role of ZFPM2 antisense RNA 1 (ZFPM2-AS1) in LUAD has not been elucidated. Using GEPIA online dataset, we identified the amplification of ZFPM2-AS1 in LUAD tissues. Through quantitative real-time reverse transcription-polymerase chain reaction analysis, we observed an upregulation of ZFPM2-AS1 in LUAD cell lines. Conducting loss-of-function assays, we found that ZFPM2-AS1 depletion impaired cell viability, suppressed cell migration, and reversed epithelial-mesenchymal transition progress in LUAD cells. Mechanism investigation manifested that ZFPM2-AS1 was distributed in the cytoplasm of LUAD cells. Moreover, ZFPM2-AS1 functioned as a molecular sponge of miR-511-3p, which was a suppressor in LUAD. Moreover, ZFPM2-AS1 sponged miR-511-3p and thereby deregulated AF4/FMR2 family member 4 (AFF4), a target of miR-511-3p. At length, rescue assays indicated that AFF4 overexpression revived the inhibiting effects of ZFPM2-AS1 knockdown on the biological processes in LUAD. All in all, this study uncovered the function and the mechanism of ZFPM2-AS1 in LUAD.

A novel prognostic nomogram based on 5 long non-coding RNAs in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common and invasive histological subtype of all kidney malignancies, with high levels of incidence and mortality. In the present study, long non-coding (lnc)RNA expression profiles of patients with ccRCC from The Cancer Genome Atlas database were comprehensively analyzed to identify differentially expressed lncRNAs (DElncRNAs). The patients with ccRCC were then divided into training and validation cohorts. Univariate and LASSO regression analyses were performed to select the most significant survival-associated candidate DElncRNAs in the training cohort. Multivariate Cox regression analysis was then performed to develop a risk score formula and a prognostic nomogram for predicting 3- and 5-year overall survival (OS). The accuracies of the nomogram predictions were evaluated by determining the area under the receiver operating characteristic curve (AUC) and a calibration plot. Finally, functional enrichment analysis and protein-protein interaction network prediction were implemented to predict the functions and molecular mechanisms of the candidate DElncRNAs in ccRCC. A total of 1,553 DElncRNAs were identified, and 5 candidate DElncRNAs (AC026992.2, AC245041.2, LINC00524, LINC01956 and LINC02080) were included in the nomogram. The AUC values for 3- and 5-year overall survival in the training cohort were 0.768 and 0.814, respectively, which were increased compared with that based on the clinical index (0.760 and 0.694, respectively). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that the 521 mRNAs highly associated with 5 DElncRNAs were primarily involved in 17 terms and 25 pathways, respectively. Based on the 5 DElncRNAs, a novel and convenient prognostic nomogram for predicting 3- and 5-year OS for patients with ccRCC was developed. The results of the present study may be conducive to the development of a precise predictive tool for the prognosis of ccRCC and may provide information regarding the molecular mechanisms of ccRCC. However, additional experimental in vitro and in vivo studies investigating lncRNAs may be required.

LNRLMI: Linear neighbour representation for predicting lncRNA-miRNA interactions

LncRNA and miRNA are key molecules in mechanism of competing endogenous RNAs(ceRNA), and their interactions have been discovered with important roles in gene regulation. As supplementary to the identification of lncRNA‐miRNA interactions from CLIP‐seq experiments, in silico prediction can select the most potential candidates for experimental validation. Although developing computational tool for predicting lncRNA‐miRNA interaction is of great importance for deciphering the ceRNA mechanism, little effort has been made towards this direction. In this paper, we propose an approach based on linear neighbour representation to predict lncRNA‐miRNA interactions (LNRLMI). Specifically, we first constructed a bipartite network by combining the known interaction network and similarities based on expression profiles of lncRNAs and miRNAs. Based on such a data integration, linear neighbour representation method was introduced to construct a prediction model. To evaluate the prediction performance of the proposed model, k‐fold cross validations were implemented. As a result, LNRLMI yielded the average AUCs of 0.8475 ± 0.0032, 0.8960 ± 0.0015 and 0.9069 ± 0.0014 on 2‐fold, 5‐fold and 10‐fold cross validation, respectively. A series of comparison experiments with other methods were also conducted, and the results showed that our method was feasible and effective to predict lncRNA‐miRNA interactions via a combination of different types of useful side information. It is anticipated that LNRLMI could be a useful tool for predicting non‐coding RNA regulation network that lncRNA and miRNA are involved in.

A nomogram based on 9-lncRNAs signature for improving prognostic prediction of clear cell renal cell carcinoma

Background

Abnormal expressions of long noncoding RNAs (lncRNAs) are very common in clear cell renal cell carcinoma (ccRCC), and some of these have been reported to be highly correlated with prognosis of ccRCC patients.

Methods

“edgeR” AND “DEseq” R packages were used to explore differentially expressed genes (DEGs) between normal and tumor tissues of ccRCC samples from The Cancer Genome Atlas (TCGA). Univariable Cox survival analysis, robust likelihood-based survival model and multivariable Cox regression analysis were used to identify prognostic lncRNAs and construct lncRNAs signature. Finally, a graphic nomogram based on the lncRNAs signature was developed to predict 1-, 3- and 5-year survival probability of ccRCC patients by using rms R package.

Results

8413 DEGs including 2740 lncRNAs and 4530 mRNAs were identified between normal and tumor tissues. 395 lncRNAs were found to be associated with prognosis of ccRCC patients (P < 0.05). Among these 395 prognostic lncRNAs, 9 key prognostic lncRNAs (RP13-463N16.6, CTD-2201E18.5, RP11-430G17.3, AC005785.2, RP11-2E11.9, TFAP2A-AS1, RP11-133F8.2, RP11-297L17.2 and RP11-348J24.2) were identified by using robust likelihood-based survival model. A 9-lncRNAs signature was constructed by using estimated regression coefficients of the 9 key prognostic lncRNAs. Results of χ²-test or Fisher’s exact test indicated that the 9-lncRNAs signature was significantly associated with clinicopathological characteristics such as tumor grade, T stage, N stage, M stage, TNM stage and survival outcome of ccRCC patients. Multivariate analysis showed that the 9-lncRNAs signature, age and M stage were independent prognostic factors. Finally, a graphic nomogram based on the lncRNAs signature, age and M stage was developed to predict 1-, 3- and 5-year survival probability of ccRCC patients by using rms R package.

Conclusions

A 9-lncRNAs signature associated with prognosis of ccRCC patients was constructed and a promising prognostic nomogram based on the 9-lncRNAs signature was developed for 1-, 3- and 5-year OS prediction of ccRCC patients in this study.

Identification of a novel four-lncRNA signature as a prognostic indicator in cirrhotic hepatocellular carcinoma

Background

Many studies have shown that long noncoding RNAs (lncRNA) are closely associated with the occurrence and development of various tumors and have the potential to be prognostic markers. Moreover, cirrhosis is an important prognostic risk factors in patients with liver cancer. Some studies have reported that lncRNA-related prognostic models have been used to predict overall survival (OS) and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC). However, no one has constructed a prognostic lncRNA model only in patients with cirrhotic HCC. Thus, it is necessary to screen novel potential lncRNA markers for improve the prognosis of cirrhotic HCC patients.

Methods

The probe expression profile dataset (GSE14520–GPL3921) from the Gene Expression Omnibus (GEO), which included 204 cirrhotic HCC samples, was reannotated and the lncRNA and mRNA expression dataset was obtained. The patients were randomly assigned to either the training set (n = 103) and testing set (n = 100). Univariate cox regression and the least absolute shrinkage and selection operator (LASSO) model were applied to screen lncRNAs linked to the OS of cirrhotic HCC in the training set. The lncRNAs having significant correlation with OS were then selected and the multivariate Cox regression model was implemented to construct the prognostic score model. Whether or not this model was related to RFS in the training set was simultaneously determined. The testing set was used to validate the lncRNA risk score model. A risk score based on the lncRNA signature was used for stratified analysis of different clinical features to test their prognostic performance. The prognostic lncRNA-related protein genes were identified by the co-expression matrix of lncRNA-mRNA, and the function of these lncRNAs was predicted through the enrichment of these co-expression genes.

Results

The signature consisted of four lncRNAs:AC093797.1,POLR2J4,AL121748.1 and AL162231.4. The risk model was closely correlated with the OS of cirrhotic HCC in the training cohort, with a hazard ratio (HR) of 3.650 (95% CI [1.761–7.566]) and log-rank P value of 0.0002. Moreover, this model also showed favorable prognostic significance for RFS in the training set (HR: 2.392, 95% CI [1.374–4.164], log-rank P = 0.0015). The predictive performance of the four-lncRNA model for OS and RFS was verified in the testing set. Furthermore, the results of stratified analysis revealed that the four-lncRNA model was an independent factor in the prediction of OS and RFS of patients with clinical characteristics such as TNM (Tumor, Node, Metastasis system) stages I–II, Barcelona Clinic Liver Cancer (BCLC) stages 0–A, and solitary tumors in both the training set and testing set. The results of functional prediction showed that four lncRNAs may be potentially involve in multiple metabolic processes, such as amino acid, lipid, and glucose metabolism in cirrhotic HCC.